CN117292550B - Speed limiting early warning function detection method for Internet of vehicles application - Google Patents
Speed limiting early warning function detection method for Internet of vehicles application Download PDFInfo
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Abstract
The invention discloses a speed-limiting early-warning function detection method for Internet of vehicles application, and relates to the technical field of speed-limiting early-warning functions. The speed limiting early warning function detection method for the application of the Internet of vehicles is used by automatically monitoring the driving frequency of the daily driving track of the userDownloading and modeling buffering the driving path reaching the preset driving frequency value, and when the user runs to the driving path model again in the follow-up driving processThe road section where the traffic is located automatically calls the cached road and speed limit data to carry out speed limit early warning broadcasting, and the traffic is not required to be continuously utilized to carry out real-time transmission broadcasting through the Internet of vehicles module, so that the usage amount of the traffic is greatly saved, and the effect of self-adaptive broadcasting is realized.
Description
Technical Field
The invention relates to the technical field of speed-limiting early-warning functions, in particular to a speed-limiting early-warning function detection method for Internet of vehicles application.
Background
Both mobile phone navigation and automatic car navigation have speed limiting and early warning functions, the mobile phone navigation and the automatic car navigation are used for carrying out self-positioning on a vehicle through Beidou or GPS, the mobile phone navigation is used for calculating the speed of the vehicle through an algorithm, the car navigation can be used for obtaining the speed of the vehicle by utilizing a speed sensor of the vehicle body, the mobile phones for obtaining road condition information are networked through the mobile phones, the vehicle is networked through the car, and the road map and the speed limiting information stored by a server are needed to be obtained through a network;
in the running process of the vehicle, the vehicle machine self-navigation utilizes the flow to acquire road condition map information and speed limit information in real time, simultaneously monitors the running speed of the vehicle body in real time, compares the speed with road section speed limit information, and automatically broadcasts and reports the speed limit information in overspeed, but the existing vehicle machine speed limit early warning function has certain limitations, and is specifically as follows:
the effect of self-adaptive broadcasting cannot be achieved, namely, the map of the position of the driving track and speed limit data are required to be transmitted by utilizing the Internet of vehicles in real time, but in the daily use process, people know that the repetition rate of the driving track is higher in the daily driving process, people mostly travel back and forth between fixed starting points in the daily driving process, and the data are required to be transmitted by utilizing the traffic of the vehicle when traveling on repeated road sections each time, so that the use of the data traffic is increased.
Therefore, it is necessary to provide a speed-limiting early-warning function detection method for internet of vehicles application to solve the above technical problems.
Disclosure of Invention
(one) solving the technical problems
In order to solve the technical problems, the invention provides a speed limiting early warning function detection method for Internet of vehicles application.
(II) technical scheme
In order to achieve the above purpose, the invention is realized by the following technical scheme: the speed limiting early warning function detection method for the application of the Internet of vehicles specifically comprises the following steps:
s1, firstly, a positioning module collects the driving track of a vehicle in real time and sends the driving track to an execution module, and the execution module counts the driving frequency of the driving trackThe driving frequency->When the running frequency reaches the set value, the execution module is connected to a server through the Internet of vehicles module, and the server drives the running frequency +.>The road and speed limit data of the driving track reaching the set value are sent to an execution module, and the execution module is used for executing the driving according to the driving frequency +.>Road and speed limit data of the driving path reaching the set value to establish driving path model +.>Wherein E and W each represent the driving frequency +.>The longitude and latitude value of a certain point on the driving track reaching the set value, V represents the speed limit data on the point, and the driving frequency is +.>The combination of a plurality of consecutive points on the path reaching the set value constitutes the driving frequency +.>Map data of the wheel path reaching the set value;
s2, a driving track model established through the step S1Sequentially buffering to a first storage module to form a model set X= [ -or ]>、/>……/>]；
S3, acquiring vehicle position information in real time during driving to obtain continuously changed vehicle point positionsAt this point +.>Certain driving track model of longitude and latitude values of (a)>The execution module retrieves a track model with the point location information in the model set X according to the point location Y> The execution module is used for enabling the real-time speed of the point where the vehicle is to be according to the real-time information of the vehicle speed provided by the vehicle speed sensor>Model of the track of the driving with the point>Speed of the corresponding point in ∈>Comparison is carried out at +.>Is greater than->The alarm module is controlled to alarm, otherwise, the alarm is not carried out;
s4, any one of the step S1, the step S2 and the step S3 forms a behavior report after the execution is finished, and the behavior report is fed back to the second buffer module for buffer storage so as to be called for reference when the speed limit early warning function of the speed limit early warning APP is detected;
s5, the behavior report cached in the second cache module is fetched, so that detection work of the early warning function is completed.
