An Internet of Things platform based on multimodal emergency data and its working method

Abstract

The present invention discloses an Internet of Things platform based on multimodal emergency data and a working method thereof, and relates to the field of Internet of Things technology. The method comprises: obtaining multimodal emergency data from outside the hospital and inside the hospital, fusing them to generate a patient emergency data information library, analyzing the patient emergency data information library, generating a patient rapid emergency plan, and the Internet of Things platform monitoring the patient's multimodal emergency data outside the hospital and the multimodal emergency-related data inside the hospital in real time. If the data is abnormal, the abnormal data is marked and transmitted to the patient emergency data information library to obtain the abnormal marked patient emergency data information library, and the patient rapid emergency plan is updated according to the abnormal marked patient emergency data information library. Through the Internet of Things platform provided by the present invention, it is possible to obtain relevant data on emergency treatment inside and outside the hospital, dispatch medical staff to rescue patients, and generate a patient rapid emergency plan according to the patient's condition and the emergency data inside and outside the hospital during the transportation of patients, so as to achieve the purpose of rescuing patients in a timely manner.

Description

Technical Field

[0001] The present invention relates to the technical field of Internet of Things, and in particular to an Internet of Things platform based on multimodal emergency data and a working method thereof. Background Art

[0002] With the rapid development of Internet of Things technology, it plays an increasingly important role in medical care. Nowadays, in the process of emergency treatment for patients both inside and outside the hospital, due to poor communication and irregular records, information transmission is prone to errors, resulting in medical staff being unable to accurately hand over the patient's basic information, condition, ECG monitoring and other patient-related information in a timely manner, delaying the rescue opportunity, resulting in the patient not receiving timely treatment. In addition, the process of handing over patients between the hospital and outside the hospital is not standardized. Emergency personnel outside the hospital often hand over to medical staff in the hospital in oral form. Since the hospital staff are busy with rescue, information is inevitably missed during the handover process, resulting in confusion and omissions in the handover process, affecting the patient's treatment and care. Therefore, there is an urgent need for an information acquisition and transmission Internet of Things platform for emergency data between the hospital and outside the hospital that can transmit messages in a timely and accurate manner and standardize the handover process. Summary of the Invention

[0003] The present invention provides an Internet of Things working method based on multimodal emergency data, comprising:

[0004] Step S1: The IoT platform obtains the patient's out-of-hospital multimodal emergency data and obtains in-hospital multimodal emergency data;

[0005] Step S2: The IoT platform integrates the patient's out-of-hospital multimodal emergency data with the in-hospital multimodal emergency-related data to generate a patient emergency data information database;

[0006] Step S3: The IoT platform analyzes the patient emergency data database and generates a rapid emergency plan for the patient;

[0007] Step S4: The Internet of Things platform monitors the patient's out-of-hospital multimodal emergency data and in-hospital multimodal emergency-related data in real time. If the data is abnormal, the abnormal data will be marked and transmitted to the patient emergency data information database to obtain the abnormal marked patient emergency data information database, and the patient's rapid emergency plan will be updated according to the abnormal marked patient emergency data information database.

[0008] In the above-mentioned IoT working method based on multimodal emergency data, the IoT platform acquires the patient's out-of-hospital multimodal emergency data and acquires the in-hospital multimodal emergency-related data in the following sub-steps:

[0009] Step S11: The IoT platform obtains the patient's location information through the positioning system, obtains the data of emergency vehicles around the patient through IoT technology, and selects the optimal emergency vehicle;

[0010] Step S12: The IoT platform obtains the patient's out-of-hospital multimodal emergency data based on the optimal ambulance and other information collection equipment;

[0011] Step S13: The IoT platform obtains in-hospital multimodal emergency care related data.

[0012] In the aforementioned IoT working method based on multimodal emergency data, the IoT platform obtains patient location information through a positioning system and obtains data on emergency vehicles around the patient through IoT technology. The sub-steps for selecting the optimal emergency vehicle are as follows:

[0013] Step S111: The IoT platform receives the patient's emergency information, locates the patient's location through the positioning system, and obtains the patient's location information data;

[0014] Step S112: The IoT platform obtains the status and location of the emergency vehicles near the patient through IoT technology based on the patient's location information, and selects the optimal emergency vehicle that is idle and can reach the patient the fastest to rescue the patient.

[0015] In the aforementioned IoT working method based on multimodal emergency data, the IoT platform analyzes the patient emergency data database and generates a rapid emergency plan for the patient in the following sub-steps:

[0016] Step S31: The IoT platform generates a patient identification and handover plan upon arrival at the hospital based on the patient emergency data database;

[0017] Step S32: The IoT platform analyzes the patient emergency data information database, obtains the patient's condition analysis results, performs registration and triage in the hospital system, and creates a patient disease information details book, obtaining the registration and triage results and the patient disease information details book;

[0018] Step S33: The Internet of Things platform monitors the patient emergency data information database in real time, and updates the patient disease information details book in real time according to changes in the patient emergency data information database;

[0019] Step S34: The IoT platform generates a quick first aid plan for the patient based on the identification and handover plan for the emergency patient arriving at the hospital, the patient's disease information details book, and the registration and triage results.

[0020] The IoT working method based on multimodal emergency data as described above, wherein the IoT platform monitors the patient's out-of-hospital multimodal emergency data and in-hospital multimodal emergency-related data in real time, and if the data is abnormal, the abnormal data is annotated and transmitted to the patient emergency data information database to obtain the abnormal annotated patient emergency data information database, and the sub-steps of updating the patient's rapid emergency plan based on the abnormal annotated patient emergency data information database are as follows: Step S41, the IoT platform monitors the patient's out-of-hospital multimodal emergency data and in-hospital multimodal emergency-related data collected in real time, determines the abnormality of the data, annotates the abnormal data, and transmits it to the patient emergency data information database to obtain the abnormal annotated patient emergency data information database;

[0021] Step S42: The Internet of Things platform analyzes the emergency data information database of abnormally marked patients, generates a local emergency plan, and updates the patient's rapid emergency plan based on the local emergency plan.

[0022] The present invention also provides an Internet of Things platform based on multimodal emergency data, comprising:

[0023] Data acquisition module, which obtains the patient's out-of-hospital multimodal emergency data and in-hospital multimodal emergency related data;

[0024] The multimodal data acquisition and fusion module integrates the patient's out-of-hospital multimodal emergency data with the in-hospital multimodal emergency-related data to generate a patient emergency data information database;

[0025] The patient rapid first aid plan generation module analyzes the patient first aid data information database and generates a patient rapid first aid plan;

[0026] The multimodal data monitoring and processing module monitors the patient's out-of-hospital multimodal emergency data and in-hospital multimodal emergency-related data in real time. If the data is abnormal, the abnormal data will be marked and transmitted to the patient emergency data information database to obtain the abnormal marked patient emergency data information database, and the patient's rapid emergency plan will be updated according to the abnormal marked patient emergency data information database.

