CN112185155B - Automobile positioning management method based on big data, computer equipment and storage medium - Google Patents
Automobile positioning management method based on big data, computer equipment and storage medium Download PDFInfo
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- CN112185155B CN112185155B CN202011049900.7A CN202011049900A CN112185155B CN 112185155 B CN112185155 B CN 112185155B CN 202011049900 A CN202011049900 A CN 202011049900A CN 112185155 B CN112185155 B CN 112185155B
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- G—PHYSICS
- G08—SIGNALLING
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- G08G1/00—Traffic control systems for road vehicles
- G08G1/123—Traffic control systems for road vehicles indicating the position of vehicles, e.g. scheduled vehicles; Managing passenger vehicles circulating according to a fixed timetable, e.g. buses, trains, trams
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- B60Q—ARRANGEMENT OF SIGNALLING OR LIGHTING DEVICES, THE MOUNTING OR SUPPORTING THEREOF OR CIRCUITS THEREFOR, FOR VEHICLES IN GENERAL
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- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/16—Anti-collision systems
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Abstract
The invention relates to an automobile positioning management method based on big data, a computer device and a storage medium, which are used for monitoring the driving environment and the driving behavior of an automobile in real time and GPS positioning data generated in the driving process of the automobile; when the change of the driving environment of the vehicle is monitored, a first control instruction is sent to a vehicle-mounted alarm system, so that the sensitivity of the alarm system for collision alarm and vibration alarm is adjusted; when the driving behavior of the vehicle is monitored to be changed, a second control instruction is sent to a vehicle-mounted alarm system, so that the sensitivity of the alarm system for vehicle braking alarm and collision and vibration alarm is adjusted; and extracting commuting address information, vehicle interest point information and vehicle running area information contained in the GPS positioning data, and predicting the position of the vehicle to be searched at present. By the method and the device, attention timeliness of driving safety risk monitoring can be improved, vehicle finding success rate is improved, and loss of vehicles is reduced.
Description
Technical Field
The invention relates to the technical field of vehicle safety, in particular to an automobile positioning management method based on big data, computer equipment and a storage medium.
Background
At present, when a financing lease business is carried out by an automobile finance company, Beidou/GPS hardware positioning equipment is used for monitoring and managing vehicles. In the safety monitoring scene of vehicles such as passenger cars or logistics cars, the safety analysis of vehicle driving is relatively mature, the safety control behavior is triggered based on platform analysis or manual judgment, the hardware is not influenced from the angle of combination of the hardware of the internet of things and the platform, and intelligent linkage is lacked. When the vehicle has the risk of losing, the current general car scheme of looking for on the market is that the operation looks for the car person and looks for the car based on basic GPS location and vehicle parking position and historical parking record, lacks intelligent integrated analysis and application, and it is lower to look for the car efficiency, and relies on GPS online location alone, and the vehicle loses the risk and is higher.
Disclosure of Invention
The technical problem to be solved by the present invention is to provide a vehicle positioning management method, a computer device and a storage medium based on big data, aiming at the defects of the prior art.
The technical scheme adopted by the invention for solving the technical problems is as follows: a big data-based automobile positioning management method is constructed, and comprises the following steps:
monitoring the driving environment and driving behavior of a shared automobile in a running vehicle in real time, and GPS positioning data generated in the running process of the vehicle;
when the change of the driving environment of the vehicle is monitored, a first control instruction is sent to a vehicle-mounted alarm system, so that the sensitivity of the alarm system for collision alarm and vibration alarm is adjusted;
when the driving behavior of the vehicle is monitored to be changed, a second control instruction is sent to a vehicle-mounted alarm system, so that the sensitivity of the alarm system for vehicle braking alarm and collision and vibration alarm is adjusted;
and extracting commuting address information, vehicle interest point information and vehicle running area information contained in the GPS positioning data, integrating historical parking positions, and predicting the position of the vehicle to be searched at present.
