Missing traffic station determination method, device, and missing traffic station supplementary registration method

Abstract

A method for determining a missing traffic station of a target subject is provided, comprising: obtaining a target subject identifier corresponding to a target subject in which a traffic station missing occurs; obtaining a target subject image associated with the target subject identifier; obtaining first and second exit times of the target subject identifier, the first exit time being the exit time of the target subject identifier corresponding to the missing traffic station entry point, and the second exit time being the last exit time of the target subject identifier before the first exit time; obtaining a set of in-station subject images, which includes subject images of all subjects appearing in all traffic stations between a first cutoff time and the first exit time, the first cutoff time being selected from one of the second exit time and the vehicle start operation time on the date of occurrence of the missing traffic station entry point; determining a target in-station subject image that matches the target subject image from the in-station subject image set; and determining the traffic station at which the target in-station subject image is collected as a missing traffic station.

Description

Technical Field

[0001] The present disclosure relates to the field of artificial intelligence technology, and in particular, to a method, an apparatus, a computer storage medium, a computer program product, a computing device for determining missing transportation stations of a target entity, and a method for supplementing and registering missing transportation stations. Background Art

[0002] With the popularization of mobile Internet technology and devices, the ways in which people take transportation and pay for transportation have also changed greatly. Traditional ways of taking and paying for transportation, such as buying tickets on the spot and purchasing special bus cards, have gradually been abandoned. Entering and leaving transportation stations through QR (Quick Response) ride codes (such as two-dimensional codes, barcodes, etc.) has become the mainstream of transportation, and it is even possible to enter and leave stations by recognizing body parts such as faces, fingerprints, and palm prints. The path from the starting point to the ending point can be determined based on the stations registered by scanning codes when entering and leaving stations, and then the cost of taking transportation can be calculated.

[0003] However, when people use QR codes to take transportation, some unexpected events may also occur. For example, in some special cases (such as network anomalies, QR code recognition system or QR code failures, etc.), it may happen that the exit station is not recorded when leaving the station or the entry station is not recorded when entering the station, resulting in abnormal settlement of the transportation cost. This brings inconvenience to users for their next transportation and also poses a potential hazard to the management and normal operation of the transportation system. Summary of the Invention

[0004] The present disclosure provides a method, an apparatus, a computer storage medium, a computer program product, and a computing device for determining missing transportation stations of a target entity, aiming to overcome some or all of the above-mentioned defects of related technologies and other possible defects.

[0005] Embodiments of the present disclosure provide a method for determining a missing transportation station of a target entity. The method includes: obtaining a target entity identifier corresponding to the target entity for which the transportation station is missing, where the missing transportation station includes a missing entry station; obtaining a target entity image associated with the target entity identifier; obtaining a first departure time and a second departure time of the target entity identifier, where the first departure time is the departure time corresponding to the missing entry station of the target entity identifier, and the second departure time is the previous departure time of the target entity identifier before the first departure time; obtaining a set of in-station entity images, where the set of in-station entity images includes entity images of all entities that appear in all transportation stations between a first cut-off time and the first departure time, and the first cut-off time is selected from one of the second departure time and the starting operation time of the vehicle on the occurrence date of the missing entry station; determining a target in-station entity image that matches the target entity image from the set of in-station entity images; and determining the transportation station where the target in-station entity image is captured as the missing transportation station.

[0006] Another embodiment of the present disclosure provides another method for determining a missing transportation station of a target entity. The method includes: obtaining a target entity identifier corresponding to the target entity for which the transportation station is missing, where the missing transportation station includes a missing exit station; obtaining a target entity image associated with the target entity identifier; obtaining a first arrival time and a second arrival time of the target entity identifier, where the first arrival time is the arrival time corresponding to the missing exit station of the target entity identifier, and the second arrival time is the next arrival time of the target entity identifier after the first arrival time; obtaining a set of in-station entity images, where the set of in-station entity images includes entity images of all entities that appear in all transportation stations between the first arrival time and a second cut-off time, and the second cut-off time is selected from one of the second arrival time and the ending operation time of the vehicle on the occurrence date of the missing exit station; determining a target in-station entity image that matches the target entity image from the set of in-station entity images; and determining the transportation station where the target in-station entity image is captured as the missing transportation station.

[0007] Another embodiment of the present disclosure provides a method for supplementary registration of a missing transportation station, including: using the method for determining a missing transportation station of a target entity as described in the previous embodiments to determine the missing transportation station of the target entity; and registering the missing transportation station to the target entity identifier.

[0008] Another embodiment of the present disclosure provides an apparatus for determining a missing transportation station of a target entity, including: a target entity identifier acquisition module configured to acquire a target entity identifier corresponding to the target entity with a transportation station missing, where the transportation station missing includes an inbound station missing; a target entity image acquisition module configured to acquire a target entity image associated with the target entity identifier; a time acquisition module configured to acquire a first outbound time and a second outbound time of the target entity identifier, where the first outbound time is the outbound time corresponding to the inbound station missing of the target entity identifier, and the second outbound time is the previous outbound time of the target entity identifier before the first outbound time; an image acquisition module configured to acquire a set of in-station entity images, where the set of in-station entity images includes entity images of all entities that appear in all transportation stations between a first cut-off time and the first outbound time, and the first cut-off time is selected from one of the second outbound time and the vehicle start operation time of the occurrence date of the inbound station missing; an image matching module configured to determine a target in-station entity image matching the target entity image from the set of in-station entity images; and a missing transportation station determination module configured to determine the transportation station where the target in-station entity image is captured as the missing transportation station.

[0009] Another embodiment of the present disclosure provides another apparatus for determining a missing transportation station of a target entity, including: a target entity identifier acquisition module configured to acquire a target entity identifier corresponding to the target entity with a transportation station missing, where the transportation station missing includes an outbound station missing; a target entity image acquisition module configured to acquire a target entity image associated with the target entity identifier; a time acquisition module configured to acquire a first inbound time and a second inbound time of the target entity identifier, where the first inbound time is the inbound time corresponding to the outbound station missing of the target entity identifier, and the second inbound time is the next inbound time of the target entity identifier after the first inbound time; an image acquisition module configured to acquire a set of in-station entity images, where the set of in-station entity images includes entity images of all entities that appear in all transportation stations between the first inbound time and a second cut-off time, and the second cut-off time is selected from one of the second inbound time and the vehicle end operation time of the occurrence date of the outbound station missing; an image matching module configured to determine a target in-station entity image matching the target entity image from the set of in-station entity images; and a missing transportation station determination module configured to determine the transportation station where the target in-station entity image is captured as the missing transportation station.

[0010] Another embodiment of the present disclosure provides a computing device, including: a memory configured to store computer-executable instructions; and a processor configured to execute the method described in any one of the foregoing method embodiments when the computer-executable instructions are executed by the processor.

[0011] Another embodiment of the present disclosure provides a computer-readable storage medium storing computer-executable instructions that, when executed, perform the method described in any one of the foregoing method embodiments.

[0012] Another embodiment of the present disclosure provides a computer program product including a computer program, wherein the steps of the method described in any one of the foregoing method embodiments are implemented when the computer program is executed by a processor.