Preferably, in the step S2, two wheel path modelsThe presence of weightOverlapping road sections, wherein when the duty ratio of the overlapping road sections reaches a set value C, the two driving track models are +.>Fitting to form a new track model +.>。
Preferably, the model set in the step S2Midrange track model->The built execution module always adds the built driving track model according to the driving track information of the vehicle>Driving frequency +.>Statistics, driving frequency according to statisticsReal-time model set->Inside wheel path model->And adding and deleting.
Preferably, the step S4 of generating a behavior report specifically includes the following steps:
1) The positioning module collects road condition information and sends the information to the execution module, and the execution module successfully receives the information to complete statistics and then generates a correct behavior report with the report name ofIf the execution module does not receive the road condition information, generating an error behavior report with the report name of +.>；
2) The execution module successfully receives the road and speed limit data sent by the Internet of vehicles module and generates a correct behavior reportGenerating an error behavior report if not successfully received>；
3) The execution module successfully establishes a driving track model for the road and speed limit data of each received driving road sectionThen generate a correct behavior report +.>If not, forming error action report +_>；
4) Successfully retrieving the wheel path model in the model set XGenerating a correct behavioural report->Generating an error behavior report if unsuccessful>；
5) Generating a correct behavior report after sending out a speed limit early warning broadcasting instructionOtherwise, generating error action report +_>。
Preferably, the execution module invokes the model set XVehicle track modelThe method comprises the following specific steps:
l1, corresponding driving track model in replication model set X
L2, vehicle track model with successful copyingAnd analyzing to obtain road and speed limit data of the road section.
Preferably, in the step S5, the detection of the early warning function is specifically that when the early warning function fails, an error behavior report in the second cache module is called, and then the report is read.
Preferably, the driving frequency of the driving track in step S1And establishing a vehicle track model when the set value is reached, wherein the set value is set and adjusted by a user according to own daily driving habits.
Preferably, for the track model in the model set XThe adding and deleting steps are as follows:
p1, when the capacity of the second buffer module does not reach the upper storage limit, directly establishing a driving track modelThe second buffer module is stored in the first buffer module;
p2, storing the model set X with the track model after the upper limit is reached in the second buffer moduleAccording to the driving frequency->The magnitude of the numerical value of (2) is arranged from a nearly small scale, and a new established driving track model is +.>Driving frequency +.>A vehicle track model arranged at the end in a model set X is larger than +.>Driving frequency +.>When the vehicle track model is used, a newly built vehicle track model is adopted>Replacing one of the track models arranged at the end in model set X>；
P3, before the second buffer module does not reach the upper storage limit, a certain driving track model in the model set XDriving frequency +.>After the value is 0 and the duration is 0 and the set duration is reached, the execution module automatically deletes the driving frequency +.>Track model with value of 0 and duration of 0 reaching set duration>。
Preferably, for driving frequencyThe calculation formula of (2) is as follows:
；
h is a statistical period set by a user, and N is the number of times the vehicle travels on the counted road segments in the statistical period.
(III) beneficial effects
The invention provides a speed limiting early warning function detection method for Internet of vehicles application. Compared with the prior art, the method has the following beneficial effects:
the speed limiting early warning function detection method for the application of the Internet of vehicles provided by the invention automatically monitors the driving frequency of the daily driving track of the user when in useDownloading, modeling and caching the road and speed limit data corresponding to the driving track reaching the preset driving frequency value, and in the follow-up driving process, when the user drives to the model driving track model +_, respectively>The road section where the traffic is located automatically calls the cached road and speed limit data to carry out speed limit early warning broadcasting, and the traffic is not required to be continuously utilized to carry out real-time transmission broadcasting through the Internet of vehicles module, so that the traffic consumption is greatly reduced, and the effect of self-adaptive broadcasting is realized.
