Digital key motion trend identification method and vehicle

Abstract

The invention provides a digital key motion trend identification method and a vehicle. The method comprises the following steps: acquiring received signal strength indication values of digital keys received by a plurality of anchor points on a vehicle; establishing a sequence of a plurality of anchor points based on the received signal strength indication value; wherein the sequence of each anchor point comprises a received signal strength indication value of the digital key received by each anchor point in a preset time; on the basis of the sequence of the multiple anchor points, the first confidence interval and the second confidence interval, the change trend category of the sequence of each anchor point is detected; wherein the first confidence interval is a confidence interval corresponding to the rising trend; the second confidence interval is a confidence interval corresponding to the descending trend; and determining the motion trend of the digital key based on the change trend categories corresponding to the plurality of anchor points. According to the invention, the unlocking and locking accuracy of the vehicle can be improved.

Description

Technical Field

[0001] This application relates to vehicle control technology, and more particularly to a method for recognizing the movement trend of a digital key and a vehicle. Background Technology

[0002] With the development of intelligent vehicle technology, digital keys have gradually become an important means of vehicle access control. Among them, digital keys based on Bluetooth Low Energy (BLE) are widely used due to their advantages such as low power consumption, easy integration, and high adoption rate. In practical applications, the distance between the digital key and the vehicle is usually estimated by receiving the Received Signal Strength Indication (RSSI), and the unlocking or locking operation is triggered accordingly.

[0003] In related technologies, the RSSI value of the digital key is typically collected by one or more anchor points on the vehicle, and distance is determined based on this value. For example, a fixed RSSI threshold range is set to determine whether to perform an unlocking or locking action. However, due to problems such as large signal fluctuations and environmental interference in BLE communication, a single anchor point or a fixed threshold cannot accurately reflect the user's true location and movement status, leading to frequent delays in unlocking / locking responses or false triggering. Summary of the Invention

[0004] This application provides a method for recognizing the movement trend of a digital key, a device for recognizing the movement trend of a digital key, a vehicle, a computer-readable storage medium, and a computer program product, which can improve the accuracy of vehicle unlocking and locking.

[0005] The technical solution of this application embodiment is implemented as follows: This application provides a parameter control method, the method comprising: acquiring received signal strength indication values ​​of digital keys received by multiple anchor points on a vehicle; establishing a sequence of multiple anchor points based on the received signal strength indication values; wherein, the sequence of each anchor point includes received signal strength indication values ​​of digital keys received by each anchor point within a preset time; verifying the change trend category of the sequence of multiple anchor points, a first confidence interval, and a second confidence interval; wherein, the first confidence interval is a confidence interval corresponding to an upward trend; the second confidence interval is a confidence interval corresponding to a downward trend; and determining the movement trend of the digital key based on the change trend categories corresponding to the multiple anchor points.

[0006] Based on the aforementioned technical methods, by acquiring the RSSI of multiple anchor points and constructing a sequence, and judging the overall movement trend based on the sequence, the positional changes of the digital key relative to the vehicle can be more comprehensively reflected, effectively identifying user behaviors such as approaching, moving away, or lingering. On this basis, the unlocking or locking calibration parameters are dynamically adjusted, improving the response accuracy and user experience of contactless unlocking and locking compared to traditional fixed threshold schemes. Setting different confidence intervals helps balance the sensitivity and stability of the enhancement / degradation trend judgment, enhancing the system's adaptability in different scenarios.

[0007] In some embodiments, the method further includes: controlling the vehicle's unlocking or locking calibration parameters based on the movement trend of the digital key.

[0008] Based on the aforementioned technical means, the unlocking or locking calibration parameters are dynamically adjusted according to the movement trend of the digital key, which improves the response accuracy and user experience of contactless unlocking and locking compared to the traditional fixed threshold scheme.

[0009] In some embodiments, the step of detecting the trend category of the sequence at each anchor point based on the sequences at multiple anchor points, a first confidence interval, and a second confidence interval includes: calculating the rank statistic of the sequence at each anchor point; wherein the rank statistic is used to measure the strength of the upward trend in the entire sequence; determining the test statistic corresponding to the sequence at each anchor point based on the rank statistic; the test statistic is used to determine whether the trend in the sequence is significant; and determining the trend category of the sequence at each anchor point based on the test statistic, the first confidence interval, and the second confidence interval.

[0010] Based on the above technical means, rank statistics and test statistics are used to determine whether there is a significant monotonic trend in the data sequence, thereby improving the objectivity and accuracy of the judgment and avoiding errors caused by subjective experience.

[0011] In some embodiments, determining the trend category of the sequence at each anchor point based on the test statistic, the first confidence interval, and the second confidence interval includes: if the first test statistic corresponding to the sequence at the first anchor point is greater than zero, determining the first significance level corresponding to the first confidence interval; wherein the first significance level characterizes the probability that the estimated sequence value falls within the first confidence interval; the sequence at each anchor point includes the sequence at the first anchor point; obtaining a first critical value at the first significance level; if the first test statistic is less than the first critical value, determining the trend category of the sequence at the first anchor point as a fluctuation category; if the first test statistic is greater than or equal to the first critical value, determining the trend category of the sequence at the first anchor point as an upward category.

[0012] Based on the aforementioned technical means, by setting significance levels and threshold values, reinforcing trends and volatile trends can be distinguished, making the judgment logic clearer and facilitating subsequent classification and strategy formulation.

[0013] In some embodiments, determining the trend category of the sequence at each anchor point based on the test statistic, the first confidence interval, and the second confidence interval includes: if the first test statistic corresponding to the sequence at the first anchor point is less than zero, determining a second significance level corresponding to the second confidence interval; wherein the second significance level characterizes the probability of an error in estimating the sequence value falling within the second confidence interval; obtaining a second critical value at the second significance level; if the first test statistic is less than the second critical value, determining the trend category of the sequence at the first anchor point as a fluctuation category; if the first test statistic is greater than or equal to the second critical value, determining the trend category of the sequence at the first anchor point as a decline category.

[0014] Based on the above technical means, the judgment criteria are further refined by using negative test statistics, which makes the judgment of strengthening trend more reliable and reduces the possibility of misjudgment.

[0015] In some embodiments, determining the movement trend of the digital key based on the change trend categories corresponding to multiple anchor points includes: if the change trend categories of the sequences of multiple anchor points are all upward, determining the movement trend as a moving away trend; if the change trend categories of the sequences of multiple anchor points are all downward, determining the movement trend as a moving closer trend; if the change trend categories of the sequences of the first portion of the multiple anchor points are upward and the change trend categories of the sequences of the second portion of the multiple anchor points are all downward, determining the movement trend as a hovering trend.

[0016] Based on the above technical means, and by combining multi-anchor trend information for integrated judgment, it is possible to more accurately distinguish different user behavior states, such as approaching, moving away, and lingering, thereby providing a reliable basis for subsequent control decisions.

[0017] In some embodiments, determining the movement trend of the digital key based on the change trend categories corresponding to multiple anchor points includes: if the change trend categories of the sequences of multiple anchor points are all upward, determining the movement trend as a moving away trend; if the change trend categories of the sequences of multiple anchor points are all downward, determining the movement trend as a moving closer trend; if the change trend categories of the sequences of the first portion of the multiple anchor points are upward and the change trend categories of the sequences of the second portion of the multiple anchor points are all downward, determining the movement trend as a hovering trend.

