Visualization method and system for smart ward

Abstract

The invention discloses a visualization method and system for a smart ward, and relates to the technical field of smart wards, and the method comprises the steps: determining the comprehensive state of a patient according to the interaction event of the patient, the illness state event of the patient and the current body data of the patient, and enabling the comprehensive state of the patient to be matched with the position of a corresponding ward, and the comprehensive state of the patient can be accurately controlled. Further, a first visual layer is determined according to the comprehensive state of the patient, the position of the ward and the current time, and a second visual layer is determined according to the emergency signal of the ward, multiple environmental data of the ward and spatial data of the ward; and determining a visual model of the smart ward according to the second visual layer and the first visual layer. Therefore, the real-time picture of the ward is collected, and the dynamic monitoring system of the smart ward is determined based on the visual model of the smart ward, the real-time picture of the ward and the personnel moving track, so that dynamic early warning of the smart ward is realized.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent wards, and in particular to a visualization method and system for intelligent wards. Background Art

[0002] With the development of technology, intelligent wards have been gradually applied to people's lives and will be widely used in hospitals in the future. Intelligent wards incorporate the functions of real-time monitoring and real-time analysis. In the prior art, collecting the current physical data of patients and determining the comprehensive status of patients based on the current physical data of patients cannot accommodate factors such as the disease events of patients, which affects the accurate control of the comprehensive status of patients. Summary of the Invention

[0003] The purpose of the present invention is to overcome the deficiencies of the prior art. The present invention provides a visualization method and system for intelligent wards.

[0004] An embodiment of the present invention provides a visualization method for an intelligent ward, including: collecting a 3D model of a hospital and marking the positions of each ward in the 3D model of the hospital; determining the disease events of patients based on the bed data set of each ward and the illness status of the corresponding patients; collecting the internal images of each ward and determining the interaction events of patients according to the recognition of the internal images of each ward; determining the comprehensive status of patients according to the interaction events of patients, the disease events of patients, and the current physical data of patients, and matching the comprehensive status of the patients to the positions of the corresponding wards; determining the first visual layer according to the comprehensive status of the patients, the positions of the wards, and the current time, and determining the second visual layer according to the emergency signals of the wards, multiple environmental data of the wards, and spatial data of the wards; determining the visual model of the intelligent ward according to the second visual layer and the first visual layer; collecting the real-time video of the ward, determining the dynamic monitoring system of the intelligent ward based on the visual model of the intelligent ward, the real-time video of the ward, and the personnel movement trajectory, and triggering the corresponding warning signal according to the dynamic monitoring system of the intelligent ward.

[0005] An embodiment of the present invention provides a visualization system for an intelligent ward. The visualization system for the intelligent ward is applied to the visualization method for the intelligent ward as described above. The visualization system for the intelligent ward includes:

[0006] A collection module, configured to collect a 3D model of a hospital and mark the positions of each ward in the 3D model of the hospital;

[0007] A disease event module, configured to determine the disease events of patients based on the bed data set of each ward and the illness status of the corresponding patients;

[0008] An interaction module, configured to collect internal images of each ward and determine patient interaction events based on the recognition of the internal images of each ward;

[0009] A comprehensive status module, configured to determine the comprehensive status of a patient based on the patient's interaction events, the patient's condition events, and the patient's current physical data, and match the comprehensive status of the patient to the location of the corresponding ward;

[0010] A visual module, configured to determine a first - layer visual layer based on the comprehensive status of the patient, the location of the ward, and the current time, and determine a second - layer visual layer based on the emergency signal of the ward, multiple environmental data of the ward, and spatial data of the ward; determine a visual model of the smart ward based on the second - layer visual layer and the first - layer visual layer;

[0011] A dynamic monitoring module, configured to collect real - time images of the ward, determine a dynamic monitoring system of the smart ward based on the visual model of the smart ward, the real - time images of the ward, and the personnel movement trajectory, and trigger corresponding warning signals according to the dynamic monitoring system of the smart ward.

[0012] In the embodiments of the present invention, through the method in the embodiments of the present invention, a 3D model of a hospital is collected, and the locations of each ward are marked in the 3D model of the hospital; the condition events of patients are determined based on the set of hospital bed data of each ward and the illness status of the corresponding patients; internal images of each ward are collected, and patient interaction events are determined based on the recognition of the internal images of each ward; the comprehensive status of a patient is determined based on the patient's interaction events, the patient's condition events, and the patient's current physical data, and the comprehensive status of the patient is matched to the location of the corresponding ward, which comprehensively considers the patient's interaction events, the patient's condition events, and the patient's current physical data, and realizes the accurate control of the comprehensive status of the patient.

[0013] Furthermore, a first - layer visual layer is determined based on the comprehensive status of the patient, the location of the ward, and the current time, and a second - layer visual layer is determined based on the emergency signal of the ward, multiple environmental data of the ward, and spatial data of the ward; a visual model of the smart ward is determined based on the second - layer visual layer and the first - layer visual layer, introducing the visual model of the smart ward, and realizing the dynamic tracking of patients within the patient based on the visual model of the smart ward.

[0014] Therefore, real - time images of the ward are collected, a dynamic monitoring system of the smart ward is determined based on the visual model of the smart ward, the real - time images of the ward, and the personnel movement trajectory, and corresponding warning signals are triggered according to the dynamic monitoring system of the smart ward, realizing the dynamic warning of the smart ward. Description of the Drawings

[0015] Figure 1Schematic diagram of the application scenario of the visualization method for a smart ward in an embodiment;

[0016] Figure 2 It is a schematic flowchart of the visualization method for a smart ward in an embodiment of the present invention;

[0017] Figure 3 It is a schematic diagram of the structural composition of the visualization system for a smart ward in an embodiment of the present invention;

[0018] Figure 4 It is a hardware diagram of an electronic device shown according to an exemplary embodiment. Detailed implementation manners

[0019] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention.

[0020] Embodiment 1

[0021] The visualization method for a smart ward provided by this application is applied to an application environment as Figure 1 shown. Among them, the computer 102 communicates with the server 104 through the network. Among them, the terminal 102 is but not limited to various personal computers, servers, and smart wards, and the server 104 is implemented by an independent server or a server cluster composed of servers.

[0022] Embodiment 2

[0023] Please refer to Figures 1 to 4 , a visualization method for a smart ward, which is applied to the visualization scenario of a smart ward; the visualization method for a smart ward includes:

[0024] Step S11: Collect the 3D model of the hospital and mark the positions of each ward in the 3D model of the hospital;

[0025] Step S12: Determine the patient's condition events based on the bed data set of each ward and the patient's illness status;

[0026] Step S13: Collect the internal images of each ward and determine the patient's interaction events according to the recognition of the internal images of each ward;

[0027] Step S14: Determine the comprehensive status of the patient according to the patient's interaction events, the patient's condition events, and the patient's current physical data, and match the comprehensive status of the patient to the position of the corresponding ward;

[0028] Step S15: Determine the first - layer visible layer based on the patient's comprehensive status, the location of the ward, and the current time, and determine the second - layer visible layer based on the emergency signals in the ward, multiple environmental data of the ward, and the spatial data of the ward; determine the visual model of the smart ward according to the second - layer visible layer and the first - layer visible layer;

[0029] Step S16: Collect the real - time images of the ward, determine the dynamic monitoring system of the smart ward based on the visual model of the smart ward, the real - time images of the ward, and the personnel movement trajectories, and trigger the corresponding warning signals according to the dynamic monitoring system of the smart ward.

[0030] In step S11, collect the 3D model of the hospital and mark the locations of each ward in the 3D model of the hospital;

[0031] In the specific implementation process of the present invention, the specific steps are as follows:

[0032] S111: Collect the architectural drawings of the hospital and multiple internal images of the hospital;

[0033] S112: Determine the 3D model of the hospital according to the architectural drawings of the hospital and multiple internal images of the hospital;

[0034] S113: Collect the 3D model of the hospital;

[0035] S114: Determine the locations of each ward based on the traversal of the 3D model of the hospital and mark the locations of each ward in the 3D model of the hospital.

[0036] In the embodiment of the present application, collect the architectural drawings of the hospital and multiple internal images of the hospital; determine the 3D model of the hospital according to the architectural drawings of the hospital and multiple internal images of the hospital. By introducing the architectural drawings of the hospital and multiple internal images of the hospital and performing an overall synthesis on them, the accuracy of the 3D model of the hospital is ensured.

[0037] At this time, obtain the latest architectural drawings from the hospital's management department or architects. These drawings usually include floor plans, elevation views, sectional views, etc., which detail the hospital's building structure, floor layout, room distribution, etc. The accuracy and detail of the drawings are crucial for the subsequent construction of the 3D model. To obtain the detailed layout and environmental information inside the hospital, use a camera or professional equipment to take multiple images of the hospital interior. These images can include photos of key areas such as corridors, wards, operating rooms, equipment rooms, etc. Pay attention to light, angle, and clarity during shooting to ensure that the images can accurately reflect the actual situation inside the hospital.

