CN112148775A - Shared automobile safety management method based on big data - Google Patents
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Abstract
The invention relates to a shared automobile vehicle safety management method based on big data, computer equipment and a storage medium, which are characterized in that vehicle GPS information in the driving process of a vehicle is identified and analyzed, GPS installation information and GPS vehicle offline risk data are identified, the GPS installation information and the GPS vehicle offline risk data are compared with normal GPS installation information and signal type combinations stored in a database, whether an installation problem exists or not is determined, the current risk condition of the vehicle is judged, and the data uploading mode of the corresponding vehicle is adjusted. By the method and the device, the hysteresis of manual adjustment of sensitivity can be reduced, the attention timeliness of driving safety risk monitoring is improved, and the active prevention and control capability is improved.
Description
Technical Field
The invention relates to the technical field of vehicle safety, in particular to a shared automobile vehicle safety 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.
Disclosure of Invention
The invention aims to solve the technical problem of providing a shared automobile vehicle safety management method based on big data, a computer device and a storage medium aiming at the defects of the prior art.
The technical scheme adopted by the invention for solving the technical problems is as follows: a shared automobile safety management method based on big data is constructed, and comprises the following steps:
collecting GPS information of a vehicle to be predicted in real time; wherein the GPS information comprises GPS installation information and vehicle offline risk data;
analyzing the vehicle GPS information, identifying the information type and the information content in the GPS installation information, simultaneously analyzing the vehicle offline risk data, filtering the offline risk caused by the vehicle equipment problem, and identifying the vehicle offline risk data type after the offline risk caused by the vehicle equipment problem is filtered;
after analyzing the GPS information, analyzing and comparing the GPS installation information with corresponding normal GPS installation information stored in a preset database, determining whether installation problems exist or not, and judging the types of the installation problems;
and meanwhile, analyzing and comparing the vehicle offline risk data type with the signal type combination stored in the database, judging the current risk condition of the vehicle, and adjusting the data uploading mode of the corresponding vehicle.
The GPS installation information comprises GPS equipment power connection type information, vehicle state expression information, travel expression information, alarm rule information, vehicle-sharing equipment information and offline voltage change information; the vehicle offline risk data includes at least: the system comprises offline location positioning data, offline power connection information, equipment wiring fault information, equipment power connection information, installation position fault information, equipment power shortage information, equipment expiration information and various sensor alarm information of online equipment.
Wherein, in the step of analyzing and comparing the GPS installation information with the corresponding normal GPS installation information stored in the preset database, the method comprises the following steps:
comparing equipment type information in the GPS installation information acquired in real time with equipment type information in the GPS installation information which is normally installed, analyzing the equipment type and the installation time information, and judging whether an installation problem exists or not;
comparing vehicle state performance information in the GPS installation information acquired in real time with vehicle state performance information in the GPS installation information which is normally installed, judging whether the vehicle state moves and whether the vehicle generates a stroke, and further judging whether the installation problem of line looseness exists;
comparing the travel performance information in the GPS installation information acquired in real time with the travel performance information in the GPS installation information normally installed, associating equipment type information, and judging whether the installation problem of manual dismantling exists or not;
comparing alarm rule information in the GPS installation information acquired in real time with alarm rule information in the GPS installation information normally installed, analyzing alarm types, alarm times and alarm rules, and judging whether the installation problem is that the installation position is not concealed or long-time power is not connected;
comparing the same-vehicle equipment information in the GPS installation information collected in real time with the same-vehicle equipment information in the GPS installation information normally installed, analyzing the condition of the same-vehicle equipment, the alarm time span and the same-vehicle driving history, and judging whether the same-vehicle equipment is warehouse test scene equipment or not;
and comparing the offline voltage change information in the GPS installation information acquired in real time with the offline voltage change information in the GPS installation information which is normally installed, analyzing and judging the change condition of the external voltage, and judging whether the vehicle is normally powered off.
