CN117834136B - Quantum key dynamic management method in Internet of vehicles communication process - Google Patents
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Abstract
The invention relates to the technical field of secure communication, in particular to a quantum key dynamic management method in the communication process of the Internet of vehicles, which comprises the following steps: acquiring a traffic condition data sequence of a road to be detected; acquiring the congestion degree of a road to be detected in each sampling time period; according to the possibility of sudden accidents of the road to be detected in each sampling time period, acquiring the complexity weight of the road to be detected in each sampling time period, and further acquiring the road complexity of the road to be detected in each sampling time period; and updating the quantum key between the vehicle passing through the road to be detected and the traffic system according to the quantum key updating period in each sampling time period. The invention improves the communication safety of the Internet of vehicles system.
Description
Technical Field
The invention relates to the technical field of secure communication, in particular to a quantum key dynamic management method in the communication process of the Internet of vehicles.
Background
The communication of the Internet of vehicles is a key component of a future intelligent traffic system, and relates to information exchange among vehicles, vehicles and infrastructure, and vehicles and pedestrians; in order to ensure the safety and confidentiality of communication, the quantum key is generated, and the quantum key provides an encryption method which can not be cracked almost, but with the lapse of time and continuous use, the quantum key can face the risk of being cracked, so that the dynamic management of the quantum key is important in such an environment; as the risk of cracking the quantum key increases over time and with continued use, the quantum key needs to be updated to avoid cracking the quantum key.
In the process of updating the quantum secret key by the traditional method, the quantum secret key is generally updated by adopting a fixed period, and the problem of safety risk can be caused because the data volume needing to be interacted in the Internet of vehicles system is different in different time points in the Internet of vehicles system, and the change of the data volume of the Internet of vehicles system can not be met by using the traditional fixed quantum secret key updating period.
Disclosure of Invention
In order to solve the problems, the invention provides a quantum key dynamic management method in the communication process of the internet of vehicles, which comprises the following steps:
Acquiring a traffic condition data sequence of a road to be detected, wherein the traffic condition data sequence comprises traffic flow data of a plurality of sampling time periods and each time instant speed data of each vehicle in each sampling time period;
Acquiring the congestion degree of a road to be detected in each sampling time period according to the average value of all the instantaneous speed data of each vehicle in each sampling time period and the vehicle flow data;
Acquiring the possibility of sudden accidents of the road to be detected in each sampling time period according to the difference of the instantaneous speed data of each vehicle in each sampling time period; acquiring the road complexity of the road to be detected in each sampling time period according to the possibility of sudden accidents of the road to be detected in each sampling time period;
Acquiring a quantum key updating period in each sampling time period according to the road complexity of a road to be detected in each sampling time period; acquiring a quantum secret key between a vehicle of a road to be detected and a traffic system; and updating the quantum key between the vehicle of the road to be detected and the traffic system according to the quantum key updating period in each sampling time period.
Preferably, the specific formula for obtaining the congestion degree of the road to be detected in each sampling period according to the average value of all the instantaneous speed data of each vehicle in each sampling period and the traffic flow data is as follows:
In the method, in the process of the invention, Represents the/>The congestion degree of the road to be detected in each sampling time period; /(I)Represents the/>Traffic flow data for each sampling period; /(I)Represents the/>First/>, within a sampling periodAn average of all the secondary instantaneous speed data of the individual vehicles; /(I)Is a preset parameter; /(I)An exponential function based on a natural constant is represented.
Preferably, the obtaining the possibility of the sudden accident on the road to be detected in each sampling period according to the difference of the instantaneous speed data of each vehicle in each sampling period comprises the following specific steps:
Acquisition of the first First/>, within a sampling periodThe degree of instantaneous speed change of the vehicle, thenThe calculation method for the possibility of sudden accidents of the road to be detected in each sampling time period comprises the following steps:
In the method, in the process of the invention, Represents the/>The possibility of sudden accidents of the road to be detected in the sampling time period; /(I)Represent the firstTraffic flow data for each sampling period; /(I)Represents the/>First/>, within a sampling periodThe degree of instantaneous speed change of the vehicle; /(I)Representing a linear normalization function.
