技术领域technical field
本发明涉及一种监护急救系统，特别是涉及一种远程监护急救系统。The invention relates to a monitoring emergency system, in particular to a remote monitoring emergency system.
背景技术Background technique
随着城市化的发展，我国大部分城市的交通较为拥堵，城乡地区又交通不便利，传统救护车在患者急性发病时难以及时到达现场，错失宝贵的有效急救时间。以心血管病为例，在2015年，全球约有1770万人死于心血管疾病，据推算我国心血管病患者人数约2.9亿，占居民死亡构成的40％以上，其主要死因为突发急性事件(急性冠脉综合征、恶性心律失常等)，且超过70％发生在院外。因此，降低心血管疾病院外死亡率，对于降低心血管病整体死亡率，减轻医疗负担，意义非常重大。心血管病具有突发性、短暂性和高危险性的特点，因而早期发现、早期诊断、早期干预对降低心血管疾病的死亡率至关重要。With the development of urbanization, the traffic in most cities in my country is relatively congested, and the transportation in urban and rural areas is inconvenient. Traditional ambulances are difficult to reach the scene in time when patients are acutely ill, and valuable effective first aid time is lost. Taking cardiovascular disease as an example, in 2015, about 17.7 million people died of cardiovascular disease in the world. It is estimated that the number of patients with cardiovascular disease in my country is about 290 million, accounting for more than 40% of the residents' deaths. The main cause of death is sudden Acute events (acute coronary syndrome, malignant arrhythmia, etc.), and more than 70% occurred outside the hospital. Therefore, reducing the out-of-hospital mortality of cardiovascular diseases is of great significance for reducing the overall mortality of cardiovascular diseases and reducing the medical burden. Cardiovascular disease has the characteristics of sudden, transient and high risk, so early detection, early diagnosis and early intervention are very important to reduce the mortality of cardiovascular disease.
建立有效地延伸到家庭的远程监护急救系统，是解决以上问题的有效途径。而目前在患者远程监护、及时干预等方面还十分不足，主要体现在： 1、患者在院外期间无法得到实时有效的病程监护，发病时身处院外，家属不知所措，没有急救药品及设备，无法进行有效、正确的急救处理，错过最佳急救时间窗。2、独居患者突然发病，无人知晓。3、患者夜间突然发病晕厥，家属难以知晓。4、身处城市或城乡结合地区，由于交通拥堵或不便利造成急救车无法及时到达现场，错过急救时间。Establishing a remote monitoring emergency system that effectively extends to the family is an effective way to solve the above problems. At present, the remote monitoring and timely intervention of patients are still very insufficient, mainly reflected in: 1. The patient cannot get real-time and effective monitoring of the course of the disease during the period outside the hospital. Effective and correct first aid treatment cannot be carried out, and the best first aid time window is missed. 2. Patients who live alone suddenly become ill, and no one knows about it. 3. The patient suffers from sudden onset of syncope at night, and it is difficult for the family members to know. 4. In a city or an urban-rural combination area, the ambulance cannot reach the scene in time due to traffic congestion or inconvenience, and the emergency time is missed.
因此，目前急需一种能够缩短诊断、救治时间，有效地将监护、急救延伸到家庭的远程监护急救系统。Therefore, there is an urgent need for a remote monitoring emergency system that can shorten the diagnosis and treatment time and effectively extend monitoring and first aid to the family.
在本发明采用人工智能实时分析监测数据并应用无人机进行急救，由于无人机速度远远大于救护车，而且没有拥堵问题，不受道路条件限制，所以无人机将先于救护车到达患者所在地，赢得宝贵的抢救时间。如果值班医师审查监测数据或与患者联系后否认危险信号，则无需通知救护车前往，节约费用及宝贵的医疗资源，同时值班医师通过控制模块向无人机发送返回指令，无人机自动返回出发位置。In the present invention, artificial intelligence is used to analyze the monitoring data in real time and use drones for first aid. Since the speed of drones is much greater than that of ambulances, and there is no congestion problem and is not limited by road conditions, drones will arrive before ambulances The location of the patient wins precious rescue time. If the on-duty doctor reviews the monitoring data or denies the danger signal after contacting the patient, there is no need to notify the ambulance to go, saving costs and valuable medical resources. At the same time, the on-duty doctor sends a return instruction to the drone through the control module, and the drone automatically returns to departure Location.
发明内容Contents of the invention
本发明通过远程监测患者在院外心电、血压、血氧等生理指标，自动分析监测判断患者的实时身体状况。能够做到早发现、早治疗，并且在患者出现紧急情况时，能第一时间得到患者的各项生理指标，并使用无人机配送急救物资，争取更多的急救时间，减少因漏诊、误诊、急救车无法及时赶到现场等原因造成的急救不及时，最大限度的挽救患者的宝贵生命。The present invention automatically analyzes and monitors and judges the real-time physical condition of the patient by remotely monitoring the patient's physiological indicators such as electrocardiogram, blood pressure, and blood oxygen outside the hospital. It can achieve early detection and early treatment, and when the patient is in an emergency, he can get the patient's various physiological indicators as soon as possible, and use the drone to deliver first-aid supplies, so as to gain more first-aid time and reduce missed diagnosis and misdiagnosis. , The ambulance cannot arrive at the scene in time and other reasons, and the first aid is not timely, so as to save the precious life of the patient to the greatest extent.
本发明的目的是提供一种患者监测模块、AI分析模块、人工控制模块、无人机控制模块及无人机多端互联，能够不受患者发病地理环境影响，快速完成患者监测、诊断、急救的远程监护急救系统。The purpose of the present invention is to provide a patient monitoring module, AI analysis module, manual control module, UAV control module and UAV multi-terminal interconnection, which can quickly complete patient monitoring, diagnosis, and first aid without being affected by the geographical environment of the patient's disease. Remote monitoring emergency system.
为实现上述发明目的，本发明提供的技术方案是：For realizing above-mentioned purpose of the invention, the technical scheme that the present invention provides is:
一种远程监护急救系统，包括患者监测模块、AI分析模块、人工控制模块、无人机控制模块和无人机，所述患者监测模块获取患者的监测数据及定位数据，并实时传输至AI分析模块；A remote monitoring emergency system, including a patient monitoring module, an AI analysis module, a manual control module, an unmanned aerial vehicle control module and an unmanned aerial vehicle. The patient monitoring module obtains patient monitoring data and positioning data, and transmits them to AI analysis in real time module;
所述AI分析模块接收、存储由患者监测模块传输的数据，分析患者监测数据后生成自动诊断结果，对自动诊断结果做出判断，向无人机控制模块发送警报信号及患者定位数据，命令无人机起飞；向人工控制模块发送诊断报告，并将存储的监测数据传输至人工控制模块；The AI analysis module receives and stores the data transmitted by the patient monitoring module, generates an automatic diagnosis result after analyzing the patient monitoring data, makes a judgment on the automatic diagnosis result, sends an alarm signal and patient positioning data to the UAV control module, and commands no The man-machine takes off; sends a diagnosis report to the manual control module, and transmits the stored monitoring data to the manual control module;
所述无人机与无人机控制模块通过无线网络进行交互通信，所述无人机上携带用于急救患者的药物及设备，无人机模块控制无人机飞往患者位置进行急救；The UAV and the UAV control module communicate interactively through the wireless network, the UAV carries medicines and equipment for first aiding patients, and the UAV module controls the UAV to fly to the patient's location for first aid;
所述人工控制模块与无人机控制模块之间双向通信，人工控制模块获得无人机控制模块的控制权限对无人机飞行进行人工操纵，并能够接收由无人机控制模块传输的无人机实时信息数据；人工控制模块与患者监测模块之间能够进行视频或语音的双向通信，并且能够接收由AI分析模块传输的诊断报告和患者监测数据。Two-way communication between the manual control module and the UAV control module, the manual control module obtains the control authority of the UAV control module to manually manipulate the flight of the UAV, and can receive the unmanned information transmitted by the UAV control module. Real-time information data from the computer; two-way video or voice communication between the manual control module and the patient monitoring module, and the ability to receive diagnostic reports and patient monitoring data transmitted by the AI analysis module.
进一步地，值班医生在人工控制模块端接收到AI分析模块传输的患者异常监测数据后，认为必要时，派出救护车开往患者处进行救治，所述无人机上设有语音输出装置。Furthermore, after the doctor on duty receives the patient abnormality monitoring data transmitted by the AI analysis module at the manual control module, if necessary, he dispatches an ambulance to the patient for treatment. The drone is equipped with a voice output device.
进一步地，患者监测模块还能够向所述AI分析模块发送求救信号，所述AI分析模块在接受到求救信号后向无人机控制模块发送警报信号及患者位置数据，命令无人机飞往患者位置进行急救。Further, the patient monitoring module can also send a distress signal to the AI analysis module. After receiving the distress signal, the AI analysis module sends an alarm signal and patient location data to the UAV control module, ordering the UAV to fly to the patient. position for first aid.
一种远程监护急救系统AI分析模块对诊断结果的判断处理方法，包括以下步骤：A method for judging and processing diagnosis results by an AI analysis module of a remote monitoring emergency system, comprising the following steps:
步骤一AI分析模块判断诊断结果是否正常，如果判断结果为是，则结束一个判断周期；如果判断结果为否，则由AI分析模块按照预设的频次向人工控制模块发送诊断报告；Step 1. The AI analysis module judges whether the diagnosis result is normal. If the judgment result is yes, then end a judgment cycle; if the judgment result is no, the AI analysis module sends a diagnosis report to the manual control module according to the preset frequency;
步骤二AI分析模块判断异常诊断结果是否需要启动无人机急救，如果判断结果为否则结束，如果判断结果为是，则向无人机控制模块发送警报信号及患者位置数据，命令无人机起飞，结束。Step 2. The AI analysis module judges whether the abnormal diagnosis result needs to start the UAV first aid. If the judgment result is otherwise, it ends. If the judgment result is yes, it sends an alarm signal and patient location data to the UAV control module, and orders the UAV to take off. ,Finish.
