Parking guiding method and device, equipment and storage medium

Abstract

The embodiment of the invention provides a parking guiding method and device, equipment and a storage medium. The method comprises the steps of determining a target travel of a vehicle, wherein the target travel at least comprises a target parking position and target parking time of the vehicle; acquiring parking space information of at least one parking space around the target parking position and at least one reference information associated with the target travel; determining a corresponding predicted use state of the at least one parking space at the target parking time based on the travel information of the target travel, the parking space information and the at least one piece of reference information; and presenting parking recommendation information for the target journey based on the corresponding predicted use state of the at least one parking space at the target parking time. According to the embodiment of the invention, the parking space can be accurately recommended to the user, so that the searching difficulty of the parking space is reduced.

Description

Technical Field

[0001] The exemplary embodiments disclosed herein generally relate to the field of computers, and particularly to parking guidance methods, apparatus, devices, computer-readable storage media, and computer program products. Background Technology

[0002] Shared vehicles allow users to temporarily rent cars for short-distance travel, offering high flexibility, convenience, and practicality. After use, shared vehicles are typically required to be parked in designated parking areas to facilitate management and operation, and minimize their impact on road traffic. Therefore, providing efficient and accurate parking guidance for these vehicles is of great importance. Summary of the Invention

[0003] In a first aspect of this disclosure, a parking guidance method is provided. The method includes: determining a target trip for a vehicle, the target trip including at least a target parking location and a target parking time; acquiring parking space information of at least one parking space around the target parking location and at least one piece of reference information associated with the target trip, the parking space information indicating at least one of the historical or current usage status of each of the at least one parking space; determining a corresponding predicted usage status of the at least one parking space at the target parking time based on the trip information, parking space information, and at least one piece of reference information; and presenting parking recommendation information for the target trip based on the corresponding predicted usage status of the at least one parking space at the target parking time.

[0004] In a second aspect of this disclosure, a parking guidance device is provided. The device includes: a trip determination module configured to determine a target trip for a vehicle, the target trip including at least a target parking location and a target parking time; an information acquisition module configured to acquire parking space information of at least one parking space surrounding the target parking location and at least one piece of reference information associated with the target trip, the parking space information indicating at least one of the historical or current usage states of each of the at least one parking space; a status determination module configured to determine a corresponding predicted usage state of at least one parking space at the target parking time based on the trip information, parking space information, and at least one piece of reference information; and an information presentation module configured to present parking recommendation information for the target trip based on the corresponding predicted usage state of at least one parking space at the target parking time.

[0005] In a third aspect of this disclosure, an electronic device is provided. The device includes at least one processing unit; and at least one memory coupled to the at least one processing unit and storing instructions for execution by the at least one processing unit. When executed by the at least one processing unit, the instructions cause the electronic device to perform the method of the first aspect.

[0006] In a fourth aspect of this disclosure, a computer-readable storage medium is provided. A computer program is stored on the medium, which, when executed by a processor, implements the method of the first aspect.

[0007] In a fifth aspect of this disclosure, a computer program product is provided, comprising a computer program that, when executed by a processor, implements the method according to a first aspect of this disclosure.

[0008] It should be understood that the description in this section is not intended to limit the key or essential features of the embodiments of this disclosure, nor is it intended to restrict the scope of this disclosure. Other features of this disclosure will become readily apparent from the following description. Attached Figure Description

[0009] The above and other features, advantages, and aspects of the embodiments of this disclosure will become more apparent from the accompanying drawings and the following detailed description. In the drawings, the same or similar reference numerals denote the same or similar elements, wherein:

[0010] Figure 1 A schematic diagram of an example environment in which embodiments of the present disclosure can be implemented is shown;

[0011] Figure 2 A flowchart illustrating a vehicle guidance process according to some embodiments of the present disclosure is shown;

[0012] Figure 3 A schematic structural block diagram of a vehicle guidance device according to some embodiments of the present disclosure is shown; and

[0013] Figure 4 A block diagram of an electronic device that can implement one or more embodiments of the present disclosure is shown. Detailed Implementation

[0014] Embodiments of this disclosure will now be described in more detail with reference to the accompanying drawings. While some embodiments of this disclosure are shown in the drawings, it should be understood that this disclosure can be implemented in various forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided to provide a more thorough and complete understanding of this disclosure. It should be understood that the accompanying drawings and embodiments of this disclosure are for illustrative purposes only and are not intended to limit the scope of protection of this disclosure.

[0015] In the description of embodiments of this disclosure, the term "comprising" and similar terms should be understood as open-ended inclusion, i.e., "including but not limited to". The term "based on" should be understood as "at least partially based on". The term "one embodiment" or "the embodiment" should be understood as "at least one embodiment". The term "some embodiments" should be understood as "at least some embodiments". Other explicit and implicit definitions may also be included below.

[0016] In this document, unless explicitly stated otherwise, performing a step in response to A does not mean that the step is performed immediately after A, but may include one or more intermediate steps.

[0017] It is understood that the data involved in this technical solution (including but not limited to the data itself, the acquisition, use, storage or deletion of the data) shall comply with the requirements of relevant laws, regulations and related provisions.

