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

Abstract

The invention relates to the technical field of medical information management, in particular to a mobile tent hospital medical information management system and method based on a WeChat applet, and the system comprises the following steps: an applet login verification module, a treatment process recording module, an injury and patient classification and bed distribution module, a network topology management module, and a medical record rapid export module. According to the invention, by analyzing the behavior data when the user logs in, the abnormal behavior is identified, and the identity verification process is adjusted, so that the security threshold of data access is improved, the defense capability for data leakage events is enhanced, the automatic identification of patient information and the real-time updating of electronic medical records are realized by using the RFID tag, the response efficiency of temporary medical facilities is improved, and the user experience is improved. The continuity and efficiency of medical services in disaster response scenes are ensured, smooth transmission of data in a medical network is ensured through real-time monitoring and optimization of network topology, and rapid export of electronic medical records is achieved by combining analysis and formatting processing of electronic medical record text content.

Description

Technical Field

[0001] The present invention relates to the technical field of medical information management, and in particular to a mobile tent hospital medical information management system and method based on WeChat applet. Background Art

[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, and improve the quality and efficiency of medical services, including data security, privacy protection, data integration and interoperability. It covers the electronic management of patient data, real-time monitoring of health information, and optimal allocation of medical resources. It realizes the integration and application of multiple key technologies such as electronic medical record systems, health information exchange, clinical decision support systems, and telemedicine services, and improves 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 the efficiency of emergency response, and cover the real-time collection and update of medical information, dynamic monitoring of patient status, and allocation of medical resources. By building mobile and web platforms, it can realize instant input and access of information. Combined with location recognition and data synchronization technology, it ensures the consistency and timeliness of information at all ends, and realizes 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 and poor information timeliness. They rely on traditional static authentication methods for user identity authentication and data security, making them vulnerable to security threats. Bed management and resource allocation are inefficient, and there is a lack of effective real-time optimization mechanisms, making it impossible to quickly adapt to urgent and changing medical needs. Network management ignores the particularities of data transmission in the medical environment, affecting the transmission and processing efficiency of medical information. Summary of the Invention

[0005] In order to solve the technical problems existing in the prior art, the embodiment of the present invention provides a mobile tent hospital medical information management system and method based on WeChat applet. The technical solution is as follows:

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

[0007] The mini-program login verification module evaluates the abnormality level of the user's login request information and adjusts the verification process based on the user's login behavior, generating a login authentication status.

[0008] The treatment process recording module, based on the login authentication status, scans the target patient's RFID tag to identify the target patient's identity information, combines the information input by the nurse and physician, and uses machine vision to automatically monitor the distribution and use of drugs, including the type, quantity, and usage of the drugs, and updates the patient's electronic medical record to generate an electronic medical record update record;

[0009] The patient classification and bed allocation module updates the electronic medical record, analyzes the diagnosis information input by the physician, evaluates the bed requirements of the target patient, allocates a bed to the target patient, 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, drug use information, and patient medical history information, adjusts the data transmission queue, analyzes data transmission delays and network bottleneck nodes by real-time monitoring of data flow and node status of the data transmission network, and adjusts the data transmission path to generate a data transmission path adjustment result;

[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, formats the medical records and verifies the data integrity based on the preset template, and uploads them to the WeChat applet to generate medical record document processing records.

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

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

[0014] The behavior data collection submodule collects the user's device type, login address, access identity, and touch operation mode information based on the user's login request information to generate a login behavior data set;

[0015] The abnormality degree analysis submodule extracts the behavior pattern information of the access identity according to the user's access identity based on the login behavior data set, evaluates the consistency of the user's login device, login address, and touch operation mode, calculates the user's behavior abnormality score, and generates a behavior abnormality evaluation result;

[0016] The verification process adjustment submodule evaluates the user's behavior abnormality level and adjusts the verification process based on the behavior abnormality evaluation result, including triggering secondary identity authentication and sending a biometric verification request, and generating a login identity authentication status.

[0017] As a further solution of the present invention, the specific formula for calculating the user's behavior abnormality score is:

[0018]

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

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

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

[0022] The input information analysis submodule extracts and analyzes the information input by nurses and physicians based on the identity confirmation records, identifies the type of medical event, patient condition information, and treatment records, and uses machine vision to monitor the drug distribution process in real time, analyze drug usage, including drug type, quantity, and usage time, and generate a summary record of treatment information in combination with patient medication demand 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 drug use record and generating an electronic medical record update record.

