CN117814769A - Prompting method, device, equipment and storage medium - Google Patents
Prompting method, device, equipment and storage medium Download PDFInfo
- Publication number
- CN117814769A CN117814769A CN202311711166.XA CN202311711166A CN117814769A CN 117814769 A CN117814769 A CN 117814769A CN 202311711166 A CN202311711166 A CN 202311711166A CN 117814769 A CN117814769 A CN 117814769A
- Authority
- CN
- China
- Prior art keywords
- heart rate
- rate variability
- information
- driver
- driving
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 63
- 238000012549 training Methods 0.000 claims description 17
- 238000012795 verification Methods 0.000 claims description 13
- 238000012360 testing method Methods 0.000 claims description 11
- 238000004590 computer program Methods 0.000 claims description 5
- 238000003062 neural network model Methods 0.000 claims description 4
- 230000001133 acceleration Effects 0.000 description 7
- 230000008569 process Effects 0.000 description 7
- 238000004891 communication Methods 0.000 description 6
- 230000008451 emotion Effects 0.000 description 6
- 238000012545 processing Methods 0.000 description 6
- 238000004458 analytical method Methods 0.000 description 4
- 230000006399 behavior Effects 0.000 description 4
- 230000008901 benefit Effects 0.000 description 3
- 239000012141 concentrate Substances 0.000 description 3
- 238000010586 diagram Methods 0.000 description 3
- 230000006870 function Effects 0.000 description 3
- 230000036651 mood Effects 0.000 description 3
- 230000003287 optical effect Effects 0.000 description 3
- 230000002159 abnormal effect Effects 0.000 description 2
- 238000004364 calculation method Methods 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 238000010801 machine learning Methods 0.000 description 2
- 239000000203 mixture Substances 0.000 description 2
- 238000007619 statistical method Methods 0.000 description 2
- 238000006467 substitution reaction Methods 0.000 description 2
- 230000008448 thought Effects 0.000 description 2
- 230000010391 action planning Effects 0.000 description 1
- 238000013528 artificial neural network Methods 0.000 description 1
- 230000002567 autonomic effect Effects 0.000 description 1
- 238000004422 calculation algorithm Methods 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 238000004140 cleaning Methods 0.000 description 1
- 239000003086 colorant Substances 0.000 description 1
- 238000002790 cross-validation Methods 0.000 description 1
- 238000003066 decision tree Methods 0.000 description 1
- 238000001514 detection method Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000011156 evaluation Methods 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 230000003993 interaction Effects 0.000 description 1
- 238000012544 monitoring process Methods 0.000 description 1
- 238000000611 regression analysis Methods 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 230000004044 response Effects 0.000 description 1
- 230000003938 response to stress Effects 0.000 description 1
- 230000002889 sympathetic effect Effects 0.000 description 1
- 230000001515 vagal effect Effects 0.000 description 1
- 210000000707 wrist Anatomy 0.000 description 1
Landscapes
- Measuring Pulse, Heart Rate, Blood Pressure Or Blood Flow (AREA)
Abstract
According to the prompting method, the prompting device, the prompting equipment and the storage medium, first heart rate data of a driver when the driver drives a vehicle are obtained; determining a first heart rate variability based on the first heart rate data; based on the first heart rate variability output prompt information, whether potential risks or problems such as distraction exist or not can be accurately estimated, prompt is timely carried out, and driving safety can be improved.
Description
Technical Field
The present disclosure relates to the field of vehicle technologies, and in particular, to a prompting method, apparatus, device, and storage medium.
Background
At present, each cart enterprise has the problems of detecting the emotion and state of a driver in intelligent driving and auxiliary driving of an automobile, in the related technology, the emotion of the driver is estimated by collecting biological characteristics of sound and expression only and then performing complex algorithm analysis, whether the potential risk of unsafe factors exists or the problem of distraction exists or not is determined by emotion analysis, but certain deviation exists by analyzing the characteristic.
Disclosure of Invention
Aiming at the problems, the application provides a prompting method, a prompting device, prompting equipment and a storage medium, which can more accurately evaluate whether the problems of potential risks or distraction exist or not, prompt in time and improve driving safety.
The embodiment of the application provides a prompting method, which comprises the following steps:
acquiring first heart rate data of a driver when the driver drives a vehicle;
determining a first heart rate variability based on the first heart rate data;
and outputting prompt information based on the first heart rate variability.
