Event-driven road parking unmanned inspection vehicle path planning method and system

Abstract

The invention provides an event-driven road parking unmanned inspection vehicle path planning method and system, and relates to the technical field of path planning, and the method comprises the steps: constructing an intelligent parking management platform, and when a signal of any parking space is reported to a source device to trigger a parking space state change event, executing the intelligent parking management platform; the intelligent parking management platform sends a first parking space inspection task to the unmanned inspection vehicle connected with the intelligent parking management platform, and sorts the first parking space inspection task to a to-be-executed parking space inspection task queue of the unmanned inspection vehicle through a task sorting rule of a path planning algorithm module; and according to the updated to-be-executed parking space inspection task queue, a driving path of the unmanned inspection vehicle is re-planned, and a task sorting rule is constructed based on the key point position set in the management area. The technical problems that existing road parking inspection is low in inspection efficiency and lagged in response are solved. The technical effect of improving the inspection task processing efficiency and accuracy of the unmanned inspection vehicle is achieved.

Description

Technical Field

[0001] This invention relates to the field of path planning technology, specifically to an event-driven path planning method and system for unmanned road parking inspection vehicles. Background Technology

[0002] In traditional on-street parking operations, operators often rely on manual patrols for management due to cost considerations. Whether it's the most basic manual patrols or patrol vehicles driven by inspectors, both require personnel to be present at the parking area. However, this management model has significant problems: manual patrols are prone to favoritism, missed checks, evasion of payment, and unauthorized cash collection, leading to revenue loss and making it difficult to eliminate management loopholes; furthermore, data accuracy is low, relying on manual recording of license plates and timing is prone to errors and omissions, failing to establish a reliable data foundation. In addition, manual patrols are costly, requiring significant manpower and management resources, and are limited by the experience and workload of patrol personnel, making the results susceptible to subjective factors, resulting in errors and omissions. In recent years, with the maturity of autonomous driving technology, unmanned patrol vehicles have been continuously developed. However, current technologies mostly rely on periodic data collection or manually triggered patrols, making it difficult to achieve timely responses in event-driven situations. Patrol vehicles patrol along fixed routes, resulting in low patrol efficiency and delayed response times.

[0003] In summary, existing road parking inspection systems suffer from technical problems such as low inspection efficiency and delayed response. Summary of the Invention

[0004] The purpose of this application is to provide an event-driven path planning method and system for unmanned road parking inspection vehicles, which can solve the technical problems of low inspection efficiency and slow response in existing road parking inspections.

[0005] In view of the above problems, this application provides an event-driven path planning method and system for unmanned road parking inspection vehicles.

[0006] The first aspect of this application provides an event-driven path planning method for unmanned road parking inspection vehicles. The method includes: constructing a smart parking management platform, the smart parking management platform including signal reporting source devices associated with each parking space within a management area, wherein the signal reporting source devices deploy parking space status change events; when the signal reporting source device of any parking space triggers the parking space status change event, the smart parking management platform sends a first parking space inspection task to the connected unmanned inspection vehicle; sorting the first parking space inspection task into the unmanned inspection vehicle's queue of pending parking space inspection tasks using a task sorting rule from a path planning algorithm module, and replanning the unmanned inspection vehicle's driving path according to the updated queue of pending parking space inspection tasks; wherein the task sorting rule is constructed based on a set of key points within the management area, the set of key points including charging points and parking space center points.

[0007] Optionally, it is determined whether the unmanned inspection vehicle is at the charging point performing a charging task; if the unmanned inspection vehicle is at the charging point performing a charging task, the real-time remaining power of the unmanned inspection vehicle is received, and it is determined whether the real-time remaining power is greater than a preset remaining power, wherein the preset remaining power is the minimum remaining power required to perform the first berth inspection task; if the real-time remaining power is greater than or equal to the preset remaining power, the first berth inspection task is placed before the charging task; if the real-time remaining power is less than the preset remaining power, the first berth inspection task is placed after the charging task.

[0008] Optionally, if the unmanned inspection vehicle is not performing a charging task at the charging point, the center point of the first berth corresponding to the first berth inspection task is identified; the queue of berth inspection tasks to be executed by the unmanned inspection vehicle is traversed to determine whether the center point of the first berth is on the path between the center points of two adjacent tasks in the queue of berth inspection tasks to be executed; if it is, the first berth inspection task is inserted between two adjacent tasks in the queue of berth inspection tasks to be executed; if it is not, the first insertion position in the queue of berth inspection tasks to be executed is identified by the shortest distance algorithm, and the first berth inspection task is inserted into the first insertion position.

[0009] Optionally, the set of berth center points for each task in the queue of berth inspection tasks to be executed is traversed; the set of berth distances between the first berth center point and the set of berth center points is calculated; and the first insertion position is obtained by identifying the distance in the set of berth distances.

[0010] Optionally, the unmanned inspection vehicle's range threshold is calculated based on its real-time remaining battery power; the first berth inspection task to be executed in the queue of berth inspection tasks is analyzed; the first target range time required to execute the first berth inspection task and return to the charging point is calculated; and the queue of berth inspection tasks to be executed is continuously updated by judging the first target range time and the range threshold.

[0011] Optionally, if the first target driving time is greater than the driving time threshold, the charging task is prioritized; if the first target driving time is less than the driving time threshold, the second berth inspection task in the queue of berth inspection tasks is analyzed, and the second target driving time required to execute the second berth inspection task and return to the charging point is calculated; and so on, until the Nth berth inspection task with a driving time greater than the driving time threshold is obtained, the charging task is placed before the Nth berth inspection task.

[0012] Optionally, the berth status change event includes a vehicle entry event or a vehicle exit event.

[0013] Optionally, the signal reporting source device is a geomagnetic detection device.

[0014] A second aspect of this application provides an event-driven unmanned road parking patrol vehicle path planning system. The system includes: a management platform construction component for constructing a smart parking management platform, the smart parking management platform including a signal reporting source device associated with each parking space within the management area, wherein the signal reporting source device deploys parking space status change events; a patrol task sending component for sending a first parking space patrol task to the connected unmanned patrol vehicle when any parking space's signal reporting source device triggers the parking space status change event; and a task sorting component for sorting the first parking space patrol task into the unmanned patrol vehicle's queue of pending parking space patrol tasks using the task sorting rules of the path planning algorithm module, and replanning the unmanned patrol vehicle's driving path according to the updated queue of pending parking space patrol tasks; wherein the task sorting rules are constructed based on a set of key points within the management area, the set of key points including charging points and parking space center points.

[0015] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0016] The method provided in this application embodiment constructs a smart parking management platform, which includes a signal reporting source device associated with each parking space within the management area. The signal reporting source device deploys parking space status change events. When any parking space's signal reporting source device triggers a parking space status change event, the smart parking management platform sends a first parking space inspection task to a connected unmanned inspection vehicle. The first parking space inspection task is then sorted into the unmanned inspection vehicle's queue of pending parking space inspection tasks using a task sorting rule from a path planning algorithm module. The unmanned inspection vehicle's driving path is then replanned according to the updated queue of pending parking space inspection tasks. The task sorting rule is constructed based on a set of key points within the management area, including charging points and parking space center points. This achieves the technical effect of improving the intelligence level of unmanned inspection vehicle path planning, realizing efficient parking space event-driven inspection, and improving the efficiency and accuracy of inspection task processing.

[0017] The above description is merely an overview of the technical solution of this application. To enable a clearer understanding of the technical means of this application and to facilitate its implementation according to the description, and to make the above and other objects, features, and advantages of this application more apparent, specific embodiments of this application are described below. It should be understood that the content described in this section is not intended to identify key or important features of the embodiments of this application, nor is it intended to limit the scope of this application. Other features of this application will become readily apparent through the following description. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are merely exemplary. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0019] Figure 1 This is a flowchart illustrating an event-driven path planning method for an unmanned road parking inspection vehicle provided in this application.

[0020] Figure 2 This application provides a schematic diagram of the structure of an event-driven unmanned road parking inspection vehicle path planning system.

[0021] Figure labeling: Management platform construction component 11, inspection task sending component 12, task sorting component 13. Detailed Implementation

[0022] This application provides an event-driven path planning method and system for unmanned road parking inspection vehicles, addressing the technical problems of low inspection efficiency and slow response in existing road parking inspection systems. It achieves the technical effects of improving the intelligence level of path planning for unmanned inspection vehicles, realizing efficient event-driven inspection of parking spaces, and improving the efficiency and accuracy of inspection task processing.

[0023] The technical solutions of the present invention will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. It should be understood that the present invention is not limited to the exemplary embodiments described herein. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention. It should also be noted that, for ease of description, only the parts related to the present invention are shown in the accompanying drawings, not all of them.

[0024] Example 1, as Figure 1 As shown, this application provides an event-driven path planning method for unmanned road parking inspection vehicles, which includes:

[0025] A smart parking management platform is constructed, which includes a signal reporting source device associated with each parking space within the management area, wherein the signal reporting source device deploys parking space status change events.

[0026] Furthermore, the parking space status change event includes a vehicle entry event or a vehicle exit event.

[0027] Specifically, to achieve real-time perception of the status of roadside parking spaces within the management area, a smart parking management platform is constructed based on a cloud or edge computing architecture, according to the on-street parking lots and parking spaces within the management area. This smart parking management platform is used for unified data access and centralized event processing management of all parking spaces within the management area. Specifically, a signal reporting source device is installed for each parking space within the management area, and this signal reporting source device is connected to the smart parking management platform and associated with the on-street parking spaces it manages. The signal reporting source device is a front-end sensing unit used to collect real-time data on the occupancy status of parking spaces and actively report status change information to the smart parking management platform. The signal reporting source device is equipped with parking space status change events to indicate changes in parking space usage. These parking space status change events include two types: vehicle entry events and vehicle exit events. Vehicle entry events indicate that a vehicle occupies a parking space from none, and vehicle exit events indicate that a vehicle leaves a parking space from none.

[0028] By deploying signal reporting source devices for each parking space and associating them with the smart parking management platform, the platform can obtain the corresponding parking space status change event when a vehicle enters or leaves any parking space. This provides unmanned inspection vehicles with accurate and real-time parking space status information, improving the accuracy and efficiency of road parking management.

[0029] Furthermore, the signal reporting source device is a geomagnetic detection device.

[0030] Specifically, the signal reporting source device is preferably a geomagnetic detection device. The geomagnetic detection device uses a geomagnetic sensor to detect disturbances in the geomagnetic field. By judging the change in the geomagnetic field intensity, it identifies whether there is a metal vehicle body in the parking space. It also establishes a connection with the smart parking management platform through wireless communication and transmits the detected parking space status change event data to the smart parking management platform in real time, thereby achieving contactless, all-weather, and highly stable detection of vehicle parking behavior.

[0031] When the signal reporting source device of any parking space triggers the parking space status change event, the smart parking management platform sends the first parking space inspection task to the connected unmanned inspection vehicle.

[0032] Specifically, when the signal reporting source device corresponding to any parking space within the management area detects a vehicle entry or exit event, triggering a parking space status change event, the signal reporting source device uploads the parking space status change event data to the smart parking management platform in real time via a wireless network, such as LoRa, ad hoc network, or cellular network. The smart parking management platform generates a first parking space inspection task based on the parking space identifier corresponding to the triggered parking space status change event, including the parking space's geographical location and the area, and sends this task to an unmanned inspection vehicle connected to the smart parking management platform. The unmanned inspection vehicle is equipped with advanced communication modules and intelligent control information, enabling it to receive task instructions from the smart parking management platform in real time. The communication module uses a high-speed, stable 4G or 5G network to ensure that the inspection task can be transmitted quickly and accurately to the unmanned inspection vehicle. The first parking space inspection task includes the specific parking space location and inspection requirements, such as taking photos of the vehicle and license plate, and recording vehicle and license plate information.

[0033] By triggering a parking space status change event, the smart parking management platform sends the first parking space inspection task to the connected unmanned inspection vehicle, realizing the transformation from parking status monitoring to unmanned inspection vehicle action. This timely converts the parking space status changes monitored by the signal reporting source equipment into specific inspection tasks, ensuring the real-time performance and accuracy of road parking management.

[0034] The first berth inspection task is sorted into the queue of unmanned inspection vehicle's pending berth inspection tasks by the task sorting rules of the path planning algorithm module, and the driving path of the unmanned inspection vehicle is replanned according to the updated queue of pending berth inspection tasks; wherein, the task sorting rules are constructed based on the set of key points in the management area, and the set of key points includes charging points and berth center points.

[0035] Specifically, after the intelligent parking management platform issues the first parking space inspection task to the unmanned inspection vehicle, it sorts and optimizes the path for the first parking space inspection task through the path planning algorithm module. The path planning algorithm module has built-in task sorting rules, which are constructed based on a set of key points within the management area. The set of key points includes at least one available charging point for the unmanned inspection vehicle and multiple corresponding parking space center points. The charging point is used to characterize the fixed position where the unmanned inspection vehicle performs energy replenishment operations, and has attributes such as charging pile coordinates and availability. The parking space center points are used to characterize the actual geographical location of each parking space in the road space of the management area. The GIS information of each key point can be accurately obtained through Geographic Information System (GIS) technology and 3D map files within the management area. The task sorting rules use the parking space center points in the set of key points as spatial references for task nodes, treating each task as several target nodes, thus serving as the basis for path planning and task insertion.

[0036] Once the first parking space inspection task is assigned to the unmanned inspection vehicle by the smart parking management platform, the path planning algorithm module adds it to the vehicle's queue of pending parking space inspection tasks according to a preset task sorting rule. After the task is inserted, the path planning algorithm module recalculates the vehicle's route based on the updated queue. The route planning can employ algorithms such as Dijkstra's or A*, calculating the distance between parking space center points to generate an optimal route that minimizes the inspection path length, reduces the number of turns, and meets battery constraints, ensuring the task is not interrupted by insufficient battery power. The vehicle then replans its route according to the updated queue, completes the photo-taking and evidence collection task for the corresponding parking space, and uploads the collected data to the smart parking management platform. The intelligent parking management platform combines data reported by the signal reporting source and inspection data from the unmanned inspection vehicle to generate corresponding vehicle entry and exit records.

[0037] By driving inspection tasks through specific events, unmanned inspection vehicles can focus their inspections only on parking spaces where the status has changed, thus improving inspection efficiency. Furthermore, by combining task sequencing rules with key point sets, unmanned inspection vehicles can dynamically optimize task sequences when faced with an increasing number of real-time inspection tasks, further enhancing the targeting, accuracy, and timeliness of inspection tasks.

[0038] Furthermore, the first berth inspection task is sorted into the queue of unmanned inspection vehicle's pending berth inspection tasks using the task sorting rules of the path planning algorithm module. The method includes: determining whether the unmanned inspection vehicle is at the charging point to perform a charging task; if the unmanned inspection vehicle is at the charging point to perform a charging task, receiving the real-time remaining battery power of the unmanned inspection vehicle, and determining whether the real-time remaining battery power is greater than a preset remaining battery power, wherein the preset remaining battery power is the minimum remaining battery power required to perform the first berth inspection task; if the real-time remaining battery power is greater than or equal to the preset remaining battery power, placing the first berth inspection task before the charging task; if the real-time remaining battery power is less than the preset remaining battery power, placing the first berth inspection task after the charging task.

[0039] Specifically, in roadside parking scenarios, to avoid impacting the passage efficiency of motor vehicles and pedestrians, unmanned inspection vehicles are often small in size, and their portable batteries are also not large. The path planning algorithm module first identifies the current location and task execution mode of the unmanned inspection vehicle, determining whether it is at a charging point to perform a charging task. A charging task refers to the vehicle replenishing its power at a charging point. If the determination result indicates that the unmanned inspection vehicle is at a charging point, the real-time remaining power of the vehicle is obtained based on the high-precision power monitoring sensor inside the vehicle, and this real-time remaining power is sent to the path planning algorithm module. The path planning algorithm module compares the real-time remaining power with a preset remaining power. The preset remaining power is the minimum remaining power required to perform the inspection task at the first parking space, i.e., the minimum safe power threshold, which is obtained based on a large amount of historical data and experiments, combined with the target parking space distance, the device's own energy consumption, and the energy consumption of the return path.

[0040] If the real-time remaining battery power is greater than or equal to the preset remaining battery power, it means that the unmanned inspection vehicle has sufficient power to perform the first berth inspection task before completing the current charging task. The path planning algorithm module places the first berth inspection task before the charging task, prioritizing the unmanned inspection vehicle's berth inspection task to ensure timely execution of the inspection task. If the real-time remaining battery power is less than the preset remaining battery power, it means that the unmanned inspection vehicle's current battery power is insufficient to directly perform the first berth inspection task. The first berth inspection task is placed after the charging task, allowing the unmanned inspection vehicle to charge first. Once the battery power reaches or exceeds the preset remaining battery power, the inspection task will then be performed. This ensures that the unmanned inspection vehicle will not fail due to insufficient battery power during task execution.

[0041] By accurately determining the charging status and remaining battery power of the unmanned inspection vehicle, the inspection tasks for the first parking space are rationally prioritized based on the actual situation. This ensures that the unmanned inspection vehicle can perform inspection tasks promptly when the battery is sufficient, improving the timeliness and effectiveness of inspections. When the battery is insufficient, the charging and inspection sequence is rationally arranged to avoid task interruptions due to battery issues, thereby improving the reliability and effectiveness of the unmanned inspection vehicle's task execution.

[0042] Furthermore, the method for determining whether the unmanned inspection vehicle is performing a charging task at the charging point also includes: if the unmanned inspection vehicle is not performing a charging task at the charging point, identifying the first berth center point corresponding to the first berth inspection task; traversing the queue of berth inspection tasks to be executed by the unmanned inspection vehicle, determining whether the first berth center point is located on the path between the berth center points corresponding to two adjacent tasks in the queue of berth inspection tasks to be executed; if it is, inserting the first berth inspection task between two adjacent tasks in the queue of berth inspection tasks to be executed; if it is not, identifying the first insertion position in the queue of berth inspection tasks to be executed using the shortest distance algorithm, and inserting the first berth inspection task into the first insertion position.

[0043] Specifically, when the determination result indicates that the unmanned inspection vehicle is not at a charging point to perform a charging task, the center point of the first parking space corresponding to the first parking space inspection task issued by the intelligent parking management platform is identified. The center point of the first parking space is the geometric coordinate of the target parking space within the management area. The queue of parking space inspection tasks currently pending execution by the unmanned inspection vehicle is traversed. This queue contains all parking space inspection tasks that the inspection vehicle has accepted but not yet executed, and each task corresponds to a parking space center point. Through traversal, spatial analysis calculations in the path planning algorithm are used to sequentially calculate the path relationship between the center point of the first parking space and the center points of the two adjacent tasks in the queue, determining whether the center point of the first parking space is on a feasible path between the center points of the two adjacent tasks.

[0044] When it is determined that the center point of the first berth is on the path between the center points of two adjacent tasks in the queue of pending berth inspection tasks, it means that the unmanned inspection vehicle passes by the center point of the first berth while performing the two adjacent tasks. At this time, the inspection task of the first berth is inserted between the two adjacent tasks in the queue of pending berth inspection tasks, thereby reducing the travel path of the unmanned inspection vehicle and improving inspection efficiency.

[0045] When it is determined that the center point of the first berth is not on the path between the center points of two adjacent berths in the queue of berth inspection tasks to be executed, the path planning algorithm module uses the shortest distance algorithm, taking the center point of the first berth as the target node and the center points of the berths corresponding to each task in the queue of berth inspection tasks to be executed as the starting nodes, to calculate the shortest distance from each starting node to the target node. Based on the shortest distance information, the first insertion position in the queue of berth inspection tasks to be executed is determined, and the first berth inspection task is inserted into the first insertion position to ensure that the unmanned inspection vehicle has the shortest overall travel path when performing tasks.

[0046] By determining the relationship between the center point of the first berth and the path in the queue of tasks to be executed, and using the shortest distance algorithm to determine the insertion position, the driving path of the unmanned inspection vehicle is optimized, unnecessary travel distance and time are reduced, and inspection efficiency is improved.

[0047] Furthermore, the first insertion position in the queue of berth inspection tasks to be executed is identified by the shortest distance algorithm. The method includes: traversing the set of berth center points for each task in the queue of berth inspection tasks to be executed; calculating the set of berth distances between the first berth center point and the set of berth center points; and obtaining the first insertion position by identifying the distance in the set of berth distances.

[0048] Specifically, the set of berth center points corresponding to each task in the queue of pending berth inspection tasks is traversed. Each berth center point represents the geometric coordinates of the corresponding berth within the management area. For the first berth center point corresponding to the first berth inspection task, the distances between it and each berth center point in the set of berth center points are calculated sequentially to generate a berth distance set. The berth distance set contains the path distances from the first berth center point to each berth center point in the set of berth center points. Multiple distances in the berth distance set are identified and judged to determine the insertion position with the smallest distance between the first berth center point and the existing task node, which is then used as the first insertion position.

[0049] By using the shortest distance algorithm to identify the first insertion position in the queue of berth inspection tasks to be executed, the driving path of the unmanned inspection vehicle is optimized, unnecessary travel distance and time are reduced, inspection efficiency is improved, and the real-time response of tasks is ensured.

[0050] Furthermore, after obtaining the updated queue of unmanned berth inspection tasks to be executed, the method further includes: calculating the range threshold of the unmanned inspection vehicle based on the real-time remaining battery power of the unmanned inspection vehicle; analyzing the first unmanned berth inspection task in the queue of unmanned berth inspection tasks to be executed; calculating the first target range time required to execute the first unmanned berth inspection task and return to the charging point; and continuously updating the queue of unmanned berth inspection tasks to be executed by judging the first target range time and the range threshold.

[0051] Furthermore, by determining the first target endurance time and the endurance time threshold, the queue of pending berth inspection tasks is continuously updated. The method includes: if the first target endurance time is greater than the endurance time threshold, the charging task is prioritized; if the first target endurance time is less than the endurance time threshold, the second pending berth inspection task in the queue of pending berth inspection tasks is analyzed, and the second target endurance time required to execute the second pending berth inspection task and return to the charging point is calculated; and so on, until the Nth pending berth inspection task with an endurance time greater than the endurance time threshold is obtained, the charging task is placed before the Nth pending berth inspection task.

[0052] Specifically, after obtaining the updated queue of unmanned parking space inspection tasks, the current range threshold of the unmanned inspection vehicle is calculated based on its real-time remaining battery power. The range threshold T is... max =SOC*TAh / p, where SOC is the real-time remaining battery percentage of the unmanned inspection vehicle, TAh is the total battery capacity of the unmanned inspection vehicle, and p is the average power of the unmanned inspection vehicle during operation. The range threshold represents the upper limit of the task that the inspection vehicle can continuously perform before safely returning to the charging point under the current battery conditions.

[0053] Obtain the current mission location of the unmanned inspection vehicle, analyze the first pending berth inspection task in the queue of pending berth inspection tasks, and calculate the first target range required for the unmanned inspection vehicle to travel from its current mission location to the berth where the first pending berth inspection task is located, complete the inspection, and return to the charging point. The first target range time T1 = (D ax +D 0a ) / v+1*T s Among them, D ax To get from the current position x to the berth P where the first berth inspection task is to be performed. a Distance, D 0a To carry out the inspection task from berth P, the first berth to be inspected. a The distance to the charging point P0, v is the average speed of the unmanned inspection vehicle during its journey, and T is the distance to the charging point P0. sThe average time for an unmanned inspection vehicle to complete one photo-taking and evidence collection at the target parking space.

[0054] The first target endurance time is compared with the endurance time threshold to determine if the first target endurance time is greater than the endurance time threshold. Based on the determination result, the queue of pending berth inspection tasks is continuously updated. If the first target endurance time is greater than the endurance time threshold (i.e., the time to return to the charging point after completing the first task exceeds the current maximum cruising time of the unmanned inspection vehicle), the charging task is prioritized, and the unmanned inspection vehicle immediately returns to the charging point for charging. If the first target endurance time is less than the endurance time threshold, the second pending berth inspection task in the queue is analyzed. The second target endurance time required to travel from the current location to the berths where the first two tasks in the queue are located, complete two inspections, and return to the charging point is calculated. Wherein, the second target endurance time T2 = (D ax +D ab +D 0b ) / v+2*T s , where D ab To carry out the inspection task from berth P, the first berth to be inspected. a Arrive at berth P, where the second berth inspection task is to be carried out. b The distance, D 0b For the inspection task to be carried out at berth P, the second berth to be inspected b Return the distance to charging point P0. After obtaining the second target range, determine whether the second target cruising time is greater than the range threshold.

[0055] This process continues until the Nth berth inspection task with a range greater than the endurance threshold is obtained. The charging task is then placed before the Nth berth inspection task. If it is determined that the endurance of N berth inspection tasks in the queue is less than the endurance threshold, the charging task is placed at the end of the queue.

[0056] By combining the battery status of unmanned inspection vehicles with task scheduling, a safe inspection strategy under battery constraints was implemented. Furthermore, by dynamically calculating the target range and continuously updating the task queue, the system ensures that the inspection vehicle can safely return to its charging point while completing event-driven tasks, thereby improving the reliability and safety of unmanned roadside parking inspections.

[0057] Example 2, based on the same inventive concept as the event-driven unmanned road parking inspection vehicle path planning method in the foregoing examples, such as... Figure 2 As shown, this application provides an event-driven unmanned road parking patrol vehicle path planning system, wherein the event-driven unmanned road parking patrol vehicle path planning system includes:

[0058] The management platform construction component 11 is used to construct a smart parking management platform. The smart parking management platform includes a signal reporting source device associated with each parking space within the management area, wherein the signal reporting source device deploys parking space status change events. The inspection task sending component 12 is used to send a first parking space inspection task to the connected unmanned inspection vehicle when the signal reporting source device of any parking space triggers the parking space status change event. The task sorting component 13 is used to sort the first parking space inspection task into the unmanned inspection vehicle's queue of pending parking space inspection tasks using the task sorting rules of the path planning algorithm module, and replan the unmanned inspection vehicle's driving path according to the updated queue of pending parking space inspection tasks. The task sorting rules are constructed based on a set of key points within the management area, including charging points and parking space center points.

[0059] Furthermore, the task sorting component 13 is also used to perform the following: determining whether the unmanned inspection vehicle is at the charging point to perform a charging task; if the unmanned inspection vehicle is at the charging point to perform a charging task, receiving the real-time remaining power of the unmanned inspection vehicle, and determining whether the real-time remaining power is greater than a preset remaining power, wherein the preset remaining power is the minimum remaining power required to perform the first berth inspection task; if the real-time remaining power is greater than or equal to the preset remaining power, placing the first berth inspection task before the charging task; if the real-time remaining power is less than the preset remaining power, placing the first berth inspection task after the charging task.

[0060] Furthermore, the task sorting component 13 is also used to perform the following: if the unmanned inspection vehicle is not at the charging point to perform the charging task, identify the first berth center point corresponding to the first berth inspection task; traverse the queue of unmanned inspection vehicle's pending berth inspection tasks, and determine whether the first berth center point is on the path between the berth center points corresponding to two adjacent tasks in the queue of pending berth inspection tasks; if it is, insert the first berth inspection task between two adjacent tasks in the queue of pending berth inspection tasks; if it is not, identify the first insertion position in the queue of pending berth inspection tasks through the shortest distance algorithm, and insert the first berth inspection task into the first insertion position.

[0061] Furthermore, the task sorting component 13 is also used to perform: traversing the set of berth center points for each task in the queue of berth inspection tasks to be executed; calculating the set of berth distances between the first berth center point and the set of berth center points; and obtaining the first insertion position by identifying the distance in the set of berth distances.

[0062] Furthermore, the task sorting component 13 is also used to perform: calculating the range threshold of the unmanned inspection vehicle based on the real-time remaining battery power of the unmanned inspection vehicle; analyzing the first unexecuted berth inspection task in the queue of unexecuted berth inspection tasks; calculating the first target range time required to execute the first unexecuted berth inspection task and return to the charging point; and continuously updating the queue of unexecuted berth inspection tasks by judging the first target range time and the range threshold.

[0063] Furthermore, the task sorting component 13 is also used to perform the following: if the first target endurance time is greater than the endurance time threshold, prioritize the charging task; if the first target endurance time is less than the endurance time threshold, analyze the second berth inspection task to be executed in the queue of berth inspection tasks to be executed, calculate the second target endurance time required to execute the second berth inspection task to be executed and return to the charging point; and so on, until the Nth berth inspection task to be executed is obtained that is greater than the endurance time threshold, and place the charging task before the Nth berth inspection task to be executed.

[0064] Furthermore, the management platform construction component 11 is also used to execute: the parking space status change event includes a vehicle entry event or a vehicle exit event.

[0065] Furthermore, the management platform construction component 11 is also used to execute: the signal reporting source device is a geomagnetic detection device.

[0066] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. The event-driven unmanned road parking patrol vehicle path planning method and specific examples in the foregoing embodiment one are also applicable to the event-driven unmanned road parking patrol vehicle path planning system in this embodiment. Through the foregoing detailed description of the event-driven unmanned road parking patrol vehicle path planning method, those skilled in the art can clearly understand the event-driven unmanned road parking patrol vehicle path planning system in this embodiment. Therefore, for the sake of brevity, it will not be described in detail here.

[0067] The above description of the disclosed embodiments enables those skilled in the art to make or use this application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this application. Therefore, this application is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

[0068] Obviously, those skilled in the art can make several improvements and modifications to this application without departing from the principles of this application, and these improvements and modifications also fall within the protection scope of this application.

Claims

1. An event-driven path planning method for unmanned road parking inspection vehicles, characterized in that, The method includes: A smart parking management platform is constructed, which includes a signal reporting source device associated with each parking space within the management area, wherein the signal reporting source device deploys parking space status change events. When the signal reporting source device of any parking space triggers the parking space status change event, the smart parking management platform sends the first parking space inspection task to the connected unmanned inspection vehicle. The first berth inspection task is sorted into the queue of unmanned inspection vehicle's pending berth inspection tasks by the task sorting rules of the path planning algorithm module, and the driving path of the unmanned inspection vehicle is replanned according to the updated queue of pending berth inspection tasks. The task sorting rules are constructed based on a set of key locations within the management area, which includes charging points and berth center points.

2. The event-driven path planning method for unmanned road parking inspection vehicles as described in claim 1, characterized in that, The method of sorting the first berth inspection task into the queue of unmanned inspection vehicle's berth inspection tasks according to the task sorting rules of the path planning algorithm module includes: Determine whether the unmanned inspection vehicle is at the charging point and performing a charging task; If the unmanned inspection vehicle is at the charging point and performing a charging task, the system receives the real-time remaining power of the unmanned inspection vehicle and determines whether the real-time remaining power is greater than the preset remaining power, wherein the preset remaining power is the minimum remaining power required to perform the first berth inspection task. If the real-time remaining power is greater than or equal to the preset remaining power, the first berth inspection task will be placed before the charging task. If the real-time remaining power is less than the preset remaining power, the first berth inspection task will be placed after the charging task.

3. The event-driven path planning method for unmanned road parking inspection vehicles as described in claim 2, characterized in that, The method for determining whether the unmanned inspection vehicle is at the charging point to perform a charging task further includes: If the unmanned inspection vehicle is not performing a charging task at the charging point, identify the center point of the first berth corresponding to the first berth inspection task. Traverse the queue of unmanned inspection vehicle's pending berth inspection tasks and determine whether the center point of the first berth is located on the path between the center points of two adjacent tasks in the queue of pending berth inspection tasks. If so, insert the first berth inspection task into the queue of berth inspection tasks to be executed between two adjacent tasks. If not, the first insertion position in the queue of berth inspection tasks to be executed is identified by the shortest distance algorithm, and the first berth inspection task is inserted into the first insertion position.

4. The event-driven path planning method for unmanned road parking inspection vehicles as described in claim 3, characterized in that, The method for identifying the first insertion position in the queue of berth inspection tasks to be executed using a shortest distance algorithm includes: Iterate through the set of berth center points for each task in the queue of berth inspection tasks to be executed; Calculate the set of berth distances between the center point of the first berth and the set of berth center points, and obtain the first insertion position by identifying the distance magnitude of the set of berth distances.

5. The event-driven path planning method for unmanned road parking inspection vehicles as described in claim 3, characterized in that, After obtaining the updated queue of berth inspection tasks to be executed, the method also includes: The unmanned inspection vehicle's remaining battery power in real time is used to calculate the vehicle's endurance time threshold. Analyze the first berth inspection task in the queue of berth inspection tasks to be executed; Calculate the first target range time required to execute the first berth inspection task and return to the charging point. Continuously update the queue of berth inspection tasks by judging the first target range time and the range time threshold.

6. The event-driven path planning method for unmanned road parking inspection vehicles as described in claim 5, characterized in that, The method of continuously updating the queue of berth inspection tasks to be executed by determining the first target endurance time and the endurance time threshold includes: If the first target battery life is greater than the battery life threshold, the charging task is prioritized. If the first target range is less than the range threshold, analyze the second berth inspection task in the queue of berth inspection tasks to be executed, and calculate the second target range required to execute the second berth inspection task and return to the charging point. This process continues until the Nth berth inspection task with a range greater than the stated threshold is obtained, at which point the charging task is placed before the Nth berth inspection task.

7. The event-driven path planning method for unmanned road parking inspection vehicles as described in claim 1, characterized in that, The berth status change events include vehicle entry events or vehicle exit events.

8. The event-driven path planning method for unmanned road parking inspection vehicles as described in claim 1, characterized in that, The signal reporting source device is a geomagnetic detection device.

9. An event-driven path planning system for an unmanned road parking inspection vehicle, characterized in that, The steps for implementing the event-driven unmanned road parking inspection vehicle path planning method according to any one of claims 1 to 8 include: A management platform building component is used to build a smart parking management platform. The smart parking management platform includes a signal reporting source device associated with each parking space in the management area, wherein the signal reporting source device deploys parking space status change events. The inspection task sending component is used to send the first parking space inspection task to the connected unmanned inspection vehicle when the signal reporting source device of any parking space triggers the parking space status change event. The task sorting component is used to sort the first berth inspection task into the queue of unmanned inspection vehicle's pending berth inspection tasks according to the task sorting rules of the path planning algorithm module, and replan the driving path of the unmanned inspection vehicle according to the updated queue of pending berth inspection tasks. The task sorting rules are constructed based on a set of key locations within the management area, which includes charging points and berth center points.

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