KR102331329B1 - Internet of Things driving control system based on state change detection, and method thereof - Google Patents

Abstract

The present invention relates to a driving control system and method according to IoT-based state change detection. According to the present invention, the vehicle controller 300 through the network 5 after storing the body state information about the vehicle driver who owns the vehicle 1 according to the execution of the driving control application (APP.) in its own memory device. ) a wearable watch 100 that starts real-time transmission; When the pressure sensor detects whether the driver of the vehicle 1 is riding on the vehicle seat after the vehicle door is opened (OPEN), the driving state information of the vehicle 1 is transmitted to the vehicle controller 300 based on IoT. Vehicle driving state measuring device 200 to perform the start; and a vehicle controller 300 configured to monitor body state information and driving state information while driving while the vehicle starter is kept on. It may be characterized in that it comprises a.
As a result, since the vehicle driver's basic physical state is recorded and stored even when not in the vehicle, the vehicle driver can manage each vehicle driver's own data and link personal data to prevent a driving accident even when riding in another vehicle It provides a preventative effect.

Description

Internet of Things driving control system based on state change detection, and method thereof

The present invention relates to a driving control system and method according to IoT-based state change detection. A driving control system based on IoT-based state change detection to prevent abnormal conditions or abnormal driving while driving by collecting and processing data such as driver driving habits through driver-based data and vehicle operation-based data, and It's about the way.

Recently, due to accidents related to drunk driving, the standards for drunk driving have been strengthened, and there are cases where people are being punished for driving while on a hangover. are doing

To prevent drunk driving, a breathalyzer can be installed inside the vehicle to prevent the engine from starting when the alcohol concentration is higher than a certain level or to prevent driving due to an increase in heart rate due to drinking. Therefore, there has been a limitation in that it is not possible to make a proper reference value.

Accordingly, in the relevant technical field, after collecting body condition information based on big data, it is possible to accurately analyze the previously collected body condition information and vehicle driving condition information and other vehicle driving condition information to provide information on driving of the vehicle. Technology development is required to provide a safety device.

Domestic Patent Publication No. 10-2019-0079568 (published on July 5, 2019) Autonomous driving control system and method to improve user safety

The present invention is to solve the above problems, by storing the body state of the vehicle driver even when the vehicle is not driving through a wearable watch or equipment that uses the physical state all the time, to identify the physical state of each individual, and then the vehicle driver IoT-based state change to prevent accidents caused by drinking, drowsiness, or physical abnormalities of the vehicle driver by accumulating driving result data according to the physical condition of the vehicle driver and comparing the vehicle driver's physical condition while driving An object of the present invention is to provide an operation control system according to sensing and a method therefor.

However, the objects of the present invention are not limited to the above-mentioned objects, and other objects not mentioned will be clearly understood by those skilled in the art from the following description.

In order to achieve the above object, the driving control system according to the IoT-based state change detection according to the embodiment of the present invention is provided for the vehicle driver who owns the vehicle 1 according to the execution of the driving control application (APP.). a wearable watch 100 that stores body state information at all times in its own memory device and then starts transmission in real time to the vehicle controller 300 through the network 5; a network 5 comprising a communication network for mutually transmitting signals and data between the wearable watch 100 , the big data server 3 , and the vehicle device system 4 ; In analyzing the body condition information distributed and stored as time constants by the distributed file program, the body condition parameters constituting the body condition information for each reference first time constant are the second time constant and the first time constant before the first time constant. After extracting feature information that is different from the third time constant by more than a preset threshold, time constants before and after the second time constant and the third time constant are again based on the second time constant for the extracted feature information A method of excluding the body condition parameter of the vehicle driver, which is the extracted characteristic information, from the body condition parameter in a special situation of the vehicle driver when extracted as at least one or more characteristic information in each of the time constants before and after the third time constant again to learn using a machine learning algorithm, and to generate body state range information as a result of learning big data server (3); and a vehicle device system 4 including a vehicle driving state meter 200, a vehicle controller 300, a heart rate sensor 400 attached to a vehicle starter, and a vehicle emergency light 500; , When the pressure sensor detects whether the driver of the vehicle 1 is riding on the vehicle seat after the vehicle door is opened (OPEN), the driving state information of the vehicle 1 is transmitted to the vehicle controller 300 based on IoT a vehicle driving state meter 200 for performing the initiation of; In a state in which the vehicle starter is kept ON, monitoring of the body state information and driving state information while driving is performed, and after receiving the body state information provided from the wearable watch 100 , the network 5 It accesses the big data server 3 through , and analyzes the body condition information, which is the collected data stored based on machine learning, through a machine learning algorithm to generate body condition range information for each time constant, and a heart rate sensor attached to the vehicle starter 400 By comparing the heart rate information of the vehicle driver at the time of starting the vehicle from a vehicle controller 300 performing control to (OFF) and maintaining the ignition on (ON) for the vehicle starter in the case of a normal range; The vehicle starter-attached heart rate sensor 400 determines whether there is an abnormality in the current heart rate compared to the heart rate sensor of the wearable watch 100 by the vehicle controller 300 and whether there is an abnormality in the mutual sensor through comparison with the wearable watch 100 ; and a vehicle emergency light 500 that flickers the vehicle emergency light so that the driving vehicle 2 can move to the shoulder or a safety zone and check the body condition.

