Newborn first-aid information management method

Abstract

The invention provides a newborn first-aid information management method, which comprises the following steps: for an initial block type two-dimensional code data structure, generating a boundary pattern of a core block and an extension block by adopting a fractal pattern algorithm, marking a block boundary, and obtaining a two-dimensional code image with a pattern mark; for the two-dimensional code image with the pattern mark, an AES-256 algorithm is adopted to encrypt a core block and an expansion block respectively, encrypted core block data and expanded block data are generated, and an encrypted two-dimensional code data structure is obtained; extracting a blood type and an allergy history from the core block decoding data, generating a first-aid key information set, and if a first-aid scene needs medical history data, generating an extension block decoding instruction to obtain the first-aid key information set and a decoding instruction; and according to the first-aid key information set and the decoding instruction, decoding the extended block data in the encrypted two-dimensional code data structure, extracting medical history data, generating a first-aid supplementary information set, and obtaining a complete first-aid information set.

Description

Technical Field

[0001] The present invention relates to the technical field of neonates, and in particular to a method for managing neonatal emergency information. Background Art

[0002] Problem background:

[0003] Neonatal emergency data processing is a key research area in the field of medical information and communication technology, directly related to neonatal life safety and emergency treatment efficiency. In emergency scenarios, quickly and accurately obtaining critical neonatal medical information is crucial for medical staff to formulate treatment plans. Advances in information and communication technology have opened up new possibilities for data storage and transmission, but existing solutions still have significant limitations. Currently, medical information storage methods based on traditional barcodes or single QR codes have limited capacity, making it difficult to store complex neonatal medical data, and unable to dynamically adjust information priority based on emergency scenarios. In addition, data encryption and update mechanisms are not perfect, which can easily lead to information leakage or lack of timeliness. These shortcomings are particularly prominent in resource-constrained emergency environments and may delay treatment.

[0004] The core challenges focus on how to efficiently store and quickly retrieve newborn medical information, and how to prioritize critical data when equipment performance is limited. First, information storage needs to balance high density and fast decoding. A single encoding method is unlikely to meet the dual needs of speed and capacity in emergency scenarios. Second, the implementation of dynamic adjustment of data priority relies on block design and boundary identification, but existing technologies are insufficient in quickly locating block boundaries and isolating data between blocks. Finally, a regularly updated data management mechanism needs to ensure the timeliness of information while avoiding excessive system resource usage due to frequent updates. These unresolved technical factors lead to inefficient acquisition of emergency information and data security risks.

[0005] Therefore, how to design a block-based QR code system that reasonably divides core and extended blocks, dynamically adjusts information priority, ensures rapid positioning and independent encryption of blocks, and realizes regular data updates has become a key issue that needs to be urgently addressed in the neonatal emergency data processing system. Summary of the Invention

[0006] The present invention provides a method for managing neonatal emergency information, which mainly includes:

[0007] The FHIR standard interface is used to obtain newborn emergency data from the medical institution database, including blood type, allergy history, and medical history. A block-based QR code is generated. The core block stores blood type and allergy history, and the extended block stores medical history, resulting in the initial block-based QR code data structure.

[0008] For the initial block-type QR code data structure, a fractal pattern algorithm is used to generate the boundary pattern of the core block and the extended block, mark the block boundary, and obtain a QR code image with pattern markings;

[0009] For the QR code image with pattern mark, the core block and the extended block are encrypted using the AES-256 algorithm to generate encrypted core block data and extended block data, and obtain the encrypted QR code data structure;

[0010] In emergency scenarios, device performance parameters, including CPU frequency and memory size, are obtained. If the CPU frequency is less than 1GHz or the memory is less than 512MB, a dynamic priority algorithm is used to prioritize decoding the core block data in the encrypted QR code data structure to obtain the core block decoded data.

[0011] Extract blood type and allergy history from the core block decoded data to generate a first aid key information set. If medical history data is required for the first aid scenario, generate an extended block decoding instruction to obtain the first aid key information set and decoding instructions.

[0012] According to the first aid key information set and the decoding instruction, the extended block data in the encrypted QR code data structure is decoded, the medical history data is extracted, and the first aid supplementary information set is generated to obtain a complete first aid information set;

[0013] Based on the complete emergency information set, updated data on blood type, allergy history, and medical history are obtained from the medical institution database using the FHIR standard interface. The core block and extension block content in the initial block-based QR code data structure are replaced using an incremental update algorithm to obtain an updated block-based QR code data structure.

[0014] To update the block-based QR code data structure, a fractal pattern algorithm is used to regenerate the boundary pattern of the core block and the extended block. The updated core block and extended block are encrypted using the AES-256 algorithm to obtain an encrypted updated QR code image.

[0015] The encrypted data is extracted from the encrypted updated QR code image. In the emergency scenario, a dynamic priority algorithm is used to decode the core block based on the device performance parameters, including CPU frequency and memory size, to generate the emergency key information set and obtain the final emergency information set.

[0016] The technical solution provided by the embodiment of the present invention may have the following beneficial effects:

[0017] The present invention discloses a method for managing neonatal emergency information. In response to the need for rapid acquisition and updating of key medical information in emergency scenarios, a block-type QR code is used to store data such as blood type, allergy history, and medical history, and a fractal pattern algorithm is used to generate block boundaries. AES-256 encryption is used to protect data security. At the emergency scene, a dynamic priority algorithm is used to decode the core block based on equipment performance to quickly obtain key information such as blood type and allergy history. If necessary, the extended block is decoded to obtain the complete medical history. The present invention also supports incremental updates of QR code content from the medical institution database through the FHIR interface to ensure the timeliness of the information. This method can quickly obtain key emergency information under limited resource conditions, improve the efficiency and safety of neonatal emergency treatment, and provide timely and accurate decision support for medical personnel. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 The present invention is a flowchart of a newborn emergency information management method. DETAILED DESCRIPTION

[0019] The technical solutions of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the embodiments described are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0020] like Figure 1 In this embodiment, a method for managing neonatal emergency information may specifically include:

[0021] Step S101: Obtain newborn emergency data, including blood type, allergy history, and medical history, from the medical institution database using the FHIR standard interface. Generate a block-based QR code, with the core block storing blood type and allergy history, and the extended block storing medical history, to obtain an initial block-based QR code data structure.

