CN111341106B - Traffic early warning method, device and equipment - Google Patents
Traffic early warning method, device and equipment Download PDFInfo
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- CN111341106B CN111341106B CN202010164875.0A CN202010164875A CN111341106B CN 111341106 B CN111341106 B CN 111341106B CN 202010164875 A CN202010164875 A CN 202010164875A CN 111341106 B CN111341106 B CN 111341106B
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
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- G—PHYSICS
- G08—SIGNALLING
- G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
- G08B21/00—Alarms responsive to a single specified undesired or abnormal condition and not otherwise provided for
- G08B21/02—Alarms for ensuring the safety of persons
- G08B21/06—Alarms for ensuring the safety of persons indicating a condition of sleep, e.g. anti-dozing alarms
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/09—Arrangements for giving variable traffic instructions
- G08G1/0962—Arrangements for giving variable traffic instructions having an indicator mounted inside the vehicle, e.g. giving voice messages
- G08G1/0967—Systems involving transmission of highway information, e.g. weather, speed limits
Abstract
The embodiment of the application provides a traffic early warning method, a device and equipment, wherein the traffic early warning method comprises the following steps: acquiring driver information, vehicle condition information and road condition information of the mobile terminal; extracting target data which accord with preset evaluation indexes from the driver information, the vehicle condition information and the road condition information; inputting the target data into a preset evaluation model to obtain an evaluation result of the mobile terminal; and sending prompt information to the mobile terminal according to the evaluation result. The driving risk early warning is realized, and the traffic safety is improved.
Description
Technical Field
The application relates to the technical field of traffic, in particular to a traffic early warning method, a traffic early warning device and traffic early warning equipment.
Background
With the increase of road vehicles, traffic accidents on roads frequently occur, and the occurrence of the traffic accidents can be effectively reduced by analyzing the driving behaviors so as to predict the driving risk. However, in the existing driving risk analysis method, a camera in a vehicle is generally used to collect facial features or behavior features of a driver, such as: whether fatigue driving such as yawning and dozing exists or not, whether the driver leaves the position of the driver and talks with other people or not in the driving process or not, and the like, or whether the judgment is carried out according to the driving path of the vehicle and the accident analysis after the accident. The judgment means is single or hysteresis, and certain accidental factors exist.
Disclosure of Invention
An object of the embodiments of the present application is to provide a traffic early warning method, device and apparatus, so as to implement driving risk early warning and improve traffic safety.
In a first aspect, an embodiment of the present application provides a traffic early warning method, including: acquiring driver information, vehicle condition information and road condition information of the mobile terminal; extracting target data which accord with preset evaluation indexes from the driver information, the vehicle condition information and the road condition information; inputting the target data into a preset evaluation model to obtain an evaluation result of the mobile terminal; and sending prompt information to the mobile terminal according to the evaluation result.
In an embodiment, the sending a prompt message to the mobile terminal according to the evaluation result includes: judging whether the evaluation result is within a preset safety threshold value; and when the evaluation result is not within the preset safety threshold, sending an early warning prompt to the mobile terminal.
In an embodiment, the step of constructing the preset evaluation model includes: obtaining sample driver information of a plurality of sample drivers, and sample vehicle condition information and sample road condition information corresponding to each sample driver; determining a plurality of preset evaluation indexes of the preset evaluation model according to the sample driver information, the sample vehicle condition information and the sample road condition information; distributing a weighted value to each preset evaluation index; and constructing the preset evaluation model according to each preset evaluation index and the corresponding weight value.
In an embodiment, the determining the plurality of preset evaluation indexes of the preset evaluation model according to the sample driver information, the sample vehicle condition information, and the sample vehicle condition information includes: determining a first evaluation index of the preset evaluation model according to the sample driver information; determining a second evaluation index of the preset evaluation model according to the sample vehicle condition information; and determining a third evaluation index of the preset evaluation model according to the sample road condition information.
In an embodiment, the extracting target data meeting a preset evaluation index from the driver information, the vehicle condition information, and the road condition information includes: extracting first feature data corresponding to the first evaluation index from the driver information, extracting second feature data corresponding to the second evaluation index from the vehicle condition information, and extracting third feature data corresponding to the third evaluation index from the road condition information; and merging the first characteristic data, the second characteristic data and the third characteristic data to obtain the target data.
A second aspect of the embodiments of the present application provides a traffic early warning device, including: the acquisition module is used for acquiring driver information, vehicle condition information and road condition information of the mobile terminal; the extraction module is used for extracting target data which accord with preset evaluation indexes from the driver information, the vehicle condition information and the road condition information; the input module is used for inputting the target data into a preset evaluation model to obtain an evaluation result of the mobile terminal; and the sending module is used for sending prompt information to the mobile terminal according to the evaluation result.
