Intelligent networked vehicle data transmission method and system

Abstract

The present invention relates to the technical field of vehicle network data transmission, and specifically to a method and system for data transmission of intelligent networked vehicles. The present invention first analyzes the frequency of interaction between users and edge servers, and screens the users who need to set up mobile servers; then analyzes the similarity between any two users, and groups the users; then, according to the distribution of the number of users in the user group, combined with the matching index, sets the mobile server that matches the user group and plans the mobile path of the mobile server; then, by real-time analysis of the differences in the mobile trends of all users in each user group, adjusts the distribution of users in each user group; and replaces the mobile server according to the remaining power and remaining memory. The method and system of the present invention use mobile servers to assist edge servers in processing vehicle data in each user group, thereby balancing the load of servers in the network and ensuring efficient and stable data transmission.

Description

Technical Field

[0001] The present invention relates to the technical field of vehicle networking data transmission, and in particular to a method and system for data transmission of an intelligent networked vehicle. Background Art

[0002] The data transmission method of intelligent connected vehicles usually uses edge computing technology to transmit and process the real-time data of vehicles. That is, the edge server set up on the roadside processes the data generated by closer vehicles, thereby shortening the transmission distance of vehicle data, balancing the load, and improving the transmission efficiency of vehicle data in the local area network.

[0003] However, the actual application scenarios are often more complicated. When an emergency occurs on a certain road section in the urban Internet of Vehicles system, it may cause a sharp increase in traffic volume on that road section and its adjacent sections. At this time, the method of installing edge servers in fixed locations is difficult to efficiently handle the sudden increase in data. In order to solve the problem of a sharp increase in data within the LAN, it is usually necessary to use drone-mounted mobile servers to balance the vehicle data load within the data-intensive LAN. However, due to the limited endurance of drones, when the data within the LAN increases rapidly, the existing technology cannot accurately distribute the load of each server in the current LAN. Summary of the invention

[0004] In order to solve the above technical problems, the purpose of the present invention is to provide a data transmission method and system for intelligent connected vehicles.

[0005] According to a first aspect of an embodiment of the present invention, a method for transmitting data of an intelligent connected vehicle is provided, and the technical solution adopted is specifically as follows:

[0006] Monitor user mobility trends and interaction data with edge servers, as well as the remaining power and memory of mobile servers;

[0007] Based on the interaction data, analyzing the interaction frequency between the user and the edge server, and screening the users who need to set up the mobile server;

[0008] Based on the movement trend and the interaction frequency, the similarity between any two of the demand users is analyzed to obtain a matching index between any two of the demand users, and the demand users are grouped to obtain a plurality of user groups;

[0009] Analyze the number distribution of demand users in the user group, determine the number of mobile servers required by the user group in combination with the matching index, and plan the mobile path of the mobile server;

[0010] Based on the movement trend, the movement trend difference index of all the demand users in each of the user groups is analyzed in real time, and the distribution of the demand users in each of the user groups is adjusted;

[0011] The replacement index of the mobile server is analyzed according to the remaining power and the remaining memory, and the mobile server is replaced.

[0012] In some embodiments of the present invention, based on the interaction data, analyzing the interaction frequency between the user and the edge server, and screening the users who need to set up the mobile server includes:

[0013] The number of visits to a single edge server is placed in the sample space in chronological order to obtain a visit-time curve, and the APCA algorithm is used to segment the visit-time curve to obtain multiple time periods;

[0014] Based on the interaction data, analyzing the average number of visits to the edge server by the user in a period of time, the minimum number of visits to the current edge server, and the average number of visits to all edge servers in the network, to obtain the frequency of interaction between the user and the edge server;

[0015] An interaction frequency threshold is set, and users who need to set up a mobile server are screened according to the interaction frequency.

[0016] In some embodiments of the present invention, based on the movement trend and the interaction frequency, the similarity between any two of the demand users is analyzed to obtain a matching index between any two of the demand users, and the demand users are grouped to obtain a plurality of user groups, including:

[0017] Based on the movement trend, analyzing the movement trend difference between any two of the demand users to obtain the movement trend similarity;

[0018] Based on the interaction frequency, analyzing the difference in interaction frequency between any two of the demand users to obtain interaction frequency similarity;

[0019] Obtaining a matching index between any two of the demand users according to the movement trend similarity and the interaction frequency similarity;

[0020] A matching threshold is set, and the demand users are grouped according to the matching index to obtain a plurality of user groups.

