Mobile tent hospital medical information management system and method based on wechat applet

Abstract

This invention relates to the field of medical information management technology, specifically to a mobile tent hospital medical information management system and method based on WeChat mini-programs. The system includes the following steps: a mini-program login verification module, a treatment process recording module, a patient classification and bed allocation module, a network topology management module, and a rapid medical record export module. In this invention, abnormal behavior is identified by analyzing user login behavior data, and the identity verification process is adjusted to raise the security threshold for data access and strengthen the defense against data leakage incidents. RFID tags are used to achieve automatic identification of patient information and real-time updates of electronic medical records, improving the response efficiency of temporary medical facilities and ensuring the continuity and efficiency of medical services in disaster response scenarios. Real-time monitoring and optimization of the network topology ensures smooth data transmission within the medical network. Combined with the analysis and formatting of electronic medical record text content, rapid export of electronic medical records is achieved.

Description

Technical Field

[0001] This invention relates to the field of medical information management technology, and in particular to a mobile tent hospital medical information management system and method based on WeChat mini-programs. Background Technology

[0002] The field of medical information management technology involves multiple aspects of information collection, processing, storage, and exchange in medical services. It aims to improve the organization and accessibility of medical data through various technical means, thereby enhancing the quality and efficiency of medical services. This includes data security, privacy protection, data integration, and interoperability. It covers the electronic management of patient data, real-time monitoring of health information, and optimized allocation of medical resources. It also integrates and applies key technologies such as electronic medical record systems, health information exchange, clinical decision support systems, and telemedicine services to improve the quality of medical services and patient safety.

[0003] Among them, the mobile tent hospital medical information system is an information management system designed for temporary medical facilities. It is used to optimize data flow and resource management in disaster response and temporary medical services, improve emergency response efficiency, and cover real-time collection and updating of medical information, dynamic monitoring of patient status, and allocation of medical resources. By building mobile and web platforms, it enables instant input and access to information. Combined with location recognition and data synchronization technologies, it ensures the consistency and timeliness of information across all terminals, and achieves efficient processing of medical data and rapid transmission of information.

[0004] Traditional hospital medical information systems cannot effectively handle large-scale data streams and real-time information updates in disaster response and temporary medical service scenarios, resulting in slow data processing speeds, poor information timeliness, reliance on traditional static authentication methods for user authentication and data security, making them vulnerable to security threats, inefficient bed management and resource allocation, lack of effective real-time optimization mechanisms, inability to quickly adapt to rapidly changing medical needs, and neglect of the special characteristics of data transmission in the medical environment in network management, affecting the efficiency of medical information transmission and processing. Summary of the Invention

[0005] To address the technical problems existing in the prior art, embodiments of the present invention provide a mobile tent hospital medical information management system and method based on WeChat mini-programs. The technical solution is as follows:

[0006] On the one hand, a mobile tent hospital medical information management system based on WeChat mini-programs is provided, which includes:

[0007] The mini-program login verification module assesses the level of abnormal behavior and adjusts the verification process based on the user's login request information and login behavior, and generates a login identity verification status.

[0008] Based on the login authentication status, the treatment process recording module identifies the target patient's identity information by scanning the RFID tag of the target patient. Combined with the information input by nurses and doctors, it uses machine vision to automatically monitor the distribution and use of medicines, including the type, quantity, and usage of medicines, and updates the patient's electronic medical record to generate an electronic medical record update record.

[0009] The patient classification and bed allocation module, based on the updated electronic medical records, analyzes the diagnostic information input by physicians, assesses the bed needs of target patients, allocates beds to target patients, and generates a bed allocation list.

[0010] The network topology management module analyzes the importance of various data in real time based on the bed allocation list, including physiological monitoring data, diagnostic information, medication use information, and patient medical record information. It adjusts the data transmission queue, analyzes data transmission latency and network bottleneck nodes by monitoring the data traffic and node status of the data transmission network in real time, and adjusts the data transmission path to generate data transmission path adjustment results.

[0011] The medical record quick export module adjusts the results according to the data transmission path, extracts and analyzes the electronic medical records of multiple patients, combines preset templates to format the medical records and verify data integrity, and uploads them to the WeChat mini program to generate medical record document processing records.

[0012] As a further aspect of the present invention, the login authentication status specifically includes an abnormal behavior score, an abnormality level, and a verification process adjustment record; the electronic medical record update record includes patient identification information, medical event information, and medical record update data; the bed allocation list includes bed demand assessment information, real-time bed occupancy status, and patient bed matching results; the data transmission path adjustment result specifically refers to delay analysis results, network bottleneck node identification information, and data transmission path adjustment records; and the medical record document processing record includes medical record information extraction records, text information formatting records, and document data integrity verification results.

[0013] As a further aspect of the present invention, the mini-program login verification module includes:

[0014] The behavior data collection submodule collects user device type, login address, access identity and touch operation mode information based on user login request information, and generates login behavior dataset;

[0015] The anomaly analysis submodule, based on the login behavior dataset, extracts the behavioral pattern information of the user's access identity according to the user's access identity, evaluates the consistency of the user's login device, login address, and touch operation mode, calculates the user's behavioral anomaly score, and generates behavioral anomaly evaluation results.