[0018] Based on the aforementioned technical means, the calibration parameters can be dynamically adjusted according to different movement trends, which can optimize the triggering timing of unlocking and locking, avoid unexpected operations caused by user hesitation, and improve user experience.

[0019] In some embodiments, establishing a sequence of multiple anchor points based on the received signal strength indication value includes: performing compensation processing on the received signal strength indication value to obtain a compensated received signal strength indication value; performing smoothing processing on the compensated received signal strength indication value to obtain a smoothed received signal strength indication value; and establishing a sequence of multiple anchor points based on the smoothed received signal strength indication value.

[0020] Based on the above technical methods, by using the missing forest method to compensate for signal loss and then using Gaussian filtering to smooth the signal, noise interference can be effectively eliminated, signal quality can be improved, and a more stable data foundation can be provided for subsequent trend judgment.

[0021] In some embodiments, determining the motion trend of the digital key based on the change trend categories corresponding to multiple anchor points includes: if the change trend categories of the sequence of multiple anchor points are all fluctuation categories, determining the motion trend as a stationary trend; correspondingly, controlling the unlocking or locking calibration parameters of the vehicle based on the motion trend of the digital key includes: if the motion trend is a stationary trend, controlling the unlocking or locking calibration parameters of the vehicle to remain unchanged.

[0022] Based on the above technical means, when all anchor points show a fluctuating trend, it is considered that the user is in a static state. At this time, the calibration parameters are not adjusted and the original settings are maintained, thereby preventing accidental triggering of unlocking or locking actions.

[0023] This application provides a device for recognizing the movement trend of a digital key, the device comprising: The acquisition unit is used to acquire the received signal strength indication values ​​of the digital key received at multiple anchor points on the vehicle; The processing unit is configured to establish a sequence of multiple anchor points based on the received signal strength indication value; wherein, the sequence of each anchor point includes the received signal strength indication value of the digital key received by each anchor point within a preset time. The processing unit is further configured to detect the trend category of the sequence at each anchor point based on the sequence of multiple anchor points, a first confidence interval, and a second confidence interval; wherein the first confidence interval is the confidence interval corresponding to an upward trend; and the second confidence interval is the confidence interval corresponding to a downward trend. The processing unit is also used to determine the movement trend of the digital key based on the change trend categories corresponding to multiple anchor points.

[0024] This application provides a vehicle, the vehicle comprising: Memory is used to store executable instructions or computer programs. The processor, when executing computer-executable instructions or computer programs stored in the memory, implements the digital key movement trend recognition method provided in the embodiments of this application.

[0025] This application provides a computer-readable storage medium storing a computer program or computer-executable instructions, which, when executed by a processor, implements the digital key movement trend recognition method provided in this application.

[0026] This application provides a computer program product, including a computer program or computer executable instructions. When the computer program or computer executable instructions are executed by a processor, they implement the digital key movement trend recognition method provided in this application. Attached Figure Description

[0027] Figure 1 This is a first flowchart illustrating the method for recognizing the movement trend of a digital key provided in this application embodiment; Figure 2 This is a schematic diagram of the arrangement of the main anchor point and the secondary anchor point provided in the embodiments of this application; Figure 3 This is a second flowchart illustrating the method for recognizing the movement trend of a digital key provided in an embodiment of this application; Figure 4 This is a schematic diagram of the third process of the digital key movement trend recognition method provided in the embodiments of this application; Figure 5 This is a schematic diagram illustrating the RSSI trend judgment results of a single anchor point during multiple approach and departure processes provided in the embodiments of this application; Figure 6 This is a schematic diagram of the structure of the digital key movement trend recognition device provided in the embodiments of this application; Figure 7 This is a schematic diagram of the vehicle structure provided in the embodiments of this application.

[0028] It should be noted that the terms "first" and "second" mentioned above are only used to distinguish between different options and do not represent the degree of superiority or inferiority of the options or their priority in the implementation process. Detailed Implementation

[0029] To make the objectives, technical solutions, and advantages of this application clearer, the application will be further described in detail below with reference to the accompanying drawings. The described embodiments should not be regarded as limitations on this application. All other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0030] In the following description, references are made to “some embodiments,” which describe a subset of all possible embodiments. However, it is understood that “some embodiments” may be the same subset or different subsets of all possible embodiments and may be combined with each other without conflict.

[0031] In the following description, the terms "first, second, third" are used merely to distinguish similar objects and do not represent a specific ordering of objects. It is understood that "first, second, third" may be interchanged in a specific order or sequence where permitted, so that the embodiments of this application described herein can be implemented in an order other than that illustrated or described herein.

[0032] In the embodiments of this application, the terms "module" or "unit" refer to a computer program or part of a computer program that has a predetermined function and works with other related parts to achieve a predetermined goal, and can be implemented wholly or partially using software, hardware (such as processing circuitry or memory), or a combination thereof. Similarly, a processor (or multiple processors or memory) can be used to implement one or more modules or units. Furthermore, each module or unit can be part of an overall module or unit that includes the functionality of that module or unit.

[0033] Unless otherwise defined, all technical and scientific terms used in the embodiments of this application have the same meaning as commonly understood by one of ordinary skill in the art. The terminology used in the embodiments of this application is for the purpose of describing the embodiments of this application only and is not intended to limit this application.

[0034] In the implementation of this application, the collection and processing of relevant data should strictly comply with the requirements of relevant laws and regulations, obtain the informed consent or separate consent of the personal information subject, and carry out subsequent data use and processing within the scope of laws and regulations and the authorization of the personal information subject.

[0035] Before providing a further detailed description of the embodiments of this application, the nouns and terms used in the embodiments of this application are explained, and the nouns and terms used in the embodiments of this application shall be interpreted as follows: 1) RSSI is an indicator used to measure the signal strength between wireless communication devices. It is usually expressed as a negative decibel milliwatt (dBm). The smaller the value, the weaker the signal and the farther the distance.

[0036] 2) Anchor Point: In this application, it refers to the Bluetooth module installed on the vehicle, which is used to receive signals from the digital key and measure its RSSI value as a source of basic data for determining position and movement trend.

[0037] 3) Mann-Kendall test: A nonparametric statistical method used to detect whether time series data exhibits a monotonically increasing or decreasing trend, widely applied in environmental science, meteorology, and other fields. This application uses the Mann-Kendall test to analyze the changing trend of RSSI over time.

[0038] 4) Motion Trend Category: Based on the trend analysis results of RSSI at each anchor point, the relative motion state between the digital key and the vehicle is divided into categories such as approaching, moving away, or hovering, which serve as the basis for subsequent adjustments to the vehicle calibration parameters.

[0039] 5) Unlock / Lock Calibration Parameter: This parameter sets the trigger conditions for automatic unlocking or locking of the vehicle, such as the RSSI threshold at a specific distance. This application dynamically adjusts this parameter to enable the system to adapt to user behavior in different scenarios.

[0040] To address the issues of delayed unlocking and unintended locking caused by large RSSI fluctuations and inaccurate positioning in related technologies, this application provides a method for recognizing the movement trend of a digital key. This method can be applied to a digital key movement trend recognition device in a vehicle. In some embodiments, the digital key movement trend recognition device can be located in the vehicle's infotainment system. Figure 1 A schematic diagram illustrating the implementation process of a digital key movement trend recognition method provided in this application embodiment is shown below. Figure 1 As shown, the method for identifying the movement trend of the digital key can be implemented through steps 101 to 104: Step 101: Obtain the signal strength indication values ​​of the digital key received at multiple anchor points on the vehicle.

