Charging method, device, medium and equipment based on movable power supply vehicle

Abstract

This application provides a charging method, apparatus, medium, and device based on a mobile power supply vehicle, relating to the field of vehicle charging, including: controlling the mobile power supply vehicle to charge a target vehicle according to the target charging pose; in response to receiving a charging completion notification from the target vehicle, acquiring point cloud data; if a target point cloud cluster exists in the point cloud data, acquiring a target point cloud cluster distance list J; based on J, obtaining a target point cloud cluster average distance set P; if P i If the distance is less than the preset average distance threshold, then P is determined. i The corresponding target point cloud is noise; the mobile power supply vehicle is controlled to plan its movement path and move to the mobile power supply vehicle compartment according to the movement path. This application comprehensively considers multiple factors, resulting in a more accurate judgment.

Description

Technical Field

[0001] This application relates to the field of vehicle charging, and in particular to a charging method, apparatus, medium and equipment based on a mobile power supply vehicle. Background Technology

[0002] In modern society, cars have become commonplace in households. With the development of the automotive industry, electric vehicles (EVs) have emerged. EVs are a type of new energy vehicle, a zero-emission mode of transportation with advantages such as zero pollution and zero emissions. Where there are EVs, there must be corresponding charging facilities, such as charging stations. Currently, in parking lots, the number of charging stations is often limited, and spaces designated for charging are frequently occupied by other non-charging vehicles, preventing EVs from using the charging stations properly. This charging problem is particularly prominent in areas with heavy traffic. Therefore, mobile power supply vehicles have emerged. After completing charging, these vehicles need to return to the charging station for recharging. This requires route planning and obstacle avoidance. To achieve rapid return for these mobile power supply vehicles, an efficient obstacle identification method is urgently needed. Summary of the Invention

[0003] To address the aforementioned technical problems, this application provides a charging method, apparatus, medium, and device based on a mobile power supply vehicle, which at least partially solves the problems existing in the prior art.

[0004] In one aspect of this application, a charging method based on a mobile power supply vehicle is provided, the method comprising the following steps:

[0005] S100, determine the target charging pose of the mobile power supply vehicle to the target vehicle based on the image acquisition device of the mobile power supply vehicle and the license plate of the target vehicle;

[0006] S200, control the mobile power supply vehicle to charge the target vehicle according to the target charging posture;

[0007] S300, in response to receiving a charging completion notification from the target vehicle, acquire point cloud data generated by the lidar device installed on the mobile power supply vehicle; wherein, the lidar device on the mobile power supply vehicle is installed on the side of the mobile power supply vehicle facing the target vehicle; the application range of the lidar device is a preset position of the front of the target vehicle; the side of the mobile power supply vehicle facing the target vehicle is a plane; when the target vehicle is charging, the distance between the front of the vehicle and the mobile power supply vehicle is less than a preset distance threshold;

[0008] S400, if a target point cloud cluster exists within the point cloud data, then obtain the target point cloud cluster distance list set J = (J1, J2, ..., J...). i , ..., J n); i = 1, 2, ..., n; where n is the number of target point clouds; J i J is a list of distances corresponding to the i-th target point cloud cluster; i =(J i1 J i2 , ..., J ij , ..., J im ); j = 1, 2, ..., m; m is the number of point cloud data contained in the i-th target point cloud cluster; J ij The distance between the j-th point cloud data in i target point cloud clusters and the center point of the i-th target point cloud cluster; the number of point cloud data in the target point cloud cluster is greater than a first quantity threshold and less than a second quantity threshold; the height corresponding to the point cloud data of the target point cloud cluster is less than the upper edge height of the license plate of the target vehicle and greater than the lower edge height of the license plate of the target vehicle;

[0009] S500, according to J, the average distance set of the target point cloud cluster is obtained as P = (P1, P2, ..., P...). i , ..., P n ); where P i P represents the average distance corresponding to the i-th target point cloud cluster; i =avg(J i ); avg() is a preset function to determine the average value;

[0010] S600, if P i If the distance is less than the preset average distance threshold, then P is determined. i The corresponding target point cloud is noise;

[0011] S700 controls the mobile power supply vehicle to plan its movement path and move to the mobile power supply vehicle compartment according to the movement path; wherein, the target point cloud clusters identified as noise are used as non-obstacles during path planning.

[0012] In a second aspect of this application, a charging device based on a mobile power supply vehicle is provided, characterized in that the device comprises:

[0013] The adjustment unit is used to adjust the pose of the mobile power supply vehicle according to the license plate position of the target vehicle, so as to obtain the target charging pose of the mobile power supply vehicle adapted to the target vehicle.

[0014] A charging unit is used to control the mobile power supply vehicle to charge the target vehicle according to the target charging posture;

[0015] The point cloud data acquisition unit is used to acquire point cloud data generated by the lidar device installed on the mobile power supply vehicle in response to receiving a charging completion notification from the target vehicle; wherein, the lidar device of the mobile power supply vehicle is installed on the side of the mobile power supply vehicle facing the target vehicle; the application range of the lidar device is a preset position of the front of the target vehicle; the side of the mobile power supply vehicle facing the target vehicle is a plane; when the target vehicle is charging, the distance between the front of the vehicle and the mobile power supply vehicle is less than a preset distance threshold.

[0016] The distance acquisition unit is used to acquire a distance list set J = (J1, J2, ..., J...) of the target point cloud cluster if the target point cloud cluster exists in the point cloud data. i , ..., J n ); i = 1, 2, ..., n; where n is the number of target point clouds; J i J is a list of distances corresponding to the i-th target point cloud cluster; i =(J i1 J i2 , ..., J ij , ..., J im ); j = 1, 2, ..., m; m is the number of point cloud data contained in the i-th target point cloud cluster; J ij The distance between the j-th point cloud data in i target point cloud clusters and the center point of the i-th target point cloud cluster; the number of point cloud data in the target point cloud cluster is greater than a first quantity threshold and less than a second quantity threshold; the height corresponding to the point cloud data of the target point cloud cluster is less than the upper edge height of the license plate of the target vehicle and greater than the lower edge height of the license plate of the target vehicle;

[0017] The average distance acquisition unit is used to obtain the average distance set P = (P1, P2, ..., P) of the target point cloud based on J. i , ..., P n ); where P i P represents the average distance corresponding to the i-th target point cloud cluster; i =avg(J i ); avg() is a preset function to determine the average value;

[0018] Noise determination unit, used if P i If the distance is less than the preset average distance threshold, then P is determined. i The corresponding target point cloud is noise;

[0019] A mobile unit is used to control the mobile power supply vehicle to plan a movement path and move to the mobile power supply vehicle compartment according to the movement path; wherein, the target point cloud clusters identified as noise during path planning are used as non-obstacles.

[0020] In a third aspect of this application, a non-transitory computer-readable storage medium is provided, wherein at least one instruction or at least one program is stored in the storage medium, and the at least one instruction or at least one program is loaded and executed by a processor to implement the aforementioned charging method based on a mobile power supply vehicle.

[0021] In a fourth aspect of this application, an electronic device is provided, including a processor and the aforementioned non-transitory computer-readable storage medium.

[0022] This application has at least the following beneficial effects:

[0023] The charging method based on a mobile power supply vehicle provided in this application firstly determines the target charging pose of the mobile power supply vehicle to the target vehicle by using the image acquisition device of the mobile power supply vehicle and the license plate of the target vehicle. Secondly, the mobile power supply vehicle is controlled to charge the target vehicle according to the target charging pose. After the target vehicle completes charging, the mobile power supply vehicle should return to its corresponding position. At this time, path planning is required. First, point cloud data generated by the lidar device set on the mobile power supply vehicle is acquired. Here, the side of the mobile power supply vehicle facing the target vehicle is a plane, and the lidar device is set on the side of the mobile power supply vehicle facing the target vehicle. The application range of the lidar device is a preset position of the front of the target vehicle. Several point cloud data are acquired based on the lidar device. Then, if a target point cloud cluster exists in the point cloud data, a distance list J of the target point cloud cluster is acquired. Here, the number of point cloud data of the target point cloud cluster is greater than a first quantity threshold and less than a second quantity threshold; the height corresponding to the point cloud data of the target point cloud cluster is less than the upper edge height of the license plate of the target vehicle and greater than the lower edge height of the license plate of the target vehicle. Based on J, the average distance set P of the target point cloud is obtained. Finally, if P i If the distance is less than the preset average distance threshold, then P is determined. i The corresponding target point cloud cluster is a non-obstacle. Here, because the number of point cloud data for ghost images (point cloud noise - non-real objects) is smaller and more concentrated compared to the number of point cloud data for actual objects, when P... iIf the distance is less than a preset average threshold and the amount of point cloud data it contains is less than a second quantity threshold, it is likely a ghost image (point cloud noise - non-real object), i.e., a non-obstacle. This application is based on the principle that laser point cloud ghosts may form near high-emissivity objects (the surface of a car or the license plate itself is a highly reflective object; when a laser beam is sent to the target object and reflected, the returned signal may propagate back to the lidar device through multiple paths. This includes light directly reflected from the target object and light reflected from the target object's surface. At this time, multiple light rays will intersect and interfere with each other, thus forming a ghost image). It also considers the characteristic that the number of point cloud data points in a ghost image is relatively small and concentrated compared to the number of point cloud data points in a real object's point cloud cluster. The distance of each point cloud data point within the target vehicle's license plate height range and containing relatively little point cloud data is obtained from the center of the point cloud. Then, a preset distance threshold is used to determine whether it is a ghost image (non-obstacle). By comprehensively considering multiple factors, the judgment result is more accurate. Attached Figure Description

[0024] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0025] Figure 1 A flowchart illustrating a charging method based on a mobile power supply vehicle, as provided in an embodiment of this application.

[0026] Figure 2 This is a structural block diagram of a charging device based on a mobile power supply vehicle, provided in an embodiment of this application. Detailed Implementation

[0027] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0028] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or server that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or devices.

[0029] It should be noted that various aspects of embodiments within the scope of the appended claims are described below. It will be apparent that the aspects described herein can be embodied in a wide variety of forms, and any particular structure and / or function described herein is merely illustrative. Based on this disclosure, those skilled in the art will understand that one aspect described herein can be implemented independently of any other aspect, and two or more of these aspects can be combined in various ways. For example, any number of aspects set forth herein can be used to implement the device and / or practice the method. Additionally, this device and / or method can be implemented using structures and / or functionalities other than one or more of the aspects set forth herein.

[0030] Please refer to Figure 1 As shown, an embodiment of this application provides a charging method based on a mobile power supply vehicle, the method comprising the following steps:

[0031] S100: Determine the target charging pose of the mobile power supply vehicle to be adapted to the target vehicle based on the image acquisition device of the mobile power supply vehicle and the license plate of the target vehicle.

[0032] Specifically, step S100 includes:

[0033] S110, in response to receiving the charging request information of the target vehicle, control the mobile power supply vehicle to move to the corresponding initial charging position; the charging request information includes the parking space identifier and the vehicle model; each parking space identifier has a corresponding initial charging position; the initial charging position is a position close to the side where the preset front position of the parking space is located, and outside the parking space line, at a certain distance from the edge line of the parking space; the side where the mobile power supply vehicle image acquisition device is located at the initial charging position is parallel to the edge line of the parking space on the side where the preset front position of the vehicle is located.

[0034] Specifically, if a charging request is received from a target vehicle, the charging request includes the parking space identifier where the target vehicle is parked (each parking space has a unique identifier) ​​and the vehicle model of the target vehicle; the mobile power supply vehicle is controlled to move to the corresponding initial charging position; here, the initial charging position is set for each parking space; as an example: for a rectangular parking space, the initial charging position is set on the side where the preset vehicle front position is located (the wider side in the longer direction), at a certain distance from the edge line of the parking space outside the parking line. Furthermore, the side where the mobile power supply vehicle's image acquisition device is located at the initial charging position is parallel to the edge line of the parking space on the side where the preset vehicle front position is located. That is, the mobile power supply vehicle at the initial charging position is parallel to the edge line of the parking space on the side where the vehicle front is located.

[0035] S120: Obtain the current image from the image acquisition device pre-installed on the mobile power supply vehicle.

[0036] Specifically, the mobile power supply vehicle is equipped with an image acquisition device. This device acquires the current image, specifically an image of the front of the target vehicle. Here, there can be at least one image acquisition device, or two. If there are two, the images acquired by the two devices are fused using a relevant image fusion algorithm.

[0037] S130, if the current image contains a complete image of the target vehicle's license plate, then obtain the current license plate image information X = (MX, WX, PX) of the target vehicle according to the vehicle model and the preset image recognition algorithm; where MX is the proportion of the area of ​​the target vehicle's license plate in the current image to the area of ​​the current image; WX is the position coordinate of the geometric center point of the target vehicle's license plate in the current image; and PX is the outline of the target vehicle's license plate in the current image.

[0038] Specifically, if the current image contains a complete image of the target vehicle's license plate, then the current license plate image information is acquired. This includes acquiring the proportion of the target vehicle's license plate area to the total area of ​​the current image, the coordinates of the geometric center point of the target vehicle's license plate, and the outline of the target vehicle's license plate. Since different vehicle models may have different heights, the height of the license plate above the ground may also differ. However, the angle and image acquisition range of the image acquisition device on the mobile charging vehicle are fixed. Therefore, the position of the license plate in the current image may differ for different vehicle models. Further, step S130 includes the following steps:

[0039] S131, if the current image contains a complete image of the target vehicle's license plate, measure the current distance between the mobile power supply vehicle and the target vehicle using the distance measuring device pre-installed on the mobile power supply vehicle.

[0040] Here, if the current image contains a complete image of the target vehicle's license plate, the current distance between the mobile power supply vehicle and the target vehicle is first measured using a distance measuring device pre-installed on the mobile power supply vehicle. The size of the license plate in the current image varies depending on the distance.

[0041] S132, determine the location distribution area of ​​the complete image of the target vehicle's license plate within the current image based on the current distance, vehicle model, and a preset mapping table; wherein, the location distribution area includes the upper layer, middle layer, and lower layer of the image; the preset mapping table includes different location distribution areas corresponding to each vehicle model at different distances.

[0042] Here, the preset mapping table includes the different positional distribution areas of each vehicle model at different distances. For example, vehicle model A, at a distance of 0.5 meters, is located in the upper layer of the image. Vehicles with higher heights likely have license plates that are relatively higher off the ground, thus their positional distribution area is likely in the upper layer; conversely, at the same distance, vehicles with lower heights likely have license plates that are relatively lower off the ground, thus their positional distribution area is likely in the lower layer. The preset mapping table records the corresponding mapping relationships. Therefore, after obtaining the current distance and vehicle model, the positional distribution area of ​​the target vehicle's license plate within the current image can be determined according to the preset mapping table.

[0043] S133, Based on the location distribution area and the preset image recognition algorithm, obtain the current license plate image information X = (MX, WX, PX) of the target vehicle.

[0044] Here, after determining the location distribution area of ​​the target vehicle's license plate within the current image, a preset image recognition algorithm can prioritize recognition based on the corresponding location distribution area, saving image recognition time and reducing the image recognition area, thus conserving resources. This improves image recognition efficiency and ultimately yields the target vehicle's current license plate image information X, including the proportion of the target vehicle's license plate area to the current image area, the position coordinates of the geometric center point of the target vehicle's license plate, and the outline of the target vehicle's license plate. A smaller proportion of the target vehicle's license plate area to the current image area indicates a greater distance between the mobile charging vehicle and the target vehicle's license plate; conversely, a larger proportion indicates a closer distance. The position coordinates of the geometric center point of the target vehicle's license plate reflect its position within the current image. The outline of the target vehicle's license plate indicates whether the mobile charging vehicle is parallel to the plane containing the license plate; if the outline of the target vehicle's license plate is deformed, it indicates that the mobile charging vehicle is not parallel to the plane containing the license plate.

[0045] After step S130, the license plate number of the target vehicle can also be obtained, and the license plate number of the target vehicle can be checked against the current image containing the complete image of the target vehicle's license plate to see if it is correct.

[0046] In one exemplary embodiment of this application, after step S120, the method further includes:

[0047] S140: If the current image contains an incomplete image of the target vehicle's license plate, control the movable electric vehicle to move away from the target vehicle until the current image contains a complete image of the target vehicle's license plate, then proceed to step S310.

[0048] Specifically, if the current image contains an incomplete image of the target vehicle's license plate, the movable electric vehicle is controlled to move away from the target vehicle until the current image contains a complete image of the target vehicle's license plate. Then, subsequent processing is performed based on the current image containing the complete image of the target vehicle's license plate.

[0049] S150, based on X, vehicle model, and preset license plate image information Y, control the mobile power supply vehicle to adjust its pose to obtain the target charging pose; Y = (MY, WY, PY); where MY is a preset ratio of the license plate area to the image area; WY is a preset position coordinate of the geometric center point of the license plate in the image determined according to the vehicle model; PY is a preset outline of the vehicle's license plate; the plane where the image acquisition device of the mobile power supply vehicle in the target charging pose is located is parallel to the plane where the target vehicle's license plate is located.

[0050] Specifically, the optimal charging position is when the license plate area occupies a preset proportion of the image area; the geometric center point of the license plate in the image is at a preset coordinate; and the outline of the vehicle's license plate is a preset outline. For example, the preset proportion can be 1 / 4; the geometric center point of the license plate can be the geometric center point of the current image; and the preset outline can be a rectangle. It should be noted that for different vehicle models, the preset coordinates of the geometric center point of the license plate in the image may be different because the height of the license plate from the ground may be different. For example, for taller vehicles (such as Jeeps), the license plate may be relatively high above the ground, so the preset coordinates of the geometric center point of the license plate may be closer to the upper part of the image.

[0051] Understandably, the proportion of the license plate area to the image area represents the distance between the mobile power supply vehicle and the front of the target vehicle. When the proportion of the license plate area to the image area is a preset ratio, the distance between the mobile power supply vehicle and the front of the target vehicle is the optimal charging distance. The coordinates of the geometric center point of the license plate in the image represent the relative position between the mobile power supply vehicle and the target vehicle. When the geometric center point of the license plate in the image is at a preset coordinate, the mobile power supply vehicle is located in the middle of the front of the target vehicle. The outline of the vehicle's license plate represents whether the mobile power supply vehicle is parallel to the target vehicle.

[0052] During adjustment, if the area of ​​the license plate occupies a smaller proportion of the image area than a preset proportion, the movable power supply vehicle is moved closer to the target vehicle; conversely, it is moved further away from the target vehicle. If the geometric center point of the license plate in the image is not at the preset position coordinates, the movable power supply vehicle is adjusted left or right. If the outline of the target vehicle's license plate is deformed, the angle of the movable power supply vehicle is rotated to make the license plate outline conform to the preset outline (rectangle). In this way, the movable power supply vehicle obtains the target charging pose by adjusting its pose.

[0053] S200 controls the mobile power supply vehicle to charge the target vehicle according to the target charging position.

[0054] Specifically, the mobile power supply vehicle is controlled to charge the target vehicle according to the target charging position.

[0055] In this embodiment, upon receiving a charging request from a target vehicle, the mobile charging vehicle is controlled to move to the corresponding initial charging position; here, the initial charging position is a fixed charging position set for the parking space. The charging request information includes the parking space identifier and the vehicle model. Then, the current image is acquired by the image acquisition device preset on the mobile charging vehicle. If the current image contains a complete image of the target vehicle's license plate, the current license plate image information X of the target vehicle is acquired according to the vehicle model and a preset image recognition algorithm. Here, the proportion of the target vehicle's license plate area to the current image area, the position coordinates of the geometric center point of the target vehicle's license plate, and the outline of the target vehicle's license plate are acquired. Since the height of different vehicle models may be different, the height of the license plate position above the ground may also be different. However, the angle and image acquisition range of the mobile charging vehicle's image acquisition device are fixed, so the position of the license plate of different vehicle models in the current image may be different. Based on the vehicle model, the position area of ​​the license plate of different vehicle models in the current image can be acquired, and then the preset image recognition algorithm is used to quickly identify the current image to obtain the current license plate image information. Then, based on X, the vehicle model, and preset license plate image information Y, the mobile power supply vehicle is controlled to adjust its pose to obtain the target charging pose. Y includes a preset ratio of the license plate area to the image area, preset position coordinates of the geometric center point of the license plate in the image determined according to the vehicle model, and a preset outline of the vehicle's license plate. That is, each adjustment amount is determined based on the preset license plate image information, thereby enabling the mobile power supply vehicle to adjust to the target charging pose to charge the target vehicle. The plane where the image acquisition device of the mobile power supply vehicle is located in the target charging pose is parallel to the plane where the license plate of the target vehicle is located. This application first controls the mobile charging vehicle to move to the initial charging position matching the corresponding parking space, and then adjusts the mobile charging vehicle according to the actual parking angle of the target vehicle, etc. This ensures that the plane where the image acquisition device of the mobile charging vehicle is located in the target charging pose is parallel to the plane where the license plate of the target vehicle is located, and the distance from the target vehicle meets the preset ratio of the area of ​​the license plate to the area of ​​the image (to obtain a suitable charging distance). Furthermore, the geometric center point of the license plate in the image is located at a preset position coordinate (at the middle of the front of the vehicle). When charging the target vehicle based on the mobile charging vehicle in the target charging pose, the waste of charging cable length can be avoided. Moreover, the entire pose adjustment process is autonomously performed based on the license plate of the target vehicle, which is simple, efficient, and saves manpower.

[0056] S300, in response to receiving a charging completion notification from the target vehicle, acquire point cloud data generated by the lidar device installed on the mobile power supply vehicle; wherein, the lidar device on the mobile power supply vehicle is installed on the side of the mobile power supply vehicle facing the target vehicle; the application range of the lidar device is a preset position of the front of the target vehicle; the side of the mobile power supply vehicle facing the target vehicle is a plane; when the target vehicle is charging, the distance between the front of the vehicle and the mobile power supply vehicle is less than a preset distance threshold.

[0057] Specifically, after the target vehicle completes charging, the mobile power supply vehicle should return to its designated location. At this point, path planning is required. First, point cloud data generated by the lidar device installed on the mobile power supply vehicle is acquired. Here, the side of the mobile power supply vehicle facing the target vehicle is a plane, and the lidar device is installed on this side. The lidar device's application range is a preset position at the front of the target vehicle. Several point cloud data points are acquired based on the lidar device. For example, the preset distance threshold is 0.5 meters.

[0058] It's important to note that because car surfaces or license plates are highly reflective, when a laser beam is sent to the target object and reflected, the returned signal may travel back to the lidar device via multiple paths. This includes light directly reflected from the target object and light reflected from its surface. This results in multiple light rays intersecting and interfering, creating ghosting (point clouds, not real objects). Specifically, this ghosting is caused by noise from the target object's surface. For example, the properties of the surface material (high reflectivity causes excessive energy reflection in the point cloud, resulting in a similarly sized and shaped point cloud image appearing not only in the original coordinate system but also in nearby locations; examples include traffic signs, license plates, and taillights), and roughness (uneven surfaces cause changes in the emission angle of the point cloud). Another factor is noise from the external scanning environment, such as rain, snow, fog, or dust particles obscuring the point cloud and preventing it from reflecting back. The number of point cloud data for the aforementioned ghost images (point cloud clusters) is relatively small compared to the number of point cloud data for actual objects, and the data is more concentrated.

[0059] S400, if a target point cloud cluster exists within the point cloud data, then obtain the target point cloud cluster distance list set J = (J1, J2, ..., J...). i , ..., J n ); i = 1, 2, ..., n; where n is the number of target point clouds; J i J is a list of distances corresponding to the i-th target point cloud cluster; i =(J i1 J i2 , ..., Jij , ..., J im ); j = 1, 2, ..., m; m is the number of point cloud data contained in the i-th target point cloud cluster; J ij The distance between the j-th point cloud data in the i-th target point cloud cluster and the center point of the i-th target point cloud cluster; the number of point cloud data in the target point cloud cluster is greater than a first quantity threshold and less than a second quantity threshold; the height corresponding to the point cloud data of the target point cloud cluster is less than the upper edge height of the license plate of the target vehicle and greater than the lower edge height of the license plate of the target vehicle.

[0060] Specifically, the target point cloud is determined through the following steps:

[0061] S410, if the point cloud data contains at least one key point cloud cluster, then obtain the key point cloud cluster information set D = (D1, D2, ..., D...). p D q ); p = 1, 2, ..., q; where q is the number of keypoint clusters; D p D represents the point cloud information of the p-th key point cloud cluster; p =(DB) p , Ds p DG p ); DB p Ds is the identifier for the p-th keypoint cloud. p Let be the number of point cloud data for the p-th key point cloud cluster; the number of point cloud data for the key point cloud cluster is greater than the first quantity threshold and less than the second quantity threshold.

[0062] Here, the first quantity threshold is the minimum point cloud data required to form a point cloud cluster; for example, the first quantity threshold is 5. The second quantity threshold can be the maximum possible point cloud data for a ghost (point cloud cluster); for example, the second quantity threshold is 10. That is, the key point cloud cluster may be a ghost.

[0063] S420: Acquire image information corresponding to each key point cloud cluster according to the preset image acquisition device, and analyze it based on the preset image analysis algorithm to obtain the analysis result corresponding to each key point cloud cluster; wherein, the analysis result can be a real object, a non-real object, or an unidentifiable object.

[0064] Here, image information corresponding to each key point cloud is acquired using a preset image acquisition device, and the analysis result corresponding to each key point cloud is obtained based on the image information.

[0065] S430, obtain the height of each point cloud data in each unidentifiable key point cloud cluster as per the analysis result, and obtain the point cloud cluster height list set GD = (GD1, GD2, ..., GD...). x GD y); x = 1, 2, ..., y; where y is the number of keypoint clouds that cannot be identified by the analysis results; GD x This is a list of the heights of the keypoint cloud clusters for the xth analysis result that are unidentifiable; GD x =(GD x1 GD x2 GD xr GD xs ); r = 1, 2, ..., s; s is the number of point cloud data contained in the x-th unidentifiable key point cloud cluster; GD xr Let be the height of the r-th point cloud data in the x-th key point cloud cluster where the analysis result is unrecognizable.

[0066] Here, if the image analysis result is indistinguishable due to lighting or occlusion, the height of each point cloud data in each key point cloud cluster with indistinguishable analysis result is obtained.

[0067] S440, obtain the height range L = [L1, L2] corresponding to the target vehicle; L1 is the height of the upper edge of the license plate of the target vehicle; L2 is the height of the lower edge of the license plate of the target vehicle.

[0068] S450, if GD xr If ∈L, then determine GD x The target point cloud.

[0069] Here, if the height of each point cloud data in any unidentifiable key point cloud cluster is within the height range corresponding to the target vehicle's license plate, it indicates that the point cloud data in that key point cloud cluster is very likely due to reflection from the target vehicle's license plate. Alternatively, it could be an obstacle at the same height as the target license plate, requiring further investigation.

[0070] S500, according to J, the average distance set of the target point cloud cluster is obtained as P = (P1, P2, ..., P...). i , ..., P n ); where P i P represents the average distance corresponding to the i-th target point cloud cluster; i =avg(J i ); avg() is a preset function for determining the average value.

[0071] S600, if P i If the distance is less than the preset average distance threshold, then P is determined. i The corresponding target point cloud is noise.

[0072] S700 controls the mobile power supply vehicle to plan its movement path and move to the mobile power supply vehicle compartment according to the movement path; wherein, the target point cloud clusters identified as noise are used as non-obstacles during path planning.

[0073] Here, because the number of point cloud data for ghost images (point cloud clusters) is smaller and more concentrated compared to the number of point cloud data for actual objects, if P i If the distance is less than the preset average distance threshold, then P is determined. i The corresponding target point cloud is a ghost image, i.e., not an obstacle.

[0074] In one exemplary embodiment of this application, step S600 is replaced by the following step:

[0075] S800, if P i If the distance is less than the preset average distance threshold, then P is determined. i The corresponding target point cloud is suspected laser point cloud noise;

[0076] S900, if P i If the number of point cloud data points of the corresponding target point cloud cluster is greater than the first quantity threshold and less than the third quantity threshold, then the target point cloud cluster is determined to be a non-obstacle; wherein, the third quantity threshold is less than the second quantity threshold and greater than the first quantity threshold.

[0077] Specifically, if P i If the distance is less than the preset average distance threshold, then P is determined first. i The corresponding target point cloud cluster is suspected laser point cloud noise. Further, obtain P... i If the number of point cloud data points in the corresponding target point cloud cluster is less than the second threshold and further less than the third threshold, that is, if it has become a point cloud cluster and the point cloud is relatively dense, but the number of point clouds is small, the probability of it being a ghost is higher.

[0078] In this embodiment, based on the above embodiment, the determination of whether a target point cloud cluster is a ghost is further made using point cloud data. Specifically, a third quantity threshold less than the second quantity threshold is used for judgment, resulting in a more accurate result compared to the previous embodiment. As an example, the third quantity threshold can be 7.

[0079] In one exemplary embodiment of this application, after step S800, the method further includes:

[0080] S1000, if P i If the number of point cloud data points corresponding to the target point cloud cluster is greater than the third threshold and less than the second threshold, then according to J... i The geometric center point Z1 of the corresponding target point cloud and the location point Z2 of the lidar device are fitted to obtain the corresponding straight line L. i =k i x+b i , x∈[X1,X2].

[0081] Specifically, because some vehicles have a smaller, gentler curvature in their front grille, the resulting ghosting may be longer. In this case, there may be relatively more point clouds. Therefore, in P... i Based on the fact that the distance is less than the preset average threshold, according to J i The geometric center point Z1 of the target point cloud and the location point Z2 of the lidar device are fitted to obtain the corresponding straight line.

[0082] S1100, based on any key point cloud data within the point cloud cluster at the front of the target vehicle, obtain the corresponding straight line L0 = k i x+b0; the x-coordinate of the key point cloud data is g∈[X1,X2].

[0083] S1200, acquire the number of point cloud data NUM0 whose vertical distance from L0 is less than a preset threshold.

[0084] S1300, if NUM0 is greater than the preset quantity threshold, then determine J. i The corresponding target point cloud is a non-obstacle.

[0085] Specifically, this is due to noise (ghosting) caused by the surface of the target object. For example, the properties of the target object's surface material (high reflectivity causing excessive energy reflection in the point cloud) will cause not only a true point cloud image to exist in the original true coordinate system, but also a point cloud image of similar size and shape to appear in other nearby locations. Therefore, based on any key point cloud data within the point cloud cluster at the front of the target vehicle, the corresponding straight line L0 is obtained. This straight line is related to the aforementioned L... i If the slopes are the same, and the number of point cloud data NUM0 with a vertical distance of less than the preset threshold from the target vehicle's front point cloud cluster to L0 is large, it indicates that the target point cloud cluster and the target vehicle's front point cloud cluster have similar shapes (same slopes), meaning that the target point cloud cluster is likely a ghost image formed by the target vehicle's grille or license plate.

[0086] In this embodiment, for ghost shadows formed by the surface material of the target object that are similar in size and shape to the target object, two straight lines with the same fitting slope are used to determine whether the target point cloud is similar in shape to the point cloud of the actual object. If they are similar, it is very likely that the ghost shadow is formed by the actual object.

[0087] Please refer to Figure 2 As shown, an embodiment of this application provides a charging device 100 based on a mobile power supply vehicle, characterized in that the device includes:

[0088] The adjustment unit 110 is used to adjust the pose of the mobile power supply vehicle according to the license plate position of the target vehicle, so as to obtain the target charging pose of the mobile power supply vehicle adapted to the target vehicle.

[0089] The charging unit 120 is used to control the mobile power supply vehicle to charge the target vehicle according to the target charging posture;

[0090] The point cloud data acquisition unit 130 is used to acquire point cloud data generated by the lidar device installed on the mobile power supply vehicle in response to receiving a charging completion notification from the target vehicle; wherein, the lidar device on the mobile power supply vehicle is installed on the side of the mobile power supply vehicle facing the target vehicle; the application range of the lidar device is a preset position of the front of the target vehicle; the side of the mobile power supply vehicle facing the target vehicle is a plane; when the target vehicle is charging, the distance between the front of the vehicle and the mobile power supply vehicle is less than a preset distance threshold.

[0091] The distance acquisition unit 140 is used to acquire a distance list set J = (J1, J2, ..., J...) of the target point cloud cluster if a target point cloud cluster exists in the point cloud data. i , ..., J n ); i = 1, 2, ..., n; where n is the number of target point clouds; J i J is a list of distances corresponding to the i-th target point cloud cluster; i =(J i1 J i2 , ..., J ij , ..., J im ); j = 1, 2, ..., m; m is the number of point cloud data contained in the i-th target point cloud cluster; J ij The distance between the j-th point cloud data in i target point cloud clusters and the center point of the i-th target point cloud cluster; the number of point cloud data in the target point cloud cluster is greater than a first quantity threshold and less than a second quantity threshold; the height corresponding to the point cloud data of the target point cloud cluster is less than the upper edge height of the license plate of the target vehicle and greater than the lower edge height of the license plate of the target vehicle;

[0092] Average distance acquisition unit 150 is used to obtain the average distance set P = (P1, P2, ..., P) of the target point cloud based on J. i , ..., P n ); where P i P represents the average distance corresponding to the i-th target point cloud cluster; i =avg(J i ); avg() is a preset function to determine the average value;

[0093] Noise determination unit 160, used if P i If the distance is less than the preset average distance threshold, then P is determined. i The corresponding target point cloud is noise;

[0094] The mobile unit 170 is used to control the mobile power supply vehicle to plan a movement path and move to the mobile power supply vehicle compartment according to the movement path; wherein, the target point cloud clusters identified as noise are used as non-obstacles during path planning.

[0095] Embodiments of this application also provide a computer program product including program code that, when the program product is run on an electronic device, causes the electronic device to perform the steps of the methods described above according to various exemplary embodiments of this application.

[0096] Furthermore, although the steps of the method in this application are described in a specific order in the accompanying drawings, this does not require or imply that the steps must be performed in that specific order, or that all the steps shown must be performed to achieve the desired result. Additional or alternative steps may be omitted, multiple steps may be combined into one step, and / or a step may be broken down into multiple steps.

[0097] Through the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein can be implemented by software or by combining software with necessary hardware. Therefore, the technical solutions according to the embodiments of this application can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (such as a CD-ROM, USB flash drive, external hard drive, etc.) or on a network, including several instructions to cause a computing device (such as a personal computer, server, mobile terminal, or network device, etc.) to execute the method according to the embodiments of this application.

[0098] In an exemplary embodiment of this application, an electronic device capable of implementing the above-described method is also provided.

[0099] Those skilled in the art will understand that various aspects of this application can be implemented as a system, method, or program product. Therefore, various aspects of this application can be specifically implemented in the following forms: a completely hardware implementation, a completely software implementation (including firmware, microcode, etc.), or a combination of hardware and software implementations, collectively referred to herein as a "circuit," "module," or "system."

[0100] An electronic device according to this embodiment of the present application. The electronic device is merely an example and should not be construed as limiting the functionality and scope of use of the embodiments of this application.

[0101] Electronic devices are manifested in the form of general-purpose computing devices. Components of an electronic device may include, but are not limited to: at least one processor, at least one memory, and buses connecting different system components (including memory and processor).

[0102] The memory stores program code that can be executed by a processor, causing the processor to perform the steps described in the "Exemplary Methods" section above, according to various exemplary embodiments of this application.

[0103] The storage may include readable media in the form of volatile storage, such as random access memory (RAM) and / or cache memory, and may further include read-only memory (ROM).

[0104] The storage may also include programs / utilities having a set (at least one) of program modules, including but not limited to: an operating system, one or more applications, other program modules, and program data, each or some combination of these examples may include an implementation of a network environment.

[0105] A bus can represent one or more of several bus architectures, including a memory bus or memory controller, a peripheral bus, a graphics acceleration port, a processor, or a local bus that uses any of the various bus architectures.

[0106] The electronic device can also communicate with one or more external devices (e.g., keyboards, pointing devices, Bluetooth devices, etc.), one or more devices that enable a user to interact with the electronic device, and / or any device that enables the electronic device to communicate with one or more other computing devices (e.g., routers, modems, etc.). This communication can be achieved through input / output (I / O) interfaces. Furthermore, the electronic device can communicate with one or more networks (e.g., local area networks (LANs), wide area networks (WANs), and / or public networks, such as the Internet) via a network adapter. As shown in the figure, the network adapter communicates with other modules of the electronic device via a bus. It should be understood that, although not shown in the figure, other hardware and / or software modules can be used in conjunction with the electronic device, including but not limited to: microcode, device drivers, redundant processors, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0107] Through the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein can be implemented by software or by combining software with necessary hardware. Therefore, the technical solutions according to the embodiments of this application can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (such as a CD-ROM, USB flash drive, external hard drive, etc.) or on a network, including several instructions to cause a computing device (such as a personal computer, server, terminal device, or network device, etc.) to execute the method according to the embodiments of this application.

[0108] In exemplary embodiments of this application, a computer-readable storage medium is also provided, on which a program product capable of implementing the methods described above is stored. In some possible implementations, various aspects of this application may also be implemented as a program product including program code, which, when the program product is run on a terminal device, causes the terminal device to perform the steps of the various exemplary embodiments of this application described in the "Exemplary Methods" section above.

[0109] The program product may employ any combination of one or more readable media. A readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples (a non-exhaustive list) of readable storage media include: electrical connections having one or more wires, portable 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 thereof.

[0110] Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, carrying readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A readable signal medium may also be any readable medium other than a readable storage medium, capable of sending, propagating, or transmitting programs for use by or in conjunction with an instruction execution system, apparatus, or device.

[0111] The program code contained on the readable medium may be transmitted using any suitable medium, including but not limited to wireless, wired, optical fiber, RF, etc., or any suitable combination thereof.

[0112] Program code for performing the operations of this application can be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java and C++, and conventional procedural programming languages ​​such as C or similar languages. The program code can execute entirely on the user's computing device, partially on the user's computing device, as a standalone software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server. In cases involving remote computing devices, the remote computing device can be connected to the user's computing device via any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computing device (e.g., via the Internet using an Internet service provider).

[0113] Furthermore, the above figures are merely illustrative of the processes included in the method according to exemplary embodiments of this application, and are not intended to be limiting. It is readily understood that the processes shown in the above figures do not indicate or limit the temporal order of these processes. Additionally, it is readily understood that these processes may be executed synchronously or asynchronously, for example, in multiple modules.

[0114] It should be noted that although several modules or units for the device used to perform actions have been mentioned in the detailed description above, this division is not mandatory. In fact, according to the embodiments of this application, the features and functions of two or more modules or units described above can be embodied in one module or unit. Conversely, the features and functions of one module or unit described above can be further divided and embodied by multiple modules or units.

[0115] The above are merely specific embodiments of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A charging method based on a mobile power supply vehicle, characterized in that, The method includes: S100, determine the target charging pose of the mobile power supply vehicle to the target vehicle based on the image acquisition device of the mobile power supply vehicle and the license plate of the target vehicle; S200, control the mobile power supply vehicle to charge the target vehicle according to the target charging posture; S300, in response to receiving a charging completion notification from the target vehicle, acquire point cloud data generated by the lidar device installed on the mobile power supply vehicle; wherein, the lidar device on the mobile power supply vehicle is installed on the side of the mobile power supply vehicle facing the target vehicle; the application range of the lidar device is a preset position of the front of the target vehicle; the side of the mobile power supply vehicle facing the target vehicle is a plane; when the target vehicle is charging, the distance between the front of the vehicle and the mobile power supply vehicle is less than a preset distance threshold; S400, if a target point cloud cluster exists within the point cloud data, then obtain the target point cloud cluster distance list set J = (J1, J2, ..., J...). i , ..., J n ); i = 1, 2, ..., n; where n is the number of target point clouds; J i J is a list of distances corresponding to the i-th target point cloud; i =(J i1 J i2 , ..., J ij , ..., J im ); j = 1, 2, ..., m; m is the number of point cloud data contained in the i-th target point cloud cluster; J ij The distance between the j-th point cloud data in i target point cloud clusters and the center point of the i-th target point cloud cluster; the number of point cloud data in the target point cloud cluster is greater than a first quantity threshold and less than a second quantity threshold; the height corresponding to the point cloud data of the target point cloud cluster is less than the upper edge height of the license plate of the target vehicle and greater than the lower edge height of the license plate of the target vehicle; S500, according to J, the average distance set of the target point cloud is obtained as P = (P1, P2, ..., P...). i , ..., P n ); where P i P represents the average distance corresponding to the i-th target point cloud cluster; i =avg(J i ); avg() is a preset function to determine the average value; S600, if P i If the distance is less than the preset average distance threshold, then P is determined. i The corresponding target point cloud is noise; S700 controls the mobile power supply vehicle to plan its movement path and move to the mobile power supply vehicle compartment according to the movement path; wherein, the target point cloud clusters identified as noise are used as non-obstacles during path planning.

2. The charging method based on a mobile power supply vehicle according to claim 1, characterized in that, The target point cloud is determined through the following steps: S410, if the point cloud data contains at least one key point cloud cluster, then obtain the key point cloud cluster information set D = (D1, D2, ..., D...). p D q ); p = 1, 2, ..., q; where q is the number of keypoint clusters; D p D represents the point cloud information of the p-th key point cloud cluster; p =(DB p , Ds p DG p ); DB p Ds is the identifier for the p-th keypoint cloud. p Let be the number of point cloud data for the p-th key point cloud cluster; the number of point cloud data for the key point cloud cluster is greater than the first quantity threshold and less than the second quantity threshold. S420: Acquires image information corresponding to each key point cloud cluster using a preset image acquisition device, and analyzes it based on a preset image analysis algorithm to obtain the analysis result corresponding to each key point cloud cluster; wherein, the analysis result can be a real object, a non-real object, or an unidentifiable object; S430, obtain the height of each point cloud data in each unidentifiable key point cloud cluster in each analysis result, and obtain the point cloud cluster height list set GD=(GD1, GD2, ..., GD... x GD y ); x = 1, 2, ..., y; where y is the number of keypoint clouds that cannot be identified by the analysis results; GD x This is a list of the heights of the keypoint cloud clusters for the xth analysis result that are unidentifiable; GD x =(GD x1 GD x2 GD xr GD xs ); r = 1, 2, ..., s; s is the number of point cloud data contained in the x-th unidentifiable key point cloud cluster; GD xr Let r be the height of the r-th point cloud data in the x-th key point cloud cluster whose analysis result is unidentifiable. S440, obtain the height range L=[L1, L2] corresponding to the target vehicle; L1 is the height of the upper edge of the license plate of the target vehicle; L2 is the height of the lower edge of the license plate of the target vehicle; S450, if GD xr If ∈L, then determine GD x The target point cloud cluster.

3. The charging method based on a mobile power supply vehicle according to claim 1, characterized in that, Step S600 is replaced by the following steps: S800, if P i If the distance is less than the preset average distance threshold, then P is determined. i The corresponding target point cloud is suspected noise; S900, if P i If the number of point cloud data points of the corresponding target point cloud cluster is greater than the first quantity threshold and less than the third quantity threshold, then the target point cloud cluster is determined to be noise; wherein, the third quantity threshold is less than the second quantity threshold and greater than the first quantity threshold.

4. The charging method based on a mobile power supply vehicle according to claim 1, characterized in that, There is at least one preset image acquisition device.

5. A charging device based on a mobile power supply vehicle, characterized in that, The device includes: The adjustment unit is used to adjust the pose of the mobile power supply vehicle according to the license plate position of the target vehicle, so as to obtain the target charging pose of the mobile power supply vehicle adapted to the target vehicle. A charging unit is used to control the mobile power supply vehicle to charge the target vehicle according to the target charging posture; The point cloud data acquisition unit is used to acquire point cloud data generated by the lidar device installed on the mobile power supply vehicle in response to receiving a charging completion notification from the target vehicle; wherein, the lidar device of the mobile power supply vehicle is installed on the side of the mobile power supply vehicle facing the target vehicle; the application range of the lidar device is a preset position of the front of the target vehicle; the side of the mobile power supply vehicle facing the target vehicle is a plane; when the target vehicle is charging, the distance between the front of the vehicle and the mobile power supply vehicle is less than a preset distance threshold. The distance acquisition unit is used to acquire a distance list set J=(J1, J2, ..., J...) of the target point cloud cluster if a target point cloud cluster exists in the point cloud data. i , ..., J n ); i = 1, 2, ..., n; where n is the number of target point clouds; J i J is a list of distances corresponding to the i-th target point cloud; i =(J i1 J i2 , ..., J ij , ..., J im ); j = 1, 2, ..., m; m is the number of point cloud data contained in the i-th target point cloud cluster; J ij The distance between the j-th point cloud data in i target point cloud clusters and the center point of the i-th target point cloud cluster; the number of point cloud data in the target point cloud cluster is greater than a first quantity threshold and less than a second quantity threshold; the height corresponding to the point cloud data of the target point cloud cluster is less than the upper edge height of the license plate of the target vehicle and greater than the lower edge height of the license plate of the target vehicle; The average distance acquisition unit is used to obtain the average distance set P = (P1, P2, ..., P) of the target point cloud based on J. i , ..., P n ); where P i P represents the average distance corresponding to the i-th target point cloud cluster; i =avg(J i ); avg() is a preset function to determine the average value; Noise determination unit, used if P i If the distance is less than the preset average distance threshold, then P is determined. i The corresponding target point cloud is noise; A mobile unit is used to control the mobile power supply vehicle to plan a movement path and move to the mobile power supply vehicle compartment according to the movement path; wherein, the target point cloud clusters identified as noise during path planning are used as non-obstacles.

6. A non-transitory computer-readable storage medium, characterized in that, The storage medium stores at least one instruction or at least one program segment, which is loaded and executed by a processor to implement the method as described in any one of claims 1-4.

7. An electronic device, characterized in that, Includes a processor and the non-transitory computer-readable storage medium as described in claim 6.

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