[0016] The verification process adjustment submodule assesses the user's abnormal behavior level and adjusts the verification process based on the abnormal behavior assessment results, including triggering two-factor authentication and sending biometric authentication requests, and generating login authentication status.

[0017] As a further aspect of the present invention, the specific formula for calculating the user's abnormal behavior score is as follows:

[0018]

[0019] Where S represents the behavior anomaly score, p1 represents the consistency score between device type and historical records, p2 represents the consistency score between login address and historical location, p3 represents the consistency score between touch operation mode, w1 is the weighting coefficient of device type, w2 is the weighting coefficient of login address, and w3 is the weighting coefficient of touch operation mode.

[0020] As a further aspect of the present invention, the treatment process recording module includes:

[0021] Based on the login authentication status, the patient identification submodule identifies the target patient's identity information by scanning the RFID tag of the target patient and generates an identity confirmation record;

[0022] Based on the identity verification record, the input information analysis submodule extracts and analyzes the input information of nurses and doctors, identifies the type of medical event, patient condition information, and treatment records, and combines patient medication needs information. Using machine vision, it monitors the drug distribution process in real time, analyzes drug usage, including drug type, quantity, and usage time, and generates a summary record of treatment information.

[0023] The electronic medical record update submodule updates the patient's electronic medical record based on the treatment information summary record, including automatically updating the patient's medication usage record and generating an electronic medical record update record.

[0024] As a further aspect of the present invention, the patient classification and bed allocation module includes:

[0025] The injury and illness condition analysis submodule, based on the updated electronic medical record, analyzes the diagnostic information input by the physician to identify the patient's condition type, severity, and expected recovery time, and generates diagnostic information analysis results.

[0026] Based on the diagnostic information analysis results, the demand analysis submodule analyzes the patient's treatment needs, assesses the bed demand of the target patient, and generates bed demand assessment information.

[0027] The patient bed matching submodule matches beds for target patients based on the bed demand assessment information and in combination with real-time bed occupancy information, and generates a bed allocation list.

[0028] As a further aspect of the present invention, the specific formula for assessing the bed demand of the target patient is as follows:

[0029]

[0030] Where R represents the bed demand index, d1 represents the index of patient condition severity, d2 represents the index of treatment duration, d3 represents the index of demand for special medical facilities, v1 is the weighting coefficient of condition severity, v2 is the weighting coefficient of treatment duration, and v3 is the weighting coefficient of demand for special medical facilities.

[0031] As a further aspect of the present invention, the network topology management module includes:

[0032] The data network monitoring submodule analyzes the importance of various data in real time based on the bed allocation list, including physiological monitoring data, diagnostic information, medication use information, and patient medical record information, adjusts the data transmission queue, and generates a transmission queue adjustment record.

[0033] The network latency analysis submodule monitors the data traffic and node status of the data transmission network in real time based on the transmission queue adjustment record, including the data transmission volume and load status of multiple nodes over multiple time periods, analyzes the latency in data transmission, detects bottleneck nodes in the network, and generates bottleneck node identification results.

[0034] Based on the bottleneck node identification results, the topology path optimization submodule adjusts the data transmission path according to real-time data transmission requirements and network node status, and generates data transmission path adjustment results.

[0035] As a further aspect of the present invention, the rapid medical record export module includes:

[0036] Based on the data transmission path adjustment results, the medical record information extraction submodule extracts and analyzes the electronic medical records of multiple patients, identifies the patients' personal information, diagnostic information, treatment records and condition status, and generates medical record data analysis results.

[0037] Based on the analysis results of the medical record data, the patient medical record processing submodule formats the medical records of multiple patients according to a preset template to generate formatted medical record documents.

[0038] The integrity verification submodule verifies the data integrity of the processed document based on the formatted medical record document, and uploads it to the WeChat mini program to generate a medical record document processing record.

[0039] On the other hand, a method for managing medical information in a mobile tent hospital based on WeChat Mini Programs is provided. This method is applied to a mobile tent hospital medical information management system based on WeChat Mini Programs, and includes:

[0040] S1: Based on user login request information, collect user behavior data, assess the abnormality level of login behavior, adjust the verification process, and generate login authentication status.

[0041] S2: Using the login authentication status, scan the patient's RFID tag to identify the target patient's identity information. Combined with the information input by the nurse and doctor, use machine vision to automatically monitor the drug distribution and usage process, including drug type, quantity, and usage. Update the patient's electronic medical record and generate an electronic medical record update record.

[0042] S3: By updating the electronic medical record, analyzing the diagnostic information entered by the physician, including the type of illness, the severity of the illness, and the expected recovery time, assessing the patient's need for a bed, matching the patient with a bed, and generating a bed allocation list;

[0043] S4: Based on the bed allocation list, analyze the importance of various data in real time, including physiological monitoring data, diagnostic information, medication use information, and patient medical record information; adjust the data transmission queue; monitor the data flow and node status in the data transmission network in real time; analyze the data transmission delay and identify bottleneck nodes; adjust the data transmission path; and generate data transmission path adjustment results.

