Respiration data reminding system for patient with critical respiratory disease

Abstract

The invention discloses a breathing data reminding system for a patient suffering from breathing crisis, and belongs to the technical field of breathing crisis, and the breathing data reminding system comprises a breathing collection module which is used for collecting real-time breathing data of the patient suffering from breathing crisis; the data processing module is used for processing and storing the real-time breathing data of the patient suffering from the breathing critically ill disease; the respiration analysis module is used for analyzing the real-time respiration data of the patient suffering from the respiratory critically ill disease; and the reminding and intervention module is used for carrying out early warning reminding and intervention control on the abnormal breathing behavior of the patient with the critical breathing disease. The problem that in the prior art, real-time monitoring and early warning reminding cannot be carried out on the breathing condition of a patient suffering from breathing critically ill is solved. The breathing condition of the patient with the critical respiratory disease can be monitored in real time, early-warning and reminding can be carried out, abnormal breathing behaviors can be found in time, the patient with the critical respiratory disease can be intervened and controlled in time, and the survival rate of the patient is increased to a certain degree.

Description

Technical Field

[0001] The present invention relates to the technical field of critical respiratory diseases, and in particular to a respiratory data reminder system for patients with critical respiratory diseases. Background Art

[0002] Critical respiratory disease is a disease caused by lung infection. It refers to clinical signs of severe, changeable and life-threatening critical conditions. It may be accompanied by difficulty breathing and low blood oxygen saturation. In severe cases, it may be accompanied by respiratory failure or multiple organ failure, which may be life-threatening. With the advancement of modern medical technology, the monitoring and treatment of patients with critical respiratory disease are becoming more and more sophisticated.

[0003] During the treatment of existing critically ill respiratory patients, it is impossible to monitor their respiratory conditions in real time and provide early warning reminders, it is impossible to detect abnormal respiratory behavior in time, and it is impossible to intervene and control critically ill respiratory patients in time, which reduces the survival rate of patients. Summary of the invention

[0004] The purpose of the present invention is to provide a respiratory data reminder system for critically ill respiratory patients, which can monitor the respiratory condition of critically ill respiratory patients in real time and provide early warning reminders, can promptly detect abnormal respiratory behavior, and can promptly intervene and control critically ill respiratory patients, thereby improving the survival rate of patients to a certain extent and solving the problems raised in the above-mentioned background technology.

[0005] To achieve the above object, the present invention provides the following technical solutions: A respiratory data reminder system for patients with critical respiratory diseases, comprising: A breathing collection module is used to collect the breathing conditions of critically ill patients in real time using smart sensors, and determine the real-time breathing data of critically ill patients based on smart sensors; A data processing module, used for cleaning the real-time respiratory data of the critically ill patients, removing inconsistent data, invalid values ​​and missing values ​​in the real-time respiratory data of the critically ill patients, and storing the cleaned real-time respiratory data of the critically ill patients; A breathing analysis module is used to analyze the real-time breathing data of patients with severe respiratory diseases, determine whether the patients with severe respiratory diseases have abnormal breathing behavior, and determine the breathing analysis results of the patients with severe respiratory diseases; The reminder and intervention module is used to provide early warning reminders and intervention control for abnormal breathing behavior of critically ill patients, so that critically ill patients can breathe normally.

[0006] Preferably, the breathing collection module comprises: A frequency monitoring unit is used to monitor and continuously collect the respiratory frequency of patients with critical respiratory diseases in real time based on intelligent sensors to obtain the respiratory frequency parameters of patients with critical respiratory diseases; A depth monitoring unit is used to monitor and continuously collect the breathing depth of critically ill patients in real time based on intelligent sensors to obtain the breathing depth parameters of critically ill patients; The blood oxygen monitoring unit is used to monitor and continuously collect the blood oxygen saturation of patients with critical respiratory diseases in real time based on intelligent sensors to obtain the blood oxygen saturation parameters of patients with critical respiratory diseases; Arterial blood gas monitoring unit, used for real-time monitoring and continuous collection of arterial blood gas of patients with critical respiratory illness based on intelligent sensors, to obtain arterial blood gas parameters of patients with critical respiratory illness; Among them, the real-time respiratory data of critically ill patients with respiratory diseases based on smart sensors are determined based on respiratory rate parameters, respiratory depth parameters, blood oxygen saturation parameters and arterial blood gas parameters.