[0022] Neonatal emergency data is obtained from a medical institution database using the FHIR standard interface to obtain a structured emergency data set. Blood type information, allergy history records, and medical history data in the structured emergency data set are parsed using JSON format to obtain a classified data set. Using a block-based QR code generation algorithm, the core block data and extended block data in the classified data set are mapped to the core block and extended block of the QR code, respectively, to obtain an initial block-based QR code data structure. An error correction coding algorithm is used to add redundant checksum information to the initial block-based QR code data structure to obtain a final block-based QR code data structure.

[0023] Specifically, the FHIR standard interface is used to obtain newborn emergency data from the medical institution database, including blood type, allergy history and medical history information.

[0024] For example, a medical institution's database records that Newborn A's blood type is O, and his allergy history includes penicillin allergy, as well as preterm birth and neonatal jaundice. Using the FHIR interface, structured JSON data can be retrieved, with fields such as bloodType:O, allergies:penicillin, medicalHistory:preterm, and jaundice. The advantage of FHIR lies in its standardization, ensuring seamless data exchange between different systems and improving data availability in emergency scenarios.

[0025] In one possible implementation, JSON parsing separates blood type, allergy history, and medical history into categorical data sets.

[0026] Specifically, blood type is parsed as type O, allergy history is parsed as penicillin, and medical history is parsed as a text list. The parsing process ensures field integrity; missing blood type fields are marked as null. This structured processing facilitates subsequent encoding and compression, improving data processing efficiency.

[0027] Prioritize, if blood type and allergy history are complete, they are encoded into binary data to form core block data.

[0028] For example, type O is coded as 00, and penicillin allergy is coded as 01, resulting in a binary string of 0001. If the allergy history is missing, it is marked as a null value, resulting in a binary string of 0000. Binary encoding reduces storage space, making it suitable for QR code generation and ensuring efficient transmission of core information.

[0029] It should be noted that medical history data uses a text compression algorithm to generate extended block data. For example, Huffman compression is used to compress the original 10-byte medical history text for premature birth and jaundice to 6 bytes. Compression reduces data volume and optimizes QR code storage efficiency while preserving the integrity of the medical history and facilitating rapid interpretation by emergency personnel.

[0030] In one embodiment, a block-based QR code generation algorithm maps core and extended block data to different regions of the QR code. The core block, storing blood type and allergy history, is located in the center of the QR code, while the extended blocks, storing compressed medical history, are distributed around the periphery. This block-based design ensures that core information is read first, improving response speed in emergency scenarios.

[0031] For example, the error-correcting coding algorithm adds redundant checksum information to the QR code. Using Reed-Solomon code, 20% redundancy is added to the core block data, and 30% redundancy is added to the extended block data. This error-correcting code ensures that the QR code remains readable even if it is partially damaged, enhancing the reliability of emergency data.

[0032] It is understood that the QR code rendering tool converts the final data structure into a scannable image.

[0033] For example, using a QR code generation library, the core block is displayed in high contrast, while the extended block is filled with grayscale. The generated QR code can be quickly scanned by emergency medical equipment, displaying information about the newborn's blood type, O, penicillin allergy, and medical history, ensuring the accuracy of emergency medical treatment.

[0034] For example, the block-based QR code design ensures both rapid access to core information and the integrity of extended information. Emergency personnel can access critical data with a single scan, reducing manual query time and significantly improving emergency response efficiency. Error-correcting coding and compression algorithms further enhance data reliability and storage efficiency, providing technical support for neonatal emergency care.

[0035] Step S102 : for the initial block-type two-dimensional code data structure, a fractal pattern algorithm is used to generate boundary patterns between the core block and the extended block, marking the block boundaries to obtain a two-dimensional code image with pattern markings.

[0036] A fractal pattern algorithm is used to generate boundary patterns between the core block and the extended block, and the boundary coordinates of the initial block-based QR code data are obtained to obtain boundary pattern data. The boundary pattern data and the block demarcation rules are then used to determine the boundary line between the core block and the extended block, obtaining boundary line coordinates. If the boundary line coordinates match the QR code data structure, a fractal pattern is embedded into the boundary line through pattern marking, obtaining boundary data for the embedded pattern. Based on the boundary data for the embedded pattern, a QR code image with pattern markings is generated to obtain a QR code image matrix. If the boundary pattern of the QR code image matrix is ​​clear, the pattern contrast is adjusted using an image optimization algorithm to obtain an optimized QR code image. The optimized QR code image is used to verify the integrity of the pattern markings for the core block and the extended block, and the QR code image is determined to have passed the verification. The final QR code image data with pattern markings is generated from the verified QR code image to obtain an outputtable QR code image.

[0037] Specifically, the fractal pattern algorithm is used to generate the boundary pattern of the core block and the extended block. It is a pattern generation method based on mathematical recursion, which generates complex and self-similar boundary shapes through iterative rules.

[0038] For example, in the scenario of a newborn emergency data QR code, fractal patterns can generate boundary patterns through Mandelbrot sets or Koch curves.

[0039] Firstly, the Koch curve is suitable for generating clear boundary lines due to its regular triangular recursive characteristics.

[0040] Specifically, the algorithm starts with a straight line. Each iteration divides the line segment into three equal parts, replacing the middle part with an equilateral triangle. This is repeated multiple times to form a complex boundary. Assuming the initial line segment length is 100 pixels, after three iterations, a boundary pattern with fine triangular zigzags is generated. The boundary coordinates are stored in a two-dimensional array, such as [(0,0), (33,0), (50,28.9), ...]. The boundary coordinates are obtained based on the geometric output of the fractal pattern.

[0041] In one possible implementation, the algorithm maps each node of the fractal pattern to the pixel grid of the QR code to generate boundary pattern data.

[0042] It is understandable that the boundary pattern data is stored in a dot matrix format. For example, in a 200x200 pixel grid, the boundary line occupies approximately 10% of the pixels, which is approximately 4,000 coordinate points.

[0043] It should be noted that the boundary pattern data must be aligned with the block structure of the QR code to ensure the distinction between the core block and the extended block. The block boundary rule is used to determine the boundary between the core block and the extended block, and the boundary line coordinates are generated based on the boundary pattern data.

[0044] For example, the rule might dictate that the core block occupies a 50x50 pixel area in the center of the QR code, with the extended block surrounding the periphery. The boundary coordinates are extracted from a closed curve in the boundary pattern, such as a closed path consisting of 100 coordinate points. The boundary coordinates must match the QR code data structure.

