CN115914024B - Method for detecting abnormal environment of intelligent network-connected automobile data - Google Patents
Method for detecting abnormal environment of intelligent network-connected automobile data Download PDFInfo
- Publication number
- CN115914024B CN115914024B CN202211502266.7A CN202211502266A CN115914024B CN 115914024 B CN115914024 B CN 115914024B CN 202211502266 A CN202211502266 A CN 202211502266A CN 115914024 B CN115914024 B CN 115914024B
- Authority
- CN
- China
- Prior art keywords
- data
- vehicle
- speed
- time
- abnormal
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 230000002159 abnormal effect Effects 0.000 title claims abstract description 99
- 238000000034 method Methods 0.000 title claims description 32
- 238000001514 detection method Methods 0.000 claims abstract description 19
- 238000012544 monitoring process Methods 0.000 claims abstract description 13
- 230000005856 abnormality Effects 0.000 claims description 18
- 238000006243 chemical reaction Methods 0.000 claims description 7
- 230000005540 biological transmission Effects 0.000 claims description 5
- 230000003111 delayed effect Effects 0.000 claims description 4
- 206010039203 Road traffic accident Diseases 0.000 abstract description 4
- 238000004364 calculation method Methods 0.000 abstract description 3
- 230000032683 aging Effects 0.000 description 3
- 230000000694 effects Effects 0.000 description 3
- 230000003993 interaction Effects 0.000 description 3
- 238000004891 communication Methods 0.000 description 2
- 238000011161 development Methods 0.000 description 2
- 230000018109 developmental process Effects 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 230000001737 promoting effect Effects 0.000 description 2
- 206010063385 Intellectualisation Diseases 0.000 description 1
- 238000013459 approach Methods 0.000 description 1
- 238000013473 artificial intelligence Methods 0.000 description 1
- 230000000712 assembly Effects 0.000 description 1
- 238000000429 assembly Methods 0.000 description 1
- 230000001186 cumulative effect Effects 0.000 description 1
- 238000003912 environmental pollution Methods 0.000 description 1
- 230000004927 fusion Effects 0.000 description 1
- 238000005457 optimization Methods 0.000 description 1
- 230000000007 visual effect Effects 0.000 description 1
Landscapes
- Traffic Control Systems (AREA)
Abstract
The invention provides a detection method of an intelligent network-connected automobile data abnormal environment, which is characterized in that CAN bus data are collected and transmitted, the data are converted into traffic operation data of a front automobile and a host automobile and stored in a corresponding database, the calculation of the traffic operation speed of the front automobile is carried out based on the traffic operation data, the data abnormal condition monitoring classification is carried out according to different actual conditions, finally, whether a detection system carries out early warning reminding is judged, and the type of early warning reminding is determined. The detection method solves the problem of dynamically detecting whether the intelligent network-connected automobile is in a data abnormal environment in real time when the speed data of the front automobile or the distance data between two automobiles are abnormal, effectively improves the safety of drivers and passengers, reduces the occurrence of traffic accidents, and ensures the operation efficiency of a traffic system. The single error is removed, multiple judgments and certain delay are designed, so that the abnormal data in the error range is removed, and the accuracy of early warning and reminding can be ensured.
Description
Technical Field
The invention relates to the technical field of automobile data detection, in particular to a detection method of an intelligent network-connected automobile data abnormal environment, which is not limited by the technical field of automobile data detection.
Background
At present, with the rapid development of high and new technologies such as the Internet of vehicles, artificial intelligence, big data and the like, intelligent Internet-connected automobiles become the strategic direction of future development of the world automobile industry, which is helpful for promoting the automobile industry to transform and upgrade to the directions of intellectualization, internet-connected, automation and the like, accelerating the industry gathering and promoting the structure optimization. Meanwhile, the intelligent network-connected automobile can effectively solve the problems of energy shortage, environmental pollution, traffic jam, traffic safety and the like in the current traffic industry, and the traffic is enabled to be more environment-friendly, intelligent, efficient, safe and smooth.
