An automated-based shared vehicle power management system

Abstract

The application discloses a kind of shared car power management systems based on automation, including data acquisition module, power monitoring module and output module, data acquisition module is used to obtain the relevant information of shared electric vehicle system and equipment;Power monitoring system is used to monitor the actual riding efficiency of electric vehicle;The application, through brake time analysis submodule, brake time analysis submodule is accurately detected to electric vehicle in the riding process by the analysis of brake and torso in the riding process of rider, reduces the monitoring burden of system;Torso analysis submodule is accurately monitored to the force size and frequency change condition of electric vehicle pedal by rider leg in the riding process, and deployment efficiency analysis module helps tenant to quickly replace the load demand electric vehicle meeting the requirements, so that tenant can rent the most suitable electric vehicle at present, improve the utilization efficiency of electric vehicle in rental area, with the characteristics of strong real-time monitoring capability and high equipment power detection precision.

Description

Technical Field

[0001] This invention relates to the field of public transportation management technology, specifically to an automated shared vehicle power management system. Background Technology

[0002] Shared electric bikes, born from technology, have become an integral part of our lives. They represent the sharing economy and are a product of the era of big data, employing the latest management and marketing models to drive rapid economic development. However, as a popular yet problematic sector, shared electric bikes are currently a topic of considerable public concern.

[0003] Shared electric bikes use torque sensors to sense the rider's pedaling force and continuously adjust and provide corresponding power support. In existing technology, while riders can select an electric bike that can power their destination based on the battery level provided by the system after scanning a code, the power allocation system develops errors over time, reducing the power distribution capability of the shared electric bikes. This results in differences in the actual speed of different shared electric bikes when the rider applies the same pedaling force. When renters need to reach their destination within a certain time, electric bikes with significant power distribution errors cannot meet the renter's speed requirements, making it difficult for them to reach their destination within the expected time and reducing the riding experience. Furthermore, if renters find the electric bike lacking power after riding for a while and want to change bikes at a nearby rental point, they cannot quickly find a bike with the appropriate power capacity, further complicating the time constraint. Therefore, it is essential to design an automated shared electric bike power management system with strong real-time monitoring capabilities and high-precision equipment power detection. Summary of the Invention

[0004] The purpose of this invention is to provide an automated shared vehicle power management system to solve the problems mentioned in the background art.

[0005] To address the aforementioned technical problems, the present invention provides the following technical solution: an automated shared vehicle power management system, comprising a data acquisition module, a power monitoring module, and an output module. The data acquisition module is used to acquire relevant information about the shared electric vehicle system and equipment. The power monitoring system is used to monitor the actual riding efficiency of the electric vehicle through the system and equipment information from the data acquisition module. The output module is used to implement relevant measures based on the predictive analysis of the power monitoring system.

[0006] According to the above technical solution, the data acquisition module includes an electric vehicle information acquisition module and a shared APP information acquisition module. The electric vehicle information acquisition module is used to acquire information about specific devices on the shared electric vehicle; the shared APP information acquisition module is used to acquire relevant system detection information within the shared electric vehicle rental APP.

[0007] According to the above technical solution, the power monitoring module includes a riding process sampling and analysis module and a power allocation efficiency analysis module. The riding process sampling and analysis module is used to obtain the riding status of the rider through relevant parameters when the rider is riding the target electric vehicle. The power allocation efficiency analysis module is used to detect the power allocation efficiency of the electric vehicle through the analysis results of the riding process sampling and analysis module.

[0008] According to the above technical solution, the riding process sampling module further includes a braking time analysis submodule, a torso analysis submodule, and a touch detector unit; the braking time analysis submodule is used to detect the number of times the rider uses the handbrake during riding and analyze the rider's speed requirements for the electric vehicle; the torso analysis submodule is used to analyze the movement trajectory of the rider's legs on the seat during riding; the touch detector unit is used to acquire the contact positions of the rider's buttocks and thighs on the seat in real time.

