CN112398814A - Driving behavior data tamper-proofing method and device based on big data - Google Patents
Driving behavior data tamper-proofing method and device based on big data Download PDFInfo
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Abstract
The embodiment of the application discloses a driving behavior data tamper-proofing method based on big data. The method comprises the following steps: acquiring driving behavior data information of a driver, wherein the driving behavior data information comprises image data information, driving behavior track data information and driving video stream data information of the driver in the driving process; comparing a difference value between a data index value and a periodic data index value in the driver driving behavior data information, wherein the periodic data index value is calculated according to the personal driving behavior data of the driver in a daily, monthly, quarterly and annual period; judging whether the difference value meets a preset condition, if so, marking the driving behavior data of the driver as monitoring data; and sending the monitoring data to a monitoring system so that the monitoring system processes the monitoring data within a preset time. Therefore, the individual driving habit index data of the driver in the database are correlated, and the data are prevented from being falsified through the judgment result and the data monitoring.
Description
Technical Field
The embodiment of the application relates to the field of data processing, in particular to a driving behavior data tamper-proofing method based on big data.
Background
Along with the continuous promotion of the intelligent technical level of vehicles, a robot coach as a full-intelligent driving training device gradually appears in each driving school, the technology plays a role in assisting teaching through human-computer interaction, a receiver is arranged on a vehicle, inductors are arranged around the vehicle body, and a copilot in the vehicle is provided with an LED display screen and an emergency braking mode. Before a student learns, a driving school party transmits data information such as field size, training items and time into a system in advance, the course and the navigation position of a vehicle are calculated through a three-dimensional electronic map of virtual reality and a high-precision difference technology, so that the state of the vehicle is judged, and a robot coach guides the student to practice the vehicle through voice and an electronic display screen.
In the prior art, driving behavior data of a learner in a driving practice process are uploaded to driving school nodes, the driving behavior data do not have data security, and a driving school of the driving behavior data in the system can be changed privately, so that the driving behavior data of the learner in the driving practice process are inaccurate.
Disclosure of Invention
The embodiment of the application provides a big data-based driving behavior data tamper-proofing method and device, which are used for preventing driving behavior data from being tampered in a node server.
The embodiment of the application provides a method for preventing driving behavior data from being tampered based on big data in a first aspect, and the method comprises the following steps:
acquiring driving behavior data information of a driver, wherein the driving behavior data information comprises image data information, driving behavior track data information and driving video stream data information of the driver in the driving process;
comparing a difference value between a data index value and a periodic data index value in the driver driving behavior data information, wherein the periodic data index value is calculated according to the personal driving behavior data of the driver in a daily, monthly, quarterly and annual period;
judging whether the difference value meets a preset condition, if so, marking the driving behavior data of the driver as monitoring data;
and sending the monitoring data to a monitoring system so that the monitoring system processes the monitoring data within a preset time.
Optionally, the comparing a difference between a data index value in the driver behavior data information and a periodic data index value, where the periodic data index value is calculated according to the personal driving behavior data of the driver in a daily, monthly, quarterly, and yearly cycle, includes:
acquiring historical driving behavior data of the driver in a database, wherein the historical driving behavior data comprises data such as the highest speed, the braking times, the lane changing times and the like;
carrying out multi-dimensional average calculation on the historical driving behavior data in days, months, quarters and years, and taking the calculated result as the periodicity index value;
extracting data index values in the driving behavior data information of the driver, wherein the data index values comprise the values of the one-time average speed, the average braking frequency and the average lane changing frequency of the driver;
and comparing the difference value of the data index value and the periodic data index value.
Optionally, before comparing the difference between the data index value in the driver behavior data information and the periodic data index value, the method further includes:
and judging whether the identity information of the driver is consistent with the data information in the database, if so, executing decryption operation on the periodic data information of the driver in the database.
Optionally, after the determining whether the difference value meets the preset condition, the method further includes:
and if not, uploading the driving behavior data of the driver to a driving school node server.
Optionally, before uploading the driver driving behavior data to a driving school node system, the method further includes:
and performing HDFS (Hadoop Distributed File System) Distributed File system encryption operation on the driver behavior data information.
