A vehicle-mounted screen icon display method, electronic equipment and storage medium

Abstract

The application discloses a kind of vehicle screen icon display method, electronic equipment and storage medium, it is related to vehicle screen icon display technical field, including the following steps: collecting the interactive position coordinates of driver under different scenes with vehicle screen, and the use condition of software, screen interaction data and screen software use data are obtained respectively;Interaction area analysis processing is carried out, and the interactive convenient area information under different scenes is obtained;And analysis predicts the possibility of software use of driver under different scenes, and screen software prediction data is obtained;And the software icon of vehicle screen under different scenes is sorted and displayed;The application is used to solve the problem that the software icon of existing vehicle screen icon display technology is displayed on the central control screen, cannot dynamically adjust icon layout and display order according to the operation habit of driver and software use habit, ensure that the software icon that driver wants to interact is always in the screen area of convenient interaction.

Description

Technical Field

[0001] This invention relates to the field of in-vehicle screen icon display technology, specifically to an in-vehicle screen icon display method, electronic device, and storage medium. Background Technology

[0002] In-vehicle screen icon display technology refers to a technical system that uses specific algorithms, programs, and display technologies to display and arrange icons on in-vehicle screens in a reasonable manner and enable interaction with users. It aims to enable drivers or passengers to easily identify and operate in-vehicle software functions through clear and intuitive icon displays, thereby improving the user experience and driving safety.

[0003] Existing in-vehicle screen icon display technologies often display software icons on the central control screen according to the software's installation order or the user's manual sorting. To display more content, existing in-vehicle central control screens are often designed to be large, making it difficult for drivers of certain sizes to interact with certain areas of the screen without changing their posture. When the desired software icon is located in this area, the driver needs to change their posture or swipe the screen to bring the icon closer, affecting usability and safety. For example, patent application CN113031834A discloses a screen icon display method, device, electronic device, and storage medium. This solution requires swiping the screen to bring distant icons closer to the finger to achieve interaction for icons that are inaccessible on the screen, requiring a considerable swipe and impacting usability. Therefore, existing in-vehicle screen icon display technologies cannot dynamically adjust the icon layout and display order based on the driver's operating habits and software usage habits to ensure that the software icons the driver wants to interact with are always in easily accessible screen areas. Summary of the Invention

[0004] This invention aims to at least partially solve one of the technical problems in the prior art. It obtains screen interaction data and screen software usage data by collecting the driver's interaction coordinates with the in-vehicle screen in different scenarios and the software usage data. Interaction area analysis is performed to obtain convenient interaction areas in different scenarios. Furthermore, the likelihood of the driver using the software in different scenarios is analyzed and predicted to obtain screen software prediction data. Finally, the software icons on the in-vehicle screen in different scenarios are sorted and displayed. This addresses the problem that existing in-vehicle screen icon display technology cannot dynamically adjust the icon layout and display order according to the driver's operating habits and software usage habits when displaying software icons on the central control screen, ensuring that the software icons the driver wants to interact with are always in convenient interaction areas on the screen.

[0005] To achieve the above objectives, in a first aspect, this application provides a method for displaying icons on a vehicle screen, comprising the following steps: Collect the coordinates of the driver's interaction with the in-vehicle screen in different scenarios, as well as the software usage, to obtain screen interaction data and screen software usage data respectively; Based on the screen interaction coordinate data, the interaction area is analyzed and processed to obtain information on the convenient interaction area in different scenarios. Based on screen software usage data, we analyze and predict the likelihood of drivers using software in different scenarios to obtain screen software prediction data. Based on information about interactive convenience areas and screen software prediction data, the software icons on the in-vehicle screen are sorted and displayed in different scenarios.

[0006] Furthermore, the interaction coordinates between the driver and the in-vehicle screen in different scenarios, as well as the software usage, are collected to obtain screen interaction data and screen software usage data, including the following sub-steps: The stage from when the driver enters the vehicle to when the vehicle is officially started is called the departure preparation scene; the stage when the vehicle stops briefly due to temporary needs during the journey is called the intermediate stop scene; and the stage from when the vehicle arrives at its destination until the driver leaves the vehicle is called the destination closing scene. The scenarios of departure preparation, midway stop, and final conclusion are counted as screen usage scenarios, and any one of the screen usage scenarios is recorded as the first scenario.

[0007] Furthermore, collecting the driver's interaction coordinates with the in-vehicle screen in different scenarios, as well as the software usage, to obtain screen interaction coordinate data and screen software usage data, also includes the following sub-steps: The software icon display interface is denoted as the icon interface, and the display area of ​​all software icons on the vehicle screen under the icon interface is denoted as the icon display area. The coordinates of each pixel in the icon display area are obtained. For each pixel in the icon display area, when the user interacts with the in-vehicle screen at the location of the pixel, the interaction count of the pixel is incremented by 1. Repeatedly and continuously obtain the interaction count of each pixel in the icon display area under all screen usage scenarios, mark the pixels with a non-zero interaction count as interactive pixels, and obtain the corresponding screen interaction data after completion; Set the first time length to T1; for the first scenario, with T1 as the time period, periodically and continuously acquire the number of times each software is used and the usage time of each software on the vehicle screen, and record it as the screen software usage data of the first scenario. Repeatedly and continuously acquire the screen software usage data of all screen usage scenarios.