[0038] Import the obtained architectural drawings into 3D modeling software and initially construct a 3D model of the hospital based on the information in the drawings. This process usually involves converting the floor plan into a three-dimensional space and adding actual dimension information such as floor height and wall thickness. Match the collected internal images with the initially constructed 3D model to ensure the accuracy and authenticity of the model. This process requires adjusting details such as the positions of walls, the sizes of doors and windows, and the layout of furniture in the model to match the actual situation in the images. At the same time, the texture information in the images can also be used to add materials and textures to the model to improve its visual effect. Also, after completing the initial modeling and image matching, the model needs to be refined in details, such as adding corridor signs, room numbers, medical equipment, etc. Finally, through comparison and verification with the actual hospital, ensure the accuracy and integrity of the model.

[0039] Specifically, assume that we are constructing a 3D model for a large general hospital. First, we obtained the latest architectural drawings from the hospital management department. These drawings detailedly depict the five-story building structure of the hospital, and the floor plans of each floor clearly mark the key positions such as wards, operating rooms, nurse stations, elevators, etc. Then, we dispatched a photography team to take pictures inside the hospital. They used high-resolution cameras to take pictures of key areas such as corridors, wards, and operating rooms from multiple angles. These pictures not only captured the internal layout of the hospital but also recorded details such as wall colors and furniture arrangements. Import these drawings and images into a professional 3D modeling software. First, we constructed an initial 3D model of the hospital based on the floor plan, including the five-story building structure and the layout of key rooms. Then, we matched the internal images with the model and adjusted details such as the sizes of doors and windows in the wards, the widths of corridors, and the layout of furniture. During the matching process, we also used the texture information in the images to add wall materials and textures to the model to make it more realistic. Finally, we added information such as corridor signs, room numbers, and medical equipment, and carried out detailed verification and correction on the model to ensure its accuracy and integrity. Now, we already have a 3D model that is highly consistent with the actual hospital, providing a solid foundation for the subsequent visualization display.

[0040] Therefore, collect the 3D model of the hospital; determine the positions of each ward based on the traversal of the 3D model of the hospital, and mark the positions of each ward in the 3D model of the hospital, realizing the traversal of the 3D model of the hospital and ensuring the accuracy of the positions of each ward.

[0041] At this time, the 3D model of the hospital is introduced, and the 3D model of the hospital is traversed, thereby conducting a final quality inspection on the 3D model of the hospital to ensure that all wards, operating rooms, corridors, and equipment have been correctly modeled, and the geometric dimensions and material textures are consistent with the actual hospital.

[0042] Traverse the already constructed 3D model of the hospital to determine and mark the locations of each ward. At this time, traversing the 3D model of the hospital to identify the location of each ward involves manual operation or an automated script, depending on the complexity of the model and the required accuracy. During the traversal, mark the location of each ward, which can be achieved by placing labels in the model, color coding, adding annotations, or using other visualization methods. The markings should be clear, accurate, and easy to identify. After completing the markings, conduct verification to ensure that all wards have been correctly marked and that the marked information matches the actual hospital. If necessary, updates can be made to correct any errors or omissions.

[0043] Specifically, for clarity, we assigned a unique number to each ward and used color coding in the model to distinguish different ward areas. After completing the markings, we conducted verification to ensure that all wards had been correctly marked and that the marked information matched the actual hospital. Now, we have a 3D model with the locations of all wards marked, providing a solid foundation for the visualization display and patient monitoring in subsequent steps.

[0044] In step S12, determine the patient's condition event based on the set of hospital bed data for each ward and the corresponding patient's illness status;

[0045] In the specific implementation process of the present invention, the specific steps are as follows:

[0046] S121: Monitor each ward in real time;

[0047] S122: Collect multiple hospital bed data for each ward and determine the set of hospital bed data for each ward based on the multiple hospital bed data for each ward;

[0048] S123: Collect the patient's condition data based on the monitoring of the detection heads installed on the patients;

[0049] S124: Determine the patient's illness status based on the patient's condition data and the patient's facial expression;

[0050] S125: Conduct multiple interactions on the set of hospital bed data for each ward and the corresponding patient's illness status;

[0051] S126: Determine the patient's condition event based on the multiple interactions of the set of hospital bed data for each ward and the corresponding patient's illness status.

[0052] In the embodiment of the present application, each ward is monitored in real time; multiple hospital bed data for each ward are collected, and the set of hospital bed data for each ward is determined based on the multiple hospital bed data for each ward, introducing the set of hospital bed data for each ward.

[0053] At this time, high-definition cameras are installed in the ward to observe the situation in the ward in real time through the video monitoring system. The cameras can be installed in the corners or on the ceilings of the ward to ensure full coverage without blind spots. Medical staff can remotely view the real-time images in the ward through the monitoring screen or mobile devices. Various sensors (such as temperature sensors, humidity sensors, sound sensors, etc.) are used to monitor the environmental parameters in the ward in real time. These sensors can transmit the collected data to the central monitoring system in real time so that medical staff can know at any time whether the ward environment is suitable for patients to live in.

[0054] Connect the medical devices in the ward (such as electrocardiographs, sphygmomanometers, ventilators, etc.) to the monitoring system to achieve real-time collection and transmission of device data. In this way, medical staff can remotely monitor the vital signs of patients, timely detect abnormal situations and take measures. When abnormal situations occur in the ward (such as abnormal vital signs of patients, equipment failures, etc.), the system can automatically trigger an alarm to notify medical staff. The alarm information can be conveyed to relevant personnel through means such as sound, light, text message or APP push.

[0055] At the same time, collecting the data of multiple hospital beds in each ward is another important task in medical management. These data include the usage status of the beds, the basic information of the patients, treatment records, etc. By collecting these data, medical institutions can better understand the usage of the beds, optimize resource allocation and improve medical efficiency. Optionally, use an intelligent bed system or manual recording method to collect the usage status information of the beds in real time, such as whether the bed is occupied, the basic information of the occupant such as name, gender, age, etc. Collect the treatment record information of the patients through the electronic medical record system or paper medical record method, including diagnosis results, treatment plans, medication records, etc. Integrate and analyze the collected bed data and patient treatment records to form a set of bed data for each ward. These data sets can be used to evaluate the usage efficiency of the beds, the treatment effects of the patients, etc.

[0056] Furthermore, the condition data of the patient is collected based on the monitoring of the detection head installed on the patient; the sick state of the patient is determined according to the condition data of the patient and the facial expression of the patient. The condition data of the patient and the facial expression of the patient are introduced, and multiple interactions are carried out on the condition data of the patient and the facial expression of the patient, ensuring the accuracy of the sick state of the patient.

[0057] At this time, the patient will wear or install some detection devices, which can monitor the patient's physiological indicators in real time, such as heart rate, blood pressure, blood oxygen saturation, body temperature, etc. These detection heads are wearable devices (such as smart watches, smart bracelets, chest straps, etc.) and also implantable devices (such as cardiac pacemakers, blood glucose monitors, etc.), depending on the patient's condition and the indicators to be monitored.

[0058] Medical staff will select appropriate detection devices according to the specific conditions of the patients and guide the patients to wear or install them correctly. These devices usually need to be connected to the hospital's monitoring system or the patient's personal health management system for real-time data transmission. The detection head continuously collects the patients' physiological data and transmits it in real time to the central monitoring system or the patient's personal device through Bluetooth, Wi-Fi or other wireless communication technologies. These data are usually presented in digital form and are timestamped so that medical staff can track the changes in the patients' conditions.

[0059] The system uses facial recognition technology and emotion analysis algorithms to capture and analyze the patients' facial expressions. These algorithms can identify expression features such as smiling, frowning, and pain, so as to infer the patients' emotional states. The system combines the condition data with the results of facial expression analysis and uses machine learning algorithms to evaluate the patients' illness states. This comprehensive evaluation method can more comprehensively understand the patients' physical and emotional states and provide more accurate diagnostic basis for medical staff. Based on the above analysis, the system will determine the patients' illness states, such as mild discomfort, moderate pain, severe illness, etc., and provide feedback to medical staff through the monitoring system or the patients' personal devices.

[0060] Specifically, assume that a heart disease patient needs to continuously monitor his heart rate and blood pressure during hospitalization. The medical staff equipped him with a smart bracelet that can monitor the heart rate and blood pressure in real time and transmit the data to the hospital's monitoring system through Bluetooth. The patient wore the bracelet during his hospitalization, and the bracelet continuously collected and transmitted his heart rate and blood pressure data. One night, the patient's heart rate suddenly increased. The bracelet immediately captured this change and transmitted the data to the monitoring system in real time. After receiving the alarm, the medical staff quickly rushed to the ward, conducted an emergency examination and treatment on the patient, and finally avoided a potential heart event.

