CN110213720A - Unexpected prevention method in mobile phone use process based on user behavior analysis - Google Patents
Unexpected prevention method in mobile phone use process based on user behavior analysis Download PDFInfo
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L67/00—Network arrangements or protocols for supporting network services or applications
- H04L67/50—Network services
- H04L67/52—Network services specially adapted for the location of the user terminal
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
- H04W4/02—Services making use of location information
- H04W4/021—Services related to particular areas, e.g. point of interest [POI] services, venue services or geofences
- H04W4/022—Services related to particular areas, e.g. point of interest [POI] services, venue services or geofences with dynamic range variability
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
- H04W4/02—Services making use of location information
- H04W4/029—Location-based management or tracking services
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
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- H04W4/12—Messaging; Mailboxes; Announcements
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
- H04W4/30—Services specially adapted for particular environments, situations or purposes
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- H—ELECTRICITY
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- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
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- H04W4/38—Services specially adapted for particular environments, situations or purposes for collecting sensor information
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Abstract
The present invention is the unexpected prevention method in the mobile phone use process based on user behavior analysis.The present invention identifies the ongoing human body behavior of user's current time and posture by Human bodys' response module, obtains the atom behavior at user's current time；The road network information near user's current time present position is obtained by road network module, obtains the spatial positional information at user's current time；The alternative events of logical interaction identification module monitoring current time user and mobile phone obtain the mode that user's current time interacts with mobile phone；Danger situation identification model is established, obtaining user's current time handles danger coefficient under danger situation, sounds a warning by client end AP P to user according to the danger coefficient.
Description
Technical field
The present invention relates to the unexpected prevention technique fields in mobile phone use process, are a kind of hands based on user behavior analysis
Unexpected prevention method in machine use process.
Background technique
Cover many aspects based on user behavior detection and the research analyzed and application achievements.Josu é Iglesias et al.
In Josu é Iglesias, Cano J, Bernardos A M, et al.:A ubiquitous activity-monitor to
prevent sedentariness[C]//IEEE International Conference on Pervasive
It is proposed in Computing&Communications Workshops.IEEE (2011) on mobile phone for movement monitoring
Mobile applications, the acceleration of motion obtained according to sensor collection and location information real-time monitoring user behavior, whole day
User Activity is assessed, itself sports level is understood for user and considers associated privacy problem；ChangKyun Jeon et al. exists
Jeon C K,Kim N H,In H P.Situation-awareness overheating protection solution
for mobile devices[C]//IEEE International Conference on Consumer
Pass is interacted by user and mobile device in context aware technology analysis a period of time in Electronics.IEEE (2015)
System, by obtain user's interaction mode and currently used mobile phone should program, take dynamic approach prevention mobile device overheat
And then the problem of influencing energy consumption；J.Zaldivar et al. is in Zaldivar J, Calafate C T, Cano J C, et al.:
Providing accident detection in vehicular networks through OBD-II devices and
Intelligent hand is utilized in Android-based smartphones [C] //Local Computer Networks.IEEE (2011)
((OBD-II) develops the application program for detecting traffic accident, guaranteeing travel safety to the interface that machine provides, and passes through judgement
Driver and vehicle present case (performance) provide the detection of travel safety, and adopt automatically after in detection, accidents happened
Stringent effort are taken, similarly, Bankar Sanket Anil et al. is in Anil B S, Vilas K A, Jagtap S R:
Intelligent system for vehicular accident detection and notification[C]//
Also lead in International Conference on Communications&Signal Processing.IEEE (2014)
Cross the detection and early warning scheme when developing intellectual resource system gives generation driving accident (vehicular accident), Fabio
Martinelli et al. is in Fabio Martinelli, Francesco Mercaldo, Vittoria Nardone, et al.:
Context-Awareness Mobile Devices for Traffic Incident Prevention[C]//
International Conference on Pervasive Computing and Communications
It is designed in Workshops.IEEE (2018) and develops the level two group for being analyzed by driver conditions and being sent warning message
At based on context aware mobile terminal application, for taking precautions against traffic accident.Furthermore context aware technology is also extensive
For protecting the encryption application design of privacy of user, preventing the intelligence control of loss of data in cloud storage service, offer Intelligent life
The application such as system and Home Fashion & Design Shanghai.
