CN117292550B - A speed limit warning function detection method for Internet of Vehicles applications - Google Patents

Abstract

The invention discloses a speed limit early warning function detection method for Internet of Vehicles applications, and relates to the technical field of speed limit early warning functions. This speed limit warning function detection method for Internet of Vehicles applications automatically monitors the driving frequency of the user's daily driving trajectory when used. , for the driving trajectory that reaches the preset driving frequency value, the road and speed limit data corresponding to the driving trajectory are downloaded and modeled and cached. In the subsequent driving process, when the user drives to the driving trajectory model again The road section where it is located will automatically call the cached road and speed limit data for speed limit warning broadcast, without the need to continue to use traffic to transmit the broadcast in real time through the Internet of Vehicles module, thus greatly saving the usage of traffic and achieving the effect of adaptive broadcast. .

Description

A speed limit warning function detection method for Internet of Vehicles applications

Technical field

The invention relates to the technical field of speed limit early warning functions, and is specifically a speed limit early warning function detection method for Internet of Vehicles applications.

Background technique

Both mobile phone navigation and automatic car navigation have speed limit warning functions. Both use Beidou or GPS to position the vehicle itself, while mobile phone navigation calculates the vehicle speed through positioning and algorithms, while car navigation can use the vehicle body The speed sensor itself obtains the vehicle speed, and the mobile phone obtains road condition information through the mobile phone network, and the vehicle uses the Internet of Vehicles, but both need to use the network to obtain the road map and speed limit information stored in the server;

While the vehicle is driving, the vehicle's own navigation uses traffic to obtain road map information and speed limit information in real time. At the same time, it monitors the vehicle's driving speed in real time, compares the vehicle speed with the speed limit information of the road section, and automatically broadcasts when speeding. However, existing vehicles The vehicle speed limit warning function has certain limitations, as follows:

It cannot achieve the effect of adaptive broadcasting, that is, the Internet of Vehicles needs to be used in real time to transmit the map and speed limit data of the driving trajectory. However, in daily use, we know that during daily driving, the repetition rate of people’s travel trajectories is relatively high. High. In daily driving, we often drive back and forth between fixed starting points, and every time we drive on a repeated road section, we need to use vehicle-machine traffic for data transmission, thus increasing the use of data traffic.

Therefore, it is necessary to provide a speed limit warning function detection method for Internet of Vehicles applications to solve the above technical problems.

Contents of the invention

(1) Technical problems solved

In order to solve the above technical problems, the present invention provides a speed limit warning function detection method for Internet of Vehicles applications.

(2) Technical solutions

In order to achieve the above objectives, the present invention is implemented through the following technical solutions: a speed limit warning function detection method for Internet of Vehicles applications, specifically including the following steps:

S1. First, the positioning module collects the driving trajectory of the vehicle in real time and sends it to the execution module. The execution module counts the driving frequency of the driving trajectory. , the driving frequency/> reaches the set value, the execution module connects to the server through the Internet of Vehicles module, and the server will drive the frequency/> The road and speed limit data of the driving trajectory that reaches the set value are sent to the execution module, and the execution module is based on the driving frequency/> Establish a driving trajectory model based on road and speed limit data that reaches the set value of the driving trajectory/> , where E and W respectively represent the driving frequency/> The longitude and latitude value of a certain point on the driving trajectory that reaches the set value, V represents the speed limit data at that point, and the driving frequency/> The combination of multiple consecutive points on the driving trajectory that reaches the set value constitutes the driving frequency/> The map data of the driving trajectory that reaches the set value;

S2. The driving trajectory model established through step S1 Caching to the first storage module in sequence to form a model set X=[/> ,/> ……/> ];

S3. While driving, collect vehicle location information in real time and obtain constantly changing vehicle locations. , at this point/> The latitude and longitude value of a certain driving trajectory model/> The points in coincide with each other, and the execution module calls a driving trajectory model with the point information in the model set X based on the position point Y/> The execution module calculates the real-time speed of the vehicle at the point based on the real-time vehicle speed information provided by the vehicle speed sensor/> With this point in the driving trajectory model/> The speed of the corresponding point in/> To compare, go to/> Greater than/> Then control the alarm module to alarm, otherwise no alarm will be issued;

Any step in S4, step S1, step S2 and step S3 will form a behavior report after execution, and the behavior report will be fed back to the second cache module for caching in preparation for the speed limit warning function of the speed limit warning APP. Call the query when testing;

S5: Retrieve the behavior report cached in the second cache module to complete the detection of the early warning function.