[0061] When the heart rate of a heart disease patient suddenly increased, in addition to collecting this physiological data through the bracelet, the camera also captured the patient's painful expression. The system combined these two pieces of information and evaluated the patient's illness state as "moderate pain" through machine learning algorithms. The system immediately sent an alarm to the medical staff's mobile devices, notifying them that the patient's condition was deteriorating. After receiving the alarm, the medical staff quickly took action, conducted further examinations and treatments on the patient, and finally successfully stabilized the patient's condition.

[0062] In another embodiment of the present application, the patient's state is determined by matching the condition data and facial expressions with predefined illness states. The state matching table can be a simple two-dimensional table, where one row represents different ranges of condition data and one column represents different facial expression features, and the intersection points in the table correspond to specific illness states.

[0063] Suppose we have a state matching table as follows:

[0064] Condition data (heart rate, beats per minute) Facial expression Sickness status <80 Normal Healthy 80-100 Normal Mild discomfort >100 Pain Moderate pain Any value Severe pain Serious illness

[0065] Now, we have a patient's data: heart rate is 105 beats per minute and facial expression is pain. According to the matching table, we can determine that the patient's illness state is "moderate pain".

[0066] In addition, first assign a weight to each illness data index and facial expression feature, then calculate the score based on the patient's actual data, and finally determine the illness state according to the score range.

[0067] Suppose we have the following weight and score rules:

[0068] Heart rate (beats per minute): 0 points for <80, 1 point for 80 - 100, 2 points for >100.

[0069] Facial expression: 0 points for normal, 1 point for slight discomfort, 2 points for pain, 3 points for extreme pain.

[0070] Illness state score range:

[0071] Health: 0 - 1 point; Mild discomfort: 2 - 3 points; Moderate pain: 4 - 5 points; Severe illness: >5 points.

[0072] Now, we have a patient's data: heart rate is 105 beats per minute (gets 2 points) and facial expression is pain (gets 2 points). According to the weight and score rules, the total score of this patient is 4 points. Therefore, we can determine that the patient's illness state is "moderate pain".

[0073] Therefore, perform multiple interactions on the bed data sets of each ward and the corresponding illness states of patients; determine the patient's illness events based on the multiple interactions of the bed data sets of each ward and the corresponding illness states of patients, realizing the multiple interactions of the bed data sets of each ward and the corresponding illness states of patients, and ensuring the accuracy of the patient's illness events.

[0074] At this time, the system will perform multiple interactions on the bed data sets of each ward (including the usage status of the beds, the basic information of the patients, treatment records, etc.) and the corresponding illness states of patients (such as health, mild discomfort, moderate pain, severe illness, etc.). This kind of interaction involves multiple aspects such as data integration, comparative analysis, trend prediction, etc., aiming to comprehensively understand the patients' illnesses and the usage situations of the wards.

[0075] The system will integrate the hospital bed data sets of each ward and the illness status of patients to form a comprehensive data set, which will include data in multiple dimensions such as patients' basic information, physiological indicators, treatment records, facial expression analysis, etc. By conducting a comparative analysis on the integrated data set, differences and similarities among different wards and different patients can be identified. Such a comparative analysis helps medical staff better understand the changing trends of the illness conditions and treatment effects. Based on historical data and current data, the system can also conduct trend prediction, predicting the development direction of patients' illness conditions and the usage trends of wards. Such predictions can provide strong support for the decision-making of medical staff.

[0076] Illness events include the deterioration, improvement of patients' illness conditions, and the emergence of new complications, etc. The system will match the integrated data set according to preset rules, which include thresholds of physiological indicators such as heart rate and blood pressure, and the recognition of facial expression features, etc. When the data set meets a certain rule, the system will trigger the corresponding illness event. After the system identifies an illness event, it will conduct further confirmation and verification, which includes communication with medical staff and further examinations of patients, etc. The confirmed illness events will be recorded in the system for subsequent analysis and processing.

[0077] Specifically, assume that a certain hospital has two wards, A and B, and there are two patients, Jia and Yi, living in them respectively. The hospital bed data set of ward A shows that the heart rate of patient Jia has been continuously increasing, and at the same time, facial expression analysis shows that he shows a painful expression. While the hospital bed data set of ward B shows that the indicators such as the heart rate and blood pressure of patient Yi are within the normal range, and the facial expression is normal. The system conducts multiple interactions on the data of these two wards and finds that patient Jia is in a moderate pain state, while patient Yi is in a healthy state. Based on this discovery, the medical staff decides to conduct an emergency examination and treatment on patient Jia.

[0078] After the system conducts multiple interactions on the data of wards A and B, it finds that the heart rate of patient Jia has been continuously increasing and his facial expression shows pain, which meets the preset rule of illness deterioration. So, the system triggers an illness deterioration event and notifies the medical staff. The medical staff then conducts an emergency examination on patient Jia and finds that he has indeed experienced a deterioration of his heart problem. Based on this discovery, the medical staff timely adjusts the treatment plan and conducts closer monitoring and treatment on patient Jia.

[0079] In another embodiment of the present application, assume that we have an illness event matching table for determining the illness events of patients in the heart disease ward:

[0080] Characteristics of the hospital bed data set Patient's sickness status Condition event Heart rate > 120 beats per minute Moderate pain or severe Tachycardia event Blood pressure < 90 / 60 mmHg Any status Hypotension event Blood oxygen saturation < 90% Moderate pain or severe Hypoxemia event Facial expression of severe pain Any status Acute pain event Frequent adjustment of medications in the treatment record Continuous deterioration of the condition Poor treatment response event

[0081] Now, we have a specific case:

[0082] Ward: Bed No. 3 in the Cardiology Ward

[0083] Bed data set: Heart rate 130 beats per minute, blood pressure 110 / 70 mmHg, blood oxygen saturation 92%, normal facial expression, but the treatment record shows that the doctor frequently adjusted the drug dosage within the past 24 hours.

[0084] Patient's illness status: Moderate pain

[0085] According to the matching table, we can determine the following illness events:

[0086] Since the heart rate > 120 beats per minute and the patient's illness status is moderate pain, the "tachycardia event" is triggered.

[0087] Although the blood pressure is lower than the lower limit of the normal value range (but in this example, it is not lower than 90 / 60 mmHg), so the hypotension event is not triggered.

[0088] Although the blood oxygen saturation is slightly low, it does not reach the threshold (< 90%) for triggering the hypoxemia event.

[0089] The facial expression is normal, so the acute pain event is not triggered.

[0090] The drug was frequently adjusted in the treatment record, and the patient's illness status remained moderate pain (or can be regarded as continuous deterioration), triggering the "poor treatment response event".

[0091] Therefore, for the patient in Bed No. 3 in the Cardiology Ward, we have determined two illness events: the tachycardia event and the poor treatment response event.

[0092] In step S13, the internal images of each ward are collected, and the interaction events of the patients are determined according to the recognition of the internal images of each ward;

[0093] In the specific implementation process of the present invention, the specific steps are as follows:

[0094] S131: Determine the internal images of each ward based on the monitoring of each ward;

[0095] S132: Collect the internal images of each ward;

[0096] S133: Input the internal images of each ward into a preset recognition model;

[0097] S134: Determine multiple interaction features according to the internal images of each ward and the preset recognition model, and the multiple interaction features are in the same ward;

[0098] S135: Determine the interaction events of the patients based on the spatial positions of the multiple interaction features, the feature types of the multiple interaction features, and the interaction situations between the multiple interaction features.

[0099] In the embodiments of the present application, the internal images of each ward are determined based on the monitoring of each ward; the internal images of each ward are collected, ensuring the subsequent processing of the internal images of each ward.

[0100] At this time, each ward should be equipped with at least one monitoring camera to capture the real-time pictures inside the ward. The monitoring camera should be connected to the central monitoring server so that the system can centrally manage and access these images. In the monitoring system, the corresponding monitoring camera is configured for each ward, which includes setting parameters such as the viewing angle, focal length, and brightness of the camera to ensure that the captured images are clear and accurate. Configure the recording parameters of the monitoring camera, such as recording quality, recording duration, loop recording, etc., to meet the hospital's requirements for monitoring recordings. The monitoring camera captures the pictures inside the ward in real time and transmits these pictures to the central monitoring server.

[0101] The central monitoring server receives and stores these image data, and at the same time provides an access interface so that other systems or users can view the internal images of the ward in real time. The system determines and obtains the internal images of a specific ward by accessing the interface of the central monitoring server, and these images can be further processed, such as enhancing the image quality, adjusting the image size, etc., to meet the needs of subsequent analysis.

