Intelligent scheduling parking space utilization method

Abstract

The invention relates to the technical field of intelligent parking, in particular to an intelligent scheduling parking space utilization method, which comprises the steps of collecting parking space states through a wireless sensor network, dividing temporary, long-term and reserved areas through a dynamic partition algorithm, and generating an optimal allocation scheme in combination with a time window priority model. The parking lot resource utilization rate can be improved, the user waiting time is shortened, and the method is suitable for different-scale parking lot scenes.

Description

Technical Field

[0001] This invention belongs to the field of intelligent transportation and parking management technology, specifically a method for intelligent scheduling of parking space utilization. Background: With the acceleration of urbanization and the continuous growth of car ownership, the problem of parking difficulties is becoming increasingly prominent. To improve the resource utilization rate and user experience of parking lots, intelligent scheduling of parking space utilization methods has gradually become a research hotspot. However, existing intelligent scheduling technologies still have certain limitations in terms of parking space allocation efficiency, dynamic adaptability, and matching of user needs, affecting the overall operational efficiency of parking lots and user satisfaction.

[0002] A search revealed an intelligent scheduling method and system for AVP (Automated Parking Utility) with publication number CN116863748B, published on November 28, 2023. This patent achieves efficient parking scheduling by dividing a parking lot into multiple service areas and intelligently allocating parking spaces based on the estimated parking duration and parking lot congestion. However, this technical solution relies heavily on estimated parking duration and service area divisions. In practical applications, it may face a contradiction between dynamically changing parking demand and fixed area divisions, leading to some areas being idle while others are overcrowded. Furthermore, this solution does not adequately consider the actual arrival time of drivers and unforeseen circumstances, potentially resulting in untimely adjustments to the scheduling plan, thus affecting the user experience.

[0003] A search revealed a parking lot intelligent scheduling server with publication number CN105464437B, published on December 26, 2017. This patent optimizes parking lot space utilization and improves vehicle access speed through the design of linear guides and vehicle carriers. However, this technical solution focuses on hardware structure improvements and fails to incorporate intelligent algorithms for dynamic parking space scheduling, resulting in insufficient adaptability to complex parking demands during peak hours. Furthermore, its fixed mechanical structure limits the flexibility of parking space layout, potentially failing to meet the needs of parking lots of different sizes and shapes, and exhibiting weak compatibility with special vehicle types, further narrowing its applicability.

[0004] The aforementioned problems indicate that existing intelligent parking space utilization technologies still have room for improvement in terms of dynamic adaptability, user demand matching, and flexibility. Therefore, this invention provides a novel intelligent parking space utilization method, aiming to improve parking lot resource utilization, optimize user experience, and meet diverse parking needs by combining real-time data collection, dynamic scheduling algorithms, and flexible parking space management strategies. Summary of the Invention

[0005] One of the objectives of this invention is to overcome the shortcomings of existing technologies and provide an intelligent parking space utilization method that improves parking lot resource utilization through dynamic allocation and real-time adjustment strategies.

[0006] The second objective of this invention is to provide a parking space scheduling system based on multi-dimensional data collection and analysis, which optimizes user experience and meets diverse parking needs.

[0007] The third objective of this invention is to provide a specific implementation method for the above-mentioned parking space scheduling method, including hardware deployment, algorithm design, and operation process.

[0008] To achieve the above objectives, the technical principle employed in this invention is as follows: A sensor network within the parking lot is used to collect real-time data on parking space occupancy, vehicle location, and user behavior. This data is then processed using a cloud computing platform. Based on this, a dynamic zoning algorithm and queuing management mechanism are employed to divide the parking lot into multiple virtual zones, each dynamically adjusting its functional attributes according to real-time demand. Simultaneously, a time window priority model is introduced, combining user reservation information and actual arrival time to generate an optimal parking space allocation scheme.

[0009] A method for intelligently scheduling parking space utilization is characterized by its use of a wireless sensor network as a foundation. Distributed nodes collect data on parking space occupancy status and upload this data to a central processing unit (CPU). Based on preset rules and real-time data analysis results, the CPU generates dynamic zoning instructions, dividing the parking lot into temporary parking areas, long-term parking areas, and reserved areas. The functional attributes of each area are dynamically switched via electronic identification devices. Users obtain allocation information and navigate to their target parking space via mobile terminals.

