Riding route recommendation method and system based on vehicle-connected power data sharing, medium and processor

Abstract

The invention discloses a riding route recommendation method and system based on vehicle-connected power data sharing, a medium and a processor. The method comprises the steps of collecting vehicle operation and driver state data, denoising and normalizing the data, determining a potential route driving experience value, fusing multiple parameters to construct a target function to obtain a preliminary recommendation route, and selecting a final recommendation route based on user preference. The system comprises an acquisition module, a processing module, a calculation module and a selection module. According to the method, factors such as driving experience, time, charging, energy consumption and user preference can be comprehensively considered, the accuracy and individuation degree of riding route recommendation are improved, the driving state is monitored in real time to guarantee safety, route diversity is also considered, and diversified travel requirements of users are met.

Description

Technical Field

[0001] The present invention relates to the technical field of cycling navigation, and in particular to a cycling route recommendation method, system, medium and processor based on vehicle-to-vehicle electrical data sharing. Background Art

[0002] With the popularity of electric bicycles and the diversification of people's travel needs, how to provide cyclists with more scientific and reasonable cycling route recommendations has become an urgent problem to be solved. In the existing cycling route recommendation technology, there are many shortcomings.

[0003] Traditional route recommendation methods often plan routes based solely on simple geographic information and traffic conditions, such as shortest distance or shortest time, completely ignoring key information such as the driver's actual experience and vehicle operating status.

[0004] At the same time, existing technologies fail to fully utilize the data collected by various sensors on vehicles. Accelerometers, gyroscopes, grip sensors, and other sensors installed on vehicles can capture a wealth of vehicle operation and driver status data, but this data is currently not effectively integrated and utilized in cycling route recommendations, resulting in a waste of resources.

[0005] In summary, a new technical solution is urgently needed to solve these problems in order to improve the quality of cycling route recommendations and user satisfaction.

[0006] In view of this, a method, system, medium and processor for recommending cycling routes based on vehicle-to-vehicle electrical data sharing are needed. Summary of the Invention

[0007] To address the issues in existing route recommendation methods, which often rely solely on simple geographic information and traffic conditions, and the resulting need for improved route recommendation quality and user satisfaction, the present invention provides a method, system, medium, and processor for cycling route recommendation based on vehicle-to-vehicle data sharing. These methods comprehensively consider driving experience, data utilization, and user preferences, improving route recommendation quality and user satisfaction. The specific technical solution is as follows:

[0008] A method for recommending cycling routes based on vehicle-to-vehicle data sharing, comprising:

[0009] S1: Utilize various sensors installed on the vehicle to collect vehicle operation data and driver status data;

[0010] S2: De-noising and normalization of the collected data;

[0011] S3: Determine the driving experience value of each potential route based on the collected data;

[0012] S4: Construct an objective function by integrating the driving experience values of the routes and determine several preliminary recommended routes;

[0013] S5: Selecting a final recommended route based on user preferences from the preliminary recommended routes.

[0014] Furthermore, in step S3, determining the driving experience value of the route based on the collected data includes the following steps:

[0015] S31: determining a vertical inclination of the electric bicycle driven by the driver based on the acceleration data;

[0016] S32: Obtaining the driver's sitting or lying inclination according to the position information of the driver when the driver is sitting or lying down obtained by the sensor;

[0017] S33: Obtaining optimal driving power according to the grip force, driving force and gravity parameters of the electric bicycle;

[0018] S34: determining a driving experience value based on the vertical inclination, the sitting and lying inclination, and the optimal driving dynamics;

[0019] S35: When the route is regarded as a continuous process, the driving experience value of the route is obtained by performing time integration on the driving experience value.

[0020] Furthermore, in step S31, determining the vertical inclination of the electric bicycle driven by the driver according to the acceleration data includes the following steps:

[0021] Calculate the component of acceleration in the horizontal plane:

[0022]

[0023] Calculate the relationship between vertical acceleration and gravity acceleration:

[0024] a 垂直 =a z -g;

[0025] Finally, the vertical tilt is calculated using the inverse tangent function:

[0026]

[0027] In the above formula, a x 、a y 、a y are the acceleration measurements of the accelerometer in the x, y, and z axes respectively; g is the acceleration due to gravity; a 水平 is the component of acceleration on the horizontal plane; a 垂直 is the component of acceleration in the vertical plane; α is the vertical inclination angle.

