Internet of vehicles equipment information management method
By collecting vehicle position and angle information in the Internet of Vehicles (IoV), creating tags and optimizing data, correcting vehicle speed and direction, and improving the GPSR algorithm to select the optimal relay node, the communication instability problem of the IoV relay selection algorithm under dynamic vehicle movement is solved, and more reliable information transmission is achieved.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- HUBEI CHEHUBAO INFORMATION TECH CO LTD
- Filing Date
- 2023-12-26
- Publication Date
- 2026-04-21
AI Technical Summary
Existing vehicle-to-everything (V2X) relay selection algorithms, such as the GPSR algorithm, cannot effectively guarantee the reliability of information transmission when the vehicle's movement changes, leading to deviations in relay node selection and affecting communication stability.
By collecting vehicle location information and movement angle, calculating vehicle speed, creating data tags, optimizing error data through weighted decision-making, correcting the speed and direction information of vehicle nodes, and using an improved GPSR algorithm to select the optimal relay node for information transmission.
It improves the reliability and stability of vehicle-to-vehicle communication, reduces errors in the management system, and ensures the accuracy of information transmission.
Smart Images
Figure CN121908231A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of artificial intelligence technology, specifically a method for managing information in vehicle networking devices. Background Technology
[0002] In the field of vehicle networking, multiple relays need to be selected for transmission in order to achieve reliable communication between vehicles. The optimization of relay selection has also attracted much attention. Scholars have proposed many solutions for different transmission scenarios, including intersections, crossroads, and curves.
[0003] In 2000, a paper proposed a greedy perimeter stateless routing algorithm (GPSR) to solve the relay selection problem in vehicle-to-everything (V2X) networks. However, this geolocation-based greedy perimeter stateless routing algorithm only relies on distance factors to make decisions, that is, it selects the node with the farthest distance within the communication radius for transmission.
[0004] In reality, because vehicle movement is dynamic and its direction and speed are not consistent, two vehicles traveling in opposite directions, due to their higher relative speed, are more likely to exceed the communication radius, leading to information transmission interruption and a shorter communication time. Similarly, vehicles traveling in the same direction have better directional conditions and can maintain communication for a longer time. We call this ability to maintain a link connection link stability.
[0005] To achieve simple and fast information transmission between two vehicles, vehicle relays are needed to establish the optimal link. Most improved GPSR algorithms consider the stability and distance of the link and seek the optimal node for information transmission. Weighted functions are often used to consider decision information parameters from multiple aspects, taking into account parameters such as distance, link stability, and bandwidth.
[0006] However, we cannot guarantee that the vehicle position and speed information we obtain is completely accurate. Deviations can affect the decision-making algorithm and may lead to deviations in the selection of relay nodes. Summary of the Invention
[0007] To address the aforementioned technical problems, the present invention aims to provide a method for managing information of vehicle networking devices. This method can analyze vehicle trajectory data by acquiring vehicle node coordinate information, further optimize error data, and ensure the reliability of communication between vehicles.
[0008] This method targets the improved GPSR algorithm, providing an optimized data processing approach for the cloud or device sending end. On the one hand, it establishes storage tags for convenient management and statistics, and on the other hand, it performs real-time correction of data with large errors, thus providing a certain guarantee for information link transmission.
[0009] This invention provides a method for managing information of vehicle networking devices, comprising the following steps:
[0010] S1. Collect vehicle location information and movement angle;
[0011] S2. Calculate the vehicle's speed V based on its position information and angle of movement;
[0012] S3. Create data tags and standardize the information for each vehicle;
[0013] S4. Calculate the data reliability Q of vehicle speed in a series of consecutively labeled data;
[0014] S5. Set a threshold u. When the data reliability Q is greater than the set threshold u, modify the speed V in the corresponding label. i+1 ;
[0015] S6. Update tags;
[0016] S7. The optimal vehicle node is determined through weighted decision-making, and data transmission is completed.
[0017] Preferably, step S1 specifically includes:
[0018] S11 establishes a coordinate system;
[0019] S12 continuously collects the coordinate information of this vehicle on the x-axis and y-axis and maps them one by one to obtain the coordinate information that changes over time. It records them in the form of arrays, denoted as X1, X2, X3… and Y1, Y2, Y3…;
[0020] S13 obtains the vehicle's movement angle. The current vehicle's movement direction is an important parameter when calculating and deciding on the vehicle-to-everything (V2X) communication link.