In order to achieve the above object, in the driving control method according to IoT-based state change detection according to an embodiment of the present invention, the wearable watch 100 controls the vehicle 1 according to the execution of the driving control application APP. a first step of storing the physical state information about the vehicle driver who owns it in its own memory device and then starting real-time transmission to the vehicle controller 300 through the network 5; When the vehicle driving state measuring device 200 detects through the pressure sensor whether the driver of the vehicle 1 is riding on the vehicle seat after the vehicle door is opened (OPEN), the driving state information of the vehicle 1 is transferred to the vehicle controller ( 300) a second step of initiating transmission based on IoT; After the vehicle controller 300 receives the body condition information provided from the wearable watch 100 , it accesses the big data server 3 through the network 5 and performs machine learning of the body condition information, which is the collected data stored based on machine learning. Analyze through an algorithm to generate body condition range information for each time constant, but heart rate among the vehicle driver's heart rate information at the time the vehicle is started from the vehicle starter-attached heart rate sensor 400 and the body condition information provided from the wearable watch 100 By comparing the information, it analyzes the abnormality of the current heartbeat for the vehicle driver and performs the control of the vehicle starter to OFF in case of an abnormality. a third step of performing control; a fourth step of monitoring, by the vehicle controller 300, body state information and driving state information while driving while the vehicle starter is kept on; After the fourth step, the vehicle controller 300 determines whether a physical abnormality is found, and if no physical abnormality is found as a result of the determination, the vehicle controller 300 converts the driving state information and the physical state information according to the timeline. A fifth step of storing the driving state information in the storage unit 340 and outputting, by the vehicle controller 300, a body state notification and an alarm to the input/output unit 350 when a physical abnormality is found as a result of the determination; After the fifth step, the vehicle controller 300 determines whether the state of the body abnormality has improved, and when the state is improved as a result of the determination, the vehicle controller 300 releases the alarm on the input/output unit 350, Store the state information together with the time information on the storage unit 340, A sixth step of controlling the blinking of the vehicle emergency light 500 and inducing shoulder parking through a request for the autonomous driving module, by the vehicle controller 300 when the state does not improve as a result of the determination; And after the sixth step (S19), the vehicle controller 300 determines whether an accident has occurred, and when an accident occurs as a result of the determination, the vehicle controller 300 determines the physical condition of the vehicle driver, and the GPS location Based emergency contact is performed, the accident data is stored in the storage unit 350 together with time information, and when an accident does not occur as a result of determination, the vehicle controller 300 releases the alarm output to the input/output unit 350 . and a seventh step of outputting the emergency contact information to the input/output unit 350 and storing the state record on the storage unit 340;

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The driving control system and method according to the IoT-based state change detection according to an embodiment of the present invention record and store the basic body state of the vehicle driver even when not in the vehicle, so that the data of each vehicle driver is stored by the vehicle driver himself/herself This management is possible and provides the effect of preventing driving accidents by linking personal data even when riding in another vehicle.

