Differential charging method, system and equipment for parking on road

Abstract

The invention discloses a differential charging method, system and device for parking on a road, and the method comprises the steps: obtaining a chassis image of a target vehicle in response to the detection of a parking motion of the target vehicle; according to the chassis image, the vehicle type of the target vehicle is obtained, and the vehicle type is configured to be at least used for representing whether the target vehicle comprises a power battery or not; and charging parking of the target vehicle according to the vehicle type of the target vehicle. The invention provides a scheme for carrying out differentiated charging of parking on the road based on chassis image recognition, and at least solves the problems that the existing differentiated charging of parking of new energy vehicles and fuel vehicles is more convenient to realize in a parking lot and only needs to depend on license plate recognition, but the realization of the differentiated charging of parking on the road on the road is more difficult, and the charging efficiency is higher. The main reason lies in the problem that new energy vehicles and fuel vehicles are difficult to distinguish through license plate recognition during road-occupied parking.

Description

Technical Field

[0001] This application relates to the field of parking management technology, and in particular to a differentiated billing method, system and equipment for on-street parking. Background Technology

[0002] With the continuous development of urban transportation, roadside parking has become an important part of urban traffic management. Traditional roadside parking fee models mostly charge uniformly based on the time a vehicle occupies a parking space, without differentiating between different types of vehicles, such as new energy vehicles and fuel vehicles. However, with the popularization of new energy vehicles, the current roadside parking fee system has failed to effectively consider the differences between new energy vehicles and fuel vehicles.

[0003] Existing roadside parking fee systems primarily use a fee-based system based on parking space usage time. This method treats all vehicles equally, without distinguishing between vehicle type, energy efficiency, and emission characteristics. While there are significant differences in traffic burden, environmental pollution, and energy efficiency between gasoline-powered vehicles and new energy vehicles when occupying roadside parking resources, the current fee structure fails to adequately differentiate between them. This uniform pricing method fails to fully reflect the advantages of new energy vehicles and lacks the necessary differentiated pricing for the emission issues of gasoline-powered vehicles, thus failing to optimize the roadside parking fee system from an environmental and energy utilization perspective. Against this backdrop, new energy vehicles and gasoline-powered vehicles differ significantly in terms of environmental protection, energy efficiency, and their impact on urban traffic. New energy vehicles typically have lower emissions and higher energy efficiency; these advantages should be reflected in the parking fee system to incentivize more citizens to choose environmentally friendly and energy-saving new energy vehicles. However, the existing fee structure fails to consider these factors, charging new energy vehicles and gasoline-powered vehicles the same price, thus failing to fully leverage the guiding role of green transportation.

[0004] The existing differentiated parking fee system for new energy vehicles and fuel vehicles is relatively easy to implement in parking lots, as it only requires license plate recognition. However, it is much more difficult to implement in roadside parking, mainly because it is difficult to distinguish between new energy vehicles and fuel vehicles through license plate recognition when parking on the road. Summary of the Invention

[0005] This invention provides a method, system, and device for differentiated billing of on-street parking. It offers a solution for differentiated billing of on-street parking based on chassis image recognition. This solution addresses the challenge of implementing differentiated billing for new energy vehicles and fuel vehicles in parking lots, where it is relatively easy and only requires license plate recognition. The main reason is that it is difficult to distinguish between new energy vehicles and fuel vehicles in on-street parking using license plate recognition.

[0006] On the one hand, a differentiated pricing method for on-street parking includes: In response to detecting a parking action of the target vehicle, an image of the target vehicle's chassis is acquired; Based on the chassis image, the vehicle type of the target vehicle is obtained, and the vehicle type is configured to at least characterize whether the target vehicle includes a power battery. Parking fees are calculated based on the vehicle type of the target vehicle.

[0007] Optionally, in response to detecting a parking action of the target vehicle, acquiring a chassis image of the target vehicle includes: In response to detecting a parking action of the target vehicle, an image of the target vehicle's chassis is acquired; In response to the detection that the target vehicle has not left within a preset free parking period, the chassis image of the target vehicle is acquired.

[0008] Optionally, in response to detecting a parking action of the target vehicle, acquiring a chassis image of the target vehicle includes: In response to detecting that the target vehicle has completed parking, an image acquisition command is sent to the corresponding image acquisition device to obtain two sets of chassis images of the target vehicle. One set of the two sets of chassis images is configured as the image before the parking lock is activated, and the other set is configured as the image after the parking lock is activated.