[0021] S14 We can calculate the direction of vehicle movement using continuous vehicle coordinates and represent it in the coordinate system as the movement angle θ;
[0022] The angles obtained from S15 are also represented in the form of a sequence, denoted as θ1, θ2, θ3...
[0023] Preferably, step S2 specifically includes:
[0024] Since S21 has obtained the vehicle coordinate information at continuous time points, the speed V of the vehicle can be calculated.
[0025] The velocity magnitude V obtained by S22 is a parameter derived from coordinate changes. During continuous acquisition, since the velocity does not change abruptly, the velocity information is processed by a weighted average method.
[0026] S23 sets up a window to hold 3 data points. The obtained vehicle speeds are averaged by moving the window, resulting in vehicle speeds V1, V2, V3, ...
[0027] Preferably, step S3 specifically includes:
[0028] S31 calculates the optimized parameter U1 to evaluate the link stability between each vehicle node and the sending vehicle node. Specifically,
[0029]
[0030] Where R is the maximum communication radius of the vehicle node; L is the distance between two vehicle nodes, which is calculated using two coordinate Pythagorean theorems; Vf is the velocity vector of the sending vehicle node, Vl is the velocity vector of adjacent vehicle nodes within the communication radius R, and the denominator is the sum of the vectors;
[0031] S32 obtains the bandwidth between each vehicle node, denoted as optimization parameter U2;
[0032] S33 creates a tag for each vehicle node.
[0033] Vi Xi Yi Ziθi U1 U2
[0034] Where Vi represents the speed of vehicle i in the coordinate system, and Xi, Yi, and Zi represent the coordinate information of the vehicle, with Zi being 0.
[0035] S35 stores the verified tags.
[0036] Preferably, step S4 specifically includes:
[0037] For continuous data for each vehicle, the data reliability Q is calculated. Specifically, since vehicle speed is unstable, the vehicle speed obtained through the linear distance of coordinates and the sampling interval is relatively ideal when the vehicle's movement angle θ remains constant. However, when the vehicle rotates, the change in its movement angle θ will affect the actual distance traveled, resulting in the actual vehicle speed being greater than the calculated speed. Therefore, whether to correct the vehicle speed is evaluated using a data reliability Q.
[0038]
[0039] Among them, Q i+1 It is the reliability of the corresponding coordinate data of the vehicle at time i+1, as shown in the formula. This involves jointly considering the information at time i and time i-1, using the difference in velocity between the two as the denominator and the sum of the velocities as the numerator. This represents the reciprocal of the degree of velocity change at these two times, further supplemented by abs(cosθ). i-cosθ i-1 The correction is made using this formula; the greater the angle change, the stronger the amplification effect on the overall function. Similarly, the formula... It involves jointly considering the information at time i and the information at time i+1.
[0040] Preferably, in step S5, V i+1 for:
[0041]
[0042] Among them, V i+1 This refers to the magnitude of the velocity at time i+1, θ i+1 It is the angle corresponding to time i+1. Since the current speed is no longer reliable, the average speed of the previous two times is used as the basis for the speed at time i+1 in the formula. Then, the same averaging method is used to process the angle, and the angle processing only considers the previous time.
[0043] Preferably, step S6 specifically includes:
[0044] S61 uses the newly acquired V i+1 Replace the old V in the label i+1 ;
[0045] S62 sets a new threshold r, where r is defined as velocity V. i+1 The percentage change when the velocity V i+1 When the change is significant, directly adjust and move the angle θ to be the same as the previous moment.
[0046] θ i+1 =θ i It remains the same as the previous moment.
[0047] Preferably, step S7 specifically includes:
[0048] Using the updated label data, recalculate the optimized parameters U1 and U2 for that label.
[0049] The optimal vehicle node is determined through weighted decision-making, and data transmission is completed. Specifically,
[0050]
[0051] Here, the distance parameter L is the distance between two vehicle nodes, which is calculated using two Pythagorean theorems. All three parameters are assigned equal weights to form the final weighted function. This function is calculated by iterating through all vehicle nodes within each vehicle's communication radius, generating a corresponding function for each vehicle node. Select function The one with the largest value is the optimal choice.
[0052] Compared with the prior art, the beneficial effects of the present invention are:
[0053] 1. Use tags to store information for each vehicle, making information management standardized and facilitating optimization and modification;
[0054] 2. By performing sequence analysis on the stored information, a validity evaluation Q was obtained. Q was then used to determine whether the data needed correction. This involved using the vehicle node's location information to estimate the vehicle's speed, further correcting the vehicle's direction information. This reduced significant errors in the management system and corrected erroneous information. Attached Figure Description
[0055] Picture 1 This is a flowchart illustrating the present invention. Detailed Implementation
[0056] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this application. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.
