Data monitoring processing method and system based on intelligent sickbed

Abstract

The invention discloses a data monitoring and processing method and system based on an intelligent sickbed, and relates to the technical field of electric digital data processing. The data monitoring and processing method based on the intelligent sickbed comprises the following steps of sickbed partitioning, falling risk monitoring and cloud service. The pressure sensor collects the pressure feature information of the sickbed subarea of the preset patient and performs data processing to obtain the sickbed pressure data, then the sickbed pressure data of the preset patient is monitored in real time to obtain the falling risk index, and the falling risk index is compared with the falling risk threshold. If the tumble risk index is smaller than the tumble risk threshold value, monitoring continues, otherwise, the tumble risk index is fed back to medical staff through an alarm notice, and finally the tumble risk index is transmitted to a deployed patient cloud server in real time for data integration and storage, so that the effect of improving tumble risk analysis efficiency is achieved; the problem that falling risk analysis is slow in intelligent sickbed application in the prior art is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of electronic digital data processing, and in particular to a data monitoring and processing method and system based on an intelligent sickbed. Background Art

[0002] With the rapid development of information technology, the medical industry is undergoing a profound change in digital transformation. As an important part of the medical equipment system, the development and application of intelligent beds are gradually receiving attention. Intelligent beds can improve the turnover rate of hospital beds, improve medical quality, and reduce medical costs. Therefore, the market demand continues to grow. In order to improve their competitiveness, medical institutions have introduced intelligent bed systems. Data monitoring and processing plays a vital role in modern society, especially in the fields of environmental protection, medical health, industrial production, etc. With the advancement of science and technology and the rapid development of sensor technology, the ability of data monitoring and processing has been significantly improved. In the medical field, data monitoring and processing are widely used in patients. In terms of monitoring of patients' vital signs and early warning of diseases, smart beds are one of the typical applications. Information technology is used to strengthen medical quality management, reduce the incidence of medical errors, and ensure patient safety. Through data analysis and processing of patient monitoring systems, hospitals can optimize medical processes, improve the efficiency and quality of medical services, and establish a digital communication platform to facilitate timely and effective communication between doctors and patients and enhance mutual trust between doctors and patients. At the same time, the application of smart beds and patient monitoring systems will also help achieve the construction goals of digital hospitals, promote the digital transformation and upgrading of the medical industry, and through the construction of digital hospitals, realize the sharing and optimal allocation of medical resources and improve resource utilization efficiency.

[0003] In the existing technology, intelligent beds integrate sensors, Internet of Things technology, artificial intelligence algorithms and remote control systems to achieve real-time monitoring of patients' vital signs, automatic adjustment of bed posture to optimize comfort, intelligent reminders to medical staff of patients' needs and changes in health status, and seamless connection with hospital information systems, thereby greatly improving the efficiency and quality of medical care, providing patients with a more personalized, safe and comfortable rehabilitation environment, and achieving improved medical care efficiency.

[0004] For example, the invention patent with announcement number CN116776258B announces a method and system for processing monitoring data of electric power equipment, including: obtaining monitoring time series data of a first data attribute of a first electric power equipment, grouping the monitoring time series data according to equipment control parameters to obtain monitoring time series data grouping results, and then constructing a similarity matrix based on the i-th group of monitoring time series data of the monitoring time series data grouping results, performing cluster analysis on the i-th group of monitoring time series data based on the similarity matrix to obtain N clustering results, and performing outlier division on the N clustering results to obtain abnormal data division results, replacing the missing values ​​of the i-th group of monitoring time series data and the abnormal data division results with the cluster centroid mean of the N clustering results to obtain the optimization result of the i-th group of monitoring time series data, and finally transmitting the i-th group of monitoring time series data optimization result to the user terminal.

[0005] For example, the invention patent with announcement number CN115292301B discloses an artificial intelligence-based task data anomaly monitoring and processing method and system, including: obtaining first office task data obtained by recording a designated monitoring range by a monitoring system, determining the locations of X characters in the target monitoring range in the first office task data, obtaining X locations, determining the locations of designated characters corresponding to each of the X characters in the second office task data, obtaining X designated locations, determining the difference vectors between each location and each designated location, obtaining X difference vectors, and optimizing the characters in the target monitoring range in the first office task data using the X difference vectors to obtain target office task data.

[0006] However, in the process of implementing the technical solution of the invention in the embodiments of the present application, the present application found that the above technology has at least the following technical problems:

[0007] In the prior art, a highly sensitive electrocardiograph or a dedicated acquisition module integrated in medical equipment is used to accurately collect cardiac electrical signals from the body surface of a preset patient of a preset intelligent bed, and the collected electrocardiogram data is carefully preprocessed. Then, complex algorithms and technical means are used to accurately extract key information such as heart rate, heart rhythm, QRS complex, ST segment and T wave from the preprocessed electrocardiogram data. Then, according to the patient's specific condition and the doctor's diagnostic needs, a series of electrocardiogram parameter ranges such as heart rate range, identification of specific arrhythmia types and ST segment deviation degree are preset. During real-time monitoring and comparison, the system will continuously compare the collected electrocardiogram data with historical fall electrocardiogram parameters to make a preliminary diagnosis of the patient's heart state or a fall risk assessment. However, the electrocardiogram data needs to undergo complex preprocessing and feature extraction processes, which take a certain amount of time. There is a problem of slow fall risk analysis in the application of intelligent beds. Summary of the invention

[0008] The embodiment of the present application solves the problem of slow fall risk analysis in the application of intelligent beds in the prior art by providing a data monitoring and processing method and system based on an intelligent bed, thereby improving the efficiency of fall risk analysis.

