Intelligent park public parking space intelligent planning management system based on artificial intelligence

Abstract

The invention belongs to the technical field of parking space management, and particularly relates to an intelligent park public parking space intelligent planning management system based on artificial intelligence, which comprises a parking space reservation sending module, a park parking space monitoring module, an automatic parking space lock module, a park map module, a parking guide module and a park parking space planning management and control module. The park parking space planning management and control module reserves the parking space for the user based on the parking space reservation request information and locks the corresponding parking space, the time and cost for the user to search for the parking space are reduced, and the parking space occupation abnormity detection module and the zombie vehicle detection module are used for searching for abnormally parked vehicles and zombie vehicles in the smart park and processing the abnormally parked vehicles and the zombie vehicles in time. The vehicle parking management difficulty in the smart park is significantly reduced, the parking performance of the corresponding vehicle in the smart park in the detection period is analyzed through the management level distribution module to determine the non-easy-to-manage vehicle, the supervision of the non-easy-to-manage vehicle is enhanced subsequently, the vehicle management effect in the smart park is ensured, and the intelligent degree is high.

Description

Technical Field

[0001] The present invention relates to the technical field of parking space management, and in particular to an intelligent planning and management system for public parking spaces in a smart park based on artificial intelligence. Background Art

[0002] Smart Park refers to a modern industrial park that uses advanced information and communication technologies to realize intelligent and networked management and services within the park, aiming to improve the park's operational efficiency, reduce resource consumption, and enhance the competitiveness of enterprises. With the acceleration of urbanization and the continuous increase in vehicle ownership, parking problems in smart parks are becoming increasingly prominent; The traditional park parking space management method is difficult to achieve parking space reservation and automatic locking and release within the park, and it is difficult to effectively reduce the time and cost of users looking for parking spaces. It is also impossible to analyze the parking performance of each vehicle in the park and strengthen the supervision of the corresponding vehicles in a timely manner. It is also difficult to analyze the inspection management planning of the smart park to determine whether to strengthen the subsequent inspection management of the smart park. It is not conducive to ensuring the smoothness and safety of parking in the smart park. The degree of intelligence is low and the management is difficult; In view of the above technical defects, a solution is now proposed. Summary of the invention

[0003] The purpose of the present invention is to provide an intelligent planning and management system for public parking spaces in a smart park based on artificial intelligence, which solves the problems that the prior art is difficult to realize parking space reservation and automatic locking and releasing of parking spaces within the park, difficult to effectively reduce the time and cost of users looking for parking spaces, and unable to analyze the parking performance of each vehicle in the park, as well as difficult to conduct inspection management planning and analysis of the smart park, and has a low degree of intelligence and great management difficulty.

[0004] To achieve the above object, the present invention provides the following technical solutions: The intelligent planning and management system for public parking spaces in a smart park based on artificial intelligence includes a parking space reservation sending module, a park parking space monitoring module, an automatic parking space lock module, a park map module, a parking guidance module, and a park parking space planning and control module. Before entering the smart park, the user inputs a parking space reservation request through the parking space reservation sending module, and the parking space reservation sending module sends the parking space reservation request information to the park parking space planning and control module. The park parking space monitoring module monitors the parking spaces in the smart park, obtains the vacant parking spaces in the smart park, and sends all the vacant parking spaces to the park parking space planning and control module; The park parking space planning and control module reserves an empty parking space for the user based on the parking space reservation request information, marks the reserved empty parking space as a matching parking space, controls the automatic parking space lock module on the matching parking space to automatically rise to lock the matching parking space, and sends the matching parking space to the user and the parking guidance module; After the user enters the smart park, the park map module sends the map of the smart park to the parking guidance module. The parking guidance module generates parking navigation based on the user's vehicle position and the corresponding matching parking space, and sends the parking navigation to the user's vehicle terminal via the park parking space planning and control module; the user drives in the park according to the parking navigation, and when the user's vehicle enters the vicinity of the corresponding matching parking space, the automatic parking lock module automatically lowers and sounds a prompt, and the user drives the vehicle into the corresponding matching parking space.

