Intelligent shared electric bicycle management method, device, equipment and storage medium

Abstract

The application discloses a kind of management method, device and equipment of intelligent shared electric bicycle and storage medium, including in the current riding vehicle of user appears lock abnormality, judge current riding vehicle whether it is blue tooth failure vehicle;If yes, obtain other vehicle position information;According to other vehicle position information, determine several candidate vehicles, select target vehicle from several candidate vehicles, and control target vehicle to send car-seeking guide information;Through car-seeking guide information, it is guided that user moves to target vehicle, and receives the riding order of current riding vehicle uploaded by target vehicle, and according to the vehicle identification in riding order, sends lock car instruction to current riding vehicle, to control current riding vehicle to lock up.Solve the technical problem that user cannot normally return car in the case that terminal equipment exists network exception in prior art, improve user experience.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of shared bicycles, in particular to a management method, device and equipment of intelligent shared electric bicycles and a storage medium. BACKGROUND

[0002] The intelligent electric bicycle is favored by people due to its fast and convenient characteristics, and more and more users use the intelligent electric bicycle as a short-distance transportation tool. However, the intelligent electric bicycle brings convenience to people's travel, but also has many problems that lead to poor user experience. For example, the current intelligent electric bicycle generally uses an electronic lock. If the network of the user's terminal device is abnormal, the user cannot normally return the bicycle, thereby generating additional bicycle usage fees and leading to poor user experience. Therefore, how to ensure normal return of the bicycle in the case of network abnormality of the terminal device becomes a technical problem to be solved. SUMMARY

[0003] The main purpose of the present application is to provide a management method, device and equipment of intelligent shared electric bicycles and a storage medium, which aims to solve the technical problem that the existing technology cannot normally return the bicycle in the case of network abnormality of the terminal device.

[0004] To achieve the above purpose, the present application provides a management method of intelligent shared electric bicycles, which comprises the following steps:

[0005] When the current riding vehicle of the user appears lock abnormality, it is judged whether the current riding vehicle is a Bluetooth fault vehicle;

[0006] If the current riding vehicle is a Bluetooth fault vehicle, the position information of other vehicles within a preset range of the current riding vehicle is obtained;

[0007] According to the position information of the other vehicles, a plurality of candidate vehicles are determined, a target vehicle is selected from the plurality of candidate vehicles, and the target vehicle is controlled to send a vehicle searching guide information;

[0008] The user is guided to move to the target vehicle through the vehicle searching guide information, and the riding order of the current riding vehicle uploaded by the target vehicle is received. The target vehicle establishes a Bluetooth connection with the terminal device of the user, and the terminal device sends the riding order to the target vehicle through the Bluetooth connection in response to the return instruction triggered by the user;

[0009] The vehicle identification is parsed from the riding order, and the lock vehicle instruction is sent to the current riding vehicle according to the vehicle identification, so as to control the current riding vehicle to lock.

[0010] Optionally, when the current vehicle of the user has a lock-off abnormality, the method comprises:

[0011] When the current vehicle of the user has a lock-off abnormality, the vehicle driving characteristics of the current vehicle within a preset time period are obtained.

[0012] According to the vehicle driving characteristics, the driving health degree of the current vehicle is predicted by a driving health degree prediction algorithm.

[0013] If the driving health degree is greater than a preset health degree, the lock-on time characteristics of the current vehicle are obtained.

[0014] According to the lock-on time characteristics, the Bluetooth health degree of the current vehicle is determined by a Bluetooth abnormality detection algorithm.

[0015] If the Bluetooth health degree is less than a preset Bluetooth health degree, the current vehicle is determined as a Bluetooth failure vehicle.

[0016] Optionally, the vehicle driving characteristics comprise single average riding time, single average riding distance, and moving time proportion.

[0017] According to the vehicle driving characteristics, the driving health degree of the current vehicle is predicted by a driving health degree prediction algorithm, which comprises:

[0018] The usage frequency of the current vehicle and the historical riding data of each vehicle in the delineated riding area are obtained.

[0019] According to the usage frequency and the frequency adjustment factor corresponding to the delineated riding area, the regional frequency screening interval corresponding to the current vehicle is determined.

[0020] According to the regional frequency screening interval, the historical riding data are cleaned to obtain target historical riding data.

[0021] The target historical riding data are clustered to obtain historical riding time data, historical riding frequency data, and historical riding distance data.

[0022] According to the historical riding frequency data and the historical riding distance data, historical average riding distance data are determined.

[0023] According to the historical riding time data, historical average riding time data are determined, and according to the historical riding time data, historical moving proportion data are determined.

[0024] normalize the historical average riding distance data, the historical average riding time data, the historical moving proportion data, the single average riding time, the single average riding distance and the moving time proportion, to obtain target average riding distance data, target average riding time data, target average moving proportion data, target average riding time, target average riding distance and target average moving time proportion;

[0025] According to the target average riding distance data, the target average riding time data, the target average moving proportion data, the target average riding time, the target average riding distance and the target average moving time proportion, the driving health degree of the current riding vehicle is predicted by a driving health degree prediction algorithm:

[0026] The driving health degree prediction algorithm is:

[0027]

[0028] In the formula, k is the driving health degree; s is the target average riding distance; s i is the target average riding distance of the i-th vehicle in the target average riding distance data; t is the target average riding time; t i is the target average riding time of the i-th vehicle in the target average riding time data; y is the target average moving time proportion; y i is the target average moving proportion of the i-th vehicle in the target average moving proportion data; a, b and c are proportional coefficients, and the sum of a, b and c is 1; M is a dynamic parameter.

[0029] Optionally, the method further comprises:

[0030] According to the delineated riding area, a target frequency value is found in a preset mapping relationship table;

[0031] Obtaining weather information and competitor price information within the delineated riding area in a preset historical time period;

[0032] According to the weather information and the competitor price information, the target frequency value is corrected to obtain a frequency adjustment factor;

[0033] According to the use frequency and the frequency adjustment factor, a frequency lower limit value and a frequency upper limit value are determined;

[0034] According to the frequency lower limit value and the frequency upper limit value, a region frequency screening interval corresponding to the current riding vehicle is determined.

[0035] Optionally, before the determining the Bluetooth health degree of the current riding vehicle according to the unlocking time feature through the Bluetooth anomaly detection algorithm, comprising:

[0036] Obtaining historical unlocking duration data of the current riding vehicle in a preset time period;

[0037] Sorting the historical unlocking duration in the historical unlocking duration data in chronological order to obtain a historical unlocking duration sequence;

[0038] Mapping the historical unlocking duration sequence into a sequence point in a target coordinate system;

[0039] Fitting the sequence point through a linear fitting algorithm to obtain an initial Bluetooth anomaly detection algorithm;

[0040] Correcting the initial Bluetooth anomaly detection algorithm according to a correction coefficient to obtain a Bluetooth anomaly detection algorithm.

