CN111311912A - Internet of vehicles detection data determination method and device and electronic equipment - Google Patents
Internet of vehicles detection data determination method and device and electronic equipment Download PDFInfo
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Abstract
The application provides a method and a device for determining Internet of vehicles detection data and electronic equipment, wherein the method comprises the following steps: acquiring a monitoring data frame set of a set time period, wherein the monitoring data frame set is communication data of a target vehicle in the set time period; setting a time tag for the monitoring data frame set; and determining a data monitoring interval of data transmission of a target monitoring data frame set in the monitoring data frame set according to the time tag of the monitoring data frame set, wherein the data monitoring interval is used as a data basis for communication monitoring of the target vehicle.
Description
Technical Field
The application relates to the technical field of computers, in particular to a method and a device for determining Internet of vehicles detection data and electronic equipment.
Background
Along with the rapid development of the car networking, the communication capacity and the information interaction ability of the car and the external environment are gradually enhanced, a plurality of entertainment functions are added, and the driving experience of the car is greatly improved. However, the network environment in the automobile is more and more exposed to people while the automobile functions are rich, and a lawless person can take the automobile.
Currently, there are also a series of safeguards against malicious attacks by attackers. However, the environment inside the vehicle is often uncontrollable, and the detection result in the real environment inside the vehicle may be insufficient due to the adoption of a fixed detection standard or the detection standard which is detected and implemented by a security manufacturer in a simulated environment.
Disclosure of Invention
In view of this, an object of the present application is to provide a method and an apparatus for determining internet of vehicles detection data, and an electronic device. The effect of improving the existing detection standard of the environment in the vehicle can be achieved.
In a first aspect, an embodiment of the present application provides a method for determining internet of vehicles detection data, including:
acquiring a monitoring data frame set of a set time period, wherein the monitoring data frame set is communication data of a target vehicle in the set time period;
setting a time tag for the data frame in the monitoring data frame set;
and determining a data monitoring interval of data transmission of a target monitoring data frame set in the monitoring data frame set according to the time tag of the data frame in the monitoring data frame set, wherein the data monitoring interval is used as a data basis for communication monitoring of the target vehicle.
In an optional implementation manner, the step of determining, according to the time tag of the data frame in the monitoring data frame set, a data monitoring interval of data transmission of a target monitoring data frame set in the monitoring data frame set includes:
calculating a monitoring time rule of data transmission of a target monitoring data frame set in the monitoring data frame set according to the time labels of the data frames in the monitoring data frame set;
and determining a data monitoring interval of data transmission of the target monitoring data frame set according to the time rule.
The method for determining the vehicle networking detection data can also set a data monitoring interval according to the time rule of the collected data frame set, so that the data monitoring interval can be more adaptive to the detection requirement of data, and the accuracy of vehicle internal environment detection can be improved.
In an optional implementation manner, the step of determining a data monitoring interval of data transmission of the target monitoring data frame set according to the time law includes:
acquiring a time interval from the monitoring time law;
determining an initial monitoring interval of the target monitoring data frame set according to the time interval;
and dynamically adjusting the initial monitoring interval according to the target monitoring data frame set to obtain a data monitoring interval for data transmission of the target monitoring data frame set.
In an optional implementation manner, the step of dynamically adjusting the initial monitoring interval according to the target monitoring data frame set to obtain a data monitoring interval for data transmission of the target monitoring data frame set includes:
detecting the data frame corresponding to the target monitoring data frame set by using a current monitoring interval, wherein the current monitoring interval is the initial monitoring interval during first detection; the data frames corresponding to the target monitoring data frame set are as follows: data frames in the target monitoring data frame set or data frames which are acquired and have the same identification as the data frames in the target monitoring data frame set;
if the detection result is that the data frame corresponding to the target monitoring data frame set is a normal data frame, reducing the current monitoring interval to obtain an updated current monitoring interval, and detecting data frames corresponding to other target monitoring data frame sets by using the updated current monitoring interval;
and if the detection result is that the data frame corresponding to the target monitoring data frame set is an abnormal data frame, taking the monitoring interval obtained by the previous detection node of the current monitoring interval as the data monitoring interval.
In an optional implementation manner, the step of dynamically adjusting the initial monitoring interval according to the target monitoring data frame set to obtain a data monitoring interval for data transmission of the target monitoring data frame set includes:
detecting the data frame corresponding to the target monitoring data frame set by using a current monitoring interval, wherein the current monitoring interval is the initial monitoring interval during first detection; the data frames corresponding to the target monitoring data frame set are as follows: data frames in the target monitoring data frame set or data frames which are acquired and have the same identification as the data frames in the target monitoring data frame set;
if the detection result is that the data frame corresponding to the target monitoring data frame set is a normal data frame, reducing the current monitoring interval to obtain an updated current monitoring interval, and detecting data frames corresponding to other target monitoring data frame sets by using the updated current monitoring interval, wherein the reduced interval of the current monitoring interval is in a current limited interval;
if the detection result is that the data frame corresponding to the target monitoring data frame set is an abnormal data frame, restoring the current monitoring interval to the monitoring interval of the previous detection node, and setting the current monitoring interval as the current limited interval;
and taking the current monitoring interval as the data monitoring interval until the distance between the end point of the current limited interval and the end point corresponding to the current monitoring interval is smaller than the set minimum adjusting value.
