CN117475665A - Method, equipment and storage medium for judging normal return of shared vehicle - Google Patents
Method, equipment and storage medium for judging normal return of shared vehicle Download PDFInfo
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- G—PHYSICS
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- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/123—Traffic control systems for road vehicles indicating the position of vehicles, e.g. scheduled vehicles; Managing passenger vehicles circulating according to a fixed timetable, e.g. buses, trains, trams
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- G06V2201/00—Indexing scheme relating to image or video recognition or understanding
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Abstract
The invention relates to the field of intelligent transportation, and discloses a method, equipment and storage medium for judging standard returning of a shared vehicle. The method comprises the following steps: when a parking picture is received, performing multi-feature extraction operation on the parking picture to obtain a feature attribute set; detecting whether the characteristic attribute set contains a site identifier, a whole vehicle identifier and a license plate number; if the characteristic attribute set comprises a site identifier, a whole vehicle identifier and a license plate number, calculating the geometric position relation between the site identifier and the whole vehicle identifier; judging whether parking is standard or not according to the geometric position relation; if the parking specification is judged, the vehicle returning state associated with the license plate number is set as the returned vehicle. In the embodiment of the invention, the judgment of the standard returning of the shared vehicle is quickly and accurately completed.
Description
Technical Field
The present invention relates to the field of intelligent transportation, and in particular, to a method, an apparatus, and a storage medium for determining a standard return of a shared vehicle.
Background
In the field of vehicle sharing, some parking management technologies exist at present, however, these prior technologies generally have some important drawbacks, which limit the effects and feasibility of the prior technologies in practical applications.
For example, bluetooth spike identification technology is used to identify the vehicle location and thus determine whether parking is normal. However, the bluetooth signal may be affected by the site environment, so that the signal is interfered, and whether parking is standard cannot be accurately determined, and then human participation in auxiliary determination is required. Under the condition of limited manpower, whether the vehicle is stopped normally cannot be judged rapidly.
Disclosure of Invention
The invention mainly aims to solve the technical problem that whether a vehicle is normally parked or not cannot be judged rapidly under the condition of limited manpower.
The first aspect of the present invention provides a method for determining a standard return of a shared vehicle, where the method for determining a standard return of a shared vehicle includes:
when a parking picture is received, performing multi-feature extraction operation on the parking picture to obtain a feature attribute set;
detecting whether the characteristic attribute set comprises a site identifier, a whole vehicle identifier and a license plate number;
if the characteristic attribute set comprises the station identifier, the whole vehicle identifier and the license plate number, calculating the geometric position relationship between the station identifier and the whole vehicle identifier;
judging whether parking is standard or not according to the geometric position relation;
and if the parking specification is judged, setting the vehicle returning state associated with the license plate number as the returned vehicle.
Optionally, in a first implementation manner of the first aspect of the present invention, when the parking picture is received, the step of calling a preset image recognition algorithm to perform an image recognition operation on the parking picture to obtain the feature attribute set includes:
when a parking picture is received, a first feature extraction operation of a station identifier is executed on the parking picture, a second feature extraction operation of a whole vehicle identifier is executed on the parking picture, and a third feature extraction operation of a license plate number is executed on the parking picture, so that a feature attribute set is obtained.
Optionally, in a second implementation manner of the first aspect of the present invention, the step of setting the license plate number-related returning state to be the returned vehicle if the parking specification is determined includes:
if the parking specification is judged, inquiring a lease order associated with the license plate number;
and setting the car returning state associated with the lease order as the returned car.
Optionally, in a third implementation manner of the first aspect of the present invention, after the step of detecting whether the feature attribute set includes a site identifier, a whole vehicle identifier, and a license plate number, the method further includes:
and if the characteristic attribute set does not contain at least one of a station identifier, a whole vehicle identifier and a license plate number, outputting prompt information for uploading the parking picture again.
Optionally, in a fourth implementation manner of the first aspect of the present invention, after the step of determining whether parking is normal according to the geometric position relationship, the method further includes:
if the stopping is judged to be out of specification, outputting prompt information for stopping again.
Optionally, in a fifth implementation manner of the first aspect of the present invention, when the parking picture is received, the step of calling a preset image recognition algorithm to perform an image recognition operation on the parking picture to obtain the feature attribute set includes:
when a parking picture is received, executing image authenticity detection on the parking picture to judge whether the parking picture is a physical picture or not;
and if the parking picture is a physical picture, invoking a preset image recognition algorithm to execute image recognition operation on the parking picture to obtain a characteristic attribute set.
