Plateau acute critical disease monitoring information management system and implementation method thereof

Abstract

According to the plateau acute critical disease monitoring information management system and the implementation method thereof, physiological parameters, such as heart rate, blood pressure and oxyhemoglobin saturation, of a patient suffering from plateau acute critical diseases are accurately obtained in real time through a data acquisition module, data timeliness is ensured, and the data are stored in a database; the chaos entropy calculation module evaluates the complexity and instability of the physiological state of a patient by utilizing a chaos theory, captures subtle changes and provides comprehensive illness state information for a doctor, the depolarization value processing module reduces abnormal or deviation values in data and improves data reliability, and the information integration and analysis module compares the analyzed and processed data with a judgment standard and provides a judgment result for the doctor. A monitoring report and an emergency processing suggestion are automatically generated, the working efficiency is improved, a scientific decision basis is provided for doctors, the report and the suggestion are visually displayed on a monitoring management interface, medical staff can make a quick response, the response time is shortened, the treatment efficiency is improved, and the risk of patients is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of monitoring information management, and in particular to a high-altitude critical care monitoring information management system and an implementation method thereof. Background Art

[0002] In plateau areas, due to special environmental factors such as high altitude, thin oxygen, low air pressure, and large temperature difference, the conditions of critically ill patients are often more complicated and changeable than in plains. Traditional monitoring information management systems may be able to meet basic monitoring needs in plains, but in plateau environments, their accuracy and reliability are often seriously affected.

[0003] Specifically, the physiological parameter data of critically ill patients in plateau environments often show greater volatility and instability. This is mainly due to the impact of environmental factors such as plateau hypoxia and cold on the patient's physiological state. Traditional monitoring information management systems often find it difficult to accurately identify the patient's true physiological state when processing these data, which may lead to misdiagnosis or missed diagnosis.

[0004] In addition, data collection in plateau environments also faces many challenges. For example, plateau hypoxia may cause sensor performance to deteriorate, thereby affecting the accuracy and stability of data collection. At the same time, factors such as large temperature differences and low humidity in plateau environments may also cause damage to data collection equipment, thereby affecting data collection and transmission. Summary of the invention

[0005] In view of this, the present invention proposes a plateau critical care information management system and an implementation method thereof, which can effectively solve the defects of the prior art that it is difficult to accurately identify the patient's true physiological state and the accuracy and stability of data collection are relatively poor.

[0006] The technical solution of the present invention is achieved in this way:

[0007] A high-altitude critical care information management system, comprising:

[0008] The data acquisition module is used to collect the physiological parameter data of critically ill patients in the plateau environment in real time to form a raw data sequence;

[0009] A chaos entropy calculation module, used for receiving the original data sequence and evaluating the complexity and instability of the patient's physiological state by calculating the chaos entropy of the data;

[0010] The debiasing value processing module is used to perform debiasing value processing on the data sequence according to the chaotic entropy calculation result;

[0011] The information integration and analysis module is used to compare and analyze the data sequence after debiasing with the preset emergency and critical disease judgment criteria, generate monitoring reports, and provide emergency treatment suggestions;

[0012] The monitoring management interface is used to display the monitoring report and emergency treatment suggestions for medical staff to monitor and respond in real time.

[0013] As a further optional solution of the plateau critical care information management system, the data acquisition module collects physiological parameter data of critical patients in the plateau environment in real time to form an original data sequence, which specifically includes:

[0014] The portable physiological parameter monitoring device is used to collect physiological parameter data of critically ill patients in a plateau environment in real time, and the physiological parameter data includes blood oxygen saturation, heart rate, respiratory rate and blood pressure;

[0015] The physiological parameter data is stored in time series to form an initial raw data sequence;

[0016] Processing the initial raw data to calculate derived data, wherein the derived data includes blood oxygen saturation change rate, heart rate variability index, respiratory rate abnormality index and blood pressure change rate;

[0017] The derived data is stored together with the initial raw data to form the final raw data.

