Electric bicycle charging method and system based on mutual recognition mechanism

Abstract

The invention provides an electric bicycle charging method and system based on a mutual recognition mechanism, and relates to the technical field of battery management, and the method comprises the steps: detecting the state of a battery through a multi-sensor fusion algorithm, predicting the endurance mileage and charging demands through deep reinforcement learning, and predicting the destination and arrival time through track analysis; a charging pile is optimized based on a multi-constraint path planning model and an improved multi-target genetic algorithm, a target charging pile adopts dual encryption identity authentication, a self-adaptive charging control algorithm based on a fuzzy neural network is used for optimizing a charging process, and data is uploaded to a charging scheduling server.

Description

Technical Field

[0001] The present invention relates to the technical field of battery management, and in particular to an electric bicycle charging method and system based on a mutual recognition mechanism. Background Art

[0002] Existing technologies lack accurate assessment of battery health status and range. Most charging systems rely only on simple voltage detection, which cannot accurately reflect the actual performance and remaining range of the battery. As a result, users cannot reasonably plan charging time and often face the risk of sudden power outages, which seriously affects the travel experience.

[0003] Traditional charging pile selection methods lack intelligence and personalization. Users usually need to manually search for nearby charging facilities and are unable to comprehensively consider multiple factors such as route planning, charging efficiency, and waiting time. This not only increases the user's time cost, but may also lead to unreasonable allocation of charging resources, causing some charging piles to be idle for a long time while other charging piles are overcrowded.

[0004] Existing charging control algorithms lack adaptability and security. Most charging systems use fixed charging curves and cannot be dynamically adjusted according to the real-time status of the battery and environmental conditions. This not only affects the charging efficiency, but may also cause problems such as overcharging or undercharging, shortening the battery life. At the same time, there is a lack of effective identity authentication mechanism, which poses a security risk and cannot prevent unauthorized use and potential power theft.

[0005] Therefore, a solution is urgently needed to solve the problems existing in the prior art. Summary of the invention

[0006] The embodiments of the present invention provide an electric bicycle charging method and system based on a mutual recognition mechanism, which can at least solve some of the problems existing in the prior art.

[0007] A first aspect of an embodiment of the present invention provides an electric bicycle charging method based on a mutual recognition mechanism, comprising:

[0008] The current position coordinates and driving trajectory data of the electric bicycle are collected, the battery status is detected through a multi-sensor fusion algorithm, and a battery health status assessment model is established. A cruising range prediction model is constructed based on the battery health status assessment model using a deep reinforcement learning algorithm. The historical riding data, ambient temperature, road condition information, and riding habits are used as input features, and the remaining cruising range and charging demand prediction results are output. The charging demand prediction results are sent to a charging scheduling server, and trajectory features are extracted through a hierarchical time memory structure and a spatial attention mechanism. In combination with road network topology constraints and historical trajectory similarity calculations, the driving trajectory data is deeply analyzed to generate destination prediction results and arrival time prediction results;

[0009] A multi-constraint path planning model is constructed based on the current position coordinates and the destination prediction result, an improved Dijkstra algorithm is used to introduce an adaptive penalty factor to dynamically avoid congested sections, and the path weight is updated online in combination with real-time traffic information, and a candidate path is searched and obtained, and the charging piles on the candidate path are optimized by an improved multi-objective genetic algorithm, and the charging power matching degree, path deviation degree and waiting time are used as optimization objectives, and an adaptive crossover operator and a mutation operator are combined to perform multi-objective solving, generate a Pareto solution set, and score each solution in the Pareto solution set, and select the charging pile with the highest score as the target charging pile;

[0010] The target charging pile adopts an identity authentication mechanism based on double encryption, and authenticates the electric bicycle through a dynamic key generation algorithm and an identity feature extraction algorithm. During the charging process, an adaptive charging control algorithm based on a fuzzy neural network is used to learn the battery charging characteristics online, and a charging condition evaluation model is established in combination with temperature compensation and voltage compensation. A fuzzy inference engine is used to optimize the charging current in real time, and the charging curve is adaptively adjusted according to the battery health status. After charging is completed, the charging process data is uploaded to the charging scheduling server.

[0011] In an optional embodiment,

[0012] The current position coordinates and driving trajectory data of the electric bicycle are collected, the battery status is detected through a multi-sensor fusion algorithm, and a battery health status assessment model is established. Based on the battery health status assessment model, a deep reinforcement learning algorithm is used to build a range prediction model. The historical riding data, ambient temperature, road condition information and riding habits are used as input features, and the output remaining range and charging demand prediction results include:

[0013] The current position coordinates of the electric bicycle are collected according to a positioning module pre-set in the electric bicycle, and the acceleration and angular velocity are collected as riding status data according to a pre-set inertial measurement unit. The controller in the electric bicycle reads the data uploaded by the positioning module and the inertial measurement unit through a bus and generates the corresponding driving track data of the electric bicycle in combination with an electronic map;

[0014] The voltage, current and temperature of the battery are collected respectively according to a pre-set sensor array, wherein the sensor array includes a voltage sensor, a current sensor and a temperature sensor, and the collected signals are filtered, normalized and feature extracted by a multi-sensor fusion algorithm to obtain a multi-dimensional feature vector representing the battery state, and the multi-dimensional feature vector is input into a pre-built battery health state assessment model, wherein the battery health state assessment model is obtained by training according to a support vector machine algorithm, and the health state corresponding to the multi-dimensional feature vector is divided into different levels, and a corresponding battery discharge rate is assigned to each level;

[0015] The historical riding data of the electric bicycle is obtained, including riding time, riding mileage, average speed and average power. At the same time, the ambient temperature data collected by the ambient temperature sensor, the road condition data collected by the road condition sensor and the user riding habit data are obtained, wherein the road condition data includes road type, road slope and road curvature, and the user riding habit data includes riding time period, riding frequency and pedaling force. The historical riding data, ambient temperature, road condition information and riding habits are used as input features, and the battery discharge rate, remaining cruising range and charging demand are used as output targets to construct a cruising range prediction model. The cruising range prediction model is trained according to the deep Q learning algorithm, and a convolutional neural network is used as an approximate function of the Q function and environmental interaction is performed to learn the mileage prediction strategy and obtain the remaining cruising range and charging demand prediction results.

[0016] In an optional embodiment,

[0017] The charging demand prediction result is sent to the charging scheduling server, and the trajectory features are extracted through the hierarchical time memory structure and spatial attention mechanism. The driving trajectory data is deeply analyzed in combination with the road network topology constraints and the historical trajectory similarity calculation to generate the destination prediction result and the arrival time prediction result, including:

[0018] The charging demand prediction result output by the range prediction model is sent to a preset charging scheduling server through a controller set in the electric bicycle. The charging scheduling server parses the demand prediction result and obtains the position coordinate sequence and timestamp sequence of the electric bicycle, and performs feature learning on the driving trajectory of the electric bicycle in combination with a hierarchical time memory structure. The hierarchical time memory structure includes multiple levels of neuron nodes, each neuron node learns the local spatiotemporal features of the driving trajectory, and obtains a hierarchical abstract representation corresponding to the driving trajectory;

[0019] Based on the hierarchical abstract representation, the local spatiotemporal features are weightedly aggregated in combination with the attention mechanism to obtain the context-related representation corresponding to the driving trajectory, and the context-related representation is added to three parallel fully connected networks to predict the destination position, arrival time and remaining power after arrival of the electric bicycle respectively. During the prediction process, the charging scheduling server obtains the road network topology information from the pre-acquired electronic map and maps and constrains the trajectory points. At the same time, the similarity between the current trajectory and the historical trajectory is calculated based on the Hamming distance, and the historical trajectory with the highest similarity is selected as the candidate trajectory. Each candidate trajectory is weighted and fused into the destination position to obtain the destination prediction result and the arrival time prediction result.

[0020] In an optional embodiment,

[0021] A multi-constraint path planning model is constructed based on the current position coordinates and the destination prediction result, an improved Dijkstra algorithm is used to introduce an adaptive penalty factor to dynamically avoid congested sections, and the path weight is updated online in combination with real-time traffic information, and a candidate path is searched, and the charging piles on the candidate path are optimized by an improved multi-objective genetic algorithm, and the charging power matching degree, path deviation degree and waiting time are used as optimization objectives, and an adaptive crossover operator and a mutation operator are combined to perform multi-objective solution, generate a Pareto solution set, and score each solution in the Pareto solution set, and select the charging pile with the highest score as the target charging pile, including:

[0022] After obtaining the current position coordinates and predicted destination position of the electric bicycle, the charging scheduling server constructs a multi-constraint path planning model, which abstracts the road network into a weighted directed graph. The nodes in the graph represent intersections, the edges represent road sections, and the weights of the edges represent the passing cost of the road section corresponding to the current edge. The passing cost is determined according to the length of the road section, the travel time and the energy consumption. The optimization goal is to minimize the total path cost, and the constraints are set in combination with the mileage limit and the available information of the charging pile.