[0028] Furthermore, in step S32, the formula for obtaining the driver's sitting or lying inclination according to the position information of the driver when the driver is sitting or lying down obtained by the sensor is as follows:

[0029]

[0030] In the above formula, Cotγ is the sitting or lying inclination; Lb is the horizontal length of the bicycle; Lp is the horizontal distance from the driver's hips to the knees when sitting or lying; H is the vertical distance from the driver's head to the pedals when sitting or lying.

[0031] In step S33, the formula for obtaining the optimal driving force according to the electric bicycle's grip force, driving force, and gravity parameters is as follows:

[0032] E=m1×W1+m2×W2+m3×W3;

[0033] In the above formula, W1 and m1 are the driver's grip force and the corresponding weight coefficient respectively; W2 and m2 are the driving force and the corresponding weight coefficient respectively; W3 and m3 are gravity and the corresponding weight coefficient respectively;

[0034] In step S34, the formula for determining the driving experience value based on the vertical inclination, the sitting and lying inclination, and the optimal driving dynamics is as follows:

[0035] D=k1×Tanα+k2×Cotγ+E;

[0036] In the above formula, D is the driving experience value; Cotγ and k2 are the sitting and lying inclinations and their corresponding weight coefficients respectively; Tanα and k1 are the vertical inclinations and their corresponding weight coefficients respectively; and E is the optimal driving dynamics.

[0037] Furthermore, in step S35, the driving experience value of the route is obtained by performing time integration on the above driving experience value as follows:

[0038]

[0039] In the above formula, D(t) is the driving experience value D as a function of time t; D route is the driving experience value of the route; T is the total driving time of the route.

[0040] Furthermore, in step S4, the driving experience values of the integrated routes are used to construct an objective function to determine several preliminary recommended routes, including the following steps:

[0041] S41: Obtaining time, charge and energy consumption parameters of each potential route respectively;

[0042] S42: The objective function is constructed by integrating the driving experience values of time, charging, energy consumption, and route. The objective function is as follows:

[0043]

[0044] In the above formula, T time For time; C cost For charging; E energy is energy consumption; c, d, e, and f are time T time 、Charge C cost Energy consumption E energy , the weight coefficient corresponding to the driving experience value D; m is the number of potential historical routes; O route is the objective function.

[0045] S43: Calculate the objective function value of each potential route and determine several preliminary recommended routes.

[0046] Furthermore, in step S5, the final recommended route is selected based on the user's preference from the preliminary recommended routes. The formula for selecting the final recommended route is as follows:

[0047] f(x)=limL specific -limL average ;

[0048] In the above formula, limL specific Indicates the maximum value of the route within a specific time period; limL average Represents the average probability of a route being selected within a certain period.

[0049] A cycling route recommendation system based on vehicle-to-vehicle data sharing, applied to the above-mentioned cycling route recommendation method based on vehicle-to-vehicle data sharing, comprises:

[0050] The acquisition module is used to collect vehicle operation data and driver status data using various sensors installed on the vehicle;

[0051] A processing module, which is used to perform denoising and normalization processing on the collected data;

[0052] A first calculation module is used to determine the driving experience value of each potential route based on the collected data;

[0053] The second calculation module is used to integrate the driving experience values of the routes to construct an objective function and determine several preliminary recommended routes;

[0054] The selection module is used to select a final recommended route based on user preferences in the preliminary recommended routes.

[0055] A computer-readable storage medium includes a stored program, wherein when the program is executed, the device where the computer-readable storage medium is located is controlled to execute the above-mentioned method for recommending cycling routes based on vehicle-to-vehicle data sharing.

[0056] A processor is used to run a program, wherein when the program is run, the above-mentioned cycling route recommendation method based on vehicle-to-vehicle data sharing is executed.

[0057] Compared with the prior art, the present invention has the following beneficial effects:

[0058] 1. Integrating multiple factors to improve recommendation accuracy: Existing technologies often recommend cycling routes based on a single factor, such as distance or time. This technical solution integrates vehicle operation data and driver status data, and also incorporates multiple factors such as time, tolls, energy consumption, and driving experience to construct an objective function. The recommended route is determined through multiple rounds of calculations. When calculating the objective function, the weights of different factors are fully considered to fully reflect the actual route conditions, providing users with more tailored recommendations and far greater accuracy than traditional methods.

[0059] 2. Real-time monitoring ensures driving safety: Utilizing real-time data collected by vehicle sensors, the system monitors the driver's posture and vehicle status. Acceleration data is used to calculate vertical tilt, determining whether the driver's sitting position is correct. Once a threshold is exceeded, a warning is issued to correct bad driving habits, reduce accident risks, and ensure safe riding. This is real-time safety monitoring that traditional technologies cannot achieve.