[0009] According to the above technical solution, the output module includes a vehicle selection recommendation module and a power allocation module. The vehicle selection recommendation module is used to recommend suitable vehicles to renters based on the prediction results of the time and battery power prediction module. The power allocation module is used to allocate the power of the electric vehicle in real time to meet the rider's riding speed requirements.

[0010] According to the above technical solution, the operation method of the power operation and maintenance management system mainly includes the following steps:

[0011] Step S1: The renter enters the destination location information in the rental system. The system detects the displayed battery level of the vehicle when the renter scans the code in the rental area. When the predicted driving distance of the displayed battery level exceeds 120% of the distance from the current location to the entered destination location, the power monitoring module is activated and monitors the riding process of the target electric vehicle and the rider in real time.

[0012] Step S2: The braking time analysis submodule detects the number of times the rider uses the handbrake during riding and analyzes the rider's speed requirements for the electric vehicle by analyzing the proportion of the rider's braking time.

[0013] Step S3: The torso analysis submodule detects the movement trajectory of the rider's torso on the electric vehicle seat during the riding process, analyzes the real-time situation of the rider's desire to maintain or accelerate the current vehicle speed, and analyzes the power distribution error of the target electric vehicle.

[0014] Step S4: During the ride, after the power distribution module learns the upper limit of the power caused by the current power distribution error of the electric vehicle, it dynamically adjusts the power of the electric vehicle according to the analysis results of steps S2 and S3, so that the power distribution of the electric vehicle meets the rider's riding speed requirements in real time. When the predicted upper limit of the power cannot meet the rider's riding speed requirements and the predicted time from the current location to the destination is lower than the minimum value, the module obtains the real-time location of the nearby rental area and analyzes the parameters of the electric vehicles in the rental area, and recommends suitable electric vehicles to the rider.

[0015] Step S5: When the rider scans the code to rent a vehicle for the second time within a short period of time, extract the actual error rate information of electric vehicles around the target rental vehicle, and recommend qualified electric vehicles to the renter.

[0016] According to the above technical solution, step S2 further includes:

[0017] Step S21: During a single braking maneuver while riding, the minimum distance between the handbrake and the handlebars is L. The normal distance between the handbrake and the handlebars of an electric vehicle is L1, which is divided into three levels: Level 1: 0.9L1 < L ≤ L1, Level 2: 0.7L1 < L ≤ 0.9L1, and Level 3: 0 < L ≤ 0.7L1.

[0018] Step S22: Obtain the number of times the handbrake changes but remains in the first gear during braking (t1), the number of times the minimum braking distance is in the second gear (t2), and the number of times the minimum braking distance is in the third gear (t3). If t3 > t2 - C and t1 > t2 + t3, proceed to step S3. Here, C is the time predicted by big data when the rider needs to stop and uses a small amount of braking during the journey from the rental location to the destination. Based on the regional changes of the current electric vehicle's route, C is 0.4t2 when the rider is traveling in an urban area; 0.3t2 when the rider is traveling in a town area; and 0.1t2 when the rider is traveling in a suburban area.

[0019] According to the above technical solution, step S3 further includes:

[0020] Step S31: During the rider's riding process, when the balance detection submodule inside the electric vehicle acquires the time period when the electric vehicle tilt angle is less than 10° or the tilt angle is higher than 10° for less than 2 seconds, the tactile detector set under the seat acquires the contact position between the rider's buttocks and the seat during the riding process. Combining the connecting line between the buttocks and thighs in the contact position, the angle φ between the midline of the thigh extending outward from the seat and the direction facing directly in front of the seat is sensed. The torso analysis submodule continuously observes the change in the magnitude of φ based on the swing of the thigh when the rider pedals the electric vehicle, and draws a plane rectangular coordinate system with time t as the horizontal axis and the angle φ as the vertical axis, where φ = 0 is the φ value corresponding to the average change of the angle when the rider's thigh pedals the electric vehicle, t = 0 is the time when the observation begins, and the unit of t is seconds.

