Pension management system based on collaborative recommendation and multi-dimensional situation

Abstract

The invention relates to the field of information technology and intelligent old-age care service, and discloses an old-age care management system based on collaborative recommendation and a multi-dimensional situation, which comprises a data acquisition module, a situation perception module, a collaborative recommendation module and a real-time feedback and dynamic adjustment module, the data acquisition module acquires health data of old people in real time through the smart bracelet, the situation sensing module establishes a blood pressure fluctuation prediction model according to the acquired health data and situation data of the old people and predicts the risk of blood pressure fluctuation, and the collaborative recommendation module establishes a dynamic situation adjustment blood pressure monitoring model and predicts the risk of blood pressure fluctuation. The real-time feedback and dynamic adjustment module dynamically combines real-time measured blood pressure data and situation data, comprehensively reflects health risks of the old people under different environmental conditions, more accurately reflects actual health conditions of the old people, generates dynamically adjusted blood pressure values, and feeds back the blood pressure values to the real-time feedback and dynamic adjustment module according to the adjusted real-time blood pressure data of the old people. And managers and old people are helped to make reasonable health management strategies.

Description

Technical Field

[0001] The present invention relates to the field of information technology and intelligent elderly care service management, and more specifically, to an elderly care management system based on collaborative recommendation and multi-dimensional context. Background Art

[0002] The existing elderly care management system mainly manages the blood pressure health of the elderly through manual records and static data monitoring by nursing staff. Nursing staff measure the blood pressure of the elderly regularly according to a set schedule, and then manually record the measurement results in paper forms or electronic documents. This method not only consumes a lot of manpower and time, but also lacks real-time performance. Since the measurement time interval is relatively long, once the blood pressure of the elderly fluctuates between two measurements, the nursing staff cannot detect it in time. The existing elderly care management system mainly analyzes and manages the data of the elderly's historical blood pressure measurements, and cannot dynamically combine situational data to accurately reflect the actual health status of the elderly.

[0003] In order to solve the above problems, a technical solution is now provided. Summary of the invention

[0004] In order to overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides a pension management system based on collaborative recommendation and multi-dimensional contexts, in which the health data and context data of the elderly are collected through a data collection module, the context perception module establishes a blood pressure fluctuation prediction model to predict the risk of blood pressure fluctuations, and the collaborative recommendation module establishes a dynamic context-adjusted blood pressure monitoring model to dynamically combine the real-time measured blood pressure data and context data to generate a dynamically adjusted blood pressure value to reflect the health status of the elderly. The real-time feedback and dynamic adjustment module helps managers and the elderly themselves make reasonable health management strategies based on the adjusted real-time blood pressure data of the elderly to solve the problems raised in the above-mentioned background technology.

[0005] To achieve the above object, the present invention provides the following technical solutions: A pension management system based on collaborative recommendation and multi-dimensional context includes a data collection module, a context perception module, a collaborative recommendation module and a real-time feedback and dynamic adjustment module. The data collection module collects health data of the elderly in real time through a smart bracelet, and the health data includes blood pressure data, heart rate data, step data and sleep duration data, and integrates context data related to blood pressure fluctuations, and the context data includes ambient temperature data, air humidity data and air quality index data; the context perception module establishes a blood pressure fluctuation prediction model based on the collected health data and context data of the elderly, predicts the risk of blood pressure fluctuations, and issues an alarm in time; the collaborative recommendation module establishes a dynamic context adjustment blood pressure monitoring model, dynamically combines the real-time measured blood pressure data and context data, generates a dynamically adjusted blood pressure value, and accurately reflects the actual health status of the elderly; the real-time feedback and dynamic adjustment module helps managers and the elderly themselves make reasonable health management strategies based on the real-time blood pressure data of the elderly dynamically adjusted by the context data; the real-time feedback and dynamic adjustment module helps managers and the elderly themselves make reasonable health management strategies based on the real-time blood pressure data of the elderly dynamically adjusted by the context data; the formula of the dynamic context adjustment blood pressure monitoring model is: , Where: For time point, For at time point The real-time blood pressure value of the elderly after dynamic adjustment of the situational data. For at time point Real-time blood pressure data of the elderly collected at is the total number of context data, satisfy , is the ambient temperature data in the scenario data, is the air humidity data in the scenario data, is the air quality index data in the scenario data, For the Situational data at a point in time The value of For the The linear weight of the context data, For at time point The ambient temperature at For at time point The air humidity at For at time point The air quality index at is the interaction weight between the ambient temperature data and the air humidity data in the scenario data, is the interaction weight between the ambient temperature data and the air quality index data in the scenario data, It is the interaction weight between the air humidity data and the air quality index data in the situational data. If the real-time blood pressure value of the elderly after dynamic adjustment of the situational data is higher than the preset safe blood pressure value, the collaborative recommendation module triggers an alarm and notifies the elderly’s family members and caregivers via mobile phones.

