Intelligent management method for parking lot

Abstract

The invention discloses an intelligent management method for a parking lot, and the method comprises the steps: carrying out the target parking space parking difficulty evaluation of idle parking spaces in a parking region through a parking space reservation application, carrying out the sorting in a descending order after the difficulty screening of all target parking space difficulty evaluation values, and selecting the first three optimal parking spaces after sorting as idle pre-target parking spaces, and performing same-parking-space multi-path difficulty calculation on the idle pre-target parking space, and finally selecting an appropriate value as a parking space path to drive to the target parking space. Efficient utilization and intelligent distribution of parking spaces can be realized, and the parking difficulty and time cost of drivers can be effectively reduced. Meanwhile, the system can monitor the parked vehicles in real time and guard the vehicles safely, so that the vehicles are ensured to be safe and worry-free. According to the intelligent parking lot management method, the overall management efficiency and the intelligent level of the parking lot are improved, and more convenient and efficient parking experience is brought to drivers.

Description

Technical Field

[0001] The present invention relates to the technical field of parking lot intelligent management, and in particular to a parking lot intelligent management method. Background Art

[0002] In the current urbanization process, parking lots are an important part of urban transportation. Their management efficiency and intelligence level are directly related to drivers' parking experience and the smoothness of urban transportation. However, traditional parking lots often face many challenges, such as uneven parking space utilization, difficulty in parking, long time to find parking spaces, and difficulty in ensuring vehicle safety. These problems not only increase drivers' parking troubles, but also aggravate urban traffic congestion.

[0003] Traditional parking lots usually only guide drivers through simple parking space displays, lacking an intelligent parking space allocation and guidance system. Drivers often need to spend a lot of time looking for vacant parking spaces on their own, especially during peak hours when parking spaces are tight. This inefficient search process not only wastes time, but can also lead to safety issues such as traffic congestion and vehicle scratches. In addition, the uneven use of parking spaces in parking lots is also a major problem. Some areas have tight parking spaces, while other areas have more vacant spaces. This uneven use of parking spaces reduces the overall efficiency of parking lots.

[0004] More importantly, traditional parking lots have obvious deficiencies in vehicle safety supervision. Most parking lots rely only on post-event surveillance videos to trace vehicle damage or theft, and are unable to ensure vehicle safety in real time. This passive management method is difficult to meet drivers' urgent needs for vehicle safety. To this end, this application proposes a parking lot intelligent management method. Summary of the invention

[0005] The present application provides an intelligent parking lot management method, which realizes intelligent management of parking lots through functions such as real-time data collection, parking difficulty assessment, parking space self-recommendation and route planning, improves parking efficiency, balances parking space utilization, and strengthens the safety supervision of vehicles, thereby effectively solving the problems existing in the background technology.

[0006] This application provides a parking lot intelligent management, including: S101, connect the parking lot with the smart parking management system. When the driver needs to park the vehicle, he / she registers and logs into the smart parking management system through the 5G user terminal. When the parking spaces are tight, he / she queues for the parking spaces by sending a parking request. S102, evaluating the parking difficulty of a target parking space for the available parking spaces in the parking area; S103, screening the difficulty evaluation values ​​of all target parking spaces and sorting them in descending order, and selecting the top three best parking spaces after sorting as the vacant pre-target parking spaces; S104, performing multi-path difficulty calculation on the idle pre-target parking space; S105, choose the right The value is used as the parking space path to drive to the target parking space; S106: The driver registers and logs into the smart parking management system through the 5G user terminal and sends a parking request when parking spaces are tight. After receiving the request, the smart parking management system pushes corresponding parking space information and parking space route to the driver based on the optimal parking space and path selected in S105. S107, the driver parks the vehicle at the selected target parking space according to the guided route, and the parked vehicle is monitored and safely guarded in real time.

