CN116778717A - Fault identification method and server for shared vehicle - Google Patents
Fault identification method and server for shared vehicle Download PDFInfo
- Publication number
- CN116778717A CN116778717A CN202311002056.6A CN202311002056A CN116778717A CN 116778717 A CN116778717 A CN 116778717A CN 202311002056 A CN202311002056 A CN 202311002056A CN 116778717 A CN116778717 A CN 116778717A
- Authority
- CN
- China
- Prior art keywords
- vehicle
- probability
- ridden
- target
- riding
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 238000000034 method Methods 0.000 title claims abstract description 30
- 238000001514 detection method Methods 0.000 claims abstract description 68
- 238000012423 maintenance Methods 0.000 claims abstract description 11
- 230000006399 behavior Effects 0.000 claims description 22
- 238000004364 calculation method Methods 0.000 claims description 13
- 238000013507 mapping Methods 0.000 claims description 7
- 230000008901 benefit Effects 0.000 claims description 5
- 238000012790 confirmation Methods 0.000 claims description 5
- 238000010586 diagram Methods 0.000 description 4
- 230000003203 everyday effect Effects 0.000 description 2
- 230000007257 malfunction Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
Classifications
-
- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
- G08G1/0125—Traffic data processing
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/10—Complex mathematical operations
- G06F17/18—Complex mathematical operations for evaluating statistical data, e.g. average values, frequency distributions, probability functions, regression analysis
-
- G—PHYSICS
- G07—CHECKING-DEVICES
- G07F—COIN-FREED OR LIKE APPARATUS
- G07F17/00—Coin-freed apparatus for hiring articles; Coin-freed facilities or services
- G07F17/0042—Coin-freed apparatus for hiring articles; Coin-freed facilities or services for hiring of objects
- G07F17/0057—Coin-freed apparatus for hiring articles; Coin-freed facilities or services for hiring of objects for the hiring or rent of vehicles, e.g. cars, bicycles or wheelchairs
-
- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
- G08G1/0137—Measuring and analyzing of parameters relative to traffic conditions for specific applications
-
- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/065—Traffic control systems for road vehicles by counting the vehicles in a section of the road or in a parking area, i.e. comparing incoming count with outgoing count
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L67/00—Network arrangements or protocols for supporting network services or applications
- H04L67/01—Protocols
- H04L67/12—Protocols specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks
Landscapes
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Engineering & Computer Science (AREA)
- Data Mining & Analysis (AREA)
- Mathematical Optimization (AREA)
- Analytical Chemistry (AREA)
- Theoretical Computer Science (AREA)
- Mathematical Physics (AREA)
- Chemical & Material Sciences (AREA)
- Computational Mathematics (AREA)
- Pure & Applied Mathematics (AREA)
- Mathematical Analysis (AREA)
- Bioinformatics & Computational Biology (AREA)
- Life Sciences & Earth Sciences (AREA)
- Probability & Statistics with Applications (AREA)
- Evolutionary Biology (AREA)
- Algebra (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Databases & Information Systems (AREA)
- Software Systems (AREA)
- General Engineering & Computer Science (AREA)
- Operations Research (AREA)
- Health & Medical Sciences (AREA)
- Computing Systems (AREA)
- General Health & Medical Sciences (AREA)
- Medical Informatics (AREA)
- Computer Networks & Wireless Communication (AREA)
- Signal Processing (AREA)
- Traffic Control Systems (AREA)
Abstract
The invention provides a fault identification method and a server of a shared vehicle, wherein the fault identification method comprises the following steps: acquiring an initial vehicle number of a target area in a detection period at a preset time interval; acquiring the inflow number and outflow number of the vehicle in a preset time interval; calculating the non-riding probability of the target vehicle according to the initial vehicle number, the vehicle inflow number and the vehicle outflow number; acquiring the initial non-ridden probability of the target vehicle; and calculating the final non-ridden probability of the detection period according to the initial non-ridden probability and the multiple non-ridden probabilities in the detection period. The method comprises the steps that through calculating the non-ridden probability of a target vehicle, when the non-ridden probability is larger than a preset value, the target vehicle is judged to be a non-fault vehicle; and when the probability of not being rided is not greater than a preset value, judging that the target vehicle is a fault vehicle. Therefore, the system can be utilized to automatically identify the faulty vehicle, an Paiyun maintenance personnel are not required to patrol, and the operation cost is reduced while the faulty vehicle is identified.
Description
Technical Field
The invention relates to the technical field of shared vehicles, in particular to a fault identification method and a server for a shared vehicle.
Background
The operators of the shared vehicles put the shared vehicles in towns for short-term rentals by users. The user may be scattered in various locations of the town during the use of the shared vehicle. At this time, if the shared vehicle is damaged or otherwise rendered unusable by the user, the operator cannot discover such a faulty vehicle in time. If the operations Shang An are followed by maintenance personnel, a significant amount of costs are added. Accordingly, there is a need to provide a method that automatically identifies a faulty vehicle and that timely processes and puts the faulty vehicle into service.
Disclosure of Invention
The invention aims to provide a fault identification method and a server for a shared vehicle, which are used for solving the problem that a current operator cannot find a fault vehicle in time.
In one aspect, a fault identification method for a shared vehicle is provided, including: acquiring an initial vehicle number of a target area in a detection period at a preset time interval; acquiring the inflow number of vehicles flowing into the target area and the outflow number of vehicles flowing out of the target area in the preset time interval; calculating the non-ridden probability of the target vehicle in the target area at the preset time interval according to the initial vehicle number, the vehicle inflow number and the vehicle outflow number; acquiring the initial non-ridden probability of the target vehicle of the detection period; calculating the final non-ridden probability of the detection period according to the initial non-ridden probability and a plurality of non-ridden probabilities in the detection period; if the final non-ridden probability is not greater than a preset value, judging that the target vehicle is a fault vehicle; if the period end non-ridden probability is larger than a preset value, judging that the target vehicle is a non-fault vehicle, and setting the period end non-ridden probability as the period initial non-ridden probability of the next detection period; if the target vehicle performs riding behavior in the detection period, setting the final non-riding probability to be 1; the non-riding probability is calculated as follows: p=1-LC/(a+lr); wherein P is the probability of not being ridden, A is the initial number of vehicles, LC is the number of vehicle outflow, LR is the number of vehicle inflow; the calculation mode of the final non-ridden probability is as follows: pz=ps× (p1×p2× … … ×pn); wherein PZ is the final non-ridden probability, PS is the initial non-ridden probability, and P1, P2, … …, PN are the non-ridden probabilities of different predetermined time intervals.
The method comprises the steps that through calculating the non-ridden probability of a target vehicle, when the non-ridden probability is larger than a preset value, the target vehicle is judged to be a non-fault vehicle; and when the probability of not being rided is not greater than a preset value, judging that the target vehicle is a fault vehicle. Therefore, the system can be utilized to automatically identify the faulty vehicle, an Paiyun maintenance personnel are not required to patrol, and the operation cost is reduced while the faulty vehicle is identified.