一种远程监护急救系统控制无人机急救的方法，包括以下步骤：A method for controlling unmanned aerial vehicle first aid by a remote monitoring emergency system, comprising the following steps:
步骤一无人机控制模块接收AI控制模块的飞行指令，开始；Step 1: The UAV control module receives the flight instruction from the AI control module and starts;
步骤二无人机控制模块根据患者位置目的地信息指定选定无人机；Step 2: The UAV control module designates the selected UAV according to the patient's location and destination information;
步骤三无人机控制模块向无人机传输目的地信息和患者信息，无人机起飞，无人机将实时信息回传给无人机控制模块；Step 3: The UAV control module transmits destination information and patient information to the UAV, the UAV takes off, and the UAV sends real-time information back to the UAV control module;
步骤四无人机控制模块根据回传的信息进行航路规划的控制参数计算，并将参数通过网络传输给无人机；Step 4: The UAV control module calculates the control parameters of route planning according to the returned information, and transmits the parameters to the UAV through the network;
步骤五无人机通过自动驾驶仪进行控制飞行；Step 5: The UAV controls the flight through the autopilot;
步骤六无人机控制模块根据无人机的定位信息判断是否到达目的地，判断结果为否，则返回步骤三；Step 6: The drone control module judges whether it has reached the destination according to the positioning information of the drone, and if the judgment result is no, then return to step 3;
步骤七判断结果为是，无人机落地，无人机控制模块向人工控制模块发送到达信号；Step 7 judges that the result is yes, the UAV lands, and the UAV control module sends an arrival signal to the manual control module;
步骤八无人机控制模块判断是否结束急救，判断结果为否返回步骤七，判断结果为是，进入步骤九；Step 8 UAV control module judges whether to end the first aid, if the judgment result is no, return to step 7, if the judgment result is yes, enter step 9;
步骤九无人机控制模块控制无人机或无人机自动返回基站，结束。Step 9 The UAV control module controls the UAV or the UAV automatically returns to the base station, and ends.
进一步地，在步骤六和七之间，还包括步骤六a：判断结果为是，则判断人工控制模块是否进行控制，如果判断结果为是，则无人机进行人工语音播报，并由人工控制模块人工操纵无人机飞行，然后进入是否结束急救的判断，判断结果为否，则继续人工语音播报并由人工操纵无人机飞行的步骤，直到判断结果为是，则进入步骤九；人工控制模块是否进行控制的判断结果为否，无人机控制模块向无人机下达语音指令，自动播报患者信息；Further, between steps 6 and 7, step 6a is also included: if the result of the judgment is yes, it is judged whether the manual control module is controlling, if the result of the judgment is yes, the unmanned aerial vehicle performs artificial voice broadcast, and is manually controlled The module manually controls the drone to fly, and then enters the judgment of whether to end the first aid. If the judgment result is no, continue the steps of artificial voice broadcast and manually control the drone to fly until the judgment result is yes, then enter step nine; manual control If the judgment result of whether the module is controlled is no, the UAV control module issues voice instructions to the UAV and automatically broadcasts patient information;
步骤六b：无人机控制模块根据传感器回传信息判断患者是否得到帮助，判断结果为否，则返回无人机控制模块向无人机下达语音指令，播报患者信息的步骤。Step 6b: The UAV control module judges whether the patient has been helped according to the information sent back by the sensor. If the judgment result is no, then return to the UAV control module to issue a voice command to the UAV and broadcast the patient information.
一种远程监护急救系统控制无人机急救的方法，包括以下步骤：A method for controlling unmanned aerial vehicle first aid by a remote monitoring emergency system, comprising the following steps:
步骤一无人机控制模块接收AI分析模块的飞行指令，开始；Step 1: The UAV control module receives the flight instruction from the AI analysis module and starts;
步骤二无人机控制模块根据患者位置目的地信息选定无人机；Step 2: The UAV control module selects the UAV according to the patient's location and destination information;
步骤三无人机控制模块向无人机传输目的地信息和患者信息，无人机起飞，无人机将实时信息回传给无人机控制模块；Step 3: The UAV control module transmits destination information and patient information to the UAV, the UAV takes off, and the UAV sends real-time information back to the UAV control module;
步骤四无人机控制模块根据回传的信息进行航路规划的控制参数计算，并将参数通过网络传输给无人机；Step 4: The UAV control module calculates the control parameters of route planning according to the returned information, and transmits the parameters to the UAV through the network;
步骤五无人机通过自动驾驶仪控制飞行；Step 5: The UAV controls the flight through the autopilot;
步骤六无人机控制模块判断是否到达目的地，判断结果为否，则返回步骤三；Step 6: The UAV control module judges whether it has reached the destination, if the judgment result is no, then return to step 3;
步骤七判断结果为是，无人机控制模块向无人机下达语音指令，自动播报患者信息，无人机控制模块向人工控制模块发送到达信号；Step 7 determines that the result is yes, the UAV control module sends a voice command to the UAV, automatically broadcasts the patient information, and the UAV control module sends an arrival signal to the manual control module;
步骤八判断人工控制是否需要介入，判断结果为是则进入步骤九，判断结果为否则进入步骤十；Step 8: Judging whether manual control needs to be involved, if the judgment result is yes, then go to step 9, if the judgment result is otherwise, go to step 10;
步骤九人工控制模块操控无人机飞行；Step 9 The manual control module controls the flight of the drone;
步骤十无人机控制模块判断是否结束急救，判断结果为否返回步骤七，判断结果为是，进入步骤十一；Step 10: UAV control module judges whether to end the first aid, if the judgment result is no, return to step 7, if the judgment result is yes, enter step 11;
步骤十一无人机控制模块控制无人机自动返回基站，结束。Step 11: The UAV control module controls the UAV to automatically return to the base station, and the end is over.
进一步地，所述实时信息包括GPS定位信息，惯组信息，位置、姿态、速度信息，超声波定位器信息、摄像头拍摄信息。Further, the real-time information includes GPS positioning information, inertial group information, position, attitude, speed information, ultrasonic locator information, and camera shooting information.
一种控制参数计算的航路规划方法，包括以下步骤：A route planning method for calculating control parameters, comprising the following steps:
步骤1以无人机当前位置作为规划起始点qstart，初始化搜索树T；Step 1 takes the current position of the UAV as the planning starting point qstart, and initializes the search tree T;
步骤2以概率p选择目标点作为采样点，以概率1-p在整个规划窗口内随机选择采样点qrand；Step 2 select the target point as the sampling point with probability p, and randomly select the sampling point qrand within the entire planning window with probability 1-p;
步骤3通过随机采样点qrand，找到已存在扩展树T的树节点q中离随机采样点距离最近的一个树节点qnear，在qnear和qrand的连线上，计算从qnear以最小航迹段长度L到达的最新点qnew；Step 3 Find the tree node qnear closest to the random sampling point in the tree node q of the existing expanded tree T through the random sampling point qrand, and calculate the minimum track segment length L from qnear on the connection line between qnear and qrand The latest point qnew reached;
步骤4判断qnew是否满足避障和无人机自身性能约束，若满足，则将qnew加入到扩展树T中，否则返回步骤2；Step 4. Determine whether qnew satisfies the obstacle avoidance and UAV performance constraints. If so, add qnew to the extended tree T, otherwise return to step 2;
步骤5判断，若满足，则进行步骤6，否则返回步骤2；Step 5 judges, if satisfied, proceed to step 6, otherwise return to step 2;
步骤6通过形成的扩展树T，获得从起始点qstart到终点qnear的可行路径；Step 6 Obtain the feasible path from the starting point qstart to the end point qnear through the formed extended tree T;
步骤7对冗余航迹节点进行剪裁，得到最终航迹。Step 7 trims redundant track nodes to obtain the final track.
采用上述技术方案，本发明具有如下有益效果：Adopt above-mentioned technical scheme, the present invention has following beneficial effect:
第一，本发明提供了一套由远程智能监测预警设备及无人机设备组成的急救系统，对患者进行院外监测，能够做到早发现、早治疗，危急情况发生时自动进行判断并命令无人机搭载急救药品及设备即刻前往患者所处地点进行及时救治，本发明的远程监护急救系统在医院、家庭、体检中心和社区中都有着广泛的应用空间，本发明能及时挽救包括突发心脏疾病、脑卒中病症在内的患者生命，大大降低社会对于心脑血管疾病的医疗负担。First, the present invention provides a set of emergency rescue system composed of remote intelligent monitoring and early warning equipment and unmanned aerial vehicle equipment. Monitoring patients outside the hospital can achieve early detection and early treatment. When a critical situation occurs, it can automatically judge and order medical personnel The man-machine equipped with first-aid medicines and equipment immediately goes to the place where the patient is located for timely treatment. The remote monitoring emergency system of the present invention has a wide application space in hospitals, families, physical examination centers and communities. The life of patients including disease and stroke, greatly reducing the medical burden of the society on cardiovascular and cerebrovascular diseases.
第二，本发明通过AI分析模块对监测数据进行分析、诊断，得出可靠数据，根据危急程度由系统自动派发无人机飞往患者所处地，将急救设备及时送达病患处，克服了患者家属对于患者实际情况判断不准确，并且自身没有急救能力错失最佳急救时间的问题。Second, the present invention analyzes and diagnoses the monitoring data through the AI analysis module to obtain reliable data. According to the degree of criticality, the system automatically dispatches drones to the patient's location, and delivers the first aid equipment to the patient in time to overcome the It solves the problem that the patient's family members are inaccurate in judging the actual situation of the patient, and they have no emergency rescue ability and miss the best emergency time.
第三，本发明不受交通环境的影响，无人机在急救车派出前快速到达病患处，无人机受控制平台的控制自动到达或返航，克服传统急救车辆到达现场的时间难以预估，救助时间难以保证的缺点。Third, the present invention is not affected by the traffic environment. The UAV can reach the patient quickly before the ambulance is dispatched, and the UAV will automatically arrive or return under the control of the control platform, which overcomes the difficulty of predicting the time when the traditional emergency vehicle arrives at the scene. , the shortcoming that the rescue time is difficult to guarantee.
