Nursing alarm system for ward

Abstract

The invention belongs to the technical field of ward nursing, and provides a ward nursing alarm system comprising a physiological parameter monitoring module provided with a non-contact sensor; an artificial intelligence algorithm is built in the data analysis and processing unit; the alarm module adopts a multi-stage alarm mechanism; the environment monitoring and adjusting module is used for monitoring environment parameters in the ward; the patient behavior analysis module monitors the activity state of the patient through a camera and an action recognition algorithm; an integrated management platform; by integrating a plurality of modules such as a physiological parameter monitoring module, a data analysis and processing module, a multi-stage alarm module, an environment monitoring and adjusting module, a patient behavior analysis module and an integrated management module, continuous non-inductive monitoring of physiological parameters of a patient, intelligent identification and graded alarm of an abnormal state and automatic adjustment and monitoring of a ward environment are realized; and real-time monitoring and abnormal alarm of patient behaviors are realized, so that a convenient and efficient patient management and nursing means is provided for medical staff.

Description

Technical Field

[0001] The invention belongs to the technical field of ward nursing, in particular to a nursing alarm system for a ward. Background Art

[0002] In the current medical environment, ward care is an important part of medical work. However, the traditional ward care method mainly relies on regular inspections by medical staff and active reports from patients, which has many shortcomings. For example, when a patient has an emergency, if the medical staff fails to discover and deal with it in time, it may lead to worsening of the condition or even endanger life. In addition, for some patients who need to continuously monitor physiological parameters, the traditional care method not only increases the workload of medical staff, but also makes it difficult to ensure the accuracy and timeliness of monitoring.

[0003] In order to solve the above problems, some ward nursing monitoring systems have appeared on the market; however, most of these systems have problems such as single function, low intelligence, and imperfect alarm mechanism.

[0004] Therefore, those skilled in the art have proposed a nursing alarm system for wards to solve the problems raised by the background technology. Summary of the invention

[0005] In order to solve the above technical problems, the present invention provides a nursing alarm system for wards to solve the problems of single function, low intelligence level, imperfect alarm mechanism and so on in most systems in the prior art.

[0006] A nursing alarm system for a ward, comprising:

[0007] The physiological parameter monitoring module is equipped with a non-contact sensor for continuously and non-sensitively monitoring the patient's heart rate, respiratory rate, blood pressure and body temperature, and transmitting the monitoring data to the data analysis unit in real time;

[0008] Data analysis and processing unit, with built-in artificial intelligence algorithm, is used to analyze the data transmitted by the physiological parameter monitoring module, identify abnormal physiological states, and automatically classify them according to the degree of abnormality;

[0009] An alarm module, wherein the alarm module adopts a multi-level alarm mechanism, including a primary sound and light alarm, an intermediate ward broadcast alarm, and an advanced remote communication alarm. The primary alarm is activated when the data analysis unit identifies a slight abnormality, the intermediate alarm is activated when a moderate abnormality is identified and the abnormal information is broadcast at the same time, and the advanced alarm is activated when a severe abnormality is identified, and an emergency notification is automatically sent to a preset medical staff communication device, which includes the patient's location information and abnormal physiological parameters;

[0010] Environmental monitoring and adjustment module, which is used to monitor environmental parameters in the ward, such as temperature, humidity, light intensity and noise level, and automatically adjust them to a suitable range according to preset standards, while triggering environmental alarms when environmental parameters are abnormal;

[0011] The patient behavior analysis module uses cameras and motion recognition algorithms to monitor the patient's activity status, such as getting up, walking, and falling. For abnormal behaviors such as long periods of inactivity or sudden falls, a behavior alarm is immediately triggered;

[0012] The integrated management platform integrates all the above modules and provides a graphical user interface for medical staff to view patient status, receive alarm information, adjust alarm thresholds and manage patient files.

[0013] Preferably, the non-contact sensors include but are not limited to infrared sensors, radar sensors and image recognition sensors to ensure monitoring without affecting the patient's rest.

[0014] Preferably, in the physiological parameter monitoring module, by introducing a signal filtering algorithm, the state of the dynamic system is estimated by using the state equation and the observation equation through the prediction and update steps;

[0015] At the same time, the moving average filtering algorithm is introduced: the average value of the data in a certain window is calculated as the estimated value of the current point

[0016] Preferably, in the data analysis and processing unit, a machine learning algorithm is introduced to build a prediction model to identify abnormal physiological states.

