Digital key calibration data determination method and apparatus, vehicle, and storage medium

Abstract

The present disclosure relates to a digital key calibration data determination method and device, a vehicle and a storage medium, and relates to the technical field of vehicle communication. The method comprises: in response to an electronic device carrying a digital key establishing a communication connection with a target vehicle, determining whether the scene where the target vehicle is located belongs to a calibration scene; in response to the scene belonging to the calibration scene, determining the signal strength distribution data set of the target calibration scene; and based on the signal strength distribution data set, determining the calibration data of the digital key corresponding to the target calibration scene. The present disclosure can generate calibration data suitable for the calibration scene, realize calibration optimization of the calibration scene, thereby improving the calibration effect and further improving the user experience.

Description

Technical Field

[0001] This disclosure relates to the field of vehicle communication technology, and in particular to a method, apparatus, vehicle, and storage medium for determining digital key calibration data. Background Technology

[0002] Digital keys, also often called Bluetooth keys or virtual keys, allow users to replace traditional car keys via smart devices like smartphones. They enable unlocking, locking, and starting of the vehicle, offering a faster and smarter experience. To ensure the reliability, accuracy, and user experience of digital keys, calibration is necessary during use, as signal strength indicators are affected by various factors, including environmental noise, multipath effects, and device characteristics. This calibration helps the vehicle more accurately estimate signal strength, reducing errors and improving reliability.

[0003] In related technologies, calibration data is generated through pre-testing and pre-written into the vehicle system or adapted calibration data is sent to the vehicle system via the cloud to complete the digital key calibration. However, due to the instability of the communication connection signal, the functional experience provided to the user after calibration may be unstable in different scenarios. For example, sometimes the key may unlock when the user is far away from the vehicle, while at other times it may unlock when the user is very close to the vehicle, affecting the user experience.

[0004] It should be noted that the information disclosed in the background section above is only used to enhance the understanding of the background of this disclosure, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0005] This disclosure provides a method, apparatus, vehicle, and storage medium for determining digital key calibration data.

[0006] According to a first aspect of the present disclosure, a method for determining digital key calibration data is provided, comprising: In response to the electronic device carrying the digital key establishing a communication connection with the target vehicle, it is determined whether the scene in which the target vehicle is located belongs to the calibrated scene; In response to the fact that the current scene belongs to the calibration scene, determine the signal strength distribution dataset of the target calibration scene; Based on the signal strength distribution dataset, the calibration data of the digital key corresponding to the target calibration scenario is determined.

[0007] In some embodiments of this disclosure, determining whether the scene in which the target vehicle is located belongs to the calibration scene includes: Obtain the location information and parking perception information of the target vehicle; Based on the parking perception information, at least one signal influence factor is determined; Based on the location information and the at least one signal influence factor, determine from the calibration scenario database whether there is a suitable target calibration scenario; The calibration scenario database includes scenario data for multiple pre-stored calibration scenarios; the scenario data includes location information and corresponding signal influence factors. If it exists, then the scene in which the target vehicle is located is determined to be a calibration scene, and the scene in which the target vehicle is located is the target calibration scene.

[0008] In some embodiments of this disclosure, the at least one signal influencing factor includes: a user holding the electronic device.

[0009] In some embodiments of this disclosure, determining the signal strength distribution dataset includes: Based on the parking perception information, multiple travel points of the user during the travel process are determined; Determine the distance between each travel point and the target vehicle; Record the communication connection signal strength corresponding to each of the travel points; Multiple data groups are generated based on the distance between each travel point and the target vehicle and the communication connection signal strength corresponding to each travel point; The signal strength distribution dataset includes the plurality of data groups.

[0010] In some embodiments of this disclosure, the signal strength distribution dataset stores the distance between the electronic device and the target vehicle and the corresponding communication connection signal strength.

[0011] In some embodiments of this disclosure, determining whether the scene in which the target vehicle is located belongs to the calibration scene includes: Obtain the location information and parking perception information of the target vehicle; Based on the location information, the system filters the calibration scene database to determine if there are any suitable calibration scenes. If it exists, the initial screening and calibration scenario is obtained; Based on the parking perception information, at least one signal influence factor is determined; Based on the at least one signal influence factor, determine whether there is a suitable target calibration scenario from the initial screening calibration scenario; If it exists, then the scene in which the target vehicle is located is determined to be a calibration scene, and the scene in which the target vehicle is located is the target calibration scene; The calibration scenario database includes scenario data for multiple pre-stored calibration scenarios; the scenario data includes location information and corresponding signal influence factors.

[0012] In some embodiments of this disclosure, the digital key calibration data determination method further includes: In response to the scenario where the target vehicle is located not belonging to the calibrated scenario, based on the location information, the parking perception information, and the identification scenario database, it is determined whether the scenario where the target vehicle is located belongs to the identification scenario; wherein, the identification scenario database includes scenario data of multiple identification scenarios stored in advance and the cumulative number of identifications corresponding to each identification scenario; In response to the fact that the current scene belongs to the recognition scene, the cumulative recognition count is incremented by one and then updated to the recognition scene database; If the current scene does not belong to the identified scene, scene data of the current scene is generated based on the location information and the parking perception information, and stored in the identified scene database.

[0013] In some embodiments of this disclosure, the digital key calibration data determination method further includes: In response to the cumulative number of recognitions corresponding to the first recognition scenario exceeding a preset threshold, the first recognition scenario is determined as the calibration scenario, and the scenario data of the first recognition scenario is stored in the calibration scenario database.

[0014] In some embodiments of this disclosure, a method for determining digital key calibration data further includes: The multiple recognition scenarios stored in the recognition scenario database are sorted from most to least according to their corresponding cumulative recognition counts; The preset number of identified scenarios that are ranked first are determined as the calibration scenarios, and the scenario data corresponding to the preset number of identified scenarios are stored in the calibration scenario database.

[0015] In some embodiments of this disclosure, determining the calibration data of the digital key corresponding to the target calibration scenario based on the signal strength distribution dataset includes: Obtain historical signal intensity distribution dataset; Based on the historical signal strength distribution dataset and the signal strength distribution dataset, data distribution analysis is performed to obtain the calibration data; The historical signal strength distribution dataset includes multiple historical signal strength distribution data subsets. Each historical signal strength distribution data subset is a signal strength distribution dataset determined during the historical digital key calibration data determination process, where the target calibration scenario is located.