Optionally, in a sixth implementation manner of the first aspect of the present invention, when the parking picture is received, the step of calling a preset image recognition algorithm to perform an image recognition operation on the parking picture to obtain the feature attribute set includes:
when a parking picture is received, generating a color histogram of the parking picture;
calculating a cross entropy of the color histogram and the preset color histogram based on the preset color histogram of the real image to obtain the difference degree between the parking picture and the real image;
and judging whether the parking picture is a physical picture or not according to the numerical value of the difference degree.
Optionally, in a seventh implementation manner of the first aspect of the present invention, the step of determining whether parking is normal according to the geometric positional relationship includes:
comparing the shape similarity between the geometric position relationship and a preset geometric position relationship;
and judging whether parking is standard or not according to the shape similarity.
A second aspect of the present invention provides a shared vehicle return-to-standard determination apparatus, including: a memory and at least one processor, the memory having instructions stored therein, the memory and the at least one processor being interconnected by a line; the at least one processor invokes the instructions in the memory to cause the shared vehicle specification return determination device to perform the shared vehicle specification return determination method described above.
A third aspect of the present invention provides a computer-readable storage medium having instructions stored therein that, when executed on a computer, cause the computer to perform the above-described shared vehicle specification return determination method.
In the embodiment of the invention, when a parking picture is received, multi-feature extraction operation is carried out on the parking picture to obtain a feature attribute set; detecting whether the characteristic attribute set comprises a site identifier, a whole vehicle identifier and a license plate number; if the characteristic attribute set comprises the station identifier, the whole vehicle identifier and the license plate number, calculating the geometric position relationship between the station identifier and the whole vehicle identifier; judging whether parking is standard or not according to the geometric position relation; and if the parking specification is judged, setting the vehicle returning state associated with the license plate number as the returned vehicle. The judgment device for the standard returning of the shared vehicle can autonomously evaluate the parking standardization through feature extraction and attribute detection, reduces the need of human intervention, judges whether the parking is standard or not by utilizing the geometric position relationship, and can improve the accuracy of the evaluation; setting the vehicle returning state associated with the license plate number as the returned vehicle, enabling a user to realize vehicle returning operation only by sending pictures, and enabling the judgment equipment sharing the vehicle to normally return to the vehicle to quickly respond to the vehicle returning request without inputting other redundant information; the judgment device for the standard return of the shared vehicle realizes the rapid and accurate judgment of the standard return of the shared vehicle.
Drawings
Fig. 1 is a schematic diagram of a first embodiment of a method for determining whether a vehicle is normal or not in an embodiment of the present invention;
FIG. 2 is a schematic diagram of a second embodiment of a method for determining whether a vehicle is normal or not in an embodiment of the present invention;
fig. 3 is a schematic diagram of an embodiment of a device for determining whether a vehicle is normal or not in an embodiment of the present invention.
Detailed Description
The embodiment of the invention provides a method, equipment and storage medium for judging whether a shared vehicle is normal or not.
Embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While the present disclosure has been illustrated in the drawings in some form, it is to be understood that the present disclosure may be embodied in various forms and should not be construed as limited to the embodiments set forth herein, but are provided to provide a more thorough and complete understanding of the present disclosure. It should be understood that the drawings and examples of the present disclosure are for illustrative purposes only and are not intended to limit the scope of the present disclosure.
In describing embodiments of the present disclosure, the term "comprising" and its like should be taken to be open-ended, i.e., including, but not limited to. The term "based on" should be understood as "based at least in part on". The term "one embodiment" or "the embodiment" should be understood as "at least one embodiment". The terms "first," "second," and the like, may refer to different or the same object. Other explicit and implicit definitions are also possible below.
For easy understanding, the following describes a specific flow of an embodiment of the present invention, referring to fig. 1, and an embodiment of a method for determining whether a vehicle is in a vehicle with shared vehicle specification in the embodiment of the present invention includes:
101. when a parking picture is received, performing multi-feature extraction operation on the parking picture to obtain a feature attribute set;
specifically, the determining device for returning the shared vehicle specification to the vehicle may be a cloud server. And preprocessing the received parking pictures, including image denoising, graying, size adjustment and other operations, so as to facilitate the accuracy and efficiency of subsequent feature extraction.