[0027] In the aforementioned IoT platform based on multimodal emergency data, the data acquisition module specifically includes:

[0028] The optimal ambulance selection submodule obtains the patient's location information through the positioning system and the data of ambulances around the patient through the Internet of Things technology to select the optimal ambulance;

[0029] The submodule for acquiring multimodal data of patients outside the hospital acquires multimodal emergency data of patients outside the hospital based on the optimal ambulance and other information collection equipment;

[0030] The submodule for acquiring data related to multimodal emergency care within the hospital acquires data related to multimodal emergency care within the hospital.

[0031] The patient positioning submodule receives the patient's emergency information, locates the patient's position through the positioning system, and obtains the patient's position information data;

[0032] The optimal ambulance selection submodule obtains the status and location of ambulances near the patient through the Internet of Things technology based on the patient's location information, and selects the optimal ambulance that is idle and can reach the patient the fastest to rescue the patient.

[0033] In the aforementioned IoT platform based on multimodal emergency data, the optimal emergency vehicle selection submodule specifically includes:

[0034] The patient location acquisition submodule receives the patient's emergency information, locates the patient's position through the positioning system, and obtains the patient's location information data;

[0035] The optimal ambulance selection submodule obtains the status and location of ambulances near the patient through the Internet of Things technology based on the patient's location information, and selects the optimal ambulance that is idle and can reach the patient the fastest to rescue the patient.

[0036] In the aforementioned IoT platform based on multimodal emergency data, the patient rapid emergency plan generation module specifically includes:

[0037] The patient arrival hospital identification and handover plan generation submodule generates a patient arrival hospital identification and handover plan based on the patient emergency data information database;

[0038] The registration and triage results and patient disease information details book acquisition submodule analyzes the patient emergency data information database, obtains the patient's condition analysis results, performs registration and triage in the hospital system, and establishes a patient disease information details book, obtains the registration and triage results and the patient disease information details book;

[0039] The data monitoring and updating submodule monitors the emergency patient condition data and basic emergency measures in the emergency patient information in real time, and updates the patient disease information details book in real time according to the changes in the emergency patient condition data and basic emergency measures;

[0040] The patient rapid first aid plan generation sub-module generates a patient rapid first aid plan based on the identification and handover plan for emergency patients arriving at the hospital, the patient's disease information details book, and the registration and triage results.

[0041] In the aforementioned IoT platform based on multimodal emergency data, the multimodal data monitoring and processing module specifically includes:

[0042] The abnormal data monitoring submodule monitors the real-time collected multimodal emergency data of patients outside the hospital and the multimodal emergency data related to the hospital in real time, determines the abnormality of the data, marks the abnormal data and transmits it to the patient emergency data information database to obtain the abnormal marked patient emergency data information database;

[0043] The abnormal data processing submodule analyzes the emergency data information database of abnormally marked patients, generates a local emergency plan, and updates the patient's rapid emergency plan based on the local emergency plan.

[0044] The beneficial effects achieved by the present invention are as follows: Through the Internet of Things platform provided by the present invention, relevant data on emergency treatment both inside and outside the hospital can be obtained, and when the patient contacts and needs emergency treatment, medical staff can be quickly dispatched to rescue the patient. During the process of transporting the patient, a rapid first aid plan for the patient can be generated in time according to the patient's detailed condition and real-time first aid related data both inside and outside the hospital, so that medical staff can respond in time according to the patient's rapid first aid plan, thereby achieving the purpose of rescuing the patient in time. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments described in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0046] Figure 1 This is a flow chart of an Internet of Things working method based on multimodal emergency data provided in Example 1 of the present application;

[0047] Figure 2 This is a schematic diagram of an Internet of Things platform based on multimodal emergency data provided in Example 2 of the present application. DETAILED DESCRIPTION

[0048] The following is a clear and complete description of the technical solutions in the embodiments of the present invention, in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0049] Example 1

[0050] like Figure 1 As shown, the first embodiment of the present application provides an Internet of Things working method based on multimodal emergency data, which includes the following steps:

[0051] Step S1: The IoT platform obtains the patient's out-of-hospital multimodal emergency data and obtains in-hospital multimodal emergency data;

[0052] Specifically, during the data acquisition and transmission process, multi-channel transmission technology is used to establish a data transmission channel, improve the data transmission speed, and achieve efficient transmission between data; during the data transmission process, asymmetric encryption technology is used to encrypt the data to achieve secure data transmission; and network topology optimization technology is used to achieve stable data transmission.

[0053] Furthermore, the IoT platform acquires the patient's out-of-hospital multimodal emergency data and the in-hospital multimodal emergency data in the following sub-steps:

[0054] Step S11: The IoT platform obtains the patient's location information through the positioning system, obtains the data of emergency vehicles around the patient through IoT technology, and selects the optimal emergency vehicle;

[0055] Positioning systems can include the GPS system, Beidou satellite system, GLONASS system, Galileo system, and Indian Regional Navigation Satellite System.

[0056] Furthermore, the IoT platform obtains the patient's location information through the positioning system and the data of emergency vehicles around the patient through IoT technology. The sub-steps for selecting the optimal emergency vehicle are as follows:

[0057] Step S111: The IoT platform receives the patient's emergency information, locates the patient's location through the positioning system, and obtains the patient's location information data;

[0058] Step S112: The IoT platform obtains the status and location of emergency vehicles near the patient through IoT technology based on the patient's location information, and selects the optimal emergency vehicle that is available and can reach the patient the fastest to rescue the patient.

[0059] Specifically, a usage status sensor is installed on the ambulance and connected to the Internet of Things platform. Its usage status is set to idle and occupied. When a patient needs an ambulance, the Internet of Things platform identifies the usage status of the ambulance based on the usage status sensor on the ambulance; a positioning sensor is installed on the ambulance, which uses 5G technology to obtain the location information of the ambulance around the patient's location and obtains map information data around the patient through the positioning system. By calculating the ambulance dispatch value, the optimal ambulance is selected to rescue the patient.

[0060] Furthermore, the emergency vehicle dispatch value expression is as follows: in, on duty for emergency vehicle dispatch; The state value of the ambulance is 1 if the ambulance is idle and 0 if the ambulance is occupied. is the traffic condition value, and its value range is , the best traffic condition is 1, and the worst traffic condition is 10; Select the shortest possible distance from the emergency vehicle to the patient's location. is the average speed of emergency vehicles.