Preferably, the acquiring a firstFirst/>, within a sampling periodThe specific formula of the instantaneous speed change degree of the vehicle is as follows:
In the method, in the process of the invention, Represents the/>First/>, within a sampling periodTotal number of all secondary instantaneous speed data of the individual vehicle; /(I)Represents the/>First/>, within a sampling periodFirst/>, of individual vehicleSecondary instantaneous speed data; /(I)Represents the/>First/>, within a sampling periodAverage of all secondary instantaneous speed data of individual vehicles.
Preferably, the method for obtaining the road complexity of the road to be detected in each sampling period according to the possibility of occurrence of the sudden accident on the road to be detected in each sampling period includes the following specific steps:
acquiring the complexity weight of the road to be detected in each sampling time period according to the possibility of sudden accidents of the road to be detected in each sampling time period; will be the first Complexity weight and/>, of road to be detected in each sampling time periodTaking the product of the congestion degree of the road to be detected in the sampling time period as the/>And the road complexity of the road to be detected in each sampling time period.
Preferably, the method for obtaining the complexity weight of the road to be detected in each sampling period according to the possibility of occurrence of the sudden accident on the road to be detected in each sampling period includes the following specific steps:
Acquisition of the first A plurality of clusters of the sampling time periods and a cluster center of each cluster; for/>Any cluster in the sampling time period is used for acquiring a first distance of the cluster and the possibility of occurrence of a sudden accident; then/>The calculating method of the complexity weight of the road to be detected in each sampling time period comprises the following steps:
In the method, in the process of the invention, Represents the/>Complexity weights of roads to be detected in the sampling time periods; /(I)Represents the/>The total number of all clusters for each sampling period; /(I)Represents the/>First/>, of the sampling periodA first distance of the clusters; /(I)Represents the/>First/>, of the sampling periodThe possibility of occurrence of sudden accidents of the clustering clusters; /(I)An exponential function based on a natural constant is represented.
Preferably, the acquiring a firstThe specific method comprises the following steps of:
will go from the first sampling period to the first The sequence consisting of the possibility of occurrence of sudden accidents on the road to be detected in all the sampling time periods of the sampling time periods is recorded as the/>The probability sequence of the road accident to be detected in each sampling time period takes the sampling time period as a horizontal axis and takes the probability of the accident of the road to be detected as a vertical axis, and the/>Inputting the road accident probability sequences to be detected in the sampling time periods into a two-dimensional coordinate system, obtaining a plurality of data points and marking the data points as the/>Accident potential data points for the individual sampling periods; couple/>, by iterative self-organizing clustering algorithmClustering all accident possibility data points of each sampling time period to obtain the/>A plurality of clusters of each sampling period and a cluster center of each cluster.
Preferably, the method for obtaining the first distance of the cluster and the possibility of occurrence of the sudden accident includes the following specific steps:
For the first Taking the average value of Euclidean distances from the cluster center of the cluster to the cluster centers of all other clusters as the first distance of the cluster, and taking the average value of the possibility of occurrence of sudden accidents of the to-be-detected roads of all accident possibility data points in the cluster as the possibility of occurrence of sudden accidents of the cluster.
Preferably, the method for obtaining the quantum key update period in each sampling period according to the road complexity of the road to be detected in each sampling period includes the following specific steps:
acquiring quantum key updating parameters in each sampling time period according to the road complexity of the road to be detected in each sampling time period; presetting a quantum key updating period Update period of Quantum Key/>A quantum key update period as a first sampling period; recording the product of the quantum key updating period of the first sampling time period and the quantum key updating parameter in the second sampling time period as a first product, and taking the result of upward rounding of the first product as the quantum key updating period of the second sampling time period; recording the product of the quantum key updating period of the second sampling time period and the quantum key updating parameter in the third sampling time period as a second product, and taking the result of the second product after upward rounding as the quantum key updating period of the third sampling time period; and so on until a quantum key update period is obtained for all sampling periods.
Preferably, the method for obtaining the quantum key update parameter in each sampling time period according to the road complexity of the road to be detected in each sampling time period includes the following specific steps:
Will be the first Road complexity and/>, of the road to be detected within the sampling periodThe ratio of the road complexity of the road to be detected in the sampling time period is taken as the/>The quantum key updates the parameters over a sampling period.