The intelligent network-connected automobile is an organic fusion of a plurality of advanced technologies, has the characteristics of an information physical system and a plurality of open interfaces, can provide convenient and comfortable driving experience for people, and can improve the vulnerability of the intelligent network-connected automobile system. Because of the characteristics of sharing, interactivity, openness, etc. of the communication network, the intelligent internet-connected vehicle may suffer from a malicious network attack during the process of data interaction with the outside, especially by utilizing a vulnerability of an open interface, for example: the malicious attacker can launch deception attack on the target vehicle, tamper the real data sent to the receiving device through fictitious and transmission related data, and launch interference attack on the target vehicle, obstruct the transmission of the real data through interference wireless channels, and finally cause the transmitted data to be distorted, delayed or lost. In addition, the aging of the sensor, bad weather conditions, geographical positions and other factors can greatly influence the communication and interaction between the intelligent network-connected automobile and the outside. When the situation occurs, the intelligent network-connected automobile may leak vehicle information data, each control system in the intelligent network-connected automobile fails, even serious traffic accidents are induced, and the safe operation of the whole traffic system is endangered. Under the background that the abnormal data environment becomes a normal state, the reality and the accuracy of data interaction must be ensured in order to develop intelligent network-connected automobiles greatly. Therefore, the method for detecting the abnormal environment of the intelligent network-connected automobile data is actively developed, and the progress and reform of the intelligent network-connected automobile industry can be greatly promoted.
In view of the above, there is a need to provide a new approach in an attempt to solve at least some of the above problems.
Disclosure of Invention
Aiming at one or more problems in the prior art, the invention provides a detection method of an intelligent network-connected automobile data abnormal environment, which is characterized in that CAN bus data are collected and transmitted, the data are converted into traffic operation data of a front automobile and a host automobile and stored in a corresponding database, the traffic operation data are based on the traffic operation data, the calculation of the traffic operation speed of the front automobile is carried out, the data abnormal condition monitoring classification is carried out according to different actual conditions, finally, whether a detection system carries out early warning reminding is judged, and the type of early warning reminding is determined.
The technical solution for realizing the purpose of the invention is as follows:
a detection method of an intelligent network-connected automobile data abnormal environment comprises the following steps:
s1, data acquisition and transmission: acquiring the data of the CAN buses of the front vehicle and the host vehicle in real time at regular time intervals G, and transmitting the data of the CAN buses of the front vehicle to the host vehicle;
S2, data conversion and storage: the host vehicle converts the data of the CAN buses of the front vehicle and the host vehicle obtained in the step S1 to obtain traffic operation data of the front vehicle and the host vehicle, and the traffic operation data are stored in a database of the traffic operation data;
s3, calculating traffic running speed: calculating the traffic running speed of the front vehicle at the moment t by using the traffic running data of the front vehicle and the self vehicle obtained by the S2 conversion;
S4, monitoring abnormal conditions of data:
When (when) And speed of the preceding vehicle at time t/>And S3, calculating the traffic running speed estimated value/>, of the preceding vehicleThe relative deviation of (2) is smaller than the allowable relative deviation epsilon, i.e./>When the intelligent network connection automobile is not in an environment with abnormal data, wherein epsilon is an allowable relative deviation parameter, and the units are%,/>Time for CAN bus data acquisition of preceding vehicle,/>The CAN bus data acquisition time of the vehicle;
When (when) Or traffic speed estimate of preceding vehicle/>Speed of vehicle before t time/>There is a null value in the vehicle, or the speed of the vehicle before time t/>And S3, calculating the traffic running speed estimated value/>, of the preceding vehicleThe relative deviation of (2) is greater than or equal to the allowable relative deviation epsilon, i.e./>The intelligent network-connected automobile is in an environment with abnormal data;
S5, early warning and reminding of a detection system: carrying out real-time early warning and reminding on the monitoring result of the abnormal condition of the data in the S4, and when the abnormal condition occurs Or/>Or when N is more than or equal to b, the intelligent network-connected automobile detection system carries out early warning reminding of data abnormal conditions, wherein sigma is an allowable limit value of relative deviation, K is the number of times of occurrence of the data abnormal conditions at the current moment, i is an integer which is more than or equal to 1 and less than or equal to K, t i is the time corresponding to the occurrence of the data abnormal conditions, a is an allowable limit value of accumulated relative deviation in a time interval (t K-t1), N is the number of times of occurrence of data delay in unit time, and b is an allowable limit value of occurrence of data delay in unit time.
Furthermore, in the method for detecting abnormal environments of intelligent network-connected automobile data, in S1, the unit of the time interval G is millisecond, and G is {4,5,8,10}.
In the method for detecting the abnormal environment of the intelligent network-connected automobile data, in S2, when the automobile receives the data of the CAN buses of a plurality of groups of front automobiles from the moment t-G to the moment t, the automobile only converts the data of the CAN buses of a group of front automobiles closest to the moment t into traffic operation data.