[0023] Solving the driving path of the electric bicycle based on the multi-constraint path planning model and the improved Dijkstra algorithm, obtaining the congestion score of the road section in real time, classifying the traffic status of each road section according to a preset congestion threshold, and if the congestion score is higher than the congestion threshold, the current road section is considered to be a congested road section, and the weight corresponding to the congested road section is increased according to the congestion score and dynamic avoidance is performed, and the weight corresponding to each road section is repeatedly updated and solved to obtain a candidate path;

[0024] Based on the candidate path, the charging piles on the candidate path are selected according to the improved genetic algorithm, and the charging piles on the candidate path are encoded as individuals through real number coding to generate an initial population. For each individual in the initial population, the charging power matching degree, the path deviation degree and the waiting time are used as the optimization objectives of the genetic algorithm, the corresponding fitness value is calculated and non-dominated sorting is performed to determine the non-dominated individuals. For each non-dominated individual, descending sorting is performed based on the fitness value, and the individuals in the top 10% are selected for binary tournament selection, adaptive crossover and mutation operations to generate a child population and repeat the iteration until the preset maximum number of iterations is reached, and the non-dominated individual combination generated by the last iteration is selected to obtain a Pareto solution set. For each solution in the Pareto solution set, the corresponding score is calculated based on the optimization objective of the genetic algorithm, and the charging pile corresponding to the solution with the highest score is selected as the target charging pile.

[0025] In an optional embodiment,

[0026] The weight corresponding to each road section is updated according to the improved Dijkstra algorithm as shown in the following formula:

[0027]

[0028] Among them, W n represents the edge weight after update, W0 represents the edge weight before update, α represents the congestion penalty weight, I(SC>TH) represents the indicator function, which is used to indicate whether the congestion score of the current road section is higher than the preset congestion threshold. If it is higher, the indicator function takes the value of 1, otherwise it takes 0. SC represents the congestion score corresponding to the current road section, TH represents the preset congestion threshold, CP represents the congestion penalty factor, β represents the dynamic adjustment weight, and DT represents the dynamic adjustment factor.

[0029] In an optional embodiment,

[0030] The target charging pile adopts an identity authentication mechanism based on double encryption, and the identity authentication of the electric bicycle is performed through a dynamic key generation algorithm and an identity feature extraction algorithm, including:

[0031] The target charging pile determines the precise timestamp of receiving the identity authentication request of the electric bicycle through an identity authentication mechanism based on double encryption and in combination with a pre-selected key generation algorithm, converts the precise timestamp into a text string in a fixed format and records it as a timestamp text string, generates a random number, converts the random number into a random number text string and concatenates it with the timestamp text string, generates an intermediate key in combination with a substitution operation and an obfuscation operation and adds the intermediate key to a pre-set one-way mapping function, generates the dynamic key, sends the dynamic key to the electric bicycle through a secure channel, and after receiving the dynamic key, the electric bicycle encrypts the dynamic key through a key encryption algorithm to obtain an encrypted authentication key;

[0032] The electric bicycle sends the encrypted authentication key and identity feature information to the target charging pile, wherein the identity feature information includes the unique identification code of the electric bicycle, the battery type and the rated voltage. After receiving the information sent by the electric bicycle, the target charging pile decrypts the authentication key through a key encryption algorithm and compares it with the dynamic key before sending. If they are consistent, it is considered that the electric bicycle has passed the key verification, extracts the unique identification code from the identity feature information and matches it with a pre-set legal identity feature library. If the match is successful, it is considered that the electric bicycle has passed the identity authentication and is allowed to access charging. Otherwise, the charging request is rejected.

[0033] In an optional embodiment,

[0034] During the charging process, an adaptive charging control algorithm based on a fuzzy neural network is used to learn the battery charging characteristics online, a charging condition evaluation model is established in combination with temperature compensation and voltage compensation, a fuzzy inference engine is used to optimize the charging current in real time, and the charging curve is adaptively adjusted according to the battery health status. After charging is completed, the charging process data is uploaded to the charging scheduling server, including:

[0035] If the electric bicycle passes the identity authentication, the target charging pile is charged. During the charging process, the target charging pile obtains the voltage parameter, current parameter and temperature parameter in the charging process through the adaptive charging control algorithm based on the fuzzy neural network and the pre-set sensor and uses them as the battery charging characteristics. The battery charging characteristics are added to the pre-set fuzzy neural network model, and the battery charging characteristics are mapped to the fuzzy set through the membership function. The decision is made through the fuzzy inference rule to obtain the defuzzified control output;

[0036] Based on the defuzzified control output, combined with the temperature parameters and voltage parameters during charging, piecewise linear fitting and polynomial fitting are performed according to the theoretical model and experimental data of the current electric bicycle to generate voltage compensation rules and temperature compensation rules. Based on the voltage compensation rules and the temperature compensation rules, a voltage correction coefficient and a temperature correction coefficient are generated and sent to the fuzzy reasoning engine for voltage optimization and temperature optimization, and real-time optimization of the charging current is achieved according to the optimization results;

[0037] Obtaining the battery usage time and charging and discharging records of the electric bicycle, evaluating the battery health status of the electric bicycle based on the Kalman filter algorithm to obtain the battery health, and adaptively adjusting the charging curve based on the battery health in combination with the control method corresponding to the charging curve, if the battery health is greater than a preset health threshold, increasing the constant current charging current and constant voltage charging voltage, otherwise reducing the constant current charging current and constant voltage charging voltage until charging is completed;

[0038] After charging is completed, the charging start time, charging end time, charging quantity and average charging power are sent to the charging scheduling server as charging process data.

[0039] A second aspect of an embodiment of the present invention provides an electric bicycle charging system based on a mutual recognition mechanism, comprising:

[0040] The first unit is used to collect the current position coordinates and driving trajectory data of the electric bicycle, detect the battery status through a multi-sensor fusion algorithm, establish a battery health status assessment model, and build a cruising range prediction model based on the battery health status assessment model using a deep reinforcement learning algorithm. The model takes historical riding data, ambient temperature, road condition information, and riding habits as input features, outputs the remaining cruising range and charging demand prediction results, and sends the charging demand prediction results to the charging scheduling server. The trajectory features are extracted through a hierarchical time memory structure and a spatial attention mechanism, and the driving trajectory data is deeply analyzed in combination with road network topology constraints and historical trajectory similarity calculations to generate destination prediction results and arrival time prediction results;

[0041] The second unit is used to build a multi-constraint path planning model based on the current position coordinates and the destination prediction result, use the improved Dijkstra algorithm to introduce an adaptive penalty factor to dynamically avoid congested sections, and update the path weight online in combination with real-time traffic information, search for candidate paths, and optimize the charging piles on the candidate paths through an improved multi-objective genetic algorithm, using charging power matching, path deviation and waiting time as optimization objectives, combining adaptive crossover operators and mutation operators to perform multi-objective solutions, generate a Pareto solution set, score each solution in the Pareto solution set, and select the charging pile with the highest score as the target charging pile;

[0042] The third unit is used for the target charging pile to adopt an identity authentication mechanism based on double encryption, and authenticates the electric bicycle through a dynamic key generation algorithm and an identity feature extraction algorithm. During the charging process, an adaptive charging control algorithm based on a fuzzy neural network is used to learn the battery charging characteristics online, and a charging condition evaluation model is established in combination with temperature compensation and voltage compensation. A fuzzy inference engine is used to optimize the charging current in real time, and the charging curve is adaptively adjusted according to the battery health status. After charging is completed, the charging process data is uploaded to the charging scheduling server.

[0043] According to a third aspect of the embodiments of the present invention,

[0044] An electronic device is provided, comprising:

[0045] processor;

[0046] a memory for storing processor-executable instructions;

[0047] The processor is configured to call the instructions stored in the memory to execute the aforementioned method.

[0048] A fourth aspect of the embodiments of the present invention is:

[0049] A computer-readable storage medium is provided, on which computer program instructions are stored. When the computer program instructions are executed by a processor, the aforementioned method is implemented.

