Tail gate control method and device, electronic equipment and vehicle

Abstract

The invention provides a tail gate control method and device and a vehicle, and relates to the technical field of vehicles, the method comprises the steps that transportation information and frequency information corresponding to a target article associated with the vehicle are obtained, the transportation information comprises a transportation address corresponding to the target article, and the frequency information is used for indicating the operation frequency corresponding to the transportation address of the target article; then intention prediction is carried out based on the transportation information and the frequency information to obtain a target intention prediction result, and the target intention prediction result is used for indicating loading and transporting operation and / or unloading operation for the target article; and according to the target intention prediction result and the arrival condition corresponding to the transportation address, determining whether to open the tail gate. Through the arrangement, whether the tail door needs to be opened or not can be determined according to the intention of the user and the travel destination, manual operation of the user is not needed, the operation convenience of the tail door of the vehicle is effectively improved, convenience is provided for daily vehicle use of people, and the product competitiveness of the vehicle is also improved while the user experience is improved.

Description

Technical Field

[0001] The present application relates to the technical field of tailgate control, and in particular to a tailgate control method, device, electronic device and vehicle. Background Art

[0002] Vehicles are increasingly becoming a popular means of transportation due to their high speed, high comfort, and time-saving travel capabilities. However, users frequently need to open the tailgate to place or retrieve items. Therefore, optimizing tailgate control methods and improving tailgate operation convenience have become pressing technical challenges for those skilled in the art. Summary of the Invention

[0003] In view of this, the present application proposes a tailgate control method, device, electronic device, vehicle and computer program product, which can optimize the control method of the tailgate and improve the operating convenience of the tailgate.

[0004] The technical solutions proposed in this application are as follows:

[0005] In a first aspect, an embodiment of the present application provides a tailgate control method, comprising:

[0006] Acquire transportation information and frequency information corresponding to a target item associated with the vehicle, wherein the transportation information includes a transportation address corresponding to the target item, and the frequency information is used to indicate an operating frequency corresponding to the target item at the transportation address;

[0007] performing intention prediction based on the transportation information and the frequency information to obtain a target intention prediction result, wherein the target intention prediction result is used to indicate a shipping operation and / or an unloading operation for the target item;

[0008] Determine whether to open the tailgate of the vehicle based on the target intention prediction result and the arrival status corresponding to the transportation address.

[0009] Optionally, in a possible implementation, performing intention prediction based on the transportation information and the frequency information to obtain a target intention prediction result includes:

[0010] Determine the delivery location and itinerary destination corresponding to the transportation information;

[0011] Determining a first frequency in the frequency information according to an item shipping condition corresponding to the delivery location;

[0012] inputting the transportation information of the target item and the first frequency into the shipping intention prediction model to obtain a shipping intention prediction result output by the shipping intention prediction model;

[0013] and / or; determining the second frequency in the frequency information according to the unloading status of the items corresponding to the travel destination;

[0014] Inputting the item information of the target item, the travel destination, and the second frequency into the unloading intention prediction model to obtain an unloading intention prediction result output by the unloading intention prediction model;

[0015] Correspondingly, determining whether to open the tailgate of the vehicle according to the target intention prediction result and the arrival status corresponding to the transportation address includes:

[0016] Determine whether to open the tailgate of the vehicle based on the target intention prediction result and the arrival status corresponding to the travel destination, and the target intention prediction result includes at least one of the shipping intention prediction result and the unloading intention prediction result.

[0017] It can be seen that in this implementation, since the loading and unloading of items are strongly related to the address, the loading and unloading frequency information in the corresponding scenarios is counted by determining the delivery place and the travel destination; since the frequency information is obtained by collecting statistics on multiple user operations, the user's loading and unloading habits can be simulated, so the loading and unloading intentions at the corresponding address can be obtained through the frequency information, thereby controlling the opening of the vehicle tailgate and improving the convenience of tailgate control.

[0018] Optionally, in a possible implementation, the method further includes:

[0019] Determine the travel destination based on navigation information; or predict the travel destination based on travel information; wherein the travel information includes at least one of travel time, physical characteristics of the traveler, and schedule data.

[0020] It can be seen that in this implementation, since users often use navigation or travel reservations to set route information during driving, determining the travel destination and related itinerary arrangements through navigation information and travel information can ensure the accuracy of the information.

[0021] Optionally, in a possible implementation, predicting a travel destination based on travel information includes:

[0022] The travel information is input into a pre-trained destination prediction model so that the destination prediction model predicts the trip destination based on the travel information and outputs the prediction.

[0023] It can be seen that in this implementation, since the process of users setting destinations is regular, that is, users have frequently visited destinations, targeted predictions of frequently visited destinations can be made through the pre-trained destination prediction model, which can improve the accuracy of destination predictions.

[0024] Optionally, in one possible implementation, the target intention prediction result includes the loading intention prediction result and the unloading intention prediction result, and determining whether to open the tailgate of the vehicle based on the target intention prediction result and the arrival status corresponding to the trip destination includes:

[0025] Determining loading and unloading intentions and probabilities of the loading and unloading intentions based on the loading and unloading intention prediction result, the unloading intention prediction result, and the arrival status corresponding to the trip destination; the loading and unloading intentions include loading intentions and / or unloading intentions;

[0026] Determining whether to open a tailgate of the vehicle is determined based on the loading and unloading intention and the probability of the loading and unloading intention.

[0027] It can be seen that in this implementation, since the relative relationship between the object and the vehicle involves the process of loading and unloading, the adaptability of the tailgate control in various scenarios can be improved by calculating the loading and unloading intentions in different operation scenarios.

[0028] Optionally, in a possible implementation, determining whether to open a tailgate of a vehicle based on the loading and unloading intention and the probability of the loading and unloading intention includes:

[0029] If the probability of the loading and unloading intention is within a first probability interval, automatically opening the tailgate of the vehicle when detecting that the user enters the tailgate opening range;

[0030] If the loading and unloading intention includes a shipping intention and the probability of the shipping intention is within a second probability interval, outputting a query message regarding whether to open the tailgate; and automatically opening the tailgate of the vehicle when a response message to the query message includes opening the tailgate and the user enters a tailgate opening range.

[0031] The lower limit of the first probability interval is greater than or equal to the upper limit of the second probability interval.

[0032] It can be seen that in this implementation, since the shipping intention is a probabilistic value, different probability sizes indicate the possibility of the user having the corresponding intention. In order to realize the control process within different numerical ranges, targeted query configuration is performed through different probability intervals, thereby avoiding miscontrol in low-probability scenarios during the shipping process and improving the accuracy of tailgate control during the shipping process.

[0033] Optionally, in a possible implementation, determining whether to open a tailgate of a vehicle based on the loading and unloading intention and the probability of the loading and unloading intention includes:

[0034] If the loading and unloading intention includes the unloading intention and the probability of the unloading intention is in the second probability interval, then when it is detected that the user has the intention to get off the vehicle, an inquiry message is output as to whether to open the tailgate; when the reply information to the inquiry message includes opening the tailgate and the user enters the tailgate opening range, the tailgate of the vehicle is automatically opened.