In some embodiments, the hint information includes: first alarm information, based on first heart rate variability output prompt message includes:
comparing the first heart rate variability with a safety threshold value to obtain a comparison result;
and outputting first alarm information under the condition that the comparison result indicates that the first heart rate variability exceeds the safety threshold value.
In some embodiments, the hint information includes: driving advice information, which outputs advice information based on the first heart rate variability, includes:
inputting the first heart rate variability into a driving habit model, and determining predicted driving information;
and outputting driving advice information based on the predicted driving information.
In some embodiments, the method further comprises:
acquiring second heart rate data of the driver when the driver drives the vehicle;
determining a second heart rate variability based on the second heart rate data;
determining whether to relate a second heart rate variability to the driving habits of the driver;
acquiring driving information of a driver under the condition that driving information of the driver related to the second heart rate variability is determined;
establishing a corresponding relation between the second heart rate variability and the driving information;
and establishing the driving habit model based on the corresponding relation.
In some embodiments, the establishing the driving habit model based on the correspondence includes:
establishing a sample data set based on the corresponding relation;
dividing the sample data set into a training set and a testing set;
training the neural network model based on the training set to obtain an initial model;
verifying the initial model based on the test set to obtain a verification result;
and under the condition that the verification result characterizes the prediction accuracy to be larger than a preset threshold value, determining the initial model as the driving habit model.
In some embodiments, the hint information includes: the second alarm information, the output prompt information based on the first heart rate variability includes:
determining that the driver is a common user if the difference between the first heart rate variability and the driver's historical heart rate variability is greater than a difference threshold;
and outputting second alarm information.
In some embodiments, the determining heart rate variability based on the heart rate data comprises:
calculating root mean square between two adjacent heartbeats;
and summing root mean square between two adjacent heart states to obtain heart rate variability.
The embodiment of the application provides a prompting device, which comprises:
the acquisition module is used for acquiring first heart rate data when a driver drives the vehicle;
a determination module for determining a first heart rate variability based on the first heart rate data;
and the output module is used for outputting prompt information based on the first heart rate variability.
Embodiments of the present application provide a computer readable storage medium storing a computer program executable by one or more processors for implementing the above-described prompting method.
According to the prompting method, the prompting device, the prompting equipment and the storage medium, first heart rate data of a driver when the driver drives a vehicle are obtained; determining a first heart rate variability based on the first heart rate data; based on the first heart rate variability output prompt information, whether potential risks or problems such as distraction exist or not can be accurately estimated, prompt is timely carried out, and driving safety can be improved.
Drawings
The present application will be described in more detail hereinafter based on embodiments and with reference to the accompanying drawings.
Fig. 1 is a schematic implementation flow chart of a prompting method provided in an embodiment of the present application;
fig. 2 is a schematic implementation flow chart of another prompting method provided in an embodiment of the present application;
fig. 3 is a schematic diagram of a composition structure of an electronic device according to an embodiment of the present application.
In the drawings, like elements are denoted by like reference numerals, and the drawings are not drawn to scale.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the present application more apparent, the present application will be described in further detail with reference to the accompanying drawings, and the described embodiments should not be construed as limiting the present application, and all other embodiments obtained by those skilled in the art without making any inventive effort are within the scope of the present application.
In the following description, reference is made to "some embodiments" which describe a subset of all possible embodiments, but it is to be understood that "some embodiments" can be the same subset or different subsets of all possible embodiments and can be combined with one another without conflict.
If a similar description of "first\second\third" appears in the application document, the following description is added, in which the terms "first\second\third" are merely distinguishing between similar objects and do not represent a particular ordering of the objects, it being understood that the "first\second\third" may be interchanged in a particular order or precedence, where allowed, so that the embodiments of the application described herein may be practiced in an order other than that illustrated or described herein.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of the present application only and is not intended to be limiting of the present application.
Based on the problems existing in the related art, the embodiment of the application provides a prompting method, an execution subject of the method can be electronic equipment, and the electronic equipment can be a mobile terminal, a computer, intelligent wearable equipment, vehicle-mounted equipment and the like. The illustrated computer may be an edge computing device, and in some embodiments, the electronic device may be a controller of a mobile terminal, a computer, a vehicle-mounted device, or a smart wearable device.
