A communication transmission control system for electric bicycles

Abstract

The present invention discloses a communication transmission control system for electric bicycles, the system includes: a data acquisition module for acquiring electric bicycle data; a data packet acquisition module for obtaining battery status data packets and update data packets; a status acquisition module for obtaining the connectivity index and communication reception rate of the electric bicycle according to the update data packet, and obtaining the networking status and communication transmission data of the electric bicycle and other vehicles; a communication rate acquisition module for obtaining the actual communication rate; a parameter calculation module for calculating the control variables, communication reception signal-to-noise ratio and signal-to-noise ratio variance of the corresponding electric bicycle according to the actual communication rate; an instruction acquisition module for constructing and training a reinforcement learning model, and determining the model output as a communication control instruction; and a vehicle-to-vehicle communication module for controlling the electric bicycle according to the communication control instruction to complete the communication. The system can realize high-efficiency communication transmission between electric bicycles.

Description

Technical Field

[0001] The present invention relates to the technical field of vehicle communication transmission, and in particular to a communication transmission control system for an electric bicycle. Background Art

[0002] With the popularity and development of electric bicycles, efficient communication transmission control between bicycles has become an important issue. Traditional communication transmission control methods mainly rely on hardware devices such as bicycle locks and bracelets to perform identity recognition and data transmission through near-field communication. Although these methods meet basic needs to a certain extent, they are unable to meet the growing communication needs of electric bicycles due to factors such as high hardware equipment costs, limited communication distances, and unstable data transmission.

[0003] In one prior art, a wireless charging method for electric bicycles mainly solves the reliability problem of the electric bicycle charging system and improves the communication reliability between the vehicle and the charging system. However, this method still relies on traditional short-range communication technologies, such as Bluetooth, which is prone to interference in dense traffic environments and fails to fully utilize the real-time communication and big data analysis capabilities of the Internet of Vehicles. In addition, this prior art usually relies on static communication protocols and is difficult to adapt to different communication environments.

[0004] The existing technology cannot meet the actual transportation needs of electric bicycles and has the problem of low communication and data transmission efficiency. Summary of the invention

[0005] The present invention provides a communication transmission control system for an electric bicycle, aiming to solve the problem that the existing technology cannot meet the actual transportation needs of electric bicycles and has low communication data transmission efficiency.

[0006] In a first aspect, the present invention provides a communication transmission control system for an electric bicycle, which is configured at a data receiving end and includes:

[0007] An electric bicycle data acquisition module is used to acquire electric bicycle data, wherein the electric bicycle data includes vehicle location data, speed data, battery status data, own historical data and historical communication rate;

[0008] An update data packet acquisition module is used to perform data processing operations on the battery status data based on the electric bicycle vehicle network to obtain a data packet of the battery status, and perform data update operations on the data packet and its own historical data to obtain an update data packet;

[0009] A network status acquisition module, used to perform calculation operations according to the update data packet to obtain a connectivity index and a communication reception rate of the electric bicycle, and to analyze and obtain a network status and communication transmission data between the electric bicycle and other vehicles according to the connectivity index and the communication reception rate;

[0010] An actual communication rate acquisition module, used to perform calculation and analysis operations according to the networking status and the communication transmission data to obtain the actual communication rate of the electric bicycle;

[0011] A parameter calculation module, used for calculating the control variable, the communication reception signal-to-noise ratio and the signal-to-noise ratio variance corresponding to the electric bicycle according to the actual communication rate;

[0012] A communication control instruction acquisition module, used to construct and train a reinforcement learning model according to the actual communication rate, the control variable and the signal-to-noise ratio variance, and determine the output result of the reinforcement learning model as a communication control instruction; the communication control instruction includes a communication rate adjustment instruction, a signal enhancement instruction, a connection management instruction, a data packet sending instruction, a dynamic adjustment instruction and an exception handling instruction;

[0013] The vehicle-to-vehicle communication module is used to control the electric bicycle according to the communication control instruction to complete the vehicle-to-vehicle communication.

[0014] In a second aspect, in order to solve the above technical problems, the present invention provides a communication transmission control method for an electric bicycle, which is executed by a data receiving end and includes:

[0015] Acquire electric bicycle data, wherein the electric bicycle data includes vehicle location data, speed data, battery status data, own historical data and historical communication rate;

[0016] Based on the electric bicycle Internet of Vehicles, the battery status data is processed to obtain a data packet of the battery status, and the data packet is updated with the own historical data to obtain an updated data packet;

[0017] Performing calculation operations according to the update data packet to obtain a connectivity index and a communication reception rate of the electric bicycle, and analyzing the connectivity index and the communication reception rate to obtain a networking status and communication transmission data between the electric bicycle and other vehicles;

[0018] Performing calculation and analysis operations according to the networking status and the communication transmission data to obtain an actual communication rate of the electric bicycle;

[0019] According to the actual communication rate, calculating the control variable, the communication reception signal-to-noise ratio and the signal-to-noise ratio variance corresponding to the electric bicycle;

[0020] According to the actual communication rate, the control variable and the signal-to-noise ratio variance, a reinforcement learning model is constructed and trained, and an output result of the reinforcement learning model is determined as a communication control instruction, wherein the communication control instruction includes a communication rate adjustment instruction, a signal enhancement instruction, a connection management instruction, a data packet sending instruction, a dynamic adjustment instruction and an exception handling instruction;

[0021] The electric bicycle is controlled according to the communication control instruction to complete vehicle-to-vehicle communication.

[0022] As an optional implementation, based on the electric bicycle Internet of Vehicles, the battery status data is subjected to data processing operations to obtain a data packet of the battery status, and the data packet is subjected to data updating operations with the own historical data to obtain an updated data packet, including:

[0023] According to the battery status data, the battery status data packet is input into the electric bicycle vehicle network to construct the battery status data packet;

[0024] The electric bicycle receives the battery status data packet, stores the own historical data and the historical data of the battery status data packet in two queues respectively, and calculates the data packet length by the following formula:

[0025]

[0026] Among them, α represents the length of the data packet, k represents the time when a data packet is received, and d i represents the historical data update value of the received data packets, d represents the standardized distance constant, and n represents the distance between the two queues;

[0027] According to the data packet length and the update time, a data update operation is performed to obtain the update data packet.