In an optional implementation manner, the step of dynamically adjusting the initial monitoring interval according to the target monitoring data frame set to obtain a data monitoring interval for data transmission of the target monitoring data frame set includes:
detecting the data frame corresponding to the target monitoring data frame set by using a current monitoring interval, wherein the current monitoring interval is the initial monitoring interval during first detection; the data frames corresponding to the target monitoring data frame set are as follows: data frames in the target monitoring data frame set or data frames which are acquired and have the same identification as the data frames in the target monitoring data frame set;
if the detection result is that the data frame corresponding to the target monitoring data frame set is a normal data frame, increasing the left end point of the current monitoring interval to obtain an updated current monitoring interval, and detecting data frames corresponding to other target monitoring data frame sets by using the updated current monitoring interval;
if the detection result is that the data frame corresponding to the target monitoring data frame set is an abnormal data frame, taking the left end point of the monitoring interval obtained by the previous detection node of the current monitoring interval as the target left end point of the data monitoring interval;
detecting the data frame corresponding to the target monitoring data frame set by using a current monitoring interval, wherein a left endpoint of the current monitoring interval is equal to the target left endpoint; the data frames corresponding to the target monitoring data frame set are as follows: data frames in the target monitoring data frame set or data frames which are acquired and have the same identification as the data frames in the target monitoring data frame set;
if the detection result is that the data frame corresponding to the target monitoring data frame set is a normal data frame, reducing the right end point of the current monitoring interval to obtain an updated current monitoring interval, and detecting data frames corresponding to other target monitoring data frame sets by using the updated current monitoring interval;
and if the detection result is that the data frame corresponding to the target monitoring data frame set is an abnormal data frame, taking the right end point of the monitoring interval obtained by the previous detection node of the current monitoring interval as the target right end point of the data monitoring interval.
The method for determining the vehicle networking detection data, provided by the embodiment of the application, can also use the temporary monitoring interval to detect the data so as to realize the dynamic adjustment monitoring interval, thereby obtaining a data monitoring interval which can be matched with the target monitoring data frame set, and further realizing the improvement of the monitoring effect of the data monitoring interval.
In an optional embodiment, each of the monitoring data frames corresponds to an identity; the step of calculating a monitoring time law of data transmission of a target monitoring data frame set in the monitoring data frame set according to the time tag of the data frame in the monitoring data frame set includes:
screening a target monitoring data frame set with a target identity from the monitoring data frame set;
and calculating the monitoring time rule of data transmission of the target monitoring data frame set according to the time label of each data frame in the target monitoring data frame set.
The method for determining the Internet of vehicles detection data can also independently determine the monitoring time law with respect to the data frames of different identity marks, so that the corresponding data monitoring interval of the target identity mark can be determined, the data monitoring interval can only serve as the monitoring standard of the data corresponding to the target identity mark, and the detection of the data can be more accurate.
In an optional embodiment, the method further comprises:
monitoring communication data of the target vehicle;
determining a detection result of the communication data according to the data monitoring interval;
judging whether the error number of the detection result in a specified monitoring time period exceeds a set value or not;
and if the error number of the detection result in the specified time period exceeds a set value, updating the data monitoring interval to obtain an updated data monitoring interval, wherein the updated data monitoring interval comprises the data monitoring interval before updating.
The method for determining the vehicle networking detection data provided by the embodiment of the application can also realize the updating of the data monitoring interval in the monitoring process, so that the data monitoring interval can be more suitable for the requirement of data detection.
In an optional implementation manner, the step of updating the data monitoring interval to obtain an updated data monitoring interval includes:
and decreasing the left end point of the data monitoring interval by a specified value, or/and increasing the right end point of the data monitoring interval by the specified value to obtain an updated data monitoring interval.
The method for determining the vehicle networking detection data can further limit the range of adjusting the data monitoring interval every time, so that the condition that the detection effect of the data monitoring interval obtained by adjustment is reduced due to the fact that the adjustment range is too large can be reduced.
In an optional embodiment, the method further comprises:
acquiring current communication data of the target vehicle;
judging whether the time interval of the adjacent data frames of the current communication data is in the data monitoring interval or not;
and if the time interval of the adjacent data frames of the current communication data is not in the data monitoring interval, judging that the current communication data is abnormal.
The method for determining the internet of vehicles detection data can also detect the data condition in the target vehicle by using the obtained data monitoring interval, so that the safety of the network environment in the vehicle is improved.
In a second aspect, an embodiment of the present application further provides a device for determining internet of vehicles detection data, including:
the system comprises an acquisition module, a processing module and a display module, wherein the acquisition module is used for acquiring a monitoring data frame set of a set time period, and the monitoring data frame set is communication data of a target vehicle in the set time period;
the setting module is used for setting a time tag for the data frame in the monitoring data frame set;
and the determining module is used for determining a data monitoring interval of data transmission of a target monitoring data frame set in the monitoring data frame set according to the time tag of the data frame in the monitoring data frame set, wherein the data monitoring interval is used as a data basis for communication monitoring of the target vehicle.
In a third aspect, an embodiment of the present application further provides an electronic device, including: a processor, a memory, the memory storing machine readable instructions executable by the processor, the machine readable instructions being executed by the processor when the electronic device is running to perform the steps of the above first aspect, or the method for determining internet of vehicles detection data in any possible implementation manner of the first aspect.
In a fourth aspect, the present embodiment also provides a vehicle, including the electronic device provided in the foregoing third aspect.
In a fifth aspect, the present application further provides a computer-readable storage medium, where a computer program is stored on the computer-readable storage medium, and the computer program is executed by a processor to perform the steps of the foregoing first aspect, or the method for determining vehicle networking detection data in any possible implementation manner of the first aspect.
According to the method and the device for determining the detection data of the internet of vehicles, the electronic equipment, the vehicle and the computer readable storage medium, the time labels of all data frames in the monitoring data frame set are adopted to determine the data monitoring interval for monitoring the data, and compared with a fixed detection standard in the prior art or a detection standard which is measured and implemented by a safety manufacturer in a simulation environment, the method and the device combine the monitoring data frame set in an actual transmission process to enable the determined data monitoring interval to better meet the requirement of data monitoring.
In order to make the aforementioned objects, features and advantages of the present application more comprehensible, embodiments accompanied with figures are described in detail below.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are required to be used in the embodiments will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present application and therefore should not be considered as limiting the scope, and for those skilled in the art, other related drawings can be obtained from the drawings without inventive effort.