[0050] In the present invention, through multi-sensor fusion and deep reinforcement learning algorithm, the battery status and cruising range are accurately predicted, and combined with destination prediction and multi-constraint path planning, the optimal charging pile is recommended to the user, which not only improves the charging efficiency, but also greatly improves the user experience, making the charging process more convenient and intelligent. An identity authentication mechanism based on double encryption is adopted to effectively prevent illegal charging and information leakage. The adaptive charging control algorithm based on fuzzy neural network can dynamically adjust the charging strategy according to battery characteristics and health status, improve charging safety and battery life, and upload the charging demand prediction results and charging process data to the charging scheduling server to realize intelligent scheduling and optimal allocation of charging resources. In summary, the present invention not only improves the operating efficiency of the entire charging network, but also provides strong support for the planning and management of future electric bicycle charging infrastructure. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 This is a flow chart of an electric bicycle charging method based on a mutual recognition mechanism according to an embodiment of the present invention;

[0052] Figure 2It is a structural schematic diagram of an electric bicycle charging system based on a mutual recognition mechanism according to an embodiment of the present invention. DETAILED DESCRIPTION

[0053] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings 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.

[0054] The technical solution of the present invention is described in detail with specific embodiments below. The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described in detail in some embodiments.

[0055] Figure 1 FIG. 1 is a flow chart of an electric bicycle charging method based on a mutual recognition mechanism according to an embodiment of the present invention. Figure 1 As shown, the method includes:

[0056] S1. Collect the current position coordinates and driving trajectory data of the electric bicycle, detect the battery status through a multi-sensor fusion algorithm, establish a battery health status assessment model, and build a range prediction model based on the battery health status assessment model using a deep reinforcement learning algorithm. Use historical riding data, ambient temperature, road condition information, and riding habits as input features, output the remaining range and charging demand prediction results, send the charging demand prediction results to the charging scheduling server, extract trajectory features through a hierarchical time memory structure and a spatial attention mechanism, combine the road network topology constraints and historical trajectory similarity calculations, and perform in-depth analysis on the driving trajectory data to generate destination prediction results and arrival time prediction results;

[0057] The hierarchical time memory structure is an architecture for capturing long-term and short-term dependencies in time series data, and is capable of storing and processing time information at different levels. The road network topology constraint refers to considering the geometric relationship between road connections, nodes and paths when analyzing the traffic network to ensure that the model conforms to the actual traffic flow laws.

[0058] In an optional embodiment,

[0059] The current position coordinates and driving trajectory data of the electric bicycle are collected, the battery status is detected through a multi-sensor fusion algorithm, and a battery health status assessment model is established. Based on the battery health status assessment model, a deep reinforcement learning algorithm is used to build a range prediction model. The historical riding data, ambient temperature, road condition information and riding habits are used as input features, and the output remaining range and charging demand prediction results include:

[0060] The current position coordinates of the electric bicycle are collected according to a positioning module pre-set in the electric bicycle, and the acceleration and angular velocity are collected as riding status data according to a pre-set inertial measurement unit. The controller in the electric bicycle reads the data uploaded by the positioning module and the inertial measurement unit through a bus and generates the corresponding driving track data of the electric bicycle in combination with an electronic map;

[0061] The voltage, current and temperature of the battery are collected respectively according to a pre-set sensor array, wherein the sensor array includes a voltage sensor, a current sensor and a temperature sensor, and the collected signals are filtered, normalized and feature extracted by a multi-sensor fusion algorithm to obtain a multi-dimensional feature vector representing the battery state, and the multi-dimensional feature vector is input into a pre-built battery health state assessment model, wherein the battery health state assessment model is obtained by training according to a support vector machine algorithm, and the health state corresponding to the multi-dimensional feature vector is divided into different levels, and a corresponding battery discharge rate is assigned to each level;

[0062] The historical riding data of the electric bicycle is obtained, including riding time, riding mileage, average speed and average power. At the same time, the ambient temperature data collected by the ambient temperature sensor, the road condition data collected by the road condition sensor and the user riding habit data are obtained, wherein the road condition data includes road type, road slope and road curvature, and the user riding habit data includes riding time period, riding frequency and pedaling force. The historical riding data, ambient temperature, road condition information and riding habits are used as input features, and the battery discharge rate, remaining cruising range and charging demand are used as output targets to construct a cruising range prediction model. The cruising range prediction model is trained according to the deep Q learning algorithm, and a convolutional neural network is used as an approximate function of the Q function and environmental interaction is performed to learn the mileage prediction strategy and obtain the remaining cruising range and charging demand prediction results.

[0063] The support vector machine algorithm is a supervised learning model that achieves data classification by finding the optimal hyperplane and is suitable for high-dimensional data. The deep Q learning algorithm is a method that combines deep learning and reinforcement learning. It uses a deep neural network to approximate the Q function to optimize the decision-making process. The Q function represents the expected reward of taking an action in a specific state and is a key concept in reinforcement learning.

[0064] A positioning module and an inertial measurement unit are installed on the electric bicycle. The positioning module uses the GPS or Beidou satellite positioning system to obtain the real-time latitude and longitude coordinates of the electric bicycle. The inertial measurement unit includes a three-axis accelerometer and a three-axis gyroscope for measuring the acceleration and angular velocity of the electric bicycle. The controller is connected to the positioning module and the inertial measurement unit through the CAN bus to read the position and motion status data in real time.

[0065] The controller matches the collected position coordinates with the pre-stored electronic map to generate the driving trajectory of the electric bicycle. Specifically, the controller samples the position coordinates every 1 second and connects continuous coordinate points to form a trajectory line. At the same time, the controller calculates the speed and direction of the electric bicycle based on the acceleration and angular velocity data to supplement the trajectory information when the GPS signal is weak or interrupted. For example, if the acceleration measured at a certain moment is 2m / s^2 and the duration is 5 seconds, the speed change can be calculated to be 10m / s.

[0066] A sensor array is set up in the battery management system, including a voltage sensor, a current sensor and a temperature sensor. The voltage sensor uses a high-precision ADC chip with a sampling rate of 1kHz and a measurement range of 0-100V. The current sensor uses a Hall effect sensor with a sampling rate of 1kHz and a measurement range of -100A to 100A. The temperature sensor uses an NTC thermistor with a sampling rate of 1Hz and a measurement range of -20℃ to 80℃.

[0067] The collected raw signals are subjected to Kalman filtering algorithm to remove noise, and then normalized. Statistical features such as mean, variance, peak value, etc. are extracted for voltage, current and temperature data respectively to form a 30-dimensional feature vector. The feature vector is input into the pre-trained support vector machine model to divide the battery health status into five levels: excellent, good, fair, poor and extremely poor, with corresponding health measurement values ​​of 1.0, 0.8, 0.6, 0.4 and 0.2 respectively.

[0068] The historical riding data of the last 30 days is extracted from the controller of the electric bicycle, including daily riding time, riding mileage, average speed and average power. The ambient temperature data is collected by the temperature sensor installed on the outside of the vehicle body, and the sampling interval is 10 minutes. The road condition data is obtained through pre-stored map information, including road type (flat road, uphill, downhill), slope angle and road curvature radius. The user's riding habit data is obtained by analyzing historical riding records, such as common riding time periods, weekly riding frequency, average pedaling power, etc.

[0069] Taking the above data as input features, the battery discharge rate, remaining mileage and charging requirements as output targets, a deep reinforcement learning model is constructed, and a convolutional neural network is used as the approximate function of the Q function. The input layer contains 64 neurons, the hidden layer contains 3 convolutional layers and 2 fully connected layers, and the output layer contains 3 neurons corresponding to the remaining mileage, charging requirements and reward value respectively.

[0070] The model training process is as follows: Initialize the Q network parameters, then in each training round, select actions (predicting mileage and charging requirements) based on the current state, and interact with the environment to obtain rewards and the next state. Store the transfer samples of (state, action, reward, next state) in the experience replay pool, randomly sample a batch of samples from the experience replay pool, calculate the mean square error between the target Q value and the current Q value as the loss function, and use the stochastic gradient descent method to update the network parameters, and iterate until convergence.

[0071] By inputting features such as the current battery status, environmental conditions, and user habits, the model can output the predicted remaining range and charging requirements. For example, if the battery health is 0.8, the ambient temperature is 20°C, the road is flat, and the user is used to riding at a moderate intensity, the model may predict that the remaining range is 45km and recommends finding a charging station within 10km.

[0072] In this embodiment, through multi-sensor fusion and deep learning algorithms, accurate prediction of the range of electric bicycles is achieved, which improves the user's riding experience and battery utilization efficiency. Multi-dimensional information such as battery discharge rate, environmental factors and user habits are taken into consideration to make the prediction results more personalized and accurate, effectively avoiding the problem of mileage anxiety. In summary, this embodiment realizes accurate perception of the operating status and battery health status of electric bicycles. Through multi-sensor fusion and deep reinforcement learning, an accurate and reliable range prediction system is established, providing users with scientific charging decision support.