[0021] Step S32: Calculate all maximum and minimum values ​​within the coordinate axis. The two corresponding extreme values ​​constitute a monitoring period. Remove the period time or the period time during which the amplitude difference is less than 20% of the maximum value of the corresponding data within the coordinate axis.

[0022] Step S33: Using curve processing technology, based on the slope information of the curves at both ends of the periodic time interval, a smooth analog line is added to splice the curves at both ends of the target, and the irregular amplitude and periodic data within the coordinate axis are processed by mean smoothing to obtain a regular periodic curve in which the included angle φ changes with time t.

[0023] Step S34: Obtain the number of wheels driven by the rider's leg pedaling the electric vehicle through one periodic curve, time T. The number of times the electric vehicle's wheels are driven by the rider's normal pedaling during the distance Q is greater than the distance traveled in this instance. When the time comes, immediately execute step S4, where S is the normal driving efficiency distance of the electric vehicle within one cycle of pedaling obtained through big data, and μ is the error ratio of the electric vehicle's inability to convert the distance that can be moved within one cycle of pedaling into the actual distance of movement due to abnormal driving conditions such as waiting for street lights, going uphill, and turning during the entire driving journey. It is determined according to the road conditions during the current electric vehicle driving process, and 0.7 < μ < 1.

[0024] According to the above technical solution, step S5 further includes:

[0025] Step S51: The vehicle selection recommendation module uses satellite positioning technology to obtain the specific location and corresponding vehicle number of all shared electric vehicles in the current rental area, and extracts the historical usage information of each vehicle through the mobile terminal;

[0026] Step S52: When the error rate between the dispatch efficiency of the target electric vehicle scanned and the accurate dispatch efficiency in step S4 is higher than the controllable error rate but lower than the error rate of the locked electric vehicles in the previous rental order, other shared electric vehicles are judged in turn. Based on the input fastest time standard to reach the destination, electric vehicles that are close to the error requirement among the detected target electric vehicles are recommended to the rider.

[0027] Compared with existing technologies, the beneficial effects achieved by this invention are as follows: This invention, through a braking time analysis submodule, solves the problem that due to varying congestion levels in different areas, cyclists frequently brake in congested sections, resulting in the same riding speed and time data obtained from the speed detector, leading to inaccurate judgment of the cyclist's actual riding speed requirements. This ensures accurate detection of electric vehicles during riding, making the research object more precise and reducing the system's monitoring burden. The torso analysis submodule accurately monitors the magnitude and frequency changes of the force applied by the rider's legs to the electric vehicle pedals during riding. This not only determines whether the rider's riding efficiency of the target electric vehicle is normal, but also assesses the rider's riding speed requirement through the magnitude of T. The allocation efficiency analysis module helps renters quickly change to electric vehicles that meet their load requirements, allowing renters to rent the most suitable electric vehicle at the moment, thus improving the utilization efficiency of electric vehicles in the rental area. Attached Figure Description

[0028] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings:

[0029] Figure 1 This is a schematic diagram of the system module composition of the present invention. Detailed Implementation

[0030] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0031] Please refer to Figure 1 The present invention provides a technical solution: an automated shared vehicle power management system, comprising:

[0032] The system comprises a data acquisition module, a power monitoring module, and an output module. The data acquisition module is used to obtain relevant information about the shared electric vehicle system and equipment. The power monitoring system is used to monitor the actual riding efficiency of the electric vehicles through the system and equipment information obtained from the data acquisition module. The output module is used to implement relevant measures based on the predictive analysis of the power monitoring system.