[0102] Specifically, assume that a monitoring system is installed in the cardiology ward of a certain hospital, and each ward is equipped with a high-definition monitoring camera. The monitoring system in the cardiology ward has been correctly installed and connected to the hospital's network. The monitoring camera in each ward is connected to the central monitoring server.

[0103] In the monitoring system, the monitoring camera is configured for each ward in the cardiology department. The viewing angle of the camera is set to cover the entire ward, and the focal length and brightness are also adjusted to the best state. The recording parameters of the monitoring camera are set to high-definition recording, and the recording duration is 24-hour loop recording.

[0104] The monitoring camera in the cardiology ward captures the pictures inside the ward in real time and transmits these pictures to the central monitoring server in the form of a high-definition video stream. The central monitoring server receives and stores these high-definition video streams, and at the same time provides an access interface to the hospital's management system and medical staff. When it is necessary to view the internal images of a specific ward, the hospital's management system determines and obtains the real-time images of that ward by accessing the interface of the central monitoring server. For example, assume that it is necessary to view the internal images of ward 301 in the cardiology department. The management system will send a request to the central monitoring server, and the server will return the real-time high-definition images of ward 301.

[0105] Further, input the internal images of each ward into a preset recognition model; determine multiple interaction features based on the internal images of each ward and the preset recognition model. The multiple interaction features are in the same ward, ensuring the accuracy of the multiple interaction features.

[0106] At this time, before inputting the image into the model, some preprocessing steps are required, such as image scaling, cropping, denoising, etc., to ensure that the image meets the input requirements of the model. Select a pre-trained image recognition model from the system. This model should have been optimized and trained for the hospital ward environment and can accurately identify various elements in the ward. Input the preprocessed internal images of the ward into the recognition model frame by frame or at a certain time interval. The recognition model infers the input image and outputs recognition results, which include the identities of people in the ward, the types of objects, behavioral characteristics, etc.

[0107] Extract the feature information related to interaction from the output of the recognition model. These features include the positions, actions, and facial expressions of people, and the types, positions, and states of objects. Analyze the extracted feature information to determine which features belong to the interaction events within the same ward. This involves the association of spatial positions (such as people and objects being in the same room), the continuity in time (such as a series of actions occurring within the same time period), and the logical relationships between features (such as a person's operation on an object). Based on the results of feature association, construct the interaction events inside the ward. These events include the communication between patients and their families, patients using medical equipment, medical staff examining or treating patients, etc.

[0108] Specifically, assume that in a cardiovascular ward, the monitoring system captured the following scenario:

[0109] There is a patient lying in bed in the ward, and next to the bed is an electrocardiogram monitor. A medical staff member enters the ward, walks to the electrocardiogram monitor, and starts checking the monitoring data. The medical staff member then adjusts some settings of the electrocardiogram monitor and has a brief conversation with the patient.

[0110] At this time, the system captured the real-time images inside the ward and input these images into the pre-trained image recognition model. The recognition model identified the patient, the electrocardiogram monitor, and the medical staff member in the ward, as well as their positions, actions, and other features. The system extracted the feature information related to interaction from the output of the model, including the entry of the medical staff member, checking the electrocardiogram monitoring data, adjusting the settings, and communicating with the patient. These features were associated within the same ward to construct an interaction event: The medical staff member monitored the electrocardiogram of the patient and made corresponding setting adjustments and communications.

[0111] Therefore, by determining the interaction events of the patient based on the spatial positions of multiple interaction features, the feature types of multiple interaction features, and the interaction situations among multiple interaction features, the spatial positions of multiple interaction features, the feature types of multiple interaction features, and the interaction situations among multiple interaction features are introduced, and the spatial positions of multiple interaction features, the feature types of multiple interaction features, and the interaction situations among multiple interaction features are considered as a whole, ensuring the accuracy of the patient's interaction events.

[0112] At this time, by synthesizing the information of multiple interaction features, including their spatial positions, feature types, and the interaction situations among them, the specific interaction events that occur to the patient in the ward are determined. This step is the advanced analysis function of the hospital monitoring system, which can provide detailed information about the patient's activities and interactions for medical staff.

[0113] The system first analyzes the spatial positions of each interaction feature in the ward, which includes determining the positional relationships of people, objects, etc., such as relative distances, directions, etc. Through spatial position analysis, the system can judge which features interact in the same space, and which features are independent or have no direct interaction with other features. The system identifies the specific types of each interaction feature, which includes the identities of people (such as patients, family members, medical staff), the types of objects (such as medical equipment, daily necessities), and the types of behaviors or actions (such as walking, sitting, operating equipment, etc.). The identification of feature types helps the system understand the specific content and background of the interaction events.

[0114] The system further analyzes the interaction situations among each interaction feature, which includes determining the interactions, dependencies, or causal relationships among the features. For example, the system can identify interaction situations such as medical staff operating medical equipment, patients communicating with family members, or patients using a certain device by themselves. Based on the above analysis, the system synthesizes the information of multiple interaction features to determine specific interaction events, which involves analyzing the features at multiple time points to construct a complete sequence of interaction events.

[0115] Specifically, assume that in a surgical ward, the monitoring system captures the following scenario: There is a patient sitting on the bed in the ward, and next to it is a postoperative care bed with some medical supplies placed on it. A nurse enters the ward, walks to the patient's bedside, and starts checking the patient's wound. The nurse then picks up a bottle of disinfectant from the care bed and disinfects the patient's wound. During the process, the nurse has a short conversation with the patient, asking about the patient's feelings.

[0116] Meanwhile, the system identifies that the patient is sitting on the bed, the nurse is standing beside the bed, and the nursing bed and medical supplies are located on one side of the ward. Through spatial position analysis, the system determines that the nurse and the patient are in the same space, and the nurse is approaching the patient for interaction. The system identifies the human characteristics as the patient and the nurse, the object characteristics as the nursing bed and medical supplies, and the behavioral characteristics as wound inspection, picking up disinfectant, disinfection treatment, etc. The system analyzes that the nurse is operating medical supplies (disinfectant) to inspect and disinfect the patient's wound. At the same time, the system identifies the communication behavior between the nurse and the patient, indicating that they communicate during the interaction. Based on the above analysis, the system determines the specific interaction event: The nurse inspected and disinfected the wound of the postoperative patient and communicated with the patient.

[0117] In step S14, determine the comprehensive state of the patient based on the patient's interaction event, the patient's condition event, and the patient's current physical data, and match the comprehensive state of the patient to the location of the corresponding ward;

[0118] In the specific implementation process of the present invention, the specific steps are as follows:

[0119] S141: Obtain the interaction event of the patient;

[0120] S142: Collect the medical record report of the patient and determine the patient's condition event based on the medical record report of the patient;

[0121] S143: Determine the current physical data of the patient based on the physical examination of the patient by the body detector;

[0122] S144: Determine the comprehensive state of the patient based on the patient's interaction event, the patient's condition event, and the patient's current physical data;

[0123] S145: Match the comprehensive state of the patient and the location of the ward; match the comprehensive state of the patient to the location of the corresponding ward.

[0124] In the embodiment of the present application, obtaining the interaction event of the patient; collecting the medical record report of the patient and determining the patient's condition event based on the medical record report of the patient; further controlling the medical record report of the patient ensures the accuracy of the patient's condition event.

[0125] At this time, obtain the interaction event information of the patient. These interaction events include the patient's activities in the ward, communication with medical staff, use of medical equipment and other behaviors. Optionally, the system retrieves or receives in real time the interaction event data about the patient, which exists in the form of event logs marked with timestamps and records the activities or interactions of the patient at specific time points.

[0126] Collect the patient's medical record reports and determine the patient's medical events based on this information in the medical record reports. Medical events include the onset of diseases, exacerbation or remission of symptoms, feedback on treatment effects, etc. The system obtains the patient's reports from the electronic medical record system or other data sources. Extract key medical information from the medical record reports through natural language processing or manual review. Determine the specific medical events of the patient based on the extracted information.

[0127] Screen out the information directly related to the patient's current health status from the collected medical record reports. Based on the screened information, combined with medical knowledge and clinical experience, evaluate the patient's condition, which involves judging the severity of symptoms, determining the disease stage, etc.

[0128] Event determination: According to the evaluation results, determine the specific medical events, which include the onset of diseases, exacerbation or remission of symptoms, appearance of complications, evaluation of treatment responses, etc. Record the determined medical events in the patient's medical record and update them as the patient's health status changes.

[0129] Furthermore, determine the patient's current physical data based on the physical examination of the patient by the body detector; determine the patient's comprehensive status based on the patient's interaction events, the patient's medical events, and the patient's current physical data. The patient's interaction events, the patient's medical events, and the patient's current physical data are introduced, achieving an overall grasp of the patient's interaction events, the patient's medical events, and the patient's current physical data, ensuring the accuracy of the patient's comprehensive status.