[0073] In an optional embodiment,

[0074] The charging demand prediction result is sent to the charging scheduling server, and the trajectory features are extracted through the hierarchical time memory structure and spatial attention mechanism. The driving trajectory data is deeply analyzed in combination with the road network topology constraints and the historical trajectory similarity calculation to generate the destination prediction result and the arrival time prediction result, including:

[0075] The charging demand prediction result output by the range prediction model is sent to a preset charging scheduling server through a controller set in the electric bicycle. The charging scheduling server parses the demand prediction result and obtains the position coordinate sequence and timestamp sequence of the electric bicycle, and performs feature learning on the driving trajectory of the electric bicycle in combination with a hierarchical time memory structure. The hierarchical time memory structure includes multiple levels of neuron nodes, each neuron node learns the local spatiotemporal features of the driving trajectory, and obtains a hierarchical abstract representation corresponding to the driving trajectory;

[0076] Based on the hierarchical abstract representation, the local spatiotemporal features are weightedly aggregated in combination with the attention mechanism to obtain the context-related representation corresponding to the driving trajectory, and the context-related representation is added to three parallel fully connected networks to predict the destination position, arrival time and remaining power after arrival of the electric bicycle respectively. During the prediction process, the charging scheduling server obtains the road network topology information from the pre-acquired electronic map and maps and constrains the trajectory points. At the same time, the similarity between the current trajectory and the historical trajectory is calculated based on the Hamming distance, and the historical trajectory with the highest similarity is selected as the candidate trajectory. Each candidate trajectory is weighted and fused into the destination position to obtain the destination prediction result and the arrival time prediction result.

[0077] The neuron node is the basic computing unit in the neural network, responsible for receiving input, performing weighted calculation and activation, so as to generate output. The local spatiotemporal features refer to features extracted in two dimensions of time and space, which can capture dynamic changes and local relationships. The hierarchical abstract representation is to convert data into a higher-level conceptual representation through feature extraction at different levels to facilitate the processing of complex tasks. The Hamming distance is an indicator used to measure the difference between two strings or binary numbers, indicating how many different bits are required to transform one string into another.

[0078] A controller with a GPS positioning function is installed on the electric bicycle to collect the real-time location information and power information of the electric bicycle. The controller is pre-loaded with a range prediction model, which is trained based on historical driving data and can predict the remaining mileage based on factors such as the current power, weather, and road conditions.

[0079] When the controller detects that the battery level of the electric bicycle is lower than a preset threshold (such as 20%), it triggers a charging demand prediction. The controller calls the range prediction model, inputs the current battery level, weather, road conditions and other information, and obtains the predicted remaining mileage. For example, if the current battery level is 15%, the weather is clear, and the road is flat, the predicted remaining mileage is 5 kilometers.

[0080] The controller sends the prediction result together with the unique identification ID of the electric bicycle via the wireless network to a pre-set charging scheduling server. After receiving the data, the charging scheduling server parses the electric bicycle ID and the predicted remaining mileage, queries the GPS trajectory data of the vehicle in the last hour based on the ID, and obtains the location coordinate sequence and the corresponding timestamp sequence.

[0081] The charging scheduling server uses a hierarchical time memory structure to learn the features of trajectory data. The structure consists of three levels, each of which contains multiple neuron nodes. The first layer of neurons learns the spatiotemporal features between two adjacent trajectory points, such as speed, acceleration, direction change, etc. The second layer of neurons learns the local road section features composed of multiple consecutive trajectory points, such as turning, acceleration, deceleration and other behavior patterns. The third layer of neurons learns trajectory features with longer time spans, such as recurring path patterns. Through layer-by-layer abstraction, a hierarchical representation of the trajectory is finally obtained.

[0082] The charging scheduling server uses the spatial attention mechanism to perform weighted aggregation of features at different levels. Specifically, it calculates the similarity between the current trajectory point and all historical trajectory points as the attention weight. The higher the similarity, the greater the feature weight corresponding to the historical trajectory point. By weighted summation, the feature representation related to the context of the current trajectory point is obtained.

[0083] The context-related feature representation is input into three parallel fully connected neural networks to predict the destination location, arrival time, and remaining power after arrival. During the prediction process, the charging scheduling server also considers the following constraints:

[0084] Road network topology constraints: Extract road network information from pre-acquired electronic maps and map trajectory points to the nearest roads to ensure that the prediction results are consistent with the actual road conditions.

[0085] Historical trajectory similarity: Calculate the similarity between the current trajectory and the historical trajectory. Use the similarity calculation method based on Hamming distance to discretize the trajectory into a grid sequence and calculate the overlap between the two sequences. Select the top three historical trajectories with the highest similarity as candidate trajectories.

[0086] Candidate trajectory fusion: The destinations of the three candidate trajectories are fused by weighted averaging to obtain the final destination prediction result, with the weight being proportional to the similarity.

[0087] The charging scheduling server outputs the destination prediction result (latitude and longitude coordinates), estimated arrival time and remaining power after arrival. For example, the predicted destination is (116.3, 39.9), and it is expected to arrive in 20 minutes with a remaining power of 5% upon arrival. Based on the prediction results and the distribution of charging piles around the destination, the charging scheduling server recommends the optimal charging route and charging station for the user.

[0088] In this embodiment, through the hierarchical time memory structure and spatial attention mechanism, the multi-scale spatiotemporal characteristics of the electric bicycle trajectory are effectively extracted, which improves the accuracy of destination prediction. Combined with the road network topology constraints and historical trajectory similarity, the prediction results are more in line with the actual road conditions and user habits, and the reliability of the prediction is improved. The end-to-end prediction method based on deep learning does not require artificially designed features, has strong generalization ability and adaptability, and can be applied to different types of electric bicycles and complex urban environments. In summary, this embodiment realizes the precise perception of the operating status and battery health status of electric bicycles. Through multi-sensor fusion and deep reinforcement learning, an accurate and reliable cruising range prediction system is established, providing users with scientific charging decision support.

[0089] S2. Construct a multi-constraint path planning model based on the current location coordinates and the destination prediction result, use the improved Dijkstra algorithm to introduce an adaptive penalty factor to dynamically avoid congested sections, and update the path weight online in combination with real-time traffic information, search for candidate paths, and optimize the charging piles on the candidate paths through an improved multi-objective genetic algorithm. Take the charging power matching degree, path deviation degree and waiting time as optimization goals, combine the adaptive crossover operator and the mutation operator to perform multi-objective solution, generate a Pareto solution set, score each solution in the Pareto solution set, and select the charging pile with the highest score as the target charging pile;

[0090] The multi-constraint path planning model is used to find the best path to meet specific needs while considering multiple constraints (such as time, cost, capacity, etc.). The improved Dijkstra algorithm is optimized based on the classic Dijkstra algorithm, and usually introduces additional strategies to improve the efficiency and adaptability of path search. The adaptive penalty factor is a parameter dynamically adjusted according to the current state, which is used to punish paths that violate constraints, thereby guiding the search for more compliant paths. The path deviation measures the difference between the actual path and the expected path, and is used to evaluate the rationality of path selection.

[0091] In an optional embodiment,

[0092] A multi-constraint path planning model is constructed based on the current position coordinates and the destination prediction result, an improved Dijkstra algorithm is used to introduce an adaptive penalty factor to dynamically avoid congested sections, and the path weight is updated online in combination with real-time traffic information, and a candidate path is searched, and the charging piles on the candidate path are optimized by an improved multi-objective genetic algorithm, and the charging power matching degree, path deviation degree and waiting time are used as optimization objectives, and an adaptive crossover operator and a mutation operator are combined to perform multi-objective solution, generate a Pareto solution set, and score each solution in the Pareto solution set, and select the charging pile with the highest score as the target charging pile, including:

[0093] After obtaining the current position coordinates and predicted destination position of the electric bicycle, the charging scheduling server constructs a multi-constraint path planning model, which abstracts the road network into a weighted directed graph. The nodes in the graph represent intersections, the edges represent road sections, and the weights of the edges represent the passing cost of the road section corresponding to the current edge. The passing cost is determined according to the length of the road section, the travel time and the energy consumption. The optimization goal is to minimize the total path cost, and the constraints are set in combination with the mileage limit and the available information of the charging pile.

[0094] Solving the driving path of the electric bicycle based on the multi-constraint path planning model and the improved Dijkstra algorithm, obtaining the congestion score of the road section in real time, classifying the traffic status of each road section according to a preset congestion threshold, and if the congestion score is higher than the congestion threshold, the current road section is considered to be a congested road section, and the weight corresponding to the congested road section is increased according to the congestion score and dynamic avoidance is performed, and the weight corresponding to each road section is repeatedly updated and solved to obtain a candidate path;

[0095] Based on the candidate path, the charging piles on the candidate path are selected according to the improved genetic algorithm, and the charging piles on the candidate path are encoded as individuals through real number coding to generate an initial population. For each individual in the initial population, the charging power matching degree, the path deviation degree and the waiting time are used as the optimization objectives of the genetic algorithm, the corresponding fitness value is calculated and non-dominated sorting is performed to determine the non-dominated individuals. For each non-dominated individual, descending sorting is performed based on the fitness value, and the individuals in the top 10% are selected for binary tournament selection, adaptive crossover and mutation operations to generate a child population and repeat the iteration until the preset maximum number of iterations is reached, and the non-dominated individual combination generated by the last iteration is selected to obtain a Pareto solution set. For each solution in the Pareto solution set, the corresponding score is calculated based on the optimization objective of the genetic algorithm, and the charging pile corresponding to the solution with the highest score is selected as the target charging pile.