[0018] It is understood that before using the technical solutions disclosed in the various embodiments of this disclosure, relevant users should be informed of the type, scope of use, and usage scenarios of the information involved in this disclosure through appropriate means in accordance with relevant laws and regulations, and authorization should be obtained from the relevant users. Among them, relevant users may include any type of rights holder, such as individuals, enterprises, and groups.

[0019] For example, in response to receiving an active request from a user, a prompt message is sent to the relevant user to clearly inform the user that the requested operation will require obtaining and using the user's information, thereby enabling the relevant user to choose whether to provide information to the software or hardware such as the electronic device, application, server, or storage medium that performs the operation of the technical solution disclosed herein based on the prompt message.

[0020] As an optional but non-restrictive implementation, in response to a user's active request, a prompt message can be sent to the user, such as a pop-up window, where the prompt message can be presented in text format. Furthermore, the pop-up window can also include a selection control allowing the user to choose "agree" or "disagree" to provide information to the electronic device.

[0021] It is understood that the above notification and user authorization process are merely illustrative and do not constitute a limitation on the implementation of this disclosure. Other methods that comply with relevant laws and regulations may also be applied to the implementation of this disclosure.

[0022] As used in this paper, the term "model" refers to a model that learns the relationship between inputs and outputs from training data, enabling it to generate corresponding outputs for a given input after training. Model generation can be based on machine learning techniques. Deep learning is a machine learning algorithm that uses multiple layers of processing units to process inputs and provide corresponding outputs. A neural network model is an example of a deep learning-based model. In this paper, "model" may also be referred to as a "machine learning model," "learning model," "machine learning network," or "learning network," and these terms are used interchangeably.

[0023] A neural network is a machine learning network based on deep learning. A neural network processes input and provides a corresponding output, typically consisting of an input layer, an output layer, and one or more hidden layers between the input and output layers. Neural networks used in deep learning applications often include many hidden layers, thus increasing the network's depth. The layers of a neural network are connected sequentially, so that the output of the previous layer is provided as the input to the next layer. The input layer receives the input to the neural network, while the output layer's output serves as the final output. Each layer of a neural network includes one or more nodes (also called processing nodes or neurons), each of which processes the input from the layer above.

[0024] Machine learning typically comprises three phases: training, testing, and application (also known as inference). In the training phase, a given model is trained using a large amount of training data, iteratively updating its parameter values ​​until the model can consistently generate inferences that meet the expected goals from the training data. Through training, the model can be considered to have learned the relationship between inputs and outputs (also known as the input-output mapping) from the training data. The parameter values ​​of the trained model are determined. In the testing phase, test inputs are applied to the trained model to test whether it can provide the correct output, thus determining the model's performance. In the application phase, the model can be used to process actual inputs based on the trained parameter values ​​to determine the corresponding output.

[0025] Shared vehicles allow users to temporarily rent cars for short-distance travel, offering high flexibility, convenience, and practicality. After use, shared vehicles are typically required to be parked in designated parking areas to facilitate management and operation, and minimize their impact on road traffic. However, finding parking spaces is a frequent problem, especially in areas with heavy traffic. Therefore, providing efficient and accurate parking guidance is of great importance.

[0026] According to embodiments of this disclosure, an improved vehicle guidance scheme is provided. In this scheme, a target journey of the vehicle is determined, the target journey including at least a target parking location and a target parking time. Parking space information of at least one parking space around the target parking location, and at least one piece of reference information associated with the target journey, are obtained. The parking space information indicates at least one of the historical or current usage states of each of the at least one parking space. Based on the journey information, parking space information, and the at least one piece of reference information, a corresponding predicted usage state of the at least one parking space at the target parking time is determined. Then, based on the corresponding predicted usage state of the at least one parking space at the target parking time, parking recommendation information for the target journey is presented.

[0027] According to embodiments of this disclosure, it is possible to predict the available parking spaces around the destination (i.e., the target parking location) when the vehicle arrives at its destination (i.e., the target parking time), thereby accurately recommending parking spaces to the user. In this way, the difficulty of finding parking spaces can be reduced.

[0028] Example Environment

[0029] Figure 1 A schematic diagram of an example environment 100 in which embodiments of the present disclosure can be implemented is shown. For example... Figure 1 As shown, environment 100 involves terminal equipment 110, vehicle 150, and server equipment 160.

[0030] like Figure 1 As shown, application 120 is installed in terminal device 110. Terminal device 110 can be a terminal device used by user 130, and user 130 can interact with application 120 through terminal device 110 and / or attached devices of terminal device 110.

[0031] In embodiments of this disclosure, application 120 can be any suitable vehicle service-related application. If application 120 is active, terminal device 110 can display the user interface 140 of application 130. User interface 140 may include, but is not limited to, a car rental page, a car return page, a prompt page, etc.

[0032] In some embodiments, terminal device 110 may be any type of mobile terminal, fixed terminal, or portable terminal, including mobile phones, desktop computers, laptop computers, notebook computers, netbook computers, tablet computers, media computers, multimedia tablets, personal communication system (PCS) devices, personal navigation devices, personal digital assistants (PDAs), audio / video players, digital cameras / camcorders, positioning devices, television receivers, radio receivers, e-book devices, gaming devices, or any combination thereof, including accessories and peripherals of these devices or any combination thereof. In some embodiments, terminal device 110 may also support any type of user-facing interface (such as "wearable" circuitry).