A second aspect of the embodiments of the present application provides a device for preventing driving behavior data from being tampered based on big data, including:
the driving behavior data information comprises image data information, driving behavior track data information and driving video stream data information of the driver in the driving process;
the comparison unit is used for comparing a difference value between a data index value and a periodic data index value in the driver driving behavior data information, and the periodic data index value is calculated according to the personal driving behavior data of the driver in a daily, monthly, quarterly and annual period;
the second judgment unit is used for judging whether the difference value meets a preset condition or not, and if so, marking the driving behavior data of the driver as monitoring data;
and the sending unit is used for sending the monitoring data to a monitoring system so that the monitoring system processes the monitoring data within a preset time.
Optionally, the comparison unit includes:
the acquisition module is used for acquiring historical driving behavior data of the driver in a database, wherein the historical driving behavior data comprises data such as the highest speed, the braking times, the lane changing times and the like;
the calculation module is used for carrying out multi-dimensional average calculation on the historical driving behavior data in a daily, monthly, quarterly and annual mode, and the calculated result is used as a periodicity index value;
the extraction module is used for extracting data index values in the driving behavior data information of the driver, wherein the data index values comprise the values of the one-time average speed, the average braking frequency and the average lane changing frequency of the driver;
and the comparison module is used for comparing the difference value of the data index value and the periodic data index value.
Optionally, the apparatus further comprises:
the first judgment unit is used for judging whether the driver identity information is consistent with the data information in the database or not;
and the first execution unit is used for executing decryption operation on the periodic data information of the driver in the database when the identity information of the driver is consistent with the information in the database.
Optionally, after the second determining unit, the apparatus further includes:
and the uploading unit is used for uploading the driving behavior data of the driver to a driving school node server when the difference value does not meet the preset condition.
Optionally, before the uploading unit, the apparatus further includes:
and the second execution unit is used for executing HDFS encryption operation on the driver behavior data information.
A third aspect of the embodiments of the present application provides a device for preventing driving behavior data from being tampered based on big data, including:
the device comprises a processor, a memory, an input and output unit and a bus;
the processor is connected with the memory, the input and output unit and the bus;
the processor specifically performs the following operations:
acquiring driving behavior data information of a driver, wherein the driving behavior data information comprises image data information, driving behavior track data information and driving video stream data information of the driver in the driving process;
comparing a difference value between a data index value and a periodic data index value in the driver driving behavior data information, wherein the periodic data index value is calculated according to the personal driving behavior data of the driver in a daily, monthly, quarterly and annual period;
judging whether the difference value meets a preset condition, if so, marking the driving behavior data of the driver as monitoring data;
and sending the monitoring data to a monitoring system so that the monitoring system processes the monitoring data within a preset time.
Optionally, the processor is further configured to perform the operations of any of the alternatives of the first aspect.
A fourth aspect of embodiments of the present application provides a computer-readable storage medium for tamper-proofing driving behavior data based on big data, including:
the computer-readable storage medium has stored thereon a program that, when executed on a computer, performs the aforementioned big-data-based tamper-resistant method of driving behavior.
According to the technical scheme, the embodiment of the application has the following advantages:
according to the method and the device, driving behavior data of a driver are obtained, the driving behavior data information comprises image data information, driving behavior track data information and driving video stream data information of the driver in the driving process, a difference value between a data index value and a periodic index value in the driving behavior data information of the driver is compared, whether the difference value meets a preset condition is judged, if yes, the driving behavior data of the driver are marked as monitoring data, and then the monitoring data are sent to a monitoring system, so that the monitoring system processes the monitoring data in preset time. Therefore, data in the database are correlated before being uploaded, and real-time monitoring of the data which do not meet the conditions can prevent the driving behavior data from being tampered in the node server.
Drawings
FIG. 1 is a schematic flow chart of an embodiment of a big data-based driving behavior data tamper-proofing method in an embodiment of the present application;
FIG. 2 is a schematic flow chart of another embodiment of a big data-based driving behavior data tamper-proofing method in the embodiment of the present application;
FIG. 3 is a schematic structural diagram of an embodiment of a big data-based driving behavior data tamper-proofing device in the embodiment of the present application;
fig. 4 is a schematic structural diagram of another embodiment of the big data-based driving behavior data tamper-proofing device in the embodiment of the present application.