[0044] S5: Based on the data transmission path adjustment results, extract and analyze the electronic medical record data of multiple patients, combine with the preset template, perform formatting processing and data integrity verification on the medical records, and upload them to the WeChat mini program to generate medical record document processing records.

[0045] The beneficial effects of the technical solutions provided by the embodiments of the present invention include at least the following:

[0046] By analyzing user login behavior data to identify abnormal behavior, adjusting the authentication process, raising the security threshold for data access, and strengthening the defense against data breaches, RFID tags are used to achieve automatic identification of patient information and real-time updates of electronic medical records, improving the response efficiency of temporary medical facilities and ensuring the continuity and efficiency of medical services in disaster response scenarios. Real-time monitoring and optimization of network topology ensures smooth data transmission in the medical network. Combined with the analysis and formatting of electronic medical record text content, rapid export of electronic medical records is achieved. Attached Figure Description

[0047] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0048] Figure 1 This is a system flowchart of the present invention;

[0049] Figure 2 This is a schematic diagram of the system framework of the present invention;

[0050] Figure 3 This is a flowchart of the WeChat Mini Program login verification module of the present invention;

[0051] Figure 4 This is a flowchart of the treatment process recording module of the present invention;

[0052] Figure 5 This is a flowchart of the patient classification and bed allocation module of the present invention;

[0053] Figure 6 This is a flowchart of the network topology management module of the present invention;

[0054] Figure 7 This is a flowchart of the medical record rapid export module of the present invention;

[0055] Figure 8 This is a schematic diagram of the method steps of the present invention. Detailed Implementation

[0056] The technical solution of the present invention will now be described with reference to the accompanying drawings.

[0057] In embodiments of the present invention, words such as "exemplarily," "for example," etc., are used to indicate that something is an example, illustration, or description. Any embodiment or design described as "exemplary" in the present invention should not be construed as being more preferred or advantageous than other embodiments or designs. Specifically, the use of the word "exemplary" is intended to present the concept in a concrete manner. Furthermore, in embodiments of the present invention, the meaning expressed by "and / or" can be both, or either one.

[0058] In the embodiments of this invention, the terms "image" and "picture" may sometimes be used interchangeably. It should be noted that, without emphasizing the distinction between them, their intended meanings are consistent. Similarly, the terms "of," "corresponding (relevant)," and "corresponding" may sometimes be used interchangeably. It should be noted that, without emphasizing the distinction between them, their intended meanings are consistent.

[0059] In this embodiment of the invention, sometimes a subscript such as W1 may be written in a non-subscript form such as W1. When the difference is not emphasized, the meaning they express is the same.

[0060] To make the technical problems, technical solutions and advantages of the present invention clearer, a detailed description will be given below in conjunction with the accompanying drawings and specific embodiments.

[0061] This invention provides a mobile tent hospital medical information management system based on WeChat Mini Programs. Please refer to [link / reference]. Figures 1 to 2 This invention provides a technical solution: a mobile tent hospital medical information management system based on WeChat mini-programs, comprising:

[0062] The mini-program login verification module assesses the level of abnormal behavior and adjusts the verification process based on the user's login request information and login behavior, and generates a login identity verification status.

[0063] The treatment process recording module is based on login authentication status. By scanning the RFID tag of the target patient, it identifies the patient's identity information. Combined with the information entered by nurses and doctors, it uses machine vision to automatically monitor the distribution and use of medicines, including the type, quantity, and usage of medicines. It also updates the patient's electronic medical record and generates an electronic medical record update record.

[0064] The patient classification and bed allocation module, based on the updated electronic medical records, analyzes the diagnostic information entered by physicians, assesses the bed needs of target patients, allocates beds to target patients, and generates a bed allocation list.

[0065] The network topology management module analyzes the importance of various data in real time based on the bed allocation list, including physiological monitoring data, diagnostic information, medication use information, and patient medical record information. It adjusts the data transmission queue, analyzes data transmission latency and network bottleneck nodes by monitoring the data traffic and node status of the data transmission network in real time, and adjusts the data transmission path to generate data transmission path adjustment results.

[0066] The medical record quick export module adjusts the results according to the data transmission path, extracts and analyzes the electronic medical records of multiple patients, combines preset templates to format the medical records and verify data integrity, and uploads them to the WeChat mini program to generate medical record document processing records.

[0067] The login authentication status specifically includes behavior anomaly score, anomaly level, and verification process adjustment records. Electronic medical record update records include patient identification information, medical event information, and medical record update data. The bed allocation list includes bed demand assessment information, real-time bed occupancy status, and patient bed matching results. Data transmission path adjustment results specifically refer to latency analysis results, network bottleneck node identification information, and data transmission path adjustment records. Medical record document processing records include medical record information extraction records, text information formatting records, and document data integrity verification results.