[0018] As a further optional solution of the plateau critical care information management system, the chaos entropy calculation module receives the original data sequence and evaluates the complexity and instability of the patient's physiological state by calculating the chaos entropy of the data, specifically including:

[0019] According to the set embedding dimension and time delay, the original data sequence is reconstructed in phase space to obtain a phase point sequence;

[0020] Count the frequency of each phase point in the phase point sequence to obtain the probability distribution;

[0021] According to the probability distribution, calculate the information entropy;

[0022] The chaos entropy is calculated by combining the information entropy and the phase space reconstruction results, wherein the greater the value of the chaos entropy is, the higher the complexity and instability of the patient's physiological state is.

[0023] As a further optional solution of the plateau critical care information management system, the setting of the time delay specifically includes:

[0024] The mutual information between the two time series is calculated, and the delay time corresponding to the first drop of the mutual information curve to the minimum value is found, and the delay time is the optimal time delay.

[0025] As a further optional solution of the plateau critical care information management system, the setting of the embedding dimension specifically includes:

[0026] After selecting the time delay, the dimension is gradually increased until the correlation dimension stops changing. The embedding dimension when the correlation dimension stops changing is used as the reconstructed phase space dimension.

[0027] As a further optional solution of the plateau critical care information management system, the debiasing value processing module performs debiasing value processing on the data sequence according to the chaotic entropy calculation result, specifically including:

[0028] The calculated chaotic entropy value is compared with the preset threshold. If the chaotic entropy value is higher than the threshold, it is considered that there is a large deviation value in the data sequence and debiasing processing is required;

[0029] In combination with the value of chaos entropy, a debiasing coefficient is calculated, wherein the debiasing coefficient is a function inversely proportional to the chaos entropy value;

[0030] Use the calculated debiasing coefficient to adjust each data point in the data series;

[0031] Output the adjusted data sequence.

[0032] As a further optional solution of the plateau emergency and critical care information management system, the information integration and analysis module compares and analyzes the data sequence after debiasing with the preset emergency and critical care judgment criteria, generates a monitoring report, and provides emergency treatment suggestions, specifically including:

[0033] Integrate the debiased data sequence into a unified data structure;

[0034] Compare and analyze the integrated data series with the preset emergency and critical illness judgment criteria;

[0035] Use machine learning algorithms to detect anomalies in the aggregated data series;

[0036] Generate a monitoring report based on the results of the comparative analysis, the monitoring report including the patient's real-time monitoring data, comparative analysis results, abnormal detection conditions, and condition assessment information;

[0037] Provide emergency treatment recommendations based on abnormal conditions in the monitoring report and the results of the condition assessment.

[0038] A method for implementing a high-altitude critical care information management system, specifically comprising:

[0039] The data acquisition module collects the physiological parameter data of critically ill patients in plateau environment in real time to form the original data sequence;

[0040] Using a chaotic entropy calculation module to receive the original data sequence, and to evaluate the complexity and instability of the patient's physiological state by calculating the chaotic entropy of the data;

[0041] According to the calculation result of chaos entropy, the data sequence is debiased through the debiasing value processing module;

[0042] The data sequence after debiasing is input into the information integration and analysis module, and compared with the preset emergency and critical judgment criteria to generate a monitoring report including the patient's current physiological status assessment and potential risk warning, and provide emergency treatment suggestions based on the comparative analysis results;

[0043] The monitoring report and emergency treatment suggestions are displayed through the monitoring management interface, allowing medical staff to monitor the patient's physiological state in real time and respond quickly.

[0044] A computing device 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 implementation method of the above-mentioned high-altitude critical care information management system are implemented.

[0045] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of the implementation method of the above-mentioned high-altitude critical care information management system.