[0041] Optionally, the determining a plurality of candidate vehicles according to the other vehicle position information, selecting a target vehicle from the plurality of candidate vehicles, and controlling the target vehicle to send a car-seeking guide information, comprising:

[0042] Determining a vehicle distance according to the other vehicle position information and the position information of the current riding vehicle, and determining a plurality of candidate vehicles according to the vehicle distance;

[0043] Obtaining historical adjacent unlocking time features corresponding to each candidate vehicle, and determining the Bluetooth health degree of each candidate vehicle according to the historical adjacent unlocking time features;

[0044] Determining a target vehicle according to the Bluetooth health degree of each candidate vehicle, and controlling the target vehicle to send a car-seeking guide information.

[0045] Optionally, the parsing a vehicle identifier from the ride order, and sending a lock vehicle instruction to the current riding vehicle according to the vehicle identifier to control the current riding vehicle to lock, comprising:

[0046] Parsing a vehicle identifier from the ride vehicle, and determining a current vehicle position according to the vehicle identifier;

[0047] When the current vehicle position is in a parking area, sending a lock vehicle instruction to the current riding vehicle to control the current riding vehicle to lock, and adding a Bluetooth fault label to the current riding vehicle.

[0048] In addition, to achieve the above-mentioned purpose, the application further provides a management device of intelligent shared electric bicycles, the device comprising:

[0049] The judgment module is configured to judge whether the current riding vehicle is a Bluetooth failure vehicle when the current riding vehicle has a lock abnormality.

[0050] The acquisition module is configured to acquire position information of other vehicles within a preset range of the current riding vehicle if the current riding vehicle is a Bluetooth failure vehicle.

[0051] The selection module is configured to determine a plurality of candidate vehicles according to the position information of the other vehicles, select a target vehicle from the plurality of candidate vehicles, and control the target vehicle to send a vehicle searching guide information.

[0052] The guide module is configured to guide the user to move to the target vehicle through the vehicle searching guide information, and receive a riding order of the current riding vehicle uploaded by the target vehicle, wherein the target vehicle establishes a Bluetooth connection with a terminal device of the user, and the terminal device sends the riding order to the target vehicle through the Bluetooth connection in response to a vehicle returning instruction triggered by the user.

[0053] The analysis module is configured to analyze a vehicle identifier from the riding order, and send a vehicle locking instruction to the current riding vehicle according to the vehicle identifier, so as to control the current riding vehicle to lock.

[0054] In addition, to achieve the above-mentioned purpose, the application further provides a management device of an intelligent shared electric bicycle, which comprises a memory, a processor, and a management program of an intelligent shared electric bicycle stored in the memory and executable on the processor, wherein the management program of the intelligent shared electric bicycle is configured to implement the steps of the management method of the intelligent shared electric bicycle.

[0055] In addition, to achieve the above-mentioned purpose, the application further provides a storage medium, wherein the storage medium stores a management program of an intelligent shared electric bicycle, and the management program of the intelligent shared electric bicycle implements the steps of the management method of the intelligent shared electric bicycle when executed by a processor.

[0056] The application judges whether the current riding vehicle is a Bluetooth fault vehicle when the current riding vehicle has lock abnormality; if the current riding vehicle is a Bluetooth fault vehicle, the position information of other vehicles within a preset range of the current riding vehicle is obtained; a number of candidate vehicles are determined according to the position information of the other vehicles, a target vehicle is selected from the number of candidate vehicles, and the target vehicle is controlled to send a car searching guide information; the user is guided to move to the target vehicle through the car searching guide information, and a riding order of the current riding vehicle uploaded by the target vehicle is received, the target vehicle and a terminal device of the user establish a Bluetooth connection, the terminal device sends the riding order to the target vehicle through the Bluetooth connection in response to a car returning instruction triggered by the user; a vehicle identifier is parsed from the riding order, and a lock vehicle instruction is sent to the current riding vehicle according to the vehicle identifier to control the current riding vehicle to lock. The application judges whether the Bluetooth of the vehicle has a fault when the current riding vehicle of the user has lock abnormality, if yes, a target vehicle is selected from the preset range of the current riding vehicle, the user is guided to reach the target vehicle through the car searching guide information sent by the target vehicle, the terminal device of the user and the target vehicle establish a Bluetooth connection, the riding order sent by the terminal device is received by the target vehicle through the Bluetooth connection, an order identifier is parsed from the riding order, and the current riding vehicle is controlled to lock according to the order identifier. In the case that the terminal device of the user has a fault, the riding order of the current riding vehicle is uploaded through the Bluetooth connection established between the target vehicle and the terminal device, so that the current riding vehicle is controlled to lock according to the order identifier parsed from the riding order, which solves the technical problem that the user cannot normally return the car in the prior art in the case that the terminal device has network abnormality, and improves the user experience. BRIEF DESCRIPTION OF DRAWINGS

[0057] Figure 1 is a structural schematic diagram of a management device of an intelligent shared electric bicycle of a hardware running environment involved in an embodiment scheme of the application;

[0058] Figure 2 is a flowchart of a first embodiment of a management method of an intelligent shared electric bicycle of the application;

[0059] Figure 3 is a flowchart of a second embodiment of a management method of an intelligent shared electric bicycle of the application;

[0060] Figure 4 is a flowchart of a third embodiment of a management method of an intelligent shared electric bicycle of the application;

[0061] Figure 5 is a structural block diagram of a first embodiment of a management device of an intelligent shared electric bicycle of the application.

[0062] The objectives, functional characteristics and advantages of the present application will be further illustrated in combination with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0063] It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application.

[0064] Referring to Figure 1 , Figure 1 The management device structure of the intelligent shared electric bicycle is shown in the figure.

[0065] As Figure 1 shown, the management device of the intelligent shared electric bicycle can include a processor 1001, such as a central processing unit (CPU), a communication bus 1002, a user interface 1003, a network interface 1004, and a memory 1005. The communication bus 1002 is used to realize the connection and communication between these components. The user interface 1003 can include a display screen, an input unit such as a keyboard, and can also include a standard wired interface, a wireless interface. The network interface 1004 can optionally include a standard wired interface, a wireless interface (such as a wireless fidelity (WI-FI) interface). The memory 1005 can be a high-speed random access memory (RAM), and can also be a stable non-volatile memory (NVM), such as a disk memory. The memory 1005 can also be a storage device independent of the aforementioned processor 1001.

[0066] Those skilled in the art can understand Figure 1 that the structure shown in the figure does not constitute a limitation on the management device of the intelligent shared electric bicycle, and can include more or fewer components than the figure, or combine certain components, or different component arrangements.