[0068] Please see Figure 2 and Figure 3 The mini-program login verification module includes:

[0069] The behavior data collection submodule collects user device type, login address, access identity and touch operation mode information based on user login request information, and generates login behavior dataset;

[0070] Login request information is automatically collected through the WeChat Mini Program interface on the user's end, including device type, which is determined by device management software, login address is determined by geolocation service or IP tracking, access identity is identified by user account and password authentication, touch operation mode is analyzed to analyze user interaction actions, and click speed and swipe trajectory are analyzed by behavior tracking technology. The target data is securely transmitted to the server via HTTPS protocol. The server uses a JSON parser to process the target data and store it in an SQL database to form a structured login behavior dataset, providing raw data input for subsequent abnormal behavior analysis.

[0071] The anomaly analysis submodule is based on the login behavior dataset. It extracts the behavioral pattern information of the user's access identity according to the user's access identity, evaluates the consistency of the user's login device, login address, and touch operation mode, calculates the user's behavioral anomaly score, and generates behavioral anomaly assessment results.

[0072] The specific formula for calculating a user's abnormal behavior score is as follows:

[0073]

[0074] Where S represents the behavior anomaly score, p1 represents the consistency score between device type and historical records, p2 represents the consistency score between login address and historical location, p3 represents the consistency score between touch operation mode, w1 is the weighting coefficient of device type, w2 is the weighting coefficient of login address, and w3 is the weighting coefficient of touch operation mode.

[0075] formula:

[0076]

[0077] Detailed explanation of the formula and its calculation derivation:

[0078] The formula is used to calculate a user's abnormal behavior score, and the result is used to determine whether further authentication measures are needed.

[0079] Parameter meanings and settings:

[0080] p1 is the consistency score between device type and history, assumed to be 1, reflecting that the device used by the user completely matches the history.

[0081] p2 is the consistency score between the login address and the historical location, assumed to be 0.3, reflecting a significant difference between the user's login location and historical location;

[0082] p3 is the consistency score of touch operation mode, assumed to be 0.6, which reflects that the user's operation mode is similar to the historical mode but there are differences;

[0083] w1 is the weighting coefficient for the device type, assumed to be 0.4;

[0084] w2 is the weighting coefficient of the login address, assumed to be 0.3;

[0085] w3 is the weighting coefficient for the touch operation mode, assumed to be 0.3;

[0086] Substitute the parameters into the formula to calculate:

[0087]

[0088] A result of 0.33 indicates that the user behavior is moderately abnormal. This result can be used to adjust the verification process during login, including triggering secondary verification or sending biometric verification requests, to improve the ability to respond to abnormal behavior and ensure the security of user and system data.

[0089] The verification process adjustment submodule assesses the user's abnormal behavior level and adjusts the verification process based on the abnormal behavior assessment results, including triggering two-factor authentication and sending biometric authentication requests, and generating login authentication status.

[0090] The system assesses the severity of user behavior anomalies. If the anomaly score exceeds a set threshold, it invokes a security protocol library to initiate a two-factor authentication request, including sending a one-time verification code via SMS or email. For login attempts on new devices or locations, it utilizes deep learning algorithms supported by the TensorFlow framework to further analyze behavioral patterns and determine whether biometric authentication measures, such as fingerprint or facial recognition, are required. Through a series of automated processes, the system quickly responds to user behavior anomalies, updates login authentication status in a timely manner, and enhances account security.

[0091] Please see Figure 2 and Figure 4 The treatment process recording module includes:

[0092] The patient identification submodule identifies the target patient's identity information and generates an identity confirmation record based on the login authentication status by scanning the RFID tag of the target patient.

[0093] The patient identification submodule accurately identifies the target patient's identity information by scanning the RFID tag and based on the login authentication status, generating a detailed identity verification record. The RFID scanning device automatically detects the RFID tag carried by the target patient, reading the stored patient identity data, including the patient's name, medical record number, date of birth, contact information, and other key information. This data is transmitted in real-time to the hospital's central database via an encrypted network connection, generating a patient identity verification record in the database. This process ensures that the identity verification process for each patient during medical treatment is both rapid and accurate, effectively avoiding medical accidents or data corruption caused by incorrect identification. Furthermore, thanks to the real-time reading and transmission technology of RFID tags, the identity verification process can be completed within seconds, thereby improving diagnostic and treatment efficiency. It reduces the workload of manually verifying patient information, ensuring the security, accuracy, and uniqueness of patient identities. Through this efficient automated identity identification system, medical staff can quickly and accurately identify patients in busy medical environments, ensuring the smooth progress of patient medical procedures and providing reliable identity evidence for subsequent medical services.

[0094] The input information analysis submodule extracts and analyzes nurse and physician input information based on identity verification records, identifies medical event types, patient condition information, and treatment records, combines patient medication needs information, uses machine vision to monitor the drug distribution process in real time, analyzes drug usage, including drug type, quantity, and usage time, and generates a summary record of treatment information.