[0045] Specifically, the matching process checks whether the boundary line completely covers the core block boundary and does not invade the extended block. The pattern marking process embeds the fractal pattern into the boundary line to generate the boundary data of the embedded pattern.

[0046] In one embodiment, the embedding process is achieved by adding decorative markers near the boundary coordinate points, such as drawing a 2-pixel wide triangle pattern around each coordinate point. The pattern's boundary data is embedded to enhance visual distinction, such as using red triangles to mark the core block boundary and blue to mark the extended block boundary. The generation of the QR code image with the pattern marker relies on the embedded pattern's boundary data.

[0047] First, a dot matrix rendering tool maps the boundary data to the QR code image matrix. For example, if the QR code image matrix is ​​300x300 pixels, the embedded triangle pattern occupies approximately 5% of the pixels in the boundary area. An image optimization algorithm adjusts the pattern contrast.

[0048] Specifically, by increasing the brightness of the boundary pattern (e.g., from 100 to 150), the boundary pattern is ensured to be clearly discernible during scanning. To verify the integrity of the pattern markings in the core block and the extended block, it is necessary to check whether the boundary pattern in the QR code image matrix is ​​continuous.

[0049] For example, the verification algorithm scans the boundary coordinates to ensure that all 100 coordinate points have corresponding triangle markers. If verification passes, a QR code image with a pattern marker is generated and output as a 300x300 pixel image file suitable for printing or screen display. This patterned QR code image facilitates quick identification of the emergency data area.

[0050] Step S103: Using the AES-256 algorithm to encrypt the core block and the extended block of the QR code image with the pattern mark, generate encrypted core block data and extended block data, and obtain an encrypted QR code data structure.

[0051] A QR code image containing a pattern mark is obtained, and image segmentation techniques are used to separate the core block and extended block, resulting in a core block image and an extended block image. The core block image is encrypted using the AES-256 algorithm to generate encrypted core block data. The extended block image is also encrypted using the AES-256 algorithm to generate encrypted extended block data. Based on the encrypted core block data and extended block data, an encrypted QR code data structure is constructed to obtain encrypted QR code data. If the encrypted QR code data passes the integrity check, data compression techniques are used to compress the encrypted QR code data to obtain compressed encrypted QR code data. Data reassembly techniques are used to integrate the compressed encrypted QR code data with the pattern mark information to generate the final encrypted QR code image.

[0052] Exemplarily, for a two-dimensional code image containing pattern marks, image segmentation technology is used to separate the core block and the extended block.

[0053] It can be understood that image segmentation identifies pixel regions of core blocks and extended blocks based on the boundary features of pattern markers.

[0054] In one possible implementation, assuming a 512x512 pixel QR code image, the core block occupies the central 256x256 pixel area, and the extended block occupies the peripheral area. Using an edge detection algorithm, the boundary lines of the pattern markers are extracted to generate core and extended block images. This method ensures accurate block separation, facilitating subsequent encryption processing.

[0055] Specifically, the AES-256 algorithm is used to encrypt the core block image. AES-256 is a symmetric encryption algorithm that uses a 256-bit key to ensure data security.

[0056] In one embodiment, pixel data of the core block image is converted into a binary stream, which is encrypted with a preset key to generate encrypted core block data.

[0057] For example, the core block contains critical information such as user IDs. After encryption, the data length remains unchanged but the content is unreadable. This encryption method protects the security of core information and facilitates safe transmission.

[0058] First, the extended block image is also encrypted using the AES-256 algorithm.

[0059] In one possible implementation, the extended block contains auxiliary information such as a timestamp or a checksum, and the encryption process is similar to that of the core block.

[0060] It should be noted that the extended block data may be large, for example, containing 512KB of auxiliary information, which is encrypted to generate irreversible ciphertext data. This encryption method ensures the confidentiality of the extended block information during transmission.

[0061] For example, when constructing an encrypted QR code data structure, the encrypted core block data and extended block data are integrated.

[0062] It is understandable that the data structure needs to retain the pattern marking information to maintain the recognizability of the QR code.

[0063] In one embodiment, encrypted data is arranged in block order, with the core block data placed at the head, followed by the extended block data, and an additional check bit is added to verify integrity. If the integrity check passes, such as through a SHA-256 hash, it confirms that the data has not been tampered with. This structural design facilitates subsequent decoding and verification.

[0064] Specifically, data compression technology is used to process encrypted QR code data.

[0065] In one possible implementation, the LZW compression algorithm is used to compress the encrypted data to 70% of its original size, for example, from 1MB to 700KB. This compressed data retains all information, making it easier to store and transmit. This compression method optimizes data transmission efficiency.

[0066] In one embodiment, the data reassembly technology integrates the compressed encrypted two-dimensional code data with the pattern mark information.

[0067] As you can understand, the pattern mark information is embedded in the QR code image as metadata, for example, by integrating the mark information into the border area through a specific pixel encoding method. The resulting encrypted QR code image contains both the encrypted data and the visible pattern mark. This integration ensures that the QR code can still be scanned and recognized while being encrypted.

[0068] Step S104, in the emergency scenario, obtain device performance parameters, including CPU frequency and memory size. If the CPU frequency is lower than 1GHz or the memory is lower than 512MB, a dynamic priority algorithm is used to preferentially decode the core block data in the encrypted QR code data structure to obtain the core block decoded data.

[0069] Obtain device performance parameters, including processor frequency and memory capacity, query the hardware configuration through the system interface, and obtain performance parameter data. If the processor frequency is lower than the preset threshold or the memory capacity is lower than the preset threshold, the low performance state is determined through the performance judgment module, and a performance deficiency flag is obtained. Based on the performance deficiency flag, a dynamic priority algorithm is used to schedule decoding tasks, adjust the task queue priority, and obtain an optimized task sequence. Extract the data structure from the encrypted QR code, and obtain the data structure information by parsing the QR code image. For the core blocks in the data structure, a priority algorithm is used to perform decoding processing to obtain the core block decoding data. The decoding data verification module performs integrity verification on the core block decoding data to obtain verified data. Based on the verified data, the core information required for the emergency scenario is generated to obtain structured emergency data.

[0070] For example, to obtain device performance parameters, hardware information can be retrieved through the system interface. For example, in an emergency device, processor frequency and memory capacity can be queried. Assuming the processor frequency is 1.8GHz and the memory capacity is 4GB, the system interface can directly read the hardware configuration file based on the API provided by the operating system to generate performance parameter data. This approach ensures accurate data and reflects device status in real time.