[0005] Furthermore, when the park parking space planning and control module determines a matching parking space for the user, the corresponding moment is marked as the first moment, and the timing is started with the first moment as the starting point, thereby obtaining the real-time locking duration of the parking space; within the time period when the real-time locking duration of the parking space does not exceed the first preset real-time locking duration threshold of the parking space, if the user cancels the reservation lock for the matching parking space, no fee will be deducted; when the real-time locking duration of the parking space exceeds the first preset real-time locking duration threshold of the parking space, if the user cancels the reservation lock for the matching parking space, the fee will be deducted and the corresponding deduction information will be sent to the user's vehicle terminal.

[0006] Furthermore, when the real-time locking time of the parking space exceeds the second preset real-time locking time threshold and the user has not entered the corresponding matching parking space, parking space billing will be started, and the real-time parking space billing information will be sent to the user's vehicle terminal, and the second preset parking space real-time locking time threshold>the first preset parking space real-time locking time threshold>3min.

[0007] Furthermore, the park parking space monitoring module is communicatively connected to the occupancy abnormality detection module, which sends the real-time monitoring images within the smart park to the occupancy abnormality detection module. The occupancy abnormality detection module identifies vehicles that are not properly parked in public parking spaces within the smart park based on the real-time monitoring images and marks them as occupancy abnormality vehicles. After identifying the occupancy abnormality vehicles, they are sent to the park supervision end, and the supervisors at the park supervision end notify the users of the corresponding vehicles to drive out the vehicles or adjust the vehicle's position.

[0008] Furthermore, the park parking space monitoring module is communicatively connected to the zombie vehicle detection module, and the park parking space monitoring module sends the real-time monitoring images within the smart park to the zombie vehicle detection module. The zombie vehicle detection module marks the time difference between the start parking time and the current time of the corresponding vehicle in the public parking space in the smart park as the parking time. When the parking time exceeds the preset parking time threshold, the corresponding vehicle is marked as a zombie vehicle, and the zombie vehicle is sent to the park supervision end. The supervisor at the park supervision end notifies the user of the corresponding vehicle to drive the vehicle out.

[0009] Furthermore, the park supervision end communicates with the management level allocation module, which is used to set the inspection period, analyze the parking performance of the corresponding vehicles in the smart park during the inspection period, determine the non-easy-to-manage vehicles through analysis, and send the non-easy-to-manage vehicles to the park supervision end. The supervisors at the park supervision end strengthen the subsequent supervision of the non-easy-to-manage vehicles.

[0010] Furthermore, the specific analysis process of the management level allocation module includes: The number of times the corresponding vehicle in the smart park is marked as an abnormally occupied vehicle and the number of times it is marked as a zombie vehicle during the detection period are collected and the sum of the two is marked as the park parking warning coefficient. The park parking warning coefficient is numerically compared with the preset park parking warning coefficient threshold. If the park parking warning coefficient exceeds the preset park parking warning coefficient threshold, the corresponding vehicle is marked as a non-easy-to-manage vehicle. If the park parking warning coefficient does not exceed the preset park parking warning coefficient threshold, the time starts when the corresponding vehicle is marked as an abnormal parking vehicle or a zombie vehicle, and continues until the corresponding user completes the required improvement measures, and the parking abnormality resolution time is obtained accordingly; The stop error resolution time is compared with the preset stop error resolution time threshold. If the stop error resolution time exceeds the preset stop error resolution time threshold, the corresponding stop error resolution time is marked as the stop error alarm time. The number of stop error alarm times corresponding to the corresponding vehicles in the smart park during the detection period is obtained and marked as the stop error alarm coefficient, and the average of all the stop error resolution times of the corresponding vehicles during the detection period is calculated to obtain the stop error improvement coefficient. And through the park driving risk analysis to obtain the driving risk coefficient of the corresponding vehicle during the detection period, the park parking warning coefficient, the abnormal parking alarm coefficient, the abnormal parking improvement coefficient and the driving risk coefficient are numerically calculated to obtain the grade allocation judgment value, and the grade allocation judgment value is numerically compared with the preset grade allocation judgment threshold. If the grade allocation judgment value exceeds the preset grade allocation judgment threshold, the corresponding vehicle will be marked as a non-easy-to-manage vehicle.