[0016] In some embodiments of this disclosure, it also includes: The signal strength distribution dataset is stored as a subset of historical signal strength distribution data in the historical signal strength distribution dataset.

[0017] In some embodiments of this disclosure, it also includes: Obtain the historical calibration data of the digital key; the historical calibration data is determined based on the historical signal strength distribution dataset; Monitor the real-time communication connection signal strength between the electronic device and the target vehicle; Based on the historical calibration data and the real-time communication connection signal strength, the real-time distance between the electronic device and the target vehicle is estimated. In response to the real-time distance being within a preset distance range, a preset action is executed.

[0018] In some embodiments of this disclosure, the communication connection includes at least: Bluetooth connection, ultra-wideband connection, and near-field communication.

[0019] In some embodiments of this disclosure, in response to the communication connection being the ultra-wideband connection, the method further includes: Based on the signal strength distribution dataset, data imputation is performed to obtain the blind spot signal strength distribution dataset corresponding to the parking perception blind spot; Specifically, based on the signal strength distribution dataset, determining the calibration data of the digital key corresponding to the target calibration scenario includes: Based on the signal strength distribution dataset and the blind zone signal strength distribution dataset, the calibration data of the digital key is determined.

[0020] According to a second aspect of the present disclosure, a digital key calibration data determination apparatus is provided, comprising: The target scene determination unit is used to determine whether the scene in which the target vehicle is located belongs to the calibration scene in response to the electronic device carrying the digital key establishing a communication connection with the target vehicle; The data determination unit is used to determine the signal strength distribution dataset of the target calibration scene in response to the fact that the scene in question belongs to the calibration scene. The data calibration unit is used to determine the calibration data of the digital key corresponding to the target calibration scenario based on the signal strength distribution dataset.

[0021] According to a third aspect of the present disclosure, a vehicle is provided, comprising: processor; Memory used to store processor-executable instructions; The processor is configured to implement the digital key calibration data determination method described in the first aspect above.

[0022] According to a fourth aspect of the present disclosure, a non-transitory computer-readable storage medium is provided, which, when instructions in the storage medium are executed by a processor of a terminal, enables the terminal to perform the digital key identification data determination method described in the first aspect.

[0023] The technical solutions provided by the embodiments of this disclosure may include the following beneficial effects: This disclosure addresses the issue of an electronic device carrying a digital key establishing a communication connection with a target vehicle. It determines whether the target vehicle's current location falls within a calibration scenario. If it does, it determines a signal strength distribution dataset for that scenario and, based on this dataset, determines the calibration data for the digital key corresponding to that scenario. By identifying the target vehicle's current location and collecting and generating a signal strength distribution dataset for that scenario, it generates calibration data suitable for the digital key, thus optimizing the calibration process and improving the user experience.

[0024] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit this disclosure. Attached Figure Description

[0025] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this disclosure and, together with the description, serve to explain the principles of this disclosure. It is obvious that the drawings described below are merely some embodiments of this disclosure, and those skilled in the art can obtain other drawings based on these drawings without any inventive effort.

[0026] Figure 1 This is a flowchart illustrating a method for determining digital key identification data according to some embodiments of the present disclosure. Figure 1 .

[0027] Figure 2 This is an implementation process flow of step S110 shown in some embodiments of this disclosure. Figure 1 .

[0028] Figure 3 This is an implementation process flow of step S110 shown in some embodiments of this disclosure. Figure 2 .

[0029] Figure 4 This is a flowchart illustrating an implementation process for determining a signal strength distribution dataset according to some embodiments of the present disclosure.

[0030] Figure 5 This is a flowchart illustrating a method for determining digital key identification data according to some embodiments of the present disclosure. Figure 2 .

[0031] Figure 6 This is a flowchart illustrating one implementation of step S130 according to some embodiments of the present disclosure.

[0032] Figure 7 This is a flowchart illustrating a method for determining digital key identification data according to some embodiments of the present disclosure. Figure 3 .

[0033] Figure 8 This is a block diagram of a digital key calibration data determination device according to some embodiments of the present disclosure.

[0034] Figure 9 This is a block diagram illustrating a vehicle according to an exemplary embodiment of the present disclosure. Detailed Implementation

[0035] Some embodiments of this disclosure will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description refers to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. Various changes, modifications, and equivalents of the methods, apparatus, and / or systems described herein will become apparent upon understanding this disclosure. For example, the order of operations described herein is merely illustrative and is not limited to those orders set forth herein, but can be changed as will become apparent upon understanding this disclosure, except for operations that must be performed in a particular order. Furthermore, for clarity and brevity, descriptions of features known in the art may be omitted.

[0036] The embodiments described in the following examples of this disclosure are not representative of all embodiments consistent with this disclosure. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this disclosure as detailed in the appended claims.

[0037] The specific implementation methods of the embodiments of this disclosure will now be described in detail with reference to the accompanying drawings.

[0038] Figure 1 This is a flowchart illustrating a method for determining digital key identification data according to some embodiments of the present disclosure. Figure 1 ,like Figure 1 As shown, the digital key calibration data determination method can be applied to vehicles.

[0039] Figure 1 The method for determining digital key calibration data, as shown, includes the following steps.

[0040] In step S110, in response to the electronic device carrying the digital key establishing a communication connection with the target vehicle, it is determined whether the scene in which the target vehicle is located belongs to the calibration scene.

[0041] In some embodiments of this disclosure, when a user's electronic device carrying a digital key approaches a target vehicle, the vehicle's sensors or communication module detect the device's presence and establish a communication connection using short-range, low-power wireless communication. It should be noted that, to ensure security, the digital key can be authenticated before establishing a communication connection to ensure that only authorized users can access the vehicle. This can be achieved through encryption algorithms and security protocols.

[0042] It should be noted that the above communication connections include at least: Bluetooth (BLE), Ultra Wideband (UWB), and Near Field Communication (NFC).