Drawings
FIG. 1 is a schematic flow chart of the method of the present invention;
FIG. 2 is a schematic diagram of the logic of the present invention;
FIG. 3 is a schematic diagram of an embodiment of the present invention;
FIG. 4 is a schematic diagram of a route trace according to the present invention;
FIG. 5 is a graph showing the frequency of the travel track according to the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Example 1
Referring to fig. 3, an embodiment of the invention provides a technical scheme, which comprises a positioning module, a vehicle speed sensor, a vehicle networking module, a speed limit early warning APP, a server and an alarm module, wherein the speed limit early warning APP is installed on a vehicle, the vehicle networking module is connected with the server for data communication, a map of a route where a position is located and speed limit information are obtained through uploading position information, the positioning module positions the vehicle in real time and gives data to the speed limit early warning APP, the vehicle speed sensor gives the measured vehicle speed to the speed limit early warning APP, the speed limit early warning APP calculates and judges whether the vehicle is overspeed, and the overspeed gives out instructions to control the alarm module to work.
Example two
Referring to fig. 1 to 5, an embodiment of the present invention provides a technical solution: the speed limiting early warning function detection method for the application of the Internet of vehicles specifically comprises the following steps:
s1, firstly, a positioning module collects vehicle track information in real time and sends the vehicle track information to an execution module, and the execution module counts the running frequency of the trackDriving frequency for driving track +.>The calculation formula of (2) is as follows:
h is a statistical period set by a user, and N is the number of times the vehicle runs on the counted road sections in the statistical period;
statistical driving frequency of a certain driving trackThe user can set H as a circle and N as 5 times, and the user can perform setting adjustment according to the daily driving habit of the user, so that the user can perform daily driving frequency in a certain driving track>When the set value reaches 5 times per week, the execution module is connected to a server through the Internet of vehicles module, and the server drives the frequency +.>The road and speed limit data of the driving track reaching the set value are sent to an execution module, and the execution module is used for executing the driving according to the driving frequency +.>Road and speed limit data of driving track reaching set value to establish driving track modelWherein E and W each represent the driving frequency +.>The longitude and latitude value of a certain point on the running track reaching the set value, V represents the speed limit data on the point, and the running frequencyA plurality of consecutive point combinations on the driving track reaching the set value form the driving frequency +.>Track map data reaching a set value;
s2, a driving track model established through the step S1Sequentially buffering to a first storage module to form a model set X= [/>、/>……/>]When the newly built driving track model +.>Certain driving track model in model set X>If there is a repeated road section and the ratio of the overlapped road section reaches the set value C, the two driving track models are +.>Fitting to form a new track model +.>The fitting process is to make two driving track models +.>Model data of the inner overlapping road section are kept, and two driving track models are added>Fitting the internal differential road section model data with the reserved model data to form a new driving track model +.>This process is specifically described below:
first wheel path model
Second wheel path model
Then the new track model after fittingIs as follows
The calculation formula of the set value C is as follows:
wherein L is two driving track models with overlapped road sectionsThe length value of the road section corresponding to the overlapped part, J represents the two driving track models +.>Track model with longest middle road section>The corresponding road section length value, when the set value C is more than 60%, the two driving track models are added>Fitting is carried out;
the illustration is made with reference to fig. 4:
the first driving track model exists in the model set XA route track from the point A to the point Q through the point T is shown in the figure;
a second driving track model exists in the model set XA route track from the point A to the point M through the point T is shown in the figure;
due to the trackModelModel of the track of the route and the track of the driving>The overlapping part of the running tracks is smaller than 60% of the set value C, so that two running track models are +.>Do not overlap;
and a newly built third driving track modelIn the figure, it shows the path from point A to point I through point T, the path model +.>The model of the route track and the driving track>The overlapping part of the track is larger than 60% of the set value C, so the two track models are +.>Overlapping is carried out so as to save the memory of the second memory module;
model X center driving track modelThe built execution module always adds the built driving track model according to the driving track information of the vehicle>Driving frequency +.>Statistics, driving frequency according to statistics>Real-time driving track model in model set X>Adding and deleting;
for the driving track model in the model set XThe adding and deleting steps of (a) are specifically as follows:
p1, when the capacity of the second buffer module does not reach the upper storage limit, directly establishing a driving track modelThe second buffer module is stored in the first buffer module;