1 is a diagram illustrating a driving control system 1 according to IoT-based state change detection according to an embodiment of the present invention.
2 is a block diagram illustrating components of a vehicle device system 4 of the driving control system 1 according to IoT-based state change detection according to an embodiment of the present invention.
3 is a block diagram illustrating components of a vehicle controller 300 in the driving control system 1 according to IoT-based state change detection according to an embodiment of the present invention.
4 is a flowchart illustrating a driving control method according to body change detection by an IoT-based heart rate sensor according to an embodiment of the present invention.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS Hereinafter, detailed description of preferred embodiments of the present invention will be described with reference to the accompanying drawings. In the following description of the present invention, if it is determined that a detailed description of a related known function or configuration may unnecessarily obscure the gist of the present invention, the detailed description thereof will be omitted.

In the present specification, when one component 'transmits' data or signal to another component, the component may directly transmit the data or signal to another component, and through at least one other component This means that data or signals can be transmitted to other components.

1 is a diagram illustrating a driving control system 1 according to IoT-based state change detection according to an embodiment of the present invention. 2 is a block diagram illustrating components of a vehicle device system 4 of the driving control system 1 according to IoT-based state change detection according to an embodiment of the present invention.

First, referring to FIG. 1, the driving control system 1 according to the IoT-based state change detection includes a wearable watch 100, a network 5, a big data server 3, and a vehicle device system 4, , vehicle device system 4, referring to FIG. 2, the vehicle driving state meter 200, the vehicle controller 300, the heart rate sensor 400 attached to the vehicle starter, and the vehicle emergency light 500 mutually transmit and receive signals and data based on IoT can be performed.

Here, the network 5 is a high-speed backbone network of a large-scale communication network capable of large-capacity and long-distance voice and data services, and may be a next-generation wired or wireless network for providing the Internet or high-speed multimedia services. When the network 5 is a mobile communication network, it may be a synchronous mobile communication network or an asynchronous mobile communication network. As an example of the asynchronous mobile communication network, there may be a wideband code division multiple access (WCDMA) type communication network. In this case, although not shown in the drawings, the mobile communication network 700 may include a Radio Network Controller (RNC). Meanwhile, although the WCDMA network is taken as an example, it may be a 3G LTE network, a 4G network, other next-generation communication networks such as 5G, and other IP-based IP networks. The network 5 serves to mutually transmit signals and data between the wearable watch 100 , the big data server 3 , the vehicle controller 300 among the vehicle device systems 4 , and other systems.

Here, the wearable watch 100 constantly grasps the physical condition of the vehicle driver who owns and operates the vehicle 1 through the driving control application (APP.), which is an independent application, and stores it in the vehicle driver's device. Sensors are used to identify the body condition parameters corresponding to the driver's sleep, drinking, excitement, tension, etc., and store each body condition parameter as one body condition information in the internal memory, as well as the vehicle controller when boarding the vehicle. The body state information may be provided to the 300 in real time.

More specifically, the wearable watch 100 may include a heart rate sensor, an EEG sensor, and the like.

Here, the EEG measured by the EEG sensor refers to a biological signal that directly or indirectly reflects the conscious or unconscious state of the human being, and is a wavelength measured in all areas on the human scalp and mainly having a frequency of 30 Hz or less with a potential difference of several tens of microvolts. say These brain waves are classified into delta waves, theta waves, alpha waves, beta waves, and gamma waves for each frequency band. Delta wave is an EEG with a frequency of 4 Hz or less and is typically seen in normal sleep, and theta wave is an EEG with a frequency of 4 to 8 Hz. .

The EEG sensor may provide EEG information to the wearable watch 100 through the input/output interface of the wearable watch 100 or may transmit/receive signals and data with the wearable watch 100 through a short-range wireless communication method.

The vehicle driving condition measuring device 200 is mounted on the vehicle 2 and includes a gyro sensor for sensing whether the vehicle body shakes, an impact sensor for determining a driving state, etc., a speed sensor for determining driving time, etc. (1) By being formed inside, driving state information including driving state parameters including the driving state of the current vehicle 2 or an impact applied to the vehicle 2 is transmitted to the vehicle controller 300 to transmit the vehicle controller 300 ) can be stored on the

The vehicle controller 300 is currently configured based on the body state information provided from the wearable watch 100 , the driving state information provided from the vehicle driving state meter 200 , and the heart rate information provided based on the heart rate sensor 400 attached to the vehicle starter. After determining the driving state of the vehicle 1 and the current physical state of the vehicle driver, when an abnormality occurs in the driving state or the physical state, the vehicle driver may be notified so that an accident can be prevented in advance.