The technical scheme of the invention has the beneficial effects that: according to the method and the device, the road complexity of the road to be detected in each sampling time period is obtained according to the possibility of sudden accidents of the road to be detected in each sampling time period, so that the quantum key updating period in each sampling time period is obtained, and the quantum key updating period is adaptively adjusted by analyzing the change of real-time traffic conditions in the Internet of vehicles environment, so that the communication safety of the Internet of vehicles system is improved.
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In order to more clearly illustrate the embodiments of the invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, it being obvious that the drawings in the following description are only some embodiments of the invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a flow chart of steps of a method for dynamically managing quantum keys in a communication process of internet of vehicles.
Detailed Description
In order to further describe the technical means and effects adopted by the invention to achieve the preset aim, the following is a detailed description of a specific implementation, structure, characteristics and effects of a quantum key dynamic management method in the internet of vehicles communication process according to the invention in combination with the accompanying drawings and the preferred embodiment. In the following description, different "one embodiment" or "another embodiment" means that the embodiments are not necessarily the same. Furthermore, the particular features, structures, or characteristics of one or more embodiments may be combined in any suitable manner.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.
The invention provides a specific scheme of a quantum key dynamic management method in the communication process of the Internet of vehicles, which is specifically described below with reference to the accompanying drawings.
Referring to fig. 1, a step flow chart of a method for dynamically managing quantum keys in a communication process of internet of vehicles according to an embodiment of the present invention is shown, and the method includes the following steps:
step S001: and acquiring a traffic condition data sequence of the road to be detected.
It should be noted that the traffic condition data refers to the number of vehicles passing through a road or other traffic channels in a specific place and a specific time period and speed information of each vehicle; traffic condition data is typically used for the purposes of assessing traffic fluidity, planning road improvement, optimizing control of traffic signals, predicting traffic congestion, and the like.
Specifically, in order to implement the quantum key dynamic management method in the internet of vehicles communication process provided by the embodiment, firstly, a traffic condition data sequence of a road to be detected needs to be collected, and the specific process is as follows:
Every 10 minutes is a sampling time period, the total number of vehicles passing through the road to be detected in the current time period is collected by a traffic camera of the road to be detected every time, the total number of vehicles passing through the road to be detected in the current time period is recorded as vehicle flow data in the current time period, the instantaneous speed of each vehicle passing through the road to be detected in the current time period is collected by a speed sensor every 0.2 seconds, the instantaneous speed data of each vehicle in the current time period is recorded as one instantaneous speed data of each vehicle in the current time period, and the total number of vehicles is collected for 168 hours; and taking a data sequence consisting of each time instant speed data and traffic flow data of each vehicle in all the sampling time periods as a traffic condition data sequence of the road to be detected.
So far, the traffic condition data sequence of the road to be detected is obtained through the method.
Step S002: and acquiring the congestion degree of the road to be detected in each sampling time period.
It should be noted that, the traffic flow data indicates the total number of vehicles passing through the road to be detected, and if the traffic flow is large, it indicates that a plurality of vehicles pass through the road to be detected in the same time period; the vehicle speed represents the running speed of the vehicle on the road to be detected, the lower speed may represent that the vehicle material is blocked, such as congestion, road construction and the like, and the higher speed represents that the road is unobstructed, so that the vehicle can run rapidly. Therefore, the traffic jam degree can be calculated according to the traffic flow data and the vehicle speed, and when the traffic flow is large and the vehicle speed is low, the traffic jam degree indicates that a plurality of vehicles pass, but the running is slow, and the road traffic is jammed at the moment; if the traffic flow is large and the vehicle speed is high, the road is not jammed at the moment; the traffic flow is smaller, and the speed of the vehicle is slower, so that the road is in a congestion state at the moment, and when the traffic flow is smaller and the speed of the vehicle is faster, the vehicle on the road is smaller, and the road is smooth.
Presetting a parameterWherein in the present embodiment, the following is-To describe, the present embodiment is not particularly limited, wherein/>Depending on the particular implementation.