[0046] The beneficial effects of the present invention are as follows: through the data acquisition module, the system can collect physiological parameter data of critically ill patients in a plateau environment in real time and accurately, such as heart rate, blood pressure, blood oxygen saturation, etc., which ensures the timeliness and accuracy of the data; the chaos entropy calculation module uses advanced chaos theory to effectively evaluate the complexity and instability of the patient's physiological state by performing chaos entropy calculation on the original data sequence. This evaluation method can capture subtle changes in physiological parameters, provide doctors with more comprehensive disease information, and help to discover potential risks at an early stage; the debiasing value processing module performs debiasing value processing on the data sequence according to the chaos entropy calculation results, effectively reducing The absence of abnormal or deviation values ​​in the data improves the reliability and accuracy of the data. The information integration and analysis module compares and analyzes the data sequence after debiasing with the preset critical and emergency judgment criteria, automatically generates monitoring reports, and provides emergency treatment suggestions. This intelligent analysis not only improves work efficiency, but also provides doctors with a scientific decision-making basis and helps to formulate more reasonable treatment plans. The monitoring management interface displays monitoring reports and emergency treatment suggestions in an intuitive and easy-to-understand manner, allowing medical staff to monitor the patient's physiological state in real time and respond quickly, which helps to shorten response time, improve treatment efficiency, and reduce patient risks. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] 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.

[0048] Figure 1 A schematic diagram of the composition of a high-altitude critical care information management system of the present invention;

[0049] Figure 2 A schematic flow chart of a method for implementing a high-altitude critical care information management system according to the present invention;

[0050] Figure 3 The figure is a schematic diagram of the composition of a computing device according to the present invention. DETAILED DESCRIPTION

[0051] 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.

[0052] refer to Figures 1 to 3, a high-altitude critical care information management system, comprising:

[0053] The data acquisition module is used to collect the physiological parameter data of critically ill patients in the plateau environment in real time to form a raw data sequence;

[0054] A chaos entropy calculation module, used for receiving the original data sequence and evaluating the complexity and instability of the patient's physiological state by calculating the chaos entropy of the data;

[0055] The debiasing value processing module is used to perform debiasing value processing on the data sequence according to the chaotic entropy calculation result;

[0056] The information integration and analysis module is used to compare and analyze the data sequence after debiasing with the preset emergency and critical disease judgment criteria, generate monitoring reports, and provide emergency treatment suggestions;

[0057] The monitoring management interface is used to display the monitoring report and emergency treatment suggestions for medical staff to monitor and respond in real time.

[0058] In this embodiment, through the data acquisition module, the system can collect physiological parameter data of critically ill patients in a plateau environment in real time and accurately, such as heart rate, blood pressure, blood oxygen saturation, etc., which ensures the timeliness and accuracy of the data. The chaos entropy calculation module uses advanced chaos theory to effectively evaluate the complexity and instability of the patient's physiological state by performing chaos entropy calculation on the original data sequence. This evaluation method can capture subtle changes in physiological parameters, provide doctors with more comprehensive disease information, and help to discover potential risks at an early stage. The debiasing value processing module performs debiasing value processing on the data sequence according to the chaos entropy calculation results, which effectively reduces Abnormal or deviation values ​​in the data improve the reliability and accuracy of the data. The information integration and analysis module compares and analyzes the data sequence after debiasing with the preset critical and severe disease judgment standards, automatically generates monitoring reports, and provides emergency treatment suggestions. This intelligent analysis not only improves work efficiency, but also provides doctors with a scientific decision-making basis and helps to formulate more reasonable treatment plans. The monitoring management interface displays monitoring reports and emergency treatment suggestions in an intuitive and easy-to-understand manner, allowing medical staff to monitor the patient's physiological state in real time and respond quickly, which helps to shorten response time, improve treatment efficiency, and reduce patient risks.

[0059] Preferably, the data acquisition module collects physiological parameter data of critically ill patients in a plateau environment in real time to form an original data sequence, which specifically includes:

[0060] The portable physiological parameter monitoring device is used to collect physiological parameter data of critically ill patients in a plateau environment in real time, and the physiological parameter data includes blood oxygen saturation, heart rate, respiratory rate and blood pressure;

[0061] The physiological parameter data is stored in time series to form an initial raw data sequence;

[0062] Processing the initial raw data to calculate derived data, wherein the derived data includes blood oxygen saturation change rate, heart rate variability index, respiratory rate abnormality index and blood pressure change rate;

[0063] The derived data is stored together with the initial raw data to form the final raw data.