[0060] 3. Personalized service based on user preferences: This solution collects historical user data to model user preferences, then uses a specific formula to calculate and compare routes to select the route that best meets their preferences. Compared to the one-size-fits-all recommendation model of existing technologies, this solution can meet the individual needs of different users in terms of scenery, safety, and comfort, thereby improving the user experience.

[0061] 4. Efficiently utilize data to mine potential value: Existing technologies underutilize vehicle sensor data. This solution fully taps into this data's value, collecting vehicle operation and driver status data. After denoising and normalization, this data is used to calculate key indicators such as the driving experience value. This provides strong support for route recommendations, improves data utilization, and creates more value.

[0062] 5. Optimize route selection to enrich travel options: When determining the initial recommended routes, we consider not only the objective function value but also route diversity. We avoid concentrating recommended routes in the same area, providing users with more options to meet special needs such as stop-by shopping and enriching their travel plans.

[0063] 6. Personalized route recommendations improve user satisfaction: By incorporating driving experience values into route recommendations, the system takes into account the user's subjective feelings during the driving process, making recommended cycling routes more tailored to their individual needs. For users who prioritize driving comfort, the system prioritizes routes that maintain a high driving experience value. Even if these routes are not optimal in terms of time, tolls, or energy consumption, they can provide users with a better driving experience, thereby improving user satisfaction with the route recommendation service.

[0064] 7. Enhanced safety and comfort: The driving experience value is closely related to the driver's driving state. Factors such as sitting posture and grip strength reflect driving safety. Combining this with route recommendations can help avoid sections that may degrade the driving experience and increase safety risks. For example, routes with rugged roads and chaotic traffic conditions should be avoided, as these sections may reduce the driving experience value and increase the risk of accidents. This new technical solution ensures safer and more comfortable travel for users. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following briefly describes the drawings required for the specific embodiments or the description of the prior art. Similar elements or parts are generally identified by similar reference numerals throughout the drawings. Elements or parts in the drawings are not necessarily drawn to scale.

[0066] Figure 1 This is a flowchart of a method for recommending cycling routes based on vehicle-to-vehicle data sharing.

[0067] Figure 2 This is a structural diagram of a cycling route recommendation system based on vehicle-to-vehicle data sharing. DETAILED DESCRIPTION

[0068] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0069] It should be understood that when used in this application, the terms "include" and "comprising" indicate the presence of described features, integers, steps, operations, elements and / or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or collections thereof.

[0070] It should also be understood that the terms used in the present specification are only for the purpose of describing particular embodiments and are not intended to limit the present invention. As used in the present specification and the appended claims, the singular forms "a", "an", and "the" are intended to include the plural forms unless the context clearly indicates otherwise.

[0071] It should be further understood that the term "and / or" used in this application refers to and includes any and all possible combinations of one or more of the associated listed items.

[0072] Example 1

[0073] like Figure 1 A method for recommending cycling routes based on vehicle-to-vehicle data sharing includes the following steps:

[0074] S1: Utilizes various sensors installed on the vehicle, such as gyroscopes, accelerometers, speed sensors, and grip sensors, to collect vehicle operation data and driver status data. The gyroscope measures the vehicle's angular velocity along the three axes (x, y, and z), the accelerometer measures the vehicle's acceleration along the three axes, the speed sensor measures the vehicle's speed, and the grip sensor detects the driver's grip strength.

[0075] The collected data is transmitted to the data processing center through wireless communication technologies (such as 4G, 5G, etc.), ensuring the stability and timeliness of data transmission and avoiding data loss or delay.

[0076] S2: De-noise and normalize the collected data.

[0077] Remove abnormal data caused by sensor errors, interference, etc. Filtering algorithms, such as Kalman filtering, can be used to optimize gyroscope and accelerometer data to improve data accuracy.

[0078] The different types of data collected are normalized to unify the data into the same dimension and value range to facilitate subsequent calculations and analysis.