[0057] like Picture 1 As shown, the present invention provides a method for managing information of vehicle networking devices, including the following steps:
[0058] Step 1: The device acquires the vehicle's geographical location information, analyzes it, and creates a tag.
[0059] 1. Collect vehicle location information. Under the conditions of vehicle-to-everything (V2X) information transmission, we often need vehicle location information to determine the vehicle's location and the lifespan of the link, and then make decisions on the transmission link.
[0060] a) Vehicle location information can be collected in two dimensions or three dimensions. On relatively flat roads, three-dimensional information is usually converted into two-dimensional information by planarization. This embodiment is described in two dimensions.
[0061] b) Establish a coordinate system.
[0062] c) Continuously collect the coordinate information of this vehicle on the x-axis and y-axis and match them one by one to obtain the coordinate information that changes over time. Record it in the form of an array, denoted as X1, X2, X3… and Y1, Y2, Y3….
[0063] 2. Obtain the vehicle's movement angle. The current direction of the vehicle's movement is an important parameter when calculating and deciding on the vehicle-to-everything (V2X) communication link.
[0064] a) We can calculate the direction of vehicle movement using continuous vehicle coordinates, and represent it in a coordinate system as the movement angle θ.
[0065] b) The obtained angles are also represented in the form of a sequence, denoted as θ1, θ2, θ3…
[0066] 3. Calculate the vehicle's speed V and optimize it.
[0067] a) Since the vehicle coordinate information at consecutive time points is obtained, it can be approximated that the vehicle moves in a straight line between consecutive time points, and the speed V of the vehicle can be directly calculated.
[0068] b) The obtained velocity magnitude V is only a parameter derived from the coordinate change. During continuous acquisition, since the velocity will not change abruptly, the velocity information can be processed by weighted averaging.
[0069] c) Specifically, set up a window to hold 3 data points, and average the obtained vehicle speeds by moving the window to get vehicle speeds V1, V2, V3...
[0070] 4. Create data tags and standardize the information for each vehicle.
[0071] a) When we continuously upload vehicle information to the cloud or other vehicle processing nodes, we need to calculate decision parameters for each vehicle, which are collectively referred to as optimization parameters here.
[0072] b) Calculate the optimized parameter U1 to evaluate the link stability between each vehicle node and the sending vehicle node. There is no fixed calculation formula; various link stability solutions have emerged in different literatures. In this embodiment, the improved GPSR-D algorithm can be used to determine link stability. Specifically, Where R is the maximum communication radius of the vehicle node; L is the distance between two vehicle nodes, which is calculated using two coordinate Pythagorean theorems; Vf is the velocity vector of the sending vehicle node, Vl is the velocity vector of adjacent vehicle nodes within the communication radius R, and the denominator is the sum of the vectors.
[0073] c) Obtain the bandwidth between each vehicle node, denoted as optimization parameter U2.
[0074] d) There are multiple algorithms for calculating link stability, and any algorithm can be selected.
[0075] e) Create a label for each vehicle node.
[0076] Vi Xi Yi Zi θi U1 U2
[0077] f) Vi represents the speed of vehicle i in the coordinate system. The vehicle networking device is located in each vehicle and must have the ability to acquire and share location information. Xi, Yi, and Zi represent the coordinate information of the corresponding vehicle. Since the scenario in this embodiment is planar, Zi can be regarded as 0.
[0078] g) Verified tags can be stored.
[0079] Step 2: The management system optimizes the tags for each time moment to reduce errors in vehicle information and improve communication quality.
[0080] 1. Calculate the data reliability Q of vehicle speed in a series of continuously labeled data.
[0081] a) For each vehicle's continuous data, the data reliability Q must be calculated. Since vehicle speed is unstable, our calculation method only obtains the vehicle speed through the coordinate straight-line distance and sampling interval. This is relatively ideal when the vehicle's movement angle θ remains constant. However, when the vehicle rotates, the change in its movement angle θ will affect the actual distance the vehicle travels, thus causing the actual vehicle speed to be greater than the calculated vehicle speed.
[0082] b) Therefore, whether or not to correct the vehicle speed is determined by a data confidence level Q.