[0009] An embodiment of the present application provides a data monitoring and processing method based on an intelligent bed, comprising the following steps: S1, using a pressure sensor to collect pressure characteristic information of a preset patient bed sub-area and perform data processing to obtain bed pressure data; S2, real-time monitoring of the bed pressure data of the preset patient to obtain a fall risk index, and comparing it with a fall risk threshold. If the fall risk index is less than the fall risk threshold, continue monitoring, otherwise feedback is given to medical staff through an alarm notification, and the fall risk index is used to measure the fall risk level of the preset patient in the preset intelligent bed; S3, transmitting the fall risk index in real time to the deployed patient cloud server for data integration and storage.

[0010] Furthermore, the real-time monitoring of bed pressure data of preset patients to obtain a fall risk index also includes obtaining a fall risk threshold, and the specific method is as follows: obtaining a historical fall frequency based on the historical number of falls of the preset patient in a preset time period, and the historical fall frequency is used to measure the frequency of falls of the preset patient; obtaining a historical fall severity coefficient based on the historical fall injury data of the preset patient, and the historical fall severity coefficient is used to measure the impact of falls on the preset patient; obtaining a fall reference value from a preset database, and normalizing the historical fall frequency and the historical fall severity coefficient to obtain a fall risk threshold, and the fall risk threshold is used to judge the fall risk level of the preset patient in bed.

[0011] Furthermore, the fall risk threshold is specifically calculated using the following formula:

[0012]

[0013] In the formula, DF0 represents the fall risk threshold, e represents the natural constant, a represents the historical fall frequency, b represents the historical fall severity coefficient, a0 represents the historical fall frequency reference value, and b0 represents the historical fall severity reference value.

[0014] Furthermore, the specific method for obtaining the fall risk index is as follows: measuring the sub-area pressure value of the bed sub-area through a pressure sensor, and the sub-area pressure value is used to measure the pressure level of the bed sub-area; by counting the sub-area pressure values ​​and calculating the average pressure value, the average pressure value is used to measure the overall pressure level; detecting the pressure value of the bed sub-area in a preset time period through a pressure sensor to obtain the sub-area pressure fluctuation value, and the sub-area pressure fluctuation value is used to measure the pressure fluctuation level of the bed sub-area in the preset time period; measuring the pressure value distribution of the bed sub-area through a pressure sensor to obtain the pressure distribution risk coefficient, and the pressure distribution risk coefficient is used to measure the abnormality of the pressure distribution of the preset intelligent bed; obtaining the pressure deviation weight factor and the pressure fluctuation weight factor from the preset database, and combining the sub-area pressure value, the average pressure value, the sub-area pressure fluctuation value and the pressure distribution risk coefficient to obtain the fall risk index.

[0015] Furthermore, the fall risk index is specifically calculated using the following formula:

[0016]

[0017] Where DF represents the fall risk index, h represents the bed sub-area number, h = 1, 2, ..., H, H represents the total number of bed sub-areas of the preset intelligent beds, x h represents the sub-area pressure value of the h-th bed sub-area, Indicates the average pressure value, y h represents the sub-area pressure fluctuation value of the h-th bed sub-area, z represents the pressure distribution hazard coefficient, α1 represents the pressure deviation weight factor, and α2 represents the pressure fluctuation weight factor.

[0018] An embodiment of the present application provides a data monitoring and processing system based on an intelligent bed, including a bed partition module, a fall risk monitoring module and a cloud service module; wherein the bed partition module is used to use a pressure sensor to collect pressure characteristic information of a preset patient bed sub-area and perform data processing to obtain bed pressure data; the fall risk monitoring module is used to monitor the bed pressure data of a preset patient in real time to obtain a fall risk index, and compare it with a fall risk threshold. If the fall risk index is less than the fall risk threshold, monitoring continues; otherwise, feedback is given to medical staff through an alarm notification. The fall risk index is used to measure the fall risk level of the preset patient in the preset intelligent bed; the cloud service module is used to transmit the fall risk index in real time to the deployed patient cloud server for data integration and storage.

[0019] One or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages:

[0020] 1. The pressure characteristic information of the preset patient bed sub-area is collected through the pressure sensor and the data is processed to obtain the bed pressure data. Then, the bed pressure data of the preset patient is monitored in real time to obtain the fall risk index, and compared with the fall risk threshold. Finally, the fall risk index is transmitted in real time to the deployed patient cloud server for data integration and storage, thereby achieving a more accurate analysis of the fall risk, and then achieving an improvement in the efficiency of the fall risk analysis, effectively solving the problem of slow fall risk analysis in the application of intelligent beds in the prior art.