[0130] At this time, the selection of the body detector should be determined according to the patient's specific situation and the items to be detected. Common body detectors include sphygmomanometers, heart rate monitors, blood glucose meters, blood oxygen saturation monitors, thermometers, etc. These devices can measure and display the patient's vital signs and biochemical indicators.

[0131] When using the body detector, medical staff need to ensure that the device is in normal working condition and perform accurate measurements according to the operation guide. For some items that need to be detected regularly, such as blood pressure and blood glucose, medical staff should record the time and data of each detection for subsequent analysis and comparison. After the detection, medical staff need to accurately record the measurement results in the patient's medical record. These data are important bases for evaluating the patient's health status and are also the foundation for formulating and adjusting treatment plans. When collecting and recording physical data, medical staff should strictly abide by the principle of privacy protection to ensure the patient's personal information and data security.

[0132] Integrate the information obtained in steps S142 and S143, which includes the patient's interaction events (such as daily activities, communication with medical staff, etc.), disease events (such as the onset of the disease, exacerbation or remission of symptoms, etc.), and current physical data (such as vital signs, biochemical indicators, etc.). After integrating the information, evaluate the patient's comprehensive status, which involves a comprehensive analysis of the patient's health condition, including physiological, psychological, and social aspects. Based on the comprehensive evaluation, determine the patient's current comprehensive status, which includes the stability or deterioration of the disease, evaluation of the treatment effect, monitoring of the rehabilitation progress, etc. According to the patient's comprehensive status, formulate or adjust the treatment plan, as well as the subsequent monitoring and evaluation plan.

[0133] Specifically, the medical staff evaluated Mr. Zhang's comprehensive status. The evaluation process is as follows:

[0134] The medical staff first reviewed Mr. Zhang's interaction events, including his daily activity level, communication with medical staff, etc.

[0135] Then, the medical staff checked Mr. Zhang's disease events, such as the time and cause of his last blood sugar increase.

[0136] Next, the medical staff referred to the current blood sugar data of Mr. Zhang obtained in step S143.

[0137] Integrating this information, the medical staff evaluated Mr. Zhang's comprehensive status. They believed that Mr. Zhang's blood sugar control was not ideal and was related to his eating habits and daily activity level. The medical staff formulated a new treatment plan, including adjusting the diet and increasing the exercise amount, and arranged a subsequent blood sugar monitoring plan.

[0138] In another embodiment of the present application, in order to more systematically evaluate the patient's comprehensive status, we can adopt the method of a matching table. The matching table is a tool that matches various information of the patient (such as interaction events, disease events, current physical data) with preset standards or categories, and determines the patient's comprehensive status through the matching results.

[0139] Suppose we have a simple matching table for evaluating the comprehensive status of pneumonia patients. The matching table includes the following dimensions:

[0140] Interaction events: The patient's daily activity ability, communication ability, etc.

[0141] Disease events: The severity of pneumonia, whether there are complications, etc.

[0142] Current physical data: Body temperature, blood oxygen saturation, white blood cell count, etc.

[0143] Under each dimension, there are several preset categories or standards, and each category corresponds to a specific status description. For example:

[0144] Interaction event:

[0145] Able to move and communicate normally: Status A; Limited activity and difficult communication: Status B;

[0146] Illness event:

[0147] Mild pneumonia without complications: Status A; Severe pneumonia with complications: Status C

[0148] Current physical data:

[0149] Normal body temperature, high blood oxygen saturation, and normal white blood cell count: Status A; High body temperature, low blood oxygen saturation, and abnormal white blood cell count: Status D

[0150] Then, based on the actual situation of the patient, we find the most suitable category under each dimension and summarize the status descriptions of these categories to obtain the comprehensive status of the patient. For example:

[0151] Patient Mr. Zhang can move and communicate normally (Status A), has mild pneumonia without complications (Status A), and has normal body temperature, high blood oxygen saturation, and normal white blood cell count (Status A).

[0152] Comprehensive status: Mr. Zhang is in good condition (all dimensions are Status A).

[0153] Therefore, match the comprehensive status of the patient with the location of the ward; match the comprehensive status of the patient to the corresponding ward location.

[0154] At this time, the comprehensive status of the patient and the location of the ward are introduced. The comprehensive status of the patient is obtained through the previous steps (such as determined based on the patient's interaction events, illness events, and current physical data), which reflects the overall health status and needs of the patient. The location of the ward includes detailed information such as the floor, area, and room number of the ward, as well as characteristics such as the facilities, capacity, and applicable illnesses of the ward.

[0155] According to the actual situation and management requirements of the hospital, set a set of matching rules, which include:

[0156] Match between the severity of the illness and the level of ward facilities: For example, critically ill patients should be assigned to wards with more complete facilities and denser medical staff.

[0157] Match between special needs and ward characteristics: For example, patients who need isolation should be assigned to wards with isolation facilities.

[0158] Match between ward availability and patient occupancy needs: Ensure that the assigned ward is vacant and meets the patient's occupancy standards.

[0159] According to the set matching rules, match the comprehensive status of the patient with the location information of the ward. This can be automatically completed by the hospital information system or manually operated by medical staff. The matching result should be one or more eligible ward locations. After the matching is completed, medical staff need to confirm the accuracy and applicability of the matching result. Once confirmed, the ward location information of the patient should be recorded in the hospital information system, and the patient's medical record should be updated. At the same time, it should be ensured that relevant medical staff can obtain the latest ward location information of the patient in a timely manner.

[0160] Specifically, suppose there is a patient, Mr. Wang, who is admitted to the hospital for treatment of heart disease. After the previous step of assessment, Mr. Wang's comprehensive status is determined to be "needing close monitoring, but the condition is relatively stable". The hospital has a set of matching rules, and one of them is that "patients who need close monitoring should be assigned to wards close to the nurse station". Mr. Wang's comprehensive status is "needing close monitoring, but the condition is relatively stable"; the location information of the ward includes multiple floors, areas, and room numbers, and the wards close to the nurse station are 301, 302, 303, etc. According to the hospital's matching rules, select the ward close to the nurse station as the preferred option. Search for the wards close to the nurse station in the hospital information system and screen out the wards that meet Mr. Wang's comprehensive status. The results are that both Ward 301 and Ward 302 meet the conditions. The medical staff confirm that Ward 301 is more suitable for Mr. Wang (for example, the facilities in Ward 301 are more complete, or Ward 302 has been reserved), and record Mr. Wang's ward location as 301 in the hospital information system. At the same time, update Mr. Wang's medical record to ensure that other medical staff can obtain this information.

[0161] In step S15, determine the first-layer visual layer according to the comprehensive status of the patient, the location of the ward, and the current time, and determine the second-layer visual layer according to the emergency signal of the ward, multiple environmental data of the ward, and the spatial data of the ward; determine the visual model of the intelligent ward according to the second-layer visual layer and the first-layer visual layer;

[0162] In the specific implementation process of the present invention, the specific steps are as follows:

[0163] S151: Collect the comprehensive status of the patient and the location of the ward;

[0164] S152: Match the comprehensive status of the patient and the location of the ward to the same time dimension, and collect the current time;

[0165] S153: Perform multiple interactions on the comprehensive status of the patient, the location of the ward, and the current time, and determine the first-layer visual layer according to the multiple interactions of the comprehensive status of the patient, the location of the ward, and the current time;

[0166] S154: Collect the emergency signals in the ward and collect multiple environmental data of the ward based on the environmental detection of the ward;

[0167] S155: Associate the emergency signals in the ward, the multiple environmental data of the ward, and the spatial data of the ward, and determine the second visible layer based on the emergency signals in the ward, the multiple environmental data of the ward, and the spatial data of the ward;

[0168] S156: Interact with the second visible layer and the first visible layer, and determine the visual model of the smart ward according to the interaction between the second visible layer and the first visible layer.

[0169] In the embodiment of the present application, collect the comprehensive status of the patient and the location of the ward; match the comprehensive status of the patient and the location of the ward to the same time dimension, and collect the current time; perform multiple interactions on the comprehensive status of the patient, the location of the ward, and the current time, introduce the comprehensive status of the patient, the location of the ward, and the current time, and at the same time, determine the first visible layer according to the multiple interactions of the comprehensive status of the patient, the location of the ward, and the current time.

[0170] At this time, ensure that the comprehensive status data of the patient, the ward location data, and the time data are synchronized, that is, they are all collected based on the same time point or a similar time period. This is the basis for constructing the subsequent visualization model, because data at different time points cannot accurately reflect the true situation of the patient's status and the ward environment. The system needs to regularly check and update the comprehensive status data of the patient and the ward location data to ensure that they are consistent with the time data, which usually involves real-time or periodic checks of data collection and storage. Add timestamps to each data item to clarify the collection time of the data, which helps to identify and match data at the same time point in subsequent steps. The system needs to be able to collect the current time in real time, which is usually achieved through the internal clock module of the system or an external time synchronization service.

