Plateau acute and critical disease treatment information platform based on AI technology

Abstract

The invention discloses a plateau acute and critical disease treatment information platform based on an AI technology, which can visually display the health state of a patient and quickly identify abnormity by collecting vital sign data of the patient with acute and critical diseases in a plateau environment and constructing a comprehensive vital sign matrix, and can monitor and dynamically evaluate the illness state in real time by utilizing a health file established by the AI technology. The medical resource utilization efficiency is improved, a medical consultation data field is constructed, a standardized medical history, symptom description and treatment suggestion framework is provided for doctors, doctor-patient communication is enhanced, the consultation efficiency is improved, a resource scheduling data cluster is combined with a big data analysis algorithm, it is ensured that medical resources are effectively allocated in time and resource allocation is optimized in acute and critical disease treatment, and meanwhile the medical resource utilization efficiency is improved. The plateau emergency and critical disease treatment related knowledge, skill training and research results are integrated, personalized learning resources are provided for users, the professional literacy and coping ability of medical staff are improved, and the overall treatment efficiency and the medical service quality are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of plateau emergency and critical illness rescue information management, and in particular to a plateau emergency and critical illness rescue information platform based on AI technology. Background Art

[0002] Due to the unique natural environment and climatic conditions in the plateau area, the treatment of critically ill patients faces many challenges. Traditional treatment methods mainly rely on the personal experience and on-site judgment of medical staff. However, this method has many shortcomings in the plateau environment.

[0003] First, environmental factors such as low oxygen and high cold in the plateau cause large fluctuations in the vital signs of patients. Traditional monitoring methods often cannot accurately capture these changes, making it difficult for doctors to make timely and accurate judgments during the treatment process. Second, medical consultation plays a vital role in the treatment of critical and emergency diseases in the plateau, but traditional consultation methods often rely on face-to-face communication, which may be restricted by conditions such as transportation and communication in the plateau environment, resulting in low consultation efficiency and even delays in treatment. Third, medical resources in the plateau are relatively scarce and unevenly distributed, making the scheduling and optimization of medical resources a major problem in emergency situations. Traditional scheduling methods often rely on manual judgment and experience-based decision-making, and it is difficult to make the best scheduling plan in a short period of time. In addition, the professional knowledge and skills of medical staff are crucial to the treatment of critical and emergency diseases in the plateau. However, traditional training methods often lack pertinence and personalization, and it is difficult to meet the needs of medical staff with different roles and learning progress, resulting in poor training results. Summary of the invention

[0004] In view of this, the present invention proposes an AI-based high-altitude critical care information platform, which can effectively solve the defects of the existing technology, such as difficulty in accurately capturing fluctuations in vital signs data, low consultation efficiency, reliance on manual judgment and experience-based decision-making in medical resource scheduling, and lack of pertinence and personalization in training methods.

[0005] The technical solution of the present invention is achieved in this way:

[0006] An AI-based information platform for emergency and critical care treatment in high altitude areas, including:

[0007] The vital signs matrix construction module is used to construct a vital signs matrix based on multiple vital signs data of critically ill patients in plateau environments, and use AI technology to construct health records of critically ill patients based on the vital signs matrix;

[0008] The medical consultation data domain processing module is used to construct a medical consultation data domain including medical history, symptom description and treatment suggestions, and to construct an emotional doctor-patient communication method based on the medical consultation data domain;

[0009] Resource scheduling data cluster optimization module, which is used to construct resource scheduling data clusters and generate emergency scheduling plans based on resource scheduling data clusters using big data analysis algorithms;

[0010] The knowledge training coefficient dynamic adjustment module is used to integrate professional knowledge, skill training and the latest research results related to plateau emergency and critical care, and dynamically adjust the training content, difficulty and frequency according to user roles and learning progress to form a personalized knowledge training coefficient.

[0011] As a further optional solution of the AI-based high-altitude critical care information platform, the vital signs matrix construction module constructs a vital signs matrix based on multiple vital signs data of critically ill patients in a high-altitude environment, and constructs a health record of critically ill patients based on the vital signs matrix, specifically including:

[0012] Real-time collection of vital signs data of critically ill patients in plateau environments, including heart rate, blood pressure, blood oxygen saturation, body temperature, respiratory rate and electrocardiogram;

[0013] Construct a vital sign matrix based on the vital sign data of critically ill patients;

[0014] Apply AI technology to conduct real-time analysis of the vital signs matrix, identify abnormal data, and predict disease development trends;

[0015] Integrate patients' historical health records and build health records for critically ill patients.