[0067] As Figure 1 shown, the memory 1005 as a storage medium can include an operating system, a network communication module, a user interface module, and a management program of the intelligent shared electric bicycle.

[0068] In Figure 1The network interface 1004 is mainly used for data communication with a network server; the user interface 1003 is mainly used for data interaction with a user; the processor 1001 and the memory 1005 can be arranged in the management device of the intelligent shared electric bicycle, and the management device of the intelligent shared electric bicycle calls the management program of the intelligent shared electric bicycle stored in the memory 1005 through the processor 1001, and executes the management method of the intelligent shared electric bicycle provided in the embodiment of the application.

[0069] The embodiment of the application provides a management method of an intelligent shared electric bicycle. Figure 2 , Figure 2 FIG. 1 is a flowchart of the first embodiment of the management method of the intelligent shared electric bicycle.

[0070] In the embodiment, the management method of the intelligent shared electric bicycle comprises the following steps.

[0071] Step S10: When the current riding vehicle of the user appears a lock abnormality, it is judged whether the current riding vehicle is a Bluetooth fault vehicle.

[0072] It should be noted that the execution subject of the embodiment can be a computing service device with data processing, network communication and program running functions, such as a personal computer, a server, etc., or an electronic device capable of realizing the above functions, a management device of an intelligent shared electric bicycle, etc. The embodiment and the following embodiments will be illustrated below taking a server as an example.

[0073] It can be understood that the lock abnormality can be a vehicle abnormality that the user cannot control the current riding vehicle to normally lock; the Bluetooth fault vehicle can be a vehicle with a Bluetooth function fault.

[0074] In one implementation mode, when the current riding vehicle of the user is in a stationary state, the current vehicle load of the current vehicle is acquired, if the current vehicle load is less than a preset vehicle load, a first time length of the vehicle load less than the preset vehicle load is acquired, and in the case that the first time length is greater than a preset lock abnormality time length, a lock request sent by a terminal device of the user is not received, it is determined that the current riding vehicle of the user appears a lock abnormality.

[0075] It can be understood that the preset vehicle load can be a vehicle load preset for judging whether the user is separated from the vehicle, and the preset vehicle load can be updated according to the vehicle load detected in a preset time length; for example, the vehicle load is collected during the user's riding process, n vehicle loads are obtained, the maximum vehicle load and the minimum vehicle load in the n vehicle loads are removed, the average vehicle load of the remaining vehicle loads is calculated, and the preset vehicle load is updated to the average vehicle load.

[0076] It should be understood that the current riding vehicle is in a stationary state and the current vehicle load is less than the preset vehicle load, and it is determined that the user performs the lock vehicle operation through the terminal device; in the case that the first preset time length is greater than the preset lock abnormal time length, the lock vehicle request sent by the user through the terminal device is not received, and it is determined that the user has a lock vehicle demand, but cannot normally perform the return vehicle operation.

[0077] Step S20: If the current riding vehicle is a Bluetooth failure vehicle, obtaining the position information of other vehicles within a preset range of the current riding vehicle.

[0078] It can be understood that if the current riding vehicle is a Bluetooth failure vehicle, it is determined that the current riding vehicle cannot establish a Bluetooth connection with the user terminal device, i.e. the terminal device and the current riding vehicle cannot perform data transmission through the Bluetooth connection; the preset range can be a pre-set area range; the position information of other vehicles can be the positioning information of other smart shared electric bicycles within the preset range.

[0079] It should be understood that if the current riding vehicle is not a Bluetooth failure vehicle, the riding order sent by the terminal device is received through the Bluetooth connection between the current riding vehicle and the terminal device, and the current riding vehicle is controlled to be locked according to the vehicle identifier in the riding order; for example: if the current riding vehicle is not a Bluetooth failure vehicle, the user is reminded to establish a Bluetooth connection with the current riding vehicle, after the Bluetooth connection is established, the prompt information "please trigger the return vehicle button" is sent, the user triggers the return vehicle button to send the riding order to the current riding vehicle through the Bluetooth connection, the current riding vehicle uploads the riding order, and the server receives the riding order uploaded by the current riding vehicle, and analyzes the vehicle identifier from the riding order, and controls the current riding vehicle to be locked according to the vehicle identifier.

[0080] Step S30: determining a plurality of candidate vehicles according to the position information of the other vehicles, selecting a target vehicle from the plurality of candidate vehicles, and controlling the target vehicle to send a car searching guide information.

[0081] It can be understood that the car searching guide information can be information for guiding the user to reach the target vehicle, for example: the car searching guide information includes but is not limited to: sound indication information, light indication information, etc.

[0082] As an implementation manner, the target vehicle is selected from the plurality of candidate vehicles according to the vehicle distance between each candidate vehicle and the current riding vehicle and the Bluetooth health degree of each candidate vehicle; for example: the vehicle distance and the Bluetooth health degree of each candidate vehicle are weighted and summed to obtain the candidate score of each candidate vehicle, and the vehicle with the highest candidate score is determined as the target vehicle.

[0083] Step S40: guiding the user to move to the target vehicle through the vehicle searching guide information, and receiving the riding order of the current riding vehicle uploaded by the target vehicle, the target vehicle establishing a Bluetooth connection with the terminal device of the user, and the terminal device sending the riding order to the target vehicle through the Bluetooth connection in response to the return vehicle instruction triggered by the user.

[0084] It can be understood that the riding information of the current riding vehicle is recorded in the riding order, for example, the information in the riding order includes but is not limited to: vehicle identification, riding time length and riding distance, etc.

[0085] In an implementation mode, the server controls the target vehicle to send the vehicle searching guide information, so as to guide the user to reach the target vehicle through the vehicle searching guide information. After the user reaches the target vehicle, the user is reminded to establish a Bluetooth connection between the terminal device and the target vehicle. After the Bluetooth connection is established, the user is reminded to perform the return vehicle operation through the terminal device. After the user triggers the return vehicle operation through the terminal device, the terminal device sends the riding order to the target vehicle through the Bluetooth connection. The target vehicle uploads the Bluetooth connection to the server.

[0086] Step S50: parsing the vehicle identification from the riding order, and sending a lock vehicle instruction to the current riding vehicle according to the vehicle identification, so as to control the current riding vehicle to be locked.

