CN111050299B - Intelligent vehicle data mutual inspection method and device - Google Patents

Intelligent vehicle data mutual inspection method and device Download PDF

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CN111050299B
CN111050299B CN201911082818.1A CN201911082818A CN111050299B CN 111050299 B CN111050299 B CN 111050299B CN 201911082818 A CN201911082818 A CN 201911082818A CN 111050299 B CN111050299 B CN 111050299B
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CN111050299A (en
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侯琛
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Tencent Technology Shenzhen Co Ltd
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Tencent Technology Shenzhen Co Ltd
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
    • H04W4/30—Services specially adapted for particular environments, situations or purposes
    • H04W4/40—Services specially adapted for particular environments, situations or purposes for vehicles, e.g. vehicle-to-pedestrians [V2P]
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/01—Detecting movement of traffic to be counted or controlled
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L43/00—Arrangements for monitoring or testing data switching networks
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L67/00—Network arrangements or protocols for supporting network services or applications
    • H04L67/01—Protocols
    • H04L67/12—Protocols specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W24/00—Supervisory, monitoring or testing arrangements

Abstract

The embodiment of the application provides an intelligent vehicle data mutual inspection method and device. The intelligent vehicle data mutual detection method comprises the following steps: acquiring the total number of intelligent vehicles in the Internet of vehicles and a data mutual inspection probability threshold; determining the number of target intelligent vehicles in the Internet of vehicles according to the total number of the intelligent vehicles and the data mutual inspection probability threshold; and determining target intelligent vehicles in the internet of vehicles according to the number of the target intelligent vehicles, and acquiring target data based on the target intelligent vehicles to perform intelligent vehicle data mutual inspection. According to the technical scheme of the embodiment of the application, when the data uploaded by the intelligent vehicles in the Internet of vehicles can be mutually checked, the waste of network flow is reduced, and the utilization rate of the network flow in the Internet of vehicles is improved.

Description

Intelligent vehicle data mutual inspection method and device
Technical Field
The application relates to the technical field of vehicle networking, in particular to an intelligent vehicle data mutual inspection method and device.
Background
In the field of car networking, intelligent vehicle in the car networking need upload multiclass data to car networking cloud ware to the intelligent vehicle in the car networking is assisted according to the data that upload by car networking cloud ware and is driven safely. When the validity of the data uploaded by the intelligent vehicles is guaranteed, the cloud server of the internet of vehicles only needs to upload data by at least two intelligent vehicles in the internet of vehicles to perform mutual detection on the data, and therefore the validity of the data uploaded by the intelligent vehicles is guaranteed.
At present, whether intelligent vehicle in the car networking carries certain type of data that need upload is mutually independent, and the process that intelligent vehicle in the car networking uploaded certain type of data also is mutually independent, can have more than two intelligent vehicle to upload same type of data simultaneously from this, and the intelligent vehicle who uploads same type of data is more, can cause the data redundancy of uploading, and then extravagant network flow for network flow utilization ratio in the car networking is lower.
Disclosure of Invention
The embodiment of the application provides an intelligent vehicle data mutual detection method and device, and then at least to a certain extent can make the data that intelligent vehicle in the car networking uploaded can carry out mutual detection, reduce the waste of network flow, improve the utilization ratio of network flow in the car networking.
Other features and advantages of the present application will be apparent from the following detailed description, or may be learned by practice of the application.
According to an aspect of an embodiment of the present application, there is provided an intelligent vehicle data mutual inspection method, including: acquiring the total number of intelligent vehicles in the Internet of vehicles and a data mutual inspection probability threshold; determining the number of target intelligent vehicles in the Internet of vehicles according to the total number of the intelligent vehicles and the data mutual inspection probability threshold; and determining target intelligent vehicles in the Internet of vehicles according to the number of the target intelligent vehicles, and acquiring target data based on the target intelligent vehicles to perform mutual detection of intelligent vehicle data.
According to an aspect of an embodiment of the present application, there is provided an intelligent vehicle data mutual inspection device, including: the acquiring unit is used for acquiring the total number of intelligent vehicles in the Internet of vehicles and the data mutual inspection probability threshold; the execution unit is used for determining the number of target intelligent vehicles in the internet of vehicles according to the total number of the intelligent vehicles and the data mutual inspection probability threshold; and the mutual inspection unit is used for determining target intelligent vehicles in the Internet of vehicles according to the number of the target intelligent vehicles and acquiring target data based on the target intelligent vehicles so as to perform mutual inspection on the intelligent vehicle data.
In some embodiments of the present application, based on the foregoing solution, the mutual inspection unit is configured to: determining a first ratio based on the target number of smart vehicles and the total number of smart vehicles; distributing random numbers to intelligent vehicles in the Internet of vehicles; and determining the intelligent vehicle with the distributed random number smaller than or equal to the first ratio as the target intelligent vehicle.
In some embodiments of the present application, based on the foregoing solution, the mutual inspection unit is configured to: and determining the intelligent vehicles with the same number as the target intelligent vehicles from the internet of vehicles as the target intelligent vehicles.
In some embodiments of the present application, based on the foregoing solution, the execution unit is configured to: determining a first value range of the number of target intelligent vehicles in the Internet of vehicles according to the total number of the intelligent vehicles and the data mutual inspection probability threshold; and determining the number of the target intelligent vehicles in the first value range, wherein the number of the target intelligent vehicles is positively correlated with the total number of the intelligent vehicles, and the number of the target intelligent vehicles is positively correlated with the data mutual detection probability threshold.