Apply context aware in the above-mentioned background technique being related to, the technologies such as user behavior monitoring, but major defect
Be that the behavior current to user is not identified and analyzed well, time locating for user and space are considered it is less,
The behavioral value of user is not combined with safeguard measure simultaneously, is not provided with effect ground hazards prevention measure；In addition, part is answered
It is more wide in range to background description in, do not provide stringent definition.
Summary of the invention
The present invention is to solve the problems, such as above-mentioned existing, provides a kind of mobile phone based on user behavior analysis and used
Unexpected prevention method in journey, the present invention provides following technical schemes:
A kind of unexpected prevention method in the mobile phone use process based on user behavior analysis, the method includes following steps
It is rapid:
Step 1: the ongoing human body behavior of user's current time and posture are carried out by Human bodys' response module
Identification, obtains the atom behavior at user's current time；
Step 2: the road network information near user's current time present position is obtained by road network module, is used
The spatial positional information at family current time；
Step 3: the alternative events of current time user and mobile phone are monitored by interaction identification module, it is current to obtain user
The mode that moment interacts with mobile phone；
Step 4: behavior, spatial positional information and the coefficient conversion interacted with mobile phone at user's current time
For continuous random variable, danger situation identification model is established, is known the continuous random variable after conversion as danger situation
The input quantity of other model, obtaining user's current time handles danger coefficient under danger situation, is passed through according to the danger coefficient
Client end AP P sounds a warning to user.
Preferably, the step 1 specifically:
Step 1: Human bodys' response module adopts human body behavior initial data by mobile phone acceleration sensor
Collection, when the collected initial data of mobile phone is divided into 3-axis acceleration, three axis angular rates, geographical location information and data acquisition
Between stab；
Step 2: carrying out feature extraction to the initial data, obtained characteristic data set represents user's current time
Behavior；The behavior includes standing, walking, rides, drives, by bus.
Preferably, the step 2 specifically:
Road network module characterizes user control location information by the position sensor built in mobile phone, passes through tune
The contextual information that user present position periphery is obtained with third-party LBS service obtains the spatial position at user's current time
Information.
Preferably, the spatial positional information at user's current time is obtained by GPS hardware in mobile phone, uses satellite
Position obtains the spatial positional information of user；The spatial positional information of user is obtained by base station location；It is obtained by WiFi positioning
Take the spatial positional information for obtaining user；It is positioned by AGPS, in conjunction with GSM or GPRS and satellite positioning, is sent using base station generation
Assistance satellite information, reduction GPS chip obtain the delay time of satellite-signal, to the scene of indoor or high building masking by base
It stands signal, mitigates dependence of the GPS chip to satellite-signal；The location based service provided by third party, including Google
Map or Amap obtain the spatial positional information of user by the service structure that third party provides.
Preferably, the step 3 specifically:
For interactive identification module using API provided by mobile phone operating system, operating system includes Android or IOS,
The alternative events of user and mobile phone are monitored by the API that the mobile phone operating system provides, and are based on the alternative events and shape
State judges the interactive mode of user and mobile phone, obtains the coefficient that user interacts with mobile phone.
Preferably, the step 4 specifically:
Step 1: the behavior at user's current time, spatial positional information and the coefficient conversion interacted with mobile phone
For continuous random variable, danger situation identification model is established by following formula:
C=behavior*space*time (1)
Wherein, C is the output under user's current time danger situation, and behavior is the atom behavior at user's current time
Mode, space be user's current time spatial positional information, time be user's current time interacted with mobile phone be
Number；
Step 2: by C and a certain threshold value D0Comparison, when C is greater than D0When, determine that user is under danger situation, forms set
D is in danger situation；
Step 3: when set D is greater than D0When, danger coefficient is calculate by the following formula:
Level=C-D0 (2)
Wherein, level is to handle the danger coefficient under danger situation at user's current time；
When D is greater than D0When, level is equal to zero；
Step 4: client end AP P according to level value carry out early warning, when the value of level constantly increases, improve to
The warning level at family is reminded, the warning of mobile phone vibration and positive lock screen including pop-up dialog box.