Optionally, the initial number of vehicles does not include an actual faulty vehicle and a low electric vehicle, and the number of vehicle inflow does not include the low electric vehicle; the actual fault vehicle is a shared vehicle for reporting faults to a user, and the low-voltage vehicle is a shared vehicle which is unavailable to the user because the electric quantity is lower than a low-voltage threshold value.
In another aspect, a fault identification method for a shared vehicle is provided, including: when the shared vehicles in the target area naturally flow out, acquiring inventory vehicles in the target area; calculating the non-ridden probability of the target vehicle in the target area according to the stock vehicle; acquiring the initial non-ridden probability of the target vehicle; calculating the final non-ridden probability of the target vehicle according to the initial non-ridden probability and the non-ridden probability; if the final non-ridden probability is not greater than a preset value, judging that the target vehicle is a fault vehicle; if the final non-ridden probability is larger than a preset value, judging that the target vehicle is a non-fault vehicle, and setting the final non-ridden probability as the initial non-ridden probability of the next time; and if the target vehicle generates riding behaviors, setting the final non-ridden probability to be 1.
Optionally, the calculating the non-rided probability of the target vehicle in the target area according to the stock vehicle includes: acquiring an actual electric quantity value corresponding to the stock vehicle; acquiring a first mapping relation between the electric quantity value of the shared vehicle and the riding probability, and acquiring the actual riding probability corresponding to the actual electric quantity value according to the first mapping relation; calculating the non-ridden probability of the target vehicle in the target area according to the stock vehicle and the corresponding actual riding probability; the non-riding probability is calculated as follows: p=1-QM/(q1+q2+ … … +qn); wherein P is the probability of not being ridden, Q1, Q2, … …, QN are the actual probabilities of riding corresponding to the stock vehicle, QM is the actual probabilities of riding corresponding to the target vehicle; the calculation mode of the final non-ridden probability is as follows: pz=ps×p; wherein PZ is the final non-ridden probability, PS is the initial non-ridden probability, and P is the non-ridden probability.
Optionally, after the determining that the target vehicle is the faulty vehicle if the final non-ridden probability is not greater than the preset value, the method further includes: acquiring asset loss and riding loss of the target vehicle; the asset loss refers to an expected loss value of the target vehicle loss, and the riding loss refers to an expected loss value of no benefit of the target vehicle due to inability to ride; acquiring labor cost for confirming a vehicle condition of the target vehicle; determining whether a vehicle condition of the target vehicle needs to be confirmed according to the asset loss, the riding loss and the labor cost; if the vehicle condition needs to be confirmed, pushing the target vehicle to an operation and maintenance person; setting the terminal non-ridden probability to 1 after the vehicle condition confirmation; if the vehicle condition does not need to be confirmed, the final non-ridden probability is set as the initial non-ridden probability of the next detection period.
Optionally, obtaining the asset loss includes: acquiring a risk coefficient of the shared vehicle according to the target area; acquiring the non-ridden duration and the vehicle value of the target vehicle; acquiring a general loss probability of the target vehicle according to the non-ridden time length; calculating the asset loss based on the risk factor, the general loss probability, and the vehicle value; obtaining the riding loss includes: acquiring the average riding unit price and the average riding times of the shared vehicle in the target area; and calculating the riding loss according to the average riding unit price and the average riding times.
Optionally, the target area refers to a set of a plurality of adjacent parking spots; the parking spots are within a preset diameter range, or the parking spots are within the same Geohash grid range.
Optionally, a second mapping relationship between the riding duration of the shared vehicle and the vehicle condition is obtained, and the target vehicle is ridden, which means that the riding duration of the target vehicle is at least more than a preset duration.
In another aspect, there is provided a server for sharing a vehicle, including: an acquisition unit configured to acquire an initial number of vehicles of a target area in a detection period at a predetermined time interval; the acquisition unit is further configured to acquire a vehicle inflow number flowing into the target area and a vehicle outflow number flowing out of the target area within the predetermined time interval; a calculation unit configured to calculate an unswept probability of a target vehicle in the target region at the predetermined time interval based on the initial vehicle number, the vehicle inflow number, and the vehicle outflow number; the acquisition unit is further used for acquiring the initial non-ridden probability of the target vehicle in the detection period; the calculating unit is further used for calculating the final non-ridden probability of the detection period according to the initial non-ridden probability and a plurality of non-ridden probabilities in the detection period; a determination unit that determines that the target vehicle is a faulty vehicle if the terminal non-ridden probability is not greater than a preset value; if the final non-ridden probability is greater than a preset value, the judging unit judges that the target vehicle is a non-fault vehicle, and the judging unit also sets the final non-ridden probability as the initial non-ridden probability of the next detection period; if the target vehicle performs riding behavior in the detection period, the judging unit sets the final non-riding probability to be 1; the non-riding probability is calculated as follows: p=1-LC/(a+lr); wherein P is the probability of not being ridden, A is the initial number of vehicles, LC is the number of vehicle outflow, LR is the number of vehicle inflow; the calculation mode of the final non-ridden probability is as follows: pz=ps× (p1×p2× … … ×pn); wherein PZ is the final non-ridden probability, PS is the initial non-ridden probability, and P1, P2, … …, PN are the non-ridden probabilities of different predetermined time intervals.
In another aspect, there is provided a server for sharing a vehicle, including: the acquisition unit is used for acquiring inventory vehicles of a target area when the shared vehicles of the target area naturally flow out; a calculation unit for calculating a non-ridden probability of a target vehicle of the target area from the stock vehicle; the acquisition unit is also used for acquiring the initial non-ridden probability of the target vehicle; the calculating unit is further used for calculating the final non-ridden probability of the target vehicle according to the initial non-ridden probability and the non-ridden probability; a determination unit that determines that the target vehicle is a faulty vehicle if the terminal non-ridden probability is not greater than a preset value; if the final non-ridden probability is greater than a preset value, the judging unit judges that the target vehicle is a non-fault vehicle, and the judging unit also sets the final non-ridden probability as the initial non-ridden probability of the next time; if the target vehicle experiences a riding behavior, the determination unit sets the terminal non-ridden probability to 1.
Drawings
Fig. 1 is a flowchart illustrating a fault recognition method of a shared vehicle according to a first embodiment of the present invention.
Fig. 2 is a schematic diagram showing a relationship between a riding time period and a failure probability of a shared vehicle according to a first embodiment of the present invention.
Fig. 3 is a schematic view of a first embodiment of the present invention sharing a target area of a portion of an operating fence of a vehicle.
Fig. 4 is a schematic block diagram of a fault recognition method of a shared vehicle according to a first embodiment of the present invention.
Fig. 5 is a flowchart illustrating a fault recognition method of a shared vehicle according to a second embodiment of the present invention.
Fig. 6 is a schematic diagram showing a relationship between a power value and a riding probability of a shared vehicle according to a second embodiment of the present invention.
Detailed Description
In order that the above objects, features and advantages of the invention will be readily understood, a more particular description of the invention will be rendered by reference to specific embodiments thereof which are illustrated in the appended drawings.
