Intelligent reminding method and system for nursing interruption event

Abstract

According to the nursing interruption event intelligent reminding method and system and the storage medium, firstly, the nursing interruption event is accurately recognized and judged, then, the social relation between the external personnel and the medical personnel related to the nursing interruption event is recognized and judged, then, the emotion of the external personnel is monitored in time, and dangerous behaviors possibly implemented by the external personnel are predicted; according to the invention, when the nursing interruption event occurs, the medical staff is reminded, so that the confusion and no-measure state when the nursing interruption event occurs is filled up, the possible personal injury is avoided, and the influence of the nursing interruption event on the patient in the nursing process is reduced.

Description

Technical Field

[0001] The present invention relates to the field of emergency treatment, and in particular to a method and system for intelligently reminding nursing interruption events. Background Art

[0002] During the nursing, treatment, consultation and surgery process, medical staff may be interrupted, delayed, suspended or terminated due to interference, disputes or emergencies from outsiders, such as outsiders chatting, quarreling, protesting or shouting with medical staff. At this time, both medical staff and patients being cared for / treated may be at a loss due to some disputes during the interruption of nursing, and it is difficult for them to make accurate judgments and respond correctly to possible dangers. In addition, the current emergency event handling also lacks effective judgments on nursing interruptions, and does not make use of the relationship between external personnel and social relations, emotions and behaviors, making it difficult to provide reminders and emergency response in the first place.

[0003] Application Contents

[0004] The purpose of this application is to provide a method and system for intelligently reminding nursing interruption events to solve the technical problems described in the background technology.

[0005] A method for intelligently reminding nursing interruption events, comprising the following steps:

[0006] S100. Assess interruptions in care in the healthcare setting:

[0007] S110: Collect and pre-process data related to nursing interruption events, including videos, images, and medical equipment monitoring data during the nursing process;

[0008] S120: Using convolutional neural networks to automatically learn features related to nursing interruption events from preprocessed data, including: interruption, delay, suspension, and termination of nursing, treatment, consultation, and surgical procedures;

[0009] S130: constructing a neural network model suitable for identifying nursing interruption events, including: multiple convolutional layers, pooling layers, and fully connected layers; dividing the data into a training set, a validation set, and a test set, and using the training set to train the neural network model for identifying nursing interruption events;

[0010] S140: using the validation set to adjust model parameters, verifying the trained neural network model suitable for identifying nursing interruption events, and evaluating the model performance on the test set;

[0011] S150: applying a neural network model suitable for identifying nursing interruption events, identifying the occurrence of nursing interruption events, and jumping to step 200;

[0012] S200, obtaining information related to the identity of an external person within a certain distance from the medical staff involved in the nursing interruption event, identifying the identity of the external person; and identifying the relationship between the external person and the medical staff involved in the nursing interruption event based on the identity of the external person;

[0013] S300, identifying facial expressions of external personnel and monitoring negative emotions of external personnel;

[0014] S400, identifying the hand and leg movements of the external personnel, predicting that the hands and legs of the external personnel will touch the medical staff, or monitoring the actions of the external personnel throwing objects at the medical staff involved in the nursing interruption event;

[0015] S500, step 150 identifies the result as "a nursing interruption event has occurred"; based on the identity of the external personnel and the relationship between the medical staff involved in the nursing interruption event, according to the monitoring results of steps 300 and 400, if any monitoring result reaches the threshold, a reminder is issued to the medical staff involved in the nursing interruption event, and an emergency solution is provided.

[0016] Furthermore, step S200 further includes:

[0017] S210: Obtaining facial print information of an external person through a monitoring device in the medical environment; or obtaining identity-related information of an external person through an identity verification device in the medical environment; identifying the identity of the external person based on the facial print information or identity-related information of the external person;

[0018] S220: Based on the medical staff information and external personnel identity information involved in the nursing interruption event, the relationship between the external personnel and the medical staff involved in the nursing interruption event is obtained, including: the parties or relatives of the medical staff involved in the L-level medical accident in the past N years; the number of medical disturbances involved by the external personnel in the past N years reaches M times;

[0019] S230: According to the relationship between the external personnel and the medical staff involved in the nursing interruption event, the interruption event external personnel relationship value A is assigned, and the interruption event external personnel relationship value A is a grade rating value. The rating grade is set by the management expert team.