The functions implemented by the prompting method provided by the embodiment of the application can be implemented by calling program codes by a processor of the electronic device, wherein the program codes can be stored in a computer storage medium.
An embodiment of the present application provides a prompting method, and fig. 1 is a schematic implementation flow diagram of the prompting method provided in the embodiment of the present application, as shown in fig. 1, including:
step S101, first heart rate data of a driver driving a vehicle is acquired.
In this application embodiment, electronic equipment can with heart rate sensor communication connection, monitor the heart rate data of driver through heart rate monitoring bed is done.
In the embodiment of the application, the electronic device may be an edge computing device.
In the embodiment of the application, the heart rate sensor may be worn on the wrist of the driver in a wearing manner, so as to obtain the first heart rate data of the driver when the driver drives the vehicle through the heart rate sensor.
Step S102, determining a first heart rate variability based on the first heart rate data.
In the embodiment of the present application, step S102 may be implemented by the following steps: calculating root mean square between two adjacent heartbeats; and summing root mean square between two adjacent heart states to obtain heart rate variability.
In this embodiment of the present application, the manner of calculating the first heart rate variability may be expressed by a calculation formula, which may be expressed as:
where RMSSD may be heart rate variability, RRinterval i is the i-th peak in the prototype heart beat, i=1 to N.
In the present embodiment, the first heart rate variability is time-based heart rate variability. Heart rate variability can be expressed as: HRV index.
In the present embodiment, the heart rate variability converts heart rate data into RR intervals, i.e. time intervals between adjacent heart beats. The RR interval can be obtained by calculating the time difference between adjacent heart beat peaks. And analyzing the RR interval sequence to obtain a series of HRV indexes. Common HRV indicators include standard deviation, mean, low and high frequency energy in frequency domain analysis, and the like.
Research shows that HRV indicators can analyze the relevance of thought, emotion and behavior, and that an increase in HRV is related to an increase in self-control ability, social skills, and ability to cope with stress, etc. The relevance of emotion and behavior, and the increase in HRV is related to the enhancement of self-control ability, the enhancement of social skills, the enhancement of ability to cope with stress, and the like. Thus, the state of the driver can be estimated by HRV.
Step S103, outputting prompt information based on the first heart rate variability.
In this embodiment, the prompt information may include: at least one of the first warning information, the second warning information, and the driving advice information.
In this embodiment of the present application, the first alarm information may be dangerous signal alarm information, for example, the output prompt information is: please slow down driving, do not fatigue driving/please concentrate on. The second alarm information may be vehicle theft alarm information, for example, the output prompt information is: vehicles present a risk of theft. The driving advice information may include: acceleration advice, cornering advice, etc.
In this embodiment of the present application, the electronic device may output the prompt information in the following manner:
through the screen display prompt message, in the embodiment of the application, the electronic device is provided with a display screen, and the prompt message such as characters, icons, pictures and the like can be displayed on the screen.
The prompt information can be output through sound, and in the embodiment of the application, the electronic device can output the sound prompt information through a loudspeaker or an earphone, such as beeping sound, voice prompt and the like.
Through pilot lamp output prompt message, in this application embodiment, there are some pilot lamps usually on electronic equipment's the panel, and the pilot lamp of different colours represents different state or fault information.
Through vibration output prompt message, in this application embodiment, electronic equipment can vibrate the suggestion through built-in vibrating device, reminds like the vibrations of cell-phone.
The prompt information can be output in a short message or push notification mode, and the electronic equipment can remind the user of related information by sending the short message or push notification to a mobile phone or a computer of the user.
In the embodiment of the application, the mode of outputting the prompt information can be used independently or simultaneously, and the proper mode is selected to output the prompt information according to specific equipment and use scenes.
According to the prompting method, first heart rate data of a driver when the driver drives a vehicle are obtained; determining a first heart rate variability based on the first heart rate data; based on the first heart rate variability output prompt information, whether potential risks or problems such as distraction exist or not can be accurately estimated, prompt is timely carried out, and driving safety can be improved.
In some embodiments, the hint information includes: the first alarm information, step S103, may be implemented by:
and step S1031, comparing the first heart rate variability with a safety threshold value to obtain a comparison result.
In this embodiment of the present application, the safety threshold may be configured, and the safety threshold may be determined according to an average hrv value in a normal state. The safety threshold may be one interval data, for example, the safety threshold may be a to B.
