Intelligent parking charge management method and system based on artificial intelligence

Abstract

The invention is suitable for the technical field of parking charging management, and provides an intelligent parking charging management method and system based on artificial intelligence, and the method comprises the steps: obtaining the whole-course tracking video data of a target vehicle in a target parking lot when the target vehicle carries out the payment behavior; and determining a space obstruction coefficient and a moving track influence coefficient of the target vehicle to other vehicles from the whole-course tracking video data, and realizing dynamic adjustment of charging by analyzing the real-time performance of the vehicle in the parking lot in the charging management process of the vehicle driving away from the parking lot. Specifically, the influence of the to-be-paid vehicle on the space obstruction and moving track of other vehicles in the parking lot is evaluated, so that the performance of the to-be-paid vehicle in the parking lot is comprehensively judged. Besides, the real-time evaluation result can be corrected in combination with the historical parking performance of the to-be-paid vehicle, so that a dynamic fee discount index is obtained.

Description

Technical Field

[0001] The present invention belongs to the technical field of parking fee management, and in particular relates to an intelligent parking fee management method and system based on artificial intelligence. Background Art

[0002] In modern cities, parking has become a major challenge in people's daily lives. Parking lot billing management systems are key tools to solve this problem, and usually adopt fixed billing logic, such as charging by time period or by parking location. Although this method is simple and easy to implement, it often fails to take into account the actual performance and impact of vehicles during parking. For example, some vehicles may hinder the movement of other vehicles or affect the overall efficiency of parking lots due to improper parking. However, the existing billing system does not adjust for these dynamic factors, which restricts the efficiency of parking lots and the experience of car owners to a certain extent.

[0003] The existing parking fee charging system usually relies on a fixed charging model, which cannot dynamically reflect the specific performance of vehicles in the parking lot. This fixed charging method ignores the impact of the space occupied by vehicles in the parking lot and the movement trajectory on the overall environment, and fails to take into account the historical parking performance of vehicles. This leads to inflexible and unreasonable charging, which cannot effectively motivate car owners to perform more standardized operations when parking, and fails to give full play to the potential of maximizing the utilization of parking resources.

[0004] Therefore, the existing parking fee management technology has significant defects, mainly reflected in the rigidity of the fee logic. It fails to dynamically adjust according to the actual performance of vehicles in the parking lot, making the fee collection system unable to effectively respond to changes in actual parking conditions, and reducing support for parking standard management and resource optimization. Summary of the invention

[0005] The purpose of the present invention is to provide an intelligent parking fee management method and system based on artificial intelligence, aiming to solve the problems raised in the background technology.

[0006] The present invention is implemented as follows: a smart parking fee management method based on artificial intelligence, the method comprising: When the target vehicle pays the fee, the full tracking video data of the target vehicle in the target parking lot is obtained, and the spatial obstruction coefficient and movement trajectory influence coefficient of the target vehicle on other vehicles are determined from the full tracking video data; The dynamic discount index of the target vehicle is calculated based on the spatial obstruction coefficient and movement trajectory influence coefficient of the target vehicle on other vehicles; Analyze whether the target vehicle has a historical parking record in the target parking lot. If so, calculate the target vehicle's past historical dynamic discount index based on the historical parking record. A discount curve is generated based on the target vehicle's past historical dynamic discount index and this dynamic discount index, and a final discount index is calculated based on the dynamic discount curve. Based on the final discount index, the current payable fees for the target vehicle are determined.

[0007] As a further limitation of the technical solution of the embodiment of the present invention, when the target vehicle performs a payment behavior, the steps of obtaining the full tracking video data of the target vehicle in the target parking lot, and determining the spatial obstruction coefficient and the movement trajectory influence coefficient of the target vehicle on other vehicles from the full tracking video data include: When the target vehicle pays the fee, the whole tracking video data of the target vehicle in the target parking lot is obtained; Capture the video content of the target vehicle in a stationary state and the video content of the target vehicle in a moving state from the full tracking video data; The spatial obstruction coefficient and movement trajectory influence coefficient of the target vehicle on other vehicles are determined according to the video content in which the target vehicle is stationary and the video content in which the target vehicle is moving.

[0008] As a further limitation of the technical solution of the embodiment of the present invention, the step of determining the spatial obstruction coefficient and the moving trajectory influence coefficient of the target vehicle on other vehicles according to the video content of the target vehicle in a stationary state and the video content of the target vehicle in a moving state includes: Intelligently analyze the video content of the target vehicle in a stationary state, determine the parking standard degree of the target vehicle according to the specific parking position of the target vehicle in the video content, and set the parking standard degree of the target vehicle as the spatial obstruction coefficient of the target vehicle to other vehicles; Intelligently analyze the video content of the target vehicle in motion, comprehensively determine the target vehicle's driving standardization based on the specific driving trajectory and speed of the target vehicle in the video content, and set the driving standardization as the influence coefficient of the target vehicle on the moving trajectory of other vehicles.