More specifically, the vehicle controller 300 receives the body state information provided from the wearable watch 100 , and then accesses the big data server 3 through the network 5 to access each day, each day of the vehicle driver based on machine learning. According to time constants such as time zone and each season, body condition information, which is collected data distributed and stored by a distributed file program, can be analyzed through a machine learning algorithm, and body condition range information for each time constant can be generated. More specifically, the machine learning algorithm used in the big data server 3 may be one of a decision tree (DT) classification algorithm, a random forest classification algorithm, and a support vector machine (SVM) classification algorithm.

That is, when the big data server 3 analyzes the body condition information distributed and stored as a time constant by the distributed file program, the body condition parameter constituting the body condition information for each first time constant as a reference is determined before the first time constant. The second time constant and the third time constant after the first time constant and feature information that is different than a preset threshold are extracted, and the extracted feature information is again before and after the second time constant based on the second time constant When the time constant and the third time constant are again extracted as at least one or more characteristic information in each of the time constants before and after the third time constant, the physical condition parameter of the vehicle driver, which is the extracted characteristic information, is set to the special situation of the vehicle driver. In a manner of excluding from the body state parameter in , body state range information may be generated as a result of learning by using at least one or more of the following plurality of machine learning algorithms.

That is, the big data server 3 may apply an ensemble structure composed of a plurality of complementary machine learning algorithms to improve the accuracy of body state range information.

The decision tree classification algorithm is a method of deriving results by learning in a tree structure, which makes it easy to interpret and understand the results, and the data processing speed is fast, and it may be possible to derive rules based on the search tree. RF can be applied as a method to improve the low classification accuracy of DT. The random forest classification algorithm is a method of slaughtering the results of learning multiple DTs as an ensemble. SVM can be applied as a method to improve overfitting that may occur through DT or RF learning. The SVM classification algorithm classifies data belonging to different classifications on a plane-based basis, and generally has high accuracy and may have low sensitivity to structural overfitting.

The vehicle starter-attached heart rate sensor 400 is a heart rate sensor attached to the vehicle starter and provides heart rate information to the vehicle controller 300 at the time of starting the vehicle. By comparison, whether there is an abnormality in the current heart rate and whether there is an abnormality in the mutual sensor through comparison with the wearable watch 100 may be determined.

On the other hand, the vehicle controller 300 transmits the current driving state and body state information in real time while driving, through the sensors mounted on the wearable watch 100 , the driving state meter 200 , and the heart rate sensor 400 attached to the vehicle starter. After checking in real time and storing driving data and physical state in the storage unit 340, as well as monitoring the current driving state and physical state information, the driving state when the vehicle 2 and the driver of the vehicle complete the driving without any abnormality And the body state information is stored in the storage unit 340 according to the timeline, and an abnormality occurs in the driving state information of the vehicle 2 such as overspeeding, and an abnormality occurs in the heartbeat information (and brain wave information) of the vehicle driver. When an abnormality such as occurring at the same time occurs, not only the abnormal symptom information is stored on the storage unit 340, but also the notification information is outputted through the input/output unit 350 in the same driving section during the next driving. can

In addition, in order to output a body notification and an alarm to the input/output unit 350 , the vehicle controller 300 has excessively constant heart rate information sensed by the vehicle condition meter 200 compared to the heart rate range information on the body condition information while the vehicle is driving. It can monitor whether it rises or falls beyond a preset error range.

More specifically, the vehicle controller 300 may lower the heart rate of the body due to drowsy driving, and when the driving speed of the vehicle 1 is excessively increased rapidly, the vehicle controller 300 may determine the current body state inside the vehicle 1 . Notifications and alarms corresponding to guidance may be output through the input/output unit 350 .

In addition, after the vehicle controller 300 outputs a notification and an alarm, the alarm release and abnormal state information are stored in the storage unit 340, and the vehicle driver's physical condition is improved so that the vehicle can be driven without any abnormality. If it is, the notification and alarm can be canceled and the abnormal state of the body can be stored separately to identify the singularity.

In addition, the vehicle controller 300 detects erroneous alarm and notification states according to individual differences in the vehicle driver's body, so that the vehicle driver's You can request an analysis of your physical condition information.