Fig. 1 is a flowchart of a method for determining vehicle networking detection data according to an embodiment of the present application.
Fig. 2 is a detailed flowchart of step 103 of the internet of vehicles detection data determination method provided in the embodiment of the present application.
Fig. 3 is a partial flowchart of a method for determining vehicle networking detection data according to an embodiment of the present application.
Fig. 4 is a partial flowchart of a method for determining vehicle networking detection data according to an embodiment of the present application.
Fig. 5 is a functional module schematic diagram of a device for determining internet of vehicles detection data provided by an embodiment of the application.
Fig. 6 is a block diagram of an electronic device according to an embodiment of the present application.
Detailed Description
The technical solution in the embodiments of the present application will be described below with reference to the drawings in the embodiments of the present application.
It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus, once an item is defined in one figure, it need not be further defined and explained in subsequent figures. Meanwhile, in the description of the present application, the terms "first", "second", and the like are used only for distinguishing the description, and are not to be construed as indicating or implying relative importance.
The automobile is more and more intelligent, and the automobile is mainly intelligentized by communicating with the external environment at present. While the intelligent network environment is achieved, the network environment in the automobile is exposed to people, and therefore the network environment in the automobile can be attacked by lawbreakers. A series of protective measures are also proposed to defend against malicious attacks by attackers.
The inventor of the present application has studied the above problems and found that the network environment in the vehicle can be detected in the following two ways:
the first mode is as follows: calculating the time interval of two continuous identical ID data frames, then comparing the time interval with half of the ID period, if the time interval of the two continuous identical ID data frames is greater than half of the ID period, the data frames are normal data frames, otherwise, the data frames are abnormal data frames.
The second mode is as follows: and calculating the time interval of two continuous identical ID data frames, comparing the time interval with a pre-stored time interval threshold range corresponding to the ID, and if the time interval of the two continuous identical ID data frames is within the time interval threshold range corresponding to the ID, determining the data frames as normal data frames, otherwise, determining the data frames as abnormal data frames.
In the first method, one half of the frame period is used as the minimum time interval, and the data is determined by using one half of the frame period as the threshold, which lacks pertinence, and the accuracy of the detection result may be different for different data frames.
In the second method, a preset time interval is measured in a specific simulation environment, which is not targeted, and the in-vehicle network is affected by driving habits of users or access to other installation devices, which may affect the accuracy of detection. And the phenomenon of missing report or false report can be caused when the preset time interval is too large or too small.
Based on the above research, the embodiment of the application provides a method and a device for determining vehicle networking detection data, an electronic device, a vehicle and a computer-readable storage medium, which do not only use a minimum threshold to detect data conditions, but also use a pre-stored value interval as a unique judgment reference. This is described below by means of several examples.
Example one
Please refer to fig. 1, which is a flowchart of a method for determining internet of vehicles detection data according to an embodiment of the present application. The specific process shown in FIG. 1 will be described in detail below.
And the monitoring data frame set is communication data of the target vehicle in the set time period. The communication data may be access data of the in-vehicle device to access the external environment.
Alternatively, the set time period described above may be a time period during which the target vehicle is in a normal operating state. For example, a period of time during which the target vehicle is in a driving state. Alternatively, the set time period may be a time period including a traveling state and a standing state of the target vehicle. For example a day, a week.
And 102, setting a time tag for the data frames in the monitoring data frame set.
Illustratively, the time tag described above is used to identify the time of receipt, time of transmission, etc. of the corresponding data frame.
The data monitoring interval is used as a data basis for communication monitoring of the target vehicle.
For example, when data is detected, the data monitoring interval is used as a basis for judging whether the data is abnormal or not. For example, if the time interval between two adjacent frames of data is within the data monitoring interval, it indicates that the current data frame is not abnormal.
As shown in fig. 2, step 103 may include the following steps.
Illustratively, the monitoring time law may be a time interval of any two adjacent frames of data in the target monitoring data frame set, and a time interval distribution.
In one embodiment, each of the monitoring data frames corresponds to an identity; step 1031, comprising: and screening a target monitoring data frame set with a target identity mark from the monitoring data frame set, and calculating a monitoring time rule of data transmission of the target monitoring data frame set according to the time label of each data frame in the target monitoring data frame set.
For example, the target id may be used to identify the electronic device, or the electronic control unit, that transmitted the data frame.
Optionally, step 1032 may be implemented as:
step a, obtaining a time interval from the monitoring time law.
Illustratively, the time interval may be calculated by the following formula:
Δt＝tj-tj-1；
wherein, tj、tj-1Respectively representing time labels of two adjacent frames of data in a target monitoring data frame set; at represents the time interval between two adjacent frames of data in the target monitoring data frame set.
And b, determining an initial monitoring interval of the target monitoring data frame set according to the time interval.
Alternatively, the left end point of the initial monitoring interval and the right end point of the initial monitoring interval may be calculated according to the time interval.
Alternatively, the time interval may be used as an initial period of the target monitoring data frame set, and at this time, the left end point of the initial monitoring interval may be represented as:
Tmin(IDn)＝αT；
the right end of the initial monitoring interval may be expressed as:
Tmax(IDn)＝βT；
wherein the interval [ α T, β T ] forms the initial monitoring interval.
Wherein T represents a data frame period; IDnRepresenting a data frame with an identity n; Δ tj-(j-1)Indicating the time interval between the jth data frame and the jth-1 data frame with the same identity, α indicating the minimum coefficient for the monitoring interval, β indicating the maximum coefficient for the monitoring interval, Tmax indicating the right end point of the monitoring interval, and Tmin indicating the left end point of the monitoring interval.
Alternatively, the data frame period may be calculated from historical data.
And c, dynamically adjusting the initial monitoring interval according to the target monitoring data frame set to obtain a data monitoring interval for data transmission of the target monitoring data frame set.
In a first embodiment, the dynamic adjustment of the initial monitoring interval according to the target monitoring data frame set to obtain the data monitoring interval of data transmission of the target monitoring data frame set can be implemented as follows.