[0017] Preferably, the remote communication alarm adopts an encrypted communication protocol to ensure the security of patient information, and supports multi-channel notifications, including SMS, email and mobile application push.

[0018] Preferably, the environmental monitoring and adjustment module also has a learning function for automatically adjusting environmental parameters according to the patient's preferences and recovery stage to create the most suitable treatment environment.

[0019] Preferably, in the environmental monitoring and adjustment module, an adaptive control algorithm is introduced to automatically adjust the environmental parameters (including temperature, humidity, light intensity and noise level) according to the environmental parameters in the ward and the patient's preferences. The adaptive control algorithm adjusts the output through proportional (P), integral (I) and differential (D) control to achieve the desired set value.

[0020] Preferably, in the integrated management platform, a weighted average method is introduced to perform weighted averaging on data from multiple data sources to improve the accuracy and reliability of the data.

[0021] A processor is configured to execute the nursing alarm system for a ward according to the above.

[0022] A computer-readable storage medium stores a computer program, which implements the above-mentioned nursing alarm system for wards when executed by a processor.

[0023] Compared with the prior art, the present invention has the following beneficial effects:

[0024] 1. The present invention can continuously and imperceptibly monitor the patient's key physiological parameters such as heart rate, respiratory rate, blood pressure and body temperature by configuring a physiological parameter monitoring module with a non-contact sensor, and transmit the monitoring data to a data analysis unit in real time; it not only improves the accuracy and timeliness of monitoring, but also reduces interference to the patient and improves the patient's comfort.

[0025] 2. The data analysis and processing unit of the present invention has a built-in artificial intelligence algorithm, which can automatically identify abnormal physiological conditions and issue graded alarms according to the degree of abnormality; this intelligent analysis and alarm mechanism can help medical staff quickly and accurately judge the patient's condition and take corresponding treatment measures, thereby effectively reducing the risk of medical accidents.

[0026] 3. The present invention also has an environmental monitoring and adjustment module, which can monitor the environmental parameters such as temperature, humidity, light intensity and noise level in the ward in real time, and automatically adjust to an appropriate range according to preset standards; at the same time, when the environmental parameters are abnormal, the module can also trigger an environmental alarm to remind medical staff to deal with it in time; this comprehensive environmental monitoring and adjustment function can provide patients with a comfortable and safe treatment environment, which is conducive to the patient's recovery.

[0027] 4. Through the camera and motion recognition algorithm, the patient behavior analysis module of the present invention can monitor the patient's activity status in real time, such as getting up, walking, falling, etc.; for abnormal behaviors such as long-term immobility or sudden falling, the module can immediately trigger a behavior alarm to remind medical staff to deal with it in time; this intelligent patient behavior analysis function not only improves the safety of the ward, but also reduces the workload of medical staff.

[0028] 5. The present invention also provides an integrated management platform that can integrate the functions of all modules and provide a graphical user interface for medical staff to view patient status, receive alarm information, adjust alarm thresholds and manage patient files; this integrated management platform makes ward nursing work more convenient and efficient. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 This is a framework diagram of the nursing alarm system for wards of the present invention. DETAILED DESCRIPTION

[0030] The following embodiments of the present invention are described in further detail in conjunction with the accompanying drawings and examples. The following examples are used to illustrate the present invention, but are not intended to limit the scope of the present invention.

[0031] Embodiment: The present invention provides a nursing alarm system for a ward, such as Figure 1 As shown, including:

[0032] The physiological parameter monitoring module is equipped with a non-contact sensor for continuously and non-sensitively monitoring the patient's heart rate, respiratory rate, blood pressure and body temperature, and transmitting the monitoring data to the data analysis unit in real time;

[0033] Data analysis and processing unit, with built-in artificial intelligence algorithm, is used to analyze the data transmitted by the physiological parameter monitoring module, identify abnormal physiological states, and automatically classify them according to the degree of abnormality;

[0034] An alarm module, wherein the alarm module adopts a multi-level alarm mechanism, including a primary sound and light alarm, an intermediate ward broadcast alarm, and an advanced remote communication alarm. The primary alarm is activated when the data analysis unit identifies a slight abnormality, the intermediate alarm is activated when a moderate abnormality is identified and the abnormal information is broadcast at the same time, and the advanced alarm is activated when a severe abnormality is identified, and an emergency notification is automatically sent to a preset medical staff communication device, which includes the patient's location information and abnormal physiological parameters;

