In-vehicle person health monitoring and processing method, system, equipment and medium

Abstract

The invention discloses an in-vehicle person health monitoring and processing method, system and device and a medium. The in-vehicle person health monitoring and processing method comprises the steps that monitoring data of all indexes of in-vehicle persons are obtained, and the monitoring data of all the indexes of the in-vehicle persons are obtained through detection of a health detection device pre-configured for the in-vehicle persons; inputting the monitoring data of each index and the question and answer data of the person in the vehicle into a pre-trained health diagnosis large model to obtain a health diagnosis suggestion; and according to the health diagnosis suggestion and a preset diagnosis threshold value, obtaining an emergency processing mode so as to process the person in the vehicle according to the emergency processing mode. By adopting the embodiment of the invention, which health state the passengers and the driver in the vehicle are in can be tracked and found in time, and relevant personnel can be reminded in time and clear health suggestions can be provided when needed.

Description

Technical Field

[0001] The present application relates to the technical field of vehicle occupant safety, and in particular to a method, system, device and medium for monitoring and processing the health of vehicle occupants. Background Art

[0002] In the related art, there is currently no suitable method for dealing with health discomfort and anxiety among drivers or passengers, especially when passengers and drivers are in remote and desolate uninhabited areas, and need timely consultation and advice and actions to deal with their health. However, casualties often occur due to untimely response. Summary of the invention

[0003] Based on this, it is necessary to provide a method, system, equipment and medium for monitoring and processing the health of people in the car in response to the above-mentioned technical problems, which can timely track and discover the health status of passengers and drivers in the car, and promptly remind relevant personnel and provide clear health advice when needed. In the case that the driver is unable to drive, the automatic driving system is notified in time, and the nearest medical department is contacted at the same time, and the drivers and passengers in poor health are sent to the designated medical department for timely treatment, thereby ensuring the safety of people in the car.

[0004] In a first aspect, a method for monitoring and treating the health of people in a vehicle is provided, comprising:

[0005] Obtaining monitoring data of various indicators of the occupants of the vehicle, wherein the monitoring data of various indicators of the occupants of the vehicle are obtained by detecting a health detection device pre-configured for the occupants of the vehicle;

[0006] Input the monitoring data of each indicator and the question-and-answer data of the occupants into a pre-trained health diagnosis model to obtain health diagnosis suggestions;

[0007] An emergency treatment method is obtained based on the health diagnosis suggestion and a preset diagnosis threshold value, so that the occupant of the vehicle can be treated according to the emergency treatment method.

[0008] In some examples, obtaining monitoring data of various indicators of the occupants of the vehicle includes:

[0009] The monitoring data of various indicators of the occupants of the vehicle are detected through the pre-configured health detection device;

[0010] Receive monitoring data of various indicators of the occupants of the vehicle sent by the health detection device, wherein the monitoring data includes heart rate, blood pressure, description of the uncomfortable area and the mode of discomfort.

[0011] In some examples, the health detection device includes at least one of an in-vehicle camera, a wearable device of a person in the vehicle, and a UWB device, and the wearable device includes one or all of a smart watch and a smart bracelet.

[0012] In some examples, the monitoring data of each indicator and the question-and-answer data of the vehicle occupants are input into a pre-trained health diagnosis model to obtain health diagnosis suggestions, including:

[0013] The health diagnosis model analyzes the monitoring data of each indicator to obtain a diagnosis problem;

[0014] According to the diagnosis question, ask the occupants of the vehicle through the voice analysis, recognition and answering module, and obtain the question and answer data of the occupants of the vehicle;

[0015] The health diagnosis model analyzes and identifies the question and answer data, and obtains the diagnosis suggestions in combination with the monitoring data of each indicator.

[0016] In some examples, based on the health diagnosis suggestion and the pre-set diagnosis threshold, an emergency treatment method is obtained, including:

[0017] According to the diagnostic threshold value input by a preset formula, determining whether the diagnostic threshold value reaches an emergency situation;

[0018] If so, then in combination with the diagnostic suggestion, a corresponding emergency treatment method is obtained, wherein the emergency treatment method includes issuing a command to the vehicle's automatic driving module to urgently depart for the nearest medical institution.