[0043] It should be noted that if the scenario occurs frequently, that is, if the target vehicle is in a calibration scenario, not only can a large amount of data be obtained, and the calibration data obtained based on this data is more accurate, but users also frequently park in this scenario, so the calibration data obtained is more likely to be applied, and they can also frequently receive corresponding digital key services, resulting in a better user experience.

[0044] In step S120, in response to the fact that the current scene belongs to the calibration scene, the signal strength distribution dataset of the target calibration scene is determined.

[0045] It should be noted that the signal strength distribution dataset stores the distance between the electronic device and the target vehicle and the corresponding communication connection signal strength. It can contain multiple data sets, with each data set representing a distance value and a corresponding communication connection signal strength.

[0046] In step S130, based on the signal strength distribution dataset, the calibration data of the digital key corresponding to the target calibration scenario is determined.

[0047] It should be noted that the calibration data corresponding to the target calibration scenario determined in this digital key calibration data determination is based on the signal strength distribution dataset acquired this time. Understandably, in specific implementations, multiple digital key calibration data determinations can be performed. For example, each time a user leaves home, they typically go to a fixed parking space to unlock their vehicle. If the scenario corresponding to the fixed parking space is determined to be a calibration scenario, each time the user goes to the fixed parking space and uses their mobile phone to unlock the vehicle, a digital key calibration data determination will be performed. Each digital key calibration data determination process will execute steps S110 to S130 to obtain the calibration data corresponding to the current target calibration scenario, which can be applied to the next digital key usage process, providing users with more accurate and stable services. Furthermore, the calibration data is generated based on user personalization and applied to user use. The process from calibration to application is closed-loop and personalized, which can improve the low flexibility of providing services after applying general calibration data, providing personalized services to users in specific scenarios.

[0048] As can be seen from the above steps, the digital key calibration data determination method provided in this embodiment of the present disclosure, in response to the establishment of a communication connection between the electronic device carrying the digital key and the target vehicle, determines whether the scene in which the target vehicle is located belongs to a calibration scene. If it belongs to a calibration scene, it determines the signal strength distribution dataset of the target calibration scene, and based on the signal strength distribution dataset, determines the calibration data of the digital key corresponding to the target calibration scene. By determining the scene in which the target vehicle is located, and collecting and generating a signal strength distribution dataset for the calibration scene, calibration data suitable for the digital key in the calibration scene is generated, thus achieving calibration optimization of the calibration scene, thereby improving the calibration effect and enhancing the user experience.

[0049] In some exemplary embodiments of this disclosure, such as Figure 2 The following is a flowchart illustrating the implementation process of step S110, provided for some exemplary embodiments of this disclosure. Figure 1 The process includes the following steps.

[0050] In step S210, the location information and parking perception information of the target vehicle are obtained.

[0051] In some embodiments of this disclosure, when a user's electronic device carrying a digital key approaches a target vehicle, the vehicle's sensors or communication modules detect the presence of the device and establish a communication connection using short-range, low-power wireless communication. During the establishment of the communication connection, the location information and parking perception information of the target vehicle are obtained.

[0052] In some embodiments of this disclosure, the location information of the target vehicle can be obtained through a Global Navigation Satellite System (GNSS) to determine the location of the target vehicle. The target vehicle may be equipped with various sensors, such as cameras, ultrasonic radar, and lidar, to perceive the surrounding environment and detect obstacles, pedestrians, and other vehicles around the vehicle, thereby obtaining parking perception information.

[0053] It should be noted that parking perception information represents the parking environment in which the target vehicle is located. From this information, one or more influencing factors affecting the communication connection signal can be identified. Combined with the vehicle position reflected by the location information, the scene in which the target vehicle is located can be determined.

[0054] In step S220, at least one signal influence factor is determined based on parking perception information.

[0055] In some embodiments of this disclosure, signal impact factor types can be predefined. These predefined signal impact factor types may include multiple types, and at least one signal impact factor corresponding to the scene where the target vehicle is located can be determined by combining parking perception information. For example, predefined signal impact factor types may include factors that can affect communication connection signals, such as users, pillars, other vehicles, walls, charging piles, and garage ceilings. Each type may also include different values ​​to further determine the specific signal impact factor. For example, the values ​​for the pillar factor may include: pillars to the side, pillars behind, pillars in front, etc.; the values ​​for the other vehicle factor may include: other vehicles parked on the left, other vehicles parked on the right, other vehicles parked in front, etc.; the values ​​for the garage ceiling factor may include: metal ceiling, open space, concrete ceiling, plasterboard, or other lightweight material ceiling, etc.

[0056] It is understood that the above-mentioned signal influencing factor types and different values ​​for each type are examples. They can be preset according to the influencing factors of communication connection signals, determined by big data analysis based on historical experimental data, or determined by analytical models such as machine learning models. This disclosure does not limit them here.

[0057] In step S230, based on location information and at least one signal influence factor, it is determined from the calibration scene database whether there is a suitable target calibration scene.

[0058] It should be noted that the calibration scenario database includes scenario data for multiple pre-stored calibration scenarios, and the scenario data includes location information and corresponding signal influence factors.

[0059] In step S240, if the scenario exists, it is determined that the scenario in which the target vehicle is located belongs to the calibration scenario, and the scenario in which the target vehicle is located is the target calibration scenario.

[0060] In some exemplary embodiments of this disclosure, such as Figure 3 The following is a flowchart illustrating the implementation process of step S110, provided for some exemplary embodiments of this disclosure. Figure 2 The process includes the following steps.

[0061] In step S310, the location information and parking perception information of the target vehicle are obtained.

[0062] In step S320, based on the location information, a search is conducted in the calibration scene database to determine if a suitable calibration scene exists.

[0063] In step S330, if the scenario exists, the initial screening and calibration scenario is obtained.

[0064] In step S340, at least one signal influence factor is determined based on parking perception information.

[0065] In step S350, based on at least one signal influence factor, it is determined whether there is a suitable target calibration scenario from the initial screening calibration scenarios.

[0066] In step S360, if the scenario exists, it is determined that the scene in which the target vehicle is located belongs to the calibration scenario, and the scene in which the target vehicle is located is the target calibration scenario.

[0067] It should be noted that the calibration scenario database includes scenario data for multiple pre-stored calibration scenarios, and the scenario data includes location information and corresponding signal influence factors.

