Automation-based Shared Vehicle Power Management System

Abstract

The present invention discloses an automated shared vehicle power management system, including a data acquisition module, a power monitoring module, and an output module. The data acquisition module is used to obtain relevant information of the shared electric vehicle system and equipment; the power monitoring system is used to monitor the actual riding efficiency of the electric vehicle. In the present invention, through the braking time analysis sub-module, the braking time analysis sub-module accurately detects the electric vehicle during the riding process by analyzing the braking and torso of the rider during the riding process, reducing the monitoring burden of the system; the torso analysis sub-module accurately monitors the magnitude and frequency changes of the force exerted by the rider's legs on the electric vehicle pedal during the riding process, and the allocation efficiency analysis module helps the rental car user quickly replace the electric vehicle that meets the required load demand, enabling the rental car user to rent the most suitable electric vehicle currently, improving the utilization efficiency of the electric vehicles in the rental area, and having the characteristics of strong real-time monitoring ability and high equipment power detection accuracy.

Description

Technical Field

[0001] The present invention relates to the technical field of public travel management, and specifically to an automated shared vehicle power management system. Background Art

[0002] Shared electric vehicles bred by technology have penetrated into our lives. They are representatives of the sharing economy and products of the era under the background of big data. They drive the rapid development of the economy by adopting the latest management and marketing models. Shared electric vehicles, which are popular among the public but have many problems, are currently a concern for people.

[0003] Shared electric vehicles sense the force exerted by the rider on the pedals through a torque sensor and continuously adjust to provide corresponding power support. In the prior art, although riders can select an electric vehicle that can supply enough power to reach the destination based on the battery level provided by the system after scanning the code, after long-term use of shared electric vehicles, the power distribution system has a certain degree of error, resulting in a reduction in the power distribution ability of shared electric vehicles. When riders apply the same pedaling force to different shared electric vehicles, the actual traveling speed of the electric vehicles varies. When a renter needs to reach the destination within a certain time, an electric vehicle with a large error in power distribution cannot meet the renter's speed requirement for the electric vehicle, making it difficult for the renter to reach the destination within the expected time and reducing the renter's riding experience; if the renter finds that the power of the electric vehicle is insufficient after riding for a period of time and is about to change the vehicle at a nearby rental point, it is also impossible to quickly replace an electric vehicle that meets the load requirement, resulting in a more urgent situation. Therefore, it is necessary to design an automated shared vehicle power management system with strong real-time monitoring ability and high equipment power detection accuracy. Summary of the Invention

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

[0005] To solve the above technical problems, the present invention provides the following technical solution: An automated shared vehicle power management system includes a data acquisition module, a power monitoring module, and an output module. The data acquisition module is used to obtain relevant information of 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 of the data acquisition module; the output module is used to implement relevant measures for 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 obtain information of specific equipment on the shared electric vehicle; the shared APP information acquisition module is used to obtain relevant system detection information in 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 distribution efficiency analysis module. The riding process sampling and analysis module is used to obtain the riding state of the rider by relevant parameters when the rider rides the target electric vehicle. The power distribution efficiency analysis module is used to detect the power distribution efficiency of the electric vehicle through the analysis result 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 sub-module, a torso analysis sub-module, and a touch detector unit. The braking time analysis sub-module is used to detect the number of times the rider uses the handbrake during the riding process and analyze the rider's speed requirement for the electric vehicle. The torso analysis sub-module is used to analyze the movement trajectory of the rider's legs on the seat during the riding process. The touch detector unit is used to obtain 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 distribution module. The vehicle selection recommendation module is used to recommend a suitable vehicle to the renter through the prediction result of the time and power prediction module. The power distribution module is used to adjust the power of the electric vehicle in real time to meet the riding speed requirement of the rider.

[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 inputs the destination location information in the car rental system. The system detects the displayed power of the vehicle scanned by the renter in the car rental area. When the predicted travelable distance of the displayed power exceeds 120% of the distance from the current location to the input destination location, the power monitoring module is activated and the riding process of the target electric vehicle and the rider is monitored in real time.