[0016] As a further optional solution of the AI-based high-altitude critical care information platform, the medical consultation data domain processing module constructs a medical consultation data domain containing medical history, symptom description and treatment suggestions, and constructs an emotional doctor-patient communication method based on the medical consultation data domain, specifically including:

[0017] Receive and process medical consultation information from patients, first responders and remote medical experts, and extract key medical information through natural language processing technology and deep learning algorithms;

[0018] Constructing a medical consultation data domain based on the extracted key medical information, wherein the medical consultation data domain includes a description of the patient's symptoms, medical history, treatment needs, and the diagnosis suggestions and treatment plans of remote medical experts;

[0019] Apply sentiment analysis technology to identify sentiment tendencies in the medical consultation data domain;

[0020] Match the emotional doctor-patient communication method based on the medical consultation data domain and the emotional tendency in the medical consultation data domain.

[0021] As a further optional solution of the AI-based high-altitude critical care information platform, the resource scheduling data cluster optimization module constructs a resource scheduling data cluster and generates an emergency scheduling plan based on the resource scheduling data cluster using a big data analysis algorithm, specifically including:

[0022] Construct a resource scheduling data cluster based on the urgency assessment of the vital signs matrix, the treatment demand analysis of the medical consultation data domain, and the distribution of surrounding medical resources;

[0023] Apply big data analysis algorithms to generate emergency dispatch plans that include the best treatment path, rescue team allocation, medical equipment deployment, and drug supply information;

[0024] Monitor the scheduling execution status in real time and dynamically adjust the scheduling strategy based on the execution status.

[0025] As a further optional solution of the AI-based high-altitude critical care information platform, the platform also includes:

[0026] The data security and privacy protection module is used for the encrypted storage and transmission of patient vital signs data, medical consultation information, resource scheduling data and knowledge training content.

[0027] As a further optional solution for the AI-based high-altitude critical care information platform, the data security and privacy protection module encrypts and stores and transmits patient vital signs data, medical consultation information, resource scheduling data, and knowledge training content, specifically including:

[0028] Designing a hybrid encryption algorithm based on chaos theory for generating random key sequences and encryption standards for the actual encryption of data;

[0029] The data to be encrypted is divided into blocks of fixed size, each block is encrypted using a different key generated by chaos theory, and the encrypted data blocks are stored together with the corresponding key index;

[0030] For each encrypted data block, a hash value is generated using a hash function based on chaotic mapping and stored in the database as an integrity check code;

[0031] Before data transmission, both parties generate a dynamic session key through a key negotiation protocol;

[0032] Using dynamic session keys, the data blocks to be transmitted are hybrid encrypted, and the encrypted data is transmitted through the communication channel;

[0033] After receiving the encrypted data, the receiver uses the chaotic mapping function and the session key for mixed decryption. After decryption, the decrypted data block is hashed using a hash function based on the chaotic mapping and compared with the hash value provided by the sender to verify the integrity of the data.

[0034] A method for a high-altitude critical care information platform based on AI technology, specifically comprising:

[0035] Steps for constructing the vital signs matrix: construct a vital signs matrix based on multiple vital signs data of critically ill patients in plateau environments, and use AI technology to construct health records of critically ill patients based on the vital signs matrix;

[0036] Medical consultation data domain processing steps: construct a medical consultation data domain including medical history, symptom description and treatment suggestions, and construct an emotional doctor-patient communication method based on the medical consultation data domain;

[0037] Resource scheduling data cluster optimization steps: construct a resource scheduling data cluster, and use big data analysis algorithms to generate emergency scheduling plans based on the resource scheduling data cluster;

[0038] Steps for dynamic adjustment of knowledge training coefficient: Integrate professional knowledge, skill training and the latest research results related to plateau emergency and critical care, and dynamically adjust training content, difficulty and frequency according to user roles and learning progress to form a personalized knowledge training coefficient.

[0039] As a further optional solution of the method of the high altitude critical illness treatment information platform based on AI technology, the method also includes:

[0040] Data security and privacy protection steps: encrypted storage and transmission of patient vital signs data, medical consultation information, resource scheduling data, and knowledge training content.