[0035] It can be seen that in this implementation, since the unloading intention is a probabilistic value, different probability sizes indicate the possibility of the user having the corresponding intention. In order to realize the control process within different numerical ranges, targeted query configuration is performed through different probability intervals, avoiding the situation of miscontrol in low-probability scenarios during the unloading process and improving the accuracy of tailgate control during the unloading process.

[0036] In a second aspect, an embodiment of the present application provides a tailgate control device, comprising:

[0037] An input unit, configured to obtain transportation information and frequency information corresponding to a target item associated with the vehicle, wherein the transportation information includes a transportation address corresponding to the target item, and the frequency information indicates an operating frequency corresponding to the target item at the transportation address;

[0038] The input unit is further configured to perform intention prediction based on the transportation information and the frequency information to obtain a target intention prediction result, wherein the target intention prediction result is used to indicate a shipping operation and / or an unloading operation for the target item;

[0039] A determination unit is used to determine whether to open the tailgate of the vehicle based on the target intention prediction result and the arrival status corresponding to the transportation address.

[0040] In a third aspect, an embodiment of the present application provides an electronic device, including:

[0041] A memory and a processor; wherein the memory is used to store a program; and the processor is used to implement any of the above methods by running the program in the memory.

[0042] In a fourth aspect, an embodiment of the present application provides a vehicle, comprising: a tailgate control device;

[0043] The tailgate control device is configured to implement any one of the above methods.

[0044] In a fifth aspect, embodiments of the present application provide a computer program product, comprising a computer program. When executed by a processor, the computer program implements any of the above methods. Optionally, the computer program may be stored on a computer-readable storage medium or in the cloud; the processor of the computer device reads the computer program from the computer-readable storage medium or the cloud.

[0045] The tailgate control method proposed in this application obtains transportation information and frequency information corresponding to a target item associated with a vehicle. The transportation information includes the transportation address corresponding to the target item, and the frequency information indicates the operation frequency of the target item at the transportation address. Then, based on the transportation information and frequency information, an intention prediction is performed to obtain a target intention prediction result, which indicates the loading and / or unloading operation for the target item. The method then determines whether to open the vehicle's tailgate based on the target intention prediction result and the arrival status of the transportation address. With this configuration, the user's intention to operate the target item at the transportation address can be simulated by statistically analyzing the operation frequency of the target item at the transportation address. This allows skill-based tailgate control based on this intention to achieve a process of determining whether to open the tailgate based on the user's intention and travel destination. This process does not require manual operation by the user, effectively improving the convenience of tailgate operation and providing convenience for people's daily use of the vehicle. While improving the user experience, the vehicle's own product competitiveness is also enhanced. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following is a brief introduction to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are merely embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without any creative work.

[0047] Figure 1 It is a flow chart of a tailgate control method provided in an embodiment of the present application.

[0048] Figure 2 It is a flowchart of determining whether to open the tailgate of a vehicle provided in an embodiment of the present application.

[0049] Figure 3 Schematic diagram of the structure of a tailgate control device provided in an embodiment of the present application.

[0050] Figure 4 This is a structural diagram of an electronic device provided in an embodiment of the present application.

[0051] Figure 5 It is a structural schematic diagram of a vehicle provided in an embodiment of the present application. DETAILED DESCRIPTION

[0052] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0053] Vehicles have high speeds and high comfort levels, which can effectively save travel time. More and more people are choosing vehicles as their means of transportation. When using a vehicle, users often need to open the tailgate to place or take items. The traditional opening method requires the user to put down the items in their hands, which is not convenient enough. Although the existing kick sensing and projection technologies can free the user's hands to a certain extent, when there are complex pedestrians around the vehicle, it is easy to have incorrect kick sensing, that is, there is a risk of inaccurate recognition; and this type of physical sensing operation requires the user to interact in a specific way, which is not friendly to novice users and has the problem of complex operation. The user experience still needs to be improved.

[0054] Therefore, optimizing the tailgate control method and improving the tailgate's operability have become technical problems urgently needed to be solved by those skilled in the art. Based on this, the present application proposes a tailgate control method, device, electronic device, vehicle, and computer program product. This technical solution improves the operability of a vehicle's tailgate by determining whether to open the tailgate based on the user's intention and travel destination.

[0055] An embodiment of the present application proposes a tailgate control method, which can be executed by an electronic device. The electronic device can be any device with data and instruction processing functions, for example, it can be various types of user terminals such as a laptop computer, a tablet computer, a desktop computer, a car computer, a mobile device (for example, a mobile phone, a personal digital assistant, a dedicated messaging device), or a combination of any two or more of these electronic devices, or it can be a server.

[0056] See also Figure 1 As shown, the method includes:

[0057] S101. Acquire transportation information and frequency information corresponding to a target item associated with a vehicle. The transportation information includes a transportation address corresponding to the target item, and the frequency information is used to indicate an operating frequency corresponding to the transportation address of the target item.

[0058] In this embodiment, the target items associated with the vehicle include items associated with the user corresponding to the vehicle, such as online shopping items, express delivery items or items obtained by other means; the target items associated with the vehicle may also include items carried by the vehicle, such as items in the trunk, items in the front row or items loaded in other storage spaces of the vehicle; that is, the items in this embodiment may be a combination of one or more of the above examples, and the specific type of items depends on the actual scenario.

[0059] Specifically, for the transportation information and frequency information corresponding to the target items, since the types of target items are different, the specific meaning of the transportation information is also different accordingly; for the target items that are items associated with the user corresponding to the vehicle, its transportation information is used to indicate the method and address (transportation address) for the user to obtain the item, such as for online shopping items, its transportation information is the logistics information; in addition, for the target items that are carried by the vehicle, its transportation information is used to indicate the path, destination (transportation address) for the user to transport the item, such as for trunk items, its transportation information is the itinerary or the itinerary including the destination. Accordingly, based on the definition of the above transportation information in different scenarios, the frequency information is the frequency of the user's operation on the target item in the corresponding scenario. The specific operation may include loading or unloading, that is, the operation of controlling the loading and unloading of items.

[0060] Therefore, based on the statistics of the transportation information and frequency information of the target items by the above-mentioned vehicles in different scenarios, it is possible to reflect the user's operating habits for various types of items and provide data support for subsequent intention recognition.

[0061] S102. Perform intention prediction based on the transportation information and frequency information to obtain a target intention prediction result, where the target intention prediction result is used to indicate a shipping operation and / or unloading operation for the target item.

[0062] In this embodiment, intention prediction based on transportation information and frequency information is a frequency statistical prediction process based on the user in different shipping item scenarios. That is, if the user performs a certain number of shipping operations in the shipping item scenario, the shipping operation is considered to be the user's habitual operation, and intention prediction can be performed in this scenario based on the habitual operation.

[0063] Specifically, considering the diversity of shipping scenarios and target items, the intention prediction process can be carried out using an intention prediction model. For the scenarios in which the intention prediction model is executed, the models for the corresponding scenarios can be configured specifically, such as the shipping intention prediction model and the unloading intention prediction model.