The invention has the following advantages:
It can be from geospatial location locating for user, user's human body behavior, user and hand when user uses mobile phone
The interaction mode many aspects of the machine contextual information current to user is portrayed to obtain the danger situation user portrait of user, and
Based on reflecting between the coefficient that the machine learning models such as artificial neural network establish user's portrait and user is under danger situation
Relationship is penetrated, the coefficient that user is under danger situation can be obtained in real time in the actual environment based on this mapping relations, according to
The numerical values recited of this coefficient carries out user to be classified other prompt and warning, to ensure the security of the lives and property of user.
Detailed description of the invention
Fig. 1 is the unexpected prevention method flow chart in the mobile phone use process based on user behavior analysis.
Fig. 2 is that three axis of human body behavior adds/angular speed time series chart.
Fig. 3 is road network information figure.
Fig. 4 is client end AP P architecture diagram.
Fig. 5 is experimental result comparative diagram.Fig. 5-(1) is n_trees control experiment results figure, and Fig. 5-(2) is n_
Feature_used control experiment results figure.
Fig. 6 is that surface chart is reminded in danger situation identification and transmission.
Specific embodiment
Below in conjunction with specific embodiment, describe the invention in detail.
Specific embodiment one:
A kind of unexpected prevention method in the mobile phone use process based on user behavior analysis, the method includes following steps
It is rapid:
Step 1: the ongoing human body behavior of user's current time and posture are carried out by Human bodys' response module
Identification, obtains the atom behavior at user's current time；
Step 2: the road network information near user's current time present position is obtained by road network module, is used
The spatial positional information at family current time；
Step 3: the alternative events of logical interaction identification module monitoring current time user and mobile phone, obtain user it is current when
Carve the mode interacted with mobile phone；
Step 4: behavior, spatial positional information and the coefficient conversion interacted with mobile phone at user's current time
For continuous random variable, danger situation identification model is established, is known the continuous random variable after conversion as danger situation
The input quantity of other model, obtaining user's current time handles danger coefficient under danger situation, and the danger coefficient passes through client
End APP sounds a warning to user.
As shown in Figure 1, human body unexpected injury prevention method of the introduction of the present invention based on context aware, provides whole frame first
Frame, from functional module for, be divided into Human bodys' response module, road network module and interaction identification module.
In order to be prevented using the unexpected injury being likely to occur during mobile phone user, first to mobile phone use process
In the situation of unexpected injury that is likely to occur be defined.Before carrying out danger situation perception and identification, first to danger situation
Perception and identification process are modeled.Spatial position where comprehensively considering human body behavior, user and mobile phone interaction and user
Three son aspects, the definition of formalization: C=(behavior, space, time) is provided to danger situation identification process
Wherein, C (Context) is the output that continuous variable indicates danger situation identification model, as the big Mr. Yu of the value of C
One threshold value D0When, it is believed that user is under danger situation, remembers that this collection is combined into D (Danger).Behavior, space, time difference
Indicate the output of three subproblems: behavior indicates the atom behavior of mobile phone holder, and such as standing walking, rides, drives
Vehicle, by bus etc.；Space indicate mobile phone holder current spatial location information, such as crossroad, crossing, by bus；time
The coefficient that expression mobile phone holder current time is interacting with mobile phone.Danger situation identification model receives above three ginseng
Number is used as mode input, and output mobile phone holder is currently at the danger coefficient under danger situation, the mobile phone based on context aware
The research of use process accident prevention method is the solution to danger situation identification model.
After the output of three submodules is converted to continuous random variable, further to danger situation identification model
It is defined: C=behavior*space*time
Work as D > D0When, level=C-D0, as D≤D0When, level=0.
The output of danger situation identification model is defined as the product of three submodule output results, for danger coefficient
Level, value depend on the output result C and threshold value D of danger situation identification model0Relationship, when the value of level constantly increases
When big, corresponding warning level, such as pop-up dialog box prompting, mobile phone vibration, positive lock screen can be improved.
Judge whether user is in danger situation and (provides correlation using contextual information by context aware technology for user
Information or service, correlation therein depend on user's currently ongoing task) it realizes, in order to whether determine user
Under possible unexpected injury, need to perceive following information using context aware technology:
1) user behavior posture.It is distinguished compared to behavior, under behavior and behavior of the same race
The posture of user distinguish it is more meaningful, this is because user may there are many hold the posture of mobile phone under behavior of the same race.