[0007] Preferably, the data processing module includes: A data cleaning unit is used to clean the real-time respiratory data of patients with critical respiratory diseases; Obtain real-time respiratory data of critically ill patients based on smart sensors; Perform consistency checks on each parameter of real-time respiratory data of critically ill patients; Check whether there is inconsistent data in the real-time respiratory data of the patient with critical respiratory disease that is useless for respiratory data reminder. If there is inconsistent data, remove the inconsistent data in the real-time respiratory data of the patient with critical respiratory disease; Check invalid and missing values ​​for each parameter of the real-time respiratory data of critically ill patients; Check whether there are invalid values ​​and missing values ​​that are useless for respiratory data reminders in the real-time respiratory data of patients with critical respiratory illness. If there are invalid values ​​and missing values, remove the invalid values ​​and missing values ​​in the real-time respiratory data of patients with critical respiratory illness; Identify real-time respiratory data of critically ill patients that are useful for respiratory data alerts.

[0008] Preferably, the data processing module further includes: A data storage unit, used for storing real-time respiratory data of patients with critical respiratory diseases; Acquire real-time respiratory data of critically ill patients that are useful for respiratory data reminders after cleaning; According to different times, the real-time respiratory data of the critically ill patients for which the respiratory data reminder is useful are grouped, and the grouped real-time respiratory data of the critically ill patients are stored.

[0009] Preferably, the breathing analysis module comprises: A threshold setting unit, used to set threshold respiratory data for patients with critical respiratory illness; According to the respiratory data reminder requirements of the critically ill patients, the threshold respiratory data of the critically ill patients are pre-set, and the threshold respiratory data of the critically ill patients are stored; A respiratory analysis unit, used to analyze real-time respiratory data of critically ill patients; Extract threshold respiratory data from patients with critical respiratory illness; According to the threshold respiratory data of the critically ill respiratory patient, the real-time respiratory data of the critically ill respiratory patient is analyzed to determine whether the critically ill respiratory patient has abnormal respiratory behavior and determine the respiratory analysis result of the critically ill respiratory patient.

[0010] Preferably, determining whether a patient with critical respiratory disease has abnormal breathing behavior includes: When the real-time respiratory data of the critically ill patient is within the threshold respiratory data range of the critically ill patient, the respiratory analysis result of the critically ill patient is that the critically ill patient does not have abnormal respiratory behavior; When the real-time respiratory data of the critically ill patient is not within the threshold respiratory data range of the critically ill patient, the respiratory analysis result of the critically ill patient is that the critically ill patient has abnormal respiratory behavior.

[0011] Preferably, the reminder intervention module includes: The early warning reminder unit is used to immediately issue early warning reminder information based on the respiratory analysis results of critically ill patients, to warn of abnormal respiratory behavior of critically ill patients, and to remind medical staff to pay attention to the condition of critically ill patients in a timely manner; The intervention and control module is used to intervene and control the abnormal breathing behavior of patients with critical respiratory diseases; The wireless network connects the equipment of critically ill respiratory patients and medical staff, and remotely intervenes in the equipment of critically ill respiratory patients and medical staff to control the operation of the ventilator and help critically ill respiratory patients breathe normally.

[0012] Preferably, the reminder intervention module further includes: The recording and display unit is used to record the real-time respiratory data and early warning reminder information of critically ill respiratory patients, and to visually display the recorded real-time respiratory data and early warning reminder information of critically ill respiratory patients, so that medical staff can view and monitor the condition of critically ill respiratory patients in real time.

[0013] Preferably, the respiratory data reminder system for critically ill patients further comprises: a supervision module for conducting supervision and analysis on the behavior of associated personnel after the early warning reminder; Among them, the supervision module conducts supervision and analysis on the behavior of related personnel, including: Obtain the location of the associated person and track the location to determine whether the location change complies with the pre-configured rules. If not, remind again; and / or, Plan routes based on the locations of connected people and pre-configured maps; Analyze the planned path based on the historical movement data of the associated personnel to determine the movement path; Determine the arrival time based on the moving path and moving speed; When the arrival time is within the threshold time configured for patients with critical respiratory illness, no reminder will be given; otherwise, a reminder will be given; Wherein, based on the moving path and moving speed, the arrival time is determined, including: Segmenting the moving path based on the path parameters and determining the moving speed corresponding to each segment respectively; Determine the transition mode between each road section and determine the transition time according to the transition mode and the pre-configured transition time correspondence table; The arrival time is calculated based on the length of each road section, the moving speed and the transition time between each road section. The calculation formula is as follows: ; In the formula, Indicates the arrival time; Indicates The length of the road section; Indicates The moving speed of each road segment; Indicates Section and The transition time between sections is configured. When ; Indicates the total number of road segments.