p2, storing the model set X with the track model after the upper limit is reached in the second buffer moduleAccording to the driving frequency->The magnitude of the numerical value of (2) is arranged from a nearly small scale, and a new established driving track model is +.>Driving frequency +.>A vehicle track model arranged at the end in a model set X is larger than +.>Frequency of->When the vehicle track model is used, a newly built vehicle track model is adopted>Replacing one of the track models arranged at the end in model set X>；
According to the driving frequencyThe number of pairs of the wheel path models in the model set X +.>The sorting is performed according to the rule that the sorting refers to, namely sorting is performed according to the output frequency value from large to small, the sorting is performed by adopting a sorting algorithm in the prior art, which is not described in detail herein, and the second storage module stores a new vehicle track model which is continuously built after reaching the upper limit->Driving frequency ∈>Driving frequency in model set X>Minimum number one driving track modelDriving frequency +.>Comparison is carried out at +.></>Deleting the newly established driving track model +.>In->>When the vehicle is running, a new running rail is usedTrace model->Replacing the travel track model in the model set X>；
P3, before the second buffer module does not reach the upper storage limit, a certain driving track model in the model set XDriving frequency +.>After the value is 0 and the duration is 0 and the set duration is reached, the execution module automatically deletes the driving frequency +.>Track model with value of 0 and duration of 0 reaching set duration>Certain driving track model in model set X>The track of the route is never passed in the set time range, and the track model is +.>Is deleted, the set time range is set according to the user's needs, for example, the user sets that a certain wheel path model is never travelled in one month +.>When the corresponding route is the route, deleting the track model +.>The time range can be set in other time ranges such as two months, and the value is set by the user according to the own needs of the user;
s3, in the driving process, real-timeCollecting vehicle position information to obtain continuously changed vehicle point positionsCertain driving track model of longitude and latitude value of point position +.>The execution module retrieves a track model with the point location information in the model set X according to the point location Y>And the execution module invokes the track model in the model set X +.>The method specifically comprises the following steps:
l1, corresponding driving track model in replication model set X；
L2, vehicle track model with successful copyingThe road and speed limit data of the road section are obtained by utilizing the existing analysis algorithm, wherein the analysis algorithm is the prior art and is not described in detail herein;
after the calling, the execution module calculates the real-time speed of the point where the vehicle is located according to the real-time information of the vehicle speed provided by the vehicle speed sensorModel of the track of the driving with the point>Speed of the corresponding point in ∈>Comparison is carried out at +.>>/>The alarm module is controlled to alarm, otherwise, the alarm is not carried out;
the map of the road section of the daily high-frequency travel of the user and the speed limit data are constructed and stored in the speed limit early warning APP, so that the speed limit information can be automatically called out from the storage to broadcast the speed limit early warning when the user repeatedly walks the road section, online data acquisition through the Internet of vehicles is not needed to be carried out by utilizing the flow again, and the use of the flow is greatly saved;
s4, any one of the steps S1, S2 and S3 forms a behavior report after the execution is finished, and the report generation steps are as follows:
1) The positioning module collects road condition information and sends the information to the execution module, and the execution module successfully receives the information to complete statistics and then generates a correct behavior report with the report name ofIf the execution module does not receive the road condition information, generating an error behavior report with the report name of +.>；
2) The execution module successfully receives the road and speed limit data sent by the Internet of vehicles module and generates a correct behavior reportGenerating an error behavior report if not successfully received>；
3) The execution module successfully establishes a driving track model for the road and speed limit data of each received driving road sectionThen generate a correct behavior report +.>If not, forming error action report +_>；
4) Successfully retrieving the wheel path model in the model set XGenerating a correct behavioural report->Generating an error behavior report if unsuccessful>；
5) Generating a correct behavior report after sending out a speed limit early warning broadcasting instructionOtherwise, generating error action report +_>；
After all reports are generated, feeding back the reports to the second buffer module for buffer storage so as to be called for reference when the speed limit early warning function of the speed limit early warning APP is detected;
s5, the behavior report cached in the second cache module is fetched, so that detection work of an early warning function is completed, and the detection is specifically as follows: when the early warning function fails, the error behavior report in the second buffer module is called, and then the report is read, so that the early warning function is detected.