[0008] Furthermore, based on the screen interaction coordinate data, the interaction area is analyzed and processed to obtain the interaction convenience area information in different scenarios, including the following sub-steps: The screen interaction data of all screen usage scenarios are merged according to the corresponding pixels to obtain the full-scene interaction data, and the screen interaction data of the first scenario is recorded as the single-scene interaction data. Divide the icon display area evenly into multiple small squares of size a1*a1, and denote them as square points; Based on full-scene interaction data, for each square point, if it does not contain interactive pixels, it is marked as a regular square point; if it contains interactive pixels, it is marked as an interactive square point. For each interactive block point, after counting the number of interactions of all interactive pixels contained therein, record it as the number of interactions of the corresponding interactive block point; Sort all interactive blocks by the number of interactions in ascending order, and denote this as the first interaction sequence. Set the sliding window size to n1 and the sliding step size to 1. Using the sliding window, slide it sequentially from the starting position of the first interaction sequence and calculate the standard deviation of the number of interactions within each sliding window. Find the sliding window corresponding to the smallest standard deviation and denote it as the minimum standard window. The minimum number of interactions within the smallest standard window is denoted as the interaction threshold. Interactive blocks with an interaction count greater than or equal to the interaction threshold are denoted as core blocks. The total number of core blocks is denoted as k1.

[0009] Furthermore, the process of analyzing and processing the interaction area based on the screen interaction coordinate data to obtain the interaction convenience area information in different scenarios includes the following sub-steps: For each interactive block point, calculate the straight-line distance to the three nearest interactive block points and take the average value, which is recorded as the average neighbor distance of the corresponding block. Arrange all the average neighbor distances in ascending order and record them as the average neighbor distance sequence. Obtain the average value of the first k2% of the average neighbor distances in the average neighbor distance sequence and record it as the extended width KW, where k2 is the set percentage. For each core cube point, calculate the sum of the straight-line distances to all other core cube points, then divide by (k1-1) to record the average core distance of the corresponding core cube point; record the core cube point with the smallest average core distance as the center cube point; record the geometric center of the center cube point as the center of the region corresponding to the full scene interaction data. Using the geometric center of the central square as the center, draw concentric circles outwards with radii of KW, 2*KW, 3*KW, ..., n*KW respectively; denote the formed annular regions from the inside out as annular regions 1-n; denote any annular region as annular region i, where n is a set positive integer; Calculate the area of ​​the annular region i, denoted as RS. i And obtain the sum of the number of interactions of all interactive cube points within the annular region i, denoted as HD. i Calculate the interaction density MD of the first ring. i , among which, MD i =HD i / RS i ; Calculate the interaction density of all annular regions sequentially from the inside out, and then calculate the interaction density ratio MD of each annular region sequentially. i / MD i-1， When the interaction density ratio of a certain annular region is less than k3, obtain the annular region number at this time and denote it as V0; denote (V0-1)*KW as the region radius corresponding to the full scene interaction data, where k3 is the set ratio threshold; Then, based on the single-scene interaction data, obtain the corresponding region center and region radius. Then, based on the region center and region radius corresponding to the full-scene interaction data, calculate the average value of the region center and region radius respectively to obtain the average center and average radius. The circular area with the average center and the average radius is denoted as the interactive convenience area of ​​the first scene; the interactive convenience areas of all screen usage scenes are repeatedly obtained to obtain the interactive convenience area information.

[0010] Furthermore, based on screen software usage data analysis, the likelihood of drivers using the software in different scenarios is predicted. The resulting screen software prediction data includes the following sub-steps: Based on the screen software usage data of the first scenario, any software is denoted as the first software, and the usage time and number of times of the first software are obtained in the most recent k4 time periods, where k4 is the set number; Calculate the product of usage time and usage frequency within each time period, and record it as the usage intensity of the corresponding time period; record the k4 time periods in order from farthest to nearest as period 1-k4; For any period j, calculate the corresponding time weight SQj, where SQj = 2*i / k4; calculate the product of the usage intensity and the time weight for each time period, and sum them up, which is denoted as the weighted intensity and QA of the first software. Assuming an initial coherence value of 0, for the k4 time periods of the first software, in order from oldest to newest, if the number of times the first software is used within a time period is not 0, then the coherence value of the corresponding time period = the coherence value of the previous time period + 1 * the corresponding time weight; if the number of times the first software is used within a time period is 0, then the coherence value of the corresponding time period = the coherence value of the previous time period * 0.8 * the corresponding time weight; the coherence value of the most recent time period is calculated sequentially and denoted as the coherence accumulation value QB of the first software.