[0014] Preferably, the supervision module is also used to analyze the status and location of all patients associated with each associated person to determine the standby area; analyze the standby area, determine the best standby position and output it; The supervision module performs the following operations: Based on the pre-configured patient analysis library, the patient's status data is analyzed to determine the threshold radius of the standby and the range of the standby area; The intersection area of ​​the waiting area ranges of each patient is taken as the waiting area; Carry out point sampling in the standby area to obtain multiple points to be analyzed; According to the distance between each point to be analyzed and each patient, and the distance between each point to be analyzed and the pre-configured facility point, the priority value of each point to be analyzed is evaluated. The evaluation formula is as follows: ; In the formula, Indicates the priority value; Indicates the distance between the point to be analyzed and The distance between patients; Indicates the distance between the point to be analyzed and The distance between the facility locations; For the pre-configured Conversion factor for each patient; For the pre-configured corresponding The conversion factor of each facility point; The number of patients associated with the associated person; is the total number of facility points; The point to be analyzed with the largest priority value is taken as the best standby position and output.

[0015] Compared with the prior art, the present invention has the following beneficial effects: 1. The present invention collects the respiratory conditions of critically ill respiratory patients in real time through intelligent sensors, determines the real-time respiratory data of critically ill respiratory patients based on intelligent sensors, cleans the real-time respiratory data of critically ill respiratory patients, removes inconsistent data, invalid values ​​and missing values ​​in the real-time respiratory data of critically ill respiratory patients, thereby improving the subsequent analysis accuracy and efficiency of the real-time respiratory data of critically ill respiratory patients, and stores the cleaned real-time respiratory data of critically ill respiratory patients for subsequent viewing and analysis.

[0016] 2. The present invention analyzes the real-time respiratory data of critically ill respiratory patients to determine whether the critically ill respiratory patients have abnormal breathing behavior and determine the respiratory analysis results of the critically ill respiratory patients. When the critically ill respiratory patients have abnormal breathing behavior, an early warning reminder message is immediately issued to warn of the abnormal breathing behavior of the critically ill respiratory patients, and medical staff are reminded to pay attention to the condition of the critically ill respiratory patients in time. The critically ill respiratory patients and the medical staff's equipment are connected through a wireless network, and the critically ill respiratory patients and the medical staff's equipment are remotely intervened to control the operation of the ventilator, and help the critically ill respiratory patients breathe normally. The breathing condition of the critically ill respiratory patients can be monitored in real time and early warning reminders can be provided, abnormal breathing behavior can be discovered in time, and timely intervention and control of the critically ill respiratory patients can be carried out, thereby improving the survival rate of patients to a certain extent. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1A structural framework diagram of a respiratory data reminder system for critically ill patients of the present invention; Figure 2 The present invention is an algorithm flow chart of the respiratory data reminder system for critically ill respiratory patients. DETAILED DESCRIPTION

[0018] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. 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.

[0019] In order to solve the problem that the current treatment of critically ill patients cannot monitor their breathing conditions in real time and provide early warning reminders, cannot detect abnormal breathing behavior in time, and cannot intervene and control critically ill patients in time, which reduces the survival rate of patients, please refer to Figure 1-Figure 2 , this embodiment provides the following technical solutions: A respiratory data reminder system for patients with critical respiratory diseases comprises a respiratory collection module, a data processing module, a respiratory analysis module and a reminder intervention module.

[0020] It should be noted that the real-time respiratory data of critically ill respiratory patients based on intelligent sensors are collected through the respiratory collection module; the real-time respiratory data of critically ill respiratory patients are cleaned and stored through the data processing module; the real-time respiratory data of critically ill respiratory patients are analyzed through the respiratory analysis module to determine whether the critically ill respiratory patients have abnormal respiratory behavior; the abnormal respiratory behavior of critically ill respiratory patients is warned and intervened and controlled through the reminder intervention module, so that the critically ill respiratory patients can breathe normally. The respiratory condition of critically ill respiratory patients can be monitored and warned in real time, abnormal respiratory behavior can be discovered in time, and timely intervention and control can be carried out on critically ill respiratory patients, which, to a certain extent, improves the survival rate of patients.

[0021] Among them, the breathing collection module is used to use intelligent sensors to collect the breathing conditions of critically ill patients in real time, and determine the real-time breathing data of critically ill patients based on intelligent sensors; In this embodiment, the breathing collection module includes: A frequency monitoring unit is used to monitor and continuously collect the respiratory frequency of patients with critical respiratory diseases in real time based on intelligent sensors to obtain the respiratory frequency parameters of patients with critical respiratory diseases; It should be noted that respiratory rate is a medical term that describes the number of breaths per minute. One rise and fall of the chest is a breath, that is, one inhalation and one exhalation. The number of breaths per minute is called the respiratory rate.

[0022] A depth monitoring unit is used to monitor and continuously collect the breathing depth of critically ill patients in real time based on intelligent sensors to obtain the breathing depth parameters of critically ill patients; It should be noted that the depth of breathing refers to the intensity of breathing, that is, the degree of gas exchange in one breath during calm breathing. The respiratory movement is driven by the contraction and relaxation of respiratory muscles such as the diaphragm and intercostal muscles to drive the movement of the chest cavity to expand and shrink, thereby driving the expansion and contraction of the lungs.