In some embodiments of the present application, based on the foregoing solution, if the target data has a plurality of categories, the execution unit is configured to: determining the maximum value of a data mutual detection probability threshold value corresponding to the categories of the target data; determining a second value range of the number of target intelligent vehicles in the internet of vehicles according to the total number of the intelligent vehicles and the maximum value of the data mutual inspection probability threshold; and determining the number of the target intelligent vehicles in the second value range, wherein the number of the target intelligent vehicles is positively correlated with the total number of the intelligent vehicles, and the number of the target intelligent vehicles is positively correlated with the maximum value of the data mutual inspection probability threshold.
In some embodiments of the present application, based on the foregoing solution, the intelligent vehicle data mutual inspection device further includes: and the updating unit is used for updating the target intelligent vehicle number according to the mutual detection result of the intelligent vehicle data mutual detection.
In some embodiments of the present application, based on the foregoing solution, the updating unit is configured to: according to the mutual detection result of the intelligent vehicle data mutual detection, determining a second ratio between the successful times of the mutual detection and the total mutual detection times; and if the second ratio is smaller than the data mutual inspection probability threshold, updating the number of the target intelligent vehicles according to the second ratio.
In some embodiments of the present application, based on the foregoing solution, the updating unit is configured to: determining a difference value between the second ratio and the data mutual detection probability threshold; and updating the target intelligent vehicle number according to the difference value.
According to an aspect of the embodiments of the present application, there is provided a computer readable medium, on which a computer program is stored, the computer program, when executed by a processor, implementing the intelligent vehicle data mutual inspection method as described in the above embodiments.
According to an aspect of an embodiment of the present application, there is provided an electronic device including: one or more processors; a storage device to store one or more programs that, when executed by the one or more processors, cause the one or more processors to implement the intelligent vehicle data mutual inspection method as described in the above embodiments.
According to the technical scheme provided by some embodiments of the application, the total number of intelligent vehicles and the data mutual detection probability threshold value in the internet of vehicles are obtained, the number of target intelligent vehicles in the internet of vehicles is determined according to the total number of intelligent vehicles and the data mutual detection probability threshold value, the number of intelligent vehicles required when the probability of obtaining certain types of target data from at least two intelligent vehicles in the internet of vehicles can reach the data mutual detection probability threshold value is determined according to the number of target intelligent vehicles, the target intelligent vehicles are determined in the internet of vehicles based on the target intelligent vehicles, the target data are obtained based on the target intelligent vehicles to carry out intelligent vehicle data mutual detection, the target data uploaded by more intelligent vehicles is avoided while the intelligent vehicle data mutual detection is carried out on the target data uploaded by the intelligent vehicles, the uploading of data redundancy is reduced, the waste of network traffic is further reduced, and the utilization rate of the network traffic in the internet of vehicles is improved.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the application.
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The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and together with the description, serve to explain the principles of the application. It is obvious that the drawings in the following description are only some embodiments of the application, and that for a person skilled in the art, other drawings can be derived from them without inventive effort. In the drawings:
fig. 1 shows a schematic diagram of an exemplary system architecture to which the technical solution of the embodiments of the present application can be applied.
FIG. 2 shows a flow diagram of an intelligent vehicle data mutual inspection method according to one embodiment of the present application.
FIG. 3 shows a flow diagram of an intelligent vehicle data mutual inspection method in one embodiment of the present application.
Fig. 4 shows a detailed flowchart of step S220 of the intelligent vehicle data mutual inspection method in an embodiment of the present application.
Fig. 5 shows a detailed flowchart of step S230 of the intelligent vehicle data mutual inspection method in an embodiment of the present application.
FIG. 6 shows a flow diagram of an intelligent vehicle data mutual inspection method in one embodiment of the present application.
FIG. 7 shows a flow diagram of an intelligent vehicle data mutual inspection method in one embodiment of the present application.
Fig. 8 shows a detailed flowchart of step S250 of the intelligent vehicle data mutual inspection method in an embodiment of the present application.
Fig. 9 shows a detailed flowchart of step S820 of the intelligent vehicle data mutual inspection method in an embodiment of the present application.
FIG. 10 shows a block diagram of an intelligent vehicle data cross-checking device according to one embodiment of the present application.
FIG. 11 illustrates a schematic structural diagram of a computer system suitable for use in implementing the electronic device of an embodiment of the present application.
Detailed Description
Example embodiments will now be described more fully with reference to the accompanying drawings. Example embodiments may, however, be embodied in many different forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of example embodiments to those skilled in the art.
Furthermore, the described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments. In the following description, numerous specific details are provided to give a thorough understanding of embodiments of the application. One skilled in the relevant art will recognize, however, that the subject matter of the present application can be practiced without one or more of the specific details, or with other methods, components, devices, steps, and so forth. In other instances, well-known methods, devices, implementations, or operations have not been shown or described in detail to avoid obscuring aspects of the application.
The block diagrams shown in the figures are functional entities only and do not necessarily correspond to physically separate entities. I.e. these functional entities may be implemented in the form of software, or in one or more hardware modules or integrated circuits, or in different networks and/or processor means and/or microcontroller means.
The flow charts shown in the drawings are merely illustrative and do not necessarily include all of the contents and operations/steps, nor do they necessarily have to be performed in the order described. For example, some operations/steps may be decomposed, and some operations/steps may be combined or partially combined, so that the actual execution sequence may be changed according to the actual situation.
Fig. 1 shows a schematic diagram of an exemplary system architecture to which the technical solution of the embodiments of the present application can be applied.
