A parking lot scheduling method and system based on big data

Abstract

This application proposes a parking lot scheduling method and system based on big data, relating to the field of parking scheduling technology. Firstly, based on the historical parking records of a first parking lot, multiple first permanent parking profiles and first temporary parking profiles are determined. Then, the first parking lot is divided into multiple first parking areas according to these parking profiles. When a vehicle enters the first parking lot, parking instruction information is generated based on whether the vehicle is a permanent resident vehicle. During the generation of parking instruction information, the first real-time parking information of the first parking lot and the corresponding parking profiles are integrated. Through the technical solution of this application, big data analysis of the historical parking records of the parking lot can be performed to provide highly appropriate parking guidance information for permanent and temporary vehicles, thereby reducing congestion caused by overcrowding.

Description

Technical Field

[0001] This invention belongs to the field of parking scheduling technology, and in particular relates to a parking lot scheduling method and system based on big data. Background Technology

[0002] Existing technologies can already achieve parking space reservation, which can realize the orderly scheduling of parking lots to a certain extent. However, when the real-time traffic flow is large, it may cause traffic congestion because too many vehicles want to park in the same area of ​​the parking lot. Summary of the Invention

[0003] The purpose of this invention is to provide a parking lot scheduling method and system based on big data to solve the technical problem of vehicle congestion caused by overcrowding in existing technologies.

[0004] This application proposes a parking lot scheduling method based on big data, characterized in that the method includes:

[0005] S1: Based on the historical parking records of the first parking lot, determine multiple parking profiles of the first permanent resident vehicle and the first temporary parking vehicle;

[0006] S2: Based on the first location distribution information of the first parking lot, multiple first permanent parking profiles and first temporary parking profiles, multiple first parking areas are determined;

[0007] S3: Determine whether the first vehicle entering is a parked vehicle. If so, proceed to S4; otherwise, proceed to S5.

[0008] S4: Generate first parking instruction information for each first permanent vehicle based on the parking profile and real-time parking information of each first permanent vehicle;

[0009] S5: Based on the first temporary parking profile and the first real-time parking information, generate second parking instruction information for each first temporary parking location.

[0010] Preferably, step S1 includes the following sub-steps:

[0011] S11: Extract historical parking records of the first parking lot within a preset time period and identify multiple first parking records;

[0012] S12: Cluster the multiple first parking records according to the first vehicle identifier to obtain multiple first parking record sets, and determine the first parking record set that meets the preset conditions as the first permanent parking record set;

[0013] S13: Determine multiple first permanent vehicle parking profiles based on multiple sets of first permanent vehicle parking records;

[0014] S14: Based on the multiple sets of first parking records and the multiple sets of first permanent parking records, determine multiple first temporary parking records, and generate a first temporary parking profile based on the multiple first temporary parking records.

[0015] Preferably, step S13 includes the following sub-steps:

[0016] S131: For each of the first set of parking records for a permanent vehicle, calculate the first average entry time and the first average exit time;

[0017] S132: For each of the first permanent parking record sets, obtain the first parking area;

[0018] S133: Generate multiple parking profiles of the first permanent parking vehicle based on multiple first average entry times, first average exit times, and first parking areas.

[0019] Preferably, step S14 includes the following sub-steps:

[0020] S141: Remove the multiple sets of first permanent parking records from the multiple sets of first parking records to obtain multiple sets of first temporary parking records;

[0021] S142: Analyze multiple first temporary parking records to obtain the first temporary parking profile.

[0022] Preferably, step S2 includes the following sub-steps:

[0023] S21: Based on multiple first permanent parking profiles and first temporary parking profiles, determine multiple first parking coordinate points;

[0024] S22: Based on multiple first parking coordinate points and a first parking area threshold, multiple first parking areas are determined.

[0025] Preferably, step S4 includes the following sub-steps:

[0026] S41: Determine the first parking priority based on the matching degree between the first permanent parking profile and the current time;

[0027] S42: Based on the first real-time parking information, determine the first number of available parking spaces. If the first number of available parking spaces is lower than a preset value, proceed to S43; otherwise, proceed to S44. The first number of available parking spaces refers to the number of available parking spaces in the parking area preferred by the first entering vehicle.

[0028] S43: Provide first parking instruction information to the first entering vehicle based on the first parking priority and multiple first parking areas.

[0029] Preferably, step S5 includes the following sub-steps:

[0030] S51: Determine the first location information to be guided based on the matching degree between the first entering vehicle and the first temporary parking profile;

[0031] S52: Determine second parking instruction information based on the first real-time parking information, multiple first parking areas, and the first location to be guided.

[0032] This application also proposes a parking lot scheduling system based on big data to implement the above-mentioned parking lot scheduling method based on big data.

[0033] This application proposes a parking lot scheduling method and system based on big data, relating to the field of parking scheduling technology. Firstly, based on the historical parking records of a first parking lot, multiple first permanent parking profiles and first temporary parking profiles are determined. Then, the first parking lot is divided into multiple first parking areas according to these parking profiles. When a vehicle enters the first parking lot, parking instruction information is generated based on whether the vehicle is a permanent resident vehicle. During the generation of parking instruction information, the first real-time parking information of the first parking lot and the corresponding parking profiles are integrated. Through the technical solution of this application, big data analysis of the historical parking records of the parking lot can be performed to provide highly appropriate parking guidance information for permanent and temporary vehicles, thereby reducing congestion caused by overcrowding. Attached Figure Description

[0034] To more clearly illustrate the embodiments of the present invention or the technical solutions in 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, and those skilled in the art can derive other embodiments based on the provided drawings without creative effort.

[0035] Figure 1 This is an execution flowchart of a parking lot scheduling method based on big data according to the present invention.

[0036] Figure 2 This is an execution flowchart of the present invention for determining multiple first permanent parking vehicle portraits and first temporary parking portraits. Detailed Implementation

[0037] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0038] The present invention will now be described in detail with reference to the accompanying drawings and specific embodiments. The illustrative embodiments and descriptions are only used to explain the present invention and are not intended to limit the present invention.

[0039] The following is a detailed description of a parking lot scheduling method and system based on big data according to the present invention.

[0040] This embodiment proposes a parking lot scheduling method based on big data. The method flow is as follows: Figure 1 As shown, the specific steps include the following:

[0041] S1: Based on the historical parking records of the first parking lot, determine multiple parking profiles of the first permanent resident vehicle and the first temporary parking vehicle.

[0042] In order to adapt to the characteristics of the first parking lot and provide customized parking guidance information for the first permanent vehicle and the first temporary vehicle, this step requires determining multiple parking profiles of the first permanent vehicle and the first temporary vehicle based on the historical parking records of the first parking lot.

[0043] S1 specifically includes the following sub-steps:

[0044] S11: Extract the historical parking records of the first parking lot within a preset time period and identify multiple first parking records.

[0045] During the operating hours of the first parking lot, parking records are generated for all vehicles entering and exiting. In this step, it is necessary to extract the historical parking records within the preset time period from the database one by one.

[0046] Preferably, in order to ensure that the parking profile has certain statistical significance, the preset time period is preferably one week or longer.

[0047] S12: Cluster multiple first parking records according to the first vehicle identifier to obtain multiple first parking record sets, and determine the first parking record set that meets the preset conditions as the first permanent parking record set.

[0048] Since multiple first parking records include both permanent and temporary parking records, in order to classify multiple first parking records into permanent parking records and temporary parking records, it is necessary to first cluster them according to vehicle identifiers such as license plate numbers to obtain multiple sets of first parking records, where each set of first parking records corresponds to one vehicle.

[0049] Next, multiple sets of the first parking records can be filtered according to preset conditions to select multiple sets of the first permanent parking records. The preset conditions can be the first parking records that have been parked more than a specified number of times within the preset time period.

[0050] S13: Determine multiple first permanent vehicle parking profiles based on multiple sets of first permanent vehicle parking records.

[0051] Since each set of first permanent parking records corresponds to one vehicle, multiple first permanent parking vehicles can be identified based on multiple sets of first permanent parking records. Furthermore, multiple first permanent parking profiles corresponding to multiple first permanent parking vehicles can be determined using the first set of parking records.

[0052] S13 may include the following sub-steps:

[0053] S131: For each of the first set of parking records for a permanent vehicle, calculate the first average entry time and the first average exit time.

[0054] In this step, multiple parking records can be extracted from the first set of permanent parking records, and multiple entry time information and multiple exit time information can be extracted from the multiple parking records, thereby using a specific algorithm to calculate the first average entry time and the average exit time.

[0055] S132: For each of the first permanent parking record sets, obtain the first parking area.

[0056] In this step, multiple parking locations of the first permanent vehicle can be obtained through historical image information, thereby determining the first parking area based on the multiple parking locations. The first parking area can be a preset range of areas where the first permanent vehicle prefers to park.

[0057] S133: Generate multiple parking profiles of the first permanent parking vehicle based on multiple first average entry times, first average exit times, and first parking areas.

[0058] In this step, the first average entry time, the first average exit time, and the first parking area can be used as labels to form multiple parking profiles of the first permanently parked vehicle.

[0059] S14: Based on the multiple sets of first parking records and the multiple sets of first permanent parking records, determine multiple first temporary parking records, and generate a first temporary parking profile based on the multiple first temporary parking records.

[0060] The multiple sets of first parking records include parking records for permanent vehicles and parking records for temporary vehicles. Therefore, in this step, the multiple sets of first permanent vehicle parking records are removed from the multiple sets of first parking records to obtain multiple sets of first temporary parking records.

[0061] S14 includes the following sub-steps:

[0062] S141: Remove the multiple sets of first permanent parking records from the multiple sets of first parking records to obtain multiple sets of first temporary parking records.

[0063] Multiple sets of the first parking records form a complete set. After removing multiple sets of the first permanent parking records from this set, multiple sets of the first temporary parking records can be obtained.

[0064] S142: Analyze multiple first temporary parking records to obtain the first temporary parking profile.

[0065] In analyzing multiple first temporary parking records, the main focus is on obtaining the changing patterns of the number of parking spaces, entry time, exit time, and parking location over time. After assigning labels to these factors, a profile of the first temporary parking space can be obtained.

[0066] S2: Based on the first location distribution information of the first parking lot, multiple first permanent parking profiles and first temporary parking profiles, multiple first parking areas are determined.

[0067] In this step, the first parking lot is divided into multiple first parking areas, which facilitates the provision of parking guidance information to vehicles waiting to park.

[0068] S2 includes the following sub-steps:

[0069] S21: Based on multiple first permanent parking profiles and first temporary parking profiles, multiple first parking coordinate points are determined.

[0070] In multiple first permanent parking profiles and first temporary parking profiles, there are tags related to the parking location. Therefore, in this step, the location information in the corresponding tags can be statistically analyzed to obtain multiple first parking coordinate points.

[0071] Preferably, the multiple first parking coordinate points can be obtained by taking the geometric center of the parking location information in multiple tags.

[0072] S22: Based on multiple first parking coordinate points and a first parking area threshold, multiple first parking areas are determined.

[0073] Typically, the area where car owners habitually park will not exceed a certain area threshold. Therefore, in this step, multiple first parking areas can be determined based on the multiple first parking coordinate points determined in S21 and the preset first parking area threshold.

[0074] Preferably, the determination of multiple first parking areas can be based on multiple first parking coordinate points as centers and the first parking area threshold as a range. In addition, in the process of determining multiple first parking areas, it should be ensured that different first parking areas do not overlap or that the overlapping areas are minimized.

[0075] S3: Determine whether the first vehicle entering is a parked vehicle. If so, proceed to S4; otherwise, proceed to S5.

[0076] In this step, it can be determined whether the first input vehicle matches the vehicle identification information in the first permanent parking record set. If so, it indicates that the first entering vehicle is a permanent parking vehicle, and then proceeds to S4; otherwise, it indicates that the first entering vehicle is a temporary parking vehicle, and then proceeds to S5.

[0077] S4: Generate first parking instruction information for each first permanent vehicle based on the parking profile and real-time parking information of each first permanent vehicle.

[0078] In this step, the parking profile of the first permanently parked vehicle and the first real-time parking information of the first parking lot are referenced to generate the first parking instruction information for each of the first permanently parked vehicles.

[0079] S4 includes the following sub-steps:

[0080] S41: Determine the first parking priority based on the matching degree between the first permanent parking profile and the current time.

[0081] In this step, the first parking priority is determined based on the matching degree between the entry time and exit time in the first permanent parking profile and the current time.

[0082] The first parking priority is used to characterize the degree to which the first entering vehicle can be preferentially guided to the location indicated in the first permanent parking profile in the first parking instruction information.

[0083] S42: Based on the first real-time parking information, determine the first number of available parking spaces. If the first number of available parking spaces is lower than a preset value, proceed to S43; otherwise, proceed to S44.

[0084] The first number of available parking spaces refers to the number of available parking spaces in the parking area preferred by the first entering vehicle.

[0085] S43: Provide first parking instruction information to the first entering vehicle based on the first parking priority and multiple first parking areas.

[0086] When there are few available parking spaces in the preferred parking area, the vehicle with the highest parking priority should be guided to the preferred parking area first. If the first parking priority is not sufficient, the vehicle should be guided to another first parking area adjacent to the signalized parking area.

[0087] S5: Based on the first temporary parking profile and the first real-time parking information, generate second parking instruction information for each first temporary parking location.

[0088] In this step, the first parking instruction information needs to be generated for each first temporary parking space by referring to the first temporary parking profile and the first real-time parking information of the first parking lot.

[0089] S5 includes the following sub-steps:

[0090] S51: Determine the first location information to be guided based on the matching degree between the first entering vehicle and the first temporary parking profile.

[0091] When the entry time of the first entering vehicle matches the first temporary parking profile with a high degree of accuracy, it is preferentially guided to the parking location indicated by the first temporary parking profile, which is the first location information to be guided.

[0092] S52: Determine second parking instruction information based on the first real-time parking information, multiple first parking areas, and the first location to be guided.

[0093] When the number of available parking spaces at the first location to be guided is less than a preset value, the first vehicle entering the vehicle needs to be guided to another first parking area that is close to the first location to be guided.

[0094] The first and second parking instruction information can be displayed on billboards in the first parking lot, or sent to the mobile terminal of the corresponding car owner and displayed thereon.

[0095] This application also proposes a big data-based parking lot scheduling system for executing the aforementioned big data-based parking lot scheduling method.

[0096] This application proposes a parking lot scheduling method and system based on big data, relating to the field of parking scheduling technology. Firstly, based on the historical parking records of a first parking lot, multiple first permanent parking profiles and first temporary parking profiles are determined. Then, the first parking lot is divided into multiple first parking areas according to these parking profiles. When a vehicle enters the first parking lot, parking instruction information is generated based on whether the vehicle is a permanent resident vehicle. During the generation of parking instruction information, the first real-time parking information of the first parking lot and the corresponding parking profiles are integrated. Through the technical solution of this application, big data analysis of the historical parking records of the parking lot can be performed to provide highly appropriate parking guidance information for permanent and temporary vehicles, thereby reducing congestion caused by overcrowding.

[0097] The above description is only a preferred embodiment of the present invention. Therefore, all equivalent changes or modifications made to the structure, features and principles described in the claims of this patent application are included in the scope of this patent application.

Claims

1. A parking lot scheduling method based on big data, characterized in that, The method includes: S1: Based on the historical parking records of the first parking lot, determine multiple parking profiles of the first permanent resident vehicle and the first temporary parking vehicle; S2: Based on the first location distribution information of the first parking lot, multiple first permanent parking profiles and first temporary parking profiles, multiple first parking areas are determined; S3: Determine whether the first vehicle entering is a parked vehicle. If so, proceed to S4; otherwise, proceed to S5. S4: Generate first parking instruction information for each first permanent vehicle based on the parking profile and real-time parking information of each first permanent vehicle; S5: Based on the first temporary parking profile and the first real-time parking information, generate second parking instruction information for each first temporary parking location; S1 includes the following sub-steps: S11: Extract historical parking records of the first parking lot within a preset time period and identify multiple first parking records; S12: Cluster the multiple first parking records according to the first vehicle identifier to obtain multiple first parking record sets, and determine the first parking record set that meets the preset conditions as the first permanent parking record set; S13: Determine multiple first permanent vehicle parking profiles based on multiple sets of first permanent vehicle parking records; S14: Based on the multiple sets of first parking records and the multiple sets of first permanent parking records, determine multiple first temporary parking records, and generate a first temporary parking profile based on the multiple first temporary parking records; S13 includes the following sub-steps: S131: For each of the first set of parking records for a permanent vehicle, calculate the first average entry time and the first average exit time; S132: For each of the first permanent parking record sets, obtain the first parking area; S133: Generate multiple parking profiles of the first permanent parking vehicle based on multiple first average entry times, first average exit times, and first parking areas; S14 includes the following sub-steps: S141: Remove the multiple sets of first permanent parking records from the multiple sets of first parking records to obtain multiple sets of first temporary parking records; S142: Analyze multiple first temporary parking records to obtain the first temporary parking profile; S4 includes the following sub-steps: S41: Determine the first parking priority based on the matching degree between the first permanent parking profile and the current time; S42: Based on the first real-time parking information, determine the first number of available parking spaces. If the first number of available parking spaces is lower than a preset value, proceed to S43. The first number of available parking spaces refers to the number of available parking spaces in the parking area preferred by the first entering vehicle. S43: Provide first parking instruction information to the first entering vehicle based on the first parking priority and multiple first parking areas.

2. The parking lot scheduling method based on big data according to claim 1, characterized in that, S2 includes the following sub-steps: S21: Based on multiple first permanent parking profiles and first temporary parking profiles, determine multiple first parking coordinate points; S22: Based on multiple first parking coordinate points and a first parking area threshold, multiple first parking areas are determined.

3. The parking lot scheduling method based on big data according to claim 2, characterized in that, S5 includes the following sub-steps: S51: Determine the first location information to be guided based on the matching degree between the first entering vehicle and the first temporary parking profile; S52: Determine second parking instruction information based on the first real-time parking information, multiple first parking areas, and the first location to be guided.

4. A parking lot scheduling system based on big data, used to implement the parking lot scheduling method based on big data in claims 1-3 above.

Images