Parking space management method, system and equipment based on big data analysis and medium

Abstract

The invention relates to a parking space management method, system and device based on big data analysis and a medium, and belongs to the technical field of parking space management. The system comprises a comprehensive information acquisition module, a parking space matching module, an optimal path recommendation module and a vehicle reputation scoring module, and parking space reservation request information and parking space information are processed through a bilateral matching model to obtain a standardized decision matrix, optimal parking space information and a parking space ground lock state. And obtaining parking lot path node information and parking space lock information, processing the parking lot path node information and the optimal parking space information through an ant colony algorithm to obtain an optimal path, generating navigation prompt information according to the optimal path, and sending the navigation prompt information to the handheld terminal. And obtaining a parking reputation level according to the parking space reservation request information and the parking space ground lock information. The parking space utilization rate, the parking space management efficiency and the parking space searching efficiency of the driver are improved, the local traffic jam phenomenon is relieved, and the parking behavior of the driver is standardized.

Description

Technical Field

[0001] This invention belongs to the field of parking space management technology, specifically relating to a parking space management method, system, equipment, and medium based on big data analysis. Background Technology

[0002] Currently, facing the problem of parking difficulties, most parking lots still suffer from low parking space utilization, low parking space management efficiency, long time for drivers to find parking spaces, and parking queues in popular areas during peak hours that disrupt traffic. This further leads to a large waste of resources, road congestion, and air pollution. Summary of the Invention

[0003] To address the aforementioned problems in existing technologies, this invention provides a parking space management method, system, device, and medium based on big data analysis. It processes parking space reservation request information and parking space information using a bilateral matching model to obtain a standardized decision matrix, optimal parking space information, and parking lock status. It also acquires parking lot path node information and parking lock information. An ant colony algorithm is used to process the parking lot path node information and optimal parking space information to obtain the optimal path. Navigation prompts are generated based on the optimal path and sent to the handheld terminal. Finally, a parking credit rating is obtained based on the parking space reservation request information and parking lock information. This invention improves parking space utilization, parking space management efficiency, and driver parking space search efficiency, thereby alleviating localized traffic congestion and regulating driver parking behavior.

[0004] The objective of this invention can be achieved through the following technical solutions:

[0005] A parking space management method based on big data analysis includes the following steps:

[0006] S1: Obtain parking space reservation request information and parking space information. The parking space reservation request information includes estimated arrival time, reservation duration, current location information, destination location information, and vehicle license plate information. The parking space information includes vacant parking space information, non-vacant parking space information, parking space fee information, and parking convenience.

[0007] S2: Process the parking space reservation request information and the parking space information through a bilateral matching model to obtain a normalized decision matrix. Obtain the optimal parking space information and parking space lock status based on the normalized decision matrix. Send the optimal parking space information to the handheld terminal. The parking space lock status includes the binding status. The optimal parking space information includes the parking space location node.

[0008] S3: The terminal identifies vehicle license plate information, uploads vehicle entry information for vehicles that have submitted the parking space reservation request, and sends reservation prompt information to the handheld terminal for vehicles that have not submitted the parking space reservation request. The vehicle entry information includes the terminal location node.

[0009] S4: Obtain parking lot path node information, process the parking lot path node information and the best parking space information using the ant colony algorithm based on the vehicle entry information to obtain the optimal path, generate navigation prompt information based on the optimal path and send the navigation prompt information to the handheld terminal, wherein the parking lot path node information includes road segment nodes and road segment node distances;

[0010] S5: Obtain parking space lock information, and obtain parking credit level based on the parking space reservation request information and the parking space lock information. The parking space lock information includes unlocking time and locking time, and the parking credit level includes high credit, medium credit, and low credit.

[0011] Preferably, step S2 includes the following steps:

[0012] S201: Obtain a matching decision matrix based on the parking space reservation request information and the parking space information, and process the matching decision matrix through the bilateral matching model to obtain a normalized decision matrix;

[0013] S202: Calculate the optimal parking space coefficient based on the normalized decision matrix to obtain the optimal parking space information, generate parking space lock binding success information based on the optimal parking space information, and update the parking space lock status to the binding status.

