Passage detection method and device, electronic equipment and storage medium

Abstract

The present disclosure provides a passageway detection method and device, electronic equipment and storage medium, relates to the technical field of artificial intelligence, specifically to the technical field of intelligent transportation and automatic driving. The specific implementation scheme is: obtaining an entrance and exit of a region; obtaining pass data of the entrance and exit, and determining a pass detection result of the entrance and exit; and determining a pass entrance and exit of the region according to the pass data and the pass detection result. The embodiment of the present disclosure can improve the accuracy of passageway detection.

Description

Technical Field

[0001] This disclosure relates to the field of image processing technology, specifically to the fields of artificial intelligence, intelligent transportation and deep learning technology, and particularly to methods, apparatuses, electronic devices and storage media for access point detection. Background Technology

[0002] In the field of navigation, users can enter a region from its entrance and leave it from its exit.

[0003] The real area has a large number of entrances and exits. Due to various reasons (such as regional construction and the epidemic), some entrances and exits of the area will be restricted. Summary of the Invention

[0004] This disclosure provides a method, apparatus, electronic device, and storage medium for detecting access points.

[0005] According to one aspect of this disclosure, a method for detecting access points is provided, comprising:

[0006] Get the area's entrances and exits;

[0007] Obtain the passage data of the entrance / exit and determine the passage detection result of the entrance / exit;

[0008] Based on the passage data and the passage detection results, the passage entrances and exits of the area are determined.

[0009] According to one aspect of this disclosure, a passageway detection device is provided, comprising:

[0010] The entrance / exit acquisition module is used to acquire the entrances and exits of the area;

[0011] The access detection module is used to acquire access data of the entrance / exit and determine the access detection result of the entrance / exit;

[0012] The access point determination module is used to determine the access points and exits of the area based on the access data and the access detection results.

[0013] According to another aspect of this disclosure, an electronic device is provided, comprising:

[0014] At least one processor; and

[0015] A memory communicatively connected to the at least one processor; wherein,

[0016] The memory stores instructions that can be executed by the at least one processor, which, when executed, enable the at least one processor to perform the access point detection method according to any embodiment of this disclosure.

[0017] According to another aspect of this disclosure, a non-transitory computer-readable storage medium storing computer instructions is provided, wherein the computer instructions are used to cause the computer to perform the access point detection method according to any embodiment of this disclosure.

[0018] According to another aspect of this disclosure, a computer program product is provided, including a computer program that, when executed by a processor, implements the access point detection method described in any embodiment of this disclosure.

[0019] The embodiments disclosed herein can improve the accuracy of access point detection.

[0020] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of this disclosure, nor is it intended to limit the scope of this disclosure. Other features of this disclosure will become readily apparent from the following description. Attached Figure Description

[0021] The accompanying drawings are provided to better understand this solution and do not constitute a limitation of this disclosure. Wherein:

[0022] Figure 1 This is a schematic diagram of a passageway detection method provided according to an embodiment of this disclosure;

[0023] Figure 2 This is a schematic diagram of another access point detection method provided according to an embodiment of this disclosure;

[0024] Figure 3 This is a schematic diagram of another access point detection method provided according to an embodiment of this disclosure;

[0025] Figure 4 This is a scene diagram of a passageway detection method provided according to an embodiment of this disclosure;

[0026] Figure 5 This is a schematic diagram of a region provided according to an embodiment of the present disclosure;

[0027] Figure 6 This is a schematic diagram of an entrance / exit provided according to an embodiment of this disclosure;

[0028] Figure 7 This is a schematic diagram of an entrance / exit provided according to an embodiment of this disclosure;

[0029] Figure 8 This is a schematic diagram of an entrance / exit provided according to an embodiment of this disclosure;

[0030] Figure 9 This is a schematic diagram of an entrance / exit provided according to an embodiment of this disclosure;

[0031] Figure 10This is a schematic diagram of a passageway detection device provided according to an embodiment of the present disclosure;

[0032] Figure 11 This is a block diagram of an electronic device used to implement the access point detection method of the embodiments of this disclosure. Detailed Implementation

[0033] The exemplary embodiments of this disclosure are described below with reference to the accompanying drawings, including various details of the embodiments to aid understanding, and should be considered merely exemplary. Therefore, those skilled in the art will recognize that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of this disclosure. Similarly, for clarity and brevity, descriptions of well-known functions and structures are omitted in the following description.

[0034] Figure 1 This is a flowchart of a passageway detection method disclosed in an embodiment of this disclosure. This embodiment can be applied to detecting whether the entrance and exit of an area are passable. The method of this embodiment can be executed by a passageway detection device, which can be implemented in software and / or hardware and specifically configured in an electronic device with a certain data processing capability. The electronic device can be a client device or a server device, such as a mobile phone, tablet computer, vehicle terminal, and desktop computer.

[0035] S101, obtain the entrance and exit points of the area.

[0036] A region can refer to a specific area on a map. The map can be divided to define regions. For example, a region may include a residential area, a shopping mall, a logistics center, a factory, or a functional area comprising at least one of the aforementioned features. An entrance / exit refers to a passageway or doorway leading into or out of a region. For example, an entrance / exit can be an open intersection or a closed doorway. A region may include at least one entrance / exit. For example, a residential area may include a north entrance, a south entrance, and a west entrance. Multiple regions on the map can be selected, and the entrances / exits of that region recorded on the map can be retrieved. The map includes at least one region and at least one entrance / exit for each region.

[0037] S102, acquire the passage data of the entrance / exit, and determine the passage detection result of the entrance / exit.

[0038] Access data is used to determine whether entrances and exits are open to traffic. Access data typically consists of data collected from entrances and exits by a data source, and may specifically include at least one of the following: user behavior at entrances and exits, access status at entrances and exits, and user suggestions at entrances and exits. Access data for entrances and exits can be requested from at least one data source.

[0039] The access control results at entrances and exits are used to determine whether the entrance or exit is passable. The access control data includes data of at least one data type. At least one access control result can be obtained from the access control data. For example, the access control result may include "passable" or "not passable." Alternatively, the access control result may include "entrance," "exit," or "not passable," etc.; furthermore, the access control result may include "vehicle entrance," "vehicle exit," "pedestrian entrance," "pedestrian exit," or "not passable," etc.

[0040] Determining the access control result of an entrance / exit based on access data can be achieved by determining the processing method based on the data type and applying the corresponding processing method to different data types to obtain the access control result. For example, if the data type is an image, image recognition can be performed on the image to detect the presence of entrance / exit identification text. If the text exists, the access control result for that entrance / exit is determined to be passable; otherwise, it is determined to be impassable. Similarly, the presence of an open door in the image can be detected; if it exists, the access control result for that entrance / exit is determined to be passable; otherwise, it is determined to be impassable. For example, if the data type is "route," the system detects routes passing through the entrance / exit. If a route exists, the entrance / exit is determined to be passable; otherwise, it is determined to be impassable. Alternatively, the system can detect the direction of movement along the route passing through the entrance / exit. If the movement direction is from outside the area to inside, the entrance / exit is determined to be an entrance; if the movement direction is from inside the area to outside, it is determined to be an exit. Other detection methods are also available and can be determined as needed.

[0041] S103, determine the access points and exits of the area based on the access data and the access detection results.

[0042] Access points are used to determine the entrances to and exits from an area. Based on access data, the reliability of each access detection result at each entrance / exit is determined. Then, based on the reliability of the access detection results and each individual detection result, the final access detection result for that entrance / exit can be determined. Based on the final access detection results of each entrance / exit in the area, multiple entrances / exits are filtered to determine the final access points for that area.

[0043] For example, the reliability of the access data can be determined based on the reliability of the data source, the reliability of the data type, and a custom reliability calculation method, and the reliability of the corresponding access detection results can also be determined. The access detection result with the highest reliability is selected and determined as the final access detection result for that entrance / exit. Based on the reliability of the final access detection results for each entrance / exit, the entrance / exit to which the final access detection result with the highest reliability belongs is selected and determined as the access entrance / exit.

[0044] Optionally, the access point detection method further includes: updating map data based on the access points of the area; and performing route planning based on the updated map data.

[0045] Access points are used to update map data. Map data is typically used for route planning. Updating map data using access points involves marking access points for corresponding areas on the map. When a user needs to plan a route, the updated map data is used. If the route passes through a certain area, the access points can be used to accurately plan the user's travel method, improving route planning accuracy and the real-time nature of map data updates, thereby enhancing the real-time performance of route updates.

[0046] Existing access control checks can be performed manually, but this method requires very high labor costs and is time-consuming.

[0047] In fact, the method provided in this disclosure can acquire passage data and passage detection results, determine passage entrances and exits, and integrate passage data to detect passable entrances and exits, thereby improving the passage detection efficiency of entrances and exits and reducing detection costs.

[0048] According to the technical solution disclosed herein, by acquiring the entrances and exits of a region and obtaining the passage data of those entrances and exits, the passage detection results are determined to filter the passage entrances and exits of the region. It is possible to detect whether the entrances and exits are passable based on online data, accurately detect the passability of entrances and exits, improve the passage detection efficiency of entrances and exits, reduce the passage detection cost, and improve the accuracy of passage detection.

[0049] Figure 2 This is a flowchart of another access point detection method disclosed in this embodiment, which is further optimized and extended based on the above technical solution and can be combined with the above optional implementation methods. The step of determining the access point / exit of the area based on the access data and the access detection results is specifically implemented as follows: based on the data type of the access data, calculating the access point / exit type weight of at least one access point / exit detection result; and based on the access point / exit type weight of each access point / exit detection result, determining the access point / exit among at least one access point / exit in the area.

[0050] S201, Obtain the entrance and exit points of the area.

[0051] S202, obtain the passage data of the entrance / exit and determine the passage detection result of the entrance / exit.

[0052] S203, based on the data type of the passage data, calculate the entrance / exit type weight of at least one passage detection result of the entrance / exit.

[0053] The number of data types for access control data must be at least one. Access control data includes data of at least one data type. Different data types have different reliability, and consequently, different reliability in determining access control detection results. Access point / exit type weights are used to determine the reliability of access control detection results and to filter these results, thereby filtering access points / exits. Access point / exit type weights can be calculated based on the correspondence between data types and weights. For example, an image data type corresponds to a weight of 0.8; a route data type corresponds to a weight of 0.6.

[0054] Typically, data of the same data type is used to determine a single access detection result. Data of different data types are processed independently, and at least one access detection result can be determined. However, due to the varying reliability of different data types, the accuracy of the corresponding access detection results differs. Therefore, it is necessary to consider the reliability of different data types and select a reliable access detection result. This embodiment of the disclosure uses entrance / exit type weighting to evaluate the reliability of the access detection result.

[0055] S204, based on the entrance / exit type weights of each of the passage detection results, determine a passage entrance / exit in at least one entrance / exit in the area.

[0056] Based on the weight of the entrance / exit type in the passage detection results for each entrance / exit, the same passage detection result can be sorted according to the weight of the entrance / exit type, and the entrances / exits can be filtered to obtain the passable entrances / exits. For example, the passage detection results include passable and impassable. The passable entrance / exit type weight for entrance / exit A is 0.3, and the impassable entrance / exit type weight is 0.5; the passable entrance / exit type weight for entrance / exit B is 0.6, and the impassable entrance / exit type weight is 0.2; the passable entrance / exit type weight for entrance / exit C is 0.9, and the impassable entrance / exit type weight is 0.3. The passable entrances / exits are sorted as C, B, and A; the impassable entrances / exits are sorted as A, C, and B. It can be determined that entrance / exit C is passable. Entrance / exit A is impassable. Entrance / exit B is in the middle and can be determined as an impassable entrance / exit, or manual evaluation can be introduced.

[0057] Optionally, the step of calculating the entrance / exit type weight of at least one entrance / exit detection result based on the data type of the access data includes: obtaining the data type of at least one piece of data in the access data, the data being used to determine the access detection result; calculating the data that determines the same access detection result; and calculating the entrance / exit type weight of each access detection result based on the data type of each piece of data, the correspondence between data type and weight, and the data that determines each access detection result.

[0058] Different data can yield the same or different access control results. This is because different data types require different detection methods to determine access control results, and consequently, the reliability of the obtained access control results also differs, meaning the corresponding entrance / exit type weights differ.

[0059] The data used to determine the same access control result are statistically analyzed to identify different data that yielded that result. This data can include at least one data type. A correspondence exists between data types and weights; this correspondence is used to statistically analyze the weights of different data types that determine the same access control result, thereby calculating the entry / exit type weight of that access control result. Specifically, the weights of each data point can be determined based on its data type and the correspondence between data types and weights. Based on the data that determined the same access control result and their weights, the entry / exit type weight of that access control result can be calculated. For example, the weights of the data that determined the same access control result can be accumulated to determine the entry / exit type weight of the access control result.

[0060] In a specific example, the data types of the access detection results for determining the passability of entrance / exit A include images and routes. The weight of image data is 0.8, and the weight of route data is 0.4. Accordingly, the weight of the entrance / exit type in the access detection results for the passability of entrance / exit A is 0.4 + 0.8 = 1.2.

[0061] By acquiring the data types of the passage data and statistically determining the data for the same passage detection result, and based on the correspondence between the data data types and weights, the weight of each passage detection result can be determined. This allows for the calculation of the entrance / exit type weight for each passage detection result. The reliability of the corresponding determined passage detection result can be assessed based on the reliability of different types of data, thereby filtering out reliable passage detection results and improving the accuracy of the passage detection results.

[0062] Optionally, the step of calculating the entrance / exit type weight of each of the access detection results based on the data type of each of the data, the correspondence between data type and weight, and the data used to determine each of the access detection results includes: when the data type of the data includes image type, determining a first weight based on the image quality of the images of each of the access detection results as determined statistically; when the data type of the access data includes route type, determining a second weight based on the number of routes of each of the access detection results as determined statistically; when the data type of the access data includes user feedback type, determining a third weight based on the user feedback content of each of the access detection results as determined statistically; and determining the entrance / exit type weight of the access detection results based on at least one weight.

[0063] The first weight can refer to the weight corresponding to image-type data. The first weight is determined based on the image quality of the image-type data. The second weight can refer to the weight corresponding to route-type data, and the second weight is determined based on the quantity of route-type data. The third weight can refer to the weight corresponding to user feedback-type data, and the third weight is determined based on the content of the user feedback. Here, user feedback content refers to user feedback-type data. Determining the entrance / exit type weight based on at least one weight actually means determining the entrance / exit type weight based on the aforementioned at least one weight. Specifically, this can be achieved by accumulating the first, second, and third weights to obtain the entrance / exit type weight.

[0064] Specifically, a pre-defined correspondence between image quality and weight can be established. Based on the image quality of the image used to determine the pass detection result, the weight of each image is determined, and the image with the highest weight is selected as the first weight. For example, image quality can be determined based on the image's data source, or based on image parameters, such as at least one of the following: sharpness, size, and resolution. For example, the weight of a panoramic image is 1, and the weight of a normal image is 0.9.

[0065] A pre-defined relationship between the number of routes and their weights can be established. Based on the number of routes that result in the traffic detection and the relationship between these numbers and weights, a second weight is determined. For example, the number of routes in the traffic detection result can be determined statistically. For instance, a weight of 0.8 corresponds to a number > 20 routes, a weight of 0.6 corresponds to a number <= 20 but > 10 routes, a weight of 0.4 corresponds to a number <= 10 but > 5 routes, and a weight of 0 corresponds to a number <= 5 routes.

[0066] A pre-defined correspondence between user feedback types and weights can be established. The third weight is determined based on whether user feedback content of the specified type exists in the data from the pass detection results. For example, if user feedback content of the specified type exists in the pass detection results, the corresponding weight is 0.5; if user feedback content of the specified type does not exist in the pass detection results, the corresponding weight is 0.

[0067] By configuring multiple data types and different weight calculation methods for different data types, the weights corresponding to the data types can be calculated. The weights corresponding to the data types that determine the number of access detection results can be statistically determined. By calculating the weights of the access points, the amount of detection data at the access points can be increased, the representativeness of the detection data at the access points can be improved, and the detection accuracy of the access points can be improved.

[0068] Optionally, the step of calculating the entry / exit type weights of each of the access detection results includes: determining the entry / exit type weights of each of the access detection results based on the priority of each access detection result, the data type of each data, the correspondence between the data type and weight under each priority, and determining the data of each access detection result.

[0069] Different passage detection results can have different priorities. Understandably, if the entrance / exit is not a passageway to or from the area, there's no need to continue detecting whether the entrance / exit is passable. In fact, the passability detection is based on the premise that the entrance / exit is a passageway; if the entrance / exit is not a passageway, the passability detection results are incorrect, and correspondingly, the data determining passability or impassability is incorrect and will not be considered. Furthermore, the passability detection results include passability or impassability. The passability detection result can be further subdivided into: vehicular entrance, vehicular exit, pedestrian entrance, and pedestrian exit. In reality, the passability detection results for vehicle entrances, vehicle exits, pedestrian entrances, and pedestrian exits are based on the premise that the entrances and exits are passable passageways. If the entrances and exits are not passable passageways, the corresponding passability detection results for vehicle entrances, vehicle exits, pedestrian entrances, and pedestrian exits will be incorrect. Therefore, the data for the passability detection results for vehicle entrances, vehicle exits, pedestrian entrances, and pedestrian exits can be determined to be incorrect and will not be considered.

[0070] For example, entrances and exits obtained from map data may not be passageways connecting the area to the outside world. Consequently, there is no need to check whether these entrances and exits are passable. Similarly, entrances and exits obtained from map data may be closed and impassable. Therefore, there is no need to classify the passability detection results for these entrances and exits, i.e., there is no need to differentiate between pedestrian and / or vehicular traffic, or exits and / or entrances.

[0071] This allows for the configuration of priorities for passage detection results, and for different priorities, different correspondences between data types and weights can be configured. Alternatively, the correspondence between data types and weights can be configured to be the same for certain priorities.

[0072] For example, in high-priority access detection results, panoramic images have a weight of 1, and ordinary images have a weight of 0.9. In low-priority access detection results, panoramic images have a weight of 0.9, and ordinary images have a weight of 0.7. As another example, in high-priority access detection results, the weight corresponding to a number > 20 is 0.8, the weight corresponding to a number <= 20 and > 10 is 0.6, the weight corresponding to a number <= 10 and > 5 is 0.4, and the weight corresponding to a number <= 5 is 0. In low-priority access detection results, the weight corresponding to a number > 20 is 1, the weight corresponding to a number <= 20 and > 10 is 0.7, the weight corresponding to a number <= 10 and > 5 is 0.5, and the weight corresponding to a number <= 5 is 0. As yet another example, in high-priority access detection results, if the access detection results data contains user feedback content of the user feedback type, the corresponding weight is 0.5; if the access detection results data does not contain user feedback content of the user feedback type, the corresponding weight is 0. In the low-priority access detection results, if it is determined that the data in the access detection results contains user feedback content of the user feedback type, the corresponding weight is 0.3; if it is determined that the data in the access detection results does not contain user feedback content of the user feedback type, the corresponding weight is 0.

[0073] The system can prioritize calculating high-priority access detection results. Based on the entrance / exit type weights of high-priority access detection results, the data type of each data item, the correspondence between low-priority data types and weights, and the data used to determine each access detection result, the entrance / exit type weights of low-priority access detection results are determined. Specifically, the priority of a "whether it is a passageway" access detection result is higher than the priority of a "whether it is passable" access detection result. The priority of a "whether it is passable" access detection result is higher than the priority of passable access detection results such as vehicle entrances, vehicle exits, pedestrian entrances, and pedestrian exits.

[0074] In a specific example, if the weight of the entrance / exit type for a passage detection result that is not a passageway is greater than or equal to a preset first weight threshold, and / or if the weight of the entrance / exit type for a passage detection result that is not a passageway is greater than or equal to the weight of the entrance / exit type for a passage detection result that is a passageway, the entrance / exit type weights for passage detection results such as passable, impassable, vehicle entrance, vehicle exit, pedestrian entrance, and pedestrian exit are determined to be 0. If the weight of the entrance / exit type for a passage detection result that is impassable is greater than or equal to a preset second weight threshold, and / or if the weight of the entrance / exit type for a passage detection result that is impassable is greater than the weight of the entrance / exit type for a passage detection result that is passable, the entrance / exit type weights for passage detection results such as vehicle entrance, vehicle exit, pedestrian entrance, and pedestrian exit are determined to be 0.

[0075] By configuring the priority of access detection results and the correspondence between the data types and weights of the priorities, the weights of the access detection results for entrance and exit types can be determined in order of priority. This can reduce the weight of low-reliability access detection results, decrease the amount of data processing for detection weights, and improve the efficiency and accuracy of entrance and exit type weights.

[0076] According to the technical solution disclosed herein, by determining the entrance / exit type weight of each entrance / exit detection result based on the data type of the access data, reliability detection of access detection results based on different data types is achieved. Entrances / exits are then filtered based on the entrance / exit type weight of the access detection results, thereby determining the access entrances / exits. Reliable access detection results can be selected to determine the access entrances / exits, improving the detection accuracy of entrances / exits while also improving detection efficiency.

[0077] Figure 3 This is a flowchart of another access point detection method disclosed in this embodiment, which is further optimized and extended based on the above technical solution and can be combined with the above optional implementation methods. The determination of the access point detection result is specifically defined as follows: based on the data type of at least one data in the access data, each of the data is processed to obtain the access detection result corresponding to each of the data.

[0078] S301, Obtain the entrance and exit points of the area.

[0079] S302, acquire the passage data of the entrance / exit, and process each of the data according to the data type of at least one of the data to obtain the passage detection result corresponding to each of the data.

[0080] Different data types require different processing methods. Data of the same data type is acquired and processed using the same method to obtain at least one pass detection result corresponding to that data type.

[0081] Optionally, the data type is an image type; the step of processing the data to obtain the corresponding access detection result includes: performing image recognition on the data to obtain access sign detection data; and determining the access detection result corresponding to the data based on the access sign detection data.

[0082] Traffic sign detection data is used to identify whether an entrance / exit is passable and / or the direction of passage. Traffic sign detection data can be the identification of entrances / exits in an image, along with the target area indicating passability and / or direction of passage. Based on the traffic sign detection data, it is determined whether the entrance / exit corresponding to the image is a passageway, its passability, and the direction of passage. The presence or absence of traffic sign detection data can determine whether the entrance / exit is a passageway; for example, if traffic sign detection data exists, the entrance / exit is determined to be passable; if not, it is determined to be impassable. Alternatively, the image content within the traffic sign detection data can determine whether the entrance / exit is a passageway; for example, if the image content indicates passability, the entrance / exit is determined to be passable; if the image content indicates impassability, it is determined to be impassable. Specifically, the direction of passage is determined based on the content of the traffic sign detection data. For example, if the image content indicates an intention to enter, the access detection result of the corresponding entrance / exit is determined as an entrance; if the image content indicates an intention to leave, the access detection result of the corresponding entrance / exit is determined as an exit.

[0083] For example, traffic sign detection data may include at least one of the following: license plate cameras, ground traffic arrows, traffic notification text, and gates.

[0084] In a specific example, the presence of a door in an image can determine whether the corresponding entrance / exit is passable. Specifically, if a door exists, the entrance / exit is determined to be passable; if no door exists, its passability cannot be determined. Alternatively, the presence or absence of a door in an image can also determine whether the corresponding entrance / exit is passable. Specifically, if a door is closed, the entrance / exit is determined to be impassable; if a door is open, it is determined to be impassable. Detecting whether a door is closed can involve checking for the presence of a locking device, such as a padlock or metal plate.

[0085] For example, the presence of a license plate camera in an image can determine whether the corresponding entrance / exit is passable, and the orientation of the camera can determine the direction of passage. Specifically, if a license plate camera is present, the entrance / exit is determined to be passable; if no camera is present, its passability cannot be determined. If the camera is within the area it is facing, the entrance / exit is determined to be a vehicle entrance; if it is outside the area it is facing, it is determined to be a vehicle exit. The license plate camera is typically a camera mounted on a pole near the vehicle entrance, used to detect the license plates of vehicles entering and exiting the area.

[0086] For example, the presence of ground-level traffic arrows in an image can determine whether the corresponding entrance / exit is passable, and the direction of traffic at the entrance / exit can be determined based on the direction of these arrows. Specifically, if ground-level traffic arrows are present, the entrance / exit is determined to be passable; if no arrows are present, its passability cannot be determined. If the entrance / exit is within the area indicated by the arrows, it is determined to be a vehicular entrance and / or a pedestrian entrance; if it is outside the area indicated by the arrows, it is determined to be a vehicular exit and / or a pedestrian exit. Ground-level traffic arrows are typically arrows on the ground near the entrance.

[0087] For example, the accessibility of an entrance / exit can be determined based on the text content of the access notice in an image, and the direction of passage can be determined based on the passage-allowed text content in the access notice. Specifically, if the access notice contains passage-allowed text, the access detection result for the corresponding entrance / exit is determined to be passable; if the access notice contains impassable text, the access detection result is determined to be impassable. If the text content identified in the access notice includes "allow entry," the access detection result for the corresponding entrance / exit is determined to be a vehicular entrance and / or a pedestrian entrance; if the text content identified in the access notice includes "allow exit," the access detection result for the corresponding entrance / exit is determined to be a vehicular exit and / or a pedestrian exit. The access notice text is typically the text displayed on a sign placed near the entrance. Furthermore, the text content can also indicate the passage time, allowing for the determination of time-limited access entrances / exits.

[0088] In addition, if at least one of the following is absent: a license plate camera, ground traffic arrows, traffic notification text, or a door, the access point / exit corresponding to the image can be determined to be impassable. Conversely, if a license plate camera, ground traffic arrows, traffic notification text, or a door is present, the access point / exit corresponding to the image can be determined to be passable.

[0089] By processing image-type data, access sign detection data is obtained. Based on the existence and specific content of the access sign detection data, the access detection result is determined. This allows for accurate analysis of access detection results from image-type data, resulting in image-based access detection and improving the accuracy of access detection results.

[0090] Optionally, the data type is a route type; the step of processing the data to obtain the corresponding traffic detection result includes: determining the traffic detection result based on the route direction and route movement mode of the data.

[0091] The data represents routes. If a route includes an entrance / exit, the accessibility detection result for that entrance / exit is determined to be passable; otherwise, it is determined to be impassable. Based on the route direction and movement mode, the route direction determines whether the route moves from outside the area to within the area or from within the area to outside. The movement mode determines whether it's for vehicles or pedestrians. For example, driving, taxis, and public transportation routes determine the accessibility detection result as a vehicle entrance / exit; walking routes determine the accessibility detection result as a pedestrian entrance / exit. The route direction determines whether an entrance / exit is an entrance / exit, and the movement mode determines whether it's a vehicle entrance / exit or a pedestrian entrance / exit.

[0092] Specifically, in acquiring entrance / exit traffic data, if a route exists that passes through the entrance / exit, the traffic detection result for that entrance / exit is determined to be passable; otherwise, it is determined to be impassable. For passable entries, the data can be categorized into data classes with the same route direction and movement mode. From each data class, one route is extracted for route direction and movement mode determination. If there is movement from outside the area to inside, the traffic detection result is determined to be an entrance; if there is movement from inside the area to outside, the traffic detection result is determined to be an exit. If the movement mode includes vehicle movement, the traffic detection result is determined to be a vehicular entrance; if the movement mode includes pedestrian movement, the traffic detection result is determined to be a pedestrian entrance.

[0093] By detecting route direction and movement mode in route type data, the traffic detection results can be determined. This allows for accurate analysis of traffic detection results from route type data, resulting in route analysis traffic detection results and improving the accuracy of traffic detection results.

[0094] Furthermore, the data type is user feedback type; the processing of the data to obtain the corresponding access detection result includes: performing intent recognition on the user feedback content of each data to determine the access detection result.

[0095] User feedback data typically refers to user suggestions and comments regarding entrances and exits. User feedback data can include at least one of the following data types: text, voice, image, and video. It is usually associated with an entrance or exit, allowing for identification of the corresponding entrance or exit, and includes information on whether passage is permitted, how to pass, and when to pass. User feedback content (User Generated Content, UGC) can be obtained by processing the data using at least one of the following methods: text recognition, voice recognition, and image recognition. For example, a user's comment regarding entrance A of a residential complex might be, "Cars cannot pass through entrance A after 6 PM."

[0096] When user feedback data exists, the accessibility of the relevant entrance / exit is determined based on the user feedback content identified from the data, along with the direction and method of passage. Without user feedback data, accessibility cannot be determined for that entrance / exit.

[0097] If the user feedback indicates that entry is permitted, the access control result for the corresponding entrance / exit in the image is determined to be a vehicular entrance and / or a pedestrian entrance; if the user feedback indicates that exit is permitted, the access control result for the corresponding entrance / exit in the image is determined to be a vehicular exit and / or a pedestrian exit. Furthermore, the user feedback can also indicate the passage time, allowing for the determination of time-limited access entrances / exits based on the passage time, and the application of time-limited access markings.

[0098] As in the previous example, the user feedback was "Cars cannot pass through Entrance A of the community after 6 pm". This confirms that the access control result of Entrance A of the community is that it is passable, and that this passability is time-limited.

[0099] By analyzing user feedback data and determining the pass detection results, the pass detection results can be accurately analyzed from the user feedback data. This allows for the extraction of pass detection results from real-time user feedback, thereby improving the accuracy of the pass detection results.

[0100] In summary, different types of data can be processed in different ways, and the detection methods for access control results can be configured according to different situations. This allows for targeted determination of access control results, enrichment of the source data for access control results, improvement of detection accuracy, and increased reliability of access control results.

[0101] S303, Based on the passage data and the passage detection results, determine the passage entrances and exits of the area.

[0102] According to the technical solution of this disclosure, by processing data according to different data types and using the corresponding data type method, the corresponding passage detection results can be obtained. This can enrich the data types of passage data, improve the representativeness of the data, and thus accurately detect passages.

[0103] Figure 4 This is a scene diagram of another access point detection method disclosed in an embodiment of this disclosure.

[0104] S401 aggregates the entrances and exits of the region to obtain the region's entrances and exits.

[0105] Establishing connections between various entrances and exits within the area facilitates subsequent comprehensive and weighted judgments. For example... Figure 5 As shown, A, B, C, D, and E are entrances and exits of a community.

[0106] S402: Obtain data from multiple data sources and verify whether there is passage data for each entrance and exit.

[0107] For example, the verification results can be found in Table 1.

[0108] Among them, the field-collected data refers to image data collected on-site. In the field-collected street view images, the street view image of entrance / exit A is as follows: Figure 6 As shown, the street view image of entrance / exit B is as follows: Figure 7 As shown, the street view image of entrance / exit C is as follows: Figure 8 As shown, the street view image of entrance / exit E is as follows: Figure 9 As shown.

[0109] Table 1

[0110] Entrance / Exit Field collection trajectory User Feedback A have B have have have C have have D none have E have have

[0111] S403: Obtain passage data at entrances and exits, and check the passage detection results at each entrance and exit.

[0112] Based on traffic data from various data sources, the system detects whether an entrance / exit is open, whether passage is permitted, the direction of passage, and the mode of transport. Specifically, data collected from the field is primarily used to determine if it is a pedestrian / vehicle lane, the direction of passage, and whether vehicles can pass; trajectory data is primarily used to determine if it is a pedestrian / vehicle lane, the direction of passage, and the number of passages; and user feedback data is primarily used to determine whether passage is permitted.

[0113] As shown in Table 2 below, the characteristics determined by the data are the passage detection results, and the data that statistically determined the same passage detection result are also included.

[0114] Table 2

[0115]

[0116] S403: The weight of each access detection result at each entrance and exit is determined by weighting the access data from each data source.

[0117] The weighting distinction takes into account the following factors:

[0118] a) Field-collected data: Field-collected data has the highest accuracy and is therefore given the highest weight. Weights are further differentiated based on the quality of different types of field-collected data.

[0119] b) Attribute data: Weights are differentiated according to attributes. Data collected in the field has a higher accuracy in detecting vehicle traffic and / or pedestrian traffic, while trajectory data has a higher accuracy in detecting traffic direction.

[0120] c) Data frequency: Trajectory data and user feedback data usually have multiple features. The more times they are displayed, the higher their weight.

[0121] The example using Table 3 as weights will be adjusted in practice:

[0122] Table 3

[0123]

[0124] The weighting of passage detection results is calculated first for passage entrances and non-passage entrances, then for passage that is passable and impassable, and finally for vehicle entrances, vehicle exits, pedestrian entrances, and pedestrian exits. If the weight of a passage detection result that is not a passage entrance is greater than or equal to a preset first weight threshold, and / or the weight of a passage detection result that is not a passage entrance is greater than or equal to the weight of a passage detection result that is a passage entrance, the weights of the passable, impassable, vehicle entrance, vehicle exit, pedestrian entrance, and pedestrian exit passage detection results are 0 or do not need to be calculated. If the weight of an impassable passage detection result is greater than or equal to a preset second weight threshold, and / or the weight of an impassable passage detection result is greater than or equal to the weight of a passable passage detection result, the weights of the vehicle entrance, vehicle exit, pedestrian entrance, and pedestrian exit passage detection results are 0 or do not need to be calculated. If the weight of the passage detection result that is not a passage entrance is less than the preset first weight threshold, the weight of the passage detection result that is not a passage entrance is less than the weight of the passage detection result that is a passage entrance, the weight of the passage detection result that is impassable is less than the preset second weight threshold, and the weight of the passage detection result that is impassable is less than the weight of the passage detection result that is passable, then calculate the weight of the passage detection results for the pedestrian entrance, vehicle exit, pedestrian entrance, and pedestrian exit.

[0125] S405: Based on the weight of each access point's access detection result, select the access point corresponding to the communication detection result with the highest weight and determine it as the access point.

[0126] The accessibility characteristics of each gate are determined by combining their weights. The gates with the highest weights are then selected as the main exit and main entrance.

[0127] For example, the filtering results are shown below.

[0128] Vehicle entrance weight ranking: B, E

[0129] Pedestrian entrance weight ranking, B, E, A

[0130] Vehicle export weight ranking: E, B

[0131] Pedestrian exit weight ranking: B, E, A

[0132] Existing verification methods are generally based on single-point verification. For example, if an area has multiple entrances and exits, each entrance and exit is verified individually, without considering the correlation of accessibility features. If the original area had one exit and one entrance, and now one exit becomes an entrance, then there is a high probability that the other exit will also become an entrance.

[0133] According to the technical solution disclosed herein, by combining multiple data sources and aggregating various entrances and exits of a region, the access characteristics of the region's entrances and exits are verified, and accessibility detection is performed. By treating a region as a whole for comprehensive judgment, the access entrances and exits of the region can be accurately detected. Furthermore, by providing a data foundation for accessibility detection based on data from multiple data sources, the amount of verifiable data at entrances and exits can be increased, thereby improving the accuracy and reliability of accessibility detection.

[0134] According to embodiments of this disclosure, Figure 10 This is a structural diagram of the access control device according to an embodiment of this disclosure. This embodiment is applicable to situations where access control is detected in images within a video stream. The device is implemented using software and / or hardware and is specifically configured in an electronic device with certain data processing capabilities.

[0135] like Figure 10 The illustrated access point detection device 500 includes: an entrance / exit acquisition module 501, a access detection module 502, and an access point determination module 503; wherein,

[0136] The entrance / exit acquisition module 501 is used to acquire the entrances and exits of the area.

[0137] The passage detection module 502 is used to acquire the passage data of the entrance and exit and determine the passage detection result of the entrance and exit;

[0138] The access point determination module 503 is used to determine the access points and exits of the area based on the access data and the access detection results.

[0139] According to the technical solution disclosed herein, by acquiring the entrances and exits of a region and obtaining the passage data of those entrances and exits, the passage detection results are determined to filter the passage entrances and exits of the region. It is possible to detect whether the entrances and exits are passable based on online data, accurately detect the passability of entrances and exits, improve the passage detection efficiency of entrances and exits, reduce the passage detection cost, and improve the accuracy of passage detection.

[0140] Furthermore, the access point determination module 503 includes: a weight determination unit, used to calculate the access point type weight of at least one access point detection result based on the data type of the access data; and an access point detection unit, used to determine the access point in at least one access point in the area based on the access point type weight of each access point detection result.

[0141] Furthermore, the weight determination unit includes: a data type determination subunit, used to obtain the data type of at least one data in the passage data, the data being used to determine the passage detection result; a data statistics subunit, used to statistically determine the data that determines the same passage detection result; and an entrance / exit type weight determination subunit, used to statistically determine the entrance / exit type weight of each passage detection result based on the data type of each data, the correspondence between data type and weight, and the data that determines each passage detection result.

[0142] Furthermore, the entrance / exit type weight determination subunit is configured to: determine a first weight based on the image quality of the images of each passage detection result when the data type includes image type; determine a second weight based on the number of routes of each passage detection result when the data type includes route type; determine a third weight based on the user feedback content of each passage detection result when the data type includes user feedback type; and determine the entrance / exit type weight of the passage detection result based on at least one weight.

[0143] Furthermore, the entrance / exit type weight determination subunit is used to: determine the entrance / exit type weight of each entrance / exit detection result based on the priority of each passage detection result, the data type of each data, the correspondence between the data type and weight under each priority, and the data of each passage detection result.

[0144] Furthermore, the passage detection module 502 includes: a data processing unit, used to process each of the data according to the data type of at least one of the data in the passage data, and obtain the passage detection result corresponding to each of the data.

[0145] Furthermore, the data type is an image type; the data processing unit includes: an image recognition subunit, used to perform image recognition on the data to obtain access sign detection data; and an access sign detection subunit, used to determine the access detection result corresponding to the data based on the access sign detection data.

[0146] Furthermore, the data type is a route type; the data processing unit includes a route detection subunit, used to determine the passage detection result based on the route direction and route movement mode of the data.

[0147] The aforementioned access point detection device can execute the access point detection method provided in any embodiment of this disclosure, and has the corresponding functional modules and beneficial effects for executing the access point detection method.

[0148] The collection, storage, use, processing, transmission, provision, and disclosure of user personal information involved in the technical solution disclosed herein comply with the provisions of relevant laws and regulations and do not violate public order and good morals.

[0149] According to embodiments of this disclosure, this disclosure also provides an electronic device, a readable storage medium, and a computer program product.

[0150] Figure 11 A schematic area diagram of an example electronic device 600 that can be used to implement embodiments of the present disclosure is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the present disclosure described and / or claimed herein.

[0151] like Figure 11 As shown, device 600 includes a computing unit 601, which can perform various appropriate actions and processes based on a computer program stored in read-only memory (ROM) 602 or a computer program loaded from storage unit 608 into random access memory (RAM) 603. RAM 603 may also store various programs and data required for the operation of device 600. The computing unit 601, ROM 602, and RAM 603 are interconnected via bus 604. Input / output (I / O) interface 605 is also connected to bus 604.

[0152] Multiple components in device 600 are connected to I / O interface 605, including: input unit 606, such as keyboard, mouse, etc.; output unit 607, such as various types of monitors, speakers, etc.; storage unit 608, such as disk, optical disk, etc.; and communication unit 609, such as network card, modem, wireless transceiver, etc. Communication unit 609 allows device 600 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0153] The computing unit 601 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the computing unit 601 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 601 performs the various methods and processes described above, such as the access control method. For example, in some embodiments, the access control method may be implemented as a computer software program tangibly contained in a machine-readable medium, such as storage unit 608. In some embodiments, part or all of the computer program may be loaded and / or installed on device 600 via ROM 602 and / or communication unit 609. When the computer program is loaded into RAM 603 and executed by the computing unit 601, one or more steps of the access control method described above may be performed. Alternatively, in other embodiments, the computing unit 601 may be configured to perform the access control method by any other suitable means (e.g., by means of firmware).

[0154] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0155] The program code used to implement the methods of this disclosure may be written in any combination of one or more programming languages. This program code may be provided to a processor or controller of a general-purpose computer, special-purpose computer, or other programmable data processing apparatus, such that when executed by the processor or controller, the program code causes the functions / operations specified in the flowcharts and / or area diagrams to be implemented. The program code may be executed entirely on a machine, partially on a machine, as a standalone software package partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0156] In the context of this disclosure, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can be, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0157] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device for displaying information to the user (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor); and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the computer. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0158] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as a data server), or computing systems that include middleware components (e.g., an application server), or computing systems that include frontend components (e.g., a user computer with a graphical user interface or web browser through which a user can interact with embodiments of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., a communication network). Examples of communication networks include local area networks (LANs), wide area networks (WANs), and the Internet.

[0159] Computer systems can include clients and servers. Clients and servers are generally located far apart and typically interact via communication networks. Client-server relationships are created by computer programs running on the respective computers and having a client-server relationship with each other. Servers can be cloud servers, servers in distributed systems, or servers incorporating blockchain technology.

[0160] It should be understood that the various forms of processes shown above can be used to rearrange, add, or delete steps. For example, the steps described in this disclosure can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution disclosed in this disclosure can be achieved, and this is not limited herein.

[0161] The specific embodiments described above do not constitute a limitation on the scope of protection of this disclosure. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this disclosure should be included within the scope of protection of this disclosure.

Claims

1. A method for detecting access points, comprising: Get the area's entrances and exits; Obtain the passage data of the entrance / exit and determine the passage detection result of the entrance / exit. The passage data includes at least one of the following: user passage behavior of the entrance / exit, passage status of the entrance / exit, and user suggestions of the entrance / exit. The passage detection result includes whether the passage is permitted or not. Based on the passage data and the passage detection results, the passage entrances and exits of the area are determined; The step of determining the access points and exits of the area based on the access data and the access detection results includes: Based on the data type of the access data, the access type weight of at least one access detection result of the access point is calculated. The number of data types of the access data is at least one, and the data types include image type and route type. Based on the entrance / exit type weights of each of the aforementioned access detection results, an access point / exit is determined in at least one of the entrances / exits in the area.

2. The method according to claim 1, wherein, The step of calculating the entrance / exit type weight of at least one entrance / exit detection result based on the data type of the access data includes: Obtain the data type of at least one piece of data in the passage data, the data being used to determine the passage detection result; Statistical analysis of data that confirms the same passage detection result; Based on the data types of each data, the correspondence between data types and weights, and the data used to determine each passage detection result, the entry / exit type weights of each passage detection result are statistically calculated.

3. The method according to claim 2, wherein, The step of calculating the entrance / exit type weights of each of the access detection results based on the data type of each of the data, the correspondence between data type and weight, and the data used to determine each of the access detection results includes: When the data type includes image type, a first weight is determined based on the image quality of each of the passage detection results determined statistically; When the data type of the passage data includes route type, a second weight is determined based on the statistical determination of the number of routes in each passage detection result; When the data type of the access data includes user feedback type, a third weight is determined based on the statistical determination of the user feedback content of each access detection result; The entrance / exit type weight of the passage detection result is determined based on at least one weight.

4. The method according to claim 2, wherein, The statistical analysis of the entrance / exit type weights for each of the passage detection results includes: Based on the priority of each access detection result, the data type of each data, the correspondence between the data type and weight under each priority, and the data used to determine each access detection result, the entry / exit type weight of each access detection result is determined.

5. The method according to claim 1, wherein, The determination of the access detection results for the entrance and exit includes: Based on the data type of at least one of the passage data, each of the data is processed to obtain the passage detection result corresponding to each of the data.

6. The method according to claim 5, wherein, The data is of image type; The process of processing each of the data to obtain the passage detection result corresponding to each of the data includes: For each of the aforementioned data, image recognition is performed on the data to obtain access sign detection data; Based on the access sign detection data, determine the access detection result corresponding to the data.

7. The method according to claim 5, wherein, The data type is route type; The process of processing each of the data to obtain the passage detection result corresponding to each of the data includes: For each of the data, the passage detection result is determined based on the route direction and movement mode of the data.

8. A passageway detection device, comprising: The entrance / exit acquisition module is used to acquire the entrances and exits of the area; The passage detection module is used to acquire the passage data of the entrance and exit and determine the passage detection result of the entrance and exit. The passage data includes at least one of the following: user passage behavior of the entrance and exit, passage status of the entrance and exit, and user suggestions of the entrance and exit. The passage detection result includes whether the passage is permitted or not. The access point determination module is used to determine the access points and exits of the area based on the access data and the access detection results; The access point determination module includes: The weight determination unit is used to calculate the entrance / exit type weight of at least one entrance / exit detection result based on the data type of the access data. The number of data types of the access data is at least one, and the data types include image type and route type. The access point detection unit is used to determine the access point in at least one access point in the area based on the access point type weight of each access detection result.

9. The apparatus according to claim 8, wherein, The weight determination unit includes: A data type determination subunit is used to obtain the data type of at least one data in the passage data, the data being used to determine the passage detection result; The data statistics subunit is used to statistically analyze data that determines the same passage detection result. The entrance / exit type weight determination subunit is used to calculate the entrance / exit type weight of each passage detection result based on the data type of each data, the correspondence between data type and weight, and the data used to determine each passage detection result.

10. The apparatus according to claim 9, wherein, The entrance / exit type weight determination subunit is used for: When the data type includes image type, a first weight is determined based on the image quality of each of the passage detection results determined statistically; When the data type of the passage data includes route type, a second weight is determined based on the statistical determination of the number of routes in each passage detection result; When the data type of the access data includes user feedback type, a third weight is determined based on the statistical determination of the user feedback content of each access detection result; The entrance / exit type weight of the passage detection result is determined based on at least one weight.

11. The apparatus according to claim 9, wherein, The entrance / exit type weight determination subunit is used for: Based on the priority of each access detection result, the data type of each data, the correspondence between the data type and weight under each priority, and the data used to determine each access detection result, the entry / exit type weight of each access detection result is determined.

12. The apparatus according to claim 8, wherein, The access detection module includes: The data processing unit is used to process each of the data according to the data type of at least one of the data in the passage data, and obtain the passage detection result corresponding to each of the data.

13. The apparatus according to claim 12, wherein, The data is of image type; The data processing unit includes: An image recognition subunit is used to perform image recognition on the data to obtain access sign detection data; The passage sign detection subunit is used to determine the passage detection result corresponding to the passage sign detection data based on the passage sign detection data.

14. The apparatus according to claim 12, wherein, The data type is route type; The data processing unit includes: The route detection subunit is used to determine the passage detection result based on the route direction and route movement mode of the data.

15. An electronic device comprising: At least one processor; as well as A memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor to enable the at least one processor to perform the access detection method according to any one of claims 1-7.

16. A non-transitory computer-readable storage medium storing computer instructions, wherein, The computer instructions are used to cause the computer to execute the access point detection method according to any one of claims 1-7.

17. A computer program product comprising a computer program that, when executed by a processor, implements the access point detection method according to any one of claims 1-7.

Images