[0071] In one possible implementation, the performance judgment module analyzes device status based on preset thresholds. For example, if the thresholds are 2.0GHz for processor frequency and 6GB for memory capacity, if a device parameter falls below any of these thresholds, such as 1.8GHz falling below 2.0GHz, the module identifies the device as in a low-performance state and generates a performance deficiency flag. This clear judgment logic allows for rapid identification of device bottlenecks, providing a basis for subsequent task scheduling.

[0072] Specifically, a dynamic priority algorithm is used to schedule decoding tasks. In low-performance states, the algorithm prioritizes high-priority tasks (such as core block decoding) over low-priority tasks (such as logging).

[0073] For example, the task queue includes core block decoding, extended block decoding, and logging. The algorithm has been adjusted to create an optimized sequence: core block decoding is prioritized. This scheduling optimizes resource allocation and improves the efficiency of key tasks.

[0074] It should be noted that the data structure can be extracted from the encrypted QR code through image analysis technology.

[0075] For example, a camera can scan an emergency QR code and parse it to extract the data structure of the core block and the extended block. Assuming the core block stores patient emergency information and the extended block stores auxiliary data, the parsing process ensures data integrity and provides a foundation for subsequent decoding.

[0076] In one embodiment, a hierarchical decoding strategy may be adopted for priority decoding of core blocks.

[0077] For example, if the core data block contains a patient's heart rate and blood oxygen levels, the priority algorithm decodes the heart rate data (higher priority) first, followed by the blood oxygen data. This strategy ensures that critical information is quickly available, meeting the timeliness requirements of emergency scenarios.

[0078] Prior to this, the decoded data verification module verifies the integrity of the core block decoded data through a checksum.

[0079] For example, core block data is accompanied by a CRC checksum. The verification module compares the decoded data with the checksum to see if they are consistent. If they are consistent, the module generates verified data. This verification method ensures data reliability and prevents erroneous information from affecting emergency decision-making.

[0080] For example, when generating structured emergency medical data, verified core block data can be reorganized into an emergency medical information table. Assuming the decoded core block data contains a patient's heart rate of 90 beats per minute and blood oxygen saturation of 95%, this reorganization generates a structured table with timestamps and data types. This data format facilitates quick access by emergency personnel, improving information transmission efficiency.

[0081] It's understandable that the above examples form a complete chain, from performance parameter acquisition to emergency data generation. Each link utilizes technologies like priority scheduling and data verification to ensure efficient and reliable information processing in emergency scenarios. These examples from different perspectives support each other and collectively ensure the practicality of the solution.

[0082] For example, task scheduling when performance is insufficient is combined with priority decoding to ensure that critical information is processed first; data verification is combined with structured output to improve information accuracy and readability. This progressive logic makes the solution more efficient in emergency scenarios.

[0083] Step S105: extract blood type and allergy history from the core block decoded data to generate a first aid key information set. If the first aid scenario requires medical history data, generate an extended block decoding instruction to obtain the first aid key information set and decoding instruction.

[0084] Obtain the coded data from the core block, parse it using the preset decoding algorithm, extract blood type information and allergy history, and generate an initial first aid key information set. If the first aid scenario involves medical history data, trigger the extended block parsing based on the scenario identifier to generate decoding instructions. Obtain supplementary data from the extended block, parse it using the decoding instructions, extract medical history data, and obtain an extended information set. For the initial first aid key information set and the extended information set, use the data fusion algorithm to generate a complete first aid key information set. Use the information verification mechanism to determine the accuracy of the complete first aid key information set and obtain the verified information set. Based on the verified information set, use a structured output format to generate the final data packet of the first aid key information set and decoding instructions. In the data storage module, obtain the final data packet, save it to the preset database, and obtain a persistent storage result.

[0085] Specifically, in first aid scenarios, obtaining coded data from the core block and parsing blood type information and allergy history are key links.

[0086] For example, the core block may contain biomedical information encoded in a specific format, and the preset decoding algorithm is usually based on standardized medical data encoding specifications, such as the HL7 standard.

[0087] In one embodiment, the algorithm identifies the header identifier of the core block, extracts the coding field, and decodes it to obtain the blood type as AB+ and the allergy history as penicillin allergy.

[0088] Prioritize, the decoding process will prioritize fixed-length fields to ensure rapid extraction of blood type information and generate an initial set of key first aid information, such as JSON format data containing blood type and allergy history.

[0089] Specifically, if the first aid scenario involves medical history data, the scenario identifier will trigger extended block parsing.

[0090] For example, if the identifier indicates that the patient has a history of chronic disease, the system will generate a decoding instruction pointing to a specific offset address in the extended block. The extended block may contain medical history data such as diabetes or hypertension. In one possible implementation, the decoding instruction locates the medical history field based on the block's metadata and parses it to obtain an extended information set, such as the year of diagnosis of diabetes in 2018. This hierarchical parsing method ensures that core information is processed first, while supplementing medical history data as needed. For data fusion algorithms, the initial information set and the extended information set need to be integrated into a complete emergency key information set.

[0091] It is understood that the fusion algorithm merges data based on field priority.

[0092] For example, blood type information has higher priority than medical history, and after fusion, a structured dataset containing blood type, allergy history, and medical history is generated.

[0093] It's important to note that the fusion process removes redundant fields, such as duplicate patient identifiers, to ensure data compactness. The accuracy of the complete information set is crucial for information verification. For example, the verification mechanism verifies that blood type and medical history fields are not missing or tampered with by comparing the data's checksum.

[0094] In one embodiment, if a medical history field is found to be incomplete, the system will mark it as a validation failure, triggering re-parsing. The validated information set is then output in a standard format, such as an XML-formatted emergency data package containing a complete record of blood type, allergies, and medical history. During the structured output phase, the final data package must meet the compatibility requirements of the emergency system.

[0095] For example, the data packet adopts the FHIR standard format and contains metadata such as blood type, allergy history, medical history and decoding instructions, so that emergency equipment can directly read it.

[0096] Preferably, the data packet will also be accompanied by a timestamp, such as 2025-04-24 10:00, to ensure data timeliness. In the data storage module, the final data packet is saved to a preset database, such as a local medical database based on SQLite.

[0097] For example, data packets are stored using the patient ID as the index, and the persistent result includes the ID of the record that was successfully stored. This storage method facilitates subsequent query and tracing, ensuring the reliability and accessibility of emergency information.

