CN111127949B - Vehicle high-risk road section early warning method and device and storage medium - Google Patents
Vehicle high-risk road section early warning method and device and storage medium Download PDFInfo
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- CN111127949B CN111127949B CN201911313106.6A CN201911313106A CN111127949B CN 111127949 B CN111127949 B CN 111127949B CN 201911313106 A CN201911313106 A CN 201911313106A CN 111127949 B CN111127949 B CN 111127949B
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- G08G1/165—Anti-collision systems for passive traffic, e.g. including static obstacles, trees
Abstract
The invention discloses a vehicle high-risk road section early warning method, which comprises the following steps: preprocessing the acquired vehicle basic data to obtain preprocessed basic data; constructing a high-risk road section early warning model based on the preprocessed basic data, wherein the rule for constructing the high-risk road section early warning model is as follows: when the distance between the vehicle and the dangerous marker is smaller than or equal to the preset distance, determining that the vehicle is in the preset range of the dangerous marker, and sending early warning information to a user; running a high-risk road section early warning model through vehicle track point data in the preprocessed basic data to obtain historical road section information network data, and storing the historical road section information network data in a database; and the vehicle terminal acquires and outputs a query result corresponding to the query information based on the historical road section information network data and the received query information. By the method, the accuracy and the timeliness of the high-risk road section early warning can be improved, and data of each region can be shared in real time.
Description
Technical Field
The invention relates to the technical field of vehicle networking, in particular to a vehicle high-risk road section early warning method, a vehicle high-risk road section early warning device and a storage medium.
Background
With the development of internet technology, the number of networked automobiles is increasing, and a dynamic mobile communication system for realizing network communication between an automobile and the public is realized through interconnection and information exchange between the automobile and people, the automobile, roads and surrounding environments, and the information can be used in multiple aspects of automobile safety, information entertainment, comfort improvement and the like, so that an integrated network of intelligent traffic management, intelligent dynamic information service and automobile control is realized.
At present, in the method for carrying out high-risk road section early warning by utilizing the internet of vehicles, a unified management platform and a data entry platform are not provided, so that data in each area cannot be shared and uniformly called, the available existing data volume is small, road data is incomplete, and accidents are easy to occur to be indefinite, therefore, the accuracy and the effectiveness of carrying out high-risk road section early warning are required to be improved, a complete system is not formed, and data support for large data volume is deficient.
Disclosure of Invention
The embodiment of the disclosure provides a vehicle high-risk road section early warning method, a vehicle high-risk road section early warning device and a storage medium. The following presents a simplified summary in order to provide a basic understanding of some aspects of the disclosed embodiments. This summary is not an extensive overview and is intended to neither identify key/critical elements nor delineate the scope of such embodiments. Its sole purpose is to present some concepts in a simplified form as a prelude to the more detailed description that is presented later.
In some optional embodiments, a vehicle high-risk road segment early warning method includes:
preprocessing the acquired vehicle basic data to obtain preprocessed basic data;
constructing a high-risk road section early warning model based on the preprocessed basic data, wherein the rule for constructing the high-risk road section early warning model is as follows: when the distance between the vehicle and the dangerous marker is smaller than or equal to the preset distance, determining that the vehicle is in the preset range of the dangerous marker, and sending early warning information to a user;
running a high-risk road section early warning model through vehicle track point data in the preprocessed basic data to obtain historical road section information network data, and storing the historical road section information network data in a database;
and the vehicle terminal acquires and outputs a query result corresponding to the query information based on the historical road section information network data and the received query information.
Optionally, before preprocessing the acquired vehicle basic data, the method further includes:
vehicle base data is acquired.
Optionally, the vehicle base data comprises:
vehicle trajectory point data, GIS data, and freight road data.
Optionally, the preprocessing the acquired vehicle basic data includes:
filtering information missing data;
filtering error data;
filtering data that the current license plate number cannot be matched with the road information;
filtering data of positions where road information cannot be matched with dangerous markers in GIS data;
and obtaining filtered basic data.