[0021] In some embodiments of the present invention, based on the movement trend, analyzing the movement trend difference between any two of the demand users to obtain the movement trend similarity includes:

[0022] Based on the movement trend, analyzing the movement direction difference and displacement difference between any two of the demand users, and analyzing the average displacement of all demand users in the network, to obtain the movement trend difference between any two of the demand users;

[0023] According to the movement trend difference and in combination with the Euclidean distance between any two of the demand users, the movement trend similarity between any two of the demand users is obtained.

[0024] In some embodiments of the present invention, analyzing the distribution of the number of demand users in the user group and determining the number of mobile servers required by the user group in combination with the matching index includes:

[0025] Analyze the number of demand users in the user group and the average number of demand users in all the user groups, and combine with the total number of all demand users in the network to obtain the distribution of the number of demand users in the user group;

[0026] Based on the matching index, obtaining a minimum matching index between any two of the demanding users in the user group;

[0027] According to the quantity distribution and in combination with the minimum matching index, obtaining a demand index of the user group for the mobile server;

[0028] The number of mobile servers required by the user group is determined according to the demand index and the number of edge servers in the network.

[0029] In some embodiments of the present invention, planning the moving path of the mobile server includes:

[0030] Allocating mobile servers to the user group according to the required quantity;

[0031] For each mobile server, a moving path of the mobile server is planned according to the sum vector of the moving directions of all the demand users accessing the mobile server and the displacement mean.

[0032] In some embodiments of the present invention, based on the movement trend, the movement trend difference index of all the demand users in each of the user groups is analyzed in real time, and the distribution of the demand users in each of the user groups is adjusted, including:

[0033] Based on the movement trend, analyzing the movement trend difference index of all the demand users in each of the user groups in real time;

[0034] Setting a difference threshold, and obtaining the user to be adjusted according to the mobile trend difference index;

[0035] Analyze the update matching index between the user to be adjusted and all users in the network to obtain a maximum update matching index, and assign the user to be adjusted to a user group corresponding to the maximum update matching index;

[0036] All the users with demand in all the user groups are traversed, and the distribution of the users with demand in each of the user groups is adjusted.

[0037] In some embodiments of the present invention, after obtaining the user to be adjusted, the following steps are further included:

[0038] Analyze the interaction frequency between the user to be adjusted and the edge server to determine whether the user to be adjusted is a demand user;

[0039] If not, the interactive server corresponding to the user to be adjusted is modified to be an edge server closest to the user to be adjusted.

[0040] According to a second aspect of an embodiment of the present invention, a data transmission system for an intelligent connected vehicle is provided, comprising: a memory and a processor, wherein:

[0041] The memory is used to store program codes;

[0042] The processor is used to read the program code stored in the memory and execute the method described in the first aspect of the embodiment of the present invention.

[0043] In some embodiments of the present invention, the processor comprises:

[0044] The data monitoring module is used to monitor the user's mobility trends and interaction data with the edge server, as well as the remaining power and remaining memory of the mobile server;

[0045] A mobile server demand user acquisition module is used to analyze the interaction frequency between the user and the edge server based on the interaction data, and screen the demand users who need to set up the mobile server;

[0046] A user grouping module is used to analyze the similarity between any two of the demand users based on the movement trend and the interaction frequency, obtain a matching index between any two of the demand users, and group the demand users to obtain a plurality of user groups;

[0047] A mobile server distribution planning module is used to analyze the number distribution of demand users in the user group, determine the number of mobile servers required by the user group in combination with the matching index, and plan the mobile path of the mobile server;

[0048] A user grouping adjustment module is used to analyze the mobile trend difference index of all the demand users in each user group in real time based on the mobile trend, and adjust the distribution of the demand users in each user group;

[0049] The mobile server replacement module is used to replace the mobile server according to the remaining power and the remaining memory.

[0050] Compared with the prior art, the intelligent connected vehicle data transmission method and system provided by the present invention has the following beneficial effects:

[0051] 1. Based on the data of users in a certain Internet of Vehicles system, the present invention obtains multiple user groups with similar mobility trends, high aggregation and high access frequency to the current local area network, and sets mobile servers matching the user groups and plans the mobile paths of the mobile servers, that is, the mobile servers (drones) are used to assist the edge servers in processing the vehicle data in each user group, thereby balancing the load of the servers in the network and making the data transmission efficient and stable;

[0052] 2. The present invention updates and adjusts the specific matching mobile server by analyzing the difference in the movement trends of all the required users in each user group in real time, and promptly removes the user nodes with unmatched movement trends, thereby ensuring the efficient operation of the mobile server and the efficient processing of user data in real time;

[0053] 3. The present invention adjusts the distribution of mobile servers within the current local area network by combining the endurance and remaining memory of the mobile servers (drones), and replaces mobile servers with insufficient power and remaining memory in a timely manner to ensure stable transmission of user data within the current network. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0055] Figure 1 A basic flow chart of a method for transmitting data of an intelligent networked vehicle provided by an embodiment of the present invention;