In one embodiment, the sending module is configured to: judging whether the evaluation result is within a preset safety threshold value; and when the evaluation result is not within the preset safety threshold, sending an early warning prompt to the mobile terminal.
In an embodiment, the traffic warning apparatus further includes a construction module, configured to: obtaining sample driver information of a plurality of sample drivers, and sample vehicle condition information and sample road condition information corresponding to each sample driver; determining a plurality of preset evaluation indexes of the preset evaluation model according to the sample driver information, the sample vehicle condition information and the sample road condition information; distributing a weighted value to each preset evaluation index; and constructing the preset evaluation model according to each preset evaluation index and the corresponding weight value.
In an embodiment, the determining the plurality of preset evaluation indexes of the preset evaluation model according to the sample driver information, the sample vehicle condition information, and the sample vehicle condition information includes: and determining a first evaluation index of the preset evaluation model according to the sample driver information, determining a second evaluation index of the preset evaluation model according to the sample vehicle condition information, and determining a third evaluation index of the preset evaluation model according to the sample vehicle condition information.
In one embodiment, the extraction module is configured to: extracting first feature data corresponding to the first evaluation index from the driver information, extracting second feature data corresponding to the second evaluation index from the vehicle condition information, and extracting third feature data corresponding to the third evaluation index from the road condition information; and merging the first characteristic data, the second characteristic data and the third characteristic data to obtain the target data.
A third aspect of embodiments of the present application provides an electronic device, including: a memory to store a computer program; a processor configured to perform the method of the first aspect of the embodiments of the present application and any of the embodiments of the present application.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are required to be used in the embodiments of the present application will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present application and therefore should not be considered as limiting the scope, and that those skilled in the art can also obtain other related drawings based on the drawings without inventive efforts.
Fig. 1 is a schematic structural diagram of an electronic device according to an embodiment of the present application;
fig. 2 is a schematic flow chart of constructing a preset evaluation model in an embodiment of the present application;
fig. 3 is a schematic flow chart of a traffic warning method according to an embodiment of the present application;
fig. 4 is a schematic structural diagram of a traffic warning device according to an embodiment of the present application;
fig. 5 is a schematic structural diagram of a traffic warning device according to an embodiment of the present application.
Reference numerals:
100-electronic equipment, 110-bus, 120-processor, 130-memory, 400-traffic early warning device, 410-acquisition module, 420-extraction module, 430-input module, 440-sending module and 450-construction module.
Detailed Description
The technical solutions in the embodiments of the present application will be described below with reference to the drawings in the embodiments of the present application.
In the description of the present application, the terms "first," "second," and the like are used for distinguishing between descriptions and do not denote an order of magnitude, nor are they to be construed as indicating or implying relative importance.
Please refer to fig. 1, which is a schematic structural diagram of an electronic device 100 according to an embodiment of the present application, and includes at least one processor 120 and a memory 130, where fig. 1 illustrates one processor as an example. The processor 120 and the memory 130 are connected by a bus 110, and the memory 130 stores instructions executable by the at least one processor 120, the instructions being executed by the at least one processor 120 to cause the at least one processor 120 to perform a traffic warning method as in the embodiments described below.
As shown in fig. 2, which is a schematic flowchart illustrating a process of constructing a preset evaluation model according to an embodiment of the present application, the method may be executed by the electronic device 100 shown in fig. 1, and the method includes the following steps:
step 210: sample driver information for a plurality of sample drivers is obtained, as well as sample vehicle condition information and sample road condition information corresponding to each sample driver.
In the above steps, the sample driver information of the sample driver includes, but is not limited to: the body health condition, the fatigue condition, the driving habit, the violation of regulations, and the illegal driving record such as drunk driving or poisonous driving. Sample vehicle condition information for sample drivers includes, but is not limited to: vehicle maintenance information, vehicle annual inspection information, vehicle mileage, and the like. Sample traffic information includes, but is not limited to: the weather condition of the area where the vehicle is located, the road congestion condition, the frequency of traffic accidents and the like.
In one embodiment, the physical health status can be obtained by recognizing the sign of the sample driver through a vehicle-mounted or road-side camera, or can be obtained through recent medical information or physical examination information of the sample driver. The fatigue condition can be obtained by directly collecting and identifying the mental state of the driver through the last vehicle using end time, the continuous driving time or a camera. The driving habits can be detected through the vehicle-mounted sensor, and the detection contents include but are not limited to bad driving habits such as sudden braking, sudden acceleration, sudden steering, unlocking of a vehicle door in the driving process, releasing of a hand brake, pulling of a safety belt, long-term load operation of an engine and the like. The violation and illegal driving records may be obtained from an information database of the traffic management department according to a license number corresponding to the sample driver.