[0013] By using the method, device, computer storage medium, computer program product, computing device for supplementing missing transportation stations provided by the embodiments of the present disclosure, and the method for supplementing registration of missing transportation stations, the missing transportation stations of the target entity can be automatically determined in a reliable and timely manner, and the information of the determined missing transportation stations can be supplemented and registered to the target entity identifier, so as to accurately and timely settle the expenses of the target entity taking transportation means, avoid adverse effects on the subsequent travel plan of the entity taking transportation means, improve the travel experience of the entity taking transportation means, and at the same time reduce the risk of economic losses suffered by the transportation system operation or management agency. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Embodiments of the present disclosure will now be described in more detail with reference to the drawings, wherein:

[0015] Figure 1 A flowchart of a method for determining missing transportation stations of a target entity according to an embodiment of the present disclosure is shown;

[0016] Figure 2 An exemplary implementation environment of a method for determining missing transportation stations of a target entity according to some embodiments of the present disclosure is shown;

[0017] Figure 3 Used to illustrate a first cut-off time and a first departure time involved in a method for determining missing transportation stations of a target entity according to some embodiments of the present disclosure;

[0018] Figure 4 A flowchart of a method for determining missing transportation stations of a target entity according to another embodiment of the present disclosure is shown;

[0019] Figure 5Shows the steps included in determining a target in-station subject image that matches a target subject image from a set of in-station subject images in a method for determining missing transportation stops of a target subject according to some embodiments of the present disclosure;

[0020] Figure 6 Shows the steps included in determining a target in-station subject image that matches a target subject image from a set of in-station subject images in a method for determining missing transportation stops of a target subject according to another embodiment of the present disclosure;

[0021] Figure 7 Shows a flowchart of a method for determining missing transportation stops of a target subject according to yet another embodiment of the present disclosure;

[0022] Figure 8 For illustrating a first entry time and a second cut-off time involved in a method for determining missing transportation stops of a target subject according to some embodiments of the present disclosure;

[0023] Figure 9 Illustrates the process of a method for supplementary registration of missing transportation stops according to some embodiments of the present disclosure;

[0024] Figure 10 Shows a block diagram of an apparatus for determining missing transportation stops of a target subject according to an embodiment of the present disclosure; and Figure 11 Illustrates an example system that includes an example computing device representing one or more systems and / or devices that can implement the various methods or apparatuses described herein. Detailed Description of the Invention

[0025] The technical solutions in the present disclosure will be clearly and completely described below with reference to the accompanying drawings. The described embodiments are only a part of the embodiments of the present invention, rather than all embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present disclosure without creative efforts fall within the scope of protection of the present invention.

[0026] Artificial Intelligence (AI) is the theory, method, technology, and application system that uses digital computers or machines controlled by digital computers to simulate, extend, and expand human intelligence, perceive the environment, acquire knowledge, and use knowledge to obtain the best results. In other words, artificial intelligence is a comprehensive technology of computer science that attempts to understand the essence of intelligence and produce a new intelligent machine that can respond in a way similar to human intelligence. Artificial intelligence also studies the design principles and implementation methods of various intelligent machines to enable the machines to have the functions of perception, reasoning, and decision-making.

[0027] Artificial intelligence technology is an interdisciplinary subject with a wide range of fields involved, including both hardware-level and software-level technologies. The basic technologies of artificial intelligence generally include technologies such as sensors, dedicated artificial intelligence chips, cloud computing, distributed storage, big data processing technology, operation / interaction systems, and mechatronics. The software technologies of artificial intelligence mainly include several major directions such as computer vision technology, speech processing technology, natural language processing technology, and machine learning / deep learning, autonomous driving, intelligent transportation, and automatic control.

[0028] Machine Learning (ML) is an interdisciplinary subject that involves multiple disciplines such as probability theory, statistics, approximation theory, convex analysis, and algorithm complexity theory. It specifically studies how computers simulate or implement human learning behaviors to acquire new knowledge or skills and reorganize the existing knowledge structure to continuously improve their own performance. Machine learning is the core of artificial intelligence and the fundamental way to make computers intelligent, and its applications cover all fields of artificial intelligence. Machine learning and deep learning usually include technologies such as artificial neural networks, belief networks, reinforcement learning, transfer learning, inductive learning, and rote learning.

[0029] With the research and progress of artificial intelligence technology, artificial intelligence technology has been studied and applied in multiple fields. For example, common ones include smart homes, smart wearable devices, virtual assistants, smart speakers, smart marketing, driverless, autonomous driving, drones, robots, intelligent healthcare, intelligent customer service, vehicle networking, autonomous driving, and intelligent transportation. It is believed that with the development of technology, artificial intelligence technology will be applied in more fields and play an increasingly important role.

[0030] To facilitate the understanding of the embodiments of the present disclosure, several concepts will be briefly introduced below.

[0031] Missing transportation station: The "missing transportation station" mentioned in this article refers to a transportation station that is not recognized or registered for calculating the fare of taking a transportation vehicle when the subject travels by a transportation vehicle. For example, when the subject takes the subway, bus, ship, or train, etc., if the subject is not recognized or registered by the corresponding management or operation organization (such as the subway operation company, etc.) when entering the station, then the subject's entry station is a missing transportation station; similarly, if the subject is not recognized or registered by the corresponding management or operation organization when leaving the station, then the subject's exit station is a missing transportation station. For such a subject, it means that a transportation station is missing. Correspondingly, the missing of a transportation station includes the missing of the entry station and the missing of the exit station.

[0032] Target entity: The entity mentioned in this article refers to the entity to be charged for taking a transportation vehicle (for example, generally a passenger, although other entities are not excluded. For example, in a transportation vehicle that requires payment for pets, the pet can also be the target entity), and the above-mentioned lack of transportation station occurs during the process of the entity taking the transportation vehicle.

[0033] Entity identifier: An entity identifier refers to an identifier corresponding to the entity and used to allow the entity to enter and leave a transportation station or a transportation vehicle. Examples of entity identifiers include, but are not limited to, QR codes, human faces, palm prints, fingerprints, transportation cards (cards or card numbers), etc. An entity identifier usually has multiple attributes. For example, it at least includes the entry time and the exit time.

[0034] Target entity identifier: The target entity identifier is the entity identifier corresponding to the above-mentioned target entity. It can be understood that the specific contents of the "entity identifier" and the "target entity identifier" may change over time and with the development and progress of technology.

[0035] Figure 1 A flowchart of a method for determining a missing transportation station of a target entity according to some embodiments of the present disclosure is schematically shown. As Figure 1 shown, according to some embodiments of the present disclosure, the method for determining a missing transportation station of a target entity includes: S110, obtaining a target entity identifier corresponding to the target entity for which the transportation station is missing, where the lack of transportation station includes a lack of entry station; S120, obtaining a target entity image associated with the target entity identifier; S130, obtaining a first exit time and a second exit time of the target entity identifier, where the first exit time is the exit time of the target entity identifier corresponding to the lack of entry station, and the second exit time is the previous exit time of the target entity identifier before the first exit time; S140, obtaining a set of in-station entity images, where the set of in-station entity images includes entity images of all entities that appear in all transportation stations between a first cut-off time and the first exit time, and the first cut-off time is selected from one of the second exit time and the starting operation time of the vehicle on the date when the lack of entry station occurs; S150, determining a target in-station entity image that matches the target entity image from the set of in-station entity images; and S160, determining the transportation station where the target in-station entity image is collected as the missing transportation station.

