Parking space dynamic optimization method and system based on multidimensional data analysis

Abstract

The present invention relates to the technical field of parking space optimization, and in particular to a parking space dynamic optimization method and system based on multidimensional data analysis; the method comprises: dividing the parking space to obtain a plurality of parking areas, and obtaining the real-time parking space utilization rate of each parking area; obtaining the driving distance between each parking area and the entrance, and combining the driving distance with the real-time parking space utilization rate of the corresponding parking area to obtain a parking area feature heterogeneous vector; identifying an incoming vehicle and obtaining parking behavior data information of the incoming vehicle; extracting the number of parking times of the incoming vehicle based on the parking behavior data information of the incoming vehicle; and traversing a pre-constructed weight space according to the number of parking times of the incoming vehicle to obtain an evaluation weight vector of the incoming vehicle, wherein the evaluation weight vector comprises a distance evaluation weight and a utilization rate evaluation weight.

Description

Technical Field

[0001] The present invention relates to the technical field of parking space optimization, and in particular to a parking space dynamic optimization method and system based on multi-dimensional data analysis. Background Art

[0002] With the acceleration of urbanization, parking issues have gradually become an important issue in urban management. In large commercial areas, residential areas or public activity venues, the contradiction between the rapid growth of parking demand and limited parking resources has become increasingly prominent. Therefore, how to effectively utilize and manage parking space, improve parking efficiency, and reduce the time and effort of finding parking spaces has become an urgent problem to be solved.

[0003] Existing parking management methods are often limited to simple parking space status display and allocation, lacking in-depth analysis and efficient utilization of the personalized needs of vehicle users and the real-time status of each area in the parking space. Therefore, a parking space dynamic optimization method based on multidimensional data analysis is urgently needed to solve the above problems. Summary of the invention

[0004] In order to solve the above technical problems, the present invention provides a parking space dynamic optimization method and system based on multidimensional data analysis, which can effectively improve parking efficiency, reduce parking time and resource waste, and thus effectively alleviate the urban parking problem.

[0005] In a first aspect, the present invention provides a parking space dynamic optimization method based on multidimensional data analysis, the method comprising:

[0006] Divide the parking space into several parking areas and obtain the real-time parking space utilization rate of each parking area;

[0007] Obtain the driving distance between each parking area and the entrance, and combine the driving distance with the real-time parking space utilization rate of the corresponding parking area to obtain the parking area feature heterogeneous vector;

[0008] Identify the vehicles entering the parking lot and obtain the parking behavior data information of the vehicles entering the parking lot;

[0009] Extracting the parking times of the vehicle entering the parking lot based on the parking behavior data information of the vehicle entering the parking lot;

[0010] According to the parking times of the vehicle entering the parking lot, traverse the pre-constructed weight space to obtain the evaluation weight vector of the vehicle entering the parking lot, wherein the evaluation weight vector includes a distance evaluation weight and a usage rate evaluation weight;

[0011] Performing weighted calculation based on the evaluation weight vector and the parking area feature heterogeneous vector to obtain a parking convenience evaluation index for the corresponding parking area;

[0012] A parking area with the largest parking convenience evaluation index is extracted as the recommended parking area for the incoming vehicle.

[0013] Furthermore, when the parking times of the incoming vehicle exceeds a preset parking times threshold, the following optimization recommendations are made for the incoming vehicle:

[0014] Based on the parking behavior data information of the entering vehicle, calculating the parking ratio of the entering vehicle in each parking area; the parking ratio is the ratio between the number of parking times of the entering vehicle in the corresponding parking area and the total number of parking times;

[0015] The parking ratios of several parking areas are adjusted in a serial manner to obtain a parking area priority sequence; in the parking area priority sequence, the higher the parking ratio, the higher the ranking;

[0016] Determine whether the real-time parking space utilization rate of the parking area ranked first in the parking area priority sequence is less than 100%. If so, use the parking area ranked first as the recommended parking area for the incoming vehicle. If the real-time parking space utilization rate is 100%, determine whether the real-time parking space utilization rate of the parking area ranked second is less than 100%. Repeat the determination process until the recommended parking area for the incoming vehicle is determined.

[0017] Furthermore, the parking space division method and the method for obtaining the real-time parking space utilization rate of the parking area include:

[0018] Using field measurement data, the parking space area is divided by using any one of grid division, cluster analysis and geometric segmentation algorithms;

[0019] Mark and number each divided parking area;

[0020] Install sensors and cameras in each demarcated parking area;

[0021] The parking space status of each parking area is divided into occupied, free and unavailable states;

[0022] Real-time parking space status information is collected through sensors and cameras installed in the parking area to obtain the real-time parking space utilization rate of each parking area.

[0023] Furthermore, the method for obtaining the driving distance between each parking area and the entrance includes:

[0024] Using map data and path planning algorithms, the shortest path from each parking area to the entrance of the parking space is calculated to determine the driving distance;

[0025] With the help of computer simulation technology, the movement trajectory of vehicles in the parking space is simulated, and the driving distance of vehicles to different parking areas is calculated;

[0026] Sensor equipment is deployed inside the parking space to monitor the real-time location and movement trajectory of the vehicle and calculate the driving distance of the vehicle to reach different parking areas.

[0027] Furthermore, the method for acquiring the parking behavior data information of the vehicles entering the parking lot includes:

[0028] When a vehicle enters a parking space, the vehicle's identity information is automatically recognized;

[0029] Through sensors and cameras installed in the parking area, the vehicle's driving trajectory inside the parking space is tracked in real time, recording the vehicle's movement path from the entrance to the final parking point;

[0030] Calculate the parking time of the vehicle in the parking space based on the time points when the vehicle enters and leaves the parking space;

[0031] Integrate the parking history data of vehicles in the parking space and obtain the parking behavior data information of the vehicles entering the space.

[0032] Furthermore, the method for obtaining the parking times of the vehicles entering the parking lot includes:

[0033] Clean the parking behavior data of vehicles entering the parking lot, including removing incorrect license plate recognition results and correcting incorrect timestamps;

[0034] Combine license plate recognition results with parking space occupancy data to identify and track vehicle parking behavior;

[0035] Identify each parking event based on vehicle entry and exit records;

[0036] Select the time range for analysis according to the needs and calculate the number of vehicle stops within the time range;

[0037] All recorded parking events are counted to extract the total number of parking times of the vehicle within the selected time range.

[0038] Furthermore, the recommended parking area for the entering vehicle is used to provide to the user of the entering vehicle to guide the entering vehicle to park quickly. The method of providing the recommended parking area for the entering vehicle to the user includes intelligent navigation system push, visual screen display, voice broadcast reminder, SMS and APP notification and parking space reservation service.

[0039] On the other hand, the present application also provides a parking space dynamic optimization system based on multi-dimensional data analysis, the system comprising:

[0040] The parking space division module is used to divide the parking space into several parking areas, and monitor and record the parking space usage of each parking area in real time, and obtain the real-time parking space usage rate of each parking area;

[0041] A feature extraction module is used to obtain the driving distance between each parking area and the entrance, and combine the driving distance with the real-time parking space utilization rate of the corresponding parking area to obtain a parking area feature heterogeneous vector;

[0042] A vehicle identification module is used to identify an incoming vehicle and obtain parking behavior data information of the incoming vehicle, and extract the parking times of the incoming vehicle based on the parking behavior data information of the incoming vehicle;

[0043] An evaluation weight calculation module, according to the parking times of the vehicle entering the parking lot, traverses the pre-constructed weight space to obtain the evaluation weight vector of the vehicle entering the parking lot, wherein the evaluation weight vector includes a distance evaluation weight and a usage rate evaluation weight;

[0044] A convenience evaluation index calculation module, used to perform weighted calculation based on the evaluation weight vector and the parking area feature heterogeneous vector to obtain a parking convenience evaluation index for the corresponding parking area;

[0045] The recommended parking area determination module extracts a parking area with a maximum parking convenience evaluation index according to the parking convenience evaluation index as the recommended parking area for the incoming vehicle.

[0046] In a third aspect, the present application provides an electronic device, comprising a bus, a transceiver, a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the transceiver, the memory, and the processor are connected via the bus, and the computer program, when executed by the processor, implements the steps of any one of the above methods.

[0047] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program implements the steps in any one of the above-mentioned methods when executed by a processor.

[0048] Compared with the prior art, the present invention has the following beneficial effects: by analyzing the parking behavior data of vehicles entering the parking lot, the method can provide personalized parking recommendations for each vehicle, meet the specific needs of vehicle users, and improve the parking experience;

[0049] This method can monitor the parking space usage and real-time parking space utilization rate of each parking area in real time, ensure the real-time and accuracy of parking information, and help vehicles quickly find available parking spaces;

[0050] Through multi-dimensional data analysis, we can deeply explore the real-time status of each area in the parking space, conduct in-depth analysis based on vehicle parking behavior data, thereby realizing dynamic optimization management of parking space and improving parking efficiency;

[0051] By calculating the evaluation index, the parking area with the highest parking convenience evaluation index can be quickly determined, and accurate and efficient parking location recommendations can be provided for vehicles, thereby reducing the time and effort of vehicles in finding parking spaces; parking resources can be reasonably allocated based on the real-time parking space utilization rate and vehicle parking behavior data of the parking area, so as to achieve efficient use of parking space and meet parking needs to the greatest extent;

[0052] Through weight calculation and evaluation index calculation, it can provide decision support for managers to help them reasonably allocate and manage parking resources according to real-time conditions;

[0053] In summary, this method provides an efficient and intelligent solution for urban parking management by comprehensively utilizing multidimensional data analysis technology, which can effectively improve parking efficiency, reduce parking time and resource waste, and thus effectively alleviate urban parking problems. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] Figure 1 is a flow chart of the present invention;

[0055] Figure 2 It is a flow chart of a method for dividing parking space and a method for obtaining the real-time parking space utilization rate of a parking area;

[0056] Figure 3 It is a structural diagram of the parking space dynamic optimization system based on multi-dimensional data analysis. DETAILED DESCRIPTION

[0057] In the description of this application, those skilled in the art should know that this application can be implemented as a method, an apparatus, an electronic device, and a computer-readable storage medium. Therefore, this application can be specifically implemented in the following forms: complete hardware, complete software (including firmware, resident software, microcode, etc.), a combination of hardware and software. In addition, in some embodiments, this application can also be implemented in the form of a computer program product in one or more computer-readable storage media, and the computer-readable storage medium contains computer program code.

[0058] The above-mentioned computer-readable storage medium may adopt any combination of one or more computer-readable storage media. Computer-readable storage media include: electrical, magnetic, optical, electromagnetic, infrared or semiconductor systems, devices or devices, or any combination of the above. More specific examples of computer-readable storage media include: portable computer disks, hard disks, random access memories, read-only memories, erasable programmable read-only memories, flash memories, optical fibers, optical disc read-only memories, optical storage devices, magnetic storage devices, or any combination of the above. In the present application, computer-readable storage media can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, device, or device.

[0059] The acquisition, storage, use, and processing of data in the technical solution of this application comply with the relevant provisions of national laws.

[0060] The present application describes the provided methods, devices, and electronic devices through flowcharts and / or block diagrams.

[0061] It should be understood that each box in the flowchart and / or block diagram and the combination of boxes in the flowchart and / or block diagram can be implemented by computer-readable program instructions. These computer-readable program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer or other programmable data processing device to produce a machine, and these computer-readable program instructions are executed by a computer or other programmable data processing device to produce a device that implements the functions / operations specified by the boxes in the flowchart and / or block diagram.

[0062] These computer-readable program instructions may also be stored in a computer-readable storage medium that enables a computer or other programmable data processing device to work in a specific manner. In this way, the instructions stored in the computer-readable storage medium produce an instruction device product including functions / operations specified in the blocks in the flowchart and / or block diagram.

[0063] Computer-readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device, so that a series of operational steps are performed on the computer, other programmable data processing apparatus, or other device to produce a computer-implemented process, thereby enabling the instructions executed on the computer or other programmable data processing apparatus to provide a process for implementing the functions / operations specified in the blocks in the flowchart and / or block diagram.

[0064] The present application is described below in conjunction with the drawings in the present application.

[0065] Embodiment 1: Figure 1 to Figure 2 As shown, the parking space dynamic optimization method based on multidimensional data analysis of the present invention specifically includes the following steps:

[0066] S1. Divide the parking space to obtain several parking areas, and obtain the real-time parking space utilization rate of each parking area;

[0067] In step S1, the parking space is divided into several areas for more refined management and optimization. It can realize the division of parking space and obtain the real-time parking space utilization rate, and provide basic data and conditions for subsequent parking space optimization and recommendation;

[0068] The parking space division method and the method for obtaining the real-time parking space utilization rate of the parking area include:

[0069] S11. Use field measurement data, grid division, cluster analysis, and geometric segmentation algorithms to divide parking spaces. The granularity of the space division should be reasonably determined based on the size of the parking space, traffic flow, and demand characteristics to ensure that the divided areas can be effectively managed and utilized.

[0070] S12, marking and numbering each divided parking area;

[0071] S13, installing sensors and cameras in each of the divided parking areas to monitor the occupancy of parking spaces in each parking area in real time;

[0072] S14, dividing the parking space status of each parking area into occupied, idle and unavailable states;

[0073] S15. Collect real-time parking space status information through sensors and cameras installed in the parking area to obtain real-time parking space utilization rate of each parking area.

[0074] In this step, by dividing the parking space into several areas, the management personnel can allocate and manage parking resources more finely to meet the parking needs of different areas; by real-time monitoring of the occupancy of parking spaces in each parking area, the vacant parking spaces can be found in time and vehicles can be guided to park, thereby improving the utilization rate of parking spaces and reducing the vacancy rate;

[0075] By obtaining the real-time parking space utilization rate of each parking area, drivers can know the parking situation of each area before entering the parking space, thereby reducing the time and effort of finding parking spaces; by dividing the parking space and real-time monitoring, it can provide users with a more convenient parking experience, reduce parking uncertainty, and improve user satisfaction;

[0076] Using real-time parking space utilization data, managers can make more accurate decisions, such as adjusting the division of parking areas, optimizing parking flow lines, etc., to improve the overall efficiency and service level of parking spaces;

[0077] In summary, the implementation of step S1 provides important basic data and conditions for the management and optimization of parking space, which helps to solve parking problems and improve the operational efficiency and user experience of parking space.

[0078] S2. Obtain the driving distance between each parking area and the entrance, and combine the driving distance with the real-time parking space utilization rate of the corresponding parking area to obtain a parking area feature heterogeneous vector;

[0079] In step S2, the parking area feature heterogeneous vector is obtained by combining the driving distance between each parking area and the parking space entrance and the real-time parking space utilization rate. By comprehensively considering the driving distance and the real-time parking space utilization rate, the parking area feature heterogeneous vector can fully reflect the parking convenience of each area in the parking space and the actual parking resource utilization. Such a feature vector provides an important data basis for the subsequent dynamic optimization of parking space;

[0080] The key is to obtain the driving distance between each parking area and the entrance, which can be achieved through a variety of technical solutions. The methods for obtaining the driving distance between each parking area and the entrance include:

[0081] S21, path planning based on map data, using the map data and path planning algorithm to calculate the shortest path between each parking area and the parking space entrance, and determine the driving distance accordingly;

[0082] S22, based on the simulation of parking space layout, using computer simulation technology to simulate the movement trajectory of vehicles in the parking space, and calculate the driving distance of the vehicle to reach different parking areas accordingly;

[0083] S23, based on real-time monitoring of sensor data, deploying sensor equipment inside the parking space to monitor the real-time position and movement trajectory of the vehicle, thereby calculating the driving distance of the vehicle to different parking areas in real time;

[0084] The driving distance and real-time parking space utilization rate are taken as two important characteristics of the parking area;

[0085] The driving distance and the real-time parking space utilization rate are combined into a feature vector to form a parking area feature heterogeneous vector;

[0086] The dimension of the feature vector depends on the number of selected features and the data granularity, and usually includes two dimensions: driving distance and real-time parking space utilization rate.

[0087] In this step, the parking area feature heterogeneous vector is constructed by integrating the driving distance between the parking area and the entrance and the real-time parking space utilization rate. This method realizes the multi-dimensional and in-depth analysis of the parking convenience and resource utilization rate of each area in the parking space, and improves the comprehensiveness and accuracy of the evaluation of parking area efficiency.

[0088] The driving distance directly reflects the difficulty and time cost of a vehicle entering a parking area, while the real-time parking space utilization rate reveals the current busyness of the area and the remaining parking resources. The feature vector formed by combining the two helps to achieve refined management and optimal allocation of parking space, effectively improving the efficiency of parking resource utilization.

[0089] Due to the use of real-time parking space utilization data, this method has good dynamic adaptability and can make dynamic adjustments and optimization suggestions based on the real-time status of parking spaces, and respond to changes in parking demand in a timely manner;

[0090] The parking area feature heterogeneous vector provides key data support for the subsequent parking space dynamic optimization algorithm. By calculating the parking convenience evaluation index of each area, the most suitable parking area for the incoming vehicle can be quickly found, which greatly saves the driver's time in finding a parking space, improves parking efficiency, and alleviates parking problems.

[0091] In summary, this step successfully achieved a quantitative description of parking area characteristics by combining multiple technologies and data sources, laying a solid foundation for realizing intelligent and efficient dynamic optimization of parking spaces.

[0092] S3, identifying the vehicles entering the parking lot, and obtaining parking behavior data information of the vehicles entering the parking lot;

[0093] In step S3, the purpose of identifying the incoming vehicles and obtaining their parking behavior data information is to understand the parking behavior of each vehicle, including the stay time, parking location and parking frequency information, so as to facilitate the subsequent parking space optimization and dynamic management;

[0094] The method for acquiring the parking behavior data information of the vehicles entering the parking lot includes:

[0095] S31, when a vehicle enters a parking space, the vehicle identity information is automatically recognized by the license plate recognition system;

[0096] S32, using sensors and cameras installed in the parking area, tracking the driving trajectory of the vehicle inside the parking space in real time, and recording the movement path of the vehicle from the entrance to the final parking point;

[0097] S33, calculating the time the vehicle stays in the parking space, i.e., the parking duration, according to the time points when the vehicle enters and leaves the parking space;

[0098] S34. Integrate the parking history data of the vehicle in the parking space, the parking history data including parking frequency, time period preference, common parking area and average parking duration, and obtain parking behavior data information of the incoming vehicle.

[0099] In this step, by identifying the vehicles entering the parking lot and obtaining their parking behavior data information, we can understand the parking preferences and habits of each vehicle and provide personalized parking recommendations for each vehicle, making parking more convenient and efficient;

[0100] By recording the parking behavior data of vehicles, it is possible to analyze the parking demand and usage of different areas in the parking space, which helps to optimize the allocation of parking space resources, reasonably arrange the use of parking spaces, improve the utilization rate of parking resources, and reduce idle parking spaces;

[0101] By tracking the driving trajectory and dwell time of vehicles in real time, it is possible to identify bottlenecks and congestion points in parking spaces, improve parking processes and management strategies in a targeted manner, reduce congestion in parking spaces, and improve parking efficiency.

[0102] Through personalized parking recommendations and optimized parking resource allocation, users’ parking convenience and comfort can be improved, the time and effort of users in finding parking spaces can be reduced, and users’ satisfaction with parking services can be improved;

[0103] Based on the analysis of vehicle parking behavior data, it can provide data support for parking space managers, help them formulate more accurate and effective parking management strategies, and improve the overall operation efficiency and service quality of parking spaces;

[0104] In summary, the beneficial effects of step S3 include personalized parking recommendations, parking space resource optimization, parking process improvement, user experience enhancement, and data-driven decision-making, thereby achieving dynamic optimization and management of parking space.

[0105] S4, extracting the parking times of the vehicle entering the parking lot based on the parking behavior data information of the vehicle entering the parking lot;

[0106] In step S4, the number of parking times of vehicles entering the parking lot is extracted by analyzing the parking behavior data information of the vehicles. This process involves multiple links of data collection, processing and analysis;

[0107] The method for obtaining the parking times of the entry vehicles includes:

[0108] S41, cleaning the parking behavior data information of the vehicles entering the parking lot, including removing the wrong license plate recognition results and correcting the wrong timestamps;

[0109] S42, combining the license plate recognition result with the parking space occupancy data to identify and track the parking behavior of the vehicle;

[0110] S43, identifying each parking event according to the vehicle entry and exit records;

[0111] S44, selecting a time range for analysis according to demand, and calculating the number of parking times of the vehicle within the time range;

[0112] S45. Count all recorded parking events and extract the total number of parking times of the vehicle within the selected time range.

[0113] In this step, by analyzing the number of parking times for each vehicle in detail, managers can better understand the usage frequency and peak hours of parking spaces, thereby allocating and scheduling parking spaces more effectively, reducing parking space congestion, and improving overall operational efficiency.

[0114] By statistically analyzing the number of vehicle parking times, vehicles that frequently use parking spaces can be identified and provided with specific parking discounts or priority parking services, which can not only improve user satisfaction but also promote the rational use of parking resources;

[0115] Based on the parking behavior of vehicles, parking spaces can provide car owners with more personalized parking services, such as recommending the best parking space or providing customized parking solutions based on the frequency of vehicle entry and exit, increasing user convenience and satisfaction;

[0116] Statistical analysis of parking times provides a quantitative way to evaluate the usage and efficiency of parking spaces, providing data support for parking space management, which can be used to support decision-making;

[0117] Effective parking management and optimization not only improves parking efficiency, but also helps reduce carbon emissions generated by vehicles looking for parking spaces, which has a positive impact on alleviating urban traffic congestion and reducing environmental pollution;

[0118] The analysis results of vehicle parking times can also provide reference data for urban traffic planning and management, helping urban planners understand the distribution and changing trends of parking demand, and thus optimize the layout of urban traffic and parking facilities;

[0119] In summary, the S4 step can not only improve the management efficiency of parking space and user experience, but also promote the development of a more intelligent, efficient and sustainable urban transportation environment.

[0120] S5. According to the parking times of the vehicle entering the parking lot, traverse the pre-constructed weight space to obtain an evaluation weight vector of the vehicle entering the parking lot, wherein the evaluation weight vector includes a distance evaluation weight and a utilization rate evaluation weight; (the distance evaluation weight represents the influence of the driving distance on the parking convenience of the parking area, and the utilization rate evaluation weight represents the influence of the real-time parking space utilization rate on the parking convenience of the parking area);

[0121] In the field of parking space optimization, the purpose of constructing a weight space is to assign corresponding weight values ​​according to different parking-related factors to quantify the influence of different factors on parking convenience. In step S5, the weight space is mainly constructed based on the number of parking times of vehicle users through pre-set distance evaluation weights and usage rate evaluation weights; the following is a weight space construction method:

[0122] S51. Define weight indicators, and define key indicators that affect parking convenience, including distance evaluation weight and utilization rate evaluation weight; the distance evaluation weight reflects the impact of driving distance on user parking convenience, and the utilization rate evaluation weight reflects the impact of real-time parking space utilization rate on user parking convenience;

[0123] S52, setting a weight range for the distance evaluation weight and the usage rate evaluation weight;

[0124] S53, dynamically adjusting the distance evaluation weight and the usage rate evaluation weight through a function according to the change in the number of parking times of the user;

[0125] S54. In the early stage of system operation, an initial weight space is set through experience and preliminary data analysis; as the system continues to collect more user parking behavior data, the weight space can self-learn and optimize according to new data feedback to ensure that the weight distribution is always close to actual needs and scenario changes;

[0126] According to the parking times of vehicles entering the parking lot, the distance evaluation weight and the utilization rate evaluation weight are dynamically adjusted through a pre-designed algorithm or model; for example, for users who park for the first time or have parked less frequently, they pay more attention to the driving distance, that is, the distance evaluation weight is larger; while for frequent visitors or users who park more frequently, they are more concerned about the real-time parking space utilization rate of the parking area, that is, the utilization rate evaluation weight is larger;

[0127] Traversing the weight space, according to the number of parking times of the vehicle user, traversing the pre-constructed weight space, and finding the weight value corresponding to the number of parking times; this step involves a variety of mathematical methods such as lookup tables, linear interpolation, and nonlinear regression to ensure that the selected weight value can truly reflect the user's parking convenience preference in the current situation;

[0128] Generate an evaluation weight vector, and combine the distance evaluation weight and the usage evaluation weight found into a vector, namely the evaluation weight vector.

[0129] In this step, the distance evaluation weight and the usage rate evaluation weight can be dynamically adjusted for each user according to the number of parking times of the user. For users who park for the first time or have parked less frequently, the system will pay more attention to the driving distance, while for frequent users or users who have parked more frequently, the system will pay more attention to the real-time parking space utilization rate. The personalized weight adjustment can better meet the parking needs of users and improve the user experience;

[0130] By traversing the weight space and combining the number of times the user has parked, an evaluation weight vector for each user can be generated. The evaluation weight vector can be used to evaluate the parking convenience of different parking areas, and then recommend the most suitable parking area for the user; by considering the user's parking preference and number of times of parking, the parking area can be recommended more accurately, reducing the user's time and energy in finding a parking space, and improving parking efficiency;

[0131] In summary, by traversing the weight space and obtaining the evaluation weight vector according to the parking times of the incoming vehicles in step S5, personalized weight adjustment, optimized parking recommendation, and continuous optimization and learning can be achieved, thereby significantly improving the efficiency of parking services and user satisfaction.

[0132] S6. Performing weighted calculation based on the evaluation weight vector and the parking area feature heterogeneous vector to obtain a parking convenience evaluation index for the corresponding parking area;

[0133] In step S6, the evaluation weight vector includes a distance evaluation weight and a utilization rate evaluation weight, which are used to reflect the impact of the driving distance and the real-time parking space utilization rate on the parking convenience; the parking area feature heterogeneous vector includes the driving distance and the real-time parking space utilization rate, which are used to describe the characteristics of the parking area;

[0134] For each parking area, the distance evaluation weight and the utilization rate evaluation weight in the evaluation weight vector are weighted with the driving distance and the real-time parking space utilization rate in the corresponding parking area feature heterogeneous vector;

[0135] The result of the weighted calculation is the parking convenience evaluation index of the parking area;

[0136] The parking convenience evaluation index takes into account distance and real-time parking space utilization, reflects the parking convenience of the parking area, and helps determine the most suitable area for incoming vehicles to park.

[0137] In this step, the distance evaluation weight and the utilization rate evaluation weight are combined with the driving distance and the real-time parking space utilization rate through weighted calculation, which comprehensively considers multiple factors of parking convenience and can more comprehensively evaluate the actual situation of the parking area, rather than just considering a single indicator;

[0138] The parking convenience evaluation index of each parking area is calculated based on its specific distance and utilization rate, so it can provide personalized parking recommendations for different parking areas, thereby meeting the different parking needs of users and improving parking experience and satisfaction;

[0139] The distance evaluation weight and utilization rate evaluation weight in the evaluation weight vector can be adjusted according to the actual situation to ensure that the evaluation process is more objective and fair, thereby avoiding the subjective influence of a single factor on the evaluation results and improving the accuracy and credibility of the evaluation;

[0140] The parking convenience evaluation index can be used as a reference for decision-making, helping managers or users to choose the most suitable parking area, optimize the use of parking resources, reduce parking waiting time, and improve parking efficiency and management level;

[0141] In summary, the beneficial effect of this step is that it provides a comprehensive and personalized parking area evaluation method, which provides strong support and reference for parking management and user parking selection, and helps to optimize urban parking management and improve parking experience.

[0142] S7, extracting a parking area with a maximum parking convenience evaluation index as a recommended parking area for the incoming vehicle;

[0143] The parking convenience evaluation index reflects the quantitative evaluation of the parking convenience of vehicle users in each parking area after comprehensively considering the personalized needs of users and actual site conditions;

[0144] Traverse all parking areas, compare their parking convenience evaluation indexes, and select the parking area with the largest parking convenience evaluation index as the recommended parking area to ensure the convenience and satisfaction of incoming vehicles;

[0145] Providing the optimal parking area to the user as a recommendation result;

[0146] Considering that the real-time conditions in the parking space may change, when screening parking areas, the parking convenience evaluation index is updated in real time, and the parking area with the largest index is re-selected as the recommended parking area;

[0147] Collect user parking experience feedback data, including parking convenience, waiting time, and congestion in parking areas, to continuously optimize parking recommendation algorithms and services; user feedback can help further improve the dynamic optimization method of parking space, improve user satisfaction and parking efficiency;

[0148] The recommended parking area for the vehicle entering the parking lot is provided to the user of the vehicle entering the parking lot, and is used to guide the vehicle entering the parking lot to park quickly. The method for providing the recommended parking area for the vehicle entering the parking lot to the user includes:

[0149] S71. Intelligent navigation system push: The built-in intelligent navigation software pushes the recommended optimal parking area information to the user. The user sees the recommended parking area on the navigation interface and follows the navigation instructions. This method is intuitive and easy to operate, reducing the time cost of users looking for parking spaces.

[0150] S72, visual screen display: a visual display screen is installed at a prominent position at the entrance of the parking space. When a vehicle enters the parking space, the system automatically identifies the vehicle and immediately displays the recommended parking area on the screen. The user can quickly find the target area to park according to the prompts.

[0151] S73, voice broadcast reminder, the parking space system tells the user which parking area to go to through voice broadcast, so that the driver can receive information at any time during driving, improving safety;

[0152] S74, SMS and APP notification, when the user registers for the parking service, the user downloads the supporting APP and binds the mobile phone number. After the system analyzes the optimal parking area, it sends the recommended information to the user through SMS and APP messages;

[0153] S75, parking space reservation service. If the parking space supports online reservation service, the system can lock the recommended optimal parking space for the user before the user enters the parking space, and directly guide the user to the reserved parking space when the user arrives at the parking space.

[0154] In this step, by selecting the parking area with the largest parking convenience evaluation index as the recommended parking area, the user can find a parking space more quickly and conveniently, thereby improving the convenience and satisfaction of parking;

[0155] By using intelligent navigation systems, visual screen displays, voice announcements and other methods, users can intuitively learn about recommended parking areas and quickly find the target area to park by following the instructions, thus reducing the time cost of finding parking spaces for users.

[0156] Providing multiple ways to recommend parking areas to users, so that users can choose the appropriate way to obtain recommended information according to their preferences. At the same time, voice broadcast reminders also improve the convenience of drivers receiving information during driving, which helps to improve the safety of parking spaces;

[0157] Recommended information is sent to users through APP messages, SMS push, etc., which increases the interaction and participation of users with parking services. At the same time, users can choose the most suitable parking method according to their needs and convenience, which improves user satisfaction.

[0158] The parking space reservation service can lock in the best recommended parking space for users before they enter the parking lot, further improving parking efficiency, reducing vehicle congestion in parking spaces, and helping to improve parking space management and service levels;

[0159] To sum up, this step can effectively improve parking convenience, reduce time costs, improve user experience and safety, increase user participation and satisfaction, and at the same time improve parking efficiency and management effects.

[0160] Embodiment 2: Figure 3 As shown, the parking space dynamic optimization system based on multi-dimensional data analysis of the present invention specifically includes the following modules:

[0161] The parking space division module is used to divide the parking space into several parking areas, and monitor and record the parking space usage of each parking area in real time, and obtain the real-time parking space usage rate of each parking area;

[0162] A feature extraction module is used to obtain the driving distance between each parking area and the entrance, and combine the driving distance with the real-time parking space utilization rate of the corresponding parking area to obtain a parking area feature heterogeneous vector;

[0163] A vehicle identification module is used to identify an incoming vehicle and obtain parking behavior data information of the incoming vehicle, and extract the parking times of the incoming vehicle based on the parking behavior data information of the incoming vehicle;

[0164] An evaluation weight calculation module, according to the parking times of the vehicle entering the parking lot, traverses the pre-constructed weight space to obtain the evaluation weight vector of the vehicle entering the parking lot, wherein the evaluation weight vector includes a distance evaluation weight and a usage rate evaluation weight;

[0165] A convenience evaluation index calculation module, used to perform weighted calculation based on the evaluation weight vector and the parking area feature heterogeneous vector to obtain a parking convenience evaluation index for the corresponding parking area;

[0166] The recommended parking area determination module extracts a parking area with a maximum parking convenience evaluation index according to the parking convenience evaluation index as the recommended parking area for the incoming vehicle.

[0167] By analyzing the parking behavior data of vehicles entering the parking lot, the system can provide personalized parking recommendations to meet the specific needs of vehicle users and improve user satisfaction. The system monitors and records the parking space usage of each parking area in real time, and can provide timely feedback on the real-time status of parking spaces to ensure the timeliness and accuracy of parking information.

[0168] The system uses multi-dimensional data analysis to deeply explore the real-time status of each area in the parking space, and conducts in-depth analysis based on vehicle parking behavior data to achieve dynamic optimization management of parking space and improve parking efficiency;

[0169] The recommended parking area determination module can quickly determine the parking area with the highest parking convenience evaluation index, and provide accurate and efficient parking location recommendations for vehicles, thereby reducing the time and effort of vehicles in finding parking spaces;

[0170] The system can reasonably allocate parking resources based on the real-time parking space utilization rate and vehicle parking behavior data in the parking area, realize efficient use of parking space, and meet parking needs to the greatest extent;

[0171] In summary, the system provides an efficient and intelligent solution for urban parking management by comprehensively utilizing multidimensional data analysis technology, which can effectively improve parking efficiency, reduce parking time and resource waste, and thus effectively alleviate urban parking problems.

[0172] The various variations and specific embodiments of the parking space dynamic optimization method based on multidimensional data analysis in the aforementioned embodiment 1 are also applicable to the parking space dynamic optimization system based on multidimensional data analysis in this embodiment. Through the aforementioned detailed description of the parking space dynamic optimization method based on multidimensional data analysis, those skilled in the art can clearly know the implementation method of the parking space dynamic optimization system based on multidimensional data analysis in this embodiment, so for the sake of brevity of the specification, it will not be described in detail here.

[0173] In addition, the present application also provides an electronic device, including a bus, a transceiver, a memory, a processor, and a computer program stored in the memory and executable on the processor. The transceiver, the memory, and the processor are respectively connected via a bus. When the computer program is executed by the processor, each process of the above-mentioned method for controlling output data is implemented, and the same technical effect can be achieved. To avoid repetition, it will not be described here.

[0174] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the technical principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A parking space dynamic optimization method based on multidimensional data analysis, It is characterized in that The method comprises: Divide the parking space into several parking areas and obtain the real-time parking space utilization rate of each parking area; Obtain the driving distance between each parking area and the entrance, and combine the driving distance with the real-time parking space utilization rate of the corresponding parking area to obtain the parking area feature heterogeneous vector; Identify the vehicles entering the parking lot and obtain the parking behavior data information of the vehicles entering the parking lot; Extracting the parking times of the vehicle entering the parking lot based on the parking behavior data information of the vehicle entering the parking lot; According to the parking times of the vehicle entering the parking lot, traverse the pre-constructed weight space to obtain the evaluation weight vector of the vehicle entering the parking lot, wherein the evaluation weight vector includes a distance evaluation weight and a usage rate evaluation weight; Performing weighted calculation based on the evaluation weight vector and the parking area feature heterogeneous vector to obtain a parking convenience evaluation index for the corresponding parking area; Extracting the parking area with the largest parking convenience evaluation index as the recommended parking area for the incoming vehicle; The method for constructing the weight space comprises: Define weight indicators and key indicators that affect parking convenience, including distance evaluation weight and utilization rate evaluation weight. The distance evaluation weight is used to reflect the impact of driving distance on user parking convenience, and the utilization rate evaluation weight is used to reflect the impact of real-time parking space utilization rate on user parking convenience. Set weight ranges for distance evaluation weights and usage rate evaluation weights; According to the change of the number of parking times of users, the distance evaluation weight and the utilization rate evaluation weight are dynamically adjusted through the preset function.

2. The parking space dynamic optimization method based on multidimensional data analysis according to claim 1, It is characterized in that The method for dividing the parking space and the method for obtaining the real-time parking space utilization rate of the parking area include: Using field measurement data, the parking space area is divided by using any one of grid division, cluster analysis and geometric segmentation algorithms; Mark and number each divided parking area; Install sensors and cameras in each demarcated parking area; The parking space status of each parking area is divided into occupied, free and unavailable states; Real-time parking space status information is collected through sensors and cameras installed in the parking area to obtain the real-time parking space utilization rate of each parking area.

3. The parking space dynamic optimization method based on multidimensional data analysis according to claim 1, It is characterized in that The method for obtaining the driving distance between each parking area and the entrance includes: Using map data and path planning algorithms, the shortest path from each parking area to the entrance of the parking space is calculated to determine the driving distance; With the help of computer simulation technology, the movement trajectory of vehicles in the parking space is simulated, and the driving distance of vehicles to different parking areas is calculated; Sensor equipment is deployed inside the parking space to monitor the real-time location and movement trajectory of the vehicle and calculate the driving distance of the vehicle to reach different parking areas.

4. The parking space dynamic optimization method based on multidimensional data analysis as claimed in claim 3, It is characterized in that The method for acquiring the parking behavior data information of the vehicles entering the parking lot includes: When a vehicle enters a parking space, the vehicle's identity information is automatically recognized; Through sensors and cameras installed in the parking area, the vehicle's driving trajectory inside the parking space is tracked in real time, recording the vehicle's movement path from the entrance to the final parking point; Calculate the parking time of the vehicle in the parking space based on the time points when the vehicle enters and leaves the parking space; Integrate the parking history data of vehicles in the parking space and obtain the parking behavior data information of the vehicles entering the space.

5. The parking space dynamic optimization method based on multidimensional data analysis according to claim 1, It is characterized in that The method for obtaining the parking times of the entry vehicles includes: Clean the parking behavior data of vehicles entering the parking lot, including removing incorrect license plate recognition results and correcting incorrect timestamps; Combine license plate recognition results with parking space occupancy data to identify and track vehicle parking behavior; Identify each parking event based on vehicle entry and exit records; Select the time range for analysis according to the needs and calculate the number of vehicle stops within the time range; All recorded parking events are counted to extract the total number of parking times of the vehicle within the selected time range.

6. The parking space dynamic optimization method based on multidimensional data analysis according to claim 1, It is characterized in that The recommended parking area for the entering vehicle is used to provide to the user of the entering vehicle, and guides the entering vehicle to park quickly. The methods of providing the recommended parking area for the entering vehicle to the user include intelligent navigation system push, visual screen display, voice broadcast reminder, SMS and APP notification, and parking space reservation service.

7. A parking space dynamic optimization system based on multi-dimensional data analysis, It is characterized in that The system is applied to the parking space dynamic optimization method based on multidimensional data analysis according to claim 1, and the system comprises: The parking space division module is used to divide the parking space into several parking areas, and monitor and record the parking space usage of each parking area in real time, and obtain the real-time parking space usage rate of each parking area; A feature extraction module is used to obtain the driving distance between each parking area and the entrance, and combine the driving distance with the real-time parking space utilization rate of the corresponding parking area to obtain a parking area feature heterogeneous vector; A vehicle identification module is used to identify an incoming vehicle and obtain parking behavior data information of the incoming vehicle, and extract the parking times of the incoming vehicle based on the parking behavior data information of the incoming vehicle; An evaluation weight calculation module, according to the parking times of the vehicle entering the parking lot, traverses the pre-constructed weight space to obtain the evaluation weight vector of the vehicle entering the parking lot, wherein the evaluation weight vector includes a distance evaluation weight and a usage rate evaluation weight; A convenience evaluation index calculation module, used to perform weighted calculation based on the evaluation weight vector and the parking area feature heterogeneous vector to obtain a parking convenience evaluation index for the corresponding parking area; The recommended parking area determination module extracts a parking area with a maximum parking convenience evaluation index according to the parking convenience evaluation index as the recommended parking area for the incoming vehicle.

8. An electronic device for dynamic optimization of parking space based on multi-dimensional data analysis, comprising a bus, a transceiver, a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the transceiver, the memory, and the processor are connected via the bus. It is characterized in that When the computer program is executed by the processor, the steps in the method according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, It is characterized in that When the computer program is executed by a processor, the steps in the method according to any one of claims 1 to 6 are implemented.

Images