[0096] The weighted directed graph represents the structure of nodes and edges in the network. The weight of the edge usually represents the cost or distance of passing through the edge. The passing cost refers to the cost or resource consumption that needs to be considered in path planning. The congestion score reflects the congestion of the path and helps to optimize the path selection. The binary tournament selection is a selection mechanism that randomly selects two individuals for comparison and selects individuals with higher fitness to improve the overall quality of the population.

[0097] The road network is abstracted into a weighted directed graph, where nodes represent intersections and edges represent road sections. The weight of each edge is calculated, that is, the cost of passing through the road section. The cost of passing through is determined based on the length of the road section, travel time, and energy consumption. The optimization goal is to minimize the total path cost, and constraints are set in combination with mileage restrictions and available information of charging piles.

[0098] Based on the multi-constrained path planning model and the improved Dijkstra algorithm, the congestion score of each road section is obtained in real time, and the traffic status of the road section is classified according to the preset congestion threshold. If the congestion score of the road section is higher than the threshold, the road section is considered congested, and the weight corresponding to the road section is increased according to the congestion score, and dynamic avoidance is performed. The weight of each road section is repeatedly updated and solved to obtain a candidate path;

[0099] Based on the candidate path, an improved genetic algorithm is used to select the charging piles on the path. The charging piles on the candidate path are encoded as individuals through real number coding to generate an initial population. For each individual in the initial population, the charging power matching degree, path deviation degree and waiting time are used as the optimization objectives of the genetic algorithm. The corresponding fitness value is calculated and non-dominated sorting is performed to determine the non-dominated individuals. For each non-dominated individual, descending sorting is performed based on the fitness value. The individuals in the top 10% are selected for binary tournament selection, adaptive crossover and mutation operations to generate a child population. The iteration is repeated until the preset maximum number of iterations is reached. The non-dominated individual combination generated by the last iteration is selected to obtain the Pareto solution set. For each solution in the Pareto solution set, the corresponding score is calculated based on the genetic algorithm optimization objective, and the charging pile corresponding to the solution with the highest score is selected as the target charging pile.

[0100] For example, suppose a shared electric bicycle needs to travel from location A to location B, and the battery is insufficient to support the entire journey. The system constructs a weighted directed graph with 50 nodes and 80 edges based on the current location coordinates of the electric bicycle and the predicted destination location as a multi-constraint path planning model. The weight of each edge is calculated based on the length, travel time and energy consumption of the corresponding road section. The constraint condition is set to the maximum mileage of the electric bicycle of 20 kilometers, and there must be available charging piles on the path;

[0101] The improved Dijkstra algorithm is used to solve the problem, and three candidate paths P1, P2 and P3 are obtained. The congestion score of the road section is obtained in real time. If the score is higher than 0.6, the road section is considered congested. For example, if the congestion score of the third road section on P1 is 0.8, its weight is increased by 50% and solved again, and finally three candidate paths after dynamic avoidance are obtained;

[0102] The charging piles on the candidate paths are encoded to generate an initial population of 50 individuals. The fitness value of the individuals is calculated by charging power matching, path deviation and waiting time. After non-dominated sorting, the top 10% of individuals are selected for genetic operations to generate a new offspring population. After 50 iterations, a Pareto solution set is obtained, which contains 8 non-dominated solutions. The score of each solution is calculated, and it is found that the charging pile corresponding to the solution numbered 15 has the highest score, the shortest distance, the best power matching, and the shortest waiting time. Therefore, this charging pile is selected as the target charging pile;

[0103] The system plans an optimal path for the electric bicycle from position A to position B via the target charging station, and sends the navigation information to the electric bicycle.

[0104] In this embodiment, a multi-constraint path planning model is used to comprehensively consider factors such as road section length, travel time and energy consumption, and plan a driving path with the lowest total cost, so that electric bicycles can reach their destinations by the optimal route, reduce power consumption, and improve cruising range. The dynamic update of congestion conditions and real-time adjustment of the path ensure that the driving path is always optimal. Even if sudden congestion occurs, it can be avoided in time to improve traffic efficiency. The genetic algorithm comprehensively considers factors such as the power matching degree of the charging pile, the distance from the vehicle position, and the vehicle waiting time, and selects the best charging pile from the candidate charging piles, so that electric bicycles can get charging services in the shortest time, reduce charging waiting time, and optimize the user's charging experience. The reasonable allocation of charging resources can shorten the waiting time of each vehicle, while improving the utilization rate of charging stations and realizing efficient utilization of charging resources. In summary, this embodiment can plan the optimal driving path and the best charging plan for electric bicycles, comprehensively improve the user experience and operation efficiency of electric bicycles, and has significant technical effects and application value.

[0105] In an optional embodiment,

[0106] The weight corresponding to each road section is updated according to the improved Dijkstra algorithm as shown in the following formula:

[0107]

[0108] Among them, W nrepresents the edge weight after update, W0 represents the edge weight before update, α represents the congestion penalty weight, I(SC>TH) represents the indicator function, which is used to indicate whether the congestion score of the current road section is higher than the preset congestion threshold. If it is higher, the indicator function takes the value of 1, otherwise it takes 0. SC represents the congestion score corresponding to the current road section, TH represents the preset congestion threshold, CP represents the congestion penalty factor, β represents the dynamic adjustment weight, and DT represents the dynamic adjustment factor.

[0109] In this embodiment, by introducing congestion penalties and dynamic adjustment mechanisms, the algorithm can better reflect real-time traffic conditions, avoid congested sections, provide users with better driving routes, effectively reduce driving time and fuel consumption, dynamically adjust parameters according to different time periods, weather conditions and other factors, adapt to complex and changeable traffic environments, and improve the accuracy and practicality of path planning. Through historical data analysis and prediction, the parameter settings are continuously optimized, the algorithm performance is improved, and strong support is provided for the development of intelligent transportation systems. In summary, this embodiment realizes the accurate quantification and dynamic response of the congestion status of road sections, provides a more accurate weight reference for path planning, and improves the practicality of the planning results.

[0110] S3. The target charging pile adopts an identity authentication mechanism based on double encryption, and authenticates the electric bicycle through a dynamic key generation algorithm and an identity feature extraction algorithm. During the charging process, an adaptive charging control algorithm based on a fuzzy neural network is used to learn the battery charging characteristics online, and a charging condition evaluation model is established in combination with temperature compensation and voltage compensation. A fuzzy inference engine is used to optimize the charging current in real time, and the charging curve is adaptively adjusted according to the battery health status. After charging is completed, the charging process data is uploaded to the charging scheduling server.

[0111] The dual encryption-based identity authentication mechanism improves the security of identity authentication by using two encryption algorithms at the same time, ensuring the authenticity of user identity and confidentiality of data. The dynamic key generation algorithm generates a new encryption key in each communication to enhance security and prevent the key from being reused or leaked. The identity feature extraction algorithm is used to extract key features from the user's biometrics or behavior to support the identity authentication process. The fuzzy neural network-based adaptive charging control algorithm combines fuzzy logic and neural network technology to dynamically adjust the charging strategy according to the battery status and environmental conditions to optimize the battery charging efficiency and life. The battery charging characteristics describe the behavior and response of the battery during the charging process, including factors such as charging rate, efficiency and temperature.

[0112] In an optional embodiment,

[0113] The target charging pile adopts an identity authentication mechanism based on double encryption, and the identity authentication of the electric bicycle is performed through a dynamic key generation algorithm and an identity feature extraction algorithm, including:

[0114] The target charging pile determines the precise timestamp of receiving the identity authentication request of the electric bicycle through an identity authentication mechanism based on double encryption and in combination with a pre-selected key generation algorithm, converts the precise timestamp into a text string in a fixed format and records it as a timestamp text string, generates a random number, converts the random number into a random number text string and concatenates it with the timestamp text string, generates an intermediate key in combination with a substitution operation and an obfuscation operation and adds the intermediate key to a pre-set one-way mapping function, generates the dynamic key, sends the dynamic key to the electric bicycle through a secure channel, and after receiving the dynamic key, the electric bicycle encrypts the dynamic key through a key encryption algorithm to obtain an encrypted authentication key;

[0115] The electric bicycle sends the encrypted authentication key and identity feature information to the target charging pile, wherein the identity feature information includes the unique identification code of the electric bicycle, the battery type and the rated voltage. After receiving the information sent by the electric bicycle, the target charging pile decrypts the authentication key through a key encryption algorithm and compares it with the dynamic key before sending. If they are consistent, it is considered that the electric bicycle has passed the key verification, extracts the unique identification code from the identity feature information and matches it with a pre-set legal identity feature library. If the match is successful, it is considered that the electric bicycle has passed the identity authentication and is allowed to access charging. Otherwise, the charging request is rejected.