[0020] Furthermore, step S300 further includes:

[0021] S310: Collect a dataset of facial images with different expressions, including multiple expression labels, such as "anger", "hate", "anger", "disgust", "fear", "sadness", "surprise", "happy", "neutral", etc.;

[0022] S320: Locate the face in the image, pre-process the input image, adjust the image size, and obtain the face position detection result;

[0023] S330: Construct an expression classification model, input the extracted features into the fully connected layer for classification, and use a labeled facial expression image dataset for training;

[0024] S340: Use the YOLO framework to train the model and adjust the training parameters, including learning rate, batch size, and training rounds, to optimize model performance;

[0025] S350: Applying an external person's facial expression recognition model to obtain a recognition result.

[0026] Furthermore, step S400 further includes:

[0027] S410: defining the space of the hand and leg states, including parameters such as the position, speed, acceleration, etc. of the hand and leg;

[0028] S420: randomly sampling and generating a series of possible hand and leg states, representing possible positions and motion states of the hands and legs at different time points;

[0029] S430: For each randomly generated state, simulate the movement of the hands and legs;

[0030] S440: For each simulated trajectory, evaluate its likelihood and plausibility using prior knowledge;

[0031] S450: estimating the probability of state transition by statistically calculating the frequency of occurrence of each state in the simulation trajectory;

[0032] S460: Aggregate all simulated trajectories to estimate the overall probability distribution of hand and leg movements;

[0033] S470: Predict the next movement of the hands and legs based on the aggregated probability distribution, thereby predicting that the hands and legs of the external person will touch the medical staff, or monitoring the external person's action of throwing objects at the medical staff involved in the nursing interruption event.

[0034] Furthermore, step S500 further includes:

[0035] It is detected that the external personnel relationship value A of the interruption event is lower than the threshold, or the negative emotions of the external personnel are detected, or it is predicted that the hands or legs of the external personnel will touch the medical staff, or it is detected that the external personnel has the action of throwing objects at the medical staff involved in the nursing interruption event; a reminder message is sent to the medical staff terminal, and a voice alarm sounds at the medical staff terminal to remind the medical staff to evacuate the patient, and the medical staff terminal displays the evacuation route; and a reminder message is sent to the security personnel to guide the security personnel to check the on-site situation.

[0036] Furthermore, the medical environment is any one of a hospital, a clinic, a research institute, a pharmacy, and a physiotherapy center.

[0037] Furthermore, nursing interruption events include: interrupting or delaying current medical work; and external behaviors that distract doctors and nurses.

[0038] A nursing interruption event intelligent reminder system is used to implement the nursing interruption event intelligent reminder method described above.

[0039] Furthermore, the intelligent reminder system for nursing interruption events also includes:

[0040] Medical staff terminal, used to receive control information sent by the nursing interruption event reminder system server; issue warnings to medical staff; and display emergency solutions;

[0041] Cameras, used to record and transmit videos of medical staff’s nursing work, to assist in determining the distance between outsiders and medical staff involved in the nursing interruption incident;

[0042] Authentication equipment, used to verify the identity-related information of external persons;

[0043] The server is used to run the intelligent reminder method for nursing interruption events; obtain, process, and store nursing work videos of medical staff; obtain, process, and store identity-related information; and send control information to medical staff terminals.

[0044] A storage medium for intelligent reminder of nursing interruption events, on which a computer program (instructions) is stored, and the computer program (instructions) is used to implement the above-mentioned intelligent reminder method for nursing interruption events.

[0045] Compared with the prior art, the advantages and positive effects of the present invention are: first, accurately identifying and judging the nursing interruption event, then identifying and judging the social relationship between the external personnel and the medical staff involved in the nursing interruption event, and then timely monitoring the emotions of the external personnel, predicting the dangerous behaviors that may be implemented by the external personnel, and reminding the medical staff, thus filling the confusion and helplessness when the nursing interruption event occurs, avoiding possible personal injuries, and reducing the impact of the nursing interruption event on patients in the nursing process. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0047] Figure 1The present invention is a schematic diagram of the structure of an intelligent reminder system for nursing interruption events.

[0048] Figure 2 The present invention is a flowchart of an intelligent reminder method for nursing interruption events.