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

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

[0026] The demand level analysis submodule analyzes the treatment needs of the patient based on the analysis results of the diagnostic information, evaluates 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 combines the real-time bed usage to generate a bed allocation list.

[0028] As a further embodiment of the present invention, the specific formula for evaluating the bed demand of the target patient is:

[0029]

[0030] Among them, 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 weight coefficient of the severity of the condition, v2 is the weight coefficient of the duration of treatment, and v3 is the weight coefficient of the demand for special medical facilities.

[0031] As a further solution 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 usage information, and patient medical history information, adjusts the data transmission queue, and generates a transmission queue adjustment record;

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

[0034] The topology path optimization submodule adjusts the data transmission path based on the bottleneck node identification result, according to the real-time data transmission demand and the network node status, and generates a data transmission path adjustment result.

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

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

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

[0038] The integrity verification submodule performs data integrity verification on the processed document based on the formatted medical record document, uploads it to the WeChat applet, and generates a medical record document processing record.

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

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

[0041] S2: Using the login authentication status, scan the patient's RFID tag to identify the target patient's identity information, combine the information input by the nurse and physician, and use machine vision to automatically monitor the distribution and use of drugs, including the type, quantity, and usage of drugs, update the patient's electronic medical record, and generate an electronic medical record update record;

[0042] S3: updating the electronic medical record, analyzing the diagnosis information input by the physician, including the type of illness, severity of illness, and 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, drug use information, and patient medical history information, adjust the data transmission queue, monitor the data flow and node status in the data transmission network in real time, adjust the data transmission path by analyzing the data transmission delay and identifying the bottleneck node, and generate a data transmission path adjustment result;

[0044] S5: According to the data transmission path adjustment result, extract and analyze the electronic medical record data of multiple patients, combine with the preset template, format the medical records and verify the data integrity, and upload them to the WeChat applet to generate medical record document processing records.

[0045] The beneficial effects brought about by the technical solution provided by the embodiment of the present invention include at least:

[0046] By analyzing user login behavior data to identify behavioral anomalies and adjusting the identity verification process, the security threshold for data access is raised, and the defense capability against data leakage incidents is strengthened. RFID tags are used to automatically identify patient information and update electronic medical records in real time, 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 ensure smooth data transmission in the medical network. Combined with the analysis and formatting of electronic medical record text content, the rapid export of electronic medical records is achieved. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0048] Figure 1 is a system flow chart of the present invention;

[0049] Figure 2 Schematic diagram of the system framework of the present invention;

[0050] Figure 3 This is a flowchart of the mini-program login verification module of the present invention;

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

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

[0053] Figure 6 This is a flow chart 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 Schematic diagram of the method steps of the present invention. DETAILED DESCRIPTION

[0056] The technical solution of the present invention is described below in conjunction with the accompanying drawings.

[0057] In the embodiments of the present invention, words such as "exemplarily" and "for example" are used to indicate examples, illustrations, or explanations. Any embodiment or design described as an "exemplary" in the present invention should not be interpreted as being preferred or advantageous over other embodiments or designs. Rather, the use of the word "exemplary" is intended to present concepts in a concrete manner. Furthermore, in the embodiments of the present invention, "and / or" can mean both or either of the two.

[0058] In the embodiments of the present invention, the terms "image" and "picture" may be used interchangeably. It should be noted that, when the distinction between them is not emphasized, their intended meanings are the same. The terms "of," "corresponding," and "corresponding" may be used interchangeably. It should be noted that, when the distinction between them is not emphasized, their intended meanings are the same.

[0059] In the embodiments of the present invention, sometimes a subscript such as W1 may be written as a non-subscript such as W1. When the difference is not emphasized, the meanings to be expressed are the same.

[0060] In order to make the technical problems, technical solutions and advantages to be solved by the present invention clearer, a detailed description will be given below with reference to the accompanying drawings and specific embodiments.

[0061] The embodiment of the present invention provides a mobile tent hospital medical information management system based on WeChat applet, please refer to Figures 1 to 2 The present invention provides a technical solution, a mobile tent hospital medical information management system based on WeChat applet, including:

[0062] The mini-program login verification module evaluates the abnormality level of the user's login request information and adjusts the verification process based on the user's login behavior, generating a login authentication status.