[0116] The text string refers to a sequence of characters used to represent text information. The obfuscation operation transforms the data to make it difficult to identify in order to protect information security. The substitution operation adjusts the position of elements in the data to increase complexity. The one-way mapping function is a mathematical function whose input value can be calculated but the original input cannot be reversed, and is widely used in cryptography.

[0117] The target charging pile receives the authentication request sent by the electric bicycle. The charging pile records the precise timestamp of receiving the request through a precise time synchronization mechanism, accurate to milliseconds. For example, the recorded timestamp is "2023-05-20, 14:30:25.789".

[0118] The charging pile converts the timestamp into a text string in a fixed format, such as "20230520143025789", uses a secure random number generator to generate a 128-bit random number, such as "9a7b3c1d5e8f2g6h", converts the random number into a text string, and concatenates it with the timestamp text string to obtain "20230520143025789-9a7b3c1d5e8f2g6h".

[0119] The charging pile performs substitution and obfuscation operations on the concatenated character string. The substitution operation rearranges the characters in the character string according to a predetermined rule, such as swapping the positions of every three characters. The obfuscation operation replaces the characters, such as replacing numbers with corresponding letters, to generate an intermediate key "2c5b0a3d1e4f7g9h-9a7B3C1D5E8F2G6H".

[0120] The charging pile substitutes the intermediate key into a preset one-way mapping function, such as the SHA-256 hash function, to generate a final 256-bit dynamic key, and sends the dynamic key to the electric bicycle through a secure encrypted channel. After receiving the dynamic key, the electric bicycle encrypts it using the AES-256 encryption algorithm to obtain the encrypted authentication key. The electric bicycle prepares identity feature information, including a unique 16-bit identification code "EV20230520001234", battery type "lithium-ion battery" and rated voltage "48V".

[0121] The electric bicycle packages the encrypted authentication key and identity feature information and sends it to the target charging pile through a secure channel. After receiving the information, the charging pile uses the same AES-256 algorithm to decrypt the authentication key and compare it with the previously generated dynamic key. If they are exactly the same, it means that the electric bicycle has passed the key verification.

[0122] The unique identification code "EV20230520001234" of the electric bicycle is extracted from the identity feature information. The charging pile maintains a pre-set legal identity feature database, which contains the unique identification codes of all electric bicycles allowed to charge. The charging pile matches the extracted identification code with the database. If a match is found, it means that the electric bicycle has passed the identity authentication.

[0123] Only when the electric bicycle passes both the key verification and identity authentication steps, the charging pile will allow access to charging. Otherwise, the charging pile will reject the charging request and send a message of refusal to charge to the electric bicycle.

[0124] In this embodiment, double encryption and dynamic key mechanism are adopted, which greatly increases the difficulty of forgery or cracking, effectively prevents unauthorized devices from accessing charging, and avoids the risk of static keys being easily stolen by dynamically generating keys. At the same time, the authentication mechanism can be updated frequently as needed. The authentication process has a high degree of automation and does not require human intervention. Identity authentication can be completed quickly, which improves the utilization efficiency of charging piles. In summary, this embodiment implements a dual authentication mechanism based on dynamic keys and identity features, provides high-security charging access control through multiple encryption and identity matching, and effectively ensures the safety and reliability of the charging process.

[0125] In an optional embodiment,

[0126] During the charging process, an adaptive charging control algorithm based on a fuzzy neural network is used to learn the battery charging characteristics online, a charging condition evaluation model is established in combination with temperature compensation and voltage compensation, a fuzzy inference engine is used to optimize the charging current in real time, and the charging curve is adaptively adjusted according to the battery health status. After charging is completed, the charging process data is uploaded to the charging scheduling server, including:

[0127] If the electric bicycle passes the identity authentication, the target charging pile is charged. During the charging process, the target charging pile obtains the voltage parameter, current parameter and temperature parameter in the charging process through the adaptive charging control algorithm based on the fuzzy neural network and the pre-set sensor and uses them as the battery charging characteristics. The battery charging characteristics are added to the pre-set fuzzy neural network model, and the battery charging characteristics are mapped to the fuzzy set through the membership function. The decision is made through the fuzzy inference rule to obtain the defuzzified control output;

[0128] Based on the defuzzified control output, combined with the temperature parameters and voltage parameters during charging, piecewise linear fitting and polynomial fitting are performed according to the theoretical model and experimental data of the current electric bicycle to generate voltage compensation rules and temperature compensation rules. Based on the voltage compensation rules and the temperature compensation rules, a voltage correction coefficient and a temperature correction coefficient are generated and sent to the fuzzy reasoning engine for voltage optimization and temperature optimization, and real-time optimization of the charging current is achieved according to the optimization results;

[0129] Obtaining the battery usage time and charging and discharging records of the electric bicycle, evaluating the battery health status of the electric bicycle based on the Kalman filter algorithm to obtain the battery health, and adaptively adjusting the charging curve based on the battery health in combination with the control method corresponding to the charging curve, if the battery health is greater than a preset health threshold, increasing the constant current charging current and constant voltage charging voltage, otherwise reducing the constant current charging current and constant voltage charging voltage until charging is completed;

[0130] After charging is completed, the charging start time, charging end time, charging quantity and average charging power are sent to the charging scheduling server as charging process data.

[0131] The defuzzified control output refers to the output result of the fuzzy control system, which is converted into clear control instructions for practical application. The membership function is used to define the degree of membership of an element in a fuzzy set, and characterizes the degree to which it belongs to a certain fuzzy concept. The piecewise linear fitting is a data fitting method that divides the data into multiple intervals and fits them with linear functions respectively to improve the fitting accuracy. The polynomial fitting uses polynomial functions to model the data to capture the trends and characteristics of the data.

[0132] After passing the certification, the charging pile starts the adaptive charging control algorithm based on fuzzy neural network, and uses the pre-installed voltage, current and temperature sensors to collect the relevant parameters of the charging process in real time. These parameters are input into the preset fuzzy neural network model as battery charging characteristics.

[0133] The fuzzy neural network model uses a membership function to map the collected parameters such as voltage, current and temperature into fuzzy sets. For example, the voltage can be mapped into three fuzzy sets of "low", "medium" and "high". The specific mapping interval can be set according to the battery specifications. Decision analysis is performed through preset fuzzy inference rules. The fuzzy inference rules can be set in the form of "if the voltage is low and the temperature is normal, increase the charging current". After defuzzification processing, the specific control output value, such as the charging current adjustment amount, is obtained.

[0134] The charging pile combines the temperature and voltage parameters collected in real time with the theoretical model and historical experimental data of this model of electric bicycle to generate voltage compensation rules and temperature compensation rules through piecewise linear fitting and polynomial fitting. For example, the voltage interval is divided into multiple sub-intervals, and linear fitting is used in each sub-interval. Quadratic polynomial fitting can be used for temperature effects. Based on the compensation rules, the system calculates the voltage correction coefficient and the temperature correction coefficient and sends them to the fuzzy inference engine.

[0135] The fuzzy inference engine comprehensively considers the control output value, voltage correction factor and temperature correction factor to optimize and adjust the charging current in real time. For example, it appropriately increases the charging current when the battery voltage is low, and reduces the charging current when the temperature is high, so as to balance the charging efficiency and safety.

[0136] The charging pile obtains the battery usage time and historical charging and discharging records of the electric bicycle, and uses the Kalman filter algorithm to combine these historical data with the current charging status to conduct a real-time evaluation of the battery health status, and obtain a battery health value between 0 and 100. The system adaptively adjusts the standard charging curve for this health. Specifically, if the health is higher than the preset threshold (such as 80), the current value of the constant current charging stage (such as from 1C to 1.2C) and the voltage value of the constant voltage charging stage (such as from 4.2V to 4.25V) are appropriately increased. Conversely, if the health is lower than the threshold, the charging parameters are reduced accordingly to extend the battery life.

[0137] The charging process continues until the termination condition is reached. After charging is completed, the charging pile will send the recorded charging start time, end time, charging capacity (such as 2kWh) and average charging power (such as 500W) data to the charging scheduling server for subsequent analysis and optimization.