[0083] c) Among them, Q i+1 This represents the reliability of the vehicle's coordinate data at time i+1. (Formula) This involves jointly considering the information from time i and time i-1, using the difference in velocity between the two as the denominator and the sum of their velocities as the numerator, signifying the reciprocal of the degree of velocity change at these two times. This is further supplemented by abs(cosθ). i -cosθ i-1 The correction is made using this formula; the greater the angle change, the stronger the amplification effect on the overall function. Similarly, the formula... It involves jointly considering the information at time i and the information at time i+1.
[0084] d) The data reliability Q established in this way is not only related to the speed magnitudes of the first two moments, but also takes into account the degree of angle change. When the angle increases, the value of the data reliability Q also changes. Generally speaking, the larger the value of Q, the less reliable the evaluated vehicle speed V is, and the greater the deviation. Whether the current vehicle speed V needs optimization can be determined by judging the degree of change in data reliability Q.
[0085] 2. Set a threshold u. When the data confidence level Q is greater than the set threshold u, modify the speed V in the corresponding label. i+1 .
[0086] a) The speed correction needs to consider the speeds of the previous two moments and the vehicle's movement angles at the current and previous moments. Due to the uncertainty of vehicle rotation, the movement angle θ at earlier moments is not considered.
[0087] b) Among them, V i+1 This refers to the magnitude of the velocity at time i+1, θ i+1 It is the angle corresponding to time i+1. Since the current speed is no longer reliable, the average speed of the previous two times is used as the basis for the speed at time i+1 in the formula. Then, the same averaging method is used to process the angle, and the angle processing only considers the previous time.
[0088] c) This process describes the velocity V i+1 The recalculation, this process is for the velocity V i+1 The optimization does not consider the speed at this moment, because according to the judgment of data reliability Q, the geographical location at this moment may have deviated.
[0089] 3. Further update the tag information.
[0090] a) Use the newly acquired V i+1 Replace the old V in the label i+1 .
[0091] b) Simply completing this replacement is not enough; optimization of the movement angle θ is also required.
[0092] c) Set a new threshold r, where r is defined as velocity V. i+1 The percentage change when the velocity V i+1 When the change is significant, directly adjust and move the angle θ to be the same as the previous moment.
[0093] d)θ i+1 =θ i It remains the same as the previous moment.
[0094] 4. Update the tags. This completes the optimization.
[0095] 5. Using the optimized label data, recalculate the optimized parameters U1 and U2 for the label.
[0096] 6. Further, the optimal vehicle node is determined through weighted decision-making to complete data transmission. This embodiment's weighted decision-making utilizes the link stability optimization parameter U1 and the bandwidth optimization parameter U2, along with the distance parameter L from the GPSR algorithm (GreedyPerimeter Stateless Routing for Wireless Networks). Distance parameter L is the distance between two vehicle nodes, calculated using two Pythagorean theorems. These three parameters are given equal weights to form the final weighted function. The formula is: These three parameters are frequently used in literature on improvements to the GPSR algorithm. They are calculated by iterating through all vehicle nodes within the communication radius of each vehicle, and a function is generated for each vehicle node. Select function The one with the largest value is the optimal choice.
[0097] In this embodiment, on the one hand, information for each vehicle is stored in the form of tags, making information management standardized and facilitating optimization and modification. On the other hand, by performing sequence analysis on the stored information, a validity evaluation Q is obtained. Q is then used to determine whether the data needs correction. This allows for the estimation of the vehicle's speed using the vehicle node's location information, further correcting the vehicle's direction information. This reduces significant errors in the management system and corrects erroneous information.
[0098] The above embodiments are only used to illustrate the technical methods of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical methods of the present invention without departing from the spirit and scope of the technical methods of the present invention.
Claims
1. A method for managing information in vehicle networking devices, characterized in that, Includes the following steps: S1. Collect vehicle location information and movement angle; S2. Calculate the vehicle's speed V based on its position information and angle of movement; S3. Create data tags and standardize the information for each vehicle; S4. Calculate the data reliability Q of vehicle speed in a series of consecutively labeled data; S5. Set a threshold u. When the data reliability Q is greater than the set threshold u, modify the speed V in the corresponding label. i+1 ; S6. Update tags; S7. The optimal vehicle node is determined through weighted decision-making, and data transmission is completed.