As shown in fig. 1, the system architecture may include a plurality of intelligent vehicles 101 and a server 102, where the intelligent vehicle 101 may be an intelligent vehicle in a car networking, and the server 102 may be a car networking cloud server for performing data interaction with the intelligent vehicle in the car networking, and the intelligent vehicle 101 and the server 102 are connected through a network.
It should be understood that the number of smart vehicles 101 and servers 102 in fig. 1 is merely illustrative. There may be any number of smart vehicles 101 and servers 102, as desired for implementation. For example, the server 102 may be a server cluster composed of a plurality of servers, and the like.
The smart vehicle 101 interacts with the server 102 over the network to receive or send messages or the like. For example, server 102 determines the total number of intelligent vehicles in the Internet of vehicles; acquiring a preset data mutual detection probability threshold value aiming at target data according to the target data to be uploaded, wherein the preset data mutual detection probability threshold value is the probability when the target data are acquired from at least two intelligent vehicles 101 in the internet of vehicles; the server 102 determines the number of target intelligent vehicles in the internet of vehicles according to the total number of the intelligent vehicles and the data mutual inspection probability threshold, the number of the target intelligent vehicles is used as the number of the intelligent vehicles when the probability that the server acquires the target data from at least two intelligent vehicles 101 in the internet of vehicles can reach the data mutual inspection probability threshold corresponding to the target data, the target intelligent vehicles are determined in the internet of vehicles according to the number of the target intelligent vehicles, the required target data is acquired based on the target intelligent vehicles to carry out the intelligent vehicle data mutual inspection, the server can carry out the intelligent vehicle data mutual inspection on the target data, the target data is prevented from being uploaded by more intelligent vehicles, the data redundancy uploading is reduced, the waste of network flow is reduced, and the utilization rate of the network flow in the internet of vehicles is improved.
It should be noted that the intelligent vehicle data mutual inspection method provided in the embodiment of the present application is generally executed by the server 102, and accordingly, the intelligent vehicle data mutual inspection device is generally disposed in the server 102. However, in other embodiments of the present application, the smart vehicle 101 may also have a similar function as the server, so as to execute the solution of the smart vehicle data mutual detection method provided in the embodiment of the present application.
The implementation details of the technical solution of the embodiment of the present application are set forth in detail below:
fig. 2 shows a flowchart of an intelligent vehicle data mutual-inspection method according to an embodiment of the present application, which may be performed by a server, which may be the server 102 shown in fig. 1. Referring to fig. 2, the intelligent vehicle data mutual inspection method at least includes steps S210 to S230, which are described in detail below.
In step S210, a total number of intelligent vehicles in the internet of vehicles and a threshold value of data mutual inspection probability are obtained.
The total number of the intelligent vehicles refers to the number of all intelligent vehicles which can upload target data in the internet of vehicles, and the target data is certain data required by the server to assist the vehicles in the internet of vehicles in driving decision, such as road viscosity, road curvature, road gradient, visibility and the like which reflect road conditions.
When the server determines the total number of the intelligent vehicles in the Internet of vehicles, the total number of the intelligent vehicles in the Internet of vehicles can be determined in a real-time monitoring mode. Specifically, a detection signal can be sent to the intelligent vehicles in the internet of vehicles, and a response signal of the intelligent vehicles for the detection signal is received, so that the total number of the intelligent vehicles in the internet of vehicles can be determined according to the received response signal. When the server determines the total number of the intelligent vehicles in the internet of vehicles, the server can also acquire the total number of the intelligent vehicles in the internet of vehicles from historical intelligent vehicle statistical data.
The probability that a certain type of target data is obtained from at least two intelligent vehicles in the internet of vehicles is preset by the data mutual detection probability threshold value, so that the possibility that the server can obtain the target data from the at least two intelligent vehicles in the internet of vehicles is high, the intelligent vehicle data mutual detection is convenient for the target data, the target data is prevented from being obtained from only one intelligent vehicle in the internet of vehicles, and the accuracy and the effectiveness of the target data obtained by the server are improved.
The data mutual detection probability threshold corresponding to the target data can be set according to the importance degree of the target data in the driving decision of the server, if the importance degree of the target data in the driving decision assistance of the server is high, the corresponding data mutual detection probability threshold can be set to be larger, otherwise, the corresponding data mutual detection probability threshold can be set to be smaller.
In step S220, the number of target intelligent vehicles in the internet of vehicles is determined according to the total number of intelligent vehicles and the data mutual inspection probability threshold.
After the total number of the intelligent vehicles in the internet of vehicles and the data mutual inspection probability threshold corresponding to the target data are obtained, in order to enable the probability that the server obtains the target data from at least two intelligent vehicles in the internet of vehicles to reach the data mutual inspection probability threshold corresponding to the target data, the number of the target intelligent vehicles in the internet of vehicles is determined according to the total number of the intelligent vehicles and the data mutual inspection probability threshold corresponding to the target data. The target intelligent vehicle number is determined by calculation, and the vehicle number can enable the probability that the server acquires the target data from at least two intelligent vehicles in the internet of vehicles to reach the corresponding data mutual detection probability threshold value.
Referring to fig. 3, fig. 3 shows a flowchart of an intelligent vehicle data mutual inspection method in an embodiment of the present application, and the step S220 may include steps S310 to S320, which are described in detail below.
In step S310, a first value range of the target number of intelligent vehicles in the internet of vehicles is determined according to the total number of intelligent vehicles and the data mutual inspection probability threshold.