[0138] In this embodiment, by real-time monitoring and adjustment of charging parameters, the charging speed can be maximized and the charging time can be shortened while ensuring safety. The charging strategy can be dynamically adjusted according to the battery health status, which can effectively slow down battery degradation and extend the service life. Uploading the charging data to the server helps to analyze the charging mode and predict charging needs, thereby optimizing the resource allocation and scheduling of the entire charging network. In summary, this embodiment realizes safe and efficient adaptive charging management, which not only ensures charging performance, but also extends battery life and provides all-round charging process optimization.

[0139] Figure 2 FIG. 1 is a schematic diagram of the structure of an electric bicycle charging system based on a mutual recognition mechanism according to an embodiment of the present invention. Figure 2 As shown, the system comprises:

[0140] The first unit is used to collect the current position coordinates and driving trajectory data of the electric bicycle, detect the battery status through a multi-sensor fusion algorithm, establish a battery health status assessment model, and build a cruising range prediction model based on the battery health status assessment model using a deep reinforcement learning algorithm. The model takes historical riding data, ambient temperature, road condition information, and riding habits as input features, outputs the remaining cruising range and charging demand prediction results, and sends the charging demand prediction results to the charging scheduling server. The trajectory features are extracted through a hierarchical time memory structure and a spatial attention mechanism, and the driving trajectory data is deeply analyzed in combination with road network topology constraints and historical trajectory similarity calculations to generate destination prediction results and arrival time prediction results;

[0141] The second unit is used to build a multi-constraint path planning model based on the current position coordinates and the destination prediction result, use the improved Dijkstra algorithm to introduce an adaptive penalty factor to dynamically avoid congested sections, and update the path weight online in combination with real-time traffic information, search for candidate paths, and optimize the charging piles on the candidate paths through an improved multi-objective genetic algorithm, using charging power matching, path deviation and waiting time as optimization objectives, combining adaptive crossover operators and mutation operators to perform multi-objective solutions, generate a Pareto solution set, score each solution in the Pareto solution set, and select the charging pile with the highest score as the target charging pile;

[0142] The third unit is used for the target charging pile to adopt an identity authentication mechanism based on double encryption, and authenticates the electric bicycle through a dynamic key generation algorithm and an identity feature extraction algorithm. During the charging process, an adaptive charging control algorithm based on a fuzzy neural network is used to learn the battery charging characteristics online, and a charging condition evaluation model is established in combination with temperature compensation and voltage compensation. A fuzzy inference engine is used to optimize the charging current in real time, and the charging curve is adaptively adjusted according to the battery health status. After charging is completed, the charging process data is uploaded to the charging scheduling server.

[0143] According to a third aspect of the embodiments of the present invention,

[0144] An electronic device is provided, comprising:

[0145] processor;

[0146] a memory for storing processor-executable instructions;

[0147] The processor is configured to call the instructions stored in the memory to execute the aforementioned method.

[0148] According to a fourth aspect of the embodiments of the present invention,

[0149] A computer-readable storage medium is provided, on which computer program instructions are stored. When the computer program instructions are executed by a processor, the aforementioned method is implemented.

[0150] The present invention may be a method, an apparatus, a system and / or a computer program product. The computer program product may include a computer-readable storage medium carrying computer-readable program instructions for executing various aspects of the present invention.

[0151] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. An electric bicycle charging method based on a mutual recognition mechanism, characterized in that: include: The current position coordinates and driving trajectory data of the electric bicycle are collected, the battery status is detected through a multi-sensor fusion algorithm, and a battery health status assessment model is established. A cruising range prediction model is constructed based on the battery health status assessment model using a deep reinforcement learning algorithm. The historical riding data, ambient temperature, road condition information, and riding habits are used as input features, and the remaining cruising range and charging demand prediction results are output. The charging demand prediction results are sent to a charging scheduling server, and trajectory features are extracted through a hierarchical time memory structure and a spatial attention mechanism. In combination with road network topology constraints and historical trajectory similarity calculations, the driving trajectory data is deeply analyzed to generate destination prediction results and arrival time prediction results; A multi-constraint path planning model is constructed based on the current position coordinates and the destination prediction result, an improved Dijkstra algorithm is used to introduce an adaptive penalty factor to dynamically avoid congested sections, and the path weight is updated online in combination with real-time traffic information, and a candidate path is searched and obtained, and the charging piles on the candidate path are optimized by an improved multi-objective genetic algorithm, and the charging power matching degree, path deviation degree and waiting time are used as optimization objectives, and an adaptive crossover operator and a mutation operator are combined to perform multi-objective solving, generate a Pareto solution set, and score each solution in the Pareto solution set, and select the charging pile with the highest score as the target charging pile; The target charging pile adopts an identity authentication mechanism based on double encryption, and authenticates the electric bicycle through a dynamic key generation algorithm and an identity feature extraction algorithm. During the charging process, an adaptive charging control algorithm based on a fuzzy neural network is used to learn the battery charging characteristics online, and a charging condition evaluation model is established in combination with temperature compensation and voltage compensation. A fuzzy inference engine is used to optimize the charging current in real time, and the charging curve is adaptively adjusted according to the battery health status. After charging is completed, the charging process data is uploaded to the charging scheduling server.

2. The method according to claim 1, characterized in that The current position coordinates and driving trajectory data of the electric bicycle are collected, the battery status is detected through a multi-sensor fusion algorithm, and a battery health status assessment model is established. Based on the battery health status assessment model, a deep reinforcement learning algorithm is used to build a range prediction model. The historical riding data, ambient temperature, road condition information and riding habits are used as input features, and the output remaining range and charging demand prediction results include: The current position coordinates of the electric bicycle are collected according to a positioning module pre-set in the electric bicycle, and the acceleration and angular velocity are collected as riding status data according to a pre-set inertial measurement unit. The controller in the electric bicycle reads the data uploaded by the positioning module and the inertial measurement unit through a bus and generates the corresponding driving track data of the electric bicycle in combination with an electronic map; The voltage, current and temperature of the battery are collected respectively according to a pre-set sensor array, wherein the sensor array includes a voltage sensor, a current sensor and a temperature sensor, and the collected signals are filtered, normalized and feature extracted by a multi-sensor fusion algorithm to obtain a multi-dimensional feature vector representing the battery state, and the multi-dimensional feature vector is input into a pre-built battery health state assessment model, wherein the battery health state assessment model is obtained by training according to a support vector machine algorithm, and the health state corresponding to the multi-dimensional feature vector is divided into different levels, and a corresponding battery discharge rate is assigned to each level; The historical riding data of the electric bicycle is obtained, including riding time, riding mileage, average speed and average power. At the same time, the ambient temperature data collected by the ambient temperature sensor, the road condition data collected by the road condition sensor and the user riding habit data are obtained, wherein the road condition data includes road type, road slope and road curvature, and the user riding habit data includes riding time period, riding frequency and pedaling force. The historical riding data, ambient temperature, road condition information and riding habits are used as input features, and the battery discharge rate, remaining cruising range and charging demand are used as output targets to construct a cruising range prediction model. The cruising range prediction model is trained according to the deep Q learning algorithm, and a convolutional neural network is used as an approximate function of the Q function and environmental interaction is performed to learn the mileage prediction strategy and obtain the remaining cruising range and charging demand prediction results.

3. The method according to claim 1, characterized in that The charging demand prediction result is sent to the charging scheduling server, and the trajectory features are extracted through the hierarchical time memory structure and spatial attention mechanism. The driving trajectory data is deeply analyzed in combination with the road network topology constraints and the historical trajectory similarity calculation to generate the destination prediction result and the arrival time prediction result, including: The charging demand prediction result output by the range prediction model is sent to a preset charging scheduling server through a controller set in the electric bicycle. The charging scheduling server parses the demand prediction result and obtains the position coordinate sequence and timestamp sequence of the electric bicycle, and performs feature learning on the driving trajectory of the electric bicycle in combination with a hierarchical time memory structure. The hierarchical time memory structure includes multiple levels of neuron nodes, each neuron node learns the local spatiotemporal features of the driving trajectory, and obtains a hierarchical abstract representation corresponding to the driving trajectory; Based on the hierarchical abstract representation, the local spatiotemporal features are weightedly aggregated in combination with the attention mechanism to obtain the context-related representation corresponding to the driving trajectory, and the context-related representation is added to three parallel fully connected networks to predict the destination position, arrival time and remaining power after arrival of the electric bicycle respectively. During the prediction process, the charging scheduling server obtains the road network topology information from the pre-acquired electronic map and maps and constrains the trajectory points. At the same time, the similarity between the current trajectory and the historical trajectory is calculated based on the Hamming distance, and the historical trajectory with the highest similarity is selected as the candidate trajectory. Each candidate trajectory is weighted and fused into the destination position to obtain the destination prediction result and the arrival time prediction result.