[0056] Figure 2 A schematic diagram of a result of grouping demand users provided by an embodiment of the present invention;

[0057] Figure 3 A schematic diagram of mobile server distribution provided by an embodiment of the present invention;

[0058] Figure 4 A schematic diagram of the basic composition of a smart connected vehicle data transmission system provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0059] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the following is a detailed description of the intelligent networked vehicle data transmission method and system proposed by the present invention, its specific implementation method, structure, features and effects, in combination with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" does not necessarily refer to the same embodiment. In addition, specific features, structures or characteristics in one or more embodiments may be combined in any suitable form.

[0060] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art to which the present invention belongs. Terms such as "comprises", "comprising" or any other variants thereof are intended to cover non-exclusive inclusion, so that a circuit structure, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such article or device. In the absence of further restrictions, an element defined by the phrase "comprising a ..." does not exclude the presence of other identical elements in the article or device including the element.

[0061] The specific scheme of the intelligent networked vehicle data transmission method provided by the present invention is described in detail below with reference to the accompanying drawings.

[0062] See also Figure 1 , which shows the basic process of a smart connected vehicle data transmission method provided by an embodiment of the present invention.

[0063] like Figure 1 As shown, an embodiment of the present invention provides a method for transmitting data of an intelligent connected vehicle, which specifically includes:

[0064] S100: Monitor the user's mobility trend and interaction data with the edge server, and monitor the remaining power and remaining memory of the mobile server.

[0065] Take the edge computing network assisted by drones within a certain road range in the city as an example, where drones are mobile servers in the network. The network structure diagram is as follows: Figure 2As shown in the figure. In the network structure, each mobile vehicle is equipped with a wireless communication unit (which can obtain the location of the corresponding vehicle) and has a certain computing power. Each mobile vehicle matches the nearest edge server. The edge server corresponding to each mobile vehicle processes the real-time data generated by the vehicle. The mobile vehicle can also obtain the required information by accessing the edge server.

[0066] The movement trend of the user (moving vehicle) is monitored by the wireless communication unit installed in each moving vehicle, wherein the movement trend includes the displacement and the moving direction per unit time. Users (mobile vehicles) in the The moving direction at the moment is: The location at the moment points to the The direction vector of the position at the moment is recorded as , vector norm , for The displacement of the user (mobile vehicle) at a certain moment.

[0067] The server in the network records the number of times a user (mobile vehicle) accesses the server every 30 seconds. At the same time, the remaining memory and remaining power of each mobile server (drone) are recorded every 30 seconds.

[0068] S200: Based on the interaction data, the interaction frequency between the user and the edge server is analyzed, and the users who need to set up the mobile server are screened.

[0069] A large amount of real-time data is generated during the operation of users (mobile vehicles). Among them, different interactive behaviors of mobile vehicles have different degrees of impact on data processing pressure. For example, if a mobile vehicle repeatedly downloads real-time traffic reports or high-resolution maps, the corresponding edge server will need to process a large amount of data. If the traffic volume on the corresponding road section is relatively dense at this time, the corresponding edge server will need to process a large amount of data. Therefore, it is necessary to increase the number of mobile servers to process user data with a high degree of aggregation in the current road section and a high frequency of sending requests to the edge server, so as to balance the load of the edge servers in the network.

[0070] Combined with the above logic, if a single user (mobile vehicle) accesses its corresponding edge server at a high frequency within a certain period of time, and the current edge server is accessed many times during this period, it is necessary to set a higher degree of mobile server for the current user node.

[0071] Therefore, in the embodiment of the present invention, based on the interaction data, the interaction frequency between the user and the edge server is analyzed, and the users who need to set up the mobile server are screened. The specific implementation method is as follows:

[0072] First, the number of visits to a single edge server is placed in the sample space in chronological order to obtain a visit volume-time curve, and the visit volume-time curve is segmented using the APCA algorithm to obtain multiple time periods.

[0073] Then, based on the interaction data, the average number of times users visit the edge server in a period of time, the minimum number of times the current edge server is visited, and the average number of times all edge servers in the network are visited are analyzed to obtain the frequency of interaction between users and edge servers. The calculation formula for the frequency of interaction between users and edge servers is constructed as follows:

[0074]

[0075] In the formula, Indicated in In the period The frequency of interaction between a user and the edge server; Indicates In the period The average number of times a user visits the current edge server; Indicated in The minimum number of times the current edge server is accessed by multiple users within a period; Indicates The average number of visits to all edge servers in the network within a period; Represents an S-shaped growth curve function, which is used to map variables between 0 and 1.