When the number of the target data needing to be uploaded is within the first value range, the probability that the server acquires the target data of the category from at least two intelligent vehicles in the internet of vehicles can reach a data mutual inspection probability threshold value, so that the server can perform intelligent vehicle data mutual inspection on the target data of the category, the situation that the target data of the category is acquired from only one intelligent vehicle in the internet of vehicles is avoided, and the accuracy and the effectiveness of the server for acquiring the target data of the category are improved.
In one implementation of the present application, the method may be based on the inequality
Figure BDA0002264488270000071
Determining a first value range of the number of target intelligent vehicles in the Internet of vehicles, wherein a, b, c and d are all normal numbers larger than zero, n is the total number of the intelligent vehicles in the Internet of vehicles, p is a data mutual inspection probability threshold value corresponding to the target data of the category, and K is 1 A first value range for the determined number of target smart vehicles.
It is noted that K 1 Is a positive integerAnd the number of the target intelligent vehicles is less than the total number of the intelligent vehicles in the internet of vehicles. Specifically, a in the inequality may be set to 1,b to 8,c to 1,d to 2, that is, may be set according to the inequality
Figure BDA0002264488270000081
And determining a first value range of the number of target intelligent vehicles in the Internet of vehicles.
In step S320, the number of target smart vehicles is determined in the first value range, the number of target smart vehicles is positively correlated with the total number of smart vehicles, and the number of target smart vehicles is positively correlated with the data mutual detection probability threshold.
According to the inequality, the number of the target intelligent vehicles is positively correlated with the total number of the intelligent vehicles, namely, the larger the number of the target intelligent vehicles is, the larger the corresponding total number of the intelligent vehicles is; the number of the target intelligent vehicles is positively correlated with the data mutual detection probability threshold, namely the larger the data mutual detection probability threshold is, the larger the total number of the corresponding intelligent vehicles is. When the target intelligent vehicle number is determined according to the first value range of the target intelligent vehicle number in the internet of vehicles, the minimum value in the first value range can be used as the target intelligent vehicle number, the minimum value in the first value range is selected as the target intelligent vehicle number, the target intelligent vehicle number of the target data of the category which needs to be uploaded in the internet of vehicles can be reduced, the target data of the category which needs to be uploaded by more intelligent vehicles is avoided, the data redundancy uploading is reduced, and the waste of network flow is reduced.
Referring to fig. 4, fig. 4 is a detailed flowchart illustrating step S220 of the intelligent vehicle data mutual inspection method in an embodiment of the present application, where if the target data has a plurality of categories, the step S220 may include steps S410 to S430, and these steps are described in detail below.
In step S410, the maximum value of the data mutual detection probability threshold corresponding to the plurality of categories of the target data is determined.
In an embodiment of the application, when the types of target data needing to be uploaded in the internet of vehicles are multiple, in order that probabilities that the server acquires the multiple types of target data from at least two intelligent vehicles in the internet of vehicles can reach data mutual detection probability thresholds corresponding to the multiple types of target data, a maximum value of the data mutual detection probability thresholds corresponding to the target data of each type may be determined first, and the number of the target intelligent vehicles needing to send the target data of the types may be determined according to the maximum value of the data mutual detection probability thresholds.
In step S420, a second value range of the number of target intelligent vehicles in the internet of vehicles is determined according to the total number of the intelligent vehicles and the maximum value of the data mutual inspection probability threshold.
After the maximum value of the data mutual detection probability threshold value corresponding to each type of the target data is determined, determining a second value range of the number of the target intelligent vehicles in the internet of vehicles according to the total number of the intelligent vehicles and the maximum value of the data mutual detection probability threshold value, wherein the second value range is the determined range of the number of the target intelligent vehicles of the target data of the multiple types sent in the internet of vehicles. When the number of the target intelligent vehicles needing to send the target data of the multiple categories in the internet of vehicles is within a second value range, the probability that the server obtains the target data of the multiple categories from at least two intelligent vehicles in the internet of vehicles can reach the data mutual detection probability threshold corresponding to the target data of the multiple categories.
In this embodiment, the inequality may be based on
Figure BDA0002264488270000091
Determining a second value range of the number of target intelligent vehicles in the Internet of vehicles, wherein a, b, c and d are all normal numbers larger than zero, n is the total number of the intelligent vehicles in the Internet of vehicles and is a data mutual inspection probability threshold value p corresponding to preset ith category of target data i ，K 2 And the second value range is the second value range of the determined target intelligent vehicle number.
It is noted that K 2 Is a positive integer, and the number of target smart vehicles is less than the total number of smart vehicles in the internet of vehicles. Specifically, a in the inequality may be set to 1,b to 8,c to 1,d to 2, i.e., aCan be according to inequality
Figure BDA0002264488270000092
And determining a second value range of the target intelligent vehicle number in the Internet of vehicles.
In step S430, the number of target smart vehicles is determined in the second value range, the number of target smart vehicles is positively correlated with the total number of smart vehicles, and the number of target smart vehicles is positively correlated with the maximum value of the data mutual detection probability threshold.
According to the inequality, the number of the target intelligent vehicles is positively correlated with the total number of the intelligent vehicles, namely the larger the number of the target intelligent vehicles is, the larger the corresponding total number of the intelligent vehicles is; the number of the target intelligent vehicles is positively correlated with the data mutual detection probability threshold, namely the larger the data mutual detection probability threshold is, the larger the total number of the corresponding intelligent vehicles is. When the target intelligent vehicle number is determined according to the second value range of the target intelligent vehicle number in the internet of vehicles, the minimum value in the second value range can be used as the target intelligent vehicle number, the minimum value in the second value range is selected as the target intelligent vehicle number, the target intelligent vehicle number of target data of each category which needs to be uploaded in the internet of vehicles can be reduced, therefore, the target data of each category is prevented from being uploaded by more intelligent vehicles, when the target data category is more, network flow blockage of a server is avoided, and the stability of the server for providing driving decision service for the intelligent vehicles in the internet of vehicles is improved.