[0033] In some embodiments, terminal device 110 can communicate with server device 160 to provide services to application 120. Server device 160 can be a standalone physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, content delivery networks, and big data and artificial intelligence platforms. Server device 160 may include, for example, computing systems / servers, such as mainframes, edge computing nodes, computing devices in a cloud environment, etc.

[0034] In some embodiments, terminal device 110 may invoke machine learning model 170 to provide services to application 120. Machine learning model 170 may be deployed on terminal device 110, server device 160, or other remote devices. When machine learning model 170 is deployed on terminal device 110, terminal device 110 may locally invoke machine learning model 170 to provide services to application 120 based on the output of machine learning model 170. When machine learning model 170 is deployed on server device 160, server device 160 may invoke machine learning model 170 to provide services to application 120 based on the output of machine learning model 170. Machine learning model 170 may be of different types, and the embodiments of this disclosure do not specifically limit its application.

[0035] In some embodiments, vehicle 150 may include, but is not limited to, bicycles, motorcycles, automobiles, or other means of transportation that comply with laws and regulations. Automobiles here may include, for example, sedans, SUVs, MPVs, buses, trucks, and other freight or passenger vehicles, including hybrid vehicles, electric vehicles, gasoline vehicles, plug-in hybrid vehicles, fuel cell vehicles, and other alternative fuel vehicles. Hybrid vehicles refer to vehicles with two or more power sources, and electric vehicles include pure electric vehicles, range-extended electric vehicles, etc., which are not specifically limited in the embodiments of this disclosure.

[0036] It should be understood that the structure and function of the various elements in environment 100 are described for illustrative purposes only and do not imply any limitation on the scope of this disclosure.

[0037] Example process

[0038] Some exemplary embodiments of this disclosure will now be described in detail with reference to the accompanying drawings.

[0039] Figure 2 A flowchart of a vehicle guidance process 200 according to some embodiments of the present disclosure is shown. For the convenience of discussion, the following will be combined with... Figure 1 Some embodiments of this disclosure are described in the context of environment 100 and from the perspective of terminal device 110, but these are merely exemplary.

[0040] In block 210 of process 200, terminal device 110 determines the target trip of vehicle 150. The target trip includes at least the target parking location and target parking time of vehicle 150. Alternatively or additionally, the target trip may also include, for example, the current location of vehicle 150, the route, the distance traveled, the duration of travel, etc. Vehicle 150 may include any vehicle associated with terminal device 110. In one example, vehicle 150 may be a vehicle rented by user 130 of terminal device 110 through application 120 (e.g., a rented shared bicycle). In another example, vehicle 150 may be a vehicle bound to terminal device 110 (e.g., a private vehicle bound to terminal device 110).

[0041] In some embodiments, the target trip may be a trip input by user 130 using terminal device 110 or an attachment device of terminal device 110. For example, during the rental of vehicle 150, user 130 may use terminal device 110 to rent vehicle 150, and user 130 may input location information indicating the target parking location to terminal device 110. Terminal device 110 may determine the predicted target parking time for vehicle 150 to reach the target parking location based on the current location of vehicle 150 and the target parking location.

[0042] In some embodiments, the target trip may be a predicted trip determined by the terminal device 110. For example, the terminal device 110 may, in response to a request to use the vehicle 150, obtain historical usage data of the vehicle 150. This historical usage data may include, but is not limited to, the user's historical driving routes, historical driving distances, historical usage frequencies, historical usage times, historical parking locations, etc. Based on the starting location of the vehicle 150 and the historical usage data, the terminal device 110 may determine the predicted parking location and predicted parking time of the vehicle 150, as the target parking location and target parking time for this use of the vehicle 150.

[0043] At block 220 of process 200, terminal device 110 acquires parking space information for at least one parking space surrounding the target parking location, and at least one piece of reference information associated with the target trip. In some embodiments, terminal device 110 may acquire trip information for the target trip, which may include target location information indicating the target parking location and target time information indicating the target parking time. Of course, trip information may also include, for example, route information, distance information, and duration information for the target trip. Terminal device 110 may acquire parking space information for at least one parking space surrounding the target parking location based on the target location information.

[0044] Parking space information can indicate at least one of the historical or current usage status of each parking space. Alternatively or additionally, parking space information may include parking space location information, current status information, historical status information, etc. Current status information can indicate the current usage status of the parking space (e.g., available, occupied, or reserved). Historical status information can indicate the historical turnover rate, parking pattern, parking time, etc. Of course, the above parking space information is only illustrative; parking space information can also include other information, such as parking requirements, parking feedback policies, etc. Here, parking feedback policies refer to the feedback or incentives (e.g., points or fee reductions) that can be obtained by parking a vehicle in a corresponding parking space.

[0045] In some embodiments, the terminal device 110 can obtain parking space information based on the area where the target parking location is located. That is, the terminal device 110 can obtain parking location information of parking spaces located in the area where the target parking location is located. This area can be an area centered on the target parking location with a predetermined distance as its radius. Of course, this area can also be an area determined according to, for example, administrative divisions or other methods, and the embodiments of this disclosure are not limited in this respect.