[0076] No. In the period The average number of times a user visits the current edge server The larger the value is, the more frequently the user interacts with the current edge server during this period; The larger the The minimum number of times the current edge server is accessed by multiple users within a period of time is larger, that is, the number of times the current edge server is accessed as a whole must be larger, and in the The average number of visits to all edge servers in the network during a period is smaller, that is, the number of times other edge servers in the network are accessed is less, so The larger the The traffic volume on the road section where the edge server is currently located is denser than that on other roads in the network.

[0077] Finally, set the interaction frequency threshold. The interaction frequency threshold value can be based on the degree of interaction frequency. Users with a value greater than 0.5 are users who need to set up mobile servers, and thus users who need to set up mobile servers are screened. The remaining unmarked users interact with the edge server less frequently, and the traffic flow on the road section is sparse, so the current load of their corresponding edge servers is relatively idle compared to the rest of the edge servers in the network.

[0078] S300: Based on the mobile trend and the interaction frequency, the similarity between any two demand users is analyzed to obtain a matching index between any two demand users, and the demand users are grouped to obtain a plurality of user groups.

[0079] Demand users (mobile vehicles) have a certain mobility trend within the network range. When the mobile server undertakes the calculation task of the real-time data of the mobile vehicle, in order to reduce the transmission delay and energy consumption of the information, it is necessary to reduce the data transmission distance as much as possible. Therefore, the location of the mobile server needs to change with the movement of the mobile vehicle. However, the movement trends of different mobile vehicles are different. Therefore, it is necessary to determine the coverage of the mobile server based on the similarity of the movement trends of different mobile vehicles.

[0080] Therefore, in the embodiment of the present invention, based on the mobile trend and the interaction frequency, the similarity between any two demand users is analyzed to obtain the matching index between any two demand users, and the demand users are grouped to obtain a number of user groups. Further including:

[0081] First, based on the movement trend, the movement trend difference between any two demand users is analyzed to obtain the movement trend similarity. The specific implementation method is: based on the movement trend, the movement direction difference and displacement difference between any two demand users are analyzed; wherein, the movement direction difference between any two demand users is analyzed, that is, the first The demand user and The angle between the direction vectors (defined in step S100) and the vectors of the demand user at all times in the time period (the time period obtained by the APCA algorithm) at the current time is recorded as , and Respectively represent The demand user and The sum vector of the direction vectors of the demand users at all times in the time period at the current time; analyze the displacement difference between any two demand users, that is, calculate the first The demand user and The absolute value of the difference in the average displacement of a demand user at all times in the time period at the current time is recorded as , and Respectively represent The demand user and The average displacement of all demand users at all times in the time period at the current time. And, analyze the average displacement of all demand users in the network, that is, calculate the average value of the average displacement of all demand users in the network at all times in the corresponding time period (the average displacement of all demand users in the network at the current time The average value of . Combined and as well as , we get the difference in mobility trends between any two demand users, which is , Represents a constant, which can be 0.1, to prevent the denominator from being 0. Based on the difference in mobility trends, combined with the Euclidean distance between any two demand users, the mobility trend similarity between any two demand users is obtained. ,in Indicates that at the current moment The demand user and The Euclidean distance between the demand users is Represents a constant, which can be 0.1, to prevent the denominator from being 0.

[0082] Then, based on the interaction frequency, the difference in the interaction frequency between any two demand users is analyzed to obtain the interaction frequency similarity. The demand user and The ratio of the interaction frequency between the demand users , and Respectively represent The demand user and The interaction frequency of the demand user in the current time period, and then calculate the difference in interaction frequency: , and the interaction frequency similarity is ,in, Represents a constant, which can be 0.1, to prevent the denominator from being 0.

[0083] Then, based on the similarity of mobile trends and interaction frequencies, the matching index between any two demand users is obtained. The demand user and The formula for calculating the matching index between demand users is:

[0084]

[0085] In the formula, Indicates The demand user and The matching index of the demand user at the current moment; Indicates The frequency of interaction of each demand user in the current time period; Indicates The frequency of interaction of each demand user in the current time period; It represents the average displacement of all demand users in the network during the current time period; and Respectively represent The demand user and The sum vector of the direction vectors of the demand user at all times in the time period at the current time; and Respectively represent The demand user and The average displacement of a demand user at all times in the time period at the current time; Indicates that at the current moment The demand user and The Euclidean distance of the location of the user in need; represents the linear normalization function; and Represents a constant, which can be 0.1, to prevent the denominator from being 0.

