Intelligent navigation method and system suitable for electric motorcycle

Abstract

The invention provides an intelligent navigation method and system suitable for an electric motorcycle. The method comprises the following steps: acquiring traffic information of a target area, habit preference data of a user during driving of the electric motorcycle, a battery state of the electric motorcycle and real-time position data; determining a plurality of possible paths according to a starting point and an ending point set by a user based on the map data of the target area; screening the possible paths according to the traffic information to obtain a plurality of first optimized paths; screening the first optimization paths according to the habit preference data to obtain a plurality of second optimization paths; screening each second optimization path according to the battery state and the real-time position data to obtain a third optimization path; and displaying the third optimized path on the intelligent helmet based on an augmented reality technology. According to the method, the path is optimized through the real-time traffic information, congestion and construction road sections can be effectively bypassed, the travel time and the waiting time are shortened, and therefore the travel efficiency is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent navigation, and in particular to an intelligent navigation method and system suitable for electric motorcycles. Background Art

[0002] With the increasing complexity of urban traffic and the popularity of electric motorcycles, the application of traditional navigation systems on electric motorcycles faces many challenges, such as insufficient real-time performance, inflexible path planning, and inability to effectively respond to dynamic changes in urban traffic.

[0003] The invention patent with application number 202410319106.1 discloses a method for simple navigation of a shared electric vehicle, which includes: in response to a navigation request initiated by a user terminal, obtaining the current position and navigation destination of the user terminal, and generating a navigation route based on the current position and navigation destination; extracting navigation key points from the navigation route, generating simple navigation data based on the navigation key points, and sending the simple navigation data to the shared electric vehicle so that the shared electric vehicle can navigate according to the preset central control prompts, mileage and simple navigation data, and the preset central control prompts include prompt patterns and prompt voices. This patent extracts navigation key points from the navigation route, simplifies the navigation route, generates simple navigation data, and facilitates the shared electric vehicle to navigate based on the existing preset central control prompts, mileage and simple navigation data. The route is simplified by extracting navigation key points, resulting in insufficient route information. Especially in complex road conditions, users need more details to ensure correct navigation. Summary of the invention

[0004] In order to overcome the shortcomings of the prior art, the purpose of the present invention is to provide an intelligent navigation method and system suitable for electric motorcycles, which can provide electric motorcycle riders with a safe, economical, efficient and comfortable travel experience, in line with the needs and development direction of modern urban transportation.

[0005] To achieve the above object, the present invention provides the following solutions:

[0006] An intelligent navigation method applicable to an electric motorcycle, comprising:

[0007] Obtaining traffic information of the target area, the user's habit preference data when driving an electric motorcycle, the battery status of the electric motorcycle, and real-time location data; the traffic information includes real-time traffic flow data, road construction and accident data; the habit preference data includes multiple frequency routes and riding speed;

[0008] Based on the map data of the target area, determining multiple possible paths according to the starting point and the end point set by the user;

[0009] Screening the possible paths according to the traffic information to obtain a plurality of first optimized paths;

[0010] Screening each of the first optimization paths according to the habit preference data to obtain a plurality of second optimization paths;

[0011] Filter each of the second optimization paths according to the battery status and the real-time location data to obtain a third optimization path;

[0012] Based on augmented reality technology, the third optimization path is displayed on the smart helmet.

[0013] Preferably, based on the map data of the target area, multiple possible paths are determined according to the starting point and the end point set by the user, including:

[0014] Acquire the coordinates of the starting point and the end point set by the user, and unify the coordinates of the starting point and the end point into the coordinate system of the map data;

[0015] The coordinates of the starting point and the end point are input into a preset A* path search model to obtain the multiple possible paths.

[0016] Preferably, the possible paths are screened according to the traffic information to obtain a plurality of first optimized paths, including:

[0017] Calculating a traffic resistance coefficient of each possible path according to the traffic information;

[0018] The possible paths are sorted according to the traffic resistance coefficient, and several paths with the lowest traffic resistance coefficient are selected as the first optimized paths.