[0095] This submodule extracts and analyzes treatment information input by nurses and physicians, identifying medical event types, patient condition information, and treatment records. Combined with patient medication needs, it further refines treatment plans. This submodule performs deep analysis of medical information, using natural language processing technology to automatically classify and organize the treatment data input by nurses and physicians. For example, for textual information such as condition descriptions, diagnoses, and treatment measures, the system can automatically extract key information and categorize and store it. Furthermore, the input information analysis submodule incorporates machine vision technology to monitor the medication distribution process in real time, analyzing information such as medication type, quantity, and usage time. Through cameras, it automatically identifies the actual use of medications and updates the system in real time, ensuring the accuracy and completeness of medication usage records. In this process, all medication usage data is closely integrated with patient treatment records, forming a complete summary record of treatment information. The application of this submodule not only improves the accuracy of treatment information but also provides real-time decision support for medical staff, helping to ensure the continuity and consistency of the treatment process and further optimizing the quality of patient treatment.

[0096] The electronic medical record update submodule updates patients' electronic medical records based on the summary records of treatment information, including automatically updating patients' medication usage records and generating electronic medical record update records;

[0097] Based on the summary records of treatment information, the system promptly updates patients' electronic medical records, including automatically updating medication usage records and generating new electronic medical record update records. This submodule automatically triggers the electronic medical record update process, mapping all data in the summary records of treatment information to the corresponding fields in the electronic medical record system, including the patient's condition progress, treatment measures, medical order execution status, and medication usage records. By employing electronic medical record management software, the system accurately processes data from the input information analysis submodule, ensuring that each patient's electronic medical record information is always up-to-date. After each update, the system generates a new record version in the database and retains historical data for subsequent queries and backtracking. This not only effectively reduces errors caused by manual operations but also improves the transparency and accuracy of medical record records. Furthermore, the automated update process ensures the integrity and consistency of medical records, enabling medical staff to quickly access the latest patient information at any time, avoiding treatment delays caused by information lag. Through this automated system, medical institutions not only improve work efficiency but also optimize the accuracy and timeliness of data during patient treatment, enhancing the overall quality and safety of medical services.

[0098] Please see Figure 2 and Figure 5 The patient classification and bed allocation module includes:

[0099] The injury and illness condition analysis submodule is based on the updated electronic medical records. By analyzing the diagnostic information input by the physician, it identifies the patient's condition type, severity, and expected recovery time, and generates diagnostic information analysis results.

[0100] Using disease analysis algorithms, including condition judgment and data classification processing, the algorithm analyzes the patient's disease type, such as infection, chronic disease, or acute injury, and assesses the severity of the disease and the expected recovery time. During the process, the algorithm considers the patient's historical disease records and current treatment response to automatically determine the severity of the disease. The expected recovery time is calculated by comparing the recovery process of similar cases. All analysis results are summarized to generate diagnostic information analysis results, which have direct guiding significance for subsequent treatment plans and resource allocation.

[0101] The demand analysis submodule, based on the diagnostic information analysis results, analyzes the patient's treatment needs, assesses the bed demand of the target patients, and generates bed demand assessment information.

[0102] The specific formula for assessing the bed demand of target patients is as follows:

[0103]

[0104] Where R represents the bed demand index, d1 represents the index of the severity of the patient's condition, d2 represents the index of the duration of treatment, d3 represents the index of the demand for special medical facilities, v1 is the weighting coefficient of the severity of the condition, v2 is the weighting coefficient of the duration of treatment, and v3 is the weighting coefficient of the demand for special equipment.

[0105] formula:

[0106]

[0107] Detailed explanation of the formula and its calculation derivation:

[0108] The formula is used to calculate the bed demand index, and the value is used to assess the urgency of patients' need for medical resources and the priority of bed allocation.

[0109] Parameter meanings and settings:

[0110] d1 is the severity index of the patient's condition, assumed to be 8;

[0111] d2 is the treatment duration index, assumed to be 5;

[0112] d3 is the demand index for special medical facilities, assumed to be 7;

[0113] v1 represents the weight of the severity of the illness, assumed to be 0.5;

[0114] v2 represents the weight of treatment time, assumed to be 0.3;

[0115] v3 represents the weight for special equipment requirements, assumed to be 0.2;

[0116] Substitute the parameters into the formula to calculate:

[0117]

[0118] The result R = 4.49 indicates that the patient's demand score for medical resources is 4.49. This value is used to determine the priority of patients in bed allocation, and the calculation process is used to improve the accuracy and efficiency of bed allocation strategies.

[0119] The patient bed matching submodule matches beds for target patients based on bed demand assessment information and real-time bed occupancy information, and generates a bed allocation list.

[0120] The algorithm uses a bed allocation system that matches patients with different needs and bed types, prioritizing patients with severe or urgent conditions. During the matching process, bed status is updated in real time to reflect the latest occupancy, ensuring accuracy and timeliness. Once a match is successful, the bed data is automatically updated and relevant medical personnel are notified, completing the allocation of patient beds. The generated bed allocation list provides medical staff with immediate bed arrangement information, promoting the rational allocation of medical resources and improving the efficiency of patient care.

[0121] Please see Figure 2 and Figure 6 The network topology management module includes:

[0122] The data network monitoring submodule analyzes the importance of various data in real time based on the bed allocation list, including physiological monitoring data, diagnostic information, medication use information, and patient medical record information, adjusts the data transmission queue, and generates a transmission queue adjustment record.