[0063] The treatment process recording module, based on the login authentication status, scans the target patient's RFID tag to identify the target patient's identity information. Combined with the information entered by the nurse and physician, it uses machine vision to automatically monitor the distribution and use of drugs, including drug type, quantity, and usage. It then updates the patient's electronic medical record and generates an electronic medical record update record.

[0064] The patient classification and bed allocation module updates records based on 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 usage information, and patient medical history information, adjusts the data transmission queue, and analyzes data transmission delays and network bottleneck nodes by monitoring the data flow and node status of the data transmission network in real time. It then adjusts the data transmission path and generates 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, formats the medical records and verifies the data integrity based on preset templates, and uploads them to the WeChat applet to generate medical record document processing records.

[0067] The login authentication status specifically includes the behavior anomaly score, anomaly level, and verification process adjustment record. The electronic medical record update record includes patient identity identification information, medical event information, and medical record update data. The bed allocation list includes bed demand assessment information, real-time bed usage, and patient bed matching results. The data transmission path adjustment result specifically refers to the delay analysis result, network bottleneck node identification information, and data transmission path adjustment record. The medical record document processing record includes medical record information extraction records, text information formatting records, and document data integrity verification results.

[0068] See also Figure 2 and Figure 3 , the mini program login verification module includes:

[0069] The behavior data collection submodule collects the user's device type, login address, access identity, and touch operation mode information based on the user's login request information to generate a login behavior data set;

[0070] Login request information, including device type, is automatically collected through the user-side WeChat mini-program interface. The type of device the user is using is determined through device management software. The login address is determined using geolocation services or IP tracking. Access identity is authenticated through user account and password authentication. The touch operation mode analyzes user interaction actions. Behavior tracking technology is used to analyze click speed and sliding trajectory. The target data is securely transmitted to the server via the HTTPS protocol. The server uses a JSON parser to process the target data and stores it in a SQL database, forming a structured login behavior dataset, which provides raw data input for subsequent abnormal behavior analysis.

[0071] The abnormality analysis submodule extracts the behavioral pattern information of the access identity based on the login behavior dataset, evaluates the consistency of the user's login device, login address, and touch operation mode, calculates the user's behavioral anomaly score, and generates a behavioral anomaly assessment result;

[0072] The specific formula for calculating the user's behavior anomaly score is:

[0073]

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

[0075] formula:

[0076]

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

[0078] A formula is used to calculate a user's behavioral anomaly score, and the result is used to determine whether further authentication measures are required;

[0079] Parameter meaning and setting value:

[0080] p1 is the consistency score between the device type and the historical records. It is assumed to be 1, which reflects that the device used by the user completely matches the historical records.

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

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

[0083] w1 is the weight coefficient of the device type, which is assumed to be 0.4;

[0084] w2 is the weight coefficient of the login address, assuming it is 0.3;

[0085] w3 is the weight coefficient of the touch operation mode, which is assumed to be 0.3;

[0086] Substitute the parameters into the formula for calculation:

[0087]

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

[0089] The verification process adjustment submodule evaluates the user's behavioral anomaly level and adjusts the verification process based on the behavioral anomaly assessment results, including triggering secondary authentication and sending biometric verification requests, and generating login authentication status;

[0090] The abnormal level of user behavior is assessed. If the abnormality score exceeds the set threshold, the security protocol library is called to initiate a secondary authentication request, including sending a one-time verification code via SMS or email. For login attempts from new devices or locations, deep learning algorithms supported by the TensorFlow framework are used to further analyze behavioral patterns to determine whether biometric verification measures such as fingerprint or facial recognition are required. Through a series of automated processes, we can quickly respond to abnormal user behavior, promptly update the login authentication status, and enhance account security.