[0041] In some embodiments of the BLE digital key solution, at least one BLE master anchor point needs to be deployed on the vehicle to establish a connection and communication with the digital key, complete authentication and authorization, and obtain the RSSI of the master anchor point. Deploying multiple BLE slave anchor point antennas on the exterior of the vehicle body, and obtaining the slave anchor point RSSI signals through broadcast scanning or channel listening, can increase signal coverage around the vehicle, which is beneficial for positioning. Mainstream BLE deployment schemes on the market include single master anchor point, 1 master + 3 slave, 1 master + 4 slave, and 1 master + 5 slave. Considering both hardware cost and performance, this solution recommends a 1 master + 4 slave layout, with the master module placed in the center console armrest or the center of the roof, and the slave modules placed on both sides of the front and rear bumpers.

[0042] In this embodiment, an anchor point refers to a Bluetooth module installed on the vehicle, used to receive Bluetooth signals from the digital key in the user's mobile phone or smart device, and to measure the Received Signal Strength Indication (RSSI) value of the digital key. Typically, the number of anchor points is one main anchor point and several secondary anchor points. For example, a layout of 1 main anchor point and 4 secondary anchor points is recommended. Figure 2 This is a schematic diagram of the arrangement of the main anchor point and the secondary anchor point provided in the embodiments of this application, as shown below. Figure 2 As shown, a main Bluetooth anchor point (0) is located in the center of the vehicle's center console armrest or roof, and four secondary Bluetooth anchor points (1, 2, 3, 4) are deployed on both sides of the front bumper, both sides of the rear bumper, etc., to cover different directions around the vehicle and improve the comprehensiveness and accuracy of signal acquisition.

[0043] In this embodiment, each anchor point periodically scans and records the received signal strength indicator value of the digital key, which reflects the relative distance and signal quality between the digital key and the vehicle.

[0044] In this embodiment, the received signal strength indicator is an index that measures the strength of a wireless communication signal, usually expressed in dBm (decibels per milliwatt). In Bluetooth communication, a smaller received signal strength indicator value indicates a weaker signal and a longer distance; conversely, a larger received signal strength indicator value indicates a stronger signal and a shorter distance.

[0045] In this embodiment, the vehicle controller periodically acquires received signal strength indication data from various anchor points via the Bluetooth protocol stack and forms a time series to provide basic data support for subsequent trend analysis. The vehicle controller's execution of the above data acquisition and processing process ensures continuous monitoring of the digital key's movement status and provides a reliable basis for subsequent movement trend classification and calibration parameter adjustment.

[0046] Step 102: Based on the received signal strength indication value, establish a sequence of multiple anchor points.

[0047] The sequence of each anchor point includes the received signal strength indication value of the digital key received by each anchor point within a preset time.

[0048] In this embodiment, establishing a sequence of multiple anchor points refers to arranging the received signal strength indication values ​​collected by each anchor point within a certain time window into a sequence in chronological order, serving as the basis for subsequent trend analysis. For example, if the preset time is set to 5 seconds and the signal update period is 100ms, each anchor point will generate a sequence of 50 received signal strength indication value data points. The sequence of received signal strength indication value data points generated by each anchor point is used to reflect the signal changes of the digital key over a period of time, and the system can identify the movement trend of the digital key by analyzing this sequence.

[0049] In this embodiment, the vehicle controller sorts the received signal strength indication data from different anchor points according to timestamps, constructing multiple independent time series. Each series corresponds to the signal change path of one anchor point, facilitating statistical analysis. By sorting the signal strength indication data according to timestamps and constructing time series, the structure of the data is improved, and clear data input is provided for subsequent trend judgment algorithms.

[0050] In this embodiment, the vehicle controller processes the received signal strength indication data through a fixed-length sliding window. The vehicle controller ensures synchronized data updates at all anchor points and can reflect real-time dynamic changes in the digital key. Using this sliding window-based processing method, the system improves its response speed and judgment accuracy. This allows the vehicle to more promptly recognize the user's intentions.

[0051] Combination Figure 2 The vehicle can obtain the sequence of received signal strength indication values ​​from multiple Bluetooth anchor points of the digital key: .

[0052] Step 103: Based on the sequences of multiple anchor points, the first confidence interval, and the second confidence interval, examine the category of the change trend of the sequence at each anchor point.

[0053] The first confidence interval corresponds to an upward trend, and the second confidence interval corresponds to a downward trend.

[0054] In this embodiment of the application, the first confidence interval is smaller than the second confidence interval; the trend categories include the category corresponding to the upward trend and the category corresponding to the downward trend.

[0055] In this embodiment of the application, the trend of change refers to whether a certain time series data shows a state of monotonically increasing, monotonically decreasing, or no significant trend.

[0056] Here, a higher confidence interval is assigned to the strengthening (corresponding to the downward) trend in order to more sensitively capture the strengthening trend of the signal, while a smaller confidence interval is assigned to the weakening (corresponding to the upward) trend to reduce the interference of signal fluctuations on the determination.

[0057] Step 104: Determine the movement trend of the digital key based on the change trend categories corresponding to multiple anchor points.

[0058] In this embodiment, the motion trend category is the relative motion state between the vehicle and the digital key, derived from the analysis of the received signal strength indication value sequence at multiple anchor points. This motion trend category mainly includes three types: approaching process, moving away process, and loitering process.

[0059] In actual implementation, the vehicle-mounted controller analyzes the sequence of each anchor point, outputs the trend state (enhanced, attenuated, or no obvious trend) of each anchor point, and comprehensively judges the motion trend category of the digital key according to the overall distribution characteristics of the anchor points. This method avoids the problem of misjudgment of the digital key motion trend judgment system caused by the error of a single anchor point, and improves the robustness and accuracy of the digital key motion trend judgment.

[0060] This application provides a method for identifying the motion trend of a digital key; the method includes: obtaining the received signal strength indication values of the digital key received by multiple anchor points on the vehicle; based on the received signal strength indication values, establishing sequences of multiple anchor points; wherein, each sequence of anchor points includes the received signal strength indication values of the digital key received by each anchor point within a preset time; based on the sequences of multiple anchor points, the first confidence interval, and the second confidence interval, detecting the change trend category of each sequence of anchor points; wherein, the first confidence interval is the confidence interval corresponding to the rising trend; the second confidence interval is the confidence interval corresponding to the falling trend; based on the change trend categories corresponding to multiple anchor points, determining the motion trend of the digital key; that is, by obtaining the RSSI of multiple anchor points, constructing sequences, and judging the overall motion trend based on the sequences, it can more comprehensively reflect the position change of the digital key relative to the vehicle, effectively identify behaviors such as the user approaching, moving away, or lingering; on this basis, dynamically adjusting the unlocking or locking calibration parameters, compared with the traditional fixed threshold scheme, improves the response accuracy and user experience of keyless unlocking and locking; setting different confidence intervals helps to balance the sensitivity and stability of enhanced / attenuated trend judgment, and enhances the adaptability of the system in different scenarios.

[0061] In some embodiments, as Figure 3 shown, step 103, based on the sequences of multiple anchor points, the first confidence interval, and the second confidence interval, detecting the change trend category of each sequence of anchor points, can be implemented through step 201 and step 202: Step 301, calculating the rank statistic of each sequence of anchor points.

[0062] Among them, the rank statistic is used to measure the intensity of the rising trend in the whole sequence.

[0063] In the embodiments of this application, the rank statistic is an intermediate variable used to describe the monotonic change trend of time series data, and the calculation of the rank statistic is based on the comparison relationship between each pair of data points in the sequence.