Optionally, after obtaining the filtered basic data, the method further includes:
associating the road information with the vehicle track point information by the license plate number;
associating GIS data information with road information by road name;
and obtaining the preprocessed basic data.
Optionally, after determining that the vehicle is within the preset range of the danger marker and sending the warning information to the user, the method further includes:
acquiring all intersection sections of a front road;
and judging whether dangerous markers exist on the intersection road sections, and outputting the intersection road sections without the dangerous markers.
Optionally, the step of storing the historical road section information network data in the database comprises:
and regularly modifying the historical road section information network data in the database.
In some optional embodiments, a vehicle high-risk road segment early warning device includes:
the preprocessing module is used for preprocessing the acquired vehicle basic data to obtain preprocessed basic data;
the model building module is used for building a high-risk road section early warning model based on the preprocessed basic data, and the rules for building the high-risk road section early warning model are as follows: when the distance between the vehicle and the dangerous marker is smaller than or equal to the preset distance, determining that the vehicle is in the preset range of the dangerous marker, and sending early warning information to a user;
the data storage module is used for operating the high-risk road section early warning model through the vehicle track point data in the preprocessed basic data to obtain historical road section information network data and storing the historical road section information network data in a database;
and the query module is used for acquiring and outputting a query result corresponding to the query information by the vehicle terminal based on the historical road section information network data and the received query information.
In some optional embodiments, a vehicle high-risk road segment early warning device includes a processor and a memory storing program instructions, where the processor is configured to execute the method for early warning a vehicle high-risk road segment provided in the above embodiments when executing the program instructions.
In some optional embodiments, a computer readable medium has computer readable instructions stored thereon, and the computer readable instructions can be executed by a processor to perform the method for warning a high-risk road section of a vehicle provided in the above embodiments.
The technical scheme provided by the embodiment of the disclosure can have the following beneficial effects:
according to the method, basic data are obtained and preprocessed, a high-risk road section early warning model is constructed based on the preprocessed basic data, historical road section information network data are obtained by operating the high-risk road section early warning model through vehicle track point data in the preprocessed basic data, the historical road section information network data are stored in a database, a vehicle terminal receives query information input by a user, and query results corresponding to the query information in the historical road section information network data are queried and output. The accuracy and the timeliness of the high-risk road section early warning can be greatly improved.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention, as claimed.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and together with the description, serve to explain the principles of the invention.
Fig. 1 is a schematic flow chart illustrating a vehicle high-risk road segment early warning method according to an exemplary embodiment;
FIG. 2 is a schematic flow chart illustrating a vehicle high-risk road segment warning method according to an exemplary embodiment;
FIG. 3 is a schematic flow chart diagram illustrating a vehicle high-risk road segment warning method according to an exemplary embodiment;
fig. 4 is a schematic structural diagram illustrating a vehicle high-risk road section early warning device according to an exemplary embodiment;
fig. 5 is a schematic structural diagram illustrating a vehicle high-risk road segment early warning device according to an exemplary embodiment.
Detailed Description
So that the manner in which the features and elements of the disclosed embodiments can be understood in detail, a more particular description of the disclosed embodiments, briefly summarized above, may be had by reference to the embodiments, some of which are illustrated in the appended drawings. In the following description of the technology, for purposes of explanation, numerous details are set forth in order to provide a thorough understanding of the disclosed embodiments. However, one or more embodiments may be practiced without these details. In other instances, well-known structures and devices may be shown in simplified form in order to simplify the drawing.
Fig. 1 is a schematic flow chart illustrating a vehicle high-risk road segment early warning method according to an exemplary embodiment;
in some embodiments, a vehicle high-risk road segment early warning method includes:
step S101, preprocessing the acquired vehicle basic data to obtain preprocessed basic data;
the vehicle basic data comprise vehicle track point data, GIS data and freight road data. Wherein the track point data of the vehicle includes: license plate number, license plate color, longitude, latitude, speed, time, administrative region that belongs to, GIS data includes: and the freight road data comprises road IDs, road names, administrative regions and geographical position information of all freight roads in the country.