[0064] Therefore, for the intention prediction result process in multiple scenarios, it is necessary to determine the operation frequency in the scenario respectively, that is, first determine the delivery place and travel destination corresponding to the transportation information; then determine the first frequency in the frequency information based on the shipping situation of the items corresponding to the delivery place; and determine the second frequency in the frequency information based on the unloading situation of the items corresponding to the travel destination, so as to realize the targeted parameter input process in different shipping scenarios and improve the accuracy of intention recognition.

[0065] Next is the prediction process based on the shipping intention prediction model and the unloading intention prediction model.

[0066] Regarding the prediction process of the shipping intention prediction model, the transportation information and the first frequency of the target item are input into the shipping intention prediction model to obtain the shipping intention prediction result output by the shipping intention prediction model.

[0067] The target items mentioned above include items purchased by users on shopping platforms, i.e., online shopping items. These target items can be any items, such as clothing, food, and toys, and are not limited in this embodiment. The target item's transportation information refers to data related to the target item's transportation process from the seller to the buyer, including logistics progress, product information, and delivery information. Product information includes physical attributes such as the product's volume, size, weight, and type, while delivery information includes information such as the target item's delivery location.

[0068] The first frequency mentioned above includes the frequency of shipping items at the target item's delivery address. This frequency can be calculated by recording the number of times a the vehicle's historical trip destination was the target item's delivery address, and the number of times b the vehicle's historical trip destination was the target item's delivery address and also shipped items. The first frequency is calculated by calculating the ratio of b to a. For example, if the target item is an online courier box and its delivery address is Address 1, and the vehicle records its historical trip destination, the number of times it arrived at Address 1 is 10, and the number of times it was the target item's delivery address and also shipped items is 9, then the first frequency is 9 / 10 = 0.9.

[0069] It should be noted that the presence of items being shipped can be detected by a sensing device provided in the trunk. Specifically, the sensing device can sense data on items in the trunk, including the quantity, weight, or location of items in the trunk. If the sensing device senses an increase in items in the trunk, such as an increase in weight, it indicates that items are being shipped. The sensing device may be a radar, camera, or weight sensor, and this embodiment does not limit this. Specifically, when it is detected that the vehicle's destination is the destination for the target item, and the sensing device senses an increase in items in the trunk, it indicates that the vehicle's destination is the destination for the target item and that items are being shipped.

[0070] It should also be noted that the first frequency is not a fixed value. As the number of times the user goes to the delivery location of the target item increases, the magnitude of the first frequency will change accordingly. In the embodiment of the present application, the first frequency is calculated in real time each time based on the number of times the vehicle's travel destination is the delivery location of the target item and the number of times the vehicle's travel destination is the delivery location of the target item and the item is shipped at the same time. For example, if the target item is a courier box purchased online, its delivery address is address 2. When the vehicle records the historical trip destination, the number of times it arrives at address 2 is 10, and the number of times the historical trip destination is the delivery address of the target item and the item is shipped at the same time is 8, then the first frequency is 8 / 10=0.8; and in the subsequent historical trip destination recording process, the number of times it arrives at address 2 is 12, and the number of times the historical trip destination is the delivery address of the target item and the item is shipped at the same time is 10, that is, the vehicle's subsequent two visits to address 2 both carried out the shipping operation of the courier box purchased online. At this time, the first frequency is 10 / 12=0.83, thereby realizing the real-time calculation process, improving the effectiveness of the first frequency, and thus improving the accuracy of intention recognition.

[0071] The shipping intention prediction model can be trained in advance, and the transportation information and the first frequency of the target item are input into the pre-trained shipping intention prediction model so that the shipping intention prediction model calculates and outputs the shipping intention prediction result based on the transportation information and the first frequency of the target item.

[0072] The shipping intention prediction model is trained with the transportation information and first frequency of the target items corresponding to each time the user uses the vehicle in a historical time period as training samples, with the goal of predicting whether the user has shipping intention when using the vehicle.

[0073] The transportation information and the first frequency of the target item corresponding to each use of the vehicle within the historical time period of the user can be obtained as training samples, and whether the vehicle was used to ship the item at the destination of the target item is used as a training label. During the training process, the training samples are input into the shipping intention prediction model to obtain the prediction results output by the shipping intention prediction model. By comparing the prediction results of the shipping intention prediction model and the training labels, the loss value of the shipping intention prediction model is determined. With the goal of reducing the loss of the shipping intention prediction model, the parameters of the shipping intention prediction model are adjusted until the shipping intention prediction model meets the training requirements. The above-mentioned shipping intention prediction model can be obtained by training based on any neural network model, or based on training based on a pre-trained model, such as a pre-trained large model similar to ChatGPT.

[0074] The shipping information and the first frequency of the target item are input into a trained shipping intention prediction model to obtain a shipping intention prediction result output by the shipping intention prediction model. In some embodiments, the shipping intention prediction result includes a probability of the user shipping the item.

[0075] It can be understood that since the transportation information and the first frequency of the target item reflect the user's shipping habits for the target item, that is, the targeted habit data of the user performing a specific operation (first frequency) at a specific location (transportation information), the transportation information and the first frequency of the target item are important information that affects whether the user opens the tailgate to ship the item. Using the transportation information and the first frequency of the target item to train the shipping intention prediction model can effectively improve the accuracy of the prediction results.

[0076] In addition, for the prediction process of the unloading intention prediction model, the item information, travel destination and second frequency of the target item are input into the unloading intention prediction model to obtain the unloading intention prediction result output by the unloading intention prediction model. For example, the item information, travel destination and second frequency of the trunk item are input into the unloading intention prediction model to obtain the unloading intention prediction result output by the unloading intention prediction model.

[0077] The aforementioned trunk item information includes the basic physical properties of the items in the vehicle trunk, including the volume, weight, and type of the items. Various sensors can be installed in the vehicle trunk to obtain information such as the volume, weight, and type of the items. For example, a radar device can be used to detect the volume of the trunk items, a weight sensor can be used to obtain the weight of the trunk items, and a camera can be used to capture an image of the trunk items. The image can then be processed using established classification algorithms to determine the type of the trunk items.

[0078] The above-mentioned trip destination refers to the final destination of the user's trip, and the navigation destination can be retrieved from the navigation device as the trip destination.

[0079] The second frequency includes the frequency of unloading items at the destination. The second frequency can be calculated by recording the number of times c the vehicle's destination was the destination and the number of times d the vehicle was both at the destination and unloaded items. The ratio of d to c can be used to calculate the second frequency.

[0080] It should be noted that the unloading of items can be detected by a sensing device installed in the trunk. Specifically, the sensing device can sense the data of items in the trunk, including the number, weight or location of items in the trunk. If the sensing device senses that the number of items in the trunk is reduced, such as a decrease in weight, it indicates that the items have been unloaded. The sensing device can be a radar, a camera or a weight sensor and other devices, which are not limited in this embodiment. Specifically, when it is detected that the vehicle has arrived at the destination of the trip, and the sensing device senses that the number of items in the trunk has decreased, it indicates that the vehicle has unloaded items at the destination of the trip.