[0012] Step S2: The braking time analysis sub-module detects the number of times the rider uses the handbrake during the riding process and analyzes the rider's speed requirement for the electric vehicle through the proportion of the rider's braking time.

[0013] Step S3: The torso analysis sub-module 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 wanting to maintain or accelerate the current vehicle running speed, and analyzes the power distribution error situation of the target electric vehicle.

[0014] Step S4: During riding, after the power allocation module learns the power upper limit caused by the current power allocation 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 allocation of the electric vehicle can meet the riding speed requirements of the rider in real time. When the predicted power upper limit cannot meet the riding speed requirements of the rider and the predicted time from the current position to the destination is lower than the minimum value, the real-time position of the nearby rental area is obtained in real time, the parameters of the electric vehicles in the rental area are analyzed, and a suitable electric vehicle is recommended to the rider;

[0015] Step S5: When the rider scans the code for the second time to rent a vehicle within a short period of time, the actual error rate information of electric vehicles around the target rental vehicle is extracted, and electric vehicles that meet the requirements are recommended to the renter.

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

[0017] Step S21: during a single braking process of the rider during riding, the minimum distance between the handbrake and the handlebar is L, and the sum of the normal distances between the handbrake and the handlebar of the electric vehicle is L1, which is divided into three gears: the first gear is 0.9L1<L≤L1, the second gear is 0.7L1<L≤0.9L1, and the third gear is 0<L≤0.7L1;

[0018] Step S22: Obtain the number of times the handbrake changes but is in the first gear when the rider brakes during riding, which is t1; the number of times the minimum distance is in the second gear when braking, which is t2; the number of times the minimum distance is in the third gear when braking, which is t3. If t3>t2-C and t1>t2+t3, execute step S3, where C is the time predicted by big data for the rider to use a small brake when it is necessary to stop during the process of driving from the rental location to the destination. According to the regional changes of the current electric vehicle driving section, when the rider drives in the urban area section, C is specifically 0.4t2; when the rider drives in the town area section, C is specifically 0.3t2; when the rider drives in the suburban area section, C is specifically 0.1t2.

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

[0020] Step S31: During the rider's cycling, when the balance detection sub-module in the electric vehicle obtains a continuous time period in which the tilt angle of the electric vehicle is lower than 10° or the tilt angle is higher than 10° for less than 2 seconds, the touch detector set under the seat cushion acquires the contact position between the rider's buttocks and the seat cushion during cycling. Combining the connecting line between the buttocks and the thighs in the contact position, it senses the included angle φ between the midline extending from the thigh to the outside of the seat cushion and the forward direction of the front of the seat cushion. The torso analysis sub-module continuously observes the change in the magnitude of φ according to the swing of the thigh when the rider pedals the electric vehicle pedal, and draws a plane rectangular coordinate system with the time t as the horizontal axis and the included angle φ as the vertical axis, where φ = 0 is the φ value corresponding to the average value of the included angle change when the rider's thigh pedals the electric vehicle pedal, and t = 0 is the start time of observation, and the unit of t is seconds;

[0021] Step S32: Calculate all the maximum and minimum values within the coordinate axes. The corresponding two extreme values form a monitoring period, and cut off the period time or the period time segment whose amplitude difference within the period is lower than 20% of the maximum value of the corresponding data within the coordinate axes;

[0022] Step S33: Through curve processing technology, according to the slope information of the curves at both ends of the period time segment cut off in the curve, add a smooth simulation line to splice the target curves at both ends, and perform mean smoothing processing on the irregular amplitude and period data within the coordinate axes to obtain a regular periodic curve of the included angle φ changing with time t;

[0023] Step S34: Through the periodic curve, obtain the period time T of the curve. When the number of wheel rotations of the rider's leg pedaling the electric vehicle pedal to drive the wheel to travel is greater than the number of wheel rotations of the normal part of the rider's cycling distance Q when pedaling the electric vehicle pedal to drive the wheel to travel, immediately execute Step S4, where S is the movement distance of the electric vehicle under normal driving efficiency per cycle of pedaling obtained through big data, and μ is the error ratio of the electric vehicle not being able to convert the movement distance per cycle of pedaling into the actual movement distance due to abnormal driving conditions such as waiting for traffic lights, going uphill, and turning during the entire driving journey, which is determined according to the road conditions during the current driving of the electric vehicle, and 0.7 < μ < 1.