[0011] Furthermore, based on screen software usage data analysis, predicting the likelihood of drivers using the software in different scenarios and obtaining screen software prediction data also includes the following sub-steps: Repeatedly obtain the weighted strength and coherent cumulative value of all software, and arrange them in descending order, and record them as the strength size sequence and coherent size sequence respectively; Obtain the ranking number of QA in the intensity size sequence and the ranking number of QB in the continuous size sequence of the first software respectively, and calculate the average value, which is recorded as the usage prediction ranking of the first software. Repeatedly obtain the usage prediction ranking of all software, and mark the software with the top k5% usage prediction ranking as high-frequency software and the software with the bottom k5% usage prediction ranking as low-frequency software; obtain the screen software prediction data for the first scenario; and repeatedly obtain the screen software prediction data for all scenarios, where k5% is a set percentage.

[0012] Furthermore, based on information about interactive convenience areas and screen software prediction data, the sorting and display of software icons on the in-vehicle screen in different scenarios includes the following sub-steps: In the first scenario, obtain the display area of ​​each software icon in the icon interface and record it as the icon display square. Record the icon display square that is in the interactive convenience area as the convenient icon square and record the icon display square that is not in the interactive convenience area as the inconvenient icon square. Based on the distance from the driver, in order from closest to farthest, all the convenience icon squares on the icon interface are sequentially labeled as convenience squares 1-k6; and the inconvenience icon squares are sequentially labeled as inconvenience squares 1-k7. When a user opens the icon display interface, frequently used apps are displayed in the convenience grid (1-k6) according to their predicted usage ranking, while infrequently used apps are displayed in the inconvenience grid (1-k7) according to their predicted usage ranking. When the user swipes to the next page of the icon display interface, the previously undisplayed app icons are displayed in the order of the predicted usage ranking of frequently used apps corresponding to the convenience grid, and the order of the predicted usage ranking of infrequently used apps corresponding to the inconvenience grid. Repeatedly sort and display software icons for all screen usage scenarios.

[0013] Secondly, this application provides an electronic device including a processor and a memory, wherein the memory stores computer-readable instructions, and when the computer-readable instructions are executed by the processor, the steps in the method described above are performed.

[0014] Thirdly, this application provides a storage medium on which a computer program is stored, which, when executed by a processor, performs the steps of the method described above.

[0015] The beneficial effects of this invention are as follows: This invention collects the coordinates of the driver's interaction with the in-vehicle screen in different scenarios, as well as the software usage, to obtain screen interaction data and screen software usage data. Based on the screen interaction coordinate data, it performs interaction area analysis to obtain convenient interaction area information for different scenarios. Based on the screen software usage data, it predicts the likelihood of the driver using the software in different scenarios, obtaining screen software prediction data. Based on the convenient interaction area information and the screen software prediction data, it sorts and displays the software icons on the in-vehicle screen in different scenarios. When displaying software icons on the central control screen, it can dynamically adjust the icon layout and display order according to the driver's operating habits and software usage habits, ensuring that the software icons the driver wants to interact with are always in convenient interaction areas on the screen, thus improving operational convenience. This invention sets an interaction threshold by calculating the standard deviation of the number of interactions within a sliding window, and uses this threshold to divide core square points. This automatically identifies the region with the most stable distribution of interaction counts, making the division independent of manually set thresholds, thus improving the accuracy and robustness of the division. The size of the convenient interaction area is determined by sequentially drawing concentric circles and calculating the interaction density ratio of the annular regions. The advantage is that using the density of interaction counts to locate the convenient interaction area is more in line with the natural habits of human operation, improving the accuracy and scientific nature of the region division. The invention also calculates the software's weighted strength and continuous cumulative value, and then obtains the software's predicted ranking through ranking averaging. The advantage is that the weighted strength reflects total usage, and the continuous cumulative value emphasizes recent consecutive calls; combining these two factors more accurately predicts the driver's current software needs. Attached Figure Description

[0016] Figure 1 This is a flowchart illustrating the steps of the method of the present invention; Figure 2 This is a flowchart of the interactive area analysis and processing of the present invention; Figure 3 This is a flowchart illustrating the predictive ranking analysis process of the present invention. Figure 4 This is a schematic diagram of the electronic device of the present invention. Detailed Implementation

[0017] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0018] Example 1, please refer to Figure 1 As shown, this application provides a method for displaying icons on a vehicle screen, including the following steps: Step S1 involves collecting the coordinates of the driver's interaction with the in-vehicle screen in different scenarios, as well as the software usage, to obtain screen interaction data and screen software usage data. Step S1 includes the following sub-steps: Step S101: The stage from when the driver enters the vehicle to when the vehicle is officially started is recorded as the departure preparation scenario; the stage when the vehicle is briefly stopped due to temporary needs during the journey is recorded as the intermediate stop scenario; and the stage from when the vehicle arrives at the destination until the driver leaves the vehicle is recorded as the destination closing scenario. Step S102: Record the departure preparation scenario, the midway stop scenario and the end-of-journey closing scenario as screen usage scenarios, and record any one of the screen usage scenarios as the first scenario. Step S103: The software icon display interface is referred to as the icon interface, and all the display areas of the software icons on the vehicle screen under the icon interface are referred to as the icon display area. The coordinates of each pixel in the icon display area are obtained. Subsequent interactive data can be accurately mapped to the position of any pixel on the screen.