[0033] This invention, through a braking time analysis submodule, solves the problem of inaccurate system judgment of riders' actual riding speed requirements due to frequent braking in congested areas caused by varying levels of congestion. This results in the riding speed and time data obtained by the speed detector being identical to the actual riding speed. This ensures accurate detection of electric vehicles during riding, making the research object more precise and reducing the system's monitoring burden. The torso analysis submodule accurately monitors the magnitude and frequency of force applied by the rider's legs to the electric vehicle pedals during riding. This not only determines whether the rider's riding efficiency of the target electric vehicle is normal, but also assesses the rider's riding speed requirement through the value of T. The allocation efficiency analysis module helps renters quickly switch to electric vehicles that meet their load requirements, allowing renters to rent the most suitable electric vehicle at the moment, thus improving the utilization efficiency of electric vehicles in the rental area.

[0034] The data acquisition module includes an electric vehicle information acquisition module and a shared APP information acquisition module. The electric vehicle information acquisition module is used to obtain information about the specific equipment on the shared electric vehicle; the shared APP information acquisition module is used to obtain relevant system detection information within the shared electric vehicle rental APP.

[0035] The power monitoring module includes a riding process sampling and analysis module and a power allocation efficiency analysis module. The riding process sampling and analysis module is used to obtain the rider's riding status through relevant parameters when the rider is riding the target electric vehicle. The power allocation efficiency analysis module is used to detect the power allocation efficiency of the electric vehicle based on the analysis results of the riding process sampling and analysis module.

[0036] The cycling process sampling module further includes a braking time analysis submodule, a torso analysis submodule, and a touch detector unit; the braking time analysis submodule is used to detect the number of times the rider uses the handbrake during the ride and analyze the rider's speed requirements for the electric vehicle; the torso analysis submodule is used to analyze the movement trajectory of the rider's legs on the seat during the ride; the touch detector unit is used to acquire the contact position of the rider's buttocks and thighs on the seat in real time.

[0037] The output module includes a vehicle recommendation module and a power allocation module. The vehicle recommendation module recommends suitable vehicles to renters based on the prediction results of the time and battery level prediction module. The power allocation module allocates the power of the electric vehicle in real time to meet the rider's riding speed requirements.

[0038] In a preferred embodiment, the operation method of the power operation and maintenance management system mainly includes the following steps:

[0039] Step S1: The renter enters the destination location information in the rental system. The system detects the displayed battery level of the vehicle when the renter scans the code in the rental area. When the predicted driving distance of the displayed battery level exceeds 120% of the distance from the current location to the entered destination location, the power monitoring module is activated and monitors the riding process of the target electric vehicle and the rider in real time.

[0040] Step S2: The braking time analysis submodule detects the number of times the rider uses the handbrake during riding and analyzes the rider's speed requirements for the electric vehicle by analyzing the proportion of the rider's braking time.

[0041] Step S3: The torso analysis submodule detects the movement trajectory of the rider's torso on the electric vehicle seat during the riding process, analyzes the real-time situation of the rider's desire to maintain or accelerate the current vehicle speed, and analyzes the power distribution error of the target electric vehicle.

[0042] Step S4: During the ride, after the power distribution module learns the upper limit of the power caused by the current power distribution error of the electric vehicle, it dynamically adjusts the power of the electric vehicle according to the analysis results of steps S2 and S3, so that the power distribution of the electric vehicle meets the rider's riding speed requirements in real time. When the predicted upper limit of power cannot meet the rider's riding speed requirements and the predicted time from the current location to the destination is lower than the minimum value, the module obtains the real-time location of the nearby rental area and analyzes the parameters of the electric vehicles in the rental area, recommending suitable electric vehicles to the rider, thereby minimizing the operating burden of the electric vehicle and improving the utilization efficiency of the electric vehicle's power.

[0043] Step S5: When the rider scans the code to rent a vehicle for the second time within a short period of time, extract the actual error rate information of electric vehicles around the target rental vehicle, and recommend qualified electric vehicles to the renter.