And d1, detecting the data frame corresponding to the target monitoring data frame set by using the current monitoring interval.
And when the initial monitoring interval is detected for the first time, the current monitoring interval is the initial monitoring interval.
Optionally, the data frame corresponding to the target monitoring data frame set may be a data frame in the target monitoring data frame set. The target monitoring data frame set is data acquired by a target vehicle in a normal state.
Optionally, the data frame corresponding to the target monitoring data frame set may also be a data frame which is acquired when the target vehicle is in a normal state and has the same identifier as the data frame in the target monitoring data frame set.
Illustratively, the time interval of two adjacent data frames in the data frames corresponding to the target monitoring data frame set is calculated, and whether the time interval is within the current monitoring interval is judged to obtain the detection result. Wherein, the time interval is in the current monitoring interval, and the detection result is that the data frame corresponding to the target monitoring data frame set is a normal data frame; and if the time interval is not within the current monitoring interval, the detection result indicates that the data frame corresponding to the target monitoring data frame set is an abnormal data frame.
And d2, if the detection result is that the data frame corresponding to the target monitoring data frame set is a normal data frame, reducing the current monitoring interval to obtain an updated current monitoring interval.
In this embodiment, after the updated current monitoring interval is obtained in step d2, the step d1 may be returned, so that the data frames corresponding to other target monitoring data frame sets are detected by using the updated current monitoring interval.
In one embodiment, the above reduction process may increase the left end point of the current monitoring interval. Optionally, the left endpoint of the current monitoring interval may be added with a specified value to obtain an updated left endpoint of the current monitoring interval. For example, the specified values may be 0.1, 0.15, 0.2, 0.08, etc. Alternatively, the specified multiple of the left end point of the current monitoring interval may be used as the left end point of the updated current monitoring interval. For example, the specified multiple may be 2 times, 2.5 times, 1.8 times, and the like.
In another embodiment, the above-mentioned reduction process may reduce the right end point of the current monitoring section. Optionally, the specified value may be subtracted from the right end point of the current monitoring interval to obtain the updated right end point of the current monitoring interval. For example, the specified values may be 0.1, 0.15, 0.2, 0.08, etc. Alternatively, a specified multiple of the right end point of the current monitoring interval may be used as the right end point of the updated current monitoring interval. For example, the specified multiple may be 1/2 times, 2/3 times, 3/5 times, or the like.
And d3, if the detection result is that the data frame corresponding to the target monitoring data frame set is an abnormal data frame, taking the monitoring interval obtained by the previous detection node of the current monitoring interval as the data monitoring interval.
In this embodiment, the detection data is acquired when the target vehicle is in a normal state, but when the detection is performed according to the current monitoring interval, the detection result is abnormal, which indicates that the contraction of the current monitoring interval exceeds the normal range, and the monitoring requirement cannot be met, and no adjustment is required.
In a second embodiment, the above dynamically adjusting the initial monitoring interval according to the target monitoring data frame set to obtain the data monitoring interval of data transmission of the target monitoring data frame set may be implemented by:
and d4, detecting the data frame corresponding to the target monitoring data frame set by using the current monitoring interval.
And when the initial monitoring interval is detected for the first time, the current monitoring interval is the initial monitoring interval.
Optionally, the data frame corresponding to the target monitoring data frame set may be a data frame in the target monitoring data frame set. Optionally, the data frame corresponding to the target monitoring data frame set may be a data frame which is acquired and has the same identifier as the data frame in the target monitoring data frame set.
And d5, if the detection result is that the data frame corresponding to the target monitoring data frame set is a normal data frame, reducing the current monitoring interval.
And the interval after the current monitoring interval is reduced is within the current limited interval.
Alternatively, an adjustment value interval may be preset.
In one embodiment, the above reduction process may increase the left end point of the current monitoring interval. Each time the increase processing is performed on the left end point, any value may be taken in the above adjustment value interval and added to the current value of the left end point.
In another embodiment, the above-mentioned reduction process may reduce the right end point of the current monitoring section. Each time the left end point is reduced, any value can be obtained by subtracting the adjustment value interval from the current right end point.
The detection interval reduction method in this embodiment is similar to the reduction method in the first embodiment, and is not described herein again.
In this embodiment, after the updated current monitoring interval is obtained in step d5, the process may return to step d4, so that the current monitoring interval obtained in step d5 is used to detect data frames corresponding to other target monitoring data frame sets.
And d6, if the detection result is that the data frame corresponding to the target monitoring data frame set is an abnormal data frame, restoring the current monitoring interval to the monitoring interval of the previous detection node, and setting the current monitoring interval as the current limited interval.
And taking the current monitoring interval as the data monitoring interval until the distance between the end point of the current limited interval and the end point corresponding to the current monitoring interval is smaller than the set minimum adjusting value.
For example, the adjustment value for adjusting the endpoint of the monitoring interval may be within the [0.08, 0.18] interval. The current defined interval is [ a1, b1], and the current monitoring interval is [ a2, b2 ]. If (a1-a2) <0.08, the left end point of the monitored interval cannot be adjusted any more, and the left end point of the latest and valid monitored interval is set as the target left end point of the data monitored interval. If (b1-b2) <0.08, the right end point of the monitored interval cannot be readjusted, and the right end point of the latest and valid monitored interval is set as the target right end point of the data monitored interval.
The effective monitoring interval indicates that the detection structures of the data frames corresponding to the target monitoring data frame set detected by using the monitoring interval are all normal data frames.
In a third embodiment, the left end point of the monitoring interval may be adjusted first, and then the right end point may be adjusted. The above dynamic adjustment of the initial monitoring interval according to the target monitoring data frame set to obtain the data monitoring interval of data transmission of the target monitoring data frame set may be implemented by:
and d7, detecting the data frame corresponding to the target monitoring data frame set by using the current monitoring interval.
