Online hailing car abnormal stay behavior identification method and device, equipment and storage medium
A technology for abnormal stays and online car-hailing, which is applied in character and pattern recognition, instruments, data processing applications, etc., and can solve problems such as error-prone judgments, low accuracy, and no perception of abnormal stays
Pending Publication Date: 2020-10-30
BEIJING DIDI INFINITY TECH & DEV
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[0004] Through active reporting to the online car-hailing platform by drivers, passengers, and police, there is a lag in the identification of abnormal stay behaviors in online car-hailing vehicles. If a major case occurs, it is easy to miss the best time for rescue, and there is no awareness of abnormal stays that have not been actively reported. The recall rate is not high; and the accuracy of the method of identifying abnormal staying behavior by comparing with the fixed staying time threshold is not high, and it is very easy to make wrong judgments. For example, the red light time at some urban intersections is longer than the fixed The dwell time threshold is misjudged as an abnormal stay, which is likely to cause trouble to drivers and passengers
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[0048] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only some, not all, embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts belong to the protection scope of the present invention.
[0049]In order to solve the problem of low real-time and low recall rate in the existing technology through the driver, passenger, and police to actively report to the online car-hailing platform, and then identify the abnormal stay behavior of the online car-hailing car, and the problem of passing and fixed length of stay There is a problem of low accuracy in the identification method of abnormal staying behavior based on threshold comparison. The embodiment o...
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The embodiment of the invention provides an online hailing car abnormal stay behavior identification method and device, equipment and a storage medium. The method comprises the steps of collecting real-time data when an online hailing car stops; obtaining a first historical data set and a second historical data set from a database, wherein the first historical data set comprises the historical stay duration of at least one driver in the same service state as the real-time data, and the second historical data set comprises the historical stay duration of at least one driver in the same time period as the real-time data and in the same area as the real-time data; and according to the real-time data, the first historical data set, the second historical data set and a preset model, identifyingwhether the online hailing car currently has an abnormal stay behavior or not. According to the invention, the online hailing car travel multi-dimensional historical data and the preset model are utilized, the multi-dimensional feature information such as the stay duration, the position and the service state is considered, whether an online hailing car currently has abnormal stay behaviors or notcan be accurately recognized in real time, the safety perception capacity of an online hailing car platform is improved, and safety of drivers and passengers is guaranteed.
Description
technical field [0001] The present invention relates to the technical field of artificial intelligence, in particular to a method, device, equipment and storage medium for identifying abnormal stop behaviors of online car-hailing vehicles. Background technique [0002] For online car-hailing platforms, safety is the guarantee for the sustainable development of the platform. Abnormal stays in online car-hailing trips are the most common and accounted for the largest proportion of all safety scenarios. The vast majority of abnormal stays are caused by conflicts between drivers and passengers, or traffic accidents resulting in casualties. [0003] In the prior art, judging whether there is abnormal stay behavior in online car-hailing vehicles usually requires drivers, passengers, and police to actively report to the online car-hailing platform, or collect the dwell time of online car-hailing vehicles in real time and compare them with fixed dwell time thresholds to detect abnor...
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IPC IPC(8): G06K9/62G06Q50/30
CPCG06F18/214G06Q50/40
Inventor 张腾剑陈奥
Owner BEIJING DIDI INFINITY TECH & DEV