[0046] Alternatively or additionally, terminal device 110 may obtain parking location information locally. For example, sensors, such as image acquisition devices, radio frequency identification (RFID) tags, etc., may be pre-deployed around the parking space. Server device 110 may receive sensing information from sensors around at least one parking space and determine the current occupancy status of each parking space within the at least one parking space based on the sensing information, as at least part of the parking space information. Terminal device 110 may pre-download a parking space database from server device 160 and periodically update the parking space information in the database. In response to a vehicle 150 request for parking, terminal device 110 may retrieve parking space information for parking spaces located in that area from its local parking space database based on the target parking location.

[0047] Alternatively or additionally, terminal device 110 may also send an information query request to server device 160 based on the target parking location. In response to the information query request, server device 160 may query parking space information in the parking space database based on the target parking location and send the queried parking space information back to terminal device 110.

[0048] The reference information may include information that could affect the parking demand of other users in the area where the target parking location is located, and may also include information that could affect the parking demand of user 130 of vehicle 150 in the area where the target parking location is located. Alternatively or additionally, at least one piece of reference information may include at least one of the following: information on activities related to the target trip, weather information related to the target trip, traffic information related to the target trip, or historical usage data of vehicle 150.

[0049] Activities related to the target trip include those that may affect parking demand in the area where the target parking location is located. Examples include sporting events, concerts, festivals, large conferences, school events, etc. Terminal device 110 can obtain activity information from a network or server device 160 based on the target parking location. Activity information may include the activity theme, location, time, type, and comments related to the activity.

[0050] Weather information related to the target trip can indicate the weather conditions in the area where the target parking location is located, such as sunny, rainy, snowy, foggy, strong wind, etc. Terminal device 110 can access the weather information platform or server device 160 based on the target parking location to obtain weather information related to the target trip.

[0051] Traffic information related to the target trip can indicate the traffic conditions in the area where the target parking location is located. For example, traffic information can indicate the degree of traffic congestion, traffic accidents, construction and maintenance, public transportation adjustments, etc., in the area where the target parking location is located. Terminal device 110 can obtain traffic information related to the target trip from server device 160, traffic information platform, etc.

[0052] In some embodiments, upon obtaining the at least one piece of reference information, the terminal device 110 can determine the credibility of the at least one piece of reference information and select reference information whose credibility meets predetermined conditions from the at least one piece of reference information. This allows for filtering of the at least one piece of reference information based on credibility, avoiding interference from unreliable reference information and improving the accuracy of predicting the usage status of parking spaces.

[0053] Alternatively or additionally, terminal device 110 may determine the credibility of the at least one piece of reference information based on the consistency of multiple data sources. For example, terminal device 110 may obtain activity information from multiple network platforms. Then, it is determined whether the activity information obtained from the multiple network platforms is consistent. If multiple sets of information are consistent, the activity information is determined to have a high credibility score. If multiple sets of information are inconsistent, the activity information is determined to have a low credibility.

[0054] Alternatively or additionally, terminal device 110 may determine the credibility of the at least one reference information based on the credibility of the data source of the at least one reference information. For example, the credibility of each data source may be determined based on the credibility of historical information from each data source.

[0055] Alternatively or additionally, terminal device 110 may pre-acquire the text pattern of false information. Terminal device 110 may determine the text pattern of the at least one piece of reference information based on text recognition, and compare the text pattern of the at least one piece of reference information with the text pattern of false information to determine the credibility of the at least one piece of reference information.

[0056] Alternatively or additionally, terminal device 110 may also perform sentiment analysis on the at least one piece of reference information, detect extreme sentiment values ​​for the at least one piece of reference information, and determine the credibility of the at least one piece of reference information based on the extreme sentiment values. Here, extreme sentiment values ​​can indicate extreme positive or extreme negative emotions, such as extreme joy, excitement, or enthusiasm, or extreme anger, frustration, or anxiety. For example, for events such as concerts, sporting events, or festivals, if extreme sentiment values ​​indicate that users express extreme joy, excitement, or enthusiasm for the event, it indicates that the event has high credibility.

[0057] Alternatively or additionally, terminal device 110 can also perform topic modeling on the at least one piece of reference information to identify information that is irrelevant to the target parking location. Of course, in practical applications, terminal device 110 can also determine the credibility of the at least one piece of reference information based on one or more of the aforementioned credibility assessment methods. For example, terminal device 110 can determine the credibility of the at least one piece of reference information based on the evaluation results and weights of each credibility assessment method.

[0058] In block 230 of process 200, terminal device 110 can determine the corresponding predicted usage status of the at least one parking space at the target parking time based on the trip information of the target trip, parking location information, and the at least one reference information. The predicted usage status can indicate whether each parking space is available or unavailable (e.g., occupied or reserved) at the target parking time.

[0059] In some embodiments, where the at least one piece of reference information includes traffic information related to the target trip, the terminal device 110 can determine the predicted traffic flow of the target area surrounding the target parking space during the target parking time based on the traffic information. Then, based on the formation information, parking information, and predicted traffic flow, the predicted usage status of the at least one parking space during the target parking time is determined. For example, the target area can be a circular area centered on the target parking space with a radius of a predetermined distance. Alternatively, the target area can be a rectangular area centered on the target parking space. Furthermore, the target area can be an area defined according to administrative divisions. Embodiments of this disclosure do not limit this. As an example, the terminal device 110 can provide traffic information, activity information, and weather information to a trained traffic flow prediction model to determine the predicted traffic flow of the target area during the target parking time.