[0019] Step S104: For each pixel in the icon display area, when the user interacts with the vehicle screen at the location of the pixel, such as by clicking, long-pressing, or swiping, the interaction count of the pixel is incremented by 1; by quantifying the frequency of each pixel being touched, objective data is provided for subsequent processing. Step S105: Repeatedly and continuously obtain the number of interactions for each pixel in the icon display area under all screen usage scenarios, mark the pixels with non-zero interaction counts as interactive pixels, and obtain the corresponding screen interaction data after completion; Step S106: Set the first time length to T1; for the first scenario, with T1 as the time period, periodically and continuously acquire the number of times each software is used and the usage time of each software on the vehicle screen, and record it as the screen software usage data of the first scenario. Repeatedly acquire the screen software usage data of all screen usage scenarios; in this embodiment, T1=1 day, that is, count the number of times each software is used and the usage time of each software on the vehicle screen on the day. In practice, different screen usage scenarios result in significant differences in driver attention distribution and software operation needs. Scenario segmentation allows the system to analyze the operating habits and software requirements at each stage in a targeted manner, ensuring that the subsequent icon sorting and display results are more scenario-adaptable.

[0020] Step S2 involves analyzing and processing the interaction area based on the screen interaction coordinate data to obtain convenient interaction area information for different scenarios. Step S2 includes the following sub-steps: For step S201, please refer to... Figure 2 As shown, screen interaction data from all screen usage scenarios are merged according to their corresponding pixels to obtain full-scene interaction data, and the screen interaction data of the first scenario is recorded as single-scene interaction data. Step S202: Divide the icon display area evenly into multiple small squares of size a1*a1, denoted as square points; a1 can be flexibly set. In this embodiment, a1=9 pixels. Because there are many pixels on the screen, the probability of a single pixel being clicked repeatedly is small. Directly using pixels as the unit is not conducive to subsequent analysis. Therefore, the interaction data of the pixels is summarized into coarser-grained squares, which reduces the subsequent computational complexity and balances spatial resolution and performance overhead, providing a convenient unit for subsequent statistics. Step S203: Based on the full-scene interaction data, for each square point, if it does not contain interactive pixels, it is marked as a general square point; if it contains interactive pixels, it is marked as an interactive square point. Step S204: For each interactive block point, count the number of interactions of all interactive pixels contained therein, and record it as the number of interactions of the corresponding interactive block point; this lays the data foundation for subsequent analysis.

[0021] Step S205: Sort all interactive block points in ascending order of interaction counts, and record this as the first interaction count sequence; Step S206: Set the sliding window size to n1 and the sliding step size to 1. Using the sliding window, slide it sequentially from the starting position of the first interaction sequence and calculate the standard deviation of the interaction count within each sliding window. Find the sliding window corresponding to the smallest standard deviation and denot it as the smallest standard window. n1 can be flexibly set according to the actual application scenario. In this embodiment, n1=5. The standard deviation is the core indicator for measuring the concentration of data: the smaller the standard deviation, the closer the click counts within the window are. The click counts of frequently interacting interactive blocks are usually stable and concentrated, while the click counts of occasional clicks are scattered. The standard deviation can effectively distinguish between these two situations. Step S207: The minimum number of interactions within the minimum standard window is recorded as the interaction threshold. Interaction blocks with an interaction count greater than or equal to the interaction threshold are recorded as core blocks. The total number of core blocks is recorded as k1. The minimum standard window is the window with the smallest standard deviation among all windows, i.e., the interval with the highest local density. This window is the core signal of user operating habits: In the in-vehicle scenario, the high-frequency operations of the driver in a normal sitting posture are often stable and do not fluctuate. Therefore, the number of clicks will be concentrated in a certain interval. Therefore, the minimum number of interactions within the minimum standard window is recorded as the interaction threshold. Points with an interaction count greater than the interaction threshold can be regarded as points with high-frequency interactions.

[0022] Step S208: For each interactive block point, calculate the straight-line distance to the three nearest interactive block points and take the average value, which is recorded as the average neighbor distance of the corresponding block. Arrange all the average neighbor distances in ascending order and record them as the average neighbor distance sequence. Obtain the average value of the first k2% of the average neighbor distances in the average neighbor distance sequence, which is recorded as the extended width KW, where k2 is a set percentage. The average neighbor distance is used to quantify the spatial crowding around the point: if the average value is small, it means that the point is in a dense area; if the average value is large, it means that it is in a sparse area. In this embodiment, k2%=50%. Generally, k2%<50%, which can filter out a very small number of abnormal data and also ensure that the subsequent ring width can capture the interaction density difference between adjacent ring areas. Step S209: For each core square point, calculate the sum of the straight-line distances to all other core square points, and then divide by (k1-1) to record the average core distance of the corresponding core square point; record the core square point with the smallest average core distance as the center square point; record the geometric center of the center square point as the center of the area corresponding to the full-scene interaction data; the average core distance reflects the degree of clustering of the point among the core square points; if a point is surrounded more densely by other core square points, it means that it is closer to the geometric center of the driver's convenient interaction area, because the core square points of the driver's convenient interaction area will cluster around the center.