Specifically, the firstThe calculation method of the congestion degree of the road to be detected in each sampling time period comprises the following steps:
In the method, in the process of the invention, Represents the/>The congestion degree of the road to be detected in each sampling time period; /(I)Represents the/>Traffic flow data for each sampling period; /(I)Represents the/>First/>, within a sampling periodAn average of all the secondary instantaneous speed data of the individual vehicles; /(I)Is a preset parameter; /(I)An exponential function based on a natural constant is represented.
It should be noted that the number of the substrates,As a reference index of speed, i.e. if the average speed of a certain vehicle is greater than the value, the speed of the vehicle is considered to be faster, i.e. the value is positive, the speed of the vehicle is considered to be faster, and the faster the speed is, the greater the value is, whereas if the value is negative, the speed of the vehicle is considered to be slower, and the slower the speed is, the smaller the value is. In summary, when the vehicle speed is low and the vehicle flow is low in the period of time, it is indicated that the vehicle in the period of time is difficult to travel, i.e. the congestion degree is high; when the vehicle speed in the period is lower and the vehicle flow is larger, the condition that the road is jammed even though the vehicle speed in the period is lower is indicated, but the vehicle is still slowly and orderly advancing, namely the jam degree is slightly lower at the moment; when the speed of the vehicle is higher and the traffic flow is larger in the period of time, the interval is smoother, and the vehicle can keep normal speed to travel, namely the congestion degree is inferior; when the traffic jam is high and the traffic flow is small in the time period, the fact that the traffic is small in the interval at the moment is indicated, the road is smooth, and the congestion degree is the lowest at the moment.
So far, the congestion degree of the road to be detected in each sampling time period is obtained through the method.
Step S003: and acquiring the complexity weight of the road to be detected in each sampling time period according to the possibility of sudden accidents of the road to be detected in each sampling time period, and further acquiring the road complexity of the road to be detected in each sampling time period.
1. And acquiring the possibility of sudden accidents of the road to be detected in each sampling time period.
It should be noted that, the traffic fluidity and the congestion condition on the road have important influence on the occurrence of accidents; if the road is congested, the vehicle speed may change continuously, increasing the risk of collision with each other; by detecting the instantaneous speed change of the vehicle, the behavior of the driver, such as sudden acceleration, sudden braking, frequent lane change and the like, can be known; these behaviors may be precursors to the occurrence of accidents, so that the possibility of the occurrence of sudden accidents on the road needs to be calculated through the instantaneous speed change condition of each vehicle.
Specifically, the firstThe calculation method for the possibility of sudden accidents of the road to be detected in each sampling time period comprises the following steps:
In the method, in the process of the invention, Represents the/>The possibility of sudden accidents of the road to be detected in the sampling time period; /(I)Represent the firstTraffic flow data for each sampling period; /(I)Represents the/>First/>, within a sampling periodTotal number of all secondary instantaneous speed data of the individual vehicle; /(I)Represents the/>First/>, within a sampling periodFirst/>, of individual vehicleSecondary instantaneous speed data; /(I)Represents the/>First/>, within a sampling periodAn average of all the secondary instantaneous speed data of the individual vehicles; /(I)Representing a linear normalization function.
It should be noted that the number of the substrates,Represents the/>The larger the value of the instantaneous speed change degree of the vehicle is, the more severe the instantaneous speed change of the vehicle is, otherwise, the more gradual the instantaneous speed change of the vehicle is; the possibility of the sudden accident of the road in the time period is measured by acquiring the instantaneous speed change condition of each vehicle, and the larger the value is, the larger the possibility of the sudden accident of the road in the time period is, and the smaller the value is, the smaller the possibility of the sudden accident of the road in the time period is.
So far, the possibility of sudden accidents on the road to be detected in each sampling time period is obtained through the method.
2. And acquiring the complexity weight of the road to be detected in each sampling time period.
It should be noted that, for a road with a higher possibility of occurrence of an accident, and the more times the road has an accident in all time periods, the higher the complexity of the road is, so that road data with a relatively close possibility of occurrence of an accident on the road is required to be clustered together according to the possibility of occurrence of an accident on the road in each sampling time period, the data in the same cluster are relatively close to each other, and for data points in different clusters, if the distances of the data points in different clusters are relatively close to each other, the possibility of occurrence of an accident on the road to be detected in the last 7 days is relatively stable, and the higher the possibility of an accident corresponding to the clusters is, the higher the complexity of the road to be detected is.