[0064] In this embodiment, through the portable physiological parameter monitoring device, the system can collect key physiological parameters of critically ill patients in a plateau environment in real time and comprehensively, such as blood oxygen saturation, heart rate, respiratory rate and blood pressure; the physiological parameter data is stored in time series to form an initial raw data sequence. This storage method not only retains the integrity of the data, but also facilitates subsequent tracking and analysis of the data. Through time series analysis, doctors can clearly see the changing trend of the patient's physiological parameters, providing a basis for disease assessment and treatment decisions; when processing the initial raw data, the system calculates derived data, such as blood oxygen saturation change rate, heart rate variability index, respiratory rate abnormality index and blood pressure change rate. These derived data can reflect the dynamic change characteristics of the patient's physiological parameters and provide richer information than the original data. By comprehensively analyzing the original data and derived data, doctors can more accurately evaluate the patient's physiological state and disease changes; the derived data is stored together with the initial raw data to form the final raw data. This data integration method ensures the integrity and accuracy of the data. At the same time, it is also convenient for doctors to trace back and review the data when necessary to ensure the accuracy and traceability of the diagnosis and treatment process.

[0065] It should be noted that the blood oxygen saturation change rate is used to evaluate the changing trend of the patient's blood oxygen saturation. The specific calculation formula is:

[0066] Blood oxygen saturation change rate = ((current blood oxygen saturation - initial blood oxygen saturation) / initial blood oxygen saturation) × 100%;

[0067] Heart rate variability index is used to reflect the fluctuation of heart rate over a period of time. The specific calculation formula is:

[0068] Heart rate variability index = (heart rate standard deviation / heart rate mean) × 100;

[0069] The respiratory rate abnormality index is used to assess whether the respiratory rate exceeds the normal range. The specific calculation formula is:

[0070] Abnormal respiratory rate index = ((actual respiratory rate - upper limit of normal respiratory rate range) / (upper limit of normal respiratory rate range - lower limit of normal respiratory rate range)) × 100%;

[0071] The blood pressure change rate is used to calculate the change rate of systolic pressure and diastolic pressure respectively. The specific calculation formula is:

[0072] Systolic blood pressure change rate = ((current systolic blood pressure - initial systolic blood pressure) / (initial systolic blood pressure)) × 100%;

[0073] Diastolic pressure change rate=((current diastolic pressure-initial diastolic pressure) / (initial diastolic pressure))×100%.

[0074] Preferably, the chaos entropy calculation module receives the original data sequence and evaluates the complexity and instability of the patient's physiological state by calculating the chaos entropy of the data, specifically including:

[0075] According to the set embedding dimension and time delay, the original data sequence is reconstructed in phase space to obtain a phase point sequence;

[0076] Count the frequency of each phase point in the phase point sequence to obtain the probability distribution;

[0077] According to the probability distribution, calculate the information entropy;

[0078] The chaos entropy is calculated by combining the information entropy and the phase space reconstruction results, wherein the greater the value of the chaos entropy is, the higher the complexity and instability of the patient's physiological state is.

[0079] In this embodiment, firstly, the original data sequence is reconstructed in phase space according to the set embedding dimension and time delay to obtain a phase point sequence. This process is a key step in chaos theory. It can convert one-dimensional time series data into multi-dimensional phase space data, so as to better reveal the inherent chaotic characteristics of the data. Through phase space reconstruction, the system can capture the nonlinear relationship and dynamic change characteristics in the patient's physiological parameter data; after obtaining the phase point sequence, the system counts the frequency of occurrence of each phase point to obtain a probability distribution. This step is the premise for calculating information entropy. Information entropy is an important indicator for measuring the randomness and uncertainty of data. By calculating information entropy, the system can quantify the complexity and randomness of the patient's physiological state data, and provide a scientific basis for evaluating the complexity and instability of the physiological state; combining the information entropy and the results of phase space reconstruction, the system calculates chaos entropy. Chaos entropy is an indicator that describes the complex orbital structure in chaotic phenomena. It reflects the complexity and instability of the system state. The larger the value of chaos entropy, the higher the complexity and instability of the patient's physiological state. This evaluation result can provide doctors with important disease information, which is helpful for early detection of potential risks and taking corresponding treatment measures.

[0080] Preferably, the setting of the time delay specifically includes:

[0081] The mutual information between the two time series is calculated, and the delay time corresponding to the first drop of the mutual information curve to the minimum value is found, and the delay time is the optimal time delay.