[0087] In an implementation mode, if the current riding vehicle of the user is in a stationary state, the current vehicle load of the current riding vehicle is obtained. The first time length in which the current vehicle load is less than the preset vehicle load is obtained. If the first time length is greater than the preset abnormal locking time length, it is determined that the current riding vehicle has an abnormal locking. It is further determined whether the current riding vehicle is a Bluetooth fault vehicle. If yes, a plurality of candidate vehicles are determined according to the position information of other vehicles within the preset range of the current riding vehicle. The target vehicle is selected from the plurality of candidate vehicles according to the vehicle distance between each candidate vehicle and the current riding vehicle and the Bluetooth health degree of each candidate vehicle. The server controls the target vehicle to send the vehicle searching guide information, so as to guide the user to reach the target vehicle through the information. After the user reaches the target vehicle, the Bluetooth connection is established. After the Bluetooth connection between the terminal device of the user and the target vehicle is established, the server controls the target vehicle to send the return vehicle prompt information to the terminal device through the Bluetooth connection. The target vehicle receives the riding order transmitted by the terminal device through the Bluetooth connection, and uploads the riding order to the server. The server parses the vehicle identification from the riding order, and sends the lock vehicle instruction according to the vehicle identification, so as to control the current riding vehicle to be locked.

[0088] Further, in order to avoid the problem that the determined target vehicle is still a Bluetooth failure vehicle, thereby causing the user to fail to complete the drop-off, the step S30 comprises: determining a vehicle distance according to the other vehicle position information and the current riding vehicle position information, and determining a plurality of candidate vehicles according to the vehicle distance; obtaining a historical adjacent unlocking time feature corresponding to each candidate vehicle, and determining a Bluetooth health degree of each candidate vehicle according to the historical adjacent unlocking time feature; determining a target vehicle according to the Bluetooth health degree of each candidate vehicle, and controlling the target vehicle to send a car searching guide information.

[0089] It can be understood that the historical adjacent unlocking time feature comprises a last unlocking time; the historical adjacent unlocking time feature of each candidate vehicle is sequentially input into a Bluetooth anomaly detection algorithm to obtain a predicted unlocking time consumption of each candidate vehicle output by the Bluetooth anomaly detection algorithm, and the predicted unlocking time consumption is taken as the Bluetooth health degree of each candidate vehicle. The shorter the predicted unlocking time consumption is, the higher the Bluetooth health degree is.

[0090] In another implementation manner, the other vehicle with a vehicle distance less than a preset vehicle distance is taken as a candidate vehicle; the historical adjacent unlocking time feature further comprises an actual unlocking time consumption of the last unlocking; the unlocking time is input into the Bluetooth anomaly detection algorithm to obtain the predicted unlocking time consumption, and if both the predicted unlocking time consumption and the actual unlocking time consumption are greater than a preset failure unlocking time, it is determined that the current riding vehicle is a Bluetooth failure vehicle.

[0091] Further, in order to control the current riding vehicle to be locked, the step S50 comprises: analyzing a vehicle identifier from the riding vehicle, and determining a current vehicle position according to the vehicle identifier; when the current vehicle position is in a parking area, sending a lock vehicle instruction to the current riding vehicle to control the current riding vehicle to be locked, and adding a Bluetooth failure label to the current riding vehicle.

[0092] It can be understood that the vehicle identifier can be an identifier capable of uniquely representing a vehicle, for example, the vehicle identifier comprises but is not limited to a vehicle number, a vehicle identification number, a vehicle code, etc.

[0093] The embodiment is characterized in that when the current riding vehicle of the user appears abnormal lock-off, it is judged whether the current riding vehicle is a Bluetooth fault vehicle; if the current riding vehicle is a Bluetooth fault vehicle, the position information of other vehicles within a preset range of the current riding vehicle is acquired; a plurality of candidate vehicles are determined according to the position information of the other vehicles, a target vehicle is selected from the plurality of candidate vehicles, and the target vehicle is controlled to send a vehicle searching guide information; the user is guided to move to the target vehicle through the vehicle searching guide information, and a riding order of the current riding vehicle uploaded by the target vehicle is received, the target vehicle and a terminal device of the user establish a Bluetooth connection, and the terminal device sends the riding order to the target vehicle through the Bluetooth connection in response to a vehicle returning instruction triggered by the user; a vehicle identifier is parsed from the riding order, and a lock vehicle instruction is sent to the current riding vehicle according to the vehicle identifier to control the current riding vehicle to lock. The embodiment is characterized in that when the current riding vehicle of the user appears abnormal lock-off, it is judged whether the vehicle Bluetooth is faulty, if so, a target vehicle is selected from a preset range of the current riding vehicle, the user is guided to reach the target vehicle through the vehicle searching guide information sent by the target vehicle, a Bluetooth connection is established between the terminal device of the user and the target vehicle, the riding order sent by the terminal device is uploaded by the target vehicle through the Bluetooth connection, an order identifier is parsed from the riding order, and the current riding vehicle is controlled to lock according to the order identifier. In the case that the terminal device of the user is faulty, the riding order of the current riding vehicle is uploaded through the Bluetooth connection established between the target vehicle and the terminal device, so that the current riding vehicle is controlled to lock according to the order identifier parsed from the riding order, thereby solving the technical problem that the user cannot normally return the vehicle in the prior art in the case that the terminal device has network abnormity, and improving the user experience.

[0094] Reference Figure 3 , Figure 3 The flowchart of the second embodiment of the management method of the intelligent shared electric bicycle.

[0095] According to the first embodiment, in the embodiment, the step S10 comprises:

[0096] Step S101: When the current riding vehicle of the user appears abnormal lock-off, the vehicle driving characteristics of the current riding vehicle within a preset time period are acquired.

[0097] It can be understood that the preset time period can be a pre-set historical time period, and the vehicle driving characteristics can be characteristics capable of representing the driving state of the current riding vehicle.

[0098] Step S102: The driving health degree of the current riding vehicle is predicted by a driving health degree prediction algorithm according to the vehicle driving characteristics.

[0099] It can be understood that the driving health degree prediction algorithm can be an algorithm preset for predicting the health degree of the vehicle; and the driving health degree can be the health degree of the current riding vehicle.

[0100] Step S103: If the driving health degree is greater than the preset health degree, the unlocking time feature of the current riding vehicle is acquired.

[0101] It can be understood that the preset health degree can be a health degree preset for judging whether the vehicle is a faulty vehicle; if the driving health degree is greater than the preset health degree, the current riding vehicle is determined to be a driving normal vehicle; if the driving health degree is less than or equal to the preset health degree, the current riding vehicle is determined to be a driving abnormal vehicle; and if the current riding vehicle is a driving abnormal vehicle, the current riding vehicle is directly controlled to be locked.

[0102] It should be understood that if the driving health degree is less than or equal to the preset health degree, it indicates that the vehicle is a driving abnormal vehicle, and a safety accident can occur if the riding continues. In order to improve the riding safety, the current riding vehicle is directly controlled to be locked in the case that the driving health degree is less than or equal to the preset health degree.

[0103] Step S104: The Bluetooth health degree of the current riding vehicle is determined according to the unlocking time feature through a Bluetooth anomaly detection algorithm.