Further, in the method for detecting abnormal conditions of intelligent network-connected automobile data, in S2, the traffic operation data at least comprises the current time t and the CAN bus data acquisition time of the preceding automobileSpeed of front vehicle/>CAN bus data acquisition time/>, of own vehicleSpeed/>, of own vehicleThe distance L t between the front vehicle and the host vehicle is obtained by collecting the vehicle-mounted laser radar of the host vehicle, wherein the distance L t between the front vehicle and the host vehicle is obtained by collecting the vehicle-mounted laser radar of the host vehicle.
Further, the method for detecting the abnormal environment of the intelligent network-connected automobile data in the invention comprises the following steps of calculating the traffic running speed of the automobile before the moment t in S3:
At time t, the estimated traffic speed of the preceding vehicle is Wherein L t-G is the distance between the preceding vehicle and the host vehicle corresponding to the time t-G,/>For the speed of the host vehicle corresponding to the time t-G,/>The speed of the preceding vehicle corresponding to the time t-G.
Further, the method for detecting abnormal conditions of data of the intelligent network-connected automobile in the invention specifically comprises the following steps:
Determining corresponding data abnormal conditions according to traffic operation data and traffic operation speed estimated values of the front vehicles:
case 1) traffic speed estimate for current vehicle Or speed of the vehicle before time t/>When the value is null, the corresponding data abnormal conditions comprise front vehicle speed delay, two-vehicle distance delay and front vehicle speed and two-vehicle distance delay simultaneously;
Case 2) distance L t between the current car and the own car exists, speed of the preceding car There is a need for a system that, When the two vehicle speeds are simultaneously satisfied, corresponding abnormal data conditions comprise inaccurate front vehicle speed, inaccurate distance between the two vehicles, simultaneous inaccuracy of the front vehicle speed and the distance between the two vehicles, repeated front vehicle speed, repeated distance between the two vehicles, simultaneous repetition of the front vehicle speed and the distance between the two vehicles;
Case 3) distance L t between the current car and the own car exists, speed of the preceding car There is a need for a system that, When the two vehicle speeds are simultaneously satisfied, the corresponding data abnormal conditions comprise repeated front vehicle speed, repeated front vehicle speed and two vehicle distance, delayed front vehicle speed and two vehicle distance.
Further, in the method for detecting abnormal conditions of intelligent network-connected vehicle data, in S5, the number of times of data delay occurring in unit time N, the number of times of data abnormality occurring up to the current time K and the determination of time t i corresponding to the occurrence of data abnormality are determined:
initializing N, K and i, namely, letting: n=0, k=0, i=0;
When the abnormal data condition of the case 1) occurs, updating N, namely, making: n=n+1;
When abnormal data conditions of the case 2) and the case 3) occur, updating K and i, namely, making: k=k+1, i=i+1, and the time t at this time is recorded as t i.
Further, in the method for detecting abnormal environments of intelligent network-connected automobile data according to the present invention, in S5, the early warning reminding of the detection system includes:
<1> when N is greater than or equal to b, the early warning prompt is a data delay condition;
<2> when When the warning is a special case of single data abnormality;
<3> when And when N is more than or equal to b, the early warning prompt is data delay and single data abnormal special condition;
<4> when And/>When the data is abnormal, the early warning prompt is a data abnormal general condition;
<5> when When N is more than or equal to b and is simultaneously met, the early warning prompt is data delay and data abnormality general condition;
<6> when And/>When the data is abnormal, the early warning prompt is a data abnormal extreme condition;
<7> when And when N is more than or equal to b and is simultaneously met, the early warning prompt is data delay and data abnormality extreme conditions.
Compared with the prior art, the technical scheme provided by the invention has the following technical effects:
The method for detecting the abnormal environment of the intelligent network-connected automobile data can solve the problem of how to dynamically detect whether the intelligent network-connected automobile is in the abnormal environment of the data in real time when the speed data of the front automobile or the distance data between two automobiles are abnormal. The safety of drivers and passengers can be effectively improved, the occurrence of traffic accidents is reduced, and the running efficiency of a traffic system is ensured while the monitoring and early warning of the abnormal data conditions are realized. In addition, the accuracy of early warning and reminding of the detection system can be guaranteed, and abnormal data in the error range can be removed by removing single errors, designing multiple judgment and certain delay. Because of factors such as malicious network attack, weather conditions, aging damage of instruments and the like, the intelligent network-connected automobile is in a data abnormality condition and becomes a normal state, and therefore the intelligent network-connected automobile has strong practical significance and popularization value.