第四，在任何急救系统中，患者误报、系统阈值等造成的假警报是无法完全避免的，对警报的鉴别时间越长，虽然假警报鉴别率越高，但是越容易错失宝贵的救治时间；对警报的鉴别时间越短，假警报率就会增加，包括救护车、急救人员在内的大量医疗资源将被浪费。该系统完美地解决了这一困境，无人机的使用成本大大低于救护车，所以在无法完全避免假警报的情况下，该系统采用复合等级反应信号阈值，在排除明显假警报的前提下，对警报积极反应，既赢得宝贵的抢救时间，又节约医疗资源。Fourth, in any emergency system, false alarms caused by patient false alarms and system thresholds cannot be completely avoided. The longer the identification time for alarms, the higher the identification rate of false alarms, but it is easier to miss precious treatment time ; The shorter the identification time of the alarm, the false alarm rate will increase, and a large amount of medical resources including ambulances and first responders will be wasted. This system perfectly solves this dilemma. The cost of using drones is much lower than that of ambulances. Therefore, when false alarms cannot be completely avoided, the system adopts a composite level response signal threshold. On the premise of excluding obvious false alarms , Actively respond to the alarm, which not only wins precious rescue time, but also saves medical resources.
第五，无人机到达定位位置后，无人机由值班医师远程操纵，指令无人机发出声光警报，通过机载广播系统播报患者姓名，登记住址，请求现场人员援助，通过机载摄像头控制无人机降落。如果患者犯病后有家属在身边，操作人员根据与家属沟通的情况，可以准确控制无人机穿过门窗、阳台降落到患者附近；如果患者犯病后身边没有人员帮助，无人机能起到现场报警作用，迅速在门卫、物业、广场等人员较多的地方降落或空投急救药品，并播报患者姓名、住址，使周围人员及时携带无人机前往救治患者Fifth, after the UAV arrives at the positioning location, the UAV is remotely operated by the doctor on duty, instructing the UAV to issue sound and light alarms, broadcast the patient’s name and address through the onboard broadcasting system, request assistance from on-site personnel, and pass the onboard camera Control the drone to land. If the patient has a family member by his side after the patient falls ill, the operator can accurately control the drone to pass through the doors, windows and balconies and land near the patient according to the communication with the family; The function is to quickly land or airdrop first-aid medicines in places with many people such as guards, properties, squares, etc., and broadcast the patient's name and address, so that people around can carry drones to treat patients in time
第六，本发明提供了患者与医生之间的语音或视频通讯技术，医生能够远程指导患者使用无人机携带的急救设备，提高了急救的效率。Sixth, the present invention provides a voice or video communication technology between the patient and the doctor, and the doctor can remotely guide the patient to use the first aid equipment carried by the drone, which improves the efficiency of first aid.
附图说明Description of drawings
图1为本发明的远程监护急救系统的交互示意图；Fig. 1 is the interactive schematic diagram of the remote monitoring emergency system of the present invention;
图2为本发明实施例中的AI分析模块对诊断结果的判断处理方法拓扑图；Fig. 2 is a topological diagram of the method for judging and processing the diagnosis result by the AI analysis module in the embodiment of the present invention;
图3为RRT算法的扩展示意图；Fig. 3 is the extended schematic diagram of RRT algorithm;
图4为本发明实施例中的无人机控制模块控制无人机急救的方法拓扑图；Fig. 4 is the topological diagram of the method for controlling the first aid of the UAV by the UAV control module in the embodiment of the present invention;
图5为本发明另一实施例中无人机控制模块控制无人机急救的方法拓扑图；Fig. 5 is the topological diagram of the method for controlling the first aid of the UAV by the UAV control module in another embodiment of the present invention;
图6为本发明另一实施例中无人机控制模块控制无人机急救的方法拓扑图。Fig. 6 is a topological diagram of a method for controlling drone first aid by the drone control module in another embodiment of the present invention.
具体实施方式Detailed ways
为了使本发明的目的、技术方案及优点更加清楚明白，下面结合附图及实施例，对本发明进行进一步详细说明。应当理解，此处所描述的结构图及具体实施例仅用以解释本发明，并不用于限定本发明。In order to make the object, technical solution and advantages of the present invention clearer, the present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the structural diagrams and specific embodiments described here are only used to explain the present invention, and are not intended to limit the present invention.
实施例1Example 1
图1为本发明的远程监护急救系统的交互示意图，如图1所示，本发明提供了一种患者的远程监护急救系统，包括患者监测模块、AI分析模块、无人机控制模块、人工控制模块和无人机。Figure 1 is an interactive schematic diagram of the remote monitoring emergency system of the present invention. As shown in Figure 1, the present invention provides a patient remote monitoring emergency system, including a patient monitoring module, an AI analysis module, a UAV control module, and a manual control module. modules and drones.
患者佩戴患者监测模块正常生活，患者监测模块中的检测设备获取患者的监测数据及定位数据，患者监测模块通过集成的检测设备将患者的心电图、血压、心电图、血氧饱和度等监测数据以及患者的定位数据实时或定时地传输至AI分析模块。The patient wears the patient monitoring module to live a normal life. The detection equipment in the patient monitoring module obtains the monitoring data and positioning data of the patient. The positioning data is transmitted to the AI analysis module in real time or regularly.
AI分析模块接收、存储由患者监测模块传输的数据，分析患者监测数据后生成自动诊断结果，对自动诊断结果做出判断，当判断结果具有危险性时，向无人机控制模块发送警报信号及患者定位数据，命令无人机起飞；向人工控制模块发送诊断报告，并将存储的监测数据和患者定位数据传输至人工控制模块。The AI analysis module receives and stores the data transmitted by the patient monitoring module, generates automatic diagnosis results after analyzing the patient monitoring data, and makes judgments on the automatic diagnosis results. When the judgment results are dangerous, it sends an alarm signal to the UAV control module and Patient positioning data, order the drone to take off; send a diagnosis report to the manual control module, and transmit the stored monitoring data and patient positioning data to the manual control module.
例如，系统可以设置为在自动诊断结果为低危时，AI分析模块每 12～24小时向人工控制模块发送一次诊断报告，诊断结果为中危时，AI 分析模块每4～12小时向人工控制模块发送一次诊断报告，诊断结果为高危时，AI分析模块立即向人工控制模块发生诊断报告，并向无人机控制模块发送警报信号，命令无人机起飞，前往患者位置进行急救。For example, the system can be set so that when the automatic diagnosis result is low risk, the AI analysis module sends a diagnosis report to the manual control module every 12 to 24 hours; when the diagnosis result is medium risk, the AI analysis module sends a report to the manual control module every 4 to 12 hours. The module sends a diagnosis report. When the diagnosis result is high risk, the AI analysis module immediately sends a diagnosis report to the manual control module, and sends an alarm signal to the UAV control module, ordering the UAV to take off and go to the patient's location for first aid.
无人机与无人机控制模块通过网络进行交互通信，无人机上携带用于急救患者的药物及设备，急救设备可以是微型除颤仪等，急救药物可以是阿司匹林、波立维、硝酸甘油片、心痛定、β受体阻滞剂、钙离子拮抗剂、 ACEI类药物、吗啡针剂、肾上腺素针剂等，无人机上设有语音输出装置，喇叭等。The UAV and the UAV control module communicate interactively through the network. The UAV carries medicines and equipment for first aid to patients. The first aid equipment can be a miniature defibrillator, etc. The first aid medicine can be aspirin, Plavix, and nitroglycerin tablets. , Nifedin, β receptor blockers, calcium ion antagonists, ACEI drugs, morphine injections, epinephrine injections, etc. The drone is equipped with voice output devices, speakers, etc.
人工控制模块能够与无人机或患者监测模块双向通信，值班医生可以通过人工控制模块操纵无人机的飞行，获得由无人机的摄像装置回传的拍摄图像，并通过无人机的喇叭进行音频输出，指挥无人机附近的人进行急救。值班医生还可以通过互联网、移动网络或wifi与患者监测模块及无人机进行视频或语音通信，及时指导患者使用无人机上的急救设备对患者进行急救。The manual control module can communicate with the drone or the patient monitoring module in two directions. The doctor on duty can control the flight of the drone through the manual control module, obtain the captured images returned by the camera device of the drone, and pass the drone’s horn Perform audio output to direct people near the drone to give first aid. The doctor on duty can also conduct video or voice communication with the patient monitoring module and the drone through the Internet, mobile network or wifi, and guide the patient in time to use the first aid equipment on the drone to give first aid to the patient.
系统可以设置为人工控制模块收到诊断报告后，由值班医师查看AI 分析模块传输的患者监测实时或既往数据，并通过自动监测模块直接与患者通话，判断病情，如果值班医师判断为假警报，则向无人机控制模块发送返航命令，无人机返航；如果判断为真警报，则通知急救车出发救援，无人机到达现场上空后，值班医师通过无人机的喇叭传输语音播报，请求现场人员协助并指挥现场救援，并能够在无人机飞行中将无人机自动飞行切换至人工操纵飞行。The system can be set so that after the manual control module receives the diagnosis report, the on-duty physician will check the real-time or past patient monitoring data transmitted by the AI analysis module, and directly communicate with the patient through the automatic monitoring module to judge the condition. If the on-duty physician judges that it is a false alarm, Then send a return command to the UAV control module, and the UAV will return; if it is judged to be a true alarm, the emergency vehicle will be notified to start rescue. On-site personnel assist and direct on-site rescue, and can switch the drone's automatic flight to manual control flight during the flight of the drone.
优选地，患者监测模块还能够向AI分析模块发送求救信号，AI分析模块在接受到求救信号后向无人机控制模块发送警报信号及患者位置数据，命令无人机飞往患者位置进行急救。Preferably, the patient monitoring module can also send a distress signal to the AI analysis module. After receiving the distress signal, the AI analysis module sends an alarm signal and patient location data to the UAV control module, ordering the UAV to fly to the patient's location for first aid.
系统可以设置为在AI分析模块对诊断结果的判断处理方法步骤一之前，对是否接收到求救信号进行判断。The system can be configured to judge whether a distress signal is received before the AI analysis module judges the diagnosis result in Step 1 of the processing method.
AI分析模块对获得的患者监测的自动诊断方法可以使用任何现有的分析诊断方法。The AI analysis module can use any existing analysis and diagnosis method for the automatic diagnosis method of patient monitoring obtained.
例如，患者监测模块实时将患者的心电图、心率、血压、血氧饱和度、呼吸情况等传输到AI分析模块，模块对连续变量数据进行阈值分析：For example, the patient monitoring module transmits the patient's ECG, heart rate, blood pressure, blood oxygen saturation, and respiratory conditions to the AI analysis module in real time, and the module performs threshold analysis on continuous variable data:
1、心率，心率大于160次/分钟为高危、159-120次/分钟为中危，大于119-90次/分钟为低危，89-46次/分钟为正常，45-30为中危、小于29 为高危。1. Heart rate, heart rate greater than 160 beats/min is high risk, 159-120 beats/min is medium risk, greater than 119-90 beats/min is low risk, 89-46 beats/min is normal, 45-30 is medium risk, Less than 29 is high risk.