[0011] Furthermore, the specific analysis process of park driving risk analysis is as follows: When the corresponding vehicle is traveling in the smart park, the real-time driving speed of the corresponding vehicle is collected, and the real-time driving speed is compared with the preset real-time driving speed threshold. If the real-time driving speed exceeds the preset real-time driving speed threshold, it is determined that the corresponding vehicle is in a speeding state; When it is determined that the corresponding vehicle is in a speeding state, the timing is started to obtain a single speeding duration, and the sum of all single speeding durations of the corresponding vehicle in the smart park during the detection period is marked as the speeding detection value, and the number of single speeding durations corresponding to the corresponding vehicle during the detection period that exceeds a preset single speeding duration threshold is marked as a speeding high risk value; The number of scratches and collisions that occur when the corresponding vehicle is driving in the smart park during the detection period is collected and marked as the scratch and collision detection value, and the total amount of losses caused by scratches and collisions of the corresponding vehicle during the detection period is marked as the scratch and collision damage inspection value; the driving risk coefficient is obtained by numerically calculating the speeding detection value, the speeding high risk value, the scratch and collision detection value and the scratch and collision damage inspection value.

[0012] Furthermore, the park supervision end communicates with the inspection management planning and analysis module, which performs inspection management planning analysis on the smart park, and determines whether to generate an inspection management enhancement signal based on this, and sends the inspection management enhancement signal to the park supervision end. When the supervisors at the park supervision end receive the inspection management enhancement signal, they strengthen the subsequent inspection management of the smart park.

[0013] Furthermore, the specific analysis process of the inspection management planning analysis is as follows: The number of vehicles parked in the public parking spaces of the smart park during the detection period is collected and marked as the parking vehicle inspection value, and the number of non-easy-to-manage vehicles is calculated by ratio with the parking vehicle inspection value to obtain the non-easy-to-manage detection value; the non-easy-to-manage detection value is numerically compared with the preset non-easy-to-manage detection threshold, and if the non-easy-to-manage detection value exceeds the preset non-easy-to-manage detection threshold, a patrol management enhancement signal is generated; If the non-easy-to-manage detection value does not exceed the preset non-easy-to-manage detection threshold, the percentage of the number of times the corresponding automatic parking lock module fails to automatically rise or automatically fall within the corresponding specified time period during the detection period is collected and marked as the parking lock abnormality detection value, the parking lock abnormality detection values ​​of all automatic parking lock modules on the public parking spaces in the smart park are averaged to obtain the locking risk value, and the percentage of the number of automatic parking lock modules in the smart park whose parking lock abnormality detection values ​​exceed the preset parking lock abnormality detection threshold is marked as the parking lock non-optimal value; The enhanced emergency coefficient is obtained by numerically calculating the non-easy-to-manage detection value, the locking risk value and the parking lock non-optimal value, and the enhanced emergency coefficient is numerically compared with the preset enhanced emergency coefficient threshold. If the enhanced emergency coefficient exceeds the preset enhanced emergency coefficient threshold, an inspection management enhancement signal is generated.

[0014] Compared with the prior art, the present invention has the following beneficial effects: 1. In the present invention, the parking space planning and control module of the park reserves parking spaces for users and locks corresponding parking spaces based on parking space reservation request information, and sends parking navigation to users, thereby reducing the time and cost of users looking for parking spaces. In addition, the abnormal parking detection module and the zombie vehicle detection module are used to find abnormally parked vehicles and zombie vehicles in the smart park and handle them in time, thereby significantly reducing the difficulty of vehicle parking management in the smart park; 2. In the present invention, the parking performance of corresponding vehicles in the smart park during the detection period is analyzed through the management level allocation module to determine the non-easy-to-manage vehicles, and the supervision of non-easy-to-manage vehicles is strengthened in the subsequent period, so as to facilitate the adoption of appropriate management measures for vehicles of different levels and ensure the vehicle management effect in the smart park. In addition, the inspection planning and analysis module is used to perform inspection management planning analysis on the smart park to determine whether to generate an inspection management enhancement signal. When the inspection management enhancement signal is generated, the subsequent inspection management of the smart park is strengthened to ensure the smoothness and safety of parking in the smart park, with a high degree of intelligence. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to facilitate understanding by those skilled in the art, the present invention is further described below in conjunction with the accompanying drawings; Figure 1 This is a system block diagram of Embodiment 1 of the present invention; Figure 2 This is a system block diagram of Embodiment 2 and Embodiment 3 of the present invention. DETAILED DESCRIPTION