[0104] As an implementation manner, the unlocking time feature includes a current request unlocking time, the current request unlocking time is a time for determining that the current riding vehicle of the user has a lock abnormality, the current request unlocking time is input into the Bluetooth anomaly detection algorithm, a predicted unlocking consumption time length of the current riding vehicle output by the Bluetooth anomaly detection algorithm is obtained, and the predicted unlocking consumption time length is taken as the Bluetooth health degree of the current riding vehicle.

[0105] Step S105: If the Bluetooth health degree is less than a preset Bluetooth health degree, the current riding vehicle is determined to be a Bluetooth faulty vehicle.

[0106] In an example, for example, the preset health degree is a, the preset Bluetooth health degree is b, when the current riding vehicle of the user has a lock abnormality, the vehicle driving feature of the current riding vehicle within a preset time period is acquired, it is assumed that the driving health degree m is obtained by the driving health degree prediction algorithm according to the vehicle driving feature, m is greater than the preset health degree a, the current request unlocking time is acquired, the current request unlocking time is input into the Bluetooth anomaly detection algorithm, the predicted unlocking consumption time length n is obtained, 1 / n is taken as the Bluetooth health degree of the current riding vehicle, 1 / n is less than b, and the current riding vehicle is determined to be a Bluetooth faulty vehicle.

[0107] Further, in order to accurately determine whether the current riding vehicle is a Bluetooth failure vehicle, before the Bluetooth health degree of the current riding vehicle is determined by the Bluetooth anomaly detection algorithm according to the unlocking time feature, the method comprises: acquiring historical unlocking duration data of the current riding vehicle within a preset time period; sorting the historical unlocking duration in the historical unlocking duration data in chronological order to obtain a historical unlocking duration sequence; mapping the historical unlocking duration sequence into a sequence point in a target coordinate system; fitting the sequence point by a linear fitting algorithm to obtain an initial Bluetooth anomaly detection algorithm; and correcting the initial Bluetooth anomaly detection algorithm according to a correction coefficient to obtain a Bluetooth anomaly detection algorithm.

[0108] It can be understood that the historical unlocking duration data can be data composed of historical unlocking durations corresponding to a plurality of historical riding orders of the current riding vehicle; the historical unlocking duration is arranged in chronological order in the historical unlocking duration sequence; the target coordinate system can be a coordinate system with time as the horizontal coordinate and unlocking duration as the vertical coordinate; the initial Bluetooth anomaly detection algorithm can be an algorithm represented by a function obtained by linear fitting of the sequence points in the target coordinate system by a linear fitting algorithm; and the correction coefficient can be a parameter for correcting the initial Bluetooth anomaly detection algorithm, which can be set according to specific scenarios.

[0109] In one example, for example: 100 historical unlocking durations of the current riding vehicle are acquired, a target coordinate system is constructed with time as the horizontal coordinate and each unit of the horizontal coordinate set as 1 hour, and unlocking duration as the vertical axis, the earliest historical unlocking duration in time among the 100 historical unlocking durations is taken as the first sequence point in the target coordinate system, the remaining historical unlocking durations are mapped into the target coordinate system according to the time difference between the unlocking time corresponding to the remaining historical unlocking durations and the unlocking time corresponding to the first sequence point, after all the sequence points are mapped, the sequence points in the target coordinate system are linearly fitted by a linear fitting algorithm to obtain an initial Bluetooth anomaly detection algorithm, and the initial Bluetooth detection algorithm is multiplied by a correction coefficient to obtain a Bluetooth anomaly detection algorithm.

[0110] The embodiment is characterized in that when the current riding vehicle of the user has a lock abnormality, the vehicle driving characteristics of the current riding vehicle in a preset time period are acquired; the driving health degree of the current riding vehicle is predicted by a driving health degree prediction algorithm according to the vehicle driving characteristics; if the driving health degree is greater than a preset health degree, the unlocking time characteristics of the current riding vehicle are acquired; the Bluetooth health degree of the current riding vehicle is determined by a Bluetooth abnormality detection algorithm according to the unlocking time characteristics; and if the Bluetooth health degree is less than a preset Bluetooth health degree, the current riding vehicle is determined as a Bluetooth fault vehicle. The embodiment is characterized in that when the current riding vehicle of the user has a lock abnormality, the driving health degree of the vehicle is determined according to the vehicle driving characteristics; if the driving health degree is greater than a preset health degree, the Bluetooth health degree of the vehicle is determined by a Bluetooth abnormality detection algorithm according to the unlocking characteristics of the current riding vehicle; if the Bluetooth health degree is less than a preset Bluetooth health degree, the vehicle is determined as a Bluetooth fault vehicle; and when the vehicle is a normal driving vehicle but the Bluetooth thereof has a fault, the vehicle can be accurately determined as a Bluetooth fault vehicle, thereby guiding the user to establish a Bluetooth connection with other vehicles with normal Bluetooth functions, and improving the user experience.

[0111] Reference Figure 4 , Figure 4 It is a flowchart of the third embodiment of the management method of the intelligent shared electric bicycle.

[0112] Based on the above embodiments, in the embodiment, the vehicle driving characteristics include single average riding time, single average riding distance and moving time proportion, and the step S102 includes:

[0113] In step 1021, the use frequency of the current riding vehicle and the historical riding data of each vehicle in the delineated riding area are acquired.

[0114] It can be understood that the use frequency can be the riding frequency of the current riding vehicle in a preset time period; the delineated riding area can be a vehicle that is allowed to drive a shared electric bicycle and is delineated in advance; the historical riding data can be the riding data of each shared intelligent electric bicycle; for example, the use frequency is obtained by counting the number of times of using the current riding vehicle in a month.

[0115] In step 1022, the region frequency screening interval corresponding to the current riding vehicle is determined according to the use frequency and the frequency adjustment factor corresponding to the delineated riding area.

[0116] It can be understood that the frequency adjustment factor can be a factor for adjusting the use frequency, and different frequency adjustment factors can be set for each delineated riding area; and the region frequency screening interval can be an interval for data screening of the historical riding data.

[0117] Step S1023: data cleaning is performed on the historical riding data according to the region frequency screening interval, and target historical riding data is obtained.

[0118] It can be understood that the historical use frequency of each vehicle is determined according to the historical riding data corresponding to each vehicle, and the historical riding data belonging to the region frequency screening interval is retained to obtain the target historical riding data.

[0119] Step S1024: clustering processing is performed on the target historical riding data to obtain historical riding time data, historical riding frequency data and historical riding distance data.

[0120] It can be understood that the clustering processing of the target historical riding data can be to integrate the historical riding time of each vehicle in the target historical riding data together to obtain the historical riding time data, and to integrate the historical riding distance of each vehicle together to obtain the historical riding distance data.

[0121] Step S1025: historical average riding distance data is determined according to the historical riding frequency data and the historical riding distance data.