[0028] As an optional implementation manner, the update time includes:

[0029] The data packet length is used as input to perform a time update operation. The time update method is defined as follows:

[0030] d i,k =d i,k-1 +(g0·α)·Y k

[0031] Among them, d i,k represents the update time at time k, d i,k-1 represents the historical moment of the previous moment k-1, g0 represents the reference gain factor, Y k Indicates that the obedience parameter is σ 2 A Gaussian distributed random variable.

[0032] As an optional implementation, performing a calculation operation according to the update data packet to obtain a connectivity index and a communication reception rate of the electric bicycle includes:

[0033] The calculation formula of the connectivity index is expressed as:

[0034]

[0035] Where N represents the connectivity index of the electric bicycle, n represents the number of all other electric bicycles within the communication range, and w ij represents the connection weight of the electric bicycle, R ij represents the communication reception rate of the electric bicycle;

[0036] The connection weight of the electric bicycle, when the electric bicycle i establishes communication with the electric bicycle j, w ij The value is 1; when the electric bicycles have not established communication with each other, w ij The value is 0;

[0037] The communication reception rate of the electric bicycle is calculated as follows:

[0038]

[0039] Among them, y ij,k represents the indicator variable. When electric bicycle i successfully receives the data packet from electric bicycle j, y ij,k The value is 1; when electric bicycle i fails to successfully receive the data packet from electric bicycle j, y ij,k The value is 0; m represents the total number of data packets sent by electric bicycle j to electric bicycle i in a given time period.

[0040] As an optional implementation manner, the obtaining of the networking status and communication transmission data between the electric bicycle and other vehicles according to the connectivity index and the communication reception rate analysis includes:

[0041] The determination method of the networking status is expressed as follows:

[0042] If N ≥ θ, the electric bicycle remains connected to the Internet of Vehicles;

[0043] If N<θ, the electric bicycle is in a disconnected state;

[0044] Among them, θ represents the preset connection threshold;

[0045] According to the communication reception rate R ij and the rate of each connection, calculate the total communication transmission data of the electric bicycle i, and the calculation formula is expressed as:

[0046]

[0047] Among them, D i represents the total communication transmission data of the electric bicycle i, s j represents the communication rate of each connected bicycle j, R ij represents the communication reception rate of the electric bicycle i, and T represents the communication time.

[0048] As an optional implementation manner, performing a calculation and analysis operation according to the networking state and the communication transmission data to obtain an actual communication rate of the electric bicycle includes:

[0049] If the electric bicycle i establishes a connection with other vehicles, the actual communication rate of the electric bicycle is determined by the maximum data transmission rate;

[0050] The calculation formula of the data transmission rate of the electric bicycle i is expressed as:

[0051] c i =s j ·w ij

[0052] Among them, c i represents the data transmission rate of the electric bicycle i, s j represents the data transmission rate of electric bicycle j, w ij represents the connection weight of the electric bicycle;

[0053] According to the data transmission rate, the actual communication rate is obtained:

[0054] As an optional implementation manner, according to the actual communication rate, the control variable corresponding to the electric bicycle, the communication reception signal-to-noise ratio and the signal-to-noise ratio variance are calculated, including:

[0055] The control variable is calculated as follows:

[0056]

[0057] Among them, u i represents the control variable, s i represents the actual communication rate of the electric bicycle i, which is regarded as the state variable of the system, p i represents the power consumption of the electric bicycle i, and A represents the control gain constant that affects the communication rate;

[0058] The communication reception signal-to-noise ratio is calculated as follows:

[0059]

[0060] Among them, γ ij It is represented by the communication receiving signal-to-noise ratio of the electric bicycle i, S ij represents the signal power of the electric bicycle i receiving the electric bicycle j, N ij represents the noise power;

[0061] The signal-to-noise ratio variance is calculated as follows:

[0062] Ω=E[(γ ij -E[γ ij ]) 2 ]

[0063] Among them, E[γ ij ] is expressed as the expected value of all signal-to-noise ratios.

[0064] As an optional implementation manner, constructing and training a reinforcement learning model according to the actual communication rate, the control variable and the signal-to-noise ratio variance, and determining the output result of the reinforcement learning model as a communication control instruction, including:

[0065] Construct a reinforcement learning model (R,M,γ,r), where

[0066] State R represents the current communication state, including the actual communication rate S i and signal-to-noise ratio variance Ω;

[0067] Action M represents the control variable u i The choice of affects the communication rate and signal-to-noise ratio;

[0068] The discount factor γ represents the influence factor of future rewards on current decisions;

[0069] The reward function r represents the communication quality Q i,j and signal-to-noise ratio optimization function;

[0070] The communication quality Q i,j is the trade-off model between the communication rate and the communication quality of electric bicycle i, and the calculation formula is expressed as:

[0071]

[0072] in:

[0073] U1=[U1(t1),U1(t2),…,U1(t N )] represents the communication rate vector;

[0074] U2=[U2(t1),U2(t2),…,U2(t N )] represents the communication quality vector;

[0075] U1(t a ) represents the electric bicycle i at t a The communication rate at the moment, U2(t a ) indicates that the electric bicycle i is at t a The communication quality at the moment, W t Expressed as learning rate;

[0076] The reinforcement learning model is trained according to the state R, the action M, the discount factor γ and the reward function r, and the training is determined to be completed when the number of training times is greater than a preset number threshold, or the reward value of the reward function is greater than a preset reward threshold, to obtain a trained reinforcement learning model;

[0077] The output result of the trained reinforcement learning model is determined as a communication control instruction.

[0078] In a third aspect, the present invention also provides a computer-readable storage medium, which includes a stored computer program, wherein when the computer program is running, the device where the computer-readable storage medium is located is controlled to execute any one of the above-mentioned communication transmission control methods for electric bicycles.

[0079] Compared with the prior art, the present invention has the following beneficial effects:

[0080] The present invention provides a communication transmission control system and method for an electric bicycle, the method comprising: acquiring electric bicycle data, the electric bicycle data comprising vehicle position data, speed data, battery status data, own historical data and historical communication rate; based on the electric bicycle vehicle network, performing data processing operations on the battery status data to obtain a data packet of the battery status, performing data updating operations on the data packet and the own historical data to obtain an updated data packet; performing calculation operations based on the updated data packet to obtain a connectivity index and a communication reception rate of the electric bicycle, and analyzing the connectivity index and the communication reception rate to obtain the connectivity of the electric bicycle with other vehicles. status and communication transmission data; performing calculation and analysis operations according to the networking status and the communication transmission data to obtain the actual communication rate of the electric bicycle; calculating the control variables, communication reception signal-to-noise ratio and signal-to-noise ratio variance corresponding to the electric bicycle according to the actual communication rate; constructing and training a reinforcement learning model according to the actual communication rate, the control variables and the signal-to-noise ratio variance, and determining the output result of the reinforcement learning model as a communication control instruction, the communication control instruction including a communication rate adjustment instruction, a signal enhancement instruction, a connection management instruction, a data packet sending instruction, a dynamic adjustment instruction and an exception handling instruction; controlling the electric bicycle according to the communication control instruction to complete vehicle-to-vehicle communication.