Specifically, the first sampling period is from the first sampling period to the first sampling periodThe sequence consisting of the possibility of occurrence of sudden accidents on the road to be detected in all the sampling time periods of the sampling time periods is recorded as the/>The probability sequence of the road accident to be detected in each sampling time period takes the sampling time period as a horizontal axis and takes the probability of the accident of the road to be detected as a vertical axis, and the/>Inputting the road accident probability sequences to be detected in the sampling time periods into a two-dimensional coordinate system, obtaining a plurality of data points and marking the data points as the/>Accident potential data points for the individual sampling periods; couple/>, by iterative self-organizing clustering algorithm (ISODATA)Clustering all accident possibility data points of each sampling time period to obtain the/>A plurality of clusters of the sampling time periods and a cluster center of each cluster; for/>Taking an average value of Euclidean distances from a cluster center of the cluster to cluster centers of all other clusters as a first distance of the cluster, and taking an average value of the possibility of occurrence of sudden accidents of a road to be detected of all accident possibility data points in the cluster as the possibility of occurrence of sudden accidents of the cluster; then/>The calculating method of the complexity weight of the road to be detected in each sampling time period comprises the following steps:
In the method, in the process of the invention, Represents the/>Complexity weights of roads to be detected in the sampling time periods; /(I)Represents the/>The total number of all clusters for each sampling period; /(I)Represents the/>First/>, of the sampling periodA first distance of the clusters; /(I)Represents the/>First/>, of the sampling periodThe possibility of occurrence of sudden accidents of the clustering clusters; /(I)An exponential function based on a natural constant is represented.
It should be noted that, the iterative self-organizing clustering algorithm is in the prior art, and this embodiment is not described in detail here; if the distance between a certain cluster and other clusters is closer, the confidence level of the data in the cluster is higher, namely the complexity weight of the road finally approaches to the cluster, namely the distance is closer, the complexity weight is higher, otherwise, the distance is farther, and the complexity weight is lower.
So far, the complexity weight of the road to be detected in each sampling time period is obtained through the method.
3. And acquiring the road complexity of the road to be detected in each sampling time period.
If the road congestion degree in the period is higher and the possibility of occurrence of an accident is higher, the complexity of the current road is higher, and conversely, the complexity of the current road is lower.
Specifically, will beComplexity weight and/>, of road to be detected in each sampling time periodTaking the product of the congestion degree of the road to be detected in the sampling time period as the/>And the road complexity of the road to be detected in each sampling time period.
So far, the road complexity of the road to be detected in each sampling time period is obtained through the method.
Step S004: and updating the quantum key between the vehicle passing through the road to be detected and the traffic system according to the quantum key updating period in each sampling time period.
1. And acquiring a quantum key updating parameter in each sampling time period.
It should be noted that, if the road in the current time period is more complex, in order to ensure the safe running of the vehicle, the more data information needs to be interacted between the vehicle and the public facilities in the current time period, so the more frequently the quantum key is used, the shorter the update period of the quantum key is required, that is, the higher the road complexity is, the shorter the update period of the quantum key in the current time period is, otherwise, the lower the road complexity is, the longer the update period of the quantum key in the current time period is.
Specifically, will beRoad complexity and/>, of the road to be detected within the sampling periodThe ratio of the road complexity of the road to be detected in the sampling time period is taken as the/>The quantum key updates the parameters over a sampling period.
Wherein, when calculating the quantum key updating parameter in the first sampling time period, presetting an experience valueWherein the present embodiment is described as/>To describe the example, the present embodiment is not particularly limited, wherein/>Depending on the particular implementation.
So far, the quantum key updating parameters in each sampling time period are obtained through the method.
2. And acquiring a quantum key updating period in each sampling time period.
Presetting a quantum key updating periodWherein the present embodiment is described as/>To describe the example, the present embodiment is not particularly limited, wherein/>Depending on the particular implementation.