[0023] Step S210: Using the geometric center of the central square as the center, draw concentric circles outwards with radii of KW, 2*KW, 3*KW, ..., n*KW respectively; denote the formed annular regions from the inside out as annular regions 1-n; denote any annular region as annular region i, where n is a set positive integer; n can be flexibly set according to the actual application scenario, as long as the circular region with a radius of n*KW can cover all interactive square points; Step S211: Calculate the area of ​​the annular region i, denoted as RS. i And obtain the sum of the number of interactions of all interactive cube points within the annular region i, denoted as HD. i Calculate the interaction density MD of the first ring. i, among which, MD i =HD i / RS i Since an interactive block is a small block, for ease of calculation, if more than half of an interactive block's area is located in the circular region i, it can be considered an interactive block of the circular region i; otherwise, it is considered an interactive block of the adjacent circular region i.

[0024] Step S212: Calculate the interaction density of all annular regions sequentially from the inside out, and calculate the interaction density ratio MD of the annular regions sequentially. i / MD i-1， When the interaction density ratio of a certain annular area is less than k3, the annular area number at this time is obtained and denoted as V0; (V0-1)*KW is denoted as the area radius corresponding to the full scene interaction data, where k3 is the set ratio threshold; in this embodiment, k3=0.3; the interaction density ratio is the number of clicks per unit area, eliminating the influence of area differences and more accurately reflecting the ease of operation of the annular area; the higher the density, the higher the frequency of clicks in the annular area per unit space, that is, the more convenient the operation; when the interaction density ratio is less than k3, it means that the annular area has entered the area where the driver needs to change posture to click, and at this time the inner edge radius of the annular area is taken as the corresponding area radius; Step S213: Based on the single-scene interaction data, obtain the corresponding region center and region radius. Then, based on the region center and region radius corresponding to the full-scene interaction data, calculate the average value of the region center and region radius to obtain the average center and average radius.

[0025] Step S214: The circular area with the average center as the center and the average radius as the radius is recorded as the interactive convenience area of ​​the first scene; the interactive convenience areas of all screen usage scenarios are repeatedly obtained to obtain the interactive convenience area information; because the amount of data in some screen usage scenarios may be small and cannot support the data analysis and processing process alone, the data is summarized and analyzed once, and then the data of the corresponding scenario is analyzed and processed again, and then the results are averaged. This can make up for the deficiency of insufficient data in a single scenario to a certain extent, and can also be more in line with the actual screen usage scenarios. In practice, the area of ​​the screen that a driver can stably reach with their arm naturally extended while in a normal sitting posture is the convenient interaction area. This range is determined by human physiological structure and screen size. Operations outside this range require the driver to deliberately lean forward, twist their arm, or release the steering wheel, which increases the difficulty of operation and safety risks. Therefore, without significant intervention in the interface design, the number of clicks on interactive pixels located within the convenient interaction area will be significantly higher than that of pixels outside the range. Based on this characteristic, analysis can determine the convenient interaction area for each driver on the screen.

[0026] Step S3 involves analyzing and predicting the likelihood of driver software usage in different scenarios based on screen software usage data, thereby obtaining screen software prediction data. Step S3 includes the following sub-steps: For step S301, please refer to... Figure 3 As shown, based on the screen software usage data of the first scenario, any software is denoted as the first software, and the usage time and number of times of the first software are obtained in the most recent k4 time periods, where k4 is the set number; in this embodiment, k4 is 30, that is, one month's data is obtained; Step S302: Calculate the product of usage time and usage frequency within each time period, and record it as the usage intensity of the corresponding time period; record the k4 time periods as period 1-k4 in order from farthest to near; usage intensity is used to quantify the actual usage intensity of the software within a day, avoiding the one-sidedness of a single indicator; Step S303: For any period j, calculate the corresponding time weight SQj, where SQj = 2*i / k4; calculate the product of the usage intensity and the time weight for each time period, and sum them up, denoted as the weighted intensity and QA of the first software; through the time weight, the influence of recent usage behavior on the prediction is made greater, which conforms to the law that user habits have recent dependence, and the weighted intensity is used to quantify the degree of user use of the software within a certain period.

[0027] Step S304: Set the initial coherence value to 0. For the k4 time periods of the first software, in order from oldest to newest, if the number of times the first software is used within a time period is not 0, then the coherence value of the corresponding time period = the coherence value of the previous time period + 1 * the corresponding time weight; if the number of times the first software is used within a time period is 0, then the coherence value of the corresponding time period = the coherence value of the previous time period * 0.8 * the corresponding time weight; calculate the coherence value of the most recent time period in sequence, and record it as the coherence accumulation value QB of the first software. For example, if the number of times the first software is used in the first three time periods are 2, 0, and 1 respectively, and the corresponding time weights are 0.1, 0.2, and 0.3 respectively, then the continuous cumulative value = (0 + 1 * 0.1) * 0.8 * 0.2 + 1 * 0.3 = 0.316. The inertia superposition value quantifies the user's persistence in using the software by superimposing and strengthening it during continuous use and slowly decaying it during interruption. Software that is used continuously and frequently recently has a high inertia superposition value, and users are more likely to continue using it; software that is frequently interrupted has a low inertia superposition value, and users are less likely to use it.