The shared vehicle is inevitably damaged or becomes unclean during operation. Because shared vehicles are commonly exposed to outdoor environments, they are subjected to more severe environments than household electric bicycles and bicycles, and thus are also more susceptible to damage or becoming unclean. When the shared vehicle is damaged or becomes unclean, the user will not select the shared vehicle, and the shared vehicle cannot bring convenience to the user or bring benefit to the operator. Therefore, it is necessary to find such a shared vehicle in time and repair or clean it so that it can meet the travel demands of the user. Here, a shared vehicle that a user cannot ride due to damage and uncleanness is collectively referred to as a faulty vehicle, and how to quickly identify the faulty vehicle is a problem that is currently in need of solving.
First embodiment.
Referring to fig. 1, the present embodiment provides a fault recognition method of a shared vehicle.
S101, acquiring the initial number of vehicles in a preset time interval of a target area in a detection period, and acquiring the number of vehicles flowing into the target area and the number of vehicles flowing out of the target area in the preset time interval.
The detection period includes a plurality of predetermined time intervals. For example, the detection period is 1 day and the predetermined time interval is 1 hour, then the detection period includes 24 predetermined time intervals. The detection period may also be 1 week, the predetermined time interval being 1 day, then the detection period comprises 7 predetermined time intervals.
The target area may be a set of a plurality of adjacent parking spots, for example, all parking spots within a plurality of preset diameter ranges are set as one set as the target area. The size of the predetermined diameter is generally set between 20 meters and 50 meters. This is because when no shared vehicles are available at the closest parking spot to the user, the user is generally willing to go to other parking spots within this range to use the vehicle. However, once this range is exceeded, the user is no longer willing to take more time to go to an out-of-range parking spot to select a shared vehicle. That is, the shared vehicles in the target area are of an alternative nature, and the user in the target area can select; the shared vehicles outside the target area and the shared vehicles inside the target area are of non-replaceable nature, and the user does not go to use the shared vehicles outside the target area when no shared vehicles are available inside the target area.
In addition, a plurality of parking points within the same Geohash grid range may be set as a set as the target area. Geohash is an algorithm that converts geographic coordinates into a comparable string, and Geohash grids represent a rectangular or polygonal geographic area of an actual geographic location.
In addition, the target area may be just one parking spot.
In the present embodiment, the vehicle inflow number refers to the number of shared vehicles naturally flowing into and scheduled to flow into the target area, and the vehicle outflow number refers to the number of shared vehicles naturally flowing out of the target area. That is, the shared vehicle in which the user naturally rides the inflow target region and the shared vehicle in which the operation and maintenance are passively scheduled to flow into the target region both belong to the vehicle inflow number; only shared vehicles naturally flowing out of the target area belong to the vehicle outflow number.
S102, calculating the non-riding probability of the target vehicle in the target area at a preset time interval according to the initial vehicle number, the vehicle inflow number and the vehicle outflow number.
From the initial number of vehicles and the number of inflow vehicles, the total number of vehicles in the target area within a predetermined time interval can be calculated. From the vehicle outflow number and the total vehicle number, the riding probability and the non-riding probability of each vehicle in the target area during a predetermined time interval can be calculated.
Specifically, the riding probability of the target vehicle is calculated as follows: m=lc/(a+lr); the non-riding probability of the target vehicle is calculated as follows: p=1-m=1-LC/(a+lr); where P is the probability of not being rided, A is the initial number of vehicles, LC is the number of vehicle outflow, and LR is the number of vehicle inflow.
S103, acquiring the initial non-ridden probability of the target vehicle in the detection period.
The server also obtains a period-initial-non-ridden probability of the target vehicle at the beginning of the detection period.
Specifically, if the target vehicle does not have a riding behavior in the previous detection period, the initial non-riding probability of the target vehicle is the final non-riding probability of the target vehicle at the end of the previous detection period. If the target vehicle has a riding behavior in the last detection period, the initial non-riding probability of the target vehicle is 1.
S104, calculating the final non-ridden probability of the detection period according to the initial non-ridden probability and the multiple non-ridden probabilities in the detection period.
By detecting each predetermined time interval within a cycle, a probability of not being rided may be calculated. Since there are a plurality of predetermined time intervals within the detection period, a plurality of non-ridden probabilities can be calculated. If the target vehicle does not have riding behavior in the detection period, the probability of the target vehicle not being rided in the detection period is the product of a plurality of probabilities of not being rided. The final non-ridden probability of the target vehicle is a product of the initial non-ridden probability of the target vehicle and the non-ridden probability of the target vehicle in the detection period. If the target vehicle experiences a riding behavior during the detection period, the target vehicle's terminal non-ridden probability is 1.
Specifically, the calculation method of the final non-riding probability of the target vehicle is as follows: pz=ps×pt=ps× (p1×p2× … … ×pn); wherein PZ is the final non-ridden probability of the target vehicle, PS is the initial non-ridden probability of the target vehicle, PT is the non-ridden probability of the target vehicle in the detection period, and P1, P2, … … and PN are the non-ridden probabilities of different preset time intervals. If the target vehicle has a riding behavior in the previous detection period, the initial period of the target vehicle is not rided with the riding probability of 1. If the target vehicle has a riding behavior in the detection period, the final non-ridden probability of the target vehicle is 1.
The target vehicle riding behavior means that the length of the target vehicle being rided is at least longer than a preset length of time. For example, a ride period of less than 1 minute is considered to be an ineffective ride, and although the shared vehicle is being ridden, there is still a possibility of malfunction.
Specifically, a reasonable riding time period can be set according to the historical riding behavior. Referring to fig. 2, a map between the riding time period and the vehicle condition (vehicle failure probability) is shown. When the riding time is between 0s and 60s, the probability of the vehicle failure according to the historical data is more than 50 percent, and when the riding time is more than 60s, the probability of the vehicle failure according to the historical data is less than 50 percent. Thus, the riding time period may be set to 1 minute.
S105, if the final non-ridden probability is not greater than a preset value, judging that the target vehicle is a fault vehicle; if the period end non-ridden probability is larger than a preset value, judging that the target vehicle is a non-fault vehicle, and setting the period end non-ridden probability as the period initial non-ridden probability of the next detection period; if the target vehicle is ridden in the detection period, the probability of the target vehicle not being ridden at the end of the period is set to be 1.
The user selects the behavior of the shared vehicle randomly, if the shared vehicle is a fault-free available vehicle, the shared vehicle is selected greatly in a longer period of time, and the probability that the target vehicle is not rided at the end of the period is not too small. If the shared vehicle is a faulty vehicle, the user actively bypasses the faulty vehicle when selecting the shared vehicle, which results in a smaller probability that the target vehicle will not be rided at the end of the journey until the set value is exceeded.
Therefore, the present embodiment can determine whether the shared vehicle is a faulty vehicle by the probability of not being ridden at the end. Specifically, after the detection period is finished, calculating to obtain the probability that the target vehicle is not ridden at the end of the period, and if the probability that the target vehicle is not ridden at the end of the period is not greater than a preset value, judging that the target vehicle is a fault vehicle. If the final non-ridden probability is greater than the preset value, the target vehicle is judged to be a non-fault vehicle, and the final non-ridden probability is set as the initial non-ridden probability of the next detection period. If the target vehicle is riding in the detection period, the target vehicle is confirmed to be a non-fault vehicle, and the final non-riding probability of the target vehicle is set to be 1.