It should be noted that, for the communication data in the internet of vehicles, the complexity of the road traffic in the current time period needs to be determined, where the complexity of the road traffic in the current time period determines the update period of the quantum key, the more the road traffic in the current time period is complex, the more the data that needs to be interacted in the internet of vehicles system, the more the number of times the quantum key between the vehicle and the traffic system of the road to be detected is used, the more the number of times the quantum key is used, the risk that the quantum key is cracked, and in order to prevent the quantum key from being cracked, the quantum key needs to be updated and replaced again, so as to avoid that the quantum key is used too many times and is cracked; when the quantum secret key is updated conventionally, a fixed period is adopted, and because the data volume needed to be interacted in the internet of vehicles system in different time periods in the road traffic data is different, the complexity degree of the current road traffic needs to be obtained by analyzing the real-time traffic condition data, and the updating period is adjusted adaptively according to the complexity degree of the current road traffic.
Specifically, the quantum key is updated in a periodA quantum key update period as a first sampling period; recording the product of the quantum key updating period of the first sampling time period and the quantum key updating parameter in the second sampling time period as a first product, and taking the result of upward rounding of the first product as the quantum key updating period of the second sampling time period; recording the product of the quantum key updating period of the second sampling time period and the quantum key updating parameter in the third sampling time period as a second product, and taking the result of the second product after upward rounding as the quantum key updating period of the third sampling time period; the product of the quantum key updating period of the third sampling time period and the quantum key updating parameter in the fourth sampling time period is recorded as a third product, and the result obtained by upwardly rounding the third product is used as the quantum key updating period of the fourth sampling time period; and so on until a quantum key update period is obtained for all sampling periods.
So far, the quantum key update period in all sampling time periods is obtained.
Specifically, the specific method for updating the quantum secret key between the vehicle and the traffic system of the road to be detected comprises the following steps:
Acquiring the last update time of a quantum key between a vehicle and a traffic system of a road to be detected, marking the last update time as an initial time, marking a quantum key update period in the last sampling time period as a target quantum key update period, and marking a difference value between the current time and the initial time as a first difference value; if the first difference value is smaller than the target quantum key updating period, not updating the quantum key between the vehicle and the traffic system of the road to be detected, and if the first difference value is equal to the target quantum key updating period, updating the quantum key between the vehicle and the traffic system of the road to be detected; and if the first difference value is greater than or equal to the target quantum key updating period, updating the quantum key between the vehicle of the road to be detected and the traffic system.
The quantum key updating method is that a quantum key distribution technology generates a new quantum key by using protocol parameters distributed by the quantum key; the quantum key distribution technology is the prior art, and the embodiment is not described herein in detail.
This embodiment is completed.
The above description is only of the preferred embodiments of the present invention and is not intended to limit the invention, but any modifications, equivalent substitutions, improvements, etc. within the principles of the present invention should be included in the scope of the present invention.
Claims (6)
1. A quantum key dynamic management method in the communication process of the Internet of vehicles is characterized by comprising the following steps:
Acquiring a traffic condition data sequence of a road to be detected, wherein the traffic condition data sequence comprises traffic flow data of a plurality of sampling time periods and each time instant speed data of each vehicle in each sampling time period;
Acquiring the congestion degree of a road to be detected in each sampling time period according to the average value of all the instantaneous speed data of each vehicle in each sampling time period and the vehicle flow data;
Acquiring the possibility of sudden accidents of the road to be detected in each sampling time period according to the difference of the instantaneous speed data of each vehicle in each sampling time period; acquiring the road complexity of the road to be detected in each sampling time period according to the possibility of sudden accidents of the road to be detected in each sampling time period;
Acquiring a quantum key updating period in each sampling time period according to the road complexity of a road to be detected in each sampling time period; acquiring a quantum secret key between a vehicle of a road to be detected and a traffic system; according to the quantum key updating period in each sampling time period, the quantum key between the vehicle of the road to be detected and the traffic system is updated;
According to the possibility of occurrence of sudden accidents on the road to be detected in each sampling time period, the road complexity of the road to be detected in each sampling time period is obtained, and the specific method comprises the following steps:
acquiring the complexity weight of the road to be detected in each sampling time period according to the possibility of sudden accidents of the road to be detected in each sampling time period; will be the first Complexity weight and/>, of road to be detected in each sampling time periodTaking the product of the congestion degree of the road to be detected in the sampling time period as the/>Road complexity of the road to be detected in the sampling time periods;
According to the possibility of occurrence of sudden accidents on the road to be detected in each sampling time period, the complexity weight of the road to be detected in each sampling time period is obtained, and the specific method comprises the following steps:
Acquisition of the first A plurality of clusters of the sampling time periods and a cluster center of each cluster; for/>Any cluster in the sampling time period is used for acquiring a first distance of the cluster and the possibility of occurrence of a sudden accident; then/>The calculating method of the complexity weight of the road to be detected in each sampling time period comprises the following steps:
In the method, in the process of the invention, Represents the/>Complexity weights of roads to be detected in the sampling time periods; /(I)Represents the/>The total number of all clusters for each sampling period; /(I)Represents the/>First/>, of the sampling periodA first distance of the clusters; /(I)Represents the/>First/>, of the sampling periodThe possibility of occurrence of sudden accidents of the clustering clusters; /(I)An exponential function based on a natural constant;
according to the road complexity of the road to be detected in each sampling time period, the quantum key updating period in each sampling time period is acquired, and the specific method comprises the following steps:
acquiring quantum key updating parameters in each sampling time period according to the road complexity of the road to be detected in each sampling time period; presetting a quantum key updating period Update period of Quantum Key/>A quantum key update period as a first sampling period; recording the product of the quantum key updating period of the first sampling time period and the quantum key updating parameter in the second sampling time period as a first product, and taking the result of upward rounding of the first product as the quantum key updating period of the second sampling time period; recording the product of the quantum key updating period of the second sampling time period and the quantum key updating parameter in the third sampling time period as a second product, and taking the result of the second product after upward rounding as the quantum key updating period of the third sampling time period; and the like until the quantum key updating period in all the sampling time periods is obtained;
According to the road complexity of the road to be detected in each sampling time period, the quantum key updating parameters in each sampling time period are obtained, and the specific method comprises the following steps:
Will be the first Road complexity and/>, of the road to be detected within the sampling periodThe ratio of the road complexity of the road to be detected in the sampling time period is taken as the/>The quantum key updates the parameters over a sampling period.
2. The method for dynamically managing quantum keys in a communication process of internet of vehicles according to claim 1, wherein the specific formula for obtaining the congestion degree of the road to be detected in each sampling period according to the average value of all the instantaneous speed data of each vehicle in each sampling period and the traffic flow data is as follows:
In the method, in the process of the invention, Represents the/>The congestion degree of the road to be detected in each sampling time period; /(I)Represents the/>Traffic flow data for each sampling period; /(I)Represents the/>First/>, within a sampling periodAn average of all the secondary instantaneous speed data of the individual vehicles; Is a preset parameter; /(I) An exponential function based on a natural constant is represented.
3. The method for dynamically managing quantum keys in a communication process of internet of vehicles according to claim 1, wherein the obtaining the possibility of occurrence of an accident on the road to be detected in each sampling period according to the difference of each instantaneous speed data of each vehicle in each sampling period comprises the following specific steps:
Acquisition of the first First/>, within a sampling periodThe degree of instantaneous speed change of the vehicle, thenThe calculation method for the possibility of sudden accidents of the road to be detected in each sampling time period comprises the following steps:
In the method, in the process of the invention, Represents the/>The possibility of sudden accidents of the road to be detected in the sampling time period; /(I)Represents the/>Traffic flow data for each sampling period; /(I)Represents the/>First/>, within a sampling periodThe degree of instantaneous speed change of the vehicle; Representing a linear normalization function.
4. A method for dynamically managing quantum keys in a communication process of internet of vehicles according to claim 3, wherein the obtaining of the first keyFirst/>, within a sampling periodThe specific formula of the instantaneous speed change degree of the vehicle is as follows:
In the method, in the process of the invention, Represents the/>First/>, within a sampling periodTotal number of all secondary instantaneous speed data of the individual vehicle; represents the/> First/>, within a sampling periodFirst/>, of individual vehicleSecondary instantaneous speed data; /(I)Represents the/>First/>, within a sampling periodAverage of all secondary instantaneous speed data of individual vehicles.