[0006] As a further solution of the present invention, the data acquisition module collects health data of the elderly in real time through a smart bracelet, and the health data includes blood pressure data, heart rate data, step data and sleep duration data, and integrates context data related to blood pressure fluctuations, and the context data includes ambient temperature data, air humidity data and air quality index data, including the following specific contents: As a terminal device for data collection, the smart bracelet can measure the blood pressure of the elderly in real time and understand the changes in blood pressure in time. The smart bracelet can accurately measure the heart rate of the elderly and reflect the health of the heart. At the same time, the smart bracelet can obtain the number of steps, exercise distance data, sleep time data and sleep quality data of the elderly. In addition to the health data of the elderly themselves, the smart bracelet also integrates the situational data related to blood pressure fluctuations, and uses real-time transmission to send the collected data to the server side in the data collection module in time to ensure the timeliness of the data, so that caregivers and family members can understand the health status of the elderly in real time. In order to ensure the accuracy of the data, the smart bracelet also has the function of data calibration and verification, and will regularly calibrate the sensors in the smart bracelet to ensure the accuracy of the measurement data.

[0007] As a further solution of the present invention, the context perception module establishes a blood pressure fluctuation prediction model based on the collected health data and context data of the elderly, predicts the risk of blood pressure fluctuation, and issues an alarm in time, including: The situational awareness module obtains real-time health data and situational data of the elderly from the smart bracelet, removes abnormal values ​​of the collected data, and establishes a blood pressure fluctuation prediction model. Through the elderly's historical blood pressure data, heart rate data, step data, sleep duration data, ambient temperature data, air humidity data and air quality data, the blood pressure fluctuation prediction model is predicted. The blood pressure fluctuation prediction model combines multidimensional data to predict future blood pressure changes. When the blood pressure exceeds the safe value, the early warning mechanism is triggered. The formula of the blood pressure fluctuation prediction model is: , Where: For time point, For time point The blood pressure value, For time point The blood pressure value at the next time point, is a constant term, indicating the blood pressure baseline without input, For time point Blood pressure at the next time point The weight of blood pressure, For time point Heart rate of the elderly, For time point The heart rate of the elderly will change at the next time point The weight of blood pressure, For time point The walking distance of the elderly is For time point The walking distance of the elderly at the next time point The weight of blood pressure, For time point The sleep duration of the elderly For time point The sleep duration of the elderly will affect the next time point The weight of blood pressure, For time point The ambient temperature value, For time point The ambient temperature value at the next time point The weight of blood pressure, For time point The air humidity value, For time point The air humidity value at the next time point The weight of blood pressure, For time point The air quality index, For time point The air quality index for the next time point Effect weight of blood pressure.