[0019] Preferably, the calculation formula of the traffic resistance coefficient is:

[0020]

[0021] Where: T ij For path P i The traffic flow coefficient of the jth segment, C ij For path P i The construction coefficient of the jth segment, A ij For path P i The accident coefficient of the jth segment, W t , W c , W a are the weight coefficients of traffic flow, construction and accidents respectively, and n is the path P i The number of road sections; Among them, F current is the traffic flow of the current road section, F maxis the maximum traffic flow capacity of the road section, D impact is the duration of the construction impact, S is the accident weight coefficient of the severity of the construction impact, T current is the current time after the accident, T resolution is the estimated time to complete the accident handling, and A is the severity of the accident.

[0022] Preferably, the number of the first optimization paths is ten.

[0023] Preferably, the first optimization paths are screened according to the habit preference data to obtain a plurality of second optimization paths, including:

[0024] Performing feature analysis on each first optimized path to extract key features of the first optimized path; the key features include: path length, average speed, number of traffic lights, and degree of scenic beauty;

[0025] The habit preference data is matched with the key features, and the matching degree between each of the first optimization paths and the user preference is calculated.

[0026] The first optimization paths are sorted according to the matching degree, and several paths with the highest matching degree are selected as the second optimization paths.

[0027] Preferably, the calculation formula of the matching degree is:

[0028]

[0029] Among them, F ik is the path P i The eigenvalue of the kth key feature, W k is the user's preference weight for the kth key feature, and m is the total number of key features.

[0030] Preferably, the number of the second optimization paths is 3.

[0031] Preferably, each of the second optimization paths is screened according to the battery status and the real-time location data to obtain a third optimization path, including:

[0032] Calculate the remaining distance according to the real-time position data;

[0033] Performing energy consumption estimation for each second optimized path according to the remaining distance and the battery status; the battery status includes the current battery power and the battery usage efficiency coefficient;

[0034] The second optimization path corresponding to the minimum estimated energy consumption is determined as the third optimization path.

[0035] Preferably, the formula for estimating energy consumption is:

[0036]

[0037] Among them, D ij For path P i The remaining distance of the jth segment, C ij For path P i The energy consumption per unit distance of the jth segment, n is the path P i The number of road sections, B current is the current battery power, and η is the battery efficiency coefficient.

[0038] An intelligent navigation system suitable for an electric motorcycle, comprising:

[0039] A data acquisition module, used to acquire traffic information of a target area, user's habit preference data when driving an electric motorcycle, battery status of the electric motorcycle, and real-time location data; the traffic information includes real-time traffic flow data, road construction and accident data; the habit preference data includes multiple frequency routes and riding speed;

[0040] A path planning module, for determining a plurality of possible paths according to a start point and an end point set by a user based on map data of the target area;

[0041] A first screening module, used for screening the possible paths according to the traffic information to obtain a plurality of first optimized paths;

[0042] A second screening module, used for screening each of the first optimization paths according to the habit preference data to obtain a plurality of second optimization paths;

[0043] A third screening module, configured to screen each of the second optimization paths according to the battery status and the real-time location data to obtain a third optimization path;

[0044] The display module is used to display the third optimization path on the smart helmet based on augmented reality technology.

[0045] According to the specific embodiments provided by the present invention, the present invention discloses the following technical effects:

[0046] The present invention provides an intelligent navigation method and system suitable for electric motorcycles, the method comprising: obtaining traffic information of a target area, habit preference data of a user when driving an electric motorcycle, battery status of the electric motorcycle and real-time location data; the traffic information includes real-time traffic flow data, road construction and accident data; the habit preference data includes multi-frequency routes and riding speed; based on the map data of the target area, multiple possible paths are determined according to the starting point and end point set by the user; the possible paths are screened according to the traffic information to obtain multiple first optimized paths; the first optimized paths are screened according to the habit preference data to obtain multiple second optimized paths; the second optimized paths are screened according to the battery status and real-time location data to obtain a third optimized path; based on augmented reality technology, the third optimized path is displayed on a smart helmet. The present invention optimizes the path through real-time traffic information, can effectively bypass congestion and construction sections, reduce travel time and waiting time, and thus improve travel efficiency. And considering the user's riding habit preferences, such as common routes and speed preferences, the navigation results are more in line with the user's personalized needs, and the user's riding experience and satisfaction are improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0048] Figure 1 A flow chart of a method provided by an embodiment of the present invention;