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

[0025] Step S51: The vehicle selection and recommendation module uses satellite positioning technology to obtain the specific positions and corresponding vehicle numbers 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 deployment efficiency of the target electric vehicle scanned and the precise deployment efficiency in Step S4 is higher than the controllable error rate and lower than the error rate of the locked electric vehicles in the previous car rental order, other shared electric vehicles are judged in sequence. According to the fastest time standard for arriving at the destination, the electric vehicles close to the error requirements in the detected target electric vehicles are recommended to the rider.

[0027] Compared with the prior art, the beneficial effects achieved by the present invention are as follows: In the present invention, through the braking time analysis sub-module, the problem that due to different degrees of congestion in different sections, riders frequently brake in congested sections, and finally the riding speed and riding time data obtained by the speed detector are the same, resulting in inaccurate judgment of the actual riding speed demand of the rider by the system, is solved. It ensures accurate detection of the electric vehicle during the riding process, makes the research object precise, and reduces the monitoring burden of the system; the torso analysis sub-module accurately monitors the magnitude and frequency changes of the force exerted by the rider's legs on the electric vehicle pedal during the riding process of the electric vehicle. It can not only judge whether the driving efficiency of the rider riding the target electric vehicle is normal, but also evaluate the demand value of the rider for the riding speed of the electric vehicle through the size of T. The deployment efficiency analysis module helps car renters quickly replace electric vehicles that meet the required load demand, enabling car renters to rent the most suitable electric vehicle currently, and improving the utilization efficiency of electric vehicles in the car rental area. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] The drawings are used to provide a further understanding of the present invention and constitute a part of the specification. They are used together with the embodiments of the present invention to explain the present invention and do not constitute a limitation to the present invention. In the drawings:

[0029] Figure 1 is a schematic diagram of the system module composition of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0030] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0031] Please refer to Figure 1 , the present invention provides a technical solution: An automated shared vehicle power management system, including:

[0032] A data acquisition module, a power monitoring module and an output module. The data acquisition module is used to obtain relevant information of 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 of the data acquisition module; the output module is used to implement relevant measures for the predictive analysis of the power monitoring system.

[0033] In the present invention, through the braking time analysis sub-module, the problem that due to different degrees of congestion in different sections, riders frequently brake in congested sections, and finally the riding speed and riding time data obtained by the speed detector are the same, resulting in inaccurate judgment of the actual riding speed demand of the rider by the system, is solved. It ensures accurate detection of the electric vehicle during the riding process, makes the research object precise, and reduces the monitoring burden of the system; the torso analysis sub-module accurately monitors the magnitude and frequency change of the force exerted by the rider's legs on the electric vehicle pedal during the riding process of the electric vehicle. It can not only judge whether the driving efficiency of the rider's target electric vehicle is normal, but also evaluate the demand value of the rider for the riding speed of the electric vehicle through the magnitude of T. The deployment efficiency analysis module helps the renter quickly replace the electric vehicle that meets the required load demand, enabling the renter to rent the most suitable electric vehicle currently, and improving the utilization efficiency of the 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 of specific equipment on the shared electric vehicle; the shared APP information acquisition module is used to obtain relevant system detection information in the shared electric vehicle rental APP.

[0035] The power monitoring module includes a riding process sampling analysis module and a deployment efficiency analysis module. The riding process sampling analysis module is used to obtain the riding state of the rider through relevant parameters when the rider rides the target electric vehicle; the deployment efficiency analysis module is used to detect the power deployment efficiency of the electric vehicle through the analysis results of the riding process sampling analysis module.