[0060] In some embodiments, terminal device 110 can generate model input for machine learning model 170 based on trip information, parking space information, and the at least one reference information of the target trip, to obtain model output for machine learning model 170. Based on the model output, the corresponding predicted usage status of at least one parking space at the target parking time is determined. Utilizing machine learning model 170 helps improve the accuracy of predicting the usage status of parking spaces and simplifies the reasoning process. As an example, terminal device 110 can determine the predicted traffic flow of the target area at the target parking time based on traffic information, activity information, and weather information. Terminal device 110 can generate model input for machine learning model 170 based on trip information, parking space information, predicted traffic flow, and historical vehicle usage data.

[0061] In some embodiments, upon obtaining trip information, parking location information, and the at least one reference information, the terminal device 110 can preprocess the trip information, parking location information, and the at least one reference information (e.g., removing abnormal data, duplicate data, etc.). The terminal device 110 can perform feature extraction on the preprocessed trip information, parking location information, and the at least one reference information to obtain vector representations of the trip information, parking location information, and the at least one reference information. Then, the terminal device 110 can use multiple vector representations as model input to the machine learning model 170, or it can fuse multiple vector representations to obtain fused features, and use the fused features as model input to the machine learning model 170.

[0062] For example, terminal device 110 can perform data structuring processing on the preprocessed trip information, parking space information, and the at least one reference information to obtain structured data. The structured data may include fields and field values ​​such as indicating time, location, activity content, traffic conditions, and weather conditions. Terminal device 110 can use a sliding event window to select data corresponding to the target parking time from the structured data and generate a vector representation based on the selected data.

[0063] As an example, the machine learning model 170 may include a deep neural network model. This deep neural network model may include a multimodal input layer, a feature extractor, an attention mechanism unit, a fusion layer, and a decision layer. The multimodal input layer may include an input layer for vector representations of different types of data; for example, the multimodal input layer may include a densely connected layer, a text embedding layer, a sequence input layer, and so on.

[0064] The feature extractor may include multiple feature extractors, each used to extract features from multimodal data. These feature extractors may include multilayer perceptrons, convolutional neural networks, Transformer models, Long Short-Term Memory (LSTM) networks, gated recurrent units, etc. The feature extractors are used to extract vector features from corresponding vector representations. For example, vector features can be extracted from the vector representation input to a densely connected layer using a multilayer perceptron. Similarly, vector features can be extracted from the vector representation input to a text embedding layer in a convolutional neural network. Furthermore, vector features can be extracted from the vector representation input to a sequence input layer using a gated recurrent unit.

[0065] Attention mechanisms can be used to capture the relationships between multimodal input data and adjust the weights between them. The fusion layer performs feature fusion on the vector features of the multimodal input data and dynamically adjusts the weights between them. The decision layer can include multiple fully connected networks to output the model based on the fused features.

[0066] In some embodiments, the terminal device 110 may also send trip information, parking space information, and the at least one reference information to the server device 160, instructing the server device 160 to generate model input for the machine learning model 170. The server device 160 provides the model input to the machine learning model 170 and receives the model output from the machine learning model. Then, based on the model output, the predicted usage status of the at least one parking space at the target parking time is determined. Alternatively, the terminal device 110 may also receive the predicted usage status of the at least one parking space at the target parking time determined by the server device 160 based on the model output.

[0067] In some embodiments, the terminal device 110 may also determine the confidence level of each parking space being in a predicted usage state at the target parking time. For example, the confidence level may range from 1% to 100%. If the confidence level of a parking space being available at the target parking time is 80%, it indicates that the parking space has a high probability of being available at the target parking time. If the confidence level of a parking space being available at the target parking time is 40%, it indicates that the probability of the parking space being available at the target parking time is relatively low.

[0068] In block 240 of process 200, terminal device 110 presents parking recommendation information for the target trip based on the predicted occupancy status of at least one parking space at the target parking time. For example, the predicted occupancy probability of each parking space at the target parking time may be presented. In some embodiments, terminal device 110 may select a target number of available parking spaces from the at least one parking space as target parking spaces. Then, terminal device 110 may present parking recommendation information for recommending target parking spaces. For example, terminal device 110 may select 10, 5, 3, or 1 available parking spaces as target parking spaces. It is understood that the above target number is merely exemplary, and any appropriate number of available parking spaces can be selected as target parking spaces according to actual needs; the embodiments of this disclosure are not limited in this respect.

[0069] In some embodiments, terminal device 110 can determine the recommendation level of each available parking space, and select a target number of parking spaces as target parking spaces based on the recommendation level. For example, the model output of machine learning model 170 may include a list of parking spaces, which may include available parking spaces and the recommendation level of each parking space. Terminal device 110 can select a target number of parking spaces as target parking spaces based on the recommendation level. Alternatively, terminal device 110 can determine information such as the distance between each parking space and the target parking space, and the walking time between each parking space and the target parking space, to determine the recommendation level of each available parking space. Then, terminal device 110 can select a target number of parking spaces as target parking spaces based on the recommendation level.

[0070] It is understood that the above method for selecting target parking spaces is merely an example, and in practical applications, any appropriate method can be used to select target parking spaces according to actual needs. For example, target parking spaces can also be selected based on factors such as the vehicle usage resources required for vehicle use, the parking feedback resources that can be obtained by parking the vehicle in each parking space, and the confidence level that each parking space is available.