[0081] It should also be noted that the second frequency is not a fixed value. As the number of times a user visits the trip destination increases, the magnitude of the second frequency will change accordingly. In the embodiments of the present application, the second frequency is calculated in real time based on the vehicle's trip destination and the number of times the vehicle unloads items. For example, if the target item is a bicycle in the trunk and its trip destination is address 3, the vehicle calculates the second frequency in real time based on the trip destination and the number of times the vehicle unloads items. This is done by counting the proportion of items unloaded at address 3. That is, if the number of times the user reaches address 3 is 10, and the number of times the user has unloaded bicycles at address 3 in the past trip destination is 9, then the proportion of items unloaded is 9 / 10 = 0.9, i.e., the second frequency is 0.9.

[0082] The unloading intention prediction model can be trained in advance, and the item information of the trunk items, the travel destination and the second frequency can be input into the pre-trained unloading intention prediction model, so that the loading and unloading map prediction model can calculate and output the unloading intention prediction result based on the item information of the trunk items, the travel destination and the second frequency.

[0083] The unloading intention prediction model is trained using the trunk item information, travel destination, and second frequency of each time the user uses the car within a historical time period as training samples, with the goal of predicting whether the user has the intention to unload the car during that time.

[0084] The item information, travel destination, and second frequency of the corresponding trunk items each time the user uses the car during the historical time period can be obtained as training samples, and whether the items are unloaded at the travel destination during this use of the car can be used as a training label. During the training process, the training samples are input into the unloading intention prediction model to obtain the prediction results output by the unloading intention prediction model. By comparing the prediction results of the unloading intention prediction model and the training labels, the loss value of the unloading intention prediction model is determined. With the goal of reducing the loss of the unloading intention prediction model, the parameters of the unloading intention prediction model are adjusted until the unloading intention prediction model meets the training requirements. The above-mentioned unloading intention prediction model can be obtained through training based on any neural network model, or can be obtained through training based on a pre-trained model, such as a pre-trained large model similar to ChatGPT.

[0085] The trunk item information, the travel destination, and the second frequency are input into a trained unloading intention prediction model to obtain an unloading intention prediction result output by the unloading intention prediction model. In some embodiments, the unloading intention prediction result includes a probability that the user will unload the item.

[0086] The trunk item information, travel destination, and second frequency are all important information that influence whether the user opens the tailgate to unload items. Using the trunk item information, travel destination, and second frequency to train the unloading intention prediction model can effectively improve the accuracy of the prediction results.

[0087] It should be noted that the embodiments of the present application do not limit the execution order of the shipping intention prediction and the unloading intention prediction. The shipping intention prediction can be executed first and then the unloading intention prediction, the unloading intention prediction can be executed first and then the shipping intention prediction, or the shipping intention prediction and the unloading intention prediction can be executed at the same time.

[0088] S103: Determine whether to open the tailgate of the vehicle based on the target intention prediction result and the arrival status corresponding to the transportation address.

[0089] In this embodiment, since the target intention prediction result can include at least one of the shipping intention prediction result and the unloading intention prediction result, it is possible to only execute the tailgate control process in the shipping scenario, or only execute the tailgate control process in the unloading scenario, or execute the tailgate control process in all scenarios.

[0090] Therefore, the need to open the tailgate for the user can be determined based on the loading and unloading intention prediction results, combined with the trip destination. In some embodiments, the need to open the tailgate for the user can also be determined based on the loading and unloading intention prediction results, combined with the trip destination and trip origin. The trip origin refers to the location of the vehicle before the trip begins, and the trip origin can be determined using a positioning device.

[0091] Specifically, if the shipping intention prediction results indicate a high probability that the user is shipping items and is greater than a set probability, and the target item's delivery address is the same as the trip's destination, then the user has shipping intentions, and the tailgate can be automatically opened when the user approaches the tailgate at the trip's destination. If the shipping intention prediction results indicate a high probability that the user is shipping items and is greater than a set probability, and the target item's delivery address is the same as the trip's starting point, then the user has shipping intentions, and the tailgate can be automatically opened when the user approaches the tailgate before the trip begins.

[0092] If the shipping intention prediction result shows that the probability of the user shipping items is low and less than or equal to the set probability, or if the delivery location of the target item is different from the starting point and destination of the trip, it means that the user has no shipping intention and the tailgate does not need to be automatically opened.

[0093] If the predicted probability of the user unloading items is high and greater than the set probability, the tailgate can be automatically opened when the user reaches the destination and approaches the tailgate. If the predicted probability of the user unloading items is low and less than or equal to the set probability, the user has no intention to load the items, and automatic tailgate opening is not required.

[0094] The above-mentioned set probability can be set according to actual conditions and is not limited in this embodiment.

[0095] In the above embodiment, the target item's transportation information and a first frequency can be input into a shipping intention prediction model to obtain a shipping intention prediction result output by the shipping intention prediction model. The trunk item information, the travel destination, and a second frequency can be input into an unloading intention prediction model to obtain an unloading intention prediction result output by the unloading intention prediction model, where the first frequency includes the frequency of loading items at the target item's delivery address, and the second frequency includes the frequency of unloading items at the travel destination. Whether to open the vehicle's tailgate is then determined based on the shipping intention prediction result, the unloading intention prediction result, and the travel destination. This configuration allows for statistical analysis of the frequency of operations performed on the target item at the transportation address to simulate the user's intention to operate the target item at the transportation address. This allows for skill-based tailgate control based on this intention to determine whether to open the tailgate based on the user's intention and travel destination. This process eliminates the need for manual user operation, effectively improving the vehicle's tailgate's operational convenience and providing convenience for daily vehicle use. This improves the user experience while also enhancing the vehicle's product competitiveness.

[0096] In one possible scenario, considering that a user may use a navigation application when configuring a historical itinerary, the navigation information recorded by the navigation application can be used to intuitively reflect the itinerary status. Therefore, based on the description of the above embodiment, as an optional implementation method, another embodiment of the present application discloses that the method of the above embodiment may specifically include the following steps:

[0097] Determine a trip destination based on navigation information; or predict a trip destination based on travel information.

[0098] Specifically, a navigation destination may be retrieved from a navigation device as the travel destination.

[0099] The itinerary destination can also be predicted based on travel information, wherein the travel information includes at least one of travel time, physical characteristics of the traveler, and to-do schedule data.

[0100] Travel time refers to the time of using the car; travelers include drivers and passengers, and the physical characteristics of travelers include information such as gender, height and weight of each traveler; schedule to-do data refers to the user's to-do information on the day of travel, including the content of the to-do items, processing time, etc. The schedule to-do data can be obtained from the user's memo and other applications, and this embodiment does not limit it.

[0101] Furthermore, given the complexity of user itineraries, destination prediction can also be performed using a prediction model to integrate and manage large amounts of travel data, improving the accuracy of trip destination predictions. This involves inputting travel information into a pre-trained destination prediction model, which then predicts and outputs the trip destination based on the travel information.