Drawings
The accompanying drawings are included to provide a further understanding of the invention, and together with the description serve to explain the embodiments of the invention, and do not constitute a limitation of the invention. In the drawings:
FIG. 1 shows a flow chart of a method for detecting abnormal conditions of intelligent network-connected automobile data.
Detailed Description
For a further understanding of the present invention, preferred embodiments of the invention are described below in conjunction with the examples, but it should be understood that these descriptions are merely intended to illustrate further features and advantages of the invention, and are not limiting of the claims of the invention.
The description of this section is intended to be illustrative of only exemplary embodiments and is not intended to be limiting of the scope of the embodiments described herein. Combinations of the different embodiments, and alternatives of features from the same or similar prior art means and embodiments are also within the scope of the description and protection of the invention.
The embodiment provides a method for detecting abnormal environments of intelligent network-connected automobile data, which is shown by referring to fig. 1, and comprises the following steps:
Step 1, data acquisition and transmission: and acquiring the data of the CAN buses of the front vehicle and the host vehicle in real time at regular time intervals G, and transmitting the data of the CAN buses of the front vehicle to the host vehicle. Preferably, the time interval G takes a value of 10 milliseconds.
Step 2, data conversion and storage: the data of the CAN buses of the front vehicle and the host vehicle obtained in the step 1 are converted to obtain traffic operation data of the front vehicle and the host vehicle, and the traffic operation data in this embodiment is shown in table 1, where the traffic operation data at least includes a current time T, a front vehicle CAN bus data acquisition time T t F, a speed v t F of the front vehicle, a host vehicle CAN bus data acquisition time T t B, a speed v t B of the host vehicle, and a distance L t between the front vehicle and the host vehicle. The distance L t between the front vehicle and the host vehicle is acquired through the vehicle-mounted laser radar of the host vehicle.
And storing the data into a database of traffic operation data. In addition, when the host vehicle receives the data of the CAN buses of a plurality of groups of front vehicles from the time t-0.01 to the time t, the host vehicle only converts the data of the CAN buses of a group of front vehicles closest to the time t into traffic operation data.
The specific data conversion process comprises the following steps: the computer is connected with the OBD interface of the host vehicle through the adapter to obtain the message data on the CAN bus of the host vehicle, and then the message data CAN be analyzed and extracted by using Python, C language or C# language and the like to obtain visual traffic operation data.
TABLE 1 partial information stored in database of traffic data of preceding vehicle and own vehicle
Step 3, traffic running speed calculation: and (3) calculating the traffic running speed of the front vehicle at the moment t by using the traffic running data of the front vehicle and the own vehicle obtained by the conversion in the step (2). The estimated traffic speed of the preceding vehicle is Wherein L t-G is the distance between the front vehicle and the own vehicle corresponding to the time t-G,For the speed of the host vehicle corresponding to the time t-G,/>The speed of the preceding vehicle corresponding to the time t-G.
Step 4, monitoring abnormal data conditions:
When (when) And speed of the preceding vehicle at time t/>And 3. Calculating the traffic running speed/>, of the front vehicle according to the stepThe relative deviation of (2) is smaller than the allowable relative deviation epsilon, i.e./>When the intelligent network-connected automobile is not in an environment with abnormal data, wherein epsilon is an allowable relative deviation parameter, the unit is percent, and the value is 2 percent;
When (when) Or traffic speed of preceding vehicle/>Speed with front vehicle/>There is a null value in the vehicle or the speed of the vehicle before time t/>And 3. Calculating the traffic running speed/>, of the front vehicle according to the stepThe relative deviation of (2) is greater than or equal to the allowable relative deviation epsilon, i.e When the intelligent network connection automobile is in an environment with abnormal data.
For the occurrence of data anomaly, the specific monitoring method is as follows:
And 4-1, determining abnormal conditions of intelligent network-connected automobile data, wherein the abnormal conditions comprise front automobile speed delay, front automobile speed repetition, front automobile speed inaccuracy, two-automobile distance delay, two-automobile distance repetition, two-automobile distance inaccuracy, front automobile speed and two-automobile distance simultaneous delay, front automobile speed and two-automobile distance simultaneous repetition, and front automobile speed and two-automobile distance simultaneous inaccuracy.