Wherein, in the step of extracting commute address information, vehicle interest point information and vehicle driving area information that contain in GPS positioning data, include the step:
identifying positioning information related to all stop point information of the vehicle to be searched in the GPS positioning data, and setting the positioning information as suspected commuting address information;
judging the stay time of the vehicle to be searched at the position of each suspected commuting address information, and filtering invalid stay points to obtain valid stay points; wherein, the stay point with the stay time not more than 5 minutes is judged as an invalid stay point;
judging whether stop points with similar positions exist in the suspected commuting address information after the invalid stop points are filtered by combining a map so as to integrate the stop points;
after integrating the stopping points, judging the stopping time and the time interval of the vehicle to be searched at each effective stopping point, and judging the functions of the effective stopping points by combining a map;
judging the stay frequency of the vehicle to be searched in the position of each suspected commuting address information within a specified time range, and determining a commuting address;
through the analysis of the driving track of the vehicle to be searched and the analysis of the situation of the map information point poi, the interest consumption category preference and the interest point staying area of the renter are known, and the possible interest point address of the renter is predicted;
and analyzing the running track of the vehicle to be searched to obtain the running area range of the vehicle and the frequency distribution of the stop addresses, so as to predict the running area of the renter which is possible to travel.
In the step of integrating the stop points, coordinate position information of the stop points is obtained through a map, and the linear distance between every two stop points is calculated; and if the linear distance between the two stop points is smaller than the preset distance threshold, integrating the stop points with the distance smaller than the preset distance threshold.
Judging the stay time and the stay time period of the integrated stay point in the step of judging the effective stay point function, and if the stay time exceeds five hours and the stay time period is in the daytime of a working day, judging the stay point as a working place; if the stay time exceeds five hours and the stay time period is at night of the working day, the stay point is judged to be the address place; according to the situation of map information points poi, a stop point which stops for more than 1 hour on a rest day is judged as an interest point, and a stop point which stops for more than 1 hour and less than five hours on a working day at night is judged as an interest point.
In the step of predicting the current position of the vehicle to be searched by integrating the historical parking position according to the extracted commuting address information, the vehicle interest point information and the vehicle driving area information, the position of the vehicle to be searched in the current time period is determined according to the current date time period, and the prediction of the current position of the vehicle to be searched is completed.
Wherein the change in the driving environment of the vehicle includes at least:
the driving time of the vehicle enters a night mode;
when the driving time of the vehicle is judged to enter the night mode, the driving time point is compared with the real-time; when a first preset time point is passed in the process of setting the driving time, judging that the driving mode enters a night mode; when the second preset time point is passed in the driving time process, judging that the vehicle is driven to leave the night mode;
the driving mode of the vehicle driving is changed from the normal mode to the slow speed mode;
and the real-time running speed is reduced to a first preset speed, and the keeping time exceeds a first preset time interval, so that the driving mode of the vehicle driving is changed from the normal mode to the slow mode.
Wherein the change in the driving behavior of the vehicle comprises at least:
the driving area of the vehicle is changed;
if the coincidence degree of the running path of the vehicle and the running track in the previous week of the current time node are lower than the preset distance, judging that the running area of the vehicle is changed;
the driving mode of the vehicle is changed from the normal mode to the high-speed mode;
and increasing the real-time running speed to a second preset speed, and changing the driving mode of vehicle driving from the normal mode to the high-speed mode if the keeping time exceeds a second preset time interval.
The adjustment of the vehicle brake alarm during the vehicle running at least comprises the adjustment of the alarm sensitivity during the rapid acceleration, the rapid deceleration and the rapid turning of the vehicle running.
Furthermore, the present invention constructs a computer device, comprising an input/output unit, a memory and a processor, wherein the memory stores computer readable instructions, and the computer readable instructions, when executed by the processor, cause the processor to execute the steps of the big data based car positioning management method according to the foregoing technical solution.
The present invention also provides a storage medium storing computer-readable instructions, which when executed by one or more processors, cause the one or more processors to perform the steps of the method for managing vehicle location based on big data according to the foregoing technical solutions.