[0123] First, real-time monitoring of different nodes in the network is performed to analyze the load of each node and its transmission volume at different time periods. Monitoring data includes the overall network activity level, real-time traffic and load of each node, assessing whether any nodes are overloaded and adjusting resource allocation and network scheduling accordingly. This ensures the priority processing of medical data, especially critical treatment data, preventing data delays or loss due to network congestion. Furthermore, the data network monitoring submodule utilizes the Sniffer Pro network analyzer to deeply analyze network traffic, providing accurate network load status reports. By continuously monitoring each node and data flow in the medical environment, the submodule can generate detailed monitoring information for each node, displaying the traffic and load status of each node at different time periods. This data is crucial for identifying potential network bottlenecks, especially during disaster responses and emergencies when hospital network infrastructure may face sudden high loads. By monitoring network status in real time, the data network monitoring submodule can adjust resource allocation promptly, ensuring network stability and efficiency.

[0124] The network latency analysis submodule monitors the data traffic and node status of the data transmission network in real time based on the transmission queue adjustment record, including the data transmission volume and load status of multiple nodes over multiple time periods. It analyzes the latency in data transmission, detects bottleneck nodes in the network, and generates bottleneck node identification results.

[0125] By leveraging network latency analysis tools such as Wireshark, the network status monitoring submodule delves into the data, analyzing the response time, data transmission efficiency, and peak network latency of each node. Tools like Wireshark capture and analyze data packets at every stage of data flow transmission, providing real-time response times for each node and identifying latency peaks and their causes. Through statistical analysis of latency, this submodule calculates the average and standard deviation of network latency, further identifying latency data points exceeding the normal range. In disaster emergency scenarios, latency can prevent the timely transmission of patient medical data, causing delays in medical decision-making. By accurately calculating latency values ​​and identifying latency anomalies, the system can provide precise data for subsequent network optimization. When latency anomalies exceed preset thresholds, the network latency analysis submodule automatically generates bottleneck node identification results. These results accurately pinpoint bottleneck nodes in the network, helping network administrators to effectively optimize and maintain them. The bottleneck node identification results not only provide the time period and related nodes of the latency but also indicate the potential causes of the latency. Network administrators can take corresponding measures based on these results, such as increasing bandwidth, optimizing routing paths, or adjusting node load to resolve latency issues.

[0126] The topology path optimization submodule adjusts the data transmission path based on the bottleneck node identification results, real-time data transmission requirements, and network node status, and generates data transmission path adjustment results.

[0127] The dynamic path optimization software NetBalancer is used to analyze and adjust data transmission paths. It operates based on real-time data transmission needs and the current status of network nodes. By simulating different network configuration scenarios, it evaluates the impact of various path configurations on data transmission efficiency. During the optimization process, it automatically calculates and suggests the best data routing path to reduce data transmission latency and avoid network congestion. The generated data transmission path adjustment results include new routing schemes, and the target scheme is immediately deployed in the network to ensure the rapid and secure transmission of critical data in medical information.

[0128] Please see Figure 2 and Figure 7 The medical record quick export module includes:

[0129] The medical record information extraction submodule extracts and analyzes the electronic medical records of multiple patients based on the data transmission path adjustment results, identifies the patients' personal information, diagnostic information, treatment records and condition status, and generates medical record data analysis results;

[0130] Using the Text Analytics Toolbox, text data was processed and analyzed to extract key information from electronic medical records, such as personal information, diagnostic information, treatment records, and disease status. The process included using natural language processing technology to identify and parse the medical record text content, extracting key data, and using pattern recognition technology to confirm the type of disease and the urgency of treatment. Data mining algorithms were applied to analyze treatment effects and patient responses. The resulting medical record data analysis results provided a comprehensive report detailing each patient's health status and treatment history.

[0131] The patient medical record processing submodule, based on the results of medical record data analysis and according to a preset template, formats the medical records of multiple patients and generates formatted medical record documents.

[0132] Using document processing software such as Microsoft Word and Adobe Acrobat, along with preset medical document templates, medical record data is formatted. This includes standardizing the original medical record data to the target medical document format, ensuring that all medical record documents follow the same visual and content structure. The process ensures the consistency and professionalism of medical information. Through automated scripting, multiple medical records can be processed in batches, improving processing efficiency and accuracy. The generated formatted medical record documents completely record each patient's detailed medical history and current condition, making it easy for medical staff to quickly obtain key information.

[0133] The integrity verification submodule is based on formatted medical record documents. It performs data integrity verification on the processed documents and uploads them to the WeChat mini program to generate medical record document processing records.

[0134] The integrity verification submodule employs data verification technology to ensure the accuracy and integrity of formatted medical record documents. Specific technologies include data validation and error detection. Validation algorithms are used to verify the document content, ensuring no errors occur during data transmission or format conversion. Error detection algorithms are applied to identify any possible data inconsistencies or missing information. Each document is automatically scanned, and the original data is compared with the formatted data to ensure that all important information is complete and accurate. The data is then uploaded to a WeChat mini-program. The generated medical record document processing log details all verification steps and any issues discovered, providing a comprehensive guarantee of document integrity.