[0028] Step S305: Repeatedly obtain the weighted strength and coherent cumulative value of all software, and arrange them in descending order, and record them as the strength size sequence and coherent size sequence respectively. Step S306: Obtain the ranking number of QA in the intensity sequence and the ranking number of QB in the coherence sequence of the first software, and calculate the average value, which is recorded as the usage prediction ranking of the first software. Usage intensity and coherence are equally important. Using the average ranking avoids imbalance of a single indicator. Alternatively, a weighted average can be used according to the actual application scenario, and the weights can be flexibly adjusted. Step S307: Repeatedly obtain the usage prediction ranking of all software, and record the software with the top k5% usage prediction ranking as high-frequency software and the software with the bottom k5% usage prediction ranking as low-frequency software; obtain the screen software prediction data for the first scenario; and repeatedly obtain the screen software prediction data for all scenarios, where k5% is a set percentage; in this embodiment, k5=50%, which can be flexibly set. In practice, calculating the intensity of use over a time period by only looking at the number of times may misjudge high-frequency but short-term light use from low-frequency but long-term deep use. For example, clicking the radio 10 times a day for 1 minute each time, or clicking the navigation twice a day for 30 minutes each time. If only time is considered, short-term but high-frequency essential scenarios may be overlooked, such as adjusting the air conditioner 5 times a day for 1 minute each time, which actually has a high degree of dependence.

[0029] Step S4: Based on the interactive convenience area information and screen software prediction data, sort and display the software icons on the in-vehicle screen in different scenarios; Step S4 includes the following sub-steps: Step S401: In the first scenario, obtain the display area of ​​each software icon in the icon interface and record it as an icon display square. Record the icon display squares that are in the convenient interaction area as convenient icon squares and record the icon display squares that are not in the convenient interaction area as inconvenient icon squares. Clearly distinguish which icon display positions on the screen are most easily accessible to the driver in this scenario and which icon display positions are relatively inconvenient. Step S402: Based on the distance to the driver, in order from closest to farthest, all the convenience icon squares on the icon interface are sequentially labeled as convenience squares 1-k6; and the inconvenience icon squares are sequentially labeled as inconvenience squares 1-k7. The closer the square is and the easier it is to reach, the earlier its number will be, which can ensure that the most important software icon appears in the closest position.

[0030] In step S403, when the user opens the icon display interface, frequently used apps are displayed in the convenience squares 1-k6 according to their predicted usage ranking, and in the inconvenience squares 1-k7 according to their predicted usage ranking. When the user swipes the screen to the next page of the icon display interface, the icons of apps that have not been displayed before are displayed in the order of the predicted usage ranking of frequently used apps corresponding to the convenience squares, and the order of the predicted usage ranking of inconvenience squares corresponding to the inconvenience squares. If the convenience squares on the homepage can only hold 6 frequently used apps, the 7th frequently used app will automatically appear in the first convenience icon square on the next page, concentrating the most likely apps in the most accessible convenience icon squares to improve the user interaction experience. Step S404: Repeat the sorting and display of software icons for all screen usage scenarios; In practice, although low-frequency applications are not frequently used, they are still needed in certain situations. Placing the icons of low-frequency applications in the inconvenient icon grid and displaying them together with the icons of other low-frequency applications avoids occupying core space. This allows users to easily access high-demand software while driving, and also allows them to quickly find it when the driver needs it occasionally.

[0031] Example 2, please refer to Figure 4 As shown, Figure 4 A schematic diagram of an electronic device is provided, which may include a processor, a communication interface, a memory, and a communication bus. The processor, communication interface, and memory communicate with each other via the communication bus. The memory stores computer-readable instructions, and the processor can call these instructions. When the processor executes a computer-readable instruction, it performs steps similar to those in a vehicle-mounted screen icon display method to achieve the following functions: collecting the driver's interaction coordinates with the vehicle-mounted screen in different scenarios, as well as software usage data, to obtain screen interaction data and screen software usage data; performing interaction area analysis based on the screen interaction coordinate data to obtain convenient interaction area information for different scenarios; analyzing and predicting the driver's likelihood of software use in different scenarios based on screen software usage data to obtain screen software prediction data; and sorting and displaying the software icons on the vehicle-mounted screen in different scenarios based on the convenient interaction area information and the screen software prediction data.

[0032] Furthermore, when the logical instructions in the aforementioned memory can be implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer 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 steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0033] Example 3: This application also provides a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, it performs the steps of the above-described method for displaying icons on a vehicle screen to achieve the following functions: collecting the coordinates of the driver's interaction with the vehicle screen in different scenarios, as well as the software usage, to obtain screen interaction data and screen software usage data respectively; performing interaction area analysis processing based on the screen interaction coordinate data to obtain interaction convenience area information in different scenarios; analyzing and predicting the driver's software usage probability in different scenarios based on the screen software usage data to obtain screen software prediction data; and sorting and displaying the software icons on the vehicle screen in different scenarios based on the interaction convenience area information and the screen software prediction data.