[0009] Optionally, obtaining the vehicle type of the target vehicle based on the chassis image includes: Based on the chassis image, and based on the texture of at least a portion of the area in the chassis image, the vehicle type of the target vehicle is obtained.

[0010] Optionally, obtaining the vehicle type of the target vehicle based on the chassis image and the texture of at least a portion of the area in the chassis image includes: Based on the chassis image, obtain the region to be identified that meets the preset conditions; Based on the region to be identified, obtain the texture of the region to be identified; Based on the texture of the region to be identified, obtain the symmetry parameters of the texture of the region to be identified; Based on the symmetry parameters of the texture of the region to be identified, and whether the symmetry parameters meet preset symmetry conditions, the vehicle type of the target vehicle is obtained.

[0011] Optionally, obtaining the region to be identified that meets preset conditions based on the chassis image includes: Based on the chassis image, the entity boundaries in the chassis image are obtained using an edge detection algorithm; Based on the entity boundaries in the chassis image, determine whether there is a whole area in the chassis image with a size larger than a preset size; When there is a whole area with a size larger than the preset size, the whole area is taken as the area to be identified.

[0012] Optionally, obtaining the vehicle type of the target vehicle based on the symmetry parameters of the texture of the region to be identified, and based on whether the symmetry parameters meet preset symmetry conditions, includes: Based on the symmetry parameters of the texture of the region to be identified, the vehicle type of the target vehicle is determined to be a vehicle including a power battery when the symmetry parameters of the texture of the region to be identified satisfy at least one of the following conditions: Condition 1: The texture of the region to be identified is an axisymmetric graphic; Condition 2: The angle between the axis of symmetry of the texture of the region to be identified and the shooting direction is less than a preset angle threshold; Condition 3: The symmetry of the texture of the region to be identified is greater than a preset symmetry threshold.

[0013] Optional, also includes: In response to detecting the parking action of the target vehicle, the voiceprint information of the target vehicle during the parking process is acquired; The step of obtaining the vehicle type of the target vehicle based on the chassis image includes: The vehicle type of the target vehicle is obtained based on the chassis image and the voiceprint information.

[0014] On the other hand, a differentiated billing system for on-street parking includes a management platform and an underground flat parking space lock connected to the management platform; The underground flatbed parking space lock is configured as follows: In response to detecting a parking action of the target vehicle, an image of the target vehicle's chassis is acquired; Send the chassis image of the target vehicle to the management platform; The management platform is configured as follows: Based on the chassis image, the vehicle type of the target vehicle is obtained, and the vehicle type is configured to at least characterize whether the target vehicle includes a power battery. Parking fees are calculated based on the vehicle type of the target vehicle.

[0015] On the other hand, there is a device, a computer device including a memory and a processor, wherein the memory stores a computer program and the processor executes the computer program to implement the above-described method.

[0016] On the other hand, a computer storage medium storing a computer program, wherein a processor executes the computer program to implement the above-described method.

[0017] Compared with the prior art, the present invention has the following advantages and beneficial effects: This invention discloses a method, system, and device for differentiated billing of on-street parking, comprising: in response to detecting a parking action of a target vehicle, acquiring a chassis image of the target vehicle; obtaining the vehicle type of the target vehicle based on the chassis image, wherein the vehicle type is configured to at least characterize whether the target vehicle includes a power battery; and billing the parking of the target vehicle based on the vehicle type. The above solution provides a method for differentiated billing of on-street parking based on chassis image recognition, which at least solves the problem that while differentiated billing for new energy vehicles and fuel vehicles is relatively easy to implement in parking lots, relying only on license plate recognition, it is much more difficult to implement in on-street parking, mainly because it is difficult to distinguish between new energy vehicles and fuel vehicles through license plate recognition. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in the specific embodiments of this application or the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. In all the drawings, similar elements or parts are generally identified by similar reference numerals. In the drawings, the elements or parts are not necessarily drawn to scale.

[0019] Figure 1 This is a flowchart illustrating a differentiated billing method for on-street parking as described in this application; Figure 2 This is a schematic diagram of the structure of a computer device according to this application.

[0020] The diagram is labeled as follows: 101-Processor, 102-Communication bus, 103-Network interface, 104-User interface, 105-Memory.