[0036] Each of the above steps S110 to S160 can be executed entirely by a terminal device, or entirely by a server, or some steps can be executed by a terminal device and other steps can be executed by a server, that is, jointly executed by the terminal device and the server. Figure 2An exemplary implementation environment of a method for determining missing transportation stations of a target entity according to some embodiments of the present disclosure is illustrated. As Figure 2 shown, various types of terminal devices communicate with a server via a network. Examples of terminal devices include, but are not limited to, mobile phones, desktop computers, tablet computers, laptop computers, and handheld computers. The server can be, for example, an independent physical server, a server cluster or a distributed system composed of multiple physical servers, or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, content delivery networks, and big data and artificial intelligence platforms. The terminal device can obtain a target entity identifier corresponding to the target entity where a transportation station is missing. The missing transportation station includes a missing inbound station and a missing outbound station, and send the target entity identifier to the server. The server can retrieve a target entity image associated with the target entity identifier based on the received target entity identifier. The server can obtain a first outbound time and a second outbound time of the target entity identifier based on the target entity identifier. For example, each time the entity uses the target entity identifier to enter and exit the station, the corresponding inbound time and outbound time can be recorded and saved, so that the server can query the corresponding inbound time and outbound time through the target entity identifier. The server can also obtain the above-mentioned in-station entity image set through an image acquisition device in the transportation station, and determine a target in-station entity image that matches the target entity image from the in-station entity image set; finally, the server determines the transportation station where the target in-station entity image is collected as the above-mentioned missing transportation station. This disclosure does not limit which steps in the method for determining the missing transportation station of the target entity are executed by the server and which steps are executed by the terminal device. For the sake of simplicity, hereinafter, the above steps in the method for determining the missing transportation station of the target entity are taken as an example where they are executed by the server for detailed description.

[0037] In step S110, the server can receive the target subject identifier corresponding to the target subject with missing transportation stations from the terminal device or other servers, or the server can also send a request command to the terminal device or other servers to receive the target subject identifier corresponding to the target subject with missing transportation stations. In some embodiments, the target subject identifier can be automatically screened out from the transportation record data of all subjects within a certain period (e.g., one day). Of course, the target subject identifier can also be obtained through manual investigation. Examples of the target subject identifier include, but are not limited to, QR codes, transportation card numbers, etc. In practice, the missing of transportation stations may occur due to various reasons. For example, the machine system for identifying QR codes malfunctions, the generated QR codes have problems, etc. In some cases, some subjects may intentionally avoid having the QR code recognized by the machine system and not pay for the transportation, that is, intentional fare evasion behavior. After obtaining the target subject identifier corresponding to the target subject with missing transportation stations, in step S120, the server can obtain the target subject image associated with the target subject identifier. In some embodiments, a database including the correspondence between the subject and the subject image can be established in advance. For example, when a subject obtains a subject identifier for the convenience of taking transportation, the subject usually needs to provide subject information including the subject image to the relevant management or operation structure, and the subject image includes the subject's facial image. Therefore, there can be a one-to-one association between the subject image and the subject identifier. Thus, when the target subject identifier is obtained, the target subject image associated with the target subject identifier can be obtained. When the subject enters and leaves the transportation station through the subject identifier, the entry time of the transportation station (i.e., the time when the subject identifier is recognized when entering the station) and the departure time of the transportation station (i.e., the time when the subject identifier is recognized when leaving the station) can be recorded and stored in real time by the terminal device or directly stored by the server in real time. Therefore, in step S130, the server can obtain the first departure time and the second departure time of the target subject identifier. The first departure time is the departure time corresponding to the missing of the entry station of the target subject identifier, and the second departure time is the previous departure time of the target subject identifier before the first departure time.

[0038] By Figure 3 the meaning of the first departure time and the second departure time can be better understood. Figure 3The first outbound time Tout1 and the second outbound time Tout2 are shown. The first outbound time Tout1 is the outbound time corresponding to the absence of an entry station for the target subject identifier, that is, there is an absence of an entry station for the target subject identifier (for example, due to some reason, the target subject identifier is not recognized when entering a certain transportation station, and correspondingly, the time when the target subject enters the above-mentioned certain transportation station cannot be known through the target subject identifier), but the time when the target subject identifier leaves the transportation station (for example, another different transportation station) is recorded as Tout1. A single subject enters a certain transportation station and leaves from another transportation station to complete a single transportation trip. The time from when the subject enters the above-mentioned certain transportation station to when it leaves the above-mentioned another transportation station can be called the cycle of taking a vehicle. Therefore, each cycle of taking a vehicle can correspond to an entry time and an outbound time. The first outbound time Tout1 can correspond to the end time of a certain cycle of taking a vehicle. However, since there is an absence of an entry station for the target subject, the start time of the above-mentioned certain cycle of taking a vehicle is not recorded. Figure 3 The second outbound time Tout2 is illustrated in the figure: the previous outbound time of the target subject identifier before the first outbound time Tout1. In other words, the second outbound time Tout2 corresponds to the end time of the previous cycle of taking a vehicle of the target subject. Figure 3 The time Ts is also shown, which represents the starting operation time of the vehicle on the date when the target subject has a transportation station absence. Although Figure 3It is shown that the second outbound time Tout2 is later than the vehicle's starting operation time Ts. However, obviously, the second outbound time Tout2 can also be earlier than the vehicle's starting operation time Ts. In step S140, the server can obtain a set of in-station subject images, which includes subject images of all subjects that appear within all transportation stations between the first cut-off time and the first outbound time. The first cut-off time is selected from one of the above-mentioned second outbound time Tout2 and the vehicle's starting operation time Ts on the occurrence date of the missing entry into the station. According to some embodiments of the present disclosure, image acquisition devices are installed within each transportation station or within the vehicle. The image acquisition devices can acquire images of the subjects entering the transportation station or the vehicle and upload them to the server or store them in a certain storage device. The server can obtain the above-mentioned set of in-station subject images from the storage device. The first cut-off time can be the above-mentioned second outbound time Tout2. Alternatively, the first cut-off time is the vehicle's starting operation time Ts on the occurrence date of the missing entry into the station of the target subject. In step S150, based on the target subject image obtained in step S120, the server can determine, from the set of in-station subject images obtained in step S140, the in-station subject image that matches the target subject image, and this station subject image is the target in-station subject image. In step S160, the server can retrieve, based on the target in-station subject image, the transportation station where the target in-station subject image was acquired. Since the subject images in the set of in-station subject images are acquired by the image acquisition devices within each transportation station or within the vehicle, there is a corresponding relationship between the subject images in the set of in-station subject images and the location of the image acquisition devices (the transportation station where the image acquisition device is located). Thus, the transportation station corresponding to the target in-station subject image, that is, the transportation station where the target in-station subject image was acquired, can be determined based on the target in-station subject image, and the server determines this transportation station as the missing transportation station of the target subject.