[0034] Based on the above description of the embodiments, the embodiments of the present invention can be provided as methods, systems, or computer program products. Based on this understanding, the above technical solutions, in essence or in terms of their contribution to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or certain parts of the embodiments.

[0035] In the embodiments provided in this application, it should be understood that the disclosed system or method can be implemented in other ways. The embodiments described above are merely illustrative. For example, the division of modules or units is only a logical functional division, and there may be other division methods in actual implementation. Furthermore, multiple modules or units may be combined or integrated into another system, or some features may be ignored or not executed. Additionally, the coupling or direct coupling or communication connection shown or discussed may be through some communication interfaces. The indirect coupling or communication connection between systems, modules, and units may be electrical, mechanical, or other forms.

[0036] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

Claims

1. A method for displaying icons on a vehicle screen, characterized in that, Includes the following steps: Collect the coordinates of the driver's interaction with the in-vehicle screen in different scenarios, as well as the software usage, to obtain screen interaction data and screen software usage data respectively; Based on the screen interaction coordinate data, the interaction area is analyzed and processed to obtain information on the convenient interaction area in different scenarios. Based on screen software usage data analysis, predict the likelihood of drivers using software in different scenarios to obtain screen software prediction data; Based on information about interactive convenience areas and screen software prediction data, the software icons on the in-vehicle screen are sorted and displayed in different scenarios. Based on screen interaction coordinate data, the interaction area analysis and processing are performed to obtain convenient interaction area information for different scenarios, including the following sub-steps: The screen interaction data of all screen usage scenarios are merged according to the corresponding pixels to obtain the full-scene interaction data, and the screen interaction data of the first scenario is recorded as the single-scene interaction data. Divide the icon display area evenly into multiple small squares of size a1*a1, and denote them as square points; Based on full-scene interaction data, for each square point, if it does not contain interactive pixels, it is marked as a regular square point; if it contains interactive pixels, it is marked as an interactive square point. For each interactive block point, after counting the number of interactions of all interactive pixels contained therein, record it as the number of interactions of the corresponding interactive block point; Sort all interactive blocks by the number of interactions in ascending order, and denote this as the first interaction sequence. Set the sliding window size to n1 and the sliding step size to 1. Using the sliding window, slide it sequentially from the starting position of the first interaction sequence and calculate the standard deviation of the number of interactions within each sliding window. Find the sliding window corresponding to the smallest standard deviation and denote it as the minimum standard window. The minimum number of interactions within the minimum standard window is denoted as the interaction threshold. Interaction blocks with an interaction count greater than or equal to the interaction threshold are denoted as core blocks. The total number of core blocks is denoted as k1. For each interactive block point, calculate the straight-line distance to the three nearest interactive block points and take the average value, which is recorded as the average neighbor distance of the corresponding block. Arrange all the average neighbor distances in ascending order and record them as the average neighbor distance sequence. Obtain the average value of the first k2% of the average neighbor distances in the average neighbor distance sequence and record it as the extended width KW, where k2 is the set percentage. For each core cube point, calculate the sum of the straight-line distances to all other core cube points, then divide by (k1-1) to record the average core distance of the corresponding core cube point; record the core cube point with the smallest average core distance as the center cube point; record the geometric center of the center cube point as the center of the region corresponding to the full scene interaction data. Using the geometric center of the central square as the center, draw concentric circles outwards with radii of KW, 2*KW, 3*KW, ..., n*KW respectively; denote the formed annular regions from the inside out as annular regions 1-n; denote any annular region as annular region i, where n is a set positive integer; Calculate the area of ​​the annular region i, denoted as RS. i And obtain the sum of the number of interactions of all interactive cube points within the annular region i, denoted as HD. i Calculate the interaction density MD of the first ring. i , among which, MD i =HD i / RS i ; Calculate the interaction density of all annular regions sequentially from the inside out, and then calculate the interaction density ratio MD of each annular region sequentially. i / MD i-1， When the interaction density ratio of a certain annular region is less than k3, obtain the annular region number at this time and denote it as V0; denote (V0-1)*KW as the region radius corresponding to the full scene interaction data, where k3 is the set ratio threshold; Then, based on the single-scene interaction data, obtain the corresponding region center and region radius. Then, based on the region center and region radius corresponding to the full-scene interaction data, calculate the average value of the region center and region radius respectively to obtain the average center and average radius. The circular area with the average center and the average radius is denoted as the interactive convenience area of ​​the first scene; the interactive convenience areas of all screen usage scenes are repeatedly obtained to obtain the interactive convenience area information.