[0086] Indicates the current moment The demand user and The similarity of the average displacement of the demand users is as large as the value, the more similar they are; Indicates the current moment The demand user and The angle between the moving directions of the users with different needs. The smaller the angle, the more similar they are. Indicates the current moment The demand user and Demand users The smaller the value, the greater the similarity.

[0087] Finally, set the matching threshold, which can be 0.8. According to the matching index (value range is (0, 1)), the demand users are grouped, that is, the demand users with a matching index greater than 0.8 are combined with the current demand users into one user group, and the demand users in the group do not participate in the subsequent grouping calculation of the remaining demand users. Repeat the above steps to determine the grouping of all demand users, and finally obtain several user groups, such as Figure 3 shown.

[0088] S400: Analyze the quantity distribution of the demanding users in the user group, determine the quantity of the mobile server required by the user group in combination with the matching index, and plan the moving path of the mobile server.

[0089] The more mobile servers are added to the network at the current moment, the less data each mobile server and edge server needs to process. Adding too many mobile servers will cause a waste of resources. Therefore, it is necessary to set the number of mobile servers that need to be added in the current state according to the actual complexity of the data in the network at the current moment.

[0090] Combined with the result of grouping the demand users in step S300, if the number of demand users who need mobile servers in the network at the current moment is greater, the number of mobile servers that need to be added is greater; if the number of demand users in a single user group is greater, the amount of data that the user group needs to process is greater; if the degree of similarity of the access behaviors and mobility trends of the demand users in the user group is higher, the complexity of the data that needs to be processed in the user group is more consistent, and the degree to which the number of mobile servers needs to be increased is greater.

[0091] Based on the above analysis, in the embodiment of the present invention, by analyzing the number distribution of demand users in the user group and combining the matching index, the number of mobile servers required by the user group is determined.

[0092] First, analyze the number of demand users in the user group and the average number of demand users in all user groups, and combine them with the total number of all demand users in the network to obtain the number distribution of demand users in the user group; based on the matching index, obtain the minimum matching index between any two demand users in the user group; according to the number distribution, combined with the minimum matching index, obtain the user group's demand index for the mobile server. The calculation formula for the user group's demand index for the mobile server is constructed as follows:

[0093]

[0094] In the formula, Indicates the current moment The demand index of the number of mobile servers for each user group; Indicates the current moment The number of demand users in a user group; Represents the mean number of demand users in all user groups in the network at the current moment; Indicates the total number of demand users in the network at the current moment; Indicates The minimum value of the matching index between any two demand users in a user group at the current moment; represents the linear normalization function.

[0095] The larger the value, the more The number of demand users in a user group is large, and the total number of demand users in the network at the current moment is relatively small, indicating that there are more demand users in the current user group; The larger it is, the more users who need mobile servers are in the network at the current moment, and the more mobile servers need to be added; The larger the The matching index of any two demand users in a user group at the current moment is relatively large, which means that the higher the similarity of the access behaviors and mobile trends of the demand users in the user group, the more consistent the complexity of the data that needs to be processed in the user group, and the greater the need to increase the number of mobile servers.

[0096] Then, according to the demand index and the number of edge servers in the network, the number of mobile servers required by the user group is determined. The number of mobile servers required by a user group at the current moment is:

[0097]

[0098] in, Indicates The number of mobile servers required by each user group at the current moment; Indicates the current moment The demand index of the number of mobile servers for each user group; Indicates the number of edge servers in the network at the current moment; Represents the floor function.

[0099] After determining the number of mobile servers required by the user group, plan the mobile paths of the mobile servers in each user group. The specific implementation method is as follows:

[0100] Mobile servers are allocated to user groups based on the number of demands, wherein the initial position coordinates of each mobile server within the range of the user group are determined by the method of determining uniform sampling points. Before the mobile server (drone) moves to the initial position, the data of all demand users (mobile vehicles) are processed by the nearest server (including edge servers and mobile servers). After the mobile server (drone) moves to the initial position, the data of the demand users in the user group are processed by the mobile servers in the user group. The mobile servers corresponding to the different demand users in the user group also adopt the principle of proximity matching.

[0101] In order to reduce the delay of information transmission within the group, the coordinates of the mobile server need to change with the movement of the demand users (mobile vehicles) in each user group. Take a single mobile server in a user group as an example: for each mobile server, the position change of a single mobile server should be consistent with the movement trend of all demand users accessing the mobile server at the current moment. Therefore, the mobile server should start moving in the direction indicated by the sum vector of the movement directions of all demand users accessing the mobile server at the current moment. The movement displacement should be the mean of the displacements of all demand users accessing the mobile server, and the movement path of the mobile server is planned. The movement direction of all demand users accessing a single mobile server at the current moment is: the movement direction of a single demand user at the first The direction of movement at a moment The sum vector That is, a single mobile server is connected to the moving directions and vectors of all the users who need to access the mobile server at the current moment. direction, the average displacement of all demand users at the current moment The displacement size moves; among them, due to the limited moving speed of the mobile server (drone), if If the displacement exceeds the current maximum displacement of the drone, the drone is moved in the currently determined direction to the maximum displacement that the drone can achieve.