[0019] In some examples, this also includes:

[0020] The autonomous driving module instructs the high-precision map and path planning modules to determine the shortest route to the medical institution;

[0021] sending a notification to the medical institution;

[0022] According to the shortest route to the medical institution, automatically drive to the medical institution.

[0023] In some examples, this also includes:

[0024] Notify the medical helicopter for emergency rescue.

[0025] In a second aspect, a system for monitoring and processing the health of people in a vehicle is provided, comprising:

[0026] An acquisition module is used to obtain monitoring data of various indicators of the occupants of the vehicle, wherein the monitoring data of various indicators of the occupants of the vehicle are obtained by detecting a health detection device pre-configured for the occupants of the vehicle;

[0027] A diagnosis suggestion module, used to input the monitoring data of each indicator and the question and answer data of the vehicle occupants into a pre-trained health diagnosis model to obtain health diagnosis suggestions;

[0028] The processing module is used to obtain an emergency treatment method according to the health diagnosis suggestion and a preset diagnosis threshold, so as to treat the occupant of the vehicle according to the emergency treatment method.

[0029] In a third aspect, a computer device is provided, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the method for monitoring and processing the health of occupants in a vehicle according to the first aspect above is implemented.

[0030] In a fourth aspect, a computer-readable storage medium is provided, on which a computer program is stored, and the program is executed by a processor to implement the method for monitoring and processing the health of occupants in a vehicle according to the first aspect above.

[0031] By adopting the embodiments of the present application, it is possible to timely track and discover the health status of passengers and drivers in the vehicle, and promptly remind relevant personnel and provide clear health advice when necessary. In the case that the driver is unable to drive, the automatic driving system is promptly notified and the nearest medical department is contacted to send drivers and passengers in poor health to the designated medical department for timely treatment, thereby ensuring the safety of people in the vehicle. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] Other features, objects and advantages of the present application will become more apparent by reading the detailed description of non-limiting embodiments made with reference to the following drawings:

[0033] Figure 1 A flow chart of a method for monitoring and treating the health of people in a vehicle provided in an embodiment of the present application;

[0034] Figure 2 A schematic diagram of an application of the method for monitoring and treating the health of people in a vehicle provided in an embodiment of the present application;

[0035] Figure 3 A structural block diagram of a system for monitoring and processing the health of people in a vehicle provided in an embodiment of the present application;

[0036] Figure 4 A structural block diagram of a computer device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0037] The present application is further described in detail below in conjunction with the embodiments and drawings. It is to be understood that the specific embodiments described herein are only used to explain the relevant application, rather than to limit the application. It is also necessary to explain that, for ease of description, only the parts related to the application are shown in the drawings.

[0038] It should be noted that, in the absence of conflict, the embodiments of the present application, that is, the features of the embodiments, can be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0039] The following is a detailed description of the method, system, device and medium for monitoring and processing the health of occupants in a vehicle according to an embodiment of the present application in conjunction with the accompanying drawings.

[0040] Figure 1 FIG. 1 is a flow chart of a method for monitoring and processing the health of people in a vehicle according to an embodiment of the present application. Figure 1 As shown, according to an embodiment of the present application, a method for monitoring and processing the health of people in a vehicle includes the following steps:

[0041] S101: Obtain monitoring data of various indicators of the occupants of the vehicle, wherein the monitoring data of various indicators of the occupants of the vehicle are obtained by detecting a health detection device pre-configured for the occupants of the vehicle.

[0042] In one embodiment of the present application, monitoring data of various indicators of people in the vehicle are obtained, including: detecting the monitoring data of various indicators of people in the vehicle through a pre-configured health detection device; receiving the monitoring data of various indicators of people in the vehicle sent by the health detection device, wherein the monitoring data includes heartbeat, blood pressure, and a description of the discomfort area and the mode of discomfort.