2. The method for displaying icons on a vehicle screen according to claim 1, characterized in that, Collecting the coordinates of the driver's interaction with the in-vehicle screen in different scenarios, as well as the software usage, to obtain screen interaction data and screen software usage data includes the following sub-steps: The stage from when the driver enters the vehicle to when the vehicle is officially started is called the departure preparation scene; the stage when the vehicle stops briefly due to temporary needs during the journey is called the intermediate stop scene; and the stage from when the vehicle arrives at its destination until the driver leaves the vehicle is called the destination closing scene. The scenarios of departure preparation, midway stop, and final conclusion are counted as screen usage scenarios, and any one of the screen usage scenarios is recorded as the first scenario.

3. The method for displaying icons on a vehicle screen according to claim 2, characterized in that, Collecting the coordinates of the driver's interaction with the in-vehicle screen in different scenarios, as well as the software usage, to obtain screen interaction coordinate data and screen software usage data also includes the following sub-steps: The software icon display interface is denoted as the icon interface, and the display area of ​​all software icons on the vehicle screen under the icon interface is denoted as the icon display area. The coordinates of each pixel in the icon display area are obtained. For each pixel in the icon display area, when the user interacts with the in-vehicle screen at the location of the pixel, the interaction count of the pixel is incremented by 1. Repeatedly and continuously obtain the interaction count of each pixel in the icon display area under all screen usage scenarios, mark the pixels with a non-zero interaction count as interactive pixels, and obtain the corresponding screen interaction data after completion; Set the first time length to T1; for the first scenario, with T1 as the time period, periodically and continuously acquire the number of times each software is used and the usage time of each software on the vehicle screen, and record it as the screen software usage data of the first scenario. Repeatedly and continuously acquire the screen software usage data of all screen usage scenarios.

4. The method for displaying icons on a vehicle screen according to claim 3, characterized in that, Based on screen software usage data analysis, predicting the likelihood of drivers using software in different scenarios involves the following sub-steps: Based on the screen software usage data of the first scenario, any software is denoted as the first software, and the usage time and number of times of the first software are obtained in the most recent k4 time periods, where k4 is the set number; Calculate the product of usage time and usage frequency within each time period, and record it as the usage intensity of the corresponding time period; record the k4 time periods in order from farthest to nearest as period 1-k4; For any period j, calculate the corresponding time weight SQj, where SQj = 2*i / k4; calculate the product of the usage intensity and the time weight for each time period, and sum them up, which is denoted as the weighted intensity and QA of the first software. Assuming the initial coherence value is 0, for the k4 time periods of the first software in order from oldest to newest, if the number of times the first software is used within a time period is not 0, then the coherence value of the corresponding time period = the coherence value of the previous time period + 1 * the corresponding time weight; if the number of times the first software is used within a time period is 0, then the coherence value of the corresponding time period = the coherence value of the previous time period * 0.8 * the corresponding time weight. The continuous value of the most recent time period is calculated sequentially and denoted as the continuous cumulative value QB of the first software.

5. The method for displaying icons on a vehicle screen according to claim 4, characterized in that, Predicting driver software usage likelihood in different scenarios based on screen software usage data analysis includes the following sub-steps: Repeatedly obtain the weighted strength and coherent cumulative value of all software, and arrange them in descending order, and record them as the strength size sequence and coherent size sequence respectively; Obtain the ranking number of QA in the intensity size sequence and the ranking number of QB in the continuous size sequence of the first software respectively, and calculate the average value, which is recorded as the usage prediction ranking of the first software. Repeatedly obtain the usage prediction ranking of all software, and mark the software with the top k5% usage prediction ranking as high-frequency software and the software with the bottom k5% usage prediction ranking as low-frequency software; obtain the screen software prediction data for the first scenario; and repeatedly obtain the screen software prediction data for all scenarios, where k5% is a set percentage.

6. The method for displaying icons on a vehicle screen according to claim 5, characterized in that, Based on information about interactive convenience areas and screen software prediction data, the sorting and display of software icons on in-vehicle screens in different scenarios includes the following sub-steps: In the first scenario, obtain the display area of ​​each software icon in the icon interface and record it as the icon display square. Record the icon display square that is in the interactive convenience area as the convenient icon square and record the icon display square that is not in the interactive convenience area as the inconvenient icon square. Based on the distance from the driver, in order from closest to farthest, all the convenience icon squares on the icon interface are sequentially labeled as convenience squares 1-k6; and the inconvenience icon squares are sequentially labeled as inconvenience squares 1-k7. When a user opens the icon display interface, frequently used apps are displayed in the convenient grids 1-k6 according to their predicted usage ranking, while infrequently used apps are displayed in the inconvenient grids 1-k7 according to their predicted usage ranking. When the user swipes the screen to the next page of icon display, the previously undisplayed software icons continue to be displayed in the order of the predicted usage ranking of high-frequency software and the order of the convenience squares, while the predicted usage ranking of low-frequency software and the order of the inconvenience squares are displayed. Repeatedly sort and display software icons for all screen usage scenarios.

7. An electronic device, characterized in that, It includes a processor and a memory, the memory storing computer-readable instructions that, when executed by the processor, perform the steps of the method as described in any one of claims 1-6.

8. A storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it performs the steps of the method as described in any one of claims 1-6.

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