[0102] The destination prediction model can be trained in advance, and the travel information can be input into the pre-trained destination prediction model so that the destination prediction model can predict the travel destination based on the travel information.

[0103] The destination prediction model is trained using the travel information corresponding to each time the user uses the car in a historical period as a training sample, with the goal of predicting the user's travel destination for that trip.

[0104] The corresponding travel information of each time the user uses the car in the historical time period can be obtained as a training sample, and the actual travel destination of the car use can be used as a training label. During the training process, the training sample is input into the destination prediction model to obtain the prediction result output by the destination prediction model. By comparing the prediction result of the destination prediction model and the training label, the loss value of the destination prediction model is determined. With the goal of reducing the loss of the destination prediction model, the parameters of the destination prediction model are adjusted until the destination prediction model meets the training requirements. The above-mentioned destination prediction model can be obtained by training based on any neural network model, or based on training based on a pre-trained model, such as a pre-trained large model similar to ChatGPT.

[0105] The travel information is input into the trained destination prediction model to obtain the travel destination output by the destination prediction model.

[0106] Travel information can indicate the user's travel destination to a certain extent. For example, if the user commutes by car during rush hour, it can be predicted whether the user is going to work or going home based on the travel time, and then determine whether the travel destination is home or work. For example, based on the physical characteristics of the travelers, it can be determined that the travelers include babies, and the travel destination may be a children's amusement park. Therefore, using travel information to train the destination prediction model can effectively improve the accuracy of the prediction results.

[0107] In some embodiments, since the navigation process requires the support of a smooth network, the destination prediction process can prioritize determining the travel destination based on the navigation information. If the travel destination cannot be determined based on the navigation information (i.e., when the network fails or is congested), the travel destination can be predicted based on the travel information in accordance with the records of the above embodiments.

[0108] In the above embodiment, two methods are provided: determining the travel destination based on navigation information and predicting the travel destination based on travel information, to ensure that the travel destination can be accurately obtained, and to perform real-time updates of frequency information based on the travel destination, thereby improving the completeness and accuracy of the user's frequency information statistics at each travel destination, and avoiding the situation where the inability to obtain the travel destination affects the control of the tailgate.

[0109] As an optional implementation method, in the above embodiment, the intention prediction result obtained by intention recognition can be expressed in the form of probability to improve the representation granularity of the data results and reflect the data differences of different intention recognitions; the specific process is as follows Figure 2 As shown, in another embodiment of the present application, the steps of the above embodiment are disclosed, which determine whether to open the tailgate of the vehicle based on the shipping intention prediction result, the unloading intention prediction result and the travel destination, and may specifically include the following steps:

[0110] S201, determining the loading and unloading intentions and the probabilities of the loading and unloading intentions based on the loading and unloading intention prediction results, the unloading intention prediction results, and the trip destination;

[0111] S202: Determine whether to open the tailgate of the vehicle based on the loading and unloading intention and the probability of the loading and unloading intention.

[0112] In an embodiment of the present application, the loading and unloading intention and the probability of loading and unloading intention can be determined based on the shipping intention prediction results, the unloading intention prediction results, and the trip destination, where the loading and unloading intention includes the shipping intention and / or the unloading intention. In other words, based on the shipping intention prediction results, the unloading intention prediction results, and the trip destination, it is first determined whether the user has the shipping intention and the probability of the shipping intention, and whether the user has the unloading intention and the probability of the unloading intention.

[0113] More specifically, the shipping intention prediction results, unloading intention prediction results and travel destination can be input into the loading and unloading intention prediction model so that the loading and unloading intention prediction model determines and outputs the loading and unloading intention and the probability of loading and unloading intention based on the shipping intention prediction results, unloading intention prediction results and travel destination.

[0114] Among them, the loading and unloading intention prediction model is based on the corresponding loading intention prediction results, unloading intention prediction results and travel destinations each time the user uses the car in the historical time period as training samples, and is trained with the goal of predicting whether the user has loading and unloading intentions when using the car.

[0115] The parameters of the loading and unloading intention prediction model can be adjusted according to the description of the above embodiment until the loading and unloading intention prediction model meets the training requirements. The specific training process is not described here. The above-mentioned loading and unloading intention prediction model can be trained based on any neural network model, or based on a pre-trained model, such as a pre-trained large model similar to ChatGPT.

[0116] The loading and unloading intention prediction results, unloading intention prediction results and travel destination are input into the trained loading and unloading intention prediction model to obtain the loading and unloading intention and the probability of loading and unloading intention output by the loading and unloading intention prediction model.

[0117] Then, according to the loading and unloading intention and the probability of the loading and unloading intention, it is determined whether to open the tailgate of the vehicle. Specifically, if the loading intention includes the shipping intention and the probability of the shipping intention is high, or the loading and unloading intention includes the unloading intention and the probability of the unloading intention is high, the tailgate can be automatically opened.

[0118] More specifically, if the probability of loading and unloading intention is within the first probability interval, the tailgate is automatically opened when it is detected that the user enters the tailgate opening range; if the loading and unloading intention includes the shipping intention and the probability of the shipping intention is within the second probability interval, an inquiry message about whether to open the tailgate is output; when the reply information to the inquiry message includes opening the tailgate and the user enters the tailgate opening range, the tailgate is automatically opened; if the loading and unloading intention includes the unloading intention and the probability of the unloading intention is within the second probability interval, when it is detected that the user has the intention to get off the vehicle, an inquiry message about whether to open the tailgate is output; when the reply information to the inquiry message includes opening the tailgate and the user enters the tailgate opening range, the tailgate is automatically opened.

[0119] Specifically, the lower limit of the first probability interval is greater than or equal to the upper limit of the second probability interval. For example, the first probability interval is 90%-100%, and the second probability interval is 60%-90%, which is not limited in this embodiment.

[0120] If the probability of loading and unloading intention is in the first probability interval, it means that the user has a high loading and unloading intention. Regardless of whether loading or unloading is required, the tailgate can be automatically opened when the user is detected to enter the tailgate opening range.

[0121] If the loading and unloading intention includes the shipping intention and the probability of the shipping intention is within the second probability interval, it indicates that the user has some shipping intention, but the shipping intention is not high and needs to be confirmed with the customer. A query message regarding whether to open the tailgate can be output, for example, a voice message including "Do you want to open the tailgate?" and the user's response is awaited. If a response message is received to the query message, and the response message includes the user's consent to open the tailgate, the tailgate can be automatically opened when the user enters the tailgate opening range. If no user response message is received within a set time period, or the received response message indicates that the user does not agree to open the tailgate, the tailgate does not need to be opened.

[0122] If the loading and unloading intention includes the intention to unload, and the probability of the unloading intention is within the second probability interval, it indicates that the user has some intention to unload, but the intention is low and needs to be confirmed with the customer. When the user shows the intention to get out of the vehicle, such as when the user opens the car door or unbuckles the seat belt, a query message regarding whether to open the tailgate can be output, such as a voice message "Do you want to open the tailgate?", and the user's response is waited for. If a response to the query is received, and the response includes the user's consent to open the tailgate, the tailgate can be automatically opened when the user enters the tailgate opening range. If no user response is received within the set time, or the received response indicates that the user does not agree to open the tailgate, the tailgate does not need to be opened.