[0135] Please see Figure 8 A method for managing medical information in a mobile tent hospital based on WeChat Mini Programs. This method is applied to a mobile tent hospital medical information management system based on WeChat Mini Programs, and includes:

[0136] S1: Based on user login request information, collect user behavior data, assess the abnormality level of login behavior, adjust the verification process, and generate login authentication status.

[0137] S2: Utilizing login authentication status, scan the patient's RFID tag to identify the target patient's identity information. Combined with information input by nurses and doctors, and using machine vision, automatically monitor the drug distribution and usage process, including drug type, quantity, and usage. Update the patient's electronic medical record and generate electronic medical record update records.

[0138] S3: By updating electronic medical records and analyzing diagnostic information entered by physicians, including disease type, severity and expected recovery time, assess patients’ bed needs, match patients with beds, and generate a bed allocation list;

[0139] S4: Based on the bed allocation list, analyze the importance of various data in real time, including physiological monitoring data, diagnostic information, medication use information, and patient medical record information; adjust the data transmission queue; monitor the data flow and node status in the data transmission network in real time; analyze data transmission delay and identify bottleneck nodes; adjust the data transmission path; and generate data transmission path adjustment results.

[0140] S5: Based on the data transmission path adjustment results, extract and analyze the electronic medical record data of multiple patients, combine with the preset template, perform formatting processing and data integrity verification of the medical records, and upload them to the WeChat mini program to generate medical record document processing records.

[0141] The above embodiments can be implemented, in whole or in part, by software, hardware (such as circuits), firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, as a computer program product. A computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, all or part of the processes or functions described in the embodiments of the present invention are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more sets of available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. A semiconductor medium can be a solid-state drive.

[0142] It should be understood that the term "and / or" in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. A and B can be singular or plural. Additionally, the character " / " in this article generally indicates an "or" relationship between the preceding and following related objects, but it can also represent an "and / or" relationship. Please refer to the context for a more accurate understanding.

[0143] In this invention, "at least one" means one or more, and "more than one" means two or more. "At least one of the following" or similar expressions refer to any combination of these items, including any combination of a single item or a plurality of items. For example, at least one of a, b, or c can represent: a, b, c, ab, ac, bc, or abc, where a, b, and c can be a single item or multiple items.

[0144] It should be understood that, in various embodiments of the present invention, the order of the above-mentioned process numbers does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0145] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0146] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the devices, apparatuses, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0147] In the several embodiments provided by this invention, it should be understood that the disclosed devices, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another device, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0148] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0149] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0150] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A mobile tent hospital medical information management system based on WeChat mini-programs, characterized in that: The system includes: The mini-program login verification module assesses the level of abnormal behavior and adjusts the verification process based on the user's login request information and login behavior, and generates a login identity verification status. Based on the login authentication status, the treatment process recording module identifies the target patient's identity information by scanning the RFID tag of the target patient. Combined with the information input by nurses and doctors, it uses machine vision to automatically monitor the distribution and use of medicines, including the type, quantity, and usage of medicines, and updates the patient's electronic medical record to generate an electronic medical record update record. The patient classification and bed allocation module, based on the updated electronic medical records, analyzes the diagnostic information input by physicians, assesses the bed needs of target patients, allocates beds to target patients, and generates a bed allocation list. The network topology management module analyzes the importance of various data in real time based on the bed allocation list, including physiological monitoring data, diagnostic information, medication use information, and patient medical record information. It adjusts the data transmission queue, analyzes data transmission latency and network bottleneck nodes by monitoring the data traffic and node status of the data transmission network in real time, and adjusts the data transmission path to generate data transmission path adjustment results. The medical record quick export module adjusts the results according to the data transmission path, extracts and analyzes the electronic medical records of multiple patients, combines preset templates to format the medical records and verify data integrity, and uploads them to the WeChat mini program to generate medical record document processing records.

2. The mobile tent hospital medical information management system based on WeChat mini-program as described in claim 1, characterized in that, The login authentication status specifically includes anomaly score, anomaly level, and verification process adjustment records. The electronic medical record update records include patient identification information, medical event information, and medical record update data. The bed allocation list includes bed demand assessment information, real-time bed occupancy status, and patient bed matching results. The data transmission path adjustment results specifically refer to latency analysis results, network bottleneck node identification information, and data transmission path adjustment records. The medical record document processing records include medical record information extraction records, text information formatting records, and document data integrity verification results.

3. The mobile tent hospital medical information management system based on WeChat mini-program as described in claim 1, characterized in that, The mini-program login verification module includes: The behavior data collection submodule collects user device type, login address, access identity and touch operation mode information based on user login request information, and generates login behavior dataset; The anomaly analysis submodule, based on the login behavior dataset, extracts the behavioral pattern information of the user's access identity according to the user's access identity, evaluates the consistency of the user's login device, login address, and touch operation mode, calculates the user's behavioral anomaly score, and generates behavioral anomaly evaluation results. The verification process adjustment submodule assesses the user's abnormal behavior level and adjusts the verification process based on the abnormal behavior assessment results, including triggering two-factor authentication and sending biometric authentication requests, and generating login authentication status.