The vehicle controller 300 performs the vehicle emergency light blinking and shoulder guidance functions, but when it is determined that the vehicle driver's body is abnormal, the vehicle 2 is based on autonomous driving through control of the autonomous driving module (not shown) provided therein. ), the vehicle emergency light 500 may be flickered to stop the driving, and the driving vehicle 2 may be controlled to move to the shoulder or to a safety zone to check the body condition.

The vehicle controller 300 performs GPS-based emergency contact, but when it is determined as an accident by analyzing the body state information and driving state information, the vehicle driver's physical state is immediately grasped and in an emergency, the GPS-based, that is, the current vehicle ( In addition to controlling the ambulance vehicle or acquaintance to quickly come to the accident vehicle (1) through emergency contact at the location closest to 1), By storing the accident data in the storage unit 340, when an event that matches at least a preset number or more of the accident parameter included in the accident data, the point of the accident, time, physical state information, and driving state information, occurs later, accident data By outputting the accident parameters coincident with the input/output unit 350 in advance, it is possible to prevent another accident from occurring.

That is, the vehicle controller 300 predicts an accident by matching more than a preset number according to the comparison of the accident parameter, the current physical state parameter, and the driving state parameter, so that the vehicle 1 is evacuated to a safe place based on autonomous driving. In the case of controlling to do this, it is possible to guide contact for emergency measures to the vehicle driver and store the current state as accident prevention data in the storage unit 350 .

3 is a block diagram illustrating components of a vehicle controller 300 in the driving control system 1 according to IoT-based state change detection according to an embodiment of the present invention. Referring to FIG. 3 , the vehicle controller 300 may include a transceiver 310 , an IoT-based I/O interface 320 , a controller 330 , a storage 340 , and an input/output unit 350 .

The control unit 330 includes an information collection module 331 , a start-up control module 332 , and a body condition analysis module 333 . It may include an accident occurrence analysis module 334 .

Hereinafter, the driving control system 1 and the vehicle controller 300 according to the IoT-based state change detection will be described in detail focusing on the components of the control unit 330 .

The information collection module 331 receives constant physical state information about the vehicle driver who owns the vehicle 1 according to the execution of the driving control application (APP.) on the wearable watch 100 from the wearable watch 100 to the network 5 ), or control the transceiver 310 to receive through the network 5 from the driver's mobile terminal whose communication setting is performed through the wearable watch 100 and short-range wireless communication, and then receive body state information at all times. It may be stored on the storage unit 340 .

To this end, the information collection module 331 on the control unit 330 receives power from the constant power of the vehicle 1, and when the battery of the vehicle 1 has an amount of power less than or equal to a preset threshold, the power supply may be cut off. Also, before the power supply is cut off, the transceiver 310 may be controlled to transmit a power supply cutoff notice to the wearable watch 100 through the network 5 .

Here, the information collection module 331 stores body state information at all times as body state range information for each time constant through a request to the big data server 3 through the network 5 in the above-described manner. can be saved

In addition, when the information collection module 331 detects through the pressure sensor whether the driver of the vehicle 1 rides on the vehicle seat after the opening (OPEN) of the vehicle door configured in the vehicle 1, the After controlling the transceiver 310 to receive the driving state information from the vehicle driving state measuring device 200 , the storage unit 340 may start storing the driving state information.

The start-up control module 332 includes heart rate information (or old woman information) of the vehicle driver at the time when the vehicle is started through the vehicle starter from the vehicle starter-attached heart rate sensor 400 and body state information (each time) provided from the wearable watch 100 . By comparing the heart rate information (or EEG information) among the body condition range information by constant), it analyzes whether the current heart rate is abnormal for the driver of the vehicle, and performs the control to OFF the vehicle starter in case of an abnormality. In the case of , it is possible to control the vehicle starter to keep the ignition on (ON).

More specifically, the start control module 332 uses the constant body state information (body state range information for each time constant) stored in the storage unit 340 to determine average heart rate information (average EEG information) or current time zone information. After generating the average heart rate information (or average EEG information), if the heart rate information at the time of starting the vehicle received from the heart rate sensor 400 attached to the vehicle starter differs by more than a preset error range, the heart rate (EEG) Analyze as an abnormality, and if the difference is less than the preset error range, it can be analyzed as normal for the heartbeat (brain wave).