[0021] 2. The pressure value of the sub-area of ​​the bed is measured by a pressure sensor to obtain the average pressure value, and then the pressure sensor is used to detect the pressure value of the bed sub-area in a preset time period to obtain the sub-area pressure fluctuation value. Finally, the pressure value distribution of the bed sub-area is measured by the pressure sensor to obtain the pressure distribution hazard coefficient, and the fall risk index is obtained by combining the pressure deviation weight factor and the pressure fluctuation weight factor, thereby realizing the numerical assessment of the fall risk and then realizing a more accurate assessment of the fall risk.

[0022] 3. The maximum sub-area pressure value is obtained by comparing the sub-area pressure values, and the coordinate point of the maximum sub-area pressure value is determined according to the two-dimensional coordinate system to obtain the pressure concentration point data, and then the center coordinate point data is obtained according to the preset bed surface geometric center of the intelligent bed. Finally, the pressure distribution risk coefficient is obtained in combination with the pressure concentration point data, thereby realizing the numerical evaluation of the pressure distribution risk, and then realizing a more accurate evaluation of the pressure distribution risk. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 A flow chart of a data monitoring and processing method based on an intelligent hospital bed provided in an embodiment of the present application;

[0024] Figure 2 A statistical diagram of changes in the pressure distribution risk coefficient provided in an embodiment of the present application;

[0025] Figure 3 A schematic diagram of the structure of a data monitoring and processing system based on an intelligent hospital bed provided in an embodiment of the present application. DETAILED DESCRIPTION

[0026] The embodiment of the present application solves the problem of slow fall risk analysis in the application of intelligent beds in the prior art by providing a data monitoring and processing method and system based on an intelligent bed. The pressure characteristic information of a preset patient bed sub-area is collected by using a pressure sensor and the bed pressure data is obtained by data processing. The bed pressure data of the preset patient is then monitored in real time to obtain a fall risk index, which is compared with the fall risk threshold. If the fall risk index is less than the fall risk threshold, monitoring continues. Otherwise, the alarm is notified to the medical staff. Finally, the fall risk index is transmitted in real time to the deployed patient cloud server for data integration and storage, thereby improving the efficiency of fall risk analysis in the application of intelligent beds.

[0027] The technical solution in the embodiment of the present application is to solve the problem of slow fall risk analysis in the above-mentioned intelligent bed application, and the overall idea is as follows:

[0028] The fall risk index is obtained by real-time monitoring of the bed pressure data of preset patients and compared with the fall risk threshold. If the fall risk index is less than the fall risk threshold, monitoring will continue. Otherwise, the fall risk index will be fed back to the medical staff through an alarm notification. At the same time, the fall risk index will be transmitted in real time to the deployed patient cloud server, thereby improving the efficiency of fall risk analysis.

[0029] In order to better understand the above technical solution, the above technical solution will be described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0030] like Figure 1 As shown, it is a flow chart of the data monitoring and processing method based on the intelligent bed provided in an embodiment of the present application, and the method includes the following steps: S1, using a pressure sensor to collect pressure characteristic information of a preset patient bed sub-area and perform data processing to obtain bed pressure data, the pressure characteristic information is used to record the pressure information of the preset patient bed sub-area, the bed pressure data includes a pressure value and a pressure change value, and the bed sub-area is obtained by zoning the preset intelligent bed; S2, real-time monitoring of the bed pressure data of the preset patient to obtain a fall risk index, and compare it with the fall risk threshold, if the fall risk index is less than the fall risk threshold, continue monitoring, otherwise feedback to medical staff through an alarm notification, the fall risk index is used to measure the fall risk level of the preset patient in the preset intelligent bed; S3, the fall risk index is transmitted in real time to the deployed patient cloud server for data integration and storage, and the patient cloud server is used to feedback the real-time changes of the fall risk index to the medical team for real-time monitoring and remote access.

[0031] In this embodiment, the patient cloud server is one of the technical solutions that already exists and is widely used in reality. With the development of information technology in the medical field, many medical institutions and health management organizations have adopted cloud servers to manage and store patients' health data. These systems usually include specially designed software platforms and cloud computing infrastructure for securely storing and managing patients' electronic health records, imaging data, laboratory results, etc. The patient cloud server not only provides the ability to remotely access and share data, but also protects patients' privacy and data integrity through advanced security measures. Medical institutions using patient cloud servers can more effectively manage patient care, realize data-driven medical decisions, and support collaboration and information sharing among multidisciplinary teams. The application of this technology not only improves the quality of medical services, but also improves patients' treatment effects and satisfaction; it achieves improved data management reliability.

[0032] Furthermore, a specific method for acquiring bed pressure data is as follows: partition the preset intelligent bed according to the measured length and width of the preset intelligent bed to obtain bed sub-areas, and deploy a pressure sensor in each bed sub-area; use the pressure sensor to collect pressure characteristic information of the preset patient bed sub-area, and perform information preprocessing on the pressure characteristic information of the preset patient bed sub-area, and the information preprocessing includes deduplication and processing of missing information; use an analog-to-digital converter to convert the pressure characteristic information after information preprocessing into a numerical form to obtain bed pressure data, and convert the acquired bed pressure data into a preset format.

[0033] In this embodiment, the length and width of the bed are evenly divided into several small squares or small rectangular areas. For example, if the length of the bed is 2.2 meters and the width is 1.2 meters, it can be divided into a grid of 22 rows and 12 columns, and each grid is a sub-area; deduplication is used to delete repeated pressure characteristic information, and information processing is used to fill in the missing pressure characteristic information through the average value; the pressure characteristic information after information preprocessing is converted into a numerical form, which usually involves an analog-to-digital converter. The analog-to-digital converter is an electronic device used to convert a continuous analog signal into a digital signal for further processing and analysis by a computer or digital circuit. The analog-to-digital converter quantizes the analog signal into a discrete digital value, usually represented in binary form; the reliability of obtaining bed pressure data is improved, and the safety of the preset intelligent bed is further improved.

[0034] Furthermore, the bed pressure data of the preset patients is monitored in real time to obtain the fall risk index, which also includes obtaining the fall risk threshold. The specific method is as follows: the historical fall frequency is obtained according to the historical number of falls of the preset patients in the preset time period, and the historical fall frequency is used to measure the frequency of falls of the preset patients; the historical fall severity coefficient is obtained according to the historical fall injury data of the preset patients, and the historical fall severity coefficient is used to measure the impact of falls on the preset patients; the fall reference value is obtained from the preset database, and the historical fall frequency and the historical fall severity coefficient are normalized to obtain the fall risk threshold, the fall reference value includes the historical fall frequency reference value and the historical fall severity reference value, the fall reference value is used to measure the risk level of falls of the preset patients, and the fall risk threshold is used to judge the fall risk level of the preset patients in bed.

[0035] In this embodiment, the historical fall frequency is represented by the ratio of the historical number of falls to a preset time period, and the historical fall severity coefficient is represented by the historical fall injury data of a preset patient. The historical fall injury data comes from medical records, nursing logs or other health records, and is rated according to the type of injury caused by the fall to obtain the historical fall severity coefficient. For example, minor injuries include skin abrasions and minor bruises, and the historical fall severity coefficient is 1. Moderate injuries include sprains and minor fractures, and the historical fall severity coefficient is 2. Severe injuries include hip fractures, intracranial hemorrhage and multiple fractures, and the historical fall severity coefficient is 3. Life-threatening injuries include severe craniocerebral injuries and multi-system injuries, and the historical fall severity coefficient is 4. This achieves a more accurate analysis of the bedridden status of the preset patient, and further achieves a more accurate analysis of the risk of falling.

[0036] Furthermore, the fall risk threshold is calculated using the following formula:

[0037]

[0038] In the formula, DF0 represents the fall risk threshold, e represents the natural constant, a represents the historical fall frequency, b represents the historical fall severity coefficient, a0 represents the historical fall frequency reference value, and b0 represents the historical fall severity reference value.

[0039] In this embodiment, a historical fall frequency reference value and a historical fall severity reference value are obtained from a preset database, and data in the preset database is obtained through medical records, nursing logs or other health records. The preset database includes detailed records of fall events that occurred in the past for a preset patient, such as the time, place, cause of the fall, whether an injury was caused, etc. The algorithm of this embodiment combines the historical fall frequency and the historical fall severity coefficient, and comprehensively analyzes to obtain a fall risk threshold. In this formula, the historical fall frequency and the historical fall severity coefficient influence each other. Frequent fall events will increase the probability of the patient being injured in the future. Multiple falls usually mean that the patient faces a higher risk, which may increase the severity of the injury when falling, indicating the risk level of falls for the preset patient. Therefore, accurate analysis is performed by establishing a mathematical form.

[0040] To facilitate analysis, we define α1 is the fall frequency coefficient, defined as α2 is the fall severity coefficient, and the calculation formula for the fall risk threshold is DF0=log2[α1*α2+1]. The statistical table of the change of the fall risk threshold is shown in Table 1:

[0041] Table 1 Statistics of changes in fall risk threshold

[0042]

[0043]

[0044] Among them, the fall frequency coefficient is positively correlated with the fall risk threshold, and the fall severity coefficient is positively correlated with the fall risk threshold; patients who fall frequently obviously face a higher risk of falling, even if they are not bedridden at present, but due to frequent falls, they are more likely to fall again in the future, especially when performing general activities in daily life. Even if the frequency of falls is not high, if they have experienced serious falls in the past, their fall risk is still high, because these patients may have specific physiological or behavioral characteristics, which makes them more likely to fall again in similar situations, and the injuries caused by the fall may be more serious; thereby achieving a comprehensive assessment of the risk of falling, and further achieving a more accurate assessment of the risk of falling, effectively solving the problem of slow fall risk analysis in the application of intelligent beds.

[0045] Furthermore, a specific method for obtaining the fall risk index is as follows: measuring the sub-area pressure value of a bed sub-area through a pressure sensor, and the sub-area pressure value is used to measure the pressure level of the bed sub-area; by counting the sub-area pressure values ​​and calculating the average pressure value, the average pressure value is used to measure the overall pressure level; detecting the pressure value of the bed sub-area in a preset time period through a pressure sensor to obtain the sub-area pressure fluctuation value, and the sub-area pressure fluctuation value is used to measure the pressure fluctuation level of the bed sub-area in the preset time period; measuring the pressure value distribution of the bed sub-area through a pressure sensor to obtain the pressure distribution hazard coefficient, and the pressure distribution hazard coefficient is used to measure the degree of abnormality of the pressure distribution of the preset intelligent bed; obtaining the pressure deviation weight factor and the pressure fluctuation weight factor from a preset database, and combining the sub-area pressure value, the average pressure value, the sub-area pressure fluctuation value and the pressure distribution hazard coefficient to obtain the fall risk index, the pressure deviation weight factor is used to measure the degree of influence of the deviation between the pressure value and the average pressure value on the fall risk index, and the pressure fluctuation weight factor is used to measure the degree of influence of the pressure fluctuation on the fall risk index.

[0046] In this embodiment, the sub-area pressure fluctuation value is represented by calculating the standard deviation of the pressure value of the bed sub-area in a preset time period; the pressure deviation weight factor and the pressure fluctuation weight factor are obtained from a preset database; in a specific embodiment, the pressure deviation value is the difference between the sub-area pressure value and the average pressure value, and a mapping set of pressure deviation values ​​and their corresponding weight factors is constructed according to the relationship between historical pressure deviation values ​​and fall risk indexes, and the real-time pressure deviation values ​​are input into the mapping set to obtain the corresponding pressure deviation weight factors; a mapping set of pressure fluctuation values ​​and their corresponding weight factors is constructed according to the relationship between historical pressure fluctuation values ​​and fall risk indexes, and the real-time pressure fluctuation values ​​are input into the mapping set to obtain the corresponding pressure fluctuation weight factors; the reliability of fall risk analysis is improved, and a more accurate analysis of fall risk is further achieved.

[0047] Furthermore, the fall risk index is calculated using the following formula:

[0048]

[0049] Where DF represents the fall risk index, h represents the bed sub-area number, h = 1, 2, ..., H, H represents the total number of bed sub-areas of the preset intelligent beds, x h represents the sub-area pressure value of the h-th bed sub-area, Indicates the average pressure value, y h represents the sub-area pressure fluctuation value of the h-th bed sub-area, z represents the pressure distribution hazard coefficient, α1 represents the pressure deviation weight factor, and α2 represents the pressure fluctuation weight factor.

[0050] In this embodiment, the algorithm of this embodiment combines the sub-region pressure value, the average pressure value, the sub-region pressure fluctuation value and the pressure distribution risk coefficient, and comprehensively analyzes to obtain the fall risk index. In this formula, the sub-region pressure fluctuation value and the pressure distribution risk coefficient affect each other, and the pressure distribution risk coefficient can affect the sub-region pressure fluctuation value. For example, if the pressure distribution risk coefficient of a certain area is high, it may mean that the pressure on the area is uneven or too large, which causes the pressure fluctuation to be large when the patient changes or moves in this part. The sub-region pressure fluctuation value can directly affect the pressure distribution risk coefficient. If the pressure fluctuation value of a certain area is large, it may indicate that the pressure distribution in the area is unstable or inappropriate, which may increase the possibility of the patient falling here, so it will increase the pressure distribution risk coefficient, indicating the fall risk level of the preset patient in the preset intelligent bed, so by establishing a mathematical form, accurate analysis is performed; the sub-region pressure value reflects the pressure intensity of a specific area on the mattress or bed surface. If the pressure value of a certain area is too high or too low, it may affect the comfort and stability of the patient in the area. For example, too high a pressure value may cause the patient to feel uncomfortable when staying here for a long time, increase the frequency of moving or adjusting the body position, thereby increasing the fall. The average pressure value is the average pressure level of all areas on the mattress or bed surface. A suitable average pressure value can promote the comfort and stability of patients and reduce the frequency of movement due to discomfort. If the average pressure value is too high or too low, it may affect the overall sleep quality and body position stability, thereby increasing the risk of falling. The sub-area pressure fluctuation value indicates the range of pressure variation in a specific area on the mattress or bed surface. A larger pressure fluctuation value may indicate that the pressure distribution in the area is unstable, which may be due to structural problems of the mattress or bed surface or posture changes caused by the high frequency of the patient's own activities. Unstable pressure distribution may increase the risk of patients falling here, especially when they try to adjust their position or move. The pressure distribution hazard coefficient reflects whether the pressure distribution on the mattress or bed surface is balanced and appropriate. A high hazard coefficient may mean that the pressure in certain areas is too high or uneven, increasing the risk of pressure sores, etc. This poor pressure distribution can make patients feel uncomfortable here, thereby increasing the instability when adjusting posture or moving, and then increasing the risk of falling. Thus, a comprehensive assessment of the risk of falling is achieved, and the efficiency of the analysis of the risk of falling is further improved, which effectively solves the problem of low accuracy in the analysis of the risk level of falling of preset patients in preset intelligent beds.

[0051] Furthermore, a specific method for obtaining the pressure distribution risk coefficient is as follows: taking the lower left corner of the preset intelligent bed as the origin and the bed surface of the preset intelligent bed as the plane to establish a coordinate axis to obtain a two-dimensional coordinate system, and the two-dimensional coordinate system is used to quantify the position information of the coordinate point; obtaining the maximum sub-area pressure value by comparing the sub-area pressure values, and determining the coordinate point of the maximum sub-area pressure value according to the two-dimensional coordinate system to obtain the pressure concentration point data, and the maximum sub-area pressure value is used to measure the pressure concentration of the preset intelligent bed, and the pressure concentration point data includes the horizontal axis coordinate value of the pressure concentration point and the vertical axis coordinate value of the pressure concentration point, and the pressure concentration point data is used to quantify the sub-area pressure value concentration of the preset patient; obtaining the central coordinate point data according to the geometric center of the bed surface of the preset intelligent bed, and obtaining the pressure distribution risk coefficient in combination with the pressure concentration point data, and the central coordinate point data includes the horizontal axis coordinate value of the central coordinate point and the vertical axis coordinate value of the central coordinate point, and the central coordinate point data is used to quantify the center position of the preset intelligent bed.

[0052] In this embodiment, the pressure distribution risk coefficient is specifically calculated using the following formula:

[0053]

[0054] In the formula, e represents the natural constant, z represents the pressure distribution hazard coefficient, and p x Indicates the horizontal axis coordinate value of the center coordinate point, p y Indicates the vertical coordinate value of the center coordinate point, g x Indicates the horizontal axis coordinate value of the pressure concentration point, g y Represents the vertical axis coordinate value of the pressure concentration point; the sub-area pressure fluctuation value is obtained by calculating the standard deviation of the pressure value of the bed sub-area in a preset time period; specifically, the algorithm of this embodiment combines the horizontal axis coordinate value of the central coordinate point, the vertical axis coordinate value of the central coordinate point, the horizontal axis coordinate value of the pressure concentration point and the vertical axis coordinate value of the pressure concentration point, and comprehensively analyzes to obtain the pressure distribution risk coefficient. In this formula, the horizontal axis coordinate value of the central coordinate point and the horizontal axis coordinate value of the pressure concentration point affect each other. When the horizontal axis coordinate value of the pressure concentration point deviates from the horizontal axis coordinate value of the central coordinate point, it means that the patient's body may be in an unbalanced or unstable state. This situation increases the risk of accidents or falls of patients in bed, especially for vulnerable groups or patients who cannot move independently, such as the elderly or paralyzed patients. The deviation of the horizontal axis coordinate value of the pressure concentration point from the horizontal axis coordinate value of the central coordinate point may indicate their inappropriate body posture or problems with bed settings. These factors will increase the potential risk of falling, indicating the abnormal degree of pressure distribution of the preset intelligent bed. Therefore, accurate analysis is performed by establishing a mathematical form.

[0055] For the convenience of analysis, define β1=(p x -g x ) 2, β1 is the pressure horizontal axis coefficient, β2=(p y -g y ) 2 , β2 is the pressure vertical axis coefficient, then the calculation formula of the pressure distribution hazard coefficient is: like Figure 2 As shown, it is a statistical graph of the changes in the pressure distribution risk coefficient provided in the example of the present application. It can be seen from the figure that the pressure horizontal axis coefficient is positively correlated with the pressure distribution risk coefficient, and the pressure vertical axis coefficient is positively correlated with the pressure distribution risk coefficient; the center coordinate point is obtained according to the geometric center of the bed surface of the preset intelligent bed, and the preset patient pressure concentration point is a pressure point that may be generated when the patient's weight distribution and posture adjustment are taken into account. If the offset between these two points is large, the bed surface may be unbalanced in supporting and dispersing pressure, causing the patient to feel unstable when moving or adjusting the body position, increasing the risk of falling; a comprehensive evaluation of the pressure distribution is achieved, and a more accurate detection of the pressure distribution of the preset intelligent bed is further achieved, effectively solving the problem of low accuracy in the analysis of the abnormal degree of pressure distribution of the preset intelligent bed.

[0056] Furthermore, the specific steps of data integration and storage are as follows: the fall risk index is transmitted to the deployed patient cloud server via Ethernet, and the received fall risk index is statistically calculated using a drawn line chart, which is used to measure the changing trend of the fall risk index; the line chart of the fall risk index is monitored in real time and displayed visually, and the visual display is used to feed back the real-time changes of the fall risk index to the medical team.