[0068] As can be seen from the above steps, by initially screening the calibration scenario database to see if there are calibration scenarios that match the location information, scenarios that do not match the location information can be quickly eliminated without further processing. This reduces the amount of data processing and the processing steps, reduces the resource and computational pressure on the vehicle system, and also improves the efficiency of scene judgment.

[0069] Figure 4 This is a flowchart illustrating a method for determining a signal strength distribution dataset according to an exemplary embodiment of this disclosure, such as... Figure 4 As shown, the steps include the following.

[0070] In step S410, multiple travel points of the user during the travel process are determined based on parking perception information.

[0071] In some embodiments of this disclosure, based on parking perception information, the user's walking speed, walking time, and other walking information can be determined. Based on the walking information, the change in distance between the user and the target vehicle over time can be obtained, and multiple walking points can be determined. For example, linear walking points can be determined, that is, the distance difference between two adjacent walking points and the target vehicle is a constant value. Alternatively, sampling can be performed at the same time intervals to determine multiple walking points.

[0072] For example, the target vehicle can use cameras installed around the vehicle to acquire video data. Based on the video data, the time point when the user was detected can be determined as t0, the time point when the user opened the car door can be determined as t1, the user's average walking speed can be estimated as s, and the distance between the user and the target vehicle can be calculated as s×(t1–t0). n linear coordinate points are set as travel points, where n can be predefined through calibration. The distance between two connected travel points is s×(t1–t0) / n, and the time point corresponding to each travel point can be determined.

[0073] In step S420, the distance between each travel point and the target vehicle is determined.

[0074] In step S430, the communication connection signal strength corresponding to each travel point is recorded.

[0075] It should be noted that, based on the time point corresponding to each travel point, the communication connection signal strength corresponding to each travel point can be determined by combining the recorded timestamp of the communication connection signal strength.

[0076] In step S440, multiple data groups are generated based on the distance between each travel point and the target vehicle and the communication connection signal strength corresponding to each travel point.

[0077] It should be noted that the signal strength distribution dataset includes multiple data sets.

[0078] It should be noted that each data set can be in the form of [p, q], where p represents the distance between the travel point and the target vehicle, and q represents the corresponding communication connection signal strength, such as Bluetooth signal strength RSSI.

[0079] In some embodiments of this disclosure, Figure 5 This is a flowchart illustrating a digital key identification data determination method according to an exemplary embodiment of the present disclosure. Figure 2 .

[0080] In this embodiment of the disclosure, Figure 5 In the digital key calibration data determination method shown, steps S510 to S530 are... Figure 1 Steps S110 to S130 in the digital key calibration data determination method shown correspond to each other and will not be repeated here.

[0081] In this embodiment of the disclosure, in Figure 1 Based on the digital key calibration data determination method shown, Figure 5 The method for determining digital key calibration data shown may also include the following steps.

[0082] In step S540, in response to the fact that the scene in which the target vehicle is located does not belong to the calibration scene, it is determined whether the scene in which the target vehicle is located belongs to the identification scene based on the location information, parking perception information and the identification scene database.

[0083] In step S550, in response to the fact that the current scene belongs to the recognition scene, the cumulative recognition count is increased by one and then updated to the recognition scene database.

[0084] In step S560, in response to the fact that the current scene does not belong to the identified scene, scene data of the current scene is generated based on the location information and parking perception information and stored in the identified scene database.

[0085] In some embodiments of this disclosure, the scene identification database includes pre-stored scene data for multiple identification scenarios and the cumulative number of identifications for each scenario. If the scenario in which the target vehicle is located does not belong to the calibrated scenario, it indicates that the scenario has not appeared frequently in previous digital key uses, and can be matched with the scene identification database to determine whether the scenario has appeared before. The scene identification database stores the identified scenarios and the number of occurrences of each scenario up to the time period determined by the current digital key calibration data. To reduce unnecessary data storage and alleviate system pressure, the scene identification database only stores the scene identification identifier, scene data, and cumulative number of identifications.

[0086] In some exemplary embodiments of this disclosure, the construction of the calibration scene database may be based on the identification scene database. It is understood that in the initial stage of a user's use of the target vehicle, the accumulated data forms the identification scene database. After a period of use, once the identification scene database has accumulated enough data, a calibration scene database can be generated to characterize the frequently occurring scenes, and these scenes can be used for subsequent personalized calibration to provide users with more intelligent services.

[0087] In some exemplary embodiments of this disclosure, a calibration scenario can be determined by judging whether the cumulative number of recognitions exceeds a set threshold. Specifically, in these embodiments, the provided digital key calibration data determination method further includes: in response to the cumulative number of recognitions corresponding to the first recognition scenario exceeding a preset number threshold, determining the first recognition scenario as a calibration scenario, and storing the scenario data of the first recognition scenario in a calibration scenario database. It should be noted that the preset number threshold can be determined based on user habits, experience values, user personalized settings, etc., and the specific value is not limited here; for example, it can be 50, 100, 120, etc.

[0088] In some exemplary embodiments of this disclosure, several recognition scenarios with a high cumulative recognition count can be identified as calibration scenarios. Specifically, in these embodiments, the digital key calibration data determination method further includes: sorting multiple recognition scenarios stored in the recognition scenario database according to their corresponding cumulative recognition counts from highest to lowest; determining a preset number of recognition scenarios at the top of the sorted list as calibration scenarios; and storing the scenario data corresponding to the preset number of recognition scenarios in the calibration scenario database. It should be noted that the preset number can be determined based on user habits, experience values, user personalized settings, etc., and the specific value is not limited here; for example, it can be 3, 5, 8, etc.

[0089] In some embodiments of this disclosure, the specific implementation process of step S130 is as follows: Figure 6 As shown, the steps may include the following.

[0090] In step S610, the historical signal intensity distribution dataset is obtained.

[0091] In step S620, based on the historical signal strength distribution dataset and the signal strength distribution dataset, data distribution analysis is performed to obtain calibration data.

[0092] It should be noted that the historical signal strength distribution dataset includes multiple subsets of historical signal strength distribution data. Each subset represents the signal strength distribution dataset determined during the previous digital key calibration data determination process, specifically for the target calibration scenario. It is understandable that there may be multiple determinations of digital key calibration data for the same calibration scenario. For the current digital key calibration data determination, the data included in the historical signal strength distribution dataset originates from each of the previous signal strength distribution datasets determined during those multiple digital key calibration data determination processes.