[0098] Step S106 , according to the first aid key information set and the decoding instruction, decode the extended block data in the encrypted two-dimensional code data structure, extract the medical history data, generate a first aid supplementary information set, and obtain a complete first aid information set.

[0099] Using a preset decoding instruction set, the data structure is retrieved from the encrypted QR code, and the extended block is parsed to obtain the original data stream. If the original data stream conforms to the preset encryption format, a decryption algorithm is used to process the data stream, extract the medical history data, and obtain the decoded medical history information. Based on the decoded medical history information, the missing fields in the first aid key information set are analyzed to generate first aid supplementary information. Using an information synthesis algorithm, the first aid supplementary information is integrated with the first aid key information set to generate a complete first aid information set. If the field integrity of the complete first aid information set does not meet the preset threshold, the relevant supplementary fields are obtained from the preset medical history database to update the complete first aid information set. A data verification algorithm is used to verify the consistency of the complete first aid information set to obtain the final first aid information output. Based on the final first aid information output, a structured first aid information record is generated and stored in the preset first aid information database.

[0100] For example, to obtain the data structure from an encrypted QR code, you need to first read the QR code with a scanning device and parse its encoding format. Typically, QR code storage data uses Base64 encoding, including an encryption identifier and data length field.

[0101] For example, the scanned data header contains version number v2.0 and the AES-256 encryption identifier, confirming that the data structure meets emergency information standards. Subsequently, the extension block is parsed to extract the original data stream. The extension block may contain fragmented information about medical history data, such as allergy history and chronic disease records. The data stream is stored as a byte stream with a length of 1024 bytes.

[0102] In one possible implementation, if the original data stream conforms to an encryption format, such as AES-256, it is decrypted using a preset key. This key, which may be stored in a secure module within the emergency system, is 256 bits long. After decryption, medical history data, such as the patient's penicillin allergy or asthma history, is extracted to generate decoded medical history information.

[0103] Prioritize the decryption process to verify data integrity and ensure no tampering.

[0104] Specifically, when analyzing missing fields in the emergency key information set, it was assumed that the current information set only included blood type A and allergy history, and was missing the chronic disease field. Comparing the medical history information revealed that asthma history was not included. Supplementary emergency information was generated, including asthma medication records, such as the use of albuterol spray.

[0105] It should be noted that missing field analysis needs to be based on the priority of the emergency scenario. For example, respiratory diseases have a higher priority in emergency treatment.

[0106] In one embodiment, the information synthesis algorithm fuses the supplementary information with the key information set. During the fusion, a field alignment method is used to ensure that the blood type, allergy history, and chronic disease fields are arranged according to a preset template.

[0107] For example, a complete set of emergency information is generated: blood type A, penicillin allergy, asthma history, and medication. After fusion, the field completeness is checked. If the surgical history is missing and the completeness is below the 90% threshold, the medical history database is queried.

[0108] For example, the database returns a patient's appendectomy record from 2023 and updates the information set. For example, when verifying consistency, the data validation algorithm checks field formats and logical relationships. The blood type field must be one of A / B / AB / O, and allergy history and medication records must be consistent. If a conflict is found between asthma medication and allergy history, the validation is marked as failed and the database must be requeried. Once the validation passes, the final emergency information output is generated.

[0109] It is understandable that the structured first aid information record uses JSON format and contains fields such as blood_type, allergy, and chronic_disease.

[0110] For example, a record might be {"blood_type":"A","allergy":"penicillin","chronic_disease":"asthma"}. This record is stored in an emergency information repository, using a distributed database to ensure high availability. During storage, a unique identifier, such as patient_001, is assigned to facilitate quick retrieval in emergency scenarios.

[0111] In step S107, based on the complete emergency information set, updated data on blood type, allergy history, and medical history are obtained from the medical institution database using the FHIR standard interface. The core block and extension block content in the initial block-based QR code data structure are replaced using an incremental update algorithm to obtain an updated block-based QR code data structure.

[0112] Updated data on blood type, allergy history, and medical history is retrieved from the medical institution database using the FHIR standard interface to determine the content and format of the updated data. A differential analysis algorithm is used to compare the updated data with the existing data in the block-based QR code data structure, identifying any differences and generating a differential data set. If the differential data set contains blood type data, the blood type data is written to the core block, generating updated core block data. If the differential data set contains allergy or medical history data, the allergy and medical history data are written to the extension block, generating updated extension block data. Based on the updated core block and extension block, the block-based QR code data structure is reassembled to generate a reassembled data structure. The reassembled data structure is converted into a QR code image using a QR code generation algorithm, generating an updated block-based QR code. The generated QR code image is verified to determine if it conforms to the preset encoding specifications, resulting in a verified QR code image.

[0113] For example, when obtaining updated data on blood type, allergy history, and medical history from a healthcare institution's database using the FHIR standard interface, the data format must comply with FHIR specifications. FHIR uses JSON or XML format and contains resources such as Patient, AllergyIntolerance, and Condition. For blood type, for example, the database returns the bloodType field of the Patient resource with a value of AB+; allergy history records a patient's allergy to penicillin using the AllergyIntolerance resource; and medical history records a diagnosis of hypertension using the Condition resource. This data is transmitted in JSON format to ensure interface compatibility and data parsability.

[0114] In one possible implementation, a differential analysis algorithm compares updated data with existing data in a block-based QR code to identify differences. Suppose the QR code's core block stores blood type A+, while the extended block stores penicillin allergy and hypertension. The algorithm compares the data and finds that the blood type has been updated to AB+, peanut allergy has been added to the allergy history, and the medical history remains unchanged. The differential data set includes both blood type AB+ and peanut allergy. The algorithm determines data freshness through field matching and timestamps, ensuring that only the latest data is processed.

[0115] Specifically, if the difference dataset contains blood type data, blood type AB+ is written to the core block. The core block uses a fixed-length field to store blood type to ensure fast reading.

[0116] For example, the blood type field occupies 8 bytes, with AB+ stored in coded form. After the update, the core block generates a new checksum to ensure data integrity. This approach improves the efficiency of reading blood type data in emergency scenarios.

[0117] Prioritize allergy and medical history data to be written to the extension block. The extension block uses a dynamic length field to accommodate varying amounts of data.