For a parking picture, various features such as color histogram, HOG features, vehicle edge features, license plate number region extraction and the like are extracted from the processed image by using a computer vision technology. For the site identification and the whole vehicle identification, a target detection or specific pattern matching method can be adopted to extract the characteristics.
Alternatively, a license plate detection algorithm (e.g., an end-to-end license plate detection algorithm based on a convolutional neural network) is used to locate the license plate region in the image. Once the license plate region is found, character recognition may be performed to extract the license plate number.
For the detection of the station identifier and the whole vehicle identifier, a target detection algorithm, such as a target detection algorithm based on deep learning (e.g. Faster R-CNN, YOLO, SSD, etc.), can be adopted. These algorithms may be trained to detect specific markers or tagged objects and once the relevant markers are detected, determine that the set of characteristic attributes includes both the site markers and the vehicle markers.
If the license plate number, the station identification and the whole vehicle identification are successfully extracted, integrating the information of the license plate number, the station identification and the whole vehicle identification into the characteristic attribute set.
Optionally, when a parking picture is received, a first feature extraction operation of a station identifier is performed on the parking picture, a second feature extraction operation of a whole vehicle identifier is performed on the parking picture, and a third feature extraction operation of a license plate number is performed on the parking picture, so that a feature attribute set is obtained. Specifically, a first feature of the stop-point identification may be identified and extracted in the parking picture using a target detection algorithm in computer vision technology, such as a deep-learning-based object detection algorithm (e.g., fast R-CNN, YOLO, etc.). A model may be trained to identify the identification cards of the parking lot. And likewise, identifying and extracting the visual characteristics of the whole car in the parking picture. A model may be trained to detect the overall contour and appearance characteristics of the vehicle. For license plate number extraction, image processing and Optical Character Recognition (OCR) techniques may be used. And respectively extracting the features obtained in the three steps to obtain the features of the station mark, the features of the whole vehicle mark and the feature attribute set of the license plate number.
102. Detecting whether the characteristic attribute set comprises a site identifier, a whole vehicle identifier and a license plate number;
specifically, by combining the information of the license plate number, the station identifier and the whole vehicle identifier, the logic judgment or the machine learning model is utilized to comprehensively judge whether the characteristic attribute set contains the required identifier information so as to judge whether the characteristic attribute set simultaneously contains the station identifier, the whole vehicle identifier and the license plate number.
Optionally, if the feature attribute set does not include at least one of a site identifier, a whole vehicle identifier and a license plate number, outputting prompt information for uploading the parking picture again.
103. If the characteristic attribute set comprises the station identifier, the whole vehicle identifier and the license plate number, calculating the geometric position relationship between the station identifier and the whole vehicle identifier;
specifically, feature points are extracted from images of the station identifier and the whole vehicle identifier by using a feature point extraction and matching algorithm (such as SIFT, SURF, ORB and the like), and matching is performed. These feature points may be corner points, edge points or other salient image features. Among them, SIFT (Scale-Invariant Feature Transform) is a feature detection algorithm for image processing and computer vision. SURF (speed-Up Robust Features) is a feature detection algorithm for image processing and computer vision. ORB (Oriented FAST and Rotated BRIEF) is a feature detection algorithm that combines FAST key point detectors and BRIEF descriptors.
Through the matched characteristic points, a transformation matrix, such as an affine transformation matrix or a projective transformation matrix, between the two identifications is calculated by using an RANSAC algorithm or the like. Wherein the transformation matrix describes how to transform from one identified coordinate system to another identified coordinate system.
And calculating the geometric position relationship of the station identifier and the whole vehicle identifier by using the calculated transformation matrix. For example, geometric transformation parameters such as translation, rotation, scaling, etc. between the markers may be calculated to determine the relative positional relationship therebetween.
And judging whether the relative position between the station mark and the whole vehicle mark accords with the parking standard or not according to the calculated geometric position relation, for example, whether the vehicle is parked in a specified parking space in a correct posture and angle or not.
104. Judging whether parking is standard or not according to the geometric position relation;
specifically, the geometric position relation between the station mark and the whole vehicle mark is calculated by utilizing an algorithm, and the relative position relation between the station mark and the whole vehicle mark is obtained, wherein the information comprises translation, rotation, scaling and the like.
According to the design criteria of a specific parking area, a canonical parking geometry is defined, such as the size of a parking space, the distance between a parked vehicle and a sign, the parking angle, etc. These conditions can be formulated according to the actual situation.