[0093] In some embodiments of this disclosure, data distribution analysis is performed based on the historical signal strength distribution dataset and the data groups contained in the signal strength distribution dataset. For example, Gaussian distribution, normal distribution, linear distribution, etc. can be used to obtain the calibrated communication connection signal strength corresponding to each travel point, so as to obtain the calibration data of the digital key.

[0094] Understandably, to avoid erroneous data affecting subsequent results, the signal strength distribution dataset can be filtered in advance to remove erroneous and abnormal data, thus ensuring the accuracy of the calibration data.

[0095] Accordingly, the digital key calibration data determination method provided in this disclosure further includes: storing the signal strength distribution dataset as a subset of historical signal strength distribution data in the historical signal strength distribution dataset, so as to serve as a big data source for data distribution analysis in the next digital key calibration data determination process when the scene is the same target calibration scene.

[0096] In some embodiments of this disclosure, the decision to perform data distribution analysis can be made by determining the amount of data in the currently acquired historical signal strength distribution dataset. If the data amount exceeds a preset data amount, steps S610 to S620 can be executed to obtain calibration data. If the data amount does not exceed the preset data amount, it indicates that the amount of data in the historical signal strength distribution dataset is small. In other words, for the same target calibration scenario, the number of times digital key calibration data has been determined in the past is not large, resulting in a limited amount of data. The effect of data distribution analysis may not be good enough, and the obtained calibration data may not be stable. In this case, data distribution analysis is not performed, and calibration data is not determined. Instead, the historical signal strength distribution dataset is updated to accumulate data for subsequent calibration data determination until the data amount exceeds the preset data amount. This ensures the accuracy and stability of the calibration data while minimizing data processing and resource consumption, thus reducing the pressure on the processor in the vehicle.

[0097] In this embodiment of the disclosure, Figure 7 This is a flowchart illustrating a method for determining digital key identification data according to some embodiments of the present disclosure. Figure 3 . Figure 7 In the digital key calibration data determination method shown, steps S710 to S730 are... Figure 1 Steps S110 to S130 in the digital key calibration data determination method shown correspond to each other and will not be repeated here.

[0098] In this embodiment of the disclosure, in Figure 1 Based on the digital key calibration data determination method shown, Figure 7 The method for determining digital key calibration data shown may also include the following steps.

[0099] In step S740, historical calibration data of the digital key is obtained.

[0100] It should be noted that the historical calibration data is determined based on historical signal strength distribution datasets. In other words, the historical calibration data is the calibration data obtained after the digital key calibration data was determined in the same target calibration scenario.

[0101] In some embodiments of this disclosure, historical calibration data can be the calibration data obtained after the last digital key calibration data determination, so as to utilize the most timely calibration data for calibration. The number of times digital key calibration data determination is performed can also be recorded, and historical calibration data can also be the calibration data obtained from a preset fixed number of digital key calibration data determinations. For example, if the current number of digital key calibration data determinations is 133, the calibration data obtained from the 100th determination can be used as the historical calibration data for the digital key, and it is stipulated that the historical calibration data is used for determinations 101-200, the calibration data obtained from the 200th determination is used for determinations 201-300, and so on. This ensures that the calibration data used for digital key calibration within a certain period is the same, reducing data interaction and the cumbersome calibration process, while providing users with a relatively stable digital key service for a period of time.

[0102] In step S750, the real-time communication connection signal strength between the electronic device and the target vehicle is monitored.

[0103] In some embodiments of this disclosure, the signal strength can be read directly from the Bluetooth adapter's API, typically presented as an RSSI (Received Signal Strength Indicator). Signal strength data can be periodically read from the UWB module by writing a program.

[0104] In step S760, the real-time distance between the electronic device and the target vehicle is estimated based on historical calibration data and real-time communication connection signal strength.

[0105] It should be noted that historical calibration data represents the correlation between distance values ​​and the strength of the calibrated communication connection signal. Based on the real-time communication connection signal strength, the real-time distance between the electronic device and the target vehicle can be estimated to avoid the influence of the environment on the communication connection signal strength, thereby determining a more accurate real-time distance.

[0106] In step S770, in response to the real-time distance being within a preset distance range, a preset execution action is performed.

[0107] In some embodiments of this disclosure, a preset action to be performed can be determined based on a pre-defined correspondence between real-time distance and the action. The preset actions may include unlocking, locking, etc., and the preset distance ranges for unlocking and locking may differ. It is understood that the preset distance range can be adjusted according to user needs, and the specific value is not limited; for example, it could be 2 meters to 2.5 meters. When the real-time distance is within the range of 2 meters to 2.5 meters, the vehicle's unlocking action may include functions such as automatic vehicle unlocking, activation of external lights, and gradual illumination of internal lights.

[0108] It should be noted that the area perceived by parking sensing information is limited. For example, the area that a camera can capture is limited, and there are blind spots. For blind spots, it is impossible to obtain the data set consisting of the distance between the electronic device and the target vehicle and the corresponding communication connection signal strength. To ensure that the coverage of the calibration data is strong enough and that the final calibration is accurate, in some embodiments of this disclosure, in response to the communication connection being an ultra-wideband connection, the provided digital key calibration data determination method further includes: performing data filling based on the signal strength distribution dataset to obtain the blind spot signal strength distribution dataset corresponding to the parking sensing blind spot. Accordingly, determining the calibration data of the digital key corresponding to the target calibration scenario based on the signal strength distribution dataset includes: determining the digital key calibration data based on the signal strength distribution dataset and the blind spot signal strength distribution dataset.

[0109] Specifically, the calibration area around the target vehicle can be divided into multiple circles centered on the vehicle, with each travel point located on one of these circles. Using the radial direction of the circle as the guide, and combining the calibrated communication signal strength of multiple travel points, the average value is calculated to determine how much the communication signal strength decreases for each unit increase in distance. This is used to fill the blind spot signal strength distribution. For example, the calibration area might be a circle with a radius of 10m around the vehicle. Every 0.5m in the radial direction represents a travel point, and each circle corresponds to a calibrated communication signal strength. Since the camera can only see within 7m, the signal strength beyond 7m can be extrapolated based on the data collected by the camera within that 7m range. For instance, within 7m, the calibrated communication signal strength increases by 5dB for every 0.5m increase; the signal strength beyond 7m is calculated and filled accordingly.