The preset value can be configured according to different operation fences. Specifically, the set of minimum non-ridden probabilities of the plurality of fault-free available shared vehicles may be determined by historical ride data, and the preset value may be set according to the minimum non-ridden probabilities of the plurality of fault-free available shared vehicles. For example: the minimum set of non-ridden probabilities for multiple non-faulty available shared vehicles within the same operating fence is 0.005, 0.003, 0.002, 0.007, 0.008, … …, then the preset value can be set to 0.001. When the target vehicle's terminal non-ridden probability is greater than 0.001, it is indicated that the target vehicle may still be a non-faulty vehicle; when the target vehicle, which is most likely to be a faulty vehicle, is not more than 0.001 in the future, it is indicated that the user intentionally avoided the target vehicle when selecting the shared vehicle.
The method comprises the steps that through calculating the non-ridden probability of a target vehicle, when the non-ridden probability is larger than a preset value, the target vehicle is judged to be a non-fault vehicle; and when the probability of not being rided is not greater than a preset value, judging that the target vehicle is a fault vehicle. Therefore, the system can be utilized to automatically identify the faulty vehicle, an Paiyun maintenance personnel are not required to patrol, and the operation cost is reduced while the faulty vehicle is identified.
The following is a specific case.
Referring to fig. 3, the operation fence may be divided into a plurality of target areas including one parking spot or a plurality of parking spots and a blank area including no parking spot according to the aforementioned rule. Taking the target area as an example, for example, the target area includes 3 parking spots, the detection period is set to 1 day, and the predetermined time interval is set to 1 hour, then the detection period includes 24 predetermined time intervals.
The server obtains the initial vehicle number of the target area in the detection period at a preset time interval, specifically, the initial vehicle number of the target area in the previous day at the whole point time between 0:00 and 23:00 in the early morning every day. The server may also acquire in real time the initial number of vehicles for the target area at a full-time point between 0:00 and 23:00 on the same day. The server obtains the inflow number of the vehicle flowing into the first target area and the outflow number of the vehicle flowing out of the first target area at preset time intervals, specifically, the inflow number of the vehicle and the outflow number of the vehicle flowing out of the first target area at 24 preset time intervals in the previous day can be obtained by the server in the early morning every day; the server may acquire the number of vehicle inflows and the number of vehicle outflows in the target area at 24 predetermined time intervals in real time on the same day.
In particular, a predetermined time interval of 7:00-8:00 is taken as an example. When the server obtains that the number of shared vehicles of 3 parking spots of the target area I is 4, 5 and 6 respectively, the initial number A of the target area I is 15. If the server acquires that the number of inflow vehicles LR into the target area one is 3 and the number of outflow vehicles LC out of the target area one is 5 in this predetermined time interval, the total number of vehicles in the target area one in this predetermined time interval is a+lr=18. Then, the riding probability of the shared vehicle of the target area one at the predetermined time interval is LC/(a+lr) =0.278, and the non-riding probability of the shared vehicle of the target area one at the predetermined time interval is 1-LC/(a+lr) =0.722.
Again, a predetermined time interval of 8:00-9:00 is taken as an example. When the server obtains 8:00, the number of shared vehicles in the target area I is 15+3-5=13, namely the initial number A of vehicles in the target area I is 13. If the server acquires that the number of inflow vehicles LR into the target area one is 7 and the number of outflow vehicles LC out of the target area one is 8, the total number of vehicles in the target area one at the predetermined time interval is a+lr=20. Then, the riding probability of the shared vehicle of the target area one at the predetermined time interval is LC/(a+lr) =0.4, and the non-riding probability of the shared vehicle of the target area one at the predetermined time interval is p=1-LC/(a+lr) =0.6.
Similarly, the probability of the shared vehicle of target zone one not being rided for the other 22 predetermined time intervals may be calculated. And will not be described in detail.
If the shared vehicle experiences a riding behavior within the day, the server sets the probability of the shared vehicle not being rided to 1. If the shared vehicle does not have riding behavior in the same day, the server accumulates the probability of the shared vehicle not being rided in 24 preset time intervals to obtain the probability of the target vehicle not being rided in the detection period PT=P1×P2×P3× … … ×P23×P24. The server also obtains the initial non-riding probability PS of the target vehicle in the previous period, and calculates the final non-riding probability PZ according to the initial non-riding probability PS and the non-riding probability PT in the detection period.
Specifically, if the preset value is 0.001, the non-ridden probability PT is calculated to be 0.004, and the acquisition period initial non-ridden probability PS is 1, then the period final non-ridden probability pz=pt×ps=0.004 > 0.001, and the server determines that the target vehicle is a non-faulty vehicle, and takes 0.004 as the period initial non-ridden probability PS of the next day. If the preset value is 0.001, the probability PT of not being ridden is calculated to be 0.004, the probability PS of not being ridden at the beginning of the acquisition period is 0.05, and then the probability pz=pt×ps=0.0002 < 0.001 of not being ridden at the end of the acquisition period, and the server judges that the target vehicle is a faulty vehicle.
Similarly, the server may calculate the final non-ridden probabilities of the target vehicles in the second, third, and fourth target areas, and determine whether the target vehicle is a faulty vehicle according to the final non-ridden probabilities.
In the present embodiment, the initial number of vehicles does not include the actual faulty vehicle and the low-voltage vehicle. The actual fault vehicle refers to a shared vehicle which a user has reported a fault, and the low-voltage vehicle is a shared vehicle which has an electric quantity lower than a low-voltage threshold value and cannot be used by the user. Both the actual faulty vehicle and the low-speed vehicle are vehicles that the user cannot select for use, and it is necessary to reject the above-described type of vehicles when calculating the non-rided probability of the target vehicle.
For example, if the number of shared vehicles of 3 parking spots in the target area is 4, 5, and 6, respectively, and the actual faulty vehicle 1 and the low-electric vehicle 2 are included, it is necessary to eliminate the above type of vehicles when calculating the initial number of vehicles in the target area. Initial vehicle number a=15-3=12. Meanwhile, in the detection period, the server does not need to calculate the non-ridden probability of the actual fault vehicle and does not need to calculate the non-ridden probability of the low-power vehicle. Because such shared vehicles themselves require field confirmation by service personnel.
Specifically, the vehicle inflow number does not include a low-electric vehicle either. For example, if the number of inflow vehicles of the target area one within the predetermined time interval is 3 and the low electric vehicle 1 is included, it is necessary to reject the above-described type of vehicle when calculating the number of inflow vehicles of the target area one. Vehicle inflow lr=3-1=2.
In this embodiment, after the server determines that the target vehicle is a faulty vehicle, the method further includes: the server obtains asset loss and ride loss of the target vehicle. The asset loss refers to an expected loss value of the target vehicle loss, and the riding loss refers to an expected loss value of no benefit of the target vehicle due to the fact that the target vehicle cannot ride.