Detailed Description
The embodiment of the application provides a driving behavior data tamper-proofing method and device based on big data, and the method and device are used for preventing the driving behavior data from being tampered.
In this embodiment, the driving behavior data tamper-proofing method based on big data may be implemented in a system, a server, or a terminal, and is not specifically limited.
Referring to fig. 1, an embodiment of the present application is described by taking an example of a server, and an embodiment of a method for preventing driving behavior data from being tampered based on big data in the embodiment of the present application includes:
101. the method comprises the steps that a terminal obtains driving behavior data information of a driver, wherein the driving behavior data information comprises image data information, driving behavior track data information and driving video stream data information of the driver in the driving process;
in the application, the terminal needs to perform data association on the driving behavior data of the driver to verify whether the data needing to be uploaded meets the preset conditions or not, so that the current driving behavior data of the driver needs to be acquired before data information is verified, and the basis for acquiring the driving behavior data by the general server is the driving behavior track acquired through the image and video stream data information of the face and the body part of the driver in the driving process, which is shot by the camera, and the sensor and the navigation positioning system.
In an actual scene, for example, a driver A learns to practice a car in a driving school, when face information of the driver A is recognized, a camera is started to shoot images and videos of the driver A in the driving process in real time, the shot information is automatically stored, the driver A can drive according to navigation equipment or a driving route required by the driving school, and the terminal records driving behavior data of the driver A in real time and stores the data.
102. The terminal compares the difference value between the data index and the periodic data index value in the driving behavior data information of the driver, and the periodic data index value carries out daily, monthly, quarterly and annual cycle calculation according to the personal driving behavior data of the driver;
in this embodiment, before the driving behavior data information of the driver is uploaded to the driving school node, data association processing is performed first. By comparing the associated data, the evidence of whether the data is tampered can be used. For example, when a driver a learns a car at a driving school on a certain day, the terminal uploads driving behavior data of the day to the cloud, and first acquires driving behavior related data of the driver a from devices such as a photographing device, a display screen and a sensor. Then extracting personal driving data of A in driving school at other time, such as maximum speed, cross road speed, braking frequency, left and right rearview mirror observation frequency, and periodically calculating the periodic index value of A according to day, month, quarter and year, which is equivalent to the personal driving habit index value of A formed by the data.
103. The terminal judges whether the difference value meets a preset condition,
the terminal judges whether the difference value between the calculated current-day driving behavior data index and the periodic index value of the driver exceeds 50%, the specific preset condition can be set according to the learning time period of the driver in the driving school, the specific preset condition is set to be 50%, and if the difference value exceeds 50%, the current-day driving behavior data of the driver is probably distorted.
104. When the terminal judges that the difference value meets the preset condition, marking the driving behavior data of the driver as monitoring data;
in the embodiment, the terminal compares the difference value between the data index value and the periodic index value in the driving behavior data of the driver, when the difference value exceeds 50%, the driving behavior data of the driver is proved to have larger difference with the individual driving habit data index, so that the driving behavior data is possible to be falsified, and therefore, the driving behavior data is directly marked as monitoring data so as to be further processed or the original data is automatically recovered.
105. And the terminal sends the monitoring data to the monitoring system so that the monitoring system processes the monitoring data within a preset time.
In the embodiment, after marking the driving behavior data of the driver, the monitoring data is sent to the monitoring system, the terminal stores the data in a log and a database while sending the monitoring data, the MySQL transaction and the successful message sending state are utilized to verify the reachability of the abnormal message doubly, the abnormal data is backed up redundantly, the monitoring data is processed in the abnormal data queue within more than 1 hour, the original data is automatically recovered according to the historical driving behavior data, and the recovered abnormal data is stored in the log and the database again. Therefore, the tampered data cannot be uploaded to the driving school node server, and the driving behavior data accuracy of the trainee is effectively improved.