[0110] In some embodiments of this disclosure, the signal intensity distribution for filling the blind zone can also be inferred based on the known signal intensity distribution using methods such as the K-Nearest Neighbors (KNN) algorithm, Radial Basis Function (RBF) interpolation, Kriging, and machine learning methods. This disclosure does not limit the scope of the invention.

[0111] As can be seen from the digital key calibration data determination method provided in the above embodiments, this disclosure can generate personalized calibration data according to the user's usage scenario, and thereby provide a unique digital key service for the user. The calibration effect is good for specific scenarios, and the user experience is improved. For example, when using a Bluetooth key, in related technologies, a fixed RSSI value is set, such as -60dB. As long as the mobile phone is within this signal strength range, the vehicle will greet the user, for example, by unfolding the rearview mirrors and flashing the lights. However, due to the influence of the actual environment, when the RSSI reaches this value, sometimes the user is 5m away from the car, and sometimes the user is 1m away from the car, which is not a good experience for the user. In specific scenarios, such as when there is Bluetooth signal reflection or refraction, this disclosure dynamically adjusts the threshold of the RSSI limit according to the calibration value determined in the above embodiments. For example, if there are many obstacles and severe obstruction, the threshold may be widened, such as to -70dB; if the surroundings are very open, the threshold may be tightened, such as to -55dB. However, the service provided to the user is that the rearview mirrors open and the lights appear when the user is about 5m away from the vehicle.

[0112] The following are embodiments of the apparatus disclosed herein, which can be used to execute embodiments of the method disclosed herein. For details not disclosed in the apparatus embodiments of this disclosure, please refer to the embodiments of the method disclosed herein.

[0113] Figure 8 This is a block diagram of a digital key calibration data determining device 800 according to some embodiments of the present disclosure. (Refer to...) Figure 8 The device 800 includes: a target scene determination unit 801, a data determination unit 802, and a data calibration unit 803.

[0114] The target scene determination unit 801 is used to determine whether the scene in which the target vehicle is located belongs to the calibration scene in response to the electronic device carrying the digital key establishing a communication connection with the target vehicle. The data determination unit 802 is used to determine the signal strength distribution dataset of the target calibration scene in response to the fact that the scene it is in belongs to the calibration scene. The data calibration unit 803 is used to determine the calibration data of the digital key corresponding to the target calibration scenario based on the signal strength distribution dataset.

[0115] In some exemplary embodiments of this disclosure, the target scene determination unit 801 is configured to: Acquire the target vehicle's location information and parking perception information; Based on parking perception information, at least one signal influencing factor is determined; Based on location information and at least one signal influence factor, determine from the calibration scenario database whether there is a suitable target calibration scenario; The calibration scenario database includes scenario data from multiple pre-stored calibration scenarios; the scenario data includes location information and corresponding signal influence factors. If it exists, then the scene in which the target vehicle is located is determined to be a calibration scene, and the scene in which the target vehicle is located is the target calibration scene.

[0116] In some exemplary embodiments of this disclosure, the target scene determination unit 801 is configured to: Acquire the target vehicle's location information and parking perception information; Based on location information, the system filters the calibration scenario database to determine if there are suitable calibration scenarios. If it exists, the initial screening and calibration scenario is obtained; Based on parking perception information, at least one signal influencing factor is determined; Based on at least one signal influence factor, determine whether there is a suitable target calibration scenario from the initial screening calibration scenarios; If it exists, then the scene in which the target vehicle is located is determined to be a calibration scene, and the scene in which the target vehicle is located is the target calibration scene; The calibration scenario database includes scenario data from multiple pre-stored calibration scenarios; the scenario data includes location information and corresponding signal influence factors.

[0117] It should be noted that at least one signal influencing factor includes: the user holding the electronic device. Accordingly, the data determination unit 802 is configured to: Based on parking perception information, multiple travel points of the user during the journey are determined; Determine the distance between each travel point and the target vehicle; Record the communication connection signal strength corresponding to each travel point; Multiple data sets are generated based on the distance between each travel point and the target vehicle and the communication connection signal strength corresponding to each travel point.

[0118] The signal strength distribution dataset includes multiple data groups.

[0119] In some exemplary embodiments of this disclosure, the signal strength distribution dataset stores the distance between the electronic device and the target vehicle and the corresponding communication connection signal strength.

[0120] In some exemplary embodiments of this disclosure, a scene recognition unit is further included, for: In response to the scenario where the target vehicle is located not being in the calibrated scenario, the system determines whether the scenario where the target vehicle is located is in the identified scenario based on location information, parking perception information, and the identification scenario database. The identification scenario database includes scenario data for multiple pre-stored identification scenarios and the cumulative number of identifications for each identification scenario. In response to the fact that the current scene belongs to the recognition scene, the cumulative recognition count is incremented by one and then updated to the recognition scene database; If the current scene does not belong to the identified scene, scene data of the current scene is generated based on location information and parking perception information and stored in the identified scene database.

[0121] In some exemplary embodiments of this disclosure, it further includes: a first calibration scene data maintenance unit, configured to: In response to the cumulative number of recognitions corresponding to the first recognition scenario exceeding a preset threshold, the first recognition scenario is identified as a calibration scenario, and the scenario data of the first recognition scenario is stored in the calibration scenario database.

[0122] In some exemplary embodiments of this disclosure, a second calibration scene data maintenance unit is further included, for: The multiple recognition scenarios stored in the recognition scenario database are sorted from most to least cumulative recognition count; The preset number of recognition scenarios that are ranked first are identified as calibration scenarios, and the scene data corresponding to the preset number of recognition scenarios are stored in the calibration scenario database.

[0123] In some exemplary embodiments of this disclosure, the data calibration unit 803 is configured to: Obtain historical signal intensity distribution dataset; Based on historical signal strength distribution datasets and signal strength distribution datasets, data distribution analysis is performed to obtain calibration data; The historical signal strength distribution dataset includes multiple subsets of historical signal strength distribution data. Each subset of historical signal strength distribution data is a signal strength distribution dataset determined by the target calibration scene during the historical digital key calibration data determination process.