[0023] The blood oxygen monitoring unit is used to monitor and continuously collect the blood oxygen saturation of patients with critical respiratory diseases in real time based on intelligent sensors to obtain the blood oxygen saturation parameters of patients with critical respiratory diseases; It should be noted that blood oxygen saturation is the percentage of the capacity of oxygenated hemoglobin bound to oxygen in the blood to the total capacity of hemoglobin that can be bound, that is, the concentration of blood oxygen in the blood, which is an important physiological parameter of the respiratory circulation; the human body's metabolic process is a biological oxidation process, and the oxygen needed in the metabolic process enters the human blood through the respiratory system, combines with hemoglobin in red blood cells to form oxygenated hemoglobin, and then is transported to various tissue cells in the human body. The ability of blood to carry and transport oxygen is measured by blood oxygen saturation.

[0024] Arterial blood gas monitoring unit, used to monitor and continuously collect arterial blood gas of patients with critical respiratory illness in real time based on intelligent sensors, and obtain arterial blood gas parameters of patients with critical respiratory illness; It should be noted that arterial blood gas is the gas component dissolved in arterial blood. Healthy people mainly contain nitrogen, oxygen and carbon dioxide, with an oxygen partial pressure of 80-100 mmHg and a carbon dioxide partial pressure of 35-45 mmHg. Among them, arterial blood gas analysis refers to the technical process of analyzing different types of gases and acidic and alkaline substances in various gases and liquids.

[0025] Among them, the real-time respiratory data of critically ill patients with respiratory diseases based on smart sensors are determined based on respiratory rate parameters, respiratory depth parameters, blood oxygen saturation parameters and arterial blood gas parameters.

[0026] It should be noted that, in the present embodiment, the real-time respiratory data of the critically ill respiratory patient based on the smart sensor are the real-time respiratory rate parameters, respiratory depth parameters, blood oxygen saturation parameters and arterial blood gas parameters of the critically ill respiratory patient.

[0027] Among them, smart sensors are sensors with information processing functions. Smart sensors are equipped with microprocessors and have the ability to collect, process and exchange information. They are the product of the combination of sensor integration and microprocessors. Therefore, smart sensors can be used to monitor and continuously collect the respiratory rate, respiratory depth, blood oxygen saturation and arterial blood gas of critically ill respiratory patients in real time, thereby obtaining the respiratory rate parameters, respiratory depth parameters, blood oxygen saturation parameters and arterial blood gas parameters of critically ill respiratory patients, providing data support for respiratory data reminders for critically ill respiratory patients, and facilitating timely detection of abnormal respiratory behavior in critically ill respiratory patients.

[0028] The data processing module is used to clean the real-time respiratory data of the critically ill patients, remove inconsistent data, invalid values ​​and missing values ​​in the real-time respiratory data of the critically ill patients, and store the cleaned real-time respiratory data of the critically ill patients; In this embodiment, the data processing module includes: A data cleaning unit is used to clean the real-time respiratory data of patients with critical respiratory diseases; Obtain real-time respiratory data of critically ill patients based on smart sensors; Perform consistency checks on each parameter of real-time respiratory data of critically ill patients; Check whether there is inconsistent data in the real-time respiratory data of the patient with critical respiratory disease that is useless for respiratory data reminder. If there is inconsistent data, remove the inconsistent data in the real-time respiratory data of the patient with critical respiratory disease; Check invalid and missing values ​​for each parameter of the real-time respiratory data of critically ill patients; Check whether there are invalid values ​​and missing values ​​that are useless for respiratory data reminders in the real-time respiratory data of patients with critical respiratory illness. If there are invalid values ​​and missing values, remove the invalid values ​​and missing values ​​in the real-time respiratory data of patients with critical respiratory illness; Identify real-time respiratory data of critically ill patients that are useful for respiratory data alerts.

[0029] It should be noted that data cleaning refers to the process of processing and organizing raw data during data analysis to improve the quality and availability of the data. It is a key step in data preprocessing, and its main purpose is to remove erroneous, incomplete, inaccurate and irrelevant data, ensure the quality and accuracy of the data, and make it more suitable for data analysis or data mining.

[0030] Among them, the consistency check is to check whether the data meets the requirements based on the reasonable value range and mutual relationship of each parameter, and to find data that exceeds the normal range, is logically unreasonable or contradictory. For example, a variable measured by a 1-7 scale has a value of 0, and a negative number appears in the weight, which should be regarded as exceeding the normal value range. Computer software such as SPSS, SAS and Excel can automatically identify each variable value that exceeds the range according to the defined value range. Logically inconsistent answers may appear in many forms: for example, many respondents said that they drove to work, but reported that they did not have a car; or respondents reported that they were heavy buyers and users of a certain brand, but at the same time gave a very low score on the familiarity scale.