[0171] The system will comprehensively analyze the patient's overall status, ward location, and current time to generate the first visual layer. The main purpose of this visual layer is to intuitively display the relationship among the patient's status, ward location, and time. First, the system needs to associate the patient's comprehensive status data, ward location data, and time data to form a complete dataset, which usually involves joining data tables or matching data items. The system needs to perform interactive analysis on the associated dataset to identify trends in the patient's status over time, the impact of ward location on the patient's status, etc., which involves data visualization techniques such as charts and maps. Based on the results of the interactive analysis, the system determines the content and layout of the first visual layer, which includes a timeline marked with changes in the patient's status at different time points, a ward location map showing the ward layout and the patient's location, and a status dashboard displaying the patient's current physiological indicators.

[0172] Specifically, assume there is a smart healthcare system monitoring a patient named Li Ming. At 9:30 am on October 16, 2023, the system collected Li Ming's comprehensive status data, including physiological indicators such as heart rate, blood pressure, and body temperature. At the same time, the system also determined that Li Ming's current ward location is on the 4th floor of Building 3, room number 405. The system also recorded the collection time of these data, which is 9:30 am on October 16, 2023. The system first associated Li Ming's comprehensive status data, ward location data, and time data. Then, the system performed interactive analysis on the associated dataset and found that Li Ming's heart rate slightly increased at 9:30 am but was still within the normal range; at the same time, Li Ming's ward location is in a relatively quiet area of the hospital, which is helpful for his rest and recovery.

[0173] Based on these analyses, the system determined the content and layout of the first visual layer. It generated a timeline marked with the status change of Li Ming at 9:30 am; at the same time, it also generated a ward location map showing the layout of the ward on the 4th floor of Building 3 and Li Ming's location (room 405); finally, the system also generated a status dashboard displaying Li Ming's current physiological indicators, including heart rate, blood pressure, and body temperature, etc.

[0174] In another embodiment of this application, a matching table is designed to take the patient's comprehensive status, ward location, and current time as inputs and output the corresponding visual layer content. For example:

[0175]

[0176] In this matching table, we have preset different input combinations and their corresponding visual layer contents. For example, when the patient's condition is stable, located on the 3rd floor of the North Area and the time is during the day, the system outputs the patient status graph, the ward location map, and the timeline (daytime). When the patient's condition is unstable, located on the 2nd floor of the South Area and the time is at night, the system outputs the emergency status indicator light, the ward location map, the timeline (nighttime), and the patient's detailed status data.

[0177] Furthermore, collect the emergency signals in the ward and collect multiple environmental data of the ward based on the environmental detection of the ward; associate the emergency signals in the ward, the multiple environmental data of the ward, and the spatial data of the ward, and determine the second - layer visual layer based on the emergency signals in the ward, the multiple environmental data of the ward, and the spatial data of the ward, which takes into account the overall consideration of the emergency signals in the ward, the multiple environmental data of the ward, and the spatial data of the ward, ensuring the accuracy of the second - layer visual layer.

[0178] At this time, it is crucial to monitor the emergency signals and multiple environmental parameters in the ward in real - time. The system needs to be able to instantly capture the emergency signals in the ward, such as the patient pressing the emergency call button, the triggering of the fire alarm, etc., and these signals are usually transmitted to the system through sensors or switch devices. In addition to emergency signals, the system also needs to collect multiple environmental data in the ward, such as temperature, humidity, air quality (such as carbon dioxide concentration, PM2.5 concentration), noise level, etc., and these data can be obtained through specialized environmental monitoring devices or sensors. The collected emergency signals and environmental data need to be accurately recorded and transmitted to the central processor or database of the system in real - time or regularly for subsequent analysis and visualization.

[0179] Conduct a comprehensive analysis of the collected emergency signals, environmental data, and the spatial data of the ward to generate the second - layer visual layer, which is designed to provide a comprehensive view of the emergency situation, environmental situation, and spatial layout in the ward. The system needs to associate the emergency signals, environmental data, and spatial data to form a complete data set, which involves operations such as connecting data tables, matching data items, or overlaying spatial data. The associated data set needs to be comprehensively analyzed to identify the relationships between emergency signals and environmental data, the impact of environmental data on patients, and the impact of spatial layout on emergency response, etc. Based on the results of the comprehensive analysis, the system determines the content and layout of the second - layer visual layer, which includes an emergency signal indicator light for instantly displaying the emergency situation in the ward; an environmental data dashboard for showing key environmental parameters such as temperature, humidity, and air quality in the ward; and a ward spatial layout map for displaying information such as the equipment layout and the location of emergency exits in the ward.

[0180] Specifically, assume that in a smart ward management system, Ward A is being monitored. Suddenly, Mr. Zhang, a patient, feels unwell and presses the emergency call button at the head of his bed. At the same time, the environmental monitoring equipment in the ward also detects some abnormalities: the temperature is slightly high, reaching 26 degrees Celsius (while the suitable ward temperature is usually between 22 - 24 degrees Celsius); the humidity is also high, reaching 65% (while the suitable ward humidity is usually between 50% - 60%); in addition, the noise level in the ward has also increased slightly, reaching 55 decibels (while a quiet ward environment usually requires a noise level below 45 decibels).

[0181] The system first correlates the signal of the emergency call button pressed by Mr. Zhang, the temperature, humidity, and noise level data in the ward, and the spatial layout data of the ward. Then, the system conducts a comprehensive analysis of these data and finds that the emergency call signal is directly related to Mr. Zhang's discomfort; at the same time, the temperature and humidity in the ward are slightly higher than the suitable range, which has an adverse impact on Mr. Zhang's recovery; in addition, the noise level in the ward has also increased slightly, which will interfere with Mr. Zhang's rest.

[0182] Based on these analyses, the system determines the content and layout of the second - layer visual layer. It generates an emergency signal indicator that immediately shows the emergency call status in Ward A; at the same time, it also generates an environmental data dashboard that displays key environmental parameters such as temperature, humidity, and noise level in Ward A; finally, the system also generates a ward spatial layout diagram that shows the equipment layout, the location of the emergency exit, and the location of Mr. Zhang's bed in Ward A.

[0183] In another embodiment of the present application, the matching table is an intuitive and easy - to - understand method that corresponds different combinations of input data to the corresponding visual layer content. The following is an example of a matching table based on emergency signals, environmental data, and spatial data:

[0184] Emergency signal Temperature Humidity Air quality Noise level Ward layout Second visible layer Yes High Medium Poor High Complex Emergency status diagram, environmental data alarm, ward layout diagram Yes Normal Low Good Normal Simple Emergency status diagram, ward location indication No High High Poor High Complex Environmental data alarm, ward layout diagram, ventilation advice No Normal Normal Good Normal Simple Environmental data dashboard, ward location map ... ... ... ... ... ... ...

[0185] In this matching table, each row represents a combination of input data and the corresponding visual layer content. For example, when there is an emergency signal in the ward, high temperature, moderate humidity, poor air quality, high noise level, and a complex ward layout, the system will select to output an emergency status diagram, environmental data alerts, and a ward layout diagram.

[0186] Therefore, by interacting with the second - layer visual layer and the first - layer visual layer, and determining the visual model of the smart ward based on the interaction between the second - layer visual layer and the first - layer visual layer, the interaction between the second - layer visual layer and the first - layer visual layer is realized, ensuring the accuracy of the visual model of the smart ward.

[0187] At this time, the information of the first-layer visual layer and the second-layer visual layer is integrated and interacted to construct a comprehensive, intuitive and practical visual model of the intelligent ward. This model will be able to reflect multi-dimensional information such as the status of patients in the ward, environmental conditions, emergency signals, and spatial layout in real time, providing decision-making support for medical staff.

[0188] First of all, the system needs to integrate the data of the first-layer visual layer and the second-layer visual layer, which includes the comprehensive status data of patients, ward location data, time data, emergency signal data, environmental data, and spatial data, etc. Next, the system designs the interaction logic to ensure that the data between different visual layers can be interrelated and interact with each other. For example, when the second-layer visual layer shows an emergency signal in the ward, the first-layer visual layer needs to highlight the location and status of that ward. Based on the integrated data and interaction logic, the system designs the visual model interface of the intelligent ward. This interface should be intuitive, easy to use and informative, and can display information such as the status of patients, the environment of the ward, emergency signals, and spatial layout at the same time. On the basis of the interface design, the system implements corresponding functions, which include real-time update of data, response to interactive operations, instant prompt of emergency signals, etc.

[0189] Specifically, taking the intelligent ward management system as an example, assume that there is a patient, Ms. Wang, receiving treatment in Ward B. The system has constructed the first-layer visual layer and the second-layer visual layer.