Still referring to fig. 2, in step S230, a target smart vehicle is determined in the internet of vehicles according to the number of target smart vehicles, and target data is obtained based on the target smart vehicle for smart vehicle data mutual check.
The server determines, in the internet of vehicles, target smart vehicles corresponding to the number of target smart vehicles according to the number of target smart vehicles, where the target smart vehicles serve as smart vehicles that transmit target data, and when the target data to be uploaded is of a plurality of categories, the target smart vehicles determined in the internet of vehicles for the target data of each category may be different or the same, and are not limited herein. The server sends notification information used for notifying the target intelligent vehicle of uploading the target data to the target intelligent vehicle, wherein the notification information comprises the category of the target data which needs to be uploaded by the target intelligent vehicle. After receiving the notification information, the target intelligent vehicle uploads the target data of the corresponding category to the server, so that the server can perform intelligent vehicle data mutual inspection based on the target data acquired from the target intelligent vehicle.
The server obtains the total number of the intelligent vehicles in the internet of vehicles, obtains the data mutual detection probability threshold corresponding to the required target data, and determines the target intelligent vehicle number in the internet of vehicles according to the total number of the intelligent vehicles and the data mutual detection probability threshold, wherein the target intelligent vehicle number is used as the vehicle number when the probability that the server obtains a certain type of target data from at least two intelligent vehicles in the internet of vehicles can reach the data mutual detection probability threshold corresponding to the type of target data; and finally, determining target intelligent vehicles in the Internet of vehicles according to the number of the target intelligent vehicles, and acquiring required target data based on the target intelligent vehicles to perform intelligent vehicle data mutual detection, so that the server can perform intelligent vehicle data mutual detection on the target data, and meanwhile, the target data is prevented from being uploaded by more intelligent vehicles, and therefore, the uploading of data redundancy is reduced, the waste of network flow is reduced, and the utilization rate of the network flow in the Internet of vehicles is improved.
In an embodiment of the present application, the step S230 may include: and determining the intelligent vehicles with the same number as the target intelligent vehicles from the internet of vehicles as the target intelligent vehicles.
After determining the number of target intelligent vehicles for uploading the target data, the server can select the intelligent vehicles with the number equal to that of the target intelligent vehicles from the internet of vehicles as the target intelligent vehicles.
In an embodiment of the application, the server may further select the intelligent vehicles with the same number as the target intelligent vehicles according to the current network conditions of the intelligent vehicles in the internet of vehicles, specifically, the server may first obtain the current network condition quality of the intelligent vehicles in the internet of vehicles, and select the intelligent vehicle with good network condition quality as the target intelligent vehicle according to the current network condition quality of the intelligent vehicles, and the influence of the network conditions on the server to obtain the target data may be effectively avoided through the above manner.
Referring to fig. 5, fig. 5 shows a specific flowchart of step S230 of the intelligent vehicle data mutual inspection method in an embodiment of the present application, where the step S230 may include step S510 to step S530, which are described in detail below.
In step S510, a first ratio is determined based on the target number of smart vehicles and the total number of smart vehicles.
After determining the number of target intelligent vehicles for uploading the target data, the server may determine a first ratio between the number of target intelligent vehicles and the total number of intelligent vehicles when determining the target intelligent vehicles in the internet of vehicles according to the number of target intelligent vehicles.
In step S520, a random number is assigned to the smart vehicles in the internet of vehicles.
The server allocates a random number to each intelligent vehicle in the internet of vehicles, and the random number can be specifically a random number from 0 to 1.
In step S530, the smart vehicle whose assigned random number is less than or equal to the first ratio is determined as the target smart vehicle.
The server determines the intelligent vehicles with the distributed random numbers smaller than or equal to the first ratio as target intelligent vehicles, and further randomly selects the intelligent vehicles corresponding to the target intelligent vehicle number from the internet of vehicles as the target intelligent vehicles according to the first ratio between the target intelligent vehicle number and the total number of the intelligent vehicles.
Referring to fig. 6, fig. 6 shows a flowchart of an intelligent vehicle data mutual inspection method in an embodiment of the present application, which further includes step S240, described in detail below.
In step S240, if the target data is received from a plurality of target smart vehicles, it is determined that the smart vehicle data mutual inspection is successful.
When the server carries out intelligent vehicle data mutual detection on certain type of target data, whether the type of target data is received from at least two target intelligent vehicles or not is determined, if the type of target data is received from at least two target intelligent vehicles, the fact that the intelligent vehicle data mutual detection on the type of target data is successful is determined, if the type of target data is not received from at least two target intelligent vehicles, the fact that the intelligent vehicle data mutual detection on the type of target data is failed is determined, the fact that the target data mutual detection on the target data can guarantee effectiveness and accuracy of the target data received by the server is achieved, therefore, accuracy of driving decisions of the server is improved, and safety of driving of intelligent vehicle links in the server-assisted vehicle networking is improved.
Referring to fig. 7, fig. 7 shows a flowchart of an intelligent vehicle data mutual inspection method in an embodiment of the present application, which further includes step S250, described in detail below.
In step S250, the target number of smart vehicles is updated according to the mutual inspection result of the mutual inspection of the smart vehicle data.