[0064] In some embodiments, the rank statistic is used to calculate the sum of the signs of all data pairs in the sequence; specifically, for any two observations X i and X j (where i < j), if Xi Greater than X j If X, then add 1 to S; if X i Less than X j If the two are equal, then subtract 1 from S; if they are equal, then do not change.

[0065] Step 302: Based on the rank statistic, determine the test statistic corresponding to the sequence of each anchor point.

[0066] Among them, the test statistic is used to determine whether the trend in the sequence is significant.

[0067] In this embodiment of the application, the test statistic follows a standard normal distribution.

[0068] Step 303: Based on the test statistic, the first confidence interval, and the second confidence interval, determine the trend category of the sequence at each anchor point.

[0069] In this embodiment, if the first test statistic corresponding to the sequence of the first anchor point is greater than zero, a first significance level corresponding to the first confidence interval is determined; wherein, the first significance level characterizes the probability that the estimated sequence value falls within the first confidence interval; the sequence of each anchor point includes the sequence of the first anchor point; a first critical value is obtained at the first significance level; if the first test statistic is less than the first critical value, the change trend category of the sequence of the first anchor point is determined to be the fluctuation category; if the first test statistic is greater than or equal to the first critical value, the change trend category of the sequence of the first anchor point is determined to be the rising category.

[0070] Here, a test statistic greater than zero indicates a possible upward trend in the sequence. The significance level is a probability threshold used to decide whether to reject the null hypothesis (e.g., no monotonic trend exists). Common significance levels include 0.05 and 0.01. The first critical value is a critical value obtained by looking up the selected significance level in a table; it is used to compare with the test statistic to determine whether the trend is significant.

[0071] In this embodiment, the fluctuation category refers to the digital key signal strength sequence at the anchor point exhibiting neither a significant upward trend nor a significant downward trend, but rather in a state of random fluctuation. The fluctuation category typically implies that the user's position relative to the vehicle while carrying the digital key has not changed significantly, or that the user's digital key is subject to significant environmental interference, resulting in unstable digital key signals.

[0072] In this embodiment of the application, if the first test statistic corresponding to the sequence of the first anchor point is less than zero, the second significance level corresponding to the second confidence interval is determined; wherein, the second significance level represents the probability of error in estimating the sequence value falling within the second confidence interval; a second critical value is obtained at the second significance level; if the first test statistic is less than the second critical value, the change trend category of the sequence of the first anchor point is determined to be the fluctuation category; if the first test statistic is greater than or equal to the second critical value, the change trend category of the sequence of the first anchor point is determined to be the decline category.

[0073] In this embodiment of the application, when the test statistic is less than zero, the characterization sequence may show a downward trend (signal enhancement).

[0074] In this embodiment of the application, step 104, determining the movement trend of the digital key based on the change trend categories corresponding to multiple anchor points, includes the following steps: Step A1: If the trend categories of multiple anchor point sequences are all upward, determine the movement trend as a moving away trend.

[0075] Step A2: If the trend of multiple anchor points is all downward, determine the trend as a convergence trend.

[0076] Step A3: If the trend category of the sequence of the first part of the anchor points is upward, and the trend category of the sequence of the second part of the anchor points is downward, the movement trend is determined to be a oscillating trend.

[0077] Step A4: If the changing trends of multiple anchor points are all fluctuating trends, determine the motion trend as a stationary trend.

[0078] In this embodiment of the application, the changing trend of the sequence of multiple anchor points is an upward trend, including the changing trend of the sequence of all anchor points being an upward trend, and the changing trend of the sequence of most anchor points being an upward trend, while the changing trend of the sequence of a small number of anchor points is a fluctuating trend.

[0079] In this embodiment of the application, the changing trend of the sequence of multiple anchor points is a downward trend, including the changing trend of the sequence of all anchor points being a downward trend, the changing trend of the sequence of most anchor points being a downward trend, and the changing trend of the sequence of a small number of anchor points being a fluctuating trend.

[0080] In this embodiment, an upward trend in the sequence corresponds to a state where the RSSI signal strength exhibits a monotonically decreasing / weakening trend over time, indicating that the distance between devices is gradually increasing. When the sequences corresponding to all anchor points show an upward trend, the system determines that the user is gradually moving away from the vehicle.

[0081] In this embodiment, a decreasing trend in the sequence corresponds to a state where the RSSI signal strength monotonically increases over time, indicating that the distance between devices is gradually decreasing. When the sequences corresponding to all anchor points show a decreasing trend, the system determines that the user is approaching the vehicle.

[0082] In this embodiment, a lingering trend in the sequence refers to a situation where, among multiple anchor points, some anchor points show an upward trend in their corresponding sequences, while others show a downward trend. This indicates that the user may be moving back and forth near the vehicle without a clear intention to move closer or further away. When a lingering trend occurs, the system determines that the user is in a lingering state.

[0083] In this embodiment of the application, when all anchor points show a fluctuating trend, it means that the position of the digital key relative to the vehicle has not moved significantly, that is, the user has not moved closer to or away from the vehicle. Therefore, it can be inferred that the relative motion trend between the digital key and the vehicle is a stationary trend.

[0084] Based on this, the technical solution to be protected in this application also includes the following steps: Step B1: Based on the movement trend of the data key, control the vehicle's unlocking or locking calibration parameters.

[0085] In this embodiment, the unlocking or locking calibration parameter refers to the parameter used to set the automatic unlocking or locking trigger conditions of the vehicle, such as the threshold value of the received signal strength indication value at a specific distance. The parameter used to set the automatic unlocking or locking trigger conditions of the vehicle can directly affect the conditions under which the vehicle triggers the unlocking or locking action, and is one of the key factors to achieve seamless operation.

[0086] In this embodiment, the vehicle controller dynamically adjusts the unlocking or locking trigger threshold based on the currently determined motion trend category. For example, during approach, the vehicle controller lowers the received signal strength indicator threshold required to trigger unlocking, allowing the user to trigger the unlocking action more quickly when approaching the vehicle; while during loitering, the vehicle controller raises the received signal strength indicator threshold to trigger locking, preventing incorrect locking before the user has actually left the vehicle. This adaptive adjustment mechanism based on motion trend category significantly improves the accuracy of seamless unlocking and locking and enhances the user experience.

[0087] In this embodiment, if the motion trend category is "moving away," the vehicle's locking calibration parameters are decreased; if the motion trend category is "approaching," the vehicle's unlocking calibration parameters are increased; if the motion trend category is "wandering," the vehicle's locking calibration parameters are increased. If the motion trend is "stationary," the vehicle's unlocking or locking calibration parameters remain unchanged.

[0088] In this embodiment of the application, when the movement trend is judged to be a stationary trend, it means that the user is not currently making any obvious approaching or moving away actions. Therefore, there is no need to adjust the existing unlocking or locking calibration parameters. The system maintains the current unlocking or locking calibration parameters unchanged, thereby preventing the system from mis-locking or mis-locking caused by unnecessary parameter switching.

[0089] In this embodiment, keeping the unlock or lockout calibration parameters unchanged helps maintain system stability when the user is stationary, reduces unnecessary triggering behavior, improves user experience, and reduces system resource consumption.

[0090] In this embodiment, by uniformly determining the fluctuation trends of multiple anchor points as static trends and keeping the unlocking or locking calibration parameters unchanged, the user's true intention can be reflected more accurately, thereby avoiding mis-locking or mis-locking and improving the intelligence level and user satisfaction of the digital key system.