The method comprises the steps of obtaining vehicle basic data before preprocessing the obtained vehicle basic data, specifically, collecting track point data of the vehicle in real time by a vehicle terminal, transmitting the track point data of the vehicle to a national unified freight platform database in real time, extracting the track point data in real time through a Kafka technology, and transmitting the track point data into a big data cluster Hadoop distributed file system, wherein the track point data of all vehicles in current operation can be obtained on a big data platform.
GIS data can be obtained from a free platform disclosed in the field, such as a national geographic information resource directory service system, and freight road data can also be obtained from a free platform disclosed in the field, such as a geographic national condition monitoring cloud platform, which is downloaded freely. And storing the acquired GIS data and the freight road data into a Hadoop distributed file system.
The Hadoop distributed file system has the characteristic of high fault tolerance, is used for being deployed on cheap hardware, provides high throughput for accessing data of application programs, and is suitable for the application programs with ultra-large data sets.
By the method, the used data are national freight road data and freight track point data, and the data are accurate and complete.
Generally, the acquired basic data includes a large amount of error data and useless data, and therefore, the acquired basic data is preprocessed first, and the specific preprocessing steps include: the method comprises the steps that a Python programming language is used for compiling a program, firstly, data with missing information are filtered out, for example, data with missing longitude information, missing latitude information or missing license plate number in data reported by a vehicle terminal at regular time are filtered out, and data of geographic positions without dangerous markers in GIS data are filtered out; filtering out error data, for example, filtering out data with an error in data format; and filtering data that the current license plate number cannot be matched with the road, filtering data that the road information cannot be matched with the dangerous marker information, and obtaining filtered basic data through the processing.
After the filtered basic data is obtained, associating the road information and the track point data information by the license plate number, specifically, grouping and merging the data by the license plate number, wherein the data grouped and merged by the license plate number comprises: vehicle speed, license plate number, license plate color, longitude, latitude, time, administrative region, road ID; combining and combining data by road names, wherein the data combined by road names in groups comprises: road name, longitude and latitude of dangerous markers on the road, and administrative region to which the road belongs.
The administrative regions comprise provincial administrative districts, city administrative districts and county administrative districts.
By the method, a large amount of useless data and error data are filtered to obtain preprocessed basic data.
Constructing a high-risk road section early warning model based on the preprocessed basic data, wherein the rule for constructing the high-risk road section early warning model is as follows: when the distance between the vehicle and the dangerous marker is smaller than or equal to the preset distance, determining that the vehicle is in the preset range of the dangerous marker, and sending early warning information to a user;
wherein the hazard markers on the road include: sharp turn signs, dangerous road section signs, speed limit signs, traffic jam signs, road slippery signs, construction road section signs, height limit signs and the like.
Specifically, the preprocessed basic data are obtained, a high-risk road section early warning model is built by the preprocessed basic data, a logistic regression algorithm and a decision tree algorithm are called when parameters are optimized in a modeling stage, the selected parameters are checked for the degree of correlation and the degree of influence, and the parameters with the high degree of correlation and the high degree of influence are used as the optimal parameters.
The rules for constructing the high-risk road section early warning model are as follows: and when the distance between the vehicle and the dangerous marker is smaller than or equal to the preset distance, determining that the vehicle is in the preset range of the dangerous marker, and sending early warning information to the user.
In some exemplary scenarios, the preset distance on the expressway is 1000 meters, the preset distance on the national road and the provincial road is 500 meters, and the preset distance on the urban road and the rural road is 100 meters.
The distance judgment model algorithm with the preset distance of 100 meters is as follows: l ═ R ═ arccos [ cos β 1 × (cos β 2) × (α 1- α 2) + sin β 1 × (sin β 2] < ═ 100
Wherein R is the radius of the earth, alpha 1 is the longitude of the point A, and beta 1 is the latitude of the point A.
α 2 is longitude of point B, and β 2 is latitude of point B.
Through the algorithm, whether the distance between the two points AB is less than or equal to 100 meters can be judged.
When the distance between the vehicle and the dangerous marker is smaller than or equal to the preset distance, the vehicle is determined to be within the preset range of the dangerous marker, the function reminding function is triggered, reminding is continuously carried out for 3 times, early warning information is sent to a mobile phone of a user through an SIM communication module arranged on the terminal, and the specific content of the early warning information is determined according to the semantics defined in the dangerous marker.