[0061] Specifically, calculate the emergency dispatch value of each ambulance near the patient , select the ambulance dispatch value The smallest positive number is the optimal ambulance, which is sent to rescue the patient.

[0062] Step S12: The IoT platform obtains the patient's out-of-hospital multimodal emergency data based on the optimal ambulance and other information collection equipment;

[0063] Specifically, the patient's out-of-hospital multimodal emergency data includes but is not limited to patient data collected by various medical devices on the best ambulance, on-site environmental data collected by video acquisition equipment, ambulance location collected by positioning acquisition equipment, patient personal information data collected by card reader wristbands, on-site personnel input data and other data collected by other IoT devices; among them, the patient data collected by various medical devices include the patient's heart rate, systolic blood pressure, respiratory rate, body temperature and other patient condition data, and the on-site environmental data collected by video acquisition equipment include the first aid measures taken by medical staff on the ambulance for the patient, the use of medical equipment on the ambulance, the language communication between the personnel on the ambulance, the patient's status and other video language data. Specific out-of-hospital equipment includes but is not limited to electrocardiogram monitors, oxygen supply systems, blood oxygen saturation, rapid blood glucose meters, blood pressure monitors, defibrillators, external pacemakers, respiratory equipment, chest decompression equipment, pressurized infusion devices, high-definition vehicle-mounted pan-tilt heads, infrared night vision cameras, wireless vehicle-mounted reversing cameras and PTZ cameras, high-definition vehicle-mounted recorders, vehicle-mounted locators, GPS systems, wireless communication equipment, audio and video recording and transmission equipment, drones, and AR glasses.

[0064] For example, audio and video recording and transmission equipment, drones, and AR glasses can also be used in the emergency scenarios of this application to obtain real-time information at the emergency scene.

[0065] Audio and video recording and transmission equipment can be used for on-site recording and evidence collection, and real-time collection of the situation at the medical rescue scene, including the medical staff's treatment process, changes in the patient's status, etc. The collected information can be used for subsequent medical dispute resolution and responsibility determination; in emergency situations, such as traffic accidents, natural disasters, etc., audio and video recording and transmission equipment can record the original conditions of the accident scene, provide strong support for accident cause analysis and responsibility determination, and can also be used to supervise and standardize medical behavior. Through real-time monitoring of audio and video recording and transmission equipment, the medical staff's treatment behavior can be supervised to ensure that they follow medical standards and operating procedures, reduce the occurrence of medical errors, and also help to improve the professional quality and sense of responsibility of medical staff, and promote the quality of medical services. Improvement; It can also be used for remote guidance and assistance. In complex or emergency medical rescue scenarios, audio and video recording and transmission equipment can transmit on-site images to remote expert teams in real time, provide remote guidance and support for on-site medical staff, and help improve rescue efficiency and success rate, especially in remote areas or environments with limited resources; It can also be used for emergency response and command and dispatch. Through the high-definition recording and real-time transmission functions of audio and video recording and transmission equipment, the on-site data is transmitted to the Internet of Things platform, enabling the command center to quickly understand the on-site situation and formulate a more scientific rescue plan. According to the on-site situation and emergency needs, the Internet of Things platform can also enable the command center to quickly dispatch surrounding resources, such as rescue vehicles and supplies, to ensure the smooth progress of the rescue operation.

[0066] Drones can be used for telemedicine. They can carry a variety of medical equipment and, through modern technologies like high-definition cameras, infrared detectors, and other sensors, provide high-quality diagnosis and treatment services to patients in remote and isolated areas. These services are suitable for uninhabited areas, areas affected by natural disasters, or areas affected by civil war or ethnic conflict. The fast, safe, and efficient medical services provided by drones can shorten delays and increase the success rate of treatment. Drones can also be used for emergency rescue support. Drones can be equipped with high-definition cameras and sensors, transmitting images and data from the scene to doctors or emergency personnel in real time via the Internet of Things platform, enabling remote first aid guidance. At disaster sites, drones can also conduct rapid search and rescue operations. Equipped with equipment like infrared thermal imagers and cameras, they can locate trapped people and provide real-time image and video data to guide rescue operations.

[0067] AR glasses can be used for telemedicine and emergency medical guidance. Using real-time video transmission and augmented reality technology, AR glasses enable first responders to communicate with remote doctors in real time and receive professional medical guidance. For example, in emergencies like cardiac arrest, first responders can wear AR glasses to transmit on-site information to attending doctors at the hospital in real time. Doctors can then use the glasses to provide precise treatment guidance. This telemedicine model not only improves emergency response efficiency but also allows emergency teams to quickly obtain professional medical advice, ultimately saving lives. AR glasses can also be used for emergency treatment in complex environments. Because AR glasses are lightweight and easy to use, first responders can access patient information, review medical guidelines, or receive remote guidance, even without the convenience of using mobile phones or other devices, enabling effective treatment. They can also be used for rescue site information overlay and navigation. In disaster relief scenarios, AR glasses can overlay information such as the precise location of buildings before an earthquake, the location of emergency exits, and the location of trapped survivors onto the actual scene, providing rescuers with clear navigation and positioning services. This helps rescuers more quickly and accurately grasp the situation at the scene, improving rescue efficiency.

[0068] Step S13: The IoT platform obtains in-hospital multimodal emergency care related data;

[0069] Specifically, the in-hospital multimodal emergency-related data include but are not limited to the number of various emergency-related departments and their occupancy status, data on various medical supplies in the hospital, the occupancy status of various medical equipment in the hospital and the collected patient data, in-hospital on-site environmental data collected by video acquisition equipment, and the on-duty status of medical staff. Specific in-hospital equipment includes but is not limited to X-ray diagnostic equipment, ultrasound diagnostic equipment, functional examination equipment, endoscopic examination equipment, nuclear medicine equipment, laboratory diagnostic equipment, pathological diagnostic equipment, ventilators, electrocardiogram monitors, cardiac defibrillators, oxygen cylinders, negative pressure suction devices, fully automatic gastric lavage machines, endotracheal intubation and tracheotomy kits, simple respirators, ultrasonic nebulizers, electrocardiographs, blood glucose meters, electric suction devices, blood gas analyzers, electroencephalograms, B-ultrasound machines, bedside line adjustment machines, routine blood and urine analyzers, blood biochemistry analyzers, microsurgery equipment, and surgical lighting equipment.