Higher heart rate variability generally indicates that the body has a stronger tolerance to stress. That is, the higher the heart rate variability, the more autonomic Shen Qingdiao is able, the faster the stress response, but not the higher the better. Heart rate variability is a balanced relationship that reflects the vagal and sympathetic interactions. The heart rate variability is optimal in the normal range, and is unfavorable in either too high or too low, so the safety threshold is the average hrv value in the normal state.
In this embodiment of the present application, the comparison result includes: the first heart rate variability is within the safety threshold and the first heart rate variability is not within the safety threshold, i.e., the first heart rate variability exceeds the safety threshold.
In this embodiment of the present application, if the first heart rate variability is within the safety threshold, it is indicated that the state of the driver is normal, and no reminding may be performed at this time.
Step S1032, outputting first alarm information when the comparison result represents that the first heart rate variability exceeds the safety threshold.
In this embodiment of the present application, when the comparison result indicates that the first heart rate variability exceeds the safety threshold, it indicates that the driver is abnormal at this time, so that first alarm information may be sent out, and the first alarm information may be danger signal warning information. Illustratively, the first alert information may include: please slow down driving, do not fatigue driving or concentrate on.
In some embodiments, the hint information includes: the driving advice information, step S103 may be implemented by:
step S1033, inputting the first heart rate variability into a driving habit model, and determining predicted driving information.
In this embodiment of the present application, the input of the driving habit model is heart rate variability, and the output of the driving habit model is predicted driving information.
For example, information such as cornering, acceleration, etc. may be predicted.
Step S1034, outputting driving advice information based on the predicted driving information.
In the embodiment of the application, the above example is accepted, and then information of recommended turning and recommended acceleration can be output.
The method provided by the embodiment of the application can provide driving advice for the driver, and in some embodiments, the advice for the safe route can also be provided.
In some embodiments, prior to step S1033, the method further comprises:
step S1, obtaining second heart rate data when the driver drives the vehicle.
In an embodiment of the present application, the second heart rate data may be historical heart rate data before the first heart rate data is acquired.
And step S2, determining second heart rate variability based on the second heart rate data.
In this embodiment, the manner of calculating the second heart rate variability may be expressed by a calculation formula, which may be expressed as:
where RMSSD may be heart rate variability, RRinterval i is the i-th peak in the prototype heart beat, i=1 to N. Heart rate variability can be expressed as: HRV index.
Step S3, determining whether to relate the second heart rate variability to the driving habit of the driver.
In the embodiment of the application, whether the associated selection information is input for the user to select.
And step S4, acquiring driving information of the driver under the condition that driving information of the driver related to the second heart rate variability is determined.
In the embodiment of the application, if the association is selected, the association is performed with the driving habit after the heart rate variability is acquired.
And S5, establishing a corresponding relation between the second heart rate variability and the driving information.
In the embodiment of the application, the second heart rate variability and the driving information are time aligned, so that the second heart rate variability and the driving information are ensured to be in the same time period. Features related to the second heart rate variability are then extracted from the driving information. For example, the correlation of the heart rate variability with the vehicle speed, acceleration, or the change in heart rate variability is observed while turning, so that the driving information can be determined. A correspondence model between the second heart rate variability and the driving information may be established using machine learning or statistical analysis methods.
And S6, establishing the driving habit model based on the corresponding relation.
In the embodiment of the present application, step S6 may be implemented by the following steps:
step S61, a sample data set is established based on the correspondence.
In this embodiment of the present application, after the correspondence is established, the collected data may be preprocessed, including data cleaning, missing value processing, outlier processing, and the like. Ensuring the quality and integrity of the data and thus creating a sample dataset.
Step S62, dividing the sample data set into a training set and a test set.
Illustratively, the training set may comprise 80% of the sample data set and the test set may comprise 20% of the sample data set.
Step S63, training the neural network model based on the training set to obtain an initial model,
in the embodiment of the application, a machine learning or statistical method is used to establish a corresponding relation model between the second heart rate variability and the driving information. Regression analysis, neural networks, decision trees, etc. may be used for modeling. When training the model, the second heart rate variability is taken as a characteristic variable, driving information is taken as a target variable, namely, the second heart rate variability is taken as input, and the driving information is taken as output to train the model.
And step S64, verifying the initial model based on the test set to obtain a verification result.
In this embodiment of the present application, after training is completed, a test set may be used to perform a test, to obtain a verification result, where the verification result may include: accuracy of prediction.
