CN-121982925-A - Intelligent garage management method and system for collaborative dynamic guidance

CN121982925ACN 121982925 ACN121982925 ACN 121982925ACN-121982925-A

Abstract

The invention discloses an intelligent garage management method and system for collaborative dynamic guidance, and aims to improve the utilization rate of parking resources and the guidance efficiency. The method receives a parking request containing user preferences and vehicle attributes, recommends and reserves a target parking lot. After a vehicle enters a field, the system acquires real-time data through the Internet of things, combines historical data and generates in-field dynamic situation information by utilizing a space-time fusion prediction model. Based on the method, the system executes multi-target dynamic optimal parking space allocation, and plans the driving path with the lowest comprehensive passing cost. In the guiding process, the system continuously monitors, and when the system is jammed or the parking space is occupied, the system immediately triggers the rescheduling of the parking space and the path. The corresponding system comprises modules of request processing, in-field sensing, data fusion and situation prediction, dynamic parking space allocation, real-time path planning, state monitoring, re-planning, charging updating and the like, and achieves automatic and intelligent management from reservation, guidance to off-site charging through a cloud, so that parking efficiency and user experience are remarkably improved.

Inventors

LI JIANG
LIU SHUANGSHENG
HE XINGLIANG
LI XIYUAN

Assignees

湖北安信数智信息技术有限公司

Dates

Publication Date: 20260505
Application Date: 20260205

Claims (9)

1. The intelligent garage management method for collaborative dynamic guidance is characterized by comprising the following steps of: S1, receiving a parking request of a user side, and acquiring information of a plurality of surrounding candidate parking lots based on destination information in the request; s2, determining and reserving a target parking lot for a user based on user preference information and information of the candidate parking lots; S3, acquiring real-time multidimensional state data in the target parking lot, and carrying out fusion analysis based on the data and historical data to generate and continuously update in-field dynamic situation information for representing future parking space occupation and traffic conditions; S4, responding to the vehicle entering the target parking lot, and dynamically distributing the optimal parking space for the vehicle based on multi-target decision by combining the in-field dynamic situation information, the vehicle attribute and the user preference; S5, dynamically planning a driving path from the current position to the optimal parking space for the vehicle based on the in-field dynamic situation information and the vehicle attribute; s6, the optimal parking space information and the running path are issued to a user side for guiding, and a guiding strategy is dynamically adjusted according to environmental changes in the guiding process; S7, updating the parking space state and completing the charging operation.
2. The collaborative dynamic guided intelligent garage management method according to claim 1, wherein in step S3, the fusion analysis is implemented by modeling a parking space topology to capture a space dependency relationship and combining historical and real-time sequence data analysis to capture a time law.
3. The intelligent garage management method for collaborative dynamic guidance according to claim 1, wherein the step S4 of dynamically allocating an optimal parking space for a vehicle based on a multi-objective decision comprises screening candidate parking spaces meeting physical constraints of the vehicle from current free parking spaces; Calculating a comprehensive evaluation value for each candidate parking space by calculating a weighted average, wherein the comprehensive evaluation value at least comprises estimated traffic time based on real-time and predicted traffic flow, walking distance reaching user preference facilities, future idle probability of the parking space based on the in-situ dynamic situation information and matching degree of the parking space attribute and user preference; And the parking space with the highest comprehensive evaluation value is allocated as the optimal parking space.
4. The intelligent garage management method according to claim 1, wherein in the step S5, when a driving path is dynamically planned for a vehicle, the path cost evaluation integrates a static physical length of a road section, a traffic time cost obtained based on real-time sensor data, a predicted congestion influence based on the on-site dynamic situation information, and a traffic difficulty coefficient based on vehicle attributes.
5. The intelligent garage management method according to claim 1, wherein in the step S6, the guiding strategy is dynamically adjusted according to the environmental change, and the method comprises continuously monitoring traffic flow and target parking space state in the field during the running process of the vehicle, triggering the re-planning process if the congestion degree of the planned path is detected to exceed the specified threshold or the parking space state is abnormal, and re-executing the parking space allocation and path planning by taking the current position of the vehicle as a new starting point and combining the latest environmental data.
6. A collaborative dynamic boot intelligent garage management system for implementing the method of any one of claims 1-5, comprising: The request processing and recommending module is deployed at the cloud and is used for executing the steps S1 and S2; the in-field sensing module is distributed in the parking lot and used for executing the step S3; The data fusion and situation prediction module is deployed at the cloud end and is used for executing the step S3; the dynamic parking space allocation module is deployed at the cloud end and is used for executing the step S4; The real-time path planning module is deployed at the cloud end and is used for executing the step S5; the state monitoring and rescheduling module is deployed at the cloud end and is used for executing the step S6; the charging and status updating module is deployed at the cloud end and is used for executing the step S7; The system comprises a request processing and recommending module, an in-field sensing module, a data fusion and situation prediction module, a dynamic parking space allocation module, a real-time path planning module, a state monitoring and rescheduling module and a charging and state updating module, wherein data interaction and instruction transmission are carried out through a network, and the whole-course dynamic guiding and management from receiving a user request to completing charging are completed cooperatively.
7. The collaborative dynamic boot intelligent garage management system of claim 6, wherein the request processing and recommendation module comprises a user interface unit and a recommendation engine unit; the user interface unit is used for interacting with a user terminal to receive a structured parking request data packet and transmitting the data packet to the recommendation engine unit; The recommendation engine unit is used for calculating and determining a target parking lot based on destination information, user preference and multi-source external data in the data packet, and issuing a reservation result to a user side through the user interface unit; the data fusion and situation prediction module comprises a sensor network unit and a prediction analysis unit; the sensor network units are distributed in the parking lot and are used for collecting multidimensional state data in real time; The prediction analysis unit is used for receiving and fusing the real-time data and the historical data uploaded by the sensor network unit, and generating in-field dynamic situation information through a space-time fusion prediction model; The dynamic parking space allocation module and the real-time path planning module comprise a decision unit and a path calculation unit; The decision unit is used for receiving the in-field dynamic situation information, the vehicle attribute and the user preference, and outputting an optimal parking space allocation result according to a multi-objective decision model; the path calculation unit is used for receiving the optimal parking space information, the real-time position of the vehicle and the dynamic situation information in the field and calculating a driving path based on a dynamic cost map; the state monitoring and rescheduling module comprises an abnormality detection unit and a rescheduling triggering unit; The anomaly detection unit is used for continuously receiving feedback from the sensor network and the vehicle positioning data and monitoring traffic flow and parking space states; The re-planning triggering unit is used for triggering the decision unit and the path calculation unit to re-execute calculation when the abnormality detection unit judges that the re-planning condition is met.
8. The collaborative dynamic boot intelligent garage management system of claim 7, wherein the sensor network element in the in-field awareness module comprises: the parking space state sensing subunit is deployed in each parking space and used for detecting the occupation state of the parking space and identifying the vehicle license plate; The traffic flow sensing subunit is deployed in the key channel and is used for collecting traffic flow, speed and queuing length data; The environment and event sensing subunit is used for collecting facility state and safety event data; and the vehicle positioning beacon subunit is deployed in the field and is used for providing real-time positioning data for the vehicle.
9. The collaborative dynamic guided intelligent garage management system of claim 7, wherein the real-time path planning module plans a travel path by: Constructing a dynamic cost map, wherein the cost of each road section integrates the static physical length, the passing time based on real-time sensor data, the predicted congestion influence based on the in-field dynamic situation information and the passing difficulty coefficient based on the vehicle attribute; And searching an optimal path from the current position of the vehicle to the optimal parking space on the dynamic cost map by adopting a D Lite algorithm.

Description

Intelligent garage management method and system for collaborative dynamic guidance Technical Field The invention relates to the field of intelligent garage management, in particular to a collaborative dynamic guiding intelligent garage management method and system. Background With the acceleration of the urban process and the rapid increase of the quantity of motor vehicles held, the 'parking difficulty' has become a common problem which plagues urban traffic management and daily travel of residents. The traditional parking lot management mode is extensive and mainly depends on manual guidance and static identification, so that a vehicle owner often faces the dilemma of difficult position finding, slow passing and messy management after entering a garage, and a large amount of time is required to blindly find an idle parking place, thereby not only reducing parking experience, but also exacerbating traffic jam and potential safety hazard in the garage. To address the above challenges, smart parking systems have been developed and developed rapidly. The prior art mainly evolves along several directions, namely, firstly, the occupied state of the parking space is detected in real time by deploying a sensor network (such as ultrasonic waves, geomagnetism and video piles), and idle parking space information is issued to a vehicle owner in a mode of a parking space guide screen, an indicator light and the like, so that basic parking space guide is realized. Secondly, the technology of the Internet of things and the technology of the mobile Internet are combined, so that a user is allowed to remotely inquire the parking space and reserve the parking space through a mobile phone application program, and navigation is performed in the field through an electronic map or an indicator lamp. Third, more advanced image recognition, artificial intelligence algorithms are introduced in an attempt to more accurately recognize and schedule vehicles. For example, some schemes evaluate parking space suitability by analyzing historical vehicle images, or dynamically optimize garage space layout and vehicle access sequence using algorithms. However, the existing intelligent garage management system still has a plurality of limitations, and cannot realize real collaborative dynamic guidance. The specific expression is as follows: the system is isolated, and most of system functional modules are relatively independent. Links such as parking space detection, path guidance, reverse vehicle searching, cost payment and the like are often realized by different subsystems, data flow is broken, and closed loop collaborative optimization is difficult to form. For example, real-time traffic flows, user personalized preferences (e.g., approaching elevators, charging piles) and future occupancy probabilities of the parking spaces are rarely comprehensively considered when the parking spaces are allocated, so that the recommended parking spaces may not be globally optimal. Static or semi-static guidance has poor dynamic adaptability, and the existing guidance strategy is mostly based on static parking space state data at a certain moment. Once planning is completed, the system is difficult to adjust in real time according to the transient traffic conditions (such as sudden congestion and vehicle crossing) in the field. Studies have shown that incorrect road selection consumes significant driver time, and existing systems lack predictive and responsive mechanisms for dynamic traffic flow. The data is shallow, lacks situation awareness and prediction capability, and multidimensional data (such as parking space state, vehicle passing speed and history rule) collected by the system are not deeply fused and deeply mined. The prediction capability of the future short-time parking space occupation and the traffic jam situation in the parking lot cannot be constructed. This results in the system only reacting "when it is down" and not being able to proceed with prospective planning and scheduling to prevent congestion or increase the turnover rate of the parking space. The characteristics of the users and the vehicles are not considered enough, the vehicles are mostly regarded as homogeneous objects in the existing scheme, and whether gaps exist or not is mainly considered when the parking spaces are allocated, so that the individual requirements of the vehicle size, the vehicle type and the users are ignored. Meanwhile, the whole-course personalized service chain from outside to inside is not yet opened, for example, the most convenient parking lot and parking space are intelligently recommended for the user according to the final destination of the user. In summary, the current intelligent garage management system has obvious defects in the aspect of collaborative dynamic guidance. Therefore, a novel intelligent garage management method and system capable of integrating multi-source information, realizing on-site and off-site coordination and perf