Preferably, in step S2, the two driving trajectory models If there is an overlapping road section, and the proportion of the overlapping road section reaches the set value C, the two driving trajectory models/> Perform fitting to form a new driving trajectory model/> .

Preferably, the model set in step S2 Mid-travel trajectory model/> After establishment, the execution module always evaluates the established driving trajectory model based on vehicle driving trajectory information/> Driving frequency/> Statistics, driving frequency based on statistics Real-time pairing of model sets/> Driving trajectory model within/> Make additions and deletions.

Preferably, generating a behavior report in step S4 specifically includes the following steps:

1) The positioning module collects traffic information and sends the information to the execution module. The execution module successfully receives the information and completes statistics to generate a correct behavior report. The report name is , if the execution module does not receive the traffic information, it will generate an error behavior report. The report name is/> ;

2) The execution module successfully receives the road and speed limit data sent through the Internet of Vehicles module and generates a correct behavior report. , if the reception is not successful, an error behavior report is generated/> ;

3) The execution module successfully establishes a driving trajectory model for each received road and speed limit data of a driving section. then generate a correct behavior report/> , if not generated, an erroneous behavior report will be generated/> ;

4) Successfully retrieve the driving trajectory model in model set X Generate correct behavior reports/> , if unsuccessful, an error behavior report is generated/> ;

5) Generate a correct behavior report after issuing the speed limit warning broadcast command , otherwise an erroneous behavior report is generated/> .

Preferably, the execution module calls the driving trajectory model in the model set X Specific steps include:

L1. Copy the corresponding driving trajectory model in model set X

L2. The successful driving trajectory model will be copied. Perform analysis to obtain the road and speed limit data of the road segment.

Preferably, the detection of the early warning function in step S5 is specifically: when the early warning function fails, the error behavior report in the second cache module is retrieved, and then the report is read.

Preferably, the driving frequency of the driving trajectory in step S1 When the set value is reached, the driving trajectory model is established, and the user can adjust the setting value according to his or her daily driving habits.

Preferably, for the driving trajectory model in model set The specific additions and deletions are:

P1. When the capacity of the second cache module does not reach the upper limit of storage, the established driving trajectory model will be directly Stored in the second cache module;

P2. After the storage in the second cache module reaches the upper limit, the driving trajectory model in the model set According to driving frequency/> The numerical values are arranged roughly from small to large, and the newly created driving trajectory model/> Driving frequency/> Greater than the last driving trajectory model in model set X/> Driving frequency/> When , the newly established driving trajectory model/> Replace a driving trajectory model ranked at the end in model set X/> ;

P3. Before the second cache module reaches the storage upper limit, a certain driving trajectory model in the model set Driving frequency/> After the value is 0 and continues to be 0 for the set time, the execution module automatically deletes the driving frequency/> The driving trajectory model after the value is 0 and continues to be 0 for the set time period/> .

Preferably, for driving frequency The calculation formula is as follows:

;

H is the statistical period set by the user, and N is the number of times the vehicle travels on the counted road section during the statistical period.

(3) Beneficial effects

The invention provides a speed limit warning function detection method for Internet of Vehicles applications. Compared with existing technology, it has the following beneficial effects:

The invention provides a speed limit warning function detection method for Internet of Vehicles applications that automatically monitors the driving frequency of the user's daily driving trajectory when in use. , for the driving trajectory that reaches the preset driving frequency value, the road and speed limit data corresponding to the driving trajectory are downloaded and modeled and cached. In the subsequent driving process, when the user drives to the model driving trajectory model again/> The road section where it is located will automatically call the cached road and speed limit data for speed limit warning broadcast, without continuing to use the traffic to transmit the broadcast in real time through the Internet of Vehicles module, thus greatly saving the usage of traffic and realizing the adaptive broadcast. Effect.