[0049] Figure 3 The present invention is a schematic diagram of a nursing interruption event identification process of a nursing interruption event intelligent reminder method.

[0050] Figure 4 A flowchart for identifying external personnel's social relationships for an intelligent reminder method for nursing interruption events.

[0051] Figure 5 This is a schematic diagram of the external personnel emotion recognition process for an intelligent reminder method for nursing interruption events.

[0052] Figure 6 The present invention is a flowchart of monitoring and predicting dangerous actions of external personnel in an intelligent reminder method for nursing interruption events.

[0053] Figure 7 This is a path distance diagram for an intelligent reminder method for nursing interruption events. DETAILED DESCRIPTION

[0054] The embodiments of the present invention are described below in conjunction with the accompanying drawings in the embodiments of the present invention. The terms used in the implementation mode of the present invention are only used to explain the specific embodiments of the present invention, and are not intended to limit the present invention.

[0055] The embodiments of the present application are described below in conjunction with the accompanying drawings. Those skilled in the art will appreciate that, with the development of technology and the emergence of new scenarios, the technical solutions provided in the embodiments of the present application are also applicable to similar technical problems.

[0056] The terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and need not be used to describe a specific order or sequential order. It should be understood that the terms used in this way can be interchangeable under appropriate circumstances, which is only to describe the distinction mode adopted by the objects of the same attributes when describing in the embodiments of the present application. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, so that the process, method, system, product or equipment comprising a series of units need not be limited to those units, but may include other units that are not clearly listed or inherent to these processes, methods, products or equipment.

[0057] The embodiments of the present invention are described below in conjunction with the accompanying drawings in the embodiments of the present invention. The terms used in the implementation mode of the present invention are only used to explain the specific embodiments of the present invention, and are not intended to limit the present invention.

[0058] As the instruction manual Figure 1-7 As shown:

[0059] A method for intelligently reminding nursing interruption events, comprising the following steps:

[0060] S100. Assess interruptions in care in the healthcare setting:

[0061] Specifically, the medical environment includes: hospitals, clinics, research institutes, pharmacies, physiotherapy clinics, etc.

[0062] S110: Collect and pre-process data related to nursing interruption events, including videos, images, and medical equipment monitoring data during the nursing process.

[0063] Specifically, the preprocessing of data related to nursing interruption events includes: cropping and scaling videos and images; cleaning medical equipment monitoring data; normalization, etc., so that it can be input into the convolutional neural network model.

[0064] S120: Use Convolutional Neural Networks (CNN) to automatically learn features related to nursing interruption events from pre-processed data, including: interruption, delay, suspension, and termination of nursing, treatment, consultation, and surgical process.

[0065] Specifically, nursing interruption events include: interruption or delay of current medical work; external behavior that distracts doctors and nurses; and other emergencies. For example, outsiders chatting, arguing, protesting, shouting, etc. with medical staff, interrupting the medical staff's current nursing, treatment, consultation, surgery and other medical work.

[0066] S130: Construct a neural network model suitable for identifying nursing interruption events, including: multiple convolutional layers, pooling layers, and fully connected layers; divide the data into a training set, a validation set, and a test set, and use the training set to train the neural network model for identifying nursing interruption events.

[0067] Specifically, during the training process of the neural network model suitable for identifying nursing interruption events, a labeled data set is used and the network parameters are continuously adjusted through the back-propagation algorithm to minimize the prediction error.

[0068] S140: Use the validation set to adjust model parameters, validate the trained neural network model suitable for identifying nursing interruption events, and evaluate the model performance on the test set. Thus, the accuracy and generalization ability of the model in identifying nursing interruption events are evaluated to prevent overfitting.

[0069] S150: Apply a neural network model suitable for identifying nursing interruption events, identify the occurrence of a nursing interruption event, and jump to step 200.

[0070] S200, obtaining identity-related information of external personnel within a certain distance from the medical staff involved in the nursing interruption event, identifying the identity of the external personnel; and identifying the relationship between the external personnel and the medical staff involved in the nursing interruption event based on the identity of the external personnel.