[0031] Therefore, by cleaning the real-time respiratory data of critically ill respiratory patients, inconsistent data, invalid values ​​and missing values ​​in the real-time respiratory data of critically ill respiratory patients can be removed, and then the real-time respiratory data of critically ill respiratory patients that are useful for respiratory data reminders can be determined, which can improve the processing accuracy and efficiency of subsequent real-time respiratory data of critically ill respiratory patients, and facilitate subsequent analysis of the real-time respiratory data of critically ill respiratory patients.

[0032] In this embodiment, the data processing module further includes: A data storage unit, used for storing real-time respiratory data of patients with critical respiratory diseases; Acquire real-time respiratory data of critically ill patients that are useful for respiratory data reminders after cleaning; According to different times, the real-time respiratory data of the critically ill patients for which the respiratory data reminder is useful are grouped, and the grouped real-time respiratory data of the critically ill patients are stored.

[0033] Among them, the respiratory analysis module is used to analyze the real-time respiratory data of patients with critical respiratory diseases, determine whether the patients with critical respiratory diseases have abnormal respiratory behavior, and determine the respiratory analysis results of the patients with critical respiratory diseases; In this embodiment, the breathing analysis module includes: A threshold setting unit, used to set threshold respiratory data for patients with critical respiratory illness; According to the respiratory data reminder requirements of the critically ill patients, the threshold respiratory data of the critically ill patients are pre-set, and the threshold respiratory data of the critically ill patients are stored; A respiratory analysis unit, used to analyze real-time respiratory data of critically ill patients; Extract threshold respiratory data from patients with critical respiratory illness; According to the threshold respiratory data of the critically ill respiratory patient, the real-time respiratory data of the critically ill respiratory patient is analyzed to determine whether the critically ill respiratory patient has abnormal respiratory behavior and determine the respiratory analysis result of the critically ill respiratory patient.

[0034] In this embodiment, determining whether a patient with critical respiratory disease has abnormal breathing behavior includes: When the real-time respiratory data of the critically ill patient is within the threshold respiratory data range of the critically ill patient, the respiratory analysis result of the critically ill patient is that the critically ill patient does not have abnormal respiratory behavior; When the real-time respiratory data of the critically ill patient is not within the threshold respiratory data range of the critically ill patient, the respiratory analysis result of the critically ill patient is that the critically ill patient has abnormal respiratory behavior.

[0035] Specifically, according to the threshold respiratory data of the critically ill patient, the real-time respiratory data of the critically ill patient is analyzed, wherein the respiratory analysis results of the critically ill patient are shown in Table 1: Table 1: Respiratory analysis results of critically ill patients Analyze the real-time respiratory data of critically ill patients based on their threshold respiratory data Breathing analysis results of critically ill patients The real-time respiratory data of the critically ill patient is within the threshold respiratory data range of the critically ill patient Patients with critical respiratory illness do not have abnormal breathing behavior The real-time respiratory data of the patient with critical respiratory disease is not within the threshold respiratory data range of the patient with critical respiratory disease Abnormal breathing behavior in critically ill patients Therefore, based on the threshold respiratory data of critically ill respiratory patients, the real-time respiratory data of critically ill respiratory patients are analyzed to determine whether the critically ill respiratory patients have abnormal respiratory behavior, and then the respiratory analysis results of the critically ill respiratory patients are determined. The respiratory condition of the critically ill respiratory patients can be monitored in real time, which is convenient for timely early warning reminders and intervention control of critically ill respiratory patients.

[0036] Among them, the reminder and intervention module is used to provide early warning reminders and intervention control for abnormal breathing behaviors of critically ill respiratory patients, so that critically ill respiratory patients can breathe normally.

[0037] In this embodiment, the reminder intervention module includes: The early warning reminder unit is used to immediately issue early warning reminder information based on the respiratory analysis results of critically ill patients, to warn of abnormal respiratory behavior of critically ill patients, and to remind medical staff to pay attention to the condition of critically ill patients in a timely manner; The intervention and control module is used to intervene and control the abnormal breathing behavior of patients with critical respiratory diseases; The wireless network connects the equipment of critically ill respiratory patients and medical staff, and remotely intervenes in the equipment of critically ill respiratory patients and medical staff to control the operation of the ventilator and help critically ill respiratory patients breathe normally.

[0038] In this embodiment, the reminder intervention module further includes: The recording and display unit is used to record the real-time respiratory data and early warning reminder information of critically ill respiratory patients, and to visually display the recorded real-time respiratory data and early warning reminder information of critically ill respiratory patients, so that medical staff can view and monitor the condition of critically ill respiratory patients in real time.