Description of the drawings

Figure 1 is a schematic flow chart of the method of the present invention;

Figure 2 is a logical schematic diagram of the present invention;

Figure 3 is a schematic diagram of Embodiment 1 of the present invention;

Figure 4 is a schematic diagram of the route trajectory of the present invention;

Figure 5 is an example of a travel trajectory frequency table according to the present invention.

Detailed ways

The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only some of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts fall within the scope of protection of the present invention.

Embodiment 1

Please refer to Figure 3. The embodiment of the present invention provides a technical solution, an Internet of Vehicles speed limit early warning system, including a positioning module, a vehicle speed sensor, an Internet of Vehicles module, a speed limit early warning APP, a server and an alarm module. The speed limit early warning APP is installed On the vehicle, it connects to the server through the Internet of Vehicles module, and the two exchange data. By uploading the location information, the map of the route and the speed limit information of the location are obtained. The positioning module locates the vehicle location in real time and sends the data to the speed limit warning APP. The vehicle speed sensor sends the measured vehicle speed to the speed limit warning APP. The speed limit warning APP determines whether the vehicle is overspeeding through calculation. If the speed is exceeded, it will issue instructions to control the alarm module.

Embodiment 2

Referring to Figures 1 to 5, an embodiment of the present invention provides a technical solution: a speed limit warning function detection method for Internet of Vehicles applications, which specifically includes the following steps:

S1. First, the positioning module collects vehicle driving trajectory information in real time and sends it to the execution module. The execution module counts the driving frequency of the driving trajectory. , for the driving frequency of the driving trajectory/> The calculation formula is as follows:

H is the statistical period set by the user, and N is the number of times the vehicle drove on the counted road section during the statistical period;

The statistical driving frequency of a certain driving trajectory Reach the set value. The user can set the set value according to his or her daily driving habits. For example, the user can set H as one week, N as 5 times, and the daily driving frequency on a certain driving track/> reaches the set value of 5 times per week, the execution module will connect to the server through the Internet of Vehicles module, and the server will set the driving frequency/> The road and speed limit data of the driving trajectory that reaches the set value are sent to the execution module, and the execution module is based on the driving frequency/> Establish a travel trajectory model based on the road and speed limit data of the driving trajectory that reaches the set value. , where E and W respectively represent the driving frequency/> The longitude and latitude value of a certain point on the driving trajectory that reaches the set value. V represents the speed limit data at that point. The driving frequency The combination of multiple consecutive points on the driving trajectory that reaches the set value constitutes the driving frequency/> Driving trajectory map data that reaches the set value;

S2. The driving trajectory model established through step S1 Caching to the first storage module in sequence to form a model set X=[/> ,/> ……/> ], when the newly created driving trajectory model/> With a certain driving trajectory model in model set X/> If there are overlapping road sections, and the proportion of overlapping road sections reaches the set value C, the two driving trajectory models/> Perform fitting to form a new driving trajectory model/> , the fitting process is to combine the two driving trajectory models/> The model data of the overlapping road sections is retained, and the two driving trajectory models/> The differentiated road section model data and the retained model data are fitted to form a new driving trajectory model/> , this process is introduced in detail below:

The first driving trajectory model

The second driving trajectory model

Then the new driving trajectory model after fitting as follows

The calculation formula for setting the value C is as follows:

where L is two driving trajectory models with overlapping road sections The length value of the road segment corresponding to the overlapping part, J represents the two driving trajectory models/> The longest driving trajectory model in the middle section/> The length value of the corresponding road segment. When the set value C is greater than 60%, the two driving trajectory models/> perform fitting;

See Figure 4 for an example:

Model set X is stored in the first driving trajectory model , which in the figure represents the route trajectory from point A through point T to point Q;

Model set X is stored in the second driving trajectory model , which in the figure represents the route trajectory from point A to point M through point T;