[0091] In this embodiment, when the vehicle's digital key system tends to move away from the vehicle, it indicates that the user's approach / distance behavior is gradually leaving the vehicle area. To prevent security risks caused by the inability to lock the vehicle promptly after Bluetooth disconnection, the method should take measures in advance to ensure the vehicle automatically locks within a suitable distance. Therefore, the method dynamically adjusts the locking calibration parameters to reduce their value, i.e., lowering the locking calibration parameters that trigger the locking operation. For example, under normal circumstances, the locking calibration parameter might be set to 8 meters, but after detecting that the vehicle's digital key system is moving away, the method can adjust the locking calibration parameter to 5 meters, thereby improving response speed and enhancing security.

[0092] In this embodiment, when the vehicle's digital key system is approaching the vehicle, it indicates that the user's approach / away movement is about to reach the vehicle. To avoid excessively long waiting times due to the digital key system's positioning delay (i.e., the user has reached the vehicle but has not been recognized), the method should improve unlocking sensitivity. Therefore, this method increases the unlocking calibration parameter, that is, increases the calibration parameter that triggers the unlocking operation. For example, under normal circumstances, the unlocking calibration parameter may be set to 5 meters, but after detecting that the vehicle's digital key system is approaching, the method can adjust the unlocking calibration parameter to 8 meters, allowing the user to receive the unlocking prompt earlier, thereby improving the user experience.

[0093] In this embodiment, when the vehicle's digital key system is in a loitering state, it indicates that the user's approach / distance actions involve repeated movement around the vehicle, which can easily trigger unexpected locking / unlocking ping-pong phenomena (i.e., frequent locking and unlocking). To avoid this problem, the digital key system should increase the locking threshold, i.e., increase the locking calibration parameter, so that locking is only triggered when the user's approach / distance actions clearly indicate moving a considerable distance away. For example, under normal circumstances, the digital key system sets the locking calibration parameter to 5 meters, while in a loitering state, the digital key system can adjust the locking calibration parameter to 7 meters, thereby reducing the false alarm rate and improving the stability and reliability of the digital key system.

[0094] In this embodiment, by dynamically adjusting the calibration parameters for unlocking or locking based on the movement trend between the vehicle's digital key and the vehicle, the system effectively addresses the need for seamless unlocking and locking in different scenarios, improves the user experience, and enables a more intelligent, secure, and efficient control strategy for the vehicle's digital key.

[0095] In some embodiments, step 102, establishing a sequence of multiple anchor points based on the received signal strength indication value, can be achieved through steps C1 to C3: Step C1: Compensate the received signal strength indication value to obtain the compensated received signal strength indication value.

[0096] Step C2: Smooth the compensated received signal strength indication to obtain a smoothed received signal strength indication value.

[0097] Step C3: Based on the smoothed received signal strength indication value, establish a sequence of multiple anchor points.

[0098] In this embodiment, compensation processing refers to repairing missing or abnormal data in the Received Signal Strength Indicator (RSSI) using algorithmic methods to improve data continuity and usability. This application employs the Missing Forest method as the compensation approach. The Missing Forest method is a machine learning-based interpolation algorithm that can predict reasonable values ​​for missing points based on historical data. By setting the maximum number of features to 5 and the maximum depth to 12, the Missing Forest method ensures sufficient generalization ability of the model while avoiding overfitting. The compensated RSSI is more stable, providing a reliable foundation for subsequent processing.

[0099] By employing a specific signal compensation mechanism, this application enables the control system to reduce the risk of misjudgment caused by signal loss, improve the accuracy of motion trend recognition, and enhance the response performance and user experience of contactless unlocking.

[0100] In this embodiment, smoothing is used to reduce the instantaneous fluctuations of the Received Signal Strength Indication (RSSI), making the data trend clearer and facilitating subsequent trend analysis. This application employs a Gaussian filtering method for smoothing. This method suppresses noise and preserves trend information by weighted averaging of the RSSI over a certain time window. Specifically, the parameters are set to a standard deviation of 2 and a window length of 8. This parameter setting effectively removes high-frequency noise while maintaining data sensitivity.

[0101] In this embodiment, after smoothing, the trend of the Received Signal Strength Indicator (RSSI) sequence is more obvious, which is beneficial for relevant testing methods to accurately determine the trend of signal enhancement or attenuation. In addition, the smoothed RSSI also provides a more stable input for the construction of subsequent anchor sequences, which helps to improve the robustness of the overall system.

[0102] In this embodiment, Bluetooth RSSI positioning exhibits environmental sensitivity. Theoretically, a two-path attenuation model is typically used to describe the relationship between RSSI and the distance *d* between the transmitter and receiver. Factors related to the shape of the attenuation curve include the height of the transmitter and receiver, the free path loss factor, the gain of the receiving and transmitting antennas, and the ground reflection coefficient. Therefore, the attenuation characteristics of Bluetooth RSSI vary under different environments. Especially in multipath environments with obstructions or reflections, RSSI can fluctuate drastically or even be lost, significantly reducing the accuracy of location determination and trend recognition based on this. Therefore, it is necessary to perform jitter reduction and smoothing processing on the RSSI signal. Specifically, this application first uses the missing forest method to compensate for lost signal values, and then uses Gaussian filtering for smoothing based on the actual signal fluctuation level.

[0103] The following describes the application of the digital key movement trend recognition method provided in this application embodiment in a real-world scenario: This application proposes a digital key motion trend recognition method based on Bluetooth RSSI. First, a hypothesis testing method is used to determine the monotonically increasing or decreasing trend of RSSI at each anchor point. This hypothesis testing method is the first application of the Mann-Kendall test in the field of wireless communication, and it is improved based on the characteristics of wireless signals to give it two-sided characteristics. By judging the increasing and decreasing trends of RSSI at each anchor point and combining multi-scenario features, the method effectively identifies the user's actions such as approaching, moving away, and lingering while carrying the digital key. Based on this, corresponding adaptive contactless unlocking and locking strategies are designed for different scenarios to optimize positioning performance and improve the user's contactless unlocking and locking experience.

[0104] The Mann-Kendall test is a non-parametric statistical test used to analyze the monotonic trend of time series data, and is widely used in meteorology, hydraulics, and environmental science. Based on the fluctuating characteristics of wireless signals, under common interference environments, RSSI (Resonance Signal Indicator) tends to decrease due to interference, and rarely increases. Therefore, under the same conditions, we have a higher confidence level for an increasing trend in RSSI and a lower confidence level for an decreasing trend. Further explanation: if an increasing trend in RSSI is detected, the actual scenario is highly likely that the digital key is close to the Bluetooth antenna; if an decreasing trend in RSSI is detected, the actual scenario may not be that the digital key is far from the Bluetooth antenna, but rather that the signal is fluctuating due to interference. Therefore, a two-sided decision rule is added to the basic Mann-Kendall test: a higher confidence interval is given to the "increasing trend decision" to more sensitively capture the increasing trend, and a smaller confidence interval is given to the "decreasing trend decision" to reduce the interference of signal fluctuations on the decision.

[0105] After calculating the monotonic trend of the RSSI at each anchor point using the Mann-Kendall test, the motion trend of the digital key relative to the vehicle is further judged based on the anchor point arrangement. The basic logic is as follows: if all anchor point RSSIs show a decaying trend at the current moment, it is judged as a "moving away process"; if all anchor point RSSIs show an increasing trend at the current moment, it is judged as a "moving towards process"; if some anchor point RSSIs show a decaying trend and some show an increasing trend at the current moment, it is judged as a "wandering process". Note that because RSSI is susceptible to interference and fluctuates significantly, the Mann-Kendall test still has a certain probability of misjudgment despite signal smoothing. Therefore, the above motion trend judgment logic needs to be refined and calibrated in conjunction with the actual vehicle signal distribution.