In one embodiment of the application, in the process of uploading data of each time of intelligent vehicles in the internet of vehicles, the server performs mutual intelligent vehicle data detection on certain category of target data uploaded by the intelligent vehicles in the internet of vehicles to obtain mutual detection results of the category of target data, and then determines whether the target data obtained in each time is uploaded by at least two target intelligent vehicles in the internet of vehicles, so that the validity of the obtained target data of the category is ensured.
The times of uploading the target data of the category to the intelligent vehicles in the Internet of vehicles reach a preset threshold value, the mutual detection success rate of mutual detection of the target data of the category on the target data of the intelligent vehicles is determined according to the mutual detection result, and then whether the number of the target intelligent vehicles needs to be updated is determined according to the mutual detection success rate, so that the mutual detection success rate of mutual detection of the target data of the category on the target data of the intelligent vehicles is improved, and therefore the effectiveness of the server for acquiring the target data of the category from at least two target intelligent vehicles in the Internet of vehicles is improved.
Referring to fig. 8, fig. 8 shows a detailed flowchart of step S250 of the intelligent vehicle data mutual inspection method in an embodiment of the present application, where the step S250 may include steps S810 to S820, which are described below.
In step S810, a second ratio between the number of times of successful mutual inspection and the total number of times of mutual inspection is determined according to the mutual inspection result of the intelligent vehicle data mutual inspection.
When the number of target intelligent vehicles of a certain category, which need to be uploaded in the internet of vehicles, is updated according to the mutual inspection result of the intelligent vehicle data mutual inspection on the target data of the certain category, the server firstly determines the times of successful mutual inspection according to the mutual inspection result of the intelligent vehicle data mutual inspection on the target data of the category, calculates a second ratio between the times of successful mutual inspection and the total times of mutual inspection, and the second ratio is used as the mutual inspection success rate of the intelligent vehicle data mutual inspection on the target data.
In step S820, if the second ratio is smaller than the data mutual detection probability threshold, the target number of smart vehicles is updated according to the second ratio.
The server updates the number of the target intelligent vehicles according to the difference, if the second ratio is greater than or equal to the data mutual detection probability threshold, the mutual detection success rate of the server for obtaining the target data from at least two target intelligent vehicles in the internet of vehicles reaches the data mutual detection probability threshold, and the number of the target intelligent vehicles can be kept unchanged, namely the number of the target intelligent vehicles needing to upload the target data of the category in the internet of vehicles does not need to be updated. If the second ratio is smaller than the data mutual detection probability threshold corresponding to the target data, it is indicated that the mutual detection success rate of the server for obtaining the target data of the category from at least two target intelligent vehicles in the internet of vehicles does not reach the data mutual detection probability threshold corresponding to the target data of the category, and the number of the target intelligent vehicles needing to upload the target data of the category in the internet of vehicles needs to be updated according to the second ratio, so as to improve the probability of the server for obtaining the target data of the category from at least two target intelligent vehicles in the internet of vehicles.
The scheme shown in the embodiment of fig. 8 can enable the server to adaptively adjust the number of target intelligent vehicles needing to upload the target data, so as to improve the effectiveness of the server in acquiring the target data from at least two target intelligent vehicles in the internet of vehicles.
Referring to fig. 9, fig. 9 shows a detailed flowchart of step S820 of the intelligent vehicle data mutual inspection method in an embodiment of the present application, where the step S820 may include steps S910 to S920, which are described below.
In step S910, a difference between the second ratio and the data cross-checking probability threshold is determined.
When the data mutual inspection probability threshold is updated according to the second ratio, the difference between the second ratio and the data mutual inspection probability threshold can be determined first, and then the number of the target intelligent vehicles can be updated conveniently according to the difference.
In step S920, the target number of smart vehicles is updated according to the difference.
When the number of target intelligent vehicles is updated according to the difference, the number of target intelligent vehicles needing to upload target data can be increased, so that the mutual detection success rate of the server for obtaining the target data from at least two target intelligent vehicles in the internet of vehicles can reach a data mutual detection probability threshold value corresponding to the target data; the method for increasing the number of target intelligent vehicles may specifically be to increase the number of target intelligent vehicles in a manner of increasing a preset number, for example, one intelligent vehicle is added on the basis of the original number of target intelligent vehicles, and of course, the number of target intelligent vehicles may also be increased according to a corresponding relationship between the difference and the number of intelligent vehicles that need to be increased, which is not limited herein.
The following describes embodiments of an apparatus of the present application, which may be used to implement the intelligent vehicle data mutual inspection method in the foregoing embodiments of the present application. For details that are not disclosed in the embodiments of the apparatus of the present application, please refer to the embodiments of the intelligent vehicle data mutual inspection method described above in the present application.
FIG. 10 shows a block diagram of an intelligent vehicle data cross-checking device according to one embodiment of the present application.
Referring to fig. 10, the intelligent vehicle data mutual inspection device 1000 according to an embodiment of the present application includes an obtaining unit 1010, an executing unit 1020, and a mutual inspection unit 1030.
The acquiring unit 1010 is used for acquiring the total number of intelligent vehicles in the internet of vehicles and a data mutual inspection probability threshold; an executing unit 1020, configured to determine the number of target intelligent vehicles in the internet of vehicles according to the total number of intelligent vehicles and the data mutual inspection probability threshold; and a mutual inspection unit 1030, configured to determine target smart vehicles in the internet of vehicles according to the number of the target smart vehicles, and obtain target data based on the target smart vehicles to perform mutual inspection on smart vehicle data.