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

[0037] The output module includes a vehicle selection recommendation module and a power deployment module. The vehicle selection recommendation module is used to recommend suitable vehicles to the renter through the prediction results of the time and power prediction module; the power deployment module is used to deploy the power of the electric vehicle in real time to meet the riding speed demand of the rider.

[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 car renter inputs the destination location information in the car rental system. The system detects the displayed battery power of the vehicle scanned by the renter in the car rental area. When the predicted travelable distance of the displayed battery power exceeds 120% of the distance from the current location to the input destination location, the power monitoring module is activated, and the riding process of the target electric vehicle and the rider is monitored in real time;

[0040] Step S2: The braking time analysis sub-module detects the number of times the rider uses the handbrake during the riding process, and analyzes the rider's speed demand for the electric vehicle through the proportion of the rider's braking time;

[0041] Step S3: The torso analysis sub-module 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 wanting to maintain or increase the current vehicle running speed, and analyzes the power distribution error situation of the target electric vehicle;

[0042] Step S4: During the riding process, after learning the power upper limit generated by the current power distribution error of the electric vehicle, the power distribution module 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 can meet the rider's riding speed demand in real time. When the predicted power upper limit cannot meet the rider's riding speed demand and the predicted time from the current location to the destination is lower than the minimum value, the real-time location of the nearby car rental area is obtained in real time and the parameters of the electric vehicles in the car rental area are analyzed, and a suitable electric vehicle is recommended to the rider, which reduces the running burden of the electric vehicle to the greatest extent and improves the utilization efficiency of the electric power in the electric vehicle;

[0043] Step S5: When the rider scans the code to rent a vehicle for the second time within a short time, the actual error rate information of the electric vehicles around the target rented vehicle is extracted, and the electric vehicles that meet the requirements are recommended to the car renter.

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

[0045] Step S21: During a single braking process of the rider during riding, the minimum distance between the brake of the handbrake and the handle of the vehicle is L, and the sum of the distances between the normal handbrake brake of the electric vehicle and the handle of the vehicle is L1, which is divided into three gears, namely: the first gear 0.9L1 < L ≤ L1, the second gear 0.7L1 < L ≤ 0.9L1, and the third gear 0 < L ≤ 0.7L1;

[0046] Step S22: Obtain the number of times t1 that the handbrake changes but is in the first gear when the rider brakes during cycling, the number of times t2 that the minimum distance during braking is in the second gear, and the number of times t3 that the minimum distance during braking is in the third gear. If t3 > t2 - C and t1 > t2 + t3, it is determined that the current rider has a relatively high speed requirement for the electric vehicle, and step S3 is executed. Here, C is the time for the rider to use a small-amplitude brake when a necessary stop occurs during the process of traveling from the rental location to the destination predicted through big data. According to the regional changes of the current electric vehicle driving section, when the rider is traveling on an urban area section, C is specifically 0.4t2; when the rider is traveling on a town area section, C is specifically 0.3t2; when the rider is traveling on a suburban area section, C is specifically 0.1t2.

[0047] When the rider has a high speed requirement for the vehicle, the braking duration of the rider is short, but the braking force during braking is large. The requirement of the rider for the vehicle speed can be monitored by the frequency of pressing the brake.

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

[0049] The first gear: 0.9L1 < L ≤ L1, the error range of no actual braking effect;

[0050] The second gear: 0.7L1 < L ≤ 0.9L1, small-amplitude braking, the vehicle speed of the electric vehicle decreases slowly, and the specific decrease speed is determined according to the size of L;

[0051] The third gear: 0 < L ≤ 0.7L1, large-amplitude braking, the vehicle speed of the electric vehicle decreases quickly, and the specific decrease speed is determined according to the size of L.