The body state analysis module 333 is configured to provide information on the body state during driving and the driving state information when the vehicle starter is kept ON by the start control module 332 and the driving of the vehicle 1 is maintained. After monitoring the body, it is possible to analyze whether abnormal signs of the body are found.

The body condition analysis module 333 stores the driving state information and the body condition information as driving state information according to the timeline in the storage unit 340 when no physical abnormality is found, and conversely, as a result of the analysis, the physical abnormality is detected. When found, a body state notification and alarm may be output to the input/output unit 350 . Here, the notification may be driving state information corresponding to the current physical abnormality and parameter information constituting the physical state information.

Here, the body anomaly is average heart rate information (or average brain wave information, more specifically, delta wave and theta wave) and the heart rate information measured by the current heart rate sensor 400 attached to the vehicle starter (or the brain wave information measured by the current vehicle driving condition measuring device 200) exceeds a preset error range. If there is a difference, it can be created.

In addition, the body condition analysis module 333 outputs a body condition notification and an alarm to the input/output unit 350, and then performs the same monitoring as to whether the condition of the body abnormality has improved within a preset time, and the body condition information and It can be analyzed using the driving status information.

When the state is improved as a result of the analysis, the body state analysis module 333 may release the alarm on the input/output unit 350 and store the abnormal state information together with the time information on the storage unit 340 . Here, the abnormal state information may be data in which time information is stored in the storage unit 340 as metadata in the above-described notification information output through the input/output unit 350 .

On the other hand, the body condition analysis module 333 controls the blinking of the vehicle emergency light 500 when the condition does not improve as a result of the analysis, and controls the shoulder parking guidance to be performed through a request to the autonomous driving module (not shown). can do.

The accident occurrence analysis module 334 analyzes the accident in which at least one of the body state information and the driving state information is preset, respectively, based on the state in which the state in which the state does not improve continues for more than a preset threshold time, or the state in which the state does not improve It is possible to analyze whether an accident has occurred by analyzing whether or not it falls above a quantitative value above a threshold.

When an accident occurs as a result of the analysis, the accident occurrence analysis module 334 may determine the physical condition of the vehicle driver, perform GPS location-based emergency contact, and store accident data in the storage unit 350 together with time information. .

That is, the accident occurrence analysis module 334 controls the transceiver 310 to directly transmit an emergency contact to the big data server 3 through the network 5 , or the wearable watch 100 connected to the network 5 . Control the transceiver 310 to transmit a request to transmit an emergency contact to the big data server 3, or network ( 5), the transceiver 310 may be controlled to transmit a request to transmit an emergency contact to the big data server 3 .

Accordingly, the big data server 3 identifies the terminal of the emergency contact network stored as the identification number of the vehicle device system 4 including the accident occurrence analysis module 334, the wearable watch 100, and the terminal identification number of the driver's mobile terminal. In addition to receiving GPS location information through a GPS-based location information request for a number, after receiving the driver's GPS location information received from the GPS location receiving module (not shown) provided in the vehicle controller 300, the emergency contact network It is possible to transmit an emergency contact by prioritizing a location located in a short distance from the driver's GPS location information among the GPS location information of the terminal corresponding to .

Here, the emergency contact provided by the big data server 3 is based on the GPS location information of the vehicle, as well as the state in which the state of which the state does not improve continues for more than a preset threshold time, or the state that the state does not improve. At least one of the information and the driving state information may include numerical information indicating whether at least one of the information and the driving state information falls to a quantitative value higher than or equal to a preset accident analysis threshold, respectively.

On the other hand, the accident occurrence analysis module 334 releases the alarm output to the input/output unit 350 when no accident occurs as a result of the analysis, outputs an emergency contact guide to the input/output unit 350, and stores the state record in the storage unit It can be stored on (340).

4 is a flowchart illustrating a driving control method according to body change detection by an IoT-based heart rate sensor according to an embodiment of the present invention. Referring to FIG. 4 , the wearable watch 100 stores the body state information about the vehicle driver who owns the vehicle 1 according to the execution of the driving control application (APP.) in its own memory device, and then 5), it is possible to start real-time transmission to the vehicle controller 300 (S11).

After step S11, when the vehicle driving state measuring device 200 detects through the pressure sensor whether the driver of the vehicle 1 rides on the vehicle seat after the vehicle door is opened (OPEN), the driving of the vehicle 1 Initiation of transmission of the state information to the vehicle controller 300 based on IoT may be performed (S12).