[0039] In the case where certain entities experience missing transportation stations when taking transportation means, the conventional solution is to adopt the method of manual supplementary registration, where the entity informs the transportation system operation or management agency on-site or registers the missing transportation station afterwards. If the entity does not inform the transportation system operation or management agency or forgets the missing transportation station, the entity will not be allowed to take the transportation means next time. In addition, some entities may also conceal the actual missing transportation station and inform a false missing transportation station that is beneficial to themselves, which also causes losses to the transportation system operation or management agency. By using the method for determining the missing transportation stations of the target entity provided in the above embodiments of the present disclosure, the missing transportation stations of the target entity can be automatically determined in a reliable and timely manner, and the information of the determined missing transportation stations can be provided to the transportation system operation or management agency and the entity. On this basis, accurate and timely settlement of the fees for the entity to take the transportation means can be achieved, avoiding adverse effects on the subsequent travel plans of taking the transportation means, improving the experience of the entity taking the transportation means, and at the same time reducing the risk of economic losses suffered by the transportation system operation or management agency.

[0040] According to some embodiments of the present disclosure, step S140 of obtaining the set of in-station entity images described above includes: in response to the second departure time being earlier than the starting operation time of the vehicle on the occurrence date of the missing entry station, determining the first cut-off time as the starting operation time of the vehicle on the occurrence date of the missing entry station. Continuing to refer to Figure 3 , if the second departure time Tout2 is earlier than the starting operation time Ts of the vehicle on the occurrence date of the missing entry station, then determine the first cut-off time as the starting operation time of the vehicle on the occurrence date of the missing entry station. That is to say, in this case, the server obtains Figure 3 the entity images of all entities that appear in all transportation stations within the time period T2 (Ts - Tout2) shown in Figure 3 . This is because it is highly unlikely that the target entity enters the transportation station before the starting operation time Ts of the vehicle on the occurrence date of the missing entry station. In addition, this also reduces the amount of data operation required to execute the method for determining the missing transportation stations of the target entity. If the second departure time Tout2 is not earlier than the starting operation time Ts of the vehicle on the occurrence date of the missing entry station, then determine the first cut-off time as the second departure time Tout2. In this case, the server obtains

[0041] According to some embodiments of the present disclosure, the method for determining a missing transportation station of a target subject further includes: collecting a plurality of subject images corresponding to a plurality of subject identifiers; and creating a correspondence between the plurality of subject identifiers and the plurality of subject images. In this case, obtaining the target subject identifier corresponding to the target subject with a missing transportation station includes: retrieving the target subject identifier from the plurality of subject identifiers; and obtaining the target subject image associated with the target subject identifier includes: determining the target subject image from the plurality of subject images according to the correspondence. Figure 4 FIG. Figure 4 shows a flowchart of a method for determining a missing transportation station of a target subject according to this embodiment, including the following steps: S410, collecting a plurality of subject images corresponding to a plurality of subject identifiers; S420, creating a correspondence between the plurality of subject identifiers and the plurality of subject images; S430, retrieving the target subject identifier corresponding to the target subject with a missing transportation station from the plurality of subject identifiers; S440, determining the target subject image from the plurality of subject images according to the above correspondence; S450, obtaining a first outbound time and a second outbound time of the target subject identifier, where the first outbound time is the outbound time of the target subject identifier corresponding to the missing inbound station, and the second outbound time is the previous outbound time of the target subject identifier before the first outbound time; S460, obtaining a set of in-station subject images, where the set of in-station subject images includes subject images of all subjects that appear in all transportation stations between a first cut-off time and the first outbound time, and the first cut-off time is selected from one of the second outbound time and the vehicle start operation time of the occurrence date of the missing inbound station; S470, determining a target in-station subject image that matches the target subject image from the set of in-station subject images; and S480, determining the transportation station where the target in-station subject image is collected as the missing transportation station.

[0042] In some embodiments, such as Figure 5As shown, determining the target in-station main body image that matches the target main body image from the in-station main body image set may include the following steps: S510. Calculate the similarity between the target main body image and each main body image in the main body images of all the main bodies in the in-station main body image set; S520. Determine the main body image in the in-station main body image set that has the highest similarity to the target main body image as the target in-station main body image. In step S510, the Euclidean distance, cosine distance, Hamming distance, etc. can be used to measure the similarity between the target main body image and the main body images in the in-station main body image set. In step S520, obtaining the main body image in the in-station main body image set that has the highest similarity to the target main body image and using this main body image as the target in-station main body image can determine the image of the target main body where the traffic station is missing captured by the image acquisition device in the traffic station in an accurate and reliable manner.

[0043] In other embodiments, determining the target in-station main body image that matches the target main body image from the in-station main body image set further includes: before calculating the similarity between the target main body image and each main body image in the main body images of all the main bodies in the in-station main body image set, performing normalization processing on the target main body image and each main body image in the in-station main body image set to obtain a normalized target main body image corresponding to the target main body image and a normalized main body image corresponding to the main body image. As Figure 6 shown, in this case, determining the target in-station main body image that matches the target main body image from the in-station main body image set may include the following steps: S610. Perform normalization processing on the target main body image and each main body image in the in-station main body image set to obtain a normalized target

[0044] main body image corresponding to the target main body image and a normalized main body image corresponding to the main body image; S620. Calculate the similarity between the normalized target main body image and the normalized main body image corresponding to the main body image in the in-station main body image set; S630. Determine the normalized main body image in the in-station main body image set that has the highest similarity to the normalized target main body image as the target in-station main body image. The above normalization processing performed on the target main body image and the main body images in the in-station main body image set may include image rotation, image enlargement, image compression, image flipping, image denoising, etc. By performing normalization processing on the target main body image and the main body images in the in-station main body image set, the efficiency of searching for the main body image in the in-station main body image set that has the highest similarity to the target main body image can be improved, and the execution of the step of determining the target in-station main body image that matches the target main body image from the in-station main body image set can be promoted.

[0045] As described above, the cosine distance can be used to measure the similarity between the target subject image and the subject images in the in-station subject image set. At this time, this similarity can be referred to as cosine similarity. In some embodiments, calculating the similarity between the target subject image and each subject image in the set of all subject images in the in-station subject image set includes: calculating the cosine similarity between the normalized target subject image and the normalized subject image, and the cosine similarity cosθ can be expressed as:

[0046]

[0047] where A i represents the feature vector of the normalized target subject image, B i represents the feature vector of the normalized subject image, and n represents the number of feature vectors. For a single normalized target subject image or normalized subject image, it can be characterized by n feature vectors. The cosine similarity measures the similarity between the normalized target subject image and the normalized subject image by calculating the cosine value of the angle between the feature vector of the normalized target subject image and the feature vector of the normalized subject image. The value range of the cosine similarity is from -1 to 1. A value of -1 may mean that the directions pointed by the two vectors are exactly opposite, and a value of 1 indicates that the directions of the two vectors are exactly the same, with the highest similarity.