In some embodiments of the present application, based on the foregoing solution, the mutual inspection unit 1030 is configured to: determining a first ratio based on the target number of smart vehicles and the total number of smart vehicles; distributing random numbers to intelligent vehicles in the Internet of vehicles; and determining the intelligent vehicle with the distributed random number smaller than or equal to the first ratio as the target intelligent vehicle.
In some embodiments of the present application, based on the foregoing solution, the mutual inspection unit 1030 is configured to: and determining the intelligent vehicles with the same number as the target intelligent vehicles from the Internet of vehicles as the target intelligent vehicles.
In some embodiments of the present application, based on the foregoing solution, the execution unit is configured 1020 as: determining a first value range of the number of target intelligent vehicles in the Internet of vehicles according to the total number of the intelligent vehicles and the data mutual inspection probability threshold; and determining the number of the target intelligent vehicles in the first value range, wherein the number of the target intelligent vehicles is positively correlated with the total number of the intelligent vehicles, and the number of the target intelligent vehicles is positively correlated with the data mutual detection probability threshold.
In some embodiments of the present application, based on the foregoing solution, if the target data has a plurality of categories, the executing unit 1020 is configured to: determining the maximum value of a data mutual detection probability threshold value corresponding to the categories of the target data; determining a second value range of the number of target intelligent vehicles in the internet of vehicles according to the total number of the intelligent vehicles and the maximum value of the data mutual inspection probability threshold; and determining the number of the target intelligent vehicles in the second value range, wherein the number of the target intelligent vehicles is positively correlated with the total number of the intelligent vehicles, and the number of the target intelligent vehicles is positively correlated with the maximum value of the data mutual inspection probability threshold.
In some embodiments of the present application, based on the foregoing solution, the intelligent vehicle data mutual inspection device further includes: and the updating unit is used for updating the target intelligent vehicle number according to the mutual detection result of the intelligent vehicle data mutual detection.
In some embodiments of the present application, based on the foregoing solution, the updating unit is configured to: according to the mutual inspection result of the intelligent vehicle data mutual inspection, determining a second ratio between the number of times of successful mutual inspection and the total number of times of mutual inspection; and if the second ratio is smaller than the data mutual inspection probability threshold, updating the number of the target intelligent vehicles according to the second ratio.
In some embodiments of the present application, based on the foregoing solution, the updating unit is configured to: determining a difference value between the second ratio and the data mutual detection probability threshold; and updating the target intelligent vehicle number according to the difference value.
FIG. 11 illustrates a schematic structural diagram of a computer system suitable for use in implementing the electronic device of an embodiment of the present application.
It should be noted that the computer system 1100 of the electronic device shown in fig. 11 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiments of the present application.
As shown in fig. 11, a computer system 1100 includes a Central Processing Unit (CPU) 1101, which can perform various appropriate actions and processes, such as performing the methods described in the above embodiments, according to a program stored in a Read-Only Memory (ROM) 1102 or a program loaded from a storage section 1108 into a Random Access Memory (RAM) 1103. In the RAM 1103, various programs and data necessary for system operation are also stored. The CPU 1101, ROM 1102, and RAM 1103 are connected to each other by a bus 1104. An Input/Output (I/O) interface 1105 is also connected to bus 1104.
The following components are connected to the I/O interface 1105: an input portion 1106 including a keyboard, mouse, and the like; an output section 1107 including a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), and the like, a speaker, and the like; a storage section 1108 including a hard disk and the like; and a communication section 1109 including a Network interface card such as a LAN (Local Area Network) card, a modem, or the like. The communication section 1109 performs communication processing via a network such as the internet. A driver 1110 is also connected to the I/O interface 1105 as necessary. A removable medium 1111, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like, is installed on the drive 1110 as necessary, so that a computer program read out therefrom is installed into the storage section 1108 as necessary.
In particular, according to embodiments of the application, the processes described above with reference to the flow diagrams may be implemented as computer software programs. For example, embodiments of the present application include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising a computer program for performing the method illustrated by the flow chart. In such an embodiment, the computer program may be downloaded and installed from a network through the communication portion 1109 and/or installed from the removable medium 1111. When the computer program is executed by a Central Processing Unit (CPU) 1101, various functions defined in the system of the present application are executed.
It should be noted that the computer readable media shown in the embodiments of the present application may be computer readable signal media or computer readable storage media or any combination of the two. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples of the computer readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a Read-Only Memory (ROM), an Erasable Programmable Read-Only Memory (EPROM), a flash Memory, an optical fiber, a portable Compact Disc Read-Only Memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the present application, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In this application, however, a computer readable signal medium may include a propagated data signal with a computer program embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. The computer program embodied on the computer readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wired, etc., or any suitable combination of the foregoing.
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present application. 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 or flowchart illustration, and combinations of blocks in the block diagrams or flowchart illustration, can be implemented by special purpose hardware-based systems that perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The units described in the embodiments of the present application may be implemented by software or hardware, and the described units may also be disposed in a processor. Wherein the names of the elements do not in some way constitute a limitation on the elements themselves.
As another aspect, the present application also provides a computer-readable medium, which may be contained in the electronic device described in the above embodiment; or may exist separately without being assembled into the electronic device. The computer readable medium carries one or more programs which, when executed by an electronic device, cause the electronic device to implement the method described in the above embodiments.
It should be noted that although in the above detailed description several modules or units of the device for action execution are mentioned, such a division is not mandatory. Indeed, the features and functionality of two or more modules or units described above may be embodied in one module or unit, according to embodiments of the application. Conversely, the features and functions of one module or unit described above may be further divided into embodiments by a plurality of modules or units.