Step 4-2, determining data abnormal conditions corresponding to the actual conditions:
case 1: traffic speed of current vehicle Or speed of front vehicle/>When the vehicle speed is null (including the situation that the data delay does not reach at the moment t and the corresponding speed or distance data is not received at the moment t), the corresponding abnormal data conditions comprise the speed delay of the front vehicle, the distance delay between two vehicles and the simultaneous delay of the speed of the front vehicle and the distance between two vehicles;
Case 2: the distance L t between the current car and the own car exists, and the speed of the front car There is a need for a system that, When the two vehicle speeds are simultaneously satisfied, corresponding abnormal data conditions comprise inaccurate front vehicle speed, inaccurate distance between the two vehicles, simultaneous inaccuracy of the front vehicle speed and the distance between the two vehicles, repeated front vehicle speed, repeated distance between the two vehicles, simultaneous repetition of the front vehicle speed and the distance between the two vehicles;
Case 3: the distance L t between the current car and the own car exists, and the speed of the front car There is a need for a system that, When the two vehicle speeds are simultaneously satisfied, the corresponding data abnormal conditions comprise repeated front vehicle speed, repeated front vehicle speed and two vehicle distance, delayed front vehicle speed and two vehicle distance.
The "presence" in cases 2 and 3 means that the speed and distance data are successfully received and converted at time t, and the corresponding values are present, in contrast to the null case in case 1.
The data delay can be further divided into two cases, data delay not arriving and data delay arriving. The case where the data delay does not arrive corresponds to the data delay in case 1, the corresponding data of which is a null value; the situation of data delay arrival corresponds to the data delay in the case 3, namely, the data delay arrival of the CAN bus of the whole front vehicle, and the corresponding data exists. When the data delay occurs, it occurs continuously with a large probability, and there is a large possibility that CAN bus data or a two-vehicle distance of a preceding vehicle at the current time t is not acquired.
The data repetition can be further divided into two cases of a single data repetition and an entire data repetition. The case of single data repetition corresponds to the data repetition in case 2, that is, only single preceding vehicle speed data repetition or single two-vehicle distance data repetition or both occur simultaneously, the time tag of the data of which is not affected; the situation of the whole data repetition corresponds to the data repetition in the case 3, namely the whole front car CAN bus data repetition, and the time tag of the data is affected. Further, the repetition of the inter-vehicle distances does not affect the time stamp of the data, so that the case where it occurs alone is not in the case 3 classification.
The value of epsilon is 2%, and the abnormal condition of the data in this example is shown in table 2.
TABLE 2 data anomaly monitoring information and related classifications
Step 5, the detection system gives an early warning and reminds:
The early warning and reminding of the monitoring result of the abnormal data condition in the step 4 can be divided into:
① When N is more than or equal to b, the early warning prompt is a data delay condition;
② When (when) When the warning is a special case of single data abnormality;
③ When (when) And when N is more than or equal to b, the early warning prompt is data delay and single data abnormal special condition;
④ When (when) And/>When the data is abnormal, the early warning prompt is a data abnormal general condition;
⑤ When (when) When the data delay and the abnormal data condition are met, the early warning prompt is 'data delay + abnormal data general condition';
⑥ When (when) And/>When the data is abnormal, the early warning prompt is a data abnormal extreme condition;
⑦ When (when) And when N is more than or equal to b and is simultaneously met, the early warning prompt is data delay and data abnormality extreme conditions.
Wherein σ is an allowable limit value of the relative deviation, K is the number of times of occurrence of the data abnormality by the current time, i is an integer of 1 or more and K or less, t i is a time corresponding to the occurrence of the data abnormality, a is an allowable limit value of the cumulative relative deviation occurring in the time interval (t K-t1), N is the number of times of occurrence of the data delay in the unit time, and b is an allowable limit value of occurrence of the data delay in the unit time. Preferably, the value of parameter σ is 5%, the value of parameter a is 10%, and the value of parameter b is 4.
In this step, the number of times N of data delay occurring in the unit time, the number of times K of occurrence of data abnormality at the current time, and the determination of time t i corresponding to occurrence of data abnormality are determined: n, K and i are initialized, namely: n=0, k=0, i=0; when the abnormal data condition of the condition 1 in the step 4-2 occurs, updating N, namely: n=n+1; when the abnormal data conditions of the case 2 and the case 3 in the step 4-2 occur, updating K and i, namely: k=k+1, i=i+1, and the time t at this time is recorded as t i. The early warning in this embodiment is shown in table 3.