[0081] The method utilizes the electric bicycle vehicle network to obtain vehicle data and construct a formula algorithm model, and uses the vehicle data as input data, which is input into the formula algorithm model for analysis and data processing to form a data model; the data model is transmitted to a central processing unit to determine parameters and communication strategies, and a vehicle-to-vehicle communication model is constructed, thereby realizing efficient communication transmission between electric bicycles. BRIEF DESCRIPTION OF THE DRAWINGS

[0082] Figure 1 It is a flow chart of a communication transmission control method for an electric bicycle provided by an embodiment of the present invention;

[0083] Figure 2 is a schematic diagram of an actual communication process of an electric bicycle provided by an embodiment of the present invention;

[0084] Figure 3 It is a structural schematic diagram of a communication transmission control system for an electric bicycle provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0085] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0086] With the popularity and development of electric bicycles, efficient communication transmission control between bicycles has become an important issue. Traditional communication transmission control methods mainly rely on hardware devices such as bicycle locks and bracelets to perform identity recognition and data transmission through near-field communication. Although these methods meet basic needs to a certain extent, they are unable to meet the growing communication needs of electric bicycles due to factors such as high hardware equipment costs, limited communication distances, and unstable data transmission.

[0087] In one prior art, a wireless charging method for electric bicycles mainly solves the reliability problem of the electric bicycle charging system and improves the communication reliability between the vehicle and the charging system. However, this method still relies on traditional short-range communication technologies, such as Bluetooth, which is prone to interference in dense traffic environments and fails to fully utilize the real-time communication and big data analysis capabilities of the Internet of Vehicles. In addition, this prior art usually relies on static communication protocols and is difficult to adapt to different communication environments.

[0088] The existing technology cannot meet the actual transportation needs of electric bicycles and has the problem of low communication and data transmission efficiency.

[0089] To solve the above problems, refer to Figure 1 The first embodiment of the present invention provides a communication transmission control method for an electric bicycle, comprising the following steps:

[0090] S1, acquiring electric bicycle data, wherein the electric bicycle data includes vehicle location data, speed data, battery status data, own historical data and historical communication rate;

[0091] S2, based on the electric bicycle Internet of Vehicles, performing a data processing operation on the battery status data to obtain a data packet of the battery status, and performing a data update operation on the data packet and the own historical data to obtain an updated data packet;

[0092] S3, performing a calculation operation according to the update data packet to obtain a connectivity index and a communication reception rate of the electric bicycle, and analyzing the connectivity index and the communication reception rate to obtain a networking status and communication transmission data between the electric bicycle and other vehicles;

[0093] S4, performing calculation and analysis operations according to the networking status and the communication transmission data to obtain an actual communication rate of the electric bicycle;

[0094] S5, calculating the control variable, communication reception signal-to-noise ratio and signal-to-noise ratio variance corresponding to the electric bicycle according to the actual communication rate;

[0095] S6, constructing and training a reinforcement learning model according to the actual communication rate, the control variable and the signal-to-noise ratio variance, and determining an output result of the reinforcement learning model as a communication control instruction;

[0096] S7, controlling the electric bicycle according to the communication control instruction to complete vehicle-to-vehicle communication.

[0097] In step S1 , electric bicycle data is acquired, wherein the electric bicycle data includes vehicle location data, speed data, battery status data, own historical data and historical communication rate.

[0098] In one implementation, a method for obtaining electric bicycle data includes:

[0099] The data acquisition of the electric bicycle itself is achieved through various sensors installed on the electric bicycle. These sensors continuously collect key data such as speed, position, battery power, etc., and transmit them to the central controller of the electric bicycle, which initially integrates and processes the received data. The integrated data is further transmitted to a remote server or the user's mobile device through a communication module such as Bluetooth, Wi-Fi or cellular network, thereby realizing real-time monitoring, data analysis and control of the electric bicycle;

[0100] The acquisition of other electric bicycle data is achieved by using network communication technologies such as Bluetooth, Wi-Fi or cellular networks to establish connections with other vehicles through communication modules. Through this connection, the electric bicycle can send requests to surrounding vehicles to obtain status data of other vehicles;

[0101] It should be noted that data requests can be coordinated through specific communication protocols (such as V2X, vehicle-to-vehicle communication protocol) to ensure the security and stability of data exchange. After the data is acquired, the system will integrate this data with its own data to provide basic support for subsequent collaborative control and path optimization.

[0102] In step S2, based on the electric bicycle Internet of Vehicles, the battery status data is subjected to data processing operations to obtain a data packet of the battery status, and the data packet is subjected to data updating operations with the own historical data to obtain an updated data packet.

[0103] In one implementation, based on the electric bicycle Internet of Vehicles, the battery status data is processed to obtain a data packet of the battery status, and the data packet is updated with the own historical data to obtain an updated data packet, including:

[0104] According to the battery status data, the battery status data packet is input into the electric bicycle vehicle network to construct the battery status data packet;

[0105] The electric bicycle receives the battery status data packet, stores the own historical data and the historical data of the battery status data packet in two queues respectively, and calculates the data packet length by the following formula:

[0106]

[0107] Among them, α represents the length of the data packet, k represents the time when a data packet is received, and d i represents the historical data update value of the received data packets, d represents the standardized distance constant, and n represents the distance between the two queues;

[0108] Performing a data update operation according to the data packet length and the update time to obtain the update data packet;

[0109] Furthermore, the update time includes:

[0110] The data packet length is used as input to perform a time update operation. The time update method is defined as follows:

[0111] d i,k =d i,k-1 +(g0·α)·Y k

[0112] Among them, d i,k represents the update time at time k, d i,k-1 represents the historical moment of the previous moment k-1, g0 represents the reference gain factor, Y k Indicates that the obedience parameter is σ 2 A Gaussian distributed random variable;

[0113] Furthermore, the constraints of the Gaussian random variable include:

[0114] Define Y k The expectation of is 0, and the expectation of its quadratic power is constrained to control the fluctuation range of the data. The constraint formula is as follows:

[0115] E[Y k ]=0,

[0116] Among them, η 2 represents the noise variance, and T represents the sampling period.