[0122] It can be understood that the historical average riding distance data is obtained by dividing the historical riding distance in the historical riding distance data by the corresponding historical riding frequency in the historical riding frequency data.

[0123] Step S1026: historical average riding time data is determined according to the historical riding time data, and historical moving proportion data is determined according to the historical riding time data.

[0124] It can be understood that the historical average riding time data is obtained by dividing the historical riding time in the historical riding time data by the corresponding historical riding frequency in the historical riding frequency data, and the historical moving proportion can be the proportion of the moving time of the vehicle in a riding cycle to the total time of the riding, the historical riding time includes the moving time and the stationary time, and the moving time is divided by the total time to obtain the historical moving proportion.

[0125] Step S1027: normalization processing is performed on the historical average riding distance data, the historical average riding time data, the historical moving proportion data, the single average riding time, the single average riding distance and the moving time proportion to obtain target average riding distance data, target average riding time data, target average moving proportion data, target average riding time, target average riding distance and target average moving time proportion.

[0126] It can be understood that the normalization processing manner can be: determining the maximum value in the historical average riding distance and the single average riding distance, dividing all data by the value to obtain the target average riding distance data and the target average riding distance; the normalization processing manner of the remaining data can refer to the above manner, and the embodiment will not be described here.

[0127] Step S1028: According to the target average riding distance data, the target average riding time data, the target average moving proportion data, the target average riding time, the target average riding distance and the target average moving time proportion, the driving health degree of the current riding vehicle is predicted by a driving health degree prediction algorithm.

[0128] The driving health degree prediction algorithm is:

[0129]

[0130] In the formula, k is the driving health degree; s is the target average riding distance; s i is the target average riding distance of the i th vehicle in the target average riding distance data; t is the target average riding time; t i is the target average riding time of the i th vehicle in the target average riding time data; y is the target average moving time proportion; y i is the target average moving proportion of the i th vehicle in the target average moving proportion data; a, b and c are proportional coefficients, and the sum of a, b and c is 1; M is a dynamic parameter.

[0131] It can be understood that M is a natural number greater than 0, which can be set according to specific scenes, and the embodiment will not be limited here.

[0132] Further, in order to accurately select the required data from the historical riding data, thereby improving the accuracy of the driving health degree calculation, the method further comprises: determining the region frequency filtering interval corresponding to the current riding vehicle according to the use frequency and the frequency adjustment factor corresponding to the delineated riding region, comprising: searching for a target frequency value in a preset mapping relationship table according to the delineated riding region; obtaining weather information and competitive product price information within a preset historical time length in the delineated riding region; correcting the target frequency value according to the weather information and the competitive product price information to obtain a frequency adjustment factor; determining a frequency lower limit value and a frequency upper limit value according to the use frequency and the frequency adjustment factor; and determining the region frequency filtering interval corresponding to the current riding vehicle according to the frequency lower limit value and the frequency upper limit value.

[0133] It can be understood that the preset mapping relationship can be a data table preset to store the corresponding relationship between the frequency value and the demarcated riding area; the weather information and the competitor price information affect the use frequency of the vehicle, for example: according to the weather information, it is determined that the proportion of rain in the preset historical time length is 50%, which will cause the use frequency of the vehicle to decrease, so the target frequency value can be increased, and according to the competitor price information, it is determined that there is a discount for taxi or online car-hailing, and the target frequency value can be increased accordingly; the use frequency minus the frequency adjustment factor is used as the lower limit value of the frequency, and the use frequency plus the frequency adjustment factor is used as the upper limit value of the frequency.

[0134] In an implementation manner, a target frequency value corresponding to the demarcated riding area is found from a preset mapping relationship table, a correction factor is found in a correction mapping relationship table according to weather information and competitor price information in a preset historical time length, the target frequency value and the correction factor are multiplied as a frequency adjustment factor, the target frequency value minus the frequency adjustment factor is used as a lower limit value of the frequency, and the target frequency value plus the frequency adjustment factor is used as an upper limit value of the frequency.

[0135] According to the target average riding distance data, the target average riding time data, the target average moving proportion data, the target average riding time, the target average riding distance and the target average moving time proportion, the driving health degree of the current riding vehicle is predicted by a driving health degree prediction algorithm, and the calculation accuracy of the driving health degree of the vehicle is improved.

[0136] In addition, the embodiment of the present application also provides a storage medium, and the storage medium stores a management program of an intelligent shared electric bicycle. When the management program of the intelligent shared electric bicycle is executed by a processor, the steps of the management method of the intelligent shared electric bicycle are realized.

[0137] Referring to Figure 5 , Figure 5 FIG. 1 is a structural block diagram of a management device for an intelligent shared electric bicycle according to an embodiment of the present application.

[0138] As Figure 5 shown, the management device for the intelligent shared electric bicycle according to the embodiment of the present application comprises:

[0139] The judgment module 10 is configured to judge whether the current riding vehicle is a Bluetooth fault vehicle when the current riding vehicle has a lock abnormality.

[0140] The acquisition module 20 is configured to acquire position information of other vehicles within a preset range of the current riding vehicle if the current riding vehicle is a Bluetooth fault vehicle.

[0141] The selecting module 30 is configured to determine a plurality of candidate vehicles according to the other vehicle position information, select a target vehicle from the plurality of candidate vehicles, and control the target vehicle to send the vehicle searching guide information;

[0142] The guiding module 40 is configured to guide the user to move to the target vehicle through the vehicle searching guide information, and receive the riding order of the current riding vehicle uploaded by the target vehicle, wherein the target vehicle establishes a Bluetooth connection with the terminal device of the user, and the terminal device sends the riding order to the target vehicle through the Bluetooth connection in response to the vehicle returning instruction triggered by the user.

[0143] The analyzing module 50 is configured to analyze a vehicle identifier from the riding order, and send a vehicle locking instruction to the current riding vehicle according to the vehicle identifier, so as to control the current riding vehicle to be locked.

[0144] Based on the first embodiment of the management device for the intelligent shared electric bicycle, the second embodiment of the management device for the intelligent shared electric bicycle is provided.

[0145] In this embodiment, the judging module 10 is further configured to, when the current riding vehicle of the user has a lock abnormality, acquire a vehicle driving feature of the current riding vehicle within a preset time length, predict a driving health degree of the current riding vehicle according to the vehicle driving feature through a driving health prediction algorithm, acquire a lock opening time feature of the current riding vehicle if the driving health degree is greater than a preset health degree, determine a Bluetooth health degree of the current riding vehicle according to the lock opening time feature through a Bluetooth abnormality detection algorithm, and determine that the current riding vehicle is a Bluetooth fault vehicle if the Bluetooth health degree is less than a preset Bluetooth health degree.