[0010] A parking space scheduling system based on multi-dimensional data collection and analysis, employing the aforementioned intelligent scheduling method, is characterized by comprising the following components:

[0011] 1. Sensor module, used to monitor parking space occupancy status in real time;

[0012] 2. Data processing module, responsible for receiving sensor data and executing dynamic partitioning algorithms;

[0013] 3. User interaction module, providing users with parking space allocation information and navigation services;

[0014] 4. Dynamic zoning control module, which switches the functional attributes of virtual areas through electronic identification devices;

[0015] 5. Time window priority model, used to generate the optimal parking space allocation scheme.

[0016] A specific step in implementing the above-mentioned intelligent parking space utilization method is as follows: a. Parking space status monitoring and data collection, the specific steps of which are: a-1. Installing pressure sensors or infrared sensors in each parking space to detect whether the parking space is occupied; a-2. Uploading the data collected by the sensors to the central processing unit through a wireless communication module; a-3. The central processing unit cleans and classifies the received data and stores it to form a real-time parking space status database.

[0017] b. Dynamic zoning and function switching, the specific steps are as follows: b-1. Based on the real-time parking space status database, count the number of vacant parking spaces and the occupancy trend in each area; b-2. Using a dynamic zoning algorithm, divide the parking lot into temporary parking areas, long-term parking areas and reserved areas; b-3. Switch the functional attributes of each area through electronic identification devices, for example, increase the proportion of temporary parking areas during peak hours.

[0018] c. Time window priority model and parking space allocation, the specific steps are as follows: c-1. Receive user reservation information, including estimated arrival time and parking duration; c-2. Generate a time window priority queue based on real-time parking space status and user reservation information; c-3. Dynamically adjust the queue order and allocate the optimal parking space based on the user's actual arrival time.

[0019] The specific implementation of step a-1 above is as follows: a-1-1. The pressure sensor is installed at the center of the parking space floor, fixed with bolts and kept horizontal to the ground; a-1-2. The infrared sensor is installed on the pillars on both sides of the parking space, at a height of 1.5 meters and tilted downwards at an angle of 45°; a-1-3. The sensor and the wireless communication module are connected through an RS485 interface, and the communication module uses LoRa technology to transmit data to the central processing unit.

[0020] The specific implementation of step b-2 above is as follows: b-2-1. The dynamic zoning algorithm counts the number of vacant parking spaces in each area at 15-minute intervals; b-2-2. If the proportion of vacant parking spaces in a certain area is less than 20%, its functional attribute is switched to temporary parking area; b-2-3. If the proportion of vacant parking spaces in a certain area is greater than 50%, its functional attribute is switched to long-term parking area or reserved area.

[0021] The specific implementation of step c-3 above is as follows: c-3-1. The time window priority model generates an initial queue based on the user's reservation time, and prioritizes allocating parking spaces closest to the entrance; c-3-2. If the user's actual arrival time is earlier than the reservation time, the user's priority is raised to the top of the queue; c-3-3. If the user's actual arrival time is more than 15 minutes later than the reservation time, a parking space is reassigned and the user is notified to update the navigation route.

[0022] The electronic identification devices involved in the above steps include LED displays and RFID tag readers, and their specific installation methods are as follows:

[0023] 1. The LED display screen is installed above the entrance of each area, fixed with a metal bracket, at a height of 2.5 meters;

[0024] 2. The RFID tag reader is installed at the entrance of the parking space, at a height of 1 meter from the ground, with the angle tilted inward at 30°;

[0025] 3. The electronic identification device is connected to the central processing unit via an RS232 interface to receive dynamic partitioning instructions and perform function switching.

[0026] The dynamic partitioning algorithm of this invention is implemented through a cloud computing platform, supporting multi-threaded concurrent processing, with a single calculation taking no more than 1 second. The time window priority model combines user behavior data and historical records to generate personalized allocation schemes. After six consecutive months of real-world application testing, parking lot resource utilization during peak hours increased to over 90%, and the average user waiting time was reduced to less than 5 minutes.

[0027] This invention addresses the shortcomings of traditional intelligent scheduling technologies in terms of dynamic adaptability, user demand matching, and flexibility by combining real-time data acquisition, dynamic partitioning algorithms, and time window priority models. The system operates without manual intervention; all operations are completed through automated processes, making it suitable for parking lots of different sizes and shapes. (See attached figures.)

[0028] Figure 1 This is a schematic diagram of the system architecture of the intelligent parking space utilization method of the present invention, showing the connection relationship and data flow direction between the sensor module, data processing module, user interaction module, dynamic partition control module, and time window priority model.

[0029] Figure 2 This is a schematic diagram of the dynamic zoning and function switching of the parking lot, showing the layout of the parking lot divided into temporary parking areas, long-term parking areas and reserved areas, as well as the installation location and function switching status of electronic signage devices at the entrance of each area.

[0030] Figure 3 The flowchart of the time window priority model describes the complete process from receiving user reservation information and generating queues to allocating parking spaces, and demonstrates the logic for dynamically adjusting the queue order.

[0031] The attached diagram is labeled as follows: 1. Sensor module; 2. Data processing module; 3. User interaction module; 4. Dynamic zoning control module; 5. Time window priority model; 6. Electronic identification device; 7. Temporary parking area; 8. Long-term parking area; 9. Reserved area. Detailed implementation method.

[0032] This invention provides an intelligent parking space utilization method and system, the core of which lies in achieving efficient utilization of parking resources through the collaborative work of sensor networks, dynamic zoning algorithms, and time window priority models. The following is in conjunction with the appendix... Figure 1 To be continued Figure 3 The specific embodiments of the present invention will be described in detail with reference to the component numbers marked in the accompanying drawings.

[0033] like Figure 1 As shown, the system architecture of this invention includes a sensor module 1, a data processing module 2, a user interaction module 3, a dynamic zoning control module 4, and a time window priority model 5. These modules are connected via data flow to form a complete closed-loop control system. Sensor module 1 is responsible for collecting parking space status information, data processing module 2 receives and processes this data, dynamic zoning control module 4 adjusts the functional attributes of the area based on the processing results, time window priority model 5 generates the optimal parking space allocation scheme, and user interaction module 3 provides navigation and allocation information to the user. The operation of the entire system relies on the coordination of the central processing unit, and all modules are connected to the central processing unit via wireless communication or wired interfaces.

[0034] In actual deployment, sensor module 1 consists of multiple distributed nodes, each installed in a single parking space to monitor the parking space occupancy status in real time. For example... Figure 1 As shown, the pressure sensor in sensor module 1 is fixed at the center of the parking space floor, bolted to ensure horizontal installation and detection accuracy. The infrared sensors are mounted on the pillars on both sides of the parking space, 1.5 meters high and tilted downwards at a 45° angle to accurately detect vehicle entry and exit. The sensors are connected to the wireless communication module via an RS485 interface, using LoRa technology to transmit data to the central processing unit. This arrangement not only reduces data transmission latency but also ensures wide and stable signal coverage.

[0035] After receiving the data uploaded by sensor module 1, data processing module 2 first cleans, classifies, and stores the data to form a real-time parking space status database. This database contains information such as the occupancy status, idle time, and historical usage records for each parking space. Based on this data, data processing module 2 calls a dynamic partitioning algorithm to statistically analyze the number of vacant parking spaces and occupancy trends in each area at 15-minute intervals. For example, when the vacancy rate of a certain area is below 20%, data processing module 2 generates an instruction to switch the area's functional attribute to temporary parking area 7; if the vacancy rate is above 50%, it switches it to long-term parking area 8 or reserved area 9. This process is as follows: Figure 2 As shown, the electronic identification device 6 displays the current functional attributes at the entrance of each area. The LED display screen is installed 2.5 meters above the entrance and fixed by a metal bracket. The RFID tag reader is installed 1 meter above the parking space entrance at an inward angle of 30°. Both are connected to the central processor via an RS232 interface to receive and execute dynamic zoning instructions.

[0036] The core of the dynamic zoning control module 4 is the dynamic zoning algorithm, which is implemented through a cloud computing platform, supports multi-threaded concurrent processing, and a single calculation takes no more than 1 second. The operating logic of the dynamic zoning algorithm is as follows: First, the number of vacant parking spaces in each area is counted based on the real-time parking space status database; second, historical data is used to predict changes in demand over a future period; finally, the functional attributes of the areas are adjusted according to preset rules. For example, during peak hours, the system automatically increases the proportion of temporary parking areas 7 to meet short-term parking demand; while during off-peak hours, the proportion of long-term parking areas 8 is appropriately increased to improve resource utilization. In addition, the dynamic zoning control module 4 also dynamically switches the functional attributes of virtual areas through electronic identification devices 6 to ensure that users can obtain the latest information in a timely manner.

[0037] Time window priority model 5 is an important component of this invention, and its process is as follows: Figure 3 As shown in the diagram, the model first receives user reservation information, including estimated arrival time and parking duration, and stores it in a queue. Based on real-time parking availability and user reservation information, the time window priority model 5 generates an initial queue, prioritizing the allocation of parking spaces closest to the entrance. If a user's actual arrival time is earlier than the reserved time, the user's priority is raised to the top of the queue; if the user's actual arrival time is more than 15 minutes later than the reserved time, a parking space is reassigned and the user is notified to update their navigation route. This mechanism not only improves the flexibility of parking space allocation but also effectively reduces user waiting time.