[0071] In some embodiments, terminal device 110 may utilize the user interface 140 of application 120 to present parking recommendation information for recommending parking spaces. For example, terminal device 110 may utilize the user interface 140 to present a map of the area where the target parking location is located, and display the target parking space and at least some of the parking recommendation information on the map. Of course, terminal device 110 may also present the parking recommendation information for the target parking space through, for example, a list or other presentation method.

[0072] In some embodiments, the parking recommendation information includes at least one of the following: location information of the target parking space, predicted usage status of the target parking space during the target parking time, distance between the target parking space and the target parking location, walking time between the target parking space and the target parking location, vehicle usage resources required for vehicle 150 to travel to the target parking space, or parking feedback resources obtainable by parking vehicle 150 in the target parking space. Vehicle usage resources may include, but are not limited to, vehicle usage fees, platform points, or virtual resources. Parking feedback resources may include positive feedback resources and negative feedback resources. Positive feedback resources may include, for example, fee reductions or exemptions for vehicle use, platform points as feedback, or additional virtual resources. Negative feedback resources may include, for example, platform points to be deducted or other resources to be deducted.

[0073] For example, terminal device 110 may present a map of the area where the target parking location is located on user interface 140, and highlight the target parking space on the map. Terminal device 110 may present parking recommendation information for the target parking space in response to selection of the target parking space. It should be understood that the parking recommendation information may also include other information, such as the confidence level that the target parking space is available during the target parking time, etc. Embodiments of this disclosure are not limited in this respect.

[0074] In some embodiments, the terminal device 110 may also perform a reservation operation for the target parking space in response to receiving a selection instruction for the target parking space. For example, the terminal device 110 may present a reservation entry for the target parking space, and in response to a selection operation for the reservation entry, the terminal device 110 may generate a selection instruction for the target parking space. Based on the selection instruction, the terminal device 110 may send a reservation request for the selected target parking space to the server device 160, and the server device 160 may modify the usage status of the target parking space in the parking space database to a reserved status.

[0075] In some embodiments, terminal device 110 may cancel the reservation for a target parking space in response to vehicle 150 failing to arrive at the reserved target parking space by the target parking time. For example, terminal device 110 may determine whether vehicle 150 has arrived at the reserved target parking space by the target parking time based on location information. If it is determined that vehicle 150 has not arrived at the target parking space by the target parking time, a prompt message may be displayed, prompting the user to confirm whether to retain the reservation for the target parking space. If the user chooses to cancel the reservation for the target parking space, terminal device 110 may send a cancellation request to server device 160. Server device 160 may, in response to the cancellation request, change the usage status of the target parking space to an available status.

[0076] In some embodiments, terminal device 110 may, in response to an instruction to select a target parking space, present navigation information from the current location of vehicle 150 to the target parking space. For example, terminal device 110 may, in response to an instruction to select a target parking space, send a reservation request for the target parking space to server device 160. Terminal device 110 may, in response to receiving a reservation success notification from server device 160, utilize user interface 140 to present navigation information from the current location of vehicle 150 to the target parking location, facilitating accurate navigation to the target parking location for the user.

[0077] In some embodiments, the terminal device 110 may also display service recommendations related to the reserved target parking space. These service recommendations can be used to suggest any suitable services related to the target parking location, such as vehicle charging services, car wash services, restaurants, shops, attractions, public transportation stops, etc. This facilitates users in accessing other services around the target parking space.

[0078] In summary, according to the embodiments of this disclosure, it is possible to predict the availability of parking spaces around the destination when a vehicle arrives at the destination, and accurately recommend parking spaces to the user, thereby reducing the difficulty of finding parking spaces.

[0079] Example devices and equipment

[0080] Embodiments of this disclosure also provide corresponding apparatus for implementing the above methods or processes. Figure 3 A schematic structural block diagram of a parking guidance device 300 according to some embodiments of the present disclosure is shown. The device 300 may be implemented in or included in the terminal device 110. Various modules / components in the device 300 may be implemented by hardware, software, firmware, or any combination thereof.

[0081] like Figure 3 As shown, the device 300 includes a trip determination module 310, an information acquisition module 320, a status determination module 330, and an information presentation module 340. The trip determination module 310 is configured to determine a target trip for the vehicle, the target trip including at least a target parking location and a target parking time. The information acquisition module 320 is configured to acquire parking space information of at least one parking space around the target parking location, and at least one piece of reference information associated with the target trip, the parking space information indicating at least one of the historical or current usage states of each of the at least one parking space. The status determination module 330 is configured to determine the corresponding predicted usage state of at least one parking space at the target parking time based on the trip information, parking space information, and at least one piece of reference information. The information presentation module 340 is configured to present parking recommendation information for the target trip based on the corresponding predicted usage state of at least one parking space at the target parking time.

[0082] In some embodiments, the state determination module 330 is further configured to: generate model input for a machine learning model based on the trip information of the target trip, the parking space information, and the at least one reference information, to obtain the model output of the machine learning model; and determine the corresponding predicted usage state of the at least one parking space at the target parking time based on the model output.