Step S65, determining the initial model as the driving habit model when the verification result characterizes the prediction accuracy to be greater than a preset threshold.
In this embodiment of the present application, the preset threshold may be configured, and an exemplary preset threshold may be configured to be 80%. If the accuracy of the verification result is greater than 80%, the initial model can be considered to be qualified for verification, and the initial model can be determined as a driving habit model.
In the embodiment of the application, if the accuracy of the verification result is less than 80%, training is continued.
In some embodiments, the built model may also be evaluated using methods such as cross-validation, etc., to check the predictive performance and generalization ability of the model. The accuracy and reliability of the model can be evaluated using indices such as mean square error, decision coefficients, etc.
In some embodiments, after the driving habit model is established, deployment may then take place.
According to the method provided by the embodiment of the application, the driving habit model can be established based on the corresponding relation. The model can be used for evaluating the driving information of the driver and providing references for driving behavior evaluation, driving risk prediction and the like.
In some embodiments, the hint information includes: the second alarm information, step S103, may be implemented by:
step S1035, determining that the driver is a very frequent user in case the difference between the first heart rate variability and the historical heart rate variability of the driver is greater than a difference threshold.
In this embodiment of the present invention, the heart rate variability of different drivers is different, for example, the value hrv of the driver is higher in the acceleration process, that is, the response speed of the driver is higher, or the historical average value hrv is a certain value X when driving at different times of day, and when driving not the vehicle owner, the difference between the first heart rate variability and the historical heart rate variability may be larger, at this time, the driver may be judged to be a common user, the common user may be the vehicle owner, and at this time the driver is not the vehicle owner.
Step S1036, outputting the second alarm information.
In this embodiment of the present application, output may be: the vehicle has warning information of theft risk.
According to the method provided by the embodiment of the application, under the condition that the difference value between the first heart rate variability and the historical heart rate variability of the driver is larger than the difference value threshold value, the driver is determined to be a common user, and the second alarm information is output, so that the risk of vehicle theft can be reduced.
Based on the foregoing embodiments, the embodiment of the present application further provides a prompting method, and fig. 2 is a schematic implementation flow chart of the prompting method provided in the embodiment of the present application, as shown in fig. 2, including:
step S201, collecting heart rate data of the current driver.
In the embodiment of the application, heart rate data of a driver is detected through a heart rate detection sensor.
In step S202, the data is analyzed and hrv value is calculated.
In the embodiment of the application, a RMSSD time domain method may be adopted to calculate the root mean square of the continuous difference between each heartbeat: rmssd=Thus, hrv was obtained.
Step S203, the driver mood status is evaluated.
Different hrv values may correspond to different mood states. HRV values can analyze the relevance of thought, emotion and behavior.
Step S204, whether the driving habits are related.
In the embodiment of the present application, if the association is performed, step S205 is performed, and if the association is not performed, step S206 is performed.
Step S205, the current driving habit of the driver is recorded.
In this embodiment, the driving habit may include: travel information.
In this embodiment of the present application, the running information includes: available data in the cockpit for speed, acceleration, cornering, deceleration, acceleration, temperature, seat angle, etc.
After step S205, step S207 is performed.
Step S206, dangerous signal feedback and recording.
In this embodiment of the present application, if it is not needed, the danger signal is directly fed back, if it exceeds, the danger signal warning is sent out, and the danger signal can be divided into: please slow down driving, do not fatigue driving/please concentrate on.
The flow ends after step S206.
Step S207, establishing an HRV value-driving habit model.
Step S208, judging whether the model is mature.
In the embodiment of the present application, if not mature, step S201 is performed, and if mature, step S209 is performed.
In the embodiment of the application, if the similarity between the predicted result and the actual result reaches 80%, the judgment model is mature, if the similarity is less than 80%, training is continued, and if the similarity reaches 80%, driving advice, safety route advice and the like can be provided for the driver.
Step S209, outputting a safety line or a travel advice.
Step S210, judging whether the driver is a vehicle owner or not, and reporting the vehicle theft risk.
The vehicle owner can be helped to judge whether the driver is the vehicle owner or not, and timely report the abnormal situation, so that the risk of vehicle theft is reduced.