[0091] See also Figure 2 and Figure 4 , the treatment process recording module includes:

[0092] The patient identification submodule, based on the login authentication status, scans the target patient’s RFID tag to identify the target patient’s identity information and generates an identity confirmation record;

[0093] The patient identification submodule scans the target patient's RFID tag and, based on their login and authentication status, accurately identifies the target patient's identity and generates a detailed identification record. The RFID scanner automatically detects the target patient's RFID tag and reads the stored patient identification data, including key information such as the patient's name, medical record number, date of birth, and contact information. This data is transmitted in real time via an encrypted network connection to the hospital's central database, where a patient identification record is generated. This process ensures rapid and accurate identification of each patient during their medical consultation, effectively preventing medical accidents or data confusion caused by identity errors. Furthermore, thanks to the RFID tag's real-time reading and transmission technology, the identification process can be completed in seconds, improving diagnosis and treatment efficiency. This reduces the manual workload of verifying patient information and ensures the security, accuracy, and uniqueness of patient identities. This efficient automated identification system allows medical staff to quickly and accurately identify patients in busy medical settings, ensuring smooth medical procedures and providing reliable identification for subsequent medical services.

[0094] The input information analysis submodule extracts and analyzes nurse and physician input based on identity confirmation records, identifies medical event types, patient condition information, and treatment records, and uses machine vision to monitor the drug distribution process in real time, analyze drug usage, including drug type, quantity, and usage time, and generate treatment information summary records.

[0095] This submodule extracts and analyzes treatment information entered by nurses and physicians, identifying medical event types, patient condition information, and treatment records. It then integrates this information with the patient's medication needs to further refine the patient's treatment plan. This submodule performs in-depth analysis of medical information and uses natural language processing to automatically classify and organize the treatment data entered by nurses and physicians. For example, the system automatically extracts key information from textual information such as condition descriptions, diagnosis results, and treatment measures, categorizing and storing it. Furthermore, the input information analysis submodule incorporates machine vision technology to monitor the drug distribution process in real time, analyzing information such as drug type, quantity, and usage duration. Using cameras, it automatically identifies actual drug usage and updates the system in real time, ensuring the accuracy and completeness of drug usage records. During this process, all drug usage data is closely integrated with the patient's treatment records to form a comprehensive summary of treatment information. This submodule not only improves the accuracy of treatment information but also provides real-time decision support for medical staff, helping to ensure continuity and consistency in the treatment process and further optimizing patient care quality.

[0096] The electronic medical record update submodule updates the patient's electronic medical record based on the summary record of treatment information, including automatically updating the patient's drug use record and generating an electronic medical record update record;

[0097] Based on the treatment information summary record, the patient's electronic medical record is promptly updated, 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 treatment information summary record to the corresponding fields in the electronic medical record system, including the patient's condition progression, treatment measures, compliance with medical orders, and medication usage records. By using electronic medical record management software, the input information analysis submodule accurately processes data, 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 query and backtracking. This not only effectively reduces errors caused by manual operations but also improves the transparency and accuracy of medical records. Furthermore, the automated update process ensures the integrity and consistency of medical records, allowing medical staff to quickly access the latest patient information at all times, avoiding treatment delays caused by outdated information. This automated system not only improves work efficiency but also optimizes the accuracy and timeliness of patient treatment data, thereby enhancing the overall quality and safety of medical services.

[0098] See also Figure 2 and Figure 5 , the patient triage and bed allocation module includes:

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

[0100] Using the disease analysis algorithm, including condition judgment and data classification processing, the patient's disease type such as infection, chronic disease or acute injury is analyzed, and the severity of the disease and the expected recovery time are assessed. During the process, the algorithm takes into account the patient's historical medical 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 level analysis submodule analyzes the patient's treatment needs based on the diagnostic information analysis results, evaluates the bed demand of the target patient, and generates bed demand assessment information;

[0102] The specific formula for assessing bed requirements for target patients is:

[0103]

[0104] Among them, 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 weight coefficient of the severity of the condition, v2 is the weight coefficient of the length of treatment time, and v3 is the weight coefficient of the demand for special equipment.

[0105] formula:

[0106]

[0107] Detailed explanation of the formula and the process of formula calculation and 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 meaning and setting value:

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

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

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

[0113] v1 is the severity weight of the disease, which is assumed to be 0.5;

[0114] v2 is the weight of treatment time length, which is assumed to be 0.3;

[0115] v3 is the special equipment demand weight, which is assumed to be 0.2;

[0116] Substitute the parameters into the formula for calculation:

[0117]

[0118] The result R = 4.49 indicates that the patient's demand score for medical resources is 4.49. The 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 the bed allocation strategy.