Specifically, the server acquires a target area in which the target vehicle is located, and determines a risk coefficient of the target vehicle according to the target area. Generally, an operator determines a first risk coefficient of an operation fence according to a loss rate of a shared vehicle in the operation fence, and determines a second risk coefficient according to a loss rate of a shared vehicle in a target area in the operation fence, wherein the higher the loss rate of the shared vehicle is, the greater the risk coefficient is. The risk factor of the target area is thus determined from the first risk factor and the second risk factor, in particular risk factor = first risk factor x second risk factor. During operation, the operator typically identifies the target area with a color. For example, a target region having a risk factor of 1 to 1.5 is marked green, a target region having a risk factor of 1.6 to 3 is marked yellow, a target region having a risk factor of 3.1 to 5 is marked orange, and a target region having a risk factor of 5.1 or more is marked red.
The server also obtains the length of the target vehicle when not being rided, and the general loss probability of the shared vehicle is generally between 0 and 0.025 percent, and the longer the length of the shared vehicle when not being rided, the higher the general loss probability.
The server also obtains a vehicle value of the target vehicle, the vehicle value being calculated from an initial value of the target vehicle and a annual usage depreciation rate of the target vehicle.
The server also acquires an average riding unit price of all the sharing vehicles flowing into or out of the target area on the previous day and an average riding frequency of the sharing vehicles in the operation fence on the previous day.
The server also obtains labor costs for confirming the vehicle condition of the target vehicle, the labor costs being mainly related to the actual distance between the operation site and the target vehicle, the more distant the labor costs are.
In this embodiment, the asset loss is calculated as follows: asset loss=risk coefficient×general loss probability×vehicle value=first risk coefficient×second risk coefficient×general loss probability×initial value×annual usage depreciation rate. The riding loss is calculated as follows: loss of ride = average riding unit price x average number of rides. The manual cost is calculated as follows: cost of labor = cost per unit distance x actual distance.
When the sum of the asset loss and the riding loss is greater than the labor cost, the vehicle condition of the target vehicle needs to be confirmed, and at this time, the server needs to push the target vehicle to the operation and maintenance personnel for confirmation by the operation and maintenance personnel. After the operation and maintenance person confirms the vehicle condition of the target vehicle, the future non-ridden probability of the target vehicle is set to 1.
When the sum of the asset loss and the riding loss is less than the labor cost, the vehicle condition of the target vehicle does not need to be confirmed, and the target vehicle does not need to be pushed to operation and maintenance personnel for confirmation. At this time, the server sets the period end non-ridden probability of the target vehicle as the period end non-ridden probability of the next detection period.
Although the target vehicle has been determined to be a faulty vehicle, if the operator does not confirm the target vehicle, the server still needs to continue to accumulate the non-ridden probability of the target vehicle. The smaller the final non-ridden probability of the target vehicle, the greater the likelihood of its failure, the more the operator is required to pay attention.
Referring to fig. 4, the present embodiment further provides a server for sharing a vehicle, the server including: an acquisition unit for acquiring an initial number of vehicles of a target area in a detection period at a predetermined time interval; the acquisition unit is also used for acquiring the inflow number of the vehicles flowing into the target area and the outflow number of the vehicles flowing out of the target area at preset time intervals; a calculation unit for calculating a non-ridden probability of a target vehicle in a target area at a predetermined time interval based on the initial vehicle number, the vehicle inflow number, and the vehicle outflow number; the acquisition unit is also used for acquiring the initial non-ridden probability of the target vehicle in the detection period; the calculating unit is also used for calculating the final non-ridden probability of the detection period according to the initial non-ridden probability and the multiple non-ridden probabilities in the detection period; a determination unit that determines that the target vehicle is a faulty vehicle if the final non-ridden probability is not greater than a preset value; if the final non-ridden probability is larger than the preset value, the judging unit judges that the target vehicle is a non-fault vehicle, and the judging unit also sets the final non-ridden probability as the initial non-ridden probability of the next detection period; if the target vehicle generates riding behavior in the detection period, the judging unit sets the probability of the terminal being not ridden to be 1; the non-riding probability is calculated as follows: p=1-LC/(a+lr); wherein P is the probability of not being ridden, A is the initial number of vehicles, LC is the number of vehicle outflow, LR is the number of vehicle inflow; the final non-ridden probability is calculated as follows: pz=ps× (p1×p2× … … ×pn); wherein PZ is the final non-ridden probability, PS is the initial non-ridden probability, and P1, P2, … …, PN are the non-ridden probabilities of different predetermined time intervals.
Second embodiment.
Referring to fig. 5, the present embodiment provides another fault recognition method of a shared vehicle.
S201, when the shared vehicles in the target area naturally flow out, the stock vehicles in the target area are acquired.
S202, calculating the non-riding probability of the target vehicle in the target area according to the stock vehicle.
S203, the initial non-riding probability of the target vehicle is acquired.
S204, calculating the final non-riding probability of the target vehicle according to the non-riding probability and the non-riding probability at the beginning of the period.
The server monitors the shared vehicles in the target area in real time, and when the shared vehicles naturally flow out, the server acquires the inventory vehicles in the target area, specifically the number A of the inventory vehicles in the target area. At this time, the riding probability of the shared vehicle of the target area may be calculated from the number of vehicles of the stock vehicle: m=1/a, the riding probability of the shared vehicle of the target region: p=1-m=1-1/a. The server acquires the initial non-ridden probability PS of the target vehicle, and calculates the final non-ridden probability of the target vehicle according to the initial non-ridden probability PS and the non-ridden probability P: pZ=PS×P=PS× (1-1/A).
The initial non-ridden probability here refers to the non-ridden probability that the target vehicle has accumulated before the natural outflow of the shared vehicle in the target area, and the final non-ridden probability refers to the non-ridden probability that the target vehicle has accumulated after the natural outflow of the shared vehicle in the target area.
S205, if the probability of the terminal being not ridden is not greater than a preset value, the target vehicle is judged to be a fault vehicle, if the probability of the terminal being not ridden is greater than the preset value, the target vehicle is judged to be a non-fault vehicle, the probability of the terminal being not ridden is set to be the probability of the next terminal being not ridden, and if the target vehicle is ridden, the probability of the terminal being not ridden is set to be 1.
The following is a specific case.
Specifically, if the preset value is 0.001. The server monitors the shared vehicles in the target area in real time, if the target area has the stock vehicles A=5, and at the moment, if the shared vehicles are rided, the riding probability M=1/5=0.2 of the shared vehicles in the target area, and the non-riding probability P=0.8 of the shared vehicles in the target area. If the initial non-ridden probability PS of the target vehicle in the target area is 0.01, the final non-ridden probability pz=pt×ps=0.008 > 0.001, and the server determines that the target vehicle is a non-faulty vehicle, and takes 0.008 as the initial non-ridden probability PS of the next day. If the initial non-ridden probability PS of the target vehicle in the target area is 0.0012, the final non-ridden probability pz=pt×ps=0.00096 < 0.001, and the server determines that the target vehicle is a faulty vehicle. The fault judging method can judge whether the target vehicle is a fault vehicle in real time, and can identify the fault vehicle more timely so as to take countermeasures more quickly.