[0070] Step S2: The IoT platform integrates the patient's out-of-hospital multimodal emergency data with the in-hospital multimodal emergency-related data to generate a patient emergency data information database;

[0071] Specifically, the following formula is used to calculate the fused emergency data:

[0072] Where, and are the fusion weights of patients’ out-of-hospital emergency data and in-hospital emergency-related data respectively; For the The sub-weight of the patient's out-of-hospital emergency data, For the Out-of-hospital emergency data of patients; For the The sub-weights of the in-hospital emergency-related data, For the Data related to in-hospital emergency care; The value range is 1 to , is the number of items of collected data.

[0073] Among them, the fusion weight and The specific method of determining is as follows: construct a data importance vector set based on the different importance of the patient's out-of-hospital emergency data and in-hospital emergency-related data, ,in, is the importance set of patients’ out-of-hospital emergency data, The importance set of the patient's in-hospital emergency data is used to construct the data importance function based on the data importance vector set. , , and , is the importance item, which is calculated based on the different importance of the data. and That is, the fusion weight of the patient's out-of-hospital emergency data and in-hospital emergency related data.

[0074] Specifically, the fusion emergency data calculated based on the patient's out-of-hospital multimodal emergency data and in-hospital multimodal emergency related data Generate a patient emergency data information database, which includes but is not limited to patient personal data, on-site personnel data, on-site situation data inside and outside the hospital, patient condition data, ambulance driving data, patient basic emergency measures data, in-hospital medical staff data, and in-hospital department data.

[0075] Step S3: The IoT platform analyzes the patient emergency data database and generates a rapid emergency plan for the patient;

[0076] Furthermore, the IoT platform analyzes the patient emergency data database and generates the following sub-steps for a rapid emergency plan for the patient:

[0077] Step S31: The IoT platform generates a patient identification and handover plan upon arrival at the hospital based on the patient emergency data database;

[0078] Specifically, the positioning system obtains traffic data and road condition information between the patient and the hospital, and generates the method and route for the patient to reach the hospital and predicts the time of the patient's arrival at the hospital through the Floyd algorithm based on the traffic data and road condition information. It also monitors the patient's location, patient transportation route and speed, road condition information and other factors that affect the patient's arrival at the hospital in real time, and adjusts and updates the patient's arrival route and predicted time according to these factors in real time, so that the hospital can arrange medical staff to meet the patient in time when the patient arrives; when the patient arrives at the hospital, the face recognition of medical staff inside and outside the hospital is performed according to the patient's emergency data information database and compared with the on-site personnel information data to ensure that the medical staff who hand over is accurate, clarify the emergency tasks of medical staff inside and outside the hospital, so that medical staff inside the hospital can quickly connect with medical staff outside the hospital to treat patients.

[0079] Step S32: The IoT platform analyzes the patient emergency data information database, obtains the patient's condition analysis results, performs registration and triage in the hospital system, and creates a patient disease information details book, obtaining the registration and triage results and the patient disease information details book;

[0080] Specifically, the patient disease information details book includes relevant information data of the patient's condition and his or her personal information data; the patient condition data information in the patient emergency data information database includes patient vital signs data, patient condition type, patient disease site and other patient condition related data; the registration and triage department is determined by the patient's condition type and patient disease site, and the patient's condition level is calculated by the vital signs data in the patient's disease data to judge the severity of the patient's condition.

[0081] Furthermore, the patient's condition level is expressed as follows:

[0082] in, Score the patient's condition; Score the patient's heart rate. For heart rate, if heart rate If it is greater than or equal to 110, the patient's heart rate score Recorded as 1, if the heart rate If the score is less than 110, the patient's heart rate score is is 0; Score the patient's systolic blood pressure. For systolic blood pressure, if systolic blood pressure If the score is less than 90, the patient's systolic blood pressure score Recorded as 2, if systolic pressure If the score is greater than or equal to 90 and less than 100, the patient's systolic blood pressure score Recorded as 1, if systolic pressure If the score is greater than or equal to 100, the patient's systolic blood pressure score Recorded as 0; Patient respiratory rate score, is the respiratory rate, if the respiratory rate If the score is greater than or equal to 30, the patient's respiratory rate score Recorded as 2, if the respiratory rate If the score is greater than or equal to 20 and less than 30, the patient's respiratory rate score is Recorded as 1, if the respiratory rate If it is less than 20, the patient's respiratory rate score Recorded as 0; Score the patient's temperature. For body temperature, if body temperature Less than 35 or body temperature If the score is greater than or equal to 39, the patient's temperature score Recorded as 2, if the body temperature Greater than or equal to 35 and less than 36.5 or body temperature If the temperature is greater than or equal to 37.5 and less than 39, the patient's temperature score Recorded as 1, if the body temperature If the temperature is greater than or equal to 36.5 and less than 37.5, the patient's temperature score Recorded as 0; Score the patient's level of consciousness. is the level of consciousness. If the level of consciousness If the patient is sleepy or drowsy, the patient's level of consciousness is scored Recorded as 2, if the level of consciousness If the patient is awake but unresponsive, the patient's level of consciousness is scored Recorded as 1, if the level of consciousness If the patient is fully awake, the patient's level of consciousness score is Recorded as 0.

[0083] Specifically, the patient's condition score Determine the severity of the patient's condition, with a total score of 0-2 points recorded as low risk, 3-4 points as medium risk, 5-7 points as high risk, and more than 8 points as extremely high risk; generate a patient disease information details book based on the emergency patient's personal data information, patient condition data information, patient condition level and other patient emergency data information.

[0084] Step S33: The Internet of Things platform monitors the patient emergency data information database in real time, and updates the patient disease information details book in real time according to changes in the patient emergency data information database;

[0085] Specifically, the patient disease information details book is updated through changes in the patient emergency data information database, so that the patient disease information details book is the patient's latest condition, so that medical staff can quickly rescue the patient according to the patient disease information details book.

[0086] Step S34: The IoT platform generates a quick first aid plan for the patient based on the identification and handover plan of the emergency patient arriving at the hospital, the patient's disease information details book, and the registration and triage results;

[0087] Specifically, a patient identification and handover vector set is constructed based on the patient's arrival at the hospital identification and handover plan ,in, To identify handover factors that influence patients’ arrival at the hospital, To identify the total number of handover factors that affect patients' arrival at the hospital; to construct a patient disease vector set based on the patient disease information details book and registration and triage results ,in, Patient disease status data, The total number of patient disease condition data; construct the in-hospital and out-of-hospital scene vector set based on the patient emergency database ,in, For on-site environmental data inside and outside the hospital, It is the total number of on-site environments inside and outside the hospital.