2、心率变化率：心率增加或减少50％所用的时间，小于10秒为高危、 11-30秒为中危、31-60秒为低危，大于61秒为正常。2. Heart rate change rate: the time taken for the heart rate to increase or decrease by 50%, less than 10 seconds is high risk, 11-30 seconds is medium risk, 31-60 seconds is low risk, and greater than 61 seconds is normal.
3、血压，血压收缩压大于180mmHg为高危，179-160mmHg为中危， 159-140mmHg为低危，140-90mmHg为正常，89-80为低危，79-70mmHg 为中危，小于69mmHg为高危。3. Blood pressure, systolic blood pressure greater than 180mmHg is high risk, 179-160mmHg is medium risk, 159-140mmHg is low risk, 140-90mmHg is normal, 89-80 is low risk, 79-70mmHg is medium risk, and less than 69mmHg is high risk .
4、呼吸频率：大于50次/分钟为高危，49-40次/分钟为中危、39-30 次/分钟为低危，29-15次/分钟为正常，小于5次/分钟为高危。5、血氧饱和度小于70％为高危，71-80％为中危，81-90％为低危，91-100％为正常。4. Respiratory frequency: more than 50 breaths/min is high risk, 49-40 breaths/min is medium risk, 39-30 breaths/min is low risk, 29-15 breaths/min is normal, and less than 5 breaths/min is high risk. 5. Blood oxygen saturation less than 70% is high risk, 71-80% is medium risk, 81-90% is low risk, and 91-100% is normal.
循环神经网络Recurrent Neural Network(RNN):是一种节点定向连接成环的深度学习人工神经网络。这种网络的内部状态可以展示动态时序行为。不同于前馈神经网络的是，RNN可以利用它内部的记忆来处理任意时序的输入序列，这让它可以更容易处理如不分段的手写识别、语音识别等。Recurrent Neural Network (RNN): It is a deep learning artificial neural network in which nodes are directional connected into a ring. The internal state of such a network can exhibit dynamic timing behavior. Different from the feedforward neural network, RNN can use its internal memory to process any sequence of input sequences, which makes it easier to handle such as non-segmented handwriting recognition, speech recognition, etc.
长短期记忆神经网络Long Short-Term Memory(LSTM):是一种时间循环神经网络，论文首次发表于1997年。由于独特的设计结构，LSTM 适合于处理和预测时间序列中间隔和延迟非常长的重要事件。Long Short-Term Memory Neural Network Long Short-Term Memory (LSTM): It is a time cyclic neural network, the paper was first published in 1997. Due to the unique design structure, LSTM is suitable for processing and predicting important events with very long intervals and delays in time series.
极联迭代分类算法Ada Boost:其核心思想是针对同一个训练集训练不同的分类器(弱分类器)，然后把这些弱分类器集合起来，构成一个更强的最终分类器(强分类器)。Jilian iterative classification algorithm Ada Boost: its core idea is to train different classifiers (weak classifiers) for the same training set, and then combine these weak classifiers to form a stronger final classifier (strong classifier) .
滤波Wave filtering:是将信号中特定波段频率滤除的操作，是抑制和防止干扰的一项重要措施。滤波分为经典滤波和现代滤波。Wave filtering: It is an operation to filter out a specific band frequency in a signal, and it is an important measure to suppress and prevent interference. Filtering is divided into classical filtering and modern filtering.
波形到波形分析Beat-to-beat:是动态心电图分析验证方法的一种，把导联上面的所有QRS波形对齐进行分析。Waveform-to-waveform analysis Beat-to-beat: It is a kind of dynamic ECG analysis and verification method, which aligns all QRS waveforms on the leads for analysis.
分析诊断算法参考ANSI/AAMI EC57制定；ANSI/AAMI EC57规定了心电图自动分析算法的评估标准。分析诊断算法的评估：使用分析诊断算法对标准参考心电数据库分析得出算法结果注释，然后将算法结果注释和参考标准心电数据库注释使用一致的比较算法，最后得出能够反映算法各项指标的统计报告。由于使用了统一的验证数据库和验证比较算法，统计报告能够客观的评估算法各方面性能指标。The analysis and diagnosis algorithm was formulated with reference to ANSI/AAMI EC57; ANSI/AAMI EC57 stipulated the evaluation criteria for the automatic analysis algorithm of ECG. Evaluation of the analysis and diagnosis algorithm: use the analysis and diagnosis algorithm to analyze the standard reference ECG database to obtain the algorithm result annotation, and then use the same comparison algorithm for the algorithm result annotation and the reference standard ECG database annotation, and finally obtain the indicators that can reflect the algorithm statistics report. Due to the use of a unified verification database and verification comparison algorithm, the statistical report can objectively evaluate the performance indicators of all aspects of the algorithm.
参考标准心电数据库Reference standard ECG database
AHA：美国心脏协会用于评价室性心律失常数据库(共80条记录，每条35分钟)。AHA: American Heart Association database for evaluating ventricular arrhythmias (80 records in total, 35 minutes each).
MIT-BIH：麻省理工心率失常数据库(共48条记录，每条30分钟)。MIT-BIH: MIT arrhythmia database (a total of 48 records, each 30 minutes).
ESC：欧洲心脏病学会ST-T数据库(90条记录，每次2小时)。ESC: European Society of Cardiology ST-T database (90 records, 2 hours each).
NST:噪声应力测试数据库(12心电记录,每条30分钟，另加3条由 MIT-BIH提供噪声)。NST: Noise stress test database (12 ECG records, 30 minutes each, plus 3 noises provided by MIT-BIH).
CU：克赖顿大学的持续性室性心律失常数据库。CU: Creighton University Sustained Ventricular Arrhythmia Database.
分析诊断算法的评估报告要求Evaluation Report Requirements for Analytical Diagnostic Algorithms
分析诊断算法的评估报告包括算法必选评估项和算法可选特性评估项。必选评估项是所有分析诊断算法必须参与评估。对可选特性评估项，如果测试算法声明支持相应的特性，则必须选择对应特性评估项。例如检测算法说明支持ST的检测，则必须进行ST幅值、ST斜率、ST改变的测试。评估检测中有四种检测结果：真阳性TP、假阴性FN、假阳性FP、真阴性TN，并由此得出常用的性能测试指标：敏感度Se＝TP/(TP+FN)阳性率+P＝TP/(TP+FP)The evaluation report of the analysis and diagnosis algorithm includes evaluation items required for the algorithm and optional evaluation items for the algorithm. The mandatory evaluation item is that all analysis and diagnosis algorithms must participate in the evaluation. For optional feature evaluation items, if the test algorithm claims to support the corresponding feature, the corresponding feature evaluation item must be selected. For example, if the detection algorithm indicates that it supports the detection of ST, then the test of ST amplitude, ST slope, and ST change must be carried out. There are four test results in the evaluation test: true positive TP, false negative FN, false positive FP, and true negative TN, and the commonly used performance test indicators are obtained from this: Sensitivity Se=TP/(TP+FN)positive rate+ P=TP/(TP+FP)
分析诊断算法的必选评估项：Required evaluation items for analytical diagnostic algorithms:
注：表格中R为必测的数据库O为选测的数据库Note: R in the table is the database to be tested and O is the database to be tested
分析诊断算法的可选特性评估项Optional feature evaluation items for analytical diagnostic algorithms
注：表格中R为必测的数据库O为选测的数据库Note: R in the table is the database to be tested and O is the database to be tested
评估比较算法Evaluation Comparison Algorithms
评估比较算法包括：心律测量评估、HRV和RRV测量评估、 Beat-by-beat比较、Run-by-run比较、VF和AF比较、ST比较Evaluation comparison algorithms include: heart rhythm measurement evaluation, HRV and RRV measurement evaluation, Beat-by-beat comparison, Run-by-run comparison, VF and AF comparison, ST comparison
Beat-by-beat比较用于推导QRS Se,QRS+P,VEB Se,VEB+P,VEB FPR,SVEB FPR,SVEB Se，SVEB+P。Beat-by-beat comparisons are used to derive QRS Se, QRS+P, VEB Se, VEB+P, VEB FPR, SVEB FPR, SVEB Se, SVEB+P.
Run-by-run比较用于推导VE couplet Se及+P,VE short run Se及+P, VE longrun Se及+P,SVE couplet Se及+P,SVE short run Se及+P,SVE long run Se及+P。Run-by-run comparison is used to derive VE couplet Se and +P, VE short run Se and +P, VE longrun Se and +P, SVE couplet Se and +P, SVE short run Se and +P, SVE long run Se and +P.
VF和AF比较用于推导:VF/AF episode Se及+P,VF/AF duration Se及 +P。VF and AF are compared for derivation: VF/AF episode Se and +P, VF/AF duration Se and +P.
ST比较用于推导ST相关评估项ST comparisons are used to derive ST-related evaluation items
心律测量评估Cardiac Rhythm Measurement Evaluation
心律测量取决于rr区间测量,但一些获得这些测量的算法对于偶尔 rr区间测量误差具有鲁棒性,而另一些则对此类错误特别敏感。RMS heart rate error用于反映此类rr区间测量算法的特性。Heart rhythm measurements depend on rr interval measurements, but some algorithms for obtaining these measurements are robust to occasional rr interval measurement errors, while others are particularly sensitive to such errors. RMS heart rate error is used to reflect the characteristics of such rr interval measurement algorithms.