And judging the position relationship between the parked vehicle and the identifier by using the calculated geometric position relationship and the defined parking geometric condition. For example, whether parking meets a specification can be determined by measuring information such as a position, an angle, etc. of a vehicle in an image in combination with a predefined specification condition.
And outputting a conclusion of whether parking is standard or not according to the judging result. If the position relation between the parked vehicle and the station mark accords with the predefined standard condition, judging that the vehicle is parked normally; otherwise, judging that the vehicle is not stopped normally.
Optionally, the method comprises the steps of. Comparing the shape similarity between the geometric position relationship and a preset geometric position relationship; and judging whether parking is standard or not according to the shape similarity. Specifically, geometric position information of a preset standard parking space is prepared as a reference. And then, comparing the geometric position information extracted from the parking picture with preset geometric position information, and calculating the shape similarity. Similarity calculation methods include, but are not limited to, contour matching algorithms, hu moments (Hu moments), and the like. The Hu moment is a set of numerical characteristics obtained based on the gray value distribution of the image and is used for describing the shape information of the image. By normalizing the second order matrix of the image, a set of eigenvectors is then constructed using these normalized moments, totaling 7 independent moments, which is the Hu moment. These moments have invariance, can describe global shape features of the image, and have invariance to translation, rotation, and scale changes.
105. If the parking specification is judged, setting the vehicle returning state associated with the license plate number as the returned vehicle;
specifically, an association database of vehicle information and license plate numbers is established, and it is ensured that license plate numbers of each vehicle are correctly associated with corresponding information.
After the vehicle is parked, whether the parking of the vehicle is standard or not is judged by utilizing the geometric position relation judging algorithm. If the standard parking is judged, executing the next step; if not, no status update is performed. After confirming the parking specification of the vehicle, the determining device for the specification of the shared vehicle to return the vehicle can inquire the related vehicle information according to the license plate number and set the returning state of the vehicle as the returned vehicle. This may be accomplished by performing an update operation on the database to update the return status field in the corresponding vehicle information to "returned".
After updating the vehicle returning state, the judging device of the shared vehicle standard vehicle returning can perform state confirmation and generate related records, such as subsequent operations of vehicle returning order, charging and the like.
Optionally, if the parking specification is determined, inquiring a lease order associated with the license plate number; and setting the car returning state associated with the lease order as the returned car. Specifically, an association database of the lease orders and license plates is established, so that each lease order is ensured to be correctly associated with corresponding vehicle information. And after judging the vehicle parking standard, inquiring the lease order information related to the shared vehicle standard returning device according to the license plate number by the shared vehicle standard returning judging device. After the renting order related to the license plate number is inquired, the decision device for the standard car returning of the shared car sets the car returning state in the corresponding renting order as the returned car. This may be accomplished by an update operation to the rental order table in the database, updating the return status field of the corresponding order to "returned". After updating the vehicle returning state, the judging device of the shared vehicle standard vehicle returning can perform state confirmation and generate related records, such as subsequent operations of vehicle returning order, charging and the like.
Optionally, if the stopping is not standard, outputting prompt information of stopping again.
In the embodiment of the invention, when a parking picture is received, multi-feature extraction operation is carried out on the parking picture to obtain a feature attribute set; detecting whether the characteristic attribute set comprises a site identifier, a whole vehicle identifier and a license plate number; if the characteristic attribute set comprises the station identifier, the whole vehicle identifier and the license plate number, calculating the geometric position relationship between the station identifier and the whole vehicle identifier; judging whether parking is standard or not according to the geometric position relation; and if the parking specification is judged, setting the vehicle returning state associated with the license plate number as the returned vehicle. The judgment device for the standard returning of the shared vehicle can autonomously evaluate the parking standardization through feature extraction and attribute detection, reduces the need of human intervention, judges whether the parking is standard or not by utilizing the geometric position relationship, and can improve the accuracy of the evaluation; setting the vehicle returning state associated with the license plate number as the returned vehicle, enabling a user to realize vehicle returning operation only by sending pictures, and enabling the judgment equipment sharing the vehicle to normally return to the vehicle to quickly respond to the vehicle returning request without inputting other redundant information; the judgment device for the standard return of the shared vehicle realizes the rapid and accurate judgment of the standard return of the shared vehicle.