In the step S2, for two wheel path models having repeated road sectionsThe purpose of the fitting is to reduce the occupation of the memory space of the first memory module if two wheel path models are +.>The number of the repeated road section information is large, so that the first storage module needs to store a large amount of repeated data, thereby greatly reducing the space utilization rate and being unfavorable for storing more driving track models +.>Thus, the repeated road section driving track model is set>Fitting function of the space wave rate is further achieved, and the effect of reducing the space wave rate is achieved;
the speed limiting early warning function detection method for the application of the Internet of vehicles provided by the invention automatically monitors the driving frequency of the daily driving track of the user when in useFor reaching the preset driving frequency +.>Downloading and modeling and caching road and speed limit data corresponding to the driving path of the value of the driving path, and in the follow-up driving process, when the user drives to the driving path model +.>The road section where the traffic is located automatically calls the cached road and speed limit data to carry out speed limit early warning broadcasting, and the traffic is not required to be continuously utilized to carry out real-time transmission broadcasting through the Internet of vehicles module, so that the usage amount of the traffic is greatly saved, and the effect of self-adaptive broadcasting is realized.
In the specific implementation process, the speed limit early warning APP is installed on the vehicle, and the user operates the speed limit early warning APP according to own daily driving habits, and is specifically as follows:
user-settable travel track frequency tableThe statistical period H of the vehicle is one month, and the speed limit early warning APP can be used for controlling the running frequency of the vehicle track according to the running frequency of the vehicle track during the running process of the user>Providing a travel track frequency table to the user for review, see the table shown in fig. 5;
daily driving frequency of user through speed limit early warning APPCounting, obtaining a statistical table through calculation, and giving the statistical table to a user, and meanwhile, counting a running track model +.>The starting point information of the running track is synchronously displayed, so that the user can know the starting point information more conveniently;
and the vehicle machines of most vehicles on the market have remote mobile phone connection function, such as the vehicle of installing the angry star function, the real-time information situation of car can be through car networking module feedback to mobile phone terminal's angry star APP in to can realize car condition information remote terminal and share, and equally, to the trip track frequency table of statistics, the user obtains through the angry star APP of operation mobile phone terminal, sends out and obtains the order back car machine speed limit early warning APP and sends the form to mobile phone terminal through car networking module, supplies the user to consult.
And all that is not described in detail in this specification is well known to those skilled in the art.
It is noted that relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, 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.
Although embodiments of the present invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made therein without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
Claims (6)
1. The speed limiting early warning function detection method for the application of the Internet of vehicles is characterized by comprising the following steps of:
s1, firstly, a positioning module collects the driving track of a vehicle in real time and sends the driving track to an execution module, and the execution module counts the driving frequency of the driving trackThe driving frequency->When the running frequency reaches the set value, the execution module is connected to a server through the Internet of vehicles module, and the server drives the running frequency +.>The road and speed limit data of the driving track reaching the set value are sent to an execution module, and the execution module is used for executing the driving according to the driving frequency +.>Road and speed limit data of the driving path reaching the set value to establish driving path model +.>Wherein E and W respectively represent the running frequencyThe longitude and latitude value of a certain point on the driving track reaching the set value, V represents the speed limit data on the point, and the driving frequency is +.>Up to a set numberMultiple consecutive combinations of points on the value wheel path form the driving frequency +.>Map data of the wheel path reaching the set value;
s2, a driving track model established through the step S1Sequentially buffering to a first storage module to form a model set X= [ -or ]>、/>……/>]；
S3, acquiring vehicle position information in real time during driving to obtain continuously changed vehicle point positionsAt this point +.>Longitude and latitude values of (a) and a certain driving track model +.>The execution module retrieves a track model with the point location information in the model set X according to the point location Y>The execution module is used for enabling the real-time speed of the point where the vehicle is to be according to the real-time speed information of the vehicle speed provided by the vehicle speed sensor>Is located on the track of the vehicleModel->Speed of the corresponding point in ∈>Comparison is carried out at +.>Is greater than->The alarm module is controlled to alarm, otherwise, the alarm is not carried out;