Step 220: and determining a plurality of preset evaluation indexes of the preset evaluation model according to the sample driver information, the sample vehicle condition information and the sample road condition information.
In the above steps, a first evaluation index of the preset evaluation model is determined according to the sample driver information, a second evaluation index of the preset evaluation model is determined according to the sample vehicle condition information, and a third evaluation index of the preset evaluation model is determined according to the sample vehicle condition information.
In an embodiment, the K-means clustering algorithm may be used to perform feature clustering analysis on the sample driver information, the sample vehicle condition information, and the sample road condition information, respectively, to obtain a plurality of evaluation dimensions respectively corresponding to the sample driver information, the sample vehicle condition information, and the sample road condition information, and then a principal component analysis method or an analytic hierarchy process is used to screen out a dimension with a large influence from the plurality of evaluation dimensions, which is used as a preset evaluation index.
In one embodiment, five evaluation dimensions of physical condition, fatigue condition, illegal driving record, illegal behavior and driving age are obtained after sample driver information is clustered, and two dimensions of the physical condition and the illegal driving record are obtained through screening according to a principal component analysis method or an analytic hierarchy process and serve as a first evaluation index.
In one embodiment, five evaluation dimensions of maintenance records, vehicle inspection records, vehicle faults, vehicle losses and vehicle ages are obtained after sample vehicle condition information is clustered, and then two dimensions of the vehicle ages and the vehicle inspection records are obtained through screening according to a principal component analysis method or an analytic hierarchy process and serve as a second evaluation index.
In one embodiment, five evaluation dimensions, namely a dangerous climate, a dangerous road section, a congested road section, an accident-prone road section and a maintenance road section, are obtained after sample road condition information is clustered, and then the dimension of the accident-prone road section is obtained through screening according to a principal component analysis method or an analytic hierarchy process and serves as a third evaluation index.
Step 230: and distributing a weighted value for each preset evaluation index.
In the above steps, a weighted value is assigned according to the influence of each preset evaluation index on traffic safety.
Step 240: and constructing a preset evaluation model according to each preset evaluation index and the corresponding weight value.
In the above steps, a regression analysis equation is established according to each preset evaluation index and the corresponding weight value, and a preset evaluation model is constructed by using regression prediction.
In one embodiment, a machine learning algorithm is adopted to train the preset evaluation model through multiple iterations, a deviation value of the preset evaluation model is calculated according to the actual accident occurrence result of a sample driver, and the weight value of each preset evaluation index is optimized according to the deviation value, so that the preset evaluation model is adjusted, and the evaluation result of the preset evaluation model changes towards the direction closer to the actual result.
As shown in fig. 3, which is a flowchart illustrating a traffic warning method according to an embodiment of the present application, the method may be executed by the electronic device 100 shown in fig. 1 to implement traffic warning and improve traffic safety. The method comprises the following steps:
step 310: and acquiring driver information, vehicle condition information and road condition information of the mobile terminal.
In the above step, driver information, vehicle condition information, and road condition information of the mobile terminal are obtained, where the mobile terminal may be a vehicle-mounted device on a vehicle, and the driver information includes but is not limited to: the body health condition, the fatigue condition, the driving habit, the violation of regulations, and the illegal driving record such as drunk driving or poisonous driving. Vehicle condition information includes, but is not limited to: vehicle maintenance information, vehicle annual inspection information, vehicle mileage, and the like. Sample traffic information includes, but is not limited to: the weather condition of the area where the vehicle is located, the road congestion condition, the frequency of traffic accidents and the like.
Step 320: and extracting target data which accord with preset evaluation indexes from the driver information, the vehicle condition information and the road condition information.
In one embodiment, first feature data corresponding to a first evaluation index is extracted from driver information, second feature data corresponding to a second evaluation index is extracted from vehicle condition information, third feature data corresponding to a third evaluation index is extracted from road condition information, and the first feature data, the second feature data and the third feature data are combined to obtain target data.
Step 330: and inputting the target data into a preset evaluation model to obtain an evaluation result of the mobile terminal.
Step 340: and sending prompt information to the mobile terminal according to the evaluation result.
In the above steps, judging whether the evaluation result is within a preset safety threshold value; and when the evaluation result is not within the preset safety threshold, sending an early warning prompt to the mobile terminal.