[0052] Even when the rider has a relatively high riding speed requirement for the electric vehicle compared with other renters, and the riding speed of the electric vehicle is increased multiple times, but due to the crowded section, frequent braking occurs, and finally the riding speed and riding time obtained by the speed detector are the same, resulting in the problem that the system's judgment of the actual riding speed requirement of the rider is inaccurate. Through this technical solution, accurate detection of the rider during cycling is ensured, the research object is made precise and simplified, and the monitoring burden on the system is reduced.

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

[0054] Step S31: During the rider's cycling, when the balance detection sub-module in the electric vehicle obtains a continuous time period in which the tilt angle of the electric vehicle is lower than 10° or the tilt angle is higher than 10° for less than 2 seconds, the touch detector set under the seat cushion acquires the contact position between the rider's buttocks and the seat cushion during cycling. Combining the connecting line between the buttocks and the thighs in the contact position, it senses the angle φ between the midline extending from the thigh to the outside of the seat cushion and the forward direction of the front of the seat cushion. The torso analysis sub-module continuously observes the change in the magnitude of φ according to the swing of the thigh when the rider pedals the electric vehicle pedal, and draws a plane rectangular coordinate system with the 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 value of the angle change when the rider's thigh pedals the electric vehicle pedal, and t = 0 is the start time of the observation, and the unit of t is seconds; when going uphill, the rider needs to apply a greater pedaling force to the pedal compared to flat ground, which may cause inaccurate detection results due to objective factors;

[0055] Step S32: Calculate all the maximum and minimum values within the coordinate axes. The corresponding two extreme values form a monitoring period, and cut off the period time or the period time segment in which the amplitude difference within the period time segment is lower than 20% of the maximum value of the corresponding data within the coordinate axes; within the period time segment with a period and amplitude lower than 20% of the maximum value, the situation of the rider pedaling the electric vehicle pedal is difficult to predict. The cut-off period time segment is invalid data and needs to be removed to achieve the accuracy of monitoring the rider's pedaling of the electric vehicle pedal.

[0056] Step S33: Through curve processing technology, according to the slope information of the curves at both ends of the period time segment cut off in the curve, add a smooth simulation line to splice the target curves at both ends, and perform mean smoothing processing on the irregular amplitude and period data within the coordinate axes to obtain a regular periodic curve of the angle φ changing with the time t;

[0057] Step S34: Through the periodic curve, obtain the period time T of the curve. When the number of wheel rotations when the rider's leg pedals the electric vehicle pedal to drive the wheel to travel is greater than the number of wheel rotations when the normal part of the current rider's cycling distance Q pedals the electric vehicle pedal to drive the wheel to travel, it is determined that the driving efficiency of the rider riding the target electric vehicle is lower than the normal value, and immediately execute Step S4, where S is the moving distance of the electric vehicle driven by one pedal stroke under normal driving efficiency obtained through big data, and μ is the error ratio of the electric vehicle not being able to convert the movable distance within one pedal stroke into the actual moving distance due to abnormal driving conditions such as waiting for traffic lights, going uphill, and turning during the entire driving distance, which is determined according to the road conditions during the current driving process of the electric vehicle, and 0.7 < μ < 1.

[0058] When the pedal cycle time of the rider on the electric vehicle is smaller than that of an ordinary rider, it indicates that the rider uses a greater force to pedal the electric vehicle, resulting in a shorter cycle time and a larger number of wheel rotations for driving the wheels. Therefore, if the actual number of driven wheels is higher than the number of wheels for the normal driving distance, it can be determined that the driving efficiency of the target electric vehicle ridden by the rider is lower than the normal value.

[0059] Through this technical solution, the problem that the force of the rider pedaling the electric vehicle pedal is inaccurate due to errors is solved. During the process of riding an electric vehicle, the magnitude and frequency changes of the force exerted by the rider's leg on the electric vehicle pedal are accurately monitored. It can not only determine whether the driving efficiency of the target electric vehicle ridden by the rider is normal, but also evaluate the required value of the riding speed of the electric vehicle by the rider through the magnitude of T. While ensuring the accurate detection of the electric vehicle during the riding process, it increases the diversity of the monitoring content and provides favorable data for the subsequent driving of other electric vehicles.