2. The method for managing information of vehicle networking devices according to claim 1, characterized in that, Step S1 is as follows: S11 establishes a coordinate system; S12 continuously collects the coordinate information of this vehicle on the x-axis and y-axis and maps them one by one to obtain the coordinate information that changes over time. It records them in the form of arrays, denoted as X1, X2, X3… and Y1, Y2, Y3…; S13 obtains the vehicle's movement angle. The current vehicle's movement direction is an important parameter when calculating and deciding on the vehicle-to-everything (V2X) communication link. S14 We can calculate the direction of vehicle movement using continuous vehicle coordinates and represent it in the coordinate system as the movement angle θ; The angles obtained from S15 are also represented in the form of a sequence, denoted as θ1, θ2, θ3...
3. The method for managing information of vehicle networking devices according to claim 2, characterized in that, Step S2 is as follows: Since S21 has obtained the vehicle coordinate information at continuous time points, the speed V of the vehicle can be calculated. The velocity magnitude V obtained by S22 is a parameter derived from coordinate changes. During continuous acquisition, since the velocity does not change abruptly, the velocity information is processed by a weighted average method. S23 sets up a window to hold 3 data points. The obtained vehicle speeds are averaged by moving the window, resulting in vehicle speeds V1, V2, V3, ...
4. The method for managing information of vehicle networking devices according to claim 3, characterized in that, Step S3 is as follows: S31 calculates the optimized parameter U1 to evaluate the link stability between each vehicle node and the sending vehicle node. Specifically, Where R is the maximum communication radius of the vehicle node; L is the distance between two vehicle nodes, which is calculated using two coordinate Pythagorean theorems; Vf is the velocity vector of the sending vehicle node, Vl is the velocity vector of adjacent vehicle nodes within the communication radius R, and the denominator is the sum of the vectors; S32 obtains the bandwidth between each vehicle node, denoted as optimization parameter U2; S33 creates a tag for each vehicle node. Vi Xi Yi Zi θi U1 U2 Where Vi represents the speed of vehicle i in the coordinate system, and Xi, Yi, and Zi represent the coordinate information of the vehicle, with Zi being 0. S35 stores the verified tags.
5. The method for managing information of vehicle networking devices according to claim 4, characterized in that, Step S4 is as follows: For continuous data for each vehicle, the data reliability Q is calculated. Specifically, since vehicle speed is unstable, the vehicle speed obtained through the linear distance of coordinates and the sampling interval is relatively ideal when the vehicle's movement angle θ remains constant. However, when the vehicle rotates, the change in its movement angle θ will affect the actual distance traveled, resulting in the actual vehicle speed being greater than the calculated speed. Therefore, whether to correct the vehicle speed is evaluated using a data reliability Q. Among them, Q i+1 It is the reliability of the corresponding coordinate data of the vehicle at time i+1, as shown in the formula. This involves jointly considering the information at time i and time i-1, using the difference in velocity between the two as the denominator and the sum of the velocities as the numerator. This represents the reciprocal of the degree of velocity change at these two times, further supplemented by abs(cosθ). i -cosθ i-1 The correction is made using this formula; the greater the angle change, the stronger the amplification effect on the overall function. Similarly, the formula... It involves jointly considering the information at time i and the information at time i+1.
6. The method for managing information of vehicle networking devices according to claim 5, characterized in that, In step S5, V i+1 for: Among them, V i+1 This refers to the magnitude of the velocity at time i+1, θ i+1 It is the angle corresponding to time i+1. Since the current speed is no longer reliable, the average speed of the previous two times is used as the basis for the speed at time i+1 in the formula. Then, the same averaging method is used to process the angle, and the angle processing only considers the previous time.
7. The method for managing information of vehicle networking devices according to claim 6, characterized in that, Step S6 is as follows: S61 uses the newly acquired V i+1 Replace the old V in the label i+1 ; S62 sets a new threshold r, where r is defined as velocity V. i+1 The percentage change when the velocity V i+1 When the change is significant, directly adjust and move the angle θ to be the same as the previous moment. θ i+1 =θ i It remains the same as the previous moment.
8. The method for managing information of vehicle networking devices according to claim 7, characterized in that, Step S7 is as follows: Using the updated label data, recalculate the optimized parameters U1 and U2 for that label. The optimal vehicle node is determined through weighted decision-making, and data transmission is completed. Specifically, Here, the distance parameter L is the distance between two vehicle nodes, which is calculated using two Pythagorean theorems. All three parameters are assigned equal weights to form the final weighted function. This function is calculated by iterating through all vehicle nodes within each vehicle's communication radius, generating a corresponding function for each vehicle node. Select function The one with the largest value is the optimal choice.