[0039] In one embodiment, the respiratory data reminder system for critically ill patients further includes: a supervision module for conducting supervision and analysis on the behavior of associated personnel after the early warning reminder; Among them, the supervision module conducts supervision and analysis on the behavior of related personnel, including: Obtain the location of the associated person and track the location to determine whether the location change complies with the pre-configured rules. If not, remind again; specifically determine whether the location change complies with the pre-configured rules, including: sampling the location change data for a preset number of times (3 times or more), and setting the time interval for each sampling to any one of 1 second to 30 seconds; determine the distance of the location change data corresponding to the patient's location for each sampling, and when the distance obtained from two adjacent samplings gradually decreases, determine that it complies with the pre-configured rules; when the distance obtained from two adjacent samplings gradually increases, determine that it does not comply with the pre-configured rules; when the number of times the distance increases in the total number of samplings is less than or equal to the number of times the distance decreases, determine that it complies; when the number of times the distance increases in the total number of samplings is greater than the number of times the distance decreases, determine that it does not comply; and / or, Planning a route based on the location of the associated person and a pre-configured map; the map is pre-configured and stored, and when planning a route, multiple routes are obtained with the location of the associated person as the starting point and the location of the patient being warned as the end point; The planned path is analyzed based on the historical movement data of the associated personnel to determine the movement path; the analysis of the historical movement data mainly analyzes the personal preferences of the associated personnel to obtain a movement path that meets the personal preferences of the associated personnel; that is, when there are multiple paths, one of them is frequently sampled by the associated personnel, and it is determined to be the movement path; Determine the arrival time based on the moving path and moving speed; When the arrival time is within the threshold time configured for patients with critical respiratory diseases, no reminder will be given; otherwise, a reminder will be given. By reminding, the related personnel are urged to move as quickly as possible and increase the moving speed to reduce the corresponding moving time, thereby improving the effectiveness of the early warning reminder response; Wherein, based on the moving path and moving speed, the arrival time is determined, including: The moving path is segmented based on the path parameters and the moving speed corresponding to each section is determined respectively; segment analysis is performed for different types of sections of the moving path (stairs, corridors, elevators, before and after gates, etc.), which improves the accuracy and effectiveness of arrival time analysis; Determine the transition mode between each road section and determine the transition time according to the transition mode and the pre-configured transition time correspondence table; for example, the transition mode includes passing through the gate, waiting for the elevator, etc.; when calculating the arrival time, consider the transition time to further achieve the accuracy of the arrival time prediction; The arrival time is calculated based on the length of each road section, the moving speed and the transition time between each road section. The calculation formula is as follows: ; In the formula, Indicates the arrival time; Indicates The length of the road section; Indicates The moving speed of each road segment; Indicates Section and The transition time between sections is configured. When ; Indicates the total number of road sections. The moving speed of the associated person on each road section, except for the moving speed of the first section of the moving path which is the current moving speed, the moving speed of the remaining sections is obtained based on the average moving speed of the associated person's historical moving data on the same type of road sections; The supervision module in this embodiment is to supervise and analyze the behavior of the associated personnel after the early warning reminder to determine whether it is necessary to issue a second early warning reminder. The supervision can be based on the location and predicted arrival time. For the supervision of the location, it is mainly to determine whether the associated personnel move towards the location of the patient who issued the early warning reminder; for the supervision of the arrival time, it is to determine whether the associated personnel can arrive at the location of the patient who issued the early warning reminder in time, so as to effectively provide timely intervention measures for the patient.