In an embodiment, the early warning prompt sent to the mobile terminal may include a specific risk factor prompt, the driver may respond to and adjust the specific risk factor, and the response or adjustment result may be uploaded to the cloud to reenter the preset evaluation model for calculation, and the driver may be reminded again according to a new evaluation result. In an embodiment, before uploading the response or adjustment result to the cloud, the confidence degree judgment can be performed first, so that the information is prevented from being tampered randomly, and the accuracy of the evaluation result is prevented from being influenced.
In one embodiment, the warning prompt may be sent when the vehicle is started, or may be sent during the running of the vehicle.
In one embodiment, when the evaluation result is not within the preset safety threshold, the vehicle driving assistance function may be further activated.
As shown in fig. 4, which is a schematic structural diagram of a traffic warning apparatus 400 according to an embodiment of the present application, the apparatus can be applied to the electronic device 100 shown in fig. 1, and includes: an acquisition module 410, an extraction module 420, an input module 430, and a sending module 440. The principle relationship of the modules is as follows:
an obtaining module 410, configured to obtain driver information, vehicle condition information, and road condition information of the mobile terminal;
the extracting module 420 is configured to extract target data meeting a preset evaluation index from the driver information, the vehicle condition information, and the road condition information;
the input module 430 is configured to input the target data into a preset evaluation model to obtain an evaluation result of the mobile terminal;
and a sending module 440, configured to send the prompt message to the mobile terminal according to the evaluation result.
For details, refer to the descriptions of step 310 to step 340 in the above embodiments.
In one embodiment, the sending module 440 is configured to: judging whether the evaluation result is within a preset safety threshold value; and when the evaluation result is not within the preset safety threshold, sending an early warning prompt to the mobile terminal. For details, see the description of step 340 in the above embodiment.
As shown in fig. 5, which is a schematic structural diagram of a traffic warning apparatus 400 according to an embodiment of the present application, the apparatus can be applied to the electronic device 100 shown in fig. 1, and includes: an acquisition module 410, an extraction module 420, an input module 430, a sending module 440, and a construction module 450.
In one embodiment, the building block 450 is configured to: acquiring sample driver information of a plurality of sample drivers, and sample vehicle condition information and sample road condition information corresponding to each sample driver; determining a plurality of preset evaluation indexes of a preset evaluation model according to the sample driver information, the sample vehicle condition information and the sample road condition information; distributing a weighted value for each preset evaluation index; and constructing a preset evaluation model according to each preset evaluation index and the corresponding weight value. For details, refer to the descriptions of step 210 to step 240 in the above embodiments.
In one embodiment, determining a plurality of preset evaluation indexes of a preset evaluation model according to the sample driver information, the sample vehicle condition information, and the sample road condition information includes: and determining a first evaluation index of the preset evaluation model according to the sample driver information, determining a second evaluation index of the preset evaluation model according to the sample vehicle condition information, and determining a third evaluation index of the preset evaluation model according to the sample road condition information. See the description of step 220 in the above embodiment for details.
In one embodiment, the extraction module 420 is configured to: extracting first feature data corresponding to a first evaluation index from the driver information, second feature data corresponding to a second evaluation index from the vehicle condition information, and third feature data corresponding to a third evaluation index from the vehicle condition information; and merging the first characteristic data, the second characteristic data and the third characteristic data to obtain target data. See the description of step 320 in the above embodiment for details.
For a detailed description of the traffic warning device 400, please refer to the description of the related method steps in the above embodiments.
An embodiment of the present invention further provides a storage medium readable by an electronic device, including: a program that, when run on an electronic device, causes the electronic device to perform all or part of the procedures of the methods in the above-described embodiments. The storage medium may be a magnetic Disk, an optical Disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a Flash Memory (Flash Memory), a Hard Disk (Hard Disk Drive, abbreviated as HDD), a Solid State Drive (SSD), or the like. The storage medium may also comprise a combination of memories of the kind described above.
The above are merely preferred embodiments of the present application and are not intended to limit the present application. Any modification, equivalent replacement, improvement and the like made within the spirit and principle of the present application shall be included in the protection scope of the present application.