[0043] In this example, the health detection device includes at least one of an in-vehicle camera, a wearable device of a person in the vehicle, and a UWB device, and the wearable device includes one or all of a smart watch and a smart bracelet.

[0044] S102: Input the monitoring data of each indicator and the question-and-answer data of the occupants into a pre-trained health diagnosis model to obtain health diagnosis suggestions.

[0045] In a specific example, the monitoring data of the indicators and the question and answer data of the occupants are input into a pre-trained health diagnosis big model to obtain health diagnosis suggestions, including: the health diagnosis big model analyzes the monitoring data of the indicators to obtain a diagnosis question; based on the diagnosis question, questions are asked to the occupants through a speech analysis, recognition and answering module to obtain the question and answer data of the occupants; the health diagnosis big model analyzes and identifies the question and answer data, and obtains the diagnosis suggestion in combination with the monitoring data of the indicators.

[0046] S103: Obtain an emergency treatment method according to the health diagnosis suggestion and a preset diagnosis threshold, so as to treat the occupant of the vehicle according to the emergency treatment method.

[0047] In one embodiment of the present application, an emergency treatment method is obtained based on the health diagnosis advice and a pre-set diagnosis threshold, including: judging whether the diagnosis threshold reaches an emergency situation based on the diagnosis threshold input by a pre-set formula; if so, obtaining a corresponding emergency treatment method in combination with the diagnosis advice, wherein the emergency treatment method includes issuing a command to the vehicle's automatic driving module to urgently depart for the nearest medical institution.

[0048] An example of emergency handling method is: the autonomous driving module instructs the high-precision map and path planning module to determine the nearest route to the medical institution; sends a notification to the medical institution; and automatically drives to the medical institution based on the nearest route to the medical institution.

[0049] Another example: The emergency response method is to notify a medical helicopter for emergency rescue.

[0050] As a specific example, in practical applications, combined with Figure 2 As shown, this method can be achieved by Figure 2 Specifically, the health observation and diagnosis device includes an in-car camera, a UWB key device, a smart health watch detection device and a health ring carried by the driver and passengers, and also includes a mobile phone device that can monitor the body status.

[0051] The central analysis and processing device includes a health consultation big model and analysis device (i.e., a medical health big model diagnosis and suggestion module), a speech semantic interpretation and reply device (i.e., a speech semantic analysis recognition and acceptance and reply module), and a central analysis and processing device (central processing and analysis module) based on the analysis opinions given by the big model and analysis device and combined with various detection data (heart rate, blood pressure, description of uncomfortable areas and uncomfortable methods) monitored by the driver and passengers. The central analysis and processing device calculates the method to be processed and the final processing work according to the numerical value of the patient's detection data and the suggestions given by the big model and the processing formula generated by combining the two. According to the processing threshold given by the final formula, several processing opinions are given, including immediately notifying the automatic driving device to quickly rush to the nearest medical facility, the automatic driving device calculates the route in combination with the high-precision map, immediately drives to the relevant medical facility and notifies the relevant medical unit by phone, and the central processing and analysis device contacts the relevant medical unit, gives the patient's basic condition and the arrival time and the relevant medical equipment that needs to be prepared, or notifies the medical helicopter to come over immediately for processing.

[0052] Medical and health model:

[0053] Input Layer

[0054] Parameter composition = analysis through cameras, health watches and rings, and in-car UWB devices

[0055] Facial contour features Body movements - Describe the patient's neurological characteristics of pain or fatigue X1, X2;

[0056] Numerical statistical analysis of blood pressure, heart rate, and electrocardiogram to analyze pathological characteristics of the body and heart X3X4, X5;

[0057] The UWB infrared device detects the changing characteristics of the respiratory heat signal, the body heat and the heat of different areas to reflect the changes in body parameters and show the characteristics of pathology X6 and X7;

[0058] The characteristic changes of vehicle driving trajectory, such as frequent lane changes, distorted trajectory, and ignoring traffic rules, etc., which are the instability characteristics of control parameters X8, X9, and X10;

[0059] When taking over during intelligent driving, the changes in body characteristics and the changes in force and heat felt by the tactile steering wheel are X11 and X12;

[0060] The health AI diagnosis model relies on speech semantic recognition to conduct detailed medical consultations with patients or drivers, including asking whether they have headaches, pain in other parts of the body, or any related discomfort. The information is sent to the health AI model, and the relevant inquiry information is input to obtain partial weights. X13, X14, X15.