[0088] Training the patient identification and handover sub-model based on the patient identification and handover vector set , using the formula Estimate the set of weights for the patient identification handoff submodel ; Train the patient disease sub-model based on the patient disease vector set , using the formula Estimate the set of weights for the patient-disease submodel ; Train the in-hospital and out-hospital situation sub-model based on the in-hospital and out-hospital situation vector set , using the formula Estimate the weights of the sub-models for on-site and off-site situations .

[0089] According to each sub-model and the set of its weights 、 、 Using the formula Train the patient rapid first aid model and generate the patient rapid first aid plan.

[0090] The IoT platform generates personalized rapid first aid plans for patients based on the patient's rapid first aid plan and combines it with the diagnosis results of the patient's condition by professional medical staff, enabling patients to be rescued in a timely manner according to the first aid plan for specific patients.

[0091] Step S4: The IoT platform monitors the patient's out-of-hospital multimodal emergency data and in-hospital multimodal emergency data in real time. If the data is abnormal, the abnormal data is annotated and transmitted to the patient emergency data information database to obtain the abnormal annotated patient emergency data information database, and the patient's rapid emergency plan is updated according to the abnormal annotated patient emergency data information database;

[0092] Furthermore, the IoT platform monitors the patient's out-of-hospital multimodal emergency data and in-hospital multimodal emergency-related data in real time. If the data is abnormal, the abnormal data is annotated and transmitted to the patient emergency data information database to obtain the abnormal annotated patient emergency data information database. The sub-steps of updating the patient's rapid emergency plan based on the abnormal annotated patient emergency data information database are as follows:

[0093] Step S41: The IoT platform monitors the collected multimodal outpatient emergency data and inpatient multimodal emergency data in real time, determines abnormalities in the data, annotates the abnormal data, and transmits it to a patient emergency data information database to obtain an abnormally annotated patient emergency data information database;

[0094] Specifically, the IoT platform uses various sensors to monitor the patient's multimodal emergency data in real time. Among them, medical equipment monitoring sensors are used to monitor the patient's heart rate, systolic blood pressure, respiratory rate, body temperature, consciousness level and other patient condition data. On-site environment monitoring sensors are used to monitor the on-site environment, such as the behavior of on-site personnel, whether the equipment is working properly, and whether the patient's medication is normal. Driving monitoring sensors are used to monitor whether the patient is driving safely during transportation and whether the transportation route is normal.

[0095] Step S42: The IoT platform analyzes the emergency data database of abnormally marked patients, generates a local emergency plan, and updates the patient's rapid emergency plan based on the local emergency plan;

[0096] Specifically, the abnormal data information marked in the emergency data information database of abnormally marked patients is analyzed. If the patient's condition is abnormal, a local emergency plan is generated based on the abnormal data analysis results and combined with the medical staff's rescue plan for the patient; if the on-site environmental data is abnormal, the abnormal scene is judged based on the abnormal data and the abnormal scene is promptly informed to the medical staff so that they can solve the on-site problem; if the patient transportation route is abnormal, the abnormal patient transportation route is judged based on the abnormal data and a new patient transportation plan is generated based on the road condition data information.

[0097] Example 2

[0098] like Figure 2 As shown, the second embodiment of the present application provides an Internet of Things platform based on multimodal emergency data, including:

[0099] The data acquisition module 21 acquires the patient's out-of-hospital multimodal emergency data and the in-hospital multimodal emergency data;

[0100] Specifically, during the data acquisition and transmission process, multi-channel transmission technology is used to establish a data transmission channel, improve the data transmission speed, and achieve efficient transmission between data; during the data transmission process, asymmetric encryption technology is used to encrypt the data to achieve secure data transmission; and network topology optimization technology is used to achieve stable data transmission.

[0101] Furthermore, the data acquisition module includes the following submodules:

[0102] The optimal ambulance selection submodule obtains the patient's location information through the positioning system and the data of ambulances around the patient through the Internet of Things technology to select the optimal ambulance;

[0103] Positioning systems can include the GPS system, Beidou satellite system, GLONASS system, Galileo system, and Indian Regional Navigation Satellite System.

[0104] Furthermore, the optimal ambulance selection submodule includes the following submodules:

[0105] The patient location acquisition submodule receives the patient's emergency information, locates the patient's position through the positioning system, and obtains the patient's location information data;

[0106] The optimal ambulance selection submodule uses IoT technology to obtain the status and location of ambulances near the patient based on the patient's location information, and selects the optimal ambulance that is available and can reach the patient the fastest to rescue the patient.

[0107] Specifically, a usage status sensor is installed on the ambulance and connected to the Internet of Things platform. Its usage status is set to idle and occupied. When a patient needs an ambulance, the Internet of Things platform identifies the usage status of the ambulance based on the usage status sensor on the ambulance; a positioning sensor is installed on the ambulance, which uses 5G technology to obtain the location information of the ambulance around the patient's location and obtains map information data around the patient through the positioning system. By calculating the ambulance dispatch value, the optimal ambulance is selected to rescue the patient.

[0108] Furthermore, the emergency vehicle dispatch value expression is as follows:

[0109] in, on duty for emergency vehicle dispatch; The state value of the ambulance is 1 if the ambulance is idle and 0 if the ambulance is occupied. is the traffic condition value, and its value range is , the best traffic condition is 1, and the worst traffic condition is 10; To select the minimum travel distance from the ambulance to the patient's location, is the average speed of emergency vehicles.

[0110] Specifically, calculate the emergency dispatch value of each ambulance near the patient , select the ambulance dispatch value The smallest positive number is the optimal ambulance, which is sent to rescue the patient.

[0111] The submodule for acquiring multimodal data of patients outside the hospital acquires multimodal emergency data of patients outside the hospital based on the optimal ambulance and other information collection equipment;

[0112] Specifically, the patient's out-of-hospital multimodal emergency data includes but is not limited to patient data collected by various medical devices on the best ambulance, on-site environmental data collected by video acquisition equipment, ambulance location collected by positioning acquisition equipment, patient personal information data collected by card reader wristbands, on-site personnel input data and other data collected by other IoT devices; among them, the patient data collected by various medical devices include the patient's heart rate, systolic blood pressure, respiratory rate, body temperature and other patient condition data, and the on-site environmental data collected by video acquisition equipment include the first aid measures taken by medical staff on the ambulance for the patient, the use of medical equipment on the ambulance, the language communication between the personnel on the ambulance, the patient's status and other video language data. Specific out-of-hospital equipment includes but is not limited to electrocardiogram monitors, oxygen supply systems, blood oxygen saturation, rapid blood glucose meters, blood pressure monitors, defibrillators, external pacemakers, respiratory equipment, chest decompression equipment, pressurized infusion devices, high-definition vehicle-mounted pan-tilt heads, infrared night vision cameras, wireless vehicle-mounted reversing cameras and PTZ cameras, high-definition vehicle-mounted recorders, vehicle-mounted locators, GPS systems, wireless communication equipment, audio and video recording and transmission equipment, drones, and AR glasses.