[0124] In some exemplary embodiments of this disclosure, the data calibration unit 803 is further configured to: The signal strength distribution dataset is stored as a subset of historical signal strength distribution data in the historical signal strength distribution dataset.

[0125] In some exemplary embodiments of this disclosure, a digital key calibration unit is also included, for: Obtain historical calibration data for the digital key; the historical calibration data is determined based on historical signal strength distribution datasets; Monitor the real-time communication signal strength between electronic devices and the target vehicle; Based on historical calibration data and real-time communication connection signal strength, the real-time distance between the electronic device and the target vehicle is estimated. In response to the real-time distance being within the preset distance range, a preset action is executed.

[0126] It should be noted that communication connections include at least: Bluetooth connection, ultra-wideband connection, and near-field communication.

[0127] In response to the communication connection being an ultra-wideband connection, some exemplary embodiments of this disclosure further include: a dead zone filling unit, configured to: Based on the signal strength distribution dataset, data imputation is performed to obtain the blind spot signal strength distribution dataset corresponding to the parking perception blind spot; Accordingly, the data calibration unit 803 is configured as follows: Based on the signal strength distribution dataset and the blind zone signal strength distribution dataset, the calibration data of the digital key is determined.

[0128] Regarding the apparatus in the above embodiments, the specific manner in which each module performs its operation has been described in detail in the embodiments related to the method, and will not be elaborated upon here.

[0129] Figure 9 This is a block diagram illustrating a vehicle 900 according to an exemplary embodiment. For example, vehicle 900 can be a hybrid vehicle, a non-hybrid vehicle, an electric vehicle, a fuel cell vehicle, or other types of vehicle. Vehicle 900 can be an autonomous vehicle, a semi-autonomous vehicle, or a non-autonomous vehicle.

[0130] Reference Figure 9 The vehicle 900 may include various subsystems, such as an infotainment system 910, a perception system 920, a decision control system 930, a drive system 940, and a computing platform 950. The vehicle 900 may also include more or fewer subsystems, and each subsystem may include multiple components. Furthermore, each subsystem and component of the vehicle 900 can be interconnected via wired or wireless means.

[0131] In some embodiments, the infotainment system 910 may include a communication system, an entertainment system, and a navigation system, etc.

[0132] The perception system 920 may include several sensors for sensing information about the environment surrounding the vehicle 900. For example, the perception system 920 may include a global positioning system (which may be GPS, BeiDou, or other positioning systems), an inertial measurement unit (IMU), lidar, millimeter-wave radar, ultrasonic radar, and a camera device.

[0133] The decision control system 930 may include a computing system, a vehicle controller, a steering system, a throttle, and a braking system.

[0134] The drive system 940 may include components that provide powered motion to the vehicle 900. In one embodiment, the drive system 940 may include an engine, an energy source, a transmission system, and wheels. The engine may be one or a combination of internal combustion engines, electric motors, and compressed air engines. The engine is capable of converting energy provided by the energy source into mechanical energy.

[0135] Some or all of the functions of the vehicle 900 are controlled by a computing platform 950. The computing platform 950 may include at least one processor 951 and a memory 952, the processor 951 being able to execute instructions 953 stored in the memory 952.

[0136] The processor 951 can be any conventional processor, such as a commercially available CPU. The processor may also include, for example, a Graphic Processing Unit (GPU), a Field Programmable Gate Array (FPGA), a System on Chip (SOC), an Application Specific Integrated Circuit (ASIC), or a combination thereof.

[0137] The memory 952 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk or optical disk.

[0138] In addition to instruction 953, memory 952 can also store data, such as road maps, route information, vehicle position, direction, speed, and other data. The data stored in memory 952 can be used by computing platform 950.

[0139] In this embodiment of the disclosure, the processor 951 may execute instructions 953 to complete all or part of the steps of the above-described digital key calibration data determination method.

[0140] In some embodiments of this disclosure, a non-transitory computer-readable storage medium is provided, which, when the instructions in the storage medium are executed by the processor of a terminal, enables the terminal to perform the digital key identification data determination method described above.

[0141] Other embodiments of this disclosure will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered exemplary only, and the true scope and spirit of this disclosure are indicated by the following claims.

[0142] It should be understood that this disclosure is not limited to the precise structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this disclosure is limited only by the appended claims.

Claims

1. A method for determining digital key calibration data, characterized in that, include: In response to the electronic device carrying a digital key establishing a communication connection with the target vehicle, based on the target vehicle's location information and parking perception information, a calibration scene database is used to determine whether the scene in which the target vehicle is located belongs to a calibration scene; the parking perception information characterizes the parking environment in which the target vehicle is located; the calibration scene database includes scene data of multiple calibration scenes, and the identification scene database includes scene data of multiple identification scenes and the cumulative number of identifications corresponding to each identification scene; the calibration scene database is constructed based on the scene data in the identification scene database accumulating to a preset number, and the calibration scene represents the identification scene that occurs frequently; In response to the fact that the current scene belongs to the calibration scene, the signal strength distribution dataset of the target calibration scene is determined; the signal strength distribution dataset is formed iteratively by collecting signal strength distribution data of the user's travel points during the use of the digital key multiple times. Based on the signal strength distribution dataset, determine the calibration data of the digital key corresponding to the target calibration scenario; The user holds the electronic device; The travel point is determined based on the parking perception information.

2. The method for determining digital key calibration data according to claim 1, characterized in that, Determining whether the scene in which the target vehicle is located belongs to the calibration scene includes: Obtain the location information and parking perception information of the target vehicle; Based on the parking perception information, at least one signal influence factor is determined; Based on the location information and the at least one signal influence factor, determine from the calibration scenario database whether there is a suitable target calibration scenario; The calibration scenario database includes scenario data for multiple pre-stored calibration scenarios; the scenario data includes location information and corresponding signal influence factors. If it exists, then the scene in which the target vehicle is located is determined to be a calibration scene, and the scene in which the target vehicle is located is the target calibration scene.