[0118] For example, peanut allergies are recorded as AllergyIntolerance resources, which include the substance code and severity, while hypertension is recorded as Condition resources, which include the diagnosis time. The updated extension block uses layered encoding to reduce storage redundancy and improve parsing efficiency.

[0119] In one embodiment, when reorganizing the block-type two-dimensional code data structure, the core blocks and the extended blocks are arranged in a preset order.

[0120] For example, the core block is placed at the head of the data structure, followed by the extension block, and a separator is added to distinguish them. After the reorganization, the data structure uses a compression algorithm to optimize storage space, making it easier to generate QR codes.

[0121] It is understandable that the QR code generation algorithm converts the restructured data structure into a QR code image. The algorithm encodes the data into a binary stream, mapping it into black and white modules.

[0122] For example, 256 bytes of data can be used to generate a 128x128 pixel QR code using the QR Code standard. After generation, the image is enhanced with an error-correcting code to enhance its damage resistance, making it suitable for rapid scanning in emergency situations. For example, when verifying a QR code image, compliance with encoding standards is checked. The verification tool scans the image to verify the module alignment and error-correcting code.

[0123] For example, images are tested according to ISO / IEC 18004 standards to ensure they remain readable even in low-light conditions or when partially damaged. Verified QR codes are stored in an emergency information database for use by first responders.

[0124] It should be noted that the above implementation ensures efficient and accurate data updates through standard interfaces and differential analysis, while block storage and QR code generation improve data readability and robustness. These designs collectively support the need for rapid access to reliable medical information in emergency scenarios.

[0125] Step S108, for updating the block-type QR code data structure, a fractal pattern algorithm is used to regenerate the boundary pattern of the core block and the extended block, and the updated core block and extended block are encrypted using the AES-256 algorithm to obtain an encrypted updated QR code image.

[0126] A fractal pattern algorithm is used to generate an initial boundary pattern. Based on the geometric features of the core and extended blocks, the fractal iteration level of the boundary pattern is determined to obtain initial boundary pattern data. Based on this initial boundary pattern data and the data structure of the block-based QR code, the storage units of the core and extended blocks are updated to determine whether the storage units meet a preset capacity threshold. If so, an updated data structure is generated. The updated core block is encrypted using the AES-256 algorithm, and the encrypted core block data is obtained, and the integrity of the encrypted data is determined. The updated extended block is also encrypted using the AES-256 algorithm, and the encrypted extended block data is obtained, and the integrity of the encrypted data is determined. Based on the encrypted core and extended block data and the encoding rules of the block-based QR code, the pixel distribution of the encrypted QR code is generated to obtain an encrypted QR code image. An edge detection algorithm is used to extract boundary pattern features from the encrypted QR code image. A determination is made as to whether the boundary pattern conforms to the generation rules of the fractal pattern algorithm. If so, the final QR code image is determined. The pixel data of the final QR code image is obtained and stored in a preset database to obtain the QR code data that can be scanned.

[0127] For example, when a fractal pattern algorithm is used to generate an initial boundary pattern, a complex boundary can be generated by iterating geometric shapes.

[0128] For example, based on the Koch snowflake curve, the initial triangle is recursively split into three segments, with the middle segment extending outward to form a new geometric feature. The core block is a fixed-size square area that stores key information such as blood type, while the extended block is a surrounding rectangular area that stores secondary information such as allergy history. The fractal iteration level can be set to three times, generating boundary pattern data with moderate complexity and easy recognition.

[0129] In one possible implementation, when updating storage units in conjunction with a block-based QR code data structure, the core block can be divided into a 64x64 pixel grid, and the extended block into a 128x128 pixel grid. When determining capacity thresholds, the core block must be able to store 8 bytes of blood type data, and the extended block must store 128 bytes of allergy history data. If the remaining capacity of the core block falls below 10%, the boundary complexity of the fractal pattern can be adjusted to free up storage space. This approach ensures efficient data storage.

[0130] Specifically, when AES-256 encrypts the core block, it converts blood type data, such as "A+," into binary and encrypts it with a 256-bit key to generate an encrypted data stream. Integrity verification verifies data consistency through a hash algorithm.

[0131] For example, the hash value of the encrypted data is compared with the hash value of the original data to ensure that it has not been tampered with. Similarly, extended block encryption is used, and allergy history data such as "penicillin allergy" is encrypted and stored to ensure privacy and security.

[0132] Prioritize generating the encrypted QR code pixel distribution by mapping the encrypted core block data to a central, high-density pixel area, and the extended block data to a peripheral, low-density area. The encoding rules can adopt the QR code standard, with a 30% error tolerance to ensure scanning stability. After the pixel distribution is generated, the QR code image is clear and readable.

[0133] In one embodiment, when the edge detection algorithm extracts boundary pattern features, the Canny algorithm may be used to identify high-contrast edges of the fractal boundary.

[0134] For example, the Koch curve boundary is checked to see if it is continuous and conforms to the three-iteration feature to ensure that the pattern conforms to the generation rules. If the test passes, the final QR code image is reliable.

[0135] For example, when storing the final QR code image in a database, the pixel data can be compressed into PNG format and stored in a MySQL database. Each QR code is assigned a unique ID, such as "QR20250424001," to facilitate retrieval and scanning verification. This approach improves data management efficiency.

[0136] As you can see, the above method enhances the aesthetics and uniqueness of QR codes through fractal patterns, ensures data security through encryption, ensures pattern accuracy through edge detection, and facilitates management through database storage. These technologies collectively enhance the reliability and practicality of QR codes in medical information scenarios.

[0137] Step S109, extracting encrypted data from the encrypted updated QR code image, decoding the core block using a dynamic priority algorithm based on device performance parameters, including CPU frequency and memory size, in the first aid scenario, generating a first aid key information set, and obtaining a final first aid information set.

[0138] Obtain encrypted data from the QR code image, use image processing technology to segment the QR code area, and obtain a first data set. If the first data set contains an encrypted identifier, process the first data set through a decryption algorithm to generate a second data set. According to the device performance parameters, obtain the CPU frequency and memory size, use a dynamic priority algorithm to allocate computing resources, and obtain a resource allocation plan. Through the resource allocation plan, execute the decoding algorithm for the core blocks in the second data set to generate a key information set. If the integrity of the key information set is higher than the preset threshold, integrate the key information set to generate an emergency information set. According to the requirements of the emergency scenario, extract the final information from the emergency information set to obtain a final information set. Send the final information set to the emergency equipment through the emergency information transmission protocol to complete the information transfer.