[0021] The realization of the purpose, functional features and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0022] To enable those skilled in the art to better understand the present disclosure, the technical solutions of the present disclosure will be clearly and completely described below with reference to the accompanying drawings of the embodiments. Obviously, the described embodiments are only some embodiments of the present disclosure, and not all embodiments. Based on the embodiments of the present disclosure, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present disclosure.

[0023] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this disclosure 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 disclosure 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 apparatus 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 apparatus.

[0024] Example 1 like Figure 1 As shown, a differentiated charging method for on-street parking includes: S1. In response to detecting the parking action of the target vehicle, acquire the chassis image of the target vehicle.

[0025] In this embodiment, the purpose of this step is to obtain the chassis image of the target vehicle in a timely and accurate manner. Under normal circumstances, parking actions can be detected by various methods such as monitoring cameras, ground sensors or vehicle-mounted sensors. However, due to the limitations of parking on the road, it is generally checked by ultrasonic sensors on the buried parking lock. When the target vehicle is parked in the designated area, the chassis image of the vehicle can be obtained by the camera installed on the buried parking lock.

[0026] S2. Based on the chassis image, obtain the vehicle type of the target vehicle.

[0027] Specifically, the vehicle type is configured to at least characterize whether the target vehicle includes a power battery. Generally, a vehicle that includes a power battery is considered a "new energy vehicle". Therefore, this solution determines the vehicle type by identifying whether the vehicle includes a power battery.

[0028] The advantages of using chassis images to determine the vehicle type of a target vehicle include: First, since this solution applies to roadside parking, which is typically unmanned and relies on in-ground parking locks for payment, there are already devices readily available at these parking spaces that can be easily modified to capture vehicle chassis images, resulting in low modification and installation costs. Second, there are significant differences between the chassis of existing new energy vehicles and gasoline vehicles. For example, new energy vehicles generally have a flatter chassis because the power battery is usually located at the bottom, and they lack exhaust pipes or have exhaust pipes located near the edge of the chassis, as well as structures such as drive shafts and fuel tanks. Therefore, the algorithmic complexity required for this solution to determine the vehicle type of the target vehicle using chassis images is lower, with lower computational power requirements and costs, making it easier to implement on a large scale. Even for plug-in hybrid or range-extended vehicles, although their chassis also have structures such as exhaust pipes and fuel tanks, the structure of the power battery can still be clearly identified in the chassis, exhibiting corresponding flatness or symmetry characteristics.

[0029] Optionally, the processing methods for the chassis image of the target vehicle include: By using machine learning or deep learning models, analyze the overall features in the chassis images to train a recognition model that can determine whether a vehicle is a new energy vehicle; or By using machine learning or deep learning models, local features in chassis images can be analyzed to identify distinctive features of different vehicles, such as battery modules, fuel systems, and engine locations, and then classified as "new energy vehicles" or "fuel vehicles".

[0030] S3. Charge parking fees for the target vehicle based on its vehicle type.

[0031] Specifically, based on the vehicle type of the target vehicle, a preset billing strategy is invoked to charge for the parking of the target vehicle.

[0032] The above solution provides a method for differentiated billing for on-street parking based on chassis image recognition. It solves the problem that while differentiated billing for new energy vehicles and fuel vehicles is relatively easy to implement in parking lots, relying only on license plate recognition, it is much more difficult to implement in on-street parking. The main reason is that it is difficult to distinguish between new energy vehicles and fuel vehicles through license plate recognition when parking on the street.

[0033] Example 2 Based on Example 1, this embodiment provides a differentiated pricing method for on-street parking, including: S1. In response to detecting the parking action of the target vehicle, acquire the chassis image of the target vehicle.

[0034] In this embodiment, the purpose of this step is to obtain the chassis image of the target vehicle in a timely and accurate manner. Under normal circumstances, parking actions can be detected by various methods such as monitoring cameras, ground sensors or vehicle-mounted sensors. However, due to the limitations of parking on the road, it is generally checked by ultrasonic sensors on the buried parking lock. When the target vehicle is parked in the designated area, the chassis image of the vehicle can be obtained by the camera installed on the buried parking lock.

[0035] Optionally, in response to detecting a parking action of the target vehicle, an image of the target vehicle's chassis is acquired, including: In response to detecting a parking action of the target vehicle, an image of the target vehicle's chassis is acquired; In response to the detection that the target vehicle has not left within a preset free parking period, the chassis image of the target vehicle is acquired.

[0036] Considering that this solution applies to roadside parking, there may be instances where parking is temporarily suspended while traffic continues normally, or where users only stop briefly and leave before the charging period ends. To conserve backend computing power, the backend server only acquires the chassis image of the target vehicle when the vehicle has been parked in the parking space for longer than the free parking period. Before the backend server acquires the chassis image, the chassis image is temporarily stored in the image acquisition device or the buffer of the buried parking lock.

[0037] S2. Based on the chassis image, obtain the vehicle type of the target vehicle.

[0038] Specifically, the vehicle type is configured to at least characterize whether the target vehicle includes a power battery. Generally, a vehicle that includes a power battery is considered a "new energy vehicle". Therefore, this solution determines the vehicle type by identifying whether the vehicle includes a power battery.

[0039] The advantages of using chassis images to determine the vehicle type of a target vehicle include: First, since this solution applies to roadside parking, which is typically unmanned and relies on in-ground parking locks for payment, there are already devices readily available at these parking spaces that can be easily modified to capture vehicle chassis images, resulting in low modification and installation costs. Second, there are significant differences between the chassis of existing new energy vehicles and gasoline vehicles. For example, new energy vehicles generally have a flatter chassis because the power battery is usually located at the bottom, and they lack exhaust pipes or have exhaust pipes located near the edge of the chassis, as well as structures such as drive shafts and fuel tanks. Therefore, the algorithmic complexity required for this solution to determine the vehicle type of the target vehicle using chassis images is lower, with lower computational power requirements and costs, making it easier to implement on a large scale. Even for plug-in hybrid or range-extended vehicles, although their chassis also have structures such as exhaust pipes and fuel tanks, the structure of the power battery can still be clearly identified in the chassis, exhibiting corresponding flatness or symmetry characteristics.

[0040] Optionally, the processing methods for the chassis image of the target vehicle include: By using machine learning or deep learning models, analyze the overall features in the chassis images to train a recognition model that can determine whether a vehicle is a new energy vehicle; or By using machine learning or deep learning models, local features in chassis images can be analyzed to identify distinctive features of different vehicles, such as battery modules, fuel systems, and engine locations, and then classified as "new energy vehicles" or "fuel vehicles".

[0041] Optionally, in response to detecting a parking action of the target vehicle, an image of the target vehicle's chassis is acquired, including: In response to detecting that the target vehicle has completed parking, an image acquisition command is sent to the corresponding image acquisition device to obtain two sets of chassis images of the target vehicle. One set of chassis images is configured as the image before the parking lock is activated, and the other set is configured as the image after the parking lock is activated.

[0042] The advantages of the above scheme are mainly as follows: First, by capturing images before and after the parking lock is activated, the images can be preserved as evidence, avoiding disputes caused by damage to the vehicle chassis. Second, the above scheme can simulate the effect of image capture by a binocular camera, enabling 3D modeling of the chassis for more detailed analysis.

[0043] Optionally, based on the two sets of chassis images collected above, the vehicle type of the target vehicle can be obtained from the chassis images, including: The chassis of the target vehicle is modeled using two sets of chassis images to obtain the flatness of the chassis. The vehicle type of the target vehicle is determined based on the flatness of the target vehicle's chassis.

[0044] Optionally, when the flatness of the target vehicle's chassis is less than a preset threshold, the target vehicle type is a vehicle excluding the power battery. Optionally, when the flatness of the target vehicle's chassis is not less than a preset threshold, the target vehicle type is a vehicle including a power battery.

[0045] Optionally, based on the chassis image, the vehicle type of the target vehicle can be obtained, including: Based on the chassis image, and based on the texture of at least a portion of the chassis image, the vehicle type of the target vehicle is obtained.

[0046] Due to the characteristics of power batteries, the chassis of new energy vehicles have protective plates to protect the power batteries. These protective plates are equipped with reinforcing ribs or similar structures to improve strength and heat dissipation performance. The arrangement of these structures is also relatively regular. The protective plates used to protect the power batteries can be identified by the texture used to represent these structures in the chassis image. In contrast, the chassis of fuel vehicles is more complex and does not have similar regular textures.

[0047] Optionally, based on the chassis image, and based on the texture of at least some areas in the chassis image, it is determined whether there are some areas in the chassis image where the texture shows a certain pattern. When there are some areas in the chassis image where the texture shows a certain pattern, the vehicle type of the target vehicle is determined to be a vehicle including a power battery.