[0009] As a further limitation of the technical solution of the embodiment of the present invention, in the process of determining the driving compliance of the target vehicle, the video content of the target vehicle in motion is intelligently analyzed, and it is analyzed whether the target vehicle has active avoidance behavior. If there is active avoidance behavior, the moving trajectory influence coefficient is adjusted accordingly.

[0010] As a further limitation of the technical solution of the embodiment of the present invention, the step of calculating the current dynamic discount index of the target vehicle according to the spatial obstruction coefficient and the moving trajectory influence coefficient of the target vehicle on other vehicles includes: Obtain the spatial obstruction coefficient and movement trajectory influence coefficient of the target vehicle on other vehicles; The spatial obstruction coefficient and the movement trajectory influence coefficient are weighted and combined, and the weights are reasonably allocated to the two, and the quantitative dynamic discount index is generated through linear combination.

[0011] As a further limitation of the technical solution of the embodiment of the present invention, the steps of comprehensively generating a discount line based on the historical dynamic discount index of the target vehicle and the current dynamic discount index, calculating a final discount index based on the dynamic discount line, and determining the current payable fee of the target vehicle based on the final discount index include: Generate a discount line based on the target vehicle's past dynamic discount index and the current dynamic discount index; Calculate the average slope of the discount line, multiply the current dynamic discount index by the average slope to get the final discount index; Determine the current payable fees for the target vehicle based on the final discount index.

[0012] An artificial intelligence-based smart parking fee management system, the system comprises: a video data parsing module, a current dynamic discount index determination module, a historical dynamic discount index determination module and a final discount index calculation module, wherein: The video data analysis module is used to obtain the full-course tracking video data of the target vehicle in the target parking lot when the target vehicle is paying the fee, and determine the spatial obstruction coefficient and movement trajectory influence coefficient of the target vehicle on other vehicles from the full-course tracking video data; The current dynamic discount index determination module is used to calculate the current dynamic discount index of the target vehicle according to the spatial obstruction coefficient and movement trajectory influence coefficient of the target vehicle on other vehicles; A historical dynamic discount index determination module is used to analyze whether the target vehicle has a historical parking record in the target parking lot. If so, the target vehicle's past historical dynamic discount index is calculated based on the historical parking record. The final discount index calculation module is used to comprehensively generate a discount curve based on the target vehicle's past historical dynamic discount index and the current dynamic discount index, and calculate the final discount index based on the dynamic discount curve, and determine the current payable fees of the target vehicle based on the final discount index.

[0013] As a further limitation of the technical solution of the embodiment of the present invention, the video data parsing module specifically includes: A video data acquisition unit, used to acquire the full tracking video data of the target vehicle in the target parking lot when the target vehicle performs the payment behavior; A video content capture unit, used to capture the video content of the target vehicle in a stationary state and the video content of the target vehicle in a moving state from the full tracking video data; The coefficient determination unit is used to determine the spatial obstruction coefficient and the moving trajectory influence coefficient of the target vehicle on other vehicles according to the video content of the target vehicle in a stationary state and the video content of the target vehicle in a moving state.

[0014] As a further limitation of the technical solution of the embodiment of the present invention, the dynamic discount index determination module specifically includes: A coefficient acquisition unit, used to obtain the spatial obstruction coefficient and movement trajectory influence coefficient of the target vehicle on other vehicles; The dynamic discount index calculation unit is used to perform weighted combination of the spatial obstruction coefficient and the movement trajectory influence coefficient, reasonably allocate weights to the two, and generate a quantified dynamic discount index through linear combination.

[0015] As a further limitation of the technical solution of the embodiment of the present invention, the final discount index calculation module specifically includes: A discount line generation unit, used to comprehensively generate a discount line according to the historical dynamic discount index of the target vehicle and the current dynamic discount index; The final discount index calculation unit is used to calculate the average slope of the discount line, and multiply the current dynamic discount index by the average slope to obtain the final discount index; The current payable fee determination unit is used to determine the current payable fee of the target vehicle according to the final discount index.

[0016] Compared with the prior art, the present invention obtains the full-course tracking video data of the target vehicle in the target parking lot when the target vehicle pays the fee, and determines the spatial obstruction coefficient and the movement trajectory influence coefficient of the target vehicle on other vehicles from the full-course tracking video data; calculates the current dynamic discount index of the target vehicle according to the spatial obstruction coefficient and the movement trajectory influence coefficient of the target vehicle on other vehicles; analyzes whether the target vehicle has a historical parking record in the target parking lot, and if so, calculates the target vehicle's past historical dynamic discount index according to the historical parking record; comprehensively generates a discount curve according to the target vehicle's past historical dynamic discount index and the current dynamic discount index, calculates the final discount index according to the dynamic discount curve, and determines the current payable fee of the target vehicle according to the final discount index.

[0017] In the process of charging management when vehicles leave the parking lot, the real-time performance of the vehicles in the parking lot can be analyzed to achieve dynamic adjustment of the billing. Specifically, the impact of the space obstruction and movement trajectory of the vehicles to be paid on other vehicles in the parking lot is evaluated, so as to comprehensively judge their performance in the parking lot. In addition, the real-time evaluation results can be corrected in combination with the historical parking performance of the vehicles to be paid, so as to obtain a dynamic fee discount index. Through this billing management method, the utilization efficiency of the parking lot can be improved, congestion can be reduced, and at the same time, car owners can be encouraged to use parking resources more reasonably, ultimately improving the overall user experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 A flowchart of a method provided by an embodiment of the present invention; Figure 2 A flow chart of obtaining video content of a target vehicle in a stationary state and video content of a target vehicle in a moving state in the method provided in an embodiment of the present invention; Figure 3 A flow chart of determining the spatial obstruction coefficient and movement trajectory influence coefficient of a target vehicle on other vehicles in the method provided in an embodiment of the present invention; Figure 4 A flow chart of calculating the current dynamic discount index of a target vehicle in the method provided in an embodiment of the present invention; Figure 5 A flow chart of calculating the current payable fee of a target vehicle in the method provided in an embodiment of the present invention; Figure 6 An application architecture diagram of a system provided by an embodiment of the present invention; Figure 7 A structural block diagram of a video data parsing module in a system provided by an embodiment of the present invention; Figure 8 A structural block diagram of a dynamic discount index determination module in a system provided in an embodiment of the present invention; Fig. 9 This is a structural block diagram of a final discount index calculation module in a system provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0019] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0020] Figure 1 A flow chart of a method provided by an embodiment of the present invention is shown.

[0021] Specifically, a smart parking fee management method based on artificial intelligence, the method specifically includes the following steps: Step S100, when the target vehicle performs payment, the whole-course tracking video data of the target vehicle in the target parking lot is obtained, and the spatial obstruction coefficient and the movement trajectory influence coefficient of the target vehicle on other vehicles are determined from the whole-course tracking video data.

[0022] In the embodiment of the present invention, multi-angle high-resolution cameras are arranged in the target parking lot to cover the entrance and exit, parking area and driving channel, so as to realize the whole process monitoring of the vehicle. When the target vehicle enters the parking lot, the vehicle identity is obtained through the license plate recognition technology, and a unique tracking ID is generated for it.

[0023] Subsequently, using the target tracking algorithm, multiple cameras can collaboratively capture the vehicle's trajectory to ensure continuous tracking of the vehicle. Even if it briefly enters the monitoring blind spot, its position can be inferred based on historical data. The real-time processing engine analyzes the video data and extracts the motion characteristics of the target vehicle, providing a basis for the subsequent calculation of the spatial obstruction coefficient and the movement trajectory influence coefficient.

[0024] Specifically, Figure 2 A flow chart of acquiring video content of a target vehicle in a stationary state and video content of a target vehicle in a moving state is shown.

[0025] When the target vehicle pays the fee, the whole-course tracking video data of the target vehicle in the target parking lot is obtained, and the spatial obstruction coefficient and the moving trajectory influence coefficient of the target vehicle on other vehicles are determined from the whole-course tracking video data. Specifically, the following steps are included: Step S101, when the target vehicle performs payment, the whole tracking video data of the target vehicle in the target parking lot is obtained; Step S101, capturing the video content of the target vehicle in a stationary state and the video content of the target vehicle in a moving state from the full tracking video data; Step S103 , determining a spatial obstruction coefficient and a moving trajectory influence coefficient of the target vehicle on other vehicles according to the video content of the target vehicle being in a stationary state and the video content of the target vehicle being in a moving state.

[0026] In the embodiment of the present invention, the target vehicle's full tracking video data is analyzed, and the target detection and motion recognition algorithms are used to distinguish the stationary and moving states of the target vehicle. Specifically, when the target vehicle is at a fixed position without moving, the video clip of this period is marked as "stationary state" and the corresponding video content is extracted; when the target vehicle starts to move or drive, it is identified as "moving state" and the video content of this section is intercepted.

[0027] Through this automated state recognition, different behaviors of vehicles can be accurately distinguished, providing data support for the subsequent calculation of spatial obstruction coefficients and moving trajectory influence coefficients.

[0028] Specifically, Figure 3 A flow chart for determining the spatial obstruction coefficient and movement trajectory influence coefficient of the target vehicle on other vehicles is shown.

[0029] The method of determining the spatial obstruction coefficient and the moving trajectory influence coefficient of the target vehicle on other vehicles according to the video content of the target vehicle in a stationary state and the video content of the target vehicle in a moving state specifically includes the following steps: Step S1031, intelligently analyzing the video content in which the target vehicle is stationary, determining the parking standard degree of the target vehicle according to the specific parking position of the target vehicle in the video content, and setting the parking standard degree of the target vehicle as the spatial obstruction coefficient of the target vehicle to other vehicles; Step S1032, intelligently analyze the video content of the target vehicle in motion, comprehensively determine the driving standardization of the target vehicle according to the specific driving trajectory and speed of the target vehicle in the video content, and set the driving standardization as the influence coefficient of the target vehicle on the moving trajectory of other vehicles.

[0030] In the process of determining the driving compliance of the target vehicle, the video content of the target vehicle in motion is intelligently analyzed, and it is analyzed whether the target vehicle has active avoidance behavior. If there is active avoidance behavior, the influence coefficient of the moving trajectory is adjusted accordingly.

[0031] In the embodiment of the present invention, when parsing the video content of the stationary state of the target vehicle, the specific position of the target vehicle is first identified by the target detection algorithm, and then it is determined whether the target vehicle is parked in accordance with the regulations. The parking standard degree of the target vehicle is calculated based on the deviation between the parking position of the target vehicle and the parking line, parking space boundary and other standards. If the target vehicle is parked neatly in the parking space and does not affect the use of adjacent parking spaces, the standard degree is high. This standard degree value is set as the spatial obstruction coefficient of the target vehicle to other vehicles.

[0032] When analyzing the video of the target vehicle's motion status, the video content will analyze the vehicle's driving path and speed performance through trajectory tracking and speed detection algorithms. The driving standardization is determined by factors such as whether the target vehicle is driving along the prescribed route, whether it avoids other vehicles, and whether it maintains a reasonable speed. The driving standardization is determined by evaluating the deviation of the driving trajectory, driving stability, and the impact on other vehicles. The standardization is set as the coefficient of the target vehicle's impact on the moving trajectory of other vehicles.

[0033] In the process of determining the target vehicle's driving compliance, the video content uses an intelligent analysis algorithm to identify the target vehicle's motion state, and further analyzes whether the target vehicle has active avoidance behavior during driving. Active avoidance behavior includes the target vehicle actively slowing down, turning, or adjusting the driving route when encountering other vehicles, pedestrians, or obstacles. If such behavior is detected, the system will consider the vehicle to have a higher driving compliance and adjust the movement trajectory influence coefficient accordingly to reduce the negative impact assessment of the target vehicle on other vehicles.

[0034] The benefit of this method is that by identifying and quantifying the vehicle's active avoidance behavior, it can more accurately reflect the vehicle's driving courtesy and safety. It not only improves the system's adaptability to dynamic driving environments, but also encourages drivers to drive more standardized and civilized in parking lots, thereby reducing potential interference with other vehicles.

[0035] Furthermore, the artificial intelligence-based smart parking fee management method further includes the following steps: Step S200, calculating the current dynamic discount index of the target vehicle according to the spatial obstruction coefficient and movement trajectory influence coefficient of the target vehicle on other vehicles.

[0036] Specifically, Figure 4 A flow chart for calculating the current dynamic discount index of the target vehicle is shown.

[0037] The calculation of the dynamic discount index of the target vehicle based on the spatial obstruction coefficient and movement trajectory influence coefficient of the target vehicle on other vehicles specifically includes the following steps: Step S201, obtaining the spatial obstruction coefficient and movement trajectory influence coefficient of the target vehicle on other vehicles; Step S202: weighted combination of the spatial obstruction coefficient and the movement trajectory influence coefficient is performed, and weights are reasonably assigned to the two, and a quantized dynamic discount index is generated through linear combination.

[0038] In an embodiment of the present invention, when generating a quantitative dynamic discount index, it is first necessary to weight the spatial obstruction coefficient and the movement trajectory influence coefficient of the target vehicle. The system extracts these two coefficients from the full tracking video, which respectively represent the impact of the space occupancy of the vehicle on other vehicles when it is stationary, and the impact on the driving path when it is moving. Next, the weights of the two are reasonably allocated according to the actual situation. For example, if the parking lot space is tight and the space occupancy impact of stationary vehicles is more important, a higher weight (such as 60%) can be assigned to the spatial obstruction coefficient, while the movement trajectory influence coefficient may be assigned a weight of 40%.

[0039] The two weighted values ​​are combined in a linear combination to generate a quantified dynamic discount index. This process can be expressed as: the dynamic discount index is equal to the spatial obstruction coefficient multiplied by its weight, plus the movement trajectory influence coefficient multiplied by its weight. For example, if the spatial obstruction coefficient of a target vehicle is 0.7, the movement trajectory influence coefficient is 0.5, and the weights are 60% and 40% respectively, then the final dynamic discount index is 0.62. This means that the comprehensive impact of the target vehicle on other vehicles in the parking lot is 0.62. In this way, the impact of vehicle behavior on the parking lot can be more reasonably evaluated, providing a basis for personalized discounts.

[0040] Furthermore, the artificial intelligence-based smart parking fee management method further includes the following steps: Step S300, analyzing whether the target vehicle has a historical parking record in the target parking lot, and if so, calculating the target vehicle's past historical dynamic discount index based on the historical parking record.

[0041] In the embodiment of the present invention, the historical parking record of the target vehicle is usually obtained through the intelligent monitoring system and vehicle identification technology in the target parking lot. When the vehicle enters the parking lot, the license plate recognition system automatically records the vehicle's identity information, including data such as entry time, exit time, and parking duration. This information will be stored in the database to form a detailed historical parking record for subsequent query and analysis.

[0042] When analyzing the historical parking records of the target vehicle, the target vehicle's past data in the target parking lot is first retrieved, including parking frequency, parking duration, payment records, and previous full-tracking video data. If there are historical parking records, further calculations can be based on these data (mainly full-tracking video data) to derive the historical dynamic discount index of the target vehicle (using step 100).

[0043] Furthermore, the artificial intelligence-based smart parking fee management method further includes the following steps: Step S400, a discount curve is generated based on the historical dynamic discount index of the target vehicle and the current dynamic discount index, and a final discount index is calculated based on the dynamic discount curve. Based on the final discount index, the current payable fee of the target vehicle is determined.

[0044] Specifically, Figure 5 A flow chart for calculating the current payable fee of the target vehicle is shown.

[0045] Among them, the discount line is generated based on the historical dynamic discount index of the target vehicle and the current dynamic discount index, and the final discount index is calculated based on the dynamic discount line. According to the final discount index, the current payable fee of the target vehicle is determined, which specifically includes the following steps: Step S401, generating a discount line based on the historical dynamic discount index and the current dynamic discount index of the target vehicle; Step S402, calculating the average slope of the discount line, multiplying the current dynamic discount index by the average slope to obtain a final discount index; Step S403, determining the current payable fee of the target vehicle according to the final discount index.

[0046] In the embodiment of the present invention, the discount fold line is an XY axis fold line, and the Y axis represents the index size, and the distance between two adjacent punctuation points on the X axis is the same and is preset.

[0047] After multiplying the dynamic discount index by the average slope, if the final discount index is greater than 1, the current payable fee will not be adjusted and will still be paid according to the initial charge. If the final discount index is less than 1, the initial charge will be multiplied by the final discount index to obtain the current payable fee for the target vehicle to leave the target parking lot.

[0048] In summary, in the process of charging management when vehicles leave the parking lot, the real-time performance of the vehicles in the parking lot can be analyzed to achieve dynamic adjustment of the billing. Specifically, the impact of the space obstruction and movement trajectory of the vehicles to be paid on other vehicles in the parking lot is evaluated, so as to comprehensively judge their performance in the parking lot. In addition, the real-time evaluation results can be corrected in combination with the historical parking performance of the vehicles to be paid, so as to obtain a dynamic fee discount index. Through this billing management method, the utilization efficiency of the parking lot can be improved, congestion can be reduced, and at the same time, car owners can be encouraged to use parking resources more reasonably, ultimately improving the overall user experience.

[0049] Furthermore, Figure 6 The application architecture diagram of the system provided by the embodiment of the present invention is shown.

[0050] Among them, in another preferred embodiment provided by the present invention, a smart parking fee management system based on artificial intelligence includes: The video data analysis module 100 is used to obtain the full-course tracking video data of the target vehicle in the target parking lot when the target vehicle performs the payment behavior, and determine the spatial obstruction coefficient and movement trajectory influence coefficient of the target vehicle on other vehicles from the full-course tracking video data.

[0051] In the embodiment of the present invention, multi-angle high-resolution cameras are arranged in the target parking lot to cover the entrance and exit, parking area and driving channel, so as to realize the whole process monitoring of the vehicle. When the target vehicle enters the parking lot, the vehicle identity is obtained through the license plate recognition technology, and a unique tracking ID is generated for it.

[0052] Subsequently, using the target tracking algorithm, multiple cameras can collaboratively capture the vehicle's trajectory to ensure continuous tracking of the vehicle. Even if it briefly enters the monitoring blind spot, its position can be inferred based on historical data. The real-time processing engine analyzes the video data and extracts the motion characteristics of the target vehicle, providing a basis for the subsequent calculation of the spatial obstruction coefficient and the movement trajectory influence coefficient.

[0053] Specifically, Figure 7 It shows a structural block diagram of the video data parsing module 100 in the system provided by the embodiment of the present invention.

[0054] Among them, in the preferred implementation manner provided by the present invention, the video data parsing module 100 specifically includes: The video data acquisition unit 101 is used to acquire the whole tracking video data of the target vehicle in the target parking lot when the target vehicle performs the payment behavior; The video content capture unit 102 is used to capture the video content of the target vehicle in a stationary state and the video content of the target vehicle in a moving state from the full tracking video data; The coefficient determination unit 103 is used to determine the spatial obstruction coefficient and the moving trajectory influence coefficient of the target vehicle on other vehicles according to the video content of the target vehicle in a stationary state and the video content of the target vehicle in a moving state.

[0055] In the embodiment of the present invention, the target vehicle's full tracking video data is analyzed, and the target detection and motion recognition algorithms are used to distinguish the stationary and moving states of the target vehicle. Specifically, when the target vehicle is at a fixed position without moving, the video clip of this period is marked as "stationary state" and the corresponding video content is extracted; when the target vehicle starts to move or drive, it is identified as "moving state" and the video content of this section is intercepted.

[0056] Through this automated state recognition, different behaviors of vehicles can be accurately distinguished, providing data support for the subsequent calculation of spatial obstruction coefficients and moving trajectory influence coefficients.

[0057] When parsing the video content of the target vehicle in a stationary state, the target vehicle's specific location is first identified through the target detection algorithm, and then it is determined whether the target vehicle is parked in accordance with regulations. The parking standardization of the target vehicle is calculated based on the deviation of the target vehicle's parking position from the parking line, parking space boundary and other standards. If the target vehicle is parked neatly in the parking space and does not affect the use of adjacent parking spaces, the standardization is high. This standardization value is set as the spatial obstruction coefficient of the target vehicle to other vehicles.

[0058] When analyzing the video of the target vehicle's motion status, the video content will analyze the vehicle's driving path and speed performance through trajectory tracking and speed detection algorithms. The driving standardization is determined by factors such as whether the target vehicle is driving along the prescribed route, whether it avoids other vehicles, and whether it maintains a reasonable speed. The driving standardization is determined by evaluating the deviation of the driving trajectory, driving stability, and the impact on other vehicles. The standardization is set as the coefficient of the target vehicle's impact on the moving trajectory of other vehicles.

[0059] In the process of determining the target vehicle's driving compliance, the video content uses an intelligent analysis algorithm to identify the target vehicle's motion state, and further analyzes whether the target vehicle has active avoidance behavior during driving. Active avoidance behavior includes the target vehicle actively slowing down, turning, or adjusting the driving route when encountering other vehicles, pedestrians, or obstacles. If such behavior is detected, the system will consider the vehicle to have a higher driving compliance and adjust the movement trajectory influence coefficient accordingly to reduce the negative impact assessment of the target vehicle on other vehicles.

[0060] The benefit of this method is that by identifying and quantifying the vehicle's active avoidance behavior, it can more accurately reflect the vehicle's driving courtesy and safety. It not only improves the system's adaptability to dynamic driving environments, but also encourages drivers to drive more standardized and civilized in parking lots, thereby reducing potential interference with other vehicles.

[0061] Furthermore, the artificial intelligence-based smart parking fee management system also includes: The current dynamic discount index determination module 200 is used to calculate the current dynamic discount index of the target vehicle according to the spatial obstruction coefficient and movement trajectory influence coefficient of the target vehicle to other vehicles.

[0062] Specifically, Figure 8 The structure block diagram of the dynamic discount index determination module 200 in the system provided by the embodiment of the present invention is shown.

[0063] Among them, in the preferred implementation mode provided by the present invention, the current dynamic discount index determination module 200 specifically includes: A coefficient acquisition unit 201 is used to acquire a spatial obstruction coefficient and a moving trajectory influence coefficient of a target vehicle on other vehicles; The current dynamic discount index calculation unit 202 is used to perform weighted combination of the spatial obstruction coefficient and the movement trajectory influence coefficient, reasonably allocate weights to the two, and generate a quantized current dynamic discount index through linear combination.

[0064] In an embodiment of the present invention, when generating a quantitative dynamic discount index, it is first necessary to weight the spatial obstruction coefficient and the movement trajectory influence coefficient of the target vehicle. The system extracts these two coefficients from the full tracking video, which respectively represent the impact of the space occupancy of the vehicle on other vehicles when it is stationary, and the impact on the driving path when it is moving. Next, the weights of the two are reasonably allocated according to the actual situation. For example, if the parking lot space is tight and the space occupancy impact of stationary vehicles is more important, a higher weight (such as 60%) can be assigned to the spatial obstruction coefficient, while the movement trajectory influence coefficient may be assigned a weight of 40%.

[0065] The two weighted values ​​are combined in a linear combination to generate a quantified dynamic discount index. This process can be expressed as: the dynamic discount index is equal to the spatial obstruction coefficient multiplied by its weight, plus the movement trajectory influence coefficient multiplied by its weight. For example, if the spatial obstruction coefficient of a target vehicle is 0.7, the movement trajectory influence coefficient is 0.5, and the weights are 60% and 40% respectively, then the final dynamic discount index is 0.62. This means that the comprehensive impact of the target vehicle on other vehicles in the parking lot is 0.62. In this way, the impact of vehicle behavior on the parking lot can be more reasonably evaluated, providing a basis for personalized discounts.

[0066] Furthermore, the artificial intelligence-based smart parking fee management system also includes: The historical dynamic discount index determination module 300 is used to analyze whether the target vehicle has a historical parking record in the target parking lot, and if so, calculate the target vehicle's past historical dynamic discount index based on the historical parking record.

[0067] In the embodiment of the present invention, the historical parking record of the target vehicle is usually obtained through the intelligent monitoring system and vehicle identification technology in the target parking lot. When the vehicle enters the parking lot, the license plate recognition system automatically records the vehicle's identity information, including data such as entry time, exit time, and parking duration. This information will be stored in the database to form a detailed historical parking record for subsequent query and analysis.

[0068] When analyzing the target vehicle’s historical parking records, the target vehicle’s past data at the target parking lot will first be retrieved, including parking frequency, parking duration, payment records, and previous full-course tracking video data. If there are historical parking records, further calculations can be based on these data (mainly full-course tracking video data) to derive the target vehicle’s historical dynamic discount index.

[0069] Furthermore, the artificial intelligence-based smart parking fee management system also includes: The final discount index calculation module 400 is used to comprehensively generate a discount curve based on the target vehicle's past historical dynamic discount index and the current dynamic discount index, and calculate the final discount index based on the dynamic discount curve, and determine the current payable fees of the target vehicle based on the final discount index.

[0070] Specifically, Fig. 9 It shows a structural block diagram of the final discount index calculation module 400 in the system provided by an embodiment of the present invention.

[0071] Among them, in the preferred implementation manner provided by the present invention, the final discount index calculation module 400 specifically includes: A discount line generating unit 401 is used to comprehensively generate a discount line according to the historical dynamic discount index and the current dynamic discount index of the target vehicle; The final discount index calculation unit 402 is used to calculate the average slope of the discount line, and multiply the current dynamic discount index by the average slope to obtain the final discount index; The current payable fee determination unit 403 is used to determine the current payable fee of the target vehicle according to the final discount index.

[0072] In the embodiment of the present invention, the discount fold line is an XY axis fold line, and the Y axis represents the index size, and the distance between two adjacent punctuation points on the X axis is the same and is preset.

[0073] After multiplying the dynamic discount index by the average slope, if the final discount index is greater than 1, the current payable fee will not be adjusted and will still be paid according to the initial charge. If the final discount index is less than 1, the initial charge will be multiplied by the final discount index to obtain the current payable fee for the target vehicle to leave the target parking lot.

[0074] In summary, in the process of charging management when vehicles leave the parking lot, the real-time performance of the vehicles in the parking lot can be analyzed to achieve dynamic adjustment of the billing. Specifically, the impact of the space obstruction and movement trajectory of the vehicles to be paid on other vehicles in the parking lot is evaluated, so as to comprehensively judge their performance in the parking lot. In addition, the real-time evaluation results can be corrected in combination with the historical parking performance of the vehicles to be paid, so as to obtain a dynamic fee discount index. Through this billing management method, the utilization efficiency of the parking lot can be improved, congestion can be reduced, and at the same time, car owners can be encouraged to use parking resources more reasonably, ultimately improving the overall user experience.

[0075] It should be understood that, although each step in the flow chart of each embodiment of the present invention is shown in sequence according to the indication of the arrow, these steps are not necessarily performed in sequence according to the order indicated by the arrow. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be performed in other orders. Moreover, at least a portion of the steps in each embodiment may include a plurality of sub-steps or a plurality of stages, and these sub-steps or stages are not necessarily performed at the same time, but can be performed at different times, and the execution order of these sub-steps or stages is not necessarily performed in sequence, but can be performed in turn or alternately with at least a portion of other steps or sub-steps or stages of other steps.

[0076] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the program can be stored in a non-volatile computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0077] The technical features of the above-described embodiments may be arbitrarily combined. To make the description concise, not all possible combinations of the technical features in the above-described embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0078] The above-mentioned embodiments only express several implementation methods of the present invention, and the description thereof is relatively specific and detailed, but it cannot be understood as limiting the scope of the patent of the present invention. It should be pointed out that, for ordinary technicians in this field, several variations and improvements can be made without departing from the concept of the present invention, which all belong to the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention shall be subject to the attached claims.

[0079] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A smart parking fee management method based on artificial intelligence, characterized in that: The method comprises: When the target vehicle pays the fee, the full tracking video data of the target vehicle in the target parking lot is obtained, and the spatial obstruction coefficient and movement trajectory influence coefficient of the target vehicle on other vehicles are determined from the full tracking video data; The dynamic discount index of the target vehicle is calculated based on the spatial obstruction coefficient and movement trajectory influence coefficient of the target vehicle on other vehicles; Analyze whether the target vehicle has a historical parking record in the target parking lot. If so, calculate the target vehicle's past historical dynamic discount index based on the historical parking record. A discount curve is generated based on the target vehicle's past historical dynamic discount index and this dynamic discount index, and a final discount index is calculated based on the dynamic discount curve. Based on the final discount index, the current payable fees for the target vehicle are determined.

2. The intelligent parking fee management method based on artificial intelligence according to claim 1 is characterized in that: When the target vehicle pays the fee, the steps of obtaining the full tracking video data of the target vehicle in the target parking lot, and determining the spatial obstruction coefficient and the moving trajectory influence coefficient of the target vehicle on other vehicles from the full tracking video data include: When the target vehicle pays the fee, the whole tracking video data of the target vehicle in the target parking lot is obtained; Capture the video content of the target vehicle in a stationary state and the video content of the target vehicle in a moving state from the full tracking video data; The spatial obstruction coefficient and movement trajectory influence coefficient of the target vehicle on other vehicles are determined according to the video content in which the target vehicle is stationary and the video content in which the target vehicle is moving.

3. The intelligent parking fee management method based on artificial intelligence according to claim 2 is characterized in that: The steps of determining the spatial obstruction coefficient and the moving trajectory influence coefficient of the target vehicle on other vehicles according to the video content of the target vehicle in a stationary state and the video content of the target vehicle in a moving state include: Intelligently analyze the video content of the target vehicle in a stationary state, determine the parking standard degree of the target vehicle according to the specific parking position of the target vehicle in the video content, and set the parking standard degree of the target vehicle as the spatial obstruction coefficient of the target vehicle to other vehicles; Intelligently analyze the video content of the target vehicle in motion, comprehensively determine the target vehicle's driving standardization based on the specific driving trajectory and speed of the target vehicle in the video content, and set the driving standardization as the influence coefficient of the target vehicle on the moving trajectory of other vehicles.

4. The intelligent parking fee management method based on artificial intelligence according to claim 3 is characterized in that: In the process of determining the driving compliance of the target vehicle, the video content of the target vehicle in motion is intelligently analyzed, and it is analyzed whether the target vehicle has active avoidance behavior. If there is active avoidance behavior, the influence coefficient of the moving trajectory is adjusted accordingly.

5. The intelligent parking fee management method based on artificial intelligence according to claim 1 is characterized in that: The steps of calculating the current dynamic discount index of the target vehicle according to the spatial obstruction coefficient and the moving trajectory influence coefficient of the target vehicle on other vehicles include: Obtain the spatial obstruction coefficient and movement trajectory influence coefficient of the target vehicle on other vehicles; The spatial obstruction coefficient and the movement trajectory influence coefficient are weighted and combined, and the weights are reasonably allocated to the two, and the quantitative dynamic discount index is generated through linear combination.

6. The intelligent parking fee management method based on artificial intelligence according to claim 1 is characterized in that: The steps of generating a discount line based on the historical dynamic discount index of the target vehicle and the current dynamic discount index, calculating the final discount index based on the dynamic discount line, and determining the current payable fee of the target vehicle based on the final discount index include: Generate a discount line based on the target vehicle's past dynamic discount index and the current dynamic discount index; Calculate the average slope of the discount line, multiply the current dynamic discount index by the average slope to get the final discount index; Determine the current payable fees for the target vehicle based on the final discount index.

7. An intelligent parking fee management system based on artificial intelligence, characterized in that: The system comprises: a video data parsing module, a current dynamic discount index determination module, a historical dynamic discount index determination module and a final discount index calculation module, wherein: The video data analysis module is used to obtain the full-course tracking video data of the target vehicle in the target parking lot when the target vehicle is paying the fee, and determine the spatial obstruction coefficient and movement trajectory influence coefficient of the target vehicle on other vehicles from the full-course tracking video data; The current dynamic discount index determination module is used to calculate the current dynamic discount index of the target vehicle according to the spatial obstruction coefficient and movement trajectory influence coefficient of the target vehicle on other vehicles; A historical dynamic discount index determination module is used to analyze whether the target vehicle has a historical parking record in the target parking lot. If so, the target vehicle's past historical dynamic discount index is calculated based on the historical parking record. The final discount index calculation module is used to comprehensively generate a discount curve based on the target vehicle's past historical dynamic discount index and the current dynamic discount index, and calculate the final discount index based on the dynamic discount curve, and determine the current payable fees of the target vehicle based on the final discount index.

8. The intelligent parking fee management system based on artificial intelligence according to claim 7 is characterized in that: The video data parsing module specifically includes: A video data acquisition unit, used to acquire the full tracking video data of the target vehicle in the target parking lot when the target vehicle performs the payment behavior; A video content capture unit, used to capture the video content of the target vehicle in a stationary state and the video content of the target vehicle in a moving state from the full tracking video data; The coefficient determination unit is used to determine the spatial obstruction coefficient and the moving trajectory influence coefficient of the target vehicle on other vehicles according to the video content of the target vehicle in a stationary state and the video content of the target vehicle in a moving state.

9. The intelligent parking fee management system based on artificial intelligence according to claim 8 is characterized in that: The dynamic discount index determination module specifically includes: A coefficient acquisition unit, used to obtain the spatial obstruction coefficient and movement trajectory influence coefficient of the target vehicle on other vehicles; The dynamic discount index calculation unit is used to perform weighted combination of the spatial obstruction coefficient and the movement trajectory influence coefficient, reasonably allocate weights to the two, and generate a quantified dynamic discount index through linear combination.

10. The intelligent parking fee management system based on artificial intelligence according to claim 9 is characterized in that: The final discount index calculation module specifically includes: A discount line generation unit, used to comprehensively generate a discount line according to the historical dynamic discount index of the target vehicle and the current dynamic discount index; The final discount index calculation unit is used to calculate the average slope of the discount line, and multiply the current dynamic discount index by the average slope to obtain the final discount index; The current payable fee determination unit is used to determine the current payable fee of the target vehicle according to the final discount index.

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