[0106] Adaptive design refers to storing multiple versions of locking / unlocking calibration parameters in the vehicle-side positioning software and managing the use of these parameters through a motion trend state machine. When the motion trend is "approaching," a larger unlocking calibration parameter is used to ensure sensitive unlocking and avoid delays. When the motion trend is "moving away," a smaller locking calibration parameter is used to control the locking distance and prevent Bluetooth disconnection from causing locking failures. When the motion trend is "loitering," a larger locking calibration parameter is used to avoid unexpected locking and unlocking issues caused by loitering near the vehicle.

[0107] Figure 4 This is a schematic diagram of a method for recognizing the movement trend of a digital key according to an embodiment of this application; as shown. Figure 4 As shown: Step 401: Obtain the multi-anchor point RSSI.

[0108] This application adopts a layout of one master Bluetooth anchor point and four slave Bluetooth anchor points. The master Bluetooth anchor point is located inside the vehicle's center console armrest or in the center of the roof, and the four slave Bluetooth anchor points are located on both sides of the front and rear bumpers of the vehicle. For ease of calculation, the obtained RSSI is taken as the absolute value of the actual physical value, i.e. k=0,1,2,3,4.

[0109] Step 402: RSSI signal processing.

[0110] In this embodiment, the signal processing method used includes missing forest method to compensate for lost RSSI and Gaussian filtering to smooth fluctuating RSSI. Specifically, the maximum feature number for the forest compensation method is 5; the maximum depth is 12; the standard deviation for the Gaussian filtering method is 2; and the window length is 8.

[0111] Step 403: Perform a bilateral Mann-Kendall test to determine the signal trend at each anchor point.

[0112] In this embodiment of the application, the following assumptions are given for the target anchor RSSI sequence: (Based on a two-sided Mann-Kendall test) Null hypothesis H0: There is no monotonic trend in the data; Alternative hypothesis H1: The data exhibits a monotonic trend.

[0113] Step 4031: Construct a multi-anchor RSSI sequence.

[0114] In this embodiment of the application, the trend is determined based on the historical RSSI of the previous 5 seconds, so an RSSI sequence window within 5 seconds needs to be constructed; wherein, the signal update period is 100ms and the window length is 50; Wherein, the RSSI sequence is: {RSSI} = , k=0,1,2,3,4; t=0,...,49; Characterizing RSSI sequences; A sequence representing the corresponding time period.

[0115] As the RSSI signal is updated, the sequence window shifts backward, and the RSSI sequence is updated accordingly. The signal trend judgment algorithm runs in real time, calculating once every 100ms.

[0116] Step 4032: Calculate the Kendall rank statistic S of the RSSI sequence at each anchor point. (k=0,1,2,3,4).

[0117] in, The calculation formula is as follows: (1) Here, k=0,1,2,3,4; n=40.

[0118] Step 4033: Calculate the test statistic Z(t) of the RSSI sequence at each anchor point. (k=0,1,2,3,4).

[0119] in, The calculation formula is as follows: (2) Here, k = 0, 1, 2, 3, 4.

[0120] Step 4034: Determine whether to accept the hypothesis test.

[0121] This application can determine whether to accept hypothesis H0 at the two-sided significance level based on the test statistic Z.

[0122] Determine the increasing or decreasing trend of each anchor point: In a standard two-sided Mann-Kendall test, at a significance level... Next, consult the standard normal distribution table to obtain the critical value for judgment. ,like If the trend change of the RSSI sequence is not significant, then the null hypothesis H0 is accepted; otherwise, the null hypothesis H0 is rejected.

[0123] This application proposes a two-sided test method, when the test statistic... At this time, the RSSI sequence may exhibit an upward trend (signal weakening), given a significance level. ,like If the trend change within 5 seconds of the kth anchor point is not significant, we accept the null hypothesis H0, which corresponds to RSSI fluctuation. Otherwise, we reject the null hypothesis H0, which indicates that the sequence has a significant upward trend, corresponding to RSSI decay.

[0124] This application proposes a two-sided test method, when the test statistic... At this time, the RSSI sequence may exhibit a downward trend (signal enhancement), given a significance level. ,like If the trend change within 5 seconds of the kth anchor point is not significant, we accept the null hypothesis H0, which corresponds to RSSI fluctuation. Otherwise, we reject the null hypothesis H0, which indicates that the sequence has a significant downward trend, corresponding to RSSI enhancement. In other words, the above steps determine the increasing or decreasing trend of each anchor point.

[0125] here, The value of is a reference value given in this invention. The actual value is between 0 and 0.5, depending on the fluctuation of the signal. The basic principle is: if the signal quality is good and you want to more sensitively detect monotonic trends, then increase the value. If the signal fluctuates significantly and you want to eliminate the fluctuation over a wider area, then reduce the adjustment. .

[0126] Figure 5 This is a schematic diagram illustrating the RSSI trend judgment results of a single anchor point during multiple approach and departure processes provided in the embodiments of this application; Figure 5 The horizontal axis represents time (t) / 100ms, and the vertical axis represents the absolute value of RSSI / dBM; for example Figure 5 As shown, based on the RSSI at anchor point 1 and the bilateral Mann-Kendall trend, an upward trend, a downward trend, or a fluctuating trend can be determined.

[0127] Step 404: Determine the movement trend of the digital key.

[0128] Determine the increasing or decreasing trend of RSSI at each anchor point. If all anchor points' RSSI are decreasing at the current moment, it is judged as "moving away process"; if all anchor points' RSSI are increasing at the current moment, it is judged as "approaching process"; if some anchor points' RSSI are decreasing and some anchor points' RSSI are increasing at the current moment, it is judged as "hovering process".

[0129] Here, we obtain the increasing or decreasing trend of each anchor point; we determine if each anchor point is in a decreasing trend, and if so, we determine it is a moving away process; if not, we determine if each anchor point is in a strengthening trend, and if so, we determine it is a moving closer process, otherwise it is a hovering process.

[0130] Step 405: Adaptive unlocking parameter adjustment.

[0131] Specifically, for a motion trend of "approaching process", increase the unlocking calibration parameters; for a motion trend of "moving away process", decrease the locking calibration parameters; and for a motion trend of "wandering process", increase the locking calibration parameters.

[0132] Figure 6 This is a schematic diagram of the structure of a digital key movement trend recognition device provided in an embodiment of this application, as shown below. Figure 6 As shown, the digital key movement trend recognition device 600 includes: The acquisition unit 601 is used to acquire the received signal strength indication value of the digital key received by multiple anchor points on the vehicle; The processing unit 602 is used to establish a sequence of multiple anchor points based on the received signal strength indication value; wherein, the sequence of each anchor point includes the received signal strength indication value of the digital key received by each anchor point within a preset time. The processing unit 602 is used to detect the trend category of the sequence at each anchor point based on the sequence at multiple anchor points, a first confidence interval, and a second confidence interval; wherein the first confidence interval is the confidence interval corresponding to an upward trend; and the second confidence interval is the confidence interval corresponding to a downward trend. The processing unit 602 is used to determine the movement trend of the digital key based on the change trend categories corresponding to multiple anchor points.