TABLE 3 related information of early warning and reminding of detection system
The method for detecting the abnormal environment of the intelligent network-connected automobile data can solve the problem of how to dynamically detect whether the intelligent network-connected automobile is in the abnormal environment of the data in real time when the speed data of the front automobile or the distance data between two automobiles are abnormal. The safety of drivers and passengers can be effectively improved, the occurrence of traffic accidents is reduced, and the running efficiency of a traffic system is ensured while the monitoring and early warning of the abnormal data conditions are realized. The accuracy of detection early warning reminding can be guaranteed, and abnormal data in an error range can be removed by removing single errors, designing multiple judgment and certain delay. Because of factors such as malicious network attack, weather conditions, aging damage of instruments and the like, the intelligent network-connected automobile is in a data abnormality condition and becomes a normal state, and therefore the intelligent network-connected automobile has strong practical significance and popularization value.
The description and applications of the present invention herein are illustrative and are not intended to limit the scope of the invention to the embodiments described above. The relevant descriptions of effects, advantages and the like in the description may not be presented in practical experimental examples due to uncertainty of specific condition parameters or influence of other factors, and the relevant descriptions of effects, advantages and the like are not used for limiting the scope of the invention. Variations and modifications of the embodiments disclosed herein are possible, and alternatives and equivalents of the various components of the embodiments are known to those of ordinary skill in the art. It will be clear to those skilled in the art that the present invention may be embodied in other forms, structures, arrangements, proportions, and with other assemblies, materials, and components, without departing from the spirit or essential characteristics thereof. Other variations and modifications of the embodiments disclosed herein may be made without departing from the scope and spirit of the invention.
Claims (8)
1. The method for detecting the abnormal environment of the intelligent network-connected automobile data is characterized by comprising the following steps:
s1, data acquisition and transmission: acquiring the data of the CAN buses of the front vehicle and the host vehicle in real time at regular time intervals G, and transmitting the data of the CAN buses of the front vehicle to the host vehicle;
S2, data conversion and storage: the host vehicle converts the data of the CAN buses of the front vehicle and the host vehicle obtained in the step S1 to obtain traffic operation data of the front vehicle and the host vehicle, and the traffic operation data are stored in a database of the traffic operation data;
s3, calculating traffic running speed: calculating the traffic running speed of the front vehicle at the moment t by using the traffic running data of the front vehicle and the self vehicle obtained by the S2 conversion;
S4, monitoring abnormal conditions of data:
When (when) And speed of the preceding vehicle at time t/>And S3, calculating the traffic running speed estimated value/>, of the preceding vehicleThe relative deviation of (2) is smaller than the allowable relative deviation epsilon, i.e./>When the intelligent network connection automobile is not in an environment with abnormal data, wherein epsilon is an allowable relative deviation parameter, and the units are%,/>Time for CAN bus data acquisition of preceding vehicle,/>The CAN bus data acquisition time of the vehicle;
When (when) Or traffic speed estimate of preceding vehicle/>Speed of vehicle before t time/>There is a null value in the vehicle, or the speed of the vehicle before time t/>And S3, calculating the traffic running speed estimated value/>, of the preceding vehicleThe relative deviation of (2) is greater than or equal to the allowable relative deviation epsilon, i.e./>The intelligent network-connected automobile is in an environment with abnormal data;
S5, early warning and reminding of a detection system: carrying out real-time early warning and reminding on the monitoring result of the abnormal condition of the data in the S4, and when the abnormal condition occurs Or/>Or when N is more than or equal to b, the intelligent network-connected automobile detection system carries out early warning reminding of data abnormal conditions, wherein sigma is an allowable limit value of relative deviation, K is the number of times of occurrence of the data abnormal conditions at the current moment, i is an integer which is more than or equal to 1 and less than or equal to K, t i is the time corresponding to the occurrence of the data abnormal conditions, a is an allowable limit value of accumulated relative deviation in a time interval (t K-t1), N is the number of times of occurrence of data delay in unit time, and b is an allowable limit value of occurrence of data delay in unit time.
2. The method for detecting abnormal conditions of intelligent network-connected automobile data according to claim 1, wherein the method comprises the following steps: in S1, the time interval G is in milliseconds, and G ε {4,5,8,10}.