The automobile positioning management method based on the big data monitors the driving environment and the driving behavior of the automobile in real time and the GPS positioning data generated in the driving process of the automobile; when the change of the driving environment of the vehicle is monitored, a first control instruction is sent to a vehicle-mounted alarm system, so that the sensitivity of the alarm system for collision alarm and vibration alarm is adjusted; when the driving behavior of the vehicle is monitored to be changed, a second control instruction is sent to a vehicle-mounted alarm system, so that the sensitivity of the alarm system for vehicle braking alarm and collision and vibration alarm is adjusted; and extracting commuting address information, vehicle interest point information and vehicle running area information contained in the GPS positioning data, integrating historical parking positions, and predicting the position of the vehicle to be searched at present. By the method and the device, attention timeliness of driving safety risk monitoring can be improved, vehicle finding success rate is improved, and loss of vehicles is reduced.
Drawings
The invention will be further described with reference to the accompanying drawings and examples, in which:
fig. 1 is a flow chart of a big data-based automobile positioning management method according to the present invention.
Fig. 2 is a schematic structural diagram of a computer device in the big data-based automobile positioning management method of the invention.
Detailed Description
For a more clear understanding of the technical features, objects and effects of the present invention, embodiments of the present invention will now be described in detail with reference to the accompanying drawings.
As shown in fig. 1, the present invention provides a big data-based automobile data management method, including:
monitoring the driving environment and driving behavior of a shared automobile in a running vehicle in real time, and GPS positioning data generated in the running process of the vehicle;
when the change of the driving environment of the vehicle is monitored, a first control instruction is sent to a vehicle-mounted alarm system, so that the sensitivity of the alarm system for collision alarm and vibration alarm is adjusted;
when the driving behavior of the vehicle is monitored to be changed, a second control instruction is sent to a vehicle-mounted alarm system, so that the sensitivity of the alarm system for vehicle braking alarm and collision and vibration alarm is adjusted;
and extracting commuting address information, vehicle interest point information and vehicle running area information contained in the GPS positioning data, integrating historical parking positions, and predicting the position of the vehicle to be searched at present.
Generally, an on-vehicle GPS device of a vehicle collects vehicle-wide information including information of a vehicle running condition, a degree of health, and the like, and transmits the vehicle-wide information to a company database as GPS data, but in the present invention, only positioning information is involved, and therefore positioning information is first extracted from a large amount of GPS data corresponding to a vehicle to be searched.
Analyzing the GPS positioning data, and extracting commuting address information, vehicle interest point information and vehicle driving area information of the corresponding vehicle.
Wherein, in the step of extracting commute address information, vehicle interest point information and vehicle driving area information that contain in GPS positioning data, include the step:
identifying positioning information related to all stop point information of the vehicle to be searched in the GPS positioning data, and setting the positioning information as suspected commuting address information;
judging the stay time of the vehicle to be searched at the position of each suspected commuting address information, and filtering invalid stay points to obtain valid stay points; wherein, the stay point with the stay time not more than 5 minutes is judged as an invalid stay point;
judging whether stop points with similar positions exist in the suspected commuting address information after the invalid stop points are filtered by combining a map so as to integrate the stop points;
after integrating the stopping points, judging the stopping time and the time interval of the vehicle to be searched at each effective stopping point, and judging the functions of the effective stopping points by combining a map;
judging the stay frequency of the vehicle to be searched in the position of each suspected commuting address information within a specified time range, and determining a commuting address;
through the analysis of the driving track of the vehicle to be searched and the analysis of the situation of the map information point poi, the interest consumption category preference and the interest point staying area of the renter are known, and the possible interest point address of the renter is predicted;
and analyzing the running track of the vehicle to be searched to obtain the running area range of the vehicle and the frequency distribution of the stop addresses, so as to predict the running area of the renter which is possible to travel.