[0049] Figure 2 A schematic diagram of the system structure provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0050] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0051] The purpose of the present invention is to provide an intelligent navigation method and system suitable for electric motorcycles, which can optimize the route through real-time traffic information, effectively bypass congestion and construction sections, reduce travel time and waiting time, and thus improve travel efficiency. And consider the user's riding habits and preferences, such as common routes and speed preferences, so that the navigation results are more in line with the user's personalized needs, and improve the user's riding experience and satisfaction.

[0052] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0053] Figure 1 A flow chart of a method provided by an embodiment of the present invention, such as Figure 1 As shown, the present invention provides an intelligent navigation method applicable to an electric motorcycle, comprising:

[0054] Step 100: Obtain traffic information of the target area, user's habit preference data when driving an electric motorcycle, battery status of the electric motorcycle, and real-time location data; traffic information includes real-time traffic flow data, road construction and accident data; habit preference data includes multiple frequency routes and riding speed;

[0055] Step 200: Based on the map data of the target area, determine multiple possible paths according to the starting point and the end point set by the user;

[0056] Step 300: Screening possible paths according to traffic information to obtain a plurality of first optimized paths;

[0057] Step 400: Screening each first optimization path according to the habit preference data to obtain a plurality of second optimization paths;

[0058] Step 500: Filter each second optimization path according to the battery status and the real-time location data to obtain a third optimization path;

[0059] Step 600: Based on augmented reality technology, the third optimization path is displayed on the smart helmet.

[0060] Specifically, step 100 of this embodiment specifically includes:

[0061] Step 101: First, traffic information is obtained through cooperation with traffic management systems or third-party data providers. These systems usually provide API interfaces that can provide real-time traffic flow data, road construction information, and accident data. By calling these APIs, real-time traffic conditions in the target area can be obtained and integrated into the navigation system so that these factors can be taken into account when planning the route.

[0062] Step 102: Secondly, the user's habit preference data can be obtained by analyzing the user's historical riding records and preference settings. When the user uses the electric motorcycle, the system can record the user's common routes, riding speed and other information, and extract the user's riding habits through data analysis technology. This data can be stored in the user's personal profile and used for personalized services during route planning to improve the user's riding experience.

[0063] Step 103: The battery status of the electric motorcycle is obtained through the vehicle's battery management system (BMS). The BMS can monitor the battery power, health status, and usage efficiency in real time, and transmit this data to the navigation system through the vehicle communication module. In this way, the system can consider the current status of the battery when planning the route, ensuring that the selected route is within the allowable range of power.

[0064] Step 104: Finally, the acquisition of real-time location data depends on the global positioning system (GPS) and the vehicle positioning module. Through GPS, the system can obtain the current location of the vehicle in real time and combine it with map data for route planning and navigation guidance. Real-time location data is not only used for route planning, but also can provide dynamic navigation services during riding, helping users adjust the driving route according to real-time road conditions.

[0065] By acquiring and integrating the above-mentioned data, the navigation system can provide accurate and personalized route planning services to meet the diverse needs of electric motorcycle riders.

[0066] Preferably, based on the map data of the target area, multiple possible paths are determined according to the starting point and the end point set by the user, including:

[0067] Acquire the coordinates of the starting point and the end point set by the user, and unify the coordinates of the starting point and the end point into the coordinate system of the map data;

[0068] The coordinates of the starting point and the end point are input into a preset A* path search model to obtain the multiple possible paths.

[0069] Specifically, the A* path search model is an algorithm for finding the optimal path on a map. It combines the advantages of the best-first search of graph search and the Dijkstra algorithm, taking into account both the current cost of the path and the estimated cost of the target, and is therefore widely used in a wide range of applications.

[0070] The starting and ending coordinates entered by the user usually come from a GPS device or are manually entered by the user. These coordinates need to be consistent with the established map data format in the system, that is, the coordinates work uniformly. This requires the implementation of a certain coordinate conversion mechanism to ensure that the user-entered coordinates can be correctly matched to the map's reference system, such as converting the geographic coordinate system (latitude and longitude) into the plane coordinate system used by the map (such as the UTM coordinate system).