3. The method for detecting abnormal conditions of intelligent network-connected automobile data according to claim 1, wherein the method comprises the following steps: and S2, when the host vehicle receives the data of the CAN buses of a plurality of groups of front vehicles from the time t-G to the time t, the host vehicle only converts the data of the CAN buses of a group of front vehicles closest to the time t into traffic operation data.
4. The method for detecting abnormal conditions of intelligent network-connected automobile data according to claim 1, wherein the method comprises the following steps: s2, the traffic operation data at least comprise the current time t and CAN bus data acquisition time of the preceding vehicleSpeed of front vehicleCAN bus data acquisition time/>, of own vehicleSpeed/>, of own vehicleThe distance L t between the front vehicle and the host vehicle is obtained by collecting the vehicle-mounted laser radar of the host vehicle, wherein the distance L t between the front vehicle and the host vehicle is obtained by collecting the vehicle-mounted laser radar of the host vehicle.
5. The method for detecting abnormal conditions of intelligent network-connected automobile data according to claim 1, wherein the method comprises the following steps: and S3, estimating the traffic running speed of the front vehicle at the moment t, wherein the traffic running speed is specifically as follows:
At time t, the estimated traffic speed of the preceding vehicle is Wherein L t-G is the distance between the preceding vehicle and the host vehicle corresponding to the time t-G,/>For the speed of the host vehicle corresponding to the time t-G,/>The speed of the preceding vehicle corresponding to the time t-G.
6. The method for detecting abnormal conditions of intelligent network-connected automobile data according to claim 1, wherein the method comprises the following steps: the monitoring of the abnormal condition of the data in the S4 specifically comprises the following steps:
Determining corresponding data abnormal conditions according to traffic operation data and traffic operation speed estimated values of the front vehicles:
case 1) traffic speed estimate for current vehicle Or speed of the vehicle before time t/>When the value is null, the corresponding data abnormal conditions comprise front vehicle speed delay, two-vehicle distance delay and front vehicle speed and two-vehicle distance delay simultaneously;
Case 2) distance L t between the current car and the own car exists, speed of the preceding car There is a need for a system that, When the two vehicle speeds are simultaneously satisfied, corresponding abnormal data conditions comprise inaccurate front vehicle speed, inaccurate distance between the two vehicles, simultaneous inaccuracy of the front vehicle speed and the distance between the two vehicles, repeated front vehicle speed, repeated distance between the two vehicles, simultaneous repetition of the front vehicle speed and the distance between the two vehicles;
Case 3) distance L t between the current car and the own car exists, speed of the preceding car There is a need for a system that, When the two vehicle speeds are simultaneously satisfied, the corresponding data abnormal conditions comprise repeated front vehicle speed, repeated front vehicle speed and two vehicle distance, delayed front vehicle speed and two vehicle distance.
7. The method for detecting abnormal conditions of intelligent network-connected automobile data according to claim 6, wherein the method comprises the following steps: in S5, the number of times N of data delay occurring in the unit time, the number of times K of occurrence of data abnormality at the current time, and the determination of time t i corresponding to occurrence of data abnormality are determined:
initializing N, K and i, namely, letting: n=0, k=0, i=0;
When the abnormal data condition of the case 1) occurs, updating N, namely, making: n=n+1;
When abnormal data conditions of the case 2) and the case 3) occur, updating K and i, namely, making: k=k+1, i=i+1, and the time t at this time is recorded as t i.