[0048] Optionally, based on the above scheme, the size of some areas where the texture exhibits a certain regularity also needs to conform to a preset size, which matches the size of power batteries on the market.

[0049] Optionally, before performing texture recognition on the chassis image, image preprocessing is required, which may include image grayscale conversion.

[0050] Optionally, based on the chassis image, and based on the texture of at least a portion of the area in the chassis image, the vehicle type of the target vehicle is obtained, including: Based on the chassis image, obtain the region to be identified that meets the preset conditions; Based on the region to be identified, obtain the texture of the region to be identified; Based on the texture of the region to be identified, obtain the symmetry parameters of the texture of the region to be identified; Based on the symmetry parameters of the texture of the region to be identified, and whether the symmetry parameters meet the preset symmetry conditions, the vehicle type of the target vehicle is obtained.

[0051] Optionally, the region to be identified that meets the preset conditions can be a region that includes or is enclosed by specified features, a region whose flatness exceeds a threshold, or the coordinates of a preset chassis image.

[0052] Specifically, the symmetry parameters of the texture of the region to be identified can be obtained through pixel difference calculation, Fourier transform, or deep learning. The symmetry parameters include at least symmetry data and the direction of the symmetry axis.

[0053] Optionally, based on the chassis image, a region to be identified that meets preset conditions is obtained, including: Based on the chassis image, the entity boundaries in the chassis image are obtained using an edge detection algorithm; Based on the entity boundaries in the chassis image, determine whether there is a whole region in the chassis image with a size larger than a preset size; When there is a whole area whose size is larger than the preset size, the whole area is used as the area to be identified.

[0054] Using the above method, large areas on the chassis image can be accurately identified. These areas can be formed by physical boundaries or by physical boundaries and the edges of the chassis image. This method avoids the situation where some gasoline vehicles also have special chassis protection plates installed, because the exhaust pipe part of the chassis of gasoline vehicles is generally not covered by chassis protection plates. By identifying physical boundaries, the exhaust pipe in the chassis image can be identified as dividing the chassis into at least two parts, left and right.

[0055] Specifically, the edges of the battery, exhaust pipe, motor, engine, or fuel tank at the bottom of the vehicle are detected using methods such as Canny edge detection and Sobel operator.

[0056] Optionally, based on the symmetry parameters of the texture of the region to be identified, and depending on whether the symmetry parameters meet preset symmetry conditions, the vehicle type of the target vehicle can be obtained, including: Based on the symmetry parameters of the texture of the region to be identified, the vehicle type of the target vehicle is determined to be a vehicle including a power battery when the symmetry parameters of the texture of the region to be identified satisfy at least one of the following conditions: Condition 1: The texture of the region to be identified is an axisymmetric graphic; Condition 2: The angle between the axis of symmetry of the texture in the area to be identified and the shooting direction is less than a preset angle threshold; Condition 3: The symmetry of the texture in the region to be identified is greater than the preset symmetry threshold.

[0057] Optionally, before obtaining the region to be identified that meets the preset conditions based on the chassis image, the chassis image may be preprocessed. The preprocessing method may include one or more of the following: noise reduction, grayscale conversion, or binarization. Preprocessing the image can further reduce the computational requirements.

[0058] By applying the above conditions, it can be ensured that the symmetry axis of the texture in the area to be identified is in the same direction as the vehicle's direction of travel, and that the preset similarity requirements are met. While some existing technologies exist for chassis identification to facilitate maintenance, traditional methods for identification or maintenance based on chassis images require precise chassis identification and matching, placing high demands on computing power and making them unsuitable for scenarios with large data volumes, such as roadside parking. This solution, however, does not require precise identification of the target vehicle's model; it only needs to identify whether the target vehicle is a new energy vehicle, reducing the demand for computing power and facilitating efficient and rapid identification.

[0059] Optional, also includes: In response to the detection of the target vehicle's parking action, the voiceprint information of the target vehicle during the parking process is obtained; Based on the chassis image, determine the vehicle type of the target vehicle, including: Based on the chassis image and voiceprint information, the vehicle type of the target vehicle is determined.

[0060] Optionally, in response to detecting a parking action of the target vehicle, the voiceprint information of the target vehicle during the parking process is acquired, including: In response to the detection of the target vehicle's parking action, the system acquires the first voiceprint information during the parking process and the second voiceprint information within a preset time period after the target vehicle finishes parking. Based on the difference analysis of the first and second voiceprint information, the system acquires the voiceprint information generated by the target vehicle during the parking process.

[0061] Based on the chassis image and voiceprint information, the vehicle type of the target vehicle is determined, including: Based on the chassis image, the vehicle type and image confidence of the target vehicle are obtained; based on the voiceprint information, the vehicle type and voiceprint confidence of the target vehicle are obtained. The vehicle type of the target vehicle is obtained based on the vehicle type, image confidence, and voiceprint confidence.

[0062] Optionally, the chassis image of the target vehicle may also be an infrared image including the chassis; Based on the chassis image, determine the vehicle type of the target vehicle, including: Based on the infrared image of the chassis, determine whether the target vehicle includes an exhaust pipe; When the target vehicle does not include an exhaust pipe, the vehicle type of the target vehicle is determined to be a vehicle including a power battery. When the target vehicle includes an exhaust pipe, the vehicle type of the target vehicle is determined according to at least one of the following indicators: Indicator 1: Location of the exhaust pipe; Indicator 2: Does the infrared image of the chassis include a heat dissipation area exceeding the preset area?

[0063] Specifically, the presence of an exhaust pipe can be determined by detecting whether a long, thin, hot strip area is present in the infrared image of the chassis. If no exhaust pipe is present, the target vehicle is definitely a new energy vehicle. If an exhaust pipe is present, the target vehicle may be a gasoline vehicle, a range-extended vehicle, or a plug-in hybrid vehicle. However, since range-extended vehicles or plug-in hybrid vehicles also include a power battery, it can only be located on the side of the chassis. At the same time, since the power battery heats up evenly, a uniformly heated area exceeding the preset area can be detected in the infrared image of the chassis.

[0064] S3. Charge parking fees for the target vehicle based on its vehicle type.

[0065] The above solution provides a differentiated pricing method for on-street parking based on chassis image recognition. This further addresses the challenge of implementing differentiated pricing for new energy vehicles and gasoline vehicles in parking lots, where it's relatively easy (relying solely on license plate recognition). The main reason is the difficulty in distinguishing between new energy and gasoline vehicles using license plate recognition alone. Furthermore, this method, by eliminating the need for deep learning or other complex methods to recognize the entire chassis (only image symmetry is required), has lower requirements for imaging equipment and computing power, and does not rely on license plate recognition technology, making it suitable for widespread deployment in various areas.

[0066] Example 3 Based on Embodiments 1 and 2, this embodiment provides a differentiated billing system for on-street parking, including a management platform and an underground flat parking space lock connected to the management platform. The underground flatbed parking space lock is configured as follows: In response to the detection of the target vehicle's parking action, an image of the target vehicle's chassis is acquired; Send the chassis image of the target vehicle to the management platform; The management platform is configured as follows: Based on the chassis image, the vehicle type of the target vehicle is obtained, and the vehicle type is configured to at least characterize whether the target vehicle includes a power battery. Parking fees are calculated based on the vehicle type of the target vehicle.

[0067] Specifically, the underground flatbed parking space lock is configured to include: A cabin structure with cavities; A flap is installed inside the cabin, and one side of the flap is movably connected to the cabin via a flap shaft; A drive unit installed inside the cabin is used to selectively raise and retract the flap. Sensors installed inside the cabin are used to detect whether a vehicle is parked above the underground flatbed parking space lock; A camera module installed on the upper surface of the cabin structure or the upper surface of the flap, the camera module may also include a supplementary lighting device; The controller, located inside the cabin, controls the drive unit to operate in response to data received from sensors or external inputs.

[0068] Optionally, in response to detecting a parking action of the target vehicle, an image of the target vehicle's chassis is acquired, including: In response to the sensor detecting that the target vehicle has stopped, the camera captures an image of the target vehicle's chassis. When the sensor detects that the target vehicle has not left within the preset free parking period, the chassis image of the target vehicle is acquired and sent to the management platform.

[0069] Optionally, in response to detecting a parking action of the target vehicle, an image of the target vehicle's chassis is acquired, including: In response to detecting that the target vehicle has completed parking, an image acquisition command is sent to the corresponding image acquisition device to obtain two sets of chassis images of the target vehicle. One set of chassis images is configured as the image before the parking lock is activated, and the other set is configured as the image after the parking lock is activated.

[0070] Specifically, the parking space lock action refers to the flap being raised.

[0071] Optionally, based on the chassis image, the vehicle type of the target vehicle can be obtained, including: Based on the chassis image, and based on the texture of at least a portion of the chassis image, the vehicle type of the target vehicle is obtained.

[0072] Optionally, based on the chassis image, and based on the texture of at least a portion of the area in the chassis image, the vehicle type of the target vehicle is obtained, including: Based on the chassis image, obtain the region to be identified that meets the preset conditions; Based on the region to be identified, obtain the texture of the region to be identified; Based on the texture of the region to be identified, obtain the symmetry parameters of the texture of the region to be identified; Based on the symmetry parameters of the texture of the region to be identified, and whether the symmetry parameters meet the preset symmetry conditions, the vehicle type of the target vehicle is obtained.

[0073] Optionally, based on the chassis image, a region to be identified that meets preset conditions is obtained, including: Based on the chassis image, the entity boundaries in the chassis image are obtained using an edge detection algorithm; Based on the entity boundaries in the chassis image, determine whether there is a whole region in the chassis image with a size larger than a preset size; When there is a whole area whose size is larger than the preset size, the whole area is used as the area to be identified.

[0074] Optionally, based on the symmetry parameters of the texture of the region to be identified, and depending on whether the symmetry parameters meet preset symmetry conditions, the vehicle type of the target vehicle can be obtained, including: Based on the symmetry parameters of the texture of the region to be identified, the vehicle type of the target vehicle is determined to be a vehicle including a power battery when the symmetry parameters of the texture of the region to be identified satisfy at least one of the following conditions: Condition 1: The texture of the region to be identified is an axisymmetric graphic; Condition 2: The angle between the axis of symmetry of the texture in the area to be identified and the shooting direction is less than a preset angle threshold; Condition 3: The symmetry of the texture in the region to be identified is greater than the preset symmetry threshold.

[0075] Optionally, the underground flatbed parking space lock is configured to also include a microphone mounted on the surface of the cabin; Also includes: In response to the sensor detecting the parking action of the target vehicle, the voiceprint information of the target vehicle during the parking process is acquired through the microphone; Based on the chassis image, determine the vehicle type of the target vehicle, including: Based on the chassis image and voiceprint information, the vehicle type of the target vehicle is determined.

[0076] Example 4 This embodiment provides a device including a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement any of the methods described above.

[0077] Specifically, such as Figure 2 As shown, Figure 2This is a schematic diagram of the structure of a device according to this application. The device may include: a processor 101, such as a central processing unit (CPU), a communication bus 102, a user interface 104, a network interface 103, and a memory 105. The communication bus 102 is used to enable communication between these components. The user interface 104 may include a display screen and an input unit such as a keyboard. Optionally, the user interface 104 may also include a standard wired interface or a wireless interface. The network interface 103 may optionally include a standard wired interface or a wireless interface (such as a Wi-Fi interface). The memory 105 may be a storage device independent of the aforementioned processor 101. The memory 105 may be a high-speed random access memory (RAM) or a stable non-volatile memory (NVM), such as at least one disk storage device. The processor 101 may be a general-purpose processor, including a central processing unit, a network processor, etc., or it may be a digital signal processor, an application-specific integrated circuit, a field-programmable gate array or other programmable logic device, discrete gate or transistor logic device, or discrete hardware component.

[0078] Those skilled in the art will understand that the appendix Figure 2 The structure shown does not constitute a limitation on the device and may include more or fewer components than shown, or combine certain components, or have different component arrangements.

[0079] like Figure 2 As shown, the memory 105, which serves as a storage medium, may include an operating system, a network communication module, a user interface module, and an application program for implementing a differentiated billing method for on-street parking.

[0080] exist Figure 2 In the device shown, the network interface 103 is mainly used for data communication with the network server; the user interface 104 is mainly used for data interaction with the user; the processor 101 and the memory 105 in this application can be set in the electronic device, and the electronic device can call the application program stored in the memory 105 through the processor 101 to implement a differentiated billing method for on-street parking to implement the above method.

[0081] Example 5 This embodiment provides a computer-readable storage medium on which a computer program is stored, and a processor executes the computer program to implement any of the methods described above.

[0082] In some embodiments, the computer-readable storage medium may be a memory such as FRAM, ROM, PROM, EPROM, EEPROM, flash memory, magnetic surface memory, optical disk, or CD-ROM; or it may be a device including one or any combination of the above-mentioned memories. The computer may be a variety of computing devices, including smart terminals and servers.

[0083] In the above embodiments of this disclosure, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0084] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. The device embodiments described above are merely illustrative; for example, the division of units can be a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual couplings, direct couplings, or communication connections may be through some interfaces; indirect couplings or communication connections between units or modules may be electrical or other forms.

[0085] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0086] Furthermore, the functional units in the various embodiments of this disclosure can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0087] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable non-volatile storage medium. Based on this understanding, the technical solution of this disclosure, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a non-volatile storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this disclosure. The aforementioned non-volatile storage medium includes various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0088] The above are merely preferred embodiments of this disclosure. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of this disclosure, and these improvements and modifications should also be considered within the scope of protection of this disclosure.

Claims

1. A method for differentiated charging of on-street parking, characterized in that, The method comprises: in response to detecting a parking action of a target vehicle, acquiring a chassis image of the target vehicle; obtaining a vehicle type of the target vehicle according to the chassis image, the vehicle type being configured to at least characterize whether the target vehicle comprises a power battery; charging the parking of the target vehicle according to the vehicle type of the target vehicle.

2. The method of claim 1, wherein, The method comprises: in response to detecting a parking action of a target vehicle, acquiring a chassis image of the target vehicle; in response to detecting that the target vehicle has not driven away within a preset free parking time, acquiring the chassis image of the target vehicle collected.

3. The method of claim 1, wherein the method further comprises: The method comprises: in response to detecting that the target vehicle has completed the parking action, sending an image collection instruction to the corresponding image collection device to acquire two sets of chassis images of the target vehicle, one set of the two sets of chassis images being configured as an image before the action of the parking lock, and the other set being configured as an image after the action of the parking lock.

4. The method of claim 1, wherein, The method comprises: obtaining the vehicle type of the target vehicle according to the texture of at least part of the region in the chassis image according to the chassis image.

5. The method of claim 4, wherein, The method comprises: obtaining a to-be-identified region that meets a preset condition according to the chassis image; obtaining the texture of the to-be-identified region according to the to-be-identified region; obtaining a symmetry parameter of the texture of the to-be-identified region according to the texture of the to-be-identified region; obtaining the vehicle type of the target vehicle based on whether the symmetry parameter of the texture of the to-be-identified region meets a preset symmetry condition.

6. The method of claim 5, wherein the method further comprises: The method comprises: obtaining an entity boundary in the chassis image based on an edge detection algorithm according to the chassis image; judging whether there is a whole region with a size greater than a preset size in the chassis image according to the entity boundary in the chassis image; when there is a whole region with a size greater than a preset size, taking the whole region as a to-be-identified region.

7. The method of claim 5, wherein the method further comprises: The method comprises: when the symmetry parameter of the texture of the to-be-identified region meets at least one of the following conditions, obtaining the vehicle type of the target vehicle as a vehicle comprising a power battery: condition 1, the texture of the to-be-identified region is an axisymmetric pattern; condition 2, the angle between the symmetry axis of the texture of the to-be-identified region and the shooting direction is less than a preset angle threshold; condition 3, the degree of symmetry of the texture of the to-be-identified region is greater than a preset symmetry threshold.

8. The method of claim 1, wherein, The method further comprises: in response to detecting a parking action of a target vehicle, acquiring a voiceprint information of the target vehicle during the parking process; The vehicle type of the target vehicle is obtained according to the chassis image, and the vehicle type is configured to at least represent whether the target vehicle comprises a power battery. The vehicle type of the target vehicle is obtained according to the chassis image and the voiceprint information.

9. A system for differentiated billing of on-street parking, characterized in that The buried platform parking lock comprises a management platform and a buried platform parking lock connected with the management platform. The buried platform parking lock is configured to: In response to detecting a parking action of a target vehicle, a chassis image of the target vehicle is acquired; The chassis image of the target vehicle is sent to the management platform; The management platform is configured to: According to the chassis image, the vehicle type of the target vehicle is obtained, and the vehicle type is configured to at least represent whether the target vehicle comprises a power battery; According to the vehicle type of the target vehicle, the parking of the target vehicle is charged.

10. An apparatus, comprising: The device comprises a memory and a processor, the memory stores a computer program, and the processor executes the computer program to realize the method according to any one of claims 1-8.

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