[0040] In order to enable the associated personnel to effectively respond to the emergency situations of each associated patient, it is necessary to know the best standby position. How to determine the best standby position? In one embodiment, the supervision module is also used to analyze the status and position of all patients associated with each associated personnel to determine the standby area; analyze the standby area, determine the best standby position and output it; The supervision module performs the following operations: Based on the pre-configured patient analysis library, the patient's status data is analyzed to determine the threshold radius of standby and the range of the standby area; the specific analysis process is: feature extraction of the patient's status data, and then based on the extracted feature parameters, the corresponding threshold radius is retrieved from the patient analysis library; wherein the extracted feature parameters include: parameters indicating the type of surgery, parameters indicating the time of surgery, parameters indicating the age of the patient, parameters indicating the weight of the patient, parameters indicating the surgeon's evaluation of the patient's surgery, etc.; The intersection area of ​​the waiting area ranges of each patient is taken as the waiting area; the waiting area belongs to the waiting area range of each patient; The standby area is sampled at points to obtain a plurality of points to be analyzed; the standby area is segmented with a preset sampling grid, and the points are determined by the intersections between the grids, and then the positions of the points are analyzed to determine whether to list them as points to be analyzed based on the analysis results; wherein the analysis steps are as follows: in the three-dimensional model map, the preset area model is placed at the points of the intersections between the determined grids, and the points that cannot be placed are deleted; then, based on the impact on the risk of passage after placement, the points with risk values ​​greater than the preset risk threshold are deleted, and the remaining points are used as points to be analyzed; wherein, the judgment of the impact on the risk of passage after placement includes: determining the minimum width of the passage area after placement, and determining the risk value based on the minimum width and the risk enhancement table pre-configured with the inherent parameters corresponding to the point; wherein the inherent parameters include: a field of parameters representing the location type (aisle area, indoor area, etc.), parameters representing the area coordinates of each location type, etc.; the area coordinates are obtained by encoding the relative positions after segmenting each location type; According to the distance between each point to be analyzed and each patient, and the distance between each point to be analyzed and the pre-configured facility point, the priority value of each point to be analyzed is evaluated. The evaluation formula is as follows: ; In the formula, Indicates the priority value; Indicates the distance between the point to be analyzed and The distance between patients; Indicates the distance between the point to be analyzed and The distance between the facility locations; For the pre-configured Conversion factor for each patient; For the pre-configured corresponding The conversion factor of each facility point; The number of patients associated with the associated person; is the total number of facility points; The point to be analyzed with the largest priority value is taken as the best standby position and output.

[0041] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0042] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A respiratory data reminder system for patients with critical respiratory diseases, characterized in that: include: A breathing collection module is used to collect the breathing conditions of critically ill patients in real time using smart sensors, and determine the real-time breathing data of critically ill patients based on smart sensors; A data processing module, used for cleaning the real-time respiratory data of the critically ill patients, removing inconsistent data, invalid values ​​and missing values ​​in the real-time respiratory data of the critically ill patients, and storing the cleaned real-time respiratory data of the critically ill patients; A breathing analysis module is used to analyze the real-time breathing data of patients with severe respiratory diseases, determine whether the patients with severe respiratory diseases have abnormal breathing behavior, and determine the breathing analysis results of the patients with severe respiratory diseases; The reminder and intervention module is used to provide early warning reminders and intervention control for abnormal breathing behavior of critically ill patients, so that critically ill patients can breathe normally.

2. A respiratory data reminder system for critically ill patients according to claim 1, characterized in that: The breathing collection module comprises: A frequency monitoring unit is used to monitor and continuously collect the respiratory frequency of patients with critical respiratory diseases in real time based on intelligent sensors to obtain the respiratory frequency parameters of patients with critical respiratory diseases; A depth monitoring unit is used to monitor and continuously collect the breathing depth of critically ill patients in real time based on intelligent sensors to obtain the breathing depth parameters of critically ill patients; The blood oxygen monitoring unit is used to monitor and continuously collect the blood oxygen saturation of patients with critical respiratory diseases in real time based on intelligent sensors to obtain the blood oxygen saturation parameters of patients with critical respiratory diseases; Arterial blood gas monitoring unit, used for real-time monitoring and continuous collection of arterial blood gas of patients with critical respiratory illness based on intelligent sensors, to obtain arterial blood gas parameters of patients with critical respiratory illness; Among them, the real-time respiratory data of critically ill respiratory patients based on smart sensors are determined based on respiratory rate parameters, respiratory depth parameters, blood oxygen saturation parameters and arterial blood gas parameters.

3. A respiratory data reminder system for critically ill patients according to claim 2, characterized in that: The data processing module comprises: A data cleaning unit is used to clean the real-time respiratory data of patients with critical respiratory diseases; Obtain real-time respiratory data of critically ill patients based on smart sensors; Perform consistency checks on each parameter of real-time respiratory data of critically ill patients; Check whether there is inconsistent data in the real-time respiratory data of the patient with critical respiratory disease that is useless for respiratory data reminder. If there is inconsistent data, remove the inconsistent data in the real-time respiratory data of the patient with critical respiratory disease; Check invalid and missing values ​​for each parameter of the real-time respiratory data of critically ill patients; Check whether there are invalid values ​​and missing values ​​that are useless for respiratory data reminders in the real-time respiratory data of patients with critical respiratory illness. If there are invalid values ​​and missing values, remove the invalid values ​​and missing values ​​in the real-time respiratory data of patients with critical respiratory illness; Identify real-time respiratory data of critically ill patients that are useful for respiratory data alerts.

4. A respiratory data reminder system for critically ill patients according to claim 3, characterized in that: The data processing module further includes: A data storage unit, used for storing real-time respiratory data of patients with critical respiratory diseases; Acquire real-time respiratory data of critically ill patients that are useful for respiratory data reminders after cleaning; According to different times, the real-time respiratory data of the critically ill patients for which the respiratory data reminder is useful are grouped, and the grouped real-time respiratory data of the critically ill patients are stored.