[0113] The submodule for acquiring data related to multimodal emergency care in hospitals is used to acquire data related to multimodal emergency care in hospitals;

[0114] Specifically, the in-hospital multimodal emergency-related data include but are not limited to the number of various emergency-related departments and their occupancy status, data on various medical supplies in the hospital, the occupancy status of various medical equipment in the hospital and the collected patient data, in-hospital on-site environmental data collected by video acquisition equipment, and the on-duty status of medical staff. Specific in-hospital equipment includes but is not limited to X-ray diagnostic equipment, ultrasound diagnostic equipment, functional examination equipment, endoscopic examination equipment, nuclear medicine equipment, laboratory diagnostic equipment, pathological diagnostic equipment, ventilators, electrocardiogram monitors, cardiac defibrillators, oxygen cylinders, negative pressure suction devices, fully automatic gastric lavage machines, endotracheal intubation and tracheotomy kits, simple respirators, ultrasonic nebulizers, electrocardiographs, blood glucose meters, electric suction devices, blood gas analyzers, electroencephalograms, B-ultrasound machines, bedside line adjustment machines, routine blood and urine analyzers, blood biochemistry analyzers, microsurgery equipment, and surgical lighting equipment.

[0115] The multimodal data acquisition and fusion module 22 fuses the patient's out-of-hospital multimodal emergency data with the in-hospital multimodal emergency-related data to generate a patient emergency data information database;

[0116] Specifically, the following formula is used to calculate the fused emergency data:

[0117] Where, and are the fusion weights of patients’ out-of-hospital emergency data and in-hospital emergency-related data respectively; For the The sub-weight of the patient's out-of-hospital emergency data, For the Out-of-hospital emergency data of patients; For the The sub-weights of the in-hospital emergency-related data, For the Data related to in-hospital emergency care; The value range is 1 to , is the number of items of collected data.

[0118] Among them, the fusion weight and The specific method of determining is as follows: construct a data importance vector set based on the different importance of the patient's out-of-hospital emergency data and in-hospital emergency-related data, ,in, is the importance set of patients’ out-of-hospital emergency data, The importance set of the patient's in-hospital emergency data is used to construct the data importance function based on the data importance vector set. , , and , is the importance item, which is calculated based on the different importance of the data. and That is, the fusion weight of the patient's out-of-hospital emergency data and in-hospital emergency related data.

[0119] Specifically, the fusion emergency data calculated based on the patient's out-of-hospital multimodal emergency data and in-hospital multimodal emergency related data Generate a patient emergency data information database, which includes but is not limited to patient personal data, on-site personnel data, on-site situation data inside and outside the hospital, patient condition data, ambulance driving data, patient basic emergency measures data, in-hospital medical staff data, and in-hospital department data.

[0120] A patient rapid first aid plan generating module 23 analyzes the patient first aid data information database and generates a patient rapid first aid plan;

[0121] Furthermore, the patient rapid emergency plan generation module 23 includes the following submodules:

[0122] The patient arrival hospital identification and handover plan generation submodule generates a patient arrival hospital identification and handover plan based on the patient emergency data information database;

[0123] Specifically, the positioning system obtains traffic data and road condition information between the patient and the hospital, and generates the method and route for the patient to reach the hospital and predicts the time of the patient's arrival at the hospital through the Floyd algorithm based on the traffic data and road condition information. It also monitors the patient's location, patient transportation route and speed, road condition information and other factors that affect the patient's arrival at the hospital in real time, and adjusts and updates the patient's arrival route and predicted time according to these factors in real time, so that the hospital can arrange medical staff to meet the patient in time when the patient arrives; when the patient arrives at the hospital, the face recognition of medical staff inside and outside the hospital is performed according to the patient's emergency data information database and compared with the on-site personnel information data to ensure that the medical staff who hand over is accurate, clarify the emergency tasks of medical staff inside and outside the hospital, so that medical staff inside the hospital can quickly connect with medical staff outside the hospital to treat patients.

[0124] The registration and triage results and patient disease information details book acquisition submodule analyzes the patient emergency data information database, obtains the patient's condition analysis results, performs registration and triage in the hospital system, and establishes a patient disease information details book, obtains the registration and triage results and the patient disease information details book;

[0125] Specifically, the patient disease information details book includes relevant information data of the patient's condition and his or her personal information data; the patient condition data information in the patient emergency data information database includes patient vital signs data, patient condition type, patient disease site and other patient condition related data; the registration and triage department is determined by the patient's condition type and patient disease site, and the patient's condition level is calculated by the vital signs data in the patient's disease data to judge the severity of the patient's condition.

[0126] Furthermore, the patient's condition level is expressed as follows:

[0127] in, Score the patient's condition; Score the patient's heart rate. For heart rate, if heart rate If it is greater than or equal to 110, the patient's heart rate score Recorded as 1, if the heart rate If the score is less than 110, the patient's heart rate score is is 0; Score the patient's systolic blood pressure. For systolic blood pressure, if systolic blood pressure If the score is less than 90, the patient's systolic blood pressure score Recorded as 2, if systolic pressure If the score is greater than or equal to 90 and less than 100, the patient's systolic blood pressure score Recorded as 1, if systolic pressure If the score is greater than or equal to 100, the patient's systolic blood pressure score Recorded as 0; Score the patient's respiratory rate. is the respiratory rate, if the respiratory rate If the score is greater than or equal to 30, the patient's respiratory rate score Recorded as 2, if the respiratory rate If the score is greater than or equal to 20 and less than 30, the patient's respiratory rate score is Recorded as 1, if the respiratory rate If it is less than 20, the patient's respiratory rate score Recorded as 0; Score the patient's temperature. For body temperature, if body temperature Less than 35 or body temperature If the score is greater than or equal to 39, the patient's temperature score Recorded as 2, if the body temperature Greater than or equal to 35 and less than 36.5 or body temperature If the temperature is greater than or equal to 37.5 and less than 39, the patient's temperature score Recorded as 1, if the body temperature If the temperature is greater than or equal to 36.5 and less than 37.5, the patient's temperature score Recorded as 0; Score the patient's level of consciousness. is the level of consciousness. If the level of consciousness If the patient is sleepy or drowsy, the patient's level of consciousness is scored Recorded as 2, if the level of consciousness If the patient is awake but unresponsive, the patient's level of consciousness is scored Recorded as 1, if the level of consciousness If the patient is fully awake, the patient's level of consciousness score is Recorded as 0.