3. The method for determining digital key calibration data according to claim 2, characterized in that, The at least one signal influencing factor includes: the user holding the electronic device.

4. The method for determining digital key calibration data according to claim 3, characterized in that, Determine the signal strength distribution dataset, including: Based on the parking perception information, multiple travel points of the user during the travel process are determined; Determine the distance between each travel point and the target vehicle; Record the communication connection signal strength corresponding to each of the travel points; Multiple data groups are generated based on the distance between each travel point and the target vehicle and the communication connection signal strength corresponding to each travel point; The signal strength distribution dataset includes the plurality of data groups.

5. The method for determining digital key calibration data according to claim 1, characterized in that, The signal strength distribution dataset stores the distance between the electronic device and the target vehicle and the corresponding communication connection signal strength.

6. The method for determining digital key calibration data according to claim 1, characterized in that, Determining whether the scene in which the target vehicle is located belongs to the calibration scene includes: Obtain the location information and parking perception information of the target vehicle; Based on the location information, the system filters the calibration scene database to determine if there are any suitable calibration scenes. If it exists, the initial screening and calibration scenario is obtained; Based on the parking perception information, at least one signal influence factor is determined; Based on the at least one signal influence factor, determine whether there is a suitable target calibration scenario from the initial screening calibration scenario; If it exists, then the scene in which the target vehicle is located is determined to be a calibration scene, and the scene in which the target vehicle is located is the target calibration scene; The calibration scenario database includes scenario data for multiple pre-stored calibration scenarios; the scenario data includes location information and corresponding signal influence factors.

7. The method for determining digital key calibration data according to claim 2 or 6, characterized in that, Also includes: In response to the scenario where the target vehicle is located not belonging to the calibrated scenario, based on the location information, the parking perception information, and the identification scenario database, it is determined whether the scenario where the target vehicle is located belongs to the identification scenario; wherein, the identification scenario database includes scenario data of multiple identification scenarios stored in advance and the cumulative number of identifications corresponding to each identification scenario; In response to the fact that the current scene belongs to the recognition scene, the cumulative recognition count is incremented by one and then updated to the recognition scene database; If the current scene does not belong to the identified scene, scene data of the current scene is generated based on the location information and the parking perception information, and stored in the identified scene database.

8. The method for determining digital key calibration data according to claim 7, characterized in that, Also includes: In response to the cumulative number of recognitions corresponding to the first recognition scenario exceeding a preset threshold, the first recognition scenario is determined as the calibration scenario, and the scenario data of the first recognition scenario is stored in the calibration scenario database.

9. The method for determining digital key calibration data according to claim 7, characterized in that, Also includes: The multiple recognition scenarios stored in the recognition scenario database are sorted from most to least according to their corresponding cumulative recognition counts; The preset number of identified scenarios that are ranked first are determined as the calibration scenarios, and the scenario data corresponding to the preset number of identified scenarios are stored in the calibration scenario database.

10. The method for determining digital key calibration data according to claim 1, characterized in that, Based on the signal strength distribution dataset, the calibration data of the digital key corresponding to the target calibration scenario is determined, including: Obtain historical signal intensity distribution dataset; Based on the historical signal strength distribution dataset and the signal strength distribution dataset, data distribution analysis is performed to obtain the calibration data; The historical signal strength distribution dataset includes multiple historical signal strength distribution data subsets. Each historical signal strength distribution data subset is a signal strength distribution dataset determined during the historical digital key calibration data determination process, where the target calibration scenario is located.

11. The method for determining digital key calibration data according to claim 10, characterized in that, Also includes: The signal strength distribution dataset is stored as a subset of historical signal strength distribution data in the historical signal strength distribution dataset.

12. The method for determining digital key calibration data according to claim 10, characterized in that, Also includes: Obtain the historical calibration data of the digital key; The historical calibration data is determined based on the historical signal strength distribution dataset; Monitor the real-time communication connection signal strength between the electronic device and the target vehicle; Based on the historical calibration data and the real-time communication connection signal strength, the real-time distance between the electronic device and the target vehicle is estimated. In response to the real-time distance being within a preset distance range, a preset action is executed.

13. The method for determining digital key calibration data according to claim 1, characterized in that, The communication connection includes at least one of the following: Bluetooth connection, ultra-wideband connection, and near-field communication.

14. The method for determining digital key calibration data according to claim 13, characterized in that, In response to the communication connection being the ultra-wideband connection, the system further includes: Based on the signal strength distribution dataset, data imputation is performed to obtain the blind spot signal strength distribution dataset corresponding to the parking perception blind spot; Specifically, based on the signal strength distribution dataset, determining the calibration data of the digital key corresponding to the target calibration scenario includes: Based on the signal strength distribution dataset and the blind zone signal strength distribution dataset, the calibration data of the digital key is determined.

15. A digital key calibration data determination device, characterized in that, include: The target scene determination unit is used to respond to the establishment of a communication connection between an electronic device carrying a digital key and a target vehicle, and to determine whether the scene in which the target vehicle is located belongs to a calibration scene based on the location information and parking perception information of the target vehicle and using a calibration scene database. The parking perception information represents the parking environment in which the target vehicle is located. The calibration scene database includes scene data of multiple calibration scenes, and the identification scene database includes scene data of multiple identification scenes and the cumulative number of identifications for each identification scene. The calibration scene database is constructed based on the scene data in the identification scene database accumulating to a preset number. The calibration scene represents the identification scene that occurs frequently. The data determination unit is used to determine the signal strength distribution dataset of the target calibration scene in response to the fact that the current scene belongs to the calibration scene; the signal strength distribution dataset is formed iteratively by collecting signal strength distribution data of the user's travel points during the use of the digital key multiple times. A data calibration unit is used to determine the calibration data of the digital key corresponding to the target calibration scenario based on the signal strength distribution dataset. The user holds the electronic device; The travel point is determined based on the parking perception information.

16. A vehicle, characterized in that, include: processor; Memory used to store processor-executable instructions; The processor is configured as follows: Implement the digital key calibration data determination method according to any one of claims 1 to 14.

17. A non-transitory computer-readable storage medium, wherein when instructions in the storage medium are executed by a processor of a terminal, the terminal is able to perform a digital key identification data determination method according to any one of claims 1 to 14.

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