And because the driving trajectory model Route trajectory and driving trajectory model/> The overlap of the driving trajectory is less than 60% of the setting value C, so the two driving trajectory models/> No overlap;

The newly established third driving trajectory model , which represents the route trajectory from point A through point T to point I in the figure. The driving trajectory model/> The route trajectory and driving trajectory model/> The overlap of the route trajectory is greater than 60% of the setting value C, so the two driving trajectory models/> Perform overlapping to save the storage memory of the second storage module;

Driving trajectory model in model set X After establishment, the execution module always evaluates the established driving trajectory model based on vehicle driving trajectory information/> Driving frequency/> Statistics, driving frequency based on statistics/> Real-time analysis of the driving trajectory model in model set X/> Make additions and deletions;

For the driving trajectory model in model set X The specific steps for adding and deleting are:

P1. When the capacity of the second cache module does not reach the upper limit of storage, the established driving trajectory model will be directly Stored in the second cache module;

P2. After the storage in the second cache module reaches the upper limit, the driving trajectory model in the model set According to driving frequency/> The numerical values are arranged roughly from small to large, and the newly created driving trajectory model/> Driving frequency/> Greater than the last driving trajectory model in model set X/> frequency/> When , the newly established driving trajectory model/> Replace a driving trajectory model ranked at the end in model set X/> ;

According to driving frequency The numerical value of is very important for the driving trajectory model in model set X/> Sorting is performed. The rules referred to in the sorting are sorting from large to small according to the travel frequency value. The sorting is specifically performed by using the sorting algorithm in the existing technology. This is the existing technology and will not be described in detail here. In the second storage module Store the new driving trajectory model that will be created after reaching the upper limit/> , change its driving frequency/> and travel frequency within model set X/> A driving trajectory model with the smallest numerical value Driving frequency/> To compare, go to/> </> , delete the newly created driving trajectory model/> , in/> > When , the new driving trajectory model/> Replace the vehicle trajectory model in model set X/> ;

P3. Before the second cache module reaches the storage upper limit, a certain driving trajectory model in the model set Driving frequency/> After the value is 0 and continues to be 0 for the set time, the execution module automatically deletes the driving frequency/> The driving trajectory model after the value is 0 and continues to be 0 for the set time period/> , a certain driving trajectory model in model set X/> The route trajectory has never been passed within the set time range. The driving trajectory model/> be deleted, and the set time range is set according to the user's needs. For example, the user sets that he has never driven a certain driving trajectory model within a month/> When corresponding to the route, delete the driving trajectory model/> , it can also be set in other time ranges such as two months. This value can be set by the user according to his or her own needs;

S3. While driving, collect vehicle location information in real time and obtain constantly changing vehicle locations. , a certain driving trajectory model of the latitude and longitude value at that point/> The points in coincide with each other, and the execution module calls a driving trajectory model with the point information in the model set X based on the position point Y/> , and the execution module calls the driving trajectory model in the model set X/> Specifically include the following:

L1. Copy the corresponding driving trajectory model in model set X ;

L2. The successful driving trajectory model will be copied. Use the existing analysis algorithm to analyze and obtain the road and speed limit data of the road section. The analysis algorithm is an existing technology and will not be described in detail here;

After retrieval, the execution module calculates the real-time speed of the vehicle location based on the real-time vehicle speed information provided by the vehicle speed sensor. With this point in the driving trajectory model/> The speed of the corresponding point in/> To compare, go to/> >/> Then control the alarm module to alarm, otherwise no alarm will be issued;

By constructing a driving trajectory model based on the map and speed limit data of the road sections that users frequently travel on daily, and storing it in the speed limit warning APP, the speed limit information can be automatically retrieved from the storage when the user repeatedly walks on this road section. To broadcast speed limit warnings, there is no need to use traffic again to obtain online data through the Internet of Vehicles, which greatly saves the use of traffic;

Any step in S4, step S1, step S2 and step S3 will form a behavior report after execution. The report generation steps are as follows:

1) The positioning module collects traffic information and sends the information to the execution module. The execution module successfully receives the information and completes statistics to generate a correct behavior report. The report name is , if the execution module does not receive the traffic information, it will generate an error behavior report. The report name is/> ;

2) The execution module successfully receives the road and speed limit data sent through the Internet of Vehicles module and generates a correct behavior report. , if the reception is not successful, an error behavior report is generated/> ;

3) The execution module successfully establishes a driving trajectory model for each received road and speed limit data of a driving section. then generate a correct behavior report/> , if not generated, an erroneous behavior report will be generated/> ;

4) Successfully retrieve the driving trajectory model in model set X Generate correct behavior reports/> , if unsuccessful, an error behavior report is generated/> ;

5) Generate a correct behavior report after issuing the speed limit warning broadcast command , otherwise an erroneous behavior report is generated/> ;

After all reports are generated, they are fed back to the second cache module for caching, so that they can be called for reference when testing the speed limit warning function of the speed limit warning APP;

S5. Retrieve the behavior report cached in the second cache module to complete the detection of the early warning function. The specific detection is: when the early warning function fails, retrieve the error behavior report in the second cache module, and then detect The report is read to realize the detection of the early warning function.

In the above step S2, for two driving trajectory models with repeated road sections, The purpose of fitting is to reduce the storage space occupied by the first storage module. If two driving trajectory models/> If the amount of repeated road section information is large, the first storage module needs to store a large amount of repeated data, which greatly reduces the utilization of space and is not conducive to storing more driving trajectory models/> , so the driving trajectory model of repeated road sections is set/> The fitting function can achieve the effect of reducing the space waste rate;

The invention provides a speed limit warning function detection method for Internet of Vehicles applications that automatically monitors the driving frequency of the user's daily driving trajectory when in use. , to reach the preset driving frequency/> The driving trajectory of the value, the road and speed limit data corresponding to the driving trajectory are downloaded and modeled and cached. During the subsequent driving process, when the user drives to the driving trajectory model again/> The road section where it is located will automatically call the cached road and speed limit data for speed limit warning broadcast, without the need to continue to use traffic for real-time transmission and broadcast through the Internet of Vehicles module, thus greatly saving the usage of traffic and achieving the effect of adaptive broadcast. .

During the specific implementation process, the speed limit warning APP is installed on the vehicle, and users operate the speed limit warning APP according to their daily driving habits, as follows:

Users can set travel trajectory frequency table The statistical period H is one month. During the user's driving process, the speed limit warning APP can monitor the driving frequency of the vehicle's driving trajectory/> Statistics, provide travel trajectory frequency table for users to check, refer to the table shown in Figure 5;

Use the speed limit warning APP to monitor users’ daily driving frequency Perform statistics, obtain a statistical table through calculation and provide it to the user, and at the same time, the calculated driving trajectory model/> The starting point information of the driving trajectory is displayed simultaneously, making it easier for users to understand;

And most vehicles on the market have remote mobile phone connection functions. For example, vehicles equipped with OnStar function, the real-time information status of the car can be fed back to the OnStar APP of the mobile phone terminal through the Internet of Vehicles module, thus enabling remote terminal of vehicle condition information. Sharing, similarly, for the statistical travel trajectory frequency table, the user can obtain it through the OnStar APP on the mobile phone terminal. After issuing the acquisition instruction, the vehicle speed limit warning APP will send the table to the mobile terminal through the Internet of Vehicles module for the user to review.

At the same time, contents not described in detail in this specification belong to the prior art known to those skilled in the art.

It should be noted that in this article, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply that these entities or operations are mutually exclusive. any such actual relationship or sequence exists between them. Furthermore, the terms "comprises," "comprises," or any other variations thereof are intended to cover a non-exclusive inclusion such that a process, method, article, or apparatus that includes a list of elements includes not only those elements, but also those not expressly listed other elements, or elements inherent to the process, method, article or equipment.

Although the embodiments of the present invention have been shown and described, those of ordinary skill in the art will understand that various changes, modifications, and substitutions can be made to these embodiments without departing from the principles and spirit of the invention. and modifications, the scope of the invention is defined by 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.