[0128] Specifically, the patient's condition score Determine the severity of the patient's condition, with a total score of 0-2 points recorded as low risk, 3-4 points as medium risk, 5-7 points as high risk, and more than 8 points as extremely high risk; generate a patient disease information details book based on the emergency patient's personal data information, patient condition data information, patient condition level and other patient emergency data information.

[0129] The data monitoring and updating submodule monitors the emergency patient condition data and basic emergency measures in the emergency patient information in real time, and updates the patient disease information details book in real time according to the changes in the emergency patient condition data and basic emergency measures;

[0130] Specifically, the patient disease information details book is updated through changes in the patient emergency data information database, so that the patient disease information details book is the patient's latest condition, so that medical staff can quickly rescue the patient according to the patient disease information details book.

[0131] The patient rapid emergency plan generation submodule generates a patient rapid emergency plan based on the identification and handover plan for emergency patients arriving at the hospital, the patient's disease information details book, and the registration and triage results;

[0132] Specifically, a patient identification and handover vector set is constructed based on the patient's arrival at the hospital identification and handover plan ,in, To identify handover factors that influence patients’ arrival at the hospital, To identify the total number of handover factors that affect patients' arrival at the hospital; to construct a patient disease vector set based on the patient disease information details book and registration and triage results ,in, Patient disease status data, The total number of patient disease data; construct the in-hospital and out-of-hospital scene vector set based on the patient emergency database ,in, For on-site environmental data inside and outside the hospital, It is the total number of on-site environments inside and outside the hospital.

[0133] Training the patient identification and handover sub-model based on the patient identification and handover vector set , using the formula Estimate the set of weights for the patient identification handoff submodel ; Train the patient disease sub-model based on the patient disease vector set , using the formula Estimate the set of weights for the patient-disease submodel ; Train the in-hospital and out-hospital situation sub-model based on the in-hospital and out-hospital situation vector set , using the formula Estimate the weights of the sub-models for on-site and off-site situations .

[0134] According to each sub-model and the set of its weights 、 、 Using the formula Train the patient rapid first aid model and generate the patient rapid first aid plan.

[0135] The IoT platform generates personalized rapid first aid plans for patients based on the patient's rapid first aid plan and combines it with the diagnosis results of the patient's condition by professional medical staff, enabling patients to be rescued in a timely manner according to the first aid plan for specific patients.

[0136] The multimodal data monitoring and processing module 24 monitors the patient's out-of-hospital multimodal emergency data and in-hospital multimodal emergency data in real time. If the data is abnormal, the abnormal data is annotated and transmitted to the patient emergency data information database to obtain the abnormal annotated patient emergency data information database, and the patient's rapid emergency plan is updated based on the abnormal annotated patient emergency data information database;

[0137] Furthermore, the multimodal data monitoring and processing module 24 includes the following submodules:

[0138] The abnormal data monitoring submodule monitors the real-time collected multimodal emergency data of patients outside the hospital and the multimodal emergency data related to the hospital in real time, determines the abnormality of the data, marks the abnormal data and transmits it to the patient emergency data information database to obtain the abnormal marked patient emergency data information database;

[0139] Specifically, the IoT platform uses various sensors to monitor the patient's multimodal emergency data in real time. Among them, medical equipment monitoring sensors are used to monitor the patient's heart rate, systolic blood pressure, respiratory rate, body temperature, consciousness level and other patient condition data. On-site environment monitoring sensors are used to monitor the on-site environment, such as the behavior of on-site personnel, whether the equipment is working properly, and whether the patient's medication is normal. Driving monitoring sensors are used to monitor whether the patient is driving safely during transportation and whether the transportation route is normal.

[0140] The abnormal data processing submodule analyzes the emergency data information database of abnormally marked patients, generates a local emergency plan, and updates the patient's rapid emergency plan based on the local emergency plan;

[0141] Specifically, the abnormal data information marked in the emergency data information database of abnormally marked patients is analyzed. If the patient's condition is abnormal, a local emergency plan is generated based on the abnormal data analysis results and combined with the medical staff's rescue plan for the patient; if the on-site environmental data is abnormal, the abnormal scene is judged based on the abnormal data and the abnormal scene is promptly informed to the medical staff so that they can solve the on-site problem; if the patient transportation route is abnormal, the abnormal patient transportation route is judged based on the abnormal data and a new patient transportation plan is generated based on the road condition data information.

[0142] The specific implementation methods described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific implementation method of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent replacements, improvements, etc. made on the basis of the technical solution of the present invention should be included in the scope of protection of the present invention.

Claims

1. An Internet of Things working method based on multimodal emergency data, characterized in that: include: Step S1: The IoT platform obtains the patient's out-of-hospital multimodal emergency data and obtains in-hospital multimodal emergency data; Step S11: The IoT platform obtains the patient's location information through the positioning system, obtains the data of emergency vehicles around the patient through IoT technology, and selects the optimal emergency vehicle; The optimal ambulance is selected based on the ambulance dispatch value, which is calculated as follows: in, on duty for emergency vehicle dispatch; The state value of the ambulance is 1 if the ambulance is idle and 0 if the ambulance is occupied. is the traffic condition value, and its value range is , the best traffic condition is 1, and the worst traffic condition is 10; To select the minimum travel distance from the ambulance to the patient's location, is the average speed of emergency vehicles; Select the emergency vehicle dispatch value The smallest positive number is the optimal ambulance, which is sent to rescue the patient; Step S12: The IoT platform obtains the patient's out-of-hospital multimodal emergency data based on the optimal ambulance and other information collection equipment; Step S13: The IoT platform obtains in-hospital multimodal emergency care related data; Step S2: The IoT platform integrates the patient's out-of-hospital multimodal emergency data with the in-hospital multimodal emergency-related data to generate a patient emergency data information database; The fusion formula of the patient's out-of-hospital multimodal emergency data and the in-hospital multimodal emergency data is: Where RH is the fusion emergency data, and are the fusion weights of patients’ out-of-hospital emergency data and in-hospital emergency-related data respectively; For the The sub-weight of the patient's out-of-hospital emergency data, For the Out-of-hospital emergency data of patients; For the The sub-weights of the in-hospital emergency-related data, For the Data related to in-hospital emergency care; The value range is 1 to , is the number of items of collected data; Among them, the fusion weight and The specific method of determining is as follows: construct a data importance vector set based on the different importance of the patient's out-of-hospital emergency data and in-hospital emergency-related data, ,in, is the importance set of patients’ out-of-hospital emergency data, The importance set of the patient's in-hospital emergency data is used to construct the data importance function based on the data importance vector set. , , and , is the importance item, which is calculated based on the different importance of the data. and That is, the fusion weight of the patient's out-of-hospital emergency data and in-hospital emergency-related data; Step S3: The IoT platform analyzes the patient emergency data database and generates a rapid emergency plan for the patient; Step S4: The Internet of Things platform monitors the patient's out-of-hospital multimodal emergency data and in-hospital multimodal emergency-related data in real time. If the data is abnormal, the abnormal data will be marked and transmitted to the patient emergency data information database to obtain the abnormal marked patient emergency data information database, and the patient's rapid emergency plan will be updated according to the abnormal marked patient emergency data information database.