[0071] Further, the working principle of the A* path search algorithm is as follows:

[0072] The core idea of ​​the A* algorithm is to calculate and maintain an estimation function f(n) = g(n) + h(n) for each node during the search process, where g(n) is the actual cost from the starting point to the current node, and h(n) is the estimated cost from the current node to the end point. The estimation function helps determine the priority of the next node to visit, so as to effectively walk through the nodes in the graph. The heuristic function h(n) uses the Euclidean distance or Manhattan distance to reasonably estimate the length of the path that has not been visited.

[0073] During the implementation process, the construction of the graph structure needs to consider the connection relationship between roads and nodes (intersections, turning points, etc.). After the search begins, the A* algorithm traverses the nodes, accumulates the visited nodes into an open node list according to certain rules, namely the heuristic function, and maintains the best path state. All neighbor nodes of a node will be examined and the estimation function will be calculated in turn, and the neighbor node with the lowest estimation value will be selected for subsequent visit. Through a reasonable implementation, the A* algorithm can list a set of reasonable alternative paths. By running the algorithm multiple times and using different heuristic adjustments and path preferences, users can obtain multiple feasible path solutions from the start point to the end point. This diverse solution can adapt to the dynamic needs of users and real-time traffic conditions. In short, the A* path search model has demonstrated superiority in supporting efficient, dynamic, and optimized multi-path solution generation, and is very suitable for application scenarios with high real-time and flexibility requirements such as electric motorcycle navigation.

[0074] Preferably, the possible paths are screened according to the traffic information to obtain a plurality of first optimized paths, including:

[0075] Calculating a traffic resistance coefficient of each possible path according to the traffic information;

[0076] The possible paths are sorted according to the traffic resistance coefficient, and several paths with the lowest traffic resistance coefficient are selected as the first optimized paths.

[0077] Preferably, the calculation formula of the traffic resistance coefficient is:

[0078]

[0079] Where: T ij For path P i The traffic flow coefficient of the jth segment, C ij For path P i The construction coefficient of the jth segment, A ij For path P i The accident coefficient of the jth segment, W t , W c , W a are the weight coefficients of traffic flow, construction and accidents respectively, and n is the path P i The number of road sections; Among them, F current is the traffic flow of the current road section, F max is the maximum traffic flow capacity of the road section, D impact is the duration of the construction impact, S is the accident weight coefficient of the severity of the construction impact, T current is the current time after the accident, T resolution is the estimated time to complete the accident handling, and A is the severity of the accident.

[0080] Furthermore, the weight coefficient of this embodiment can be set according to the actual situation to reflect the influence of different factors on the path selection. For example, S is the severity of the construction impact, with complete closure being 1 and partial closure being 0.5. A major accident is 1 and A minor accident is 0.5.

[0081] Preferably, the number of the first optimization paths is ten.

[0082] Preferably, the first optimization paths are screened according to the habit preference data to obtain a plurality of second optimization paths, including:

[0083] Performing feature analysis on each first optimized path to extract key features of the first optimized path; the key features include: path length, average speed, number of traffic lights, and degree of scenic beauty;

[0084] The habit preference data is matched with the key features, and the matching degree between each of the first optimization paths and the user preference is calculated.

[0085] The first optimization paths are sorted according to the matching degree, and several paths with the highest matching degree are selected as the second optimization paths.

[0086] Preferably, the calculation formula of the matching degree is:

[0087]

[0088] Among them, F ik is the path Pi The eigenvalue of the kth key feature, W k is the user's preference weight for the kth key feature, and m is the total number of key features.

[0089] Furthermore, the path length is a path that the user may prefer to be shorter, and its weight can be set according to the user's preference. The average speed is a path that the user may prefer to ride within a certain speed range, and the weight can be set according to the user's speed preference. The number of traffic lights is a path that the user may prefer to have fewer traffic lights, and the weight can be set according to the user's patience. The degree of scenery is a path that the user may prefer to have beautiful scenery, and the weight can be set according to the user's interests. In this way, the present embodiment can effectively screen out paths that meet the user's habitual preferences and improve the user's riding experience and satisfaction. Examples are as follows:

[0090] User preference weight setting

[0091] Path length:

[0092] Users prefer shorter paths, and the weight is set to 0.4.