In the step of integrating the stop points, coordinate position information of the stop points is obtained through a map, and the linear distance between every two stop points is calculated; and if the linear distance between the two stop points is smaller than the preset distance threshold, integrating the stop points with the distance smaller than the preset distance threshold.
Judging the stay time and the stay time period of the integrated stay point in the step of judging the effective stay point function, and if the stay time exceeds five hours and the stay time period is in the daytime of a working day, judging the stay point as a working place; if the stay time exceeds five hours and the stay time period is at night of the working day, the stay point is judged to be the address place; according to the situation of map information points poi, a stop point which stops for more than 1 hour on a rest day is judged as an interest point, and a stop point which stops for more than 1 hour and less than five hours on a working day at night is judged as an interest point.
And judging the stay time of the vehicle to be searched at the position of each suspected commuting address information, and filtering invalid stay points. The vehicle can stop and stay for various reasons in the driving process, such as traffic lights, traffic jam, temporary inspection and the like. Specifically, a dwell point at which the dwell time does not exceed 5 minutes is determined as an invalid dwell point, and the invalid dwell point is excluded.
Judging whether stop points with similar positions exist in the suspected commuting address information after the invalid stop points are filtered by combining a map so as to integrate the stop points; for example, when a vehicle arrives at a specific location, such as a parking lot of a company or a home, the vehicle may stop at different parking spaces of the same parking lot every day due to time and sequence problems, and in the GPS positioning data, the different parking spaces of the same parking lot may be determined as different parking spots. At this time, the distance information between the stop points needs to be judged. Acquiring coordinate position information of the stop points through a map, and calculating the linear distance between every two stop points; and if the linear distance between the two stop points is smaller than the preset distance threshold, integrating the stop points with the distance smaller than the preset distance threshold. In the invention, each stopping point after the invalid stopping point is eliminated is numbered, then the distance between each stopping point and the rest stopping points is calculated by adopting a traversal method, and if the distance is less than 500 meters, the stopping points belong to the same parking lot, namely, the stopping points are integrated into the same stopping point. After one traversal is completed, the remaining parking points can be determined as parking points which are not in the same parking lot.
And after integrating the stop points, judging the stop time and the time interval of the vehicle to be searched at each effective stop point, and judging the effective stop points by combining a map. And judging the stop frequency of the vehicle to be searched in the position of each suspected commuting address information within a specified time range, and determining the commuting address.
Judging the stay time and the stay time period of the integrated stay point, and if the stay time exceeds five hours and the stay time period is in the daytime of a working day, judging the stay point as a working place; and if the stay time exceeds five hours and the stay time period is at night of the working day, judging that the stay point is the address place. And further combining the stay frequency of the stay points, if the stay frequency is more than 10 times per month, determining the stay points as commuting stay points, and otherwise, judging the interest points. Wherein the stop point of the family address and the stop point of the working address are set as commuting addresses. Subsequently, if the commuting address of the renter is changed and the renter is not informed, the company can determine the change condition according to the judgment process and can electrically confirm the change condition to the renter.
In the step of predicting the current position of the vehicle to be searched by integrating the historical parking position according to the extracted commuting address information, the vehicle interest point information and the vehicle driving area information, the position of the vehicle to be searched in the current time period is determined according to the current date time period, and the prediction of the current position of the vehicle to be searched is completed.
Specifically, assuming that the current time point for searching for the vehicle is 10 am on wednesday, according to the analysis, the renter should be in a working state and the vehicle should be in a unit parking lot; assuming that the current car searching time point is 10 weekday nights, according to the analysis, if the renter is in the information state, the car of the renter is in the parking lot to which the home address belongs; if the current car-searching time is three points in saturday afternoon, the renter is in a rest state, and if the renter is judged to have a habit of going to a market in saturday according to the judgment, the car is supposed to be in a parking lot to which the home address belongs or a parking lot of the market.
Further, the method also comprises a step of judging the change condition of the renter.
Judging commuting place information, driving time information and driving heat distribution information of a vehicle to be searched, if the commuting place information, the driving time information and the driving heat distribution information are changed, further judging driving information in GPS positioning data, comparing the driving information in a specified time interval of a current period with the driving information in the same time interval of a rental initial stage, and judging whether the driving condition of a driver is changed or not by comparing acceleration, deceleration and turning information of the vehicle; since the driving habits of each driver are fixed, the behavior habits of acceleration, deceleration and turning operations should be the same during the driving, and if the difference between the front and the rear is found by analyzing the data, the change of the driver, i.e. the renter is indicated. In the invention, in order to ensure the judgment accuracy, if the front time and the rear time are inconsistent, a plurality of random time periods are intercepted between the two time periods for judgment, and the time point of the change of the driver can be determined not because only the current driver changes.
Meanwhile, images acquired by an external camera of the vehicle to be searched can be acquired, whether drivers are the same in different periods is judged, the time point of change of the drivers can be determined, and whether the leaseholder has default conditions or not can be judged according to the agreed contract.
Wherein the change in the driving environment of the vehicle includes at least:
the driving time of the vehicle enters a night mode;
when the driving time of the vehicle is judged to enter the night mode, the driving time point is compared with the real-time; when a first preset time point is passed in the process of setting the driving time, judging that the driving mode enters a night mode; when the second preset time point is passed in the driving time process, judging that the vehicle is driven to leave the night mode;
the driving mode of the vehicle driving is changed from the normal mode to the slow speed mode;
and the real-time running speed is reduced to a first preset speed, and the keeping time exceeds a first preset time interval, so that the driving mode of the vehicle driving is changed from the normal mode to the slow mode.
Wherein the change in the driving behavior of the vehicle comprises at least:
the driving area of the vehicle is changed;
if the coincidence degree of the running path of the vehicle and the running track in the previous week of the current time node are lower than the preset distance, judging that the running area of the vehicle is changed;
the driving mode of the vehicle is changed from the normal mode to the high-speed mode;
and increasing the real-time running speed to a second preset speed, and changing the driving mode of vehicle driving from the normal mode to the high-speed mode if the keeping time exceeds a second preset time interval.
The adjustment of the vehicle brake alarm during the vehicle running at least comprises the adjustment of the alarm sensitivity during the rapid acceleration, the rapid deceleration and the rapid turning of the vehicle running.
The vehicle positioning management method is aimed at the vehicle to which the vehicle leasing company belongs, and can adjust the sensitivity of the related alarm of the vehicle internal alarm system based on different driving environments and driving behaviors, improve the driving safety of the vehicle, protect the alarm system components of the vehicle, avoid loss and prolong the service life.
Specifically, in the vehicle running process, the vehicle driving environment and the vehicle driving behavior in the vehicle running process are monitored in real time. In the embodiment of the invention, the change of the driving environment of the vehicle refers to that the vehicle drives from the normal driving environment to the abnormal driving environment in the real-time driving process. The change of the driving behavior of the vehicle means that the driving behavior of a driver changes during the driving of the vehicle.
The changes in the driving environment of the vehicle include at least temporal changes and spatial changes.
Time change: in the driving process of a driver, the visibility of the driver is clear in the daytime environment, the mental state of the driver is good, fatigue driving generally cannot occur, and drivers of other vehicles in the daytime environment also have the same visibility and mental state, so that the driver belongs to a relatively safe driving environment. However, in the night time environment, due to the influence of the natural colors, the visibility of the driver is reduced, the mental state of the driver may be fatigued, and the drivers of other vehicles have the same visibility and mental state at night, so that accidents are more likely to occur to the drivers in the night time than in the daytime. In the invention, a time node for switching from daytime to night is set, and when the time reaches the set time node in the driving process of a driver, the driving environment representing the time is switched.