[0071] Specifically, the certain distance range is a path distance of 5, 10, 20 meters, etc., where the path distance is the length of a route from one location to another, not the straight-line distance between two points in a three-dimensional space, such as Figure 7 As shown in the figure, the path distance from point A to point B is the sum of the plane distance from point A to the stairs, the inclined distance of going up the stairs, and the plane distance from the stairs to point B, rather than the spatial straight-line distance from point A to point B.

[0072] S300, identifying facial expressions of external personnel and monitoring negative emotions of external personnel. Negative emotions include: "anger", "hatred", "anger", "disgust", "fear", "sadness", and "surprise".

[0073] S400, identifying the hand and leg movements of the external personnel, predicting that the hands and legs of the external personnel will touch the medical staff, or monitoring the actions of the external personnel throwing objects at the medical staff involved in the nursing interruption event.

[0074] S500, step 150 identifies the result as "a nursing interruption event has occurred"; based on the identity of the external personnel and the relationship between the medical staff involved in the nursing interruption event, according to the monitoring results of steps 300 and 400, if any monitoring result reaches the threshold, a reminder is issued to the medical staff involved in the nursing interruption event, and an emergency solution is provided.

[0075] In this embodiment, the intelligent reminder method for nursing interruption events involved in the present application first accurately identifies and determines the nursing interruption events, then identifies and determines the social relationship between the external personnel and the medical staff involved in the nursing interruption events, and then timely monitors the emotions of the external personnel, predicts the dangerous behaviors that may be implemented by the external personnel, and reminds the medical staff, thereby filling the confusion and helplessness when the nursing interruption events occur, avoiding possible personal injuries, and reducing the impact of the nursing interruption events on patients in the nursing process.

[0076] Specifically, step S200 also includes:

[0077] S210: Obtain facial print information of an external person through a monitoring device in the medical environment; or obtain identity-related information of an external person through an identity authentication device in the medical environment; and identify the identity of the external person based on the facial print information or identity-related information of the external person.

[0078] Specifically, identity-related information includes: ID card information, hospitalization card information, outpatient card information, consultation QR code information, bed number information, etc.

[0079] S220: Based on the medical staff information involved in the nursing interruption incident and the identity information of the external personnel, the relationship between the external personnel and the medical staff involved in the nursing interruption incident is obtained, including: the parties or relatives of the medical staff involved in the L-level medical accident within N years; the number of medical disturbances involved by the external personnel within N years reaches M times.

[0080] S230: According to the relationship between the external personnel and the medical staff involved in the nursing interruption event, the interruption event external personnel relationship value A is assigned. The interruption event external personnel relationship value A is a grade rating value, and the rating level is set by the management expert team. For example: if the medical staff is involved in a third-level medical accident party or relative within 0.5 years, the interruption event external personnel relationship value A = -3; if the number of medical disturbances involved by the external personnel within 2 years reaches 6, the interruption event external personnel relationship value A = -2.

[0081] In this embodiment, in the intelligent reminder method for nursing interruption events involved in this application, the social relationship between external personnel and medical staff involved in the nursing interruption events is accurately and finely identified and divided.

[0082] Specifically, step S300 also includes:

[0083] S310: Collect a dataset of facial images with different expressions, including multiple expression labels, such as "anger", "hate", "angry", "disgust", "fear", "sadness", "surprise", "happy", "grateful", "neutral", etc.

[0084] S320: Locate the face in the image, pre-process the input image, adjust the image size, and obtain the face position detection result.

[0085] S330: Build an expression classification model, input the extracted features into the fully connected layer for classification, and use the labeled facial expression image dataset for training.

[0086] S340: Use the YOLO framework to train the model and adjust the training parameters, including learning rate, batch size, and training rounds, to optimize model performance.

[0087] S350: Applying an external person's facial expression recognition model to obtain a recognition result.

[0088] In this embodiment, in the intelligent reminder method for nursing interruption events involved in this application, after a nursing interruption event occurs, the facial expression of an external person can be quickly and accurately identified, thereby improving the robustness of the method and optimizing the training strategy.

[0089] Specifically, step S400 also includes:

[0090] S410: Define the space of the hand and leg states, including parameters such as the position, speed, and acceleration of the hand and leg.

[0091] S420: Randomly sample and generate a series of possible hand and leg states, representing possible positions and motion states of the hands and legs at different time points.

[0092] S430: For each randomly generated state, simulate the movement of the hands and legs.