[0117] In step S3, a calculation operation is performed based on the update data packet to obtain the connectivity index and communication reception rate of the electric bicycle, and the networking status and communication transmission data between the electric bicycle and other vehicles are analyzed based on the connectivity index and the communication reception rate.

[0118] In one implementation, a calculation operation is performed according to the update data packet to obtain a connectivity index and a communication reception rate of the electric bicycle, and a networking status and communication transmission data between the electric bicycle and other vehicles are analyzed according to the connectivity index and the communication reception rate, including:

[0119] The calculation formula of the connectivity index is expressed as:

[0120]

[0121] Where N represents the connectivity index of the electric bicycle, n represents the number of all other electric bicycles within the communication range, and w ij represents the connection weight of the electric bicycle, R ij represents the communication reception rate of the electric bicycle;

[0122] It should be noted that the connectivity index formula calculates the sum of the weighted communication reception rates of electric bicycle i and all other electric bicycles j, with the weight w ij indicates whether there is communication between the two electric bicycles, and R ij It represents the proportion of data packets received by electric bicycle i from electric bicycle j in the case of successful communication;

[0123] The connection weight of the electric bicycle, when the electric bicycle i establishes communication with the electric bicycle j, w ij The value is 1; when the electric bicycle i and the electric bicycle j have not established communication, w ij The value is 0;

[0124] The communication reception rate of the electric bicycle is calculated as follows:

[0125]

[0126] Among them, y ij,k represents the indicator variable. When electric bicycle i successfully receives the data packet from electric bicycle j, y ij,k The value is 1; when electric bicycle i fails to successfully receive the data packet from electric bicycle j, y ij,k The value is 0; m represents the total number of data packets sent by electric bicycle j to electric bicycle i in a given time period;

[0127] Furthermore, the networking status and communication transmission data of the electric bicycle and other vehicles are obtained according to the connectivity index and the communication reception rate analysis, including:

[0128] The determination method of the networking status is expressed as follows:

[0129] If N ≥ θ, the electric bicycle remains connected to the Internet of Vehicles

[0130] If N<θ, the electric bicycle is in a disconnected state.

[0131] Among them, θ represents the preset connection threshold;

[0132] According to the communication reception rate R ij and the rate of each connection, calculate the total communication transmission data of the electric bicycle i, and the calculation formula is expressed as:

[0133]

[0134] Among them, D i represents the total communication transmission data of the electric bicycle i, s j represents the communication rate of each connected bicycle j, R ij represents the communication reception rate of the electric bicycle i, and T represents the communication time.

[0135] It should be noted that for electric bicycle i, by setting the communication rate S between it and each connected vehicle j j , receiving rate R ijThe communication data volume of a single connection can be obtained by multiplying the communication data volume of all connections by the communication time T. The total transmission data volume D of the electric bicycle i within the specified communication time can be obtained by adding the communication data volume of all connections. t .

[0136] In step S4, a calculation and analysis operation is performed according to the networking status and the communication transmission data to obtain an actual communication rate of the electric bicycle.

[0137] In one implementation, the performing of calculation and analysis operations according to the networking status and the communication transmission data to obtain the actual communication rate of the electric bicycle includes:

[0138] If the electric bicycle i establishes a connection with other vehicles, the actual communication rate of the electric bicycle is determined by the maximum data transmission rate;

[0139] The calculation formula of the data transmission rate of the electric bicycle i is expressed as:

[0140] c i =s j ·w ij

[0141] Among them, c i represents the data transmission rate of the electric bicycle i, s j represents the data transmission rate of electric bicycle j, w ij represents the connection weight of the electric bicycle;

[0142] According to the data transmission rate, the actual communication rate is obtained:

[0143] It should be noted that, among all connections, the actual communication rate of electric bicycle i is determined by the maximum value of its communication transmission rate with other vehicles, so the connection with the highest communication rate is selected to ensure the best communication performance.

[0144] In step S5, the control variable, the communication reception signal-to-noise ratio and the signal-to-noise ratio variance corresponding to the electric bicycle are calculated according to the actual communication rate.

[0145] In one implementation, according to the actual communication rate, calculating the control variable, the communication reception signal-to-noise ratio and the signal-to-noise ratio variance corresponding to the electric bicycle includes:

[0146] The control variable is calculated as follows:

[0147]

[0148] Among them, u i represents the control variable, si represents the actual communication rate of the electric bicycle i, which is regarded as the state variable of the system, p i represents the power consumption of the electric bicycle i, and A represents the control gain constant that affects the communication rate;

[0149] Furthermore, the control variables are normalized, and the calculation formula is as follows:

[0150]

[0151] in, represents the normalized control variable, P max Indicates the maximum power consumption threshold, P max =P u +P c , P u Indicates the maximum available power of the electric bicycle, P c Indicates the power consumption of the controller;

[0152] The communication reception signal-to-noise ratio is calculated as follows:

[0153]

[0154] Among them, γ ij It is represented by the communication receiving signal-to-noise ratio of the electric bicycle i, S ij represents the signal power of the electric bicycle i receiving the electric bicycle j, N ij represents the noise power;

[0155] It should be noted that

[0156] The signal-to-noise ratio variance is calculated as follows:

[0157] Ω=E[γ ij -E[γ ij ]) 2 ]

[0158] Among them, E[γ ij ] is expressed as the expected value of all signal-to-noise ratios;

[0159] It should be noted that the communication receiving signal-to-noise ratio of electric bicycle i at time k is γ k , which reflects the strength ratio of the communication signal to the noise. When electric bicycle i establishes a connection with other electric bicycles j (i.e., i≠j), the signal-to-noise ratio γ received by vehicle i k will be related to the received noise power P I At this time, the noise power received by electric bicycle i is in the signal-to-noise ratio γ kThe average value is taken to reflect the average signal quality of multiple connections. If the communication between electric bicycle i and electric bicycle j fails, the signal-to-noise ratio γ k =0, otherwise, γ k >0;

[0160] Furthermore, the noise power P1 received by vehicle i is calculated by the following formula:

[0161] P I =λ i ·N0·Δf

[0162] Among them, λ i represents the occupancy rate of the receiver receiving frequency band, N0 represents the noise power spectral density, and Δf is the bandwidth of the vehicle network signal. This formula quantifies the total noise power received by the vehicle, reflecting its relative ratio to the communication signal.