[0133] In other embodiments of this application, the processing unit 602 is used to control the unlocking or locking calibration parameters of the vehicle based on the movement trend of the digital key.

[0134] In other embodiments of this application, the processing unit 602 is used to calculate the rank statistic of the sequence at each anchor point; wherein the rank statistic is used to measure the strength of the upward trend in the entire sequence. Processing unit 602 is used to determine the test statistic corresponding to the sequence of each anchor point based on the rank statistic; the test statistic is used to determine whether the trend in the sequence is significant. Processing unit 602 is used to determine the trend category of the sequence at each anchor point based on the test statistic, the first confidence interval, and the second confidence interval.

[0135] In other embodiments of this application, the processing unit 602 is configured to determine a first significance level corresponding to a first confidence interval if the first test statistic corresponding to the sequence of the first anchor point is greater than zero; wherein, the first significance level characterizes the probability of error in estimating the sequence value falling within the first confidence interval; the sequence of each anchor point includes the sequence of the first anchor point. The acquisition unit 601 is used to acquire a first critical value at a first significance level; Processing unit 602 is used to determine the change trend category of the sequence of the first anchor point as the fluctuation category if the first test statistic is less than the first critical value; Processing unit 602 is used to determine the trend category of the sequence of the first anchor point as the rising category if the first test statistic is greater than or equal to the first critical value.

[0136] In other embodiments of this application, the processing unit 602 is configured to determine the second significance level corresponding to the second confidence interval if the first test statistic corresponding to the sequence of the first anchor point is less than zero; wherein, the second significance level characterizes the probability that the estimated sequence value falls within the second confidence interval incorrectly; The acquisition unit 601 is used to acquire the second critical value at the second significance level; Processing unit 602 is used to determine the change trend category of the sequence of the first anchor point as the fluctuation category if the first test statistic is less than the second critical value; The processing unit 602 is used to determine the trend category of the sequence of the first anchor point as the decreasing category if the first test statistic is greater than or equal to the second critical value.

[0137] In other embodiments of this application, the processing unit 602 is used to determine the movement trend as a moving away trend if the change trend categories of the sequences of multiple anchor points are all upward. Processing unit 602 is used to determine the movement trend as a convergence trend if the change trend categories of multiple anchor points are all decreasing. The processing unit 602 is used to determine the movement trend as a oscillating trend if the change trend category of the sequence of the first part of the anchor points among the multiple anchor points is an upward category and the change trend category of the sequence of the second part of the anchor points among the multiple anchor points is a downward category.

[0138] In other embodiments of this application, the processing unit 602 is used to reduce the vehicle's locking calibration parameters if the movement trend is a moving away trend; The processing unit 602 is used to increase the vehicle's unlocking calibration parameters if the movement trend is an approaching trend; The processing unit 602 is used to increase the vehicle's locking calibration parameters if the motion trend is a wandering trend.

[0139] In other embodiments of this application, the processing unit 602 is used to perform compensation processing on the received signal strength indication value to obtain a compensated received signal strength indication value; to perform smoothing processing on the compensated received signal strength indication value to obtain a smoothed received signal strength indication value; and to establish a sequence of multiple anchor points based on the smoothed received signal strength indication value.

[0140] In other embodiments of this application, the processing unit 602 is used to determine the motion trend as a stationary trend if the change trends of the sequence of multiple anchor points are all fluctuating trends; accordingly, based on the motion trend of the digital key, the unlocking or locking calibration parameters of the vehicle are controlled, including: if the motion trend is a stationary trend, the unlocking or locking calibration parameters of the vehicle are kept unchanged.

[0141] The descriptions of the apparatus embodiments above are similar to those of the method embodiments above, and have similar beneficial effects. In some embodiments, the functions or modules included in the apparatus provided in this disclosure can be used to perform the methods described in the method embodiments above. For technical details not disclosed in the apparatus embodiments of this application, please refer to the descriptions of the method embodiments of this application for understanding.

[0142] It should be noted that, in the embodiments of this application, if the above-mentioned digital key movement trend recognition method is implemented as a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the embodiments of this application, or the part that contributes to the related technology, can be embodied in the form of a software product. This software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the methods of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, mobile hard drives, read-only memory (ROM), magnetic disks, or optical disks. Thus, the embodiments of this application are not limited to any specific hardware, software, or firmware, or any combination of hardware, software, and firmware.

[0143] See Figure 7 , Figure 7 This is a structural schematic diagram of the vehicle 700 provided in the embodiments of this application. Figure 7 The vehicle 700 shown includes at least one processor 710, a memory 750, at least one network interface 720, and a user interface 730. The various components in the vehicle 700 are coupled together via a bus system 740. It is understood that the bus system 740 is used to implement communication between these components. In addition to a data bus, the bus system 740 also includes a power bus, a control bus, and a status signal bus. However, for clarity, ... Figure 7 The general labeled all buses as Bus System 740.

[0144] The processor 710 can be an integrated circuit chip with signal processing capabilities, such as a general-purpose processor, a digital signal processor (DSP), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or any conventional processor.

[0145] User interface 730 includes one or more output devices 731 that enable the presentation of media content, including one or more speakers and / or one or more visual displays. User interface 730 also includes one or more input devices 732, including user interface components that facilitate user input, such as a keyboard, mouse, microphone, touch screen display, camera, other input buttons and controls.

[0146] The memory 750 may be removable, non-removable, or a combination thereof. Exemplary hardware devices include solid-state storage, hard disk drives, optical disk drives, etc. The memory 750 may optionally include one or more storage devices physically located away from the processor 710.

[0147] The memory 750 may include volatile memory or non-volatile memory, or both. The non-volatile memory may be ROM, and the volatile memory may be random access memory (RAM). The memory 750 described in this application embodiment is intended to include any suitable type of memory.

[0148] In some embodiments, memory 750 is capable of storing data to support various operations, examples of which include programs, modules, and data structures or subsets or supersets thereof, as illustrated below.

[0149] Operating system 751 includes system programs for handling various basic system services and performing hardware-related tasks, such as the framework layer, core library layer, driver layer, etc., for implementing various basic business functions and handling hardware-based tasks; The network communication module 752 is used to reach other electronic devices via one or more (wired or wireless) network interfaces 720, exemplary network interfaces 720 including Bluetooth, wireless compatibility authentication, and Universal Serial Bus (USB); the presentation module 753 is used to enable the presentation of information (e.g., a user interface for operating peripheral devices and displaying content and information) via one or more output devices 731 associated with the user interface 730 (e.g., a display screen, a speaker, etc.). The input processing module 754 is used to detect and translate one or more user inputs or interactions from one or more input devices 432. In some embodiments, the apparatus provided in this application can be implemented in software. Figure 7 A device 755 for recognizing the movement trend of a digital key stored in a memory 750 is shown. This device can be software in the form of programs and plug-ins, and includes the following software modules: a first module 7551 and a second module 7552. These modules are logically linked and can therefore be arbitrarily combined or further separated according to the functions they implement. The functions of each module will be described below.

[0150] In other embodiments, the apparatus provided in this application can be implemented in hardware. As an example, the apparatus provided in this application can be a processor in the form of a hardware decoding processor, which is programmed to execute the digital key movement trend recognition method provided in this application. For example, the processor in the form of a hardware decoding processor can be one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), programmable logic devices (PLDs), complex programmable logic devices (CPLDs), field-programmable gate arrays (FPGAs), or other electronic components.