In some exemplary scenarios, the vehicle runs on a lane of the province, a dangerous marker is on the road, for example, the dangerous marker is a sharp turn, and when the distance between the vehicle and the dangerous marker is detected to be 300 meters and less than 500 meters, the functional reminding module is triggered, and a short message is sent to the mobile phone of the user through the SIM communication module of the terminal, for example, a short message warning message of "a sharp turn exists at a position 300 meters ahead, please pay attention" is sent.
Optionally, after determining that the vehicle is within the preset range of the danger marker, playing the early warning information through a voice playing module of the terminal, in some exemplary scenarios, the vehicle is driven on a lane, the danger marker is on the road, for example, the danger marker is a sharp turn, and it is detected that the distance between the vehicle and the danger marker is 300 meters and less than the preset distance of 500 meters, triggering a function reminding module, and playing the voice early warning information of "the sharp turn and please notice at the position 300 meters ahead of the road" through the voice playing module of the terminal, and continuously playing for three times.
By the method, the high-risk road section early warning model is built, and when the distance between the vehicle and the dangerous marker is close, the early warning information is sent, so that danger of a user is avoided, and driving safety is improved.
Optionally, after determining that the vehicle is within the preset range of the danger marker and sending the warning information to the user, the method further includes:
acquiring all intersection sections of a front road;
and judging whether dangerous markers exist on the intersection road sections, and outputting the intersection road sections without the dangerous markers.
Specifically, after the vehicle is determined to be within the preset range of the dangerous marker and early warning information is sent to a user, a GIS data interface is called, all intersections of the road ahead are obtained and are extended by a preset distance, in some exemplary scenes, each intersection is extended by 5 kilometers, whether a high-risk road section exists in the intersection road section is judged, the intersection road section with the high-risk road section is deleted, and the intersection road section without the high-risk road section is stored in a database and is called by a front end.
By the method, the optimal route screening is realized, multiple road section selections are provided for the current user, and the road prejudgment is completed.
Running a high-risk road section early warning model through vehicle track point data in the preprocessed basic data to obtain historical road section information network data, and storing the historical road section information network data in a database;
in order to improve the query timeliness, the constructed high-risk road section early warning model is operated through vehicle track point data in the preprocessed basic data, and the obtained result is used as historical road section information network data and is stored in a database.
The MySQL database group is used for statistically analyzing historical road section information network data, establishing a data set by taking provinces as units, mapping various service line tables by taking cities as units, and inquiring the historical road section information network data stored in the database by taking license plate numbers and vehicle SIM numbers as indexes.
The high-risk road section early warning model constructed by using historical data operation is used, the obtained historical road section information network data is stored in the database, the license plate number and the vehicle SIM number are used as indexes, the historical road section information network data stored in the database is inquired, and the inquiry timeliness can be greatly improved.
Usually, the road facilities are changed, so that the historical road section information network data in the database needs to be modified regularly, and the data is more accurate. In some exemplary scenarios, the historical road segment information network data in the database is modified once every two weeks.
By the method, the historical road section information network data in the database is modified regularly, so that the early warning accuracy can be improved, and good experience is brought to users.
And the vehicle terminal acquires and outputs a query result corresponding to the query information based on the historical road section information network data and the received query information.
Specifically, the vehicle-mounted terminal receives query information input by a user, wherein the query information comprises a license plate number and a vehicle SIM number, and takes the query information as an index to acquire and output a query result corresponding to the query information from historical road section information network data.
Generally, the in-vehicle terminal saves the acquired query result in a database.
Optionally, before preprocessing the acquired vehicle basic data, the method further includes:
vehicle base data is acquired.
The vehicle basic data comprise vehicle track point data, GIS data and freight road data. Wherein the track point data of the vehicle includes: license plate number, license plate color, longitude, latitude, speed, time, administrative region that belongs to, GIS data includes: and the freight road data comprises road IDs, road names, administrative regions and geographical position information of all freight roads in the country.