[0044] In this embodiment, step S2 further includes:

[0045] Step S21: During a single braking maneuver while riding, the minimum distance between the handbrake and the handlebars is L. The normal distance between the handbrake and the handlebars of an electric vehicle is L1, which is divided into three levels: Level 1: 0.9L1 < L ≤ L1, Level 2: 0.7L1 < L ≤ 0.9L1, and Level 3: 0 < L ≤ 0.7L1.

[0046] Step S22: Obtain the number of times the handbrake changes but remains in the first gear when the rider brakes during the ride (t1), the number of times the minimum braking distance is in the second gear (t2), and the number of times the minimum braking distance is in the third gear (t3). If t3 > t2 - C and t1 > t2 + t3, it is determined that the current rider has a high speed requirement for the electric vehicle, and step S3 is executed. Here, C is the time predicted by big data when the rider needs to stop and use a small amount of braking during the journey from the rental location to the destination. Depending on the area of ​​the electric vehicle's route, C is 0.4t2 when the rider is riding in an urban area; 0.3t2 when the rider is riding in a town area; and 0.1t2 when the rider is riding in a suburban area.

[0047] When a cyclist has high speed requirements, the cyclist's braking duration is short, but the braking force is large. The frequency of braking can be used to monitor the cyclist's speed requirements.

[0048] Among the three braking positions set by the system,

[0049] First gear 0.9L1<L≤L1: Error range with no actual braking effect;

[0050] Second gear 0.7L1<L≤0.9L1: Small braking, the speed of the electric vehicle decreases slowly and the specific speed of decrease is determined by the value of L;

[0051] The third gear, 0 < L ≤ 0.7L1, involves significant braking, resulting in a rapid decrease in the speed of the electric vehicle, with the specific rate of descent determined by the value of L.

[0052] Even when cyclists have a higher demand for riding speed compared to other renters, repeatedly increasing the riding speed can lead to frequent braking due to congested areas, ultimately resulting in the riding speed and time being the same as those obtained by the speed detector. This causes the system to inaccurately judge the cyclist's actual riding speed demand. This technical solution ensures accurate detection of the cyclist during the riding process, making the research object precise and simple, and reducing the monitoring burden of the system.

[0053] In this embodiment, step S3 further includes:

[0054] Step S31: During the rider's ride, when the balance detection submodule inside the electric vehicle acquires data for a duration of less than 2 seconds when the electric vehicle's tilt angle is less than 10° or more than 10°, the tactile detector located under the seat acquires the contact position between the rider's buttocks and the seat. Combining the connecting line between the buttocks and thighs at the contact position, the angle φ between the midline of the thigh extending outward from the seat and the direction the seat faces is perceived. The torso analysis submodule continuously observes the change in the magnitude of φ based on the swing of the thigh when the rider pedals the electric vehicle, and draws a Cartesian coordinate system with time t as the horizontal axis and angle φ as the vertical axis. Here, φ = 0 is the φ value corresponding to the average change of the angle when the rider's thigh pedals the electric vehicle, and t = 0 is the time when the observation begins. The unit of t is seconds. When going uphill, the rider needs to apply more pedaling force than on flat ground, which may cause the detection results to be inaccurate due to objective factors.

[0055] Step S32: Calculate all maximum and minimum values ​​within the coordinate axis. The two extreme values ​​constitute a monitoring period. Remove the period time or the period time interval where the amplitude difference is less than 20% of the maximum value of the corresponding data within the coordinate axis. Within the period time interval where the period or amplitude is less than 20% of the maximum value, the rider's pedaling behavior is difficult to predict, and the removed period time interval is invalid data and needs to be removed to achieve accuracy in monitoring the rider's pedaling behavior.

[0056] Step S33: Using curve processing technology, based on the slope information of the curves at both ends of the periodic time interval, a smooth analog line is added to splice the curves at both ends of the target, and the irregular amplitude and periodic data within the coordinate axis are processed by mean smoothing to obtain a regular periodic curve in which the included angle φ changes with time t.