[0102] S500: Based on the mobility trend, the mobility trend difference index of all demand users in each user group is analyzed in real time, and the distribution of demand users in each user group is adjusted.

[0103] As the time series increases, the movement trend of some demand users may no longer conform to the overall movement trend of the demand users in the current user group due to differences in the mobile server speed or the driving routes of the demand users. At this time, it is not suitable to use the current mobile server to process the data of the demand users. The demand users in the user group should be updated or the user group of some demand users should be changed.

[0104] Therefore, in the embodiment of the present invention, based on the mobile trend, the mobile trend difference index of all the demand users in each user group is analyzed in real time, and the distribution of the demand users in each user group is adjusted. Further including:

[0105] First, based on the mobile trend, the mobile trend difference index of all demand users in each user group is analyzed in real time. The specific implementation method is as follows: Moment The moving direction vectors and displacements of all demand users in the user group are obtained. Moment User group The moving direction vector of the demand user and displacement , and calculate the Moment The sum vector of the moving direction vectors of all demand users in the user group And the displacement mean ; and then calculate the The moving direction vector of the demand user With The sum vector corresponding to the user groups The angle between , get the Moment The difference between the moving direction of the user with demand and the overall moving direction of his user group; and calculate the Displacement of demand users With The mean displacement corresponding to each user group The absolute value of the difference between , combined with The mean displacement corresponding to each user group , get the Moment The difference between the displacement of the user with demand and the overall displacement of the user group to which he belongs; finally, combining the difference in moving direction and displacement, we get Moment The user with the need and the The mobile trend difference index among all demand users in the user group. Moment The user with the need and the The calculation formula of the mobile trend difference index among all demand users in a user group is:

[0106]

[0107] In the formula, Indicates Moment The user with the need and the The mobility trend difference index among all demand users in a user group; Indicates Moment The sum vector of the moving direction vectors of all demand users in a user group; Indicates Moment User group The moving direction vector of the demand user; Indicates Moment The mean displacement of all demand users in a user group; Indicates Moment User group The displacement of the demand users; Representation vector With vector The angle between represents the linear normalization function.

[0108] Indicates Moment The difference between the displacement of the individual demand user and the overall displacement of the user group to which he belongs is greater. Moment The user with the need and the The greater the difference in mobility trends among all demand users in a user group; Indicates Moment The difference between the moving direction of the individual demand user and the overall moving direction of his user group is larger, indicating that the Moment The user with the need and the The greater the difference in mobility trends among all demand users within a user group.

[0109] Then, a difference threshold is set, and users to be adjusted are obtained according to the mobile trend difference index. A specific implementation method is: the difference threshold may be set to 0.6, and the demand users with a mobile trend difference index greater than 0.6 are regrouped to obtain users to be adjusted.

[0110] Then, the update matching index between the user to be adjusted and all users in the network is analyzed (the specific analysis method is the same as the method provided in step S300), the maximum update matching index is obtained, and the user to be adjusted is allocated to the user group corresponding to the maximum update matching index.

[0111] In some embodiments of the present invention, after obtaining the user to be adjusted, the following further includes: analyzing the interaction frequency between the user to be adjusted and the edge server (the specific analysis method is the same as the method provided in step S200), and determining whether the user to be adjusted is a demand user; if not, modifying the interaction server corresponding to the user to be adjusted to the edge server closest to the user to be adjusted.

[0112] Traverse all the demand users in all user groups and adjust the distribution of demand users in each user group.

[0113] S600: Analyze the replacement index of the mobile server according to the remaining power and the remaining memory, and replace the mobile server.

[0114] Due to the limited battery life of mobile servers, they cannot run for a long time, and the loads of different mobile servers vary. In order to ensure stable data transmission in the current network, mobile servers with insufficient power or too little memory need to be replaced in time.

[0115] Therefore, in the embodiment of the present invention, the replacement index of the mobile server is analyzed according to the remaining power and the remaining memory, and the mobile server is replaced.