2) interactive mode of user and mobile phone.Can the interactive mode that accurately judge user and mobile phone, accurately sentence
It is disconnected go out user and mobile phone interactive mode have great importance to judge user whether to be likely to be under danger situation, but needs
Recognize only to be insufficient to whether user is in danger situation to carry out identification by using the interactive mode of user and mobile phone
, it can only constitute necessary condition, rather than sufficient and necessary condition.
3) spatial positional information locating for user.By taking walking as an example, walking is as one of the most common type in people's daily life
Behavior may occur in any occasion, but it is reasonable that under normal conditions when user is jaywalking or walking exists
When car lane periphery, it is potentially dangerous a possibility that it is higher, therefore pay close attention to user spatial positional information be necessary.
Meanwhile to be currently available that positioning means from the point of view of, meter level following precision can not be realized to the position of user based on smart phone
Positioning, therefore the how far that user distance will be paid close attention to using respective algorithms the region of unexpected injury may occur.
Human bodys' response module be to the ongoing human body behavior of user and hold mobile phone posture (such as walking, race
Walk, ride) it is identified, this system carries out the acquisition of human body behavioral data using mobile phone, and the collected data of mobile phone are determined
Justice is initial data, when raw data format is defined as 3-axis acceleration, three axis angular rates, geographical location information, data acquisition
Between stab.
Initial data can not be input in model directly as input, needed to carry out feature extraction to initial data, be obtained
To characteristic data set, and it is entered into model and is trained.Specifically, this is mention to the feature of initial data
It takes, analyzed according to the feature and data set training pattern of selection, to feature selecting and model selection.
Fig. 2 and Fig. 3 shows the collected initial data of mobile phone acceleration sensor in gait processes, has distinctness
Time series feature, this system divides initial data according to time window and to extract behavioural characteristic therein as follows:
1)/angular speed calculating mean value is added to every axis.
2)/angular speed calculating standard deviation is added to every axis.
3) every axis is added/angular speed calculates and closes plus/angular speed.
For certain specific human body behavior, its feature, this system can be captured in the time window of certain time length
Specific experiment has been carried out to the multi-parameter (time window length, model structure etc.) for influencing modelling effect, and has passed through experiment number
It is applied in system according to the corresponding model of preferably one group of experiment parameter is had chosen.
Road network module be using context aware technology obtain user be presently near position contextual information (including
Road network information etc.), and combined by spatial positional information with the contextual information on user present position periphery, it can count in real time
It calculates user and is in a possibility that Traffic accident injury easily sends out region.This system by the position sensor built in smart phone come pair
The spatial positional information of user characterizes, and obtains the situation on user present position periphery by calling third party's LBS service
Information.
Acquisition for user's space location information, this system mainly use following methods:
1) GPS positioning, needs GPS hardware supported, and utility satellite positions to obtain the latitude and longitude information of current location, tool
Have that speed is fast, feature with high accuracy, and still can be used in the absence of a network, but uses the head of GPS positioning
The secondary Connection Time is longer, and only open location outdoors can obtain preferable effect, indoors, the fields such as high building is intensive
Closing locating effect can be poor, and the energy consumption of this positioning method is highest in current mobile terminal positioning method.
2) the characteristics of base station location, the principle of base station location have been carried out introduction in front, this positioning method for by
Environment influence is smaller, can use in the range of base station covers, but base station location needs consumed flow, while positioning accuracy
It is lower.
3) Wi-Fi positioning as base station location, be protected from environmental it is smaller, there are Wi-Fi signal place
To use.
4) AGPS is positioned, and in conjunction with GSM or GPRS and satellite positioning, satellite information is sent using base station generation, to reduce GPS chip
It the delay time for obtaining satellite-signal, still can be by base station signal, to mitigate GPS to the scene of indoor or high building masking
Degree of dependence of the chip to satellite-signal.
5) location based service that third party provides, such as Google Maps, Amap, general third-party application meeting
Corresponding service interface is provided, corresponding location information can be got by the service interface that third party provides.
The continuity of the accuracy, user location variation that obtain in view of user location, this system devise following two
Mode portrays the spatial positional information of user:
1) the spatial perception method based on distance between beeline and dot is got with the immediate road of user current location
Degree of closeness (the i.e. shortest distance d) of section and user
2) the spatial perception method based on location track is got with the position rail in user's passing a period of time
The matching degree m in the highest section of mark matching degree
This system is defined user based on the weighted array of both the above calculated result and is in Traffic accident injury Yi Faqu
A possibility that domain.