After step S12 , the vehicle controller 300 compares the heart rate information of the vehicle driver at the time of starting the vehicle from the vehicle starter-attached heart rate sensor 400 with the heart rate information among the body state information provided from the wearable watch 100 . Analyzes whether the current heart rate is abnormal for the driver and performs control of the vehicle starter to OFF in case of an abnormality. There is (S13).

When the ignition ON is maintained according to step S13, the vehicle controller 300 monitors the body state information and driving state information while driving (S14).

After step S14, the vehicle controller 300 determines whether a body abnormality is found (S15).

If no physical abnormality is found as a result of the determination in step S15, the vehicle controller 300 may store the driving state information and the physical state information in the storage unit 340 as driving state information according to a timeline (S16a). ).

Conversely, when a physical abnormality is found as a result of the determination in step S15 , the vehicle controller 300 may output a body state notification and an alarm to the input/output unit 350 ( S16 ).

After the step S16, the vehicle controller 300 determines whether the state of the body abnormality has improved (S17).

When the state is improved as a result of the determination in step S17, the vehicle controller 300 may release the alarm on the input/output unit 350 and store the abnormal state information together with the time information in the storage unit 340 ( S18).

On the other hand, if the state is not improved as a result of the determination in step S17, the vehicle controller 300 controls the blinking of the vehicle emergency light 500, and induces shoulder parking through a request to the autonomous driving module (not shown). It can be done (S19).

After step S19, the vehicle controller 300 determines whether an accident has occurred (S20).

When an accident occurs as a result of the determination in step S20, the vehicle controller 300 identifies the physical condition of the vehicle driver, performs GPS location-based emergency contact, and stores accident data together with time information on the storage unit 350 It can be stored (S21).

On the other hand, if an accident does not occur as a result of determination in step S20 , the vehicle controller 300 releases the alarm output to the input/output unit 350 , outputs an emergency contact guide to the input/output unit 350 , and records the state may be stored on the storage unit 340 (S22).

The present invention can also be implemented as computer-readable codes on a computer-readable recording medium. The computer-readable recording medium includes all kinds of recording devices in which data readable by a computer system is stored.

Examples of computer-readable recording media include ROM, RAM, CD-ROM, magnetic tape, floppy disk, optical data storage device, etc. also includes

In addition, the computer-readable recording medium is distributed in a computer system connected through a network, so that the computer-readable code can be stored and executed in a distributed manner. In addition, functional programs, codes, and code segments for implementing the present invention can be easily inferred by programmers in the technical field to which the present invention pertains.

As described above, preferred embodiments of the present invention have been disclosed in the present specification and drawings, and although specific terms are used, these are only used in a general sense to easily explain the technical contents of the present invention and to help the understanding of the present invention. , it is not intended to limit the scope of the present invention. It will be apparent to those of ordinary skill in the art to which the present invention pertains that other modifications based on the technical spirit of the present invention can be implemented in addition to the embodiments disclosed herein.

1: Operation control system according to IoT-based state change detection
2: vehicle
3: Big data server
4: Vehicle device system
5: Network
100: wearable watch
200: driving condition meter
300: vehicle controller
310: transceiver
320: IoT-based I/O interface
330: control unit
331: information collection module
332: start control module
333: body state analysis module
334: accident occurrence analysis module
340: storage
350: input/output unit
400: heart rate sensor attached to the vehicle starter
500: vehicle emergency light

Claims (5)