[0163] In step S6, a reinforcement learning model is constructed and trained according to the actual communication rate, the control variable and the signal-to-noise ratio variance, and an output result of the reinforcement learning model is determined as a communication control instruction.

[0164] In one implementation, a reinforcement learning model is constructed and trained according to the actual communication rate, the control variable, and the signal-to-noise ratio variance, and an output result of the reinforcement learning model is determined as a communication control instruction, including:

[0165] Construct a reinforcement learning model (R,M,γ,r), where

[0166] State R represents the current communication state, including the actual communication rate S i and signal-to-noise ratio variance Ω;

[0167] Action M represents the control variable u i The choice of affects the communication rate and signal-to-noise ratio;

[0168] The discount factor γ represents the influence factor of future rewards on current decisions;

[0169] The reward function r represents the communication quality Q i,j and signal-to-noise ratio optimization function;

[0170] The communication quality Q i,j is the trade-off model between the communication rate and the communication quality of electric bicycle i, and the calculation formula is expressed as:

[0171]

[0172] in:

[0173] U1=[U1(t1),U1(t2),…,U1(tN )] represents the communication rate vector;

[0174] U2=[U2(t1),U2(t2),…,U2(t N )] represents the communication quality vector;

[0175] U1(t a ) represents the electric bicycle i at t a The communication rate at the moment, U2(t a ) indicates that the electric bicycle i is at t a The communication quality at the moment, W t Expressed as learning rate;

[0176] The reinforcement learning model is trained according to the state R, the action M, the discount factor γ and the reward function r, and the training is determined to be completed when the number of training times is greater than a preset number threshold, or the reward value of the reward function is greater than a preset reward threshold, to obtain a trained reinforcement learning model;

[0177] Determine the output result of the trained reinforcement learning model as a communication control instruction, wherein the communication control instruction includes a communication rate adjustment instruction, a signal enhancement instruction, a connection management instruction, a data packet sending instruction, a dynamic adjustment instruction, and an exception handling instruction;

[0178] Furthermore, with the maximum total communication quality Q i An objective function is constructed for the goal. The setting of this objective function is based on the dynamic adjustment of the time difference T and the estimation of the historical communication rate to improve the communication quality of the system. The calculation formula of the objective function is as follows:

[0179]

[0180] Among them, Q i,j represents the sum of the communication quality between electric bicycles i and j in different time periods, and t0 represents the initial time;

[0181] By maximizing the objective function Q i ,The system can find the best balance between the communication rate and ,quality of electric bicycles to achieve efficient and stable network ,communication.

[0182] In step S7, the electric bicycle is controlled according to the communication control instruction to complete vehicle-to-vehicle communication.

[0183] In one implementation, controlling the electric bicycle according to the communication control instruction to complete vehicle-to-vehicle communication includes:

[0184] The electric bicycle control system directly applies the generated communication control instructions to the communication module of the electric bicycle to control it. Through these instructions, reliable communication between the electric bicycle and other vehicles can be achieved in a dynamic driving environment;

[0185] It should be noted that the role of the communication rate adjustment instruction is to automatically adjust the rate when the signal quality fluctuates to ensure the stability and adaptability of data transmission; the role of the signal enhancement instruction is to increase the transmission power or switch to a channel with less interference when the signal weakens to ensure reliable communication; the role of the connection management instruction is to establish, maintain, switch or disconnect the communication connection to ensure the communication continuity of the vehicle during dynamic driving; the role of the data packet sending control instruction is to control the frequency and timing of data transmission to avoid congestion and improve transmission efficiency;

[0186] The function of the dynamic adjustment instruction is to adjust the communication parameters in real time based on information such as vehicle speed and position to adapt to different scenarios; the function of the exception handling instruction is to respond to emergencies such as signal loss, trigger emergency measures, and ensure stable communication.

[0187] In order to facilitate the understanding of the present invention, some preferred embodiments of the present invention are further described below.

[0188] The following describes the working process of the present invention using a relatively common scenario as an example. The working process is as follows:

[0189] Step 1: Obtain the vehicle's location, speed, temperature, humidity, wind speed, wind direction and other information;

[0190] Step 2: Preprocess the acquired vehicle data, including data collection and data cleaning;

[0191] Step 3: Construct a formula algorithm based on vehicle speed, communication signal strength and signal frequency to generate a communication transmission strategy;

[0192] Step 4: Perform communication transmission between electric bicycles according to the generated communication transmission strategy;

[0193] Step 5: Monitor the influencing factors in the communication process in real time and dynamically adjust the communication transmission strategy.

[0194] Preferably, in step 1, multiple sensors are used to obtain vehicle information, including a GPS module to obtain location information, a speed sensor to obtain speed information, a temperature sensor to obtain temperature information, a humidity sensor to obtain humidity information, a wind speed sensor to obtain wind speed information, and a wind direction sensor to obtain wind direction information. The data collection frequency can be adjusted according to actual needs, for example, once per second or once per minute;

[0195] It should be noted that the accuracy and frequency of data acquisition directly affect the effect of subsequent data processing. Therefore, high-precision sensors should be selected and the acquisition frequency should be set reasonably.

[0196] Preferably, in step 2, data preprocessing includes data cleaning and data correction. Data cleaning removes noise and outliers by filtering, smoothing and other methods to ensure the accuracy and consistency of the data. Data correction eliminates system errors by zero point correction, linear correction and other methods. Data integration integrates data from different sensors into a unified data set to facilitate subsequent processing and analysis.

[0197] Preferably, in step 3, based on the vehicle speed, communication signal strength and signal frequency, a calculation formula for constructing a comprehensive function is as follows:

[0198] S total =a·e -b·d +c·v+k·f+m

[0199] Among them, S total represents the comprehensive signal strength, a, b, c, k and m are constants, v represents the vehicle speed, f represents the signal frequency, and the parameters in the formula are continuously optimized by the least squares method, gradient descent method or genetic algorithm to ensure the accuracy of the model;

[0200] It should be noted that the construction and optimization of formula algorithms are the key to generating effective communication transmission strategies, and they need to be verified and adjusted multiple times based on actual data.

[0201] Preferably, in step 4, communication transmission between electric bicycles is performed according to the generated communication transmission strategy. The communication strategy includes the optimization settings of signal frequency, strength, modulation mode and coding mode. For example, when the vehicle speed is high, the signal frequency is appropriately reduced to reduce the influence of the Doppler effect; when the communication distance is far, the signal strength is increased to compensate for signal attenuation.