Interaction type module this system realized by using API provided by Mobile operating system (such as Android,
IOS), the alternative events of user and mobile phone are monitored by the API of operating system offer, and based on event and state come to user
Judged with the interactive mode of mobile phone.
It can be from geospatial location locating for user, user's human body behavior, user and hand when user uses mobile phone
The interaction mode many aspects of the machine contextual information current to user is portrayed to obtain the danger situation user portrait of user, and
Based on reflecting between the coefficient that the machine learning models such as artificial neural network establish user's portrait and user is under danger situation
Relationship is penetrated, the coefficient that user is under danger situation can be obtained in real time in the actual environment based on this mapping relations, according to
The numerical values recited of this coefficient carries out user to be classified other prompt and warning, to ensure the security of the lives and property of user.This system
Main Morphology will in the form of client end AP P present, as the application directly interacted with user, the frame of client end AP P
Structure is as shown in figure 4, the function of client end AP P mainly has:
1) raw data acquisition, including user behavior data, location information.
2) original data processing, to mobile phone to data be based on sliding window pre-process.
3) Behavior-based control identification module carries out user behavior recognition.
4) environmental information locating for user is provided based on customer position information and road model.
5) judgement is provided to whether user is in dangerous scene.
6) communications and data is carried out with server-side to exchange.
According to Fig.5, in the human body behavior perception algorithm experiment based on random forest, to parameter n_trees and n_
Feature_used is provided with several groups of control experiments, in control experiment, is used as training set, 1/3 use for the 2/3 of characteristic data set
Make test set, training set and test set are concentrated obtained by stratified sampling from characteristic, guarantee either training set and test
Collection all with characteristic data set data distribution having the same.According to experimental result, n_trees=30, n_feature_ are used
Used=0.2 is used as more excellent parameter.Its specific experimental result comparative diagram is as shown in Fig. 5-1 and 5-2.
Core, that is, user behavior analysis is neutralized for the present invention, acquires design data multiple groups comparative experiments.By pair
Than experiment, under the determining more excellent parameter of model, model shows 96.47% accuracy rate on test set, from its experiment effect
Fruit sees that the correctness of algorithm is preferable.
Specific embodiment two:
In conjunction with technology and algorithm mentioned above, this system gives preliminary realization effect and specifically completes user
The development of management module, data acquisition module and danger situation identification prevention module, user behavior monitoring and dangerous feelings
Border identification and the part interface for sending prompting show as shown in Figure 6:
Specifically, danger situation prevention system design has following function:
1) user management.One of support function as system, provided for user log in, register and personal information modification
Equal interfaces.Meanwhile the safety to guarantee client and server-side data interaction, for the part of interface that server-side provides, visitor
Family end needs to enclose Token in request to guarantee the legitimacy of user identity.
2) data management.For system, other each functional modules provide data service for data management, in particular it is required that completing hand
Machine carrys out the initial data of the built-in sensors such as acceleration sensor, position sensor；Complete pretreatment (this to initial data
Place pretreatment only refer to asynchronous collecting to initial data integrate)；It completes initial data and is uploaded to server-side so as to mould
Type is adjusted.
3) danger situation identification and prevention.The core function of danger situation identification and prevention as system, is adopted according to data
Collect real-time behavioral data, position data of module etc. to identify and to whether user is in danger situation according to recognition result
Take further behavior.More specifically, needing to complete real-time Human bodys' response；Complete real-time user location identification；
Complete real-time interactive mode identification.
The above is only the preferred implementation of the unexpected prevention method in the mobile phone use process based on user behavior analysis
Mode, the protection scope of the unexpected prevention method in the mobile phone use process based on user behavior analysis are not limited merely to above-mentioned
Embodiment, all technical solutions belonged under the thinking all belong to the scope of protection of the present invention.It should be pointed out that for the skill of this field
For art personnel, several improvements and changes without departing from the principles of the present invention, such modifications and variations also should be regarded as this
The protection scope of invention.
Claims (6)
1. the unexpected prevention method in a kind of mobile phone use process based on user behavior analysis, it is characterized in that: the method packet
Include following steps:
Step 1: the ongoing human body behavior of user's current time and posture are known by Human bodys' response module
Not, the atom behavior at user's current time is obtained；
Step 2: the road network information near user's current time present position is obtained by road network module, user is obtained and works as
The spatial positional information at preceding moment；
Step 3: the alternative events of logical interaction identification module monitoring current time user and mobile phone, obtain user's current time with
The mode of interaction occurs for mobile phone；
Step 4: behavior, spatial positional information and the coefficient interacted with mobile phone at user's current time are converted to company
Continuous property stochastic variable, establishes danger situation identification model, identifies mould for the continuous random variable after conversion as danger situation
The input quantity of type, obtaining user's current time handles danger coefficient under danger situation, passes through client according to the danger coefficient
End APP sounds a warning to user.
2. the unexpected prevention method in a kind of mobile phone use process based on user behavior analysis according to claim 1,
It is characterized in that: the step 1 specifically:
Step 1: Human bodys' response module is acquired human body behavior initial data by mobile phone acceleration sensor, it will
The collected initial data of mobile phone is divided into 3-axis acceleration, three axis angular rates, geographical location information and data acquisition time stamp；
Step 2: carrying out feature extraction to the initial data, obtained characteristic data set represents the behavior at user's current time
Mode；The behavior includes standing, walking, rides, drives, by bus.
3. the unexpected prevention method in a kind of mobile phone use process based on user behavior analysis according to claim 1,
It is characterized in that: the step 2 specifically:
Road network module characterizes user control location information by the position sensor built in mobile phone, by calling the
The LBS service of tripartite obtains the contextual information on user present position periphery, obtains the spatial positional information at user's current time.
4. the unexpected prevention method in a kind of mobile phone use process based on user behavior analysis according to claim 3,
It is characterized in that: the spatial positional information at user's current time is obtained by GPS hardware in mobile phone, obtained using satellite positioning
Take the spatial positional information at family；The spatial positional information of user is obtained by base station location；It is positioned and is obtained by WiFi
The spatial positional information of user；It is positioned by AGPS, in conjunction with GSM or GPRS and satellite positioning, send auxiliary to defend using base station generation
Star information, reduction GPS chip obtain the delay time of satellite-signal, are believed by base station the scene of indoor or high building masking
Number, mitigate dependence of the GPS chip to satellite-signal；The location based service provided by third party, including Google Maps
Or Amap, the spatial positional information of user is obtained by the service structure that third party provides.
5. the unexpected prevention method in a kind of mobile phone use process based on user behavior analysis according to claim 1,
It is characterized in that: the step 3 specifically:
For interactive identification module using API provided by mobile phone operating system, operating system includes Android or IOS, is passed through
The alternative events of API monitoring user and mobile phone that the mobile phone operating system provides, and it is based on the alternative events and state pair
The interactive mode of user and mobile phone judges, obtains the coefficient that user interacts with mobile phone.
6. the unexpected prevention method in a kind of mobile phone use process based on user behavior analysis according to claim 1,
It is characterized in that: the step 4 specifically:
Step 1: the behavior at user's current time, spatial positional information and the coefficient interacted with mobile phone are converted to company
Continuous property stochastic variable, establishes danger situation identification model by following formula:
C=behavior*space*time (1)
Wherein, C is the output under user's current time danger situation, and behavior is the atom behavior side at user's current time
Formula, space are the spatial positional information at user's current time, and time is the coefficient interacted with mobile phone at user's current time；
Step 2: by C and a certain threshold value D0Comparison, when C is greater than D0When, determine that user is under danger situation, is formed at set D
In danger situation；
Step 3: when set D is greater than D0When, danger coefficient is calculate by the following formula:
Level=C-D0 (2)
Wherein, level is to handle the danger coefficient under danger situation at user's current time；
When D is greater than D0When, level is equal to zero；
Step 4: client end AP P carries out early warning according to level value, when the value of level constantly increases, improve to user's
Warning level is reminded, the warning of mobile phone vibration and positive lock screen including pop-up dialog box.
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| CN110682860A (en) * | 2019-09-19 | 2020-01-14 | 佛吉亚好帮手电子科技有限公司 | Vehicle-mounted intelligent alarm method and system for communication in gas station |
| CN113192331A (en) * | 2021-04-26 | 2021-07-30 | 吉林大学 | Intelligent early warning system and early warning method for riding safety in internet environment |
| CN114627615A (en) * | 2022-03-01 | 2022-06-14 | 重庆邮电大学 | Danger prompting method for using mobile phone during walking |
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