According to the method provided by the embodiment of the application, the hrv sensor is adopted to collect the heartbeat data of the driver, the driving state of the driver is estimated more directly, the important improvement effect is achieved on the safety coefficient in the driving situation, besides the innovation in the aspect of collecting the data, modeling is provided by using the collected data, the driver portrait is constructed, the driving habit of the driver is collected, for example, when the mood of the driver is relaxed, the maximum value and the average value of the vehicle speed, the safety coefficient of the vehicle driving, the straight driving state or the turning state of the vehicle and the like are collected, linkage analysis is carried out, so that the driving preference of the driver is deduced, the method has important guiding significance in the aspect of future route planning and action planning, and more accurate prompt and early warning are provided in the aspect of danger reporting.
Based on the foregoing embodiments, the embodiments of the present application provide a prompting device, where each module included in the device and each unit included in each module may be implemented by a processor in a computer device; of course, the method can also be realized by a specific logic circuit; in practice, the processor may be a central processing unit (CPU, central Processing Unit), a microprocessor (MPU, microprocessor Unit), a digital signal processor (DSP, digital Signal Processing), or a field programmable gate array (FPGA, field Programmable Gate Array), or the like.
The embodiment of the application provides a prompting device, which comprises:
the acquisition module is used for acquiring first heart rate data when a driver drives the vehicle;
a determination module for determining a first heart rate variability based on the first heart rate data;
and the output module is used for outputting prompt information based on the first heart rate variability.
In some embodiments, the hint information includes: first alarm information, based on first heart rate variability output prompt message includes:
comparing the first heart rate variability with a safety threshold value to obtain a comparison result;
and outputting first alarm information under the condition that the comparison result indicates that the first heart rate variability exceeds the safety threshold value.
In some embodiments, the hint information includes: driving advice information, which outputs advice information based on the first heart rate variability, includes:
inputting the first heart rate variability into a driving habit model, and determining predicted driving information;
and outputting driving advice information based on the predicted driving information.
In some embodiments, the method further comprises:
acquiring second heart rate data of the driver when the driver drives the vehicle;
determining a second heart rate variability based on the second heart rate data;
determining whether to relate a second heart rate variability to the driving habits of the driver;
acquiring driving information of a driver under the condition that driving information of the driver related to the second heart rate variability is determined;
establishing a corresponding relation between the second heart rate variability and the driving information;
and establishing the driving habit model based on the corresponding relation.
In some embodiments, the establishing the driving habit model based on the correspondence includes:
establishing a sample data set based on the corresponding relation;
dividing the sample data set into a training set and a testing set;
training the neural network model based on the training set to obtain an initial model;
verifying the initial model based on the test set to obtain a verification result;
and under the condition that the verification result characterizes the prediction accuracy to be larger than a preset threshold value, determining the initial model as the driving habit model.
In some embodiments, the hint information includes: the second alarm information, the output prompt information based on the first heart rate variability includes:
determining that the driver is a common user if the difference between the first heart rate variability and the driver's historical heart rate variability is greater than a difference threshold;
and outputting second alarm information.
In some embodiments, the determining heart rate variability based on the heart rate data comprises:
calculating root mean square between two adjacent heartbeats;
and summing root mean square between two adjacent heart states to obtain heart rate variability.
It should be noted that, in the embodiment of the present application, if the foregoing prompting method is implemented in the form of a software functional module, and is sold or used as a separate product, the foregoing prompting method may also be stored in a computer readable storage medium. Based on such understanding, the technical solutions of the embodiments of the present application may be essentially or partly contributing to the prior art, and the computer software product may be stored in a storage medium, and include several instructions to cause a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the methods described in the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read Only Memory (ROM), a magnetic disk, an optical disk, or other various media capable of storing program codes. Thus, embodiments of the present application are not limited to any specific combination of hardware and software.
Accordingly, an embodiment of the present application provides a computer readable storage medium having a computer program stored thereon, wherein the computer program when executed by a processor implements the steps of the prompting method provided in the above embodiment.
The embodiment of the application provides electronic equipment; fig. 3 is a schematic diagram of a composition structure of an electronic device according to an embodiment of the present application, as shown in fig. 3, the electronic device 500 includes: a processor 501, at least one communication bus 502, a user interface 503, at least one external communication interface 504, a memory 505. Wherein the communication bus 502 is configured to enable connected communication between these components. The user interface 503 may include a display screen, and the external communication interface 504 may include a standard wired interface and a wireless interface, among others. The processor 501 is configured to execute a program of the prompting method stored in the memory to implement the steps in the prompting method provided in the above-described embodiment.