[0190] In the first-layer visual layer, the system shows the location of Ward B, the comprehensive status of Ms. Wang (such as heart rate, blood pressure, etc.), and the current time. These information help medical staff quickly understand the basic situation of Ward B.

[0191] In the second-layer visual layer, the system monitors the emergency signal (such as Ms. Wang pressing the emergency call button) in Ward B, environmental data (such as temperature, humidity, etc.), and spatial layout information. These information provide more detailed conditions inside the ward for medical staff.

[0192] Now, the system interacts with these two visual layers. When Ms. Wang presses the emergency call button, the emergency signal indicator light in the second-layer visual layer immediately lights up and emits a sound prompt. At the same time, the location of Ward B in the first-layer visual layer is highlighted, and the comprehensive status data of Ms. Wang is also updated and highlighted in real time. In this way, medical staff can immediately notice the emergency situation in Ward B and respond quickly.

[0193] Based on these interactive information, the system constructs a visual model of the intelligent ward. The visual model not only shows the status and environmental conditions of patients in the ward, but also provides instant prompts of emergency signals and spatial layout information through interactive operations. Medical staff can comprehensively understand the situation inside the ward through this model and make accurate decisions.

[0194] In step S16, the real-time picture of the ward is collected, and the dynamic monitoring system of the intelligent ward is determined based on the visual model of the intelligent ward, the real-time picture of the ward, and the personnel movement trajectory, and the corresponding warning signal is triggered according to the dynamic monitoring system of the intelligent ward;

[0195] In the specific implementation process of the present invention, the specific steps are as follows:

[0196] S161: Monitor the ward in real time and collect the real-time picture of the ward;

[0197] S162: Associate the visual model of the intelligent ward, the real-time picture of the ward, and the personnel movement trajectory;

[0198] S163: Determine the dynamic monitoring system of the intelligent ward according to the visual model of the intelligent ward, the real-time picture of the ward, and the personnel movement trajectory;

[0199] S164: Obtain the dynamic monitoring system of the intelligent ward;

[0200] S165: In the dynamic monitoring system of the intelligent ward, collect multiple data based on the dynamic monitoring of the intelligent ward;

[0201] S166: Determine the corresponding warning event according to the synthesis of multiple data, and determine the warning signal according to the warning event and the preset warning signal matching table, and trigger the corresponding warning signal.

[0202] In the embodiment of the present application, the ward is monitored in real time, and the real-time picture of the ward is collected; the visual model of the intelligent ward, the real-time picture of the ward, and the personnel movement trajectory are associated; the dynamic monitoring system of the intelligent ward is determined according to the visual model of the intelligent ward, the real-time picture of the ward, and the personnel movement trajectory, which takes into account the overall consideration of the visual model of the intelligent ward, the real-time picture of the ward, and the personnel movement trajectory, and realizes the accurate control of the dynamic monitoring system of the intelligent ward.

[0203] At this time, devices such as cameras and sensors installed in the ward are used to transmit the real-time pictures and data to the central monitoring system through the Internet of Things technology. The monitoring content includes the vital signs of patients (such as heart rate, blood pressure, body temperature, etc.), environmental parameters in the ward (such as temperature, humidity, light, etc.), the working status of medical equipment, and the behavior activities of patients, etc. The collected data will be preliminarily processed and screened to remove noise and irrelevant information to ensure the accuracy and effectiveness of the data.

[0204] The visual model is a digital ward model that displays information such as the ward layout, equipment locations, and key monitoring points. Match the real-time video with the visual model to ensure that the content in the video corresponds to the elements in the model. At the same time, integrate the movement trajectory data of personnel (including patients, medical staff, etc.), which is obtained through RFID tags, positioning systems, etc. This helps medical staff understand the situation in the ward more intuitively and improve the monitoring efficiency.

[0205] The dynamic monitoring system is a monitoring system that can reflect the dynamic changes in the ward in real time, including monitoring focuses, triggering conditions, and response mechanisms. Based on the visual model and real-time video, determine the key monitoring areas and potential risk points in the ward. Set specific conditions or thresholds, and when these conditions are met, the system will trigger corresponding warnings or alarms. Develop clear response processes and measures to ensure that medical staff can respond quickly and take corresponding actions when a warning or alarm occurs.

[0206] Specifically, multiple high-definition cameras and sensors are installed in the smart ward, which can monitor various situations in the ward in real time. For example, when an emergency occurs to a patient suddenly (such as falling, convulsing, etc.), the camera will immediately capture the video and transmit it to the central monitoring system through Internet of Things technology. Medical staff can respond quickly and take necessary treatment measures. The smart ward system uses RFID technology and positioning system to track the movement trajectory of patients in real time. At the same time, the system associates the real-time video with the visual model, and medical staff can clearly see the patient's location, status, and the equipment situation in the ward on the screen. When a patient needs emergency treatment, the system can quickly locate the patient's position and guide medical staff to reach the scene quickly. By integrating the visual model, real-time video, and personnel movement trajectory, a complete dynamic monitoring system is constructed. The system can monitor various data in the ward in real time and trigger warnings or alarms according to the set triggering conditions. For example, when the patient's vital signs show abnormal fluctuations, the system will immediately trigger a warning signal and display the patient's relevant information and location on the screen. Medical staff can respond quickly and take necessary treatment measures to ensure the patient's safety.

[0207] Therefore, obtain the dynamic monitoring system of the smart ward; in the dynamic monitoring system of the smart ward, collect multiple data based on the dynamic monitoring of the smart ward; determine the corresponding warning events according to the synthesis of the multiple data, and determine the warning signals according to the warning events and the preset warning signal matching table, and trigger the corresponding warning signals to achieve timely warnings.

[0208] At this time, the system needs to be able to access and utilize the previously established intelligent ward dynamic monitoring system, which usually means that the system needs to retrieve relevant monitoring configurations, triggering conditions, and monitoring logics from a database or memory structure. These configurations include the monitored areas, monitored devices, data threshold ranges, etc. The process of obtaining the dynamic monitoring system involves loading configuration files, initializing monitoring logics, establishing connections with data sources, etc. Once the acquisition is successful, the system can start collecting and analyzing data based on this system.

[0209] According to the configuration of the dynamic monitoring system, start collecting data from various sensors and devices in the ward. These data include the real-time vital signs of patients, environmental parameters in the ward, the operating status of medical devices, etc. The system needs to ensure the accuracy and integrity of the data and needs to preprocess the data, such as denoising and calibration. According to the configuration of the dynamic monitoring system, start collecting data from various sensors and devices in the ward. These data include the real-time vital signs of patients, environmental parameters in the ward, the operating status of medical devices, etc. The system needs to ensure the accuracy and integrity of the data and needs to preprocess the data, such as denoising and calibration. The collected data will be stored in the system's database or memory for subsequent analysis and processing.

[0210] The system will synthesize and analyze multiple collected data to determine whether a warning event has occurred. Warning events are based on the abnormality of a single data (such as too high heart rate), and also based on the correlation between multiple data (such as abnormal heart rate and blood pressure at the same time). Once a warning event is determined, the system will determine the corresponding warning signal according to the preset warning signal matching table, which contains the correspondence between various warning events and warning signals. For example, too high heart rate triggers a sound alarm, while too low blood pressure triggers a light alarm. Finally, the system will trigger the corresponding warning mechanism according to the determined warning signal, which includes displaying alarm information on the screen, sending text messages or emails to notify medical staff, triggering sound or light alarms, etc.

[0211] Embodiment III

[0212] Please refer to Figure 3 , Figure 3 which is a schematic diagram of the structural composition of the visualization system of the intelligent ward in the embodiment of the present invention.

[0213] As Figure 3 shown, a visualization system for an intelligent ward, the visualization system of the intelligent ward includes:

[0214] An acquisition module 21, configured to acquire a 3D model of a hospital and mark the positions of each ward in the 3D model of the hospital;

[0215] The medical condition event module 22 is used to determine the medical condition events of patients based on the bed data sets of each ward and the illness status of the corresponding patients;

[0216] The interaction module 23 is used to collect the internal images of each ward and determine the interaction events of patients based on the recognition of the internal images of each ward;

[0217] The comprehensive status module 24 is used to determine the comprehensive status of patients based on the interaction events of patients, the medical condition events of patients, and the current physical data of patients, and match the comprehensive status of the patients to the positions of the corresponding wards;

[0218] The visualization module 25 is used to determine the first - layer visualization layer based on the comprehensive status of the patient, the position of the ward, and the current time, and determine the second - layer visualization layer based on the emergency signals of the ward, the multiple environmental data of the ward, and the spatial data of the ward; determine the visualization model of the intelligent ward according to the second - layer visualization layer and the first - layer visualization layer;

[0219] The dynamic monitoring module 26 is used to collect the real - time images of the ward, determine the dynamic monitoring system of the intelligent ward based on the visualization model of the intelligent ward, the real - time images of the ward, and the personnel movement trajectories, and trigger the corresponding warning signals according to the dynamic monitoring system of the intelligent ward.