[0146] The judgment module 10 is also used for acquiring a use frequency of the current riding vehicle and historical riding data of each vehicle in the demarcated riding area; determining a regional frequency screening interval corresponding to the current riding vehicle according to the use frequency and a frequency adjustment factor corresponding to the demarcated riding area; performing data cleaning on the historical riding data according to the regional frequency screening interval to obtain target historical riding data; performing clustering processing on the target historical riding data to obtain historical riding time length data, historical riding frequency data and historical riding distance data; determining historical average riding distance data according to the historical riding frequency data and the historical riding distance data; determining historical average riding time length data according to the historical riding time length data, and determining historical moving proportion data according to the historical riding time length data; performing normalization processing on the historical average riding distance data, the historical average riding time length data, the historical moving proportion data, single average riding time length, single average riding distance and moving time length proportion to obtain target average riding distance data, target average riding time length data, target average moving proportion data, target average riding time length, target average riding distance and target average moving time length proportion; and predicting a driving health degree of the current riding vehicle through a driving health degree prediction algorithm according to the target average riding distance data, the target average riding time length data, the target average moving proportion data, the target average riding time length, the target average riding distance and the target average moving time length proportion.

[0147] The driving health degree prediction algorithm is as follows:

[0148]

[0149] In the formula, k represents the driving health degree; s represents the target average riding distance; s i represents the target average riding distance of the i th vehicle in the target average riding distance data; t represents the target average riding time length; t i represents the target average riding time length of the i th vehicle in the target average riding time length data; y represents the target average moving time length proportion; y i represents the target average moving proportion of the i th vehicle in the target average moving proportion data; a, b and c are proportional coefficients, and the sum of a, b and c is 1; M is a dynamic parameter; and the vehicle driving characteristics include single average riding time length, single average riding distance and moving time length proportion.

[0150] The judgment module 10 is also used for finding a target frequency value in a preset mapping relationship table according to the demarcated cycling area; obtaining weather information and competitor price information in a preset historical time length in the demarcated cycling area; correcting the target frequency value according to the weather information and the competitor price information to obtain a frequency adjustment factor; determining a frequency lower limit value and a frequency upper limit value according to the use frequency and the frequency adjustment factor; and determining a regional frequency screening interval corresponding to the current cycling vehicle according to the frequency lower limit value and the frequency upper limit value.

[0151] The judgment module 10 is also used for obtaining historical unlocking time length data of the current cycling vehicle in a preset time length; sorting historical unlocking time lengths in the historical unlocking time length data in time sequence to obtain a historical unlocking time length sequence; mapping the historical unlocking time length sequence into sequence points in a target coordinate system; fitting the sequence points by using a linear fitting algorithm to obtain an initial Bluetooth abnormality detection algorithm; and correcting the initial Bluetooth abnormality detection algorithm according to a correction coefficient to obtain a Bluetooth abnormality detection algorithm.

[0152] The selection module 30 is also used for determining vehicle distances according to other vehicle position information and position information of the current cycling vehicle, and determining a plurality of candidate vehicles according to the vehicle distances; obtaining historical adjacent unlocking time features corresponding to each candidate vehicle, and determining Bluetooth health degrees of the candidate vehicles according to the historical adjacent unlocking time features; determining a target vehicle according to the Bluetooth health degrees of the candidate vehicles, and controlling the target vehicle to send a car searching guide information.

[0153] The selection module 30 is also used for parsing a vehicle identifier from the cycling vehicle, and determining a current vehicle position according to the vehicle identifier; when the current vehicle position is in a parking area, sending a car locking instruction to the current cycling vehicle to control the current cycling vehicle to lock, and adding a Bluetooth fault label to the current cycling vehicle.

[0154] Other embodiments or specific implementations of the management device of the intelligent shared electric bicycle can refer to the above-mentioned method embodiments, which will not be described here.

[0155] It should be noted that in this document, the terms "comprising", "including", or any other variant thereof are intended to cover non-exclusive inclusion, so that processes, methods, articles, or systems that include a series of elements not only include those elements, but also include other elements not explicitly listed, or inherent to such processes, methods, articles, or systems. Without more limitations, the element defined by the statement "including a" does not exclude the presence of another identical element in the process, method, article, or system including the element.

[0156] The above-mentioned embodiment numbers of the present application are only for description, and do not represent the advantages and disadvantages of the embodiments.

[0157] Through the above description of the embodiments, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be realized by means of software and the necessary general hardware platform, of course, they can also be realized by hardware, but in many cases the former is a better embodiment. Based on such understanding, the technical solutions of the present application can be embodied in the form of a software product, which is stored in a storage medium (such as a read-only memory / random access memory, a magnetic disk, or an optical disk), and includes a plurality of instructions for causing an end device (which can be a mobile phone, a computer, a server, an air conditioner, or a network device, etc.) to execute the methods described in the various embodiments of the present application.

[0158] The above is only the preferred embodiment of the present application, and does not limit the patent scope of the present application, and any equivalent structure or equivalent flow transformation made by using the content of the specification and drawings, or direct or indirect application in other related technical fields, are also included in the patent protection scope of the present application.

Claims

1. A management method for intelligent shared electric bicycles, characterized in that, The method includes: When the user's currently ridden vehicle experiences a locking malfunction, determine whether the currently ridden vehicle is a Bluetooth malfunction vehicle. If the currently ridden vehicle is a Bluetooth malfunctioning vehicle, then obtain the location information of other vehicles within a preset range of the currently ridden vehicle; Based on the location information of other vehicles, a number of candidate vehicles are determined, a target vehicle is selected from the number of candidate vehicles, and the target vehicle is controlled to issue a vehicle-finding guidance message. The vehicle-finding guidance information guides the user to the target vehicle, and the target vehicle uploads the current riding order for the vehicle being ridden. The target vehicle establishes a Bluetooth connection with the user's terminal device, and the terminal device, in response to the user's vehicle return instruction, sends the riding order to the target vehicle through the Bluetooth connection. The vehicle identifier is parsed from the riding order, and a locking command is sent to the currently riding vehicle based on the vehicle identifier to control the currently riding vehicle to lock. When a user's currently ridden vehicle experiences a locking malfunction, determining whether the currently ridden vehicle is a Bluetooth malfunction vehicle includes: When the user's current vehicle experiences a locking malfunction, the vehicle's driving characteristics within a preset time period are obtained. The driving health of the currently ridden vehicle is predicted using a driving health prediction algorithm based on the vehicle's driving characteristics. If the driving health score is greater than the preset health score, then the unlocking time feature of the currently ridden vehicle is obtained; The Bluetooth health of the currently ridden vehicle is determined using a Bluetooth anomaly detection algorithm based on the unlocking time characteristics. If the Bluetooth health score is lower than the preset Bluetooth health score, the currently ridden vehicle is determined to be a Bluetooth malfunctioning vehicle.