[0057] Step S34: Obtain the number of wheels driven by the rider's leg pedaling the electric vehicle through one periodic curve, time T. The number of times the electric vehicle's wheels are driven by the rider's normal pedaling during the distance Q is greater than the distance traveled in this instance. If it is determined that the rider's driving efficiency of the target electric vehicle is lower than the normal value, step S4 is executed immediately. Here, S is the distance traveled by the electric vehicle within one cycle of pedaling, which is obtained through big data. Here, μ is the error ratio that the electric vehicle cannot convert the distance that can be traveled within one cycle of pedaling into the actual distance traveled due to abnormal driving conditions such as waiting for street lights, going uphill, and turning during the entire journey. It is determined according to the road conditions during the current electric vehicle driving process, and 0.7 < μ < 1.

[0058] When the cycle time of a rider pedaling an electric bike is shorter than that of a regular rider, it indicates that the rider is using greater force to pedal, resulting in a shorter cycle time and a larger number of wheels being driven. Therefore, if the actual number of driven wheels is higher than the number of wheels used to travel the distance under normal conditions, it can be determined that the rider's efficiency in riding the target electric bike is lower than normal.

[0059] This technical solution solves the problem of inaccurate pedaling force due to errors. It accurately monitors the changes in the magnitude and frequency of force applied by the rider's legs to the pedals during electric vehicle riding. This not only determines whether the rider's riding efficiency of the target electric vehicle is normal, but also assesses the rider's speed requirement by using the value of T. This ensures accurate detection of electric vehicles during riding while increasing the diversity of monitoring content and providing valuable data for the subsequent riding of other electric vehicles.

[0060] In this embodiment, step S5 further includes:

[0061] Step S51: The vehicle selection recommendation module uses satellite positioning technology to obtain the specific location and corresponding vehicle number of all shared electric vehicles in the current rental area, and extracts the historical usage information of each vehicle through the mobile terminal;

[0062] Step S52: When the error rate between the dispatch efficiency of the target electric vehicle scanned and the accurate dispatch efficiency in step S4 is higher than the controllable error rate but lower than the error rate of the locked electric vehicles in the previous rental order, other shared electric vehicles are judged in turn. Based on the input fastest time standard to reach the destination, electric vehicles that are close to the error requirement among the detected target electric vehicles are recommended to the rider.

[0063] This technology solves the problem that renters cannot quickly change to electric vehicles that meet their load requirements due to time constraints, allowing renters to rent the most suitable electric vehicle at the moment, thus improving the utilization efficiency of electric vehicles in the rental area.