Wherein, during the first detection, the current monitoring interval is the initial monitoring interval; the data frames corresponding to the target monitoring data frame set are as follows: data frames in the target monitoring data frame set or data frames which are acquired and have the same identification as the data frames in the target monitoring data frame set;
d8, if the detection result is that the data frame corresponding to the target monitoring data frame set is a normal data frame, increasing the left end point of the current monitoring interval to obtain an updated current monitoring interval, and detecting the data frames corresponding to other target monitoring data frame sets by using the updated current monitoring interval;
in this embodiment, after the updated current monitoring interval is obtained in step d8, the process may return to step d7, so that the current monitoring interval obtained in step d8 is used to detect data frames corresponding to other target monitoring data frame sets.
D9, if the detection result is that the data frame corresponding to the target monitoring data frame set is an abnormal data frame, taking the left endpoint of the monitoring interval obtained by the previous detection node of the current monitoring interval as the target left endpoint of the data monitoring interval;
and d10, detecting the data frame corresponding to the target monitoring data frame set by using the current monitoring interval.
Wherein the left end point of the current monitoring interval in the step d10 is equal to the target left end point.
Optionally, the data frame corresponding to the target monitoring data frame set may be a data frame in the target monitoring data frame set. Optionally, the data frame corresponding to the target monitoring data frame set may be a data frame which is acquired and has the same identifier as the data frame in the target monitoring data frame set.
And d11, if the detection result is that the data frame corresponding to the target monitoring data frame set is a normal data frame, reducing the right end point of the current monitoring interval to obtain an updated current monitoring interval, and detecting data frames corresponding to other target monitoring data frame sets by using the updated current monitoring interval.
In this embodiment, after the updated current monitoring interval is obtained in step d11, the process may return to step d10, so that the current monitoring interval obtained in step d11 is used to detect data frames corresponding to other target monitoring data frame sets.
And d12, if the detection result is that the data frame corresponding to the target monitoring data frame set is an abnormal data frame, taking the right endpoint of the monitoring interval obtained by the previous detection node of the current monitoring interval as the target right endpoint of the data monitoring interval.
The adjustment manner of the values of the left and right endpoints in the present embodiment may be the same as the adjustment manner of the endpoint values of the section in the first embodiment. The adjustment manner of the values of the left and right endpoints in the present embodiment may be the same as the adjustment manner of the endpoint values of the section in the second embodiment. The adjustment of the endpoint of the monitoring interval is not described herein.
Optionally, in the embodiment of the present application, a white list detection engine preset in the in-vehicle system may be used, and after the white list in the in-vehicle system is updated, an Electronic Control Unit (ECU) may be added or deleted, and when the in-vehicle environment changes, the data monitoring interval of each current electronic Control Unit in the vehicle may be updated. As shown in fig. 3, the method for determining the internet of vehicles detection data in the present embodiment may further include the following steps.
And 104, monitoring communication data of the target vehicle.
In this embodiment, the monitoring communication data is communication data carrying the target identity.
And 105, determining a detection result of the communication data according to the data monitoring interval.
Alternatively, the time interval between two adjacent frames of data in the monitoring communication data may be calculated, and the detection result may be obtained by determining whether the time interval is within the data monitoring interval.
And 106, judging whether the error number of the detection result in the appointed monitoring time period exceeds a set value or not.
If the number of errors of the detection result in the specified time period exceeds a set value, step 107 is executed.
Illustratively, the specified monitoring period described above may be a one week, three days, four days, etc. period.
The above-mentioned set value can be arbitrarily set. For example, the set value may be different according to the environment in the vehicle.
And step 107, updating the data monitoring interval to obtain an updated data monitoring interval.
Wherein the updated data monitoring interval comprises the data monitoring interval before updating.
In one embodiment, the updated data monitoring interval is obtained by decreasing the left end point of the data monitoring interval by a specified value or/and increasing the right end point of the data monitoring interval by a specified value.
If the range of the data monitoring interval is excessively reduced, the network false alarm in the vehicle can be detected frequently, so that the data monitoring interval can be adjusted according to the number of false alarms, and when the number of false alarms exceeds a set threshold value, the data monitoring interval can be updated, so that the detection of the communication data in the vehicle environment is more accurate.
Optionally, as shown in fig. 4, the method for determining the internet of vehicles in this embodiment may further include the following steps.
And step 108, acquiring the current communication data of the target vehicle.
In this embodiment, different data monitoring intervals are used to detect data frames with different ids.
Optionally, the corresponding data monitoring interval may be determined according to the identity corresponding to the current communication data, and the corresponding data monitoring interval is used as a detection basis.
And step 110, if the time interval of the adjacent data frames of the current communication data is not within the data monitoring interval, determining that the current communication data is abnormal.
By the method for determining the vehicle networking detection data, a monitoring interval for detecting abnormal conditions of the communication data in the vehicle can be dynamically adjusted, and therefore the accuracy of the detection result can be improved.
Example two
Based on the same application concept, a device for determining vehicle networking detection data corresponding to the method for determining vehicle networking detection data is further provided in the embodiment of the present application, and as the principle of solving the problem of the device in the embodiment of the present application is similar to that of the embodiment of the method for determining vehicle networking detection data, the implementation of the device in the embodiment of the present application can refer to the description in the embodiment of the method, and repeated details are omitted.
Please refer to fig. 5, which is a schematic diagram of functional modules of the device for determining internet of vehicles detection data according to the embodiment of the present application. Each module in the internet of vehicles detection data determination device in this embodiment is used to execute each step in the above method embodiment. The car networking detection data determination device includes: an acquisition module 201, a setting module 202 and a determination module 203; wherein the content of the first and second substances,
the acquiring module 201 is configured to acquire a monitoring data frame set of a set time period, where the monitoring data frame set is communication data of a target vehicle in the set time period;
a setting module 202, configured to set a time tag for a data frame in the monitoring data frame set;
a determining module 203, configured to determine, according to the time tag of the data frame in the monitoring data frame set, a data monitoring interval for data transmission of a target monitoring data frame set in the monitoring data frame set, where the data monitoring interval is used as a data basis for communication monitoring of the target vehicle.