Referring to fig. 2, fig. 2 is a third embodiment of a method for determining whether a vehicle is returned to a vehicle in a shared vehicle specification according to an embodiment of the present invention, in step 101, the following steps may be performed:
1011. when a parking picture is received, executing image authenticity detection on the parking picture to judge whether the parking picture is a physical picture or not;
specifically, image authenticity detection refers to judging whether an image is tampered or manipulated by using computer vision and image processing technologies, i.e. judging the authenticity and integrity of the image. The main tasks of image authenticity detection include: detecting whether an image is edited or tampered with: and judging whether the image is subjected to operations such as adding, deleting, modifying and the like by analyzing the characteristics such as digital trace, editing trace, pixel value distribution and the like in the image. Detecting the source and acquisition environment of an image: the source and the authenticity of the image are judged by analyzing metadata (such as Exif information) in the image, shooting environment characteristics (such as illumination, shadows), noise points, compression traces and the like.
When a parking picture is received, generating a color histogram of the parking picture; calculating a cross entropy of the color histogram and the preset color histogram based on the preset color histogram of the real image to obtain the difference degree between the parking picture and the real image; and judging whether the parking picture is a physical picture or not according to the numerical value of the difference degree. The received parking pictures are subjected to color space conversion (such as RGB to HSV), and then the number of pixels of each color in the image is counted to generate a color histogram. A preset true image color histogram is prepared as a reference. Then, cross entropy is calculated for the color histogram of the parking picture and the color histogram of the real image. The cross entropy is an index for measuring the difference between two probability distributions and can be used for measuring the difference degree between a parking picture and a real picture. According to the calculated cross entropy value, a threshold value can be set to judge whether the parking picture is a physical picture. If the cross entropy is smaller than a certain threshold value, judging that the object photo is the object photo; and otherwise, the picture is a non-physical picture.
Optionally, metadata of the picture is analyzed, including shooting device information, shooting time, geographical location, etc. Such information may help to authenticate the authenticity of the picture, for example by checking device information and time of capture to confirm whether the picture meets expectations.
Optionally, digital image processing techniques are used to identify whether there are obvious signs of modification in the picture, such as image composition, tampering, smearing, etc. The authenticity of the picture can be found by analyzing color distribution, pixel matching, edge detection, etc.
Optionally, an image authenticity detection model is built based on a deep learning technique, and the authenticity of the picture is identified by training a large number of real pictures and synthesized/tampered pictures
1012. And if the parking picture is a physical picture, invoking a preset image recognition algorithm to execute image recognition operation on the parking picture to obtain a characteristic attribute set.
In this embodiment, false or tampered pictures can be filtered out, so that the pictures processed later are ensured to have certain authenticity. Especially if subsequent analysis and decision making of the image is required. After confirming that the parking picture is a physical picture, invoking a preset image recognition algorithm to execute image recognition operation on the parking picture, and further extracting useful information and characteristic attribute sets from the picture. The accuracy and the safety of parking management can be effectively improved through image authenticity detection and image identification operation.
Fig. 3 is a schematic structural diagram of a determining device for returning to a vehicle in a shared vehicle specification, where the determining device 500 for returning to a vehicle in a shared vehicle specification may have relatively large differences due to different configurations or performances, and may include one or more processors (central processing units, CPU) 510 (e.g., one or more processors) and a memory 520, and one or more storage media 530 (e.g., one or more mass storage devices) storing application programs 533 or data 532. Wherein memory 520 and storage medium 530 may be transitory or persistent storage. The program stored in the storage medium 530 may include one or more modules (not shown), each of which may include a series of instruction operations in the determination device 500 for returning the shared vehicle specification. Still further, the processor 510 may be configured to communicate with the storage medium 530 to execute a series of instruction operations in the storage medium 530 on the shared vehicle specification return determination device 500.
The decision device 500 for returning vehicles based on the shared vehicle specification may also include one or more power sources 540, one or more wired or wireless network interfaces 550, one or more input/output interfaces 560, and/or one or more operating systems 531, such as Windows Serve, mac OS X, unix, linux, free BSD, and the like. It will be appreciated by those skilled in the art that the shared vehicle specification return determination device structure shown in fig. 3 does not constitute a limitation on the determination device for returning based on the shared vehicle specification, and may include more or less components than those illustrated, or may combine certain components, or may be a different arrangement of components.
The present invention also provides a computer readable storage medium, which may be a non-volatile computer readable storage medium, or may be a volatile computer readable storage medium, where instructions are stored in the computer readable storage medium, where the instructions, when executed on a computer, cause the computer to perform the steps of the method for determining whether the shared vehicle is still in a vehicle.