[0008] As a further solution of the present invention: the real-time feedback and dynamic adjustment module helps managers and the elderly themselves to make reasonable health management strategies based on the adjusted real-time blood pressure data of the elderly, including: If the real-time blood pressure data of the elderly after dynamic adjustment of the situational data is within the preset normal blood pressure range, the real-time feedback and dynamic adjustment module encourages the elderly to eat more fresh vegetables and fruits, and encourages the elderly to increase the intake of whole grains; if the real-time blood pressure data of the elderly after dynamic adjustment of the situational data exceeds the preset normal blood pressure range, the real-time feedback and dynamic adjustment module issues an alarm, and notifies the elderly's family members and caregivers through mobile phones, and provides a diet plan to strictly control salt intake and avoid high-oil, high-fat, and high-sugar foods; If the real-time blood pressure data of the elderly after dynamic adjustment of the situational data is within the preset normal value range, the real-time feedback and dynamic adjustment module recommends that the elderly do more aerobic exercise, formulates appropriate exercise intensity according to the elderly's physical condition and exercise ability, and gradually increases the frequency of exercise to allow the elderly's body to adapt to the intensity of exercise; if the real-time blood pressure data of the elderly after dynamic adjustment of the situational data exceeds the preset normal value range, the real-time feedback and dynamic adjustment module notifies the elderly's family members and caregivers through mobile phones, and recommends that the elderly perform low-intensity aerobic exercise and avoid strenuous exercise. Each exercise time is controlled within 30 minutes. During the elderly's exercise, real-time attention is paid to the elderly's heart rate and blood pressure changes, and personalized exercise plans are formulated according to the elderly's specific physical condition.

[0009] Technical effects and advantages of the pension management system based on collaborative recommendation and multi-dimensional context of the present invention: The present invention collects blood pressure data, heart rate data, step data, sleep duration data, ambient temperature data, air humidity data and air quality index data of the elderly through a data acquisition module. The situational perception module establishes a blood pressure fluctuation prediction model to predict the risk of blood pressure fluctuation and issue an alarm in time. A dynamic situational adjustment blood pressure monitoring model is established through a collaborative recommendation module to dynamically combine the real-time measured blood pressure data and situational data to generate a dynamically adjusted blood pressure value, which accurately reflects the actual health status of the elderly and comprehensively reflects the health risks of the elderly under different environmental conditions. The real-time feedback and dynamic adjustment module helps managers and the elderly themselves make reasonable health management strategies based on the adjusted real-time blood pressure data of the elderly. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] Figure 1 This is a structural schematic diagram of a pension management system based on collaborative recommendation and multi-dimensional scenarios according to the present invention. DETAILED DESCRIPTION

[0011] 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.

[0012] Example 1 The present invention discloses a pension management system based on collaborative recommendation and multi-dimensional context, comprising a data collection module, a context perception module, a collaborative recommendation module and a real-time feedback and dynamic adjustment module, wherein the data collection module collects health data of the elderly in real time through a smart bracelet, wherein the health data includes blood pressure data, heart rate data, step data and sleep duration data, and integrates context data related to blood pressure fluctuations, wherein the context data includes ambient temperature data, air humidity data and air quality index data; the context perception module establishes a blood pressure fluctuation prediction model according to the collected health data and context data of the elderly, predicts the risk of blood pressure fluctuations, and issues an alarm in time; the collaborative recommendation module establishes a dynamic context-adjusted blood pressure monitoring model, dynamically combines the real-time measured blood pressure data and context data, generates a dynamically adjusted blood pressure value, and accurately reflects the actual health status of the elderly; the real-time feedback and dynamic adjustment module helps managers and the elderly themselves make reasonable health management strategies according to the adjusted real-time blood pressure data of the elderly.

[0013] Furthermore, the data collection module is connected to the situational awareness module, the collaborative recommendation module is connected to the situational awareness module, and the real-time feedback and dynamic adjustment module is connected to the collaborative recommendation module.

[0014] Furthermore, the data collection module collects the health data of the elderly in real time through the smart bracelet, and the health data includes blood pressure data, heart rate data, step data and sleep duration data, and integrates the situational data related to blood pressure fluctuations, and the situational data includes ambient temperature data, air humidity data and air quality index data, including: As a terminal device for data collection, the smart bracelet can measure the blood pressure of the elderly in real time. Various changes, such as blood pressure increase or decrease, can be known in time. The smart bracelet can accurately measure the heart rate of the elderly and reflect the health of the heart. At the same time, the smart bracelet can obtain the number of steps, exercise distance data, sleep time data and sleep quality data of the elderly; in addition to the health data of the elderly themselves, the smart bracelet also integrates the situational data related to blood pressure fluctuations. In terms of data transmission, real-time transmission is adopted to send the collected data to the server side in the data collection module in time to ensure the timeliness of the data, so that caregivers and family members can understand the health status of the elderly in real time. In order to ensure the accuracy of the data, the smart bracelet also has the function of data calibration and verification, and the sensors in the smart bracelet will be calibrated regularly to ensure the accuracy of the measurement data.