[0061] Create a functional

[0062] The training of a deep neural network (DNN) is actually an optimization process involving a complex mathematical model with multiple layers and a large number of parameters. The core goal of this process is to adjust the weights and biases in the network so that the network can achieve the expected performance on a given task (such as image recognition, speech recognition, or language processing).

[0063] The basic structure of deep neural networks

[0064] In order to better understand the changes brought about by training, it is necessary to understand the basic composition of a deep neural network. A typical deep neural network includes an input layer, multiple hidden layers, and an output layer. Each layer consists of multiple neurons, each neuron is connected to all neurons in the previous layer through weights, and each neuron may also include a bias term. The output of the neuron is processed by an activation function to introduce nonlinear factors, allowing the network to learn complex patterns.

[0065] Hidden Layer

[0066] It is the internal implicit information obtained by analyzing the input layer. For example, X1 represents a painful expression, which can be 1 or 0. X2 represents fatigue, where fatigue is 1 and non-fatigue is 0. 1 represents a high degree of discomfort, and 0 represents no discomfort.

[0067] Blood pressure X3, heart rate X4, and electrocardiogram X5 represent a health index respectively. When the blood pressure is less than 140, it is normal, and above 140 is abnormal. A heart rate above 100 is abnormal. A heart rate between 100 and 50 is normal, and below 50 is abnormal. The electrocardiogram analysis is similar.

[0068] Through UWB devices or infrared devices, X6 X7 can sense high fever in the head or heart area. 39 degrees is abnormal, and below 38 degrees is normal. Of course, low temperature in the head also means abnormal.

[0069] Parameters related to driving include irregular driving, X8 represents abnormality and is represented by 1, which indicates possible drinking, and X9 driving S trajectory, which represents abnormality and is represented by 1.

[0070] The heat and torque X11 and X12 felt by the steering wheel represent whether the driver is normal or abnormal, and are represented by data between 0 and 1.

[0071] Using CNN convolutional neural network, combined with input layer and hidden layer information, and using Sigmoid function, forward propagation is performed. Input data passes through the network, moving from the input layer to the output layer. The neurons in each layer calculate the output according to the current weights and biases. Then the loss is calculated. In the output layer, the network's prediction results will be compared with the true labels, and the loss function (such as cross entropy loss) is calculated. This loss indicates the error between the current network's prediction and the actual situation. Then back propagation is performed, and the gradient of the loss function with respect to each weight is calculated. These gradients provide information about the direction and magnitude of the loss reduction. Then the weights are updated, and gradient descent or other optimization algorithms are used to update the weights and biases. The goal is to reduce the value of the loss function. At the same time, the following situations are taken into consideration for further step-by-step optimization.

[0072] Local receptive field

[0073] In an image, the connection between local pixels is relatively close, while the connection between pixels farther away is relatively weak. Therefore, each neuron does not need to perceive the entire image. It only needs to perceive local information, and then combine the local information at a higher level to obtain global information. The convolution operation is the realization of the local receptive field, and because the convolution operation can share weights, it also reduces the number of parameters.

[0074] Pooling

[0075] Pooling is to reduce the input image, reduce pixel information, and retain only important information, mainly to reduce the amount of calculation. It mainly includes maximum pooling and mean pooling.

[0076] Activation Function

[0077] The purpose of activation function is to add nonlinearity. Activation functions include sigmoid, tanh, and relu. The first two are often used in fully connected layers, and relu is common in convolutional layers. An activation function is a function added to an artificial neural network to help the network learn complex patterns in the data. Similar to the neuron-based model in the human brain, the activation function ultimately determines what to send to the next neuron. In an artificial neural network, the activation function of a node defines the output of the node for a given input or set of inputs. A standard computer chip circuit can be thought of as a digital circuit activation function that gets an on (1) or off (0) output based on the input.