[0007] Preferably, the parking difficulty assessment in S102 includes: The distance from the parking lot entrance to the target parking space, the length of the entrance path, the parking space situation, and the obstacles along the way; The parking difficulty assessment calculation formula is as follows:

[0008] Where D is the parking difficulty assessment value, and the assessment value is proportional to the parking difficulty; and They are the length and width of the parking space; and is the weight coefficient of the parking space size, reflecting the impact of parking space size on parking difficulty; is the weight coefficient of the straight-line distance from the parking space to the parking lot entrance, The straight-line distance from the parking entrance to the target free parking space is in meters. The longer the distance, the more difficult it is to park. is the influence coefficient of the vehicle situation, It is the vehicle situation coefficient on both sides of the vacant parking space. It is set according to whether there are cars on both sides of the vacant parking space or against the wall. =3. There is a car nearby =2. Against a wall or pillar =1. Both sides are empty =0; is the obstacle influence coefficient, which considers whether there are obstacles such as pillars, walls or other vehicles on the way of the vehicle, and the degree of influence of these obstacles on the parking operation; The influence coefficient in the above formula requires .

[0009] Preferably, the S104 includes: Rank all possible paths to the pre-target parking space and define the sum of the angles of all corners passed by the path to the pre-target parking space ; when The sum of is greater than If the angle threshold is 700°, it is difficult to determine the path, so the path is discarded; If more than 5 paths are judged to be difficult, the parking space will be abandoned; when The sum of is less than Angle threshold, calculate path difficulty , path difficulty The calculation formula is as follows: .

[0010] Preferably, the S104 further includes: S201, between the parking lot entrance and the parking area, select a road section without obstacles that must be passed through the entrance as an evaluation area, and set an entry point at the end of the area; S202, when the driving distance between the vacant parking space and the parking lot entrance exceeds a first distance, the entry point is enabled for detection; S203, collecting the driver's driving data and calculating the turning angle evaluation index; S204, evaluating the driver's driving ability by setting a driving evaluation index threshold interval; S205, path difficulty during path planning It is directly proportional to the driver's driving ability.

[0011] Preferably, the step S201, setting an entry point, includes: The starting point of the road section starts from the parking lot entrance and ends at the entry point. A clear entry point is set at the end of this road section, and an electronic induction sensor can be used to identify that the vehicle has reached the entry point.

[0012] Preferably, the S203 includes: When the driver drives into the parking lot from the entrance, the intelligent parking management system starts timing. When the vehicle passes the entry point, the system records the time at that moment as part of the driving time. The driving evaluation index is calculated comprehensively based on the vehicle's driving speed, driving time, and turning speed from the entrance to the entry point, and the driver's driving ability is evaluated based on the driving evaluation index.

[0013] The driving assessment index calculation formula is as follows:

[0014] Among them, the definition Driving speed, that is, the straight-line speed of the vehicle before entering the curve Turning speed, which is the average speed of the vehicle in the curve; The time taken, which is the time it takes for the vehicle to enter the curve and fully turn out; is the turning efficiency, which represents the average turning speed that the vehicle can maintain in a given time; It is the speed stability factor, which is used to evaluate the smoothness of the driver's speed change before and after entering the curve.

[0015] Preferably, the S204 includes: S301, based on the historical parking space parking driver capabilities, an average driving capability of the parking space is obtained; S302, calculating the average parking difficulty value of all parking spaces for each parking space; S303, calculating a difficulty balance index of average driving ability and average parking difficulty of parking spaces; S304: Divide the parking spaces into areas of different difficulty levels according to the difficulty balance index.

[0016] Preferably, the difficulty balance index in S303 includes:

[0017] The recommended balance coefficient in the formula is 0.6, which can be adjusted in specific practical applications.

[0018] Preferably, the S304 includes: S401, performing comprehensive regional division of the parking lot according to the physical distance characteristics of each parking space in the parking lot; S402, calculating the overall difficulty of the comprehensive partition; S403, dynamically calculating the driving level of the driver; S404: Allocate a target parking space for the driver.

[0019] Preferably, the step S402 includes: .