In one possible implementation, the determining module 203 includes a rule calculating unit and an interval determining unit:
the rule calculating unit is used for calculating a monitoring time rule of data transmission of a target monitoring data frame set in the monitoring data frame set according to the time labels of the data frames in the monitoring data frame set;
and the interval determining unit is used for determining a data monitoring interval of data transmission of the target monitoring data frame set according to the time rule.
In a possible embodiment, the interval determining unit is configured to:
acquiring a time interval from the monitoring time law;
determining an initial monitoring interval of the target monitoring data frame set according to the time interval;
and dynamically adjusting the initial monitoring interval according to the target monitoring data frame set to obtain a data monitoring interval for data transmission of the target monitoring data frame set.
In a possible embodiment, the interval determining unit is configured to:
detecting the data frame corresponding to the target monitoring data frame set by using a current monitoring interval, wherein the current monitoring interval is the initial monitoring interval during first detection; the data frames corresponding to the target monitoring data frame set are as follows: data frames in the target monitoring data frame set or data frames which are acquired and have the same identification as the data frames in the target monitoring data frame set;
if the detection result is that the data frame corresponding to the target monitoring data frame set is a normal data frame, reducing the current monitoring interval to obtain an updated current monitoring interval, and detecting data frames corresponding to other target monitoring data frame sets by using the updated current monitoring interval;
and if the detection result is that the data frame corresponding to the target monitoring data frame set is an abnormal data frame, taking the monitoring interval obtained by the previous detection node of the current monitoring interval as the data monitoring interval.
In a possible embodiment, the interval determining unit is configured to:
detecting the data frame corresponding to the target monitoring data frame set by using a current monitoring interval, wherein the current monitoring interval is the initial monitoring interval during first detection; the data frames corresponding to the target monitoring data frame set are as follows: data frames in the target monitoring data frame set or data frames which are acquired and have the same identification as the data frames in the target monitoring data frame set;
if the detection result is that the data frame corresponding to the target monitoring data frame set is a normal data frame, reducing the current monitoring interval to obtain an updated current monitoring interval, and detecting data frames corresponding to other target monitoring data frame sets by using the updated current monitoring interval, wherein the reduced interval of the current monitoring interval is in a current limited interval;
if the detection result is that the data frame corresponding to the target monitoring data frame set is an abnormal data frame, restoring the current monitoring interval to the monitoring interval of the previous detection node, and setting the current monitoring interval as the current limited interval;
and taking the current monitoring interval as the data monitoring interval until the distance between the end point of the current limited interval and the end point corresponding to the current monitoring interval is smaller than the set minimum adjusting value.
In a possible embodiment, the interval determining unit is configured to:
detecting the data frame corresponding to the target monitoring data frame set by using a current monitoring interval, wherein the current monitoring interval is the initial monitoring interval during first detection; the data frames corresponding to the target monitoring data frame set are as follows: data frames in the target monitoring data frame set or data frames which are acquired and have the same identification as the data frames in the target monitoring data frame set;
if the detection result is that the data frame corresponding to the target monitoring data frame set is a normal data frame, increasing the left end point of the current monitoring interval to obtain an updated current monitoring interval, and detecting data frames corresponding to other target monitoring data frame sets by using the updated current monitoring interval;
if the detection result is that the data frame corresponding to the target monitoring data frame set is an abnormal data frame, taking the left end point of the monitoring interval obtained by the previous detection node of the current monitoring interval as the target left end point of the data monitoring interval;
detecting the data frame corresponding to the target monitoring data frame set by using a current monitoring interval, wherein a left endpoint of the current monitoring interval is equal to the target left endpoint; the data frames corresponding to the target monitoring data frame set are as follows: data frames in the target monitoring data frame set or data frames which are acquired and have the same identification as the data frames in the target monitoring data frame set;
if the detection result is that the data frame corresponding to the target monitoring data frame set is a normal data frame, reducing the right end point of the current monitoring interval to obtain an updated current monitoring interval, and detecting data frames corresponding to other target monitoring data frame sets by using the updated current monitoring interval;
and if the detection result is that the data frame corresponding to the target monitoring data frame set is an abnormal data frame, taking the right end point of the monitoring interval obtained by the previous detection node of the current monitoring interval as the target right end point of the data monitoring interval.
In a possible implementation manner, each of the monitoring data frames corresponds to an identity; a law calculation unit to:
screening a target monitoring data frame set with a target identity from the monitoring data frame set;
and calculating the monitoring time rule of data transmission of the target monitoring data frame set according to the time label of each data frame in the target monitoring data frame set.
In a possible implementation manner, the present embodiment may further include an update module, where the update module includes a data monitoring unit, a result determining unit, and an interval updating unit:
the data monitoring unit is used for monitoring communication data of the target vehicle;
a result determining unit, configured to determine a detection result of the communication data according to the data monitoring interval;
a result determination unit configured to determine whether or not the number of errors of the detection result within a specified monitoring period exceeds a set value;
and the interval updating unit is used for updating the data monitoring interval to obtain an updated data monitoring interval if the error number of the detection result in the specified time period exceeds a set value, wherein the updated data monitoring interval comprises the data monitoring interval before updating.
In a possible implementation, the interval updating unit is configured to:
and decreasing the left end point of the data monitoring interval by a specified value, or/and increasing the right end point of the data monitoring interval by the specified value to obtain an updated data monitoring interval.
In a possible implementation manner, the device for determining internet of vehicles detection data in this embodiment further includes: a detection module to:
acquiring current communication data of the target vehicle;
judging whether the time interval of the adjacent data frames of the current communication data is in the data monitoring interval or not;
and if the time interval of the adjacent data frames of the current communication data is not in the data monitoring interval, judging that the current communication data is abnormal.