According to the execution of the driving control application (APP.), the physical state information about the vehicle driver who owns the vehicle 1 is stored in its own memory device, and then transmitted in real time to the vehicle controller 300 through the network 5 A wearable watch 100 that discloses;
a network 5 comprising a communication network for mutually transmitting signals and data between the wearable watch 100 , the big data server 3 , and the vehicle device system 4 ;
In analyzing the body condition information distributed and stored as time constants by the distributed file program, the body condition parameters constituting the body condition information for each reference first time constant are the second time constant and the first time constant before the first time constant. After extracting feature information that is different from the third time constant by more than a preset threshold, time constants before and after the second time constant and the third time constant are again based on the second time constant for the extracted feature information A method of excluding the body condition parameter of the vehicle driver, which is the extracted characteristic information, from the body condition parameter in a special situation of the vehicle driver when extracted as at least one or more characteristic information in each of the time constants before and after the third time constant again to learn using a machine learning algorithm, and to generate body state range information as a result of learning big data server (3); and
A vehicle device system 4 comprising a vehicle driving state meter 200, a vehicle controller 300, a heart rate sensor 400 attached to a vehicle starter, and a vehicle emergency light 500;
The vehicle device system 4 comprises:
When the pressure sensor detects whether the driver of the vehicle 1 is riding on the vehicle seat after the vehicle door is opened (OPEN), the driving state information of the vehicle 1 is transmitted to the vehicle controller 300 based on IoT. Vehicle driving state measuring device 200 to perform the start;
In a state in which the vehicle starter is kept ON, monitoring of the body state information and driving state information while driving is performed, and after receiving the body state information provided from the wearable watch 100 , the network 5 It accesses the big data server 3 through , and analyzes the body condition information, which is the collected data stored based on machine learning, through a machine learning algorithm to generate body condition range information for each time constant, and a heart rate sensor attached to the vehicle starter 400 By comparing the heart rate information of the vehicle driver at the time of starting the vehicle from a vehicle controller 300 performing control to (OFF) and maintaining the ignition on (ON) for the vehicle starter in the case of a normal range;
The vehicle starter-attached heart rate sensor 400 determines whether there is an abnormality in the current heart rate compared to the heart rate sensor of the wearable watch 100 by the vehicle controller 300 and whether there is an abnormality in the mutual sensor through comparison with the wearable watch 100 ; and
a vehicle emergency light 500 for flickering the vehicle emergency light so that the driving vehicle 2 can move to the shoulder or a safety zone and check the body condition;
A driving control system according to IoT-based state change detection, comprising:
delete After the wearable watch 100 stores the body state information about the vehicle driver who owns the vehicle 1 according to the execution of the driving control application (APP.) in its own memory device, the vehicle controller through the network 5 a first step of initiating real-time transmission to 300;
When the vehicle driving state measuring device 200 detects through the pressure sensor whether the driver of the vehicle 1 is riding on the vehicle seat after the vehicle door is opened (OPEN), the driving state information of the vehicle 1 is transferred to the vehicle controller ( 300) a second step of initiating transmission based on IoT;
After the vehicle controller 300 receives the body condition information provided from the wearable watch 100 , it accesses the big data server 3 through the network 5 and performs machine learning of the body condition information, which is the collected data stored based on machine learning. Analyze through an algorithm to generate body condition range information for each time constant, but heart rate among the vehicle driver's heart rate information at the time the vehicle is started from the vehicle starter-attached heart rate sensor 400 and the body condition information provided from the wearable watch 100 By comparing the information, it analyzes the abnormality of the current heartbeat for the vehicle driver and performs the control of the vehicle starter to OFF in case of an abnormality. a third step of performing control;
a fourth step of monitoring, by the vehicle controller 300, body state information and driving state information while driving while the vehicle starter is kept on;
After the fourth step, the vehicle controller 300 determines whether a physical abnormality is found, and if no physical abnormality is found as a result of the determination, the vehicle controller 300 converts the driving state information and the physical state information according to the timeline. A fifth step of storing the driving state information in the storage unit 340 and outputting, by the vehicle controller 300, a body state notification and an alarm to the input/output unit 350 when a physical abnormality is found as a result of the determination;
After the fifth step, the vehicle controller 300 determines whether the state of the body abnormality has improved, and when the state is improved as a result of the determination, the vehicle controller 300 releases the alarm on the input/output unit 350, Store the state information together with the time information on the storage unit 340, A sixth step of controlling the blinking of the vehicle emergency light 500 and inducing shoulder parking through a request for the autonomous driving module, by the vehicle controller 300 when the state does not improve as a result of the determination; and
After the sixth step (S19), the vehicle controller 300 determines whether an accident has occurred, and when an accident occurs as a result of the determination, the vehicle controller 300 determines the physical condition of the vehicle driver, and based on the GPS location Perform emergency contact, store the accident data together with time information on the storage unit 350, and if an accident does not occur as a result of determination, the vehicle controller 300 releases the alarm output to the input/output unit 350 and , a seventh step of outputting the emergency contact information to the input/output unit 350 and storing the state record on the storage unit 340;
A driving control method according to IoT-based state change detection, comprising: delete delete

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