Specifically, the vehicle terminal collects track point data of the vehicles in real time, transmits the track point data of the vehicles to a national unified freight platform database in real time, extracts the track point data in real time through a Kafka technology, transmits the track point data to a big data cluster Hadoop distributed file system, and can obtain the track point data of all vehicles currently running on a big data platform.
GIS data can be obtained from a free platform disclosed in the field, such as a national geographic information resource directory service system, and freight road data can also be obtained from a free platform disclosed in the field, such as a geographic national condition monitoring cloud platform, which is downloaded freely. And storing the acquired GIS data and the freight road data into a Hadoop distributed file system.
The Hadoop distributed file system has the characteristic of high fault tolerance, is used for being deployed on cheap hardware, provides high throughput for accessing data of application programs, and is suitable for the application programs with ultra-large data sets.
By the method, the used data are national freight road data and freight track point data, and the data are accurate and complete.
Optionally, the vehicle base data comprises:
vehicle trajectory point data, GIS data, and freight road data.
The vehicle basic data comprise vehicle track point data, GIS data and freight road data. Wherein the track point data of the vehicle includes: license plate number, license plate color, longitude, latitude, speed, time, administrative region that belongs to, GIS data includes: and the freight road data comprises road IDs, road names, administrative regions and geographical position information of all freight roads in the country.
Optionally, the preprocessing the acquired vehicle basic data includes:
filtering information missing data;
filtering error data;
filtering data that the current license plate number cannot be matched with the road information;
filtering data of positions where road information cannot be matched with dangerous markers in GIS data;
and obtaining filtered basic data.
Generally, the acquired basic data includes a large amount of error data and useless data, and therefore, the acquired basic data is preprocessed first, and the specific preprocessing steps include: the method comprises the steps that a Python programming language is used for compiling a program, firstly, data with missing information are filtered out, for example, data with missing longitude information, missing latitude information or missing license plate number in data reported by a vehicle terminal at regular time are filtered out, and data of geographic positions without dangerous markers in GIS data are filtered out; filtering out error data, for example, filtering out data with an error in data format; and filtering data that the current license plate number cannot be matched with the road, filtering data that the road information cannot be matched with the dangerous marker information, and obtaining filtered basic data through the processing.
Optionally, after obtaining the filtered basic data, the method further includes:
associating the road information with the vehicle track point information by the license plate number;
associating GIS data information with road information by road name;
and obtaining the preprocessed basic data.
After the filtered basic data is obtained, associating the road information and the track point data information by the license plate number, specifically, grouping and merging the data by the license plate number, wherein the data grouped and merged by the license plate number comprises: vehicle speed, license plate number, license plate color, longitude, latitude, time, administrative region, road ID; combining and combining data by road names, wherein the data combined by road names in groups comprises: road name, longitude and latitude of dangerous markers on the road, and administrative region to which the road belongs.
The administrative regions comprise provincial administrative districts, city administrative districts and county administrative districts.
By the method, a large amount of useless data and error data are filtered to obtain preprocessed basic data.
Optionally, after determining that the vehicle is within the preset range of the danger marker and sending the warning information to the user, the method further includes:
acquiring all intersection sections of a front road;
and judging whether dangerous markers exist on the intersection road sections, and outputting the intersection road sections without the dangerous markers.
Specifically, after the vehicle is determined to be within the preset range of the dangerous marker and early warning information is sent to a user, a GIS data interface is called, all intersections of the road ahead are obtained and are extended by a preset distance, in some exemplary scenes, each intersection is extended by 5 kilometers, whether a high-risk road section exists in the intersection road section is judged, the intersection road section with the high-risk road section is deleted, and the intersection road section without the high-risk road section is stored in a database and is called by a front end.
Optionally, the method may cause a situation that the front road has no intersection, and therefore, a road pre-judgment model may be constructed by using the preprocessed basic data, the construction method of the road pre-judgment model is the same as that of the high-risk road section early warning model, and detailed explanation is omitted, and the only difference is that whether the vehicle is in the preset range of the dangerous marker or not is judged, the preset range is changed, and the preset distance in the road pre-judgment model is 3-5 times greater than the preset distance in the high-risk road section early warning model.