EXAMPLE III
For the convenience of understanding the present embodiment, an electronic device for executing the method for determining the internet of vehicles according to the present embodiment will be described in detail.
In the present embodiment, the Electronic device is a device installed in a vehicle, and may also be an Electronic Control Unit (ECU) installed in the vehicle.
Optionally, the electronic device 300 may include a memory 311 and a processor 313.
The Memory 311 may be, but is not limited to, a Random Access Memory (RAM), a Read Only Memory (ROM), a Programmable Read-Only Memory (PROM), an Erasable Read-Only Memory (EPROM), an electrically Erasable Read-Only Memory (EEPROM), and the like. The memory 311 is configured to store a program, and the processor 313 executes the program after receiving an execution instruction, and the method executed by the electronic device 300 defined by the process disclosed in any embodiment of the present application may be applied to the processor 313, or implemented by the processor 313.
The processor 113 may be an integrated circuit chip having signal processing capability. The Processor 113 may be a general-purpose Processor, and includes a Central Processing Unit (CPU), a Network Processor (NP), and the like; the Integrated Circuit may also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other programmable logic device, a discrete gate or transistor logic device, or a discrete hardware component. The various methods, steps, and logic blocks disclosed in the embodiments of the present application may be implemented or performed. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
In this embodiment, a vehicle may also be provided. The vehicle includes a plurality of Electronic Control Units (ECUs).
The plurality of ECUs realize the interaction function of each ECU through a serial CAN bus.
Since the CAN bus is serial, there may be a case where transmission data conflicts between ECUs. In this embodiment, for the above conflict situation, a non-destructive bit arbitration bus structure mechanism is designed for the CAN bus, and when two ECUs send information to the network at the same time, the ECU with low priority actively stops sending data, while the ECU with high priority continues to transmit data without being affected. Because the data sending rules of the ECUs with different priorities may be different, different data monitoring intervals can be calculated for different ECUs to meet the data monitoring requirements of different ECUs.
Optionally, the determination of the data monitoring interval in the step 101-107 in the first embodiment may be calculated by one of the electronic devices. After the electronic device calculates the data monitoring interval, the data monitoring interval is sent to the corresponding device or electronic control unit, and the corresponding device or electronic control unit performs the step 108 of checking the communication data in the in-vehicle environment and the step 110.
In addition, the embodiment of the present application further provides a computer-readable storage medium, where a computer program is stored on the computer-readable storage medium, and when the computer program is executed by a processor, the steps of the method for determining the internet of vehicles detection data in the above-mentioned method embodiment are executed.
The computer program product of the method for determining the internet of vehicles detection data provided in the embodiment of the present application includes a computer readable storage medium storing a program code, where instructions included in the program code may be used to execute the steps of the method for determining the internet of vehicles detection data described in the above method embodiment, which may be specifically referred to in the above method embodiment and are not described herein again.
In the embodiments provided in the present application, it should be understood that the disclosed apparatus and method can be implemented in other ways. The apparatus embodiments described above are merely illustrative, and for example, the flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods and computer program products according to various embodiments of the present application. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
In addition, functional modules in the embodiments of the present application may be integrated together to form an independent part, or each module may exist separately, or two or more modules may be integrated to form an independent part.
The functions, if implemented in the form of software functional modules 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 or portions thereof that substantially contribute to the prior art may be embodied in the form of a software product stored in a storage medium and including 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 other various media capable of storing program codes. It is noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
The above description is only a preferred embodiment of the present application and is not intended to limit the present application, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, improvement and the like made within the spirit and principle of the present application shall be included in the protection scope of the present application. It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus, once an item is defined in one figure, it need not be further defined and explained in subsequent figures.
The above description is only for the specific embodiments of the present application, but the scope of the present application is not limited thereto, and any person skilled in the art can easily conceive of the changes or substitutions within the technical scope of the present application, and shall be covered by the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (14)
1. A method for determining Internet of vehicles detection data is characterized by comprising the following steps:
acquiring a monitoring data frame set of a set time period, wherein the monitoring data frame set is communication data of a target vehicle in the set time period;
setting a time tag for the data frame in the monitoring data frame set;
and determining a data monitoring interval of data transmission of a target monitoring data frame set in the monitoring data frame set according to the time tag of the data frame in the monitoring data frame set, wherein the data monitoring interval is used as a data basis for communication monitoring of the target vehicle.
2. The method of claim 1, wherein the step of determining a data monitoring interval for data transmission of a target one of the monitoring data frame sets based on the time tag of the data frame of the monitoring data frame set comprises:
calculating a monitoring time rule of data transmission of a target monitoring data frame set in the monitoring data frame set according to the time labels of the data frames in the monitoring data frame set;
and determining a data monitoring interval of data transmission of the target monitoring data frame set according to the time rule.
3. The method of claim 2, wherein the step of determining a data monitoring interval for data transmission of the target monitoring data frame set according to the time law comprises:
acquiring a time interval from the monitoring time law;
determining an initial monitoring interval of the target monitoring data frame set according to the time interval;
and dynamically adjusting the initial monitoring interval according to the target monitoring data frame set to obtain a data monitoring interval for data transmission of the target monitoring data frame set.
4. The method of claim 3, wherein the step of dynamically adjusting the initial monitoring interval according to the target monitoring data frame set to obtain a data monitoring interval for data transmission of the target monitoring data frame set comprises:
detecting the data frame corresponding to the target monitoring data frame set by using a current monitoring interval, wherein the current monitoring interval is the initial monitoring interval during first detection; the data frames corresponding to the target monitoring data frame set are as follows: data frames in the target monitoring data frame set or data frames which are acquired and have the same identification as the data frames in the target monitoring data frame set;
if the detection result is that the data frame corresponding to the target monitoring data frame set is a normal data frame, reducing the current monitoring interval to obtain an updated current monitoring interval, and detecting data frames corresponding to other target monitoring data frame sets by using the updated current monitoring interval;
and if the detection result is that the data frame corresponding to the target monitoring data frame set is an abnormal data frame, taking the monitoring interval obtained by the previous detection node of the current monitoring interval as the data monitoring interval.