[0079] S3: The shared electric vehicle human dynamic mechanical balance system is formed by combining the human body model with the friction mechanics, torque balance, and principles of dynamics. The driving experience value of each potential route is determined based on the collected data. The specific steps include:

[0080] S31: Determine the vertical inclination of the electric bicycle driven by the driver based on the acceleration data. The steps are as follows:

[0081] Assume that at a certain moment, the angular velocities of the vehicle in the x-axis, y-axis, and z-axis directions are measured by the preprocessed gyroscope as ω x、ω y 、ω z The accelerations measured by the accelerometer in the three axes are a x 、a y 、a y Taking into account the earth's gravitational acceleration g, ignoring resistance factors such as wind and road friction, and assuming the vehicle speed is at the rated normal value, the vehicle's vertical inclination α is calculated using the following formula based on the principles of dynamics:

[0082] First calculate the component of acceleration in the horizontal plane:

[0083]

[0084] Then calculate the relationship between the vertical acceleration and the acceleration due to gravity:

[0085] a 垂直 =a z -g;

[0086] Finally, the vertical tilt is calculated using the inverse tangent function:

[0087]

[0088] Tilt analysis and application: The calculated vertical tilt α can intuitively reflect the driver's body posture when riding an e-bike. The closer α is to 0°, the more vertical the vehicle is and the more correct the driver's sitting posture is. When α deviates significantly from 0°, it indicates that the driver's sitting posture is incorrect, and the vehicle's driving stability may be affected. When the driver has an incorrect sitting posture, the vehicle can provide friendly reminders through the built-in reminder system to promptly correct the driver's bad driving habits, while also improving driving safety, reducing driving risks, and enhancing the driving experience. For example, when the vertical tilt exceeds a certain threshold (such as 10°), the vehicle will vibrate or issue a voice prompt to remind the driver to adjust their sitting posture to ensure driving safety.

[0089] S32: Based on the position information of the driver when sitting or lying down obtained by the sensor, combined with the horizontal length Lb of the bicycle, the horizontal distance Lp from the driver's hips to the knees when sitting or lying down, and the vertical distance H from the driver's head to the pedals, the driver's sitting or lying inclination Cotγ is obtained. The formula is as follows:

[0090]

[0091] In the above formula, Lb is the horizontal length of the bicycle; Lp is the horizontal distance from the driver's hips to the knees when sitting or lying down; H is the vertical distance from the driver's head to the pedals when sitting or lying down.

[0092] S33: Obtain the optimal driving force based on the grip force, driving force, and gravity parameters of the electric bicycle. The formula is as follows:

[0093] E=m1×W1+m2×W2+m3×W3;

[0094] In the above formula, W1 and m1 are the driver's grip strength and the corresponding weight coefficient respectively (the driver's grip strength is mainly used to test the driver's driving concentration), W2 and m2 are the driving force (the driving force is the speed of the electric vehicle itself) and the corresponding weight coefficient respectively, and W3 and m3 are gravity (gravity comes from the data tested by the balance monitor) and the corresponding weight coefficient respectively.

[0095] S34: Determine the driving experience value based on the vertical inclination, the sitting and lying inclination, and the optimal driving dynamics. The calculation formula is as follows:

[0096] D=k1×Tanα+k2×Cotγ+E;

[0097] In the above formula, D is the driving experience value; Cotγ and k2 are the sitting and lying inclinations and their corresponding weight coefficients respectively; Tanα and k1 are the vertical inclinations and their corresponding weight coefficients respectively; and E is the optimal driving dynamics.

[0098] The following is a table of driving experience values D under different circumstances:

[0099] Situation setting value Sitting posture Driving Experience The body is tilted vertically 80° to 90°, and the sitting posture is bent 80° to 90° correct Comfort The body is tilted vertically 70° to 80°, and the sitting posture is bent 70° to 80° correct generally The body is tilted vertically below 70° and the sitting posture is bent below 70° Incorrect Difference

[0100] Furthermore, the driver's grip strength is mainly used to test the driver's driving concentration, as shown in the following table:

[0101] Situation setting value Driving status Grip strength 25mm / s or more Holding the handlebars Grip strength below 25mm / s Hands off the handlebars / Not holding the handlebars firmly enough

[0102] Furthermore, the comparison table of center of gravity measurement value, driving status and safety factor is as follows:

[0103] Center of gravity measurement value Driving status Safety factor Deviation 0~3mm normal high Deviation 3~5mm There is a serious risk of tipping, rollover, and bumping middle Deviation more than 5mm Severe tipping, rollover, bumping, and collision Low

[0104] S35: When the route is considered as a continuous process, the driving experience value D is expressed as a function of time t as D(t). If the total driving time of the route is T, the driving experience value D of the route can be calculated by integration. route :

[0105]