In some exemplary scenarios, the preset distance in the road pre-judgment model is 3 times greater than the preset distance in the high-risk road segment early warning model, the preset distance on an expressway is 3000 meters, the preset distance on a national road and a provincial road is 1500 meters, and the preset distance on an urban road and a rural road is 300 meters. After the vehicle is determined to be within the preset range of the dangerous marker and early warning information is sent to a user, a GIS data interface is called, all intersections of a road ahead are obtained, a preset distance is extended, in some exemplary scenes, each intersection extends for 5 kilometers, whether a high-risk road section exists in the intersection road section is judged, the intersection road section with the high-risk road section is deleted, and the intersection road section without the high-risk road section is stored in a database and is called by a front end.
By the method, the optimal route screening is realized, multiple road section selections are provided for the current user, and the road prejudgment is completed.
Optionally, the step of storing the historical road section information network data in the database comprises:
and regularly modifying the historical road section information network data in the database.
Usually, the road facilities are changed, so that the historical road section information network data in the database needs to be modified regularly, and the data is more accurate. In some exemplary scenarios, the historical road segment information network data in the database is modified once every two weeks.
By the method, the historical road section information network data in the database is modified regularly, so that the early warning accuracy can be improved, and good experience is brought to users.
FIG. 2 is a schematic flow chart illustrating a vehicle high-risk road segment warning method according to an exemplary embodiment;
as shown in fig. 2, firstly, data is acquired, a historical data set of a specific vehicle is acquired, parameters are adjusted according to loaded historical data, a high-risk road section early warning model is trained, the accuracy of the model is verified, then, in a data processing layer, the high-risk road section early warning model is operated by using preprocessed basic data, result data of the specific vehicle is stored in a MySQL library group, the high-risk road section early warning model is reloaded within set time, and the result data stored in the MySQL library group is updated, so that the data are more accurate.
The front end comprises a real-time query layer and a visual display layer, the real-time query layer receives input query information, a query result is obtained according to the input query information, the query result in the database is cached by Redis for the front end to call, the query speed is improved, and the obtained query result is stored in the MySQL library group. The visual display layer can display the driving route of the current vehicle, can display map information, can display the current traffic flow statistical chart, and enables a user to visually know the current traffic flow condition.
FIG. 3 is a schematic flow chart diagram illustrating a vehicle high-risk road segment warning method according to an exemplary embodiment;
as shown in fig. 3, first, basic data is collected, and the basic data includes: the method comprises the steps of storing acquired basic data into a Hadoop distributed file system by utilizing a Kafka technology, preprocessing data by utilizing a Python language compiling program, performing data preprocessing according to the specific steps, preprocessing the preprocessed data again according to application scenes of the data, constructing a high-risk road early warning model, operating the model by utilizing the preprocessed data to obtain result data, performing statistical analysis on the result data, and storing the result data into a database for front-end calling.
Fig. 4 is a schematic diagram illustrating a vehicle high-risk road segment early warning device according to an exemplary embodiment.
In some embodiments, a vehicle high-risk section early warning device includes:
the preprocessing module is used for preprocessing the acquired vehicle basic data to obtain preprocessed basic data;
the model building module is used for building a high-risk road section early warning model based on the preprocessed basic data, and the rules of the early warning model are as follows: when the distance between the vehicle and the dangerous marker is smaller than or equal to the preset distance, determining that the vehicle is in the preset range of the dangerous marker, and sending early warning information to a user;
the data storage module is used for operating the early warning model through the vehicle track point data in the preprocessed basic data to obtain historical road section information network data and storing the historical road section information network data in a database;
and the query module is used for acquiring and outputting a query result corresponding to the query information by the vehicle terminal based on the historical road section information network data and the received query information.
FIG. 5 is a schematic diagram of a vehicle high-risk road segment early warning device according to an exemplary embodiment
In some embodiments, the vehicle high-risk road segment early warning device includes a processor 51 and a memory 52 storing program instructions, and may further include a communication interface 53 and a bus 54. The processor 51, the communication interface 53 and the memory 52 may communicate with each other through the bus 54. The communication interface 53 may be used for information transfer. The processor 51 may call the logic instructions in the memory 52 to execute the method for warning a high-risk road section of a vehicle provided by the above embodiment.