[0020] One or more technical solutions provided in this application have at least the following technical effects or advantages: 1. This application comprehensively evaluates the difficulty of parking by taking into account multiple factors, such as the size of the parking space, the distance from the parking space to the entrance of the parking lot, the situation of the vehicles in front of it, and the obstacles along the way. This multi-dimensional evaluation method can more accurately reflect the difficulty of the actual parking process and provide drivers with more reliable parking selection suggestions. Secondly, the technical solution quantifies each influencing factor by setting clear evaluation formulas and coefficients, making the evaluation of parking difficulty more objective and scientific. This not only improves the accuracy of the evaluation, but also enhances the comparability of the evaluation results, making it easier for drivers to compare and choose between different parking spaces.

[0021] 2. This application ranks and calculates the difficulty of all possible paths to the pre-target parking space, providing the driver with multiple optional parking paths. This multi-path recommendation method increases the driver's choice, allowing him to choose the parking path that best suits him based on actual conditions and personal preferences. At the same time, by setting angle thresholds to screen paths, overly complex or difficult-to-drive paths are avoided from being recommended to the driver, thereby improving parking efficiency and safety. In addition, the minimum path difficulty is selected as the pre-target selected parking space path, and is pushed to the driver in real time through 5G technology, so that the driver can quickly obtain the optimal parking path guidance information, further improving the convenience and intelligence of parking.

[0022] 3. This application collects the driver's driving data in real time when the driver enters the parking lot, including driving speed, turning speed, and driving time, and then calculates the driving evaluation index (DEI). This index comprehensively reflects the driver's turning efficiency and speed stability when driving on a curve, and provides a scientific basis for subsequent parking space allocation. Based on the DEI value, the system can automatically divide drivers into different driving ability levels and formulate differentiated parking space allocation strategies for different levels. This personalized parking space allocation method not only improves the convenience of parking, but also significantly enhances the driver's parking experience.

[0023] 4. By introducing the difficulty balance index, the scientific quantification and division of parking space difficulty is achieved. First, by collecting the driving evaluation index of each driver in the historical parking space, the average driving ability of the parking space is calculated, which provides a data basis for the evaluation of parking space difficulty. Then, combined with the average parking difficulty value of the parking space, the relationship between driving ability and parking difficulty is comprehensively considered using the difficulty balance index formula, making the division of parking space difficulty more objective and accurate. This method not only improves the utilization rate of parking spaces, but also optimizes the parking experience and provides drivers with more suitable parking options.

[0024] 5. By comprehensively calculating the parking difficulty of each parking area and dynamically evaluating the driving level of the vehicle, the intelligent and personalized parking allocation is realized. It not only divides the comprehensive area according to the physical distance characteristics of the parking lot and calculates the parking difficulty of each area, but also can instantly evaluate the driver's driving level when the vehicle enters the parking lot, so as to accurately match the driver's ability with the difficulty of the parking space, improve parking efficiency and safety, and optimize the driver's parking experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1 The present invention is a flowchart of intelligent parking lot management according to an embodiment of the present invention. DETAILED DESCRIPTION

[0026] To facilitate the understanding of the present invention, the present application will be described more comprehensively below with reference to the relevant drawings; the drawings show preferred embodiments of the present invention, but the present invention can be implemented in many different forms and is not limited to the embodiments described herein; on the contrary, the purpose of providing these embodiments is to enable a more thorough and comprehensive understanding of the disclosed content of the present invention.

[0027] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by technicians in the technical field to which the present invention belongs; the terms used in the specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention; the term "and / or" used herein includes any and all combinations of one or more related listed items.

[0028] Embodiment 1: Figure 1 As shown, a parking lot intelligent management method includes the following steps: S101, connect the parking lot with the smart parking management system. When the vehicle needs to be parked, the driver registers and logs into the smart parking management system through the 5G user terminal. When parking spaces are tight, the driver queues for parking spaces by sending a parking request.