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

[0120] Using a bed allocation algorithm, the algorithm matches patient needs and bed types, giving priority to patients with serious conditions and high urgency. During the bed matching process, the bed status is updated in real time to reflect the latest usage to ensure the accuracy and timeliness of the match. After a successful match, the bed data is automatically updated and the relevant medical staff are notified to complete the allocation of patient beds. The generated bed allocation list provides medical staff with instant bed arrangement information, promoting the rational allocation of medical resources and the efficiency of patient care.

[0121] See also 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 usage information, and patient medical records, adjusts the data transmission queue, and generates transmission queue adjustment records;

[0123] First, the system monitors different nodes in the network in real time, analyzing the load on each node and its traffic volume over different time periods. This monitoring data includes the overall network activity level, real-time traffic flow and load at each node, assessing any overloaded nodes and enabling timely adjustments to resource allocation and network scheduling. This ensures that medical data, particularly critical treatment data, is prioritized, 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 and provide accurate reports on network load status. By continuously monitoring each node and data flow in the healthcare environment, the submodule generates detailed monitoring information for each node, displaying the traffic flow and load status of each node over different time periods. This data is crucial for identifying potential network bottlenecks, especially during disaster response and emergency situations, when hospital network infrastructure may face sudden and severe load increases. By monitoring network status in real time, the data network monitoring submodule can adjust resource allocation in a timely manner to ensure network stability and efficiency.

[0124] The network delay analysis submodule monitors the data flow and node status of the data transmission network in real time based on the transmission queue adjustment records, including the data transmission volume in multiple time periods and the load status of multiple nodes. It analyzes the delay 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 system can further analyze the data provided by the Network Status Monitoring submodule, analyzing each node's response time, data transmission efficiency, and network latency peaks. Tools like Wireshark can capture and analyze data packets at every stage of a data flow, providing real-time response times for each node and identifying latency spikes and their causes. By performing statistical analysis on latency, the submodule calculates the mean and standard deviation of network latency, further identifying data points with latency exceeding normal limits. In disaster response scenarios, latency can prevent timely delivery of patient medical data, leading to delayed medical decision-making. By accurately calculating latency values and identifying latency anomalies, the system provides precise guidance for subsequent network optimization. When latency anomalies exceed a preset threshold, the Network Latency Analysis submodule automatically generates bottleneck node identification results. These results pinpoint the bottleneck nodes within the network, helping network administrators effectively optimize and maintain them. The bottleneck node identification results not only provide the delay timeframe and associated nodes, but also indicate potential causes of the delay. Network administrators can use these results to take appropriate measures, such as increasing bandwidth, optimizing routing paths, or adjusting node loads, to address latency issues.

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

[0127] Dynamic path optimization software NetBalancer is used to analyze and adjust data transmission paths, operating according to real-time data transmission needs and the current status of network nodes. By simulating different network configuration scenarios, the impact of various path configurations on data transmission efficiency is evaluated. During the optimization process, the optimal data routing path is automatically calculated and recommended to reduce data transmission delays and avoid network congestion. The generated data transmission path adjustment results include a new routing plan, which is immediately deployed to the network to ensure the rapid and secure transmission of critical medical information data.

[0128] See also 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, diagnosis information, treatment records and condition status, and generates medical record data analysis results;

[0130] The Text Analytics Toolbox is used to process and analyze text data and extract key information from electronic medical records, such as personal information, diagnosis information, treatment records, and condition status. The process includes using natural language processing technology to identify and parse the medical record text content, extract key data, and confirm the type of condition and the urgency of treatment through pattern recognition technology. Data mining algorithms are applied to analyze treatment effects and patient responses. The generated medical record data analysis results provide a comprehensive report that details each patient's health status and treatment process.

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

[0132] Use document processing software Microsoft Word and Adobe Acrobat, along with preset medical document templates, to format medical record data. This includes standardizing the original medical record data into the target medical document format, ensuring that all medical record documents follow the same visual and content structure. This step ensures the consistency and professionalism of medical information. Through script automation, batch processing of multiple medical records improves processing efficiency and accuracy. The generated formatted medical record documents fully record each patient's detailed medical history and current condition, making it easier for medical staff to quickly obtain key information.