Furthermore, the logic instructions in the memory 52 may be implemented in software functional units and stored in a computer readable storage medium when sold or used as a stand-alone product.
The memory 52 is a computer-readable storage medium, and can be used for storing software programs, computer-executable programs, such as program instructions/modules corresponding to the methods in the embodiments of the present disclosure. The processor 51 executes the functional application and data processing by executing the software program, instructions and modules stored in the memory 52, that is, implements the method in the above-described method embodiments.
The memory 52 may include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function; the storage data area may store data created according to the use of the terminal device, and the like. Further, the memory 52 may include high speed random access memory, and may also include non-volatile memory.
The embodiment of the disclosure provides a computer readable medium, on which computer readable instructions are stored, and the computer readable instructions can be executed by a processor to implement the vehicle high-risk road section early warning method provided by the embodiment.
The computer-readable storage medium described above may be a transitory computer-readable storage medium or a non-transitory computer-readable storage medium.
Those of skill in the art would appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software may depend upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the disclosed embodiments. It can be clearly understood by those skilled in the art that, for convenience and brevity of description, the specific working processes of the system, the apparatus and the unit described above may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
In the embodiments disclosed herein, the disclosed methods, products (including but not limited to devices, apparatuses, etc.) may be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, a division of a unit may be merely a division of a logical function, and an actual implementation may have another division, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form. Units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to implement the present embodiment. In addition, functional units in the embodiments of the present disclosure may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit.
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). In some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. Each block of the block diagrams and/or flowchart illustrations, and combinations of blocks in the block diagrams and/or flowchart illustrations, can be implemented by special purpose hardware-based systems that perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The above description is only for the preferred embodiment of the present application, but the scope of the present application is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present application should be covered within the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (7)
1. The vehicle high-risk road section early warning method is characterized by comprising the following steps:
preprocessing the acquired basic data of the vehicle, filtering information missing data, filtering error data, filtering data that the current license plate number cannot be matched with road information, filtering data that the road information cannot be matched with the position of a dangerous marker in GIS data, acquiring the filtered basic data, and associating the road information with track point data information by the license plate number, wherein the grouping and merging data by the license plate number comprises: vehicle speed, license plate number, license plate color, longitude, latitude, time, administrative region, road ID; associating GIS data information and road information by road names, wherein the combined data by road names comprises the following data: obtaining preprocessed basic data according to the road name, the longitude and latitude of the dangerous marker on the road and the administrative region to which the road belongs;
constructing a high-risk road section early warning model based on the preprocessed basic data, wherein the rules for constructing the high-risk road section early warning model are as follows: when the distance between the vehicle and the dangerous marker is smaller than or equal to a preset distance, determining that the vehicle is in the preset range of the dangerous marker, and sending early warning information to a user, wherein the distance judgment algorithm is as follows: l ═ R × arccos [ cos β 1 × cos β 2 × cos (α 1- α 2) + sin β 1 × sin β 2] < ═ a preset safety distance; wherein R is the earth radius, alpha 1 is the longitude of the point A, beta 1 is the latitude of the point A, alpha 2 is the longitude of the point B, and beta 2 is the latitude of the point B;
running the high-risk road section early warning model through the vehicle track point data in the preprocessed basic data to obtain historical road section information network data, storing the historical road section information network data in a MySQL database, statistically analyzing the historical road section information network data by the MySQL database, establishing a data set by taking provinces as a unit, mapping various service line tables by taking cities as a unit, and inquiring the historical road section information network data stored in the database by taking license plate numbers and vehicle SIM numbers as indexes;
the vehicle terminal acquires and outputs a query result corresponding to the query information based on the historical road section information network data and the received query information, wherein the query information comprises a license plate number and a vehicle SIM number, and the vehicle terminal further comprises a visual display layer for displaying a running route of a current vehicle, displaying map information and displaying a current traffic flow statistical chart;
after the early warning information is sent to a user, a GIS data interface is called, all intersection sections of the front road are obtained, the preset distance is extended, whether high-risk sections exist in the intersection sections is judged, the intersection sections with the high-risk sections are deleted, the intersection sections without the high-risk sections are stored in a database and output, if no intersection section exists in the front road, whether a vehicle is in the preset range of the dangerous marker is judged by using a road pre-judging model, and the preset distance in the road pre-judging model is 3-5 times larger than the preset distance in the early warning model of the high-risk sections.