[0220] Embodiment 4

[0221] In this embodiment, an electronic device is provided. Its internal structure diagram is as Figure 4 shown. This electronic device includes a processor, a memory, a network interface, a display screen, and an input device connected through a system bus. Among them, the processor of this electronic device is used to provide computing and control capabilities. The memory of this electronic device includes a non - volatile storage medium and an internal memory. The non - volatile storage medium stores an operating system and a computer program, and a database is deployed on the non - volatile storage medium. The database is used to store user behavior data and user portraits. The internal memory provides an environment for the operation of the operating system and the computer program in the non - volatile storage medium. The network interface of this electronic device is used to communicate with other electronic devices on which application software is deployed. When the computer program is executed by the processor, it is used to implement a low - altitude patrol method for an unmanned aerial vehicle. The display screen of this electronic device is a liquid crystal display screen or an electronic ink display screen. The input device of this electronic device is a touch layer covering the display screen, or a button, a trackball, or a touchpad set on the shell of the electronic device, or an external keyboard, touchpad, or mouse, etc.

[0222] For any combination of the technical features of the above embodiments, for the sake of brevity of description, not all combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered to be within the scope described in this specification.

Claims

1. A visualization method for a smart ward, characterized in that, Including: Collecting the 3D model of the hospital and marking the locations of each ward in the 3D model of the hospital; Determining the patient's medical condition events based on the bed data set of each ward and the illness status of the corresponding patient; Collecting the internal images of each ward and determining the patient's interaction events based on the recognition of the internal images of each ward; Determining the comprehensive status of the patient according to the patient's interaction events, the patient's medical condition events, and the patient's current physical data, and matching the comprehensive status of the patient to the location of the corresponding ward; Determining the first visual layer according to the comprehensive status of the patient, the location of the ward, and the current time, and determining the second visual layer according to the emergency signal of the ward, the multiple environmental data of the ward, and the spatial data of the ward; Determining the visual model of the intelligent ward according to the second visual layer and the first visual layer; Collecting the real-time picture of the ward, determining the dynamic monitoring system of the intelligent ward based on the visual model of the intelligent ward, the real-time picture of the ward, and the personnel movement trajectory, and triggering the corresponding warning signal according to the dynamic monitoring system of the intelligent ward.

2. The visualization method of the intelligent ward according to claim 1, characterized in that, The collecting the 3D model of the hospital and marking the locations of each ward in the 3D model of the hospital includes: Collecting the architectural drawings of the hospital and multiple internal images of the hospital; Determining the 3D model of the hospital according to the architectural drawings of the hospital and the multiple internal images of the hospital; Collecting the 3D model of the hospital; Determining the locations of each ward based on the traversal of the 3D model of the hospital and marking the locations of each ward in the 3D model of the hospital.

3. The visualization method of the intelligent ward according to claim 2, wherein, The determining the patient's medical condition events based on the bed data set of each ward and the illness status of the corresponding patient includes: Monitoring each ward in real time; Collecting multiple bed data of each ward and determining the bed data set of each ward based on the multiple bed data of each ward; Collecting the patient's medical condition data based on the monitoring of the detection head installed on the patient; Determining the illness status of the patient according to the patient's medical condition data and the patient's facial expression; Performing multiple interactions on the bed data set of each ward and the illness status of the corresponding patient; Determining the patient's medical condition events according to the multiple interactions of the bed data set of each ward and the illness status of the corresponding patient.

4. The visualization method of the intelligent ward according to claim 3, characterized in that, The collecting the internal images of each ward and determining the patient's interaction events based on the recognition of the internal images of each ward includes: Determining the internal images of each ward based on the monitoring of each ward; Collecting the internal images of each ward; Inputting the internal images of each ward into a preset recognition model; Determining multiple interaction features according to the internal images of each ward and the preset recognition model, and the multiple interaction features are in the same ward; Determining the patient's interaction events based on the spatial location of the multiple interaction features, the feature types of the multiple interaction features, and the interaction situation between the multiple interaction features.

5. The visualization method of the intelligent ward according to any one of claims 1 to 4, characterized in that, The determining the comprehensive status of the patient according to the patient's interaction events, the patient's medical condition events, and the patient's current physical data, and matching the comprehensive status of the patient to the location of the corresponding ward includes: Obtaining the patient's interaction events; Collecting the patient's medical record report and determining the patient's medical condition events according to the patient's medical record report; Determine the current physical data of the patient based on the physical examination of the patient by the body detector; Determine the comprehensive status of the patient based on the interaction events of the patient, the disease events of the patient, and the current physical data of the patient; Match the comprehensive status of the patient with the location of the ward; match the comprehensive status of the patient to the location of the corresponding ward.

6. The visualization method of the intelligent ward according to claim 5, characterized in that, Determine the first visual layer according to the comprehensive status of the patient, the location of the ward, and the current time, and determine the second visual layer according to the emergency signal of the ward, multiple environmental data of the ward, and spatial data of the ward; Determine the visual model of the smart ward according to the second visual layer and the first visual layer, including: Collect the comprehensive status of the patient and the location of the ward; Match the comprehensive status of the patient and the location of the ward to the same time dimension, and collect the current time; Perform multiple interactions on the comprehensive status of the patient, the location of the ward, and the current time, and determine the first visual layer according to the multiple interactions of the comprehensive status of the patient, the location of the ward, and the current time.

7. The visualization method of the intelligent ward according to claim 6, characterized in that, Determine the first visual layer according to the comprehensive status of the patient, the location of the ward, and the current time, and determine the second visual layer according to the emergency signal of the ward, multiple environmental data of the ward, and spatial data of the ward; Determine the visual model of the smart ward according to the second visual layer and the first visual layer, further including: Collect the emergency signal of the ward, and collect multiple environmental data of the ward based on the environmental detection of the ward; Associate the emergency signal of the ward, multiple environmental data of the ward, and spatial data of the ward, and determine the second visual layer based on the emergency signal of the ward, multiple environmental data of the ward, and spatial data of the ward; Perform an interaction on the second visual layer and the first visual layer, and determine the visual model of the smart ward according to the interaction between the second visual layer and the first visual layer.

8. The visualization method of the intelligent ward according to claim 7, wherein, Collect the real-time picture of the ward, determine the dynamic monitoring system of the smart ward based on the visual model of the smart ward, the real-time picture of the ward, and the personnel movement trajectory, and trigger the corresponding warning signal according to the dynamic monitoring system of the smart ward, including: Monitor the ward in real time and collect the real-time picture of the ward; Associate the visual model of the smart ward, the real-time picture of the ward, and the personnel movement trajectory; Determine the dynamic monitoring system of the smart ward according to the visual model of the smart ward, the real-time picture of the ward, and the personnel movement trajectory.

9. The visualization method of the intelligent ward according to claim 8, characterized in that, Collect the real-time picture of the ward, determine the dynamic monitoring system of the smart ward based on the visual model of the smart ward, the real-time picture of the ward, and the personnel movement trajectory, and trigger the corresponding warning signal according to the dynamic monitoring system of the smart ward, further including: Obtain the dynamic monitoring system of the smart ward; In the dynamic monitoring system of the smart ward, collect multiple data based on the dynamic monitoring of the smart ward; Determine the corresponding warning event according to the synthesis of multiple data, determine the warning signal according to the warning event and the preset warning signal matching table, and trigger the corresponding warning signal.

10. A visualization system for a smart ward, characterized in that, The visualization system of the smart ward is applied to the visualization method of the smart ward as described in any one of claims 1-9, and the visualization system of the smart ward includes: A collection module, which is used to collect the 3D model of the hospital and mark the positions of each ward in the 3D model of the hospital; A disease event module, which is used to determine the disease events of patients based on the bed data sets of each ward and the illness status of the corresponding patients; An interaction module, which is used to collect the internal images of each ward and determine the interaction events of patients according to the recognition of the internal images of each ward; A comprehensive status module, which is used to determine the comprehensive status of patients according to the interaction events of patients, the disease events of patients and the current physical data of patients, and match the comprehensive status of the patients to the positions of the corresponding wards; A visualization module, which is used to determine the first - layer visualization layer according to the comprehensive status of the patient, the position of the ward and the current time, and determine the second - layer visualization layer according to the emergency signals of the ward, multiple environmental data of the ward and the spatial data of the ward; determine the visualization model of the smart ward according to the second - layer visualization layer and the first - layer visualization layer; A dynamic monitoring module, which is used to collect the real - time images of the ward, determine the dynamic monitoring system of the smart ward based on the visualization model of the smart ward, the real - time images of the ward and the personnel movement trajectories, and trigger the corresponding warning signals according to the dynamic monitoring system of the smart ward.

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