In the above case, the default user may randomly select the stock vehicle, and the probabilities of the stock vehicle being selected are all the same. In fact, however, the user may have a preference for selecting a vehicle for inventory, such as a preference for the amount of power of the vehicle. In particular, more fully charged shared vehicles are often the user's preference.
Referring to fig. 6, a schematic diagram of the relationship between the charge value of the shared vehicle and the riding probability is shown.
When the charge value of the shared vehicle is 0% -7%, the probability that the user selects the shared vehicle is 0 because the operator does not allow the user to use the shared vehicle below the low electric threshold. At this time, such shared vehicles should be excluded from the stock vehicles. When the electric quantity value of the shared vehicle is 8% -10%, the probability of the user selecting the shared vehicle is 5%, when the electric quantity value of the shared vehicle is 11% -15%, the probability of the user selecting the shared vehicle is 12% - … …, and as the electric quantity of the shared vehicle is more sufficient, the probability of the shared vehicle being selected by the user to ride is larger. Specifically, the relationship between the electric quantity value of the shared vehicle and the riding probability can be obtained according to historical riding data. The riding probability is also related to the riding habit of the user, so that different operation fences have different user groups, and therefore, different operation fences have different corresponding relations between electric quantity values and the riding probability.
In this embodiment, the calculating, by the server, the non-rided probability of the target vehicle in the target area from the stock vehicle includes: the method comprises the steps that a server obtains an actual electric quantity value corresponding to an inventory vehicle, and obtains a first mapping relation between an electric quantity value of a shared vehicle and riding probability; the server also obtains the actual riding probability corresponding to the actual electric quantity value according to the first mapping relation, and calculates the non-riding probability of the target vehicle in the target area according to the inventory vehicle and the corresponding actual riding probability. Specifically, the non-riding probability of the target vehicle is calculated as follows: p=1-QM/(q1+q2+ … … +qn); wherein P is the probability of not being ridden, Q1, Q2, … …, QN are the actual probabilities of riding corresponding to the stock vehicle, and QM is the actual probabilities of riding corresponding to the target vehicle.
The above specific examples are further described.
The server monitors the shared vehicles in the target area in real time, if 5 vehicles are stored in the target area, the target area is respectively A1, A2, A3, A4 and A5, the electric quantity values are respectively 5%, 13%, 22%, 34% and 87%, and the corresponding riding probabilities Q1, Q2, Q3, Q4 and Q5 are respectively 0%, 12%, 28%, 84% and 95%. Then, with A1 as the target vehicle, the probability of being selected by the user to ride is m1=q1/(q1+q2+q3+q4+q5) =0, and accordingly, the probability of A2 is m2=q2/(q1+q2+q3+q4+q5) =0.055%, the probability of A3 is m3=12.78%, the probability of A4 is m4=38.36%, and the probability of A5 is m5= 43.37%.
Then, A1 is the target vehicle, the probability of not being ridden p1=1-m1=1, the probability of not being ridden p2=1-m2= 99.945%, the probability of not being ridden p3=1-m3= 87.22%, the probability of not being ridden p4=1-m4= 61.64%, and the probability of not being ridden p5=1-m5= 56.63% of A5.
The electric power of the target vehicle A1 is lower than the low electric threshold value, which is 1 as the final non-ridden probability of the target vehicle. If the initial non-ridden probability PS of the target vehicle A2 is 0.0012, the final non-ridden probability pz=pt×ps=0.0012 > 0.001, and the server determines that the target vehicle is a non-faulty vehicle and takes 0.0012 as the initial non-ridden probability PS of the next day. If the initial non-ridden probability PS of the target vehicle A3 is 0.0012, the final non-ridden probability pz=pt×ps= 0.00104 > 0.001, and the server determines that the target vehicle is a non-faulty vehicle and takes 0.00104 as the initial non-ridden probability PS of the next day. If the initial non-ridden probability PS of the target vehicle A4 is 0.0012, the final non-ridden probability pz=pt×ps=0.00074 < 0.001, and the server determines that the target vehicle is a faulty vehicle.
It can be seen from the above that, for a shared vehicle having the same probability of not being ridden initially, different charge values will also lead to different determination results.
The present embodiment also provides a server for sharing a vehicle, the server including: the acquisition unit is used for acquiring inventory vehicles in the target area when the shared vehicles in the target area naturally flow out; a calculation unit for calculating a non-ridden probability of the target vehicle in the target area from the stock vehicle; the acquisition unit is also used for acquiring the initial non-ridden probability of the target vehicle; the calculating unit is also used for calculating the final non-riding probability of the target vehicle according to the initial non-riding probability and the non-riding probability; a determination unit that determines that the target vehicle is a faulty vehicle if the final non-ridden probability is not greater than a preset value; if the final non-ridden probability is larger than the preset value, the judging unit judges that the target vehicle is a non-fault vehicle, and the judging unit also sets the final non-ridden probability as the next initial non-ridden probability; if the target vehicle experiences a riding behavior, the determination unit sets the terminal non-ridden probability to 1.
Although the present invention is disclosed above, the present invention is not limited thereto. Various changes and modifications may be made by one skilled in the art without departing from the spirit and scope of the invention, and the scope of the invention should be assessed accordingly to that of the appended claims.
Claims (10)
1. A fault identification method for a shared vehicle, comprising:
acquiring an initial vehicle number of a target area in a detection period at a preset time interval;
acquiring the inflow number of vehicles flowing into the target area and the outflow number of vehicles flowing out of the target area in the preset time interval;
calculating the non-ridden probability of the target vehicle in the target area at the preset time interval according to the initial vehicle number, the vehicle inflow number and the vehicle outflow number;
acquiring the initial non-ridden probability of the target vehicle of the detection period;
calculating the final non-ridden probability of the detection period according to the initial non-ridden probability and a plurality of non-ridden probabilities in the detection period;
if the final non-ridden probability is not greater than a preset value, judging that the target vehicle is a fault vehicle;
if the period end non-ridden probability is larger than a preset value, judging that the target vehicle is a non-fault vehicle, and setting the period end non-ridden probability as the period initial non-ridden probability of the next detection period;
if the target vehicle performs riding behavior in the detection period, setting the final non-riding probability to be 1;
The non-riding probability is calculated as follows: p=1-LC/(a+lr); wherein P is the probability of not being ridden, A is the initial number of vehicles, LC is the number of vehicle outflow, LR is the number of vehicle inflow;
the calculation mode of the final non-ridden probability is as follows: pz=ps× (p1×p2× … … ×pn); wherein PZ is the final non-ridden probability, PS is the initial non-ridden probability, and P1, P2, … …, PN are the non-ridden probabilities of different predetermined time intervals.
2. The fault identification method as claimed in claim 1, wherein the initial number of vehicles does not include an actual faulty vehicle and a low electric vehicle, and the number of vehicle inflow does not include the low electric vehicle; the actual fault vehicle is a shared vehicle for reporting faults to a user, and the low-voltage vehicle is a shared vehicle which is unavailable to the user because the electric quantity is lower than a low-voltage threshold value.