[0133] The integrity verification submodule verifies the data integrity of the processed medical record based on the formatted medical record document, uploads it to the WeChat applet, and generates a medical record processing record;

[0134] The integrity verification submodule uses data verification technology to ensure the accuracy and completeness of formatted medical record documents. Specific technologies include data verification and error detection. The document content is verified using a checksum algorithm to ensure that 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 and formatted data are compared to ensure that all important information is included and correct. The data is then uploaded to the WeChat mini-program. The generated medical record document processing record details all verification steps and any problems found, providing a comprehensive guarantee of document integrity.

[0135] See also Figure 8 A mobile tent hospital medical information management method based on WeChat applet is applied to a mobile tent hospital medical information management system based on WeChat applet, and the method includes:

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

[0137] S2: Using the login authentication status, scan the patient's RFID tag to identify the target patient's identity information. Combined with the information entered by the nurse and physician, using machine vision, it automatically monitors the distribution and use of drugs, including drug type, quantity, and usage. It also updates the patient's electronic medical record and generates an electronic medical record update record.

[0138] S3: Update records through electronic medical records, analyze the diagnosis information entered by the physician, including the type of illness, severity of illness, and expected recovery time, assess the patient's bed needs, match the patient with a bed, 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 usage information, and patient medical history 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 the bottleneck nodes, adjust the data transmission path, and generate the 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, format the medical records and verify the data integrity, and upload them to the WeChat applet to generate medical record document processing records.

[0141] The above embodiments can be implemented in whole or in part through software, hardware (such as circuits), firmware, or any other combination. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. A computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer program are loaded or executed on a computer, the processes or functions described in accordance with the embodiments of the present invention are generated in whole or in part. 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 a wired method (such as infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that contains a collection of one or more available media. The available medium can be a magnetic medium (such as a floppy disk, hard disk, or tape), an optical medium (such as a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state drive.

[0142] It should be understood that the term "and / or" as used herein simply describes a relationship between associated objects, indicating that three possible relationships exist. For example, "A and / or B" can represent: A alone, A and B together, or B alone. A and B can be singular or plural. Furthermore, the character " / " as used herein generally indicates an "or" relationship between the associated objects, but it may also indicate an "and / or" relationship. For specific understanding, please refer to the context.

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

[0144] It should be understood that in various embodiments of the present invention, the size of the serial numbers of the above-mentioned processes does not mean 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 appreciate that the units and algorithm steps of each example 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 performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present invention.

[0146] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described equipment, devices and units can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0147] In the several embodiments provided by the present invention, it should be understood that the disclosed devices, apparatuses and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the units is merely a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another device, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interface, indirect coupling or communication connection of the device or unit, which can be electrical, mechanical or other forms.

[0148] The units described as separate components may or may not be physically separate, and 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 these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0149] If the functions are implemented in the form of 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 the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[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 modifications or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in the present invention should be included in the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be based on the scope of protection of the claims.

Claims

1. A mobile tent hospital medical information management system based on WeChat applet, characterized by: The system comprises: The mini-program login verification module evaluates the abnormality level of the user's login request information and adjusts the verification process based on the user's login behavior, generating a login authentication status. The treatment process recording module, based on the login authentication status, scans the target patient's RFID tag to identify the target patient's identity information, combines the information input by the nurse and physician, and uses machine vision to automatically monitor the distribution and use of drugs, including the type, quantity, and usage of the drugs, and updates the patient's electronic medical record to generate an electronic medical record update record; The patient classification and bed allocation module updates the electronic medical record, analyzes the diagnosis information input by the physician, evaluates the bed requirements of the target patient, allocates a bed to the target patient, 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, drug use information, and patient medical history information, adjusts the data transmission queue, analyzes data transmission delays and network bottleneck nodes by real-time monitoring of data flow and node status of the data transmission network, and adjusts the data transmission path to generate a data transmission path adjustment result; 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, formats the medical records and verifies the data integrity based on the preset template, and uploads them to the WeChat applet to generate medical record document processing records.

2. The mobile tent hospital medical information management system based on WeChat applet according to claim 1 is characterized in that: The login authentication status specifically includes the behavior anomaly score, anomaly level, and verification process adjustment record. The electronic medical record update record includes patient identity information, medical event information, and medical record update data. The bed allocation list includes bed demand assessment information, real-time bed usage, and patient bed matching results. The data transmission path adjustment result specifically refers to the delay analysis result, network bottleneck node identification information, and data transmission path adjustment record. The medical record document processing record includes medical record information extraction record, text information formatting record, and document data integrity verification result.

