Shared bicycle power management system based on automation

Abstract

The invention discloses an automation-based shared vehicle power management system, which comprises a data acquisition module, a power monitoring module and an output module, and is characterized in that the data acquisition module is used for acquiring related information of a shared electric vehicle system and equipment; the power monitoring system is used for monitoring the actual riding efficiency of the electric vehicle; according to the system, through the brake time analysis sub-module, the brake time analysis sub-module analyzes the brake and the trunk in the riding process of a rider, accurate detection is carried out in the riding process of the electric vehicle, and the monitoring burden of the system is reduced; the trunk analysis sub-module accurately monitors the magnitude of force applied by the legs of a rider to pedals of an electric vehicle and the frequency change condition in the riding process, and the allocation efficiency analysis module helps a renter to quickly replace the electric vehicle meeting the load requirement of the requirement, so that the renter can rent the most suitable electric vehicle at present. The system improves the utilization efficiency of the electric vehicles in the rental area, and has the characteristics of high real-time monitoring capability and high equipment power detection precision.

Description

Technical Field

[0001] The present invention relates to the technical field of public travel management, and in particular to an automation-based shared vehicle power management system. Background Art

[0002] The shared electric vehicles, which are born out of science and technology, have penetrated into our lives. They are the representative of the shared economy and the product of the era under the background of big data. They use the latest management and marketing models to drive the rapid development of the economy. As a popular shared electric vehicle with many problems, it is currently of great concern to people.

[0003] Shared electric vehicles sense the rider's pedaling force through torque sensors and continuously adjust and provide corresponding power support. In the prior art, although riders can select electric vehicles that can be supplied to the destination by scanning the electric vehicle power value provided by the system, after a long period of use, the power allocation system of shared electric vehicles will produce a certain degree of error, resulting in a reduction in the power allocation ability of shared electric vehicles, so that when riders apply the same pedaling force to different shared electric vehicles, the actual travel speed of the electric vehicles will be different. In the case that the renter needs to reach the destination within a certain period of time, the electric vehicle with a large error in power allocation cannot meet the renter's speed requirements for the electric vehicle, making it difficult for the renter to reach the destination within the expected time, reducing the renter's riding experience; if the renter finds that the electric vehicle is underpowered after riding for a period of time, when preparing to change the vehicle at a nearby rental point, it is impossible to quickly change the electric vehicle with load requirements, resulting in more urgent time. Therefore, it is very necessary to design an automated shared vehicle power management system with strong real-time monitoring capabilities 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 technology.

[0005] In order to solve the above technical problems, the present invention provides the following technical solutions: an automated shared vehicle power management system, comprising a data acquisition module, a power monitoring module and an output module, wherein 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 collection module includes an electric vehicle information collection module and a shared APP information collection module. The electric vehicle information collection module is used to obtain information about specific equipment on the shared electric vehicle; the shared APP information collection module is used to obtain 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 deployment 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 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 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 tactile 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 requirement 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 tactile detector unit is used to obtain the contact position 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 a suitable vehicle to the renter based on the prediction results of the time and 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, and the system detects the displayed power of the vehicle scanned by the renter in the rental area. When the predicted driving 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 submodule detects the number of times the rider uses the handbrake during riding, and analyzes the rider's speed requirement for the electric vehicle based on the proportion of the rider's braking time;

[0013] Step S3: the trunk analysis submodule detects the movement trajectory of the rider's trunk on the electric vehicle seat during riding, analyzes the real-time situation of the rider wanting to maintain or speed up the current vehicle speed, and analyzes the power allocation error 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 riding process of the rider, when the balance detection submodule in the electric vehicle obtains that the tilt angle of the electric vehicle is less than 10° or the tilt angle is greater than 10° for less than 2 seconds, the touch detector arranged under the seat cushion obtains the contact position between the rider's buttocks and the seat cushion during the riding process, and combines the connection lines between the buttocks and the thigh in the contact position to sense that the angle between the midline of the thigh extending outward from the seat cushion and the direction facing the front of the seat cushion is φ, and the trunk analysis submodule continuously observes the size change of φ according to the swing of the thigh when the rider steps on the pedal of 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, wherein φ=0 is the φ value corresponding to the average value of the angle change when the rider's thigh steps on the pedal of the electric vehicle, t=0 is the time to start observation, and the unit of t is second;