[0202] It should be noted that the implementation of communication transmission strategies needs to take into account changes in the actual communication environment and ensure communication quality under different conditions.

[0203] Preferably, in step 5, various influencing factors in the communication process are monitored in real time, such as communication distance, obstacles, weather, electromagnetic interference, etc. According to the monitoring results, the communication transmission strategy is dynamically adjusted to ensure the communication quality. The monitoring method includes real-time data stream processing and sensor fusion.

[0204] It should be noted that real-time monitoring and dynamic adjustment are the key to improving the robustness and adaptability of communication systems. Timeliness of monitoring and effectiveness of adjustment must be ensured;

[0205] Preferably, the present invention also adds a competition mechanism, such as setting a reward mechanism in the vehicle-to-vehicle communication model to encourage electric bicycle users to actively participate in communication and information sharing. Reward users through points, coupons, etc. to improve communication efficiency;

[0206] It should be noted that competition mechanisms and user incentives can increase user participation and system activity, and further optimize the performance of the communication system.

[0207] Through the above specific implementation methods, the present invention can effectively optimize the communication transmission strategy between electric bicycles, improve the communication quality and transmission efficiency. At the same time, by introducing competition mechanism and user incentive measures, the practicality of the system and user experience are further improved.

[0208] Reference Figure 2 The vehicle-to-vehicle communication between the electric bicycles includes two communication modes: direct communication transmission and indirect information exchange through the Internet of Vehicles.

[0209] Furthermore, in direct communication transmission, under the premise of determining the vehicle-to-vehicle network, the electric bicycle i and the electric bicycle j can directly transmit and receive signals to each other and share real-time information. This method is suitable for low-latency data exchange between vehicles, which helps to transmit key information such as driving status, location, speed, etc., and ensure safe distance and driving coordination between vehicles;

[0210] Furthermore, the electric bicycle Internet of Vehicles serves as a hub, and each vehicle uploads its own data to share with other vehicles. The electric bicycle i and the electric bicycle j send their respective status data packets (such as data packet i and data packet j) to the Internet of Vehicles, and other networked vehicles can obtain this information through the network.

[0211] Combining direct communication and vehicle networking, vehicles can share information at the local and global levels, improving the overall e-bike communication transmission efficiency.

[0212] In summary, the present invention provides a communication transmission control method for an electric bicycle, the method comprising: acquiring electric bicycle data, the electric bicycle data comprising vehicle location data, speed data, battery status data, its own historical data and historical communication rate; based on the electric bicycle vehicle networking, performing data processing operations on the battery status data to obtain a data packet of the battery status, performing data updating operations on the data packet and its own historical data to obtain an updated data packet; performing calculation operations based on the updated data packet to obtain a connectivity index and a communication reception rate of the electric bicycle, and analyzing the connectivity index and the communication reception rate to obtain a correlation between the electric bicycle and other vehicles. Network status and communication transmission data; perform calculation and analysis operations according to the network status and the communication transmission data to obtain the actual communication rate of the electric bicycle; calculate the control variables, communication reception signal-to-noise ratio and signal-to-noise ratio variance corresponding to the electric bicycle according to the actual communication rate; construct and train a reinforcement learning model according to the actual communication rate, the control variables and the signal-to-noise ratio variance, and determine the output result of the reinforcement learning model as a communication control instruction, the communication control instruction including a communication rate adjustment instruction, a signal enhancement instruction, a connection management instruction, a data packet sending instruction, a dynamic adjustment instruction and an exception handling instruction; control the electric bicycle according to the communication control instruction to complete vehicle-to-vehicle communication.

[0213] This method combines direct communication and vehicle networking, and vehicles can share information at the local and global levels, improving the overall communication transmission efficiency of electric bicycles.

[0214] Reference Figure 3 The second embodiment of the present invention provides a communication transmission control system for an electric bicycle, comprising:

[0215] 101 an electric bicycle data acquisition module, used to acquire electric bicycle data, the electric bicycle data including vehicle location data, speed data, battery status data, own historical data and historical communication rate;

[0216] 102 is an update data packet acquisition module, which is used to perform a data processing operation on the battery status data based on the electric bicycle vehicle network to obtain a data packet of the battery status, and perform a data update operation on the data packet and the own historical data to obtain an update data packet;

[0217] 103 a network status acquisition module, for performing calculation operations according to the update data packet to obtain a connectivity index and a communication reception rate of the electric bicycle, and obtaining a network status and communication transmission data between the electric bicycle and other vehicles according to the connectivity index and the communication reception rate;

[0218] 104 an actual communication rate acquisition module, used to perform calculation and analysis operations according to the networking state and the communication transmission data to obtain the actual communication rate of the electric bicycle;

[0219] 105 parameter calculation module, used for calculating the control variable, communication reception signal-to-noise ratio and signal-to-noise ratio variance corresponding to the electric bicycle according to the actual communication rate;

[0220] 106 a communication control instruction acquisition module, configured to construct and train a reinforcement learning model according to the actual communication rate, the control variable and the signal-to-noise ratio variance, and determine the output result of the reinforcement learning model as a communication control instruction; the communication control instruction includes a communication rate adjustment instruction, a signal enhancement instruction, a connection management instruction, a data packet sending instruction, a dynamic adjustment instruction and an exception handling instruction;

[0221] 107 vehicle-to-vehicle communication module, used to control the electric bicycle according to the communication control instruction to complete vehicle-to-vehicle communication.

[0222] It should be noted that a communication transmission control system for an electric bicycle provided in an embodiment of the present invention is used to execute all the process steps of a communication transmission control method for an electric bicycle in the above embodiment. The working principles and beneficial effects of the two correspond one to one, and thus will not be repeated herein.