3. The mobile tent hospital medical information management system based on WeChat applet according to claim 1 is characterized in that: The mini-program login verification module includes: The behavior data collection submodule collects the user's device type, login address, access identity, and touch operation mode information based on the user's login request information to generate a login behavior data set; The abnormality degree analysis submodule extracts the behavior pattern information of the access identity according to the user's access identity based on the login behavior data set, evaluates the consistency of the user's login device, login address, and touch operation mode, calculates the user's behavior abnormality score, and generates a behavior abnormality evaluation result; The verification process adjustment submodule evaluates the user's behavior abnormality level and adjusts the verification process based on the behavior abnormality evaluation result, including triggering secondary identity authentication and sending a biometric verification request, and generating a login identity authentication status.

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

5. The mobile tent hospital medical information management system based on WeChat applet according to claim 1 is characterized in that: The treatment process recording module includes: The patient identification submodule identifies the target patient's identity information by scanning the target patient's RFID tag based on the login authentication status and generates an identity confirmation record; The input information analysis submodule extracts and analyzes the information input by nurses and physicians based on the identity confirmation records, identifies the type of medical event, patient condition information, and treatment records, and uses machine vision to monitor the drug distribution process in real time, analyze drug usage, including drug type, quantity, and usage time, and generate a summary record of treatment information in combination with patient medication demand 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 drug use record and generating an electronic medical record update record.

6. The mobile tent hospital medical information management system based on WeChat applet according to claim 1 is characterized in that: The patient classification and bed allocation module includes: The injury and illness analysis submodule analyzes the diagnosis information input by the physician based on the updated electronic medical record to identify the patient's condition type, severity, and expected recovery time, and generates a diagnosis information analysis result; The demand level analysis submodule analyzes the treatment needs of the patient based on the analysis results of the diagnostic information, evaluates 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 combines the real-time bed usage to generate a bed allocation list.

7. The mobile tent hospital medical information management system based on WeChat applet according to claim 6 is characterized in that: The specific formula for evaluating the bed demand for target patients is: Among them, 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 weight coefficient of the severity of the condition, v2 is the weight coefficient of the duration of treatment, and v3 is the weight coefficient of the demand for special medical facilities.

8. The mobile tent hospital medical information management system based on WeChat applet according to claim 1 is 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 usage information, and patient medical history information, adjusts the data transmission queue, and generates a transmission queue adjustment record; The network delay analysis submodule monitors the data flow and node status of the data transmission network in real time based on the transmission queue adjustment record, including the data transmission volume in multiple time periods and the load status of multiple nodes, analyzes the delay in data transmission, detects bottleneck nodes in the network, and generates bottleneck node identification results; The topology path optimization submodule adjusts the data transmission path based on the bottleneck node identification result, according to the real-time data transmission demand and the network node status, and generates a data transmission path adjustment result.

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

10. A mobile tent hospital medical information management method based on WeChat applet, characterized in that: The mobile tent hospital medical information management system based on WeChat applet according to any one of claims 1 to 9, wherein the method comprises: S1: Based on the user login request information, collect user behavior data, evaluate the abnormality level of login behavior, adjust the verification process, and generate the login authentication status; S2: Using the login authentication status, scan the patient's RFID tag to identify the target patient's identity information, combine the information input by the nurse and physician, and use machine vision to automatically monitor the distribution and use of drugs, including the type, quantity, and usage of drugs, update the patient's electronic medical record, and generate an electronic medical record update record; S3: updating the electronic medical record, analyzing the diagnosis information input by the physician, including the type of illness, severity of illness, and 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, drug use information, and patient medical history information, adjust the data transmission queue, monitor the data flow and node status in the data transmission network in real time, adjust the data transmission path by analyzing the data transmission delay and identifying the bottleneck node, and generate a data transmission path adjustment result; S5: According to the data transmission path adjustment result, extract and analyze the electronic medical record data of multiple patients, combine with the preset template, format the medical records and verify the data integrity, and upload them to the WeChat applet to generate medical record document processing records.

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