[0082] In this embodiment, mutual information is a statistic that measures the amount of shared information between two random variables, and can capture the correlation between two time series at different time lags. By calculating the mutual information, the optimal time delay between the two time series can be accurately found. The delay time corresponding to the first drop of the mutual information curve to the minimum value is determined as the optimal time delay. This standard is clear and objective, avoiding the subjectivity and uncertainty of artificially set time delays. The setting of the optimal time delay helps to more accurately reflect the dynamic changes of the patient's physiological state. By considering the time delay, the system can more accurately capture the interaction and mutual influence between physiological parameters. The accurate time delay setting enables the system to perform physiological status assessment based on more reliable data, which helps doctors to more accurately judge the patient's condition and physiological state, thereby formulating a more reasonable treatment plan.

[0083] Preferably, the setting of the embedding dimension specifically includes:

[0084] After selecting the time delay, the dimension is gradually increased until the correlation dimension stops changing. The embedding dimension when the correlation dimension stops changing is used as the reconstructed phase space dimension.

[0085] In this embodiment, the correlation dimension is an important parameter for describing the geometric properties of the attractor, which reflects the structural complexity of the system in the phase space. When the embedding dimension increases to a certain extent, the correlation dimension will tend to be stable, and at this time, increasing the embedding dimension will no longer affect the correlation dimension. Therefore, taking the cessation of change of the correlation dimension as the basis for setting the embedding dimension has higher accuracy; by gradually increasing the dimension and observing the change of the correlation dimension to determine the embedding dimension, the subjectivity and uncertainty of artificially setting the embedding dimension are avoided, which makes the implementation of the technical solution more objective and reliable; the appropriate embedding dimension can ensure that the reconstructed phase space can fully reflect the dynamic characteristics of the original data, and by gradually increasing the dimension and finding the embedding dimension when the correlation dimension stops changing, it can be ensured that the reconstructed phase space contains enough information to reflect the comprehensive situation of the patient's physiological state; accurate embedding dimension setting helps to improve the accuracy of physiological state assessment. When the embedding dimension is too low, it may not be able to fully capture the interaction and mutual influence between physiological parameters, and when the embedding dimension is too high, it may introduce unnecessary noise and complexity. Therefore, choosing a suitable embedding dimension is crucial for accurately assessing the physiological state.

[0086] Preferably, the debiasing value processing module performs debiasing value processing on the data sequence according to the chaotic entropy calculation result, specifically including:

[0087] The calculated chaotic entropy value is compared with the preset threshold. If the chaotic entropy value is higher than the threshold, it is considered that there is a large deviation value in the data sequence and debiasing processing is required;

[0088] In combination with the value of chaos entropy, a debiasing coefficient is calculated, wherein the debiasing coefficient is a function inversely proportional to the chaos entropy value;

[0089] Use the calculated debiasing coefficient to adjust each data point in the data series;

[0090] Output the adjusted data sequence.

[0091] In this embodiment, firstly, by comparing the chaos entropy value with the preset threshold, it is possible to accurately identify whether there is a large deviation value in the data sequence. Chaos entropy is an indicator for measuring data complexity and instability. A higher value of chaos entropy often means that there are abnormal or unstable factors in the data. Therefore, this step is crucial to improving data accuracy. Once the deviation value is identified, the technical solution will calculate the debiasing value coefficient in combination with the chaos entropy value, and use the coefficient to adjust each data point in the data sequence. This process can effectively reduce or eliminate the impact of the deviation value on the data as a whole, thereby improving the accuracy and reliability of the data. The technical solution realizes the automation of debiasing value processing, and can complete data deviation identification and correction without human intervention, which greatly improves the efficiency and accuracy of data processing and reduces the risk of human error. Through the calculation of preset thresholds and debiasing value coefficients, the technical solution can be flexibly adjusted according to the characteristics of different data sets.