[0048] Referring to the above Figure 1 、 Figure 3 and Figure 4 The described embodiments can automatically determine the missing transportation station (missing entry station) of the target subject in a reliable and timely manner. On this basis, accurate and timely settlement of the cost of the subject taking the transportation means can be achieved, avoiding adverse effects on the subsequent travel plan of taking the transportation means, and improving the travel experience of the subject taking the transportation means.

[0049] Based on the same inventive concept, another embodiment of the present disclosure provides another method for the missing transportation station of the target subject, as Figure 7As shown, the method may include the following steps: S710. Obtain a target entity identifier corresponding to the target entity where a traffic station is missing, where the traffic station missing includes an outbound station missing; S720. Obtain a target entity image associated with the target entity identifier; S730. Obtain a first inbound time and a second inbound time of the target entity identifier, where the first inbound time is the inbound time corresponding to the outbound station missing of the target entity identifier, and the second inbound time is the next inbound time of the target entity identifier after the first inbound time; S740. Obtain a set of in-station entity images, where the set of in-station entity images includes entity images of all entities that appear in all traffic stations between the first inbound time and a second cut-off time, and the second cut-off time is selected from one of the second inbound time and the vehicle end operation time of the occurrence date of the outbound station missing; S750. Determine a target in-station entity image that matches the target entity image from the set of in-station entity images; and S760. Determine the traffic station where the target in-station entity image is captured as the missing traffic station.

[0050] In steps S710 and S720, the server may obtain a target entity identifier corresponding to the target entity where an outbound station is missing, and obtain a target entity image associated with the target entity identifier based on the target entity identifier. Steps S710 and S720 may be substantially similar to steps S110 and S120 described with reference to Figure 1 and will not be elaborated here. When the entity enters and leaves the traffic station through the entity identifier, the time of entering the traffic station (i.e., the time when the entity identifier is recognized when entering the station) and the time of leaving the traffic station (i.e., the time when the entity identifier is recognized when leaving the station) can be recorded and stored in real time by the terminal device or directly stored in real time by the server. Therefore, in step S730, the server may obtain the first inbound time and the second inbound time of the target entity identifier. The first inbound time is the inbound time corresponding to the outbound station missing of the target entity identifier, and the second inbound time is the next inbound time of the target entity identifier after the first inbound time.

[0051] The following uses Figure 8 to illustrate the meanings of the first inbound time and the second inbound time. Figure 8The first entry time Tin1 and the second entry time Tin2 are shown. The first entry time Tin1 is the entry time when the target subject identifier has no corresponding exit station, that is, the target subject identifier has an exit station missing (for example, due to some reason, the target subject identifier is not recognized when leaving a certain transportation station. Correspondingly, the time when the target subject leaves the above-mentioned certain transportation station cannot be known through the target subject identifier). However, the time when the target subject identifier enters a transportation station (for example, another different transportation station) is recorded as Tin1. The subject enters a certain transportation station and leaves from another transportation station to complete a single transportation trip. The time from when the subject enters the above-mentioned certain transportation station to when it leaves the above-mentioned another transportation station can be called the cycle of taking the transportation tool. Therefore, each cycle of taking the transportation tool can correspond to an entry time and an exit time. The first entry time Tin1 can correspond to the start time of a certain cycle of taking the transportation tool. However, due to the exit station missing of the target subject, the end time of the above-mentioned certain cycle of taking the transportation tool is not recorded. Figure 8 The second entry time Tin2 is illustrated in the figure: the next entry time of the target subject identifier after the first entry time Tin1. In other words, the second entry time Tin2 corresponds to the start time of the next cycle of taking the transportation tool of the target subject. Figure 8 The time Te is also shown, which represents the end operation time of the vehicle on the date when the target subject has a transportation station missing. Although Figure 8 it is shown in the figure that the second entry time Tin2 is earlier than the end operation time Te of the vehicle, obviously, the second entry time Tin2 can also be later than the end operation time Te of the vehicle.

[0052] In step S740, the server may obtain a set of in-station subject images, which includes subject images of all subjects that appear within all transportation stations between the first entry time and the second cut-off time. The second cut-off time is selected from one of the second entry time and the end operation time of the vehicle on the date when the exit station is missing. According to some embodiments of the present disclosure, image acquisition devices within each transportation station or within a vehicle may acquire images of subjects entering the transportation station or the vehicle and upload them to the server or store them in a certain storage device. The server may obtain the above-mentioned set of in-station subject images from the storage device. The second cut-off time may be the above-mentioned second entry time Tin2. Alternatively, the second cut-off time is the end operation time Te of the vehicle on the date when the target subject's exit station is missing. In step S750, based on the target subject image obtained in step S720, the server may determine, from the set of in-station subject images obtained in step S740, an in-station subject image that matches the target subject image, and this station subject image is the target in-station subject image. In step S760, the server may retrieve, based on the target in-station subject image, the transportation station where the target in-station subject image was acquired. Since the subject images in the set of in-station subject images are acquired by image acquisition devices within each transportation station or within a vehicle, there is a corresponding relationship between the subject images in the set of in-station subject images and the locations of the image acquisition devices (the transportation stations where the image acquisition devices are located). Thus, the transportation station where the corresponding image acquisition device is located, that is, the transportation station where the target in-station subject image was acquired, can be determined based on the target in-station subject image, and the server determines this transportation station as the missing transportation station of the target subject.

[0053] Through the method for determining the missing transportation station of the target subject provided by the above embodiments of the present disclosure, the missing transportation station (the missing exit station) of the target subject can be automatically determined in a reliable and timely manner. The information of the determined missing transportation station can be provided to the transportation system operation or management agency and the subject. On this basis, accurate and timely settlement of the fees for the subject to take the vehicle can be achieved, avoiding adverse effects on the subsequent travel plans of taking the vehicle, enhancing the experience of the subject taking the vehicle, and at the same time reducing the risk of economic losses suffered by the transportation system operation or management agency.

[0054] According to some embodiments of the present disclosure, step S740 of obtaining the set of in-station subject images includes: in response to the second entry time being later than the end operation time of the vehicle on the date when the exit station is missing, determining the second cut-off time as the end operation time of the vehicle on the date when the exit station is missing. Continue to refer to Figure 8, if the second entry time Tin2 is later than the end operation time Te of the vehicle on the occurrence date of the missing exit station, the second cut-off time is determined as the end operation time Te of the vehicle on the occurrence date of the missing exit station. That is, in this case, the server obtains Figure 8 the subject images of all subjects that appear in all traffic stations within the time period P2 (Tin1 - Te) shown in Figure 8 . This is because the possibility that the target subject leaves the traffic station after the end operation time Te of the vehicle on the occurrence date of the missing exit station is very low. Additionally, this also reduces the amount of data computation required to execute the method for determining the missing traffic station of the target subject. If the second entry time Tin2 is not later than the end operation time Te of the vehicle on the occurrence date of the missing exit station, the second cut-off time is determined as the second entry time Tin2. In this case, the server obtains Figure 8 the subject images of all subjects that appear in all traffic stations within the time period P1 (Tin1 - Tin2) shown in Figure 8 . This can increase the possibility and accuracy of determining the target in-station subject image that matches the target subject image.