[0093] Average speed:

[0094] The user prefers to ride within a certain speed range, and the weight is set to 0.2.

[0095] Number of traffic lights:

[0096] Users prefer paths with fewer traffic lights, and the weight is set to 0.1.

[0097] Scenic beauty:

[0098] Users prefer scenic routes, and the weight is set to 0.3.

[0099] Path feature value examples

[0100] Suppose we have three paths, and the characteristic values ​​of each path are as follows:

[0101] Path A:

[0102] Length of trail: 5 km

[0103] Average speed: 25 km / h

[0104] Number of traffic lights: 3

[0105] Scenic beauty: High

[0106] Path B:

[0107] Length of trail: 6 km

[0108] Average speed: 20 km / h

[0109] Number of traffic lights: 1

[0110] Scenic beauty: Medium

[0111] Path C:

[0112] Trail length: 4 km

[0113] Average speed: 22 km / h

[0114] Number of traffic lights: 5

[0115] Scenic level: Low

[0116] The matching degree of each path can be calculated by multiplying the feature value of each path by the user preference weight and summing them up.

[0117] Preferably, the number of the second optimization paths is 3.

[0118] Preferably, each of the second optimization paths is screened according to the battery status and the real-time location data to obtain a third optimization path, including:

[0119] Calculate the remaining distance according to the real-time position data;

[0120] Performing energy consumption estimation for each second optimized path according to the remaining distance and the battery status; the battery status includes the current battery power and the battery usage efficiency coefficient;

[0121] The second optimization path corresponding to the minimum estimated energy consumption is determined as the third optimization path.

[0122] Preferably, the formula for estimating energy consumption is:

[0123]

[0124] Among them, D ij For path P i The remaining distance of the jth segment, C ij For path P i The energy consumption per unit distance of the jth segment, n is the path P i The number of road sections, B current is the current battery power, and η is the battery efficiency coefficient.

[0125] Specifically, the battery efficiency coefficient is determined by the battery health status and environmental factors. The battery efficiency coefficient is a comprehensive indicator used to evaluate the actual performance of the battery under current conditions. It takes into account the impact of battery health status (such as battery aging, number of charge and discharge cycles, and internal resistance changes) and environmental factors (such as temperature, humidity, and altitude) on battery performance. The health status can be evaluated through diagnostic data provided by the battery management system (BMS), while environmental factors are obtained through sensor data or weather forecast information. By combining these factors, the battery efficiency coefficient can be dynamically adjusted to reflect the actual available power of the battery under different conditions, thereby providing a more accurate energy consumption estimate in route planning and ensuring that the electric motorcycle will not be interrupted due to lack of power during driving.

[0126] Furthermore, in this embodiment, the third optimized path is displayed on the smart helmet using augmented reality (AR) technology, aiming to provide riders with intuitive and real-time navigation information, thereby improving riding safety and user experience. The specific implementation is through the following steps:

[0127] First, smart helmets need to integrate AR display devices, such as transparent displays or projection lenses. These devices can project digital information directly into the rider's field of view without blocking the actual road conditions. The helmets also need to be equipped with sensors, such as accelerometers, gyroscopes, and GPS modules, to obtain the rider's real-time location and movement status.

[0128] At the software level, the AR navigation system needs to be closely integrated with the path planning module. The path planning module provides detailed information about the third optimized path, including turning points, distances, and directions of the path. The AR system is responsible for converting this information into visual navigation instructions, such as arrows, distance markers, and turn prompts, and overlaying them in the rider's field of view. Real-time data processing is the key to achieving AR navigation. The system needs to continuously receive data from GPS and sensors to update the rider's current position and direction. Based on this data, the AR system dynamically adjusts the display position and content of the navigation information to ensure that the rider always receives accurate navigation instructions. In addition, the system can also integrate traffic information and environmental data to provide more comprehensive navigation services.