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

[0061] Step S51: The vehicle selection and recommendation module uses satellite positioning technology to obtain the specific locations and corresponding vehicle numbers 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 deployment efficiency of the target electric vehicle scanned by the code and the precise deployment efficiency in step S4 is higher than the controllable error rate and lower than the error rate of the locked electric vehicle in the previous rental order, make a sequential judgment on other shared electric vehicles, and recommend the electric vehicle close to the error requirement in the detected target electric vehicle to the rider according to the fastest time standard for reaching the destination input.

[0063] Through this technical means, the problem that the renter cannot quickly replace the electric vehicle that meets the load requirements due to time constraints is solved, enabling the renter to rent the most suitable electric vehicle currently and improving the utilization efficiency of the electric vehicles in the rental area.

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

[0065] Finally, it should be noted that the above are only 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 perform equivalent replacements for some of the technical features. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall 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 obtain relevant information of the shared electric vehicle system and equipment; the power monitoring module is used to monitor the actual riding efficiency of the electric vehicle through the system and equipment information of the data acquisition module; the output module is used to implement relevant measures for the predictive analysis of 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 obtain information of specific equipment on the shared electric vehicle; the shared APP information acquisition module is used to obtain relevant system detection information in the shared electric vehicle rental APP; The power monitoring module includes a riding process sampling analysis module and a deployment efficiency analysis module. The riding process sampling analysis module is used to obtain the riding state of the rider through relevant parameters when the rider rides the target electric vehicle; the deployment efficiency analysis module is used to detect the power deployment efficiency of the electric vehicle through the analysis results of the riding process sampling analysis module; The riding process sampling module further includes a braking time analysis sub-module, a torso analysis sub-module, and a touch detector unit. The braking time analysis sub-module is used to detect the number of times the rider uses the handbrake during the riding process and analyze the rider's speed requirement for the electric vehicle; The torso analysis sub-module is used to analyze the movement trajectory of the rider's legs on the seat during the riding process; the touch detector unit is used to obtain the contact positions of the rider's buttocks and thighs on the seat in real time; The output module includes a vehicle selection recommendation module and a power deployment module. The vehicle selection recommendation module is used to recommend suitable vehicles to the renter through the prediction results of the time and power prediction module; the power deployment module is used to deploy the power of the electric vehicle in real time to meet the riding speed requirements of the rider; The operation method of the power operation and maintenance management system mainly includes the following steps: Step S1: The renter inputs the destination location information in the rental system. The system detects the displayed power of the vehicle scanned by the renter in the rental area. When the predicted travelable distance of the displayed power exceeds 120% of the distance from the current location to the input destination location, the power monitoring module is activated, and the target electric vehicle and the rider's riding process are monitored in real time; Step S2: The braking time analysis sub-module detects the number of times the rider uses the handbrake during the riding process, and analyzes the rider's speed requirement for the electric vehicle through the proportion of the rider's braking time; Step S3: The torso analysis sub-module 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 wanting to maintain or increase the current vehicle running speed, and analyzes the power deployment error situation of the target electric vehicle; Step S4: During the riding process, after the power distribution module learns the power upper limit generated by the current electric vehicle power distribution error, based on the analysis results of steps S2 and S3, it dynamically adjusts the power of the electric vehicle to make the power distribution of the electric vehicle meet the riding speed requirements of the rider in real time. When the predicted power upper limit cannot meet the riding speed requirements of the rider and the predicted time from the current location to the destination is lower than the minimum value, it obtains the real-time location of the nearby car rental area in real time and analyzes the parameters of the electric vehicles in the car rental area, and recommends a suitable electric vehicle 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 the electric vehicles around the target rented vehicle, and recommend the electric vehicles that meet the requirements to the car renter; The said step S2 further includes: Step S21: During the single braking process of the rider while cycling, the minimum distance between the brake of the handbrake and the handlebar held by the rider is L, and the sum of the distances between the normal handbrake brake of the electric vehicle and the handlebar is L1, which is divided into three gears, namely: the first gear , the second gear , the third gear ; Step S22: Obtain the number of times t1 when the handbrake changes but is in the first gear when the rider brakes during riding, the number of times t2 when the minimum braking distance is in the second gear when braking, and the number of times t3 when the minimum braking distance is in the third gear when braking. If , execute Step S3, where C is the time for the rider to use a small braking when making a necessary stop during the process of traveling from the car rental location to the destination predicted by big data. According to the regional changes of the current electric vehicle driving section, when the rider is driving on the urban area section, C is specifically 0.4t2; when the rider is driving on the town area section, C is specifically 0.3t2; when the rider is driving on the suburban area section, C is specifically 0.1t2; Among the three braking gears set by the system First gear : Error range without actual braking effect; Second gear : Slight braking, the speed of the electric vehicle decreases slowly, and the specific decrease speed is determined according to the size of L; Third gear : Apply a significant brake, and the speed of the electric vehicle will decrease rapidly, with the specific deceleration rate determined by the magnitude of L.