5. The method of claim 3, wherein the step of dynamically adjusting the initial monitoring interval according to the target monitoring data frame set to obtain a data monitoring interval for data transmission of the target monitoring data frame set comprises:
detecting the data frame corresponding to the target monitoring data frame set by using a current monitoring interval, wherein the current monitoring interval is the initial monitoring interval during first detection; the data frames corresponding to the target monitoring data frame set are as follows: data frames in the target monitoring data frame set or data frames which are acquired and have the same identification as the data frames in the target monitoring data frame set;
if the detection result is that the data frame corresponding to the target monitoring data frame set is a normal data frame, reducing the current monitoring interval to obtain an updated current monitoring interval, and detecting data frames corresponding to other target monitoring data frame sets by using the updated current monitoring interval, wherein the reduced interval of the current monitoring interval is in a current limited interval;
if the detection result is that the data frame corresponding to the target monitoring data frame set is an abnormal data frame, restoring the current monitoring interval to the monitoring interval of the previous detection node, and setting the current monitoring interval as the current limited interval;
and taking the current monitoring interval as the data monitoring interval until the distance between the end point of the current limited interval and the end point corresponding to the current monitoring interval is smaller than the set minimum adjusting value.
6. The method of claim 3, wherein the step of dynamically adjusting the initial monitoring interval according to the target monitoring data frame set to obtain a data monitoring interval for data transmission of the target monitoring data frame set comprises:
detecting the data frame corresponding to the target monitoring data frame set by using a current monitoring interval, wherein the current monitoring interval is the initial monitoring interval during first detection; the data frames corresponding to the target monitoring data frame set are as follows: data frames in the target monitoring data frame set or data frames which are acquired and have the same identification as the data frames in the target monitoring data frame set;
if the detection result is that the data frame corresponding to the target monitoring data frame set is a normal data frame, increasing the left end point of the current monitoring interval to obtain an updated current monitoring interval, and detecting data frames corresponding to other target monitoring data frame sets by using the updated current monitoring interval;
if the detection result is that the data frame corresponding to the target monitoring data frame set is an abnormal data frame, taking the left end point of the monitoring interval obtained by the previous detection node of the current monitoring interval as the target left end point of the data monitoring interval;
detecting the data frame corresponding to the target monitoring data frame set by using a current monitoring interval, wherein a left endpoint of the current monitoring interval is equal to the target left endpoint; the data frames corresponding to the target monitoring data frame set are as follows: data frames in the target monitoring data frame set or data frames which are acquired and have the same identification as the data frames in the target monitoring data frame set;
if the detection result is that the data frame corresponding to the target monitoring data frame set is a normal data frame, reducing the right end point of the current monitoring interval to obtain an updated current monitoring interval, and detecting data frames corresponding to other target monitoring data frame sets by using the updated current monitoring interval;
and if the detection result is that the data frame corresponding to the target monitoring data frame set is an abnormal data frame, taking the right end point of the monitoring interval obtained by the previous detection node of the current monitoring interval as the target right end point of the data monitoring interval.
7. The method of claim 2, wherein each of the monitoring data frames corresponds to an identification; the step of calculating a monitoring time law of data transmission of a target monitoring data frame set in the monitoring data frame set according to the time tag of the data frame in the monitoring data frame set includes:
screening a target monitoring data frame set with a target identity from the monitoring data frame set;
and calculating the monitoring time rule of data transmission of the target monitoring data frame set according to the time label of each data frame in the target monitoring data frame set.
8. The method according to any one of claims 1-7, further comprising:
monitoring communication data of the target vehicle;
determining a detection result of the communication data according to the data monitoring interval;
judging whether the error number of the detection result in a specified monitoring time period exceeds a set value or not;
and if the error number of the detection result in the specified time period exceeds a set value, updating the data monitoring interval to obtain an updated data monitoring interval, wherein the updated data monitoring interval comprises the data monitoring interval before updating.
9. The method of claim 8, wherein the step of updating the data monitoring interval to obtain an updated data monitoring interval comprises:
and decreasing the left end point of the data monitoring interval by a specified value, or/and increasing the right end point of the data monitoring interval by the specified value to obtain an updated data monitoring interval.
10. The method according to any one of claims 1-7, further comprising:
acquiring current communication data of the target vehicle;
judging whether the time interval of the adjacent data frames of the current communication data is in the data monitoring interval or not;
and if the time interval of the adjacent data frames of the current communication data is not in the data monitoring interval, judging that the current communication data is abnormal.
11. An internet of vehicles detection data determination device, comprising:
the system comprises an acquisition module, a processing module and a display module, wherein the acquisition module is used for acquiring a monitoring data frame set of a set time period, and the monitoring data frame set is communication data of a target vehicle in the set time period;
the setting module is used for setting a time tag for the data frame in the monitoring data frame set;
and the determining module is used for determining a data monitoring interval of data transmission of a target monitoring data frame set in the monitoring data frame set according to the time tag of the data frame in the monitoring data frame set, wherein the data monitoring interval is used as a data basis for communication monitoring of the target vehicle.
12. An electronic device, comprising:
a processor;
memory storing machine-readable instructions executable by the electronic control unit, the machine-readable instructions, when executed by the electronic control unit, performing the steps of the method according to any one of claims 1 to 10 when the electronic device is running.
13. A vehicle, characterized by comprising: the electronic device of claim 12.
14. A computer-readable storage medium, having stored thereon a computer program which, when being executed by a processor, is adapted to carry out the steps of the method according to any one of claims 1 to 10.
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| CN110784483A (en) * | 2019-11-04 | 2020-02-11 | 北京航空航天大学 | DGA abnormal domain name-based event detection system and method |
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