Parking lot saturation degree determination method and device, and electronic equipment

Abstract

This application provides a method, apparatus, and electronic device for determining parking lot saturation, relating to the field of cloud computing. The method includes: acquiring an image of the parking lot entrance and parking bill data, the parking bill data including vehicle entry and exit records; determining a first saturation of the parking lot based on the entrance image and parking bill data; and sending the first saturation to a terminal device, whereby the first saturation guides the user of the terminal device to park. In this embodiment, determining parking lot saturation using the entrance image and parking bill data does not increase hardware costs and has high accuracy. It can achieve automatic parking guidance without human intervention, avoiding traffic congestion near the parking lot caused by parking.

Description

Technical Field

[0001] This application relates to the field of cloud computing technology, and in particular to a method, apparatus and electronic device for determining parking lot saturation. Background Technology

[0002] In recent years, with the rapid development of IoT and digital image processing technologies, a large number of enclosed parking lots have been transformed into unmanned smart parking lots. These smart parking lots use underground remote sensing coils to control intelligent cameras that automatically capture images of vehicles entering and leaving, and recognize license plates. The cameras report the identified entry and exit information to the parking lot management system, generating corresponding bills. Drivers can then settle payments through a cloud-based service, achieving complete self-service entry and exit. However, the elimination of manual access control makes it impossible to manage traffic flow manually when there are long queues, easily causing secondary congestion on nearby roads. Furthermore, with the rapid advancement of urban digitalization, there is a need for unified command and dispatch of all parking lots at the city or district level; therefore, it is necessary to understand the real-time parking situation of all parking lots within the region.

[0003] Currently, common methods for detecting parking lot saturation include adding infrared sensors to the top of parking spaces or installing high-position video cameras at the entrances. However, these methods are too costly to implement and maintain and cannot be operated in the long term. Summary of the Invention

[0004] This application provides a method, apparatus, and electronic device for determining parking lot saturation, in order to obtain the saturation of the parking lot, realize parking management, and avoid traffic congestion near the parking lot caused by parking.

[0005] In a first aspect, embodiments of this application provide a method for determining parking lot saturation, the method comprising:

[0006] Acquire images of the parking lot entrance and parking bill data, wherein the parking bill data includes vehicle entry record data and vehicle exit record data of the parking lot;

[0007] Based on the entrance image and the parking bill data, determine the first saturation of the parking lot;

[0008] The first saturation is sent to the terminal device, and the first saturation is used to guide the user of the terminal device to park.

[0009] Secondly, embodiments of this application provide a parking lot saturation determination device, the device comprising:

[0010] The acquisition module is used to acquire images of the entrance to the parking lot and parking bill data, wherein the parking bill data includes vehicle entry record data and vehicle exit record data of the parking lot;

[0011] A determining module is used to determine the first saturation of the parking lot based on the entrance image and the parking bill data;

[0012] The sending module is used to send the first saturation to the terminal device, and the first saturation is used to guide the user of the terminal device to park.

[0013] Thirdly, embodiments of this application provide an electronic device, including a memory, a processor, and a computer program stored in the memory, wherein the processor implements any of the methods described above when executing the computer program.

[0014] Compared with the prior art, this application has the following advantages:

[0015] This application provides a method, apparatus, and electronic device for determining parking lot saturation. The method involves acquiring an image of the parking lot entrance and parking bill data, including vehicle entry and exit records. Based on the entrance image and parking bill data, a first saturation level is determined. This first saturation level is then sent to a terminal device to guide users on the terminal device to park. In this embodiment, determining parking lot saturation using the entrance image and parking bill data does not increase hardware costs and is highly accurate. It enables automatic parking guidance without human intervention, preventing traffic congestion near the parking lot caused by parking.

[0016] The above description is only an overview of the technical solution of this application. In order to better understand the technical means of this application, it can be implemented according to the contents of the specification. In order to make the above and other objects, features and advantages of this application more obvious and understandable, specific embodiments of this application are given below. Attached Figure Description

[0017] In the accompanying drawings, unless otherwise specified, the same reference numerals throughout the various drawings denote the same or similar parts or elements. These drawings are not necessarily drawn to scale. It should be understood that these drawings depict only some embodiments according to this application and should not be construed as limiting the scope of this application.

[0018] Figure 1 A schematic diagram illustrating a scenario for the parking lot saturation determination method provided in this application;

[0019] Figure 2 This is a flowchart of a parking lot saturation determination method according to an embodiment of this application;

[0020] Figure 3 This is a flowchart of a parking lot saturation determination method according to an embodiment of this application;

[0021] Figure 4 This is a structural block diagram of a parking lot saturation determination device according to an embodiment of this application; and

[0022] Figure 5 This is a block diagram of an electronic device used to implement embodiments of this application. Detailed Implementation

[0023] In the following description, only certain exemplary embodiments are briefly described. As those skilled in the art will recognize, the described embodiments can be modified in various ways without departing from the concept or scope of this application. Therefore, the drawings and description are considered to be exemplary in nature and not restrictive.

[0024] To facilitate understanding of the technical solutions of the embodiments of this application, the relevant technologies of the embodiments of this application are described below. The following relevant technologies are optional solutions and can be combined with the technical solutions of the embodiments of this application in any way, and all of them fall within the protection scope of the embodiments of this application.

[0025] Figure 1 This is a schematic diagram illustrating an application scenario for implementing the method of this application embodiment. In this embodiment, calculations are performed through a cloud computing server to realize the functions of a cloud server. The cloud server provides tenants with the function of calculating parking lot saturation, and tenants can push the calculated saturation to users in the parking lot to achieve parking management.

[0026] like Figure 1 As shown, the cloud platform includes an IoT device management platform, cloud storage services, a parking lot data acquisition platform, and a parking engine. Local smart network cameras capture images at the parking lot entrance, process them via a switch, and then send them to the integrated acquisition unit. Each smart network camera includes a lens, a camera application, and input / output (I / O) ports. Underground pre-embedded remote sensing coils and barriers send control commands to the smart network cameras through the I / O ports. The smart network cameras automatically capture images at the parking lot entrance and send them to the integrated acquisition unit, which performs data caching and distribution. The integrated acquisition unit contains camera manufacturer toolkits and cloud software development kits.

[0027] The integrated image acquisition device utilizes edge routing to upload acquired images to a cloud storage service via Hypertext Transfer Protocol (HTTP). The cloud storage service assigns file identification information to the images and distributes it to the integrated image acquisition device. The device then assembles the file identification information, license plate number, device identifier, parking lot information, gate information, and acquisition time into a snapshot event and reports it to the IoT device platform via Message Queuing Telemetry Transport (MQTT). The IoT device management platform sends the snapshot event to the parking lot data acquisition platform via Advanced Message Queuing Protocol (AMQP). The parking lot data acquisition platform retrieves images of the parking lot entrance from the cloud storage service and sends the license plate information and snapshot event identified from the images to the parking engine.

[0028] In addition, the intelligent network cameras collect images of vehicles entering and leaving the parking lot, and report this information to the parking management system. The parking management system generates corresponding billing data and sends the vehicle entry and exit data, along with the billing data, to the cloud-based parking engine via HTTP. Based on the captured events and parking billing data, the parking engine determines the parking lot's vehicle entry rate. Based on the vehicle entry rate, it determines the parking lot's saturation. Using the parking billing data, it corrects the parking lot's saturation, obtaining a revised saturation level, which is then sent to the tenant's terminal device. The tenant's terminal device then sends the saturation level to the user's terminal device, allowing the user to determine whether to park in that parking lot based on its saturation level.

[0029] One related technology involves installing infrared sensors above each parking space or high-position video cameras at the entrance to obtain parking information. However, the implementation and maintenance costs are extremely high, making it difficult to use as a routine method. Another related technology calculates available spaces by accumulating entry and exit records. However, due to potential over-parking and manual release phenomena in parking lots, this approach cannot guarantee the accuracy of the calculation results.

[0030] In this embodiment, the smart camera at the parking lot entrance is reused, eliminating the need for additional hardware costs. Furthermore, by utilizing parking bill data and images from the parking lot entrance, the saturation level of the parking lot is determined with high accuracy. This enables automated parking management, preventing traffic congestion near the parking lot caused by parking issues.

[0031] This application provides a method for determining parking lot saturation. In this embodiment, the executing entity can be a computing device, such as a server. Figure 2The diagram shown is a flowchart of a parking lot saturation determination method according to an embodiment of this application. The method includes:

[0032] Step S201: Obtain the image of the parking lot entrance and parking bill data. The parking bill data includes vehicle entry record data and vehicle exit record data.

[0033] The image acquisition device may include a smart camera or other form of image acquisition device installed at the entrance of the parking lot. Taking a smart camera as an example, if a vehicle is queuing up to enter the parking lot at the entrance, the image at the entrance may contain the vehicle. If there is no vehicle waiting at the entrance, the image at the entrance will not contain the vehicle.

[0034] The vehicle billing data includes vehicle entry records, such as vehicle license plate information and entry time, as well as vehicle exit records, such as exit time and parking fees.

[0035] Step S202: Determine the first saturation of the parking lot based on the entrance image and parking bill data.

[0036] The first saturation of the parking lot represents the degree of saturation of the parked vehicles. It can be expressed in different forms, such as high, medium and low levels, or different numerical values. This application does not limit this.

[0037] Step S203: The first saturation is sent to the terminal device. The first saturation is used to guide the user of the terminal device to park.

[0038] It can periodically acquire images and parking bill data at the entrance, calculate the first saturation of the parking lot in real time, and send the first saturation to the terminal device. This allows users of the terminal device to determine whether to park in the parking lot based on the first saturation, thereby realizing the parking guidance function.

[0039] This application provides a method for determining parking lot saturation. The method involves acquiring an image of the parking lot entrance and parking bill data, including vehicle entry and exit records. Based on the entrance image and parking bill data, a first saturation level is determined. This first saturation level is then sent to a terminal device to guide users on the terminal device to park. In this embodiment, determining parking lot saturation using the entrance image and parking bill data does not increase hardware costs and is highly accurate. It enables automatic parking guidance without human intervention, preventing traffic congestion near the parking lot caused by parking.

[0040] In one possible implementation, before determining the initial saturation of the parking lot based on the entrance image and parking bill data, the method further includes:

[0041] Analysis of the entrance images determined that at least one vehicle was waiting to enter.

[0042] Specifically, vehicle recognition is performed in the entrance image to determine whether the image includes vehicles waiting to enter. If waiting vehicles are present, the parking lot's first saturation is determined based on the entrance image and parking bill data. If no waiting vehicles are present in the entrance image, the parking lot's first saturation is configured to a preset value, such as zero.

[0043] In one possible implementation, the initial saturation of the parking lot is determined based on the entrance image and parking bill data, including:

[0044] Based on the entrance image and parking bill data, determine the vehicle entry rate of the parking lot. The vehicle entry rate represents the proportion of at least one waiting vehicle that has entered the parking lot.

[0045] The first saturation level of the parking lot is determined based on the vehicle entry rate and parking bill data.

[0046] The entrance image includes pictures of vehicles queuing to enter the parking lot. Some of these vehicles may enter, while others may not. Based on parking bill data, it's determined whether any vehicles in the entrance image have entered, how many have entered, and how many haven't, thus calculating the vehicle entry rate. Based on the parking lot's vehicle entry rate and parking bill data, the first saturation level of the parking lot is determined.

[0047] In one possible implementation, the vehicle entry rate of the parking lot is determined based on the entrance image and parking bill data, including:

[0048] Based on the image at the entrance, identify the identification information of at least one vehicle waiting to enter;

[0049] Based on the identification information of at least one vehicle waiting to enter, query the parking bill data to determine whether at least one vehicle waiting to enter has entered the parking lot;

[0050] The vehicle entry rate of a parking lot is determined based on the number of vehicles that have entered the parking lot from at least one waiting vehicle and the number of vehicles waiting to enter.

[0051] In practical applications, when vehicles waiting to enter are present in the entrance image, the system identifies vehicle identification information, such as license plates. Based on the license plate, the system queries the parking bill data to retrieve the vehicle's entry and exit records, thus determining whether the vehicle has entered the parking lot. The system calculates the parking lot's vehicle entry rate by comparing the number of vehicles entering the parking lot with the total number of vehicles waiting to enter. The vehicle entry rate represents the proportion of vehicles queuing in the entrance image that have actually entered the parking lot.

[0052] For example, images of the parking lot entrance captured in the last 24 hours are filtered, and records with consecutive license plates of the same type are merged into one record. For the same vehicle, the capture time of the first record is taken as the vehicle's start waiting time, and the capture time of the last record is taken as the vehicle's end waiting time. If there are no more than two records, the record is discarded. If the parking lot has multiple entrance lanes, the vehicle's entry status is checked in the parking bill data according to the entrance lane, license plate, and final capture time. The vehicle entry rate is calculated, which is the proportion of vehicles that entered the parking lot after being captured in the last 24 hours of images.

[0053] In one possible implementation, the parking lot includes multiple entry lanes, and a first saturation level is determined based on the parking lot's vehicle entry rate and parking billing data, including:

[0054] Based on parking bill data and vehicle entry rate, the third saturation corresponding to each of the multiple entry lanes is determined;

[0055] Determine the average value of multiple third saturations, and use the average value of multiple third saturations as the first saturation.

[0056] In practical applications, when a parking lot includes multiple entry lanes, the entrance image includes images of multiple entry lanes. Based on the vehicle and lane identifiers in the entry lane images, the vehicle's entry and exit records are retrieved from the parking bill data. The saturation corresponding to each entry lane, i.e., the third saturation, is calculated. The arithmetic mean of the third saturations of multiple entry lanes is then calculated as the first saturation of the parking lot. It should be noted that, in addition to the arithmetic mean, other calculation methods can also be used to calculate the first saturation of the parking lot; this application does not limit this method.

[0057] In one possible implementation, the first saturation level of the parking lot is determined based on the parking lot's vehicle entry rate and parking bill data, including:

[0058] Determine the second saturation level of the parking lot based on the vehicle entry rate;

[0059] Using parking bill data, the second saturation is corrected to obtain the first saturation.

[0060] In practical applications, the vehicle entry rate is determined based on entrance images acquired within multiple time periods of a preset time cycle. If there are waiting vehicles in the entrance image of the current time period, the initial saturation of the parking lot for that time period, i.e., the second saturation, is determined based on the vehicle entry rate. To improve the accuracy of the saturation calculation, a correction coefficient is calculated using parking bill data to correct the second saturation, resulting in the corrected saturation, i.e., the first saturation. If there are no waiting vehicles in the entrance image of the current time period, the second saturation for that time period is configured to a preset value, such as zero.

[0061] For example, the second saturation can be calculated according to the following formula (1):

[0062]

[0063] Here, enterRate represents the entry rate. When it is determined from the parking bill data that there are vehicles entering the parking space in the current time period, the second saturation S of the current time period is calculated using formula (1); when there are no vehicles entering the parking space in the current time period, the second saturation S of the current time period is calculated using formula (2). The constants 1 and 2 in formulas (1) and (2) can be set according to specific needs.

[0064] For example, if the second saturation was zero in the previous time period and the second saturation is still zero in the current time period, then the second saturation is corrected according to the following formula (3):

[0065] S n ′=S n *K c (3)

[0066] Among them, S n ′ represents the corrected second saturation, S n K represents the second saturation level before correction. c This represents the continuous waiting impact factor, which is an empirical value and can be configured according to specific needs.

[0067] In this embodiment, the second saturation is corrected using parking bill data to obtain the first saturation, which makes the saturation calculation result more accurate.

[0068] The specific implementation method for correcting the second saturation using parking bill data is shown in the following example:

[0069] In one possible implementation, parking bill data is used to correct the second saturation to obtain the first saturation, including:

[0070] Based on parking bill data, determine the number of vehicles entering and leaving the parking lot;

[0071] The number of vehicles entering and leaving the parking lot is determined based on the number of vehicles entering and leaving.

[0072] The second saturation is corrected based on the number of vehicles entering, leaving, and parked to obtain the first saturation.

[0073] In practical applications, parking bill data can be used to count the number of vehicles entering and leaving the parking lot in each time period within a preset time cycle. Based on the number of vehicles entering and leaving the parking lot in that time period, the number of vehicles currently parked can be determined.

[0074] For example, the number of vehicles entering and leaving the parking lot per minute over the past 7 days is counted. The baseline value for the number of vehicles currently parked is calculated as (number of vehicles entering per day + number of vehicles leaving per day) / 2. The total number of vehicles currently parked is calculated as: Baseline value + Number of vehicles entering the parking lot - Number of vehicles leaving the parking lot.

[0075] It is understood that the baseline value and the number of parked vehicles can also be calculated using other methods, and this application does not limit this.

[0076] Based on the number of vehicles entering, leaving, and parked, a correction coefficient can be determined to correct the second saturation, resulting in the first saturation. See the following example for details:

[0077] In one possible implementation, the second saturation is corrected based on the number of vehicles entering, leaving, and parked to obtain the first saturation, including:

[0078] Based on the number of vehicles entering, leaving, and parked in multiple time periods of the current time period, determine the average and median of the number of vehicles entering, leaving, and parked in, respectively.

[0079] The second saturation is corrected based on the average and median values ​​of the number of vehicles entering, leaving, and parking, respectively, to obtain the first saturation.

[0080] In practical applications, the number of vehicles entering and leaving the vehicle is obtained from multiple time periods, the number of parked vehicles is calculated, and then the average and median values ​​of the number of vehicles entering, leaving and parked vehicles from multiple time periods are calculated to correct the second saturation and obtain a first saturation with higher accuracy.

[0081] For example, the first saturation can be calculated according to the following formula (4):

[0082]

[0083] Among them, S n "" indicates the first degree of saturation, S n Indicates the second saturation level. `in_count` represents the average number of vehicles entering across multiple time periods within the current time period; `in_count_mid` represents the median number of vehicles entering across multiple time periods within the current time period; `parking_count` represents the average number of vehicles parked across multiple time periods within the current time period; `parking_count_mid` represents the median number of vehicles parked across multiple time periods within the current time period; `out_count` represents the average number of vehicles leaving across multiple time periods within the current time period; `out_count_mid` represents the median number of vehicles leaving across multiple time periods within the current time period; K in K represents the entry influence factor. parking K represents the influencing factor during the shutdown process. out K represents the departure impact factor. in K parking K out These are empirical values ​​and can be configured according to specific needs.

[0084] In one possible implementation, the method further includes:

[0085] Based on the number of vehicles entering and leaving in the current time period, predict the number of vehicles entering and leaving in the next time period.

[0086] The first saturation level is adjusted based on the number of vehicles entering and leaving in the next time period.

[0087] In practical applications, based on the number of vehicles entering and leaving in multiple time periods of the current time period, a differential autoregressive moving average model can be used to predict the number of vehicles entering and leaving in multiple time periods of the next time period. Then, using the number of vehicles entering and leaving in multiple time periods of the next period, the average and median of the number of vehicles entering and leaving in each time period of the next period are calculated respectively, and the first saturation is corrected. In this embodiment, using data from the next time period to correct the first saturation of the current time period can further improve the accuracy of the saturation calculation results.

[0088] For example, the first saturation can be corrected according to the following formula (5):

[0089]

[0090] Among them, S n"" indicates the first degree of saturation, S n "′" represents the corrected first saturation level, in_next_count represents the average number of vehicles entering over multiple time periods in the next time period, in_next_count_mid represents the median number of vehicles entering over multiple time periods in the next time period, out_next_count represents the average number of vehicles leaving over multiple time periods in the next time period, out_next_count_mid represents the median number of vehicles leaving over multiple time periods in the next time period, K next This represents the influence factor of the predicted value. It is an empirical value and can be configured according to specific needs. For example, it can be 1.2.

[0091] Optionally, the corrected first saturation can be normalized to ensure it falls within a preset range.

[0092] For example, the corrected first saturation can be normalized according to the following formula (6):

[0093]

[0094] Among them, S p15 S' is the result of the first saturation normalization calculated based on data from the past 15 minutes, with values ​​in the interval (0, 1). p15 The first saturation level is calculated based on data from the past 15 minutes.

[0095] In addition, the technical solution of this application also includes a step of calibrating the available parking spaces in the parking lot, as shown in the following embodiment:

[0096] In one possible implementation, the method further includes:

[0097] The first saturation of multiple time periods is sorted, and the average value of the first saturation in the first preset position is calculated to obtain the average saturation of the parking lot.

[0098] If the first saturation of the current time period is greater than the average saturation, then the available parking spaces in the parking lot will be calibrated to the preset value.

[0099] In practical applications, the peak saturation is determined based on the first saturation over multiple past time periods. When the current first saturation exceeds the peak saturation, the available parking spaces are calibrated.

[0100] For example, the saturation of each day over the past 7 days is sorted from largest to smallest, the top 5% of saturation is selected, the average value is calculated as the peak saturation, and if the current first saturation exceeds the peak saturation, the number of available parking spaces is set to zero, and a prompt message is sent to the mobile terminal indicating that there are no available parking spaces in the parking lot.

[0101] In one possible implementation, the method further includes:

[0102] Multiple influencing factors for the first saturation are determined, and these factors are adjusted according to a preset time period. The optimized first saturation is then determined using the adjusted factors.

[0103] Among them, the multiple influencing factors of the first saturation include the continuous waiting influencing factor, the entry influencing factor, the in-suspension influencing factor, the exit influencing factor, and the predicted value influencing factor involved in the above embodiments.

[0104] The availability of parking spaces in the parking lot is constantly changing. When the availability of parking spaces reaches a preset threshold, the first saturation over multiple time periods is used to calculate the loss using a loss function. Multiple influencing factors of the first saturation are adjusted, and the value of the loss function is calculated using gradient descent. When the value of the loss function is minimized, the influencing factors are at their optimal values. The first saturation of the next time period is then calculated using the optimal values.

[0105] For example, the loss can be calculated using the loss function shown in the following formula (7):

[0106]

[0107] Where cost represents the value of the loss function, s k This represents the first saturation level for each time period. If we calculate the loss over 5 time periods, the k values ​​would be 1, 2, 3, 4, and 5, respectively. max The peak saturation value can be calculated based on the first saturation value over multiple past time periods.

[0108] It is understood that the loss function can also take other forms, and this application does not limit it.

[0109] To more clearly illustrate the technical concept of this application, the method provided in this application will be described below through a specific embodiment.

[0110] Figure 3 This is a flowchart illustrating a parking lot saturation determination method according to an embodiment of this application. Figure 3As shown, firstly, capture records are collected. Specifically, images at the parking lot entrance are captured using smart cameras. Secondly, capture records are merged. Specifically, capture events from the most recent 24 hours are filtered by parking lane, and records with consecutive identical license plates are merged into one record. The capture time of the first record is used as the start waiting time, and the capture time of the last record is used as the end waiting time. If there are no more than two records, they are discarded. Thirdly, entry records are supplemented. Specifically, by lane, license plate, and final capture time, the parking bill data is checked to see if the vehicle has entered the parking lot. Using the capture records and parking bill data, the entry rate is calculated, which is the percentage of vehicles that entered the parking lot after being captured in the most recent 24 hours of capture records. Then, the initial saturation for the current time period is calculated based on the entry rate. The saturation for the previous time period is zero, and the saturation for the current time period is also zero. The initial saturation is corrected based on continuous congestion, specifically using a continuous waiting impact factor. The saturation of multiple lanes per minute over the past 15 minutes is summarized to calculate the saturation of the parking lot over the past 15 minutes. In addition, entry and exit data are collected. Based on entry and exit records from parking bills, calculate the parking lot's baseline parking capacity and the minute-by-minute entry / exit throughput. This involves calculating the number of vehicles entering and leaving the parking lot over the past 15 minutes. Using the baseline, the number of vehicles entering, and the number of vehicles leaving, calculate the number of vehicles parked per minute. Calculate the average / median entry / exit throughput and the average / median parking capacity over the past 15 minutes. Use these average / median entry / exit throughput and average / median parking capacity data to adjust for parking lot saturation. Using the number of vehicles entering and leaving over the past 15 minutes, predict the data for the next 15 minutes, i.e., predict the average / median entry / exit throughput for the next period. Use the predicted number of vehicles entering and leaving over the next 15 minutes to adjust for parking lot saturation, i.e., adjust parking lot saturation based on the data for the next period.

[0111] Corresponding to the application scenarios and methods provided in the embodiments of this application, the embodiments of this application also provide a parking lot saturation determination device. For example... Figure 4 The diagram shown is a structural block diagram of a parking lot saturation determination device according to an embodiment of this application. The device may include:

[0112] The acquisition module 401 is used to acquire images of the entrance to the parking lot and parking bill data, wherein the parking bill data includes vehicle entry record data and vehicle exit record data of the parking lot;

[0113] The determining module 402 is used to determine the first saturation of the parking lot based on the entrance image and the parking bill data;

[0114] A sending module 403 is used to send the first saturation level to a terminal device, whereby the first saturation level guides the user of the terminal device to park. This application embodiment provides a parking lot saturation determination device, which acquires an image of the parking lot entrance and parking bill data, including vehicle entry and exit records; determines a first saturation level of the parking lot based on the entrance image and parking bill data; and sends the first saturation level to the terminal device, whereby the first saturation level guides the user of the terminal device to park. In this embodiment, determining the parking lot saturation level using the entrance image and parking bill data does not increase hardware costs and has high accuracy. It can achieve automatic parking guidance without human intervention, avoiding traffic congestion near the parking lot caused by parking.

[0115] In one possible implementation, the device further includes an analysis module for:

[0116] Analysis of the entrance images determined that at least one vehicle was waiting to enter.

[0117] In one possible implementation, module 402 is specifically used for:

[0118] Based on the entrance image and parking bill data, determine the vehicle entry rate of the parking lot. The vehicle entry rate represents the proportion of at least one waiting vehicle that has entered the parking lot.

[0119] The first saturation level of the parking lot is determined based on the vehicle entry rate and parking bill data.

[0120] In one possible implementation, module 402 is specifically used for:

[0121] Based on the image at the entrance, identify the identification information of at least one vehicle waiting to enter;

[0122] Based on the identification information of at least one vehicle waiting to enter, query the parking bill data to determine whether at least one vehicle waiting to enter has entered the parking lot;

[0123] The vehicle entry rate of a parking lot is determined based on the number of vehicles that have entered the parking lot from at least one waiting vehicle and the number of vehicles waiting to enter.

[0124] In one possible implementation, the determining module 402 includes a determining unit and a correcting unit;

[0125] A determination unit is used to determine the second saturation of the parking lot based on the vehicle entry rate of the parking lot;

[0126] The correction unit is used to correct the second saturation using parking bill data to obtain the first saturation.

[0127] In one possible implementation, the correction unit is specifically used for:

[0128] Based on parking bill data, determine the number of vehicles entering and leaving the parking lot;

[0129] The number of vehicles entering and leaving the parking lot is determined based on the number of vehicles entering and leaving.

[0130] The second saturation is corrected based on the number of vehicles entering, leaving, and parked to obtain the first saturation.

[0131] In one possible implementation, when the correction unit corrects the second saturation based on the number of entering vehicles, the number of leaving vehicles, and the number of vehicles parked, to obtain the first saturation, it is used to:

[0132] Based on the number of vehicles entering, leaving, and parked in multiple time periods of the current time period, determine the average and median of the number of vehicles entering, leaving, and parked in, respectively.

[0133] The second saturation is corrected based on the average and median values ​​of the number of vehicles entering, leaving, and parking, respectively, to obtain the first saturation.

[0134] In one possible implementation, the device further includes a correction module for:

[0135] Based on the number of vehicles entering and leaving in the current time period, predict the number of vehicles entering and leaving in the next time period.

[0136] The first saturation level is adjusted based on the number of vehicles entering and leaving in the next time period.

[0137] In one possible implementation, the parking lot includes multiple entry lanes, and the determining module 402 is specifically used for:

[0138] Based on parking bill data and vehicle entry rate, the third saturation corresponding to each of the multiple entry lanes is determined;

[0139] Determine the average value of multiple third saturations, and use the average value of multiple third saturations as the first saturation.

[0140] In one possible implementation, the device further includes a calibration module for:

[0141] The first saturation of multiple time periods is sorted, and the average value of the first saturation in the first preset position is calculated to obtain the average saturation of the parking lot.

[0142] If the first saturation of the current time period is greater than the average saturation, then the available parking spaces in the parking lot will be calibrated to the preset value.

[0143] In one possible implementation, the device further includes an adjustment module for:

[0144] Multiple influencing factors for the first saturation are determined, and these factors are adjusted according to a preset time period. The optimized first saturation is then determined using the adjusted factors.

[0145] The functions of each module in each device in the embodiments of this application can be found in the corresponding description in the above method, and they have corresponding beneficial effects, which will not be repeated here.

[0146] Figure 5 This is a block diagram of an electronic device used to implement embodiments of this application. For example... Figure 5 As shown, the electronic device includes a memory 510 and a processor 520. The memory 510 stores a computer program that can run on the processor 520. When the processor 520 executes the computer program, it implements the method described in the above embodiments. The number of memories 510 and processors 520 can be one or more.

[0147] The electronic device also includes:

[0148] The communication interface 530 is used to communicate with external devices and exchange and transmit data.

[0149] If the memory 510, processor 520, and communication interface 530 are implemented independently, they can be interconnected via a bus to communicate with each other. This bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. This bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 5 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0150] Optionally, in a specific implementation, if the memory 510, processor 520, and communication interface 530 are integrated on a single chip, then the memory 510, processor 520, and communication interface 530 can communicate with each other through an internal interface.

[0151] This application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the method provided in this application.

[0152] This application also provides a chip including a processor for calling and executing instructions stored in a memory, causing a communication device with the chip installed to perform the method provided in this application.

[0153] This application also provides a chip, including: an input interface, an output interface, a processor, and a memory. The input interface, output interface, processor, and memory are connected through an internal connection path. The processor is used to execute code in the memory. When the code is executed, the processor is used to execute the method provided in the application embodiment.

[0154] It should be understood that the aforementioned processor can be a Central Processing Unit (CPU), or other general-purpose processors, Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field-Programmable Gate Arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. General-purpose processors can be microprocessors or any conventional processor. It is worth noting that the processor can be a processor supporting Advanced Reduced Instruction Set Machines (ARM) architecture.

[0155] Further, optionally, the aforementioned memory may include read-only memory and random access memory. The memory may be volatile memory or non-volatile memory, or may include both. Non-volatile memory may include read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. Volatile memory may include random access memory (RAM), which serves as an external cache. By way of example, but not limitation, many forms of RAM are available. Examples include Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), Synchronous DRAM (SDRAM), Double Data Rate SDRAM (DDR SDRAM), Enhanced Synchronous DRAM (ESDRAM), Synchronous Link DRAM (SLDRAM), and Direct Rambus RAM (DR RAM).

[0156] In the above embodiments, implementation can be achieved, in whole or in part, through software, hardware, firmware, or any combination thereof. When implemented in software, it can be implemented, in whole or in part, as a computer program product. A computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions according to this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transferred from one computer-readable storage medium to another.

[0157] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this application. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of those different embodiments or examples.

[0158] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this application, "a plurality of" means two or more, unless otherwise explicitly specified.

[0159] Any process or method described in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or more executable instructions for implementing a particular logical function or process. Furthermore, the scope of the preferred embodiments of this application includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functionality involved.

[0160] The logic and / or steps described in the flowchart or otherwise herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus or device (such as a computer-based system, a processor-included system or other system that can fetch and execute instructions from, an instruction execution system, apparatus or device).

[0161] It should be understood that various parts of this application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. All or part of the steps of the methods in the above embodiments can be implemented by a program instructing related hardware, the program being stored in a computer-readable storage medium, which, when executed, includes one or a combination of the steps of the method embodiments.

[0162] Furthermore, the functional units in the various embodiments of this application can be integrated into a processing module, or each unit can exist physically separately, or two or more units can be integrated into a module. The integrated module can be implemented in hardware or as a software functional module. If the integrated module is implemented as a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium. This storage medium can be a read-only memory, a disk, or an optical disk, etc.

[0163] The above description is merely an exemplary embodiment of this application, but the scope of protection of this application is not limited thereto. Any person skilled in the art can easily conceive of various variations or substitutions within the technical scope described in this application, and these should all be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for determining parking lot saturation, characterized in that, The method includes: Acquire images of the parking lot entrance and parking bill data, wherein the parking bill data includes vehicle entry record data and vehicle exit record data of the parking lot; Based on the entrance image, the parking bill data, and multiple influencing factors, the first saturation of the parking lot is determined. The multiple influencing factors of the first saturation are determined by the first saturation over multiple time periods when the number of available parking spaces in the parking lot reaches a preset threshold, and the value of the preset loss function is determined based on the minimum value of the preset loss function. The first saturation is sent to the terminal device, and the first saturation is used to guide the user of the terminal device to park.

2. The method of claim 1, wherein, Before determining the first saturation of the parking lot based on the entrance image and the parking bill data, the method further includes: The image at the entrance was analyzed to determine that it included at least one vehicle waiting to enter.

3. The method of claim 2, wherein, Determining the first saturation of the parking lot based on the entrance image and the parking bill data includes: Based on the entrance image and the parking bill data, the vehicle entry rate of the parking lot is determined, and the vehicle entry rate represents the proportion of the at least one waiting vehicle entering the parking lot; The first saturation level of the parking lot is determined based on the vehicle entry rate and the parking bill data.

4. The method of claim 3, wherein, Determining the vehicle entry rate of the parking lot based on the entrance image and parking bill data includes: Based on the image at the entrance, determine the identification information of the at least one vehicle waiting to enter; Based on the identification information of the at least one vehicle waiting to enter, the parking bill data is queried to determine whether the at least one vehicle waiting to enter has entered the parking lot; The vehicle entry rate of the parking lot is determined based on the number of vehicles entering the parking lot from the at least one waiting vehicle and the number of the at least one waiting vehicle.

5. The method according to claim 3 or 4, characterized in that, Determining the first saturation level of the parking lot based on the vehicle entry rate and the parking bill data includes: The second saturation level of the parking lot is determined based on the vehicle entry rate of the parking lot; Using the parking bill data, the second saturation is corrected to obtain the first saturation.

6. The method according to claim 5, characterized in that, The step of using the parking bill data to correct the second saturation to obtain the first saturation includes: Based on the parking bill data, determine the number of vehicles entering and leaving the parking lot; The number of vehicles entering and leaving the parking lot is determined based on the number of vehicles entering and leaving the parking lot. The second saturation is corrected based on the number of vehicles entering, the number of vehicles leaving, and the number of vehicles parked to obtain the first saturation.

7. The method of claim 6, wherein, The step of correcting the second saturation based on the number of vehicles entering, the number of vehicles leaving, and the number of vehicles currently parked, to obtain the first saturation, includes: Based on the number of vehicles entering, the number of vehicles leaving, and the number of vehicles in parking spaces across multiple time periods within the current time period, determine the average and median values ​​corresponding to the number of vehicles entering, the number of vehicles leaving, and the number of vehicles in parking spaces, respectively. The second saturation is corrected based on the average and median values ​​corresponding to the number of vehicles entering, the number of vehicles leaving, and the number of vehicles in parking spaces, respectively, to obtain the first saturation.

8. The method of claim 6, wherein, The method further includes: Based on the number of vehicles entering and leaving in the current time period, predict the number of vehicles entering and leaving in the next time period. The first saturation is corrected based on the number of vehicles entering and leaving in the next time period.

9. The method of claim 3, wherein, The parking lot includes multiple entry lanes. Determining the first saturation level of the parking lot based on the vehicle entry rate and parking bill data includes: Based on the parking bill data and the vehicle entry rate, the third saturation corresponding to each of the multiple entry lanes is determined; The average value of the plurality of third saturations is determined, and the average value of the plurality of third saturations is used as the first saturation.

10. The method of claim 2 or 3, wherein, The method further includes: The first saturation of multiple time periods is sorted, and the average value of the first saturation at the top preset position is calculated to obtain the average saturation of the parking lot. If the first saturation of the current time period is greater than the average saturation, then the available parking spaces in the parking lot will be calibrated to a preset value.

11. A parking lot saturation determination device, characterized in that, The device includes: The acquisition module is used to acquire images of the entrance to the parking lot and parking bill data, wherein the parking bill data includes vehicle entry record data and vehicle exit record data of the parking lot; The determination module is used to determine the first saturation of the parking lot based on the entrance image, the parking bill data, and multiple influencing factors. The multiple influencing factors of the first saturation are determined by determining the value of a preset loss function based on the first saturation over multiple time periods when the number of available parking spaces in the parking lot reaches a preset threshold, and the value of the preset loss function is determined based on the minimum value of the preset loss function. The sending module is used to send the first saturation to the terminal device, and the first saturation is used to guide the user of the terminal device to park.

12. An electronic device comprising a memory, a processor, and a computer program stored in the memory, wherein the processor, when executing the computer program, implements the method of any one of claims 1-10.

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