Community shared parking management system and method based on Internet of Things and artificial intelligence

Abstract

The invention provides a community shared parking management system and method based on Internet of Things and artificial intelligence, relates to the technical field of intelligent traffic management, and solves the problem of various limitations in community scale shared parking management. In the system, a sensing layer acquires parking space state information, vehicle characteristic information and environment parameter information in real time through multiple types of sensing devices deployed in parking spaces, and generates sensing data; the edge computing layer receives sensing data through an edge computing node, and carries out local data processing, equipment control instruction generation, abnormal state detection and local data caching; the cloud platform layer receives and integrates data from the edge computing layer through a cloud computing platform, and executes parking resource scheduling decision, user behavior analysis and demand prediction, user account management and payment settlement processing. The vehicle and parking space monitoring accuracy can be improved, and the parking experience, the parking space utilization rate and the parking lot passing efficiency of community users are smoothly improved.

Description

Technical Field

[0001] This invention relates to the field of intelligent traffic management technology, specifically to a community shared parking management system and method based on the Internet of Things and artificial intelligence. Background Technology

[0002] With the continued advancement of global urbanization and the significant increase in the number of motor vehicles owned by residents, urban static traffic management, especially the management of community parking resources, faces increasingly severe pressure. Currently, many large cities in my country suffer from a severe shortage of parking spaces. Statistics reveal that the average ratio of motor vehicles to parking spaces has fallen to approximately 1:0.8, with a nationwide parking space shortage exceeding 80 million. This contradiction is particularly acute in older communities. On the one hand, parking resources in these areas are often under-allocated and underutilized. Data shows that the vacancy rate of parking spaces can reach as high as 40% at night, resulting in a serious waste of resources. On the other hand, during peak hours, congestion at parking lot entrances and exits is frequent, with drivers waiting an average of more than 15 minutes to enter, significantly reducing traffic efficiency and impacting residents' quality of life. Meanwhile, the traditional manual management model is inefficient and costly, with the annual manpower cost for a single parking lot potentially exceeding 200,000 yuan. Drivers also spend a significant amount of time searching for available parking spaces, averaging 10 to 15 minutes, highlighting the urgent need to improve the overall user experience.

[0003] To address the "parking difficulty" problem, the concept of shared parking and related systems have emerged, aiming to revitalize existing parking resources. However, existing shared parking systems still reveal numerous technical bottlenecks in practical applications. They lack accurate, real-time sensing capabilities for parking space occupancy, with a common detection error rate exceeding 5%, making it difficult to support refined management. When coordinating and controlling multiple devices such as barriers, guidance screens, parking locks, and payment terminals, the existing systems have weak collaborative control mechanisms, resulting in a high failure rate for device linkage and impacting process smoothness. The systems are highly dependent on the network; in the event of unstable or interrupted network signals, core functions are almost paralyzed, indicating insufficient fault tolerance and an inability to guarantee service continuity. The service model is relatively simplistic, failing to conduct in-depth analysis based on user behavior data to provide personalized recommendations or services, leading to generally low user satisfaction. The value of the massive amounts of parking data collected by the system has not been effectively mined and utilized, with insufficient data utilization, failing to fully leverage the role of data in optimizing operations and decision-making.

[0004] These technological shortcomings are even more pronounced in community-scale applications. Community parking lots are typically characterized by their relatively small size, scattered spatial distribution, and relatively fixed but highly volatile user groups, with significant differences between day and night, weekdays and holidays. These characteristics necessitate parking management systems that are more adaptable and targeted. Furthermore, the community environment introduces unique challenges: limited and complex physical space for deploying sensing and control devices; potentially unstable or missing wireless network coverage; a wide age range of users with varying acceptance and operational capabilities for intelligent services; and extremely high requirements for vehicle and personnel safety management. Therefore, in urban renewal projects such as the renovation of old residential areas, the construction of new smart communities, and parking facilities for commercial complexes, traditional, general-purpose parking management systems are increasingly unable to adapt to these complex environmental characteristics and diverse user needs. There is an urgent need for a new type of parking management system that can effectively integrate new technologies, overcome the aforementioned shortcomings, and is highly adaptable to the characteristics of community scenarios, in order to achieve refined, intelligent management and efficient utilization of community parking resources. Summary of the Invention

[0005] The purpose of this invention is to address the problems in community-scale shared parking management, such as insufficient real-time accuracy of parking space status perception, low reliability of multi-device collaborative control, functional unavailability under network anomalies, lack of personalized services, and excessively high operating costs. Therefore, it proposes a community shared parking management system and method based on the Internet of Things (IoT) and artificial intelligence (AI). This invention, targeting the community scale, achieves dynamic resource allocation through a multi-layered collaborative parking resource management architecture, improving the accuracy of vehicle and parking space monitoring, and successfully enhancing the parking experience for community users, parking space utilization, and parking lot traffic efficiency.

[0006] The present invention employs the following technical solutions to achieve its objective: A community shared parking management system based on the Internet of Things and artificial intelligence, the system comprising the following hierarchical architecture: The perception layer is used to collect parking space status information, vehicle characteristic information, and environmental parameter information in real time through various types of perception devices deployed in parking spaces, and generate perception data. The edge computing layer is used to receive the sensed data through edge computing nodes deployed in the community, perform local data processing, generate device control commands, detect abnormal states and cache local data, and maintain basic operating functions when the network is abnormal. The cloud platform layer is used to receive and integrate data from the edge computing layer through the cloud computing platform, and to perform parking resource scheduling decisions based on artificial intelligence algorithms, user behavior analysis and demand prediction, user account management and payment settlement processing. The perception layer, the edge computing layer, and the cloud platform layer all use standardized interfaces for their respective communication connections and data interactions.

[0007] Preferably, the sensing layer includes: a geomagnetic sensor for detecting the occupancy status of parking spaces; a video detection device for collecting vehicle features and environmental image information; a liftable parking lock for physically controlling parking space occupancy; and an environmental sensor for monitoring environmental parameters. The geomagnetic sensor, the video detection device, the liftable ground lock device, and the environmental sensor are all connected to the edge computing layer via wired or wireless communication.

[0008] Furthermore, the edge computing layer is configured to: synchronize the processed sensing data and device status information to the cloud platform layer when the network connection is normal; perform local data storage operations when the network connection is abnormal, and cache sensing data and operation instructions for a preset time period in the local storage space; and perform data synchronization operations between the cached data and the cloud platform layer after the network connection is restored, and perform data consistency verification.

[0009] Specifically, the basic operational functions maintained by the edge computing layer when the network connection is abnormal include: continuing to generate parking space status information based on locally cached perception data; executing basic control commands for the liftable parking lock device in the perception layer; and maintaining the ability to respond locally to user reservation requests.

[0010] Preferably, the cloud platform layer executes parking resource scheduling decisions based on artificial intelligence algorithms, specifically including: training a user behavior model based on historical and real-time data and generating a user profile; generating a parking space allocation scheme using a multi-objective optimization algorithm based on user reservation requests, real-time parking space status, and the user profile; and generating a parking demand prediction result for the future time period based on a cloud-preset prediction model.

[0011] Specifically, the cloud platform layer is also configured to: generate a dynamic navigation path for users from the entrance to the assigned parking space based on real-time traffic information and the internal structure of the parking lot; and provide users with service interfaces for parking reservation, status inquiry and navigation guidance via mobile terminals.

[0012] Preferably, the system further includes: an identity authentication module for verifying the identity of resident users in the community and authorizing them to use the exclusive community channel; a visitor management module for generating and managing temporary access permissions for visitors; an electric vehicle service module for coordinating the allocation of parking spaces and charging piles and providing charging services to electric vehicle users; and a system integration interface for data interaction and functional linkage with the community security system and access control system.

[0013] Preferably, the system employs encryption algorithms to protect data during data transmission; during data storage, it desensitizes sensitive information involving user privacy based on a preset privacy type definition; during access control, it uses a multi-factor authentication mechanism to verify user identity; and during operation record management, it uses distributed ledger technology to prevent key operation records from being temporarily tampered with.

[0014] Specifically, the edge computing node is an industrial server with redundant power supply configuration and local storage array protection components; the perception layer is equipped with a liftable ground lock device in the form of an electromechanical integrated device; the perception layer is also equipped with a video detection device in the form of an embedded device with low-light enhancement components and acquisition and analysis components.

[0015] This invention also provides a parking management method based on the aforementioned community shared parking management system, the method comprising the following steps: S1. The occupancy status of parking spaces, vehicle characteristics and environmental parameters are collected by the sensing devices deployed in the parking spaces and transmitted to the edge computing nodes for processing to generate real-time parking space status data. S2. Receive and process real-time parking space status data at the edge computing node, generate equipment control commands, perform local anomaly detection, and synchronize the processing results to the cloud computing platform when the network connection is normal; when the network connection is abnormal, cache real-time parking space status data and equipment control commands locally at the edge computing node, and maintain basic parking space status updates and equipment control functions. S3. Receive and integrate data from edge computing nodes on the cloud computing platform, analyze user behavior and predict parking demand based on artificial intelligence algorithms, and use optimization algorithms to generate parking space resource allocation schemes and scheduling instructions. S4. Based on the parking space resource allocation plan and scheduling instructions, provide users with reservation, navigation, and payment service interfaces, and send control instructions to edge computing nodes to execute parking space allocation, guide users, and control physical equipment; S5. Perform data interaction and functional linkage operations with the community intelligent system, and perform secure storage, access control and privacy protection processing on the data generated during system operation.

[0016] In summary, due to the adoption of this technical solution, the beneficial effects of this invention are as follows: This invention demonstrates significant effectiveness in improving management efficiency. Through intelligent management methods, it achieves highly efficient utilization of parking space resources, with parking space utilization nearly doubling. Simultaneously, it significantly reduces manual management costs, saving considerable human resources. The time vehicles spend searching for available parking spaces is greatly shortened, improving traffic flow. Furthermore, the overall turnover rate of the parking lot is significantly increased, effectively alleviating congestion during peak hours and allowing limited parking resources to serve more vehicles.

[0017] The key equipment of this invention maintains an extremely high online rate, far exceeding industry standard requirements. Even in the event of unstable or interrupted network signals, the core functions of the system can still maintain continuous and stable operation, ensuring uninterrupted service. The system maintains a high level of accuracy in collecting key data such as parking space status, and the false alarm rate is effectively controlled. User operation commands are responded to quickly, with a short average response time, improving the system's smoothness and timeliness.

[0018] After using this invention, users maintained a high success rate in reserving parking spaces, exceeding expectations. The payment process was significantly simplified, with fewer steps, improved ease of use, and a substantial reduction in the time required for users to complete payments. Overall user satisfaction was high, and service quality was substantially improved.

[0019] The application of this invention allows for cost recovery in a relatively short time, with a shorter-than-expected investment payback period. The annual revenue generated per parking space is significantly increased, boosting operating income; the system's operation and maintenance costs are also significantly reduced. Attached Figure Description

[0020] The present invention is further described in detail with reference to the following figures, which include four figures as follows: Figure 1 This is a schematic diagram of the hierarchical architecture of the community shared parking management system of the present invention; Figure 2 This is a schematic diagram of a preferred component device of the sensing layer in the system of the present invention; Figure 3 This is a functional diagram of a preferred user service-related module in the system of the present invention; Figure 4 This is a schematic diagram illustrating the overall process of the community shared parking management method of the present invention. Detailed Implementation

[0021] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. The parts of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0022] Therefore, the following detailed description of the embodiments of the invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the invention without inventive effort are within the scope of protection of the invention.

[0023] Example 1 A community-based shared parking management system based on the Internet of Things and artificial intelligence. The system's hierarchical architecture can be found here. Figure 1 The illustration specifically includes: The perception layer is used to collect parking space status information, vehicle characteristic information, and environmental parameter information in real time through various types of perception devices deployed in parking spaces, and generate perception data. The edge computing layer is used to receive sensing data through edge computing nodes deployed in the community, perform local data processing, generate device control commands, detect abnormal states and cache local data, and maintain basic operating functions when the network is abnormal. The cloud platform layer is used to receive and integrate data from the edge computing layer through the cloud computing platform, and to perform parking resource scheduling decisions based on artificial intelligence algorithms, user behavior analysis and demand forecasting, user account management and payment settlement processing. The perception layer, edge computing layer, and cloud platform layer all use standardized interfaces for their respective communication connections and data interactions.

[0024] As a preferred embodiment, such as Figure 2 As shown, the perception layer includes: a geomagnetic sensor for detecting the occupancy status of parking spaces; a video detection device for collecting vehicle features and environmental image information; a liftable parking lock for physically controlling parking space occupancy; and an environmental sensor for monitoring environmental parameters.

[0025] Among them, the geomagnetic sensing device, video detection device, liftable ground lock device, and environmental sensing device are all connected to the edge computing layer via wired or wireless communication.

[0026] In this embodiment, the geomagnetic sensing device employs triaxial magnetoresistive technology, possessing high sensitivity for detecting magnetic field changes and accurately identifying disturbances to the geomagnetic field caused by the vehicle chassis's metal structure. To adapt to different seasons and weather conditions, the geomagnetic sensing device incorporates a temperature compensation mechanism to ensure stable measurement accuracy. Its power consumption is designed according to actual constraints, and combined with existing battery technology, it can support continuous operation for extended periods.

[0027] The video detection device can employ high-resolution imaging sensors, such as a four-megapixel image sensor, which perfectly meets the requirements, thus supporting relevant video encoding formats and reducing bandwidth consumption. To cope with nighttime or low-light environments, the device can integrate existing mature low-light enhancement technologies, enabling it to operate under extremely low light conditions. The acquired video data is not only used to confirm parking space occupancy status but also supports vehicle feature recognition, including the extraction of license plate numbers, vehicle models, and vehicle colors, providing richer information for subsequent management and scheduling.

[0028] The liftable parking lock is made of robust and durable materials, and its lifting action is designed with time constraints to ensure that it can complete unlocking or locking operations in a short time. The lock supports multiple communication protocols, allowing it to reliably receive control commands and provide status feedback via wired or wireless means. Since the lock is used directly in the parking lot environment, it is configured with a high level of protection and wide temperature range adaptability.

[0029] Environmental sensors collect real-time environmental data such as temperature, humidity, light intensity, and air quality around the parking space. This data not only helps in understanding the operating environment of the equipment but also assists in assessing the reliability of the sensing data. For example, it serves as a basis for adjusting the sensitivity of relevant detection algorithms in rainy or snowy weather and provides users with more comprehensive data services.

[0030] In the perception layer, various sensing and control devices can work collaboratively. When a geomagnetic sensor detects a change in the magnetic field, it can trigger a video detection device for verification, reducing false alarms. Raw data or pre-processed information collected by each device in the perception layer is transmitted to edge computing nodes located in the community via standardized and mature wired or wireless communication links. Wired communication links can use technologies such as Ethernet or CAN bus, while wireless connections can utilize cellular IoT technologies such as 4G, 5G, or NB-IoT to adapt to different deployment environments and wiring conditions. Edge computing nodes, as a key component of the edge computing layer, are responsible for receiving, pre-processing, and integrating these data streams from the perception layer, providing basic input for subsequent intelligent decision-making.

[0031] In this embodiment, the edge computing layer is configured to: synchronize the processed sensing data and device status information to the cloud platform layer when the network connection is normal; perform local data storage operation when the network connection is abnormal, and cache the sensing data and operation instructions for a preset time period in the local storage space; and perform data synchronization operation between the cached data and the cloud platform layer after the network connection is restored, and perform data consistency verification.

[0032] The basic operational functions maintained by the edge computing layer when network connectivity is abnormal include: continuing to generate parking space status information based on locally cached perception data; executing basic control commands for the liftable parking lock device in the perception layer; and maintaining the ability to respond locally to user reservation requests.

[0033] In this embodiment, the edge computing layer is deployed on-site in the community, typically using industrial-grade server equipment with certain computing and storage capabilities. This layer is responsible for data preprocessing, localized control, and network fault tolerance. When the network connection is good, i.e., the communication link with the cloud platform layer is unobstructed, the edge computing layer performs preliminary processing on the raw data received from the sensing layer, including filtering invalid data, making preliminary status judgments, i.e., parking space occupancy / vacancy, and synchronously uploading the processed sensing data and the status information of the sensing layer devices to the cloud platform layer in real time to ensure that the cloud has the latest on-site status.

[0034] When a network connection anomaly or interruption is detected, the edge computing layer immediately activates its fault tolerance mechanism. The core operation involves local data storage, caching the received sensing data and control commands to be sent to the sensing layer devices, primarily the raising and lowering commands for the ground locks, into their local storage space. This local storage space is configured with sufficient capacity to support caching data volumes preset for several hours or even days. In this scenario, the edge computing layer does not completely cease operation but continues to maintain basic system functionality by relying on the locally cached data and pre-defined logic.

[0035] The maintained basic operational capabilities include continuing to generate and update local parking space status information based on recently cached parking space sensing data, ensuring relatively accurate information when users query or display it locally. Simultaneously, it continues to execute basic control commands for the liftable parking locks in the sensing layer, responding to locally cached or newly received but not yet executed lifting commands. Furthermore, for new reservation requests initiated by users within the community via mobile applications, the edge computing layer can perform preliminary responses and processing based on locally stored parking space status information during network outages, maintaining limited reservation service capabilities until full synchronization with the cloud is achieved after network recovery.

[0036] Once network connectivity is restored, the edge computing layer proactively performs data synchronization, uploading the sensor data and operation commands cached locally during the network outage to the cloud platform layer. To ensure data consistency and integrity, the system performs a data consistency check after synchronization. This check involves comparing the key event sequences or state snapshots recorded by the edge computing layer and the cloud platform layer during the network outage. A hash-based verification algorithm is used to identify and correct any discrepancies or conflicts, ensuring that the cloud platform ultimately possesses complete and accurate data records, and that the system state is restored to consistency.

[0037] As a preferred embodiment, the cloud platform layer executes parking resource scheduling decisions based on artificial intelligence algorithms, specifically including: training a user behavior model based on historical and real-time data and generating a user profile; generating a parking space allocation scheme using a multi-objective optimization algorithm based on user reservation requests, real-time parking space status, and user profiles; and generating parking demand prediction results for future time periods based on a cloud-preset prediction model.

[0038] The cloud platform layer is also configured to: generate a dynamic navigation path for users from the entrance to the assigned parking space based on real-time traffic information and the internal structure of the parking lot; and provide users with service interfaces for parking reservation, status inquiry and navigation guidance via mobile terminals.

[0039] In this embodiment, the cloud platform layer, serving as the core decision-making and service hub of the system, is deployed on a remote cloud computing infrastructure. One of its core functions is to execute parking resource scheduling decisions based on artificial intelligence algorithms. The cloud computing platform continuously collects and integrates historical parking data from the edge computing layer, including parking space occupancy periods and user parking durations, while also collecting real-time status information. Using this data, the platform trains and continuously optimizes a user behavior model. This model can analyze users' parking habits, time preferences, and frequently used parking space areas, thereby building a refined user profile for each user and providing a data foundation for personalized services. The technology for constructing and characterizing user profiles is already a mature technology in the field and can be directly loaded and used according to actual needs; therefore, it will not be elaborated upon in this embodiment.

[0040] When a user submits a parking reservation request via a mobile application, the cloud platform layer combines real-time parking space status information, including the location and type of available spaces, with the user's profile information, and invokes its built-in multi-objective optimization algorithm. This algorithm comprehensively considers multiple factors, such as minimizing the user's walking distance to improve convenience, maximizing overall parking space utilization to avoid resource waste, and balancing the fairness of allocation for users with different priorities, ultimately generating an optimal or near-optimal parking space allocation scheme.

[0041] Meanwhile, the cloud platform layer also runs a demand forecasting model based on time series forecasting technology, which can be configured as a mature long short-term memory network model in the field. This model can analyze historical patterns, current trends, and upcoming events, such as holidays, to generate forecasts of parking demand in various areas of the community over a period of time, thereby predicting parking conditions for the next few hours to days and providing a basis for dynamic resource allocation and contingency planning.

[0042] In this embodiment, the cloud platform layer also undertakes the function of providing services directly to users. After a user successfully reserves a parking space, the platform combines real-time traffic information around the parking lot with a detailed structural layout diagram of the parking lot, focusing on factors such as lanes, entrances / exits, and elevator locations, to plan an optimal dynamic navigation route for the user from the parking lot entrance to their assigned parking space, and provides clear guidance through the user's mobile application interface. In addition, the platform also provides a complete set of user service interfaces, which can be integrated into the user's mobile terminal application. These interfaces assist users in reserving parking spaces, checking parking space status in real time, receiving and following navigation instructions, completing seamless or online payment of parking fees, and viewing historical orders and electronic invoices. Through these service interfaces, a smooth and convenient one-stop parking service experience can be provided to users.

[0043] As a preferred embodiment, such as Figure 3 As shown, with the support of the cloud platform layer, the system also includes the following preferred modules: an identity authentication module for verifying the identity of resident users in the community and authorizing them to use the exclusive community channel; a visitor management module for generating and managing temporary access permissions for visitors; an electric vehicle service module for coordinating the allocation of parking spaces and charging piles and providing charging services to electric vehicle users; and a system integration interface for data interaction and functional linkage with the community security system and access control system.

[0044] In this embodiment, to meet the specific needs of a community setting, the identity authentication module is designed specifically for permanent residents. This module verifies user identities by interfacing with the community's existing resident database or an independent identity verification system. Once verification is successful, the system authorizes the user to use a reserved community access point, such as a fast-access gate or a parking space in a specific residential area. This identity recognition can be based on various mature methods, such as mobile terminal authentication, automatic license plate recognition, or biometric recognition, thereby providing permanent residents with a convenient and efficient entry and exit experience.

[0045] The visitor management module handles temporary parking needs from non-resident users. Residents or property management personnel can initiate invitations through the system to generate temporary, time-limited access permissions for visitors. These permissions can be issued to visitors in the form of QR codes, digital keys, or one-time license plate authorization codes. The system manages the validity period of these permissions and synchronizes them in real-time with edge computing and sensing layer devices during the validity period to control parking lot entrance and exit gates and parking space locks, ensuring that visitor vehicles can smoothly enter and park in designated spaces. Permissions automatically expire upon their expiration, ensuring the safety of the community environment.

[0046] For the growing number of electric vehicle users, the core function of the electric vehicle service module is to coordinate the allocation of parking space and charging station resources. When an electric vehicle user reserves a parking space, the system prioritizes or automatically allocates a space equipped with a charging station. The module can monitor the availability and charging progress of charging stations in real time and integrate this information into resource scheduling decisions. After connecting to the cloud platform layer through their mobile terminal application, users can easily start charging, monitor charging status, and complete charging fee settlement. This module provides electric vehicle users with a one-stop parking and charging service.

[0047] The system integration interface defines standardized data formats and communication protocols, enabling seamless integration and data exchange with other intelligent systems within the community. The system can share video surveillance data or alarm information from abnormal events in the parking lot with the community security system; it can link with the access control system to synchronously open or close corresponding access gates when vehicles enter or exit; or it can share parking data with building management systems or energy management systems to support broader community operation optimization. This deep system integration can significantly improve the overall efficiency and intelligence level of community management.

[0048] As a preferred embodiment, the system uses encryption algorithms to protect data during data transmission; during data storage, sensitive information involving user privacy is anonymized based on the definition of preset privacy types; during access control, a multi-factor authentication mechanism is used to verify user identity; and during operation record management, distributed ledger technology is used to prevent key operation records from being temporarily tampered with.

[0049] In this embodiment, the system employs standard-compliant encryption algorithms to protect communication data during data transmission, ensuring information security along the transmission link. For data stored in the system, sensitive information involving user privacy, such as personal identification information and complete license plate numbers, is anonymized based on preset rules, allowing for partial masking or replacement. When users access system services, a multi-factor authentication mechanism is used to verify user identity, enhancing account security. Furthermore, for critical operation records, such as authorization, payment, and permission changes, the system uses mature distributed ledger technology for evidence storage. This technology ensures that records are difficult to temporarily tamper with once generated, improving the transparency and traceability of operations.

[0050] Example 2 Based on Example 1, this example provides an exemplary description of the application of the method and process of the community shared parking management system and the deployment of the system. Figure 4 A brief description of the corresponding parking management method is shown below, which can be viewed concurrently. The hardware basis of this method is the system of Example 1, and its key steps can therefore be summarized as follows: S1. The occupancy status of parking spaces, vehicle characteristics and environmental parameters are collected by the sensing devices deployed in the parking spaces and transmitted to the edge computing nodes for processing to generate real-time parking space status data. S2. Receive and process real-time parking space status data at the edge computing node, generate equipment control commands, perform local anomaly detection, and synchronize the processing results to the cloud computing platform when the network connection is normal; when the network connection is abnormal, cache real-time parking space status data and equipment control commands locally at the edge computing node, and maintain basic parking space status updates and equipment control functions. S3. Receive and integrate data from edge computing nodes on the cloud computing platform, analyze user behavior and predict parking demand based on artificial intelligence algorithms, and use optimization algorithms to generate parking space resource allocation schemes and scheduling instructions. S4. Based on the parking space resource allocation plan and scheduling instructions, provide users with reservation, navigation, and payment service interfaces, and send control instructions to edge computing nodes to execute parking space allocation, guide users, and control physical equipment; S5. Perform data interaction and functional linkage operations with the community intelligent system, and perform secure storage, access control and privacy protection processing on the data generated during system operation.

[0051] Once the above method is applied in the system, it can provide users with one-stop parking services. When the system is deployed and operated in the corresponding parking lot, it can be carried out in multiple stages in sequence, including the hardware deployment stage, the software deployment stage, and the operation and maintenance stage. This embodiment will introduce them in sequence.

[0052] During the hardware deployment phase, the first step involves a 1-2 week site survey and solution design, including detailed measurements of the parking lot's dimensions and structure, assessment of network coverage and equipment installation conditions, and development of a customized deployment plan. This is followed by a 2-3 week equipment selection and procurement phase, where compatible equipment models are chosen based on site requirements, and spare parts are prepared at the parking lot management office. Next, a 3-4 week installation, commissioning, and network testing phase takes place, with equipment installed gradually by area, single-point and system integration testing completed, and equipment parameter configuration optimized. Finally, a 1-2 week system integration and optimization phase is conducted, including full system functional testing, performance tuning, parameter optimization, and the provision of user training and management briefings.

[0053] During the software deployment phase, the basic environment setup is completed within 3 to 5 days, including server configuration, network settings, database installation and initialization, and security policy configuration. Then, the system is installed and configured within 2 to 3 days, deploying core services, setting business parameters, and completing interface integration testing. Next, data initialization is completed within 1 to 2 days, importing basic data, migrating user data, and starting system trial operation. Finally, functional testing and acceptance testing are conducted within 5 to 7 days, including unit testing, integration testing, stress testing, security testing, and user acceptance testing.

[0054] During the operation and maintenance phase, the system can be monitored 24 / 7 through the perception layer, and equipment inspections and maintenance can be carried out regularly. The system can be tested and restored and data integrity verified at different preset cycles, and the system can be optimized on a quarterly basis. System performance can be improved through version upgrades.

[0055] By applying the method of this embodiment, the community shared parking management system can promote the intelligent transformation of the parking industry, improve the efficiency of urban parking resource utilization, enhance the travel experience of residents, and provide strong support for the construction of smart cities.

Claims

1. A community shared parking management system based on the Internet of Things and artificial intelligence, characterized in that, The system includes the following hierarchical architecture: The perception layer is used to collect parking space status information, vehicle characteristic information, and environmental parameter information in real time through various types of perception devices deployed in parking spaces, and generate perception data. The edge computing layer is used to receive the sensed data through edge computing nodes deployed in the community, perform local data processing, generate device control commands, detect abnormal states and cache local data, and maintain basic operating functions when the network is abnormal. The cloud platform layer is used to receive and integrate data from the edge computing layer through the cloud computing platform, and to perform parking resource scheduling decisions based on artificial intelligence algorithms, user behavior analysis and demand prediction, user account management and payment settlement processing. The perception layer, the edge computing layer, and the cloud platform layer all use standardized interfaces for their respective communication connections and data interactions.

2. The community shared parking management system according to claim 1, characterized in that, The sensing layer includes: a geomagnetic sensor for detecting the occupancy status of parking spaces; a video detection device for collecting vehicle features and environmental image information; a liftable parking lock for physically controlling parking space occupancy; and an environmental sensor for monitoring environmental parameters. The geomagnetic sensor, the video detection device, the liftable ground lock device, and the environmental sensor are all connected to the edge computing layer via wired or wireless communication.

3. The community shared parking management system according to claim 1, characterized in that, The edge computing layer is configured to synchronize the processed sensing data and device status information to the cloud platform layer when the network connection is normal. When the network connection is abnormal, local data storage operation is performed, and the perception data and operation instructions for a preset time period are cached in the local storage space; after the network connection is restored, the cached data is synchronized with the data at the cloud platform layer, and data consistency is verified.

4. The community shared parking management system according to claim 3, characterized in that, The basic operational functions maintained by the edge computing layer when the network connection is abnormal include: continuing to generate parking space status information based on locally cached perception data; executing basic control commands for the liftable parking lock device in the perception layer; and maintaining the ability to respond locally to user reservation requests.

5. The community shared parking management system according to claim 1, characterized in that, The cloud platform layer executes parking resource scheduling decisions based on artificial intelligence algorithms, specifically including: training a user behavior model based on historical and real-time data and generating a user profile; generating a parking space allocation scheme using a multi-objective optimization algorithm based on user reservation requests, real-time parking space status, and the user profile; and generating parking demand prediction results for future time periods based on a cloud-preset prediction model.

6. The community shared parking management system according to claim 5, characterized in that, The cloud platform layer is also configured to: generate a dynamic navigation path for users from the entrance to the assigned parking space based on real-time traffic information and the internal structure of the parking lot; and provide users with service interfaces for parking reservation, status inquiry and navigation guidance via mobile terminals.

7. The community shared parking management system according to claim 1, characterized in that, The system also includes: an identity authentication module for verifying the identity of resident users in the community and authorizing them to use the exclusive community access channel; a visitor management module for generating and managing temporary access permissions for visitors; an electric vehicle service module for coordinating the allocation of parking spaces and charging piles and providing charging services to electric vehicle users; and a system integration interface for data interaction and functional linkage with the community security system and access control system.

8. The community shared parking management system according to claim 1, characterized in that: The system employs encryption algorithms to protect data during data transmission; during data storage, sensitive information involving user privacy is de-identified based on preset privacy type definitions. During access control, a multi-factor authentication mechanism is used to verify user identity; In the process of managing operation records, distributed ledger technology is used to prevent critical operation records from being temporarily tampered with.

9. The community shared parking management system according to claim 1, characterized in that: The edge computing node is an industrial server with redundant power supply configuration and local storage array protection components; the perception layer is equipped with a liftable ground lock device in the form of an electromechanical integrated device; the perception layer is also equipped with a video detection device in the form of an embedded device with low-light enhancement components and acquisition and analysis components.

10. A parking management method for a community shared parking management system according to any one of claims 1-9, characterized in that, The method includes the following steps: S1. The occupancy status of parking spaces, vehicle characteristics and environmental parameters are collected by the sensing devices deployed in the parking spaces and transmitted to the edge computing nodes for processing to generate real-time parking space status data. S2. Receive and process real-time parking space status data at the edge computing node, generate equipment control commands, perform local anomaly detection, and synchronize the processing results to the cloud platform when the network connection is normal. When the network connection is abnormal, real-time parking space status data and equipment control commands are cached locally on the edge computing node, and basic parking space status updates and equipment control functions are maintained. S3. Receive and integrate data from edge computing nodes on the cloud platform, analyze user behavior and predict parking demand based on artificial intelligence algorithms, and use optimization algorithms to generate parking space resource allocation schemes and scheduling instructions. S4. Based on the parking space resource allocation plan and scheduling instructions, provide users with reservation, navigation, and payment service interfaces, and send control instructions to edge computing nodes to execute parking space allocation, guide users, and control physical equipment; S5. Perform data interaction and functional linkage operations with the community intelligent system, and perform secure storage, access control and privacy protection processing on the data generated during system operation.

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