[0029] Specifically, sensors, cameras and other equipment are used to synchronously update the parking lot layout, number of parking spaces, and real-time parking status information on the smart parking lot management system.

[0030] S102, evaluating the parking difficulty of a target parking space for the available parking spaces in the parking area.

[0031] Among them, the parking difficulty assessment coefficient includes: The distance from the parking lot entrance to the target parking space, the length of the entrance path, the parking space surrounding the vehicle (there are cars on both sides, against the wall, there is a car on the side), and the obstacles along the way.

[0032] The parking difficulty assessment calculation formula is as follows:

[0033] Where D is the parking difficulty assessment value, and the assessment value is proportional to the parking difficulty; and They are the length and width of the parking space; and is the weight coefficient of the parking space size, reflecting the impact of parking space size on parking difficulty; is the weight coefficient of the straight-line distance from the parking space to the parking lot entrance, The straight-line distance from the parking entrance to the target free parking space is in meters. The longer the distance, the more difficult it is to park. is the influence coefficient of the vehicle situation, It is the vehicle situation coefficient on both sides of the vacant parking space. It is set according to whether there are cars on both sides of the vacant parking space or against the wall. =3. There is a car nearby =2. Against a wall or pillar =1. Both sides are empty =0; is the obstacle influence coefficient, which considers whether there are obstacles such as pillars, walls or other vehicles on the way of the vehicle, and the degree of influence of these obstacles on the parking operation; The obstacle influence coefficient can be set according to the actual situation. =0, the number of obstacles passed is within 5 and includes 5 =0.5, more than 5 obstacles passed is 1.

[0034] It should be noted that the influence coefficient in the above formula is defined by the specific actual situation and requires .

[0035] Specifically, measure the length and width of each vacant parking space and calculate the ratio to the standard vehicle size, calculate the length of the path from each parking space to the parking lot entrance, evaluate the obstacles around the parking space and assign corresponding obstacle impact coefficients, substitute the above data into the formula to calculate the parking difficulty assessment value for each parking space.

[0036] S103, screening the difficulty evaluation values ​​of all target parking spaces and sorting them in descending order, and selecting the top three best parking spaces after sorting as vacant pre-target parking spaces.

[0037] S104, calculating the difficulty of multiple paths in the same parking space for the idle pre-target parking space.

[0038] Among them, all possible paths to the pre-target parking space are ranked, and the sum of the angles of all corners passed in the path to the pre-target parking space is defined ,when The sum of is greater than If the angle threshold is 700°, it is difficult to determine the path and the path is discarded. If more than 5 paths are difficult to determine, the parking space is discarded. when The sum of is less than Angle threshold, calculate path difficulty , path difficulty The calculation formula is as follows:

[0039] S105, choose the right The value is used as the parking space path to drive to the target parking space.

[0040] S106: The driver registers and logs into the smart parking management system through the 5G user terminal and sends a parking request when parking spaces are tight. After receiving the request, the smart parking management system pushes the corresponding parking space information and guidance route to the driver based on the optimal parking space and path selected in S105.

[0041] S107, the driver parks the vehicle at the selected target parking space according to the guided route, and the parked vehicle is monitored and safely guarded in real time.

[0042] The technical solutions in the above embodiments of the present application have at least the following technical effects or advantages: 1. This application comprehensively evaluates the difficulty of parking by taking into account multiple factors, such as the size of the parking space, the distance from the parking space to the entrance of the parking lot, the situation of the vehicles in front of it, and the obstacles along the way. This multi-dimensional evaluation method can more accurately reflect the difficulty of the actual parking process and provide drivers with more reliable parking selection suggestions. Secondly, the technical solution quantifies each influencing factor by setting clear evaluation formulas and coefficients, making the evaluation of parking difficulty more objective and scientific. This not only improves the accuracy of the evaluation, but also enhances the comparability of the evaluation results, making it easier for drivers to compare and choose between different parking spaces.