The steps of analyzing the vehicle offline risk data, filtering the offline risks caused by the vehicle equipment problems, and identifying the types of the vehicle offline risk data after the offline risks caused by the vehicle equipment problems are filtered include:
if the received vehicle offline risk data contain offline location positioning data, analyzing and identifying the offline location positioning data, and if the offline location positioning data are commuting resident points or offline of the same place with platform gathering property, judging that false offline expression is performed;
if the received vehicle offline risk data contain offline power connection information, analyzing and identifying the offline power connection information, and if the offline power connection information comes from wired equipment in the vehicle and the historical voltage value is greater than or equal to 20V, indicating that the power is not long, judging that false offline performance is achieved;
if the received vehicle offline risk data contains equipment wiring fault information, analyzing and identifying the equipment wiring fault information, and if the information comes from wired equipment, corresponding to the situation that the wired equipment has power failure alarm in a specified time interval and the vehicle is in a motion state during alarm, indicating that the vehicle is offline risk caused by installation problem;
if the received vehicle offline risk data contain equipment power connection information, analyzing and identifying the equipment power connection information, and if the information comes from wired equipment and corresponds to the situation that power failure alarm occurs to the wired equipment within a specified time interval, the single-day alarm frequency exceeds the set frequency, and meanwhile, the highest value of the historical voltage is less than or equal to 15V, the offline risk caused by the installation problem is indicated;
if the received vehicle offline risk data contains mounting position fault information, analyzing and identifying the mounting position fault information, and if the information comes from the wireless equipment and the light sensing alarm time span of the corresponding wireless equipment in a single day is larger than a second preset time interval, indicating that the vehicle offline risk is caused by mounting problems;
if the received vehicle offline risk data contain equipment power shortage information, analyzing and identifying the equipment power shortage information, and if the information comes from wireless equipment and the electric quantity of the wireless equipment is 0, or the information comes from wired equipment and corresponds to the situation that the wired equipment has an over-low power alarm, and meanwhile, the electric quantity of the wired equipment is 0, indicating that the vehicle offline risk is caused by power shortage;
if the received vehicle offline risk data contains equipment expiration information, analyzing and identifying the equipment expiration information, judging whether the expiration reason is the expiration of a card or the expiration of equipment, and judging the offline risk caused by the power shortage expiration information.
In the step of judging whether the installation problem exists according to the equipment type information, judging whether the equipment type information is GPS equipment, and if no GPS equipment exists, judging that the GPS equipment is not installed; if the GPS equipment exists, judging by combining other information in the GPS installation information; if the vehicle is in a non-moving state, the problem of non-installation is solved; if the vehicle is in a moving state and a stroke is generated, judging that the installation problem of line looseness exists; if the journey is not generated, the installation problem of suspected dismantling machine exists; judging the alarm frequency within a specified time interval, if the alarm frequency exceeds a preset alarm frequency threshold value, judging the change condition of the offline voltage, and if the external voltage of the offline voltage is less than 20V, judging that the installation problem of the un-lengthened power exists; if the external voltage is 20V or more, there is no mounting problem.
The method comprises the steps of judging the type of the same-vehicle equipment in combination with other information in GPS (global positioning system) installation information, if the same-vehicle equipment is wired equipment and has multiple sections of strokes, wherein the stroke time is more than 15 minutes, further judging the span time of generating a light-sensitive alarm on the day, if the stroke time is more than 65 minutes, the installation problem that the installation position is not hidden exists, and if the stroke time is less than or equal to 65 minutes, the installation problem does not exist; if the same-vehicle equipment is wired equipment and has multiple sections of travel and the travel time is less than or equal to 15 minutes, the equipment is warehouse test equipment; if the same vehicle equipment does not have wired equipment, the span time of generating the light sensation alarm on the day is judged, if the span time is more than 65 minutes, the installation problem that the installation position is not hidden exists, and if the span time is less than or equal to 65 minutes, the installation problem does not exist.