[0151] This application provides a computer program product, which includes a computer program or computer-executable instructions stored in a computer-readable storage medium. The processor of an electronic device reads the computer-executable instructions from the computer-readable storage medium and executes the computer-executable instructions, causing the electronic device to perform the digital key movement trend recognition method described in this application.

[0152] This application provides a computer-readable storage medium storing computer-executable instructions or a computer program. When the computer-executable instructions or the computer program are executed by a processor, the processor will execute the digital key movement trend recognition method provided in this application. For example, ... Figure 1 The method for identifying the movement trend of digital keys is shown.

[0153] In some embodiments, the computer-readable storage medium may be a memory such as RAM, ROM, flash memory, magnetic surface memory, optical disk, or CD-ROM; or it may be a variety of devices including one or any combination of the above-mentioned memories.

[0154] In some embodiments, computer-executable instructions may take the form of programs, software, software modules, scripts, or code, written in any form of programming language (including compiled or interpreted languages, or declarative or procedural languages), and may be deployed in any form, including as stand-alone programs or as modules, components, subroutines, or other units suitable for use in a computing environment.

[0155] As an example, computer-executable instructions may, but do not necessarily, correspond to files in a file system. They may be stored as part of a file that holds other programs or data, for example, in one or more scripts in a Hyper Text Markup Language (HTML) document, in a single file dedicated to the program in question, or in multiple co-located files (e.g., files that store one or more modules, subroutines, or code sections).

[0156] As an example, computer-executable instructions can be deployed to execute on a single electronic device, or on multiple electronic devices located at one location, or on multiple electronic devices distributed across multiple locations and interconnected via a communication network.

[0157] In summary, the motion trend recognition method for digital keys based on Bluetooth RSSI proposed in this application transfers and applies the Mann-Kendall test method, and proposes a two-sided judgment rule. This achieves sensitive recognition of signal enhancement trends while to some extent accommodating signal attenuation fluctuations, reducing interference caused by fluctuations. Furthermore, it combines multi-anchor point measurements to judge the relative motion trend between the digital key and the vehicle. Traditional RSSI positioning technology mainly relies on calibration, using the unlocking and locking signal thresholds in a real vehicle environment to trigger the contactless unlocking and locking function. This approach cannot predict the user's intention to approach or move away from the vehicle, thus often resulting in problems such as "unlocking stuck" or "ping-pong" unlocking and locking in complex scenarios. This application, under the same hardware solution and cost control, uses software algorithms to judge the vehicle's motion trend, predicts the user's action intentions to a certain extent, and dynamically adjusts the unlocking and locking calibration parameters, thereby improving the user's contactless unlocking and locking experience.

[0158] The above description is merely an embodiment of this application and is not intended to limit the scope of protection of this application. Any modifications, equivalent substitutions, and improvements made within the spirit and scope of this application are included within the scope of protection of this application.

Claims

1. A method for recognizing the movement trend of a digital key, characterized in that, The method includes: Obtain the received signal strength indication values ​​of the digital key received at multiple anchor points on the vehicle; Based on the received signal strength indication value, a sequence of multiple anchor points is established; wherein, the sequence of each anchor point includes the received signal strength indication value of the digital key received by each anchor point within a preset time. Based on sequences with multiple anchor points, a first confidence interval, and a second confidence interval, the trend category of the sequence at each anchor point is examined; wherein, the first confidence interval is the confidence interval corresponding to an upward trend; and the second confidence interval is the confidence interval corresponding to a downward trend. The movement trend of the digital key is determined based on the change trend categories corresponding to multiple anchor points.

2. The method according to claim 1, characterized in that, The method further includes: Based on the movement trend of the digital key, the unlocking or locking calibration parameters of the vehicle are controlled.

3. The method according to claim 2, characterized in that, The sequence based on multiple anchor points, a first confidence interval, and a second confidence interval is used to examine the trend category of the sequence at each anchor point, including: Calculate the rank statistic of the sequence at each anchor point; wherein the rank statistic is used to measure the strength of the upward trend in the entire sequence; Based on the rank statistic, a test statistic corresponding to the sequence at each anchor point is determined; the test statistic is used to determine whether the trend in the sequence is significant. Based on the test statistic, the first confidence interval, and the second confidence interval, the trend category of the sequence at each anchor point is determined.

4. The method according to claim 3, characterized in that, The step of determining the trend category of the sequence at each anchor point based on the test statistic, the first confidence interval, and the second confidence interval includes: If the first test statistic corresponding to the sequence of the first anchor point is greater than zero, the first significance level corresponding to the first confidence interval is determined; wherein, the first significance level represents the probability of error in estimating the sequence value falling within the first confidence interval; the sequence of each anchor point includes the sequence of the first anchor point; Obtain the first critical value at the first significance level; If the first test statistic is less than the first critical value, the change trend category of the sequence at the first anchor point is determined to be the fluctuation category. If the first test statistic is greater than or equal to the first critical value, the trend category of the sequence at the first anchor point is determined to be the upward category.

5. The method according to claim 3, characterized in that, The step of determining the trend category of the sequence at each anchor point based on the test statistic, the first confidence interval, and the second confidence interval includes: If the first test statistic corresponding to the sequence of the first anchor point is less than zero, determine the second significance level corresponding to the second confidence interval; wherein, the second significance level represents the probability of error in estimating the sequence value falling within the second confidence interval; Obtain the second critical value at the second significance level; If the first test statistic is less than the second critical value, the change trend category of the sequence at the first anchor point is determined to be the fluctuation category. If the first test statistic is greater than or equal to the second critical value, the trend category of the sequence at the first anchor point is determined to be the decreasing category.

6. The method according to claim 2, characterized in that, The process of determining the movement trend of the digital key based on the change trend categories corresponding to multiple anchor points includes: If the change trend category of multiple anchor points is all upward, the movement trend is determined to be a moving away trend. If the change trend category of multiple anchor points is all downward, the movement trend is determined to be a convergence trend. If the trend category of the sequence of the first part of the multiple anchor points is upward, and the trend category of the sequence of the second part of the multiple anchor points is downward, then the movement trend is determined to be a oscillating trend.

7. The method according to claim 6, characterized in that, The method of controlling the unlocking or locking calibration parameters of the vehicle based on the movement trend of the digital key includes: If the movement trend is a moving away trend, decrease the locking calibration parameters of the vehicle; If the movement trend is an approaching trend, increase the unlocking calibration parameters of the vehicle; If the movement trend is a hovering trend, increase the locking calibration parameters of the vehicle.

8. The method according to claim 1, characterized in that, The step of establishing a sequence of multiple anchor points based on the received signal strength indication value includes: The received signal strength indication value is compensated to obtain a compensated received signal strength indication value; The compensated received signal strength indication is smoothed to obtain a smoothed received signal strength indication value; Based on the smoothed received signal strength indication value, a sequence of multiple anchor points is established.

9. The method according to claim 4 or 5, characterized in that, The process of determining the movement trend of the digital key based on the change trend categories corresponding to multiple anchor points includes: If the change trends of multiple anchor points are all fluctuating trends, then the motion trend is determined to be a static trend. Accordingly, the step of controlling the unlocking or locking calibration parameters of the vehicle based on the movement trend of the digital key includes: If the movement trend is a stationary trend, the unlocking or locking calibration parameters of the vehicle remain unchanged.

10. A vehicle, characterized in that, The vehicle includes: a memory for storing computer-executable instructions or computer programs; and a processor for executing the computer-executable instructions or computer programs stored in the memory to implement the digital key movement trend recognition method according to any one of claims 1 to 9.

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