[0092] It should be noted that the debiasing coefficient is a function inversely proportional to the chaotic entropy value, which means that when the chaotic entropy value is high, the debiasing coefficient will be small, and vice versa. Combined with the chaotic entropy value, the debiasing coefficient is calculated. The specific steps include:

[0093] Determine the inverse relationship: First, determine the inverse relationship between the debiasing coefficient and the chaotic entropy value;

[0094] Calculate the coefficient value: According to the determined inverse relationship, substitute the calculated chaotic entropy value into the formula to calculate the corresponding debiasing coefficient, which will be used in the subsequent data adjustment process.

[0095] Preferably, the information integration and analysis module compares and analyzes the data sequence after debiasing with the preset emergency and critical illness judgment criteria, generates a monitoring report, and provides emergency treatment suggestions, specifically including:

[0096] Integrate the debiased data sequence into a unified data structure;

[0097] Compare and analyze the integrated data series with the preset emergency and critical illness judgment criteria;

[0098] Use machine learning algorithms to detect anomalies in the aggregated data series;

[0099] Generate a monitoring report based on the results of the comparative analysis, the monitoring report including the patient's real-time monitoring data, comparative analysis results, abnormal detection conditions, and condition assessment information;

[0100] Provide emergency treatment recommendations based on abnormal conditions in the monitoring report and the results of the condition assessment.

[0101] In this embodiment, the data sequence after debiasing processing is integrated into a unified data structure, which helps to eliminate the differences in data format and structure. By comparing and analyzing with the preset emergency and critical judgment standards, the technical solution can automatically identify abnormal conditions and potential risks in the data, and complete the preliminary data analysis work without manual intervention. The integrated data sequence is detected for abnormality by using a machine learning algorithm, which helps to discover potential abnormal patterns in the data that are not easily detected by humans. The machine learning algorithm can process a large amount of data and extract useful information from it, thereby improving the accuracy of disease assessment. The monitoring report includes not only the patient's real-time monitoring data, but also includes comparative analysis results, abnormal detection conditions, and disease assessment information. This information comprehensively reflects the patient's disease condition and provides a comprehensive reference basis for doctors. Once an abnormality is detected or the patient's condition is assessed to be serious, the technical solution can immediately provide emergency treatment suggestions, which helps doctors make decisions quickly and take necessary treatment measures, thereby saving patients' lives. Through timely and accurate disease assessment and emergency treatment suggestions, the technical solution helps hospitals to reasonably allocate medical resources. For patients with serious conditions, treatment resources and medical staff can be arranged in priority to ensure that patients receive timely and effective treatment.

[0102] A method for implementing a high-altitude critical care information management system, specifically comprising:

[0103] The data acquisition module collects the physiological parameter data of critically ill patients in plateau environment in real time to form the original data sequence;

[0104] Using a chaotic entropy calculation module to receive the original data sequence, and to evaluate the complexity and instability of the patient's physiological state by calculating the chaotic entropy of the data;

[0105] According to the calculation result of chaos entropy, the data sequence is debiased through the debiasing value processing module;

[0106] The data sequence after debiasing is input into the information integration and analysis module, and compared with the preset emergency and critical judgment criteria to generate a monitoring report including the patient's current physiological status assessment and potential risk warning, and provide emergency treatment suggestions based on the comparative analysis results;

[0107] The monitoring report and emergency treatment suggestions are displayed through the monitoring management interface, allowing medical staff to monitor the patient's physiological state in real time and respond quickly.

[0108] A computing device 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 implementation method of the above-mentioned high-altitude critical care information management system are implemented.

[0109] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of the implementation method of the above-mentioned high-altitude critical care information management system.

[0110] 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. A high-altitude critical care information management system, characterized in that: include: The data acquisition module is used to collect the physiological parameter data of critically ill patients in the plateau environment in real time to form a raw data sequence; A chaos entropy calculation module, used for receiving the original data sequence and evaluating the complexity and instability of the patient's physiological state by calculating the chaos entropy of the data; The debiasing value processing module is used to perform debiasing value processing on the data sequence according to the chaotic entropy calculation result; The information integration and analysis module is used to compare and analyze the data sequence after debiasing with the preset emergency and critical disease judgment criteria, generate monitoring reports, and provide emergency treatment suggestions; The monitoring management interface is used to display the monitoring report and emergency treatment suggestions for medical staff to monitor and respond in real time.