[0078] Fully connected layer

[0079] The fully connected layer acts as a classifier in the entire convolutional neural network. The previous output needs to be flattened before the fully connected layer.

[0080] The graph of the Sigmoid function looks like an S-shaped curve.

[0081] The output range of the Sigmoid function is 0 to 1. Since the output value is limited to 0 to 1, it normalizes the output of each neuron; used for models that use predicted probabilities as output. Since the probability ranges from 0 to 1, the Sigmoid function is very suitable for gradient smoothing and avoiding "jumping" output values; the function is differentiable. This means that the slope of the sigmoid curve between any two points can be found; a clear prediction, that is, very close to 1 or 0.

[0082] Network status before training

[0083] Before training begins, the network's weights and biases are usually randomly initialized. This means that the network's output is random and the network's performance may be very poor. This initialization ensures that the starting point of network training is unbiased, and the training process gradually learns the characteristics and patterns of the data by gradually adjusting these parameters.

[0084] Training process

[0085] Training deep neural networks usually uses the backpropagation algorithm combined with gradient descent or its variants (such as the Adam optimizer) to update the weights and biases in the network.

[0086] Changes after successful training

[0087] When a deep neural network is successfully trained, it means that the parameters (weights and biases) of the network have been adjusted to a state where it can perform a specific task (such as cat recognition) well. "Successful training" here means that the network shows high accuracy on the training set and maintains similar performance on the validation set and test set to prove its generalization ability.

[0088] After the diagnosis of the intelligent AI health diagnosis model, after the medical treatment requirement is confirmed, all relevant parameters are concentrated in the central analysis and processing device, including the health consultation model and analysis device, as well as the speech semantic interpretation and reply device, and the central analysis and processing device based on the analysis opinions given by the large model and analysis device and combined with the various test data (heart rate, blood pressure, description of the uncomfortable area and the way of discomfort) monitored by the driver and passengers. The central analysis and processing device calculates the method to be processed and the final processing work according to the value of the patient's test data and the suggestions given by the large model and the processing formula generated by combining the two. Through the processing threshold given by the final formula, several processing opinions are given, including immediately notifying the automatic driving device to rush to the nearest medical facility quickly, the automatic driving device calculates the route in combination with the high-precision map, and immediately drives to the relevant medical facility and notifies the relevant medical unit by phone, and the central processing and analysis device contacts the relevant medical unit, gives the patient's basic condition and the arrival time and the relevant medical equipment that needs to be prepared, or notifies the medical helicopter to come over immediately for processing.

[0089] According to the method for monitoring and processing the health of occupants in a vehicle according to an embodiment of the present application, it is possible to timely track and discover the health status of passengers and drivers in the vehicle, and promptly remind relevant personnel and provide clear health advice when necessary. In the event that the driver is unable to drive, the automatic driving system is promptly notified and the nearest medical department is contacted to send drivers and passengers in poor health to the designated medical department for timely treatment, thereby ensuring the safety of occupants in the vehicle.

[0090] Figure 3 FIG. 1 is a structural block diagram of a system for monitoring and processing the health of passengers in a vehicle according to an embodiment of the present application. Figure 3 As shown, according to an embodiment of the present application, a system for monitoring and processing the health of people in a vehicle includes: an acquisition module 310, a diagnosis suggestion module 320 and a processing module 330, wherein:

[0091] The acquisition module 310 is used to obtain monitoring data of various indicators of the occupants of the vehicle, wherein the monitoring data of various indicators of the occupants of the vehicle are obtained by detecting a health detection device pre-configured for the occupants of the vehicle;

[0092] The diagnosis suggestion module 320 is used to input the monitoring data of each indicator and the question and answer data of the vehicle occupants into a pre-trained health diagnosis model to obtain health diagnosis suggestions;

[0093] The processing module 330 is used to obtain an emergency treatment method according to the health diagnosis suggestion and a preset diagnosis threshold, so as to treat the occupant of the vehicle according to the emergency treatment method.