HRV和RRV测量评估Evaluation of HRV and RRV Measurements
HRV和RRV测量评估需要计算以下指标：Mean、SDNN、SDANN、 ASDNN NN50、pNN50、rMSSD、VLF、LF、HFEvaluation of HRV and RRV measurements requires the calculation of the following indicators: Mean, SDNN, SDANN, ASDNN NN50, pNN50, rMSSD, VLF, LF, HF
Beat-by-beat比较Beat-by-beat comparison
Beat-by-beat比较用于计算QRS算法和心搏分类算法敏感度和阳性预测率。分类算法检测的心跳标签同标准心电数据库的标记的心跳标签，执行两两配对比较。如果心电算法检测到的心跳标签的位置同标准数据库的标签的位置在150ms窗口内，这样认为一个匹配，如果在这个窗口内没有找到一个匹配，则这个候选的心跳可能为多检或者漏检。这样直到比较结束产生一个beat-by-beat的计数矩阵如下表：Beat-by-beat comparisons were used to calculate the sensitivity and positive predictive rate of the QRS algorithm and the beat classification algorithm. The heartbeat labels detected by the classification algorithm are compared with the marked heartbeat labels of the standard ECG database, and a pairwise comparison is performed. If the location of the heartbeat label detected by the ECG algorithm is within the 150ms window of the standard database label, it is considered a match. If no match is found within this window, the candidate heartbeat may be multiple detection or missed detection. . In this way, until the end of the comparison, a beat-by-beat count matrix is generated as follows:
算法检测标签(Algorithm label)Algorithm label
分类检测算法敏感度和阳性率由上面矩阵计算得出。The sensitivity and positive rate of the classification detection algorithm are calculated from the above matrix.
在这个矩阵推导过程中，应该保持对参考标注文件和测试标注文件中被标记为不可读或者VF的片段进行跟踪。在不可读片段中，伪心跳标记为X；其他情况都标c记为O。在参考VF片段期间，产生的搏动标签不计入矩阵统计。在测试标注为VF片段期间，参考表现为搏动标签，配对为O伪标签，并像其他漏检的搏动一样计数。原则上，不可读片段和VF 片段可在开始后嵌套开始，在设计执行beat-by-beat比较的软件中，应考虑这种可能性的存在。During this matrix derivation, one should keep track of fragments marked as unreadable or VF in the reference and test annotation files. In unreadable fragments, false heartbeats are marked as X; otherwise, c is marked as O. During referencing of VF segments, generated beat labels are not counted in matrix statistics. During testing of labeled VF segments, references appear as beat labels, paired with O pseudolabels, and counted like other missed beats. In principle, unreadable fragments and VF fragments could nest start after start, and this possibility should be considered in designing software to perform beat-by-beat comparisons.
Run-by-run比较Run-by-run comparison
Run-by-run比较被用于测量算法检测连续异位心搏的能力。对于每种类型的异位心搏(VEB和SVEB)，都需要执行敏感度run-by-run比较以及阳性率run-by-run比较。run-by-run比较最终产生了一对矩阵见下表，其中每一个元素值是一个对应类型的计数。一般术语run指连续的V或F 标签序列。最后根据矩阵可以计算couplet、short run、long run的敏感度和阳性率。Run-by-run comparisons were used to measure the algorithm's ability to detect consecutive ectopic beats. For each type of ectopic beat (VEB and SVEB), a sensitivity run-by-run comparison as well as a positive rate run-by-run comparison needs to be performed. The run-by-run comparison finally produces a pair of matrices as shown in the table below, where each element value is a count of a corresponding type. The general term run refers to a contiguous sequence of V or F tags. Finally, the sensitivity and positive rate of couplet, short run, and long run can be calculated according to the matrix.
Run sensitivity summary matrix Run sensitivity summary matrix
Run positive predictivity summary matrix Run positive predictivity summary matrix
室颤室扑(VF)和房颤房扑(AF)比较Comparison of ventricular flutter (VF) and atrial fibrillation (AF)
此算法用于验证能够检测VF，AF的算法。在标准心电数据注释，标示VF进行中，如果算法也重叠检测到VF，则VF片断检测阳性敏感性为真，其他情况为阴性。类似的每一个算法标记的片段覆盖了存在的标记，被认为满足了真阳性的VF片断阳性率，其他情况的算法标记为虚假误报。This algorithm is used to verify the algorithm capable of detecting VF, AF. In the standard ECG data annotation, it is marked that VF is in progress, if the algorithm also overlaps and detects VF, the positive sensitivity of VF fragment detection is true, and it is negative in other cases. Similarly, the fragments marked by each algorithm cover the existing marks, which are considered to meet the positive rate of VF fragments of true positives, and the algorithms in other cases are marked as false false positives.
测量VF的持续敏感度和阳性预测率需要计算参考的总共持续时间以及算法标记的VF的重叠的持续时间Measuring the persistence sensitivity and positive predictive rate of VF requires calculation of the total duration of the reference as well as the overlapping duration of the algorithm-flagged VF
ST比较ST comparison
对于声明能够分析ST片断的算法需要测试ST段的振幅、ST段的斜率以及ST改变。For an algorithm to claim to be able to analyze ST segments needs to test the amplitude of the ST segment, the slope of the ST segment, and the ST change.
1)对于要求检测ST段振幅的算法，应为所有测量和测量所有导联 st振幅生成以下数据图：1) For algorithms that require detection of ST segment amplitudes, the following data plots should be generated for all measurements and measurements of ST amplitudes in all leads:
A.所有算法测量的ST振幅相对于参考ST振幅的差的散点图，以及图上的指示识别线A. Scatterplot of the difference in ST amplitude measured by all algorithms relative to the reference ST amplitude, and the indicated identification line on the plot
B.算法测量差值相对于参考st值的散点图，并有所有算法测量的平均差和标准差的指示线B. A scatter plot of the algorithmic measurement difference relative to the reference st value, with indicator lines for the mean and standard deviation of all algorithmic measurements
C.算法测量的ST振幅相对于参考ST振幅在从-200微伏到+200微伏范围内的散点图。C. Scatterplot of algorithm-measured ST amplitudes relative to reference ST amplitudes ranging from -200 microvolts to +200 microvolts.
2)对于检测st段斜率的算法，需要对所有导联的测量画出下面的数据图：2) For the algorithm for detecting the slope of the ST segment, it is necessary to draw the following data graph for the measurement of all leads:
A.测量st斜率的差相对于参考st斜率的散点图，并有算法测量st斜率的平均差和标准差指示线A. Measure the scatter plot of the difference of the st slope relative to the reference st slope, and have an algorithm to measure the mean difference and standard deviation indicator line of the st slope
B.所有算法测量的st斜率相对于参考st斜率的散点图，并有指示识别线B. A scatterplot of the st slope measured by all algorithms relative to the reference st slope, with indicative identification lines
C.算法测量的st斜率相对于参考st斜率的散点图，参考st斜率在-2.0 mV/s到+2.0mV/s的范围内。C. Scatterplot of the st slope measured by the algorithm versus the reference st slope in the range of -2.0 mV/s to +2.0 mV/s.
为了推导出ST事件的敏感度和阳性预测率，类似于run-by-run比较的Event-by-event比较是非常必要的。St改变事件在任何时间间隔内算法测试的ST改变和参考注释文件表示的ST改变有重叠。包括极值或至少 50％的参考标记的重叠期的事件匹配用于敏感度目的。包括极值或至少50％的算法测试标记的重叠期的事件匹配用于阳性预测率目的。In order to derive the sensitivity and positive predictive rate of ST events, event-by-event comparisons similar to run-by-run comparisons are necessary. St change events overlap the ST changes tested by the algorithm and the ST changes indicated by the reference annotation file in any time interval. Event matches for periods of overlap that included extreme values or at least 50% of the reference markers were used for sensitivity purposes. Event matches for periods of overlap that included extreme values or at least 50% of the algorithm's test flags were used for positive predictive rate purposes.
在运算过程中，对关键变量进行阈值分析:1、心电图RR间期大于4 秒为高危，3.9-3秒为中危，2.9-2秒为低危，2-1秒为正常。2、ST段抬高，与患者日常心电图相比较，ST段抬高超过3mm为高危、2.9-2mm为中危、 1.9-0.5为低危，0.4-0为正常。3、ST段压低，与患者日常心电图相比较，单个导联ST段压低超过3mm为高危、2.9-2mm为中危、1.9-0.5为低危，0.4-0为正常。4、出现两个导联ST段变化为中危，三个导联以上ST段变化为高危。5、突发QRS波宽度增加大于一倍为高危。6、监测到房颤波为高危。7、监测到室颤波为高危。8、患者按下紧急求救按钮为高危。During the operation process, threshold analysis was performed on the key variables: 1. An ECG RR interval greater than 4 seconds was high risk, 3.9-3 seconds was medium risk, 2.9-2 seconds was low risk, and 2-1 seconds was normal. 2. ST-segment elevation. Compared with the patient's daily ECG, an ST-segment elevation of more than 3 mm is high risk, 2.9-2 mm is moderate risk, 1.9-0.5 is low risk, and 0.4-0 is normal. 3. ST-segment depression, compared with the patient's daily ECG, a single-lead ST-segment depression exceeding 3 mm is high risk, 2.9-2 mm is medium risk, 1.9-0.5 is low risk, and 0.4-0 is normal. 4. ST-segment changes in two leads are intermediate risk, and ST-segment changes in more than three leads are high-risk. 5. Sudden increase in the width of QRS complex is more than doubled as high risk. 6. Atrial fibrillation is detected as high risk. 7. Ventricular fibrillation is detected as high risk. 8. It is high risk for the patient to press the emergency button.
实施例2Example 2
图2是本发明AI分析模块对诊断结果的判断处理方法拓扑图，如图 2所示，本发明提供了一种诊断结果的判断处理方法，包括以下步骤：Fig. 2 is a topological diagram of the method for judging and processing the diagnosis result by the AI analysis module of the present invention. As shown in Fig. 2, the present invention provides a method for judging and processing the diagnosis result, including the following steps:
步骤一AI分析模块判断诊断结果是否正常，如果判断结果为是，则结束一个判断周期；如果判断结果为否，则由AI分析模块按照预设的频次向人工控制模块发送诊断报告；Step 1. The AI analysis module judges whether the diagnosis result is normal. If the judgment result is yes, then end a judgment cycle; if the judgment result is no, the AI analysis module sends a diagnosis report to the manual control module according to the preset frequency;
例如：通常诊断结果分为正常、低危、中危及高危四类，后三种结果均为异常结果。诊断为正常的数据，则结束一个判断周期，诊断结果为低危的数据，系统设置为由AI分析模块每12～24小时向人工控制模块发送一次诊断报告，由值班医师审查患者的监测数据，并指导患者诊疗；诊断结果为中危的数据，系统设置为由AI分析模块每4～12小时向人工控制模块发送一次诊断报告，由值班医师审查患者的监测数据，并指导患者诊疗；诊断结果为高危的数据将被立即发送给人工控制模块。For example: Usually, the diagnosis results are divided into four categories: normal, low-risk, medium-risk and high-risk, and the latter three results are all abnormal results. If the data is diagnosed as normal, a judgment cycle will end, and the diagnosis result is low-risk data. The system is set so that the AI analysis module sends a diagnosis report to the manual control module every 12 to 24 hours, and the doctor on duty reviews the monitoring data of the patient. And guide the patient's diagnosis and treatment; the diagnosis result is medium-risk data, the system is set so that the AI analysis module sends a diagnosis report to the manual control module every 4 to 12 hours, and the doctor on duty reviews the patient's monitoring data and guides the patient's diagnosis and treatment; the diagnosis result Data deemed high risk will be sent immediately to the human control module.