Referring to fig. 2, an embodiment of the present application is described using a terminal as an example. Another embodiment of the driving behavior data tamper-proofing method based on big data in the embodiment of the application comprises the following steps:
201. the method comprises the steps that a terminal obtains driving behavior data information of a driver, wherein the driving behavior data information comprises image data information, driving behavior track data information and driving video stream data information of the driver in the driving process;
step 201 in this embodiment is similar to step 101 in the previous embodiment, and is not described herein again.
202. The terminal judges whether the identity information of the driver is consistent with the data information in the database, if so, decryption operation is carried out on the periodic data information of the driver in the database;
the terminal needs to verify the identity information before extracting the historical driving behavior data of the driver, specifically, the system can automatically identify the information by inputting the identity information, such as personal information of names, mobile phone numbers and the like, and can automatically decrypt the historical driving behavior data of the driver after the information verification is successful.
In this embodiment, the data is encrypted at the time of the write operation, so the data needs to be decrypted when the read operation is used.
203. The method comprises the steps that a terminal obtains historical driving behavior data of a driver in a database, wherein the historical driving behavior data comprise data such as the highest speed, the braking times and the lane changing times;
in this embodiment, after the driver successfully verifies the identity information, the terminal may automatically obtain the driver's historical driving behavior data, where the driving behavior data includes a maximum speed, a cross speed, a braking number, a number of times of observing the left and right rearview mirrors, a steering wheel rotation speed, a number of times of changing lanes, and the like.
204. The terminal calculates the multi-dimensional average value of the historical driving behavior data in days, months, quarters and years, and the calculated result is used as a periodic index value;
in this embodiment, after the terminal extracts the historical driving behavior data of the driver, the terminal performs multidimensional average value calculation on the data for days, months, quarters and years, and the calculated result is used as a periodic index value, that is, the periodic index value is the driving habit index value of the driver and can be used as basic data for comparison.
205. The method comprises the steps that a terminal extracts a data index value in driving behavior data information of a driver, wherein the data index value comprises the values of one-time average speed, average braking times, average lane changing times and the like of the driver;
and after determining the periodic index value of the driver, the terminal extracts the driving behavior data of the driver in the driving school on the current day, wherein the obtaining mode is the same as the mode, and the description is omitted here. Wherein the data index values comprise values such as average maximum speed, average crossroad speed, average braking times, average left and right rearview mirror observation times, average steering wheel rotation average speed and average lane change times of the driver on the current day.
206. The terminal compares the difference value of the data index value and the periodic data index value;
and when the terminal calculates the data index value of the driving behavior of the driver on the current day and the periodic data index value of the driver, comparing the data of the driver on the current day and the periodic data index value of the driver.
207. The terminal judges whether the difference value meets a preset condition or not;
208. the terminal marks the driving behavior data of the driver as monitoring data;
209. the terminal sends the monitoring data to the monitoring system so that the monitoring system processes the monitoring data within a preset time;
210. The terminal executes HDFS encryption operation on the driver behavior data information and uploads the driver driving behavior data to the driving school node server.
When the difference is lower than 50%, it indicates that the driving behavior data of the driver on the same day can be used as real data, in this embodiment, the data is encrypted during the write operation, a specific encryption method is an end-to-end HDFS transparent encryption method, and the encryption process is completely transparent to the client, so that the security of the data is also protected. After the data are encrypted, the driving behavior data of the driver on the same day are uploaded to a driving school node server, so that the driving school is prevented from intercepting and modifying the data at a vehicle-mounted terminal source privately.