[0035] Environmental monitoring and adjustment module, which is used to monitor environmental parameters in the ward, such as temperature, humidity, light intensity and noise level, and automatically adjust them to a suitable range according to preset standards, while triggering environmental alarms when environmental parameters are abnormal;

[0036] The patient behavior analysis module uses cameras and motion recognition algorithms to monitor the patient's activity status, such as getting up, walking, and falling. For abnormal behaviors such as long periods of inactivity or sudden falls, a behavior alarm is immediately triggered;

[0037] The integrated management platform integrates all the above modules and provides a graphical user interface for medical staff to view patient status, receive alarm information, adjust alarm thresholds and manage patient files.

[0038] From the above, we can see that by integrating multiple modules such as physiological parameter monitoring, data analysis and processing, multi-level alarm, environmental monitoring and adjustment, patient behavior analysis and integrated management, we have achieved continuous and imperceptible monitoring of patient physiological parameters, intelligent identification of abnormal conditions and graded alarms, automatic adjustment and monitoring of ward environment, and real-time monitoring of patient behavior and abnormal alarms, providing medical staff with convenient and efficient patient management and nursing methods, and effectively improving the safety and intelligence level of ward nursing.

[0039] Furthermore, the non-contact sensors include but are not limited to infrared sensors, radar sensors and image recognition sensors to ensure monitoring without affecting the patient's rest.

[0040] From the above, we can see that the non-contact sensors used in the system, such as infrared sensors, radar sensors and image recognition sensors, can continuously and imperceptibly monitor the patient's key physiological parameters without affecting the patient's rest; this monitoring method not only improves the accuracy and timeliness of the data, but also effectively improves the patient's comfort and reduces the discomfort and interference caused by traditional contact monitoring methods.

[0041] Furthermore, in the physiological parameter monitoring module, by introducing a signal filtering algorithm, the state equation and the observation equation are used to estimate the state of the dynamic system through prediction and update steps. The formula involved in the signal filtering algorithm is as follows:

[0042] State prediction equation: X(kk-1)=AX(k-1k-1)+BU(k-1);

[0043] Observation update equation: X(kk)=X(kk)+K g (Z(k)-HX(kk-1));

[0044] Among them, K g represents the Kalman gain;

[0045] At the same time, the moving average filtering algorithm is introduced: the average value of the data in a certain window is calculated as the estimated value of the current point. The formula of the moving average filtering algorithm is as follows:

[0046]

[0047] Among them, n represents the window size, k represents half of the window, and X i Represents the original data, Y i Represents the smoothed data.

[0048] From the above, it can be seen that by introducing signal filtering algorithms (such as Kalman filtering using state prediction equations and observation update equations) and moving average filtering algorithms, the patient's physiological parameter data can be more accurately estimated and smoothed; this data processing method effectively reduces noise and interference, improves data stability and reliability, and thus provides a more accurate foundation for subsequent data analysis and abnormality identification.

[0049] Furthermore, in the data analysis and processing unit, a machine learning algorithm is introduced to construct a prediction model to identify abnormal physiological states. The formula involved in the machine learning algorithm is as follows:

[0050] Support Vector Machine (SVM): Classifies data by finding the classification hyperplane that maximizes the margin. Its formula includes the decision function: Among them, K(x i ,x) represents the kernel function;

[0051] Logistic regression: Use the logistic function (sigmoid function) to convert the output of linear regression into probability. The formula is:

[0052] From the above, we can see that by introducing machine learning algorithms (such as support vector machines and logistic regression), we can build accurate prediction models to automatically identify and classify patients' abnormal physiological states; this intelligent data analysis method not only improves the accuracy and efficiency of abnormality identification, but also helps medical staff to take timely intervention measures, reduce the risk of medical accidents, and provide strong protection for patients' safety and recovery.

[0053] Furthermore, the remote communication alarm adopts an encrypted communication protocol to ensure the security of patient information, and supports multi-channel notifications, including SMS, email and mobile application push.

[0054] From the above, we can see that the remote communication alarm adopts encrypted communication protocols and supports multi-channel notifications, which ensures the security of patient information and the timeliness of alarm information; through encrypted communication protocols, the privacy and sensitive information of patients are effectively protected, avoiding the risk of information leakage; at the same time, the multi-channel notification method ensures that medical staff can receive alarm information in time no matter when and where, so as to respond and handle emergencies quickly, providing strong protection for the life safety of patients.