[0055] Figure 7 The method for determining the missing traffic station of the target subject shown in Figure 1 can automatically determine the missing exit station of the target subject. Although the overall steps are different from Figure 1 the method shown in Figure 1 , steps S710 to S760 can respectively correspond one-to-one with steps S110 to S160. The literal expressions of some steps are even exactly the same. For example, step S750 can also include: calculating the similarity between the target subject image and each subject image in the subject images of all subjects in the in-station subject image set; determining the subject image with the highest similarity to the target subject image in the in-station subject image set as the target in-station subject image. Figure 7 The method shown in Figure 7 and Figure 1 the method shown in Figure 1 are essentially different in that Figure 7 the specific contents of the target subject image and the in-station subject image set shown in Figure 7 are different from Figure 1 the specific contents of the target subject image and the in-station subject image set shown in Figure 1 .

[0056] Another embodiment of the present disclosure provides a method for supplementary registration of a missing traffic station, as shown in Figure 9As shown, the method includes: S910. The method for determining the missing transportation station of the target entity described in any of the foregoing embodiments may be used to determine the missing transportation station; S920. Register the missing transportation station to the target entity identifier. According to some embodiments of the present disclosure, after the server determines the missing transportation station of the target entity, it may send the information carrying the missing transportation station and the target entity identifier to the fee management system related to the transportation management or operation agency, and the fee management system registers the missing transportation station to the target entity identifier. In this way, the fees that the entity should pay during transportation can be accurately calculated, and the information about the missing transportation station and the fees to be paid can also be sent to the communication device of the entity to inform the entity.

[0057] According to another aspect of the present disclosure, there is provided a device for determining the missing transportation station of a target entity, as Figure 10 shown, the device 1000 includes: a target entity identifier acquisition module 1000a configured to acquire a target entity identifier corresponding to the target entity with a missing transportation station, where the missing transportation station includes a missing entry station; a target entity image acquisition module 1000b configured to acquire a target entity image associated with the target entity identifier; a time acquisition module 1000c configured to acquire a first outbound time and a second outbound time of the target entity identifier, where the first outbound time is the outbound time corresponding to the missing entry station of the target entity identifier, and the second outbound time is the previous outbound time of the target entity identifier before the first outbound time; an image acquisition module 1000d configured to acquire a set of in-station entity images, where the set of in-station entity images includes entity images of all entities that appear in all transportation stations between a first cut-off time and the first outbound time, and the first cut-off time is selected from one of the second outbound time and the vehicle start operation time of the occurrence date of the missing entry station; an image matching module 1000e configured to determine a target in-station entity image that matches the target entity image from the set of in-station entity images; and a missing transportation station determination module 1000f configured to determine the transportation station where the target in-station entity image is collected as the missing transportation station. This device can automatically determine the missing entry station of the target entity in a reliable and timely manner.

[0058] According to another embodiment of the present disclosure, an apparatus for determining a missing transportation station of a target entity includes: a target entity identifier acquisition module configured to acquire a target entity identifier corresponding to the target entity for which a transportation station is missing, where the transportation station missing includes an outbound station missing; a target entity image acquisition module configured to acquire a target entity image associated with the target entity identifier; a time acquisition module configured to acquire a first inbound time and a second inbound time of the target entity identifier, where the first inbound time is the inbound time corresponding to the outbound station missing of the target entity identifier, and the second inbound time is the next inbound time of the target entity identifier after the first inbound time; an image acquisition module configured to acquire a set of in-station entity images, where the set of in-station entity images includes entity images of all entities that appear in all transportation stations between the first inbound time and a second cut-off time, and the second cut-off time is selected from one of the second inbound time and the vehicle end-of-operation time of the occurrence date of the outbound station missing; an image matching module configured to determine a target in-station entity image that matches the target entity image from the set of in-station entity images; and a missing transportation station determination module configured to determine the transportation station where the target in-station entity image is captured as the missing transportation station. This apparatus can automatically determine the missing outbound station of the target entity in a reliable and timely manner, and its structure is similar to that of Figure 10 the apparatus shown.

[0059] Another aspect of the present disclosure provides a computing device, including: a memory configured to store computer-executable instructions; a processor configured to execute the steps in the method as described in any of the foregoing embodiments when the computer-executable instructions are executed by the processor.

[0060] In particular, according to an embodiment of the present disclosure, the process described above with reference to the flowchart can be implemented as a computer program. For example, an embodiment of the present disclosure provides a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes program code for executing at least one step in the method embodiment of the present disclosure.

[0061] Another embodiment of the present disclosure provides one or more computer-readable storage media having computer-readable instructions stored thereon, which when executed implement a method for determining missing transportation stations of a target entity or a method for supplementary registration of missing transportation stations according to some embodiments of the present disclosure. Each step of the method for determining missing transportation stations of a target entity or the method for supplementary registration of missing transportation stations can be transformed into computer-readable instructions through programming and thus stored in a computer-readable storage medium. When such a computer-readable storage medium is read or accessed by a computing device or a computer, the computer-readable instructions therein are executed by a processor on the computing device or the computer to implement the method for determining missing transportation stations of a target entity or the method for supplementary registration of missing transportation stations.

[0062] Figure 11 FIG. illustrates an example system 1100 that includes an example computing device 1110 representative of one or more systems and / or devices that may implement the techniques described in the embodiments herein. The computing device 1110 may be, for example, a server of a service provider, a device associated with the server, a system-on-a-chip, and / or any other suitable computing device or computing system. Referring above to Figure 10 The apparatus 1000 for determining missing transportation stations of a target entity described may take the form of the computing device 1110. Alternatively, the apparatus 1000 for determining missing transportation stations of a target entity may be implemented as a computer program in the form of an application 1116.

[0063] As Figure 11 The example computing device 1110 illustrated includes a processing system 1111, one or more computer-readable media 1112, and one or more I / O interfaces 1113 that are communicatively coupled to each other. Although not shown, the computing device 1110 may also include a system bus or other data and command transfer system that couples the various components to each other. The system bus may include any one or combination of different bus architectures, such as a memory bus or memory controller, a peripheral bus, a universal serial bus, and / or a processor or local bus using any of various bus architectures.

[0064] The processing system 1111 represents the functionality to perform one or more operations using hardware. Accordingly, the processing system 1111 is illustrated as including hardware elements 1114 that may be configured as a processor, functional blocks, etc. This may include being implemented in hardware as an application specific integrated circuit or other logic device formed using one or more semiconductors. The hardware elements 1114 are not limited by the materials from which they are formed or the processing mechanisms employed therein. For example, a processor may be composed of (multiple) semiconductors and / or transistors (e.g., an electronic integrated circuit (IC)). In such a context, the executable instructions of the processor may be electronically executable instructions.

[0065] The computer-readable medium 1112 is illustrated as including a memory / storage 1115. The memory / storage 1115 represents the memory / storage capacity associated with one or more computer-readable media. The memory / storage 1115 may include volatile media (such as random access memory (RAM)) and / or non-volatile media (such as read-only memory (ROM), flash memory, optical disks, magnetic disks, etc.). The memory / storage 1115 may include fixed media (e.g., RAM, ROM, fixed hard disk drive, etc.) and removable media (e.g., flash memory, removable hard disk drive, optical disk, etc.). The computer-readable medium 1112 may be configured in various other ways as further described below.