s4, any one of the step S1, the step S2 and the step S3 forms a behavior report after the execution is finished, and the behavior report is fed back to the second buffer module for buffer storage so as to be called for reference when the speed limit early warning function of the speed limit early warning APP is detected;
s5, retrieving the behavior report cached in the second cache module, so as to finish the detection work of the early warning function;
in the step S2, two wheel path modelsWhen there is an overlapping road section, the two track models are added when the ratio of the overlapping road section reaches the set value C>Fitting to form a new track model +.>The calculation formula of the set value C is as follows:
wherein L is two driving track models with overlapped road sectionsThe length value of the road section corresponding to the overlapped part, J represents the two driving track models +.>Track model with longest middle road section>The length value of the corresponding road section;
the model set in the step S2Midrange track model->The built execution module always adds the built driving track model according to the driving track information of the vehicle>Driving frequency +.>Statistics, driving frequency according to statistics>Real-time model set->Inside wheel path model->Adding and deleting;
for the driving track model in the model set XThe adding and deleting steps are as follows:
p1, the capacity of the second buffer module does not reach the storage capacityWhen the upper limit is stored, the established driving track model is directly builtThe second buffer module is stored in the first buffer module;
p2, storing the model set X with the track model after the upper limit is reached in the second buffer moduleAccording to the driving frequencyThe values of (2) are arranged from large to small, and a new track model is built +.>Driving frequency +.>A vehicle track model arranged at the end in a model set X is larger than +.>Driving frequency of>When the vehicle track model is used, a newly built vehicle track model is adopted>Replacing one of the track models arranged at the end in model set X>；
P3, before the second buffer module does not reach the upper storage limit, a certain driving track model in the model set XIs a driving frequency of (2)After the value is 0 and the duration is 0 and the set duration is reached, the execution module automatically deletes the driving frequency +.>Track model with value of 0 and duration of 0 reaching set duration>。
2. The method for detecting the speed limit early warning function for the application of the internet of vehicles according to claim 1, wherein the method comprises the following steps: the step S4 of generating the behavior report specifically comprises the following steps:
1) The positioning module collects road condition information and sends the information to the execution module, and the execution module successfully receives the information to complete statistics and then generates a correct behavior report with the report name ofIf the execution module does not receive the road condition information, generating an error behavior report with the report name of +.>；
2) The execution module successfully receives the road and speed limit data sent by the Internet of vehicles module and generates a correct behavior reportGenerating an error behavior report if not successfully received>；
3) The execution module successfully establishes a driving track model for the road and speed limit data of each received driving road sectionThen generate aCritical behavioral report->If not, forming error action report +_>；
4) Successfully retrieving the wheel path model in the model set XGenerating a correct behavioural report->Generating an error behavior report if unsuccessful>；
5) Generating a correct behavior report after sending out a speed limit early warning broadcasting instructionOtherwise, generating error action report +_>。
3. The method for detecting the speed limit early warning function for the application of the internet of vehicles according to claim 1, wherein the method comprises the following steps: the execution module invokes the wheel path model in the model set XThe method comprises the following specific steps:
l1, corresponding driving track model in replication model set X；
L2, vehicle track model with successful copyingAnd analyzing to obtain road and speed limit data of the road section.
4. The method for detecting the speed limit early warning function for the application of the internet of vehicles according to claim 2, which is characterized by comprising the following steps: in the step S5, the detection of the early warning function is specifically that when the early warning function fails, an error behavior report in the second cache module is called, and then the report is read.
5. The method for detecting the speed limit early warning function for the application of the internet of vehicles according to claim 1, wherein the method comprises the following steps: driving frequency of the driving track in step S1And establishing a vehicle track model when the set value is reached, wherein the set value is set and adjusted by a user according to own daily driving habits.
6. The method for detecting the speed limit early warning function for the application of the internet of vehicles according to claim 5, wherein the method comprises the following steps: for driving frequencyThe calculation formula of (2) is as follows:
；
h is a statistical period set by a user, and N is the number of times the vehicle travels on the counted road segments in the statistical period.
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