[0055] Furthermore, the environmental monitoring and adjustment module also has a learning function, which can automatically adjust environmental parameters according to the patient's preferences and recovery stage to create the most suitable treatment environment.

[0056] From the above, it can be seen that the learning function of the environmental monitoring and adjustment module can automatically adjust the environmental parameters in the ward, such as temperature, humidity, light intensity and noise level, according to the patient's personal preferences and recovery stage, so as to create the most suitable treatment environment; this personalized environmental adjustment method not only improves the patient's comfort and satisfaction, but also helps promote the patient's recovery process and improves the overall quality and effectiveness of medical care.

[0057] Furthermore, in the environment monitoring and adjustment module, an adaptive control algorithm is introduced to automatically adjust the environmental parameters (such as temperature, humidity, light intensity and noise level) according to the environmental parameters in the ward and the patient's preferences. The adaptive control algorithm adjusts the output through proportional (P), integral (I) and differential (D) control to achieve the desired set value. The formula is as follows:

[0058]

[0059] Wherein, e(t) represents the error signal.

[0060] From the above, we can see that by introducing adaptive control algorithms (such as PID control), it is possible to flexibly and accurately adjust environmental parameters such as temperature, humidity, light intensity and noise level in the ward according to the error signal between the actual environmental parameters and the expected set values; this control method not only improves the accuracy and response speed of environmental adjustment, but also can effectively respond to various complex and changeable environmental conditions, ensuring that the most suitable treatment environment is always maintained in the ward, further improving the patient's comfort and rehabilitation effect.

[0061] Furthermore, in the integrated management platform, by introducing the weighted average method, the data from multiple data sources are weighted averaged to improve the accuracy and reliability of the data. The formula of the weighted average method is as follows:

[0062]

[0063] Among them, w i represents the weight, X i represents the data of the data source, and Y represents the fused data.

[0064] From the above, we can see that by introducing the weighted average method to process data from multiple data sources, the accuracy and reliability of the data can be significantly improved; this method effectively integrates multi-source information by weighted averaging the data from different data sources according to their weights, reducing the errors and deviations that may be caused by a single data source; this data fusion strategy not only enhances the comprehensiveness and representativeness of the data, but also provides medical staff with more accurate and reliable patient status information, which helps to make more scientific and reasonable nursing decisions, thereby improving the quality and efficiency of ward care.

[0065] Working principle: The patient's physiological parameters are continuously monitored through non-contact sensors, and the data is transmitted to the data analysis and processing unit in real time; the data analysis and processing unit uses artificial intelligence algorithms to process the monitoring data, identify abnormal physiological states and grade them according to the degree of abnormality; once an abnormality is detected, the system will trigger the corresponding alarm mechanism, including primary sound and light alarms, intermediate ward broadcast alarms and advanced remote communication alarms; at the same time, the environmental monitoring and adjustment module will monitor and adjust the environmental parameters in the ward to ensure that a suitable treatment environment is provided for the patient; the patient behavior analysis module monitors the patient's activity status through cameras and motion recognition algorithms, and triggers an alarm when abnormal behavior is detected; the functions of all modules are integrated on the integrated management platform for medical staff to view patient status, receive alarm information, adjust alarm thresholds and manage patient files.

[0066] Furthermore, the beneficial effects of the ward nursing alarm system of this embodiment and the existing ward nursing monitoring system (comparative example) on the market are compared, and the following table is obtained:

[0067]

[0068]

[0069] It can be seen from the above table that compared with the existing ward nursing monitoring systems on the market, the ward nursing alarm system of this embodiment has shown significant beneficial effects in data analysis and processing, alarm mechanism, environmental monitoring and adjustment, patient behavior analysis, integrated management platform, security and privacy protection and personalized services, etc., providing medical staff with more convenient, efficient and intelligent patient management and nursing methods, while also improving the comfort and safety of patients.

[0070] The embodiment of the present application provides an electronic device, which is applicable to the above-mentioned nursing alarm system for wards, including:

[0071] Memory, used to protect computer programs and data;

[0072] Processor, used to run system programs.

[0073] An embodiment of the present application provides a computer storage medium, which is applicable to the above-mentioned nursing alarm system for wards, and performs hierarchical confidentiality management on the above-mentioned system and data in accordance with confidentiality management requirements.