[0057] In this embodiment, Ethernet is a commonly used local area network technology, which is suitable for transmitting large amounts of data in medical facilities or home environments. Through Ethernet connection, stable, high-speed and reliable data transmission can be achieved; the line chart of the fall risk index is used for visual display, which can not only monitor the patient's fall risk, but also provide real-time feedback to the medical team. Medical staff can view the changes in fall risk in real time through the monitoring interface, and take intervention measures or adjust the treatment plan as needed. When implementing this process, data security and privacy protection must be ensured, data is transmitted using encryption, and only authorized personnel can access and view the data; the security of data transmission is improved, and real-time monitoring of the fall risk index is further realized.

[0058] Furthermore, a specific method for drawing a line graph is as follows: use Matpl ot lib to create a preset statistical graph, which is used to count the fall risk index of a preset patient in a preset time period; input the received fall risk index into the preset statistical graph to obtain the fall risk index data points, and connect the fall risk index data points in sequence; add statistical icon annotations to obtain a line graph of the fall risk index, which is used to explain the content represented by each broken line, and the statistical icon annotations include the patient's name and the statistical time period of the fall risk index.

[0059] In this embodiment, Matplotlib is a Python drawing library, which is widely used to generate various types of charts, including line graphs, bar graphs, scatter graphs, etc., and uses the mathematical drawing library (Mathematical Drawing Library, Matplotlib) to create a preset statistical graph. There are fall risk index data points distributed in chronological order in the preset statistical graph. The line graph of the fall risk index is obtained by connecting the fall risk index data points. The line graph will show the trend of the fall risk index over time, and the changes in the fall risk index are reflected by the rise or fall of the line; a more accurate analysis of the fall risk index is achieved, and the reliability of the fall risk analysis is further improved.

[0060] like Figure 3 As shown, it is a structural schematic diagram of a data monitoring and processing system based on an intelligent bed provided in an embodiment of the present application. The data monitoring and processing system based on an intelligent bed provided in an embodiment of the present application includes: a bed partition module, a fall risk monitoring module and a cloud service module; wherein the bed partition module is used to use a pressure sensor to collect pressure characteristic information of a preset patient bed sub-area and perform data processing to obtain bed pressure data, the pressure characteristic information is used to record the pressure information of the preset patient bed sub-area, the bed pressure data includes a pressure value and a pressure change value, and the bed sub-area is obtained by partitioning the preset intelligent bed; the fall risk monitoring module is used to monitor the bed pressure data of the preset patient in real time to obtain a fall risk index, and compare it with the fall risk threshold. If the fall risk index is less than the fall risk threshold, the monitoring continues, otherwise it is fed back to the medical staff through an alarm notification, and the fall risk index is used to measure the fall risk level of the preset patient in the preset intelligent bed; the cloud service module is used to transmit the fall risk index to the deployed patient cloud server in real time for data integration and storage, and the patient cloud server is used to feed back the real-time changes of the fall risk index to the medical team for real-time monitoring and remote access.

[0061] In this embodiment, if the fall risk index is monitored to exceed the fall risk threshold, the system will trigger an alarm notification, which will be immediately fed back to the relevant medical staff or nursing stations. The alarm notification content usually includes the patient's identification information, the current fall risk status, recommended countermeasures and emergency contact information. The recommended countermeasures include increasing the monitoring frequency, checking the bed setting or adjusting the patient's posture. The emergency contact information is used for medical staff to take necessary actions immediately, such as going to the patient's bedside to check or provide support. If the fall risk index is less than the fall risk threshold, monitoring and data recording will continue, but the alarm notification will not be triggered; the real-time performance of the alarm notification is improved, and the safety of the preset patient in bed is further improved.

[0062] To summarize, the embodiment of the present application utilizes a pressure sensor to collect pressure characteristic information of a preset patient bed sub-area and performs data processing to obtain bed pressure data, then monitors the preset patient bed pressure data in real time to obtain a fall risk index, and compares it with the fall risk threshold, and finally transmits the fall risk index in real time to the deployed patient cloud server for data integration and storage, thereby achieving a more accurate analysis of the fall risk, and further improving the efficiency of the fall risk analysis, effectively solving the problem of slow fall risk analysis in the application of intelligent beds in the prior art.

[0063] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0064] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0065] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0066] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0067] Although the preferred embodiments of the present invention have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0068] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A data monitoring and processing method based on an intelligent bed, characterized in that: The following steps are involved: S1, using a pressure sensor to collect pressure characteristic information preset in a patient bed sub-area, and performing data processing to obtain bed pressure data; S2, real-time monitoring of the bed pressure data of the preset patient to obtain the fall risk index, and compare it with the fall risk threshold. If the fall risk index is less than the fall risk threshold, continue monitoring, otherwise, give feedback to the medical staff through an alarm notification. The fall risk index is used to measure the fall risk level of the preset patient in the preset intelligent bed; S3, transmits the fall risk index in real time to the deployed patient cloud server for data integration and storage; The specific method for obtaining the bed pressure data is as follows: Partition the preset intelligent bed according to the measured length and width of the preset intelligent bed to obtain bed sub-areas, and deploy a pressure sensor in each bed sub-area; Using a pressure sensor to collect pressure characteristic information of a preset patient bed sub-area, and performing information preprocessing on the pressure characteristic information of the preset patient bed sub-area; The pressure characteristic information after information preprocessing is converted into a numerical form by using an analog-to-digital converter to obtain the bed pressure data, and the acquired bed pressure data is converted into a preset format.