[0093] S440: For each simulated trajectory, evaluate its likelihood and plausibility using prior knowledge.

[0094] S450: Estimate the probability of state transition by counting the frequency of occurrence of each state in the simulation trajectory.

[0095] S460: Aggregate all simulated trajectories to estimate the overall probability distribution of hand and leg movements.

[0096] S470: Predict the next movement of the hands and legs based on the aggregated probability distribution, thereby predicting that the hands and legs of the external person will touch the medical staff, or monitoring the external person's action of throwing objects at the medical staff involved in the nursing interruption event.

[0097] In this embodiment, in the intelligent reminder method for nursing interruption events involved in this application, after a nursing interruption event occurs, the social relationship of the external personnel is identified, and the hand and leg movements of the external personnel can be identified and judged in parallel, thereby enhancing the reliability and accuracy of the identification of suspected dangerous movements.

[0098] Specifically, step S500 also includes:

[0099] It is detected that the external personnel relationship value A of the interruption event is lower than the threshold, or the negative emotions of the external personnel are detected, or it is predicted that the hands or legs of the external personnel will touch the medical staff, or it is detected that the external personnel has the action of throwing objects at the medical staff involved in the nursing interruption event; a reminder message is sent to the medical staff terminal, and a voice alarm sounds at the medical staff terminal to remind the medical staff to evacuate the patient, and the medical staff terminal displays the evacuation route; and a reminder message is sent to the security personnel to guide the security personnel to check the on-site situation.

[0100] A nursing interruption event intelligent reminder system, used to implement the above-mentioned nursing interruption event intelligent reminder method, comprising:

[0101] The medical staff terminal is used to receive control information sent by the nursing interruption event reminder system server; issue warnings to medical staff; and display emergency solutions.

[0102] Specifically, medical staff terminals include: mobile phones, notebooks, tablets, wearable devices, smart glasses, smart bracelets, smart watches, etc.

[0103] The camera is used to record and transmit the nursing work video of the medical staff to assist in determining the distance between the outsider and the medical staff involved in the nursing interruption incident.

[0104] Authentication device, used to verify the identity-related information of external persons.

[0105] Specifically, identity authentication devices include: facial information collection devices, NFC recognition devices, RFID recognition devices, code scanning devices, etc.

[0106] The server is used to run the intelligent reminder method for nursing interruption events; obtain, process, and store nursing work videos of medical staff; obtain, process, and store identity-related information; and send control information to medical staff terminals.

[0107] Specifically, the server can be a local server, a cloud server, etc.

[0108] A storage medium for intelligent reminder of nursing interruption events, on which a computer program (instructions) is stored, and the computer program (instructions) is used to implement the above-mentioned intelligent reminder method for nursing interruption events.

[0109] It should be noted that the explanations and definitions of the same steps or terms are also applicable to different embodiments. For the sake of brevity, repeated descriptions are appropriately omitted herein.

[0110] Those skilled in the art will appreciate that the functions described in conjunction with the various illustrative logic blocks, modules, and algorithm steps disclosed herein can be implemented in hardware, software, firmware, or any combination thereof. If implemented in software, the functions described in the various illustrative logic blocks, modules, and steps can be stored or transmitted as one or more instructions or codes on a computer-readable medium and executed by a hardware-based processing unit. Computer-readable media may include computer-readable storage media, which corresponds to tangible media, such as data storage media, or includes any communication media that facilitates the transfer of computer programs from one place to another (e.g., according to a communication protocol). In this manner, computer-readable media may generally correspond to (1) non-temporary tangible computer-readable storage media, or (2) communication media, such as signals or carrier waves. Data storage media may be any available media that can be accessed by one or more computers or one or more processors to retrieve instructions, codes, and / or data structures for implementing the techniques described in this application. A computer program product may include computer-readable media.