2. The Internet of Things working method based on multimodal emergency data according to claim 1, characterized in that: The IoT platform obtains the patient's location information through the positioning system and the data of emergency vehicles around the patient through IoT technology. The sub-steps for selecting the optimal emergency vehicle are as follows: Step S111: The IoT platform receives the patient's emergency information, locates the patient's location through the positioning system, and obtains the patient's location information data; Step S112: The IoT platform obtains the status and location of the emergency vehicles near the patient through IoT technology based on the patient's location information, and selects the optimal emergency vehicle that is idle and can reach the patient the fastest to rescue the patient.

3. The Internet of Things working method based on multimodal emergency data according to claim 2, characterized in that: The IoT platform analyzes the patient emergency data database and generates a rapid emergency plan for the patient in the following sub-steps: Step S31: The IoT platform generates a patient identification and handover plan upon arrival at the hospital based on the patient emergency data database; Step S32: The IoT platform analyzes the patient emergency data information database, obtains the patient's condition analysis results, performs registration and triage in the hospital system, and creates a patient disease information details book, obtaining the registration and triage results and the patient disease information details book; Step S33: The Internet of Things platform monitors the patient emergency data information database in real time, and updates the patient disease information details book in real time according to changes in the patient emergency data information database; Step S34: The IoT platform generates a quick first aid plan for the patient based on the identification and handover plan of the emergency patient arriving at the hospital, the patient's disease information details book, and the registration and triage results; Among them, the patient identification and handover vector set is constructed according to the patient arrival hospital identification and handover plan ,in, To identify handover factors that influence patients’ arrival at the hospital, To identify the total number of handover factors that affect patients' arrival at the hospital; to construct a patient disease vector set based on the patient disease information details book and registration and triage results ,in, Patient disease status data, The total number of patient disease condition data; construct the in-hospital and out-of-hospital scene vector set based on the patient emergency database ,in, For on-site environmental data inside and outside the hospital, The total number of on-site environments inside and outside the hospital; Training the patient identification and handover sub-model based on the patient identification and handover vector set , using the formula Estimate the set of weights for the patient identification handoff submodel ; Train the patient disease sub-model based on the patient disease vector set , using the formula Estimate the set of weights for the patient-disease submodel ; Train the in-hospital and out-hospital situation sub-model based on the in-hospital and out-hospital situation vector set , using the formula Estimate the weights of the sub-models for on-site and off-site situations ; According to each sub-model and the set of its weights 、 、 Using the formula Train the patient rapid first aid model and generate the patient rapid first aid plan.

4. The Internet of Things working method based on multimodal emergency data according to claim 3, characterized in that: The IoT platform monitors the patient's out-of-hospital multimodal emergency data and in-hospital multimodal emergency data in real time. If the data is abnormal, the abnormal data will be annotated and transmitted to the patient emergency data information database to obtain the abnormal annotated patient emergency data information database. The sub-steps of updating the patient's rapid emergency plan based on the abnormal annotated patient emergency data information database are as follows: Step S41: The IoT platform monitors the collected multimodal outpatient emergency data and inpatient multimodal emergency data in real time, determines abnormalities in the data, annotates the abnormal data, and transmits it to a patient emergency data information database to obtain an abnormally annotated patient emergency data information database; Step S42: The platform analyzes the emergency data information database of abnormally marked patients, generates a local emergency plan, and updates the patient's rapid emergency plan based on the local emergency plan.

5. An Internet of Things platform based on multimodal emergency data, used to execute an Internet of Things working method based on multimodal emergency data according to any one of claims 1 to 4, characterized in that: include: Data acquisition module, which obtains the patient's out-of-hospital multimodal emergency data and in-hospital multimodal emergency related data; The multimodal data acquisition and fusion module integrates the patient's out-of-hospital multimodal emergency data with the in-hospital multimodal emergency-related data to generate a patient emergency data information database; The patient rapid first aid plan generation module analyzes the patient first aid data information database and generates a patient rapid first aid plan; The multimodal data monitoring and processing module monitors the patient's out-of-hospital multimodal emergency data and in-hospital multimodal emergency-related data in real time. If the data is abnormal, the abnormal data will be marked and transmitted to the patient emergency data information database to obtain the abnormal marked patient emergency data information database, and the patient's rapid emergency plan will be updated according to the abnormal marked patient emergency data information database.

6. The Internet of Things platform based on multimodal emergency data according to claim 5, characterized in that: Data acquisition module, specifically including: The optimal ambulance selection submodule obtains the patient's location information through the positioning system and the data of ambulances around the patient through the Internet of Things technology to select the optimal ambulance; The submodule for acquiring multimodal data of patients outside the hospital acquires multimodal emergency data of patients outside the hospital based on the optimal ambulance and other information collection equipment; The submodule for acquiring data related to multimodal emergency care within the hospital acquires data related to multimodal emergency care within the hospital.

7. The Internet of Things platform based on multimodal emergency data according to claim 6, characterized in that: The optimal emergency vehicle selection submodule includes: The patient location acquisition submodule receives the patient's emergency information, locates the patient's position through the positioning system, and obtains the patient's location information data; The optimal ambulance selection submodule obtains the status and location of ambulances near the patient through the Internet of Things technology based on the patient's location information, and selects the optimal ambulance that is idle and can reach the patient the fastest to rescue the patient.

8. The Internet of Things platform based on multimodal emergency data according to claim 7, characterized in that: Multimodal data monitoring and processing module, specifically including: The abnormal data monitoring submodule monitors the real-time collected multimodal emergency data of patients outside the hospital and the multimodal emergency data related to the hospital in real time, determines the abnormality of the data, marks the abnormal data and transmits it to the patient emergency data information database to obtain the abnormal marked patient emergency data information database; The abnormal data processing submodule analyzes the emergency data information database of abnormally marked patients, generates a local emergency plan, and updates the patient's rapid emergency plan based on the local emergency plan.

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