[0021] 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 period where the amplitude difference in the period time or period time period is lower than 20% of the corresponding data maximum value in the coordinate axis;

[0022] Step S33: by using curve processing technology, according to the slope information of the curves at both ends of the truncated period time period in the curve, smooth simulation lines are added to splice the curves at both ends of the target, and the irregular amplitude and period data in the coordinate axis are averaged and smoothed to obtain a regular periodic curve of the angle φ changing with time t;

[0023] Step S34: Obtain the number of times the wheel moves when the rider steps on the pedal of the electric vehicle to drive the wheel through a periodic curve. The number of wheels driven by the normal pedaling of the electric vehicle during the riding distance Q of the rider is greater than When the electric vehicle is in a state of normal driving, step S4 is immediately executed, wherein S is the moving distance of the electric vehicle in one pedaling cycle obtained through big data, and μ is the error ratio of the electric vehicle that cannot convert the movable distance in one pedaling cycle into the actual moving distance due to abnormal driving conditions such as waiting for street lights, going uphill, turning, etc. during the entire driving journey, which is determined according to the current road conditions during the 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 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;

[0026] Step S52: When the error rate between the allocation efficiency of the scanned target electric vehicle and the precise allocation efficiency of step S4 is higher than the controllable error rate and lower than the error rate of the locked electric vehicle in the last rental order, other shared electric vehicles are judged in turn, and based on the fastest time standard to reach the destination input, the electric vehicles that are close to the error requirements among 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: the present invention, through the braking time analysis submodule, solves the problem that due to the different congestion levels in different sections, cyclists frequently brake in crowded sections, and finally the riding speed and riding time data obtained by the speed detector are the same, resulting in the system's inaccurate judgment of the cyclist's actual riding speed requirement. It ensures accurate detection of electric vehicles during riding, makes the research object precise, and reduces the monitoring burden of the system; the torso analysis submodule accurately monitors the size and frequency changes of the force applied by the cyclist's legs on the electric vehicle pedals during the riding of the electric vehicle, which can not only determine whether the driving efficiency of the cyclist's riding target electric vehicle is normal, but also evaluate the cyclist's demand value for the riding speed of the electric vehicle through the size of T. The deployment efficiency analysis module helps the renter to quickly replace the electric vehicle that meets the required load demand, so that the renter can rent the most suitable electric vehicle at the moment, thereby improving the utilization efficiency of electric vehicles in the rental area. BRIEF DESCRIPTION OF THE DRAWINGS

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

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

[0030] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

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

[0032] Data acquisition module, power monitoring module and 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] The present invention solves the problem that due to the different congestion levels in different sections, cyclists frequently brake in crowded sections, which is finally the same as the riding speed and riding time data obtained by the speed detector, resulting in inaccurate judgment of the system on the actual riding speed demand of the cyclist, through the braking time analysis submodule. It ensures the accurate detection of the electric vehicle during riding, makes the research object precise, and reduces the monitoring burden of the system; the trunk analysis submodule accurately monitors the force applied by the cyclist's legs on the pedals of the electric vehicle during the riding of the electric vehicle, which can not only determine whether the driving efficiency of the cyclist's riding of the target electric vehicle is normal, but also evaluate the cyclist's demand value for the riding speed of the electric vehicle through the size of T; the deployment efficiency analysis module helps the renter to quickly replace the electric vehicle that meets the required load demand, so that the renter can rent the most suitable electric vehicle at the moment, thereby improving the utilization efficiency of the electric vehicles in the rental area.

[0034] The data collection module includes an electric vehicle information collection module and a shared APP information collection module. The electric vehicle information collection module is used to obtain information about specific equipment on shared electric vehicles; the shared APP information collection 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 deployment efficiency analysis module. The riding process sampling and analysis module is used to obtain the riding status of the rider through the 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 and analysis module.