[0066] One or more I / O interfaces 1113 represent the functionality that allows a user to input commands and information into the computing device 1110 using various input devices and optionally also allows information to be presented to the user and / or other components or devices using various output devices. Examples of input devices include a keyboard, a cursor control device (e.g., a mouse), a microphone (e.g., for voice input), a scanner, a touch function (e.g., a capacitive or other sensor configured to detect physical touch), a camera (e.g., that can detect motion not involving touch as a gesture using visible or non-visible wavelengths such as infrared frequencies), and so on. Examples of output devices include a display device (e.g., a monitor or a projector), a speaker, a printer, a network card, a haptic response device, etc. Thus, the computing device 1110 may be configured in various ways as further described below to support user interaction.

[0067] The computing device 1110 also includes an application 1116. The application 1116 may be, for example, a software instance of the apparatus 1000 for the missing transportation stop of the target subject described with reference to Figure 10 and implements the techniques described herein in combination with other elements in the computing device 1110.

[0068] Various techniques may be described herein in the general context of software, hardware elements, or program modules. Generally, these modules include routines, programs, objects, elements, components, data structures, etc. that perform particular tasks or implement particular abstract data types. The terms “module,” “function,” and “component” as used herein generally represent software, firmware, hardware, or a combination thereof. The features of the techniques described herein are platform-independent, meaning that these techniques may be implemented on various computing platforms having a variety of processors.

[0069] Implementations of the described modules and techniques can be stored on or transmitted across some form of computer-readable medium. The computer-readable medium can include a variety of media accessible by computing device 1110. By way of example and not limitation, the computer-readable medium can include "computer-readable storage media" and "computer-readable signal media".

[0070] Contrary to mere signal transmission, carrier waves, or signals themselves, "computer-readable storage media" refers to media and / or devices that are capable of persistently storing information, and / or tangible storage devices. Thus, computer-readable storage media refers to non-signal-bearing media. Computer-readable storage media includes hardware such as volatile and non-volatile, removable and non-removable media and / or storage devices implemented in a method or technology suitable for storing information such as computer-readable instructions, data structures, program modules, logic elements / circuits, or other data. Examples of computer-readable storage media can include, but are not limited to, RAM, ROM, EEPROM, flash memory or other memory technologies, CD-ROM, digital versatile disks (DVDs) or other optical storage devices, hard disks, magnetic cassettes, magnetic tape, magnetic disk storage devices or other magnetic storage devices, or other storage devices, tangible media, or articles of manufacture suitable for storing the desired information and accessible by a computer.

[0071] "Computer-readable signal media" refers to signal-bearing media configured to send instructions to computing device 1110, such as via a network. Signal media typically can embody computer-readable instructions, data structures, program modules, or other data in a modulated data signal such as a carrier wave, data signal, or other transmission mechanism. Signal media also includes any information delivery media. The term "modulated data signal" refers to a signal in which one or more of the characteristics are set or changed in such a manner as to encode information in the signal. By way of example and not limitation, communication media includes wired media such as a wired network or direct-wired connection, and wireless media such as acoustic, RF, infrared, and other wireless media.

[0072] As previously described, hardware elements 1114 and computer-readable medium 1112 represent instructions, modules, programmable device logic, and / or fixed device logic implemented in hardware, which in some embodiments can be used to implement at least some aspects of the techniques described herein. Hardware elements can include integrated circuits or system-on-a-chip, application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), complex programmable logic devices (CPLDs), and components of other hardware devices implemented in silicon or other hardware. In this context, the hardware elements can serve as processing devices that execute program tasks defined by the instructions, modules, and / or logic embodied by the hardware elements, as well as hardware devices for storing instructions for execution, e.g., the previously described computer-readable storage media.

[0073] The foregoing combinations can also be used to implement the various techniques and modules described herein. Thus, software, hardware, or program modules and other program modules can be implemented as one or more instructions and / or logic embodied on a computer-readable storage medium of some form and / or by one or more hardware elements 1114. The computing device 1110 can be configured to implement specific instructions and / or functions corresponding to the software and / or hardware modules. Thus, for example, by using the computer-readable storage medium of the processing system and / or the hardware element 1114, the module can be implemented at least in part in hardware as a module executable by the computing device 1110 as software. The instructions and / or functions can be executable / operable by one or more articles of manufacture (e.g., one or more computing devices 1110 and / or processing systems 1111) to implement the techniques, modules, and examples described herein.

[0074] In various embodiments, the computing device 1110 can be configured in a variety of different ways. For example, the computing device 1110 can be implemented as a computer-like device including a personal computer, a desktop computer, a multi-screen computer, a laptop computer, a netbook, etc. The computing device 1110 can also be implemented as a mobile device-like device including mobile devices such as mobile phones, portable music players, portable gaming devices, tablet computers, multi-screen computers, etc. The computing device 1110 can also be implemented as a television-like device, which includes a device having or connected to a generally larger screen in a casual viewing environment. These devices include televisions, set-top boxes, gaming consoles, etc.

[0075] The techniques described herein can be supported by these various configurations of the computing device 1110 and are not limited to the specific examples of the techniques described herein. The functionality can also be implemented in whole or in part on the "cloud" 1120 by using a distributed system, such as via the platform 1122 described below. The cloud 1120 includes and / or represents a platform 1122 for resources 1124. The platform 1122 abstracts the underlying functionality of the hardware (e.g., servers) and software resources of the cloud 1120. The resources 1124 can include other applications and / or data that can be used when performing computer processing on servers remote from the computing device 1110. The resources 1124 can also include services provided via the Internet and / or via a subscriber network such as a cellular or Wi-Fi network.

[0076] Platform 1122 can abstract resources and functions to connect computing device 1110 with other computing devices. Platform 1122 can also be used to abstract a hierarchy of resources to provide a corresponding level of hierarchy for the demands encountered for resources 1124 implemented via platform 1122. Thus, in an interconnected device embodiment, the implementation of the functions described herein can be distributed throughout system 1100. For example, the functions can be implemented partially on computing device 1110 and via platform 1122 that abstracts the functions of cloud 1120.

[0077] It should be understood that, for clarity, embodiments of the present disclosure have been described with reference to different functional units. However, it will be apparent that, without departing from the present disclosure, the functionality of each functional unit can be implemented in a single unit, implemented in multiple units, or implemented as part of other functional units. For example, functionality illustrated as being performed by a single unit can be performed by multiple different units. Thus, reference to a particular functional unit is only considered as a reference to an appropriate unit for providing the described functionality, rather than indicating a strict logical or physical structure or organization. Thus, the present disclosure can be implemented in a single unit, or can be physically and functionally distributed among different units and circuits.

[0078] It will be understood that although the terms first, second, third, etc. may be used herein to describe various devices, elements, components, or parts, these devices, elements, components, or parts should not be limited by these terms. These terms are only used to distinguish one device, element, component, or part from another device, element, component, or part.