[0016] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0017] Embodiment 1: Figure 1 As shown, the intelligent planning and management system for public parking spaces in a smart park based on artificial intelligence proposed by the present invention includes a parking space reservation sending module, a park parking space monitoring module, an automatic parking space lock module, a park map module, a parking guidance module and a park parking space planning and control module; Before entering the smart park, the user inputs a parking reservation request through the parking reservation sending module, and the parking reservation sending module sends the parking reservation request information (including user identity information, license plate number information, etc.) to the park parking space planning and control module; The park parking space monitoring module monitors the parking spaces in the smart park through surveillance cameras, obtains the vacant parking spaces in the smart park, and sends all the vacant parking spaces to the park parking space planning and control module; the park parking space planning and control module reserves vacant parking spaces for users based on the parking space reservation request information, marks the reserved vacant parking spaces as matching parking spaces, and controls the automatic parking space lock module on the matching parking spaces to automatically rise to lock the matching parking spaces, and sends the matching parking spaces to users and parking guidance modules, so that parking spaces can be reserved in advance, reducing the time and cost of users looking for parking spaces, and improving parking efficiency in the smart park; After the user enters the smart park, the park map module sends the map of the smart park to the parking guidance module. The parking guidance module generates parking navigation based on the user's vehicle position and the corresponding matching parking space, and sends the parking navigation to the user's vehicle terminal via the park parking space planning and control module; the user drives in the park according to the parking navigation, and when the user's vehicle enters the vicinity of the corresponding matching parking space, the automatic parking space lock module automatically lowers and sounds a prompt sound, and the user drives the vehicle into the corresponding matching parking space, which is convenient for the user to quickly find and drive into the corresponding public parking space.

[0018] When the park parking space planning and control module determines a matching parking space for a user, the corresponding moment is marked as the first moment, and the timing is started from the first moment, thereby obtaining the real-time locking duration of the parking space; within the time period when the real-time locking duration of the parking space does not exceed the first preset real-time locking duration threshold of the parking space, if the user cancels the reservation locking of the matching parking space, no fee will be deducted; when the real-time locking duration of the parking space exceeds the first preset real-time locking duration threshold of the parking space, if the user cancels the reservation locking of the matching parking space, the fee will be deducted and the corresponding fee deduction information will be sent to the user's vehicle terminal; Furthermore, when the real-time locking time of the parking space exceeds the second preset real-time locking time threshold and the user has not entered the corresponding matching parking space, parking space billing will begin, and the real-time billing information of the parking space will be sent to the user's vehicle terminal. By formulating a reasonable reservation and cancellation deduction plan, reservation management is facilitated; it should be noted that the second preset parking space real-time locking time threshold>the first preset parking space real-time locking time threshold>3min.

[0019] In addition, the park parking space monitoring module is communicated with the occupancy abnormality detection module, and the park parking space monitoring module sends the real-time monitoring images within the smart park to the occupancy abnormality detection module. The occupancy abnormality detection module identifies the vehicles that are not properly parked in the public parking spaces in the smart park based on the real-time monitoring images and marks them as abnormally occupied vehicles. After identifying the abnormally occupied vehicles, they are sent to the park supervision end. The supervisors at the park supervision end notify the users of the corresponding vehicles to drive out the vehicles or adjust the vehicle positions to ensure that all vehicles in the smart park are correctly parked and reduce the difficulty of vehicle parking management in the smart park.

[0020] Furthermore, the park parking space monitoring module is communicatively connected to the zombie vehicle detection module, and the park parking space monitoring module sends the real-time monitoring images within the smart park to the zombie vehicle detection module. The zombie vehicle detection module marks the time difference between the start parking time and the current time of the corresponding vehicle in the public parking space in the smart park as the parking duration. When the parking time exceeds the preset parking time threshold, the corresponding vehicle will be marked as a zombie vehicle, and the zombie vehicle will be sent to the park supervision end. The supervisor at the park supervision end will notify the user of the corresponding vehicle to drive the vehicle out, further reducing the difficulty of vehicle parking management in the smart park, which is conducive to avoiding the problem of insufficient internal parking spaces due to long-term occupation of public parking spaces in the smart park.

[0021] Embodiment 2: Figure 2 As shown, the difference between this embodiment and the first embodiment is that the park supervision end is connected to the management level allocation module in communication, and the management level allocation module is used to set the detection period, preferably, the detection period is thirty days; the parking performance of the corresponding vehicles in the smart park during the detection period is analyzed, and the non-easy-to-manage vehicles are determined through analysis, and the non-easy-to-manage vehicles are sent to the park supervision end. The supervisors at the park supervision end strengthen the subsequent supervision of the non-easy-to-manage vehicles, such as temporarily not allowing the corresponding non-easy-to-manage vehicles to enter and shortening the stay time of the corresponding non-easy-to-manage vehicles in the public parking spaces of the smart park, etc., so as to facilitate the adoption of appropriate management measures for vehicles of different levels and ensure the vehicle management effect in the smart park; the specific analysis process of the management level allocation module is as follows: The number of times the corresponding vehicle in the smart park is marked as an abnormally occupied vehicle and the number of times it is marked as a zombie vehicle during the detection period are collected and the sum of the two is marked as the park parking warning coefficient. The park parking warning coefficient is numerically compared with the preset park parking warning coefficient threshold. If the park parking warning coefficient exceeds the preset park parking warning coefficient threshold, it indicates that the parking performance of the corresponding vehicle in the smart park during the detection period is poor, and the corresponding vehicle is marked as a non-easy-to-manage vehicle; If the park parking warning coefficient does not exceed the preset park parking warning coefficient threshold, the time starts when the corresponding vehicle is marked as an abnormal parking vehicle or a zombie vehicle, and continues until the corresponding user completes the required improvement measures, and the parking abnormality resolution time is obtained accordingly; wherein, the larger the parking abnormality resolution time is, the less timely the corresponding response process to the vehicle parking abnormality is; The parking anomaly resolution time is numerically compared with the preset parking anomaly resolution time threshold. If the parking anomaly resolution time exceeds the preset parking anomaly resolution time threshold, it indicates that the corresponding response process to the vehicle parking anomaly is not timely, and the corresponding parking anomaly resolution time is marked as parking anomaly alarm time; the number of parking anomaly alarm times corresponding to the corresponding vehicles in the smart park during the detection period is obtained and marked as the parking anomaly alarm coefficient, and the parking anomaly improvement coefficient is obtained by averaging all parking anomaly resolution times of the corresponding vehicles during the detection period; And through the park driving risk analysis to obtain the driving risk coefficient of the corresponding vehicle during the detection period, specifically: when the corresponding vehicle is driving in the smart park, the real-time driving speed of the corresponding vehicle is collected, and the real-time driving speed is compared with the preset real-time driving speed threshold. If the real-time driving speed exceeds the preset real-time driving speed threshold, it is determined that the corresponding vehicle is in a speeding state; When it is determined that the corresponding vehicle is in a speeding state, the timing is started to obtain a single speeding duration, and the sum of all single speeding durations of the corresponding vehicle in the smart park during the detection period is marked as the speeding detection value, and the number of single speeding durations corresponding to the corresponding vehicle during the detection period that exceeds a preset single speeding duration threshold is marked as a speeding high risk value; The number of scratches and collisions that occur when the corresponding vehicle is driving in the smart park during the detection period is collected and marked as a scratch and collision detection value, and the total amount of losses caused by scratches and collisions of the corresponding vehicle during the detection period is marked as a scratch and collision damage inspection value; The speeding detection value HP, the speeding high risk value NY, the scratch detection value SW and the scratch damage inspection value QR are numerically calculated by the formula XF=q×HP+w×NY+t×SW+e×QR to obtain the driving risk coefficient XF; wherein q, w, t, e are preset proportional coefficients with values ​​greater than zero, and the larger the value of the driving risk coefficient XF, the greater the driving safety hazard of the corresponding vehicle user in the smart park during the detection period; The park parking warning coefficient RY, the abnormal parking alarm coefficient UP, the abnormal parking improvement coefficient GF and the driving risk coefficient XF are numerically calculated by the formula FX=np×RY+hy×UP+sq×GF+hu×XF to obtain the grade distribution judgment value FX; wherein np, hy, sq, hu are preset proportional coefficients with values ​​greater than zero, and the larger the value of the grade distribution judgment value FX is, the worse the overall behavior performance of the corresponding vehicle user in the smart park is, and the more supervision of the corresponding user needs to be strengthened; The level allocation judgment value FX is numerically compared with the preset level allocation judgment threshold. If the level allocation judgment value FX exceeds the preset level allocation judgment threshold, it indicates that the overall behavior of the corresponding vehicle user in the smart park is poor, and the supervision of the corresponding user needs to be strengthened in time, then the corresponding vehicle will be marked as a non-easy-to-manage vehicle.

[0022] Embodiment 3: Figure 2As shown, the difference between this embodiment and the first and second embodiments is that the park supervision end is connected to the inspection management planning analysis module in communication, and the inspection planning analysis module performs inspection management planning analysis on the smart park, and judges whether to generate an inspection management enhancement signal based on this, and sends the inspection management enhancement signal to the park supervision end. When the supervisor of the park supervision end receives the inspection management enhancement signal, the subsequent inspection management of the smart park is strengthened to ensure the smoothness and safety of parking in the smart park, which is conducive to the reasonable formulation of subsequent inspection management plans in the smart park, and has a high degree of intelligence; the specific analysis process of the inspection management planning analysis is as follows: The number of vehicles parked in the public parking spaces of the smart park during the detection period is collected and marked as the parking vehicle inspection value, and the number of vehicles that are not easy to manage is calculated by ratio with the parking vehicle inspection value to obtain the not easy to manage detection value; the not easy to manage detection value is numerically compared with the preset not easy to manage detection threshold. If the not easy to manage detection value exceeds the preset not easy to manage detection threshold, it indicates that the management of vehicle users in the smart park during the detection period is difficult, and a patrol management enhancement signal is generated; If the non-easy-to-manage detection value does not exceed the preset non-easy-to-manage detection threshold, the percentage of the number of times the corresponding automatic parking lock module fails to automatically rise or automatically fall within the corresponding specified time (i.e., fails to rise or fall in time) during the detection period is collected and marked as the parking lock abnormality detection value, and the parking lock abnormality detection values ​​of all automatic parking lock modules on the public parking spaces in the smart park are averaged to obtain the locking risk value; and comparing the parking lock abnormality detection value with the preset parking lock abnormality detection threshold, and marking the proportion of the number of automatic parking lock modules in the smart park whose parking lock abnormality detection value exceeds the preset parking lock abnormality detection threshold as a parking lock non-optimal value; The enhanced emergency coefficient QW is obtained by numerically calculating the non-easy-to-manage detection value HX, the locking risk value GP and the parking lock non-optimal value WX through the formula QW=a×HX+b×GP+c×WX, wherein a, b, and c are preset proportional coefficients whose values ​​are greater than zero, and the larger the value of the enhanced emergency coefficient QW, the more necessary it is to strengthen the inspection management in the smart park in a timely manner to ensure the smoothness and safety of parking; the enhanced emergency coefficient QW is numerically compared with the preset enhanced emergency coefficient threshold. If the enhanced emergency coefficient QW exceeds the preset enhanced emergency coefficient threshold, it indicates that the inspection management in the smart park needs to be strengthened, and a patrol management enhancement signal is generated.

[0023] The working principle of the present invention is as follows: when in use, a parking space is reserved for a user based on the parking space reservation request information through the park parking space planning and control module, and the automatic parking space lock module on the corresponding parking space is controlled to automatically rise to lock the corresponding parking space. The user drives in the park according to the parking navigation and drives the vehicle into the corresponding matching parking space. The parking space can be booked in advance, which is convenient for the user to quickly find and drive into the corresponding public parking space, reducing the time and cost of the user to find a parking space, and improving the parking efficiency in the smart park. The abnormal parking detection module and the zombie vehicle detection module are used to find the abnormally parked vehicles and zombie vehicles in the smart park and deal with them in time to ensure the correct parking of all vehicles in the smart park, avoid the public parking spaces in the smart park from being occupied for a long time, resulting in insufficient internal parking spaces, and significantly reduce the difficulty of parking management of vehicles in the smart park. The management level allocation module is used to analyze the parking performance of the corresponding vehicles in the smart park during the detection period to determine the non-easy-to-manage vehicles, and subsequently strengthen the supervision of the non-easy-to-manage vehicles, so as to facilitate the adoption of appropriate management measures for vehicles of different levels, and ensure the vehicle management effect in the smart park, with a high degree of intelligence.

[0024] The above formulas are all dimensionless and numerical calculations. The formula is a formula obtained by collecting a large amount of data and performing software simulation to obtain the most recent real situation. The preset parameters in the formula are set by technicians in this field according to actual conditions. The preferred embodiments of the present invention disclosed above are only used to help explain the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to only specific implementation methods. Obviously, many modifications and changes can be made according to the contents of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the present invention, so that technicians in the relevant technical field can understand and use the present invention well. The present invention is only limited by the claims and their full scope and equivalents.

Claims

1. The intelligent planning and management system for public parking spaces in smart parks based on artificial intelligence is characterized by: It includes a parking reservation sending module, a park parking monitoring module, an automatic parking lock module, a park map module, a parking guidance module and a park parking planning and control module. Before entering the smart park, the user inputs a parking reservation request through the parking reservation sending module, and the parking reservation sending module sends the parking reservation request information to the park parking planning and control module. The park parking space monitoring module monitors the parking spaces in the smart park, obtains the vacant parking spaces in the smart park, and sends all the vacant parking spaces to the park parking space planning and control module; The park parking space planning and control module reserves an empty parking space for the user based on the parking space reservation request information, marks the reserved empty parking space as a matching parking space, controls the automatic parking space lock module on the matching parking space to automatically rise to lock the matching parking space, and sends the matching parking space to the user and the parking guidance module; After the user enters the smart park, the park map module sends the map of the smart park to the parking guidance module. The parking guidance module generates parking navigation based on the user's vehicle position and the corresponding matching parking space position, and sends the parking navigation to the user's vehicle terminal through the park parking space planning and control module; The user drives in the park according to the parking navigation. When the user's vehicle enters the vicinity of the corresponding matching parking space, the automatic parking lock module automatically lowers and emits a prompt sound, and the user drives the vehicle into the corresponding matching parking space.

2. According to the artificial intelligence-based intelligent planning and management system for public parking spaces in a smart park according to claim 1, it is characterized in that: When the park parking space planning and control module determines a matching parking space for a user, the corresponding moment is marked as the first moment, and the timing is started from the first moment, thereby obtaining the real-time locking time of the parking space; If the user cancels the reservation lock for the matching parking space during a time period when the real-time locking time of the parking space does not exceed the first preset real-time locking time threshold of the parking space, no fee will be deducted; When the real-time locking time of the parking space exceeds the first preset real-time locking time threshold of the parking space, if the user cancels the reservation lock of the matching parking space, the fee will be deducted and the corresponding deduction information will be sent to the user's vehicle terminal.

3. The intelligent planning and management system for public parking spaces in a smart park based on artificial intelligence according to claim 2 is characterized in that: When the real-time locking time of the parking space exceeds the second preset real-time locking time threshold and the user has not entered the corresponding matching parking space, parking space billing will begin, and the real-time parking space billing information will be sent to the user's vehicle terminal, and the second preset parking space real-time locking time threshold>the first preset parking space real-time locking time threshold>3min.

4. The intelligent planning and management system for public parking spaces in a smart park based on artificial intelligence according to claim 1 is characterized in that: The park parking space monitoring module is communicated with the occupancy abnormality detection module. The park parking space monitoring module sends the real-time monitoring images within the smart park to the occupancy abnormality detection module. The occupancy abnormality detection module identifies vehicles that are not properly parked in public parking spaces within the smart park based on the real-time monitoring images and marks them as vehicles with occupancy abnormality. After identifying the vehicles with occupancy abnormality, they are sent to the park supervision end. The supervisors at the park supervision end notify the users of the corresponding vehicles to drive out the vehicles or adjust the vehicle's position.