2. The method as described in claim 1, characterized in that, The vehicle driving characteristics include average riding time per trip, average riding distance per trip, and percentage of travel time. The step of predicting the driving health of the currently ridden vehicle based on the vehicle's driving characteristics using a driving health prediction algorithm includes: Obtain the current usage frequency of the riding vehicle and the historical riding data of each vehicle within the defined riding area; The frequency filtering interval for the current riding vehicle is determined based on the usage frequency and the frequency adjustment factor corresponding to the defined riding area. The historical cycling data is cleaned according to the regional frequency filtering interval to obtain the target historical cycling data. Clustering is performed on the target historical cycling data to obtain historical cycling duration data, historical cycling frequency data, and historical cycling distance data; The historical average cycling distance data is determined based on the historical number of rides and the historical cycling distance data. Based on the historical riding time data, determine the historical average riding time data, and based on the historical riding time data, determine the historical movement percentage data. The historical average cycling distance data, historical average cycling time data, historical movement percentage data, single average cycling time, single average cycling distance and movement time percentage are normalized to obtain target average cycling distance data, target average cycling time data, target average movement percentage data, target average cycling time, target average cycling distance and target average movement time percentage. Based on the target average riding distance data, the target average riding time data, the target average movement percentage data, the target average riding time, the target average riding distance, and the target average movement time percentage, the riding health of the current vehicle is predicted using a riding health prediction algorithm. The driving health prediction algorithm is as follows: In the formula, k represents the riding health level; s represents the target average riding distance. Let be the target average riding distance of the i-th vehicle in the target average riding distance data; t is the target average riding time. y represents the target average riding time of the i-th vehicle in the target average riding time data; y is the percentage of the target average travel time. denoted as the target average movement percentage of the i-th vehicle in the target average movement percentage data; a, b, and c are proportionality coefficients, and the sum of a, b, and c is 1; M is a dynamic parameter.

3. The method as described in claim 2, characterized in that, The step of determining the regional frequency filtering interval corresponding to the current riding vehicle based on the usage frequency and the frequency adjustment factor corresponding to the defined riding area includes: The target frequency value is found in the preset mapping table according to the defined cycling area; Obtain weather information and competitor price information for a preset historical duration within the designated cycling area; The target frequency value is corrected based on the weather information and the competitor's price information to obtain a frequency adjustment factor; The lower frequency limit and the upper frequency limit are determined based on the usage frequency and the frequency adjustment factor. The frequency filtering interval corresponding to the currently ridden vehicle is determined based on the lower frequency limit and the upper frequency limit.

4. The method as described in claim 1, characterized in that, Before determining the Bluetooth health of the currently ridden vehicle using the Bluetooth anomaly detection algorithm based on the unlocking time characteristics, the process includes: Obtain the historical unlock duration data of the currently ridden vehicle within a preset time period; Sort the historical unlocking durations in the historical unlocking duration data according to time order to obtain the historical unlocking duration sequence; Map the historical unlock duration sequence to sequence points in the target coordinate system; The sequence points are fitted using a linear fitting algorithm to obtain an initial Bluetooth anomaly detection algorithm; The initial Bluetooth anomaly detection algorithm is modified according to the correction factor to obtain the Bluetooth anomaly detection algorithm.

5. The method according to any one of claims 1-4, characterized in that, The step of determining a number of candidate vehicles based on the location information of other vehicles, selecting a target vehicle from the number of candidate vehicles, and controlling the target vehicle to issue vehicle-finding guidance information includes: The vehicle distance is determined based on the location information of other vehicles and the location information of the currently ridden vehicle, and several candidate vehicles are determined based on the vehicle distance; Obtain the historical adjacent unlock time characteristics of each candidate vehicle, and determine the Bluetooth health of each candidate vehicle based on the historical adjacent unlock time characteristics. The target vehicle is determined based on the Bluetooth health status of each candidate vehicle, and the target vehicle is controlled to issue a vehicle-finding guidance message.

6. The method according to any one of claims 1-3, characterized in that, The step of parsing the vehicle identifier from the riding order and sending a locking command to the currently ridden vehicle based on the vehicle identifier to control the locking of the currently ridden vehicle includes: The vehicle identifier is parsed from the riding vehicle, and the current vehicle position is determined based on the vehicle identifier; When the current vehicle is in a parking area, a lock command is sent to the currently ridden vehicle to control the current riding vehicle to lock, and a Bluetooth fault tag is added to the current riding vehicle.

7. A management device for intelligent shared electric bicycles, characterized in that, The device includes: The judgment module is used to determine whether the currently ridden vehicle is a Bluetooth malfunctioning vehicle when the user's currently ridden vehicle experiences a locking abnormality. The acquisition module is used to acquire the location information of other vehicles within a preset range of the currently ridden vehicle if the currently ridden vehicle is a Bluetooth malfunctioning vehicle. The selection module is used to determine a number of candidate vehicles based on the location information of the other vehicles, select a target vehicle from the number of candidate vehicles, and control the target vehicle to issue vehicle search guidance information. The guidance module is used to guide the user to the target vehicle through the vehicle search guidance information, and to receive the riding order of the currently ridden vehicle uploaded by the target vehicle. The target vehicle establishes a Bluetooth connection with the user's terminal device, and the terminal device sends the riding order to the target vehicle through the Bluetooth connection in response to the user's vehicle return instruction. The parsing module is used to parse the vehicle identifier from the riding order and send a locking command to the currently riding vehicle based on the vehicle identifier to control the currently riding vehicle to lock. When a user's currently ridden vehicle experiences a locking malfunction, determining whether the currently ridden vehicle is a Bluetooth malfunction vehicle includes: When the user's current vehicle experiences a locking malfunction, the vehicle's driving characteristics within a preset time period are obtained. The driving health of the currently ridden vehicle is predicted using a driving health prediction algorithm based on the vehicle's driving characteristics. If the driving health score is greater than the preset health score, then the unlocking time feature of the currently ridden vehicle is obtained; The Bluetooth health of the currently ridden vehicle is determined using a Bluetooth anomaly detection algorithm based on the unlocking time characteristics. If the Bluetooth health score is lower than the preset Bluetooth health score, the currently ridden vehicle is determined to be a Bluetooth malfunctioning vehicle.

8. A management device for intelligent shared electric bicycles, characterized in that, The device includes: a memory, a processor, and a management program for intelligent shared electric bicycles stored in the memory and executable on the processor, the management program for intelligent shared electric bicycles being configured to implement the steps of the management method for intelligent shared electric bicycles as described in any one of claims 1 to 6.

9. A storage medium, characterized in that, The storage medium stores a management program for intelligent shared electric bicycles. When the management program for intelligent shared electric bicycles is executed by a processor, it implements the steps of the management method for intelligent shared electric bicycles as described in any one of claims 1 to 6.

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