[0139] Exemplarily, the process of obtaining encrypted data from a QR code image can be implemented based on image processing technology.

[0140] Specifically, grayscale conversion and threshold segmentation techniques are used to convert the QR code image into a binary image so as to clearly distinguish the QR code area from the background area.

[0141] For example, in an emergency situation, first responders use a handheld device to scan a QR code wristband worn by a patient. After the device's camera captures the image, it converts the image pixel values ​​into grayscale values ​​ranging from 0 to 255 through grayscale processing. Then, an adaptive threshold algorithm is used to segment the QR code region, generating the first data set. This method effectively copes with lighting changes or image noise, ensuring accurate extraction of the QR code region.

[0142] In a possible implementation, if the first data set includes an encryption identifier, it needs to be processed using a decryption algorithm.

[0143] First, assume that the encryption flag indicates that the data is encrypted using AES-256 and the decryption process requires the use of a pre-shared key.

[0144] For example, the key pre-stored in the emergency device is consistent with the encryption key when the QR code is generated. The device processes the first data set through the AES-256 decryption algorithm to generate the second data set.

[0145] It should be noted that the integrity of the encrypted identifier must be verified before decryption to prevent data tampering. This approach ensures the security of sensitive patient information.

[0146] Specifically, when allocating computing resources based on device performance parameters, optimization can be achieved through a dynamic priority algorithm.

[0147] For example, if the emergency equipment detects that the CPU frequency is 2.4GHz and the memory size is 4GB, the dynamic priority algorithm will allocate resources based on the urgency of the task.

[0148] In one embodiment, the decoding core block has a higher priority than other background tasks, and the algorithm may allocate 70% of CPU resources and 50% of memory to decoding. This allocation scheme can quickly respond to the real-time needs of emergency scenarios.

[0149] For example, when executing a decoding algorithm on the core block in the second data set, standard QR code decoding rules, such as the error correction mechanism of QR codes, can be used. Assuming the core block contains the patient's blood type and allergy history, the decoding algorithm generates a key information set by reading the QR code's positioning pattern and data modules.

[0150] In one possible implementation, if the error correction level is H, even if the QR code is partially damaged, 30% of the data can still be recovered. This method ensures the reliability of the information.

[0151] It is understandable that when integrating the key information set to generate the emergency information set, the integrity needs to be verified.

[0152] For example, the device uses a checksum algorithm to confirm that the integrity of a critical information set is above a preset threshold of 90%, and then integrates information such as blood type and allergy history into an emergency information set. This method ensures the accuracy of the information.

[0153] In one embodiment, when extracting final information from the emergency information set, key fields may be preferentially extracted according to the requirements of the emergency scenario.

[0154] For example, at the emergency scene, the device automatically extracts information about the patient's blood type A and penicillin allergy, generating a final set of information. This targeted extraction improves emergency response efficiency.

[0155] Preferably, the Bluetooth low energy protocol may be used when sending the final message set via the emergency message transmission protocol.

[0156] For example, emergency equipment transmits the final information set to the emergency vehicle system via Bluetooth, with a transmission rate of 1Mbps and a latency of less than 100ms. This method ensures that information is quickly and stably delivered to the emergency equipment, providing reliable support for subsequent medical decision-making.

[0157] The above embodiment is only one of the preferred implementation methods of the present invention and should not be used to limit the scope of protection of the present invention. Any changes or modifications that have no substantive meaning made to the main design concept and spirit of the present invention, as long as the technical problems they solve are still consistent with the present invention, should be included in the scope of protection of the present invention.

Claims

1. A method for managing neonatal emergency information, characterized in that: The method comprises: The FHIR standard interface is used to obtain newborn emergency data from the medical institution database, including blood type, allergy history, and medical history. A block-based QR code is generated. The core block stores blood type and allergy history, and the extended block stores medical history, resulting in the initial block-based QR code data structure. For the initial block-type QR code data structure, a fractal pattern algorithm is used to generate the boundary pattern of the core block and the extended block, mark the block boundary, and obtain a QR code image with pattern markings; For QR code images with pattern marks, the AES-256 algorithm is used to encrypt the core block and extended block respectively, generating encrypted core block data and extended block data, and obtaining the encrypted QR code data structure. In emergency scenarios, device performance parameters, including CPU frequency and memory size, are obtained. If the CPU frequency is lower than 1GHz or the memory is lower than 512MB, a dynamic priority algorithm is used to prioritize decoding the core block data in the encrypted QR code data structure to obtain the core block decoded data. Extract blood type and allergy history from the core block decoded data to generate a first aid key information set. If medical history data is required for the first aid scenario, generate an extended block decoding instruction to obtain the first aid key information set and decoding instructions. According to the first aid key information set and the decoding instruction, the extended block data in the encrypted QR code data structure is decoded, the medical history data is extracted, and the first aid supplementary information set is generated to obtain a complete first aid information set; Based on the complete emergency information set, updated data on blood type, allergy history, and medical history are obtained from the medical institution database using the FHIR standard interface. The core block and extension block content in the initial block-based QR code data structure are replaced using an incremental update algorithm to obtain an updated block-based QR code data structure. To update the block-based QR code data structure, a fractal pattern algorithm is used to regenerate the boundary pattern of the core block and the extended block. The updated core block and extended block are encrypted using the AES-256 algorithm to obtain an encrypted updated QR code image. The encrypted data is extracted from the encrypted updated QR code image. In the emergency scenario, a dynamic priority algorithm is used to decode the core block based on the device performance parameters, including CPU frequency and memory size, to generate the emergency key information set and obtain the final emergency information set.

2. The method according to claim 1, characterized in that The method uses the FHIR standard interface to obtain newborn emergency data from the medical institution database, including blood type, allergy history, and medical history, and generates a block-type QR code. The core block stores blood type and allergy history, and the extended block stores medical history. The initial block-type QR code data structure includes: Obtain neonatal emergency data from the medical institution database through the FHIR standard interface to obtain a structured emergency data set; Parsing the blood type information, allergy history records, and medical history data in the structured first aid dataset using JSON format to obtain a classified data set; By using a block-type QR code generation algorithm, the core block data and the extended block data in the classified data set are mapped to the core block and the extended block of the QR code respectively to obtain an initial block-type QR code data structure; An error correction coding algorithm is used to add redundant check information to the initial block-type two-dimensional code data structure to obtain a final block-type two-dimensional code data structure.