[0116] First, obtain the Moment The remaining power of the mobile servers And remaining memory , and calculate the The average remaining memory of all mobile servers in the network at time , get the Moment The replacement index of mobile servers. Moment The replacement index calculation formula for a mobile server is:

[0117]

[0118] In the formula, Indicates Moment The replacement index of mobile servers; Indicates Moment The remaining battery power of each mobile server; Indicates Moment The remaining memory of the mobile servers; Indicates The average remaining memory of all mobile servers in the network at a certain time; represents the linear normalization function.

[0119] Then, a replacement index threshold is set, and the replacement index threshold value can be 0.8, and the mobile server with a replacement index greater than 0.8 is replaced. Before the new mobile server arrives at the designated location, it processes the data of the corresponding user nearby, and after the new mobile server moves to the location of the mobile server to be replaced, the original mobile server is removed.

[0120] Based on the same inventive concept as the above method, this embodiment also provides a smart connected vehicle data transmission system.

[0121] See also Figure 4 , which shows the basic composition of an intelligent connected vehicle data transmission system provided by an embodiment of the present invention.

[0122] like Figure 4 As shown, a data transmission system for an intelligent networked vehicle includes: a memory 10 and a processor 20, wherein:

[0123] A memory 10, used for storing program codes;

[0124] The processor 20 is used to read the program code stored in the memory 10, and execute the monitoring of the user's movement trend and the interaction data with the edge server, as well as the monitoring of the remaining power and remaining memory of the mobile server; based on the interaction data, analyze the interaction frequency between the user and the edge server, and screen the users who need to set up the mobile server; based on the movement trend and the interaction frequency, analyze the similarity between any two users, obtain the matching index between any two users, group the users, and obtain a number of user groups; analyze the number distribution of users in the user group, and determine the number of users in the user group for the mobile server in combination with the matching index, and plan the movement path of the mobile server; based on the movement trend, analyze the movement trend difference index of all users in each user group in real time, and adjust the distribution of users in each user group; according to the remaining power and remaining memory, analyze the replacement index of the mobile server and replace the mobile server.

[0125] Furthermore, the processor includes: a data monitoring module 21, a mobile server demand user acquisition module 22, a user grouping module 23, a mobile server distribution planning module 24, a user grouping adjustment module 25 and a mobile server replacement module 26. Among them:

[0126] The data monitoring module 21 is used to monitor the user's mobility trend and interaction data with the edge server, and to monitor the remaining power and remaining memory of the mobile server;

[0127] The mobile server demand user acquisition module 22 is used to analyze the interaction frequency between the user and the edge server based on the interaction data, and screen the demand users who need to set up the mobile server;

[0128] The user grouping module 23 is used to analyze the similarity between any two demand users based on the mobility trend and the interaction frequency, obtain the matching index between any two demand users, and group the demand users to obtain a number of user groups;

[0129] The mobile server distribution planning module 24 is used to analyze the number distribution of users in the user group, determine the number of mobile servers required by the user group in combination with the matching index, and plan the mobile path of the mobile server;

[0130] The user grouping adjustment module 25 is used to analyze the mobile trend difference index of all demand users in each user group in real time based on the mobile trend, and adjust the distribution of demand users in each user group;

[0131] The mobile server replacement module 26 is used to replace the mobile server according to the remaining power and remaining memory.

[0132] It should be noted that the sequence of the above embodiments of the present invention is only for description and does not represent the advantages and disadvantages of the embodiments. The processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0133] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referenced to each other, and each embodiment focuses on the differences from other embodiments.

Claims

1. A method for transmitting data of an intelligent networked vehicle, characterized in that: The method comprises: Monitor user mobility trends and interaction data with edge servers, as well as the remaining power and memory of mobile servers; Based on the interaction data, analyzing the interaction frequency between the user and the edge server, and screening the users who need to set up the mobile server; Based on the movement trend and the interaction frequency, the similarity between any two of the demand users is analyzed to obtain a matching index between any two of the demand users, and the demand users are grouped to obtain a plurality of user groups; Analyze the number distribution of demand users in the user group, determine the number of mobile servers required by the user group in combination with the matching index, and plan the mobile path of the mobile server; Based on the movement trend, the movement trend difference index of all the demand users in each of the user groups is analyzed in real time, and the distribution of the demand users in each of the user groups is adjusted; The replacement index of the mobile server is analyzed according to the remaining power and the remaining memory, and the mobile server is replaced.

2. The intelligent connected vehicle data transmission method according to claim 1, characterized in that: Based on the interaction data, the interaction frequency between the user and the edge server is analyzed, and the users who need to set up the mobile server are screened, including: The number of visits to a single edge server is placed in the sample space in chronological order to obtain a visit-time curve, and the APCA algorithm is used to segment the visit-time curve to obtain multiple time periods; Based on the interaction data, analyzing the average number of visits to the edge server by the user in a period of time, the minimum number of visits to the current edge server, and the average number of visits to all edge servers in the network, to obtain the frequency of interaction between the user and the edge server; An interaction frequency threshold is set, and users who need to set up a mobile server are screened according to the interaction frequency.