4. The mobile tent hospital medical information management system based on WeChat mini-program according to claim 3, characterized in that, The specific formula for calculating the user's abnormal behavior score is as follows: Where S represents the behavior anomaly score, p1 represents the consistency score between device type and historical records, p2 represents the consistency score between login address and historical location, p3 represents the consistency score between touch operation mode, w1 is the weighting coefficient of device type, w2 is the weighting coefficient of login address, and w3 is the weighting coefficient of touch operation mode.

5. The mobile tent hospital medical information management system based on WeChat mini-program as described in claim 1, characterized in that, The treatment process recording module includes: Based on the login authentication status, the patient identification submodule identifies the target patient's identity information by scanning the RFID tag of the target patient and generates an identity confirmation record; Based on the identity verification record, the input information analysis submodule extracts and analyzes the input information of nurses and doctors, identifies the type of medical event, patient condition information, and treatment records, and combines patient medication needs information. Using machine vision, it monitors the drug distribution process in real time, analyzes drug usage, including drug type, quantity, and usage time, and generates a summary record of treatment information. The electronic medical record update submodule updates the patient's electronic medical record based on the treatment information summary record, including automatically updating the patient's medication usage record and generating an electronic medical record update record.

6. The mobile tent hospital medical information management system based on WeChat mini-program according to claim 1, characterized in that, The patient classification and bed allocation module includes: The injury and illness condition analysis submodule, based on the updated electronic medical record, analyzes the diagnostic information input by the physician to identify the patient's condition type, severity, and expected recovery time, and generates diagnostic information analysis results. Based on the diagnostic information analysis results, the demand analysis submodule analyzes the patient's treatment needs, assesses the bed demand of the target patient, and generates bed demand assessment information. The patient bed matching submodule matches beds for target patients based on the bed demand assessment information and in combination with real-time bed occupancy information, and generates a bed allocation list.

7. The mobile tent hospital medical information management system based on WeChat mini-program according to claim 6, characterized in that, The specific formula for assessing the bed demand of the target patients is as follows: Where R represents the bed demand index, d1 represents the index of patient condition severity, d2 represents the index of treatment duration, d3 represents the index of demand for special medical facilities, v1 is the weighting coefficient of condition severity, v2 is the weighting coefficient of treatment duration, and v3 is the weighting coefficient of demand for special medical facilities.

8. The mobile tent hospital medical information management system based on WeChat mini-program according to claim 1, characterized in that, The network topology management module includes: The data network monitoring submodule analyzes the importance of various data in real time based on the bed allocation list, including physiological monitoring data, diagnostic information, medication use information, and patient medical record information, adjusts the data transmission queue, and generates a transmission queue adjustment record. The network latency analysis submodule monitors the data traffic and node status of the data transmission network in real time based on the transmission queue adjustment record, including the data transmission volume and load status of multiple nodes over multiple time periods, analyzes the latency in data transmission, detects bottleneck nodes in the network, and generates bottleneck node identification results. Based on the bottleneck node identification results, the topology path optimization submodule adjusts the data transmission path according to real-time data transmission requirements and network node status, and generates data transmission path adjustment results.

9. The mobile tent hospital medical information management system based on WeChat mini-program according to claim 1, characterized in that, The rapid medical record export module includes: Based on the data transmission path adjustment results, the medical record information extraction submodule extracts and analyzes the electronic medical records of multiple patients, identifies the patients' personal information, diagnostic information, treatment records and condition status, and generates medical record data analysis results. Based on the analysis results of the medical record data, the patient medical record processing submodule formats the medical records of multiple patients according to a preset template to generate formatted medical record documents. The integrity verification submodule verifies the data integrity of the processed document based on the formatted medical record document, and uploads it to the WeChat mini program to generate a medical record document processing record.

10. A method for managing medical information in a mobile tent hospital based on WeChat mini-programs, characterized in that: The mobile tent hospital medical information management system based on WeChat mini-program according to any one of claims 1-9, the method includes: S1: Based on user login request information, collect user behavior data, assess the abnormality level of login behavior, adjust the verification process, and generate login authentication status. S2: Using the login authentication status, scan the patient's RFID tag to identify the target patient's identity information. Combined with the information input by the nurse and doctor, use machine vision to automatically monitor the drug distribution and usage process, including drug type, quantity, and usage. Update the patient's electronic medical record and generate an electronic medical record update record. S3: By updating the electronic medical record, analyzing the diagnostic information entered by the physician, including the type of illness, the severity of the illness, and the expected recovery time, assessing the patient's need for a bed, matching the patient with a bed, and generating a bed allocation list; S4: Based on the bed allocation list, analyze the importance of various data in real time, including physiological monitoring data, diagnostic information, medication use information, and patient medical record information; adjust the data transmission queue; monitor the data flow and node status in the data transmission network in real time; analyze the data transmission delay and identify bottleneck nodes; adjust the data transmission path; and generate data transmission path adjustment results. S5: Based on the data transmission path adjustment results, extract and analyze the electronic medical record data of multiple patients, combine with the preset template, perform formatting processing and data integrity verification on the medical records, and upload them to the WeChat mini program to generate medical record document processing records.

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