[0083] In some embodiments, the at least one piece of reference information includes at least traffic information related to the target trip, and the status determination module 330 is further configured to: determine the predicted traffic flow of the target area surrounding the target parking space during the target parking time based on the traffic information; and determine the predicted usage status of the at least one parking space during the target parking time based on the trip information, the parking space information, and the predicted traffic flow.

[0084] In some embodiments, the at least one piece of reference information further includes at least one of the following: information on activities related to the target trip, weather information related to the target trip, or historical vehicle usage data for the vehicle.

[0085] In some embodiments, the device 300 further includes a parking space selection module configured to select a target parking space from at least one parking space whose predicted usage status is available, wherein the parking recommendation information includes recommendation information for the target parking space.

[0086] In some embodiments, the device 300 further includes a reservation module configured to perform a reservation operation for a target parking space in response to receiving a selection instruction for a target parking space.

[0087] In some embodiments, the device 300 further includes a cancellation module configured to cancel the reservation for the target parking space in response to the vehicle not arriving at the reserved target parking space within the target parking time.

[0088] In some embodiments, the device 300 further includes a navigation module configured to present navigation information from the vehicle’s current location to a target parking space in response to a selection instruction.

[0089] In some embodiments, the information presentation module 340 is further configured to present service recommendation information related to the reserved target parking space.

[0090] In some embodiments, the parking recommendation information includes at least one of the following: location information of the target parking space, the predicted usage status of the target parking space during the target parking time, the distance between the target parking space and the target parking location, the walking time between the target parking space and the target parking location, the vehicle usage resources required for the vehicle to drive to the target parking space, or the parking feedback resources that can be obtained by parking the vehicle in the target parking space.

[0091] The units and / or modules included in device 300 can be implemented in various ways, including software, hardware, firmware, or any combination thereof. In some embodiments, one or more units and / or modules can be implemented using software and / or firmware, such as machine-executable instructions stored on a storage medium. In addition to or as an alternative to machine-executable instructions, some or all of the units and / or modules in device 300 can be implemented at least partially by one or more hardware logic components. By way of example and not limitation, exemplary types of hardware logic components that can be used include field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), complex programmable logic devices (CPLDs), and so on.

[0092] Figure 4 A block diagram of an electronic device 400 in which one or more embodiments of the present disclosure may be implemented is shown. It should be understood that... Figure 4 The electronic device 400 shown is merely exemplary and should not be construed as limiting the functionality and scope of the embodiments described herein. Figure 4 The illustrated electronic device 400 may include or be implemented as Figure 1 Terminal equipment 110 or Figure 3 Device 300.

[0093] like Figure 4 As shown, electronic device 400 is in the form of a general-purpose electronic device. Components of electronic device 400 may include, but are not limited to, one or more processors or processing units 410, memory 420, storage device 430, one or more communication units 440, one or more input devices 450, and one or more output devices 460. Processing unit 410 may be a physical or virtual processor and is capable of performing various processes according to programs stored in memory 420. In a multiprocessor system, multiple processing units execute computer-executable instructions in parallel to improve the parallel processing capability of electronic device 400.

[0094] Electronic device 400 typically includes multiple computer storage media. Such media can be any accessible media that is accessible to electronic device 400, including but not limited to volatile and non-volatile media, removable and non-removable media. Memory 420 can be volatile memory (e.g., registers, cache, random access memory (RAM)), non-volatile memory (e.g., read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory), or some combination thereof. Storage device 430 can be removable or non-removable media and can include machine-readable media, such as flash drives, disks, or any other media that can be used to store information and / or data and can be accessed within electronic device 400.

[0095] Electronic device 400 may further include additional removable / non-removable, volatile / non-volatile storage media. Although not explicitly stated... Figure 4 As shown, disk drives for reading from or writing to removable, non-volatile disks (e.g., "floppy disks") and optical disk drives for reading from or writing to removable, non-volatile optical disks can be provided. In these cases, each drive can be connected to a bus (not shown) via one or more data media interfaces. Memory 420 may include computer program product 425 having one or more program modules configured to perform various methods or actions of various embodiments of this disclosure.

[0096] Communication unit 440 enables communication with other electronic devices via a communication medium. Additionally, the functionality of components of electronic device 400 can be implemented using a single computing cluster or multiple computing machines capable of communicating via communication connections. Therefore, electronic device 400 can operate in a networked environment using logical connections to one or more other servers, network personal computers (PCs), or another network node.

[0097] Input device 450 can be one or more input devices, such as a mouse, keyboard, trackball, etc. Output device 460 can be one or more output devices, such as a monitor, speaker, printer, etc. Electronic device 400 can also communicate with one or more external devices (not shown) via communication unit 440 as needed. These external devices include storage devices, display devices, etc., and can communicate with one or more devices that enable user interaction with electronic device 400, or with any device that enables electronic device 400 to communicate with one or more other electronic devices (e.g., network card, modem, etc.). Such communication can be performed via input / output (I / O) interface (not shown).

[0098] According to an exemplary implementation of this disclosure, a computer-readable storage medium is provided that stores computer-executable instructions thereon, wherein the computer-executable instructions are executed by a processor to implement the methods described above. According to an exemplary implementation of this disclosure, a computer program product is also provided, which is tangibly stored on a non-transitory computer-readable medium and includes computer-executable instructions, which are executed by a processor to implement the methods described above.