3. The method according to claim 1, characterized in that The method of generating a boundary pattern between a core block and an extended block using a fractal pattern algorithm for the initial block-type two-dimensional code data structure, marking the block boundary, and obtaining a two-dimensional code image with pattern markings includes: Generate boundary patterns between the core block and the extended block using a fractal pattern algorithm, obtain boundary coordinates of the initial block-type QR code data, and obtain boundary pattern data; Using the boundary pattern data and the block demarcation rules, the boundary line between the core block and the extended block is determined, and the coordinates of the boundary line are obtained; If the boundary line coordinates match the QR code data structure, the fractal pattern is embedded into the boundary line through pattern marking processing to obtain the boundary data of the embedded pattern; Generate a two-dimensional code image with pattern marks according to the boundary data of the embedded pattern, and obtain a two-dimensional code image matrix; If the boundary pattern of the QR code image matrix is ​​clear, the pattern contrast is adjusted using an image optimization algorithm to obtain an optimized QR code image; Use the optimized QR code image to verify the integrity of the pattern mark of the core block and the extended block, and determine the QR code image that has passed the verification; The final two-dimensional code image data with pattern marks is generated by verifying the two-dimensional code image, and an outputtable two-dimensional code image is obtained.

4. The method according to claim 1, wherein The AES-256 algorithm is used to encrypt the core block and the extended block of the QR code image with the pattern mark, generate encrypted core block data and extended block data, and obtain an encrypted QR code data structure, including: Obtain a QR code image containing a pattern mark, separate the core block and the extended block using image segmentation technology, and obtain a core block image and an extended block image; For the core block image, the AES-256 algorithm is used for encryption to generate encrypted core block data; For the extended block image, the AES-256 algorithm is used for encryption to generate encrypted extended block data; Construct an encrypted QR code data structure based on the encrypted core block data and the extended block data to obtain the encrypted QR code data; If the integrity check of the encrypted QR code data passes, the encrypted QR code data is compressed using data compression technology to obtain compressed encrypted QR code data; Through data reconstruction technology, the compressed encrypted QR code data is integrated with the pattern mark information to generate the final encrypted QR code image.

5. The method according to claim 1, characterized in that In the first aid scenario, device performance parameters, including CPU frequency and memory size, are obtained. If the CPU frequency is lower than 1 GHz or the memory is lower than 512 MB, a dynamic priority algorithm is used to preferentially decode the core block data in the encrypted QR code data structure to obtain the core block decoded data, including: Obtain device performance parameters, including processor frequency and memory capacity, by querying hardware configuration through the system interface to obtain performance parameter data; If the processor frequency is lower than a preset threshold or the memory capacity is lower than a preset threshold, a low performance state is determined by the performance judgment module and a performance deficiency flag is obtained; According to the performance deficiency indicator, a dynamic priority algorithm is used to schedule decoding tasks, adjust the task queue priority, and obtain an optimized task sequence; Extract the data structure from the encrypted QR code and obtain the data structure information by parsing the QR code image; For the core blocks in the data structure, a priority algorithm is used to perform decoding processing to obtain the core block decoding data; Through the decoding data verification module, the integrity of the core block decoded data is checked to obtain the verified data; Based on the verified data, the core information required for the first aid scenario is generated to obtain structured first aid data.

6. The method according to claim 1, characterized in that The blood type and allergy history are extracted from the core block decoded data to generate a first aid key information set. If the first aid scenario requires medical history data, an extended block decoding instruction is generated to obtain the first aid key information set and decoding instructions, including: Obtain coded data from the core block, parse it using a preset decoding algorithm, extract blood type information and allergy history, and generate an initial set of key first aid information; If the emergency scenario involves medical history data, the extended block parsing is triggered according to the scenario identifier to generate decoding instructions; Obtain supplementary data from the extension block, parse using decoding instructions, extract medical history data, and obtain an extended information set; For the initial first aid key information set and the extended information set, a data fusion algorithm is used to generate a complete first aid key information set; Through the information verification mechanism, the accuracy of the complete emergency key information set is judged to obtain the verified information set; Based on the verified information set, a structured output format is used to generate the final data packet of the emergency key information set and decoding instructions; In the data storage module, the final data packet is obtained and saved to the preset database to obtain a persistent storage result.

7. The method according to claim 1, characterized in that The method comprises decoding the extension block data in the encrypted QR code data structure according to the first aid key information set and the decoding instruction, extracting the medical history data, generating the first aid supplementary information set, and obtaining a complete first aid information set, including: Through the preset decoding instruction set, the data structure is obtained from the encrypted QR code, the extension block is parsed, and the original data stream is obtained; If the original data stream conforms to the preset encryption format, the decryption algorithm is used to process the data stream, extract the medical history data, and obtain the decoded medical history information; Based on the decoded medical history information, the missing fields in the emergency key information set are analyzed to generate emergency supplementary information; Through the information synthesis algorithm, the first aid supplementary information is integrated with the first aid key information set to generate a complete first aid information set; If the completeness of the fields in the complete emergency information set does not reach the preset threshold, relevant supplementary fields are obtained from the preset medical history database to update the complete emergency information set; Use data verification algorithm to verify the consistency of the complete emergency information set and obtain the final emergency information output; Based on the final first aid information output, a structured first aid information record is generated and stored in the preset first aid information database.

8. The method according to claim 1, characterized in that According to the complete emergency information set, updated data on blood type, allergy history, and medical history are obtained from the medical institution database using the FHIR standard interface, and the core block and extension block content in the initial block-type QR code data structure are replaced using an incremental update algorithm to obtain an updated block-type QR code data structure, including: Obtain updated data on blood type, allergy history, and medical history from the medical institution database through the FHIR standard interface, and determine the content and format of the updated data; A differential analysis algorithm is used to compare the updated data with the existing data in the block-type QR code data structure, determine the data differences, and obtain a differential data set; If the difference data set contains blood type data, the blood type data is written into the core block to generate updated core block data; If the difference data set contains allergy history or medical history data, the allergy history and medical history data are written into the extension block to generate updated extension block data; Reorganize the block-type QR code data structure according to the updated core block and extension block to obtain a reorganized data structure; The reorganized data structure is converted into a QR code image through a QR code generation algorithm to generate an updated block-type QR code; The generated two-dimensional code image is verified to determine whether the image conforms to the preset encoding specification, and a two-dimensional code image that passes the verification is obtained.

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