[0015] In this embodiment, the aforementioned smart bracelet has a concise and clear operation interface, and its interface layout is reasonable and the icons are clear and intuitive. On this operation interface, various health data are clear at a glance, and the elderly can easily view their blood pressure, heart rate, exercise steps, exercise distance, sleep time and sleep quality without complicated operation procedures. At the same time, the smart bracelet also has a voice prompt function. For those elderly people who are not familiar with the operation of smart devices or have poor eyesight, they can only use voice prompts to understand their current health status at any time, such as real-time blood pressure values, whether the heartbeat is normal, and other information. In terms of data security, the smart bracelet uses advanced symmetric encryption algorithms and reliable storage technology. Only authorized personnel, such as specific caregivers and family members, can access and view these data. In this way, the privacy of the elderly is effectively protected and the risk of personal health data being leaked at will is avoided. In addition, the smart bracelet also has the remarkable feature of low power consumption. It has been optimized in terms of internal circuit design and power management, and can achieve long-term battery life after a single charge. Such battery life greatly reduces the trouble of frequent charging for the elderly, allowing them to continue to wear the bracelet and monitor health data uninterruptedly. At the same time, it also has practical functions of waterproof and dustproof. Whether in the scene of daily contact with water when washing hands and washing faces, or in a dusty environment, the smart bracelet can work normally. This good adaptability fully meets the various needs of the elderly in their daily life environment, allowing them to wear smart bracelets with confidence in different environments.

[0016] Furthermore, the situational awareness module establishes a blood pressure fluctuation prediction model based on the collected health data and situational data of the elderly, predicts the risk of blood pressure fluctuation, and issues an alarm in a timely manner, including: The situational awareness module obtains real-time health data and situational data of the elderly from the smart bracelet. After obtaining the data, it removes abnormal values ​​of the collected data and establishes a blood pressure fluctuation prediction model. It makes full use of the elderly’s historical blood pressure data, heart rate data reflecting heart status, step data reflecting daily activity, sleep duration data related to physical recovery, ambient temperature data, air humidity data and air quality data. Through in-depth mining and calculation of these data, it predicts the blood pressure fluctuation of the elderly at the next time point. The blood pressure fluctuation prediction model combines multidimensional data to predict future blood pressure changes. When the blood pressure exceeds the safe value, the early warning mechanism is triggered. The formula of the blood pressure fluctuation prediction model is: , Where: For time point, For time point The blood pressure value, For time point The blood pressure value at the next time point, is a constant term, indicating the blood pressure baseline without input, For time point Blood pressure at the next time point The weight of blood pressure, For time point Heart rate of the elderly, For time point The heart rate of the elderly will change at the next time point The weight of blood pressure, For time point The walking distance of the elderly is For time point The walking distance of the elderly at the next time point The weight of blood pressure, For time point The sleep duration of the elderly For time point The sleep duration of the elderly will affect the next time point The weight of blood pressure, For time point The ambient temperature value, For time point The ambient temperature value at the next time point The weight of blood pressure, For time point The air humidity value, For time point The air humidity value at the next time point The weight of blood pressure, For time point The air quality index, For time point The air quality index for the next time point The influence weight of blood pressure, if the blood pressure fluctuation prediction model predicts the time point Blood pressure value at the next time point If the systolic blood pressure threshold is greater than 140 mmHG and the diastolic blood pressure threshold is greater than 85 mmHG, the situational awareness module triggers an alarm and notifies the elderly’s family and caregivers via mobile phones.

[0017] In this real-time example, the aforementioned context perception module obtains real-time health data and context data of the elderly from the smart bracelet and removes abnormal values ​​of the collected data, including: The blood pressure data, heart rate data, step data, sleep duration data, ambient temperature data, air humidity data and air quality data collected from smart bracelets and other devices may have abnormal values ​​due to detection errors in the sensors themselves, errors in the data transmission process and short-term physiological fluctuations of the elderly. These data deviate from the normal range and interfere with the accurate analysis of the health status of the elderly. In order to remove the outliers, the isolation forest algorithm is used. This algorithm can identify outliers that are significantly different from the distribution of most data points in multidimensional data, remove the outliers from the overall data, and obtain data that can more accurately reflect the true health status of the elderly.