[0106] S4: Integrate the driving experience values of the routes to construct an objective function and determine several preliminary recommended routes. The specific steps are as follows:

[0107] S41: Obtain the time T for each potential route respectively time 、Charge C cost and energy consumption E energy The calculation formula of the parameters is as follows:

[0108]

[0109] S42: Fusion time T time 、Charge C cost Energy consumption E energy and the driving experience value D of the route route Construct the objective function, objective function O route The expression is as follows:

[0110]

[0111] Among them, c, d, e, and f are time T time 、Charge C cost Energy consumption E energy , driving experience value D route The corresponding weight coefficient is c+d+e+f=1; based on the weight coefficient calculated above, when time T time Shortest, Charge C cost Minimum, energy consumption E energy The lowest, and the three results are the lowest among the m historical routes. At this time, the route under the comprehensive goal consideration is the cycling route initially recommended by the platform; v0 is the ideal speed of the vehicle in the absence of traffic congestion; L length is the road length; T flow is the traffic flow; a i is the traffic flow weight coefficient of time period i; a max is the traffic flow weight coefficient a i The maximum value of is the traffic flow T flow p is the charging standard per unit length; k1 is a constant determined according to the actual charging policy; e0 is the basic energy consumption per unit length; k2 is a constant determined according to the energy consumption characteristics of the vehicle.

[0112] When D is small, its impact on the objective function value is relatively small. As D increases, the impact gradually increases, but the rate of increase gradually slows down. This is suitable for scenarios where the driving experience value has little impact on the overall recommendation results when it varies within a certain range, but will have a significant impact on the recommendation results when the driving experience value reaches a high level.

[0113] Traffic flow weight coefficient calculation formula: Among them, a i represents the traffic flow weight coefficient of the i-th time period (for example, the current user's time period is i); F i represents the traffic volume of the road in the i-th time period; It represents the total traffic volume of the road in the statistical n time periods; k is the adjustment coefficient, which is set according to the actual situation and is used to adjust the size range of the weight coefficient to make the result more in line with the actual needs. Assume that the traffic volume of a certain road in the morning peak (7-9 o'clock), flat peak (9-17 o'clock), and evening peak (17-19 o'clock) time periods is F1 = 2000 vehicles, F2 = 3000 vehicles, and F3 = 2500 vehicles respectively. If k is set to 1, then the morning peak traffic flow weight coefficient is

[0114] S43: Calculate the objective function value of each potential route and determine several preliminary recommended routes, including the following determination methods:

[0115] According to the calculation results of the objective function, the top ranked routes are selected as the preliminary recommended routes. If the top 3 are selected, then O route The first three routes with the smallest values will be initially recommended.

[0116] Set an O according to actual business needs and experience route The threshold of the value, assuming the threshold is set to 10, then the calculated O route Routes with a value less than or equal to 10 can be used as preliminary recommended routes. route If the value meets the threshold requirement, it has the opportunity to become a preliminary recommended route. This can avoid focusing on only a few optimal routes and ignoring other routes that may meet the needs of some users.

[0117] Consider line diversity: In addition to O route If the first three routes are all concentrated in the same area, in order to give users more choices, the standard can be appropriately relaxed to include a route with a different location but O route The line with a slightly higher value. For example, line 4 is O route The value is 12, which is higher than the first three routes, but it passes through different areas and can meet the different needs of users (that is, users may need to purchase things along the way). It can also be included in the preliminary recommended route.

[0118] S5: Select the final recommended route based on user preferences from the initial recommended routes. Route recommendations are influenced not only by the aforementioned objective factors, but also by personal preferences and public ratings. User preferences are modeled by collecting historical user data and training a user preference model. For example, a set of data recording user route selections for a particular destination is shown in the following table:

[0119] user Time period Route selection User1 Today 12:00~18:00 Route 1 User2 Yesterday 18:00~00:00 Route 2 User3 Today 6:00~12:00 Route 1 User4 Yesterday 8:00~12:00 Route 3 …User … …

[0120] Through the following extreme value function formula:

[0121] f(x)=limL specific -limLaverage ;

[0122] limL specific This value represents the maximum route value within a specific time period, that is, the route with the highest probability of being selected within that time period. In practice, it is determined by counting the frequency of route selections by users for a particular destination within a specific time period. For example, suppose there are five routes leading to the same destination. During the time period of 12:00 PM to 6:00 PM today, Route 1 was selected 20 times, Route 2 was selected 10 times, Route 3 was selected 5 times, Route 4 was selected 8 times, and Route 5 was selected 12 times. Route 1 was selected the most frequently within that time period, and its corresponding frequency value of 20 is the maximum route value within that specific time period. (Frequency is used as a quantitative numerical example here; in actual applications, it could also be other values such as route distance or duration.) This value represents the route that users are most likely to choose within that specific time period. It does not refer to the route itself, but rather to the quantitative indicator corresponding to that route.