Wherein, the sensor type of all kinds of sensor alarm information of measuring online equipment includes at least: a sensor for monitoring whether the equipment is separated, a sensor for monitoring turnover, a sensor for monitoring power failure, a sensor for monitoring electric quantity and a sensor for monitoring risk points; and if the sensors separated by the detection equipment generate induction signals, judging that the off-line risk is generated when at least one of the other monitoring sensors generates induction signals.
After the offline risk is judged to be generated, the running mode of the wireless equipment of the vehicle is adjusted to be the vehicle following mode, and meanwhile, the signal uploading frequency of the corresponding wireless equipment is accelerated.
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 shared automobile vehicle safety management method according to the above 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 big data based shared automobile vehicle safety management method according to the above technical solution.
The shared automobile vehicle safety management method based on big data identifies and analyzes vehicle GPS information in the vehicle running process, identifies GPS installation information and GPS vehicle offline risk data in the vehicle GPS information, compares the GPS installation information with normal GPS installation information and signal type combinations stored in a database, determines whether an installation problem exists, judges the current risk condition of the vehicle, and adjusts the data uploading mode of the corresponding vehicle. By the method and the device, the hysteresis of manual adjustment of sensitivity can be reduced, the attention timeliness of driving safety risk monitoring is improved, and the active prevention and control capability is improved.
Drawings
The invention will be further described with reference to the accompanying drawings and examples, in which:
fig. 1 is a schematic structural diagram of a shared automobile vehicle safety management method based on big data according to the present invention.
Fig. 2 is a schematic structural diagram of a computer device in a big data-based shared automobile safety management method provided by 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 shared automobile vehicle safety management method, including:
collecting GPS information of a vehicle to be predicted in real time; wherein the GPS information comprises GPS installation information and vehicle offline risk data;
analyzing the vehicle GPS information, identifying the information type and the information content in the GPS installation information, simultaneously analyzing the vehicle offline risk data, filtering the offline risk caused by the vehicle equipment problem, and identifying the vehicle offline risk data type after the offline risk caused by the vehicle equipment problem is filtered;
after analyzing the GPS information, analyzing and comparing the GPS installation information with corresponding normal GPS installation information stored in a preset database, determining whether installation problems exist or not, and judging the types of the installation problems;
and meanwhile, analyzing and comparing the vehicle offline risk data type with the signal type combination stored in the database, judging the current risk condition of the vehicle, and adjusting the data uploading mode of the corresponding vehicle.
The GPS installation information comprises GPS equipment power connection type information, vehicle state expression information, travel expression information, alarm rule information, vehicle-sharing equipment information and offline voltage change information; the vehicle offline risk data includes at least: the system comprises offline location positioning data, offline power connection information, equipment wiring fault information, equipment power connection information, installation position fault information, equipment power shortage information, equipment expiration information and various sensor alarm information of online equipment.
Wherein, in the step of analyzing and comparing the GPS installation information with the corresponding normal GPS installation information stored in the preset database, the method comprises the following steps:
comparing equipment type information in the GPS installation information acquired in real time with equipment type information in the GPS installation information which is normally installed, analyzing the equipment type and the installation time information, and judging whether an installation problem exists or not;
comparing vehicle state performance information in the GPS installation information acquired in real time with vehicle state performance information in the GPS installation information which is normally installed, judging whether the vehicle state moves and whether the vehicle generates a stroke, and further judging whether the installation problem of line looseness exists;
comparing the travel performance information in the GPS installation information acquired in real time with the travel performance information in the GPS installation information normally installed, associating equipment type information, and judging whether the installation problem of manual dismantling exists or not;
comparing alarm rule information in the GPS installation information acquired in real time with alarm rule information in the GPS installation information normally installed, analyzing alarm types, alarm times and alarm rules, and judging whether the installation problem is that the installation position is not concealed or long-time power is not connected;
comparing the same-vehicle equipment information in the GPS installation information collected in real time with the same-vehicle equipment information in the GPS installation information normally installed, analyzing the condition of the same-vehicle equipment, the alarm time span and the same-vehicle driving history, and judging whether the same-vehicle equipment is warehouse test scene equipment or not;
and comparing the offline voltage change information in the GPS installation information acquired in real time with the offline voltage change information in the GPS installation information which is normally installed, analyzing and judging the change condition of the external voltage, and judging whether the vehicle is normally powered off.