[0074] Those skilled in the art will appreciate that the embodiments of the present application may be provided as a system or a computer program product. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt 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.) that contain computer-usable program code.

[0075] The present application is described with reference to the flowcharts and / or block diagrams of the devices (systems) and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes 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 generate 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 1A device that provides the functions specified in a block or multiple blocks.

[0076] 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 including 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.

[0077] 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 A step that specifies a function in one or more boxes.

[0078] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0079] The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0080] Computer readable media include permanent and non-permanent, removable and non-removable media, and can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0081] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, commodity or device including the elements.

[0082] The embodiments of the present invention are provided for the purpose of illustration and description. Although the embodiments of the present invention have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limitations of the present invention. Ordinary technicians in this field can change, modify, replace and modify the above embodiments within the scope of the present invention.

Claims

1. A nursing alarm system for a ward, characterized in that: include: The physiological parameter monitoring module is equipped with a non-contact sensor for continuously and non-sensitively monitoring the patient's heart rate, respiratory rate, blood pressure and body temperature, and transmitting the monitoring data to the data analysis unit in real time; Data analysis and processing unit, with built-in artificial intelligence algorithm, is used to analyze the data transmitted by the physiological parameter monitoring module, identify abnormal physiological states, and automatically classify them according to the degree of abnormality; An alarm module, wherein the alarm module adopts a multi-level alarm mechanism, including a primary sound and light alarm, an intermediate ward broadcast alarm, and an advanced remote communication alarm. The primary alarm is activated when the data analysis unit identifies a slight abnormality, the intermediate alarm is activated when a moderate abnormality is identified and the abnormal information is broadcast at the same time, and the advanced alarm is activated when a severe abnormality is identified, and an emergency notification is automatically sent to a preset medical staff communication device, which includes the patient's location information and abnormal physiological parameters; Environmental monitoring and adjustment module, which is used to monitor the environmental parameters in the ward, including temperature, humidity, light intensity and noise level, and automatically adjust them to the appropriate range according to preset standards, and trigger environmental alarms when environmental parameters are abnormal; The patient behavior analysis module uses cameras and motion recognition algorithms to monitor the patient's activity status, including getting up, walking, and falling. For abnormal behaviors, such as long periods of inactivity or sudden falls, behavioral alarms are immediately triggered; The integrated management platform integrates all the above modules and provides a graphical user interface for medical staff to view patient status, receive alarm information, adjust alarm thresholds and manage patient files.

2. A nursing alarm system for a ward as claimed in claim 1, characterized in that: The non-contact sensor includes but is not limited to an infrared sensor, a radar sensor, and an image recognition sensor.

3. A nursing alarm system for a ward as claimed in claim 2, characterized in that: In the physiological parameter monitoring module, by introducing the signal filtering algorithm, the state equation and observation equation are used to estimate the state of the dynamic system through prediction and update steps; At the same time, the moving average filtering algorithm is introduced: the average value of the data within a certain window is calculated as the estimated value of the current point.

4. A nursing alarm system for a ward as claimed in claim 1, characterized in that: In the data analysis and processing unit, machine learning algorithms are introduced to build predictive models and identify abnormal physiological states.

5. A nursing alarm system for a ward as claimed in claim 1, characterized in that: The remote communication alarm uses an encrypted communication protocol to ensure the security of patient information, and supports multi-channel notifications, including SMS, email, and mobile application push.

6. A nursing alarm system for a ward as claimed in claim 1, characterized in that: The environmental monitoring and adjustment module also has a learning function for automatically adjusting environmental parameters according to the patient's preferences and recovery stage to create the most suitable treatment environment.

7. A nursing alarm system for a ward as claimed in claim 1, characterized in that: In the environmental monitoring and regulation module, an adaptive control algorithm is introduced to automatically adjust the environmental parameters according to the environmental parameters in the ward and the patient's preferences.

8. A nursing alarm system for a ward as claimed in claim 1, characterized in that: In the integrated management platform, the weighted average method is introduced to perform weighted average on the data from multiple data sources.

9. A processor, characterized in that: The nursing alarm system for a ward is configured to implement the nursing alarm system for a ward according to any one of claims 1 to 8.

10. A computer-readable storage medium, characterized in that: A computer program is stored thereon, and when the computer program is executed by a processor, the nursing alarm system for a ward according to any one of claims 1 to 8 is implemented.

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