8. The method for detecting abnormal conditions of intelligent network-connected automobile data according to claim 1, wherein the method comprises the following steps: s5, the early warning reminding of the detection system comprises the following steps:
<1> when N is greater than or equal to b, the early warning prompt is a data delay condition;
<2> when When the warning is a special case of single data abnormality;
<3> when And when N is more than or equal to b, the early warning prompt is data delay and single data abnormal special condition;
<4> when And/>When the data is abnormal, the early warning prompt is a data abnormal general condition;
<5> when When N is more than or equal to b and is simultaneously met, the early warning prompt is data delay and data abnormality general condition;
<6> when And/>When the data is abnormal, the early warning prompt is a data abnormal extreme condition;
<7> when And when N is more than or equal to b and is simultaneously met, the early warning prompt is data delay and data abnormality extreme conditions.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211502266.7A CN115914024B (en) | 2022-11-28 | 2022-11-28 | Method for detecting abnormal environment of intelligent network-connected automobile data |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211502266.7A CN115914024B (en) | 2022-11-28 | 2022-11-28 | Method for detecting abnormal environment of intelligent network-connected automobile data |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN115914024A CN115914024A (en) | 2023-04-04 |
| CN115914024B true CN115914024B (en) | 2024-06-21 |
Family
ID=86485747
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202211502266.7A Active CN115914024B (en) | 2022-11-28 | 2022-11-28 | Method for detecting abnormal environment of intelligent network-connected automobile data |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN115914024B (en) |
Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109843653A (en) * | 2017-07-26 | 2019-06-04 | 松下电器（美国）知识产权公司 | Abnormal detector and method for detecting abnormality |
Family Cites Families (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110191011A (en) * | 2019-04-15 | 2019-08-30 | 厦门科灿信息技术有限公司 | Smart machine monitoring method, device and equipment based on data center's monitoring system |
| CN110248311B (en) * | 2019-06-17 | 2021-01-01 | 重庆西部汽车试验场管理有限公司 | V2I application function testing method based on intelligent networking platform |
| CN110992677B (en) * | 2019-11-20 | 2021-03-19 | 北方工业大学 | Intelligent networking automobile formation control method and system for coping with communication abnormity |
| CN113938295B (en) * | 2021-09-29 | 2022-12-13 | 国家计算机网络与信息安全管理中心 | Method and system for detecting abnormal transmission behavior of internet automobile communication data, electronic equipment and readable medium |
-
2022
- 2022-11-28 CN CN202211502266.7A patent/CN115914024B/en active Active
Patent Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109843653A (en) * | 2017-07-26 | 2019-06-04 | 松下电器（美国）知识产权公司 | Abnormal detector and method for detecting abnormality |
Non-Patent Citations (1)
| Title |
|---|
| 车联网基站定位纠偏算法研究;王明儒;阮详兵;;电信工程技术与标准化;20170515(第05期);全文 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN115914024A (en) | 2023-04-04 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN111030962B (en) | Vehicle-mounted network intrusion detection method and computer-readable storage medium | |
| RU2018111478A (en) | System and method for creating rules | |
| CN107567005B (en) | Internet of vehicles abnormal behavior detection method and system based on artificial immune system | |
| CN103632211A (en) | Motor vehicle fault pre-warning and callback prediction system | |
| CN111354193B (en) | Highway vehicle abnormal behavior early warning system based on 5G communication | |
| CN103500503A (en) | Method and system for analyzing accurate road conditions based on crowdsourcing mode | |
| CN111565361A (en) | Test method and test system of vehicle emergency braking early warning system based on V2V | |
| CN108961473A (en) | A kind of vehicle-state assessment method for early warning based on intelligent network connection automobile control centre | |
| CN107742417A (en) | A kind of car accident alarm method and device | |
| CN114157469B (en) | Vehicle-mounted network variant attack intrusion detection method based on domain antagonism neural network | |
| CN114064656B (en) | Automatic driving scene recognition and conversion method based on road end perception system | |
| CN113938295B (en) | Method and system for detecting abnormal transmission behavior of internet automobile communication data, electronic equipment and readable medium | |
| CN115691223A (en) | Cloud edge-end cooperation-based collision early warning method and system | |
| CN115914024B (en) | Method for detecting abnormal environment of intelligent network-connected automobile data | |
| CN111047835B (en) | Road passenger traffic overspeed early warning system based on block chain | |
| CN105988460A (en) | Dynamic track detection method, apparatus, and system for vehicle | |
| CN115796726A (en) | Vehicle abnormality processing method, vehicle abnormality detection method, device, system and component | |
| CN112437111B (en) | Vehicle-road cooperative system based on context awareness | |
| CN115063980A (en) | Self-adaptive vehicle abnormal driving detection method and device and terminal equipment | |
| CN115640828A (en) | Vehicle-mounted digital twin cheating detection method based on antagonistic generation network | |
| CN113868875A (en) | Method, device and equipment for automatically generating test scene and storage medium | |
| CN116189477B (en) | Safety control method and equipment for intelligent network-connected automobile data abnormal environment | |
| CN112491814B (en) | Internet of vehicles networking intersection network attack detection method and system | |
| CN115472040B (en) | Personalized anti-collision early warning method for networked vehicle based on collision probability field | |
| CN115311861B (en) | Highway fatigue driving judging method and device, electronic equipment and storage medium |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant |