Method, device, equipment and medium for calibrating electronic fence of shared vehicle

Abstract

The invention relates to a method, a device, equipment and a medium for calibrating an electronic fence of a shared vehicle. Specifically, a geographic area of the electronic fence is determined, driving data of a shared vehicle associated with the geographic area is acquired, a candidate area of the electronic fence is determined based on the driving data, and the electronic fence is calibrated using the candidate area. In this way, the accuracy of the electronic fence is improved, automatic calibration of the electronic fence is achieved, manual intervention is not needed, and the operation cost is reduced.

Description

Technical Field

[0001] Implementations of the present disclosure relate to shared vehicle management, and in particular, to a method, apparatus, electronic device, and storage medium for calibrating an electronic fence of a shared vehicle. Background Art

[0002] With the development of positioning technology and electronic information technology, electronic fence systems have been developed. For example, a virtual electronic fence can be established in a certain geographical area (for example, a city, park, business district or other range) to manage shared vehicles (for example, bicycles, electric vehicles, etc.). Operators can use electronic fences to manage shared vehicles and improve the standardization of operations. By using electronic fences, the operating platform can monitor and control the scope of use of items in real time, thereby ensuring the orderliness of shared vehicles during return, use and flow. However, with the increasing popularity of the application of electronic fences, especially the increasing number of application scenarios in large-scale environments, the accuracy requirements of electronic fences are becoming increasingly higher. Summary of the Invention

[0003] According to a first aspect of the present disclosure, a method for calibrating an electronic fence for a shared vehicle is provided. The method comprises: determining a geographic area of ​​the electronic fence; obtaining driving data of the shared vehicle associated with the geographic area; determining candidate areas for the electronic fence based on the driving data; and calibrating the electronic fence using the candidate areas.

[0004] According to a second aspect of the present disclosure, a device for calibrating an electronic fence for a shared vehicle is provided. The device includes: a geographic region determination module for determining a geographic region of the electronic fence; an acquisition module for acquiring driving data of shared vehicles associated with the geographic region; a candidate region determination module for determining candidate regions for the electronic fence based on the driving data; and a calibration module for calibrating the electronic fence using the candidate regions.

[0005] According to a third aspect of the present disclosure, an electronic device is provided, comprising: a memory and a processor; wherein the memory is used to store one or more computer instructions, wherein the one or more computer instructions are executed by the processor to implement the method according to the first aspect of the present disclosure.

[0006] According to a fourth aspect of the present disclosure, a computer-readable storage medium is provided, on which one or more computer instructions are stored, wherein the one or more computer instructions are executed by a processor to implement the method according to the first aspect of the present disclosure.

[0007] According to a fifth aspect of the present disclosure, a computer program product is provided, comprising a computer program or instructions, wherein when the computer program or instructions are executed by a processor, the method according to the first aspect of the present disclosure is implemented. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] The features, advantages and other aspects of the various implementations of the present disclosure will become more apparent with reference to the following detailed description in conjunction with the accompanying drawings, which illustrate several implementations of the present disclosure in an illustrative and non-limiting manner. In the accompanying drawings:

[0009] Figure 1 A block diagram schematically illustrates an application environment in which an implementation according to the present disclosure may be used;

[0010] Figure 2 Schematically shows a block diagram for calibrating an electronic fence of a shared item according to an exemplary implementation of the present disclosure;

[0011] Figure 3 A schematic diagram of an exemplary implementation according to the present disclosure is shown;

[0012] Figure 4 Another exemplary implementation diagram of the present disclosure is shown;

[0013] Figure 5 A flowchart schematically illustrates a method for calibrating an electronic fence of a shared item according to an exemplary implementation of the present disclosure; and

[0014] Figure 6 A block diagram of a computing device / server according to an exemplary implementation of the present disclosure is schematically shown. DETAILED DESCRIPTION

[0015] The following describes implementations of the present disclosure in more detail with reference to the accompanying drawings. Although certain implementations of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be construed as limited to the implementations described herein. Rather, these implementations are provided to provide a more thorough and complete understanding of the present disclosure. It should be understood that the drawings and implementations of the present disclosure are for illustrative purposes only and are not intended to limit the scope of protection of the present disclosure.

[0016] In the description of the implementations of the present disclosure, the term "including" and similar terms should be understood as open inclusion, i.e., "including but not limited to." The term "based on" should be understood as "based at least in part on." The term "an implementation" or "the implementation" should be understood as "at least one implementation." The term "some implementations" should be understood as "at least some implementations." Other explicit and implicit definitions may be included below.

[0017] Herein, unless explicitly stated otherwise, executing a step “in response to A” does not mean executing the step immediately after “A” but may include one or more intermediate steps.

[0018] It is understandable that the data involved in this technical solution (including but not limited to the data itself, the acquisition, use, storage or deletion of the data) shall comply with the requirements of relevant laws, regulations and relevant provisions.

[0019] It is understandable that before using the technical solutions disclosed in each implementation method of the present disclosure, the type, scope of use, usage scenarios, etc. of the information involved in the present disclosure should be informed to relevant users and authorization should be obtained from relevant users in an appropriate manner in accordance with relevant laws and regulations. The relevant users may include any type of right holders, such as individuals, enterprises, and groups.

[0020] For example, in response to receiving an active request from a user, a prompt message is sent to the relevant user to clearly prompt the relevant user that the operation requested to be performed will require obtaining and using the information of the relevant user, so that the relevant user can independently choose whether to provide information to the software or hardware such as the electronic device, application, server or storage medium that executes the operation of the technical solution of the present disclosure based on the prompt message.

[0021] As an optional but non-limiting implementation, in response to receiving an active request from a relevant user, a prompt message may be sent to the relevant user in the form of a pop-up window, in which the prompt message may be presented in text form. Furthermore, the pop-up window may also include a selection control for the user to select "agree" or "disagree" to provide information to the electronic device.

[0022] It is understandable that the above notification and the process of obtaining user authorization are merely illustrative and do not constitute a limitation on the implementation of the present disclosure. Other methods that comply with relevant laws and regulations may also be applied to the implementation of the present disclosure.

[0023] First, an application environment according to an implementation of the present disclosure is described with reference to the accompanying drawings. Figure 1 Figure 100 illustrates a method for calibrating a geo-fence for shared items. For ease of description, this disclosure uses shared bicycles and shared trams in urban transportation environments as examples of shared vehicles to describe the specific process of calibrating a geo-fence for these vehicles. Shared vehicles may also include shared shopping carts in supermarkets, shared luggage carts in airports, shared wheelchairs in hospitals, and so on.

[0024] like Figure 1As shown, the geographic area 110 is the geographic area where the electronic fence 120 is located. It can have a pre-specified range, for example, it can cover part of a city, a specific block, or have other predetermined geographic ranges. When using the electronic fence 120, it is usually necessary to first determine the geographic area 110 of the electronic fence 120 for subsequent operations. There are multiple shared vehicles distributed in the geographic area 110. In order to facilitate the management of shared vehicles, one or more electronic fences 120 can usually be set up to park shared vehicles. Although not shown, the geographic area can also include multiple electronic fences 120, for example, they can be distinguished and managed by electronic fence numbers.

[0025] It will be understood that the accuracy of the electronic fence will directly affect the management effect. It is the basis for regulating the parking and use of vehicles. In actual applications, the road conditions and vehicle usage in different geographical areas are different. For example, the driving and returning of vehicles in certain areas (for example, busy roads and / or popular return points, etc.) may have a greater impact on urban traffic; while vehicles in certain remote areas may have a smaller impact on urban traffic. Different treatments may be needed when calibrating the electronic fence based on the prosperity of each area, road network density, population density, etc.

[0026] Currently, manual calibration is commonly used to calibrate and adjust electronic fences. However, manual calibration is costly, complex, and inconvenient for dynamic adjustment. A more efficient and accurate method for calibrating shared vehicle electronic fences would improve vehicle management efficiency and thus enhance vehicle placement accuracy.

[0027] In order to at least partially solve the defects of the above technical solutions, according to an implementation of the present disclosure, a technical solution for calibrating an electronic fence is proposed. Figure 2 Describe in more detail the Figure 2 A block diagram 200 for calibrating an electric fence according to an implementation of the present disclosure is schematically shown.

[0028] like Figure 2 As shown, the geographic area 110 corresponding to the geo-fence 120 to be calibrated can first be determined. Within this geographic area 110, driving data 210 of the shared vehicle can be obtained. In some implementations, driving data 210 may include at least road network information 212 and / or vehicle return data 214. For example, road network information 212 may provide information about the road network on which the vehicle traveled or about the road network associated with the parking area, while vehicle return data 214 may record the specific location information of the shared vehicle when it was returned within the geographic area 110.

[0029] Based on the driving data 210, the candidate area 222 of the electronic fence 120 can be determined by the method of the present disclosure. In this process, the road network information 212 or the vehicle return data 214 can be analyzed and processed separately, or the road network information 212 and the vehicle return data 214 can be analyzed and processed in combination. The present disclosure does not specifically limit its implementation method. For example, the candidate areas 222 on both sides of the road can be determined based on the road network information 212; for another example, the concentrated parking points of the vehicles can be determined based on the vehicle return data 124 to determine the candidate areas 222; for another example, the association between the shared vehicle parking points and the road can be obtained based on the road network information 212, and then the concentrated parking points of the vehicles can be determined based on the vehicle return data 214, thereby determining the candidate areas 222 on both sides of the road, and so on.

[0030] After determining the candidate area 222, the geo-fence 120 to be calibrated can be calibrated using the candidate area 222. For example, the calibration process can transform the geo-fence 120 to be calibrated into a calibrated geo-fence. The intersection of the candidate area 222 and the geo-fence 120 can be used as the calibrated geo-fence. The calibrated geo-fence can be determined based on the location of the candidate area 222 and the area of ​​the geo-fence 120, and so on. Although the specific steps of the calibration process are not shown in detail in the figure, it can be understood that it is a series of processes for adjusting and optimizing the geo-fence based on driving data.

[0031] In some implementations, the geographic area 110 of the electronic fence can be pre-defined. For example, it can be set to have a clearly defined scope, including a specific block in a city, a certain park, etc. Pre-setting the geographic area 110 can refine the management granularity of the electronic fence and make calibration more targeted and operational. For example, when applied to a specific block in a city, the geographic area 110 can be determined based on urban planning and the functional division of the block. Urban planning departments typically carry out detailed planning of the block's road layout, building distribution, and the location of public facilities. This information can serve as an important basis for determining the boundaries of the geographic area 110. Alternatively and / or additionally, the usage demand and parking characteristics of shared vehicles within the block can be considered. For example, vehicles in commercial areas are frequently used and parking is concentrated on roads around shopping malls, while vehicles near residential areas are more concentrated on roads near community entrances and exits. Based on these actual conditions, the scope of the geographic area 110 can be precisely delineated so that the boundaries of the geographic area 110 cover the main vehicle usage and parking areas while avoiding unnecessary expansion.

[0032] After obtaining the geographic area 110 , the driving data 210 of the shared vehicle associated with the geographic area 110 is obtained. In some implementations, the driving data 210 may include road network information 212 and vehicle return data 214 of the shared vehicle.

[0033] The acquisition of road network information 212 usually requires the fusion and analysis of multi-source data. On the one hand, the vehicle's own positioning information can be combined with map data. The map records in detail the topological structure, road section attributes and geographical location information of the road network, such as road grade, number of lanes, road section direction, etc. Through the map matching algorithm, the real-time position of the vehicle is matched with the road in the map, and it is possible to determine which road the vehicle is currently traveling on and the relevant attribute information of the road, thereby obtaining the part of the road network information 212 that is related to the road network on which the vehicle is traveling. In some implementations, the road network information 212 can be pre-generated accurate information to ensure that the road network information 212 can provide a detailed and reliable basis for the calibration of the electronic fence, including important information such as the direction of the road, connection relationship, and association with the parking area.

[0034] The acquisition of vehicle return data 214 primarily relies on the positioning system installed on the shared vehicle. Currently, mainstream positioning systems include the Global Positioning System (GPS) or the Beidou Satellite Navigation System. These positioning systems receive satellite signals and, through complex signal processing and calculation, determine the precise coordinates of the vehicle in a geographic coordinate system. In response to a vehicle return operation, the shared vehicle's latitude and longitude information, or the shared vehicle's location coordinates, can be recorded to determine the vehicle's return location.

[0035] In some implementations, to further improve the accuracy of location data, an inertial measurement unit (IMU) on the shared vehicle can be incorporated. The IMU can measure the vehicle's acceleration and angular velocity in real time. When satellite signals are temporarily lost, such as when the vehicle enters a tunnel, underground parking lot, or other signal-blocked area, the IMU can infer the vehicle's position and attitude changes based on inertial principles, ensuring the continuity and reliability of location data. In this way, the return vehicle data 214 can record the specific location information of the shared vehicle within the geographic area 110, providing a data basis for subsequent analysis of the vehicle's parking location.

[0036] The candidate area 222 of the electronic fence can be determined based on the acquired driving data 210. By analyzing the driving data 210, the range that matches the actual parking and frequently used areas of the vehicle is screened out as the candidate area 222. For example, if shared vehicles are frequently returned within a certain range, the range may be determined as a candidate area. In the specific implementation process, the corresponding candidate area determination method can be set according to the different types of driving data. For example, for driving data that reflects the location distribution of shared vehicles when they are returned, algorithms such as spatial clustering can be used to determine the candidate area; for driving data that reflects road network information, the candidate area is determined by matching with the road network topology structure and combining the road section information.

[0037] In some implementations, after candidate area 222 is determined, the geo-fence can be calibrated using candidate area 222. Candidate area 222 is compared and adjusted with the geo-fence 120 to be calibrated, allowing the geo-fence to more accurately designate the appropriate parking and usage areas for vehicles. For example, if candidate area 222 significantly exceeds the geo-fence 120 to be calibrated, but analysis of shared vehicle driving data indicates that the excess area is within the appropriate vehicle usage area, the geo-fence can be expanded accordingly.

[0038] When calibrating an electronic fence, various methods such as position calibration, shape and size adjustment, and direction calibration can be used, and this disclosure does not limit their implementation. For example, position calibration can be performed based on the center position of the determined candidate area to adjust the center position of the electronic fence to be calibrated. For example, if the center of the electronic fence to be calibrated is located at the edge of a grid, and the center of the candidate area determined by big data analysis is at the center of the grid and the vehicle return density is significantly higher, the center of the electronic fence will be moved to the center position of the candidate area.

[0039] In some implementations, the shape and size of the geo-fence can be adjusted based on the scope of the candidate area and the density of return points. If the candidate area is long and narrow with return points concentrated on both sides, the geo-fence can be adjusted from the original square to a rectangle, and the area can be appropriately expanded to cover more return points. If the candidate area is more dispersed but generally falls within a specific range, a polygonal geo-fence can be used to precisely define the densely populated return area.

[0040] Direction calibration can be achieved by analyzing the parking direction distribution of returned vehicles within a candidate area and adjusting the direction of the geo-fence based on the parking direction of the majority of vehicles. For example, if the majority of vehicles in a candidate area are parked in a north-south direction, the original east-west geo-fence can be adjusted to a north-south direction. It should be understood that when the sample size meets the preset requirements, the actual parking posture and position of most users will be consistent with the standard direction and position defined by the ground-based parking frame. In this way, the desired parking posture and position can be determined more accurately, thereby improving the accuracy of the calibrated geo-fence. The calibrated geo-fence can guide users to park in a more standardized and manageable manner.

[0041] pass Figure 2 The illustrated implementation method can achieve automatic calibration of the electronic fence without manual intervention, reducing labor costs and work complexity. At the same time, it improves calibration efficiency and accuracy, and can better adapt to complex and changing environments.

[0042] An overview of an implementation of the present disclosure has been described. Further details will be provided below. During the calibration of the electronic fence for a shared vehicle, it is expected that the electronic fence can be more closely aligned with the road network information. According to an implementation of the present disclosure, candidate areas for the calibrated electronic fence can be determined based on the road network information in a geographic area. Figure 3 Describes the situation where driving data includes calibrating geo-fences when sharing road network information of vehicles driving in a geographic area. Figure 3 A schematic diagram 300 is shown for calibrating an electronic fence using road network information according to an implementation of the present disclosure.

[0043] In such Figure 3 In the illustrated implementation, the location of the fence 120 to be calibrated is first determined. This location can be represented in a variety of ways, such as using latitude and longitude information or a custom coordinate system. For example, the center position P of the fence 120 to be calibrated is determined. In some implementations, the latitude and longitude information of point P can be obtained to match road segment information in a road network information system. Specifically, the matching road segment can be determined based on one or a combination of conditions, such as the road segment location, road segment direction, and road segment identification.

[0044] In some implementations, when matching road segment information by location, for example, the spatial distance between point P and each road segment can be calculated, and the closest road segment can be selected as the matching road segment. When determining by direction, for example, the angle between the main orientation of the fence where point P is located and the direction of each road segment can be analyzed, and the road segment with the smallest angle can be selected for matching. Matching road segment information can also be determined by road segment name. For example, if there is a road segment with a specific name (e.g., a non-motorized vehicle lane, a pedestrian street, etc.) near the area where point P is located, this road segment will be matched first. Through these methods, accurate matching of electronic fences and road segment information can be achieved.

[0045] In some implementations, after obtaining road network information, the road segment information can be pre-structured, for example, by establishing a standardized database containing road segment location, road segment direction, and road segment identification, to reduce the time consumption during the real-time parsing process. When performing matching, the "distance + direction" combination rule can be preferentially adopted. Specifically, the initial search radius is first defined with the center P of the electronic fence as the origin, and the road segments within this radius are screened out; then, the deviation angle between the direction of each road segment and the preset initial direction of the fence is calculated, and the road segment with the smallest deviation angle is selected as the matching object; if there are multiple road segments with the same deviation angle, the road segment name (for example, "XX Road Non-motorized Vehicle Lane") can be further used to achieve accurate matching. Alternatively and / or additionally, a machine learning algorithm can be used to classify and analyze the road segment data to automatically identify the road segment where the vehicle is located, and the matching rules can be dynamically adjusted based on actual usage scenarios and data feedback to adapt to different environments and conditions.

[0046] By matching electronic fence candidate areas with road network information through the above-mentioned disclosed method, the candidate areas can be made more relevant to the actual road environment, providing an accurate basis for subsequent calibration and improving the rationality and accuracy of electronic fence calibration.

[0047] When using the implementation method disclosed herein, the road network information on the map can also be used as basic data to obtain detailed road section information. Furthermore, the latest road network data can be obtained in real time to ensure the accuracy of road section information and avoid road matching errors caused by factors such as road construction, reconstruction, or new roads.

[0048] In some implementations, a geographic matching rule system can be constructed. For example, this system is centered around spatial location relevance and can include key indicators such as distance threshold, directional consistency, and topological relationships. Distance thresholds can be used to define the spatial proximity between a road segment and an electronic fence. Directional consistency can be used to measure the degree of alignment between the road segment's direction and the long side of the electronic fence. Topological relationships can be used to determine whether a road segment and the electronic fence have spatial associations such as inclusion, adjacency, or intersection.

[0049] In some implementations, the road segment information can also be converted to spatial coordinates, converting parameters such as the starting and ending point coordinates, direction angles, etc. of the road segment to the same geographic coordinate system as the electronic fence area, ensuring consistency in spatial reference between the two. Subsequently, candidate road segments around the electronic fence area can be screened based on a preset distance threshold. Specifically, the straight-line distance between the midpoint of the road segment and the center of the electronic fence is calculated, and road segments with a distance less than or equal to the threshold are retained as candidate road segments.

[0050] In some implementations, the deviation between the orientation angle of a candidate road segment and the orientation angle of the long side of the geo-fence can be calculated. If the deviation is less than a preset angle threshold, the road segment is considered to match the geo-fence orientation. Spatial topological analysis can also be used to determine the positional relationship between the road segment and the geo-fence, such as whether the road segment is partially or completely within the geo-fence area or whether the edge of the road segment touches the geo-fence boundary.

[0051] In some implementations, the matching results of distance, direction, and topology can be combined to map the road segments that meet all the rule conditions to the corresponding electronic fence areas to complete the matching process. If there are multiple road segments that meet the conditions, the final mapping relationship is determined by the priority of the road segment identification (for example, non-motorized vehicle lanes take precedence over motor vehicle lanes).

[0052] Through the above-mentioned specific implementation method, the specific method of matching the electronic fence area with the road section information can be clarified, making the matching process more standardized and operational, ensuring the reliability of the matching results, and thus improving the accuracy of the candidate area determination.

[0053] In some implementations of the present disclosure, when determining a candidate area for an electronic fence, the boundary of the candidate area may be determined based on the road section position of the road section information, or the vehicle placement direction of the candidate area may be determined based on the road section direction of the road section information.

[0054] In one implementation, when determining the boundaries of a candidate area based on the location of a road segment in road segment information, key data representing the location of the road segment, such as the coordinates of the starting and ending points and the centerline of the road segment, can be first extracted from the road segment information. Using this data as a reference and taking into account the spatial correlation requirements between vehicle parking and the road segment, the boundaries of the candidate area are delineated. For example, using the centerline of the road segment as a reference, a horizontal boundary is formed by extending a certain distance to both sides of the road segment, and a vertical boundary is determined by the starting and ending points of the road segment. This ensures that the boundaries of the candidate area cover the space around the road segment that is suitable for vehicle parking and does not exceed a reasonable geographical range.

[0055] Optionally, the vehicle placement orientation in the candidate area can be determined based on the road section direction from the road section information. For example, the road section's orientation can be obtained from the road section information, such as the angle between the road section and true north. Since the vehicle placement orientation typically needs to be aligned with the road section direction to improve space utilization and user convenience, the road section direction is used as a reference for the vehicle placement orientation in the candidate area. For example, the vehicle placement orientation can be set perpendicular to the road section direction. For example, if the road section direction is east-west, the vehicle placement orientation in the candidate area can be set to a north-south orientation perpendicular to the road section direction to ensure orderly parking.

[0056] The boundaries of the candidate area and the direction of vehicle placement are closely linked to the road section information, which meets the needs of actual parking scenarios, improves the effectiveness of electronic fences in vehicle parking management, and ensures the convenience of users returning their vehicles.

[0057] According to some implementations of the present disclosure, when determining the candidate area of ​​the electronic fence, it can be achieved in the following manner: determining the position offset according to the road section information, and then determining the center position of the candidate area based on the position offset.

[0058] When determining the position offset based on road section information, key location parameters for the road section can be first extracted from the road section information, such as the coordinates of the section centerline and the distance between the section edge and the centerline. Taking into account safe parking distance requirements (such as avoiding lane occupation and ensuring space for boarding and alighting), offset parameters are calculated from the key location on the road section to the center of the candidate area. For example, using the section centerline as a reference, and considering that parked vehicles must maintain a certain lateral distance from the road section, this lateral distance is the lateral position offset. Simultaneously, the longitudinal position offset is determined based on the distribution of regular vehicle stops on the road section. Together, these two factors constitute the position offset.

[0059] When determining the center position of a candidate area based on a position offset, the reference point of the road section can be used as the starting point and spatial coordinates can be transformed according to the determined position offset. The coordinates of the reference point are added to the coordinate increment corresponding to the position offset, and the resulting coordinates are the center position of the candidate area. For example, if the coordinates of the reference point of the road section are (x0, y0), the coordinate increment corresponding to the lateral position offset is (a, 0), and the coordinate increment corresponding to the longitudinal position offset is (0, b), then the coordinates of the center position of the candidate area are (x0+a, y0+b), thereby ensuring that the center of the candidate area is located at a location suitable for concentrated parking of vehicles around the road section.

[0060] By determining the candidate area through the position offset, the center position of the candidate area can be determined more accurately, making the calibrated electronic fence position more reasonable, further improving the accuracy of the fence position, and facilitating standardized parking of vehicles.

[0061] Continue to refer Figure 3 In some embodiments, before calibrating the electronic fence, key information of the target road section can be obtained, including the longitude and latitude coordinates of the road section (for example, the coordinates of the two end points of the target road section), the direction of the road section (for example, the angle between the target road section and the north direction), and other information to provide basic data for subsequent calibration. Based on the expected electronic fence location information, the expected distance thresholds S0 and S1 can be determined, and then the calibrated electronic fence position center P' can be determined. The expected electronic fence location information can be obtained based on a comprehensive analysis of multiple factors such as the historical driving data of shared vehicles and the distribution of return points. The determination of the distance thresholds S0 and S1 can also take into account factors such as the reasonable range of vehicle parking and the safe distance from the road section. For example, S0 can be set to the maximum distance that allows most users to conveniently return the vehicle, and S1 can be set to the minimum safe distance that ensures that the vehicle does not affect the normal traffic of the road section. Through the values ​​of S0 and S1, combined with the longitude and latitude coordinates of the road section and the direction of the road section, the center position P' of the fence after calibration can be calculated. The fence's shape is not limited; it can be rectangular or another suitable shape. Its specific form is determined by pre-defined rules (such as the center P' and the fence's dimensions). Once the fence's shape is determined, the coordinates and orientation of each endpoint are uniquely defined, ensuring that the calibrated fence can better serve the operation and management of shared vehicles.

[0062] exist Figure 3 In the electronic fence calibration scenario shown, when determining the geographical area of ​​the electronic fence, the received shared vehicle location data not only contains plane coordinates (such as latitude and longitude or x, y coordinates), but also can include elevation information (i.e., z coordinate). The system calculates the slope of the corresponding area based on the three-dimensional coordinates (x, y, z) to determine whether the area is suitable for parking. The specific implementation method is as follows.

[0063] First, extract the z coordinate information from the location data. This is done by using a shared vehicle positioning device to collect the elevation data of the shared vehicle when it is returned, or by matching the (x, y) coordinates to a preset digital elevation model to obtain the corresponding z coordinate information, thus forming the three-dimensional location information (x, y, z).

[0064] After preprocessing, the slope of a geographic area can be calculated based on its z-coordinate. Multiple feature points (e.g., vertices divided by a grid) within the candidate geo-fence area are selected, their z-coordinate values ​​are extracted, and a spatial interpolation algorithm is used to construct an elevation model for the area. Based on the elevation model, the ratio of the elevation difference between adjacent feature points to the horizontal distance is calculated. The inverse tangent of this ratio is the slope value for the corresponding road section.

[0065] In some implementations, the calculated slope value can be compared with a preset threshold value, which can be set according to vehicle parking safety standards. If the slope value exceeds the threshold value, it is determined that the area is not suitable for parking. At this time, the system can perform a correction operation and automatically narrow the range of the electronic fence to exclude the steep slope area. Optionally, an alarm mechanism can be triggered, and a reminder message containing the steep slope position (i.e., three-dimensional coordinates (x, y, z)) and the slope value can be sent to the administrator terminal through the server, prompting the administrator to conduct on-site verification and manual intervention. It should be noted that the above-mentioned specific implementation of using three-dimensional coordinates (x, y, z) to calculate the slope of the area to calibrate the electronic fence is only an exemplary explanation of the present disclosure, and the present disclosure does not limit the specific model and algorithm used to calculate the slope.

[0066] Through the above method, the z-coordinate is used to achieve quantitative analysis of the slope of the geographical area, which can effectively screen out areas that are not suitable for parking, improve the rationality and safety of electronic fence calibration, and avoid vehicle parking risks caused by terrain factors.

[0067] In addition, in some implementations of the present disclosure, a road section information caching mechanism can also be introduced. For example, through analysis, for electronic fences that are used frequently, such as those around some commercial areas and subway stations, the road section information that matches them, such as the coordinates of the first and last endpoints of the road section, the direction of the road section, and other information, can be cached to a local server or edge node. When the fence is calibrated again, the road section information can be directly retrieved from the cache without the need to repeatedly query from the road network system. At the same time, a cache update mechanism is set up. When the road network information changes, such as when a road section is diverted due to construction, the cache synchronization update can be triggered by a change notification from the road network system. In this way, the network delay and data transmission cost of repeated access to the road network system can be reduced. In particular, for scenarios that require high-frequency calibration, such as road construction, the time to obtain road section information can be shortened, significantly improving the efficiency of electronic fence calibration.

[0068] In some implementations of the present disclosure, the vertices of the candidate area can also be determined in combination with the parking area features: based on the feature that shared vehicle return points are usually located on both sides of the road section, the vertices are determined at appropriate locations on each road section (such as areas where parking is allowed). For example, next to a curved road section, vertices can be selected at different locations on the road section based on the actual range of parking. For multiple intersecting road sections, vertices can be selected at appropriate parking areas at the intersections. Specifically, vertices can be connected to form polygons. For example, all determined vertices are connected in sequence to form an irregular polygon candidate area that matches the road network distribution and the actual parking area. This area is closely related to the surrounding road network and meets the characteristics of vehicle parking being adapted to the road environment.

[0069] Figure 3 The specific implementation method is shown when the driving data 210 is the road network information 212. The driving data can also use the return data 214 of the shared vehicle being returned. Figure 4 FIG. 4 shows another schematic diagram 400 for calibrating an electronic fence according to an implementation of the present disclosure. Figure 4 As shown, the electronic fence 120 can be calibrated based on the process described above to obtain a calibrated electronic fence. Figure 4 A schematic diagram showing determination of an electronic fence based on driving data when the vehicle return data includes vehicle location data of a shared vehicle.

[0070] like Figure 4 As shown, the calibration system can receive vehicle location data of multiple shared vehicles when they are returned within a geographic area. The location data of these shared vehicles, as a key part of the return data, is obtained based on the positioning devices installed on the shared vehicles. For example, when the vehicle is returned, the latitude and longitude information or coordinate information of the shared vehicle is recorded in real time. These positioning devices receive location data multiple times at certain time intervals to ensure that the final parking location of the vehicle can be accurately determined. When a vehicle enters a geographic area and is returned, its location data is transmitted to the server via a wireless communication network (such as 4G, 5G, etc.), and the server receives and stores these data in a unified manner for subsequent processing.

[0071] In some implementations, based on the received vehicle location data of multiple shared vehicles, a big data clustering method can be used to determine the candidate areas of the electronic fence. Specifically, these vehicle location data are regarded as data points and processed using a clustering algorithm. The algorithm uses the spatial distance between data points as a metric to cluster data points that are closely adjacent in space into one category. In the clustering process, appropriate distance thresholds and density thresholds can be set as needed. If the number of data points in a certain area reaches or exceeds the density threshold, and the distance between the data points is less than the distance threshold, then these data points constitute a cluster. In Figure 4In the example, the areas covered by these clusters correspond to the centralized parking areas for shared vehicles when they are returned. By analyzing the distribution range, shape, and other characteristics of these clusters, we can determine candidate geo-fence areas. For example, if the clusters exhibit a relatively regular polygonal distribution, the shape of the candidate area can be approximately set to this polygon.

[0072] In some implementations, in addition to the big data clustering method, a classification algorithm can also be used to determine the candidate area of ​​the electronic fence. For example, classification algorithms such as decision tree, naive Bayes, logistic regression, K-nearest neighbor, support vector machine, etc. can be used. The present disclosure does not limit the choice of classification algorithm. For example, taking the decision tree algorithm as an example, the shared vehicle location data and related attribute information (such as the road section, the distribution of surrounding buildings, etc.) are used as input features to construct a decision tree model. By gradually dividing and deciding these features, it is determined whether each vehicle location data is suitable as a point within the scope of the electronic fence.

[0073] After classifying the vehicle location data of multiple shared vehicles, the areas covered by the data points belonging to the same category are integrated to identify the location and shape of the fence, and then obtain the calibrated electronic fence area. Specifically, for example Figure 4 Candidate region 410 is obtained by using the coordinates of region vertices D', E', F', and G'. Other algorithms employ similar principles, all of which accurately identify and determine candidate geo-fence regions by analyzing and learning vehicle location data and related features. The disclosed implementation utilizes a large amount of real vehicle return location data to determine candidate regions, improving the practicality and accuracy of the geo-fence.

[0074] It will be understood that the location data of multiple shared vehicles can store the vehicle location data of shared vehicles over a period of time. For example, the vehicle location data of shared vehicles in a geographical area over the past 30 days can be counted. It is also possible to count the vehicle location data of multiple time periods of other different lengths based on actual needs. In this case, the vehicle location data of each geographical area can include information such as the specific latitude and longitude information or coordinates of the vehicle. In addition, the location data of multiple shared vehicles can further cover other more specific information, such as the exact time of returning the vehicle, the precise location when returning the vehicle, the direction in which the vehicle is parked, etc.

[0075] like Figure 4 As shown, when the return data of a shared vehicle being returned within a geographic area is included, in some implementations, the return data may also include the vehicle's orientation when the vehicle was returned. When the return data includes the vehicle's orientation when the vehicle was returned, determining the candidate geo-fence area based on the driving data may determine the orientation of the vehicle within the candidate geo-fence area based on the vehicle's orientation.

[0076] In some implementations, the vehicle direction included in the vehicle return data can be obtained in a variety of ways. When the vehicle return data includes the vehicle direction at the time of return, determining the candidate geo-fence area based on the driving data can determine the vehicle placement direction within the candidate geo-fence area based on the vehicle direction.

[0077] In some implementations, the acquired vehicle direction data can be preprocessed. An algorithm is used to denoise the raw vehicle direction data, removing abnormal direction values ​​caused by sensor errors or signal interference. Furthermore, the processed vehicle direction data is validated based on the vehicle's driving trajectory and parking posture to ensure that the vehicle direction data used for analysis accurately reflects the vehicle's actual parking orientation upon return.

[0078] Based on preprocessed vehicle direction data, cluster analysis can be used to determine the vehicle orientation within a candidate area. Vehicle orientation data from the same time period within a geographic area (e.g., weekday morning and evening rush hour, weekends, etc.) is used as sample points and clustered using a density-based clustering algorithm. Each cluster center represents a major vehicle orientation, and the density and range of the cluster reflect the distribution frequency and concentration of that orientation.

[0079] After obtaining the clustering results, the vehicle placement direction within the candidate geo-fence area can be determined based on the clustering results. If the proportion of sample points in a cluster to the total sample volume exceeds a preset threshold, the direction corresponding to the cluster center is determined as the primary vehicle placement direction within the candidate area. If the number of sample points in multiple clusters exceeds the threshold, multiple primary placement directions are determined based on the density and range of each cluster, and multi-directional compatibility is considered in the geo-fence design.

[0080] In some implementations, the determined vehicle placement direction can be optimized in combination with road network information. For example, by analyzing road network features such as road directions and lane directions around the candidate area, if the vehicle direction clustering result is clearly correlated with the road network direction, for example, the vehicle direction is perpendicular to the adjacent road direction, then the rationality of that direction as the vehicle placement direction for the candidate area can be further strengthened. Through the implementation method disclosed herein, the vehicle placement direction in the candidate area of ​​the electronic fence can be accurately determined based on the vehicle direction, improving the matching degree between the electronic fence and the actual parking scene, and also facilitating the orderly management of vehicles.

[0081] According to some implementations of the present disclosure, multi-source data fusion can be used to collect vehicle driving data. Specifically, multiple data sources can be fused to collect vehicle driving data, such as GPS, Wi-Fi, Bluetooth, base station positioning, etc., so as to reduce the error caused by a single data source. It is also possible to perform a cleaning operation on the collected driving data to remove abnormal values, such as erroneous data caused by positioning drift, signal interference, etc., to ensure that the driving data is accurate and reliable. Furthermore, the obtained driving data of the shared vehicle can also be preprocessed. Data preprocessing is to preprocess the driving data to extract key information in the driving data, such as the parking position of the shared vehicle, the direction of the shared vehicle, etc., so as to better match it with the road section information.

[0082] When using the implementation of the present disclosure, the calibration system can receive real-time return requests from shared vehicle users via an onboard communication module or a mobile application associated with the shared vehicle. During this process, the onboard communication module or mobile application utilizes a mobile communication network, such as 4G, 5G, or a next-generation communication network, to send the return request information to a server. The return request includes the vehicle's location data, which can be obtained by a positioning device such as a global positioning system (GPS) installed on the vehicle.

[0083] Location data can be expressed as latitude and longitude, such as the Earth's surface coordinates expressed in degrees, minutes, seconds, or decimal fractions. It can also be expressed as three-dimensional coordinates, typically within a specific three-dimensional spatial reference frame. For example, a Cartesian coordinate system represents the vehicle's position in space, where the x and y axes represent the plane position and the z axis represents the vertical height. After receiving the return request and the location data, the server performs a preliminary format and integrity check on the data to ensure that subsequent processing is based on accurate and valid data.

[0084] In some implementations of the present disclosure, after obtaining valid shared vehicle location data, the server determines the geographical area of ​​the electronic fence. Specifically, the received location data (whether it is latitude and longitude information or coordinate information) is converted into a matching spatial coordinate format. Then, with the location as the center, the geographical area where the electronic fence is located is delineated in combination with preset rules and algorithms. For example, a circular area with the vehicle location as the center and a specific distance (such as 50 meters, 100 meters, etc., which can be adjusted according to the actual application scenario and needs) as the radius can be set as the preliminary electronic fence geographical area. Optionally, a polygonal partitioning algorithm can be used based on the surrounding roads, blocks and other geographical elements to generate a polygonal area that fits the actual geographical environment as the electronic fence geographical area. In the process of determining the geographical area, auxiliary information such as historical vehicle return data and traffic flow data in the area will also be referred to, and the preliminary demarcated area will be optimized and adjusted to ensure that the geographical area of ​​the electronic fence can not only meet the convenience of users returning the vehicle, but also adapt to the surrounding geographical environment and traffic rules.

[0085] Using the implementation method disclosed herein, the shared vehicle's electronic fence can be calibrated more quickly and accurately, providing a clear target area for subsequent fence calibration and ensuring the effectiveness of the calibration work. In this way, the standardization and effectiveness of shared vehicle management can be improved, and operational efficiency can be increased.

[0086] According to some implementations of the present disclosure, after obtaining the calibrated electronic fence, the electronic fence can be expanded according to actual needs.

[0087] In one implementation, the parking density at the boundary of the electronic fence can be determined based on the number of vehicles within a predetermined range of the calibrated electronic fence. Specifically, a predetermined distance interval is defined based on the calibrated electronic fence boundary, such as a specific range extending outward from the electronic fence boundary. The number of shared vehicles parked within this interval is counted, and the ratio of the number of vehicles to the area of ​​the interval is calculated to determine the parking density at the boundary.

[0088] In some implementations, the redundancy value of the geo-fence can also be determined based on parking density. If parking density is high, indicating a high demand for parking in the area or potential interference in the positioning environment, a larger redundancy value should be set. If parking density is low, a smaller redundancy value should be set. Furthermore, a machine learning model can be used to predict the redundancy value to obtain the optimal redundancy value for the actual scenario. For example, the redundancy value can be determined based on a regression model trained on the historical relationship between parking density and redundancy value.

[0089] According to some implementations of the present disclosure, when expanding a calibrated electronic fence based on a redundancy value, the determined redundancy value can be used as an expansion amplitude parameter, and the fence can be uniformly expanded outward along the boundary of the calibrated electronic fence, so that the expanded electronic fence can cover more reasonably parked vehicles while taking into account the traffic needs in the area. For example, after determining the calibrated electronic fence area A, the parking density ρ at the edge of area A is calculated, and the number of vehicles within the distance interval x from the edge of area A is counted. The more vehicles in this interval, the greater the redundancy value, and then the area A is expanded according to this redundancy value. Through the implementation of the present disclosure, the redundancy value of the electronic fence can be flexibly adjusted according to the actual parking density, and the fence range can be appropriately relaxed when the positioning environment is poor to ensure that users can return the vehicle smoothly, while also ensuring the flexibility of fence management.

[0090] In some implementations of the present disclosure, the road network density within the predetermined range of the calibrated electronic fence can also be obtained. For example, a predetermined spatial range centered on the calibrated electronic fence is defined, and the road network density is determined based on at least one parameter of the number of road sections, road section width, number of obstructions (buildings, tree shades, etc.), and height of obstructions within the range. Specifically, the road network density can be characterized by counting the total number of road sections within the range; or the sum of the widths of all road sections within the range can be calculated, and the road network density can be reflected by the total width; the number of obstructions can also be counted and the height of obstructions can be measured, and the distribution characteristics of the obstructions can be used to assist in determining the road network density; the above parameters can also be combined for calculation, such as multiplying the number of road sections by the average road section width to obtain the road network density.

[0091] In some implementations, when determining the redundancy value of an electronic fence based on road network density, an association rule that the road network density is positively correlated with the redundancy value can be used. That is, the greater the road network density, the more complex the road distribution in the area or the more significant the impact of obstructions, and the worse the positioning environment may be. In this case, a larger redundancy value needs to be set; the smaller the road network density, the better the positioning environment, and the smaller the redundancy value is set accordingly. In addition, by analyzing the correspondence between the road network density and the redundancy value in historical data, a mapping model can be established to more accurately determine the adaptive redundancy value based on the real-time road network density. Optionally, other association rules can be used. The present disclosure does not limit its implementation. By analyzing the negative correlation between the road network density and the redundancy value in historical data, a mapping model can be established to accurately determine the adaptive redundancy value based on the real-time road network density.

[0092] In addition, when expanding a calibrated electronic fence based on a redundancy value, the determined redundancy value can be used as an expansion parameter to uniformly expand the corresponding amplitude outward along each boundary of the calibrated electronic fence to form an expanded electronic fence. For example, if the redundancy value is a specific distance, each boundary of the calibrated electronic fence is pushed outward by that distance. For another example, the pushing distances of different boundaries may be different. When the positioning environment is poor, the difficulty of returning the vehicle for the user can be reduced by widening the range, while avoiding the problem of user return failure due to positioning deviation. Through this implementation method, the impact of the road network environment on positioning is taken into account, and the redundancy value is determined by the road network density, so that the electronic fence can adapt to different positioning environments. While ensuring management specifications, the success rate of user return of the vehicle is improved, and the reliability of the overall system is improved.

[0093] Figure 5 A flowchart schematically illustrates a method 500 for managing an operating area for a shared vehicle, according to an implementation of the present disclosure. At block 510, a geographic area for an electric fence is determined. At block 520, driving data of a shared vehicle associated with the geographic area is obtained. At block 530, candidate areas for the electric fence are determined based on the driving data. And at block 540, the electric fence is calibrated using the candidate areas.

[0094] According to an implementation of the present disclosure, the driving data includes road network information of a shared vehicle traveling within a geographic area, and determining a candidate area for an electronic fence based on the driving data includes: determining road section information within the geographic area based on the road network information, the road section information including at least any one of a road section location, a road section direction, and a road section identification; matching the area of ​​the electronic fence with the road section information; and determining the candidate area for the electronic fence based on the matching result.

[0095] According to an implementation of the present disclosure, matching the area of ​​the electronic fence with the road section information includes: mapping the road section information to the area of ​​the electronic fence based on a geographic matching rule.

[0096] According to an implementation of the present disclosure, determining the candidate area of ​​the electronic fence includes at least one of the following: determining the boundary of the candidate area based on the section position of the road section information; and determining the vehicle placement direction of the candidate area based on the section direction of the road section information.

[0097] According to an implementation of the present disclosure, determining a candidate area of ​​an electronic fence includes: determining a position offset according to road section information; and determining a center position of the candidate area based on the position offset.

[0098] According to one implementation of the present disclosure, the driving data includes return data of shared vehicles returned within a geographic area, the return data includes vehicle position data of the shared vehicles, and determining the candidate area of ​​the electronic fence based on the driving data includes: receiving vehicle position data of multiple shared vehicles when they are returned within the geographic area; and determining the candidate area of ​​the electronic fence based on the vehicle position data of the multiple shared vehicles.

[0099] According to an implementation of the present disclosure, the vehicle return data also includes the vehicle direction of the vehicle when it is returned, and determining the candidate area of ​​the electronic fence based on the driving data includes: determining the vehicle placement direction of the candidate area of ​​the electronic fence based on the vehicle direction.

[0100] According to an implementation of the present disclosure, the method 500 further includes: determining the geographical area of ​​the electronic fence includes: receiving a return request of a shared vehicle, the return request including location data of the vehicle; and determining the geographical area of ​​the electronic fence based on the location data of the shared vehicle.

[0101] According to one implementation of the present disclosure, the method 500 further includes: determining a parking density at a boundary of the electronic fence based on the number of vehicles within a predetermined range of the calibrated electronic fence; determining a redundancy value of the electronic fence based on the parking density; and extending the calibrated electronic fence based on the redundancy value.

[0102] According to an implementation of the present disclosure, the method 500 further includes: obtaining the road network density within a predetermined range of the calibrated electronic fence, the road network density being determined by at least any one of the number of road sections, the width of the road section, the number of obstacles, and the height of the obstacles; determining the redundancy value of the electronic fence based on the road network density; and expanding the calibrated electronic fence based on the redundancy value.

[0103] See above Figures 2 to 5 A method for calibrating an electronic fence for a shared vehicle is described. According to one implementation of the present disclosure, a device for calibrating an electronic fence for a shared vehicle is further provided. The device includes: a geographic area determination module for determining a geographic area for the electronic fence; an acquisition module for acquiring driving data of a shared vehicle associated with the geographic area; a candidate area determination module for determining candidate areas for the electronic fence based on the driving data; and a calibration module for calibrating the electronic fence using the candidate areas. Alternatively and / or additionally, the device may further include modules for executing other steps in method 500, which will not be described in detail.

[0104] Figure 6 Schematically illustrates a block diagram of a computing device / server according to an implementation of the present disclosure. It should be understood that Figure 6The illustrated computing device / server 600 is merely exemplary and should not be construed as limiting the functionality and scope of the implementations described herein.

[0105] like Figure 6 As shown, computing device / server 600 is in the form of a general-purpose computing device. Components of computing device / server 600 may include, but are not limited to, one or more processors or processing units 610, memory 620, storage device 630, one or more communication units 640, one or more input devices 650, and one or more output devices 660. Processing unit 610 may be a real or virtual processor and may perform various processes according to programs stored in memory 620. In a multi-processor system, multiple processing units execute computer-executable instructions in parallel to increase the parallel processing capabilities of computing device / server 600.

[0106] The computing device / server 600 typically includes a plurality of computer storage media. Such media can be any available media accessible to the computing device / server 600, including but not limited to volatile and non-volatile media, removable and non-removable media. The memory 1220 can be a volatile memory (e.g., registers, cache, random access memory (RAM)), a non-volatile memory (e.g., read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory), or some combination thereof. The storage device 630 can be a removable or non-removable medium and can include a machine-readable medium, such as a flash drive, a disk, or any other medium that can be used to store information and / or data (e.g., training data for training) and can be accessed within the computing device / server 600.

[0107] The computing device / server 600 may further include additional removable / non-removable, volatile / non-volatile storage media. Figure 6 As shown in FIG, a magnetic disk drive for reading from or writing to a removable, non-volatile magnetic disk (e.g., a "floppy disk") and an optical disk drive for reading from or writing to a removable, non-volatile optical disk may be provided. In these cases, each drive may be connected to a bus (not shown) by one or more data media interfaces. Memory 620 may include a computer program product 625 having one or more program modules configured to perform various methods or actions of various implementations of the present disclosure.

[0108] The communication unit 640 enables communication with other computing devices via a communication medium. Additionally, the functionality of the components of the computing device / server 600 can be implemented as a single computing cluster or multiple computing machines that can communicate via a communication connection. Thus, the computing device / server 600 can operate in a networked environment using logical connections to one or more other servers, network personal computers (PCs), or other network nodes.

[0109] Input device 650 may be one or more input devices, such as a mouse, keyboard, trackball, etc. Output device 660 may be one or more output devices, such as a display, speaker, printer, etc. Computing device / server 600 may also communicate with one or more external devices (not shown) via communication unit 640 as needed, such as storage devices, display devices, etc., with one or more devices that allow a user to interact with computing device / server 600, or with any device that allows computing device / server 600 to communicate with one or more other computing devices (e.g., a network card, modem, etc.). Such communication may be performed via an input / output (I / O) interface (not shown).

[0110] According to an implementation of the present disclosure, a computer-readable storage medium is provided, on which one or more computer instructions are stored, wherein the one or more computer instructions are executed by a processor to implement the method described above.

[0111] According to an implementation of the present disclosure, a computer program product is provided, including a computer program / instruction, wherein the computer program / instruction implements the method described above when executed by a processor.

[0112] Various aspects of the present disclosure are described herein with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products implemented according to the present disclosure. It should be understood that each block of the flowcharts and / or block diagrams, and combinations of blocks in the flowcharts and / or block diagrams, can be implemented by computer-readable program instructions.

[0113] These computer-readable program instructions can be provided to a processing unit of a general-purpose computer, a special-purpose computer, or other programmable data processing device, thereby producing a machine, such that when these instructions are executed by the processing unit of the computer or other programmable data processing device, a device is generated that implements the functions / actions specified in one or more blocks in the flowchart and / or block diagram. These computer-readable program instructions can also be stored in a computer-readable storage medium, where these instructions cause the computer, programmable data processing device, and / or other device to operate in a specific manner. Thus, the computer-readable medium storing the instructions comprises an article of manufacture that includes instructions for implementing various aspects of the functions / actions specified in one or more blocks in the flowchart and / or block diagram.

[0114] Computer-readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device, so that a series of operational steps are performed on the computer, other programmable data processing apparatus, or other device to produce a computer-implemented process, thereby causing the instructions executed on the computer, other programmable data processing apparatus, or other device to implement the functions / actions specified in one or more boxes in the flowchart and / or block diagram.

[0115] The flow charts and block diagrams in the accompanying drawings show the possible architecture, functions and operations of the systems, methods and computer program products according to multiple implementations of the present disclosure. In this regard, each box in the flow chart or block diagram can represent a part for a module, program segment or instruction, and a part for a module, program segment or instruction comprises one or more executable instructions for realizing the logical function of the specification. In some alternative implementations, the functions marked in the box can also occur in a sequence different from that marked in the accompanying drawings. For example, two continuous boxes can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flow chart, and the combination of the boxes in the block diagram and / or flow chart can be realized by a special hardware-based system that performs the function or action of the specification, or can be realized by a combination of special hardware and computer instructions.

[0116] While various implementations of the present disclosure have been described above, the foregoing description is intended to be illustrative, non-exhaustive, and not limited to the disclosed implementations. Many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the described implementations. The terminology used herein is selected to best explain the principles of the implementations, their practical applications, or improvements to existing technologies, or to enable others skilled in the art to understand the implementations disclosed herein.

Claims

1. A method for calibrating an electronic fence of a shared vehicle, comprising: determining a geographical area of ​​the electronic fence; obtaining driving data of shared vehicles associated with the geographic area; determining a candidate area of ​​the electronic fence based on the driving data; as well as The geo-fence is calibrated using the candidate area.

2. The method according to claim 1, wherein the driving data includes road network information of the shared vehicle traveling within the geographical area, and determining the candidate area of ​​the electronic fence based on the driving data comprises: Determine road segment information within the geographical area based on the road network information, wherein the road segment information includes at least one of a road segment location, a road segment direction, and a road segment identifier; Matching the area of ​​the electronic fence with the road section information; as well as The candidate area of ​​the electronic fence is determined according to the matching result.

3. The method according to claim 2, wherein matching the area of ​​the electronic fence with the road segment information comprises: Based on geographic matching rules, the road segment information is mapped to the area of ​​the electronic fence.

4. The method according to claim 2, wherein determining the candidate area of ​​the geo-fence comprises at least one of the following: Determining a boundary of the candidate area based on a road section position of the road section information; and The direction in which items are placed in the candidate area is determined based on the road section direction of the road section information.

5. The method according to claim 2, wherein determining the candidate area of ​​the geo-fence comprises: determining a position offset according to the road section information; as well as The center position of the candidate region is determined based on the position offset.

6. The method of claim 1 , wherein the driving data includes return data of the shared vehicle being returned within the geographic area, the return data includes item location data of the shared vehicle, and determining the candidate area for the geo-fence based on the driving data comprises: receiving item location data for a plurality of shared vehicles within the geographic area when the vehicles are returned; as well as The candidate area of ​​the electronic fence is determined based on the item location data of a plurality of the shared vehicles.

7. The method according to claim 6, wherein the vehicle return data further includes the orientation of the item when the item is returned to the vehicle, and determining the candidate area of ​​the geo-fence based on the driving data comprises: Based on the object orientation, the object placement orientation of the candidate area of ​​the electronic fence is determined.

8. The method of claim 1 , wherein determining the geographic area of ​​the geo-fence comprises: receiving a return request for the shared vehicle, the return request including the location data of the item; as well as The geographic area of ​​the electronic fence is determined based on the location data of the shared vehicle.

9. The method according to claim 1, further comprising: determining a parking density at a boundary of the electronic fence based on a calibrated number of items within a predetermined range of the electronic fence; determining a redundancy value of the electronic fence based on the parking density; as well as The calibrated geo-fence is extended based on the redundancy value.

10. The method according to claim 1, further comprising: Obtaining a calibrated road network density within a predetermined range of the electronic fence, where the road network density is determined by at least one of the number of road sections, the width of a road section, the number of obstructions, and the height of obstructions; Determining a redundancy value of the electronic fence based on the road network density; as well as The calibrated geo-fence is extended based on the redundancy value.

11. A device for calibrating an electronic fence of a shared vehicle, comprising: A geographic area determination module, configured to determine the geographic area of ​​the electronic fence; an acquisition module, configured to acquire driving data of shared vehicles associated with the geographical area; a candidate area determination module, configured to determine a candidate area of ​​the electronic fence based on the driving data; as well as A calibration module is used to calibrate the electronic fence using the candidate area.

12. An electronic device comprising: memory and processor; The memory is configured to store one or more computer instructions, wherein the one or more computer instructions are executed by the processor to implement the method according to any one of claims 1 to 10.

13. A computer-readable storage medium having one or more computer instructions stored thereon, wherein the one or more computer instructions are executed by a processor to implement the method according to any one of claims 1 to 10.

14. A computer program product comprising a computer program or instructions, wherein the computer program or instructions implement the method according to any one of claims 1 to 10 when executed by a processor.

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