步骤二AI分析模块判断异常诊断结果是否需要启动无人机急救，如果判断结果为否则结束，如果判断结果为是，则向无人机控制模块发送警报信号及患者位置数据，命令无人机起飞，结束。Step 2. The AI analysis module judges whether the abnormal diagnosis result needs to start the UAV first aid. If the judgment result is otherwise, it ends. If the judgment result is yes, it sends an alarm signal and patient location data to the UAV control module, and orders the UAV to take off. ,Finish.
例如：系统设置为将判断为高危的异常诊断结果判断为需要启动无人机急救。For example: the system is set to judge the abnormal diagnosis result judged as high risk as the need to start the drone first aid.
实施例3Example 3
无人机的航路规划是急救系统中执行系统中至关重要的一部分。航路规划是指在特定约束条件下，寻找从初始点到目标点满足某种性能指标最优的运动轨迹。航路规划必须综合考虑无人机的飞行特性，如：最小转弯半径、爬升角、实用升限等；环境因素，如：地形、雷达威胁源、禁飞区、雷雨区、链路覆盖区域、链路干扰等；任务载荷因素：视场角范围、作用距离、重叠率等。The route planning of the UAV is a crucial part of the execution system in the emergency system. Route planning refers to finding the optimal trajectory from the initial point to the target point to satisfy a certain performance index under certain constraints. Route planning must comprehensively consider the flight characteristics of UAVs, such as: minimum turning radius, climb angle, practical ceiling, etc.; environmental factors, such as: terrain, radar threat source, no-fly zone, thunderstorm area, link coverage area, link Road interference, etc.; task load factors: field of view range, operating distance, overlap rate, etc.
高效的航路规划算法可以大大提高无人机自主执行任务的效率和安全性。本发明采用基于快速扩展随机树的三维航路规划方法，该算法能够根据当前环境快速有效搜索规划空间，通过随机采样点将搜索导向空白区域，使三维航路规划能够用于实时航迹规划。基于RRT(快速扩展随机树) 方法的航路规划以状态空间中的规划起始点为根节点，通过随机采样逐渐增加叶节点的方式生成随机扩展树。当随机树的叶节点中包含了目标点或者目标区域的点时，随机树的扩展停止，便可在随机树中找到一条以根节点组成的从起始点到目标点的路径。RRT的扩展方式如图3所示。Efficient route planning algorithms can greatly improve the efficiency and safety of autonomous drone missions. The present invention adopts a three-dimensional route planning method based on a rapidly expanding random tree. The algorithm can quickly and effectively search the planning space according to the current environment, and guide the search to a blank area through random sampling points, so that the three-dimensional route planning can be used for real-time track planning. The route planning based on the RRT (rapidly expanded random tree) method takes the planning starting point in the state space as the root node, and generates a random expanded tree by gradually increasing the leaf nodes through random sampling. When the leaf node of the random tree contains the target point or the point of the target area, the expansion of the random tree stops, and a path from the starting point to the target point composed of the root node can be found in the random tree. The expansion mode of RRT is shown in Fig. 3 .
基于控制参数计算的航路规划方法，包括以下步骤：The route planning method based on control parameter calculation includes the following steps:
步骤1以无人机当前位置作为规划起始点qstart，初始化搜索树T；Step 1 takes the current position of the UAV as the planning starting point qstart , and initializes the search tree T;
步骤2以概率p选择目标点作为采样点，以概率1-p在整个规划窗口内随机选择采样点qrand；Step 2 select the target point as the sampling point with probability p, and randomly select the sampling point qrand within the entire planning window with probability 1-p;
步骤3通过随机采样点qrand，找到已存在扩展树T的树节点q中离随机采样点距离最近的一个树节点qnear，在qnear和qrand的连线上，计算从qnear以最小航迹段长度L到达的最新点qnew；Step 3 Through the random sampling point qrand , find a tree nodeqnear that is closest to the random sampling point in the tree node qof the existing extended tree T, and calculate from q nearto the minimum The latest point qnew reached by the track segment length L;
步骤4判断qnew是否满足避障和无人机自身性能约束，若满足，则将 qnew加入到扩展树T中，否则返回步骤2；Step 4. Determine whether qnew satisfies the obstacle avoidance and performance constraints of the UAV itself. If so, add qnew to the extended tree T, otherwise return to step 2;
步骤5判断|qnew-qgoal|＜＜L，若满足，则进行步骤6，否则返回步骤2；Step 5 judge |qnew -qgoal |<<L, if satisfied, go to step 6, otherwise return to step 2;
步骤6通过形成的扩展树T，获得从起始点qstart到终点qnear的可行路径；Step 6 Obtain the feasible path from the starting point qstart to the end point qnear through the formed extended tree T;
步骤7对冗余航迹节点进行剪裁，得到最终航迹。Step 7 trims redundant track nodes to obtain the final track.
采用RRT实时航路规划方法进行无人机控制参数计算，无人机根据接收到的控制参数信息，通过自动驾驶仪控制无人机飞行。因为RRT为实时航路规划方法，则有较好的规避障碍物能力。影响远程人工控制效果的因素较多，人工控制的效果较差。The RRT real-time route planning method is used to calculate the control parameters of the UAV, and the UAV controls the flight of the UAV through the autopilot according to the received control parameter information. Because RRT is a real-time route planning method, it has a better ability to avoid obstacles. There are many factors affecting the effect of remote manual control, and the effect of manual control is poor.
实施例4Example 4
无人机自动飞行，图4是实施例中的无人机控制模块控制无人机急救的方法拓扑图，如图所示，远程监护急救系统控制无人机急救的方法，包括以下步骤：UAV automatic flight, Fig. 4 is the topological diagram of the method of the UAV control module in the embodiment to control the UAV first aid, as shown in the figure, the remote monitoring emergency system controls the UAV first aid method, including the following steps:
步骤一无人机控制模块接收AI分析模块的警报信号，开始；Step 1: The UAV control module receives the alarm signal of the AI analysis module and starts;
步骤二无人机控制模块根据患者位置目的地信息选定无人机；Step 2: The UAV control module selects the UAV according to the patient's location and destination information;
步骤三无人机控制模块向无人机传输目的地信息和患者信息，无人机起飞，无人机将实时信息回传给无人机控制模块，实时信息包括但不限于：GPS定位信息，惯组信息，位置、姿态、速度信息，超声波定位器信息、摄像头拍摄信息等；Step 3: The UAV control module transmits destination information and patient information to the UAV, the UAV takes off, and the UAV sends real-time information back to the UAV control module. The real-time information includes but is not limited to: GPS positioning information, Inertial group information, position, attitude, speed information, ultrasonic locator information, camera shooting information, etc.;
步骤四无人机控制模块根据回传的信息进行航路规划的控制参数计算，并将参数通过网络传输给无人机；Step 4: The UAV control module calculates the control parameters of route planning according to the returned information, and transmits the parameters to the UAV through the network;
步骤五无人机通过自动驾驶仪进行控制飞行；Step 5: The UAV controls the flight through the autopilot;
步骤六无人机控制模块根据无人机的定位信息判断是否到达目的地，判断结果为否，则返回步骤三；Step 6: The drone control module judges whether it has reached the destination according to the positioning information of the drone, and if the judgment result is no, then return to step 3;
步骤七判断结果为是，无人机落地，无人机控制模块向人工控制模块发送到达信号，系统可以设置为医生在人工控制模块端接收到该信号后，及时与患者及家属联系，指导急救，在值班医生确定已经完成无人机的送药工作后，给无人机控制模块发送一个结束急救的信号；If the judgment result of step 7 is yes, the UAV lands, and the UAV control module sends an arrival signal to the manual control module. The system can be set so that after receiving the signal at the manual control module, the doctor will contact the patient and his family in time to guide the first aid. , after the on-duty doctor confirms that the drone’s medicine delivery work has been completed, send a signal to the drone’s control module to end the first aid;
步骤八无人机控制模块判断是否结束急救，判断结果为否，返回步骤六，判断结果为是，进入步骤九，系统可以设置为在无人机模块接收到人工控制模块发送的确认结束信号后判断为是，否则判断为否；Step eight: UAV control module judges whether to end the first aid, if the judgment result is no, return to step six, if the judgment result is yes, enter step nine, the system can be set to after the UAV module receives the confirmation end signal sent by the manual control module judged as yes, otherwise judged as no;
步骤九无人机控制模块控制无人机自动返回基站，结束。Step 9: The UAV control module controls the UAV to automatically return to the base station, and the end is over.