[0123] The tailgate opening range is defined as the distance between the tailgate and the vehicle exterior within a set distance. This set distance can be set based on actual conditions and is not limited in this embodiment. Furthermore, the set duration can also be set based on actual conditions.

[0124] In the above embodiment, whether to open the tailgate of the vehicle can be determined based on the user's loading and unloading intention and the probability of the loading and unloading intention, which improves the operating convenience of the tailgate of the vehicle and provides convenience for people's daily use of the vehicle.

[0125] Corresponding to the above tailgate control method, the present application also discloses a tailgate control device, see Figure 3 As shown, the device includes:

[0126] The input unit 100 is used to obtain transportation information and frequency information corresponding to a target item associated with the vehicle, wherein the transportation information includes a transportation address corresponding to the target item, and the frequency information indicates an operating frequency of the target item at the transportation address;

[0127] The input unit 100 is further configured to perform intention prediction based on the transportation information and the frequency information to obtain a target intention prediction result, wherein the target intention prediction result is used to indicate a shipping operation and / or unloading operation for the target item;

[0128] The determination unit 110 is configured to determine whether to open the tailgate of the vehicle based on the target intention prediction result and the arrival status corresponding to the transportation address.

[0129] Optionally, in a possible implementation, the input unit 100 is used to determine the delivery address and trip destination corresponding to the transportation information when performing intention prediction based on the transportation information and the frequency information to obtain a target intention prediction result; determine the first frequency in the frequency information according to the item shipping situation corresponding to the delivery address; input the transportation information of the target item and the first frequency into the shipping intention prediction model to obtain the shipping intention prediction result output by the shipping intention prediction model; and / or; determine the second frequency in the frequency information according to the item unloading situation corresponding to the trip destination; input the item information of the target item, the trip destination and the second frequency into the unloading intention prediction model to obtain the unloading intention prediction result output by the unloading intention prediction model; correspondingly, determining whether to open the tailgate of the vehicle according to the target intention prediction result and the arrival situation corresponding to the transportation address includes: determining whether to open the tailgate of the vehicle according to the target intention prediction result and the arrival situation corresponding to the trip destination, and the target intention prediction result includes at least one of the shipping intention prediction result and the unloading intention prediction result.

[0130] Optionally, in one possible implementation, the input unit 100 is used to determine the travel destination based on navigation information; or, to predict the travel destination based on travel information; wherein the travel information includes at least one of travel time, physical characteristics of the traveler, and schedule to-do data.

[0131] Optionally, in a possible implementation, the input unit 100 is used to input the travel information into a pre-trained destination prediction model when predicting a travel destination based on the travel information, so that the destination prediction model predicts and outputs the travel destination based on the travel information.

[0132] Optionally, in a possible implementation, the determination unit 110 is used to determine whether to open the tailgate of the vehicle based on the shipping intention prediction result, the unloading intention prediction result and the arrival status corresponding to the trip destination, and to determine the loading and unloading intention and the probability of the loading and unloading intention according to the shipping intention prediction result, the unloading intention prediction result and the arrival status corresponding to the trip destination; the loading and unloading intention includes shipping intention and / or unloading intention; and determine whether to open the tailgate of the vehicle based on the loading and unloading intention and the probability of the loading and unloading intention.

[0133] Optionally, in a possible implementation, the determination unit 110 is used to input the shipping intention prediction result, the unloading intention prediction result and the trip destination into a loading and unloading intention prediction model when determining the loading and unloading intention and the probability of the loading and unloading intention based on the shipping intention prediction result, the unloading intention prediction result and the arrival situation corresponding to the trip destination, so that the loading and unloading intention prediction model determines and outputs the loading and unloading intention and the probability of the loading and unloading intention based on the shipping intention prediction result, the unloading intention prediction result and the trip destination.

[0134] Optionally, in a possible implementation, the determination unit 110 is used to determine whether to open the tailgate of the vehicle based on the loading and unloading intention and the probability of the loading and unloading intention. If the probability of the loading and unloading intention is in a first probability interval, the tailgate of the vehicle is automatically opened when it is detected that the user enters the tailgate opening range; if the loading and unloading intention includes the shipping intention and the probability of the shipping intention is in a second probability interval, an inquiry information on whether to open the tailgate is output; when the reply information to the inquiry information includes opening the tailgate and the user enters the tailgate opening range, the tailgate of the vehicle is automatically opened; wherein the lower limit value of the first probability interval is greater than or equal to the upper limit value of the second probability interval.

[0135] Optionally, in a possible implementation, the determination unit 110 is used to determine whether to open the tailgate of the vehicle based on the loading and unloading intention and the probability of the loading and unloading intention. If the loading and unloading intention includes an unloading intention and the probability of the unloading intention is in a second probability interval, then when it is detected that the user has the intention to get off the vehicle, an inquiry message on whether to open the tailgate is output; when the reply information to the inquiry message includes opening the tailgate and the user enters the tailgate opening range, the tailgate of the vehicle is automatically opened.

[0136] Specifically, the device provided in this embodiment is based on the same application concept as the method provided in the above embodiments of this application, can execute the method provided in any of the above embodiments of this application, and has the corresponding functional modules and beneficial effects of the execution method. For technical details not fully described in this embodiment, please refer to the specific processing content of the method provided in the above embodiments of this application, and will not be repeated here.

[0137] The functions implemented by the above units can be implemented by the same or different processors respectively, and the embodiments of the present application are not limited thereto.

[0138] It should be understood that the units in the above devices can be implemented in the form of a processor calling software. For example, the device includes a processor, the processor is connected to a memory, and the memory stores instructions. The processor calls the instructions stored in the memory to implement any of the above methods or realize the functions of each unit of the device. The processor can be a general-purpose processor, such as a CPU or a microprocessor, and the memory can be a memory within the device or a memory outside the device. Alternatively, the units in the device can be implemented in the form of hardware circuits. The functions of some or all units can be realized by designing the hardware circuits. The hardware circuit can be understood as one or more processors. For example, in one implementation, the hardware circuit is an ASIC, and the functions of some or all of the above units can be realized by designing the logical relationships between the components within the circuit. For another example, in another implementation, the hardware circuit can be implemented by a PLD. For example, an FPGA can include a large number of logic gate circuits. The connection relationships between the logic gate circuits are configured through a configuration file to realize the functions of some or all of the above units. All units of the above devices can be implemented entirely in the form of a processor calling software, or entirely in the form of hardware circuits, or partially in the form of a processor calling software, with the remaining parts implemented in the form of hardware circuits.