[0223] In summary, the present invention provides a communication transmission control method for an electric bicycle, the method comprising: acquiring electric bicycle data, the electric bicycle data comprising vehicle location data, speed data, battery status data, own historical data and historical communication rate; based on the electric bicycle vehicle network, performing data processing operations on the battery status data to obtain a data packet of the battery status, performing data updating operations on the data packet and the own historical data to obtain an updated data packet; performing calculation operations based on the updated data packet to obtain a connectivity index and a communication reception rate of the electric bicycle, and analyzing the connectivity index and the communication reception rate to obtain the connectivity between the electric bicycle and other vehicles. The invention relates to a method for controlling the electric bicycle by using a plurality of control variables, a plurality of control variables and a plurality of control variables, and a plurality of control variables. The method comprises: obtaining a network status and communication transmission data; performing calculation and analysis operations according to the network status and the communication transmission data to obtain an actual communication rate of the electric bicycle; calculating a control variable, a communication reception signal-to-noise ratio and a signal-to-noise ratio variance corresponding to the electric bicycle according to the actual communication rate; constructing and training a reinforcement learning model according to the actual communication rate, the control variable and the signal-to-noise ratio variance, and determining an output result of the reinforcement learning model as a communication control instruction, wherein the communication control instruction includes a communication rate adjustment instruction, a signal enhancement instruction, a connection management instruction, a data packet sending instruction, a dynamic adjustment instruction and an exception handling instruction; and controlling the electric bicycle according to the communication control instruction to complete vehicle-to-vehicle communication.

[0224] The method utilizes the electric bicycle vehicle network to obtain vehicle data and construct a formula algorithm model, and uses the vehicle data as input data, which is input into the formula algorithm model for analysis and data processing to form a data model; the data model is transmitted to a central processing unit to determine parameters and communication strategies, and a vehicle-to-vehicle communication model is constructed, thereby realizing efficient communication transmission between electric bicycles.

[0225] The embodiment of the present invention also provides an electronic device. The electronic device includes: a processor, a memory, and a computer program stored in the memory and executable on the processor, such as a communication transmission control method program for an electric bicycle. When the processor executes the computer program, the steps in the above-mentioned embodiments of the communication transmission control method for an electric bicycle are implemented, such as Figure 1 Alternatively, when the processor executes the computer program, the functions of the modules / units in the above-mentioned device embodiments are realized, for example, 101 is an electric bicycle data acquisition module.

[0226] Exemplarily, the computer program may be divided into one or more modules / units, which are stored in the memory and executed by the processor to implement the present invention. The one or more modules / units may be a series of computer program instruction segments capable of implementing specific functions, which are used to describe the execution process of the computer program in the electronic device.

[0227] The electronic device may be a computing device such as a desktop computer, a notebook, a PDA, and a smart tablet. The electronic device may include, but is not limited to, a processor and a memory. Those skilled in the art will appreciate that the above components are merely examples of electronic devices and do not constitute a limitation on the electronic device. The electronic device may include more or fewer components than the above components, or may combine certain components, or different components. For example, the electronic device may also include input and output devices, network access devices, buses, etc.

[0228] The processor may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc. The processor is the control center of the electronic device, and uses various interfaces and lines to connect various parts of the entire electronic device.

[0229] The memory can be used to store the computer program and / or module, and the processor realizes various functions of the electronic device by running or executing the computer program and / or module stored in the memory, and calling the data stored in the memory. The memory can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, an application required for at least one function (such as a sound playback function, an image playback function, etc.), etc.; the data storage area can store data created according to the use of the mobile phone (such as audio data, a phone book, etc.), etc. In addition, the memory can include a high-speed random access memory, and can also include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), at least one disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0230] Wherein, if the module / unit integrated in the electronic device is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on such an understanding, the present invention implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. Wherein, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electric carrier signal, telecommunication signal and software distribution medium. It should be noted that the content contained in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electric carrier signals and telecommunication signals.

[0231] It should be noted that the device embodiments described above are merely schematic, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. In addition, in the accompanying drawings of the device embodiments provided by the present invention, the connection relationship between the modules indicates that there is a communication connection between them, which may be specifically implemented as one or more communication buses or signal lines. A person of ordinary skill in the art may understand and implement it without paying any creative effort.

[0232] The specific embodiments described above further illustrate the purpose, technical solutions and beneficial effects of the present invention. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. It is particularly pointed out that for those skilled in the art, any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the scope of protection of the present invention.

Claims

1. A communication transmission control method for an electric bicycle, characterized in that: Executed by the data receiving end, including: Acquire electric bicycle data, wherein the electric bicycle data includes vehicle location data, speed data, battery status data, own historical data and historical communication rate; Based on the electric bicycle Internet of Vehicles, the battery status data is processed to obtain a data packet of the battery status, and the data packet is updated with the own historical data to obtain an updated data packet; Performing calculation operations according to the update data packet to obtain a connectivity index and a communication reception rate of the electric bicycle, and analyzing the connectivity index and the communication reception rate to obtain a networking status and communication transmission data between the electric bicycle and other vehicles; Performing calculation and analysis operations according to the networking status and the communication transmission data to obtain an actual communication rate of the electric bicycle; According to the actual communication rate, calculating the control variable, the communication reception signal-to-noise ratio and the signal-to-noise ratio variance corresponding to the electric bicycle; According to the actual communication rate, the control variable and the signal-to-noise ratio variance, a reinforcement learning model is constructed and trained, and an output result of the reinforcement learning model is determined as a communication control instruction, wherein the communication control instruction includes a communication rate adjustment instruction, a signal enhancement instruction, a connection management instruction, a data packet sending instruction, a dynamic adjustment instruction and an exception handling instruction; Controlling the electric bicycle according to the communication control instruction to complete vehicle-to-vehicle communication; Wherein, based on the electric bicycle Internet of Vehicles, the battery status data is subjected to data processing operations to obtain a data packet of the battery status, and the data packet is subjected to data updating operations with the own historical data to obtain an updated data packet, including: According to the battery status data, the battery status data is input into the electric bicycle vehicle network to construct the battery status data packet; The electric bicycle receives the battery status data packet, stores the own historical data and the historical data of the battery status data packet in two queues respectively, and calculates the data packet length by the following formula: Among them, α represents the length of the data packet, k represents the time when a data packet is received, and d i represents the historical data update value of the received data packets, d represents the standardized distance constant, and n represents the distance between the two queues; Performing a data update operation according to the data packet length and the update time to obtain the update data packet; The update time includes: The data packet length is used as input to perform a time update operation. The time update method is defined as follows: d i,k =d i,k-1 +(g0·α)·Y k Among them, d i,k represents the update time at time k, d i,k-1 represents the historical moment of the previous moment k-1, g0 represents the reference gain factor, Y k Indicates that the obedience parameter is σ 2 A Gaussian distributed random variable.