5. The method for dynamically managing quantum keys in internet of vehicles according to claim 1, wherein the obtaining the first stepThe specific method comprises the following steps of:
will go from the first sampling period to the first The sequence consisting of the possibility of occurrence of sudden accidents on the road to be detected in all the sampling time periods of the sampling time periods is recorded as the/>The probability sequence of the road accident to be detected in each sampling time period takes the sampling time period as a horizontal axis and takes the probability of the accident of the road to be detected as a vertical axis, and the/>Inputting the road accident probability sequences to be detected in the sampling time periods into a two-dimensional coordinate system, obtaining a plurality of data points and marking the data points as the/>Accident potential data points for the individual sampling periods; couple/>, by iterative self-organizing clustering algorithmClustering all accident possibility data points of each sampling time period to obtain the/>A plurality of clusters of each sampling period and a cluster center of each cluster.
6. The method for dynamically managing quantum keys in a communication process of internet of vehicles according to claim 1, wherein the obtaining the first distance of the cluster and the possibility of occurrence of a sudden accident comprises the following specific steps:
For the first Taking the average value of Euclidean distances from the cluster center of the cluster to the cluster centers of all other clusters as the first distance of the cluster, and taking the average value of the possibility of occurrence of sudden accidents of the to-be-detected roads of all accident possibility data points in the cluster as the possibility of occurrence of sudden accidents of the cluster.
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Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2016036159A (en) * | 2015-10-08 | 2016-03-17 | 住友電気工業株式会社 | On-vehicle device and method for obtaining momentum to update common key |
| CN107979840A (en) * | 2018-01-23 | 2018-05-01 | 重庆邮电大学 | A kind of the car networking V2I Verification Systems and method of Key-insulated safety |
| CN112115494A (en) * | 2020-09-22 | 2020-12-22 | 安徽大学 | Data access control method suitable for interior of automatic driving vehicle |
| CN114419928A (en) * | 2022-01-27 | 2022-04-29 | 合肥工业大学 | Vehicle road cloud cooperative control system and method based on quantum communication |
| CN115001684A (en) * | 2022-07-18 | 2022-09-02 | 合肥工业大学 | Multidimensional self-adaptive dynamic key updating method |
| CN116707755A (en) * | 2023-06-13 | 2023-09-05 | 南京邮电大学 | Traffic jam prediction system and method based on homomorphic encryption and blockchain |
| WO2024002160A1 (en) * | 2022-06-29 | 2024-01-04 | 中国第一汽车股份有限公司 | Data processing method and apparatus, and electronic device and storage medium |
-
2024
- 2024-03-01 CN CN202410231442.0A patent/CN117834136B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2016036159A (en) * | 2015-10-08 | 2016-03-17 | 住友電気工業株式会社 | On-vehicle device and method for obtaining momentum to update common key |
| CN107979840A (en) * | 2018-01-23 | 2018-05-01 | 重庆邮电大学 | A kind of the car networking V2I Verification Systems and method of Key-insulated safety |
| CN112115494A (en) * | 2020-09-22 | 2020-12-22 | 安徽大学 | Data access control method suitable for interior of automatic driving vehicle |
| CN114419928A (en) * | 2022-01-27 | 2022-04-29 | 合肥工业大学 | Vehicle road cloud cooperative control system and method based on quantum communication |
| WO2024002160A1 (en) * | 2022-06-29 | 2024-01-04 | 中国第一汽车股份有限公司 | Data processing method and apparatus, and electronic device and storage medium |
| CN115001684A (en) * | 2022-07-18 | 2022-09-02 | 合肥工业大学 | Multidimensional self-adaptive dynamic key updating method |
| CN116707755A (en) * | 2023-06-13 | 2023-09-05 | 南京邮电大学 | Traffic jam prediction system and method based on homomorphic encryption and blockchain |
Non-Patent Citations (1)
| Title |
|---|
| 基于强化学习的低时延车联网群密钥分配管理技术;徐堂炜等;网络与信息安全学报;20201013(05);全文 * |
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