The steps of analyzing the vehicle offline risk data, filtering the offline risks caused by the vehicle equipment problems, and identifying the types of the vehicle offline risk data after the offline risks caused by the vehicle equipment problems are filtered include:
if the received vehicle offline risk data contain offline location positioning data, analyzing and identifying the offline location positioning data, and if the offline location positioning data are commuting resident points or offline of the same place with platform gathering property, judging that false offline expression is performed;
if the received vehicle offline risk data contain offline power connection information, analyzing and identifying the offline power connection information, and if the offline power connection information comes from wired equipment in the vehicle and the historical voltage value is greater than or equal to 20V, indicating that the power is not long, judging that false offline performance is achieved;
if the received vehicle offline risk data contains equipment wiring fault information, analyzing and identifying the equipment wiring fault information, and if the information comes from wired equipment, corresponding to the situation that the wired equipment has power failure alarm in a specified time interval and the vehicle is in a motion state during alarm, indicating that the vehicle is offline risk caused by installation problem;
if the received vehicle offline risk data contain equipment power connection information, analyzing and identifying the equipment power connection information, and if the information comes from wired equipment and corresponds to the situation that power failure alarm occurs to the wired equipment within a specified time interval, the single-day alarm frequency exceeds the set frequency, and meanwhile, the highest value of the historical voltage is less than or equal to 15V, the offline risk caused by the installation problem is indicated;
if the received vehicle offline risk data contains mounting position fault information, analyzing and identifying the mounting position fault information, and if the information comes from the wireless equipment and the light sensing alarm time span of the corresponding wireless equipment in a single day is larger than a second preset time interval, indicating that the vehicle offline risk is caused by mounting problems;
if the received vehicle offline risk data contain equipment power shortage information, analyzing and identifying the equipment power shortage information, and if the information comes from wireless equipment and the electric quantity of the wireless equipment is 0, or the information comes from wired equipment and corresponds to the situation that the wired equipment has an over-low power alarm, and meanwhile, the electric quantity of the wired equipment is 0, indicating that the vehicle offline risk is caused by power shortage;
if the received vehicle offline risk data contains equipment expiration information, analyzing and identifying the equipment expiration information, judging whether the expiration reason is the expiration of a card or the expiration of equipment, and judging the offline risk caused by the power shortage expiration information.
In the step of judging whether the installation problem exists according to the equipment type information, judging whether the equipment type information is GPS equipment, and if no GPS equipment exists, judging that the GPS equipment is not installed; if the GPS equipment exists, judging by combining other information in the GPS installation information; if the vehicle is in a non-moving state, the problem of non-installation is solved; if the vehicle is in a moving state and a stroke is generated, judging that the installation problem of line looseness exists; if the journey is not generated, the installation problem of suspected dismantling machine exists; judging the alarm frequency within a specified time interval, if the alarm frequency exceeds a preset alarm frequency threshold value, judging the change condition of the offline voltage, and if the external voltage of the offline voltage is less than 20V, judging that the installation problem of the un-lengthened power exists; if the external voltage is 20V or more, there is no mounting problem.
The method comprises the steps of judging the type of the same-vehicle equipment in combination with other information in GPS (global positioning system) installation information, if the same-vehicle equipment is wired equipment and has multiple sections of strokes, wherein the stroke time is more than 15 minutes, further judging the span time of generating a light-sensitive alarm on the day, if the stroke time is more than 65 minutes, the installation problem that the installation position is not hidden exists, and if the stroke time is less than or equal to 65 minutes, the installation problem does not exist; if the same-vehicle equipment is wired equipment and has multiple sections of travel and the travel time is less than or equal to 15 minutes, the equipment is warehouse test equipment; if the same vehicle equipment does not have wired equipment, the span time of generating the light sensation alarm on the day is judged, if the span time is more than 65 minutes, the installation problem that the installation position is not hidden exists, and if the span time is less than or equal to 65 minutes, the installation problem does not exist.