In the invention, by monitoring the time process of a driver in the driving process, comparing the driving time point with the real-time, and when the driving time point passes a first preset time point in the driving time process, judging that the driver enters a night mode; and when the second preset time point is passed in the driving time process, judging that the vehicle is driven to leave the night mode. Specifically, it is set that 7 nighttime is the first preset time point, and when the vehicle is traveling at 7 nighttime, it is determined that the vehicle enters the nighttime mode, and at this time, the sensitivity of the warning device related to the collision warning and the vibration warning to the warning system of the vehicle is adjusted, thereby improving the sensitivity thereof. The sensitivity of the vehicle alarm system is improved, so that the vehicle alarm system can sense in time when receiving slight vibration or collision under the driving condition of a night mode, give an alarm and remind a driver to take measures in time.
And meanwhile, setting a morning point 7 as a second preset time point, judging that the vehicle exits the mode when the vehicle runs at the morning point 7, and adjusting the sensitivity of relevant alarm equipment for collision alarm and vibration alarm of an alarm system of the vehicle to reduce the sensitivity. When the vehicle runs in the time environment of 7 am, the visibility and mental state of a driver are good, the danger can be observed in time, an alarm can be given, the sensitivity of the alarm is reduced, and the service life of the alarm can be prolonged.
In other embodiments of the invention, the continuous travel time may be defined. If the continuous driving time exceeds the limited time, an alarm signal needs to be sent out to remind a driver to stop the vehicle for rest, and the continuous driving time can be set to be 3 hours. Therefore, the present invention does not have the case of continuously traveling from night 7 to morning 7 and from morning 7 to night 7, and does not control the above case.
Spatial variation: during driving, the speed should be reduced when the driver drives into a congested road section, such as a congested street or a road section with many cars. The invention sets the real-time running speed to be reduced to a first preset speed, and the keeping time exceeds a first preset time interval, so that the driving mode of vehicle driving is changed from a normal mode to a slow mode. The first preset speed is set to be 30km/h, and the first preset time interval is set to be 3 minutes. When the running speed is lower than the first preset speed, the fact that the vehicle enters the congested road section is judged, at the moment, the sensitivity of related alarm equipment of collision alarm and vibration alarm of an alarm system of the vehicle is adjusted, and the sensitivity is improved. Because the congested road section has more pedestrians and vehicles, even if the speed of the vehicle is reduced, a driver is relatively closed in a cab, and compared with the common road section, the driver is more likely to collide, and accidents are caused. The sensitivity is improved at the moment, and the driver can sense the vibration or the collision in time to give an alarm and remind the driver to take measures in time.
If the running speed is higher than the first preset speed and the continuous running time exceeds the first preset time interval, the vehicle returns to normal running and is driven away from the congested road section, and at the moment, the sensitivity of relevant alarm equipment of collision alarm and vibration alarm of an alarm system of the vehicle is adjusted, so that the sensitivity is reduced. Under the road conditions of normal driving, the visibility and the mental state of a driver are good, dangers can be observed in time, an alarm can be given, the sensitivity of the alarm is reduced, and the service life can be prolonged.
The change in the driving behavior of the vehicle includes at least a change in a traveling region in which the vehicle travels, and a change in a driving mode in which the vehicle travels from a normal mode to a high-speed mode.
The rental company often installs a GPS positioning device for the rental vehicle provided by the customer, the GPS positioning device is connected to a database of the rental company, GPS positioning information is transmitted to the database in real time or at regular time for storage, and the GPS positioning information is provided for technicians of the rental company to extract data for analysis when necessary. In an embodiment of the invention, the database stores GPS data transmitted by GPS equipment of vehicles within one week, and analyzes the GPS data to determine the driving track.
In the real-time driving process, real-time GPS data is transmitted to a database through GPS positioning equipment, GPS driving track information contained in the real-time GPS data is determined through analysis, the real-time GPS data is compared with driving tracks in GPS data of corresponding vehicles stored in the database, and if the coincidence degree of the driving path of the vehicle and the driving track in the previous week of the current time node are lower than a preset distance, the driving area of the vehicle is judged to be changed. And setting the preset distance to be 1 kilometer, and if the length of the misaligned track reaches 1 kilometer, indicating that the vehicle has traveled for more than 1 kilometer under strange road conditions, and judging that the traveling area changes. In this case, on the premise that normal rental of the vehicle is confirmed, the sensitivity of the warning device related to the collision warning and the vibration warning of the warning system of the vehicle is adjusted to improve the sensitivity. And judging that the driver currently drives the vehicle to arrive at the unfamiliar environment, and the driver cannot obtain the current road condition information. The sensitivity is improved at the moment, and the driver can sense the vibration or the collision in time to give an alarm and remind the driver to take measures in time.
And if the coincidence degree of the running path of the vehicle and the running track in the previous week of the current time node exceed the preset distance, judging that the running of the vehicle returns to the normal running area. And setting the preset distance to be 1 kilometer, and judging that the driving area returns to be normal if the length of the misaligned track reaches 1 kilometer. In this case, the sensitivity of the warning device relating to the collision warning and the vibration warning to the warning system of the vehicle is adjusted to decrease the sensitivity. At this time, the driver has already driven away from the unfamiliar area, and the service life can be prolonged by reducing the sensitivity of the alarm.
During driving, the speed should be increased when the driver drives on a highway or a road section where fewer vehicles are allowed to travel at a higher speed. The invention sets the real-time running speed to be increased to a second preset speed, and the keeping time exceeds a second preset time interval, so that the driving mode of vehicle driving is changed from a normal mode to a high-speed mode. The second preset speed is set to 80km/h and the second preset time interval is set to 1 minute. When the driving speed is lower than the second preset speed, the vehicle is judged to enter the high-speed driving road section, and the sensitivity of relevant alarm equipment for collision alarm and vibration alarm of an alarm system of the vehicle is adjusted at the moment, so that the sensitivity is improved. Because the speed of the high-speed running road section is higher, compared with the common road section, the collision is more easy to occur, and the accident is caused. The sensitivity is improved at the moment, and the driver can sense the vibration or the collision in time to give an alarm and remind the driver to take measures in time.
If the running speed is lower than the second preset speed and the continuous running time exceeds the second preset time interval, the fact that the vehicle runs back to normal is indicated, the vehicle is driven away from the high-speed running road section, and at the moment, the sensitivity of relevant alarm equipment of a collision alarm and a vibration alarm of an alarm system of the vehicle is adjusted, and the sensitivity is reduced. Under the road conditions of normal driving, the visibility and the mental state of a driver are good, dangers can be observed in time, an alarm can be given, the sensitivity of the alarm is reduced, and the service life can be prolonged.
While the present invention has been described with reference to the embodiments shown in the drawings, the present invention is not limited to the embodiments, which are illustrative and not restrictive, and it will be apparent to those skilled in the art that various changes and modifications can be made therein without departing from the spirit and scope of the invention as defined in the appended claims.
Claims (9)
1. A vehicle positioning management method based on big data is characterized by comprising the following steps:
monitoring the driving environment and driving behavior of the running vehicle of the automobile in real time, and GPS positioning data generated in the running process of the vehicle;
when the change of the driving environment of the vehicle is monitored, a first control instruction is sent to a vehicle-mounted alarm system, so that the sensitivity of the alarm system for collision alarm and vibration alarm is adjusted;
when the driving behavior of the vehicle is monitored to be changed, a second control instruction is sent to a vehicle-mounted alarm system, so that the sensitivity of the alarm system for vehicle braking alarm and collision and vibration alarm is adjusted; the change in the driving behavior of the vehicle includes at least:
the driving area of the vehicle is changed;
if the coincidence degree of the running path of the vehicle and the running track in the previous week of the current time node are lower than the preset distance, judging that the running area of the vehicle is changed;
the driving mode of the vehicle is changed from the normal mode to the high-speed mode;
increasing the real-time running speed to a second preset speed, and changing the driving mode of vehicle driving from a normal mode to a high-speed mode if the keeping time exceeds a second preset time interval;
and (4) extracting commuting address information, vehicle interest point information and vehicle running area information contained in the GPS positioning data, integrating historical parking positions, and predicting the position of the vehicle to be searched currently.