[0014] Preferably, step S201 specifically includes the following steps:

[0015] Based on the parking space reservation request information and the parking space information, vehicle attribute values ​​and parking space attribute values ​​are obtained through normalization processing. The calculation formula is as follows:

[0016]

[0017] Where i = 1, 2, 3..., j = 1, 2, 3..., α and β are attribute factors, a iID For the vehicle license plate information, a it0 For the estimated arrival time, a it For the predetermined duration, a id0 For the current location information, a id For the destination location information, a i b is the vehicle attribute value. jon For the vacant parking space status information, b joff For non-vacant parking space status information, bjch For the parking space charging information, b ju For the aforementioned parking convenience, b j The parking space attribute value;

[0018] The matching decision matrix is ​​obtained by fuzzy membership matching based on the vehicle attribute values ​​and the parking space attribute values. The matching decision matrix is ​​represented as A = |a| i b j | is: where a i b is the vehicle attribute value. j Let A be the parking space attribute value, and let A be the matching decision matrix.

[0019] The normalized decision matrix is ​​obtained by normalizing the matching decision matrix using the bilateral matching model. The calculation formula is as follows: in For the matching decision matrix, Let be the normalized decision matrix.

[0020] Preferably, step S202 specifically includes the following steps:

[0021] The optimal parking space coefficient is calculated using the following formula: Among them W i c and d are the weighting coefficients, and c and d are the coefficient factors. Let SE be the normalized decision matrix, and SE be the optimal parking space coefficient.

[0022] Obtain the optimal parking space information corresponding to the parking space with the maximum value of the optimal parking space coefficient, and send the optimal parking space information to the handheld terminal.

[0023] Preferably, step S4 specifically includes the following steps:

[0024] Based on the parking space location node and the parking lot path node information, the target road segment node is obtained by calculating the node priority value. The calculation formula is as follows: Where P is the parking space location node, d1 is the coefficient factor, Si and Sj are the road segment nodes, Dij is the distance between the road segment nodes, and S is the node priority value;

[0025] When the node priority value is greater than or equal to 0.9, the road segment node is the target road segment node, and the optimal path is obtained by ant colony algorithm based on the target road segment node and the terminal location node.

[0026] Preferably, step S5 specifically includes the following steps:

[0027] The vehicle credit rating is obtained based on the parking space reservation request information and the parking space lock information. The calculation formula is: TY=|n1×(T1-T2)+n2xT0+n3×T+n4×Ci| where n1, n2, n3 and n4 are coefficient factors, T1 is the estimated arrival time, T2 is the unlocking time, T0 is the reserved duration, T is the locking time, Ci is the parking convenience, and TY is the vehicle credit rating.

[0028] When TY < 2, the vehicle credit rating is low credit; when 2 ≤ TY ≤ 6, the vehicle credit rating is medium credit; and when TY > 6, the vehicle credit rating is high credit.

[0029] A parking space management system based on big data analytics includes:

[0030] The integrated information collection module is used to acquire parking space reservation request information and parking space information. The parking space reservation request information includes estimated arrival time, reservation duration, current location information, destination location information, and vehicle license plate information. The parking space information includes vacant parking space information, non-vacant parking space information, parking space fee information, and parking convenience information.

[0031] The parking space matching module is used to obtain a normalized decision matrix by processing the parking space reservation request information and the parking space information through a bilateral matching model, obtain the optimal parking space information and parking space lock status based on the normalized decision matrix, and send the optimal parking space information to the handheld terminal. The parking space lock status includes a binding status, and the optimal parking space information includes a parking space location node.

[0032] The optimal route recommendation module is used to identify vehicle license plate information through the terminal, upload vehicle entry information for vehicles that have submitted the parking space reservation request, and send reservation prompt information to the handheld terminal for vehicles that have not submitted the parking space reservation request. The vehicle entry information includes the terminal location node. The module obtains parking lot path node information, processes the parking lot path node information and the best parking space information using an ant colony algorithm to obtain the optimal route, generates navigation prompt information based on the optimal route, and sends the navigation prompt information to the handheld terminal. The parking lot path node information includes road segment nodes and intersection node distances.

[0033] The vehicle credit rating module is used to obtain parking space lock information and obtain a parking credit rating based on the parking space reservation request information and the parking space lock information. The parking space lock information includes the unlocking time and the locking time, and the parking credit rating includes high credit, medium credit, and low credit.

[0034] An electronic device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the above-described parking space management method based on big data analysis.

[0035] A storage medium containing computer-executable instructions, which, when executed by a computer processor, are used to perform the aforementioned parking space management method based on big data analysis.

[0036] The beneficial effects of this invention are as follows:

[0037] 1. By processing the parking space reservation request information and the parking space information through a bilateral matching model to obtain a normalized decision matrix, the optimal parking space information and parking space lock status are obtained based on the normalized decision matrix, and the optimal parking space information is sent to the handheld terminal, thereby improving the utilization rate of parking spaces and the efficiency of user travel, and further alleviating road traffic congestion.

[0038] 2. By acquiring parking lot path node information, and processing the parking lot path node information and the best parking space information using an ant colony algorithm based on vehicle entry information, the optimal path is obtained. Navigation prompts are generated based on the optimal path and sent to the handheld terminal, which improves parking lot management efficiency and driver's efficiency in finding parking spaces, helps users quickly reach their reserved parking spaces, and avoids traffic congestion in the parking lot during peak parking periods. Attached Figure Description

[0039] To facilitate understanding by those skilled in the art, the present invention will be further described below with reference to the accompanying drawings.

[0040] Figure 1 This is a flowchart illustrating the parking space management method of the present invention. Detailed Implementation

[0041] To further illustrate the technical means and effects of the present invention in achieving its intended purpose, the following detailed description of the specific implementation methods, structures, features, and effects of the present invention, in conjunction with the accompanying drawings and preferred embodiments, is provided.

[0042] Please see Figure 1 A parking space management method based on big data analysis includes the following steps:

[0043] S1: Obtain parking space reservation request information and parking space information. The parking space reservation request information includes estimated arrival time, reservation duration, current location information, destination location information, and vehicle license plate information. The parking space information includes vacant parking space information, non-vacant parking space information, parking space fee information, and parking convenience.

[0044] S2: Process the parking space reservation request information and the parking space information through a bilateral matching model to obtain a normalized decision matrix. Obtain the optimal parking space information and parking space lock status based on the normalized decision matrix. Send the optimal parking space information to the handheld terminal. The parking space lock status includes the binding status. The optimal parking space information includes the parking space location node.

[0045] S3: The terminal identifies vehicle license plate information, uploads vehicle entry information for vehicles that have submitted the parking space reservation request, and sends reservation prompt information to the handheld terminal for vehicles that have not submitted the parking space reservation request. The vehicle entry information includes the terminal location node.

[0046] S4: Obtain parking lot path node information, process the parking lot path node information and the best parking space information using the ant colony algorithm based on the vehicle entry information to obtain the optimal path, generate navigation prompt information based on the optimal path and send the navigation prompt information to the handheld terminal, wherein the parking lot path node information includes road segment nodes and road segment node distances;

[0047] S5: Obtain parking space lock information, and obtain parking credit level based on the parking space reservation request information and the parking space lock information. The parking space lock information includes unlocking time and locking time, and the parking credit level includes high credit, medium credit, and low credit.

[0048] Step S1 involves a comprehensive information collection module, which obtains parking space reservation request information and parking space information. The parking space reservation request information includes estimated arrival time, reservation duration, current location information, destination location information, and vehicle license plate information. The parking space information includes vacant parking space information, non-vacant parking space information, parking space fee information, and parking convenience.

[0049] By booking parking spaces online, data collection based on user destination location, current location, estimated arrival time, booking duration, and parking space status provides a data foundation for data analysis, ensuring the accuracy of the data source and improving the precision of data analysis and decision-making.

[0050] Step S2 involves the parking space matching module, which obtains vehicle attribute values ​​and parking space attribute values ​​through normalization processing based on the parking space reservation request information and the parking space information. The calculation formula is as follows:

[0051]

[0052] The matching decision matrix is ​​obtained through fuzzy membership matching based on the vehicle attribute values ​​and the parking space attribute values. The matching decision matrix is ​​expressed as: A = ai. The normalized decision matrix is ​​obtained by normalizing the matching decision matrix through the bilateral matching model, and the calculation formula is: in For the matching decision matrix, The normalized decision matrix is ​​defined as follows;

[0053] The optimal parking space coefficient is calculated based on the normalized decision matrix. The calculation formula is as follows: The optimal parking space information is obtained based on the optimal parking space coefficient, corresponding to the parking space with the maximum value of the optimal parking space coefficient. Based on the optimal parking space information, a parking space lock binding success message is generated and the parking space lock status is updated to the binding status. The optimal parking space information is then sent to the handheld terminal.

[0054] By allocating optimal parking spaces through a two-sided allocation model, parking space decisions can increase the planning of users' trips, improve parking space utilization and user travel efficiency, thereby alleviating road traffic congestion and reducing fuel consumption.

[0055] Steps S3 and S4 involve the optimal route recommendation module, which identifies vehicle license plate information through the terminal, uploads vehicle entry information for vehicles that have submitted the parking space reservation request, and sends reservation prompt information to the handheld terminal for vehicles that have not submitted the parking space reservation request information. The vehicle entry information includes the terminal location node.

[0056] Based on the parking space location node and the parking lot path node information, the target road segment node is obtained by calculating the node priority value. The calculation formula is as follows: When the node priority value is greater than or equal to 0.9, the road segment node is the target road segment node. The optimal path is obtained by processing the target road segment node and the terminal location node through the ant colony algorithm. Navigation prompt information is generated based on the optimal path and sent to the handheld terminal. The parking lot path node information includes road segment nodes and road segment node distances.

[0057] By using the ant colony algorithm to process node information and obtain the optimal path, the efficiency of parking lot management is improved, helping users quickly reach their reserved parking spaces, avoiding traffic congestion in the parking lot during peak hours, and also preventing users from becoming frustrated because they cannot find a parking space.

[0058] Step S5 involves the vehicle credit rating module. By obtaining parking space lock information, the vehicle credit rating is obtained based on the parking space reservation request information and the parking space lock information. The calculation formula is: TY=|n1×(T1-T2)+n2×T0+n3×T+n4×Ci|

[0059] When TY < 2, the vehicle credit rating is low credit; when 2 ≤ TY ≤ 6, the vehicle credit rating is medium credit; and when TY > 6, the vehicle credit rating is high credit.

[0060] By setting up a vehicle credit rating module, parking lots can implement parking fee discounts based on vehicle credit ratings, thereby regulating user parking behavior, preventing haphazard parking, and improving parking space management efficiency and utilization.

[0061] Furthermore, this application also provides a parking space management system based on big data analysis, including:

[0062] The integrated information collection module is used to acquire parking space reservation request information and parking space information. The parking space reservation request information includes estimated arrival time, reservation duration, current location information, destination location information, and vehicle license plate information. The parking space information includes vacant parking space information, non-vacant parking space information, parking space fee information, and parking convenience information.

[0063] The parking space matching module is used to obtain a normalized decision matrix by processing the parking space reservation request information and the parking space information through a bilateral matching model, obtain the optimal parking space information and parking space lock status based on the normalized decision matrix, and send the optimal parking space information to the handheld terminal. The parking space lock status includes a binding status, and the optimal parking space information includes a parking space location node.

[0064] The optimal route recommendation module is used to identify vehicle license plate information through the terminal, upload vehicle entry information for vehicles that have submitted the parking space reservation request, and send reservation prompt information to the handheld terminal for vehicles that have not submitted the parking space reservation request. The vehicle entry information includes the terminal location node. The module obtains parking lot path node information, processes the parking lot path node information and the best parking space information using an ant colony algorithm to obtain the optimal route, generates navigation prompt information based on the optimal route, and sends the navigation prompt information to the handheld terminal. The parking lot path node information includes road segment nodes and intersection node distances.

[0065] The vehicle credit rating module is used to obtain parking space lock information and obtain a parking credit rating based on the parking space reservation request information and the parking space lock information. The parking space lock information includes the unlocking time and the locking time, and the parking credit rating includes high credit, medium credit, and low credit.

[0066] Furthermore, this application also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the above-mentioned parking space management method based on big data analysis.

[0067] Furthermore, this application also provides a storage medium containing computer-executable instructions, which, when executed by a computer processor, are used to perform the aforementioned parking space management method based on big data analysis.

[0068] Working principle and usage process of this invention:

[0069] The system acquires parking space reservation request information and parking space information through a comprehensive information collection module. It then processes the parking space reservation request information and parking space information through a two-sided matching model to obtain a normalized decision matrix, optimal parking space information, and parking space lock status. The system also acquires parking lot path node information and parking space lock information. Finally, it processes the parking lot path node information and optimal parking space information through an ant colony algorithm to obtain the optimal path. Based on the optimal path, the system generates navigation prompts and sends them to the handheld terminal. Finally, it obtains the parking credit rating based on the parking space reservation request information and parking space lock information.

[0070] The program code included in the methods of this invention can be transmitted using any suitable medium, including but not limited to wireless, wired, optical fiber, RF, etc., or any suitable combination thereof. The computer program code for performing the operations of this invention can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages—such as Java, Smalltalk, and C++—and conventional procedural programming languages—such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0071] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any simple modifications, equivalent changes and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the scope of the present invention.

Claims

1. A parking space management method based on big data analysis, characterized in that, Includes the following steps: S1: Obtain parking space reservation request information and parking space information. The parking space reservation request information includes estimated arrival time, reservation duration, current location information, destination location information, and vehicle license plate information. The parking space information includes vacant parking space information, non-vacant parking space information, parking space fee information, and parking convenience. S2: Based on the parking space reservation request information and the parking space information, a normalized decision matrix is ​​obtained through bilateral matching model processing. The optimal parking space information and parking space lock status are obtained based on the normalized decision matrix. The optimal parking space information is sent to the handheld terminal. The parking space lock status includes the binding status. The optimal parking space information includes the parking space location node. S3: The terminal identifies the vehicle license plate information, uploads vehicle entry information for vehicles that have submitted the parking space reservation request, and sends a reservation prompt message to the handheld terminal for vehicles that have not submitted the reservation request information. The vehicle entry information includes the terminal location node. S4: Obtain parking lot path node information, process the parking lot path node information and the best parking space information using the ant colony algorithm based on the vehicle entry information to obtain the optimal path, generate navigation prompt information based on the optimal path and send the navigation prompt information to the handheld terminal, wherein the parking lot path node information includes road segment nodes and road segment node distances; S5: Obtain parking space lock information, and obtain parking credit level based on the parking space reservation request information and the parking space lock information. The parking space lock information includes unlocking time and locking time, and the parking credit level includes high credit, medium credit, and low credit.

2. The parking space management method based on big data analysis according to claim 1, characterized in that, Step S2 includes the following steps: S201: Obtain a matching decision matrix based on the parking space reservation request information and the parking space information, and process the matching decision matrix through the bilateral matching model to obtain a normalized decision matrix; S202: Calculate the optimal parking space coefficient based on the normalized decision matrix to obtain the optimal parking space information, generate parking space lock binding success information based on the optimal parking space information, and update the parking space lock status to the binding status.

3. The parking space management method based on big data analysis according to claim 2, characterized in that, Step S201 specifically includes the following steps: Based on the parking space reservation request information and the parking space information, vehicle attribute values ​​and parking space attribute values ​​are obtained through normalization processing. The calculation formula is as follows: Where i = 1, 2, 3..., j = 1, 2, 3..., α and β are attribute factors, a iID For the vehicle license plate information, a it0 For the estimated arrival time, a it For the predetermined duration, a id0 For the current location information, a id For the destination location information, a i b is the vehicle attribute value. jon For the vacant parking space status information, b joff For non-vacant parking space status information, b jch For the parking space charging information, b ju For the aforementioned parking convenience, b j The parking space attribute value; The matching decision matrix is ​​obtained by fuzzy membership matching based on the vehicle attribute values ​​and the parking space attribute values. The matching decision matrix is ​​represented as: A = |a i b j |where a i b is the vehicle attribute value. j Let A be the parking space attribute value, and let A be the matching decision matrix. The normalized decision matrix is ​​obtained by normalizing the matching decision matrix using the bilateral matching model. The calculation formula is as follows: in For the matching decision matrix, Let be the normalized decision matrix.

4. The parking space management method based on big data analysis according to claim 2, characterized in that, Step S202 specifically includes the following steps: The optimal parking space coefficient is calculated using the following formula: Among them W i c and d are the weighting coefficients, and c and d are the coefficient factors. Let SE be the normalized decision matrix, and SE be the optimal parking space coefficient. Obtain the optimal parking space information corresponding to the parking space with the maximum value of the optimal parking space coefficient, and send the optimal parking space information to the handheld terminal.

5. The parking space management method based on big data analysis according to claim 1, characterized in that, Step S4 specifically includes the following steps: Based on the parking space location node and the parking lot path node information, the target road segment node is obtained by calculating the node priority value. The calculation formula is as follows: Where P is the parking space location node, d1 is the coefficient factor, Si and Sj are the road segment nodes, Dij is the distance between the road segment nodes, and S is the node priority value; When the node priority value is greater than or equal to 0.9, the road segment node is the target road segment node, and the optimal path is obtained by ant colony algorithm based on the target road segment node and the terminal location node.

6. The parking space management method based on big data analysis according to claim 1, characterized in that, Step S5 specifically includes the following steps: The vehicle credit rating is obtained based on the parking space reservation request information and the parking space lock information. The calculation formula is: TY=|n1×(T1-T2)+n2×T0+n3×T+n4×Ci| where n1, n2, n3 and n4 are coefficient factors, T1 is the estimated arrival time, T2 is the unlocking time, T0 is the reserved duration, T is the locking time, Ci is the parking convenience, and TY is the vehicle credit rating. When TY < 2, the vehicle credit rating is low credit; when 2 ≤ TY ≤ 6, the vehicle credit rating is medium credit; and when TY > 6, the vehicle credit rating is high credit.

7. A parking space management system based on big data analysis, characterized in that, include: The integrated information collection module is used to acquire parking space reservation request information and parking space information. The parking space reservation request information includes estimated arrival time, reservation duration, current location information, destination location information, and vehicle license plate information. The parking space information includes vacant parking space information, non-vacant parking space information, parking space fee information, and parking convenience information. The parking space matching module is used to obtain a normalized decision matrix by processing the parking space reservation request information and the parking space information through a bilateral matching model, obtain the optimal parking space information and parking space lock status based on the normalized decision matrix, and send the optimal parking space information to the handheld terminal. The parking space lock status includes a binding status, and the optimal parking space information includes a parking space location node. The optimal route recommendation module is used to identify vehicle license plate information through the terminal, upload vehicle entry information for vehicles that have submitted the parking space reservation request, and send reservation prompt information to the handheld terminal for vehicles that have not submitted the parking space reservation request. The vehicle entry information includes the terminal location node. The module obtains parking lot path node information, processes the parking lot path node information and the best parking space information using an ant colony algorithm to obtain the optimal route, generates navigation prompt information based on the optimal route, and sends the navigation prompt information to the handheld terminal. The parking lot path node information includes road segment nodes and intersection node distances. The vehicle credit rating module is used to obtain parking space lock information and obtain a parking credit rating based on the parking space reservation request information and the parking space lock information. The parking space lock information includes the unlocking time and the locking time, and the parking credit rating includes high credit, medium credit, and low credit.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the parking space management method based on big data analysis as described in any one of claims 1-6.

9. A storage medium containing computer-executable instructions, characterized in that, The computer-executable instructions, when executed by a computer processor, are used to perform the parking space management method based on big data analysis as described in any one of claims 1-6.

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