[0038] In a real-world application scenario, suppose a parking lot has 100 parking spaces, and traffic flow is heavy during peak hours. At this time, sensor module 1 collects real-time data on parking space occupancy and uploads it to data processing module 2. Data processing module 2 analyzes the data and finds that the vacancy rate of temporary parking area 7 has dropped to 15%, while long-term parking area 8 still has 40% vacancy. Based on this, dynamic zoning control module 4 generates instructions to switch some long-term parking area 8 to temporary parking area 7, and simultaneously updates the displayed information through electronic signage device 6. Meanwhile, time window priority model 5 generates a queue based on user reservation information and dynamically adjusts the allocation scheme according to the actual arrival time. For example, user A expects to arrive in 10 minutes but arrives 5 minutes early due to traffic congestion. Time window priority model 5 immediately increases user A's priority and allocates the nearest vacant parking space to them. The entire process requires no manual intervention; all operations are completed through automated processes.

[0039] Furthermore, this invention also relates to specific details of hardware deployment. The pressure sensor and infrared sensor in sensor module 1 require regular calibration to ensure detection accuracy. The wireless communication module uses LoRa technology, with a transmission distance of up to several kilometers, suitable for large parking lot scenarios. The central processing unit is deployed in the parking lot management center and connected to a cloud computing platform via a fiber optic network to achieve high-speed data processing and algorithm execution. The LED display screen and RFID tag reader / writer of electronic identification device 6 must be waterproof and dustproof to adapt to outdoor environments. The algorithm code for dynamic zoning control module 4 and time window priority model 5 is stored in the central processing unit and can be maintained and optimized through remote upgrades.

[0040] After six months of continuous practical application testing, this invention has improved parking lot resource utilization to over 90% during peak hours, and reduced the average user waiting time to less than 5 minutes. This achievement is attributed to the close integration of real-time data acquisition, dynamic zoning algorithms, and a time window priority model. The system operates without manual intervention; all operations are completed through automated processes, making it suitable for parking lot scenarios of different sizes and shapes. To better enable those skilled in the art to fully understand and implement this invention, the following supplementary explanation of the specific implementation principles is provided using a specific application scenario.

[0041] In practical applications, suppose a large commercial complex parking lot has 100 parking spaces, and traffic flow is high during peak hours. At this time, sensor module 1 begins to collect real-time data on the occupancy status of each parking space and uploads the detected data to data processing module 2. For example... Figure 1As shown, the pressure sensor in sensor module 1 is bolted to the center of the parking space floor, while the infrared sensors are mounted on the pillars on both sides of the parking space at a height of 1.5 meters and tilted downwards at a 45° angle to ensure accurate detection of vehicle entry and exit. The sensors are connected to the wireless communication module via an RS485 interface, using LoRa technology to transmit data to the central processing unit. This arrangement not only reduces data transmission latency but also ensures wide and stable signal coverage. Based on this hardware configuration, the system can monitor the parking space occupancy status in real time, providing a reliable data foundation for subsequent dynamic scheduling.

[0042] After receiving the data uploaded by sensor module 1, data processing module 2 first cleans, classifies, and stores the data to form a real-time parking space status database. This database contains information such as the occupancy status, idle time, and historical usage records for each parking space. Subsequently, data processing module 2 calls a dynamic partitioning algorithm to statistically analyze the number of vacant parking spaces and occupancy trends in each area at 15-minute intervals. For example, when it is found that the vacancy rate of temporary parking area 7 has dropped to 15%, while long-term parking area 8 still has 40% vacancy, data processing module 2 generates an instruction to switch part of long-term parking area 8 to temporary parking area 7. This process is as follows: Figure 2 As shown, electronic identification device 6 displays the current functional attributes at the entrance of each area. An LED display screen is installed 2.5 meters above the entrance and fixed with a metal bracket. An RFID tag reader / writer is installed 1 meter above the parking space entrance, tilted inwards at a 30° angle. Both are connected to the central processor via an RS232 interface to receive and execute dynamic zoning commands. Through these steps, the system achieves the goal of dynamically adjusting the functional attributes of areas according to real-time needs, ensuring more efficient resource allocation.

[0043] Meanwhile, time window priority model 5 generates queues based on user reservation information and dynamically adjusts the allocation scheme according to the actual arrival time. For example... Figure 3 As shown, Time Window Priority Model 5 first receives user reservation information, including estimated arrival time and parking duration, and stores it in a queue. Based on real-time parking availability and user reservation information, Time Window Priority Model 5 generates an initial queue, prioritizing the allocation of parking spaces closest to the entrance. If a user's actual arrival time is earlier than the reserved time, the user's priority is raised to the top of the queue; if the user's actual arrival time is more than 15 minutes later than the reserved time, a parking space is reassigned and the user is notified to update their navigation route. For example, User A is expected to arrive in 10 minutes but arrives 5 minutes early due to traffic congestion. Time Window Priority Model 5 immediately raises User A's priority and allocates the nearest available parking space to them. Throughout the entire process, the system completes all operations through an automated workflow, requiring no manual intervention.

[0044] In addition, regarding hardware deployment, the pressure sensor and infrared sensor in sensor module 1 need to be calibrated regularly to ensure detection accuracy. The wireless communication module adopts LoRa technology, with a transmission distance of up to several kilometers, suitable for large parking lot scenarios. The central processing unit is deployed in the parking lot management center and connected to the cloud computing platform via a fiber optic network to achieve high-speed data processing and algorithm execution. The LED display screen and RFID tag reader / writer of electronic identification device 6 need to be waterproof and dustproof to adapt to outdoor environments. The algorithm code of dynamic zoning control module 4 and time window priority model 5 is stored in the central processing unit and can be maintained and optimized through remote upgrades.

[0045] After six months of continuous practical application testing, this invention has increased parking lot resource utilization to over 90% during peak hours, and reduced the average user waiting time to less than 5 minutes. This achievement is attributed to the close integration of real-time data acquisition, dynamic zoning algorithms, and time window priority models. For example, the dynamic zoning algorithm is implemented through a cloud computing platform, supporting multi-threaded concurrent processing, with a single calculation taking no more than 1 second. Simultaneously, the time window priority model combines user behavior data and historical records to generate personalized allocation schemes. The system operates without manual intervention; all operations are completed through automated processes, making it suitable for parking lot scenarios of different sizes and shapes.

[0046] Through the implementation of the above specific application scenarios, this invention effectively solves the shortcomings of traditional intelligent scheduling technology in terms of dynamic adaptability, user demand matching and flexibility, and significantly improves parking lot resource utilization and user experience.

Claims

1. A method for intelligently scheduling parking space utilization, characterized in that... The method includes the following steps: a. Parking space status monitoring and data collection, the specific steps of which are a-1. Installing pressure sensors or infrared sensors in each parking space to detect whether the parking space is occupied a-2. Uploading the data collected by the sensors to the central processing unit through the wireless communication module a-3. The central processing unit cleans and classifies the received data and stores it to form a real-time parking space status database b. Dynamic zoning and function switching, the specific steps of which are b-1. Statistically calculating the number of vacant parking spaces and the occupancy trend in each area according to the real-time parking space status database b-2. Dividing the parking lot into temporary parking area (7), long-term parking area (8) and reserved area (9) using a dynamic zoning algorithm b-3. Switching the functional attributes of each area through electronic identification device (6) c. Time window priority model and parking space allocation, the specific steps of which are c-1. Receiving user reservation information, including the expected arrival time and parking duration c-2. Generating a time window priority queue according to the real-time parking space status and user reservation information c-3. Dynamically adjusting the queue order and allocating the optimal parking space based on the user's actual arrival time.

2. The intelligent scheduling method for parking space utilization according to claim 1, characterized in that... The specific implementation method of step a-1 is as follows: a-1-1. The pressure sensor is installed at the center of the parking space floor and fixed with bolts to keep it horizontal to the ground. a-1-2. The infrared sensor is installed on the pillars on both sides of the parking space at a height of 1.5 meters and tilted downward at an angle of 45 degrees. a-1-3. The sensor and the wireless communication module are connected through an RS485 interface, and the communication module uses LoRa technology to transmit data to the central processing unit.

3. The intelligent scheduling method for parking space utilization according to claim 1, characterized in that... The specific implementation of step b-2 is as follows: b-2-1. The dynamic partitioning algorithm counts the number of vacant parking spaces in each area at 15-minute intervals. b-2-2. If the proportion of vacant parking spaces in a certain area is less than 20%, its functional attribute is switched to temporary parking area (7). b-2-3. If the proportion of vacant parking spaces in a certain area is higher than 50%, its functional attribute is switched to long-term parking area (8) or reserved area (9).

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