实施例5Example 5
无人机飞行中介入人工操纵，图5是另一实施例中的无人机控制模块控制无人机急救的方法拓扑图，如图所示，远程监护急救系统控制无人机急救的方法，包括以下步骤：Manual manipulation is involved in the flight of the UAV. FIG. 5 is a topological diagram of the method for controlling the first aid of the UAV by the UAV control module in another embodiment. As shown in the figure, the method for controlling the first aid of the UAV by the remote monitoring and first aid system, Include the following steps:
步骤一无人机控制模块接收AI分析模块的飞行指令，开始；Step 1: The UAV control module receives the flight instruction from the AI analysis module and starts;
步骤二无人机控制模块根据患者位置目的地信息选定无人机；Step 2: The UAV control module selects the UAV according to the patient's location and destination information;
步骤三无人机控制模块向无人机传输目的地信息和患者信息，无人机起飞，无人机将实时信息回传给无人机控制模块；Step 3: The UAV control module transmits destination information and patient information to the UAV, the UAV takes off, and the UAV sends real-time information back to the UAV control module;
步骤四无人机控制模块根据回传的信息进行航路规划的控制参数计算，并将参数通过网络传输给无人机；Step 4: The UAV control module calculates the control parameters of route planning according to the returned information, and transmits the parameters to the UAV through the network;
步骤五无人机通过自动驾驶仪控制飞行；Step 5: The UAV controls the flight through the autopilot;
步骤六无人机控制模块判断是否到达目的地，判断结果为否，则返回步骤三；Step 6: The UAV control module judges whether it has reached the destination, if the judgment result is no, then return to step 3;
步骤七判断结果为是，无人机控制模块向无人机下达语音指令，自动播报患者信息，无人机控制模块向人工控制模块发送到达信号；Step 7 determines that the result is yes, the UAV control module sends a voice command to the UAV, automatically broadcasts the patient information, and the UAV control module sends an arrival signal to the manual control module;
步骤八判断人工控制是否需要介入，判断结果为是则进入步骤九，判断结果为否则进入步骤十；Step 8: Judging whether manual control needs to be involved, if the judgment result is yes, then go to step 9, if the judgment result is otherwise, go to step 10;
步骤九人工控制模块操控无人机飞行；Step 9 The manual control module controls the flight of the drone;
步骤十无人机控制模块判断是否结束急救，判断结果为否，返回步骤七，判断结果为是，进入步骤十一；Step 10: UAV control module judges whether to end the first aid, if the judgment result is no, return to step 7, if the judgment result is yes, enter step 11;
步骤十一无人机控制模块控制无人机自动返回基站，结束。Step 11: The UAV control module controls the UAV to automatically return to the base station, and the end is over.
在步骤七中，对实现无人机上的自动语音播报功能的技术手段不做限制，本发明可以使用任何一种现有技术中的方法，例如中国发明专利号为 201510757022.7，名称为：一种智能软件的语音报表系统及语音播报方法所公开的语音播报方法，语音播报功能由设置在无人机上的语音播报单元完成，将接收到的患者信息后合成固定格式的文字信息，并输出经语音合成的语音文件，通过喇叭播放，语音播报能够引起无人机附近人的注意，帮助实施救助工作。In step seven, there are no restrictions on the technical means for realizing the automatic voice broadcast function on the drone. The present invention can use any method in the prior art. For example, the Chinese invention patent number is 201510757022.7, and the name is: a smart The voice report system of the software and the voice broadcast method disclosed by the voice broadcast method, the voice broadcast function is completed by the voice broadcast unit arranged on the drone, and the received patient information is synthesized into text information in a fixed format, and output via voice synthesis The voice file is played through the speaker, and the voice broadcast can attract the attention of people near the drone and help carry out rescue work.
医生在人工控制模块接收到到达信号后，可以与患者监测模块进行语音或视频通话，指挥患者或现场人员使用合适的药物或设备进行急救，在救护车到达前争取到宝贵的救治时间。After receiving the arrival signal at the manual control module, the doctor can make a voice or video call with the patient monitoring module, instruct the patient or on-site personnel to use appropriate drugs or equipment for first aid, and gain valuable treatment time before the ambulance arrives.
在步骤九中，介入人工控制后，操纵无人机飞行的权限被人工控制模块获得，值班医生在人工控制模块端接收由无人机控制模块传输的无人机实时信息，并且能够手动操纵无人机飞行，可以控制无人机飞行到患者身边或门卫室、广场等人员密集区域，或者在降落条件限制时，直接空投急救设备。In step 9, after the manual control is involved, the authority to fly the drone is obtained by the manual control module, and the doctor on duty receives the real-time information of the drone transmitted by the drone control module at the manual control Man-machine flight can control the drone to fly to the patient's side or the guard room, square and other densely populated areas, or directly drop the first aid equipment when the landing conditions are limited.
无人机在低空悬停播报患者信息，能够引起家属及周围人员注意，获得他们的帮助，值班医生通过视频确认现场有人员协助后，值班医生可以操纵无人机降落或空投急救设备，降落地点尽可能离患者最近，由现场人员携带急救设备到患者身边开始急救。The drone hovers at low altitude and broadcasts patient information, which can attract the attention of family members and surrounding people and get their help. After the doctor on duty confirms that there are personnel assisting on the scene through video, the doctor on duty can manipulate the drone to land or airdrop emergency equipment. As close as possible to the patient, the on-site personnel carry first aid equipment to the patient to start first aid.
在步骤十中，系统可以设置为由人工控制模块向无人机控制模块发送一个确认结束急救的信号，无人机控制模块收到这个信号后则判断结束急救。In step ten, the system can be configured such that the manual control module sends a signal to the UAV control module to confirm the end of the first aid, and the UAV control module judges to end the first aid after receiving this signal.
实施例6Example 6
无人机到达目的地后接入人工控制模块的控制，图6是另一实施例中的无人机控制模块控制无人机急救的方法拓扑图，如图所示，远程监护急救系统控制无人机急救的方法，包括以下步骤：After the drone arrives at the destination, it is connected to the control of the manual control module. Figure 6 is a topological diagram of the method for the drone control module to control the first aid of the drone in another embodiment. As shown in the figure, the remote monitoring emergency system controls the drone The method of man-machine first aid includes the following steps:
步骤一无人机控制模块接收AI分析模块的警报信号，开始；Step 1: The UAV control module receives the alarm signal of the AI analysis module and starts;
步骤二无人机控制模块根据患者位置目的地信息选定无人机；Step 2: The UAV control module selects the UAV according to the patient's location and destination information;
步骤三无人机控制模块向无人机传输目的地信息和患者信息，无人机起飞，无人机将实时信息回传给无人机控制模块，实时信息包含但不限于：GPS定位信息，惯组信息，位置、姿态、速度信息，超声波定位器信息、摄像头拍摄信息等；Step 3: The UAV control module transmits destination information and patient information to the UAV, the UAV takes off, and the UAV sends real-time information back to the UAV control module. The real-time information includes but is not limited to: GPS positioning information, Inertial group information, position, attitude, speed information, ultrasonic locator information, camera shooting information, etc.;
步骤四无人机控制模块根据回传的信息进行航路规划的控制参数计算，并将参数通过网络传输给无人机；Step 4: The UAV control module calculates the control parameters of route planning according to the returned information, and transmits the parameters to the UAV through the network;
步骤五无人机通过自动驾驶仪进行控制飞行；Step 5: The UAV controls the flight through the autopilot;
步骤六无人机控制模块根据无人机的定位信息判断是否到达目的地，判断结果为否，则返回步骤三；Step 6: The drone control module judges whether it has reached the destination according to the positioning information of the drone, and if the judgment result is no, then return to step 3;
步骤六a：判断结果为是，则判断人工控制模块是否进行控制，如果判断结果为是，则无人机进行人工语音播报，并由人工控制模块的医生人工操纵无人机飞行，然后进入是否结束急救的判断，判断结果为否，则继续人工语音播报并由医生人工操纵无人机飞行的步骤，直到判断结果为是，则进入步骤九；人工控制模块是否进行人工控制的判断结果为否，无人机控制模块向无人机下达语音指令，自动播报患者信息；Step 6a: If the result of the judgment is yes, it is judged whether the manual control module is in control. End the judgment of first aid, if the judgment result is no, continue the steps of artificial voice broadcast and the doctor manually controls the drone flight until the judgment result is yes, then enter step nine; the judgment result of whether the manual control module performs manual control is no , the UAV control module issues voice commands to the UAV, and automatically broadcasts patient information;
步骤六b：无人机控制模块判断患者是否得到帮助，判断结果为否，则返回无人机控制模块向无人机下达语音指令，播报患者信息的步骤；Step 6b: The UAV control module judges whether the patient has been helped, and if the judgment result is no, then return to the UAV control module to issue a voice command to the UAV and broadcast the patient information;
步骤七判断结果为是，无人机落地，无人机控制模块向人工控制模块发送到达信号；Step 7 judges that the result is yes, the UAV lands, and the UAV control module sends an arrival signal to the manual control module;
步骤八无人机控制模块判断是否结束急救，判断结果为否，返回步骤七，判断结果为是，进入步骤九；Step eight UAV control module judges whether to end the first aid, if the judgment result is no, return to step seven, if the judgment result is yes, enter step nine;
步骤九无人机控制模块控制无人机或无人机自动返回基站，结束。Step 9 The UAV control module controls the UAV or the UAV automatically returns to the base station, and ends.
在步骤六b中，是否得到帮助的判断可以由系统任意设置，可以设置为自动语音播报达到一定预设时间后判断为得到帮助，也可在无人机上设置一个按钮，语音播报内容提示拾到无人机的人按下按钮，当按下按钮时无人机控制模块判断为得到帮助，或者可以设置为由人工控制模块向无人机控制模块发送得到帮助的信号，医生在与患者监测模块取得联系后，确认无人机到达患者处，医生在人工控制模块端向无人机控制模块发送一个得到帮助的信号，无人机控制模块接收到该信号后，判断为得到帮助。在此，不对该判断方法做限制性的规定。In step 6b, the judgment of whether to get help can be set arbitrarily by the system. It can be set to be judged as getting help after the automatic voice broadcast reaches a certain preset time. The person on the drone presses the button, and when the button is pressed, the drone control module judges that help is received, or it can be set to send a help signal from the manual control module to the drone control module, and the doctor communicates with the patient monitoring module After getting in touch and confirming that the drone has arrived at the patient, the doctor sends a help signal to the drone control module at the manual control module side, and the drone control module judges that it has been helped after receiving the signal. Here, no restrictive provisions are made on the determination method.
以上所述实施例仅表达了本发明的实施方式，其描述较为具体和详细，但并不能因此而理解为对本发明专利范围的限制。应当指出的是，对于本领域的正常技术人员来说，在不脱离本发明构思的前提下，还可以做出若干变形和改进，这些都属于本发明的保护范围。因此，本发明专利的保护范围应以所附权利要求为准。The above-mentioned embodiments only express the implementation manner of the present invention, and the description thereof is relatively specific and detailed, but should not be construed as limiting the patent scope of the present invention. It should be noted that, for those skilled in the art, several modifications and improvements can be made without departing from the concept of the present invention, and these all belong to the protection scope of the present invention. Therefore, the protection scope of the patent for the present invention should be based on the appended claims.