2. The big data based car positioning management method according to claim 1, wherein in the step of extracting commuting address information, vehicle interest point information and vehicle driving area information contained in the GPS positioning data, the method comprises the steps of:
identifying positioning information related to all stop point information of the vehicle to be searched in the GPS positioning data, and setting the positioning information as suspected commuting address information;
judging the stay time of the vehicle to be searched at the position of each suspected commuting address information, and filtering invalid stay points to obtain valid stay points; wherein, the stay point with the stay time not more than 5 minutes is judged as an invalid stay point;
judging whether stop points with similar positions exist in the suspected commuting address information after the invalid stop points are filtered by combining a map so as to integrate the stop points;
after integrating the stopping points, judging the stopping time and the time interval of the vehicle to be searched at each effective stopping point, and judging the functions of the effective stopping points by combining a map;
judging the stay frequency of the vehicle to be searched in the position of each suspected commuting address information within a specified time range, and determining a commuting address;
through the analysis of the driving track of the vehicle to be searched and the analysis of the situation of the map information point poi, the interest consumption category preference and the interest point staying area of the renter are known, and the possible interest point address of the renter is predicted;
and analyzing the running track of the vehicle to be searched to obtain the running area range of the vehicle and the frequency distribution of the stop addresses, so as to predict the running area of the renter which is possible to travel.
3. The automobile positioning management method based on big data as claimed in claim 2, wherein in the step of integrating the stop points, coordinate position information of the stop points is obtained through a map, and a straight line distance between every two stop points is calculated; and if the linear distance between the two stop points is smaller than the preset distance threshold, integrating the stop points with the distance smaller than the preset distance threshold.
4. The vehicle positioning management method based on big data according to claim 2, wherein in the step of determining the valid stop point function, the stop time and time period of the integrated stop point are determined, and if the stop time exceeds five hours and the stop time period is in the daytime of working day, the stop point is determined as the working place; if the stay time exceeds five hours and the stay time period is at night of the working day, the stay point is judged to be the address place; according to the situation of map information points poi, a stop point which stops for more than 1 hour on a rest day is judged as an interest point, and a stop point which stops for more than 1 hour and less than five hours on a working day at night is judged as an interest point.
5. The automobile positioning management method based on big data as claimed in claim 2, wherein in the step of predicting the current position of the vehicle to be searched by integrating the historical parking position according to the extracted commuting address information, the vehicle interest point information and the vehicle driving area information, the position of the vehicle to be searched in the current time period is determined according to the current date time period, and the prediction of the current position of the vehicle to be searched is completed.
6. The big data based car positioning management method according to claim 1, wherein the change of the driving environment of the vehicle at least comprises:
the driving time of the vehicle enters a night mode;
when the driving time of the vehicle is judged to enter the night mode, the driving time point is compared with the real-time; when a first preset time point is passed in the process of setting the driving time, judging that the driving mode enters a night mode; when the second preset time point is passed in the driving time process, judging that the vehicle is driven to leave the night mode;
the driving mode of the vehicle driving is changed from the normal mode to the slow speed mode;
and the real-time running speed is reduced to a first preset speed, and the keeping time exceeds a first preset time interval, so that the driving mode of the vehicle driving is changed from the normal mode to the slow mode.
7. The big data based car positioning management method of claim 3, wherein the adjustment of the vehicle braking alarm for vehicle driving comprises at least adjustment of the sensitivity of the alarm during rapid acceleration, rapid deceleration and rapid turning of the vehicle driving.
8. A computer device comprising an input-output unit, a memory and a processor, wherein the memory stores computer readable instructions which, when executed by the processor, cause the processor to perform the steps of the big data based car positioning management method according to any one of claims 1 to 7.
9. A storage medium storing computer readable instructions, which when executed by one or more processors, cause the one or more processors to perform the steps of the big data based car positioning management method of any of claims 1 to 7.
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