CN-119479355-B - Big data-based parking lot scheduling method and system

CN119479355BCN 119479355 BCN119479355 BCN 119479355BCN-119479355-B

Abstract

The application provides a big data-based parking lot scheduling method and a big data-based parking lot scheduling system, which relate to the technical field of parking scheduling, and firstly determine a plurality of first resident car parking figures and first adjacent car parking figures according to the historical parking records of a first parking lot, divide the first parking lot into a plurality of first parking areas according to the car parking figures, and further, when a vehicle is driven into the first parking lot, respectively generating parking instruction information according to whether the vehicle is a resident vehicle, wherein in the process of generating the parking instruction information, the first real-time parking information of the first parking lot and the corresponding parking image are integrated. According to the technical scheme, the historical parking record of the parking lot can be subjected to big data analysis, so that parking guide information with high adaptation degree is provided for normal parking and temporary parking respectively, and congestion caused by stacking parking is reduced.

Inventors

ZHAO JI
MEI JUAN
FU YI

Assignees

无锡城市职业技术学院

Dates

Publication Date: 20260505
Application Date: 20241111

Claims (4)

1. The parking lot scheduling method based on big data is characterized by comprising the following steps: s1, determining a plurality of first resident car parking images and first adjacent car parking images according to a historical parking record of a first parking lot; s2, determining a plurality of first parking areas according to the first position distribution information of the first parking lot, a plurality of first resident car parking images and the first temporary parking images; s3, judging whether the first incoming vehicle is a resident vehicle, if so, turning to S4, otherwise turning to S5; S4, generating first parking indication information for each first resident car according to each first resident car parking image and first real-time parking information; S5, generating second parking indication information for each first parking according to the first parking image and the first real-time parking information; the step S1 comprises the following substeps: S11, extracting historical parking records of the first parking lot in a preset time period, and determining a plurality of first parking records; S12, clustering a plurality of first parking records according to a first vehicle identifier to obtain a plurality of first parking record sets, and determining the first parking record sets meeting preset conditions as first resident vehicle parking record sets; S13, determining a plurality of first resident vehicle parking portraits according to a plurality of first resident vehicle parking record sets; s14, determining a plurality of first adjacent parking records according to the plurality of first parking record sets and the plurality of first parking record sets, and generating a first adjacent parking image according to the plurality of first adjacent parking records; the step S13 comprises the following substeps: S131, calculating a first average driving-in time and a first average driving-out time for each first parking record set; S132, acquiring a first parking area for each first parking record set; s133, generating a plurality of first resident parking images according to the plurality of first average driving-in times, the first average driving-out times and the first parking areas; the step S14 includes the following sub-steps: s141, removing a plurality of first parking record sets in the plurality of first parking record sets to obtain a plurality of first temporary parking records; S142, analyzing a plurality of the first temporary parking records to obtain the first temporary parking images; The step S4 comprises the following substeps: s41, determining a first parking priority according to the matching degree of the first parking image and the current time; S42, determining a first number of vacant parking spaces according to the first real-time parking information, and if the first number of vacant parking spaces is lower than a preset value, turning to S43, wherein the first number of vacant parking spaces refers to the number of vacant parking spaces in a parking area favored by the first entering vehicle; And S43, providing first parking indication information for the first incoming vehicle according to the first parking priority and the plurality of first parking areas.
2. The method for dispatching a parking lot based on big data according to claim 1, wherein the step S2 comprises the following sub-steps: s21, determining a plurality of first parking coordinate points according to a plurality of first parking images and the first adjacent parking images; S22, determining a plurality of first parking areas according to the first parking coordinate points and the first parking area threshold.
3. The method for dispatching a parking lot based on big data according to claim 2, wherein the step S5 comprises the following sub-steps: S51, determining first position information to be guided according to the matching degree of the first incoming vehicle and the first temporary parking image; And S52, determining second parking indication information according to the first real-time parking information, the plurality of first parking areas and the first position information to be guided.
4. A big data based parking lot scheduling system for implementing a big data based parking lot scheduling method according to any one of the preceding claims 1-3.

Description

Big data-based parking lot scheduling method and system Technical Field The invention belongs to the technical field of parking scheduling, and particularly relates to a parking lot scheduling method and system based on big data. Background In the prior art, reservation of parking spaces can be realized, so that orderly scheduling of parking spaces can be realized to a certain extent, but when the real-time traffic flow is large, vehicles can be blocked because too many vehicles expect to park in the same area of the parking spaces. Disclosure of Invention The invention aims to provide a parking lot scheduling method and system based on big data, which are used for solving the technical problem of vehicle blockage caused by bundling and parking in the prior art. The application provides a parking lot scheduling method based on big data, which is characterized by comprising the following steps: s1, determining a plurality of first resident car parking images and first adjacent car parking images according to a historical parking record of a first parking lot; s2, determining a plurality of first parking areas according to the first position distribution information of the first parking lot, a plurality of first resident car parking images and the first temporary parking images; s3, judging whether the first incoming vehicle is a resident vehicle, if so, turning to S4, otherwise turning to S5; S4, generating first parking indication information for each first resident car according to each first resident car parking image and first real-time parking information; s5, generating second parking indication information for each first parking according to the first parking image and the first real-time parking information. Preferably, the step S1 includes the following sub-steps: S11, extracting historical parking records of the first parking lot in a preset time period, and determining a plurality of first parking records; S12, clustering a plurality of first parking records according to a first vehicle identifier to obtain a plurality of first parking record sets, and determining the first parking record sets meeting preset conditions as first resident vehicle parking record sets; S13, determining a plurality of first resident vehicle parking portraits according to a plurality of first resident vehicle parking record sets; And S14, determining a plurality of first adjacent parking records according to the plurality of first parking record sets and the plurality of first parking record sets, and generating a first adjacent parking image according to the plurality of first adjacent parking records. Preferably, the step S13 includes the following substeps: S131, calculating a first average driving-in time and a first average driving-out time for each first parking record set; S132, acquiring a first parking area for each first parking record set; S133, generating a plurality of first resident parking images according to the plurality of first average driving-in times, the first average driving-out times and the first parking areas. Preferably, the step S14 includes the following substeps: s141, removing a plurality of first parking record sets in the plurality of first parking record sets to obtain a plurality of first temporary parking records; S142, analyzing the plurality of first temporary parking records to obtain the first temporary parking image. Preferably, the step S2 includes the following sub-steps: s21, determining a plurality of first parking coordinate points according to a plurality of first parking images and the first adjacent parking images; S22, determining a plurality of first parking areas according to the first parking coordinate points and the first parking area threshold. Preferably, the step S4 includes the following substeps: s41, determining a first parking priority according to the matching degree of the first parking image and the current time; S42, determining the first number of the vacant parking spaces according to the first real-time parking information, if the first number of the vacant parking spaces is lower than a preset value, turning to S43, otherwise turning to S44, wherein the first number of the vacant parking spaces refers to the number of the vacant parking spaces in a parking area favored by the first entering vehicle; And S43, providing first parking indication information for the first incoming vehicle according to the first parking priority and the plurality of first parking areas. Preferably, the step S5 includes the following substeps: S51, determining first position information to be guided according to the matching degree of the first incoming vehicle and the first temporary parking image; And S52, determining second parking indication information according to the first real-time parking information, the plurality of first parking areas and the first position information to be guided. The application also provides a parking lot scheduling system based on the big d