Referring to fig. 3, an embodiment of a device for preventing driving behavior data from being tampered based on big data in the embodiment of the present application includes:
the acquiring unit 301 is configured to acquire driving behavior data information of a driver, where the driving behavior data information includes image data information, driving behavior trajectory data information, and driving video stream data information of the driver during driving;
a first judging unit 302, configured to judge whether the driver identity information is consistent with the data information in the database;
the first executing unit 303 is configured to, when the driver identity information is consistent with information in the database, execute a decryption operation on the periodic data information of the driver in the database;
a comparison unit 304, configured to compare a difference between a data index value in the driver driving behavior data information and a periodic data index value, where the periodic data index value is calculated according to the driver's individual driving behavior data in a daily, monthly, quarterly, and yearly period;
a second judging unit 305, configured to judge whether the difference satisfies a preset condition, and if so, mark the driver driving behavior data as the monitoring data;
a sending unit 306, configured to send the monitoring data to the monitoring system, so that the monitoring system processes the monitoring data within a preset time;
a second execution unit 307 for executing an HDFS encryption operation on the driver behavior data information;
and the uploading unit 308 is configured to upload the driving behavior data of the driver to the driving school node server when the difference does not meet the preset condition.
In this embodiment, the comparison unit 304 may include an obtaining module 3041, a calculating module 3042, an extracting module 3043, and a comparing module 3044.
An obtaining module 3041, configured to obtain historical driving behavior data of a driver in a database, where the historical driving behavior data includes data such as a highest speed, a braking frequency, and a lane change frequency;
a calculating module 3042, configured to perform daily, monthly, quarterly and yearly multidimensional average calculation on historical driving behavior data, where a calculated result is used as a periodicity index value;
the extracting module 3043 is used for extracting data index values in the driving behavior data information of the driver, wherein the data index values comprise values such as driving one-time average speed, average braking times and average lane changing times;
a comparing module 3044 for comparing the difference between the data index value and the periodic data index value.
In this embodiment, after the obtaining unit 301 obtains the driving behavior data information of the driver, the first determining unit 302 determines whether the identity information of the driver is consistent with the data information in the database, if so, the first executing unit 303 performs a decryption operation on the periodic data information of the driver in the database, and the comparing unit 304 compares a difference value between a data index and a periodic data index value in the driving behavior data information of the driver, specifically, the obtaining module in the comparing unit 301 first obtains the historical driving behavior data of the driver in the database, including data such as the highest speed, the braking times, and the lane changing times; the calculation module 3042 calculates the multi-dimensional calculation according to the day, month, quarter and year, the extraction module 3043 extracts a data index value in the driving behavior data information of the driver, the comparison module 3044 compares the difference value between the data index value and the periodic index value, the second judgment unit 305 judges whether the difference value meets a preset condition, if so, marks the driving behavior data of the driver as monitoring data, and the sending unit 306 sends the monitoring data to the monitoring system, so that the monitoring system processes the monitoring data in a preset time, if not, the second execution unit 307 executes an HDFS encryption operation on the driving behavior data information, and the uploading unit 308 uploads the driving behavior data of the driver to the driving school node server.
Referring to fig. 4, another embodiment of the tamper-proofing device for driving behavior data based on big data in the embodiment of the present application includes:
a processor 401, a memory 402, an input-output unit 403, a bus 404;
the processor 401 is connected to the memory 402, the input/output unit 403, and the bus 404;
acquiring driving behavior data information of a driver, wherein the driving behavior data information comprises image data information, driving behavior track data information and driving video stream data information of the driver in the driving process;
comparing a difference value between a data index value and a periodic data index value in the driving behavior data information of the driver, and calculating the periodic data index value in a daily, monthly, quarterly and annual cycle according to the personal driving behavior data of the driver;
judging whether the difference value meets a preset condition, if so, marking the driving behavior data of the driver as monitoring data;
and sending the monitoring data to the monitoring system so that the monitoring system processes the monitoring data within a preset time.
Optionally, the functions of the processor 401 correspond to the steps in the embodiments shown in fig. 1 to fig. 2, and are not described herein again.
It is clear to those skilled in the art that, for convenience and brevity of description, the specific working processes of the above-described systems, apparatuses and units may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
In the several embodiments provided in the present application, it should be understood that the disclosed system, apparatus and method may be implemented in other manners. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the units is only one logical division, and other divisions may be realized in practice, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present application may be substantially implemented or contributed to by the prior art, or all or part of the technical solution may be embodied in a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a read-only memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and the like.