2. A communication transmission control method for an electric bicycle according to claim 1, characterized in that: The step of performing a calculation operation according to the update data packet to obtain a connectivity index and a communication reception rate of the electric bicycle includes: The calculation formula of the connectivity index is expressed as: Where N represents the connectivity index of the electric bicycle, n represents the number of all other electric bicycles within the communication range, and w ij represents the connection weight of the electric bicycle, R ij represents the communication reception rate of the electric bicycle; The connection weight of the electric bicycle, when the electric bicycle i establishes communication with the electric bicycle j, w ij The value is 1; when the electric bicycle i and the electric bicycle j have not established communication, w ij The value is 0; The communication reception rate of the electric bicycle is calculated as follows: Among them, y ij,k represents the indicator variable. When electric bicycle i successfully receives the data packet from electric bicycle j, y ij,k The value is 1; when electric bicycle i fails to successfully receive the data packet from electric bicycle j, y ij,k The value is 0; m represents the total number of data packets sent by electric bicycle j to electric bicycle i in a given time period.

3. A communication transmission control method for an electric bicycle according to claim 2, characterized in that: The obtaining of the networking status and communication transmission data between the electric bicycle and other vehicles according to the connectivity index and the communication reception rate analysis includes: The determination method of the networking status is expressed as follows: If N ≥ θ, the electric bicycle remains connected to the Internet of Vehicles; If N<θ, the electric bicycle is in a disconnected state; Among them, θ represents the preset connection threshold; According to the communication reception rate R ij and the rate of each connection, calculate the total communication transmission data of the electric bicycle i, and the calculation formula is expressed as: Among them, D i represents the total communication transmission data of the electric bicycle i, s j represents the communication rate of each connected bicycle j, R ij represents the communication reception rate of the electric bicycle i, and T represents the communication time.

4. A communication transmission control method for an electric bicycle according to claim 2, characterized in that: The performing of calculation and analysis operations according to the networking state and the communication transmission data to obtain the actual communication rate of the electric bicycle includes: If the electric bicycle i establishes a connection with other vehicles, the actual communication rate of the electric bicycle is determined by the maximum data transmission rate; The calculation formula of the data transmission rate of the electric bicycle i is expressed as: c i =s j ·w ij Among them, c i represents the data transmission rate of the electric bicycle i, s j represents the data transmission rate of electric bicycle j, w ij represents the connection weight of the electric bicycle; According to the data transmission rate, the actual communication rate is obtained:

5. A communication transmission control method for an electric bicycle according to claim 2, characterized in that: According to the actual communication rate, the control variable, the communication reception signal-to-noise ratio and the signal-to-noise ratio variance corresponding to the electric bicycle are calculated, including: The control variable is calculated as follows: Among them, u i represents the control variable, s i represents the actual communication rate of the electric bicycle i, which is regarded as the state variable of the system, p i represents the power consumption of the electric bicycle i, and A represents the control gain constant that affects the communication rate; The communication reception signal-to-noise ratio is calculated as follows: Among them, γ ij It is represented by the communication receiving signal-to-noise ratio of the electric bicycle i, S ij represents the signal power of the electric bicycle i receiving the electric bicycle j, N ij represents the noise power; The signal-to-noise ratio variance is calculated as follows: Among them, E[γ ij ] is expressed as the expected value of all signal-to-noise ratios.

6. A communication transmission control method for an electric bicycle according to claim 1, characterized in that: According to the actual communication rate, the control variable and the signal-to-noise ratio variance, a reinforcement learning model is constructed and trained, and an output result of the reinforcement learning model is determined as a communication control instruction, including: Construct a reinforcement learning model (R,M,γ,r), where State R represents the current communication state, including the actual communication rate S i and signal-to-noise ratio variance Ω; Action M represents the control variable u i The choice of affects the communication rate and signal-to-noise ratio; The discount factor γ represents the influence factor of future rewards on current decisions; The reward function r represents the communication quality Q i,j and signal-to-noise ratio optimization function; The communication quality Q i,j is the trade-off model between the communication rate and the communication quality of electric bicycle i, and the calculation formula is expressed as: in: U1=[U1(t1),U1(t2),…,U1(t N )] represents the communication rate vector; U2=[U2(t1),U2(t2),…,U2(t N )] represents the communication quality vector; U1(t a ) represents the electric bicycle i at t a The communication rate at the moment, U2(t a ) indicates that the electric bicycle i is at t a The communication quality at the moment, W t Expressed as learning rate; The reinforcement learning model is trained according to the state R, the action M, the discount factor γ and the reward function r, and the training is determined to be completed when the number of training times is greater than a preset number threshold, or the reward value of the reward function is greater than a preset reward threshold, to obtain a trained reinforcement learning model; The output result of the trained reinforcement learning model is determined as a communication control instruction.

7. A communication transmission control system for an electric bicycle, characterized in that: The method is configured at a data receiving end and is used to execute a communication transmission control method for an electric bicycle as claimed in any one of claims 1 to 6, comprising: An electric bicycle data acquisition module is used to acquire electric bicycle data, wherein the electric bicycle data includes vehicle location data, speed data, battery status data, own historical data and historical communication rate; An update data packet acquisition module is used to perform data processing operations on the battery status data based on the electric bicycle vehicle network to obtain a data packet of the battery status, and perform data update operations on the data packet and its own historical data to obtain an update data packet; A network status acquisition module, used to perform calculation operations according to the update data packet to obtain a connectivity index and a communication reception rate of the electric bicycle, and to analyze and obtain a network status and communication transmission data between the electric bicycle and other vehicles according to the connectivity index and the communication reception rate; An actual communication rate acquisition module, used to perform calculation and analysis operations according to the networking status and the communication transmission data to obtain the actual communication rate of the electric bicycle; A parameter calculation module, used for calculating the control variable, the communication reception signal-to-noise ratio and the signal-to-noise ratio variance corresponding to the electric bicycle according to the actual communication rate; A communication control instruction acquisition module, used to construct and train a reinforcement learning model according to the actual communication rate, the control variable and the signal-to-noise ratio variance, and determine the output result of the reinforcement learning model as a communication control instruction; the communication control instruction includes a communication rate adjustment instruction, a signal enhancement instruction, a connection management instruction, a data packet sending instruction, a dynamic adjustment instruction and an exception handling instruction; The vehicle-to-vehicle communication module is used to control the electric bicycle according to the communication control instruction to complete the vehicle-to-vehicle communication.

8. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored computer program, wherein when the computer program is executed, the device where the computer-readable storage medium is located is controlled to execute a communication transmission control method for an electric bicycle as described in any one of claims 1 to 6.

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