[0129] To improve the user experience, the AR navigation system should support simple user interactions, such as adjusting the displayed content or switching navigation modes through voice commands or gesture control. At the same time, the system needs to ensure the simplicity and clarity of information display to avoid excessive information distracting the rider's attention and ensure riding safety.

[0130] Through the above steps, the augmented reality navigation system of this embodiment can provide real-time and intuitive route guidance for electric motorcycle riders, helping them to reach their destination safely and conveniently in complex traffic environments.

[0131] Corresponding to the above method, such as Figure 2 As shown, this embodiment also provides an intelligent navigation system suitable for an electric motorcycle, including:

[0132] A data acquisition module, used to acquire traffic information of a target area, user's habit preference data when driving an electric motorcycle, battery status of the electric motorcycle, and real-time location data; the traffic information includes real-time traffic flow data, road construction and accident data; the habit preference data includes multiple frequency routes and riding speed;

[0133] A path planning module, for determining a plurality of possible paths according to a start point and an end point set by a user based on map data of the target area;

[0134] A first screening module, used for screening the possible paths according to the traffic information to obtain a plurality of first optimized paths;

[0135] A second screening module, used for screening each of the first optimization paths according to the habit preference data to obtain a plurality of second optimization paths;

[0136] A third screening module, configured to screen each of the second optimization paths according to the battery status and the real-time location data to obtain a third optimization path;

[0137] The display module is used to display the third optimization path on the smart helmet based on augmented reality technology.

[0138] The beneficial effects of the present invention are as follows:

[0139] (1) The present invention optimizes the route through real-time traffic information, which can effectively bypass congested and construction sections, reduce travel time and waiting time, and thus improve travel efficiency.

[0140] (2) The present invention takes into account the user's riding habits and preferences, such as commonly used routes and speed preferences, so that the navigation results are more in line with the user's personalized needs, thereby improving the user's riding experience and satisfaction.

[0141] (3) The present invention monitors the battery status in real time and considers the power and energy consumption estimation when selecting a route, thereby ensuring that the user will not face danger due to insufficient power during driving, thereby improving driving safety.

[0142] (4) The present invention dynamically adjusts the navigation path according to the real-time location and traffic information, so that the navigation system can adapt to changes in road conditions at any time, ensuring that the user always obtains the best driving plan.

[0143] (5) The present invention accurately estimates energy consumption and selects routes, reduces unnecessary routes and power consumption, and not only reduces the economic burden on users, but also has a positive impact on environmental protection.

[0144] (6) The present invention uses augmented reality technology on a smart helmet to provide intuitive and easy-to-understand navigation information, allowing users to obtain navigation guidance more safely and conveniently during riding.

[0145] (7) The method of the present invention comprehensively considers multiple factors such as traffic flow, construction conditions, accident impact, and user habits, and provides a comprehensive path optimization solution to ensure excellent performance under various conditions.

[0146] (8) The intelligent navigation method of the present invention is not only applicable to electric motorcycles, but can also be extended to other types of electric vehicles to provide high-quality services to a wider range of user groups.

[0147] In this specification, each embodiment is described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the embodiments can be referred to each other. For the system disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the method part.

[0148] The principles and implementation methods of the present invention are described in this article using specific examples. The description of the above embodiments is only used to help understand the method and core idea of ​​the present invention. At the same time, for those skilled in the art, according to the idea of ​​the present invention, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting the present invention.

Claims

1. An intelligent navigation method suitable for an electric motorcycle, characterized in that: include: Obtaining traffic information of a target area, user's habit preference data when driving an electric motorcycle, battery status of the electric motorcycle, and real-time location data; the traffic information includes real-time traffic flow data, road construction and accident data; the habit preference data includes multiple frequency routes and riding speed; Based on the map data of the target area, determining multiple possible paths according to the starting point and the end point set by the user; Screening the possible paths according to the traffic information to obtain a plurality of first optimized paths; Screening each of the first optimization paths according to the habit preference data to obtain a plurality of second optimization paths; Filter each of the second optimization paths according to the battery status and the real-time location data to obtain a third optimization path; Based on augmented reality technology, the third optimization path is displayed on the smart helmet.