2. The data monitoring and processing method based on the intelligent bed according to claim 1, characterized in that: The real-time monitoring of the bed pressure data of the preset patient to obtain the fall risk index also includes obtaining the fall risk threshold, and the specific method is as follows: Obtaining a historical fall frequency according to the number of historical falls of a preset patient in a preset time period, wherein the historical fall frequency is used to measure the frequency of falls of the preset patient; Obtaining a historical fall severity coefficient based on historical fall injury data of a preset patient, wherein the historical fall severity coefficient is used to measure the impact of the fall on the preset patient; A fall reference value is obtained from a preset database, and the historical fall frequency and the historical fall severity coefficient are normalized to obtain a fall risk threshold, where the fall risk threshold is used to determine the fall risk level of a preset patient in bed.

3. The data monitoring and processing method based on the intelligent bed according to claim 1, characterized in that: The specific method for obtaining the fall risk index is as follows: Measuring a sub-region pressure value of a bed sub-region by a pressure sensor, wherein the sub-region pressure value is used to measure a pressure level of the bed sub-region; By counting the pressure values ​​of the sub-areas and calculating the average pressure value, the average pressure value is used to measure the overall pressure level; The pressure sensor detects the pressure value of the bed sub-area in a preset time period to obtain a sub-area pressure fluctuation value, wherein the sub-area pressure fluctuation value is used to measure the pressure fluctuation level of the bed sub-area in the preset time period; The pressure distribution of the sub-areas of the bed is measured by a pressure sensor to obtain a pressure distribution risk coefficient, where the pressure distribution risk coefficient is used to measure the degree of abnormality of the pressure distribution of the preset intelligent bed; The pressure deviation weight factor and the pressure fluctuation weight factor are obtained from the preset database, and the fall risk index is obtained by combining the sub-area pressure value, the average pressure value, the sub-area pressure fluctuation value and the pressure distribution hazard coefficient.

4. The data monitoring and processing method based on the intelligent bed as claimed in claim 3, characterized in that: The specific method for obtaining the pressure distribution hazard coefficient is as follows: Taking the lower left corner of the preset intelligent bed as the origin and the bed surface of the preset intelligent bed as the plane, a coordinate axis is established to obtain a two-dimensional coordinate system; The maximum sub-region pressure value is obtained by comparing the sub-region pressure values, and the coordinate point of the maximum sub-region pressure value is determined according to the two-dimensional coordinate system to obtain the pressure concentration point data, wherein the maximum sub-region pressure value is used to measure the pressure concentration of the preset intelligent bed; The center coordinate point data is obtained according to the geometric center of the bed surface of the preset intelligent bed, and the pressure distribution hazard coefficient is obtained in combination with the pressure concentration point data.

5. The data monitoring and processing method based on the intelligent bed according to claim 1, characterized in that: The specific steps of data integration and storage are as follows: The fall risk index is transmitted to the deployed patient cloud server via Ethernet, and the received fall risk index is statistically calculated using a drawn line chart; Monitor the line chart of the fall risk index in real time and display it visually.

6. The data monitoring and processing method based on the intelligent bed according to claim 5, characterized in that: The specific method of drawing the line graph is as follows: Using Matplotlib to create a preset statistical graph, wherein the preset statistical graph is used to count the fall risk index of a preset patient in a preset time period; Input the received fall risk index into a preset statistical graph to obtain fall risk index data points, and connect the fall risk index data points in sequence; Add a statistical chart annotation to obtain a line chart of the fall risk index, where the statistical chart annotation is used to explain what each line represents.

7. The data monitoring and processing system based on intelligent hospital beds is characterized by: The system is used to execute the data monitoring and processing method based on the intelligent bed according to any one of claims 1 to 6, and the system includes a bed partition module, a fall risk monitoring module and a cloud service module; Wherein, the bed partition module is used to collect pressure characteristic information of a preset patient bed sub-area using a pressure sensor and perform data processing to obtain bed pressure data; The fall risk monitoring module is used to monitor the bed pressure data of the preset patient in real time to obtain the fall risk index, and compare it with the fall risk threshold. If the fall risk index is less than the fall risk threshold, the monitoring continues, otherwise the alarm notification is fed back to the medical staff. The fall risk index is used to measure the fall risk level of the preset patient in the preset intelligent bed; The cloud service module is used to transmit the fall risk index in real time to the deployed patient cloud server for data integration and storage.

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