3. The intelligent connected vehicle data transmission method according to claim 1, characterized in that: Based on the movement trend and the interaction frequency, the similarity between any two of the demand users is analyzed to obtain a matching index between any two of the demand users, and the demand users are grouped to obtain a number of user groups, including: Based on the movement trend, analyzing the movement trend difference between any two of the demand users to obtain the movement trend similarity; Based on the interaction frequency, analyzing the difference in interaction frequency between any two of the demand users to obtain interaction frequency similarity; Obtaining a matching index between any two of the demand users according to the movement trend similarity and the interaction frequency similarity; A matching threshold is set, and the demand users are grouped according to the matching index to obtain a plurality of user groups.

4. The intelligent connected vehicle data transmission method according to claim 3, characterized in that: Based on the movement trend, analyzing the movement trend difference between any two of the demand users to obtain the movement trend similarity includes: Based on the movement trend, analyzing the movement direction difference and displacement difference between any two of the demand users, and analyzing the average displacement of all demand users in the network, to obtain the movement trend difference between any two of the demand users; According to the movement trend difference and in combination with the Euclidean distance between any two of the demand users, the movement trend similarity between any two of the demand users is obtained.

5. The intelligent connected vehicle data transmission method according to claim 1, characterized in that: Analyzing the quantity distribution of the demanding users in the user group and determining the quantity of the mobile server demanded by the user group in combination with the matching index includes: Analyze the number of demand users in the user group and the average number of demand users in all the user groups, and combine with the total number of all demand users in the network to obtain the distribution of the number of demand users in the user group; Based on the matching index, obtaining a minimum matching index between any two of the demanding users in the user group; According to the quantity distribution and in combination with the minimum matching index, obtaining a demand index of the user group for the mobile server; The number of mobile servers required by the user group is determined according to the demand index and in combination with the number of edge servers in the network.

6. The intelligent connected vehicle data transmission method according to claim 5, characterized in that: Planning a moving path of the mobile server includes: Allocating mobile servers to the user group according to the required quantity; For each mobile server, a moving path of the mobile server is planned according to the sum vector of the moving directions of all the demand users accessing the mobile server and the displacement mean.

7. The intelligent connected vehicle data transmission method according to claim 1, characterized in that: Based on the movement trend, the movement trend difference index of all the demand users in each of the user groups is analyzed in real time, and the distribution of the demand users in each of the user groups is adjusted, including: Based on the movement trend, analyzing the movement trend difference index of all the demand users in each of the user groups in real time; Setting a difference threshold, and obtaining the user to be adjusted according to the mobile trend difference index; Analyze the update matching index between the user to be adjusted and all users in the network to obtain a maximum update matching index, and assign the user to be adjusted to a user group corresponding to the maximum update matching index; All the users with demand in all the user groups are traversed, and the distribution of the users with demand in each of the user groups is adjusted.

8. The intelligent connected vehicle data transmission method according to claim 7, characterized in that: Get the user to be adjusted, and then include: Analyze the interaction frequency between the user to be adjusted and the edge server to determine whether the user to be adjusted is a demand user; If not, the interactive server corresponding to the user to be adjusted is modified to be an edge server closest to the user to be adjusted.

9. An intelligent networked vehicle data transmission system, characterized in that: The system comprises: a memory and a processor, wherein: The memory is used to store program codes; The processor is configured to read the program code stored in the memory and execute the method according to any one of claims 1 to 8.

10. The intelligent connected vehicle data transmission system according to claim 9, characterized in that: The processor comprises: The data monitoring module is used to monitor the user's mobility trends and interaction data with the edge server, as well as the remaining power and remaining memory of the mobile server; A mobile server demand user acquisition module is used to analyze the interaction frequency between the user and the edge server based on the interaction data, and screen the demand users who need to set up the mobile server; A user grouping module is used to analyze the similarity between any two of the demand users based on the movement trend and the interaction frequency, obtain a matching index between any two of the demand users, and group the demand users to obtain a plurality of user groups; A mobile server distribution planning module is used to analyze the number distribution of demand users in the user group, determine the number of mobile servers required by the user group in combination with the matching index, and plan the mobile path of the mobile server; A user grouping adjustment module is used to analyze the mobile trend difference index of all the demand users in each user group in real time based on the mobile trend, and adjust the distribution of the demand users in each user group; The mobile server replacement module is used to replace the mobile server according to the remaining power and the remaining memory.

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