[0123] The maximum route value within a specific time period—the route with the highest probability of being selected—is determined and compared to the average route value. The smaller the difference between the two, the more closely the route matches the user's preference and is therefore the recommended route. If the difference fluctuates significantly, the route is simply the most popular route for that time period and not the optimal recommended route. It's important to note that the recommended optimal route is not fixed; it's influenced not only by the routes selected within a specific time period but also by changes in historical user data. By optimizing the model algorithm, the recommended routes become more scientific, accurate, and more tailored to the driver's needs.

[0124] limL average It represents the average probability of a route being selected within a certain period (such as a week or a month). It is the result of averaging the historical route selection data of users within a certain range.

[0125]

[0126] In the above formula, limL average represents the average probability of a route being selected within a certain period; q t Indicates the actual number of times a route in the preliminary recommended routes is selected within a certain period; q z It is the total number of route selections for all preliminary recommended routes within a certain period.

[0127] Example 2

[0128] like Figure 2 FIG2 is a schematic diagram of a cycling route recommendation system based on vehicle-to-vehicle data sharing, which is applied to the above-mentioned cycling route recommendation method based on vehicle-to-vehicle data sharing, including:

[0129] The acquisition module is used to collect vehicle operation data and driver status data using various sensors installed on the vehicle;

[0130] A processing module, which is used to perform denoising and normalization processing on the collected data;

[0131] A first calculation module is used to determine the driving experience value of each potential route based on the collected data;

[0132] The second calculation module is used to integrate the driving experience values of the routes to construct an objective function and determine several preliminary recommended routes; the selection module is used to select the final recommended route based on user preferences among the preliminary recommended routes.

[0133] Example 3

[0134] A computer-readable storage medium includes a stored program, wherein when the program is executed, the device where the computer-readable storage medium is located is controlled to execute the above-mentioned method for recommending cycling routes based on vehicle-to-vehicle data sharing.

[0135] Example 4

[0136] A processor is used to run a program, wherein when the program is run, the above-mentioned cycling route recommendation method based on vehicle-to-vehicle data sharing is executed.

[0137] Beneficial effects:

[0138] 1. Improving Cycling Comfort: Traditional cycling route recommendations focus primarily on factors like distance and time, ignoring the actual riding experience. Incorporating the driving experience value, the system can avoid bumpy, narrow, or steep sections of road, which can reduce the driving experience value. For example, based on the vertical inclination and sitting / lying inclination calculations within the driving experience value, if a section of road frequently climbs, causing the rider to lean too far, and thus reducing the driving experience value, the system may not recommend that route, thereby improving overall riding comfort.

[0139] 2. Enhanced Cycling Safety: Parameters such as grip strength and center of gravity measurements within the driving experience value reflect cycling safety. When combined with recommended cycling routes, routes with complex traffic conditions can be avoided, where riders are more likely to experience unstable center of gravity or require frequent grip adjustments. For example, at heavy traffic intersections, where riders frequently need to adjust their grip and their center of gravity is easily disturbed, the driving experience value decreases. The new recommendation system will reduce these route recommendations, minimizing safety risks.

[0140] 3. Meeting Personalized Needs: Different cyclists have varying requirements for comfort, safety, and speed. Driving experience values are combined with cycling routes, with weights assigned based on user history and preferences. For comfort-conscious users, the system prioritizes routes with high driving experience values when recommending routes. For speed-conscious users, the system recommends relatively quick routes while ensuring a consistent driving experience, providing personalized service.

[0141] 4. Optimizing Urban Cycling Planning: By leveraging a large amount of user driving experience and cycling route data, city planners can identify road sections with poor cycling experiences and areas requiring infrastructure improvements. If a particular area is found to have generally low driving experience, they can consider optimizing road slopes, widening roads, or adding bike lanes to improve the urban cycling environment.