3. A fault identification method for a shared vehicle, comprising:
when the shared vehicles in the target area naturally flow out, acquiring inventory vehicles in the target area;
calculating the non-ridden probability of the target vehicle in the target area according to the stock vehicle;
acquiring the initial non-ridden probability of the target vehicle;
Calculating the final non-ridden probability of the target vehicle according to the initial non-ridden probability and the non-ridden probability;
if the final non-ridden probability is not greater than a preset value, judging that the target vehicle is a fault vehicle;
if the final non-ridden probability is larger than a preset value, judging that the target vehicle is a non-fault vehicle, and setting the final non-ridden probability as the initial non-ridden probability of the next time;
and if the target vehicle generates riding behaviors, setting the final non-ridden probability to be 1.
4. The method of claim 3, wherein the calculating the probability of the target vehicle of the target area not being rided based on the inventory vehicle comprises:
acquiring an actual electric quantity value corresponding to the stock vehicle;
acquiring a first mapping relation between the electric quantity value of the shared vehicle and the riding probability, and acquiring the actual riding probability corresponding to the actual electric quantity value according to the first mapping relation;
calculating the non-ridden probability of the target vehicle in the target area according to the stock vehicle and the corresponding actual riding probability;
the non-riding probability is calculated as follows: p=1-QM/(q1+q2+ … … +qn); wherein P is the probability of not being ridden, Q1, Q2, … …, QN are the actual probabilities of riding corresponding to the stock vehicle, QM is the actual probabilities of riding corresponding to the target vehicle;
The calculation mode of the final non-ridden probability is as follows: pz=ps×p; wherein PZ is the final non-ridden probability, PS is the initial non-ridden probability, and P is the non-ridden probability.
5. The fault identification method as claimed in claim 1 or 3, wherein after the target vehicle is determined to be a faulty vehicle if the final non-ridden probability is not greater than a preset value, further comprising:
acquiring asset loss and riding loss of the target vehicle;
the asset loss refers to an expected loss value of the target vehicle loss, and the riding loss refers to an expected loss value of no benefit of the target vehicle due to inability to ride;
acquiring labor cost for confirming a vehicle condition of the target vehicle;
determining whether a vehicle condition of the target vehicle needs to be confirmed according to the asset loss, the riding loss and the labor cost;
if the vehicle condition needs to be confirmed, pushing the target vehicle to an operation and maintenance person; setting the terminal non-ridden probability to 1 after the vehicle condition confirmation;
if the vehicle condition does not need to be confirmed, the final non-ridden probability is set as the initial non-ridden probability of the next detection period.
6. The fault identification method of claim 5, wherein,
acquiring the asset loss includes:
acquiring a risk coefficient of the shared vehicle according to the target area;
acquiring the non-ridden duration and the vehicle value of the target vehicle;
acquiring a general loss probability of the target vehicle according to the non-ridden time length;
calculating the asset loss based on the risk factor, the general loss probability, and the vehicle value;
obtaining the riding loss includes:
acquiring the average riding unit price and the average riding times of the shared vehicle in the target area;
and calculating the riding loss according to the average riding unit price and the average riding times.
7. A fault identification method as claimed in claim 1 or claim 3 wherein the target area refers to a collection of a plurality of adjacent parking spots; the parking spots are within a preset diameter range, or the parking spots are within the same Geohash grid range.
8. The failure recognition method according to claim 1 or 3, wherein a second map relationship between a riding time period of the shared vehicle and a vehicle condition is obtained, and the target vehicle riding behavior means that the riding time period of the target vehicle being rided is at least more than a preset time period.
9. A server for sharing a vehicle, comprising:
an acquisition unit configured to acquire an initial number of vehicles of a target area in a detection period at a predetermined time interval;
the acquisition unit is further configured to acquire a vehicle inflow number flowing into the target area and a vehicle outflow number flowing out of the target area within the predetermined time interval;
a calculation unit configured to calculate an unswept probability of a target vehicle in the target region at the predetermined time interval based on the initial vehicle number, the vehicle inflow number, and the vehicle outflow number;
the acquisition unit is further used for acquiring the initial non-ridden probability of the target vehicle in the detection period;
the calculating unit is further used for calculating the final non-ridden probability of the detection period according to the initial non-ridden probability and a plurality of non-ridden probabilities in the detection period;
a determination unit that determines that the target vehicle is a faulty vehicle if the terminal non-ridden probability is not greater than a preset value;
if the final non-ridden probability is greater than a preset value, the judging unit judges that the target vehicle is a non-fault vehicle, and the judging unit also sets the final non-ridden probability as the initial non-ridden probability of the next detection period;
If the target vehicle performs riding behavior in the detection period, the judging unit sets the final non-riding probability to be 1;
the non-riding probability is calculated as follows: p=1-LC/(a+lr); wherein P is the probability of not being ridden, A is the initial number of vehicles, LC is the number of vehicle outflow, LR is the number of vehicle inflow;
the calculation mode of the final non-ridden probability is as follows: pz=ps× (p1×p2× … … ×pn); wherein PZ is the final non-ridden probability, PS is the initial non-ridden probability, and P1, P2, … …, PN are the non-ridden probabilities of different predetermined time intervals.