[0094] According to the vehicle occupant health monitoring and processing system of the embodiment of the present application, it is possible to timely track and discover the health status of the passengers and driver in the vehicle, and promptly remind relevant personnel and provide clear health advice when necessary. In the event that the driver is unable to drive, the automatic driving system is promptly notified and the nearest medical department is contacted to send the driver and passengers in poor health to the designated medical department for timely treatment, thereby ensuring the safety of the people in the vehicle.

[0095] For the specific definition of the system for monitoring and processing the health of occupants in a vehicle, please refer to the definition of the method for monitoring and processing the health of occupants in a vehicle above, which will not be repeated here. The various modules of the above-mentioned system for monitoring and processing the health of occupants in a vehicle can be implemented in whole or in part through software, hardware and a combination thereof. The above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

[0096] Reference below Figure 4 , Figure 4 A schematic diagram of the structure of a computer device suitable for implementing the embodiments of the present application is shown.

[0097] like Figure 4 As shown, the computer system includes a central processing unit (CPU) 1001, which can perform various appropriate actions and processes according to the program stored in the read-only memory (ROM) 1002 or the program loaded from the storage part 1008 into the random access memory (RAM) 1003. In the RAM 1003, various programs and data required for the operation instructions of the system are also stored. The CPU 1001, the ROM 1002, and the RAM 1003 are connected to each other through a bus 1004. An input / output (I / O) interface 1005 is also connected to the bus 1004.

[0098] The following components are connected to the I / O interface 1005: an input section 1006 including a keyboard, a mouse, etc.; an output section 1007 including a cathode ray tube (CRT), a liquid crystal display (LCD), etc., and a speaker, etc.; a storage section 1008 including a hard disk, etc.; and a communication section 1009 including a network interface card such as a LAN card, a modem, etc. The communication section 1009 performs communication processing via a network such as the Internet. A drive 1010 is also connected to the I / O interface 1005 as needed. A removable medium 1011, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 1010 as needed, so that a computer program read therefrom is installed into the storage section 1008 as needed.

[0099] In particular, according to an embodiment of the present application, the above reference flow chart Figure 1 The described process may be implemented as a computer-readable storage medium. For example, an embodiment of the present application includes a computer-readable storage medium, which includes a computer program, and the computer program includes a program code for executing the method shown in the flowchart.

[0100] In particular, according to an embodiment of the present application, the above reference flow chart Figure 1 The described process can be implemented as a computer software program. For example, a computer program product is included in an embodiment of the present application, which includes a computer program carried on a computer readable medium, and the computer program includes a program code for executing the method shown in the flow chart. In such an embodiment, the computer program includes a program code for executing the method shown in the flow chart. In such an embodiment, the computer program can be downloaded and installed from the network through the communication part 1009, and / or installed from the removable medium 1011. When the computer program is executed by the central processing unit (CPU) 1001, the above-mentioned functions defined in the system of the present application are executed.

[0101] It should be noted that the computer-readable medium shown in the present application may be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present application, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, device or device. In the present application, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, which carries a computer-readable program code. This propagated data signal may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. Computer-readable signal media may also be any computer-readable medium such as a computer-readable storage medium that can send, propagate or transmit a program for use by or in conjunction with an instruction execution system, device or device. The program code contained on the computer-readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wire, optical cable, RF, etc., or any suitable combination of the above.

[0102] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, functions and operating instructions of the system, method and computer program product according to various embodiments of the present application. In this regard, each box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the aforementioned module, a program segment or a part of a code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some implementations as replacements, the functions marked in the box can also occur in a sequence different from that marked in the accompanying drawings. For example, the boxes represented by two connections can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flow chart, and the combination of the boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operating instruction, or can be implemented with a combination of dedicated hardware and computer instructions.