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|---|---|---|---|
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| CN202111130634.5ACN114171181A (en) | 2018-02-24 | 2018-02-24 | Remote monitoring emergency system |
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810156699.9ACN108417264B (en) | 2018-02-24 | 2018-02-24 | A remote monitoring emergency system |
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|---|---|---|---|
| CN202111130634.5ADivisionCN114171181A (en) | 2018-02-24 | 2018-02-24 | Remote monitoring emergency system |
| Publication Number | Publication Date |
|---|---|
| CN108417264Atrue CN108417264A (en) | 2018-08-17 |
| CN108417264B CN108417264B (en) | 2021-11-02 |
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
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| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
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| Country | Link |
|---|---|
| CN (2) | CN114171181A (en) |
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109101040A (en)* | 2018-08-30 | 2018-12-28 | 深圳市研本品牌设计有限公司 | A kind of unmanned plane for assisting rescuing |
| CN110364254A (en)* | 2019-07-15 | 2019-10-22 | 珠海威泓急救云科技有限公司 | A kind of automated external defibrillator intelligent assistance system and method |
| CN110955260A (en)* | 2019-12-11 | 2020-04-03 | 久心医疗科技（苏州）有限公司 | Intelligent scheduling system and method for portable emergency medical equipment |
| WO2020073225A1 (en)* | 2018-10-10 | 2020-04-16 | 广东医动科技有限公司 | Remote detecion and integrated medical system |
| CN111916189A (en)* | 2020-07-02 | 2020-11-10 | 北京国安广传网络科技有限公司 | Safety early warning method and device for physical examination vehicle |
| ES2881148A1 (en)* | 2021-10-08 | 2021-11-26 | Ostirion S L U | PROCEDURE AND EQUIPMENT FOR VISUAL RECOGNITION AND AUTOMATIC MEDICAL DIAGNOSIS THROUGH DRONE (Machine-translation by Google Translate, not legally binding) |
| CN113838584A (en)* | 2021-09-23 | 2021-12-24 | 厦门大学 | A community intelligent traditional Chinese medicine diagnosis and treatment system |
| CN116072274A (en)* | 2023-03-06 | 2023-05-05 | 四川互慧软件有限公司 | Automatic dispatch system for medical care of ambulance |
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2010013284A1 (en)* | 2008-08-01 | 2010-02-04 | Chic Trading S.R.L. | First ai d kit |
| CN102749080A (en)* | 2012-06-18 | 2012-10-24 | 北京航空航天大学 | Unmanned aerial vehicle three-dimensional air route generation method based on hydrodynamics |
| CN104359473A (en)* | 2014-10-24 | 2015-02-18 | 南京航空航天大学 | Collaborative flight path intelligent planning method for formation flying of unmanned planes under dynamic environment |
| CN105068486A (en)* | 2015-09-02 | 2015-11-18 | 杨珊珊 | Unmanned aerial vehicle emergency medical rescue system and unmanned aerial vehicle emergency medical rescue method |
| CN204904034U (en)* | 2015-09-02 | 2015-12-23 | 杨珊珊 | Urgent medical rescue system and first -aid centre and first aid unmanned aerial vehicle thereof |
| CN205433651U (en)* | 2016-02-23 | 2016-08-10 | 济宁中科大象医疗电子科技有限公司 | Wearable motion and electrocardiosignal are real -time to be gathered and guardianship equipment |
| US20160311532A1 (en)* | 2013-11-10 | 2016-10-27 | Google Inc. | Methods And Systems For Providing Aerial Assistance |
| CN106066931A (en)* | 2016-05-25 | 2016-11-02 | 张福林 | A kind of active-mode intelligent emergency system |
| CN106081137A (en)* | 2016-06-08 | 2016-11-09 | 锦州医科大学骨外科学研究所 | Novel emergency first aid unmanned plane |
| CN106296536A (en)* | 2016-08-04 | 2017-01-04 | 秦卿 | A kind of harmful influence emergency management and rescue commander and technical specialist's remote support system |
| CN107085437A (en)* | 2017-03-20 | 2017-08-22 | 浙江工业大学 | A UAV trajectory planning method based on EB‑RRT |
| CN107169710A (en)* | 2017-06-02 | 2017-09-15 | 杭州市第人民医院 | Rescue articles for use unmanned plane delivering system based on rights management |
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2010013284A1 (en)* | 2008-08-01 | 2010-02-04 | Chic Trading S.R.L. | First ai d kit |
| CN102749080A (en)* | 2012-06-18 | 2012-10-24 | 北京航空航天大学 | Unmanned aerial vehicle three-dimensional air route generation method based on hydrodynamics |
| US20160311532A1 (en)* | 2013-11-10 | 2016-10-27 | Google Inc. | Methods And Systems For Providing Aerial Assistance |
| CN104359473A (en)* | 2014-10-24 | 2015-02-18 | 南京航空航天大学 | Collaborative flight path intelligent planning method for formation flying of unmanned planes under dynamic environment |
| CN105068486A (en)* | 2015-09-02 | 2015-11-18 | 杨珊珊 | Unmanned aerial vehicle emergency medical rescue system and unmanned aerial vehicle emergency medical rescue method |
| CN204904034U (en)* | 2015-09-02 | 2015-12-23 | 杨珊珊 | Urgent medical rescue system and first -aid centre and first aid unmanned aerial vehicle thereof |
| CN205433651U (en)* | 2016-02-23 | 2016-08-10 | 济宁中科大象医疗电子科技有限公司 | Wearable motion and electrocardiosignal are real -time to be gathered and guardianship equipment |
| CN106066931A (en)* | 2016-05-25 | 2016-11-02 | 张福林 | A kind of active-mode intelligent emergency system |
| CN106081137A (en)* | 2016-06-08 | 2016-11-09 | 锦州医科大学骨外科学研究所 | Novel emergency first aid unmanned plane |
| CN106296536A (en)* | 2016-08-04 | 2017-01-04 | 秦卿 | A kind of harmful influence emergency management and rescue commander and technical specialist's remote support system |
| CN107085437A (en)* | 2017-03-20 | 2017-08-22 | 浙江工业大学 | A UAV trajectory planning method based on EB‑RRT |
| CN107169710A (en)* | 2017-06-02 | 2017-09-15 | 杭州市第人民医院 | Rescue articles for use unmanned plane delivering system based on rights management |
| Title |
|---|
| 刘新 等: "无人机快速航迹规划算法", 《华中科技大学学报（自然科学版）》* |
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109101040A (en)* | 2018-08-30 | 2018-12-28 | 深圳市研本品牌设计有限公司 | A kind of unmanned plane for assisting rescuing |
| WO2020073225A1 (en)* | 2018-10-10 | 2020-04-16 | 广东医动科技有限公司 | Remote detecion and integrated medical system |
| CN113557575A (en)* | 2018-10-10 | 2021-10-26 | 深圳市智慧健康产业发展有限公司 | A remote detection and medical integrated system |
| CN110364254A (en)* | 2019-07-15 | 2019-10-22 | 珠海威泓急救云科技有限公司 | A kind of automated external defibrillator intelligent assistance system and method |
| CN110955260A (en)* | 2019-12-11 | 2020-04-03 | 久心医疗科技（苏州）有限公司 | Intelligent scheduling system and method for portable emergency medical equipment |
| CN111916189A (en)* | 2020-07-02 | 2020-11-10 | 北京国安广传网络科技有限公司 | Safety early warning method and device for physical examination vehicle |
| CN113838584A (en)* | 2021-09-23 | 2021-12-24 | 厦门大学 | A community intelligent traditional Chinese medicine diagnosis and treatment system |
| ES2881148A1 (en)* | 2021-10-08 | 2021-11-26 | Ostirion S L U | PROCEDURE AND EQUIPMENT FOR VISUAL RECOGNITION AND AUTOMATIC MEDICAL DIAGNOSIS THROUGH DRONE (Machine-translation by Google Translate, not legally binding) |
| CN116072274A (en)* | 2023-03-06 | 2023-05-05 | 四川互慧软件有限公司 | Automatic dispatch system for medical care of ambulance |
| Publication number | Publication date |
|---|---|
| CN114171181A (en) | 2022-03-11 |
| CN108417264B (en) | 2021-11-02 |
| Publication | Publication Date | Title |
|---|---|---|
| CN108417264A (en) | A remote monitoring emergency system | |
| CN110840425B (en) | Health monitoring system and method for emergency patients in treatment | |
| CN104952447B (en) | Intelligent wearable device for elderly people's health service and voice recognition method | |
| Jin et al. | Predicting cardiovascular disease from real-time electrocardiographic monitoring: An adaptive machine learning approach on a cell phone | |
| US11600182B2 (en) | Vehicle allocation service system, vehicle allocation service method, program, and moving object | |
| KR101768624B1 (en) | System for responding of emergency patient and method thereof | |
| EP3196836A1 (en) | System for predicting risk of onset of cerebrovascular disease | |
| CN106357810B (en) | Networked system and its application method are cured before traffic first aid institute | |
| CN110840424B (en) | Early warning type in-diagnosis monitoring device and method | |
| US20210338174A1 (en) | Method and system for assessing emergency risk for patients | |
| CN116330256A (en) | an emergency robot | |
| Rodriguez et al. | Towards automatic sensor-based triage for individual remote monitoring during mass casualty incidents | |
| CN111862532A (en) | Alarm system facing emergency | |
| CN119989104B (en) | Rapid injury detection classification method and medium for emergency group events | |
| CN104867083A (en) | Mobile medical system based on mobile terminals | |
| CN211124084U (en) | Body temperature monitoring assistance monitoring system | |
| JP6592633B1 (en) | Emergency call system | |
| CN112635081A (en) | Remote electrocardiogram monitoring and health management platform based on cloud platform | |
| US12417844B2 (en) | System and method for contactless monitoring and early prediction of a person | |
| CN119724512A (en) | Medical resource dynamic allocation method and system driven by patient status data | |
| US8203447B2 (en) | Telemetry system and method | |
| WO2021205672A1 (en) | Emergency call system | |
| KR20200047128A (en) | Casualties Classification Method, And Rescue Robots for Performing the Method | |
| CN104933319A (en) | Mobile medical system using mobile terminal and based on cloud | |
| CN109920526A (en) | A kind of artificial intelligence early warning system |
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