[0043] 2. This application ranks and calculates the difficulty of all possible paths to the pre-target parking space, providing the driver with multiple optional parking paths. This multi-path recommendation method increases the driver's choice, allowing him to choose the parking path that best suits him based on actual conditions and personal preferences. At the same time, by setting angle thresholds to screen paths, overly complex or difficult-to-drive paths are avoided from being recommended to the driver, thereby improving parking efficiency and safety. In addition, the minimum path difficulty is selected as the pre-target selected parking space path, and is pushed to the driver in real time through 5G technology, so that the driver can quickly obtain the optimal parking path guidance information, further improving the convenience and intelligence of parking.

[0044] Embodiment 2: The above embodiment 1 builds a comprehensive parking difficulty assessment system and path planning algorithm, comprehensively considers multiple factors of parking spaces and quantifies the difficulty in real time, providing drivers with scientific parking space selection; at the same time, it combines angle thresholds to select the optimal path, realizes visualization and intelligent guidance throughout the parking process, and significantly improves parking efficiency and experience. In order to further improve parking efficiency and optimize parking experience, further improvements are made to embodiment 1, setting the entry point of the parking area, calculating the time from the parking entrance to the entry point, to evaluate the driver's driving ability, and recommend target parking spaces.

[0045] Now, further improvements are made on the basis of step S105 in the first embodiment, specifically: S201, between the parking lot entrance and the parking area, select the obstacle-free road section that the entrance must pass through as the evaluation area, and set an entry point at the end of the area.

[0046] The starting point of the road section starts from the parking lot entrance and ends at the entry point. A clear entry point is set at the end of this road section, and an electronic induction sensor can be used to identify that the vehicle has reached the entry point.

[0047] S202: When the driving distance between the vacant parking space and the parking lot entrance exceeds a first distance, the entry point is enabled for detection.

[0048] Among them, the first distance is defined as a driving distance of 200 meters. If the proportion of vacant parking spaces within 200 meters of the entrance area is 40%, parking spaces within 200 meters of the entrance will be recommended first. When the proportion of vacant parking spaces is 10%, the entry point will be started for detection, and vacant parking spaces at long distances (driving distance of 200 meters) will be recommended.

[0049] S203, collecting the driver's driving data and calculating the turning angle evaluation index.

[0050] Specifically, when the driver drives into the parking lot from the entrance, the intelligent parking management system starts timing, and when the vehicle passes the entry point, the system records the time at that moment as part of the driving time; The driving evaluation index is calculated comprehensively based on the vehicle's driving speed, driving time, and turning speed from the entrance to the entry point, and the driver's driving ability is evaluated based on the driving evaluation index.

[0051] The driving assessment index calculation formula is as follows:

[0052] Among them, the definition Driving speed, i.e. the straight-line speed of the vehicle before entering the curve; Turning speed, which is the average speed of the vehicle in the curve; The time taken is the time it takes for the vehicle to enter the curve and completely turn out.

[0053] is the turning efficiency, which indicates the average turning speed that the vehicle can maintain in a given time. Turning at high speed and in a short time will receive a higher score; is the speed stability factor, which is used to evaluate how smoothly the driver changes speed before and after entering a curve. If the driving speed and the turning speed are similar, it means that the driver can smoothly slow down or accelerate into the curve, which will result in a higher stability score. If the speed changes greatly, the stability score will be reduced.

[0054] Based on the driving time and speed of the driving evaluation index, the evaluation standard time threshold and speed range are set to evaluate the driver's driving ability level.

[0055] S204: Evaluate the driver's driving ability based on a preset driving evaluation index threshold range.

[0056] Specifically, the DEI value range for excellent (strong driving ability) is ≥ 0.8. When the DEI value reaches or exceeds 0.8, it means that the driver has demonstrated efficient and stable speed control in cornering, with high cornering efficiency, smooth speed changes, and strong driving ability; Good (strong driving ability) DEI value range is 0.6-0.79. Drivers with DEI values ​​within this range perform well in curve driving, with relatively high turning efficiency and speed stability, and strong driving ability; General (medium driving ability) DEI value range is 0.4-0.59. When the DEI value is in this range, the driver's performance in cornering is medium, and there is room for improvement in cornering efficiency and speed stability. The driving ability is at a medium level. Poor (weak driving ability): DEI value range is 0.2-0.39. Drivers with DEI values ​​in this range may have problems such as unstable speed control and low turning efficiency when driving on curved roads, and their driving ability is relatively weak; Poor (poor driving ability) DEI value range <0.2. When the DEI value is lower than 0.2, it indicates that the driver's speed control is extremely unstable when driving on a curve, the turning efficiency is low, and the driving ability is poor.

[0057] S205, path difficulty during path planning It is directly proportional to the driver's driving ability.

[0058] The technical solutions in the above embodiments of the present application have at least the following technical effects or advantages: 1. This application collects the driver's driving data in real time when the driver enters the parking lot, including driving speed, turning speed, and driving time, and then calculates the driving evaluation index (DEI). This index comprehensively reflects the driver's turning efficiency and speed stability when driving on a curve, and provides a scientific basis for subsequent parking space allocation. Based on the DEI value, the system can automatically divide drivers into different driving ability levels and formulate differentiated parking space allocation strategies for different levels. This personalized parking space allocation method not only improves the convenience of parking, but also significantly enhances the driver's parking experience.

[0059] Example 3: The above-mentioned Example 1 and Example 2 use the parking difficulty evaluation formula in step S102, and the difficulty of multiple paths to the pre-target parking space is calculated in step S104. Steps S203 to S205 evaluate the driver's driving ability and formulate the allocation rules of the idle pre-target parking space based on the evaluation results. The comprehensive evaluation and decision-making of multiple steps and dimensions achieves a balance between the difficulty of parking and provides the driver with the best parking space recommendation. In order to better improve the driver's parking efficiency and parking experience, after calculating the driver's driving ability, the specific steps are further improved as follows: S301, based on the historical parking space parking driver capabilities, obtain the average driving capability of the parking space.

[0060] Specifically, the driving evaluation index DEL of each driver in the historical parking space is collected to calculate the average driving ability of the parking space. .

[0061] S302: Calculate the average parking difficulty value of all parking spaces for each parking space.

[0062] The calculation formula for the average parking difficulty value of all parking spaces is as follows:

[0063] S303, calculating a difficulty balance index of average driving ability and average parking difficulty of parking spaces.

[0064] The difficulty balance index calculation formula is as follows:

[0065] The balance coefficient in the formula is defined as 0.6 in this embodiment, and can be adjusted in specific practical applications.

[0066] S304: Divide the parking spaces into areas of different difficulty levels according to the difficulty balance index.

[0067] The difficulty balance index is inversely proportional to the difficulty of parking. The smaller the difficulty balance value, the more difficult parking is. When the difficulty balance coefficient is ≤0.3, the parking space is defined as difficult. When the difficulty balance coefficient is in the range of (0.3, 0.5], the parking space is defined as difficult. When the difficulty balance coefficient is in the range of (0.5, 0.8], the parking space is defined as medium. When the difficulty balance coefficient is greater than 0.8, the parking space is defined as easy.

[0068] It should be noted that all historical data statistics need to specify a window period, and all collected data are within the window period. If the time period exceeds the window period, the data needs to be collected again to update the average difficulty value.

[0069] The technical solutions in the above embodiments of the present application have at least the following technical effects or advantages: 1. The technical solution in the embodiment of the present application realizes the scientific quantification and division of parking space difficulty by introducing the difficulty balance index. First, by collecting the driving evaluation index of each driver in the historical parking space, the average driving ability of the parking space is calculated, which provides a data basis for the evaluation of parking space difficulty. Then, combined with the average parking difficulty value of the parking space, the relationship between driving ability and parking difficulty is comprehensively considered using the difficulty balance index formula, making the division of parking space difficulty more objective and accurate. This method not only improves the utilization rate of parking spaces, but also optimizes the parking experience and provides drivers with more suitable parking options.

[0070] Embodiment 4: The above embodiment 3 divides the parking spaces in the parking lot into zones according to the difficulty level, and greatly reduces the parking difficulty of the driver when the parking space is subsequently allocated to the driver according to the driving ability, thereby enhancing the driver's parking experience. In order to further improve driving efficiency and enhance the parking experience, the embodiment 3 is further improved to calculate the overall difficulty level of the large-area partition, dynamically calculate the driving level of the vehicle driver, and obtain the final target parking space.

[0071] Now, further improvements are made on the basis of step S304 in the third embodiment, specifically: S401, dividing the parking lot into comprehensive areas according to the physical distance characteristics of each parking space in the parking lot.

[0072] Among them, the principle of comprehensive area zoning is that 50-100 parking spaces within the same geographical location are one comprehensive zone.

[0073] S402, calculating the overall difficulty of the comprehensive partition.

[0074] in, .

[0075] S403, dynamically calculating the driver's driving level.

[0076] Specifically, when the driver drives through the entry point, the driver's driving level is immediately obtained.

[0077] S404: Allocate a target parking space for the driver.

[0078] Specifically, after calculating the driver's driving level, first find the nearest first parking comprehensive zone, query the difficulty D of the parking spaces in the comprehensive zone, and combine the driver's ability level. If the difficulty of the vacant parking spaces exceeds the driver's ability level, query the difficulty and driving ability of the vacant parking spaces in the second parking comprehensive zone that is farther away from the first parking comprehensive zone until a target parking space that meets the driver's level is matched; plan the driving route according to the target parking space, and give priority to the path with the least turning angle; after planning the optimal path, push the path and the target parking space to the driver.

[0079] The technical solutions in the above embodiments of the present application have at least the following technical effects or advantages: 1. By comprehensively calculating the parking difficulty of each parking lot and dynamically evaluating the driving level of the vehicle, the intelligent and personalized parking allocation is realized. It not only divides the comprehensive area according to the physical distance characteristics of the parking lot and calculates the parking difficulty of each area, but also can instantly evaluate the driver's driving level when the vehicle enters the parking lot, so as to accurately match the driver's ability with the difficulty of the parking space, improve parking efficiency and safety, and optimize the driver's parking experience.

[0080] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A parking lot intelligent management method, characterized in that: include: S101, connect the parking lot with the smart parking management system. When the driver needs to park the vehicle, he / she registers and logs into the smart parking management system through the 5G user terminal. When the parking spaces are tight, he / she queues for the parking spaces by sending a parking request. S102, evaluating the parking difficulty of a target parking space for the available parking spaces in the parking area; S103, screening the difficulty evaluation values ​​of all target parking spaces and sorting them in descending order, and selecting the top three best parking spaces after sorting as the vacant pre-target parking spaces; S104, performing multi-path difficulty calculation on the idle pre-target parking space; S105, choose the right The value is used as the parking space path to drive to the target parking space; S106, the driver registers and logs into the intelligent parking management system through the 5G user terminal, and sends a parking request when parking spaces are tight; After receiving the request, the intelligent parking management system pushes corresponding parking space information and parking space route to the driver according to the optimal parking space and route selected in S105; S107, the driver parks the vehicle at the selected target parking space according to the guided route, and the parked vehicle is monitored and safely guarded in real time.