[0103] The units or modules involved in the embodiments described in the present application may be implemented by software or hardware. The units or modules described may also be arranged in a processor. The names of these units or modules do not, in some cases, constitute limitations on the units or modules themselves.

[0104] The technical features of the above embodiments may be arbitrarily combined. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0105] The above embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the patent application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the patent application shall be subject to the attached claims.

Claims

1. A method for monitoring and processing the health of people in a vehicle, characterized in that: include: Obtaining monitoring data of various indicators of the occupants of the vehicle, wherein the monitoring data of various indicators of the occupants of the vehicle are obtained by detecting a health detection device pre-configured for the occupants of the vehicle; Input the monitoring data of each indicator and the question-and-answer data of the occupants into a pre-trained health diagnosis model to obtain health diagnosis suggestions; An emergency treatment method is obtained based on the health diagnosis suggestion and a preset diagnosis threshold value, so that the occupant of the vehicle can be treated according to the emergency treatment method.

2. The method for monitoring and treating the health of people in a vehicle according to claim 1, characterized in that: The monitoring data of various indicators of the occupants of the vehicle are obtained, including: The monitoring data of various indicators of the occupants of the vehicle are detected through the pre-configured health detection device; Receive monitoring data of various indicators of the occupants of the vehicle sent by the health detection device, wherein the monitoring data includes heart rate, blood pressure, description of the uncomfortable area and the mode of discomfort.

3. The method for monitoring and treating the health of people in a vehicle according to claim 2, characterized in that: The health detection device includes at least one of an in-vehicle camera, a wearable device of a person in the vehicle, and a UWB device, and the wearable device includes one or all of a smart watch and a smart bracelet.

4. The method for monitoring and treating the health of people in a vehicle according to claim 1, characterized in that: The monitoring data of each indicator and the question-and-answer data of the vehicle occupants are input into a pre-trained health diagnosis model to obtain health diagnosis suggestions, including: The health diagnosis model analyzes the monitoring data of each indicator to obtain a diagnosis problem; According to the diagnosis question, ask the occupants of the vehicle through the voice analysis, recognition and answering module, and obtain the question and answer data of the occupants of the vehicle; The health diagnosis model analyzes and identifies the question and answer data, and obtains the diagnosis suggestions in combination with the monitoring data of each indicator.

5. The method for monitoring and treating the health of people in a vehicle according to claim 1, characterized in that: Based on the health diagnosis suggestions and the pre-set diagnosis threshold, emergency treatment methods are obtained, including: According to the diagnostic threshold value input by a preset formula, determining whether the diagnostic threshold value reaches an emergency situation; If so, then in combination with the diagnostic suggestion, a corresponding emergency treatment method is obtained, wherein the emergency treatment method includes issuing a command to the vehicle's automatic driving module to urgently depart for the nearest medical institution.

6. The method for monitoring and treating the health of people in a vehicle according to claim 5, characterized in that: Also includes: The autonomous driving module instructs the high-precision map and path planning modules to determine the shortest route to the medical institution; sending a notification to the medical institution; According to the shortest route to the medical institution, automatically drive to the medical institution.

7. The method for monitoring and treating the health of people in a vehicle according to claim 5, characterized in that: Also includes: Notify the medical helicopter for emergency rescue.

8. A system for monitoring and processing the health of people in a vehicle, characterized in that: include: An acquisition module is used to obtain monitoring data of various indicators of the occupants of the vehicle, wherein the monitoring data of various indicators of the occupants of the vehicle are obtained by detecting a health detection device pre-configured for the occupants of the vehicle; A diagnosis suggestion module, used to input the monitoring data of each indicator and the question and answer data of the vehicle occupants into a pre-trained health diagnosis model to obtain health diagnosis suggestions; The processing module is used to obtain an emergency treatment method according to the health diagnosis suggestion and a preset diagnosis threshold, so as to treat the occupant of the vehicle according to the emergency treatment method.

9. A computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, the method for monitoring and processing the health of occupants in a vehicle according to any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the method for monitoring and processing the health of occupants in a vehicle according to any one of claims 1 to 7 is implemented.

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