Claims (9)
1. A traffic early warning method is characterized by comprising the following steps:
acquiring driver information, vehicle condition information and road condition information of the mobile terminal;
extracting target data which accord with preset evaluation indexes from the driver information, the vehicle condition information and the road condition information;
inputting the target data into a preset evaluation model to obtain an evaluation result of the mobile terminal;
sending prompt information to the mobile terminal according to the evaluation result;
determining a plurality of preset evaluation indexes of the preset evaluation model according to the sample driver information, the sample vehicle condition information and the sample road condition information, including: determining a first evaluation index of the preset evaluation model according to the sample driver information; determining a second evaluation index of the preset evaluation model according to the sample vehicle condition information; determining a third evaluation index of the preset evaluation model according to the sample road condition information;
the preset evaluation model comprises a plurality of preset evaluation indexes, and the preset evaluation indexes are obtained by performing feature clustering analysis on the sample driver information, the sample vehicle condition information and the sample road condition information by using a K-means clustering algorithm;
the early warning prompt sent to the mobile terminal comprises a specific danger factor prompt, a driver can respond to and adjust the specific danger factor, the response or adjustment result is uploaded to the cloud end to input the preset evaluation model again for calculation, and the driver reminds again according to a new evaluation result;
before uploading the response or adjustment result to the cloud, the confidence degree is judged, so that the information is prevented from being tampered randomly, and the accuracy of the evaluation result is prevented from being influenced.
2. The method according to claim 1, wherein the sending a prompt message to the mobile terminal according to the evaluation result comprises:
judging whether the evaluation result is within a preset safety threshold value;
and when the evaluation result is not within the preset safety threshold, sending an early warning prompt to the mobile terminal.
3. The method of claim 1, wherein the step of constructing the pre-set evaluation model comprises:
obtaining sample driver information of a plurality of sample drivers, and sample vehicle condition information and sample road condition information corresponding to each sample driver;
determining a plurality of preset evaluation indexes of the preset evaluation model according to the sample driver information, the sample vehicle condition information and the sample road condition information;
distributing a weighted value to each preset evaluation index;
and constructing the preset evaluation model according to each preset evaluation index and the corresponding weight value.
4. The method according to claim 1, wherein the extracting target data meeting a preset evaluation index from the driver information, the vehicle condition information and the road condition information comprises:
extracting first feature data corresponding to the first evaluation index from the driver information, extracting second feature data corresponding to the second evaluation index from the vehicle condition information, and extracting third feature data corresponding to the third evaluation index from the road condition information;
and merging the first characteristic data, the second characteristic data and the third characteristic data to obtain the target data.
5. A traffic early warning device is characterized by comprising;
the acquisition module is used for acquiring driver information, vehicle condition information and road condition information of the mobile terminal;
the extraction module is used for extracting target data which accord with preset evaluation indexes from the driver information, the vehicle condition information and the road condition information;
the input module is used for inputting the target data into a preset evaluation model to obtain an evaluation result of the mobile terminal;
the sending module is used for sending prompt information to the mobile terminal according to the evaluation result;
the early warning prompt sent to the mobile terminal comprises a specific danger factor prompt, a driver can respond to and adjust the specific danger factor, the response or adjustment result is uploaded to the cloud end to input the preset evaluation model again for calculation, and the driver reminds again according to a new evaluation result;
before uploading the response or adjustment result to the cloud, the confidence degree is judged, so that the information is prevented from being tampered randomly, and the accuracy of the evaluation result is prevented from being influenced.
6. The apparatus of claim 5, wherein the sending module is configured to:
judging whether the evaluation result is within a preset safety threshold value;
and when the evaluation result is not within the preset safety threshold, sending an early warning prompt to the mobile terminal.
7. The apparatus of claim 5, further comprising a construction module to:
obtaining sample driver information of a plurality of sample drivers, and sample vehicle condition information and sample road condition information corresponding to each sample driver;
determining a plurality of preset evaluation indexes of the preset evaluation model according to the sample driver information, the sample vehicle condition information and the sample road condition information;
distributing a weighted value to each preset evaluation index;
constructing the preset evaluation model according to each preset evaluation index and the corresponding weight value; wherein the content of the first and second substances,
the determining a plurality of preset evaluation indexes of the preset evaluation model according to the sample driver information, the sample vehicle condition information and the sample road condition information includes:
and determining a first evaluation index of the preset evaluation model according to the sample driver information, determining a second evaluation index of the preset evaluation model according to the sample vehicle condition information, and determining a third evaluation index of the preset evaluation model according to the sample vehicle condition information.
8. The apparatus of claim 7, wherein the extraction module is configured to:
extracting first feature data corresponding to the first evaluation index from the driver information, extracting second feature data corresponding to the second evaluation index from the vehicle condition information, and extracting third feature data corresponding to the third evaluation index from the road condition information;
and merging the first characteristic data, the second characteristic data and the third characteristic data to obtain the target data.
9. An electronic device, comprising:
a memory to store a computer program;
a processor to perform the method of any one of claims 1 to 4.
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