2. The intelligent navigation method for electric motorcycles according to claim 1, characterized in that: Based on the map data of the target area, multiple possible paths are determined according to the starting point and the end point set by the user, including: Acquire the coordinates of the starting point and the end point set by the user, and unify the coordinates of the starting point and the end point into the coordinate system of the map data; The coordinates of the starting point and the end point are input into a preset A* path search model to obtain the multiple possible paths.

3. The intelligent navigation method for electric motorcycles according to claim 1, characterized in that: The possible paths are screened according to the traffic information to obtain a plurality of first optimized paths, including: Calculating a traffic resistance coefficient of each possible path according to the traffic information; The possible paths are sorted according to the traffic resistance coefficient, and several paths with the lowest traffic resistance coefficient are selected as the first optimized paths.

4. The intelligent navigation method for electric motorcycles according to claim 3, characterized in that: The calculation formula of the traffic resistance coefficient is: Where: T ij For path P i The traffic flow coefficient of the jth segment, C ij For path P i The construction coefficient of the jth segment, A ij For path P i The accident coefficient of the jth segment, W t , W c , W a are the weight coefficients of traffic flow, construction and accidents respectively, and n is the path P i The number of road sections; Among them, F current is the traffic flow of the current road section, F max is the maximum traffic flow capacity of the road section, D impact is the duration of the construction impact, S is the accident weight coefficient of the severity of the construction impact, T current is the current time after the accident, T resolution is the estimated time to complete the accident handling, and A is the severity of the accident.

5. The intelligent navigation method for electric motorcycles according to claim 3, characterized in that: The number of the first optimization paths is 10.

6. The intelligent navigation method for electric motorcycles according to claim 1, characterized in that: The first optimization paths are screened according to the habit preference data to obtain a plurality of second optimization paths, including: Performing feature analysis on each first optimized path to extract key features of the first optimized path; the key features include: path length, average speed, number of traffic lights, and degree of scenic beauty; The habit preference data is matched with the key features, and the matching degree between each of the first optimization paths and the user preference is calculated. The first optimization paths are sorted according to the matching degree, and several paths with the highest matching degree are selected as the second optimization paths.

7. The intelligent navigation method for electric motorcycles according to claim 5, characterized in that: The calculation formula of the matching degree is: Among them, F ik is the path P i The eigenvalue of the kth key feature, W k is the user's preference weight for the kth key feature, and m is the total number of key features.

8. The intelligent navigation method for electric motorcycles according to claim 1, characterized in that: The second optimization paths are screened according to the battery status and the real-time location data to obtain a third optimization path, including: Calculate the remaining distance according to the real-time position data; Performing energy consumption estimation for each second optimized path according to the remaining distance and the battery status; the battery status includes the current battery power and the battery usage efficiency coefficient; The second optimization path corresponding to the minimum estimated energy consumption is determined as the third optimization path.

9. The intelligent navigation method for electric motorcycles according to claim 8, characterized in that: The formula for energy consumption estimation is: Among them, D ij For path P i The remaining distance of the jth segment, C ij For path P i The energy consumption per unit distance of the jth segment, n is the path P i The number of road sections, B current is the current battery power, and η is the battery efficiency coefficient.

10. An intelligent navigation system suitable for an electric motorcycle, characterized in that: include: A data acquisition module, used to acquire traffic information of a target area, user's habit preference data when driving an electric motorcycle, battery status of the electric motorcycle, and real-time location data; the traffic information includes real-time traffic flow data, road construction and accident data; the habit preference data includes multiple frequency routes and riding speed; A path planning module, for determining a plurality of possible paths according to a start point and an end point set by a user based on map data of the target area; A first screening module, used for screening the possible paths according to the traffic information to obtain a plurality of first optimized paths; A second screening module, used for screening each of the first optimization paths according to the habit preference data to obtain a plurality of second optimization paths; A third screening module, configured to screen each of the second optimization paths according to the battery status and the real-time location data to obtain a third optimization path; The display module is used to display the third optimization path on the smart helmet based on augmented reality technology.

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