[0018] Furthermore, the collaborative recommendation module establishes a dynamic situation-adjusted blood pressure monitoring model, dynamically combines the real-time measured blood pressure data with situational data, and generates dynamically adjusted blood pressure values ​​to accurately reflect the actual health status of the elderly, including: Combining the real-time blood pressure data and situational data of the elderly, a dynamic situational adjustment blood pressure monitoring model is established to monitor the health status of the elderly in real time. The formula of the dynamic situational adjustment blood pressure monitoring model is: , Where: For time point, For at time point The real-time blood pressure value of the elderly after dynamic adjustment of the situational data. For at time point Real-time blood pressure data of the elderly collected at is the total number of context data, satisfy , is the ambient temperature data in the scenario data, is the air humidity data in the scenario data, is the air quality index data in the scenario data, For the Situational data at a point in time The value of For the The linear weight of the context data, For at time point The ambient temperature at For at time point The air humidity at For at time point The air quality index at is the interaction weight between the ambient temperature data and the air humidity data in the scenario data, is the interaction weight between the ambient temperature data and the air quality index data in the scenario data, It is the interaction weight between the air humidity data and the air quality index data in the situational data. If the real-time blood pressure value of the elderly after dynamic adjustment of the situational data is higher than the preset safe blood pressure value, the collaborative recommendation module triggers an alarm and notifies the elderly’s family members and caregivers via mobile phones.

[0019] Furthermore, the real-time feedback and dynamic adjustment module helps managers and the elderly themselves make reasonable health management strategies based on the real-time blood pressure data of the elderly that has been dynamically adjusted by the context data, including: If the real-time blood pressure data of the elderly after dynamic adjustment of the situational data is within the preset normal blood pressure range, the real-time feedback and dynamic adjustment module encourages the elderly to eat more fresh vegetables and fruits, and encourages the elderly to increase the intake of whole grains; if the real-time blood pressure data of the elderly after dynamic adjustment of the situational data exceeds the preset normal blood pressure range, the real-time feedback and dynamic adjustment module issues an alarm, and notifies the elderly's family members and caregivers through mobile phones, and provides a diet plan to strictly control salt intake and avoid high-oil, high-fat, and high-sugar foods; If the real-time blood pressure data of the elderly after dynamic adjustment of the situational data is within the preset normal value range, the real-time feedback and dynamic adjustment module recommends that the elderly do more aerobic exercise, formulates appropriate exercise intensity according to the elderly's physical condition and exercise ability, and gradually increases the frequency of exercise to allow the elderly's body to adapt to the intensity of exercise; if the real-time blood pressure data of the elderly after dynamic adjustment of the situational data exceeds the preset normal value range, the real-time feedback and dynamic adjustment module notifies the elderly's family members and caregivers through mobile phones, and recommends that the elderly perform low-intensity aerobic exercise and avoid strenuous exercise. Each exercise time is controlled within 30 minutes. During the elderly's exercise, real-time attention is paid to the elderly's heart rate and blood pressure changes, and personalized exercise plans are formulated according to the elderly's specific physical condition.

[0020] The present invention collects blood pressure data, heart rate data, step data, sleep duration data, ambient temperature data, air humidity data and air quality index data of the elderly through a data acquisition module. The data acquisition module covers the health data of the elderly in their daily lives and the situational data in the environment, providing a rich data basis for subsequent analysis. The situational perception module establishes a blood pressure fluctuation prediction model, conducts a comprehensive analysis based on the collected multi-faceted data, predicts the risk of blood pressure fluctuations, and issues an alarm in time to avoid the occurrence of potential dangers. A dynamic situational adjustment blood pressure monitoring model is established through a collaborative recommendation module, and the real-time measured blood pressure data and situational data are dynamically combined to generate a dynamically adjusted blood pressure value, which accurately reflects the actual health status of the elderly and comprehensively reflects the health risks of the elderly under different environmental conditions. The real-time feedback and dynamic adjustment module helps managers and the elderly themselves make reasonable health management strategies based on the adjusted real-time blood pressure data of the elderly.