[0036] The riding 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 riding, and analyze the rider's speed requirement 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 tactile detector unit is used to obtain the contact position 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 allocation module. The vehicle selection recommendation module is used to recommend suitable vehicles to car renters based on the prediction results of the time and power prediction module; the power allocation module is used to allocate the power of the electric vehicle in real time to meet the riding speed requirements 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 renter enters the destination location information in the rental system, and the system detects the displayed power of the vehicle scanned by the renter in the rental area. When the predicted driving 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;

[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 requirement for the electric vehicle based on the proportion of the rider's braking time;

[0041] Step S3: the trunk analysis submodule detects the movement trajectory of the rider's trunk on the electric vehicle seat during riding, analyzes the real-time situation of the rider wanting to maintain or speed up the current vehicle speed, and analyzes the power allocation error of the target electric vehicle;

[0042] 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, thereby reducing the operating burden of the electric vehicle to the greatest extent and improving the utilization efficiency of the electric power in the electric vehicle;

[0043] 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.

[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 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;

[0046] Step S22: The number of times the handbrake changes but is in the first gear when the rider brakes during riding is t1, the number of times the minimum distance is in the second gear when braking is t2, and the number of times the minimum distance is in the third gear when braking is 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, wherein C is the time 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 predicted by big data. 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.

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

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

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

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

[0051] The third gear 0<L≤0.7L1: large braking, the electric vehicle's speed drops quickly and the specific descent speed is determined according to the size of L.

[0052] Even when the rider has a higher riding speed requirement than other renters, and increases the riding speed several times, the riding speed is frequently braked due to crowded areas, and finally the riding speed and riding time obtained by the speed detector are the same, resulting in the system's inaccurate judgment of the rider's actual riding speed requirement. Through this technical solution, accurate detection of riders during riding is guaranteed, making the research object precise and simplified, and reducing the monitoring burden of the system.

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

[0054] Step S31: During the riding process of the rider, when the balance detection submodule in the electric vehicle obtains that the electric vehicle is tilted at an angle less than 10° or the tilt angle is higher than 10° for less than 2 seconds, the touch detector arranged under the seat cushion obtains the contact position between the rider's buttocks and the seat cushion during the riding process, and combines the connection lines between the buttocks and the thigh in the contact position to sense that the angle between the midline of the thigh extending outward from the seat cushion and the direction facing the front of the seat cushion is φ, and the trunk analysis submodule continuously observes the size change 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 time t as the horizontal axis and the angle φ as the vertical axis, wherein 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, t=0 is the time to start observation, and the unit of t is seconds; when going uphill, the rider needs to apply a greater pedaling force to the pedal than on flat ground, so that the detection result is inaccurate due to objective factors;

[0055] Step S32: Calculate all the maximum and minimum values ​​in the coordinate axis, and the corresponding two extreme values ​​are a monitoring period. Cut off the period time period in which the cycle time or the amplitude difference in the period time period is lower than 20% of the corresponding data maximum value in the coordinate axis; in the period time period in which the cycle and amplitude are lower than 20% of the maximum value, the pedaling of the electric vehicle by the rider is difficult to predict, and the cut period time period is invalid data, which needs to be removed to achieve the accuracy of monitoring the rider's pedaling of the electric vehicle.

[0056] Step S33: by using curve processing technology, according to the slope information of the curves at both ends of the truncated period time period in the curve, smooth simulation lines are added to splice the curves at both ends of the target, and the irregular amplitude and period data in the coordinate axis are averaged and smoothed to obtain a regular periodic curve of the angle φ changing with time t;

[0057] Step S34: Obtain the number of times the wheel moves when the rider steps on the pedal of the electric vehicle to drive the wheel through a periodic curve. The number of wheels driven by the normal pedaling of the electric vehicle during the riding distance Q of the rider is greater than When it is determined that the driving efficiency of the target electric vehicle ridden by the rider is lower than the normal value, step S4 is immediately executed, wherein S is the movement distance of the electric vehicle with normal driving efficiency within one pedaling cycle obtained through big data, and μ is the error ratio of the electric vehicle being unable to convert the movable distance within one pedaling cycle into the actual movement distance due to abnormal driving conditions such as waiting for street lights, going uphill, and turning during the entire driving journey, which is determined according to the current road conditions during the driving of the electric vehicle, and 0.7<μ<1.