10. A server for sharing a vehicle, comprising:
the acquisition unit is used for acquiring inventory vehicles of a target area when the shared vehicles of the target area naturally flow out;
a calculation unit for calculating a non-ridden probability of a target vehicle of the target area from the stock vehicle;
the acquisition unit is also used for acquiring the initial non-ridden probability of the target vehicle;
the calculating unit is further used for calculating the final non-ridden probability of the target vehicle according to the initial non-ridden probability and the non-ridden probability;
A determination unit that determines that the target vehicle is a faulty vehicle if the terminal non-ridden probability is not greater than a preset value;
if the final non-ridden probability is greater than a preset value, the judging unit judges that the target vehicle is a non-fault vehicle, and the judging unit also sets the final non-ridden probability as the initial non-ridden probability of the next time;
if the target vehicle experiences a riding behavior, the determination unit sets the terminal non-ridden probability to 1.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311002056.6A CN116778717B (en) | 2023-08-10 | 2023-08-10 | Fault identification method and server for shared vehicle |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311002056.6A CN116778717B (en) | 2023-08-10 | 2023-08-10 | Fault identification method and server for shared vehicle |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN116778717A true CN116778717A (en) | 2023-09-19 |
| CN116778717B CN116778717B (en) | 2024-01-19 |
Family
ID=88013651
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202311002056.6A Active CN116778717B (en) | 2023-08-10 | 2023-08-10 | Fault identification method and server for shared vehicle |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN116778717B (en) |
Citations (14)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107742243A (en) * | 2017-10-18 | 2018-02-27 | 深圳市沃特沃德股份有限公司 | Shared bicycle labeling method and device |
| US20180376305A1 (en) * | 2017-06-23 | 2018-12-27 | Veniam, Inc. | Methods and systems for detecting anomalies and forecasting optimizations to improve smart city or region infrastructure management using networks of autonomous vehicles |
| CN110363306A (en) * | 2018-04-04 | 2019-10-22 | 韩巧珍 | Shared bicycle method for diagnosing faults |
| US20200090427A1 (en) * | 2018-09-13 | 2020-03-19 | International Business Machines Corporation | Malfunction detection of shared vehicles |
| CN111846046A (en) * | 2019-04-03 | 2020-10-30 | 北京嘀嘀无限科技发展有限公司 | System, method and device for detecting safety of bicycle |
| CN111881980A (en) * | 2020-07-29 | 2020-11-03 | 上海钧正网络科技有限公司 | Vehicle fault detection method and device, computer equipment and storage medium |
| CN111882759A (en) * | 2019-05-24 | 2020-11-03 | 北京嘀嘀无限科技发展有限公司 | Vehicle use control device and method and vehicle |
| WO2021169165A1 (en) * | 2020-02-28 | 2021-09-02 | 平安国际智慧城市科技股份有限公司 | Trajectory monitoring-based shared vehicle monitoring method and apparatus, and computer device |
| CN113823040A (en) * | 2020-06-19 | 2021-12-21 | 中国移动通信集团福建有限公司 | Management method and system for shared bicycle |
| US20220012995A1 (en) * | 2021-09-23 | 2022-01-13 | Intel Corporation | Active vehicle safety system for cyclists and pedestrians |
| CN115526549A (en) * | 2022-11-23 | 2022-12-27 | 宁波小遛共享信息科技有限公司 | Shared vehicle scheduling profit calculation method, server, and storage medium |
| CN115526548A (en) * | 2022-11-23 | 2022-12-27 | 宁波小遛共享信息科技有限公司 | Shared vehicle scheduling loss calculation method, server, and storage medium |
| CN115798078A (en) * | 2022-11-29 | 2023-03-14 | 永安行科技股份有限公司 | Vehicle accurate fault diagnosis method and device |
| CN116112532A (en) * | 2023-01-12 | 2023-05-12 | 中国联合网络通信集团有限公司 | Shared bicycle fault processing method, mobile terminal and shared bicycle operation server |
-
2023
- 2023-08-10 CN CN202311002056.6A patent/CN116778717B/en active Active
Patent Citations (14)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20180376305A1 (en) * | 2017-06-23 | 2018-12-27 | Veniam, Inc. | Methods and systems for detecting anomalies and forecasting optimizations to improve smart city or region infrastructure management using networks of autonomous vehicles |
| CN107742243A (en) * | 2017-10-18 | 2018-02-27 | 深圳市沃特沃德股份有限公司 | Shared bicycle labeling method and device |
| CN110363306A (en) * | 2018-04-04 | 2019-10-22 | 韩巧珍 | Shared bicycle method for diagnosing faults |
| US20200090427A1 (en) * | 2018-09-13 | 2020-03-19 | International Business Machines Corporation | Malfunction detection of shared vehicles |
| CN111846046A (en) * | 2019-04-03 | 2020-10-30 | 北京嘀嘀无限科技发展有限公司 | System, method and device for detecting safety of bicycle |
| CN111882759A (en) * | 2019-05-24 | 2020-11-03 | 北京嘀嘀无限科技发展有限公司 | Vehicle use control device and method and vehicle |
| WO2021169165A1 (en) * | 2020-02-28 | 2021-09-02 | 平安国际智慧城市科技股份有限公司 | Trajectory monitoring-based shared vehicle monitoring method and apparatus, and computer device |
| CN113823040A (en) * | 2020-06-19 | 2021-12-21 | 中国移动通信集团福建有限公司 | Management method and system for shared bicycle |
| CN111881980A (en) * | 2020-07-29 | 2020-11-03 | 上海钧正网络科技有限公司 | Vehicle fault detection method and device, computer equipment and storage medium |
| US20220012995A1 (en) * | 2021-09-23 | 2022-01-13 | Intel Corporation | Active vehicle safety system for cyclists and pedestrians |
| CN115526549A (en) * | 2022-11-23 | 2022-12-27 | 宁波小遛共享信息科技有限公司 | Shared vehicle scheduling profit calculation method, server, and storage medium |
| CN115526548A (en) * | 2022-11-23 | 2022-12-27 | 宁波小遛共享信息科技有限公司 | Shared vehicle scheduling loss calculation method, server, and storage medium |
| CN115798078A (en) * | 2022-11-29 | 2023-03-14 | 永安行科技股份有限公司 | Vehicle accurate fault diagnosis method and device |
| CN116112532A (en) * | 2023-01-12 | 2023-05-12 | 中国联合网络通信集团有限公司 | Shared bicycle fault processing method, mobile terminal and shared bicycle operation server |
Also Published As
| Publication number | Publication date |
|---|---|
| CN116778717B (en) | 2024-01-19 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Dow et al. | A novel approach for evaluating the impact of electric vehicles on the power distribution system | |
| EP1668450B1 (en) | Wide-area electrical demand and supply management | |
| CN109445972A (en) | Data recovery method, device, equipment and storage medium | |
| CN112035544B (en) | Power distribution network data anomaly monitoring and diagnosis method | |
| Ferrario et al. | Predictive capacity of topological measures in evaluating seismic risk and resilience of electric power networks | |
| CN114819665A (en) | Distributed energy management-based abnormity early warning method and system | |
| CN101715203B (en) | Method and device for automatically positioning fault points | |
| KR102271244B1 (en) | LOAD TRANSFER SYSTEM and METHOD for POWER TRANSMISSION and POWER SUPPLY | |
| CN116011827B (en) | Power failure monitoring analysis and early warning system and method for key cells | |
| JP6870615B2 (en) | Equipment management system, equipment, method and program | |
| CN110738415A (en) | Electricity stealing user analysis method based on electricity utilization acquisition system and outlier algorithm | |
| CN116778717B (en) | Fault identification method and server for shared vehicle | |
| CN115526355A (en) | Intelligent monitoring management method and system for shared vehicles and electronic equipment | |
| CN113505931A (en) | Charger robot dynamic scheduling optimization method based on genetic algorithm | |
| CN111226254A (en) | Reliability of network maps | |
| McGarry et al. | A high-resolution geospatial and socio-technical methodology for assessing the impact of electrified heat and transport on distribution network infrastructure | |
| CN110163668A (en) | A kind of Products Show method based on correlation rule and collaborative filtering | |
| Christodoulou et al. | Urban water distribution network asset management using spatio-temporal analysis of pipe-failure data | |
| CN113379497A (en) | Order regulation and control method and device, computer equipment and computer readable storage medium | |
| CN111626460B (en) | Regional water network topology relationship confirmation method, device and system | |
| CN113706103B (en) | Data screening method and system for power users | |
| CN116862088B (en) | Intelligent community service system based on information acquisition | |
| CN116644334B (en) | Reliability monitoring and increasing method for in-service nuclear power unit and nuclear power turbine | |
| CN117691743B (en) | Distribution network power failure judging method and system considering depth effect | |
| KR102616314B1 (en) | Electric vehicle charger operating device |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |