Layout adjustment information determination method and device for parking lot, equipment and medium

Abstract

The embodiment of the invention discloses a layout adjustment information determination method for a parking lot, and relates to the technical field of parking lot management, and the method comprises the steps: carrying out the recognition of a target vehicle entering a target parking lot in a preset period through a pre-trained vehicle recognition model, and obtaining the vehicle attribute information of the target vehicle; determining vehicle statistical data of the target parking lot in the preset period according to the vehicle attribute information identified in the preset period; and acquiring site layout data of the parking lot, and determining layout adjustment information corresponding to the target parking lot according to the vehicle statistical data and the site layout data. The accuracy of vehicle identification can be improved, and the operation efficiency of the parking lot is improved.

Description

Technical Field

[0001] The embodiments of the present invention relate to the field of computer software application technology, and in particular to a method, device, electronic device, storage medium and program product for determining layout adjustment information of a parking lot. Background Art

[0002] With the development of the times, driving has become a more common way for people to travel. With more and more vehicles, how to reasonably plan parking lots is an urgent problem to be solved.

[0003] In the prior art, a simple vehicle recognition technology is generally used to recognize the license plate information of the vehicle, thereby completing the recognition of the vehicles entering and leaving the parking lot, and optimizing the parking lot space according to the recognition results.

[0004] In the above-mentioned solution for optimizing the parking lot site, the quality and quantity of vehicle data collected by vehicle identification technology are insufficient, and the data collection is also relatively limited. The operating efficiency of the optimized parking lot is low and cannot maximize the utilization efficiency of parking lot resources. Summary of the invention

[0005] The embodiments of the present invention provide a method, device, electronic device, storage medium and program product for determining layout adjustment information of a parking lot, which can improve the accuracy of vehicle recognition and improve the operation efficiency of the parking lot.

[0006] According to one aspect of the present invention, a method for determining layout adjustment information of a parking lot is provided, comprising:

[0007] Identify the target vehicle entering the target parking lot within a preset period through a pre-trained vehicle recognition model to obtain vehicle attribute information of the target vehicle;

[0008] Determine the vehicle statistics of the target parking lot within the preset period according to the vehicle attribute information identified within the preset period;

[0009] Acquire the site layout data of the parking lot, and determine the layout adjustment information corresponding to the target parking lot according to the vehicle statistical data and the site layout data.

[0010] According to another aspect of the present invention, there is provided a device for determining layout adjustment information of a parking lot, comprising:

[0011] A vehicle identification module is used to identify a target vehicle entering a target parking lot within a preset period through a pre-trained vehicle identification model to obtain vehicle attribute information of the target vehicle;

[0012] A data statistics module, used to determine the vehicle statistics of the target parking lot within the preset period according to the vehicle attribute information identified within the preset period;

[0013] The layout adjustment module is used to obtain the site layout data of the parking lot, and determine the layout adjustment information corresponding to the target parking lot according to the vehicle statistical data and the site layout data.

[0014] According to another aspect of the present invention, there is provided an electronic device, the electronic device comprising:

[0015] at least one processor; and

[0016] a memory communicatively connected to the at least one processor; wherein,

[0017] The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the method for determining layout adjustment information for a parking lot according to any embodiment of the present invention.

[0018] According to another aspect of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the method for determining layout adjustment information for a parking lot described in any embodiment of the present invention when executed.

[0019] According to another aspect of the present invention, a computer program product is provided, including a computer program, wherein when the computer program is executed by a processor, the method for determining layout adjustment information of a parking lot according to any embodiment of the present invention is implemented.

[0020] The embodiment of the present invention can identify the target vehicles entering the target parking lot within a preset period through a pre-trained vehicle recognition model, and can obtain vehicle attribute information of the target vehicle with high accuracy. According to the vehicle attribute information recognized within the preset period, the vehicle statistics data of the target parking lot within the preset period are determined, which can more clearly connect to the vehicle entry and exit situation of the target parking lot. The site layout data of the parking lot is obtained, and the layout adjustment information corresponding to the target parking lot is determined according to the vehicle statistics data and the site layout data, which can improve the accuracy of vehicle recognition and improve the operation efficiency of the parking lot.

[0021] It should be understood that the contents described in this section are not intended to identify the key or important features of the embodiments of the present invention, nor are they intended to limit the scope of the present invention. Other features of the present invention will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0023] Figure 1 is a flow chart of a method for determining parking lot layout adjustment information provided by Embodiment 1 of the present invention;

[0024] Figure 2 is a flow chart of another method for determining parking lot layout adjustment information provided by Embodiment 2 of the present invention;

[0025] Figure 3 is a flow chart of a device for determining parking lot layout adjustment information provided by Embodiment 3 of the present invention;

[0026] Figure 4 A schematic diagram of the structure of an electronic device provided in Embodiment 4 of the present invention. DETAILED DESCRIPTION

[0027] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0028] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0029] Embodiment 1

[0030] Figure 1This is a flow chart of a method for determining layout adjustment information of a parking lot provided by the first embodiment of the present invention. This embodiment is applicable to the case where layout adjustment information corresponding to a target parking lot is determined based on vehicle statistical data and site layout data. The method can be executed by a device for determining layout adjustment information of a parking lot. The device can be implemented by software and / or hardware and can generally be integrated in an electronic device, which can be a terminal device or a server device. Figure 1 As shown, the method includes:

[0031] S110 , identifying a target vehicle that enters a target parking lot within a preset period using a pre-trained vehicle recognition model to obtain vehicle attribute information of the target vehicle.

[0032] The vehicle recognition model may be a model for identifying vehicles under different conditions. After the recognition model is trained using a sample set constructed using existing vehicle data, the recognition model is determined as a vehicle recognition model when the recognition accuracy of the recognition model for the vehicle meets the requirements. By identifying the vehicle using the pre-trained vehicle-connected recognition model, it is possible to obtain vehicle attribute information with high accuracy. This provides a high-accuracy data basis for optimizing the layout of the parking lot, thereby improving the utilization rate of the parking lot.

[0033] The preset period may be a pre-set time period, such as a week or a month. Within the preset period, the vehicle information entering the parking lot needs to be identified by the vehicle identification model, and the vehicle information identified by the vehicle identification model is recorded. The target parking lot may be a parking lot that needs to be adjusted in layout. The target vehicle may be a vehicle that enters the target parking lot and parks within the preset period. The vehicle attribute information may be information used to characterize the vehicle attributes after the target vehicle is identified by the vehicle connection identification model. The vehicle attribute information may include but is not limited to at least one of vehicle model information, vehicle size information, and vehicle energy type information.

[0034] S120: Determine vehicle statistics of the target parking lot within a preset period according to the vehicle attribute information identified within the preset period.

[0035] The vehicle statistical data may be statistical data of vehicle attribute information corresponding to vehicles entering the target parking lot within a preset period, obtained by the vehicle recognition model. The vehicle statistical data may be used to characterize vehicle characteristics of target vehicles entering the target parking lot within a preset period.

[0036] Optionally, vehicle statistics of the target parking lot within a preset period are determined based on vehicle attribute information identified within the preset period, including: determining a vehicle category corresponding to the target vehicle based on the vehicle attribute information identified within the preset period, and determining vehicle statistics of the target parking lot based on the target vehicle and the vehicle category corresponding to the target vehicle; wherein the vehicle attribute information includes at least one of vehicle model information, vehicle size information and vehicle energy type information.

[0037] The vehicle category may be a comprehensive classification result involving multiple factors, and the comprehensive classification result may be obtained by classification based on at least one of the attribute information such as vehicle model information, vehicle size information, and vehicle energy type information. The vehicle category may be used to characterize the comprehensive characteristics of vehicles of the same type. For example, vehicles with vehicle sizes within the same range and vehicle energy types belonging to the same energy type. Vehicle energy types may include fuel type, hybrid type, electric type, and other energy types.

[0038] Specifically, the vehicle identification model may be used to identify the vehicles that enter the target parking lot within a preset period, and obtain the vehicle attribute information corresponding to each vehicle. The vehicle type of each vehicle is determined based on the vehicle attribute information corresponding to each vehicle. The vehicle statistics of the target parking lot are determined based on the number of vehicles corresponding to each vehicle type that enter the target parking lot within the preset period, and the total number of vehicles that enter the target parking lot within the preset period. Based on the vehicle statistics of the target parking lot, the characteristics of the vehicles that enter the target parking lot within the preset period can be obtained, thereby providing a data basis for the optimization and adjustment of the target parking lot.

[0039] Optionally, vehicle statistics of the target parking lot are determined based on the target vehicle and the vehicle category corresponding to the target vehicle, including: for each vehicle category, determining the vehicle ratio corresponding to the vehicle category based on a first total number of target vehicles corresponding to the vehicle category and a second total number of target vehicles in the target parking lot; and determining the vehicle statistics of the target parking lot based on the vehicle ratio corresponding to the vehicle category.

[0040] The first total number may be the sum of all target vehicles of a specific vehicle category that enter the target parking lot within a preset period. The second total number may be the sum of all target vehicles of a specific vehicle category that enter the target parking lot within a preset period. The vehicle proportion may be the proportion of all target vehicles of a specific vehicle category that enter the target parking lot within a preset period to the total of all target vehicles that enter the target parking lot within a preset period.

[0041] Specifically, for each target vehicle that enters the target parking lot within a preset period, the vehicle attribute information of each target vehicle can be obtained through the vehicle identification model. The vehicle category to which each target vehicle belongs is determined based on the vehicle attribute information, and after the preset period ends, the number of each vehicle category is counted. According to the number of each vehicle category and the total number of target vehicles that enter the target parking lot within the preset period, the proportion of each vehicle category to the total number of target vehicles that enter the target parking lot within the preset period is determined. According to the proportion of each vehicle category to the total number of target vehicles that enter the target parking lot within the preset period, the vehicle statistical data of the target parking lot within the preset period is obtained. Through the vehicle statistical data, the vehicle characteristics of the target vehicles that enter the target parking lot within the preset period can be determined, thereby providing users with more intuitive vehicle statistical data. It can help users understand the parking situation and vehicle characteristics of the target parking lot within the preset period, and provide accurate vehicle characteristic data for the layout optimization of the parking lot.

[0042] Optionally, after determining the vehicle statistics of the target parking lot within a preset period according to the vehicle attribute information identified within the preset period, the method further includes: displaying the vehicle statistics in a preset form in the target interface.

[0043] The preset form may be a display method or format preset for displaying vehicle statistical data. For example, a chart type and a data arrangement type. The embodiment of the present invention does not specifically limit the preset form, and it may be any form customized according to different requirements to ensure the readability, accuracy and comprehensibility of the data. The target interface may be a specific display interface for displaying vehicle statistical data in a preset form.

[0044] For example, after determining the vehicle statistics data of the target parking lot within a preset period, the vehicle statistics data may be converted into more intuitive chart data. The chart data may be displayed in a preset specific display interface to intuitively display the vehicle statistics data to the user, so that the user can understand the parking situation of the target parking lot within the preset period through the vehicle statistics data.

[0045] S130: Acquire the site layout data of the parking lot, and determine the layout adjustment information corresponding to the target parking lot according to the vehicle statistical data and the site layout data.

[0046] The site layout data may be information used to characterize the physical space layout and structure of the parking lot. The site layout data may include but is not limited to at least one of parking space information, channel and path information, obstacle and facility information, and area division information.

[0047] The layout adjustment information may be reference information for layout adjustment proposed to optimize the use efficiency and user experience of the parking lot, determined based on vehicle statistical data and site layout data. The layout adjustment information may include, but is not limited to, at least one of adjustment information such as parking space reallocation, channel optimization, and area adjustment.

[0048] Specifically, the original site layout data of the parking lot may be obtained, and a comprehensive analysis may be performed manually or by machine on the original site layout data of the parking lot and the vehicle statistical data of the target parking lot identified by the vehicle recognition model within a preset time, and the layout adjustment information corresponding to the target parking lot may be determined according to the analysis results.

[0049] Optionally, layout adjustment information corresponding to the target parking lot is determined based on vehicle statistical data and site layout data of the target parking lot, including: inputting the vehicle statistical data and site layout data into a pre-trained layout adjustment model, and determining the layout adjustment information corresponding to the target parking lot based on an output result of the layout adjustment model.

[0050] Optionally, after determining the layout adjustment information corresponding to the target parking lot according to the vehicle statistical data and the site layout data, the method further includes: adjusting the layout of the target parking lot according to the layout adjustment information.

[0051] The layout adjustment model can be a machine learning model used to predict the optimal layout adjustment solution of the target parking lot by learning a large amount of parking lot data (including vehicle statistics data and site layout data) and the corresponding optimized layout solutions. The output result of the layout adjustment model can be a specific layout adjustment suggestion or solution derived by the layout adjustment model based on the input data (vehicle statistics data and site layout data).

[0052] For example, the vehicle identification model can be used to collect and analyze the vehicle statistics of the target parking lot within a preset period, and obtain the site layout data of the target parking lot, including vehicle entry and exit records, parking duration, vehicle type distribution, parking space layout, channel width, barrier-free facilities and other information. The vehicle statistics and site layout data are then input into the layout adjustment model, and the model predicts the optimal layout adjustment plan based on the learned rules and patterns. For example, measures such as optimizing parking space layout, widening channels, and adding barrier-free facilities can be taken. By determining the layout adjustment information corresponding to the target parking lot based on the vehicle statistics and site layout data of the target parking lot, and optimizing and adjusting the target parking lot based on the site layout data, it is possible to improve parking efficiency and user experience, and improve the utilization rate of parking lot resources.

[0053] The technical solution of this embodiment determines the vehicle category corresponding to the target vehicle through the vehicle attribute information identified within a preset period. For each vehicle category, the vehicle proportion corresponding to the vehicle category is determined according to the first total number of target vehicles corresponding to the vehicle category and the second total number of target vehicles in the target parking lot, and the vehicle statistical data of the target parking lot is determined according to the vehicle proportion corresponding to the vehicle category. The vehicle statistical data is displayed in the target interface in a preset form. It enables users to see the parking situation of the target parking lot within a preset period more intuitively. By inputting the vehicle statistical data and the site layout data into the pre-trained layout adjustment model, the layout adjustment information corresponding to the target parking lot is determined based on the output result of the layout adjustment model, and the layout of the target parking lot is adjusted according to the layout adjustment information. It can improve parking efficiency and user experience, and improve the utilization rate of parking lot resources.

[0054] Embodiment 2

[0055] Figure 2 This is a flowchart of another method for determining the layout adjustment information of a parking lot provided by the second embodiment of the present invention. This embodiment is specific based on the above embodiment. In this embodiment, after obtaining the site layout data of the parking lot and determining the layout adjustment information corresponding to the target parking lot according to the vehicle statistical data and the site layout data, it also includes: generating a target site layout diagram of the parking lot according to the layout adjustment information and the site layout data of the parking lot, and displaying the target site layout diagram on a preset display page according to a preset display method. Figure 2 As shown, the method of this embodiment may include:

[0056] S210: Identify a target vehicle that enters the target parking lot within a preset period using a pre-trained vehicle recognition model to obtain vehicle attribute information of the target vehicle.

[0057] Optionally, the vehicle recognition model can be trained in the following manner: obtaining multiple parking image data of a sample parking lot under multiple lighting conditions, multiple shooting angles, and multiple occlusion conditions, generating extended image data based on at least part of the parking image data, and constructing a sample data set based on the parking image data and the extended image data; training a pre-constructed recognition model with the sample data set, and determining the trained recognition model as a vehicle recognition model when a preset training end condition is met.

[0058] The sample parking lot may be a parking lot used as a data collection source. The sample parking lot may include a variety of different lighting conditions, shooting angles, and occlusion conditions, thereby providing a variety of parking image data. The vehicle image data collected from the sample parking lot may be used to train and verify the vehicle recognition model.

[0059] Among them, multiple lighting conditions can be when collecting parking image data, covering a variety of different lighting environments from strong light (such as sunlight at noon on a sunny day) to weak light (such as weak light at dusk or at night). By training the model with a sample set containing multiple lighting conditions, the vehicle recognition accuracy of the model under different lighting conditions can be improved. Multiple shooting angles can be images of multiple shooting angles such as front, side and oblique directions generated by different positions, heights and orientations of the camera when collecting images. By training the model with a sample set containing multiple shooting angles, the recognition accuracy of the model in identifying vehicles from multiple different perspectives can be improved. Multiple occlusion conditions can be vehicle image data in which the vehicle in the image is partially occluded by other objects (such as trees, buildings, other vehicles, etc.). By training the model with a sample set containing multiple occlusion conditions, the recognition accuracy of the model can be improved when the vehicle is partially invisible.

[0060] The extended image data may be image data obtained by transforming the original parking image data. Training the model with a sample set including the extended image data can improve the generalization ability of the model and the breadth of model recognition.

[0061] The plurality of parking image data may be parking image data collected from sample parking lots under various lighting conditions, various shooting angles, and various occlusion conditions. The sample data set may be a data set composed of original parking image data and extended image data, and is used to train a vehicle recognition model.

[0062] The end training condition may be one or more criteria set during the model training process, which are used to determine whether the model meets the usage requirements and the conditions for stopping training. The end training condition may include at least one of the accuracy of the model, the value of the loss function, the training time or the number of iterations, etc. When the end training condition is met, the model training process will end, and the trained model will be determined as the vehicle recognition model. The recognition model may be a model pre-built according to a certain deep learning architecture (such as a convolutional neural network and a recurrent neural network) before starting training.

[0063] Optionally, generating extended image data based on the parking image dataset includes: inputting at least part of the parking image data into an image generation model to generate the extended image data, wherein the image generation model is a generation model obtained by training a generative adversarial network.

[0064] Among them, the image generation model can be a model for generating extended image data constructed by adversarial generative network technology.

[0065] Optionally, the sample data set includes a training sample data set and a test sample data set. A pre-constructed recognition model is trained using the sample data set, and when a preset end-training condition is met, the trained recognition model is determined as a vehicle recognition model, including: training the recognition model using the training sample data set to obtain an initial vehicle recognition model; inputting the test sample data set into the initial vehicle recognition model to obtain a model verification result of the initial vehicle recognition model; if the model verification result does not meet the preset end-training condition, adjusting the feature weights of the initial vehicle recognition model, and when the model verification result meets the preset end-training condition, determining the initial vehicle recognition model as the vehicle recognition model.

[0066] The training sample dataset may be the original dataset used to train the vehicle recognition model. The training sample dataset may include vehicle images or other features related to vehicle images, and corresponding labels, such as vehicle type labels. The test sample dataset may be a dataset independent of the training sample dataset, used to evaluate the performance of the model after the model training is completed.

[0067] The initial vehicle recognition model may be a vehicle recognition model obtained before the start of the training process or at a certain stage during the training process and has not been fully verified and adjusted. The initial vehicle recognition model may be a model that is constructed based on a predefined architecture (such as a convolutional neural network) but has not been fully trained with a training sample data set, or has been trained with a training sample data set but the training results do not meet the training conditions.

[0068] The model validation result may be a comparison result between the prediction result of the model on the test data and the true label after the test sample data set is input into the initial vehicle recognition model. The model validation result may be used to characterize the performance of the model on the test data.

[0069] The preset end training condition may be one or more conditions set during the model training process to determine when to stop training. These conditions may be based on factors such as the performance of the model on the validation set (such as the accuracy reaching a certain level), training time, number of iterations, etc. If the model validation result meets the preset end training condition, the model training is terminated, and the trained model is determined as the vehicle recognition model.

[0070] The feature weight may be the contribution of each input feature to the model output result. By adjusting the feature weight, the performance of the model can be improved to better adapt to the training data.

[0071] Specifically, multiple parking image data under multiple lighting conditions, multiple shooting angles, and multiple occlusion conditions can be collected from multiple sample parking lots. Part of the image data in the multiple parking image data is input into the image generation model to generate multiple extended image data to enhance the model's adaptability to actual complex scenes. A sample data set for training the model is constructed based on the multiple extended image data and the multiple parking image data. The pre-constructed recognition model is trained with the training sample data set, and the training result of the model is verified with the test sample data set. When the recognition model meets the preset end training conditions, the trained recognition model is determined as a vehicle recognition model. The vehicle recognition ability of the vehicle recognition model under different lighting conditions, different shooting angles, and different occlusion conditions can be improved, thereby improving the recognition accuracy of the vehicle recognition model, and obtaining vehicle attribute information with higher accuracy.

[0072] S220: Determine vehicle statistics of the target parking lot within a preset period based on vehicle attribute information identified within a preset period.

[0073] S230: Acquire the site layout data of the parking lot, and determine the layout adjustment information corresponding to the target parking lot according to the vehicle statistical data and the site layout data.

[0074] S240: Generate a target site layout diagram of the parking lot according to the layout adjustment information and the site layout data of the parking lot, and display the target site layout diagram on a preset display page according to a preset display method.

[0075] The target site layout diagram may be a graphical representation of the adjusted parking lot generated after calculation and adjustment based on the layout adjustment information and the original site layout data of the parking lot, such as layout adjustment information such as the redistribution of parking spaces, adjustment of channel widths, and addition of barrier-free facilities.

[0076] The preset display mode may be a specific presentation mode and format used when displaying the target site layout map. The preset display mode may include display information such as the zoom ratio, color coding, annotation mode and interactive functions of the target site layout map. The preset display page may be a specific web page or interface for displaying the target site layout map. The page usually contains necessary navigation elements, layout map display area, related explanatory text and other information, so that users can easily view and understand the target site layout map.

[0077] Specifically, by generating a target site layout diagram of the parking lot according to the layout adjustment information and the site layout data of the parking lot, and displaying the target site layout diagram on a preset display page according to a preset display method, the target site layout diagram can be made easier for parking lot managers to understand and analyze, while providing necessary interactive functions to improve the efficiency of optimizing and adjusting the parking lot layout.

[0078] The technical solution of this embodiment obtains multiple parking image data of a sample parking lot under multiple lighting conditions, multiple shooting angles and multiple occlusion conditions, generates extended image data based on at least part of the parking image data, and constructs a sample data set based on the parking image data and the extended image data; trains a pre-constructed recognition model through the sample data set, and determines the trained recognition model as a vehicle recognition model when a preset end training condition is met. The accuracy of vehicle recognition by the vehicle recognition model in different situations can be improved. The vehicle statistics data of the target parking lot within a preset period are determined based on the vehicle attribute information identified within a preset period; the site layout data of the parking lot are obtained, and the layout adjustment information corresponding to the target parking lot is determined based on the vehicle statistics data and the site layout data. The target site layout diagram of the parking lot is generated based on the layout adjustment information and the site layout data of the parking lot, and the target site layout diagram is displayed on a preset display page according to a preset display method. The target site layout diagram can be made easier for parking lot managers to understand and analyze, and necessary interactive functions are provided to improve the efficiency of optimizing and adjusting the parking lot layout.

[0079] Embodiment 3

[0080] Figure 3 is a flow chart of a device for determining parking lot layout adjustment information provided by Embodiment 3 of the present invention, such as Figure 3 As shown, the device includes: a vehicle identification module 310, a data statistics module 320 and a layout adjustment module 330.

[0081] Among them, the vehicle identification module 310 is used to identify the target vehicles entering the target parking lot within a preset period through a pre-trained vehicle identification model to obtain the vehicle attribute information of the target vehicle; the data statistics module 320 is used to determine the vehicle statistical data of the target parking lot within the preset period according to the vehicle attribute information identified within the preset period; the layout adjustment module 330 is used to obtain the site layout data of the parking lot, and determine the layout adjustment information corresponding to the target parking lot according to the vehicle statistical data and the site layout data.

[0082] Furthermore, the device for determining the layout adjustment information of the parking lot also includes: a vehicle recognition model training module, which is used to train the vehicle recognition model in the following manner: obtaining multiple parking image data of a sample parking lot under multiple lighting conditions, multiple shooting angles and multiple occlusion conditions, generating extended image data based on at least part of the parking image data, and constructing a sample data set based on the parking image data and the extended image data; training a pre-constructed recognition model through the sample data set, and determining the trained recognition model as the vehicle recognition model when a preset training end condition is met.

[0083] Furthermore, the vehicle recognition model training module is specifically used to: input at least part of the parking image data into an image generation model to generate extended image data, wherein the image generation model is a generation model obtained by generative adversarial network training.

[0084] Furthermore, the vehicle recognition model training module is specifically used to: train the recognition model through a training sample data set to obtain an initial vehicle recognition model; input the test sample data set into the initial vehicle recognition model to obtain a model verification result of the initial vehicle recognition model; if the model verification result does not meet the preset end training condition, adjust the feature weight of the initial vehicle recognition model, and when the model verification result meets the preset end training condition, determine the initial vehicle recognition model as the vehicle recognition model.

[0085] Furthermore, the data statistics module 320 is specifically used to: determine the vehicle category corresponding to the target vehicle based on the vehicle attribute information identified within the preset period, and determine the vehicle statistical data of the target parking lot based on the target vehicle and the vehicle category corresponding to the target vehicle; wherein the vehicle attribute information includes at least one of vehicle model information, vehicle size information and vehicle energy type information.

[0086] Furthermore, the data statistics module 320 is also specifically used to: for each vehicle category, determine the vehicle ratio corresponding to the vehicle category according to the first total number of the target vehicles corresponding to the vehicle category and the second total number of the target vehicles in the target parking lot; determine the vehicle statistical data of the target parking lot according to the vehicle ratio corresponding to the vehicle category.

[0087] Furthermore, the layout adjustment module 330 is specifically used to: input the vehicle statistical data and the site layout data into a pre-trained layout adjustment model, and determine the layout adjustment information corresponding to the target parking lot based on the output result of the layout adjustment model.

[0088] Furthermore, the device for determining layout adjustment information of a parking lot also includes: a target site layout map display module, which is used to obtain the site layout data of the parking lot, determine the layout adjustment information corresponding to the target parking lot according to the vehicle statistical data and the site layout data, generate a target site layout map of the parking lot according to the layout adjustment information and the site layout data of the parking lot, and display the target site layout map on a preset display page according to a preset display method.

[0089] Furthermore, the device for determining the layout adjustment information of a parking lot also includes: a vehicle statistical data display module, which is used to display the vehicle statistical data in a preset form in a target interface after determining the vehicle statistical data of the target parking lot within the preset period based on the vehicle attribute information identified within the preset period.

[0090] The above-mentioned device for determining the layout adjustment information of a parking lot can execute the method for determining the layout adjustment information of a parking lot provided in any embodiment of the present invention, and has the functional modules and beneficial effects corresponding to the execution method. For technical details not described in detail in this embodiment, please refer to the method for determining the layout adjustment information of a parking lot provided in any embodiment of the present invention.

[0091] Since the above-mentioned layout adjustment information determination device for parking lots is a device that can execute the layout adjustment information determination method for parking lots in the embodiment of the present invention, based on the layout adjustment information determination method for parking lots introduced in the embodiment of the present invention, the technical personnel of the field can understand the specific implementation of the layout adjustment information determination device for parking lots in this embodiment and its various variations, so how the layout adjustment information determination device for parking lots implements the layout adjustment information determination method for parking lots in the embodiment of the present invention is not described in detail here. As long as the technical personnel of the field implement the device used by the layout adjustment information determination method for parking lots in the embodiment of the present invention, it belongs to the scope of protection of this application.

[0092] Embodiment 4

[0093] Figure 4A schematic diagram of the structure of an electronic device 10 that can be used to implement an embodiment of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or required herein.

[0094] like Figure 4 As shown, the electronic device 10 includes at least one processor 11, and a memory connected to the at least one processor 11, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., wherein the memory stores a computer program that can be executed by at least one processor, and the processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or the computer program loaded from the storage unit 18 to the random access memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 can also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other through a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0095] A number of components in the electronic device 10 are connected to the I / O interface 15, including: an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a disk, an optical disk, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0096] The processor 11 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The processor 11 executes the various methods and processes described above, such as a method for determining layout adjustment information for a parking lot.

[0097] In some embodiments, the method for determining the layout adjustment information for a parking lot may be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as a storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 10 via the ROM 12 and / or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of the method for determining the layout adjustment information for the parking lot described above may be performed. Alternatively, in other embodiments, the processor 11 may be configured to execute the method for determining the layout adjustment information for the parking lot in any other appropriate manner (e.g., by means of firmware).

[0098] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0099] Computer programs for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that when the computer program is executed by the processor, the functions / operations specified in the flow chart and / or block diagram are implemented. The computer program may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0100] In the context of the present invention, a computer-readable storage medium may be a tangible medium that may contain or store a computer program for use by or in combination with an instruction execution system, device or equipment. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices or equipment, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0101] To provide interaction with a user, the systems and techniques described herein may be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or trackball) through which the user can provide input to the electronic device. Other types of devices may also be used to provide interaction with the user; for example, the feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form (including acoustic input, voice input, or tactile input).

[0102] The systems and techniques described herein may be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0103] A computing system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The client and server relationship is generated by computer programs running on the corresponding computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and VPS services.

[0104] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps recorded in this disclosure can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solutions disclosed in this disclosure can be achieved, and this document does not limit this.

[0105] The above specific implementations do not constitute a limitation on the protection scope of the present disclosure. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present disclosure shall be included in the protection scope of the present disclosure.

Claims

1. A method for determining layout adjustment information of a parking lot, characterized in that: include: Identify the target vehicle that enters the target parking lot within a preset period through a pre-trained vehicle recognition model to obtain vehicle attribute information of the target vehicle; Determine the vehicle statistics of the target parking lot within the preset period according to the vehicle attribute information identified within the preset period; The site layout data of the parking lot is acquired, and the layout adjustment information corresponding to the target parking lot is determined according to the vehicle statistical data and the site layout data.

2. The method according to claim 1, characterized in that The vehicle recognition model is obtained by training in the following way: Acquire a plurality of parking image data of a sample parking lot under a plurality of lighting conditions, a plurality of shooting angles, and a plurality of occlusion conditions, generate extended image data according to at least a portion of the parking image data, and construct a sample data set according to the parking image data and the extended image data; The pre-constructed recognition model is trained using the sample data set, and when a preset training termination condition is met, the trained recognition model is determined as the vehicle recognition model.

3. The method according to claim 2, characterized in that The generating the extended image data according to the parking image dataset comprises: At least part of the parking image data is input into an image generation model to generate extended image data, wherein the image generation model is a generation model obtained by training a generative adversarial network.

4. The method according to claim 2, characterized in that: The sample data set includes a training sample data set and a test sample data set, and the pre-built recognition model is trained by the sample data set, and when a preset end training condition is met, the trained recognition model is determined as the vehicle recognition model, including: Training the recognition model through a training sample data set to obtain an initial vehicle recognition model; Inputting the test sample data set into an initial vehicle recognition model to obtain a model verification result of the initial vehicle recognition model; If the model verification result does not meet the preset end training condition, the feature weight of the initial vehicle recognition model is adjusted. When the model verification result meets the preset end training condition, the initial vehicle recognition model is determined as the vehicle recognition model.

5. The method according to claim 1, characterized in that The determining of vehicle statistics of the target parking lot within the preset period according to the vehicle attribute information identified within the preset period includes: The vehicle category corresponding to the target vehicle is determined based on the vehicle attribute information identified within the preset period, and the vehicle statistical data of the target parking lot is determined based on the target vehicle and the vehicle category corresponding to the target vehicle; wherein the vehicle attribute information includes at least one of vehicle model information, vehicle size information and vehicle energy type information.

6. The method according to claim 5, characterized in that The determining the vehicle statistical data of the target parking lot according to the target vehicle and the vehicle category corresponding to the target vehicle includes: For each of the vehicle categories, determining a vehicle proportion corresponding to the vehicle category according to a first total number of the target vehicles corresponding to the vehicle category and a second total number of the target vehicles in the target parking lot; The vehicle statistics data of the target parking lot are determined according to the vehicle proportion corresponding to the vehicle category.

7. The method according to claim 1, characterized in that The determining, according to the vehicle statistics data of the target parking lot and the site layout data, the layout adjustment information corresponding to the target parking lot includes: The vehicle statistical data and the site layout data are input into a pre-trained layout adjustment model, and layout adjustment information corresponding to the target parking lot is determined based on an output result of the layout adjustment model.

8. The method according to claim 1, characterized in that: After acquiring the site layout data of the parking lot and determining the layout adjustment information corresponding to the target parking lot according to the vehicle statistical data and the site layout data, the method further includes: A target site layout diagram of the parking lot is generated according to the layout adjustment information and the site layout data of the parking lot, and the target site layout diagram is displayed on a preset display page according to a preset display method.

9. The method according to claim 1, characterized in that: After determining the vehicle statistics of the target parking lot within the preset period according to the vehicle attribute information identified within the preset period, the method further includes: The vehicle statistical data is displayed in a target interface in a preset form.

10. A computer program product comprising a computer program / instructions, wherein: When the computer program / instructions are executed by a processor, the method for determining layout adjustment information for a parking lot according to any one of claims 1 to 9 is implemented.

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