[0021] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0022] Finally: 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 pension management system based on collaborative recommendation and multi-dimensional context, comprising a data collection module, a context awareness module, a collaborative recommendation module and a real-time feedback and dynamic adjustment module, characterized in that: The data acquisition module collects the health data of the elderly in real time through the smart bracelet. The health data includes blood pressure data, heart rate data, step data and sleep duration data, and integrates the situational data related to blood pressure fluctuations. The situational data includes ambient temperature data, air humidity data and air quality index data; the situational perception module establishes a blood pressure fluctuation prediction model based on the collected health data and situational data of the elderly, predicts the risk of blood pressure fluctuations, and issues an alarm in time; the collaborative recommendation module establishes a dynamic situational adjustment blood pressure monitoring model, dynamically combines the real-time measured blood pressure data and situational data, and generates a dynamically adjusted blood pressure value. The formula of the dynamic situational adjustment blood pressure monitoring model is: ， Where: For time point, For at time point The real-time blood pressure value of the elderly after dynamic adjustment of the situational data. For at time point Real-time blood pressure data of the elderly collected at is the total number of context data, satisfy , is the ambient temperature data in the scenario data, is the air humidity data in the scenario data, is the air quality index data in the scenario data, For the Situational data at a point in time The value of For the The linear weight of the context data, For at time point The ambient temperature at For at time point The air humidity at For at time point The air quality index at is the interaction weight between the ambient temperature data and the air humidity data in the scenario data, is the interaction weight between the ambient temperature data and the air quality index data in the scenario data, It is the interaction weight between the air humidity data and the air quality index data in the situational data. If the real-time blood pressure value of the elderly after dynamic adjustment of the situational data is higher than the preset safe blood pressure value, the collaborative recommendation module triggers an alarm and notifies the elderly’s family members and caregivers via mobile phones.

2. The pension management system based on collaborative recommendation and multi-dimensional context according to claim 1, characterized in that ,The situation perception module builds a blood pressure fluctuation prediction model based on the ,collected health data and situation data of the elderly, predicts the risk of ,blood pressure fluctuation and issues an alarm in time. The formula of the ,blood pressure fluctuation prediction model is as follows: ， Where: For time point, For time point The blood pressure value, For time point The blood pressure value at the next time point, is a constant term, indicating the blood pressure baseline without input, For time point Blood pressure at the next time point The weight of blood pressure, For time point Heart rate of the elderly, For time point The heart rate of the elderly will change at the next time point The weight of blood pressure, For time point The walking distance of the elderly is For time point The walking distance of the elderly at the next time point The weight of blood pressure, For time point The sleep duration of the elderly For time point The sleep duration of the elderly will affect the next time point The weight of blood pressure, For time point The ambient temperature value, For time point The ambient temperature value at the next time point The weight of blood pressure, For time point The air humidity value, For time point The air humidity value at the next time point The weight of blood pressure, For time point The air quality index, For time point The air quality index for the next time point The influence weight of blood pressure, if the time point predicted by the blood pressure fluctuation prediction model Blood pressure value at the next time point If the blood pressure is greater than the set safe value, an alarm will be triggered through the emotional module to notify the elderly person's family and caregivers.

3. The pension management system based on collaborative recommendation and multi-dimensional context according to claim 1, characterized in that: The situational awareness module is connected to the data collection module, the collaborative recommendation module is connected to the situational awareness module, and the real-time feedback and dynamic adjustment module is connected to the collaborative recommendation module.

4. The pension management system based on collaborative recommendation and multi-dimensional context according to claim 1, characterized in that: The data acquisition module collects the health data of the elderly in real time through smart bracelets, and integrates the situational data related to blood pressure fluctuations. The smart bracelet serves as a data acquisition terminal to measure the elderly's blood pressure and heart rate health data, step data, and sleep duration data in real time, integrate the blood pressure fluctuation situational data and transmit it to the server in real time.

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