Roadside vehicle owner autonomous parking system and method based on dynamic electronic fence

Abstract

The invention relates to a roadside vehicle owner autonomous parking system and method based on a dynamic electronic fence, and relates to the technical field related to intelligent traffic, and the system comprises a geomagnetic sensing layer which is used for detecting the change of a magnetic field signal, generating a vehicle entry event when detecting that a vehicle enters a berth, and uploading the vehicle entry event to a business engine layer through an edge computing gateway; the business engine layer is used for analyzing the vehicle entry event, triggering parking rule evaluation and outputting a parking behavior guide instruction; and the cloud decision-making layer is used for generating a parking bill when the vehicle leaves the parking space and pushing the parking bill to the vehicle owner mobile terminal. The technical problems that in the prior art, a service closed loop cannot be formed by vehicle detection, charging rule application and bill generation and pushing, so that the intelligent level of parking management, the satisfaction degree of vehicle owners and the parking space turnover efficiency are insufficient, and the operation cost is high are solved. The technical effects of reducing the operation cost and improving the intelligent level of parking management, the satisfaction degree of vehicle owners and the parking space turnover efficiency are achieved.

Description

Technical Field

[0001] This invention relates to the field of intelligent transportation technology, specifically to a roadside vehicle owner autonomous parking system and method based on dynamic electronic fences. Background Technology

[0002] Traditional roadside parking management generally relies on manual patrols, parking meters, or simple sensor detection, which suffers from low management efficiency, inaccurate billing, poor driver experience, and insufficient resource utilization. For example, drivers need to actively find and operate payment devices, which can easily lead to disputes when leaving; management cannot monitor parking space status in real time, making dynamic control and guidance impossible, resulting in low parking space turnover. In recent years, the integrated development of IoT, edge computing, and cloud computing technologies has provided new technological paths for upgrading smart parking. Among them, geomagnetic sensors are widely used for parking space status sensing due to their simple deployment, low cost, and reliable detection. However, simple parking space status detection only solves the basic problem of "whether there is a car" and still cannot achieve full-process, automated, and service-oriented parking management.

[0003] Therefore, current technologies suffer from the inability to form a service loop between vehicle detection, billing rule application, and bill generation and delivery, resulting in insufficient intelligence in parking management, low owner satisfaction, low parking space turnover efficiency, and high operating costs. Summary of the Invention

[0004] This application provides a roadside self-parking system and method based on dynamic electronic fences, which solves the technical problems in the prior art where vehicle detection, billing rule application, bill generation and push cannot form a service loop, resulting in insufficient intelligent level of parking management, low owner satisfaction and parking space turnover efficiency, and high operating costs. It achieves the technical effect of reducing operating costs and improving the intelligent level of parking management, owner satisfaction and parking space turnover efficiency.

[0005] This application provides a roadside autonomous parking system based on dynamic electronic fences. The system includes: a geomagnetic sensing layer, which detects changes in magnetic field signals. When a vehicle enters a parking space, a vehicle entry event is generated and uploaded to the business engine layer via an edge computing gateway; a business engine layer, which parses the vehicle entry event, triggers parking rule evaluation, and outputs parking behavior guidance instructions; and a cloud decision layer, which generates a parking bill when the vehicle leaves the parking space and pushes the parking bill to the vehicle owner's mobile device.

[0006] In a possible implementation, the roadside vehicle owner autonomous parking system based on dynamic electronic fences also performs the following processing: the geomagnetic sensing layer includes an edge computing gateway and multiple geomagnetic sensors, the multiple geomagnetic sensors adopt NB-IoT wireless communication, and the edge computing gateway is used to preprocess and locally cache the data of the multiple geomagnetic sensors.

[0007] In a possible implementation, the roadside vehicle owner autonomous parking system based on dynamic electronic fences also performs the following processes: a dynamic electronic fence engine, which receives and caches cloud-based electronic fence configurations and dynamically detects signals within the fence, as well as acquires vehicle entry and exit fence synchronization signals; a multi-segment rule engine, which performs parking rule logic judgments, processes vehicle owner autonomous operation requests, parses rule configurations issued by the cloud management platform, performs parking rule evaluations, and generates parking behavior guidance instructions; and a parking space status synchronization engine, which updates parking space status and synchronizes the parking space status to the cloud management platform.

[0008] In a possible implementation, the roadside vehicle owner autonomous parking system based on dynamic electronic fences also performs the following processing: when the geomagnetic sensing layer detects that a vehicle has left the parking space, it generates a vehicle departure event and uploads the vehicle departure event to the business engine layer and the cloud decision layer through the edge computing gateway.

[0009] In a possible implementation, the roadside vehicle owner autonomous parking system based on dynamic electronic fences also performs the following processing: the cloud decision layer includes a PDA inspection terminal, and the PDA inspection terminal's departure PDA review includes three modes, namely, the PDA full review mode, the PDA review mode that is only activated during free time, and the trusted vehicle owner mode.

[0010] In a possible implementation, the roadside vehicle owner autonomous parking system based on dynamic electronic fences also performs the following processing: if the vehicle owner fails to pay the parking bill within a predetermined time, a PDA inspection reminder is automatically triggered.

[0011] In a possible implementation, the roadside vehicle owner autonomous parking system based on dynamic electronic fences also performs the following processing: if the network is interrupted when the vehicle owner pays the parking bill, the edge computing gateway caches the vehicle departure event and automatically synchronizes after the network is restored.

[0012] This application also provides a method for autonomous parking of roadside vehicles based on dynamic electronic fences. The method includes: constructing a negative space based on absolutely normal data as a prerequisite threshold for a target monitoring area according to the area operation and maintenance rules; accessing monitoring stream data, triggering the prerequisite threshold, and performing data entropy change judgment based on the negative space as a baseline to perform abnormal alarm management; wherein, the abnormal alarm management steps include: if the data entropy change is an entropy increase regression type, executing a no-response or direct alarm response based on a preset risk control level; if the data entropy change is an entropy increase non-regression type, triggering a deep discriminator, and executing a multi-channel directional alarm response based on feature causal processing of video and audio and the classification of risk control event types; wherein, the prerequisite threshold and the deep discriminator are embedded plug-ins of an AI analysis alarm system.

[0013] This application proposes a roadside autonomous parking system and method based on dynamic electronic fences. The system comprises a geomagnetic sensing layer to detect changes in magnetic field signals. When a vehicle enters a parking space, a vehicle entry event is generated and uploaded to the business engine layer via an edge computing gateway. The business engine layer parses the vehicle entry event, triggers parking rule evaluation, and outputs parking behavior guidance instructions. A cloud-based decision layer generates a parking bill when the vehicle leaves the parking space and pushes the bill to the owner's mobile device. This system solves the technical problems in existing technologies where vehicle detection, billing rule application, bill generation, and push cannot form a service loop, resulting in insufficient parking management intelligence, low owner satisfaction, low parking space turnover efficiency, and high operating costs. It achieves the technical effect of reducing operating costs while improving parking management intelligence, owner satisfaction, and parking space turnover efficiency. Attached Figure Description

[0014] To more clearly illustrate the technical solutions of the embodiments of this disclosure, the accompanying drawings of the embodiments of this disclosure will be briefly described below. Flowcharts are used in this application to illustrate the operations performed by the system according to the embodiments of this application. It should be understood that the preceding or following operations are not necessarily performed precisely in sequence. Instead, various steps can be processed in reverse order or simultaneously as needed. Furthermore, other operations can be added to these processes, or one or more steps can be removed from these processes.

[0015] Figure 1 A schematic diagram of the roadside vehicle owner autonomous parking system based on dynamic electronic fence provided in this application embodiment.

[0016] Figure 2 This is a schematic diagram of a roadside vehicle owner autonomous parking method based on a dynamic electronic fence, provided as an embodiment of this application.

[0017] Figure labeling: Geomagnetic sensing layer 10, business engine layer 20, cloud decision layer 30. Detailed Implementation

[0018] To further illustrate the technical means and effects of the present invention in achieving its intended purpose, the following detailed description is provided in conjunction with the accompanying drawings and preferred embodiments, based on the specific implementation methods, structure, features, and effects of the present invention.

[0019] This application provides a roadside vehicle owner autonomous parking system based on dynamic electronic fences, such as... Figure 1 As shown, the system includes:

[0020] The geomagnetic sensing layer 10 is used to detect changes in magnetic field signals. When a vehicle is detected entering a parking space, a vehicle entry event is generated, and the vehicle entry event is uploaded to the business engine layer through the edge computing gateway.

[0021] Preferably, the geomagnetic sensing layer refers to a hardware and data acquisition layer consisting of physically deployed geomagnetic sensors and edge computing gateways. Specifically, a geomagnetic sensor node is buried underground in each roadside parking space, and each node is equipped with an NB-IoT wireless communication module. An edge computing gateway device is deployed in the area covered by every 30 to 60 parking spaces. The geomagnetic sensor continuously monitors the magnetic field strength at its location. When a vehicle enters and parks in the parking space where the sensor is located, the metal vehicle body causes a distortion in the local Earth's magnetic field, and the sensor detects this specific pattern of magnetic field signal change. The processing unit within the sensor or the associated edge computing gateway determines the signal strength based on threshold values ​​and other parameters. The matching algorithm identifies that the magnetic field change matches the characteristics of "vehicle entry" rather than environmental interference, and then generates a structured "vehicle entry event" data record, which includes at least the unique ID of the sensor that triggered the event, the timestamp of the event, and the event type identifier. The generated event data is sent to the edge computing gateway in the area via the NB-IoT wireless network. The edge computing gateway collects, verifies, and performs necessary data formatting and invalid data filtering preprocessing on the data from multiple sensors. Subsequently, the edge computing gateway uploads the verified "vehicle entry event" data packet to the business engine layer server located in the network backend or cloud via uplinks such as 4G / Ethernet.

[0022] Furthermore, the specific configuration of the geomagnetic sensing layer 10 also includes an edge computing gateway and multiple geomagnetic sensors. The multiple geomagnetic sensors use NB-IoT wireless communication, and the edge computing gateway is used to preprocess and locally cache the data from the multiple geomagnetic sensors.

[0023] Preferably, the geomagnetic sensing layer includes an edge computing gateway and multiple geomagnetic sensors. The multiple geomagnetic sensors use NB-IoT wireless communication. One geomagnetic sensor is buried in each berth to detect changes in the berth status in real time and report vehicle entry / exit signals. It adopts the low-power wide-area network communication protocol NB-IoT, with power consumption of <1W, battery life of 5 years, IP68 waterproof rating, and can work in environments ranging from -40 to 85℃. Depending on the road section, 30 to 60 berths are equipped with one edge computing gateway to receive data from geomagnetic sensors within a 500-meter radius, and to preprocess and locally cache the data from multiple geomagnetic sensors. The processor is a quad-core ARM Cortex-A53 with a main frequency of 1.5GHz, 2GB DDR4 memory, 32GB eMMC storage, and communication interfaces including an NB-IoT module, a 4G module, and an Ethernet interface.

[0024] Business engine layer 20 is used to parse the vehicle entry event, trigger parking rule evaluation, and output parking behavior guidance instructions.

[0025] Preferably, the business engine layer refers to the software service layer deployed on the edge or cloud server, which is responsible for converting physical sensing events into business logic. Through real-time rule calculation, it generates and issues specific operation instructions such as starting billing, information prompts, and violation alarms. That is, the business engine layer receives and processes vehicle entry event data uploaded by the geomagnetic sensing layer, executes multiple preset business logic judgments, and finally generates and outputs instructions to guide or inform vehicle owners. Specifically, after the interface service of the business engine layer receives the "vehicle entry event" data packet, it parses it and extracts key information such as event type, sensor ID, and timestamp, and confirms that the data comes from the correct edge computing gateway.

[0026] Preferably, after parsing the event, the rule evaluation engine is invoked to perform logical judgments based on dynamic data and preset rules. This includes associating the vehicle with the owner, attempting to bind the parking space occupancy event with the owner's account and vehicle information that initiated a parking request via mobile application within the same time period and geofence, matching the dynamic geofence, and verifying whether the GPS location reported by the vehicle falls within the dynamic geofence generated by the parking space coordinates and preset tolerance range to confirm whether the vehicle is parked compliantly in the designated grid. Parking rules are applied, and based on information such as the road segment to which the parking space belongs, the current time, and the vehicle type, the corresponding rule library configured in the cloud is invoked for automated judgment. The rule content includes, but is not limited to, whether parking is allowed, such as whether it is during a no-parking period; the applicable billing standard, such as a rate increase after the first hour; the time limit for allowed parking, such as a 2-hour limit; and whether there are special controls, such as clearing the area during an event.

[0027] Preferably, based on the evaluation results output by the rule evaluation engine, structured parking behavior guidance instructions are generated, which may include guidance for car owners and push them to the car owner's mobile application. The content may be "You have successfully parked in parking space A001, billing has started, this section has a time limit of 2 hours" or "It was detected that you have not completely parked in the designated area, please move forward" or "This is a no-parking period, please leave immediately"; for instructions to the billing system, a "start billing" instruction is sent to the billing system, and a "update parking space status to occupied" instruction is sent to the parking space status management system.

[0028] Furthermore, the specific configuration of the business engine layer 20 also includes: a dynamic electronic fence engine, which is used to receive and cache cloud-based electronic fence configurations and dynamically detect signals within the fence, as well as obtain vehicle entry and exit fence synchronization signals; a multi-segment rule engine, which is used to execute parking rule logic judgments, process vehicle owner autonomous operation requests, parse rule configurations issued by the cloud management platform, perform parking rule evaluations, and generate the parking behavior guidance instructions; and a parking space status synchronization engine, which is used to update parking space status and synchronize the parking space status to the cloud management platform.

[0029] Preferably, the dynamic electronic fence engine is a software service responsible for creating and managing virtual parking boundaries and performing real-time spatial matching. It receives and caches cloud-based electronic fence configurations and dynamically detects signals within the fence, as well as acquiring vehicle entry and exit synchronization signals. Specifically, it obtains electronic fence configuration information defined by logical parameters such as geographic coordinates, parking space numbers, and boundary tolerance ranges from the cloud management platform and caches it in local memory or a database to ensure rapid rule invocation and business continuity during network interruptions. Simultaneously, it continuously monitors and judges location signals from the vehicle owner's mobile device. When a signal is received, it calculates in real time whether the coordinates fall within the range of a configured electronic fence polygon. Based on the "vehicle entry / exit" physical events reported by the geomagnetic sensor, it performs spatiotemporal correlation and logical matching with the location information reported by the vehicle owner's mobile device to generate an accurate and business-usable "vehicle entry / exit electronic fence" synchronization signal.

[0030] Preferably, the multi-segment rule engine is a configurable software rule execution core embedded in the business logic, responsible for making complex business decisions. It is used to execute parking rule logic judgments, process vehicle owner autonomous operation requests, parse rule configurations issued by the cloud management platform, perform parking rule evaluations, and generate parking behavior guidance instructions. Specifically, based on the input vehicle entry event, electronic fence matching results, vehicle information, and current time, it executes preset "IF-THEN" logic rules, receives and processes operation requests from the vehicle owner's mobile terminal, such as "apply for parking" and "apply for departure," and uses them as one of the input conditions for rule judgment; it receives and parses dynamically updatable rule configuration files or data streams from the cloud management platform. The rules may include time-sharing rates, parking time limits, vehicle type restrictions, special date rules, etc.; finally, it performs a final evaluation by integrating all inputs and rules, and generates structured guidance instructions based on the evaluation results, such as "Guidance Instruction Type - Start Billing, Parameter - Rate = 5 yuan / hour, Time Limit = 2 hours" or "Guidance Instruction Type - Violation Alarm, Parameter - Reason = No Parking Period."

[0031] Preferably, the parking space status synchronization engine is a software status management service responsible for maintaining and distributing the real-time occupancy status of parking spaces. It is used to update the parking space status and synchronize the parking space status to the cloud management platform. Specifically, based on events from the geomagnetic sensing layer and confirmation instructions from the business engine, it instantly changes the parking space status records it manages. The status includes "idle", "occupied (pending confirmation)", "occupied (billed)", "faulty", "disabled", etc. Then, it actively and in real time pushes or reports the changes in the local parking space status to the cloud management platform to ensure that the cloud management backend, data dashboard, and public parking space query service can obtain the latest parking space usage information.

[0032] The cloud-based decision layer 30 is used to generate a parking bill when a vehicle leaves a parking space and push the parking bill to the vehicle owner's mobile device.

[0033] Preferably, the cloud-based decision layer refers to the core service and data processing layer deployed on the cloud computing platform. It is responsible for performing the final billing calculation based on the end-to-end data at the end of the parking period, generating a standardized electronic bill, and proactively and accurately pushing the bill to the car owner through the network. At the same time, it manages the payment and settlement status. That is, at the end of the parking event, i.e. when the vehicle leaves, it serves as the final billing, settlement, and user interaction hub, completing the closed loop of the parking transaction. Specifically, when the business engine layer forwards the "vehicle departure event" generated by the geomagnetic sensing layer, the cloud-based decision layer triggers the management service to collect and verify the key data of the entire parking chain, including verifying the entry event, departure event, related information, and usage rules. Among them, the entry event records the entry time and parking space ID of the business engine layer, and the departure event records the departure time and parking space ID of the geomagnetic sensing layer. The related information refers to the car owner account and vehicle information bound to the parking space this time. The applicable rules refer to the billing rules actually applied during this parking period obtained from the business engine layer, such as the rate, free time, and capped amount.

[0034] Preferably, the system accurately calculates the parking duration based on the entry and exit times, calls the established billing rules, automatically calculates the amount due, and formats the calculation results to generate a complete and tamper-proof electronic parking bill record. This record typically includes details such as license plate number, parking space number, entry time, exit time, total parking duration, free time, and rates for each time period, as well as the final amount due, bill number, and generation time. Then, through a message push service combined with a mobile push SDK, the bill data package is instantly sent to the vehicle owner's mobile application associated with the parking record. After the push is completed, the cloud updates the status of the parking record to "pending payment" and starts the payment timeout countdown.

[0035] Preferably, the cloud-based decision-making layer is also responsible for or coordinates payment status management, audit process triggering, and data storage and analysis. Payment status management refers to receiving callbacks from car owners' mobile devices when payments are successful, updating the bill status to "paid," and completing the order loop. Audit process triggering refers to automatically completing settlement based on bill information in modes such as "trusted car owner." In other modes, the bill or departure event may be synchronized to the PDA inspection terminal to trigger a manual audit process. Data storage and analysis refers to storing complete parking transaction records in the cloud database to provide a data foundation for operational analysis, reconciliation, and reporting.

[0036] Furthermore, the specific configuration of the cloud decision layer 30 also includes a PDA inspection terminal, and the PDA inspection terminal's departure PDA review includes three modes: PDA full review mode, PDA review mode only activated during free time, and trusted car owner mode.

[0037] Preferably, the cloud-based decision-making layer includes a PDA inspection terminal. The PDA inspection terminal is a handheld mobile device that extends and executes cloud-based decision-making and management capabilities on-site. Its application software connects in real-time with the backend services of the cloud-based decision-making layer. Inspection personnel receive instructions from the cloud, view parking space and order statuses, and can transmit on-site verification results back to the cloud, thus influencing cloud-based decisions, such as confirming departure times and modifying order statuses. The PDA inspection terminal's departure verification includes three modes: administrators can preset and select three different verification strategies from the cloud based on road segment attributes, management requirements, or manpower costs to determine when and how the PDA intervenes. These include a full-process PDA verification mode, a PDA verification mode activated only during free time, and a trusted vehicle owner mode.

[0038] Preferably, the trigger condition for the PDA full-process verification mode is that every time a vehicle leaves, regardless of the parking duration and fee, after the vehicle owner completes the "apply to leave" or "pay to leave" operation on the mobile terminal, the leave request will not take effect immediately. A record pending verification will be generated and pushed to the PDA inspection terminal of the inspector responsible for the area in real time. The inspector must go to the site in person or verify through video or other means that the vehicle has indeed left, and manually click "confirm" on the PDA inspection terminal. Only then will the parking record officially end. In this mode, the cloud takes the final confirmation time of the inspector on the PDA inspection terminal as the authoritative leave time. This mode is suitable for road sections with strict management, high risk of violations, or temporary traffic control.

[0039] Preferably, the trigger condition for activating the PDA verification mode only during the free period is when the vehicle's parking time exceeds the preset free period and incurs a fee. If the parking is within the free period, the system automatically completes the process without PDA verification after the vehicle owner applies to leave. If the parking exceeds the free period and incurs a fee, the payment and departure record will be pushed to the PDA inspection terminal after the vehicle owner completes the payment, requiring the inspector to conduct a post-event confirmation, which may involve on-site verification or review based on the record. This approach is suitable for balancing efficiency and cost, allowing limited manpower to focus on orders that have already generated transactions and ensuring revenue verification.

[0040] The preferred "Trusted Owner" mode is triggered when all vehicles depart. After the owner completes the "Apply to Depart" or "Pay to Depart" operation on their mobile device, the system immediately and automatically adopts this operation time as the departure time and completes the order settlement. The PDA inspection terminal does not receive mandatory review tasks; the system may only push the departure record to the PDA for inspectors to review or only issue an alert when there are abnormal data in the background. This mode is suitable for road sections with good credit environments and low violation rates, aiming to achieve the highest degree of automation and unmanned operation, minimizing labor costs. By integrating the PDA terminal and pre-setting three review strategies, it achieves refined and flexible control over the management of vehicle departure. Administrators can flexibly switch between three management modes—"Ensure Absolute Control," "Efficiency and Cost Balance," and "Maximize Automation"—according to actual conditions.

[0041] Furthermore, the specific configuration of the cloud decision layer 30 also includes automatically triggering a PDA inspection reminder if the car owner fails to pay the parking bill within the predetermined time.

[0042] Preferably, after the vehicle owner drives away, they receive a parking bill pushed by the cloud decision-making layer via their mobile device, but fail to complete the online payment within the system's preset payment deadline. The order status is then determined to be "overdue payment," automatically triggering a PDA inspection reminder. Specifically, this overdue payment order is marked as an abnormal order and a pending task with a "payment reminder" or "on-site verification" label is generated. Based on the geographical location of the parking space corresponding to the order, the system automatically assigns the task and pushes it in real-time to the PDA inspection terminal held by the inspector responsible for that area. The reminder information received by the PDA inspection terminal includes the parking space number, The system records key information such as license plate number, entry / exit time, amount due, and current status (timeout). It also clearly instructs inspectors to handle the situation on-site, such as confirming whether the vehicle has actually left or whether there is a case of "skipping payment" with the vehicle still remaining. If the owner is still on-site, the inspector will remind and guide them to complete the payment. If the owner has left, the inspector will record the situation on-site through the PDA inspection terminal and may change the order status to "outstanding" or initiate subsequent collection procedures according to management rules, such as sending SMS reminders. This seamlessly connects the online payment process with offline manual collection capabilities to ensure the collection rate of parking fees.

[0043] Furthermore, the specific configuration of the cloud decision layer 30 also includes that if the network is interrupted when the car owner pays the parking bill, the edge computing gateway caches the vehicle leaving event and automatically synchronizes it after the network is restored.

[0044] Preferably, if the uplink network between the edge computing gateway and the cloud decision layer is interrupted when the owner attempts to pay the parking bill on the mobile device after the vehicle leaves, the edge computing gateway will activate its fault tolerance function, cache the vehicle departure event, and then continuously monitor the status of its uplink network link. Once the network connection is detected to be available again, the gateway's built-in synchronization service will automatically start, retrieve all locally cached data that has not yet been successfully uploaded to the cloud, including re-uploading the "vehicle departure event" cached due to the network interruption to the cloud decision layer, and match it with the corresponding parking record. This will re-trigger or correct the bill generation and status update process, ensuring that the cloud billing system can ultimately obtain complete and accurate parking duration data, thereby avoiding the loss or error of billing information due to network problems.

[0045] Furthermore, the specific configuration of the roadside vehicle owner autonomous parking system based on dynamic electronic fences also includes generating a vehicle departure event when the geomagnetic sensing layer detects that a vehicle has left the parking space, and uploading the vehicle departure event to the business engine layer and the cloud decision layer through the edge computing gateway.

[0046] Preferably, when the geomagnetic sensing layer detects a vehicle leaving a parking space, the geomagnetic sensor buried beneath the parking space detects a restorative change in magnetic field strength caused by the removal of the metal vehicle body. The sensor or its associated processor identifies this specific change pattern and determines it as a valid "vehicle departure" behavior. Subsequently, a structured vehicle departure event data record is generated. This record contains key fields, including the sensor ID corresponding to the unique parking space, the event type "departure", and a precise timestamp. The generated "vehicle departure event" data packet is sent to the edge computing gateway responsible for this area via the NB-IoT wireless network. The edge computing gateway receives the data, verifies the data integrity, and performs necessary data format standardization.

[0047] Preferably, after verification, the same "vehicle departure event" data packet is uploaded to two different logical layers simultaneously or almost simultaneously via its uplink network link: the business engine layer and the cloud decision layer. The business engine layer is used to trigger near real-time business logic processing, that is, the business engine layer performs rule evaluation based on the end of billing logic and updates the local parking space status to "vacant", while sending a departure confirmation request to the car owner's APP. The cloud decision layer is used for final billing adjudication and record archiving, that is, the cloud decision layer uses it as the authoritative "departure time" basis for calculating parking duration and generating the final bill, thereby ensuring the independence and reliability of the core billing data.

[0048] In the above text, refer to Figure 1 This paper describes in detail a roadside vehicle owner autonomous parking system based on a dynamic electronic fence according to an embodiment of the present invention. Next, reference will be made to... Figure 2This invention describes a roadside autonomous parking method based on a dynamic electronic fence, according to embodiments of the present invention. The roadside autonomous parking method based on a dynamic electronic fence, such as... Figure 2 As shown, the method includes: detecting changes in the magnetic field signal; when a vehicle is detected entering a parking space, generating a vehicle entry event and uploading the vehicle entry event to the business engine layer through an edge computing gateway; parsing the vehicle entry event, triggering parking rule evaluation, and outputting parking behavior guidance instructions; when the vehicle leaves the parking space, generating a parking bill and pushing the parking bill to the car owner's mobile device.

[0049] In one possible implementation, the roadside vehicle owner autonomous parking method based on dynamic electronic fence further includes: the geomagnetic sensing layer includes an edge computing gateway and multiple geomagnetic sensors, the multiple geomagnetic sensors adopt NB-IoT wireless communication, and the edge computing gateway is used to preprocess and locally cache the data of the multiple geomagnetic sensors.

[0050] In one possible implementation, the roadside vehicle owner autonomous parking method based on dynamic electronic fences further includes: a dynamic electronic fence engine, which receives and caches cloud-based electronic fence configurations and dynamically detects signals within the fence, as well as acquiring vehicle entry and exit synchronization signals; a multi-segment rule engine, which performs parking rule logic judgments, processes vehicle owner autonomous operation requests, parses rule configurations issued by the cloud management platform, performs parking rule evaluations, and generates the parking behavior guidance instructions; and a parking space status synchronization engine, which updates the parking space status and synchronizes the parking space status to the cloud management platform.

[0051] In one possible implementation, the roadside vehicle owner autonomous parking method based on dynamic electronic fence further includes: when the geomagnetic sensing layer detects that a vehicle has left the parking space, it generates a vehicle departure event and uploads the vehicle departure event to the business engine layer and the cloud decision layer through the edge computing gateway.

[0052] In one possible implementation, the roadside vehicle owner autonomous parking method based on dynamic electronic fences further includes: the cloud decision layer includes a PDA inspection terminal, and the PDA inspection terminal's departure PDA review includes three modes, namely, PDA full review mode, PDA review mode only activated during free time, and trusted vehicle owner mode.

[0053] In one possible implementation, the roadside vehicle owner autonomous parking method based on dynamic electronic fences further includes: automatically triggering a PDA inspection reminder if the vehicle owner fails to pay the parking bill within a predetermined time.

[0054] In one possible implementation, the roadside vehicle owner autonomous parking method based on dynamic electronic fences further includes: if the network is interrupted when the vehicle owner pays the parking bill, the edge computing gateway caches the vehicle departure event and automatically synchronizes after the network is restored.

[0055] The roadside vehicle owner autonomous parking system based on dynamic electronic fence provided in the embodiments of the present invention can execute the roadside vehicle owner autonomous parking method based on dynamic electronic fence provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0056] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any modifications, equivalent changes, and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the scope of the present invention.

Claims

1. A roadside vehicle owner-controlled parking system based on dynamic electronic fences, characterized in that, The system includes: The geomagnetic sensing layer is used to detect changes in magnetic field signals. When a vehicle is detected entering a parking space, a vehicle entry event is generated and uploaded to the business engine layer through the edge computing gateway. The business engine layer is used to parse the vehicle entry event, trigger parking rule evaluation, and output parking behavior guidance instructions. The cloud-based decision-making layer generates a parking bill when a vehicle leaves a parking space and pushes the parking bill to the vehicle owner's mobile device.

2. The roadside vehicle owner autonomous parking system based on dynamic electronic fence as described in claim 1, characterized in that, The geomagnetic sensing layer includes an edge computing gateway and multiple geomagnetic sensors. The multiple geomagnetic sensors use NB-IoT wireless communication. The edge computing gateway is used to preprocess and locally cache the data from the multiple geomagnetic sensors.

3. The roadside vehicle owner autonomous parking system based on dynamic electronic fence as described in claim 1, characterized in that, The business engine layer includes: The dynamic electronic fence engine is used to receive and cache cloud-based electronic fence configurations and dynamically detect signals within the fence, as well as acquire vehicle entry and exit synchronization signals. A multi-segment rule engine is used to execute parking rule logic judgments, process vehicle owner autonomous operation requests, parse rule configurations issued by the cloud management platform, perform parking rule evaluations, and generate parking behavior guidance instructions. The parking space status synchronization engine is used to update the parking space status and synchronize the parking space status to the cloud management platform.

4. The roadside vehicle owner autonomous parking system based on dynamic electronic fence as described in claim 1, characterized in that, When the geomagnetic sensing layer detects that a vehicle has left the parking space, it generates a vehicle departure event and uploads the vehicle departure event to the business engine layer and the cloud decision layer through the edge computing gateway.

5. The roadside vehicle owner autonomous parking system based on dynamic electronic fence as described in claim 1, characterized in that, The cloud-based decision-making layer includes a PDA inspection terminal. The PDA inspection terminal's departure PDA verification includes three modes: PDA full-process verification mode, PDA verification mode that is only activated during free time, and trusted car owner mode.

6. The roadside vehicle owner autonomous parking system based on dynamic electronic fence as described in claim 1, characterized in that, If the car owner fails to pay the parking bill within the scheduled time, a PDA inspection reminder will be automatically triggered.

7. The roadside vehicle owner autonomous parking system based on dynamic electronic fence as described in claim 1, characterized in that, If the network is interrupted when the car owner pays the parking bill, the edge computing gateway caches the vehicle departure event and automatically synchronizes it after the network is restored.

8. A method for autonomous parking of roadside vehicles based on dynamic electronic fences, characterized in that: The method is applied to the roadside vehicle owner autonomous parking system based on dynamic electronic fence as described in any one of claims 1-7, and the method includes: The system detects changes in the magnetic field signal. When a vehicle is detected entering a parking space, a vehicle entry event is generated and uploaded to the business engine layer via the edge computing gateway. Analyze the vehicle entry event, trigger parking rule evaluation, and output parking behavior guidance instructions; When a vehicle leaves a parking space, a parking bill is generated and pushed to the vehicle owner's mobile device.

Images