[0058] When the cycle time of a rider pedaling an electric vehicle is shorter than that of an ordinary rider, it means that the rider has used greater force to pedal the electric vehicle, which shortens the cycle time and increases the number of driven wheels. Therefore, if the actual number of driven wheels is higher than the number of wheels for the distance traveled under normal circumstances, it can be judged that the riding efficiency of the rider's riding the target electric vehicle is lower than normal.

[0059] Through this technical solution, the problem of inaccurate force of the rider stepping on the pedal of the electric vehicle due to errors is solved. During the riding of the electric vehicle, the size and frequency changes of the force applied by the rider's legs to the pedal of the electric vehicle 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 rider's demand for the riding speed of the electric vehicle through the size of T. While ensuring accurate detection of the electric vehicle during riding, it increases the diversity of monitoring content and provides favorable data for subsequent driving 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 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 allocation efficiency of the scanned target electric vehicle and the precise allocation efficiency of step S4 is higher than the controllable error rate and lower than the error rate of the locked electric vehicle in the last rental order, other shared electric vehicles are judged in turn, and based on the fastest time standard to reach the destination input, the electric vehicles that are close to the error requirements among the detected target electric vehicles are recommended to the rider.

[0063] This technical means solves the problem that car renters cannot quickly change electric vehicles to meet load requirements due to time constraints, so that car renters can rent the most suitable electric vehicle at the moment, thereby improving the utilization efficiency of electric vehicles in the rental area.

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

[0065] Finally, it should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art can still modify the technical solutions described in the aforementioned embodiments or replace some of the technical features therein by equivalents. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. An automated shared vehicle power management system, characterized by: It includes 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 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 prediction 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 about specific equipment on a shared electric vehicle; the shared APP information acquisition module is used to obtain relevant system detection information in a 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 through the relevant parameters of the rider 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; The riding process sampling module further includes a braking time analysis submodule, a trunk 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 obtain the contact position 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 allocation module. The vehicle selection recommendation module is used to recommend a suitable vehicle to the renter based on the prediction results of the time and power prediction module; the power allocation module is used to allocate 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 enters the destination location information in the rental system, and the system detects the displayed power of the vehicle scanned by the renter in the rental area. When the predicted driving 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; 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 requirement for the electric vehicle based on the proportion of the rider's braking time; Step S3: the trunk analysis submodule detects the movement trajectory of the rider's trunk on the electric vehicle seat during riding, analyzes the real-time situation of the rider wanting to maintain or speed up the current vehicle speed, and analyzes the power allocation error of the target electric vehicle; 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; 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 the electric vehicles around the target rental vehicle is extracted, and the electric vehicles that meet the requirements are recommended to the renter; The step S2 further comprises: 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, and 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; Step S22: The number of times when the handbrake of the rider changes but is in the first gear when braking during riding is t1, the number of times when the minimum distance is in the second gear when braking is t2, and the number of times when the minimum distance is in the third gear when braking is t3. If t3>t2-C and t1>t2+t3, execute step S3, wherein C is the time for the rider to use a small brake when it is necessary to stop when driving from the 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 in the urban area section, C is specifically 0.4t2; when the rider is driving in the town area section, C is specifically 0.3t2; when the rider is driving in the suburban area section, C is specifically 0.1t2; Among the three brake positions set by the system, First gear 0.9L1<L≤L1: error range with no actual braking effect; The second gear is 0.7L1<L≤0.9L1: small braking, the electric vehicle's speed decreases slowly and the specific speed is determined according to the size of L; The third gear 0<L≤0.7L1: large-scale braking, the electric vehicle's speed drops quickly and the specific speed of descent is determined by the size of L; The step S5 further comprises: Step S51: The vehicle selection 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; Step S52: When the error rate between the allocation efficiency of the scanned target electric vehicle and the precise allocation efficiency of step S4 is higher than the controllable error rate and lower than the error rate of the locked electric vehicle in the last rental order, other shared electric vehicles are judged in turn, and based on the fastest time standard to reach the destination input, the electric vehicles that are close to the error requirements among the detected target electric vehicles are recommended to the rider.

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