It should be noted here that: the description of the storage medium and apparatus embodiments above is similar to that of the method embodiments described above, with similar benefits as the method embodiments. For technical details not disclosed in the embodiments of the storage medium and the apparatus of the present application, please refer to the description of the method embodiments of the present application for understanding.
It should be appreciated that reference throughout this specification to "one embodiment" or "an embodiment" means that a particular feature, structure or characteristic described in connection with the embodiment is included in at least one embodiment of the present application. Thus, the appearances of the phrases "in one embodiment" or "in an embodiment" in various places throughout this specification are not necessarily all referring to the same embodiment. Furthermore, the particular features, structures, or characteristics may be combined in any suitable manner in one or more embodiments. It should be understood that, in various embodiments of the present application, the sequence numbers of the foregoing processes do not mean the order of execution, and the order of execution of the processes should be determined by the functions and internal logic thereof, and should not constitute any limitation on the implementation process of the embodiments of the present application. The foregoing embodiment numbers of the present application are merely for describing, and do not represent advantages or disadvantages of the embodiments.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
In the several embodiments provided in this application, it should be understood that the disclosed apparatus and method may be implemented in other ways. The above described device embodiments are only illustrative, e.g. the division of the units is only one logical function division, and there may be other divisions in practice, such as: multiple units or components may be combined or may be integrated into another system, or some features may be omitted, or not performed. In addition, the various components controlled or discussed may be coupled or directly coupled or communicatively coupled to each other via some interface, whether indirectly coupled or communicatively coupled to devices or units, whether electrically, mechanically, or otherwise.
The units described above as separate components may or may not be physically separate, and components for controlling the units may or may not be physical units; can be located in one place or distributed to a plurality of network units; some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in each embodiment of the present application may be integrated in one processing unit, or each unit may be separately used as one unit, or two or more units may be integrated in one unit; the integrated units may be implemented in hardware or in hardware plus software functional units.
Those of ordinary skill in the art will appreciate that: all or part of the steps for implementing the above method embodiments may be implemented by hardware related to program instructions, and the foregoing program may be stored in a computer readable storage medium, where the program, when executed, performs steps including the above method embodiments; and the aforementioned storage medium includes: a mobile storage device, a Read Only Memory (ROM), a magnetic disk or an optical disk, or the like, which can store program codes.
Alternatively, the integrated units described above may be stored in a computer readable storage medium if implemented in the form of software functional modules and sold or used as a stand-alone product. Based on such understanding, the technical solutions of the embodiments of the present application may be essentially or partly contributing to the prior art, embodied in the form of a software product stored in a storage medium, comprising several instructions for causing a controller to execute all or part of the methods described in the embodiments of the present application. And the aforementioned storage medium includes: various media capable of storing program codes, such as a removable storage device, a ROM, a magnetic disk, or an optical disk.
The foregoing is merely an embodiment of the present application, but the protection scope of the present application is not limited thereto, and any person skilled in the art can easily think about changes or substitutions within the technical scope of the present application, and the changes and substitutions are intended to be covered in the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (10)
1. A method of prompting, comprising:
acquiring first heart rate data of a driver when the driver drives a vehicle;
determining a first heart rate variability based on the first heart rate data;
and outputting prompt information based on the first heart rate variability.
2. The method of claim 1, wherein the hint information comprises: first alarm information, based on first heart rate variability output prompt message includes:
comparing the first heart rate variability with a safety threshold value to obtain a comparison result;
and outputting first alarm information under the condition that the comparison result indicates that the first heart rate variability exceeds the safety threshold value.
3. The method of claim 1, wherein the hint information comprises: driving advice information, which outputs advice information based on the first heart rate variability, includes:
inputting the first heart rate variability into a driving habit model, and determining predicted driving information;
and outputting driving advice information based on the predicted driving information.
4. A method according to claim 3, characterized in that the method further comprises:
acquiring second heart rate data of the driver when the driver drives the vehicle;
determining a second heart rate variability based on the second heart rate data;
determining whether to relate a second heart rate variability to the driving habits of the driver;
acquiring driving information of a driver under the condition that driving information of the driver related to the second heart rate variability is determined;
establishing a corresponding relation between the second heart rate variability and the driving information;
and establishing the driving habit model based on the corresponding relation.