[0111] As an example and not limitation, such computer-readable storage media may include RAM, ROM, EEPROM, CD-ROM or other optical disk storage, magnetic disk storage or other magnetic storage, flash memory, or any other medium that can be used to store the desired program code in the form of instructions or data structures and can be accessed by a computer. Also, any connection is properly referred to as a computer-readable medium. For example, if instructions are transmitted from a website, server or other remote source using a coaxial cable, fiber optic cable, twisted pair, digital subscriber line (DSL), or wireless technologies such as infrared, radio and microwave, then the coaxial cable, fiber optic cable, twisted pair, DSL, or wireless technologies such as infrared, radio and microwave are included in the definition of media. However, it should be understood that the computer-readable storage media and data storage media do not include connections, carrier waves, signals or other temporary media, but are actually directed to non-temporary tangible storage media. As used herein, disks and optical disks include compact disks (CDs), laser optical disks, optical optical disks, digital versatile disks (DVDs), and Blu-ray disks, where disks typically reproduce data magnetically, while optical disks reproduce data optically using lasers. Combinations of the above should also be included within the scope of computer-readable media.

[0112] The corresponding functions may be performed by one or more processors such as one or more digital signal processors (DSPs), general-purpose microprocessors, application-specific integrated circuits (ASICs), field programmable logic arrays (FPGAs), or other equivalent integrated or discrete logic circuits. Therefore, the term "processor" as used herein may refer to any of the aforementioned structures or any other structures suitable for implementing the techniques described herein. In addition, in some aspects, the functions described by the various illustrative logic blocks, modules, and steps described herein may be provided in dedicated hardware and / or software modules configured for encoding and decoding, or incorporated in a combined codec. Moreover, the technology may be fully implemented in one or more circuits or logic elements. In one example, the various illustrative logic blocks, units, and modules in the video encoder 20 and the video decoder 30 may be understood as corresponding circuit devices or logic elements.

[0113] The techniques of the present application may be implemented in a variety of devices or apparatuses, including a wireless handset, an integrated circuit (IC), or a set of ICs (e.g., a chipset). Various components, modules, or units are described in this application to emphasize functional aspects of devices for performing the disclosed techniques, but do not necessarily need to be implemented by different hardware units. In fact, as described above, the various units may be combined in a codec hardware unit in conjunction with appropriate software and / or firmware, or provided by interoperating hardware units (including one or more processors as described above).

[0114] The above is only an exemplary embodiment of the present application, but the protection scope of the present application is not limited thereto. Any changes or substitutions that can be easily thought of by a person skilled in the art within the technical scope disclosed in the present application 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.

Claims

1. A method for intelligently reminding nursing interruption events, characterized in that the steps include: S100. Assess interruptions in care in the healthcare setting: S110: Collect and pre-process data related to nursing interruption events, including videos, images, and medical equipment monitoring data during the nursing process; S120: Using convolutional neural networks to automatically learn features related to nursing interruption events from preprocessed data, including: interruption, delay, suspension, and termination of nursing, treatment, consultation, and surgical procedures; S130: constructing a neural network model suitable for identifying nursing interruption events, including: multiple convolutional layers, pooling layers, and fully connected layers; dividing the data into a training set, a validation set, and a test set, and using the training set to train the neural network model for identifying nursing interruption events; S140: using the validation set to adjust model parameters, verifying the trained neural network model suitable for identifying nursing interruption events, and evaluating the model performance on the test set; S150: applying a neural network model suitable for identifying nursing interruption events, identifying the occurrence of nursing interruption events, and jumping to step 200; S200, obtaining information related to the identity of an external person within a certain distance from the medical staff involved in the nursing interruption event, identifying the identity of the external person; and identifying the relationship between the external person and the medical staff involved in the nursing interruption event based on the identity of the external person; S300, identifying facial expressions of external personnel and monitoring negative emotions of external personnel; S400, identifying the hand and leg movements of the external personnel, predicting that the hands and legs of the external personnel will touch the medical staff, or monitoring the actions of the external personnel throwing objects at the medical staff involved in the nursing interruption event; S500, step 150 identifies the result as "a nursing interruption event has occurred"; based on the identity of the external personnel and the relationship between the medical staff involved in the nursing interruption event, according to the monitoring results of steps 300 and 400, if any monitoring result reaches the threshold, a reminder is issued to the medical staff involved in the nursing interruption event, and an emergency solution is provided.