5. The intelligent planning and management system for public parking spaces in a smart park based on artificial intelligence according to claim 4 is characterized in that: The park parking space monitoring module is communicatively connected to the zombie vehicle detection module. The park parking space monitoring module sends the real-time monitoring images within the smart park to the zombie vehicle detection module. The zombie vehicle detection module marks the time difference between the start parking time and the current time of the corresponding vehicle in the public parking space in the smart park as the parking duration. When the parking time exceeds the preset parking time threshold, the corresponding vehicle will be marked as a zombie vehicle and sent to the park supervision end. The supervisor at the park supervision end will notify the user of the corresponding vehicle to drive the vehicle out.

6. The intelligent planning and management system for public parking spaces in a smart park based on artificial intelligence according to claim 5 is characterized in that: The park supervision end communicates with the management level allocation module. The management level allocation module is used to set the inspection period, analyze the parking performance of the corresponding vehicles in the smart park during the inspection period, determine the non-easy-to-manage vehicles through analysis, and send the non-easy-to-manage vehicles to the park supervision end. The supervisors at the park supervision end strengthen the subsequent supervision of the non-easy-to-manage vehicles.

7. The intelligent planning and management system for public parking spaces in a smart park based on artificial intelligence according to claim 6 is characterized in that: The specific analysis process of the management level allocation module includes: The number of times the corresponding vehicle in the smart park is marked as an abnormally occupied vehicle and the number of times it is marked as a zombie vehicle during the detection period are collected and the sum of the two is marked as the park parking warning coefficient. If the park parking warning coefficient exceeds the preset park parking warning coefficient threshold, the corresponding vehicle is marked as a non-easy-to-manage vehicle; If the park parking warning coefficient does not exceed the preset park parking warning coefficient threshold, the park driving risk analysis is performed to obtain the driving risk coefficient of the corresponding vehicle during the detection period, and the park parking warning coefficient, parking abnormality alarm coefficient, parking abnormality improvement coefficient and driving risk coefficient are numerically calculated to obtain the level allocation judgment value. If the level allocation judgment value exceeds the preset level allocation judgment threshold, the corresponding vehicle will be marked as a non-easy-to-manage vehicle.

8. The intelligent planning and management system for public parking spaces in a smart park based on artificial intelligence according to claim 7 is characterized in that: The specific analysis process of park driving risk analysis is as follows: The sum of all single speeding durations of the corresponding vehicle in the smart park during the detection period is marked as the speeding detection value, and the number of single speeding durations corresponding to the corresponding vehicle during the detection period that exceeds the preset single speeding duration threshold is marked as the speeding high risk value; The number of scratches and collisions that occur when the corresponding vehicle is driving in the smart park during the detection period is collected and marked as the scratch and collision detection value, and the total amount of losses caused by scratches and collisions of the corresponding vehicle during the detection period is marked as the scratch and collision damage inspection value; the driving risk coefficient is obtained by numerically calculating the speeding detection value, the speeding high risk value, the scratch and collision detection value and the scratch and collision damage inspection value.

9. The intelligent planning and management system for public parking spaces in a smart park based on artificial intelligence according to claim 6 is characterized in that: The park supervision end communicates with the inspection management planning and analysis module. The inspection planning and analysis module performs inspection management planning analysis on the smart park, and determines whether to generate an inspection management enhancement signal based on this, and sends the inspection management enhancement signal to the park supervision end. When the supervisors at the park supervision end receive the inspection management enhancement signal, they strengthen the subsequent inspection management of the smart park.

10. The intelligent planning and management system for public parking spaces in a smart park based on artificial intelligence according to claim 9 is characterized in that: The specific analysis process of inspection management planning analysis is as follows: The number of vehicles parked in the public parking spaces of the smart park during the detection period is collected and marked as the parking vehicle inspection value, and the number of non-easy-to-manage vehicles is calculated by ratio with the parking vehicle inspection value to obtain the non-easy-to-manage detection value; If the non-easy-to-manage detection value exceeds the preset non-easy-to-manage detection threshold, a patrol management enhancement signal is generated; If the non-easy-to-manage detection value does not exceed the preset non-easy-to-manage detection threshold, the enhanced emergency coefficient is obtained by numerically calculating the non-easy-to-manage detection value, the locking risk value and the parking lock non-optimal value. If the enhanced emergency coefficient exceeds the preset enhanced emergency coefficient threshold, an inspection management enhancement signal is generated.

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