5. The method of claim 4, wherein the establishing the driving habit model based on the correspondence relationship comprises:
establishing a sample data set based on the corresponding relation;
dividing the sample data set into a training set and a testing set;
training the neural network model based on the training set to obtain an initial model;
verifying the initial model based on the test set to obtain a verification result;
and under the condition that the verification result characterizes the prediction accuracy to be larger than a preset threshold value, determining the initial model as the driving habit model.
6. The method of claim 5, wherein the hint information comprises: the second alarm information, the output prompt information based on the first heart rate variability includes:
determining that the driver is a common user if the difference between the first heart rate variability and the driver's historical heart rate variability is greater than a difference threshold;
and outputting second alarm information.
7. The method according to any one of claims 1 to 6, wherein said determining heart rate variability based on said heart rate data comprises:
calculating root mean square between two adjacent heartbeats;
and summing root mean square between two adjacent heart states to obtain heart rate variability.
8. A reminder device, comprising:
the acquisition module is used for acquiring first heart rate data when a driver drives the vehicle;
a determination module for determining a first heart rate variability based on the first heart rate data;
and the output module is used for outputting prompt information based on the first heart rate variability.
9. An electronic device comprising a memory and a processor, the memory having stored thereon a computer program which, when executed by the processor, performs the prompting method of any one of claims 1 to 7.
10. A storage medium storing a computer program executable by one or more processors for implementing a prompting method according to any one of claims 1 to 5.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311711166.XA CN117814769A (en) | 2023-12-12 | 2023-12-12 | Prompting method, device, equipment and storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311711166.XA CN117814769A (en) | 2023-12-12 | 2023-12-12 | Prompting method, device, equipment and storage medium |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN117814769A true CN117814769A (en) | 2024-04-05 |
Family
ID=90523535
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202311711166.XA Pending CN117814769A (en) | 2023-12-12 | 2023-12-12 | Prompting method, device, equipment and storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN117814769A (en) |
-
2023
- 2023-12-12 CN CN202311711166.XA patent/CN117814769A/en active Pending
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US10121345B1 (en) | Vehicle operator emotion management system and method | |
| US10121205B1 (en) | Risk evaluation based on vehicle operator behavior | |
| JP4505619B2 (en) | Psychosomatic state judgment system | |
| Manawadu et al. | Multiclass classification of driver perceived workload using long short-term memory based recurrent neural network | |
| EP2434466A1 (en) | Driving assistance method and device | |
| US20230211780A1 (en) | Operation support method, operation support system, and operation support server | |
| US10877444B1 (en) | System and method for biofeedback including relevance assessment | |
| Panagopoulos et al. | Forecasting markers of habitual driving behaviors associated with crash risk | |
| CN106913333B (en) | Method for selecting sensitivity characteristic index of continuous attention level | |
| CN109716411A (en) | Method and apparatus to monitor the activity level of driver | |
| US11772674B2 (en) | Systems and methods for increasing the safety of voice conversations between drivers and remote parties | |
| JP5643142B2 (en) | Driving ability determination device and driving ability determination method | |
| CN110638473A (en) | Method, device, electronic device and storage medium for determining fatigue driving | |
| CN114601478A (en) | Method, device and equipment for improving alertness of driver and readable storage medium | |
| Murata | Proposal of a method to predict subjective rating on drowsiness using physiological and behavioral measures | |
| Yang et al. | Real-time driver cognitive workload recognition: Attention-enabled learning with multimodal information fusion | |
| US20210323559A1 (en) | Data processing device, human-machine interface system including the device, vehicle including the system, method for evaluating user discomfort, and computer-readable medium for carrying out the method | |
| Vasudevan et al. | Driver drowsiness monitoring by learning vehicle telemetry data | |
| Tsai et al. | Machine learning model for aberrant driving behaviour prediction using heart rate variability: a pilot study involving highway bus drivers | |
| CN109249931A (en) | Control method and device for vehicle drive | |
| JP2022051216A (en) | Safe operation support method, safe operation support system and safe operation support sever | |
| CN117814769A (en) | Prompting method, device, equipment and storage medium | |
| CN107007292B (en) | Method for learning fatigue | |
| Damousis et al. | A fuzzy expert system for the early warning of accidents due to driver hypo-vigilance | |
| WO2020225956A1 (en) | System for predicting risk of dangerous driving |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |