Stereo garage intelligent scheduling management method and system under real-time data processing

Abstract

The application discloses a stereo garage intelligent scheduling management method and system under real-time data processing, relates to the technical field of scheduling management, and comprises the following steps: collecting multi-source data through a vehicle recognition system, a parking space state monitoring matrix and a user demand instruction collector, obtaining vehicle warehouse entry and exit information, parking space state monitoring results and a user demand instruction set; performing overall consistency analysis on the vehicle warehouse entry and exit information, the parking space state monitoring results and the user demand instruction set, executing parking path planning according to an analysis result, and obtaining a parking path set; performing linkage equipment response matching based on the parking path set, and obtaining a linkage equipment response operation set; performing one-to-one association on the parking path set and the linkage equipment response operation set, and obtaining a target management scheme. The application solves the technical problem that the existing technology cannot respond to scheduling in a stereo garage in time and has low management reliability, and achieves the technical effect of improving scheduling management accuracy and timeliness.

Description

Technical Field

[0001] This invention relates to the field of scheduling and management technology, specifically to a method and system for intelligent scheduling and management of automated parking garages under real-time data processing. Background Technology

[0002] With the acceleration of urbanization, parking has become a major problem for most cities. Multi-level parking garages, as an efficient space-utilizing parking solution, are gradually being widely adopted. To improve the operational efficiency of parking garages, intelligent parking scheduling systems have emerged, utilizing multi-source data acquisition technology to combine vehicle information, parking space status monitoring, and user needs for dynamic scheduling and route planning. However, existing intelligent parking systems still face many challenges, such as the efficient allocation of parking space resources, real-time response to user needs, and optimization of route planning. Especially when multiple vehicle demands occur simultaneously, ensuring priority scheduling and reasonable allocation of parking space resources remains a problem that urgently needs to be solved. Summary of the Invention

[0003] This application provides a method and system for intelligent scheduling and management of automated parking garages under real-time data processing, which is used to address the technical problems of existing technologies that cannot respond to scheduling within automated parking garages in a timely manner and have low management reliability.

[0004] In view of the above problems, this application provides a method and system for intelligent scheduling and management of automated parking garages under real-time data processing.

[0005] The first aspect of this application provides a method for intelligent scheduling and management of automated parking garages under real-time data processing, the method comprising:

[0006] Multi-source data is collected through a vehicle identification system, a parking space status monitoring matrix, and a user demand command collector to obtain vehicle entry and exit information, parking space status monitoring results, and a set of user demand commands.

[0007] The system integrates vehicle entry and exit information, parking space status monitoring results, and user demand command set for overall consistency analysis. Based on the analysis results, it performs parking path planning to obtain a set of parking paths.

[0008] Based on the parking path set, the response of the linkage equipment is matched to obtain the set of linkage equipment response operations.

[0009] By associating the parking path set and the linkage device response operation set one-to-one, a target management scheme is obtained.

[0010] A second aspect of this application provides an intelligent scheduling and management system for automated parking garages under real-time data processing, the system comprising:

[0011] The user demand instruction acquisition module is used to collect multi-source data through the vehicle identification system, parking space status monitoring matrix and user demand instruction collector to obtain vehicle entry and exit information, parking space status monitoring results and user demand instruction set.

[0012] The parking path set acquisition module is used to integrate the vehicle entry and exit information, parking space status monitoring results and user demand command set for overall consistency analysis, and perform parking path planning based on the analysis results to obtain the parking path set;

[0013] The module for obtaining the set of responses to the linked devices is used to match the responses of the linked devices based on the set of parking paths to obtain the set of responses to the linked devices.

[0014] The target management scheme acquisition module is used to associate the parking path set and the linkage equipment response operation set one-to-one to obtain the target management scheme.

[0015] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0016] This application utilizes a vehicle identification system, a parking space status monitoring matrix, and a user demand command collector to collect multi-source data, obtaining vehicle entry / exit information, parking space status monitoring results, and a set of user demand commands. It then integrates these data for overall consistency analysis, performs parking path planning based on the analysis results, and obtains a set of parking paths. Based on this set, it matches responses from linked equipment to obtain a set of linked equipment response operations. Finally, it establishes a one-to-one association between the parking path set and the linked equipment response operation set to obtain a target management solution. This achieves the technical effect of improving management reliability by scheduling parking resources and planning paths based on real-time data. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 A schematic diagram of the intelligent scheduling and management method for automated parking garages under real-time data processing provided in this application embodiment;

[0019] Figure 2 A schematic diagram of the structure of the intelligent scheduling and management system for three-dimensional parking garages under real-time data processing provided in the embodiments of this application.

[0020] Explanation of reference numerals in the attached diagram: Module 11 for obtaining user demand instructions, Module 12 for obtaining parking path set, Module 13 for obtaining set of coordinated equipment response operations, and Module 14 for obtaining target management scheme. Detailed Implementation

[0021] This application provides a method and system for intelligent scheduling and management of automated parking garages under real-time data processing, addressing the technical problem of existing technologies that cannot respond to scheduling within automated parking garages in a timely manner and have low management reliability.

[0022] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.

[0023] It should be noted that any variation of the terms "comprising" and "having" is intended to cover non-exclusive inclusion, for example, a process, method, system, product, or server that includes a series of steps or units is not necessarily limited to those steps or units that are explicitly listed, but may include other steps or modules that are not explicitly listed or that are inherent to such processes, methods, products, or devices.

[0024] Example 1, as Figure 1 As shown, this application provides a method for intelligent scheduling and management of automated parking garages under real-time data processing, the method comprising:

[0025] Step S100: Collect multi-source data through the vehicle identification system, parking space status monitoring matrix and user demand command collector to obtain vehicle entry and exit information, parking space status monitoring results and user demand command set;

[0026] Furthermore, by collecting multi-source data through a vehicle identification system, a parking space status monitoring matrix, and a user demand command collector, vehicle entry and exit information, parking space status monitoring results, and a set of user demand commands are obtained. In this embodiment, step S100 further includes:

[0027] The vehicle information set is collected by the vehicle identification system, the vehicle condition information is scanned by traversing the vehicle information set, and the vehicle entry and exit information is determined based on the scanning results.

[0028] The parking space status monitoring matrix is ​​used to perform dual monitoring of the target multi-level parking garage using parking space images and parking space sensor status to obtain the parking space status monitoring results;

[0029] Based on vehicle entry and exit information, and combined with user requests collected by the interactive screen of the target automated parking garage, a set of user request instructions is obtained, wherein each user request instruction corresponds to a license plate number in the vehicle entry and exit information.

[0030] Furthermore, by collecting a vehicle information set through a vehicle identification system, scanning the vehicle condition information through the vehicle information set, and determining the vehicle entry and exit information based on the scanning results, step S100 of this embodiment also includes:

[0031] The vehicle condition information set is collected by traversing the vehicle information set to obtain the vehicle condition information set.

[0032] The vehicle condition information set is parsed using a vehicle calibrator. If the parsing result indicates that the verification meets the conditions, the corresponding license plate number is added to the vehicle entry and exit information.

[0033] If the parsing result indicates that the verification does not meet the requirements, an operation prohibition instruction is generated. Based on the operation prohibition instruction, a pop-up window is displayed to prohibit the operation and show the non-compliance information.

[0034] In one possible embodiment, the parking space status monitoring matrix consists of sensors and cameras within the automated parking garage, used to monitor the status of each parking space in real time, thereby determining whether it is vacant or occupied, and other characteristics of the parking space, such as whether it is a charging parking space or whether car wash services are provided. The vehicle recognition system is used to identify vehicle information entering or leaving the automated parking garage. The user demand instruction collector is used to collect users' parking, charging, car wash, and other needs through interactive screens, mobile terminals, and other applications. Vehicle entry and exit data reflects the entry and exit of vehicles within the target automated parking garage. The parking space status monitoring results reflect the vacancy or occupancy status of parking spaces within the target automated parking garage.

[0035] In one possible embodiment, a vehicle recognition system's camera collects information on vehicles entering and exiting the parking garage, including license plate number, vehicle type, color, vehicle malfunction, and battery level. A vehicle verifier then analyzes and verifies the scanned vehicle information to determine if it meets requirements. If the verification is successful, the vehicle is allowed to enter or exit the garage, and the corresponding license plate number is added to the vehicle entry / exit information. If the analysis result indicates that the verification is unsuccessful, a prohibition instruction is obtained. This prohibition instruction serves to remind users and staff that the vehicle cannot enter or exit the target automated parking garage. After obtaining the prohibition instruction, a pop-up window is displayed on the interactive screen, indicating that the vehicle does not meet the requirements—that is, displaying the non-compliance information.

[0036] Preferably, the vehicle verifier is an analytical verification module built based on support vector machines. A large amount of vehicle operating condition information data (including fault status, battery level, etc.) is collected to obtain a set of sample operating condition information data that meets the conditions and a set of sample operating condition information data that does not meet the conditions. These two sets of data are input into a framework built based on SVM, and a linear kernel is selected as the kernel function for training. During training, the framework parameters are updated based on the degree of similarity between the framework output and the sample operating condition information data sets that meet and do not meet the conditions, until the requirements are met, thus obtaining the trained vehicle verifier.

[0037] The parking space status monitoring matrix uses cameras to capture images of parking spaces. Based on these images, it automatically identifies whether a parking space is vacant, whether there are obstacles, or whether it meets needs such as charging or car washing requirements. Sensors detect the occupancy status of parking spaces in real time, and the results are cross-validated with the image recognition results to obtain the parking space status monitoring results.

[0038] Preferably, the trained convolutional neural network is used to analyze the captured parking space images to automatically identify the status of the parking spaces. Multiple sample images of vacant parking spaces, multiple sample images of parking spaces with obstacles, and multiple sample images of occupied parking spaces, along with the corresponding parking space status monitoring results, are acquired as training data. The framework built on the convolutional neural network is then subjected to supervised training until the training converges, resulting in a trained convolutional neural network.

[0039] Pressure sensors or infrared sensors are installed under each parking space. Changes in pressure or reflection can identify whether a parking space is vacant or occupied. This dual monitoring system reliably identifies the parking space status within the automated parking system, providing reliable data support for subsequent scheduling and management.

[0040] Next, the license plate number is extracted from the vehicle entry and exit information, and the user request corresponding to the license plate number is extracted from the interactive screen of the target automated parking garage to obtain the user request instruction set. The interactive screen is a display terminal inside the parking garage, typically a touchscreen or display screen, used to input parking requests, check available parking spaces, obtain parking assistance, and display vehicle parking instructions and status information.

[0041] Step S200: Integrate the vehicle entry and exit information, parking space status monitoring results, and user demand command set for overall consistency analysis, and perform parking path planning based on the analysis results to obtain a parking path set;

[0042] Furthermore, by integrating the vehicle entry and exit information, parking space status monitoring results, and user demand command set for overall consistency analysis, and performing parking path planning based on the analysis results to obtain a parking path set, step S200 of this embodiment further includes:

[0043] Based on the vehicle entry and exit information, basic vehicle information is identified to determine the set of basic vehicle information.

[0044] By combining the user demand instruction set, the vehicle basic information set, and the parking space status monitoring results, an overall inconsistency identification is performed to obtain the overall inconsistency coefficient.

[0045] When the overall inconsistency coefficient is greater than or equal to a preset coefficient threshold, a scheduling management early warning instruction is generated;

[0046] When the overall inconsistency coefficient is less than a preset coefficient threshold, parking path planning is performed based on the user demand instruction set, the vehicle basic information set, and the parking space status monitoring results to obtain the parking path set.

[0047] In one possible implementation, the allocation of parking spaces and parking routes within the target automated parking garage are optimized by combining vehicle entry and exit information, parking space status, and user needs. Based on vehicle entry and exit information, basic vehicle information is extracted, such as vehicle type, license plate number, vehicle size, whether it is an electric vehicle requiring charging, and whether it requires a car wash.

[0048] The user demand instruction set indicates the actions a vehicle with a specific license plate number needs to perform in the parking garage, such as regular parking, car washing, and charging. The vehicle's basic information reflects the basic characteristics of the vehicle, such as the required parking space size. The parking space status monitoring results reflect the availability and occupancy of regular parking spaces, charging spaces, and car wash spaces within the target automated parking garage. The overall inconsistency coefficient reflects the degree of inconsistency between user demand and parking space status. If multiple users need to use charging spaces, but the garage is full and waiting times are long, the overall inconsistency coefficient is high. Conversely, if only a few users need to use charging spaces, and there are many available charging spaces to meet their needs, the overall inconsistency coefficient is low.

[0049] Therefore, the overall inconsistency coefficient reflects the degree of inconsistency between the available resources and user demand within the target automated parking garage. When the overall inconsistency coefficient is greater than or equal to a preset threshold set by those skilled in the art, it indicates that the user demand that needs to be processed within the target automated parking garage has exceeded the garage's available capacity. At this point, a dispatch management early warning instruction is generated. Based on the dispatch management early warning instruction, staff are reminded to take corresponding measures, such as guiding some vehicles out of the garage or suspending vehicle entry at the garage entrance.

[0050] When the overall inconsistency coefficient is less than the preset coefficient threshold, it indicates that although the target multi-level parking garage cannot fully meet the user's needs, it is within the schedulable range. At this time, the parking path planner is used to analyze the user demand instruction set, the vehicle basic information set, and the parking space status monitoring results to complete the parking path planning, determine the parking path corresponding to different license plate numbers, and obtain the parking path set.

[0051] Preferably, multiple sets of sample user demand instructions, multiple sets of sample vehicle basic information, and multiple sets of sample parking space status monitoring results, along with corresponding sets of multiple sample parking paths, are acquired as training data for the parking path planner. This training data is then divided into a training set and a validation set in a 3:2 ratio. The training set is used to supervise the training of a framework built on a feedforward neural network. The validation set is used to validate the framework, obtaining multiple sets of validated parking paths. The number of paths in these validated parking path sets that are identical to those in the multiple sample parking path sets in the validation set is counted. When the number of paths meets a preset requirement, the validation is successful, and the trained parking path planner is obtained.

[0052] Furthermore, by combining the user demand instruction set, the vehicle basic information set, and the parking space status monitoring results to perform overall inconsistency identification and obtain an overall inconsistency coefficient, step S200 of this embodiment further includes:

[0053] Based on regular parking, charging parking, and car wash as indexes, the user demand instruction set is divided to obtain the regular parking user demand instruction set, the charging parking user demand instruction set, and the car wash user demand instruction set.

[0054] Based on the vehicle basic information set and the ordinary parking user demand instruction set, charging parking user demand instruction set and car wash user demand instruction set, parking space demand is identified to determine the ordinary parking space demand set, charging parking space demand set and car wash parking space demand set.

[0055] Based on the set of demand for ordinary parking spaces, the set of demand for charging parking spaces, and the set of demand for car wash parking spaces, and the parking space status monitoring results, an overall inconsistency is identified to obtain the overall inconsistency coefficient.

[0056] Furthermore, step S200 in this embodiment of the application also includes:

[0057] Based on the timestamps of the demand sets for ordinary parking spaces, charging parking spaces, and car wash spaces, and combined with the parking space status monitoring results, parking space matching is performed to obtain the matching result sets for ordinary parking spaces, charging parking spaces, and car wash spaces.

[0058] Based on the set of matching results for ordinary parking spaces, charging parking spaces, and car wash spaces, demand inconsistency is identified, and the demand inconsistency coefficient is determined.

[0059] Based on the user demand urgency description information of the ordinary parking space demand set, the charging parking space demand set, and the car wash parking space demand set, demand urgency inconsistency is identified in the ordinary parking space matching result set, the charging parking space matching result set, and the car wash parking space matching result set, and a demand urgency inconsistency coefficient set is determined.

[0060] The average of the sets of demand inconsistency coefficients and demand urgency inconsistency coefficients is calculated to determine the overall inconsistency coefficient.

[0061] Furthermore, based on the user demand urgency description information of the ordinary parking space demand set, the charging parking space demand set, and the car wash parking space demand set, demand urgency inconsistency identification is performed on the ordinary parking space matching result set, the charging parking space matching result set, and the car wash parking space matching result set to determine the demand urgency inconsistency coefficient set. Step S200 in this embodiment further includes:

[0062] Extract the first user demand urgency description information of the first ordinary parking space demand from the ordinary parking space demand set;

[0063] Based on the first demand for ordinary parking spaces, the matching result set of ordinary parking spaces is mapped and matched to obtain the first ordinary parking space matching result.

[0064] The urgency of the user's needs is assessed based on the parking space status in the first user's demand urgency description information and the first ordinary parking space matching result to identify the degree of demand urgency satisfaction, thereby obtaining the first demand urgency inconsistency coefficient.

[0065] The system iterates through the sets of demand for ordinary parking spaces, charging parking spaces, and car wash spaces to extract user demand urgency description information. It then identifies demand urgency inconsistencies in the matching result sets for ordinary parking spaces, charging parking spaces, and car wash spaces, and determines a set of demand urgency inconsistency coefficients.

[0066] In one possible embodiment, the set of parking request instructions input by the user is divided according to different types of parking needs (such as regular parking, charging parking, and car wash needs). Each subset contains specific types of parking request instructions, such as "regular parking" requests, parking needs requiring "charging," or parking needs requiring "car wash." Furthermore, based on the vehicle size information contained in the vehicle basic information set, the sets of regular parking user request instructions, charging parking user request instructions, and car wash user request instructions are supplemented to obtain the sets of regular parking space requests, charging parking space requests, and car wash space requests. For example, regular parking space requests include parking space type, parking space size, and parking duration. If the user's vehicle is a standard sedan, a standard parking space is allocated, typically approximately 2.5 meters wide and 5 meters long. If the vehicle is a large SUV or truck, a larger parking space needs to be allocated.

[0067] In one embodiment, parking spaces are allocated based on the timestamp of the submitted request, in chronological order, and combined with parking space status monitoring results, resulting in a set of matching results for regular parking spaces, a set of matching results for charging parking spaces, and a set of matching results for car wash parking spaces. When all requested parking spaces are occupied, the corresponding matching result is empty.

[0068] At this point, the number of empty parking space matching results in the set of ordinary parking space matching results, the set of charging parking space matching results, and the set of car wash parking space matching results is counted, and the counted result is compared with the total number of the set of ordinary parking space matching results, the set of charging parking space matching results, and the set of car wash parking space matching results to obtain the demand inconsistency coefficient.

[0069] For example, in the target multi-level parking garage, the received demands are for regular parking, charging parking, and car wash parking, with the following statistics: Regular parking demand: Total number of matching results: 10 regular parking spaces. Number of empty matching results: 3 spaces (i.e., 3 vehicles need to park when all spaces are full, unable to meet the regular parking demand). Charging parking demand: Total number of matching results: 5 charging spaces; Number of empty matching results: 2 spaces (i.e., 2 charging demands need to charge when all charging spaces are full, unable to meet the charging demand). Car wash demand: Total number of matching results: 4 car wash spaces; Number of empty matching results: 1 space (i.e., 1 vehicle needs to wash when the space is occupied, unable to meet the car wash demand). In this case, the total number of empty matching results is 3 + 2 + 4 = 9. The total number of matching results for regular parking, charging parking, and car wash parking is 10 + 5 + 4 = 19. Therefore, the demand inconsistency coefficient is 9 / 19.

[0070] Furthermore, a parking space matching analysis is performed to determine the degree of urgency insufficiency and thus the urgency inconsistency coefficient. The urgency description information of the first user demand for a first ordinary parking space is extracted from the set of ordinary parking space demands. For example, the urgency description information of the first user demand is: license plate number: ABC123, ordinary parking, unlimited parking time. If the matching result for the first ordinary parking space is empty, it indicates that the immediate parking demand cannot be met, and the urgency inconsistency coefficient is 1. If the matching result for the first ordinary parking space is a matched space, the urgency inconsistency coefficient is 0.

[0071] The first user's urgent requirement description information is license plate number: ABC123, ordinary parking, parking within 3 minutes. If the first ordinary parking space matching result is a matching space, but the parking time exceeds 3 minutes, even if the matching is successful, the inconsistency coefficient of the first requirement's urgency is still 1.

[0072] In other words, the demand urgency inconsistency coefficient is 1 or 0. When the urgent demand can be met, the demand urgency inconsistency coefficient is 0, and when the urgent demand cannot be met, the demand urgency inconsistency coefficient is 1.

[0073] Based on the principle that the inconsistency coefficients of the first demand urgency are the same, the user demand urgency description information is extracted by traversing the demand sets of ordinary parking spaces, charging parking spaces, and car wash spaces. Demand urgency inconsistency is then identified in the matching result sets of ordinary parking spaces, charging parking spaces, and car wash spaces to determine the set of demand urgency inconsistency coefficients. Furthermore, the mean of the demand inconsistency coefficients and the demand urgency inconsistency coefficient set is calculated to determine the overall inconsistency coefficient. The overall inconsistency coefficient reflects the overall degree of demand satisfaction in the target multi-level parking garage.

[0074] Step S300: Based on the parking path set, perform linkage device response matching to obtain a linkage device response operation set;

[0075] Step S400: Associate the parking path set and the linkage equipment response operation set one-to-one to obtain the target management scheme.

[0076] Furthermore, based on the parking path set, the linkage device response is matched to obtain the linkage device response operation set. In this embodiment, step S300 further includes:

[0077] Obtain the set of callable linked devices for the target automated parking garage;

[0078] By combining the parking path set with the operational parameters of the callable linkage device set, the set of linkage device response operations is obtained.

[0079] Furthermore, a preset safety boundary is obtained. When the target management scheme is executed, the smart camera of the target multi-level parking garage identifies whether the user is outside the preset safety boundary. If not, a prompt is obtained that the user has left the parking garage.

[0080] In one possible embodiment, the target automated parking garage has multiple interconnected devices for vehicle entry, exit, and path planning, including an automated guided vehicle (AGV) for guiding vehicles to parking spaces, a lifting platform for adjusting the height of parking spaces so that vehicles can enter parking spaces of suitable height, and a robotic arm for moving objects and adjusting vehicle positions.

[0081] According to the route information of each parking path in the parking path set, identify the operation parameters for the set of callable linkage devices, where each parking path corresponds to a linkage device response operation. Preferably, by obtaining multiple sample parking paths, a sample set of callable linkage devices, and the corresponding multiple sample linkage device response operations as a training sample set, use the training sample set to perform supervised training on a framework constructed based on a feedforward neural network until the training converges to obtain a trained operation recognizer. Use the operation recognizer to identify the operation parameters for the set of callable linkage devices with respect to the parking path set to obtain the set of linkage device response operations.

[0082] Furthermore, associate each path in the parking path set with the corresponding set of linkage device response operations on a one-to-one basis. For example, for path A, the operations of the automatic guided vehicle and the lifting platform are associated. For path B, the operations of the robotic arm and the automatic guided vehicle are associated.

[0083] In a possible embodiment, the preset safety boundary refers to a virtual area set in the garage, which usually includes the working areas for vehicle parking and path planning, as well as the high-risk areas that may exist in the garage. The intelligent camera monitors the active area in the garage to identify the positions of people. The camera is equipped with advanced computer vision technology that can detect and analyze the relative positions between vehicles, people, and devices.

[0084] When the target management plan is executed, the system will monitor the user's position in real time. By analyzing the data of the intelligent camera, it is judged whether the user is within the preset safety boundary in the garage. If the user is within the preset safety boundary, continue to execute the target management plan (such as parking path planning and linkage device control). If not, obtain a prompt for the user to leave the garage, such as "Warning: Please leave the garage and do not approach the equipment working area!" or "Thank you for using. The garage management system reminds you to ensure a safe departure."

[0085] In the embodiments of the present application, in summary, the embodiments of the present application at least have the following technical effects:

[0086] The present application performs multi-source data collection through a vehicle identification system, a parking space status monitoring matrix, and a user demand instruction collector to obtain vehicle entry / exit information, parking space status monitoring results, and a set of user demand instructions; integrates the vehicle entry / exit information, parking space status monitoring results, and the set of user demand instructions for overall consistency analysis, and performs parking path planning according to the analysis results to obtain a parking path set; performs linkage device response matching based on the parking path set to obtain a set of linkage device response operations; associates the parking path set and the set of linkage device response operations on a one-to-one basis to obtain a target management plan. It achieves the technical effect of performing parking space resource scheduling and path planning based on real-time data and improving management reliability.

[0087] Example 2, based on the same inventive concept as the intelligent scheduling and management method for multi-level parking garages under real-time data processing in the foregoing examples, such as... Figure 2 As shown, this application provides an intelligent scheduling and management system for automated parking garages under real-time data processing. The system and method embodiments in this application are based on the same inventive concept. The system includes:

[0088] The user demand instruction acquisition module 11 is used to collect multi-source data through the vehicle identification system, parking space status monitoring matrix and user demand instruction collector to obtain vehicle entry and exit information, parking space status monitoring results and user demand instruction set.

[0089] The parking path set acquisition module 12 is used to integrate the vehicle entry and exit information, parking space status monitoring results and user demand command set for overall consistency analysis, and perform parking path planning based on the analysis results to obtain the parking path set;

[0090] The linkage device response operation set acquisition module 13 is used to perform linkage device response matching based on the parking path set to obtain the linkage device response operation set.

[0091] The target management scheme acquisition module 14 is used to associate the parking path set and the linkage equipment response operation set one-to-one to obtain the target management scheme.

[0092] Furthermore, the system is used to implement the following functions:

[0093] The vehicle information set is collected by the vehicle identification system, the vehicle condition information is scanned by traversing the vehicle information set, and the vehicle entry and exit information is determined based on the scanning results.

[0094] The parking space status monitoring matrix is ​​used to perform dual monitoring of the target multi-level parking garage using parking space images and parking space sensor status to obtain the parking space status monitoring results;

[0095] Based on vehicle entry and exit information, and combined with user requests collected by the interactive screen of the target automated parking garage, a set of user request instructions is obtained, wherein each user request instruction corresponds to a license plate number in the vehicle entry and exit information.

[0096] Furthermore, the system is used to implement the following functions:

[0097] The vehicle condition information set is collected by traversing the vehicle information set to obtain the vehicle condition information set.

[0098] The vehicle condition information set is parsed using a vehicle calibrator. If the parsing result indicates that the verification meets the conditions, the corresponding license plate number is added to the vehicle entry and exit information.

[0099] If the parsing result indicates that the verification does not meet the requirements, an operation prohibition instruction is generated. Based on the operation prohibition instruction, a pop-up window is displayed to prohibit the operation and show the non-compliance information.

[0100] Furthermore, the system is used to implement the following functions:

[0101] Based on the vehicle entry and exit information, basic vehicle information is identified to determine the set of basic vehicle information.

[0102] By combining the user demand instruction set, the vehicle basic information set, and the parking space status monitoring results, an overall inconsistency identification is performed to obtain the overall inconsistency coefficient.

[0103] When the overall inconsistency coefficient is greater than or equal to a preset coefficient threshold, a scheduling management early warning instruction is generated;

[0104] When the overall inconsistency coefficient is less than a preset coefficient threshold, parking path planning is performed based on the user demand instruction set, the vehicle basic information set, and the parking space status monitoring results to obtain the parking path set.

[0105] Furthermore, the system is used to implement the following functions:

[0106] Based on regular parking, charging parking, and car wash as indexes, the user demand instruction set is divided to obtain the regular parking user demand instruction set, the charging parking user demand instruction set, and the car wash user demand instruction set.

[0107] Based on the vehicle basic information set and the ordinary parking user demand instruction set, charging parking user demand instruction set and car wash user demand instruction set, parking space demand is identified to determine the ordinary parking space demand set, charging parking space demand set and car wash parking space demand set.

[0108] Based on the set of demand for ordinary parking spaces, the set of demand for charging parking spaces, and the set of demand for car wash parking spaces, and the parking space status monitoring results, an overall inconsistency is identified to obtain the overall inconsistency coefficient.

[0109] Furthermore, the system is used to implement the following functions:

[0110] Based on the timestamps of the demand sets for ordinary parking spaces, charging parking spaces, and car wash spaces, and combined with the parking space status monitoring results, parking space matching is performed to obtain the matching result sets for ordinary parking spaces, charging parking spaces, and car wash spaces.

[0111] Based on the set of matching results for ordinary parking spaces, charging parking spaces, and car wash spaces, demand inconsistency is identified, and the demand inconsistency coefficient is determined.

[0112] Based on the user demand urgency description information of the ordinary parking space demand set, the charging parking space demand set, and the car wash parking space demand set, demand urgency inconsistency is identified in the ordinary parking space matching result set, the charging parking space matching result set, and the car wash parking space matching result set, and a demand urgency inconsistency coefficient set is determined.

[0113] The average of the sets of demand inconsistency coefficients and demand urgency inconsistency coefficients is calculated to determine the overall inconsistency coefficient.

[0114] Furthermore, the system is used to implement the following functions:

[0115] Extract the first user demand urgency description information of the first ordinary parking space demand from the ordinary parking space demand set;

[0116] Based on the first demand for ordinary parking spaces, the matching result set of ordinary parking spaces is mapped and matched to obtain the first ordinary parking space matching result.

[0117] The urgency of the user's needs is assessed based on the parking space status in the first user's demand urgency description information and the first ordinary parking space matching result to identify the degree of demand urgency satisfaction, thereby obtaining the first demand urgency inconsistency coefficient.

[0118] The system iterates through the sets of demand for ordinary parking spaces, charging parking spaces, and car wash spaces to extract user demand urgency description information. It then identifies demand urgency inconsistencies in the matching result sets for ordinary parking spaces, charging parking spaces, and car wash spaces, and determines a set of demand urgency inconsistency coefficients.

[0119] Furthermore, the system is used to implement the following functions:

[0120] Obtain the set of callable linked devices for the target automated parking garage;

[0121] By combining the parking path set with the operational parameters of the callable linkage device set, the set of linkage device response operations is obtained.

[0122] Furthermore, a preset safety boundary is obtained. When the target management scheme is executed, the smart camera of the target multi-level parking garage identifies whether the user is outside the preset safety boundary. If not, a prompt is obtained that the user has left the parking garage.

[0123] It should be noted that the order of the embodiments described above is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. Furthermore, the above description focuses on specific embodiments of this specification. The processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired results. In some implementations, multitasking and parallel processing are possible or may be advantageous.

[0124] The above description is only a preferred embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

[0125] This specification and accompanying drawings are merely illustrative examples of this application and are intended to cover any and all modifications, variations, combinations, or equivalents within the scope of this application. Clearly, those skilled in the art can make various alterations and modifications to this application without departing from its scope. Therefore, if such modifications and modifications fall within the scope of this application and its equivalents, this application intends to include such modifications and modifications.

Claims

1. A method for intelligent scheduling and management of automated parking garages under real-time data processing, characterized in that: The method includes: Multi-source data is collected through a vehicle identification system, a parking space status monitoring matrix, and a user demand command collector to obtain vehicle entry and exit information, parking space status monitoring results, and a set of user demand commands. The system integrates vehicle entry and exit information, parking space status monitoring results, and user demand command set for overall consistency analysis. Based on the analysis results, it performs parking path planning to obtain a set of parking paths. Based on the parking path set, the response of the linkage equipment is matched to obtain the set of linkage equipment response operations. By associating the parking path set and the linkage device response operation set one-to-one, a target management scheme is obtained; The system integrates vehicle entry / exit information, parking space status monitoring results, and user request command sets for overall consistency analysis. Based on the analysis results, parking path planning is performed to obtain a set of parking paths, including: Based on the vehicle entry and exit information, basic vehicle information is identified to determine a set of basic vehicle information, which includes the size information of each vehicle. By combining the user demand instruction set, the vehicle basic information set, and the parking space status monitoring results, an overall inconsistency identification is performed to obtain the overall inconsistency coefficient. When the overall inconsistency coefficient is greater than or equal to a preset coefficient threshold, a scheduling management early warning instruction is generated; When the overall inconsistency coefficient is less than a preset coefficient threshold, parking path planning is performed based on the user demand instruction set, the vehicle basic information set, and the parking space status monitoring results to obtain the parking path set. The user demand instruction set includes a set of user demand instructions for regular parking, user demand instructions for charging parking, and user demand instructions for car washing. By combining the user demand command set, the vehicle basic information set, and the parking space status monitoring results, an overall inconsistency identification is performed to obtain an overall inconsistency coefficient, including: Based on the timestamps of the demand sets for ordinary parking spaces, charging parking spaces, and car wash spaces, and combined with the parking space status monitoring results, parking space matching is performed to obtain the matching result sets for ordinary parking spaces, charging parking spaces, and car wash spaces. Based on the set of matching results for ordinary parking spaces, charging parking spaces, and car wash spaces, demand inconsistency is identified, and the demand inconsistency coefficient is determined. Based on the user demand urgency description information of the ordinary parking space demand set, the charging parking space demand set, and the car wash parking space demand set, demand urgency inconsistency is identified in the ordinary parking space matching result set, the charging parking space matching result set, and the car wash parking space matching result set, and a demand urgency inconsistency coefficient set is determined. The average of the sets of demand inconsistency coefficients and demand urgency inconsistency coefficients is calculated to determine the overall inconsistency coefficient.

2. The intelligent scheduling and management method for automated parking garages under real-time data processing as described in claim 1, characterized in that, Multi-source data is collected through a vehicle identification system, a parking space status monitoring matrix, and a user demand command collector to obtain vehicle entry and exit information, parking space status monitoring results, and a set of user demand commands, including: The vehicle information set is collected by the vehicle identification system, the vehicle condition information is scanned by traversing the vehicle information set, and the vehicle entry and exit information is determined based on the scanning results. The parking space status monitoring matrix is ​​used to perform dual monitoring of the target multi-level parking garage using parking space images and parking space sensor status to obtain the parking space status monitoring results; Based on vehicle entry and exit information, and combined with user requests collected by the interactive screen of the target automated parking garage, a set of user request instructions is obtained, wherein each user request instruction corresponds to a license plate number in the vehicle entry and exit information.

3. The intelligent scheduling and management method for automated parking garages under real-time data processing as described in claim 2, characterized in that, A vehicle information set is collected through a vehicle identification system, and the vehicle condition information is scanned by traversing the vehicle information set. Based on the scanning results, the vehicle entry and exit information is determined, including: The vehicle condition information set is collected by traversing the vehicle information set to obtain the vehicle condition information set. The vehicle condition information set is parsed using a vehicle calibrator. If the parsing result indicates that the verification meets the conditions, the corresponding license plate number is added to the vehicle entry and exit information. If the parsing result indicates that the verification does not meet the requirements, an operation prohibition instruction is generated. Based on the operation prohibition instruction, a pop-up window is displayed to prohibit the operation and show the non-compliance information.

4. The intelligent scheduling and management method for automated parking garages under real-time data processing as described in claim 1, characterized in that, By combining the user demand command set, the vehicle basic information set, and the parking space status monitoring results, an overall inconsistency identification is performed to obtain an overall inconsistency coefficient, including: Based on regular parking, charging parking, and car wash as indexes, the user demand instruction set is divided to obtain the regular parking user demand instruction set, the charging parking user demand instruction set, and the car wash user demand instruction set. Based on the vehicle basic information set and the ordinary parking user demand instruction set, charging parking user demand instruction set and car wash user demand instruction set, parking space demand is identified to determine the ordinary parking space demand set, charging parking space demand set and car wash parking space demand set. Based on the set of demand for ordinary parking spaces, the set of demand for charging parking spaces, and the set of demand for car wash parking spaces, and the parking space status monitoring results, an overall inconsistency is identified to obtain the overall inconsistency coefficient.

5. The intelligent scheduling and management method for automated parking garages under real-time data processing as described in claim 4, characterized in that, Based on the user demand urgency description information of the aforementioned sets of demand for ordinary parking spaces, charging parking spaces, and car wash spaces, the demand urgency inconsistencies of the matching result sets for ordinary parking spaces, charging parking spaces, and car wash spaces are identified, and a set of demand urgency inconsistency coefficients is determined, including: Extract the first user demand urgency description information of the first ordinary parking space demand from the ordinary parking space demand set; Based on the first demand for ordinary parking spaces, the matching result set of ordinary parking spaces is mapped and matched to obtain the first ordinary parking space matching result. The urgency of the user's needs is assessed based on the parking space status in the first user's demand urgency description information and the first ordinary parking space matching result to identify the degree of demand urgency satisfaction, thereby obtaining the first demand urgency inconsistency coefficient. The system iterates through the sets of demand for ordinary parking spaces, charging parking spaces, and car wash spaces to extract user demand urgency description information. It then identifies demand urgency inconsistencies in the matching result sets for ordinary parking spaces, charging parking spaces, and car wash spaces, and determines a set of demand urgency inconsistency coefficients.

6. The intelligent scheduling and management method for automated parking garages under real-time data processing as described in claim 1, characterized in that, Based on the parking path set, the response of the linkage equipment is matched to obtain a set of linkage equipment response operations, including: Obtain the set of callable linkage devices for the target automated parking garage; By combining the parking path set with the operational parameters of the callable linkage device set, the set of linkage device response operations is obtained.

7. The intelligent scheduling and management method for automated parking garages under real-time data processing as described in claim 1, characterized in that, A preset safety boundary is obtained. When the target management scheme is executed, the smart camera of the target three-dimensional parking garage identifies whether the user is outside the preset safety boundary. If not, a prompt is obtained that the user has left the parking garage.

8. A three-dimensional parking garage intelligent dispatch and management system with real-time data processing, characterized in that: The system is used to execute the intelligent scheduling and management method for automated parking garages under real-time data processing as described in any one of claims 1-7, and the system includes: The user demand instruction acquisition module is used to collect multi-source data through the vehicle identification system, parking space status monitoring matrix and user demand instruction collector to obtain vehicle entry and exit information, parking space status monitoring results and user demand instruction set. The parking path set acquisition module is used to integrate the vehicle entry and exit information, parking space status monitoring results and user demand command set for overall consistency analysis, and perform parking path planning based on the analysis results to obtain the parking path set; The module for obtaining the set of responses to the linked devices is used to match the responses of the linked devices based on the set of parking paths to obtain the set of responses to the linked devices. The target management scheme acquisition module is used to associate the parking path set and the linkage equipment response operation set one-to-one to obtain the target management scheme.

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