2. The method of claim 1, wherein prior to preprocessing the acquired vehicle-based data, further comprising:
vehicle base data is acquired.
3. The method of claim 2, wherein the vehicle grounding data comprises:
vehicle trajectory point data, GIS data, and freight road data.
4. The method of claim 1, wherein the storing the historical road segment information network data in a database comprises:
and regularly modifying the historical road section information network data in the database.
5. The utility model provides a vehicle high-risk highway section early warning device which characterized in that includes:
the preprocessing module is used for preprocessing the acquired basic data of the vehicle, filtering information missing data, filtering error data, filtering data that the current license plate number cannot be matched with road information, filtering data that the road information cannot be matched with the position of a dangerous marker in GIS data, obtaining filtered basic data, associating the road information with track point data information by the license plate number, wherein the data are combined by grouping the license plate numbers, and the data comprise: vehicle speed, license plate number, license plate color, longitude, latitude, time, administrative region, road ID; associating GIS data information and road information by road names, wherein the combined data by road names comprises the following data: obtaining preprocessed basic data according to the road name, the longitude and latitude of the dangerous marker on the road and the administrative region to which the road belongs;
the model building module is used for building a high-risk road section early warning model based on the preprocessed basic data, and the rules for building the high-risk road section early warning model are as follows: when the distance between the vehicle and the dangerous marker is smaller than or equal to a preset distance, determining that the vehicle is in the preset range of the dangerous marker, and sending early warning information to a user, wherein the distance judgment algorithm is as follows: l ═ R × arccos [ cos β 1 × cos β 2 × cos (α 1- α 2) + sin β 1 × sin β 2] < ═ a preset safety distance; wherein R is the earth radius, alpha 1 is the longitude of the point A, beta 1 is the latitude of the point A, alpha 2 is the longitude of the point B, and beta 2 is the latitude of the point B;
the data storage module is used for operating the high-risk road section early warning model through vehicle track point data in the preprocessed basic data to obtain historical road section information network data, storing the historical road section information network data in a MySQL database, performing statistical analysis on the historical road section information network data by the MySQL database, establishing a data set by taking provinces as units, mapping various service line tables by taking cities as units, and inquiring the historical road section information network data stored in the database by taking license plate numbers and vehicle SIM numbers as indexes;
the inquiry module is used for acquiring and outputting an inquiry result corresponding to the inquiry information by the vehicle terminal based on the historical road section information network data and the received inquiry information, wherein the inquiry information comprises a license plate number and a vehicle SIM number, and the vehicle terminal also comprises a visual display layer which is used for displaying a running route of a current vehicle, displaying map information and displaying a current traffic flow statistical chart; after the early warning information is sent to a user, a GIS data interface is called, all intersection sections of the front road are obtained, the preset distance is extended, whether high-risk sections exist in the intersection sections is judged, the intersection sections with the high-risk sections are deleted, the intersection sections without the high-risk sections are stored in a database and output, if no intersection section exists in the front road, whether a vehicle is in the preset range of the dangerous marker is judged by using a road pre-judging model, and the preset distance in the road pre-judging model is 3-5 times larger than the preset distance in the early warning model of the high-risk sections.
6. A vehicle high-risk road section early warning device is characterized by comprising a processor and a memory, wherein program instructions are stored in the memory, and the processor is configured to execute the vehicle high-risk road section early warning method according to any one of claims 1 to 4 when executing the program instructions.
7. A computer readable medium having computer readable instructions stored thereon, the computer readable instructions being executable by a processor to implement a method for vehicle high risk segment warning according to any one of claims 1 to 4.
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