[0139] In an embodiment of the present application, a processor is a circuit with the ability to process signals. In one implementation, the processor may be a circuit with the ability to read and execute instructions, such as a CPU, a microprocessor, a GPU, or a DSP. In another implementation, the processor may implement certain functions through the logical relationship of a hardware circuit, and the logical relationship of the hardware circuit may be fixed or reconfigurable, such as a hardware circuit implemented by an ASIC or PLD, such as an FPGA. In a reconfigurable hardware circuit, the process of the processor loading a configuration document to implement the configuration of the hardware circuit can be understood as the process of the processor loading instructions to implement the functions of some or all of the above units. In addition, it can also be a hardware circuit designed for artificial intelligence, which can be understood as an ASIC, such as an NPU, TPU, DPU, etc.

[0140] It can be seen that each unit in the above device can be one or more processors (or processing circuits) configured to implement the above method, such as: CPU, GPU, NPU, TPU, DPU, microprocessor, DSP, ASIC, FPGA, or a combination of at least two of these processor forms.

[0141] In addition, the various units in the above apparatus may be fully or partially integrated together, or may be implemented independently. In one implementation, these units are integrated together and implemented in the form of a system-on-chip (SOC). The SOC may include at least one processor for implementing any of the above methods or implementing the functions of the various units of the apparatus. The at least one processor may be of different types, such as a CPU and an FPGA, a CPU and an artificial intelligence processor, a CPU and a GPU, etc.

[0142] An embodiment of the present application further provides a control device, which includes a processor and an interface circuit. The processor in the control device is connected to an input and output component through the interface circuit of the control device.

[0143] The input and output components specifically refer to hardware components that enable users to input information and output information to users, such as microphones, keyboards, handwriting tablets, touch screens, displays, speakers, printers, etc.

[0144] The above-mentioned interface circuit can be any interface circuit that can realize data communication function, for example, it can be a USB interface circuit, a Type-C interface circuit, a serial port circuit, a PCIE circuit, etc.

[0145] The processor in the control device is a circuit with signal processing capabilities. By executing any of the tailgate control methods described in the above embodiments, it improves the convenience of operating the vehicle tailgate. The specific implementation of the processor can be found in the above-mentioned processor implementation, and the embodiments of this application are not strictly limited thereto.

[0146] When the control device is applied to a device with human-computer interaction function, the input and output components of the control device can be the input components and output components on the device, such as a microphone, keyboard, handwriting tablet, touch screen, display, audio player, etc. At the same time, the processor of the control device can be the CPU or GPU of the device, etc., and the interface circuit of the control device can be the interface circuit between the information input component of the device and the processor such as the CPU or GPU.

[0147] Corresponding to the above tailgate control method, the present application embodiment also discloses an electronic device, see Figure 4 As shown, the electronic device includes:

[0148] Memory 200 and processor 210;

[0149] The memory 200 is connected to the processor 210 and is used to store programs;

[0150] The processor 210 is configured to implement the tailgate control method disclosed in any of the above embodiments by running the program stored in the memory 200 .

[0151] Specifically, the electronic device may further include: a bus, a communication interface 220 , an input device 230 and an output device 240 .

[0152] The processor 210, the memory 200, the communication interface 220, the input device 230 and the output device 240 are interconnected via a bus.

[0153] A bus may include a pathway that transfers information between components of a computer system.

[0154] Processor 210 can be a general-purpose processor, such as a general-purpose central processing unit (CPU), a microprocessor, or the like, or an application-specific integrated circuit (ASIC), or one or more integrated circuits for controlling the execution of the program of the present application. It can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic device, discrete gate or transistor logic device, or discrete hardware component.

[0155] The processor 210 may include a main processor, and may also include a baseband chip, a modem, and the like.

[0156] The memory 200 stores a program for executing the technical solution of the present application, and may also store an operating system and other key services. Specifically, the program may include program code, and the program code includes computer operating instructions. More specifically, the memory 200 may include a read-only memory (ROM), other types of static storage devices that can store static information and instructions, a random access memory (RAM), other types of dynamic storage devices that can store information and instructions, disk storage, flash, etc.

[0157] The input device 230 may include a device for receiving data and information input by a user, such as a keyboard, a mouse, a camera, a scanner, a light pen, a voice input device, a touch screen, a pedometer, or a gravity sensor.

[0158] Output device 240 may include devices that allow information to be output to a user, such as a display screen, printer, speakers, etc.

[0159] The communication interface 220 may include any device such as a transceiver to communicate with other devices or communication networks, such as Ethernet, Radio Access Network (RAN), Wireless Local Area Network (WLAN), etc.

[0160] The processor 210 executes the program stored in the memory 200 and calls other devices to implement the various steps of the tailgate control method provided in the above embodiments of the present application.

[0161] Another embodiment of the present application also provides a vehicle, see Figure 5 As shown, the vehicle includes a tailgate control device 300, and the tailgate control device 300 is configured to implement the various steps of the tailgate control method provided in the above embodiments of the present application.

[0162] The tailgate control device 300 can be located in the vehicle's center console or any other location, such as the vehicle's engine compartment. The tailgate control device 300 can be an electronic device embedded in the vehicle's electronic control unit (ECU), or a processing chip independent of the ECU. Alternatively, the tailgate control device 300 can be one or more ECUs specifically designed to control the tailgate.

[0163] The vehicle provided in this embodiment belongs to the same application concept as the tailgate control method provided in the above embodiments of this application, can execute the tailgate control method provided in any of the above embodiments of this application, and has the corresponding functional modules and beneficial effects for executing the above tailgate control method. The technical details that are not fully described in this embodiment can be referred to the specific processing content of the tailgate control method provided in the above embodiments of this application, and will not be repeated here.

[0164] In addition to the above methods and devices, embodiments of the present application may also be computer program products. The computer program product includes a computer program that, when executed by a processor, can execute the tailgate control method provided in any of the above embodiments of the present application. Optionally, the computer program can be stored on a computer-readable storage medium or in the cloud; the computer device's processor reads the computer program from the computer-readable storage medium or the cloud.

[0165] The computer program product may be written in any combination of one or more programming languages to implement the program code of the embodiments of the present application, including object-oriented programming languages such as Java, C++, and conventional procedural programming languages such as C or similar programming languages. The program code may be executed entirely on the user's computing device, partially on the user's computing device, as a standalone software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server.

[0166] The computer program product may be implemented in hardware, software, or a combination thereof. In one embodiment, the computer program product is implemented as a computer storage medium. In another embodiment, the computer program product is implemented as a software product, such as a software development kit (SDK).

[0167] In addition, the embodiment of the present application may also be a computer-readable storage medium having computer program instructions stored thereon. When the computer program instructions are executed by a processor, the processor executes the various steps of the tailgate control method provided in the above embodiment.

[0168] The computer-readable storage medium can adopt any combination of one or more readable media. The readable medium can be a readable signal medium or a readable storage medium. The readable storage medium can include, for example, but is not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples (non-exhaustive list) of readable storage media include: an electrical connection with one or more wires, a portable 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 above.

[0169] Specifically, the specific working contents of each part of the above-mentioned electronic device, computer program product and storage medium, as well as the specific processing contents of the computer program product or the computer program on the above-mentioned storage medium when being executed by the processor, can be found in the contents of the various embodiments of the above-mentioned tailgate control method, and will not be repeated here.