5. A respiratory data reminder system for critically ill patients according to claim 4, characterized in that: The breathing analysis module comprises: A threshold setting unit, used to set threshold respiratory data for patients with critical respiratory illness; According to the respiratory data reminder requirements of the critically ill patients, the threshold respiratory data of the critically ill patients are pre-set, and the threshold respiratory data of the critically ill patients are stored; A respiratory analysis unit, used to analyze real-time respiratory data of critically ill patients; Extract threshold respiratory data from patients with critical respiratory illness; According to the threshold respiratory data of the critically ill respiratory patient, the real-time respiratory data of the critically ill respiratory patient is analyzed to determine whether the critically ill respiratory patient has abnormal respiratory behavior and determine the respiratory analysis result of the critically ill respiratory patient.

6. A respiratory data reminder system for critically ill patients according to claim 5, characterized in that: Determine whether critically ill patients have abnormal breathing behavior, including: When the real-time respiratory data of the critically ill patient is within the threshold respiratory data range of the critically ill patient, the respiratory analysis result of the critically ill patient is that the critically ill patient does not have abnormal respiratory behavior; When the real-time respiratory data of the critically ill patient is not within the threshold respiratory data range of the critically ill patient, the respiratory analysis result of the critically ill patient is that the critically ill patient has abnormal respiratory behavior.

7. A respiratory data reminder system for critically ill patients according to claim 6, characterized in that: The reminder intervention module comprises: The early warning reminder unit is used to immediately issue early warning reminder information based on the respiratory analysis results of critically ill patients, warn of abnormal respiratory behavior of critically ill patients, and remind medical staff to pay attention to the condition of critically ill patients in a timely manner; The intervention and control module is used to intervene and control the abnormal breathing behavior of patients with critical respiratory diseases; The wireless network connects the equipment of critically ill respiratory patients and medical staff, and remotely intervenes in the equipment of critically ill respiratory patients and medical staff to control the operation of the ventilator and help critically ill respiratory patients breathe normally.

8. A respiratory data reminder system for critically ill patients according to claim 7, characterized in that: The reminder intervention module further includes: The recording and display unit is used to record the real-time respiratory data and early warning reminder information of critically ill respiratory patients, and to visually display the recorded real-time respiratory data and early warning reminder information of critically ill respiratory patients, so that medical staff can view and monitor the condition of critically ill respiratory patients in real time.

9. A respiratory data reminder system for critically ill patients according to claim 1, characterized in that: Also includes: The supervision module is used to supervise and analyze the behavior of related personnel after early warning reminders; Among them, the supervision module conducts supervision and analysis on the behavior of related personnel, including: Obtain the location of the associated person and track the location to determine whether the location change complies with the pre-configured rules. If not, remind again; and / or, Plan routes based on the locations of connected people and pre-configured maps; Analyze the planned path based on the historical movement data of the associated personnel to determine the movement path; Determine the arrival time based on the moving path and moving speed; When the arrival time is within the threshold time configured for patients with critical respiratory illness, no reminder will be given; otherwise, a reminder will be given; Wherein, based on the moving path and moving speed, the arrival time is determined, including: Segmenting the moving path based on the path parameters and determining the moving speed corresponding to each segment respectively; Determine the transition mode between each road section and determine the transition time according to the transition mode and the pre-configured transition time correspondence table; The arrival time is calculated based on the length of each road section, the moving speed and the transition time between each road section. The calculation formula is as follows: ； In the formula, Indicates the arrival time; Indicates The length of the road section; Indicates The moving speed of each road segment; Indicates Section and The transition time between sections is configured. When ; Indicates the total number of road segments.

10. A respiratory data reminder system for critically ill patients according to claim 9, characterized in that: The supervision module is also used to analyze the status and location of all patients associated with each associated person to determine the standby area; analyze the standby area, determine the best standby position and output it; The supervision module performs the following operations: Based on the pre-configured patient analysis library, the patient's status data is analyzed to determine the threshold radius of the standby and the range of the standby area; The intersection area of ​​the waiting area ranges of each patient is taken as the waiting area; Carry out point sampling in the standby area to obtain multiple points to be analyzed; According to the distance between each point to be analyzed and each patient, and the distance between each point to be analyzed and the pre-configured facility point, the priority value of each point to be analyzed is evaluated. The evaluation formula is as follows: ； In the formula, Indicates the priority value; Indicates the distance between the point to be analyzed and The distance between patients; Indicates the distance between the point to be analyzed and The distance between the facility locations; For the pre-configured Conversion factor for each patient; For the pre-configured corresponding The conversion factor of each facility point; The number of patients associated with the associated person; is the total number of facility points; The point to be analyzed with the largest priority value is taken as the best standby position and output.

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