Claims (10)
1. A driving behavior data tamper-proofing method based on big data is characterized by comprising the following steps:
acquiring driving behavior data information of a driver, wherein the driving behavior data information comprises image data information, driving behavior track data information and driving video stream data information of the driver in the driving process;
comparing a difference value between a data index value and a periodic data index value in the driver driving behavior data information, wherein the periodic data index value is calculated according to the personal driving behavior data of the driver in a daily, monthly, quarterly and annual period;
judging whether the difference value meets a preset condition, if so, marking the driving behavior data of the driver as monitoring data;
and sending the monitoring data to a monitoring system so that the monitoring system processes the monitoring data within a preset time.
2. The method of claim 1, wherein said comparing the difference between data index values in said driver behavior data information and periodic data index values, said periodic data index values being calculated from said driver's individual driving behavior data for a daily, monthly, quarterly, and yearly period, comprises:
acquiring historical driving behavior data of the driver in a database, wherein the historical driving behavior data comprises data such as the highest speed, the braking times, the lane changing times and the like;
carrying out multi-dimensional average calculation on the historical driving behavior data in days, months, quarters and years, and taking the calculated result as the periodicity index value;
extracting data index values in the driving behavior data information of the driver, wherein the data index values comprise the values of the one-time average speed, the average braking frequency and the average lane changing frequency of the driver;
and comparing the difference value of the data index value and the periodic data index value.
3. The method according to claim 2, characterized in that before comparing the difference of the data index value in the driver behavior data information with a periodic data index value, the method further comprises:
and judging whether the identity information of the driver is consistent with the data information in the database, if so, executing decryption operation on the periodic data information of the driver in the database.
4. The method according to claim 1, wherein after the determining whether the difference value satisfies a preset condition, the method further comprises:
and if not, uploading the driving behavior data of the driver to a driving school node server.
5. The method of claim 4, wherein prior to said uploading said driver driving behavior data to a driving school node system, said method further comprises:
and performing HDFS (Hadoop Distributed File System) Distributed File system encryption operation on the driver behavior data information.
6. A big data-based driving behavior data tamper-proofing device, comprising:
the driving behavior data information comprises image data information, driving behavior track data information and driving video stream data information of the driver in the driving process;
the comparison unit is used for comparing a difference value between a data index value and a periodic data index value in the driver driving behavior data information, and the periodic data index value is calculated according to the personal driving behavior data of the driver in a daily, monthly, quarterly and annual period;
the second judgment unit is used for judging whether the difference value meets a preset condition or not, and if so, marking the driving behavior data of the driver as monitoring data;
and the sending unit is used for sending the monitoring data to a monitoring system so that the monitoring system processes the monitoring data within a preset time.
7. The apparatus of claim 6, wherein the comparison unit comprises:
the acquisition module is used for acquiring historical driving behavior data of the driver in a database, wherein the historical driving behavior data comprises data such as the highest speed, the braking times, the lane changing times and the like;
the calculation module is used for carrying out multi-dimensional average calculation on the historical driving behavior data in a daily, monthly, quarterly and annual mode, and the calculated result is used as a periodicity index value;
the extraction module is used for extracting data index values in the driving behavior data information of the driver, wherein the data index values comprise the values of the one-time average speed, the average braking frequency and the average lane changing frequency of the driver;
and the comparison module is used for comparing the difference value of the data index value and the periodic data index value.
8. The apparatus of claim 7, wherein prior to the comparison unit, the apparatus further comprises:
the first judgment unit is used for judging whether the driver identity information is consistent with the data information in the database or not;
and the first execution unit is used for executing decryption operation on the periodic data information of the driver in the database when the identity information of the driver is consistent with the information in the database.
9. The apparatus according to claim 6, wherein after the second determination unit, the apparatus further comprises:
and the uploading unit is used for uploading the driving behavior data of the driver to a driving school node server when the difference value does not meet the preset condition.
10. The apparatus of claim 9, wherein prior to the uploading unit, the apparatus further comprises:
and the second execution unit is used for executing HDFS encryption operation on the driver behavior data information.
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| CN202011154649.0A CN112398814B (en) | 2020-10-26 | 2020-10-26 | Driving behavior data tamper-proof method and device based on big data |
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