2. The intelligent reminder method for nursing interruption events according to claim 1 is characterized in that: Step S200 also includes: S210: Obtaining facial print information of an external person through a monitoring device in the medical environment; or obtaining identity-related information of an external person through an identity verification device in the medical environment; identifying the identity of the external person based on the facial print information or identity-related information of the external person; S220: Based on the medical staff information and external personnel identity information involved in the nursing interruption event, the relationship between the external personnel and the medical staff involved in the nursing interruption event is obtained, including: the parties or relatives of the medical staff involved in the L-level medical accident in the past N years; the number of medical disturbances involved by the external personnel in the past N years reaches M times; S230: According to the relationship between the external personnel and the medical staff involved in the nursing interruption event, the interruption event external personnel relationship value A is assigned, and the interruption event external personnel relationship value A is a grade rating value. The rating grade is set by the management expert team.

3. The intelligent reminder method for nursing interruption events according to claim 2 is characterized in that: Step S300 also includes: S310: Collecting a dataset of facial images with different expressions, including multiple expression labels, such as "angry", "hate", "angry", "disgusted", "fear", "sad", "surprised", "happy", "neutral", etc.; S320: Locate the face in the image, pre-process the input image, adjust the image size, and obtain the face position detection result; S330: Construct an expression classification model, input the extracted features into the fully connected layer for classification, and use a labeled facial expression image dataset for training; S340: Use the YOLO framework to train the model and adjust the training parameters, including learning rate, batch size, and training rounds, to optimize model performance; S350: Applying an external person's facial expression recognition model to obtain a recognition result.

4. The intelligent reminder method for nursing interruption events according to claim 3 is characterized in that: Step S400 also includes: S410: defining the space of the hand and leg states, including parameters such as the position, speed, acceleration, etc. of the hand and leg; S420: randomly sampling and generating a series of possible hand and leg states, representing possible positions and motion states of the hands and legs at different time points; S430: For each randomly generated state, simulate the movement of the hands and legs; S440: For each simulated trajectory, evaluate its likelihood and plausibility using prior knowledge; S450: estimating the probability of state transition by statistically calculating the frequency of occurrence of each state in the simulation trajectory; S460: Aggregate all simulated trajectories to estimate the overall probability distribution of hand and leg movements; S470: Predict the next movement of the hands and legs based on the aggregated probability distribution, thereby predicting that the hands and legs of the external person will touch the medical staff, or monitoring the external person's action of throwing objects at the medical staff involved in the nursing interruption event.

5. The intelligent reminder method for nursing interruption events according to claim 4 is characterized in that: Step S500 also includes: It is detected that the external personnel relationship value A of the interruption event is lower than the threshold, or the negative emotions of the external personnel are detected, or it is predicted that the hands or legs of the external personnel will touch the medical staff, or it is detected that the external personnel has the action of throwing objects at the medical staff involved in the nursing interruption event; a reminder message is sent to the medical staff terminal, and a voice alarm sounds at the medical staff terminal to remind the medical staff to evacuate the patient, and the medical staff terminal displays the evacuation route; and a reminder message is sent to the security personnel to guide the security personnel to check the on-site situation.

6. The intelligent reminder method for nursing interruption events according to any one of claims 1 to 5, characterized in that: The medical environment is any one of a hospital, clinic, research institute, pharmacy, and physical therapy center.

7. The intelligent reminder method for nursing interruption events according to any one of claims 1 to 5, characterized in that: Nursing interruption events include: interrupting or delaying current medical work; external behaviors that distract doctors and nurses.

8. A nursing interruption event intelligent reminder system, used to implement the nursing interruption event intelligent reminder method according to any one of claims 1 to 7.

9. The intelligent reminder system for nursing interruption events according to claim 8, characterized in that: Also includes: Medical staff terminal, used to receive control information sent by the nursing interruption event reminder system server; Alerts to medical staff; Display solutions to emergencies; Cameras, used to record and transmit videos of medical staff’s nursing work, to assist in determining the distance between outsiders and medical staff involved in the nursing interruption incident; Authentication equipment, used to verify the identity-related information of external persons; A server, used for running a smart reminder method for nursing interruption events; Acquire, process, and store videos of medical staff’s nursing work; acquire, process, and store identity-related information; Control information sent to medical staff terminals.

10. A storage medium for intelligent reminder of nursing interruption events, on which a computer program (instructions) is stored, and the computer program (instructions) is used to implement the intelligent reminder method for nursing interruption events according to any one of claims 1 to 6.

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