[0142] 5. Alleviate traffic congestion and improve urban traffic efficiency: The driving experience value can reflect the actual traffic conditions of a road section. When combined with cycling route recommendations, the system can guide cyclists to avoid congested sections based on real-time driving experience value data. For example, if a section of road has a traffic accident or road construction that causes the driving force in the driving experience value to decrease and traffic flow to increase, the system will incorporate this information into route planning and divert cyclists to other unobstructed sections. As more cyclists are properly guided, the distribution of urban traffic flow will become more balanced, effectively alleviating local congestion and improving overall traffic efficiency.

[0143] 6. Assist in the operation and management of shared bicycles and optimize vehicle dispatch: For shared bicycle operating companies, the data combining driving experience values and riding routes is of great value. By analyzing the driving experience values in different regions and routes, companies can understand which vehicle distribution areas have a poor riding experience, and there may be cases of vehicle damage or unreasonable deployment. For example, if the driving experience values of riding routes in a certain area are generally low, and the main reason is abnormal grip or balance problems caused by vehicle failure, the company can carry out targeted inspections and replacements of vehicles in that area. At the same time, combined with riding route data, the vehicle deployment strategy can be optimized, and vehicles can be concentrated in areas with high demand and good driving experience, thereby improving vehicle utilization efficiency and turnover rate.

[0144] 7. Improve the scientific nature of user decision-making and promote green travel: When choosing a cycling route, users often find it difficult to comprehensively consider multiple factors. Combining driving experience values with cycling routes provides users with a more comprehensive basis for decision-making. When planning a trip, users can not only see general information such as route distance and time, but also intuitively understand the driving experience of different routes. This allows users to more scientifically select routes that not only meet their travel needs but also provide a good cycling experience. When users discover that green and comfortable cycling routes provide a better driving experience, they will be more inclined to choose cycling, further promoting the promotion of green travel modes, reducing urban carbon emissions, and improving urban environmental quality.

[0145] The present invention discloses a method, system, medium and processor for recommending cycling routes based on vehicle-to-vehicle data sharing. The method includes collecting vehicle operation and driver status data, denoising and normalizing the data, determining the driving experience value of potential routes, fusing multiple parameters to construct an objective function to obtain a preliminary recommended route, and then selecting the final recommended route based on user preferences. The system includes modules such as collection, processing, calculation and selection. Through the present invention, factors such as driving experience, time, charges, energy consumption and user preferences can be comprehensively considered to improve the accuracy and personalization of cycling route recommendations, monitor driving status in real time to ensure safety, and take into account route diversity to meet users' diverse travel needs.

[0146] Those skilled in the art will appreciate that the units of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition of each example has been generally described in terms of function in the above description. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0147] In the embodiments provided by the present invention, it should be understood that the division of units is merely a logical function division, and there may be other division methods in actual implementation, for example, multiple units can be combined into one unit, one unit can be split into multiple units, or some features can be ignored, etc.

[0148] In addition, the functional units in the various embodiments of the present invention may be integrated into a single processing unit, each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0149] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server or network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, read-only memory (ROM, Read-0nly Memory), random access memory (RAM, Random Access Memory), mobile hard disk, magnetic disk or optical disk, etc., various media that can store program code.

[0150] 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 above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above embodiments, or make equivalent replacements for some or all of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the scope of the technical solutions of the embodiments of the present invention, and they should all be included in the scope of this application.

Claims

1. A cycling route recommendation method based on vehicle-to-vehicle data sharing, characterized in that: include: S1: Utilize various sensors installed on the vehicle to collect vehicle operation data and driver status data; S2: De-noising and normalization of the collected data; S3: Determine the driving experience value of each potential route based on the collected data; S4: Construct an objective function by integrating the driving experience values of the routes and determine several preliminary recommended routes; S5: Selecting a final recommended route based on user preferences from the preliminary recommended routes.

2. The cycling route recommendation method based on vehicle-to-vehicle data sharing according to claim 1 is characterized in that: In step S3, determining the driving experience value of the route based on the collected data includes the following steps: S31: determining a vertical inclination of the electric bicycle driven by the driver based on the acceleration data; S32: Obtaining the driver's sitting or lying inclination according to the position information of the driver when the driver is sitting or lying down obtained by the sensor; S33: Obtaining optimal driving power according to the grip force, driving force and gravity parameters of the electric bicycle; S34: determining a driving experience value based on the vertical inclination, the sitting and lying inclination, and the optimal driving dynamics; S35: When the route is regarded as a continuous process, the driving experience value of the route is obtained by performing time integration on the driving experience value.