2. The parking lot intelligent management method according to claim 1, characterized in that: The parking difficulty assessment in S102 includes: The distance from the parking lot entrance to the target parking space, the length of the entrance path, the parking space situation, and the obstacles along the way; The parking difficulty assessment calculation formula is as follows: ; Where D is the parking difficulty assessment value, and the assessment value is proportional to the parking difficulty; and They are the length and width of the parking space; α and β are the weight coefficients of the parking space size, reflecting the impact of parking space size on parking difficulty; is the weight coefficient of the straight-line distance from the parking space to the parking lot entrance, The straight-line distance from the parking entrance to the target free parking space is in meters. The longer the distance, the more difficult it is to park. is the influence coefficient of the vehicle situation, It is the vehicle situation coefficient on both sides of the vacant parking space. It is set according to whether there are cars on both sides of the vacant parking space or against the wall. =3. There is a car nearby =2. Against a wall or pillar =1. Both sides are empty =0; is the obstacle influence coefficient, which considers whether there are obstacles such as pillars, walls or other vehicles on the way of the vehicle, and the degree of influence of these obstacles on the parking operation; The influence coefficient in the above formula requires .

3. The parking lot intelligent management method according to claim 1, characterized in that: The S104 includes: Rank all possible paths to the pre-target parking space and define the sum of the angles of all corners passed by the path to the pre-target parking space ; when The sum of is greater than If the angle threshold is 700°, it is difficult to determine the path, so the path is discarded; If more than 5 paths are judged to be difficult, the parking space will be abandoned; when The sum of is less than Angle threshold, calculate path difficulty , path difficulty The calculation formula is as follows: 。 4. The parking lot intelligent management method according to claim 1, characterized in that: The S104 further includes: S201, between the parking lot entrance and the parking area, select a road section without obstacles that must be passed through the entrance as an evaluation area, and set an entry point at the end of the area; S202, when the driving distance between the vacant parking space and the parking lot entrance exceeds a first distance, the entry point is enabled for detection; S203, collecting the driver's driving data and calculating the turning angle evaluation index; S204, evaluating the driver's driving ability by setting a driving evaluation index threshold interval; S205, path difficulty during path planning It is directly proportional to the driver's driving ability.

5. The parking lot intelligent management method according to claim 4, characterized in that: The step S201, setting an entry point, includes: The starting point of the road section starts from the parking lot entrance and ends at the entry point. A clear entry point is set at the end of this road section, and the electronic induction sensor can be used to identify the vehicle reaching the entry point.

6. The parking lot intelligent management method according to claim 4, characterized in that: The S203 includes: When the driver drives into the parking lot from the entrance, the intelligent parking management system starts timing. When the vehicle passes the entry point, the system records the time at that moment as part of the driving time. The driving evaluation index is calculated based on the vehicle's driving speed, driving time, and turning speed from the entrance to the entry point. The driver's driving ability is evaluated based on the driving evaluation index. The driving evaluation index calculation formula is as follows: ; Among them, the definition Driving speed, that is, the straight-line speed of the vehicle before entering the curve Turning speed, which is the average speed of the vehicle in the curve; The time taken, which is the time it takes for the vehicle to enter the curve and fully turn out; is the turning efficiency, which represents the average turning speed that the vehicle can maintain in a given time; It is the speed stability factor, which is used to evaluate the smoothness of the driver's speed change before and after entering the curve.

7. The parking lot intelligent management method according to claim 4, characterized in that: The S204 includes: S301, obtaining an average driving ability of the parking space based on the historical parking driver abilities of the parking space; S302, calculating the average parking difficulty value of all parking spaces for each parking space; S303, calculating a difficulty balance index of average driving ability and average parking difficulty of parking spaces; S304: Divide the parking spaces into areas of different difficulty levels according to the difficulty balance index.

8. The parking lot intelligent management method according to claim 7, characterized in that: The S303, difficulty balance index, includes: ; Among them, the recommended balance coefficient in the formula is 0.6, which can be adjusted in specific practical applications.

9. The parking lot intelligent management method according to claim 8, characterized in that: The S304 includes: S401, performing comprehensive regional division of the parking lot according to the physical distance characteristics of each parking space in the parking lot; S402, calculating the overall difficulty of the comprehensive partition; S403, dynamically calculating the driving level of the driver; S404: Allocate a target parking space for the driver.

10. The parking lot intelligent management method according to claim 8, characterized in that: The S402 includes: 。

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