4. The method according to claim 1, characterized in that: A multi-constraint path planning model is constructed based on the current position coordinates and the destination prediction result, an improved Dijkstra algorithm is used to introduce an adaptive penalty factor to dynamically avoid congested sections, and the path weight is updated online in combination with real-time traffic information, and a candidate path is searched, and the charging piles on the candidate path are optimized by an improved multi-objective genetic algorithm, and the charging power matching degree, path deviation degree and waiting time are used as optimization objectives, and an adaptive crossover operator and a mutation operator are combined to perform multi-objective solution, generate a Pareto solution set, and score each solution in the Pareto solution set, and select the charging pile with the highest score as the target charging pile, including: After obtaining the current position coordinates and predicted destination position of the electric bicycle, the charging scheduling server constructs a multi-constraint path planning model, which abstracts the road network into a weighted directed graph. The nodes in the graph represent intersections, the edges represent road sections, and the weights of the edges represent the passing cost of the road section corresponding to the current edge. The passing cost is determined according to the length of the road section, the travel time and the energy consumption. The optimization goal is to minimize the total path cost, and the constraints are set in combination with the mileage limit and the available information of the charging pile. Solving the driving path of the electric bicycle based on the multi-constraint path planning model and the improved Dijkstra algorithm, obtaining the congestion score of the road section in real time, classifying the traffic status of each road section according to a preset congestion threshold, and if the congestion score is higher than the congestion threshold, the current road section is considered to be a congested road section, and the weight corresponding to the congested road section is increased according to the congestion score and dynamic avoidance is performed, and the weight corresponding to each road section is repeatedly updated and solved to obtain a candidate path; Based on the candidate path, the charging piles on the candidate path are selected according to the improved genetic algorithm, and the charging piles on the candidate path are encoded as individuals through real number coding to generate an initial population. For each individual in the initial population, the charging power matching degree, the path deviation degree and the waiting time are used as the optimization objectives of the genetic algorithm, the corresponding fitness value is calculated and non-dominated sorting is performed to determine the non-dominated individuals. For each non-dominated individual, descending sorting is performed based on the fitness value, and the individuals in the top 10% are selected for binary tournament selection, adaptive crossover and mutation operations to generate a child population and repeat the iteration until the preset maximum number of iterations is reached, and the non-dominated individual combination generated by the last iteration is selected to obtain a Pareto solution set. For each solution in the Pareto solution set, the corresponding score is calculated based on the optimization objective of the genetic algorithm, and the charging pile corresponding to the solution with the highest score is selected as the target charging pile.

5. The method according to claim 1, characterized in that The weight corresponding to each road section is updated according to the improved Dijkstra algorithm as shown in the following formula: Among them, W n represents the edge weight after update, W0 represents the edge weight before update, α represents the congestion penalty weight, I(SC>TH) represents the indicator function, which is used to indicate whether the congestion score of the current road section is higher than the preset congestion threshold. If it is higher, the indicator function takes the value of 1, otherwise it takes 0. SC represents the congestion score corresponding to the current road section, TH represents the preset congestion threshold, CP represents the congestion penalty factor, β represents the dynamic adjustment weight, and DT represents the dynamic adjustment factor.

6. The method according to claim 1, characterized in that The target charging pile adopts an identity authentication mechanism based on double encryption, and the identity authentication of the electric bicycle is performed through a dynamic key generation algorithm and an identity feature extraction algorithm, including: The target charging pile determines the precise timestamp of receiving the identity authentication request of the electric bicycle through an identity authentication mechanism based on double encryption and in combination with a pre-selected key generation algorithm, converts the precise timestamp into a text string in a fixed format and records it as a timestamp text string, generates a random number, converts the random number into a random number text string and concatenates it with the timestamp text string, generates an intermediate key in combination with a substitution operation and an obfuscation operation and adds the intermediate key to a pre-set one-way mapping function, generates the dynamic key, sends the dynamic key to the electric bicycle through a secure channel, and after receiving the dynamic key, the electric bicycle encrypts the dynamic key through a key encryption algorithm to obtain an encrypted authentication key; The electric bicycle sends the encrypted authentication key and identity feature information to the target charging pile, wherein the identity feature information includes the unique identification code of the electric bicycle, the battery type and the rated voltage. After receiving the information sent by the electric bicycle, the target charging pile decrypts the authentication key through a key encryption algorithm and compares it with the dynamic key before sending. If they are consistent, it is considered that the electric bicycle has passed the key verification, extracts the unique identification code from the identity feature information and matches it with a pre-set legal identity feature library. If the match is successful, it is considered that the electric bicycle has passed the identity authentication and is allowed to access charging. Otherwise, the charging request is rejected.

7. The method according to claim 1, characterized in that During the charging process, an adaptive charging control algorithm based on a fuzzy neural network is used to learn the battery charging characteristics online, a charging condition evaluation model is established in combination with temperature compensation and voltage compensation, a fuzzy inference engine is used to optimize the charging current in real time, and the charging curve is adaptively adjusted according to the battery health status. After charging is completed, the charging process data is uploaded to the charging scheduling server, including: If the electric bicycle passes the identity authentication, the target charging pile is charged. During the charging process, the target charging pile obtains the voltage parameter, current parameter and temperature parameter in the charging process through the adaptive charging control algorithm based on the fuzzy neural network and the pre-set sensor and uses them as the battery charging characteristics. The battery charging characteristics are added to the pre-set fuzzy neural network model, and the battery charging characteristics are mapped to the fuzzy set through the membership function. The decision is made through the fuzzy inference rule to obtain the defuzzified control output; Based on the defuzzified control output, combined with the temperature parameters and voltage parameters during charging, piecewise linear fitting and polynomial fitting are performed according to the theoretical model and experimental data of the current electric bicycle to generate voltage compensation rules and temperature compensation rules. Based on the voltage compensation rules and the temperature compensation rules, a voltage correction coefficient and a temperature correction coefficient are generated and sent to the fuzzy reasoning engine for voltage optimization and temperature optimization, and real-time optimization of the charging current is achieved according to the optimization results; Obtaining the battery usage time and charging and discharging records of the electric bicycle, evaluating the battery health status of the electric bicycle based on the Kalman filter algorithm to obtain the battery health, and adaptively adjusting the charging curve based on the battery health in combination with the control method corresponding to the charging curve, if the battery health is greater than a preset health threshold, increasing the constant current charging current and constant voltage charging voltage, otherwise reducing the constant current charging current and constant voltage charging voltage until charging is completed; After charging is completed, the charging start time, charging end time, charging quantity and average charging power are sent to the charging scheduling server as charging process data.

8. An electric bicycle charging system based on a mutual recognition mechanism, used to implement the method described in any one of claims 1 to 7, characterized in that: include: The first unit is used to collect the current position coordinates and driving trajectory data of the electric bicycle, detect the battery status through a multi-sensor fusion algorithm, establish a battery health status assessment model, and build a cruising range prediction model based on the battery health status assessment model using a deep reinforcement learning algorithm. The model takes historical riding data, ambient temperature, road condition information, and riding habits as input features, outputs the remaining cruising range and charging demand prediction results, and sends the charging demand prediction results to the charging scheduling server. The trajectory features are extracted through a hierarchical time memory structure and a spatial attention mechanism, and the driving trajectory data is deeply analyzed in combination with road network topology constraints and historical trajectory similarity calculations to generate destination prediction results and arrival time prediction results; The second unit is used to build a multi-constraint path planning model based on the current position coordinates and the destination prediction result, use the improved Dijkstra algorithm to introduce an adaptive penalty factor to dynamically avoid congested sections, and update the path weight online in combination with real-time traffic information, search for candidate paths, and optimize the charging piles on the candidate paths through an improved multi-objective genetic algorithm, using charging power matching, path deviation and waiting time as optimization objectives, combining adaptive crossover operators and mutation operators to perform multi-objective solutions, generate a Pareto solution set, score each solution in the Pareto solution set, and select the charging pile with the highest score as the target charging pile; The third unit is used for the target charging pile to adopt an identity authentication mechanism based on double encryption, and authenticates the electric bicycle through a dynamic key generation algorithm and an identity feature extraction algorithm. During the charging process, an adaptive charging control algorithm based on a fuzzy neural network is used to learn the battery charging characteristics online, and a charging condition evaluation model is established in combination with temperature compensation and voltage compensation. A fuzzy inference engine is used to optimize the charging current in real time, and the charging curve is adaptively adjusted according to the battery health status. After charging is completed, the charging process data is uploaded to the charging scheduling server.

9. An electronic device, characterized in that: include: processor; a memory for storing processor-executable instructions; The processor is configured to call the instructions stored in the memory to execute the method described in any one of claims 1 to 7.

10. A computer-readable storage medium having computer program instructions stored thereon, characterized in that: When the computer program instructions are executed by a processor, the method according to any one of claims 1 to 7 is implemented.

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