[0170] For the sake of simplicity, the aforementioned method embodiments are described as a series of action combinations. However, those skilled in the art should be aware that this application is not limited by the order of the actions described, because according to this application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in this specification are all preferred embodiments, and the actions and modules involved are not necessarily required for this application.

[0171] It should be noted that the various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. Similar or identical parts between the various embodiments can be referred to in conjunction with each other. For device embodiments, since they are generally similar to method embodiments, their description is relatively simplified. For relevant parts, refer to the description of the method embodiments.

[0172] The steps in the methods of each embodiment of the present application can be adjusted in sequence, merged, and deleted according to actual needs, and the technical features recorded in each embodiment can be replaced or combined.

[0173] The modules and sub-modules in the devices and terminals in the various embodiments of the present application can be merged, divided, and deleted according to actual needs.

[0174] In the several embodiments provided in this application, it should be understood that the disclosed terminals, devices, and methods can be implemented in other ways. For example, the terminal embodiments described above are merely illustrative. For example, the division of modules or submodules is merely a logical function division. In actual implementation, there may be other division methods, such as multiple submodules or modules can be combined or integrated into another module, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interface, device or module, which can be electrical, mechanical or other forms.

[0175] The modules or submodules described as separate components may or may not be physically separate, and the components of the modules or submodules may or may not be physical modules or submodules, that is, they may be located in one place or distributed across multiple network modules or submodules. Some or all of the modules or submodules may be selected to achieve the purpose of this embodiment according to actual needs.

[0176] In addition, each functional module or submodule in each embodiment of the present application may be integrated into a processing module, or each module or submodule may exist physically separately, or two or more modules or submodules may be integrated into a single module. The above-mentioned integrated modules or submodules may be implemented in the form of hardware or software functional modules or submodules.

[0177] Professionals may further appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the above description has generally described the components and steps of each example according to their functions. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians may use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0178] The steps of the methods or algorithms described in conjunction with the embodiments disclosed herein may be implemented directly using hardware, software units executed by a processor, or a combination of the two. The software units may be placed in random access memory (RAM), internal memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disks, removable disks, CD-ROMs, or any other form of storage medium known in the art.

[0179] Finally, it should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or device comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or elements inherent to such process, method, article, or device. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of additional identical elements in the process, method, article, or device comprising the element.

[0180] The above description of the disclosed embodiments is intended to enable one skilled in the art to implement or use the present application. Various modifications to these embodiments will be readily apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application is not limited to the embodiments shown herein, but is intended to conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A tailgate control method, characterized in that: include: Acquire transportation information and frequency information corresponding to a target item associated with the vehicle, wherein the transportation information includes a transportation address corresponding to the target item, and the frequency information is used to indicate an operating frequency corresponding to the target item at the transportation address; performing intention prediction based on the transportation information and the frequency information to obtain a target intention prediction result, wherein the target intention prediction result is used to indicate a shipping operation and / or an unloading operation for the target item; Determine whether to open the tailgate of the vehicle based on the target intention prediction result and the arrival status corresponding to the transportation address.

2. The method according to claim 1, characterized in that The performing intention prediction based on the transportation information and the frequency information to obtain a target intention prediction result includes: Determine the delivery location and itinerary destination corresponding to the transportation information; Determining a first frequency in the frequency information according to an item shipping condition corresponding to the delivery location; inputting the transportation information of the target item and the first frequency into the shipping intention prediction model to obtain a shipping intention prediction result output by the shipping intention prediction model; and / or; determining the second frequency in the frequency information according to the unloading status of the items corresponding to the travel destination; Inputting the item information of the target item, the travel destination, and the second frequency into the unloading intention prediction model to obtain an unloading intention prediction result output by the unloading intention prediction model; Correspondingly, determining whether to open the tailgate of the vehicle according to the target intention prediction result and the arrival status corresponding to the transportation address includes: Determine whether to open the tailgate of the vehicle based on the target intention prediction result and the arrival status corresponding to the travel destination, and the target intention prediction result includes at least one of the shipping intention prediction result and the unloading intention prediction result.

3. The method according to claim 2, characterized in that Also includes: Determine the travel destination based on navigation information; or predict the travel destination based on travel information; wherein the travel information includes at least one of travel time, physical characteristics of the traveler, and schedule data.

4. The method according to claim 3, characterized in that The predicting of the travel destination based on the travel information includes: The travel information is input into a pre-trained destination prediction model so that the destination prediction model predicts the trip destination based on the travel information and outputs the prediction.

5. The method according to claim 2, characterized in that The target intention prediction result includes the loading intention prediction result and the unloading intention prediction result, and determining whether to open the tailgate of the vehicle according to the target intention prediction result and the arrival status corresponding to the trip destination includes: Determining loading and unloading intentions and probabilities of the loading and unloading intentions based on the loading and unloading intention prediction result, the unloading intention prediction result, and the arrival status corresponding to the trip destination; the loading and unloading intentions include loading intentions and / or unloading intentions; Determining whether to open a tailgate of the vehicle is determined based on the loading and unloading intention and the probability of the loading and unloading intention.

6. The method according to claim 5, characterized in that The determining whether to open the tailgate of the vehicle according to the loading and unloading intention and the probability of the loading and unloading intention includes: If the probability of the loading and unloading intention is within a first probability interval, automatically opening the tailgate of the vehicle when detecting that the user enters the tailgate opening range; If the loading and unloading intention includes a shipping intention and the probability of the shipping intention is within a second probability interval, outputting a query message regarding whether to open the tailgate; and automatically opening the tailgate of the vehicle when a response message to the query message includes opening the tailgate and the user enters a tailgate opening range. The lower limit of the first probability interval is greater than or equal to the upper limit of the second probability interval.

7. The method according to claim 5, characterized in that The determining whether to open the tailgate of the vehicle according to the loading and unloading intention and the probability of the loading and unloading intention includes: If the loading and unloading intention includes the unloading intention and the probability of the unloading intention is in the second probability interval, then when it is detected that the user has the intention to get off the vehicle, an inquiry message is output as to whether to open the tailgate; when the reply information to the inquiry message includes opening the tailgate and the user enters the tailgate opening range, the tailgate of the vehicle is automatically opened.

8. A tailgate control device, characterized in that: include: An input unit, configured to obtain transportation information and frequency information corresponding to a target item associated with the vehicle, wherein the transportation information includes a transportation address corresponding to the target item, and the frequency information indicates an operating frequency corresponding to the target item at the transportation address; The input unit is further configured to perform intention prediction based on the transportation information and the frequency information to obtain a target intention prediction result, wherein the target intention prediction result is used to indicate a shipping operation and / or an unloading operation for the target item; A determination unit is used to determine whether to open the tailgate of the vehicle based on the target intention prediction result and the arrival status corresponding to the transportation address.

9. An electronic device, characterized in that: include: memory and processor; Wherein, the memory is used to store programs; The processor is configured to implement the method according to any one of claims 1 to 7 by running the program in the memory.

10. A vehicle, characterized in that: include: Tailgate control equipment; The tailgate control device is configured to implement the method according to any one of claims 1 to 7.

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