In the context of this disclosure, a machine-readable medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. The machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
Moreover, although operations are depicted in a particular order, this should be understood as requiring that such operations be performed in the particular order shown or in sequential order, or that all illustrated operations be performed, to achieve desirable results. In certain circumstances, multitasking and parallel processing may be advantageous. Likewise, while several specific implementation details are included in the above discussion, these should not be construed as limiting the scope of the present disclosure. Certain features that are described in the context of separate embodiments can also be implemented in combination in a single implementation. Conversely, various features that are described in the context of a single implementation can also be implemented in multiple implementations separately or in any suitable subcombination.
Although the subject matter has been described in language specific to structural features and/or methodological acts, it is to be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or acts described above. Rather, the specific features and acts described above are example forms of implementing the claims.
Claims (10)
1. The method for judging whether the shared vehicle is normal or not is characterized by comprising the following steps of:
when a parking picture is received, performing multi-feature extraction operation on the parking picture to obtain a feature attribute set;
detecting whether the characteristic attribute set comprises a site identifier, a whole vehicle identifier and a license plate number;
if the characteristic attribute set comprises the station identifier, the whole vehicle identifier and the license plate number, calculating the geometric position relationship between the station identifier and the whole vehicle identifier;
judging whether parking is standard or not according to the geometric position relation;
and if the parking specification is judged, setting the vehicle returning state associated with the license plate number as the returned vehicle.
2. The method for determining whether the shared vehicle is normal or not according to claim 1, wherein the step of calling a preset image recognition algorithm to perform an image recognition operation on the parking picture when the parking picture is received, and obtaining the feature attribute set comprises:
when a parking picture is received, a first feature extraction operation of a station identifier is executed on the parking picture, a second feature extraction operation of a whole vehicle identifier is executed on the parking picture, and a third feature extraction operation of a license plate number is executed on the parking picture, so that a feature attribute set is obtained.
3. The method according to claim 1, wherein the step of setting the license plate number-associated return state to the returned vehicle if the parking specification is determined includes:
if the parking specification is judged, inquiring a lease order associated with the license plate number;
and setting the car returning state associated with the lease order as the returned car.
4. The method for determining whether the shared vehicle is returning to the vehicle according to claim 1, wherein after the step of detecting whether the feature attribute set includes a station identifier, a whole vehicle identifier, and a license plate number, the method further comprises:
and if the characteristic attribute set does not contain at least one of a station identifier, a whole vehicle identifier and a license plate number, outputting prompt information for uploading the parking picture again.
5. The method for determining whether the vehicle stop is normal or not according to claim 1, wherein after the step of determining whether the vehicle stop is normal or not according to the geometric positional relationship, the method further comprises:
if the stopping is judged to be out of specification, outputting prompt information for stopping again.
6. The method for determining whether the shared vehicle is normal or not according to claim 1, wherein the step of calling a preset image recognition algorithm to perform an image recognition operation on the parking picture when the parking picture is received, and obtaining the feature attribute set comprises:
when a parking picture is received, executing image authenticity detection on the parking picture to judge whether the parking picture is a physical picture or not;
and if the parking picture is a physical picture, invoking a preset image recognition algorithm to execute image recognition operation on the parking picture to obtain a characteristic attribute set.
7. The method for determining whether the shared vehicle is normal or not according to claim 6, wherein the step of calling a preset image recognition algorithm to perform an image recognition operation on the parking picture when the parking picture is received, and obtaining the feature attribute set comprises:
when a parking picture is received, generating a color histogram of the parking picture;
calculating a cross entropy of the color histogram and the preset color histogram based on the preset color histogram of the real image to obtain the difference degree between the parking picture and the real image;
and judging whether the parking picture is a physical picture or not according to the numerical value of the difference degree.
8. The method according to claim 1, wherein the step of determining whether parking is normal based on the geometric positional relationship comprises:
comparing the shape similarity between the geometric position relationship and a preset geometric position relationship;
and judging whether parking is standard or not according to the shape similarity.
9. A shared vehicle specification returning determination apparatus, characterized in that the shared vehicle specification returning determination apparatus includes: a memory and at least one processor, the memory having instructions stored therein, the memory and the at least one processor being interconnected by a line;
the at least one processor invoking the instructions in the memory to cause the shared vehicle specification return determination apparatus to perform the shared vehicle specification return determination method of any one of claims 1-8.
10. A computer-readable storage medium, on which a computer program is stored, characterized in that the computer program, when executed by a processor, implements the method for determining whether a shared vehicle has been returned according to any one of claims 1-8.
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