[0099] Various aspects of this disclosure are described herein with reference to flowchart illustrations and / or block diagrams of methods, apparatuses, devices, and computer program products implemented according to this disclosure. It should be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer-readable program instructions.

[0100] These computer-readable program instructions can be provided to a processing unit of a general-purpose computer, a special-purpose computer, or other programmable data processing apparatus to produce a machine such that, when executed by the processing unit of the computer or other programmable data processing apparatus, they create means for implementing the functions / actions specified in one or more blocks of the flowchart and / or block diagram. These computer-readable program instructions can also be stored in a computer-readable storage medium that causes a computer, programmable data processing apparatus, and / or other device to operate in a particular manner. Thus, the computer-readable medium storing the instructions comprises an article of manufacture that includes instructions for implementing aspects of the functions / actions specified in one or more blocks of the flowchart and / or block diagram.

[0101] Computer-readable program instructions can be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational steps to be performed on the computer, other programmable data processing apparatus, or other device to produce a computer-implemented process, thereby causing the instructions that execute on the computer, other programmable data processing apparatus, or other device to perform the functions / actions specified in one or more boxes of a flowchart and / or block diagram.

[0102] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of an instruction, which contains one or more executable instructions for implementing the specified logical function. In some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, may be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0103] Various implementations of this disclosure have been described above. These descriptions are exemplary and not exhaustive, nor are they limited to the disclosed implementations. Many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the described implementations. The terminology used herein is chosen to best explain the principles, practical applications, or improvements to technology in the market, or to enable others skilled in the art to understand the various implementations disclosed herein.

Claims

1. A parking guidance method, comprising: Determine the target route for the vehicle, which includes at least the target parking location and the target parking time for the vehicle; Acquire parking space information for at least one parking space around the target parking location and at least one piece of reference information associated with the target trip, the parking space information indicating at least one of the historical or current usage status of each of the at least one parking space; Based on the trip information of the target trip, the parking space information, and the at least one reference information, determine the corresponding predicted usage status of the at least one parking space at the target parking time; as well as Based on the predicted usage status of the at least one parking space at the target parking time, parking recommendation information is presented for the target trip.

2. The method of claim 1, wherein determining the corresponding predicted usage status of the at least one parking space at the target parking time comprises: Based on the trip information of the target trip, the parking space information, and the at least one reference information, generate the model input of the machine learning model to obtain the model output of the machine learning model; as well as Based on the model output, the predicted usage status of the at least one parking space during the target parking time is determined.

3. The method of claim 1, wherein the at least one piece of reference information includes at least traffic information related to the target trip, and wherein determining the corresponding predicted usage status of the at least one parking space at the target parking time comprises: Based on the traffic information, the predicted traffic flow of the target area surrounding the target parking space during the target parking time is determined; as well as Based on the trip information, the parking space information, and the predicted traffic flow, the predicted usage status of the at least one parking space during the target parking time is determined.

4. The method of claim 3, wherein the at least one reference information further comprises at least one of the following: Information on activities related to the target itinerary, Weather information related to the target itinerary, or Historical vehicle usage data for the aforementioned vehicle.

5. The method according to claim 1, further comprising: Select a target parking space whose predicted usage status is available from the at least one parking space, wherein the parking recommendation information includes recommendation information for the target parking space.

6. The method according to claim 5, further comprising: In response to receiving a selection instruction for the target parking space, a reservation operation for the target parking space is performed.

7. The method according to claim 6, further comprising: If the vehicle does not arrive at the reserved parking space by the target parking time, the reservation for the target parking space is cancelled.

8. The method according to claim 6, further comprising: In response to the selection instruction, navigation information from the vehicle's current location to the target parking space is presented.

9. The method according to claim 6, further comprising: It displays service recommendations related to the reserved target parking space.

10. The method of claim 5, wherein the parking recommendation information includes at least one of the following: The location information of the target parking space. The predicted usage status of the target parking space during the target parking time. The distance between the target parking space and the target parking location. The walking time between the target parking space and the target parking location. The vehicle resources required for the vehicle to travel to the target parking space, or The parking feedback resources that can be obtained by parking the vehicle in the target parking space.

11. A parking guidance device, comprising: The trip determination module is configured to determine the target trip of the vehicle, the target trip including at least the target parking location and the target parking time of the vehicle; The information acquisition module is configured to acquire parking space information of at least one parking space around the target parking location and at least one piece of reference information associated with the target trip, wherein the parking space information indicates at least one of the historical usage status or current usage status of each of the at least one parking space. The status determination module is configured to determine the corresponding predicted usage status of the at least one parking space at the target parking time based on the trip information of the target trip, the parking space information, and the at least one reference information. The information presentation module is configured to present parking recommendation information for the target trip based on the corresponding predicted usage status of the at least one parking space at the target parking time.

12. An electronic device, comprising: At least one processing unit; as well as At least one memory, coupled to the at least one processing unit and storing instructions for execution by the at least one processing unit, the instructions causing the electronic device to perform the method according to any one of claims 1 to 10 when executed by the at least one processing unit.

13. A computer-readable storage medium having a computer program stored thereon, the computer program being executable by a processor to implement the method according to any one of claims 1 to 10.

14. A computer program product comprising a computer program, wherein the computer program, when executed by a processor, implements the method according to any one of claims 1 to 10.

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