Through the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein may be implemented by software, or by software in combination with necessary hardware. Therefore, the technical solution according to the embodiments of the present application can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a usb disk, a removable hard disk, etc.) or on a network, and includes several instructions to enable a computing device (which can be a personal computer, a server, a touch terminal, or a network device, etc.) to execute the method according to the embodiments of the present application.
Other embodiments of the present application will be apparent to those skilled in the art from consideration of the specification and practice of the embodiments disclosed herein. This application is intended to cover any variations, uses, or adaptations of the invention following, in general, the principles of the application and including such departures from the present disclosure as come within known or customary practice within the art to which the invention pertains.
It will be understood that the present application is not limited to the precise arrangements described above and shown in the drawings and that various modifications and changes may be made without departing from the scope thereof. The scope of the application is limited only by the appended claims.

Claims (10)

1. An intelligent vehicle data mutual inspection method is characterized by comprising the following steps:
acquiring the total number of intelligent vehicles in the internet of vehicles and a data mutual inspection probability threshold, wherein the data mutual inspection probability threshold is the probability when target data are acquired from at least two intelligent vehicles in the internet of vehicles;
determining the number of target intelligent vehicles in the Internet of vehicles according to the total number of the intelligent vehicles and the data mutual inspection probability threshold;
determining a target smart vehicle in the Internet of vehicles according to the number of target smart vehicles, an
And acquiring the target data based on the target intelligent vehicle to perform intelligent vehicle data mutual inspection.
2. The intelligent vehicle data mutual inspection method according to claim 1, wherein the determining target intelligent vehicles in the internet of vehicles according to the target intelligent vehicle number comprises:
determining a first ratio based on the target number of smart vehicles and the total number of smart vehicles;
distributing a random number to the intelligent vehicles in the Internet of vehicles;
and determining the intelligent vehicle with the distributed random number smaller than or equal to the first ratio as the target intelligent vehicle.
3. The intelligent vehicle data mutual inspection method according to claim 1, wherein the determining target intelligent vehicles in the internet of vehicles according to the target intelligent vehicle number comprises:
and determining the intelligent vehicles with the same number as the target intelligent vehicles from the Internet of vehicles as the target intelligent vehicles.
4. The intelligent vehicle data mutual inspection method according to claim 1, wherein the determining the number of target intelligent vehicles in the internet of vehicles according to the total number of intelligent vehicles and the data mutual inspection probability threshold value comprises:
determining a first value range of the number of target intelligent vehicles in the Internet of vehicles according to the total number of the intelligent vehicles and the data mutual inspection probability threshold;
and determining the number of the target intelligent vehicles in the first value range, wherein the number of the target intelligent vehicles is positively correlated with the total number of the intelligent vehicles, and the number of the target intelligent vehicles is positively correlated with the data mutual detection probability threshold.
5. The intelligent vehicle data mutual inspection method according to claim 1, wherein if the types of the target data are multiple, the determining the number of target intelligent vehicles in the internet of vehicles according to the total number of intelligent vehicles and the data mutual inspection probability threshold value comprises:
determining the maximum value of a data mutual detection probability threshold value corresponding to the categories of the target data;
determining a second value range of the number of target intelligent vehicles in the internet of vehicles according to the total number of the intelligent vehicles and the maximum value of the data mutual inspection probability threshold;
and determining the number of the target intelligent vehicles in the second value range, wherein the number of the target intelligent vehicles is positively correlated with the total number of the intelligent vehicles, and the number of the target intelligent vehicles is positively correlated with the maximum value of the data mutual inspection probability threshold.
6. The intelligent vehicle data mutual inspection method according to claim 1, further comprising:
and if the target data are received from the plurality of target intelligent vehicles, determining that the mutual inspection of the intelligent vehicle data is successful.
7. The intelligent vehicle data mutual inspection method according to claim 1, further comprising:
and updating the target intelligent vehicle number according to the mutual detection result of the intelligent vehicle data mutual detection.
8. The intelligent vehicle data mutual inspection method according to claim 7, wherein the updating of the data target intelligent vehicle number according to the mutual inspection result of the intelligent vehicle data mutual inspection comprises:
according to the mutual inspection result of the intelligent vehicle data mutual inspection, determining a second ratio between the number of times of successful mutual inspection and the total number of times of mutual inspection;
and if the second ratio is smaller than the data mutual inspection probability threshold, updating the number of the target intelligent vehicles according to the second ratio.
9. The intelligent vehicle data mutual inspection method according to claim 8, wherein the updating the target number of intelligent vehicles according to the second ratio comprises:
determining a difference value between the second ratio and the data mutual detection probability threshold;
and updating the target intelligent vehicle number according to the difference value.
10. The utility model provides an intelligent vehicle data mutual inspection device which characterized in that includes:
the device comprises an acquisition unit, a processing unit and a processing unit, wherein the acquisition unit is used for acquiring the total number of intelligent vehicles in the internet of vehicles and a data mutual inspection probability threshold, and the data mutual inspection probability threshold is the probability when target data are acquired from at least two intelligent vehicles in the internet of vehicles;
the execution unit is used for determining the number of target intelligent vehicles in the internet of vehicles according to the total number of the intelligent vehicles and the data mutual inspection probability threshold;
and the mutual inspection unit is used for determining target intelligent vehicles in the internet of vehicles according to the number of the target intelligent vehicles and acquiring the target data based on the target intelligent vehicles so as to perform mutual inspection of intelligent vehicle data.
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