2. The automated shared vehicle power management system according to claim 1, wherein: The said step S3 further includes: Step S31: During the rider's riding process, when the balance detection sub-module in the electric vehicle obtains that the tilt angle of the electric vehicle is lower than 10° or the duration of the tilt angle higher than 10° is less than 2 seconds, the touch detector set under the seat cushion obtains the contact position between the rider's buttocks and the seat cushion during the riding process. Combining the connecting line between the buttocks and the thigh in the contact position, it senses the included angle φ between the midline extending from the thigh to the outside of the seat cushion and the forward direction of the front of the seat cushion. The torso analysis sub-module continuously observes the change in the size of φ according to the swing of the rider's thigh when pedaling the electric vehicle pedal, and draws a plane rectangular coordinate system with the time t as the horizontal axis and the included angle φ as the vertical axis, where φ = 0 is the φ value corresponding to the average value of the included angle change when the rider's thigh pedals the electric vehicle pedal, and t = 0 is the start time of the observation, and the unit of t is second; Step S32: Calculate all the maximum and minimum values in the coordinate axis. The corresponding two extreme values are a monitoring period, and cut off the period time or the period time period whose amplitude difference within the period is lower than 20% of the maximum value of the corresponding data in the coordinate axis; Step S33: Through curve processing technology, according to the slope information of the curves at both ends of the period time period cut off in the curve, add a smooth simulation line to splice the target curves at both ends, and perform mean smoothing processing on the irregular amplitude and period data in the coordinate axis to obtain a regular periodic curve of the included angle φ changing with time t; Step S34: Obtain the time T of one period of the curve through the periodic curve. When the number of wheel rotations of the rider pedaling the electric vehicle pedal to drive the wheel is greater than the number of wheel rotations of the rider pedaling the electric vehicle pedal to drive the wheel during the normal leg pedaling in the riding distance Q of this ride immediately execute Step S4, where S is the moving distance of the electric vehicle with normal driving efficiency per period of pedaling obtained through big data, and μ is the error ratio of the electric vehicle not being able to convert the movable distance per period of pedaling into the actual moving distance due to waiting for traffic lights, going uphill, and turning during the entire driving distance, which is determined according to the road conditions during the current driving process of the electric vehicle. .

3. The automated shared vehicle power management system according to claim 2, wherein: The said step S5 further includes: Step S51: The vehicle selection and recommendation module uses satellite positioning technology to obtain the specific locations and corresponding vehicle numbers of all shared electric vehicles in the current car rental area, and extracts the historical usage information of each vehicle through the mobile terminal; Step S52: When the deployment efficiency error rate of the target electric vehicle scanned with the code is higher than the controllable error rate and lower than the error rate of the locked electric vehicle in the previous car rental order, make a judgment on other shared electric vehicles in turn. According to the fastest time standard for input to reach the destination, recommend the electric vehicle that is close to the error requirement in the detected target electric vehicle to the rider.

Images