3. The cycling route recommendation method based on vehicle-to-vehicle data sharing according to claim 2 is characterized in that: In step S31, determining the vertical inclination of the electric bicycle driven by the driver according to the acceleration data includes the following steps: Calculate the component of acceleration in the horizontal plane: Calculate the relationship between vertical acceleration and gravity acceleration: a 垂直 =a z -g; Finally, the vertical tilt is calculated using the inverse tangent function: In the above formula, a x 、a y 、a y are the acceleration measurements of the accelerometer in the x, y, and z axes respectively; g is the acceleration due to gravity; a 水平 is the component of acceleration on the horizontal plane; a 垂直 is the component of acceleration in the vertical plane; α is the vertical inclination angle.

4. The cycling route recommendation method based on vehicle-to-vehicle data sharing according to claim 3 is characterized in that: In step S32, the formula for obtaining the driver's sitting or lying inclination according to the position information of the driver when sitting or lying down obtained by the sensor is as follows: In the above formula, Cotγ is the sitting or lying inclination; Lb is the horizontal length of the bicycle; Lp is the horizontal distance from the driver's hips to the knees when sitting or lying; H is the vertical distance from the driver's head to the pedals when sitting or lying. In step S33, the formula for obtaining the optimal driving force according to the electric bicycle's grip force, driving force, and gravity parameters is as follows: E=m1×W1+m2×W2+m3×W3; In the above formula, W1 and m1 are the driver's grip force and the corresponding weight coefficient respectively; W2 and m2 are the driving force and the corresponding weight coefficient respectively; W3 and m3 are gravity and the corresponding weight coefficient respectively; In step S34, the formula for determining the driving experience value based on the vertical inclination, the sitting and lying inclination, and the optimal driving dynamics is as follows: D=k1×Tanα+k2×Cotγ+E; In the above formula, D is the driving experience value; Cotγ and k2 are the sitting and lying inclinations and their corresponding weight coefficients respectively; Tanα and k1 are the vertical inclinations and their corresponding weight coefficients respectively; and E is the optimal driving dynamics.

5. The cycling route recommendation method based on vehicle-to-vehicle data sharing according to claim 4 is characterized in that: In step S35, the driving experience value of the route is obtained by performing time integration on the above driving experience value as follows: In the above formula, D(t) is the driving experience value D as a function of time t; D route is the driving experience value of the route; T is the total driving time of the route.

6. The method for recommending cycling routes based on vehicle-to-vehicle data sharing according to claim 1, characterized in that: In step S4, the driving experience values of the integrated routes are used to construct an objective function to determine several preliminary recommended routes, including the following steps: S41: obtaining time, charge and energy consumption parameters of each potential route respectively; S42: The objective function is constructed by integrating the driving experience values of time, charging, energy consumption, and route. The objective function is as follows: In the above formula, T time For time; C cost For charging; E energy is energy consumption; c, d, e, and f are time T time 、Charge C cost Energy consumption E energy , the weight coefficient corresponding to the driving experience value D; m is the number of potential historical routes; O route is the objective function; S43: Calculate the objective function value of each potential route and determine several preliminary recommended routes.

7. The method for recommending cycling routes based on vehicle-to-vehicle data sharing according to claim 1, characterized in that: In step S5, the final recommended route is selected based on the user's preference from the preliminary recommended routes. The formula for selecting the final recommended route is as follows: f(x)=limL specific -limL average ; In the above formula, limL specific Indicates the maximum value of the route within a specific time period; limL average Represents the average probability of a route being selected within a certain period.

8. A cycling route recommendation system based on vehicle-to-vehicle data sharing, characterized in that: The method for recommending cycling routes based on vehicle-to-vehicle data sharing as claimed in any one of claims 1 to 7 comprises: The acquisition module is used to collect vehicle operation data and driver status data using various sensors installed on the vehicle; A processing module, which is used to perform denoising and normalization processing on the collected data; A first calculation module is used to determine the driving experience value of each potential route based on the collected data; The second calculation module is used to integrate the driving experience values of the routes to construct an objective function and determine several preliminary recommended routes; The selection module is used to select a final recommended route based on user preferences in the preliminary recommended routes.

9. A computer-readable storage medium, characterized in that The computer-readable storage medium includes a stored program, wherein when the program is executed, the device where the computer-readable storage medium is located is controlled to execute the cycling route recommendation method based on vehicle-to-vehicle data sharing according to any one of claims 1 to 7.

10. A processor, characterized in that: The processor is used to run a program, wherein the program, when running, executes the cycling route recommendation method based on vehicle-to-vehicle data sharing according to any one of claims 1 to 7.

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