2. A high-altitude critical care information management system according to claim 1, characterized in that: The data acquisition module collects physiological parameter data of critically ill patients in a plateau environment in real time to form an original data sequence, specifically including: The portable physiological parameter monitoring device is used to collect physiological parameter data of critically ill patients in a plateau environment in real time, and the physiological parameter data includes blood oxygen saturation, heart rate, respiratory rate and blood pressure; The physiological parameter data is stored in time series to form an initial raw data sequence; Processing the initial raw data to calculate derived data, wherein the derived data includes blood oxygen saturation change rate, heart rate variability index, respiratory rate abnormality index and blood pressure change rate; The derived data is stored together with the initial raw data to form the final raw data.

3. A high-altitude critical care information management system according to claim 2, characterized in that: The chaos entropy calculation module receives the original data sequence and evaluates the complexity and instability of the patient's physiological state by calculating the chaos entropy of the data, specifically including: According to the set embedding dimension and time delay, the original data sequence is reconstructed in phase space to obtain a phase point sequence; Count the frequency of each phase point in the phase point sequence to obtain the probability distribution; According to the probability distribution, calculate the information entropy; The chaos entropy is calculated by combining the information entropy and the phase space reconstruction results, wherein the greater the value of the chaos entropy is, the higher the complexity and instability of the patient's physiological state is.

4. A high-altitude critical care information management system according to claim 3, characterized in that: The setting of the time delay specifically includes: The mutual information between the two time series is calculated, and the delay time corresponding to the first drop of the mutual information curve to the minimum value is found, and the delay time is the optimal time delay.

5. A high-altitude critical care information management system according to claim 4, characterized in that: The setting of the embedding dimension specifically includes: After selecting the time delay, the dimension is gradually increased until the correlation dimension stops changing. The embedding dimension when the correlation dimension stops changing is used as the reconstructed phase space dimension.

6. A high-altitude critical care information management system according to claim 5, characterized in that: The debiasing value processing module performs debiasing value processing on the data sequence according to the chaotic entropy calculation result, specifically including: The calculated chaotic entropy value is compared with the preset threshold. If the chaotic entropy value is higher than the threshold, it is considered that there is a large deviation value in the data sequence and debiasing processing is required; In combination with the value of chaos entropy, a debiasing coefficient is calculated, wherein the debiasing coefficient is a function inversely proportional to the chaos entropy value; Use the calculated debiasing coefficient to adjust each data point in the data series; Output the adjusted data sequence.

7. A high-altitude critical care information management system according to claim 6, characterized in that: The information integration and analysis module compares and analyzes the data sequence after debiasing with the preset emergency and critical illness judgment criteria, generates a monitoring report, and provides emergency treatment suggestions, including: Integrate the debiased data sequence into a unified data structure; Compare and analyze the integrated data series with the preset emergency and critical illness judgment criteria; Use machine learning algorithms to detect anomalies in the aggregated data series; Generate a monitoring report based on the results of the comparative analysis, the monitoring report including the patient's real-time monitoring data, comparative analysis results, abnormal detection conditions, and condition assessment information; Provide emergency treatment recommendations based on abnormal conditions in the monitoring report and the results of the condition assessment.

8. A method for implementing a high-altitude critical care information management system, characterized in that: Specifically include: The data acquisition module collects the physiological parameter data of critically ill patients in plateau environment in real time to form the original data sequence; Using a chaotic entropy calculation module to receive the original data sequence, and to evaluate the complexity and instability of the patient's physiological state by calculating the chaotic entropy of the data; According to the calculation result of chaos entropy, the data sequence is debiased through the debiasing value processing module; The data sequence after debiasing is input into the information integration and analysis module, and compared with the preset emergency and critical judgment criteria to generate a monitoring report including the patient's current physiological status assessment and potential risk warning, and provide emergency treatment suggestions based on the comparative analysis results; The monitoring report and emergency treatment suggestions are displayed through the monitoring management interface, allowing medical staff to monitor the patient's physiological state in real time and respond quickly.

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 implementation method of the high-altitude critical care information management system described in claim 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 method for implementing the high-altitude critical care information management system of claim 8.

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