[0079] Although the present disclosure has been described in connection with some embodiments, it is not intended to be limited to the specific forms set forth herein. Rather, the scope of the present disclosure is limited only by the appended claims. Additionally, although individual features may be included in different claims, these may possibly be advantageously combined, and including in different claims does not imply that a combination of features is not feasible and / or advantageous. The order of features in the claims does not imply that the features must be in any particular order in which they work. Further, in the claims, the word "comprising" does not exclude other elements, and the terms "a" or "an" do not exclude a plurality.

Claims

1. A method for determining a missing transportation station of a target entity, comprising: Obtaining a target entity identifier corresponding to the target entity with a missing transportation station, where the missing transportation station includes a missing entry station; Obtaining a target entity image associated with the target entity identifier; Obtaining a first departure time and a second departure time of the target entity identifier, where the first departure time is the departure time of the target entity identifier corresponding to the missing entry station, and the second departure time is the previous departure time of the target entity identifier before the first departure time; Obtaining a set of in-station entity images, where the set of in-station entity images includes entity images of all entities that appear in all transportation stations between a first cut-off time and the first departure time, and the first cut-off time is selected from one of the second departure time and the starting operation time of the vehicle on the date of the occurrence of the missing entry station; Determining a target in-station entity image that matches the target entity image from the set of in-station entity images; And Determining the transportation station where the target in-station entity image is captured as the missing transportation station.

2. The method according to claim 1, wherein obtaining the set of in-station entity images includes: In response to the second departure time being earlier than the starting operation time of the vehicle on the date of the occurrence of the missing entry station, determining the first cut-off time as the starting operation time of the vehicle on the date of the occurrence of the missing entry station.

3. The method according to claim 1, further comprising: Collecting a plurality of entity images corresponding to a plurality of entity identifiers; And Creating a correspondence between the plurality of entity identifiers and the plurality of entity images, where obtaining the target entity identifier corresponding to the target entity with a missing transportation station includes: retrieving the target entity identifier from the plurality of entity identifiers, where obtaining the target entity image associated with the target entity identifier includes: determining the target entity image from the plurality of entity images according to the correspondence.

4. The method according to claim 1, wherein determining the target in-station entity image that matches the target entity image from the set of in-station entity images includes: Calculating the similarity between the target entity image and each entity image in the set of entity images of all entities in the set of in-station entity images; Determining the entity image with the highest similarity to the target entity image in the set of in-station entity images as the target in-station entity image.

5. The method according to claim 4, wherein determining the target in-station entity image that matches the target entity image from the set of in-station entity images further includes: Before calculating the similarity between the target entity image and each entity image in the set of entity images of all entities in the set of in-station entity images, performing normalization processing on the target entity image and each entity image in the set of in-station entity images to obtain a normalized target entity image corresponding to the target entity image and a normalized entity image corresponding to the entity image.

6. The method according to claim 5, wherein calculating the similarity between the target subject image and each subject image in the subject images of all the subjects in the in-station subject image set includes: Calculate the cosine similarity between the standardized target subject image and the standardized subject image, where the cosine similarity includes: Among them, A i represents the feature vector of the standardized target subject image, and B i represents the feature vector of the standardized subject image, and n represents the number of feature vectors.

7. A method for a missing transportation station of a target subject, including: Obtain a target subject identifier corresponding to the target subject with a missing transportation station, where the missing transportation station includes a missing departure station; Obtain a target subject image associated with the target subject identifier; Obtain a first entry time and a second entry time of the target subject identifier, where the first entry time is the entry time corresponding to the target subject identifier and the missing departure station, and the second entry time is the next entry time of the target subject identifier after the first entry time; Obtain a set of in-station subject images, where the set of in-station subject images includes subject images of all subjects that appear in all transportation stations between the first entry time and a second cut-off time, and the second cut-off time is selected from one of the second entry time and the vehicle end operation time of the occurrence date of the missing departure station; Determine a target in-station subject image that matches the target subject image from the set of in-station subject images; And Determine the transportation station where the target in-station subject image is collected as the missing transportation station.

8. The method according to claim 7, where obtaining the set of in-station subject images includes: In response to the second entry time being later than the vehicle end operation time of the occurrence date of the missing departure station, determine the second cut-off time as the vehicle end operation time of the occurrence date of the missing departure station.

9. The method according to claim 7, where determining a target in-station subject image that matches the target subject image from the set of in-station subject images includes: Calculate the similarity between the target subject image and each subject image in the subject images of all subjects in the set of in-station subject images; Determine the subject image with the highest similarity to the target subject image in the set of in-station subject images as the target in-station subject image.

10. A method for supplementary registration of a missing transportation station, including: Use the method for determining the missing transportation station of the target subject according to any one of claims 1-9 to determine the missing transportation station of the target subject; And Register the missing transportation station to the target subject identifier.

11. A device for determining a missing transportation station of a target subject, including: A target subject identifier acquisition module configured to obtain a target subject identifier corresponding to the target subject with a missing transportation station, where the missing transportation station includes a missing arrival station; A target subject image acquisition module configured to obtain a target subject image associated with the target subject identifier; A time acquisition module configured to obtain a first departure time and a second departure time of the target subject identifier, where the first departure time is the departure time corresponding to the target subject identifier and the missing arrival station, and the second departure time is the previous departure time of the target subject identifier before the first departure time, An image acquisition module, configured to acquire a set of in-station subject images, where the set of in-station subject images includes subject images of all subjects that appear within all transportation stations between a first cut-off time and the first outbound time, and the first cut-off time is selected from one of the second outbound time and the starting operation time of the vehicle on the date when the in-station absence occurs; An image matching module, configured to determine a target in-station subject image that matches the target subject image from the set of in-station subject images; And A missing transportation station determination module, configured to determine the transportation station where the target in-station subject image is acquired as the missing transportation station.

12. An apparatus for determining a missing transportation station of a target subject, comprising: A target subject identifier acquisition module, configured to acquire a target subject identifier corresponding to the target subject for which a transportation station is missing, where the transportation station missing includes an outbound station missing; A target subject image acquisition module, configured to acquire a target subject image associated with the target subject identifier; A time acquisition module, configured to acquire a first inbound time and a second inbound time of the target subject identifier, where the first inbound time is the inbound time corresponding to the outbound station missing of the target subject identifier, and the second inbound time is the next inbound time of the target subject identifier after the first inbound time, An image acquisition module, configured to acquire a set of in-station subject images, where the set of in-station subject images includes subject images of all subjects that appear within all transportation stations between the first inbound time and a second cut-off time, and the second cut-off time is selected from one of the second inbound time and the ending operation time of the vehicle on the date when the outbound station missing occurs; An image matching module, configured to determine a target in-station subject image that matches the target subject image from the set of in-station subject images; And A missing transportation station determination module, configured to determine the transportation station where the target in-station subject image is acquired as the missing transportation station.

13. A computing device, comprising A memory, configured to store computer-executable instructions; A processor, configured to execute the method according to any one of claims 1-10 when the computer-executable instructions are executed by the processor.

14. A computer-readable storage medium, storing computer-executable instructions, which when executed, execute the method according to any one of claims 1-10.

15. A computer program product, comprising a computer program, where when the computer program is executed by a processor, the steps of the method according to any one of claims 1-10 are implemented.

Images