[0041] A computing device comprises a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the computer program, implements the steps of any one of the above-mentioned methods for establishing an AI-based highland critical care information platform.

[0042] A computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the steps of any one of the above-mentioned methods for establishing an AI-based highland critical care information platform are implemented.

[0043] The beneficial effects of the present invention are as follows: by collecting multiple vital signs data of critically ill patients in a plateau environment, a comprehensive vital signs matrix can be constructed. This matrixed data processing method helps to more intuitively display the patient's health status and quickly identify abnormal indicators. The health record constructed using AI technology can achieve real-time monitoring and dynamic evaluation of the patient's condition, and also help to improve the utilization efficiency of medical resources, because it can allocate medical resources based on the patient's actual condition to avoid waste and mismatch of resources. By constructing a medical consultation data domain, it can provide a standardized medical history, symptom description and treatment recommendation framework, which helps doctors understand the patient's condition more accurately. The construction of an emotional doctor-patient communication method can enhance the trust and understanding between doctors and patients, improve the satisfaction and quality of medical services, and also help to improve the efficiency of medical consultation, because it can be based on the patient's The medical history and symptom description can be used to quickly generate possible treatment suggestions and provide decision support for doctors. By building a resource scheduling data cluster, it can achieve comprehensive monitoring and dynamic management of medical resources. The emergency scheduling plan generated by the big data analysis algorithm can ensure that medical resources are deployed in a timely and effective manner during the treatment of critical and emergency diseases, thereby improving the success rate of treatment. It can also help optimize the allocation of medical resources, reduce idleness and waste of resources, and improve the overall efficiency of medical services. By integrating professional knowledge, skill training and the latest research results related to the treatment of critical and emergency diseases in the plateau, it can provide users with comprehensive learning resources and dynamically adjust the training content, difficulty and frequency according to the user's role and learning progress, ensuring that users get a personalized learning experience and improve learning effects. It can also help improve the professional quality and skill level of medical staff and improve their ability to respond to critical and emergency diseases in the plateau. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. 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 paying creative work.

[0045] Figure 1 This is a schematic diagram of the composition of an information platform for treating critical and severe diseases in high altitude areas based on AI technology in the present invention;

[0046] Figure 2 It is a flow chart of a high altitude critical illness rescue information method based on AI technology of the present invention;

[0047] Figure 3 The figure is a schematic diagram of the composition of a computing device according to the present invention. DETAILED DESCRIPTION

[0048] The technical solutions in the embodiments of the present invention are described clearly and completely below. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0049] refer to Figures 1 to 3 , an AI-based information platform for emergency and critical care treatment in high altitude areas, including:

[0050] The vital signs matrix construction module is used to construct a vital signs matrix based on multiple vital signs data of critically ill patients in plateau environments, and use AI technology to construct health records of critically ill patients based on the vital signs matrix;

[0051] The medical consultation data domain processing module is used to construct a medical consultation data domain including medical history, symptom description and treatment suggestions, and to construct an emotional doctor-patient communication method based on the medical consultation data domain;

[0052] Resource scheduling data cluster optimization module, which is used to construct resource scheduling data clusters and generate emergency scheduling plans based on resource scheduling data clusters using big data analysis algorithms;

[0053] The knowledge training coefficient dynamic adjustment module is used to integrate professional knowledge, skill training and the latest research results related to plateau emergency and critical care, and dynamically adjust the training content, difficulty and frequency according to user roles and learning progress to form a personalized knowledge training coefficient.

[0054] In this embodiment, by collecting multiple vital signs data of critically ill patients in a plateau environment, a comprehensive vital signs matrix can be constructed. This matrix data processing method helps to more intuitively display the patient's health status and quickly identify abnormal indicators. The health record constructed using AI technology can achieve real-time monitoring and dynamic evaluation of the patient's condition, and also help to improve the utilization efficiency of medical resources, because it can allocate medical resources based on the patient's actual condition to avoid waste and mismatch of resources. By constructing a medical consultation data domain, it can provide a standardized medical history, symptom description and treatment recommendation framework, which helps doctors understand the patient's condition more accurately. The construction of an emotional doctor-patient communication method can enhance the trust and understanding between doctors and patients, improve the satisfaction and quality of medical services, and also help to improve the efficiency of medical consultation, because it can be based on the patient's Medical history and symptom descriptions can quickly generate possible treatment suggestions and provide decision support for doctors. By building a resource scheduling data cluster, it can achieve comprehensive monitoring and dynamic management of medical resources. The emergency scheduling plan generated by the big data analysis algorithm can ensure that medical resources are deployed in a timely and effective manner during the treatment of critical and emergency diseases, thereby improving the success rate of treatment. It can also help optimize the allocation of medical resources, reduce idle and waste resources, and improve the overall efficiency of medical services. By integrating professional knowledge, skill training and the latest research results related to high-altitude critical and emergency treatment, it can provide users with comprehensive learning resources and dynamically adjust the training content, difficulty and frequency according to the user's role and learning progress, ensuring that users have a personalized learning experience and improve learning outcomes. It can also help improve the professional quality and skill level of medical staff and improve their ability to respond to high-altitude critical and emergency treatment.

[0055] Preferably, the vital signs matrix construction module constructs a vital signs matrix based on multiple vital signs data of critically ill patients in a plateau environment, and constructs a health record of the critically ill patients based on the vital signs matrix, specifically including:

[0056] Real-time collection of vital signs data of critically ill patients in plateau environments, including heart rate, blood pressure, blood oxygen saturation, body temperature, respiratory rate and electrocardiogram;

[0057] Construct a vital sign matrix based on the vital sign data of critically ill patients;

[0058] Apply AI technology to conduct real-time analysis of the vital signs matrix, identify abnormal data, and predict disease development trends;

[0059] Integrate patients' historical health records and build health records for critically ill patients.

[0060] In this embodiment, the vital signs data such as heart rate, blood pressure, blood oxygen saturation, body temperature, respiratory rate and electrocardiogram of critically ill patients in a plateau environment are collected in real time to ensure the timeliness and accuracy of the data. These data are an important basis for evaluating the patient's health status and the development trend of the disease. The collected data items comprehensively cover the patient's basic vital signs, which helps to have a more comprehensive understanding of the patient's health status. A vital signs matrix is ​​constructed based on the vital signs data of critically ill patients. This matrix display method makes the data more intuitive and easy to understand. Doctors can quickly capture changes in the patient's vital signs from the matrix and respond more quickly. Through matrix data processing, the efficiency of data processing is improved, allowing doctors to obtain a large amount of useful information in a short time. The application of AI technology to perform real-time analysis of the vital signs matrix can automatically identify abnormal data and predict the development trend of the disease. This intelligent analysis method is great. It has greatly improved the accuracy and timeliness of disease monitoring, helping doctors to take treatment measures in advance and reduce the risk of disease worsening. The predictive nature of AI technology enables doctors to assess patients' conditions more accurately, providing a scientific basis for formulating personalized treatment plans, integrating patients' historical health records, and building health records for critically ill patients, ensuring the integrity and continuity of patients' health information. Doctors can consult health records to understand patients' past medical history, treatment experience and other information, providing important reference for current treatment. Health records can also be personalized according to the patient's specific circumstances, recording the patient's special needs, allergy history and other information, providing doctors with more comprehensive patient information, and helping to formulate more personalized treatment plans.

[0061] It should be noted that real-time data stream processing technology is used to continuously collect data streams from vital signs monitoring devices, obtain and update users' vital signs data in real time. These data may include key indicators such as heart rate, blood pressure, and body temperature. The collected raw data is preprocessed, including denoising, standardization, classification and other operations to improve the quality and reliability of the data. The preprocessing stage also requires data quality testing, using specific formulas to score data points, and encryption processing to ensure the security of data during transmission. Machine learning algorithms are used to extract features from the preprocessed vital signs data. The goal of this stage is to automatically learn and extract key features from the data, which will be used for subsequent model training and prediction. Based on the extracted features, a machine learning model is built for real-time prediction. Time series analysis, these models can capture long-term dependencies in vital signs data and establish predictive models of health conditions. By comparing real-time vital signs data with the health trends predicted by the model, abnormal patterns in the data can be identified, which involves setting a threshold or using statistical methods to determine whether the data deviates from the normal range. When potential health problems are detected, the early warning mechanism is automatically triggered, which includes sending alerts to users, notifying medical professionals, or initiating emergency response processes. Machine learning models are used to conduct in-depth analysis of vital signs data to predict the development trend of the disease, which involves predicting the rate of disease progression, severity, or potential complications. Based on the prediction results, patients are provided with personalized health advice and treatment plans, which include recommendations such as adjusting lifestyle habits, drug therapy, or surgical treatment.

[0062] Preferably, the medical consultation data domain processing module constructs a medical consultation data domain including medical history, symptom description and treatment suggestions, and constructs an emotional doctor-patient communication method based on the medical consultation data domain, specifically including:

[0063] Receive and process medical consultation information from patients, first responders and remote medical experts, and extract key medical information through natural language processing technology and deep learning algorithms;

[0064] Constructing a medical consultation data domain based on the extracted key medical information, wherein the medical consultation data domain includes a description of the patient's symptoms, medical history, treatment needs, and the diagnosis suggestions and treatment plans of remote medical experts;

[0065] Apply sentiment analysis technology to identify sentiment tendencies in the medical consultation data domain;

[0066] Match the emotional doctor-patient communication method based on the medical consultation data domain and the emotional tendency in the medical consultation data domain.

[0067] In this embodiment, through natural language processing technology and deep learning algorithms, the technical solution can automatically extract key medical information from medical consultation information, such as patient symptom descriptions, medical history, treatment needs, and remote medical experts' diagnostic suggestions and treatment plans. Compared with traditional manual extraction, this method not only greatly improves the efficiency of information processing, but also reduces the possibility of human errors. The extracted key medical information is used to construct a medical consultation data domain. The construction of the data domain makes the medical information more structured, easy to understand and analyze. By applying sentiment analysis technology, the technical solution can identify emotional tendencies in the medical consultation data domain, such as patients' anxiety, fear, helplessness and other emotions, as well as medical experts' sympathy, understanding and other attitudes towards patients. The results of sentiment analysis help doctors better understand the patient's psychological state. In order to provide more humane medical services, according to the medical consultation data domain and the emotional tendencies therein, this technical solution can match the most appropriate emotional doctor-patient communication method. This method not only helps to relieve patients' tension, but also enhances trust and cooperation between doctors and patients, thereby improving the quality and satisfaction of medical services. The key medical information in the medical consultation data domain provides doctors with comprehensive patient information. By analyzing the patient's medical history and symptom description, doctors can have a deeper understanding of the patient's condition and thus develop a more personalized treatment plan. By automatically processing and analyzing medical consultation information, the workload of doctors is reduced, allowing them to focus more on the treatment of patients. At the same time, by optimizing the doctor-patient communication method, unnecessary medical consultations and repeated examinations are reduced, thereby improving the utilization efficiency of medical resources.

[0068] Preferably, the resource scheduling data cluster optimization module constructs a resource scheduling data cluster, and uses a big data analysis algorithm to generate an emergency scheduling plan based on the resource scheduling data cluster, specifically including:

[0069] Construct a resource scheduling data cluster based on the urgency assessment of the vital signs matrix, the treatment demand analysis of the medical consultation data domain, and the distribution of surrounding medical resources;

[0070] Apply big data analysis algorithms to generate emergency dispatch plans that include the best treatment path, rescue team allocation, medical equipment deployment, and drug supply information;

[0071] Monitor the scheduling execution status in real time and dynamically adjust the scheduling strategy based on the execution status.

[0072] In this embodiment, through the urgency assessment based on the vital signs matrix, the urgency of the medical event can be quickly determined to ensure that emergency dispatch resources can be allocated to the patients who need them most. This accurate assessment helps to reduce delays in the dispatch process and improve the response speed of emergency dispatch. Through the treatment demand analysis in the medical consultation data domain, the patient's treatment needs can be fully understood, including the required medical equipment, drugs, rescue teams, etc. This comprehensive analysis helps to ensure that the dispatch plan can meet the patient's actual treatment needs and improve the accuracy of dispatch. Through the big data analysis algorithm, an emergency dispatch plan including the optimal treatment path can be generated. The generation of the optimal treatment path helps to reduce the rescue team's Time consumption on the way can improve rescue efficiency. Through the optimization of rescue team allocation and medical equipment and drug allocation, it can ensure that emergency dispatch resources are reasonably allocated. This reasonable allocation helps to avoid waste and shortage of resources and improve the utilization efficiency of medical resources. Through real-time monitoring of dispatch execution, the dispatch strategy can be dynamically adjusted according to the execution situation. This real-time monitoring and dynamic adjustment mechanism can help to deal with emergencies in the dispatch process and enhance the flexibility and adaptability of the dispatch strategy. Through real-time monitoring and dynamic adjustment, problems in the dispatch process can be discovered and solved in time, and dispatch efficiency can be improved. At the same time, dynamic adjustment can also ensure that the dispatch plan always meets the actual treatment needs of patients and improves the quality of dispatch.

[0073] It should be noted that data is collected from multiple channels such as vital signs monitoring systems, medical consultation systems, geographic information systems (GIS), and medical resource management systems. The data content includes patients' vital signs data, treatment needs, geographic location information, and the distribution of surrounding medical resources (such as hospitals, emergency centers, medical equipment, and drugs). The collected data is cleaned, deduplicated, and converted to improve the quality and availability of the data. The data is standardized to ensure that data from different sources can be matched and compared with each other. The pre-processed data is integrated to form a resource scheduling data cluster that contains multi-dimensional information such as vital signs data, treatment needs, geographic location, and medical resource distribution. Cluster analysis is performed on the resource scheduling data cluster to group similar data points into one category in order to better understand and analyze the data. According to the needs and goals of emergency scheduling, appropriate big data analysis algorithms are selected, such as path optimization algorithms, resource allocation algorithms, etc., and the resource scheduling data cluster is input into the selected big data analysis algorithm. , execute the algorithm to generate an emergency dispatch plan, according to the patient's geographical location and treatment needs, combined with the distribution of surrounding medical resources, use the path optimization algorithm to generate the best treatment path, according to the patient's urgency and treatment needs, combined with the distribution of rescue teams and available resources, use the resource allocation algorithm to allocate rescue teams, according to the treatment needs, combined with the distribution of medical equipment and available resources, generate a medical equipment allocation plan, according to the treatment needs, combined with the inventory and supply channels of drugs, generate drug supply information to ensure that patients can get the required drugs in time, evaluate the generated emergency dispatch plan, including the feasibility, efficiency, cost and other aspects of the plan, according to the evaluation results, optimize and adjust the emergency dispatch plan to ensure that the plan is more in line with actual needs, during the execution of the emergency dispatch plan, monitor the dispatch execution in real time, including the location of the rescue team, the use of medical equipment, etc., according to the real-time monitoring results, dynamically adjust the emergency dispatch plan to ensure that the plan can respond to emergencies and changes.

[0074] Preferably, the platform further comprises:

[0075] The data security and privacy protection module is used for the encrypted storage and transmission of patient vital signs data, medical consultation information, resource scheduling data and knowledge training content.

[0076] Preferably, the encrypted storage and transmission of patient vital signs data, medical consultation information, resource scheduling data and knowledge training content by the data security and privacy protection module specifically includes:

[0077] Designing a hybrid encryption algorithm based on chaos theory for generating random key sequences and encryption standards for the actual encryption of data;

[0078] The data to be encrypted is divided into blocks of fixed size, each block is encrypted using a different key generated by chaos theory, and the encrypted data blocks are stored together with the corresponding key index;

[0079] For each encrypted data block, a hash value is generated using a hash function based on chaotic mapping and stored in the database as an integrity check code;

[0080] Before data transmission, both parties generate a dynamic session key through a key negotiation protocol;

[0081] Using dynamic session keys, the data blocks to be transmitted are hybrid encrypted, and the encrypted data is transmitted through the communication channel;

[0082] After receiving the encrypted data, the receiver uses the chaotic mapping function and the session key for mixed decryption. After decryption, the decrypted data block is hashed using a hash function based on the chaotic mapping and compared with the hash value provided by the sender to verify the integrity of the data.

[0083] In this embodiment, a hybrid encryption algorithm based on chaos theory and Advanced Encryption Standard (AES) is designed. Chaos theory is used to generate a highly random key sequence, while AES is used for the actual encryption of data. The chaotic key generation formula is: K_i = f(K_{i-1}), where f is a chaotic mapping function (such as Logistic mapping), K_i is the i-th key, K_{i-1} is the previous key, and the initial key K_0 is generated by the user's biometric features (such as fingerprints or iris scans) to ensure the uniqueness and non-replicability of the key;

[0084] The data to be encrypted is divided into blocks of fixed size, each block is encrypted by AES using a different key generated by the chaotic key generation formula, and the encrypted data block is stored together with the corresponding chaotic key index so that it can be correctly matched during decryption;

[0085] For each encrypted data block, a hash value is generated using a hash function based on chaotic mapping and stored in the database as an integrity check code;

[0086] Before data transmission, the two parties generate a dynamic session key through a key negotiation protocol (such as key exchange based on chaos synchronization). The chaotic mapping function and initial conditions in the key negotiation protocol are agreed upon by both parties in advance to ensure the uniqueness and security of the key.

[0087] Using dynamic session keys, the data blocks to be transmitted are encrypted by CEA, and the encrypted data is transmitted through a secure communication channel (such as a quantum key distribution network or a VPN with advanced encryption);

[0088] After receiving the encrypted data, the receiver uses the same chaotic mapping function and session key to perform CEA decryption. After decryption, the receiver uses a self-created hash function to perform hash calculation on the decrypted data block and compares it with the hash value provided by the sender to verify the integrity of the data.

[0089] It should be noted that the chaotic mapping function (Logistic mapping): x_{n+1}=r*x_n*(1-x_n), where r is the chaotic parameter (usually between 3.57 and 4), x_n is the nth state value (between 0 and 1), is used to generate a highly random key sequence;

[0090] Self-created hash function: H(M)=g(f_1(M),f_2(M),...,f_k(M)), where f_1,f_2,...,f_k are k different chaotic mapping functions, g is a combinatorial function (such as XOR, addition or multiplication), and M is the input data. This function is used to generate a hash value of the data to verify the integrity of the data.

[0091] A method for a high-altitude critical care information platform based on AI technology, specifically comprising:

[0092] Steps for constructing the vital signs matrix: construct a vital signs matrix based on multiple vital signs data of critically ill patients in plateau environments, and use AI technology to construct health records of critically ill patients based on the vital signs matrix;

[0093] Medical consultation data domain processing steps: construct a medical consultation data domain including medical history, symptom description and treatment suggestions, and construct an emotional doctor-patient communication method based on the medical consultation data domain;

[0094] Resource scheduling data cluster optimization steps: construct a resource scheduling data cluster, and use big data analysis algorithms to generate emergency scheduling plans based on the resource scheduling data cluster;

[0095] Steps for dynamic adjustment of knowledge training coefficient: Integrate professional knowledge, skill training and the latest research results related to plateau emergency and critical care, and dynamically adjust training content, difficulty and frequency according to user roles and learning progress to form a personalized knowledge training coefficient.

[0096] Preferably, the method further comprises:

[0097] Data security and privacy protection steps: encrypted storage and transmission of patient vital signs data, medical consultation information, resource scheduling data, and knowledge training content.

[0098] A computing device comprises a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the computer program, implements the steps of any one of the above-mentioned methods for establishing an AI-based highland critical care information platform.

[0099] A computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the steps of any one of the above-mentioned methods for establishing an AI-based highland critical care information platform are implemented.

[0100] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. An AI-based high-altitude critical care information platform, characterized by: include: The vital signs matrix construction module is used to construct a vital signs matrix based on multiple vital signs data of critically ill patients in plateau environments, and use AI technology to construct health records of critically ill patients based on the vital signs matrix; The medical consultation data domain processing module is used to construct a medical consultation data domain including medical history, symptom description and treatment suggestions, and to construct an emotional doctor-patient communication method based on the medical consultation data domain; Resource scheduling data cluster optimization module, which is used to construct resource scheduling data clusters and generate emergency scheduling plans based on resource scheduling data clusters using big data analysis algorithms; The knowledge training coefficient dynamic adjustment module is used to integrate professional knowledge, skill training and the latest research results related to plateau emergency and critical care, and dynamically adjust the training content, difficulty and frequency according to user roles and learning progress to form a personalized knowledge training coefficient.

2. According to claim 1, a high-altitude critical care information platform based on AI technology is characterized in that: The vital sign matrix construction module constructs a vital sign matrix based on multiple vital sign data of critically ill patients in a plateau environment, and constructs a health record of critically ill patients based on the vital sign matrix, specifically including: Real-time collection of vital signs data of critically ill patients in plateau environments, including heart rate, blood pressure, blood oxygen saturation, body temperature, respiratory rate and electrocardiogram; Construct a vital sign matrix based on the vital sign data of critically ill patients; Apply AI technology to conduct real-time analysis of the vital signs matrix, identify abnormal data, and predict disease development trends; Integrate patients' historical health records and build health records for critically ill patients.

3. According to claim 2, a high-altitude critical care information platform based on AI technology is characterized in that: The medical consultation data domain processing module constructs a medical consultation data domain including medical history, symptom description and treatment suggestions, and constructs an emotional doctor-patient communication method based on the medical consultation data domain, specifically including: Receive and process medical consultation information from patients, first responders and remote medical experts, and extract key medical information through natural language processing technology and deep learning algorithms; Constructing a medical consultation data domain based on the extracted key medical information, wherein the medical consultation data domain includes a description of the patient's symptoms, medical history, treatment needs, and the diagnosis suggestions and treatment plans of remote medical experts; Apply sentiment analysis technology to identify sentiment tendencies in the medical consultation data domain; Match the emotional doctor-patient communication method based on the medical consultation data domain and the emotional tendency in the medical consultation data domain.

4. According to claim 3, a high-altitude critical care information platform based on AI technology is characterized in that: The resource scheduling data cluster optimization module constructs a resource scheduling data cluster and generates an emergency scheduling plan based on the resource scheduling data cluster using a big data analysis algorithm, specifically including: Construct a resource scheduling data cluster based on the urgency assessment of the vital signs matrix, the treatment demand analysis of the medical consultation data domain, and the distribution of surrounding medical resources; Apply big data analysis algorithms to generate emergency dispatch plans that include the best treatment path, rescue team allocation, medical equipment deployment, and drug supply information; Monitor the scheduling execution status in real time and dynamically adjust the scheduling strategy based on the execution status.

5. According to claim 4, a high-altitude critical care information platform based on AI technology is characterized in that: The platform also includes: The data security and privacy protection module is used for the encrypted storage and transmission of patient vital signs data, medical consultation information, resource scheduling data and knowledge training content.

6. According to claim 5, a high-altitude critical care information platform based on AI technology is characterized in that: The data security and privacy protection module encrypts the storage and transmission of patient vital signs data, medical consultation information, resource scheduling data and knowledge training content, specifically including: Designing a hybrid encryption algorithm based on chaos theory for generating random key sequences and encryption standards for the actual encryption of data; The data to be encrypted is divided into blocks of fixed size, each block is encrypted using a different key generated by chaos theory, and the encrypted data blocks are stored together with the corresponding key index; For each encrypted data block, a hash value is generated using a hash function based on chaotic mapping and stored in the database as an integrity check code; Before data transmission, both parties generate a dynamic session key through a key negotiation protocol; Using dynamic session keys, the data blocks to be transmitted are hybrid encrypted, and the encrypted data is transmitted through the communication channel; After receiving the encrypted data, the receiver uses the chaotic mapping function and the session key for mixed decryption. After decryption, the decrypted data block is hashed using a hash function based on the chaotic mapping and compared with the hash value provided by the sender to verify the integrity of the data.

7. A high-altitude critical care information method based on AI technology, characterized in that: Specifically include: Steps for constructing the vital signs matrix: construct a vital signs matrix based on multiple vital signs data of critically ill patients in plateau environments, and use AI technology to construct health records of critically ill patients based on the vital signs matrix; Medical consultation data domain processing steps: construct a medical consultation data domain including medical history, symptom description and treatment suggestions, and construct an emotional doctor-patient communication method based on the medical consultation data domain; Resource scheduling data cluster optimization steps: construct a resource scheduling data cluster, and use big data analysis algorithms to generate emergency scheduling plans based on the resource scheduling data cluster; Steps for dynamic adjustment of knowledge training coefficient: Integrate professional knowledge, skill training and the latest research results related to plateau emergency and critical care, and dynamically adjust training content, difficulty and frequency according to user roles and learning progress to form a personalized knowledge training coefficient.

8. The high altitude critical illness rescue information method based on AI technology according to claim 7 is characterized in that: The method further comprises: Data security and privacy protection steps: encrypted storage and transmission of patient vital signs data, medical consultation information, resource scheduling data, and knowledge training content.

9. A computing device, characterized in that It includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the high-altitude critical illness rescue information method based on AI technology described in any one of claims 7-8 are implemented.

10. A computer-readable storage medium, characterized in that: The storage medium stores a computer program, which, when executed by a processor, implements the steps of the high-altitude critical illness rescue information method based on AI technology as described in any one of claims 7-8.

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