Wherein, the sensor type of all kinds of sensor alarm information of measuring online equipment includes at least: a sensor for monitoring whether the equipment is separated, a sensor for monitoring turnover, a sensor for monitoring power failure, a sensor for monitoring electric quantity and a sensor for monitoring risk points; and if the sensors separated by the detection equipment generate induction signals, judging that the off-line risk is generated when at least one of the other monitoring sensors generates induction signals.
After the offline risk is judged to be generated, the running mode of the wireless equipment of the vehicle is adjusted to be the vehicle following mode, and meanwhile, the signal uploading frequency of the corresponding wireless equipment is accelerated.
Specifically, the combination of signal types corresponding to the high risk condition includes the following conditions:
the method comprises the following steps of online equipment, equipment separation alarm and turnover alarm;
the method comprises the following steps of online equipment, equipment separation alarm and turnover alarm;
the method comprises the following steps of online equipment, equipment separation alarm and power-off alarm;
online equipment, equipment separation alarm and low-power alarm exist;
the method comprises the steps of online equipment, equipment separation alarm and risk point alarm;
the method comprises the following steps of online equipment, equipment separation alarm, turnover alarm and power-off alarm;
the method comprises the following steps of online equipment, equipment separation alarm, turnover alarm and low-power alarm;
the method comprises the steps of online equipment, equipment separation alarm, turnover alarm and risk point alarm;
the method comprises the following steps of online equipment, equipment separation alarm, low-power alarm and power-off alarm;
the method comprises the steps of online equipment, equipment separation alarm, power-off alarm and risk point alarm;
the method comprises the steps of online equipment, equipment separation alarm, low-power alarm and risk point alarm;
the method comprises the following steps of online equipment, equipment separation alarm, low-power alarm, power-off alarm and turnover alarm;
the method comprises the following steps of online equipment, equipment separation alarm, power-off alarm, turnover alarm and risk point alarm;
the method comprises the following steps of online equipment, equipment separation alarm, low-power alarm, turnover alarm and risk point alarm;
the method comprises the following steps of online equipment, equipment separation alarm, power-off alarm, low-power alarm, turnover alarm and risk point alarm;
and if the data type combination of the real-time vehicle offline risk data is one of the modes, determining the offline risk. And after the offline risk is judged to be generated, adjusting the running mode of the wireless equipment of the vehicle into a car tracking mode, and accelerating the signal uploading frequency of the corresponding wireless equipment.
Furthermore, the present invention provides a computer device 1, comprising a storage medium 11 and a processor 12, wherein the storage medium 11 stores computer-readable instructions 111, and when the computer-readable instructions 111 are executed by one or more processors 12, the one or more processors 12 execute the steps of the online vehicle finding method based on big data analysis according to the foregoing technical solution.
As shown in fig. 2, the present invention provides a storage medium 11, where the storage medium 11 can be read and written by a processor 12, the storage medium 11 stores computer instructions 111, and when the computer instructions 111 are executed by one or more processors 12, the one or more processors 12 execute the steps of the online vehicle finding method based on big data analysis according to the foregoing technical solution.
According to the shared automobile vehicle safety management method based on the big data, the driving environment and the driving behavior of the vehicle in the driving process of the vehicle are monitored, and the alarming system of the vehicle is adjusted through real-time comparison and judgment when the driving environment and/or the driving behavior of the vehicle are judged to change and reach the preset condition, so that the sensitivity of the alarming system reaches the degree required by the current driving environment and/or the driving behavior of the vehicle. By the method and the device, the hysteresis of manual adjustment of sensitivity can be reduced, the attention timeliness of driving safety risk monitoring is improved, and the active prevention and control capability is improved.
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 (10)
1. A shared automobile vehicle safety management method based on big data is characterized by comprising the following steps:
collecting GPS information of a vehicle to be predicted in real time; wherein the GPS information comprises GPS installation information and vehicle offline risk data;
analyzing the vehicle GPS information, identifying the information type and the information content in the GPS installation information, simultaneously analyzing the vehicle offline risk data, filtering the offline risk caused by the vehicle equipment problem, and identifying the vehicle offline risk data type after the offline risk caused by the vehicle equipment problem is filtered;
after analyzing the GPS information, analyzing and comparing the GPS installation information with corresponding normal GPS installation information stored in a preset database, determining whether installation problems exist or not, and judging the types of the installation problems;
and meanwhile, analyzing and comparing the vehicle offline risk data type with the signal type combination stored in the database, judging the current risk condition of the vehicle, and adjusting the data uploading mode of the corresponding vehicle.
2. The big data based shared automobile vehicle safety management method according to claim 1, wherein the GPS installation information includes GPS device power connection type information, vehicle state performance information, trip performance information, alarm rule information, same-vehicle device information, and offline voltage change information; the vehicle offline risk data includes at least: the system comprises offline location positioning data, offline power connection information, equipment wiring fault information, equipment power connection information, installation position fault information, equipment power shortage information, equipment expiration information and various sensor alarm information of online equipment.
3. The big data based shared automotive vehicle safety management method according to claim 2, wherein in the step of comparing the GPS installation information with the corresponding normal GPS installation information stored in the preset database, the step of:
comparing equipment type information in the GPS installation information acquired in real time with equipment type information in the GPS installation information which is normally installed, analyzing the equipment type and the installation time information, and judging whether an installation problem exists or not;
comparing vehicle state performance information in the GPS installation information acquired in real time with vehicle state performance information in the GPS installation information which is normally installed, judging whether the vehicle state moves and whether the vehicle generates a stroke, and further judging whether the installation problem of line looseness exists;
comparing the travel performance information in the GPS installation information acquired in real time with the travel performance information in the GPS installation information normally installed, associating equipment type information, and judging whether the installation problem of manual dismantling exists or not;
comparing alarm rule information in the GPS installation information acquired in real time with alarm rule information in the GPS installation information normally installed, analyzing alarm types, alarm times and alarm rules, and judging whether the installation problem is that the installation position is not concealed or long-time power is not connected;
comparing the same-vehicle equipment information in the GPS installation information collected in real time with the same-vehicle equipment information in the GPS installation information normally installed, analyzing the condition of the same-vehicle equipment, the alarm time span and the same-vehicle driving history, and judging whether the same-vehicle equipment is warehouse test scene equipment or not;
and comparing the offline voltage change information in the GPS installation information acquired in real time with the offline voltage change information in the GPS installation information which is normally installed, analyzing and judging the change condition of the external voltage, and judging whether the vehicle is normally powered off.
4. The big data based shared automotive vehicle safety management method according to claim 2, wherein the step of analyzing the vehicle offline risk data, filtering offline risks caused by vehicle own equipment problems, and identifying the type of the vehicle offline risk data after filtering offline risks caused by vehicle own equipment problems comprises:
if the received vehicle offline risk data contain offline location positioning data, analyzing and identifying the offline location positioning data, and if the offline location positioning data are commuting resident points or offline of the same place with platform gathering property, judging that false offline expression is performed;
if the received vehicle offline risk data contain offline power connection information, analyzing and identifying the offline power connection information, and if the offline power connection information comes from wired equipment in the vehicle and the historical voltage value is greater than or equal to 20V, indicating that the power is not long, judging that false offline performance is achieved;
if the received vehicle offline risk data contains equipment wiring fault information, analyzing and identifying the equipment wiring fault information, and if the information comes from wired equipment, corresponding to the situation that the wired equipment has power failure alarm in a specified time interval and the vehicle is in a motion state during alarm, indicating that the vehicle is offline risk caused by installation problem;
if the received vehicle offline risk data contain equipment power connection information, analyzing and identifying the equipment power connection information, and if the information comes from wired equipment and corresponds to the situation that power failure alarm occurs to the wired equipment within a specified time interval, the single-day alarm frequency exceeds the set frequency, and meanwhile, the highest value of the historical voltage is less than or equal to 15V, the offline risk caused by the installation problem is indicated;
if the received vehicle offline risk data contains mounting position fault information, analyzing and identifying the mounting position fault information, and if the information comes from the wireless equipment and the light sensing alarm time span of the corresponding wireless equipment in a single day is larger than a second preset time interval, indicating that the vehicle offline risk is caused by mounting problems;
if the received vehicle offline risk data contain equipment power shortage information, analyzing and identifying the equipment power shortage information, and if the information comes from wireless equipment and the electric quantity of the wireless equipment is 0, or the information comes from wired equipment and corresponds to the situation that the wired equipment has an over-low power alarm, and meanwhile, the electric quantity of the wired equipment is 0, indicating that the vehicle offline risk is caused by power shortage;
if the received vehicle offline risk data contains equipment expiration information, analyzing and identifying the equipment expiration information, judging whether the expiration reason is the expiration of a card or the expiration of equipment, and judging the offline risk caused by the power shortage expiration information.
5. The shared automotive vehicle safety management method based on big data according to claim 3, characterized in that in the step of determining whether there is an installation problem according to the device type information, it is determined whether the device type information is a GPS device, and if there is no GPS device, it is determined that the GPS device is not installed; if the GPS equipment exists, judging by combining other information in the GPS installation information; if the vehicle is in a non-moving state, the problem of non-installation is solved; if the vehicle is in a moving state and a stroke is generated, judging that the installation problem of line looseness exists; if the journey is not generated, the installation problem of suspected dismantling machine exists; judging the alarm frequency within a specified time interval, if the alarm frequency exceeds a preset alarm frequency threshold value, judging the change condition of the offline voltage, and if the external voltage of the offline voltage is less than 20V, judging that the installation problem of the un-lengthened power exists; if the external voltage is 20V or more, there is no mounting problem.
6. The big-data-based shared automobile vehicle safety management method according to claim 5, wherein in the step of determining in combination with other information in the GPS installation information, the type of the on-vehicle device is determined, if the on-vehicle device is a wired device and has multiple trips and the trip time is longer than 15 minutes, the span time for generating a light-sensitive alarm on the day is further determined, if the span time is longer than 65 minutes, an installation problem that an installation position is not hidden exists, and if the span time is less than or equal to 65 minutes, the installation problem does not exist; if the same-vehicle equipment is wired equipment and has multiple sections of travel and the travel time is less than or equal to 15 minutes, the equipment is warehouse test equipment; if the same vehicle equipment does not have wired equipment, the span time of generating the light sensation alarm on the day is judged, if the span time is more than 65 minutes, the installation problem that the installation position is not hidden exists, and if the span time is less than or equal to 65 minutes, the installation problem does not exist.
7. The big data based shared automotive vehicle safety management method according to claim 2, wherein the sensor types measuring various types of sensor alarm information of the on-line device at least include: a sensor for monitoring whether the equipment is separated, a sensor for monitoring turnover, a sensor for monitoring power failure, a sensor for monitoring electric quantity and a sensor for monitoring risk points; and if the sensors separated by the detection equipment generate induction signals, judging that the off-line risk is generated when at least one of the other monitoring sensors generates induction signals.
8. The big data-based shared automotive vehicle safety management method according to claim 7, wherein after it is determined that the offline risk is generated, the operation mode of the wireless device of the vehicle is adjusted to a car-following mode, and simultaneously the signal uploading frequency of the corresponding wireless device is accelerated.
9. A computer device comprising an input-output unit, a memory and a processor, wherein the memory has stored therein computer readable instructions, which when executed by the processor, cause the processor to perform the steps of the big data based shared automotive vehicle safety management method of any one of claims 1 to 8.
10. 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 shared automotive vehicle safety management method of any one of claims 1 to 8.
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