[0064] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0065] Finally, it should be noted that the above descriptions are merely preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. An automated shared vehicle power management system, characterized in that: It includes a data acquisition module, a power monitoring module, and an output module. The data acquisition module is used to acquire relevant information about the shared electric vehicle system and equipment. The power monitoring module is used to monitor the actual riding efficiency of the electric vehicles based on the system and equipment information from the data acquisition module. The output module is used to implement relevant measures based on the predictive analysis from the power monitoring module. The data acquisition module includes an electric vehicle information acquisition module and a shared APP information acquisition module. The electric vehicle information acquisition module is used to acquire information about the specific equipment on the shared electric vehicle; the shared APP information acquisition module is used to acquire relevant system detection information within the shared electric vehicle rental APP. The power monitoring module includes a riding process sampling and analysis module and a power allocation efficiency analysis module. The riding process sampling and analysis module is used to obtain the riding status of the rider by using relevant parameters when the rider is riding the target electric vehicle. The power allocation efficiency analysis module is used to detect the power allocation efficiency of the electric vehicle based on the analysis results of the riding process sampling and analysis module. The cycling process sampling module further includes a braking time analysis submodule, a torso analysis submodule, and a tactile detector unit; the braking time analysis submodule is used to detect the number of times the rider uses the handbrake during the cycling process and analyze the rider's speed requirements for the electric vehicle; The torso analysis submodule is used to analyze the movement trajectory of the rider's legs on the saddle during riding; the tactile detector unit is used to acquire the contact position of the rider's buttocks and thighs on the saddle in real time. The output module includes a vehicle selection recommendation module and a power allocation module. The vehicle selection recommendation module is used to recommend suitable vehicles to renters based on the prediction results of the time and battery power prediction module. The power distribution module is used to adjust the power of the electric vehicle in real time to meet the rider's riding speed requirements. The operation method of the power management system includes the following steps: Step S1: The renter enters the destination location information in the rental system. The system detects the displayed battery level of the vehicle when the renter scans the code in the rental area. When the predicted driving distance of the displayed battery level exceeds 120% of the distance from the current location to the entered destination location, the power monitoring module is activated and monitors the riding process of the target electric vehicle and the rider in real time. Step S2: The braking time analysis submodule detects the number of times the rider uses the handbrake during riding and analyzes the rider's speed requirements for the electric vehicle by analyzing the proportion of the rider's braking time. Step S3: The torso analysis submodule detects the movement trajectory of the rider's torso on the electric vehicle seat during the riding process, analyzes the real-time situation of the rider's desire to maintain or accelerate the current vehicle speed, and analyzes the power distribution error of the target electric vehicle. Step S4: During the ride, after the power distribution module learns the upper limit of the power caused by the current power distribution error of the electric vehicle, it dynamically adjusts the power of the electric vehicle according to the analysis results of steps S2 and S3, so that the power distribution of the electric vehicle meets the rider's riding speed requirements in real time. When the predicted upper limit of the power cannot meet the rider's riding speed requirements and the predicted time from the current location to the destination is lower than the minimum value, the module obtains the real-time location of the nearby rental area and analyzes the parameters of the electric vehicles in the rental area, and recommends suitable electric vehicles to the rider. Step S5: When the rider scans the code to rent a vehicle for the second time within a short period of time, extract the actual error rate information of electric vehicles around the target rental vehicle, and recommend qualified electric vehicles to the renter. Step S2 further includes: Step S21: During a single braking maneuver while riding, the minimum distance between the handbrake and the handlebars is L. The normal distance between the handbrake and the handlebars on an electric bicycle is L1, which is divided into three levels: Level 1 Second gear Third gear ; Step S22: t1 is the number of times the handbrake changes position during braking but remains in the first gear; t2 is the number of times the minimum braking distance is in the second gear; and t3 is the number of times the minimum braking distance is in the third gear. If... Execute step S3, where C is the time predicted by big data when the rider needs to stop and use a small amount of braking during the journey from the rental location to the destination. Depending on the area of ​​the electric vehicle's route, C is 0.4t² when the rider is traveling in an urban area; 0.3t² when the rider is traveling in a town area; and 0.1t² when the rider is traveling in a suburban area. Of the three braking positions set by the system, First gear : The error range with no actual braking effect; Second gear With slight braking, the electric vehicle's speed decreases slowly, and the specific rate of descent depends on the value of L. Third gear : When braking sharply, the electric vehicle's speed decreases rapidly, and the specific rate of decrease depends on the value of L; Step S5 further includes: Step S51: The vehicle selection recommendation module uses satellite positioning technology to obtain the specific location and corresponding vehicle number of all shared electric vehicles in the current rental area, and extracts the historical usage information of each vehicle through the mobile terminal; Step S52: When the error rate between the dispatch efficiency of the target electric vehicle scanned and the accurate dispatch efficiency in step S4 is higher than the controllable error rate but lower than the error rate of the locked electric vehicles in the previous rental order, other shared electric vehicles are judged in turn. Based on the input fastest time standard to reach the destination, electric vehicles that are close to the error requirement among the detected target electric vehicles are recommended to the rider.

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