Data display method and device, electronic equipment, storage medium and chip

Abstract

The invention provides a data display method and device, electronic equipment, a storage medium and a chip. The method comprises the steps of obtaining predicted report point data corresponding to a target track output by a report point prediction model; wherein the report point prediction model is used for performing data prediction from at least one dimension of an equipment direction dimension, a data integrity dimension, a trajectory bending dimension, a trajectory position dimension and a trajectory repetition dimension; screening the predicted report point data according to attribute information of a display screen and a prediction period to obtain target report point data; and drawing the target report point data along the target track. In the present disclosure, the report point prediction model performs report point data prediction from the device direction dimension, the data integrity dimension, the trajectory bending dimension, the trajectory position dimension and the trajectory repetition dimension, the trajectory prediction accuracy is improved, in addition, in combination with the screen attribute information, the prediction period is dynamically adjusted and the predicted report point data is screened, and the prediction accuracy is improved. The smoothness and continuity of the track can be improved, and the overall chirality experience is improved.

Description

Technical Field

[0001] The present disclosure relates to the field of data processing, and in particular, to a data display method, an apparatus, an electronic device, a storage medium, and a chip. Background Art

[0002] With the popularization of intelligent terminal devices and the development of the data processing field, users have put forward higher requirements for the quality of the data display content from the terminal. As an important direction in the field of display technology for touch input processing of the terminal, when the terminal outputs touch content to the user, the display effect can be enhanced by predicting the handwriting. However, in the related art, due to large prediction errors in predicting the handwriting, inaccurate estimation of the number of prediction cycles, and insufficient adaptation to the writing scenario, the predicted trajectory and the real trajectory deviate greatly, resulting in poor followability of writing. Summary of the Invention

[0003] The present disclosure provides a data display method, an apparatus, an electronic device, a storage medium, and a chip to solve the problems in the related art, which can improve the accuracy of trajectory prediction, dynamically adjust the number of prediction cycles in combination with the screen attribute information, improve the smoothness and continuity of the trajectory, and enhance the overall followability experience.

[0004] A first aspect embodiment of the present disclosure provides a data display method, and the method includes:

[0005] Obtaining predicted reporting point data corresponding to a target trajectory output by a reporting point prediction model; wherein, the reporting point prediction model is used to perform data prediction from at least one dimension of the device direction dimension, the data integrity dimension, the trajectory bending dimension, the trajectory position dimension, and the trajectory repetition dimension;

[0006] Screening the predicted reporting point data according to the attribute information of the display screen and the prediction period to obtain target reporting point data;

[0007] Drawing the target reporting point data along the target trajectory.

[0008] In some embodiments of the present disclosure, the obtaining predicted reporting point data corresponding to a target trajectory output by a reporting point prediction model includes:

[0009] Performing a first adjustment on the direction of the coordinate system of the target trajectory to make it consistent with the direction of the coordinate system of the device screen;

[0010] Performing interpolation calculation on the predicted trajectory points according to a preset sampling interval and the attribute information of the predicted trajectory points to obtain first data;

[0011] Calculating the curvature of the target trajectory according to the first trajectory point at the current moment and the second trajectory points at historical moments within the preset sampling interval;

[0012] Perform differential processing on the first data to obtain second data;

[0013] Perform filtering processing on the second data to obtain the predicted reporting point data.

[0014] In some embodiments of the present disclosure, the screening of the predicted reporting point data according to the attribute information of the display screen and the prediction period to obtain the target reporting point data includes:

[0015] Convert the direction of the coordinate system of the target trajectory and the coordinates of the predicted trajectory points to obtain converted data;

[0016] Screen the converted data according to the attribute information of the display screen and the prediction period to obtain the target reporting point data.

[0017] In some embodiments of the present disclosure, the conversion of the direction of the coordinate system of the target trajectory and the coordinates of the trajectory points to obtain converted data includes:

[0018] Perform a second adjustment on the direction of the coordinate system of the target trajectory to adjust it to the direction before performing the first adjustment, and the second adjustment is the reverse process of the first adjustment;

[0019] Determine the coordinates corresponding to the target reporting point data according to the nearest visible coordinates of the target trajectory and the coordinates of the predicted trajectory points to obtain the converted data.

[0020] In some embodiments of the present disclosure, the screening of the converted data according to the attribute information of the display screen and the prediction period to obtain the target reporting point data includes:

[0021] Calculate the deviation between the predicted trajectory points and the latest reporting point of the target trajectory according to the attribute information of the predicted trajectory points;

[0022] Input the deviation and the historical trajectory into a preset recognition model to output the unavailable predicted trajectory points within the prediction period;

[0023] Screen the number of the predicted trajectory points according to the attribute information of the display screen to obtain the target reporting point data.

[0024] In some embodiments of the present disclosure, the adjustment of the direction of the coordinate system of the target trajectory to be consistent with the direction of the coordinate system of the device screen includes:

[0025] Judge whether the direction of the coordinate system of the target trajectory is consistent with the direction of the coordinate system of the device screen;

[0026] If they are inconsistent, adjust the direction of the coordinate system of the target trajectory according to the direction of the coordinate system of the device screen.

[0027] In some embodiments of the present disclosure, the training method of the reporting point prediction model includes:

[0028] Obtain trajectory features for training, and obtain a preset number of prediction deviation sequences within each training prediction period;

[0029] Divide the deviation labels of the prediction deviation sequences;

[0030] Generate eigenvalue based on the preset algorithm according to the training trajectory features and the prediction deviation sequences carrying deviation categories;

[0031] Input the eigenvalue into the reporting point prediction model for training to obtain a trained reporting point prediction model.

[0032] An embodiment of the second aspect of the present disclosure provides a data display device, the device includes:

[0033] An acquisition unit, configured to acquire prediction reporting point data corresponding to a target trajectory output by a reporting point prediction model; wherein, the reporting point prediction model is used to perform data prediction from at least one dimension of the device direction dimension, data integrity dimension, trajectory bending dimension, trajectory position dimension, and trajectory repetition dimension;

[0034] A screening unit, configured to screen the prediction reporting point data according to the attribute information of the display screen and the prediction period to obtain target reporting point data;

[0035] A drawing unit, configured to draw the target reporting point data along the target trajectory.

[0036] In some embodiments of the present disclosure, the acquisition unit includes:

[0037] An adjustment module, configured to perform a first adjustment on the direction of the coordinate system of the target trajectory to make it consistent with the direction of the coordinate system of the device screen;

[0038] A first calculation module, configured to perform interpolation calculation on the prediction trajectory points according to the preset sampling interval and the attribute information of the prediction trajectory points to obtain first data;

[0039] A second calculation module, configured to calculate the curvature of the target trajectory according to the first trajectory point at the current moment and the second trajectory point at the historical moment within the preset sampling interval;

[0040] A first processing module, configured to perform difference processing on the first data to obtain second data;

[0041] The second processing module is used to filter the second data to obtain the predicted reporting point data.

[0042] In some embodiments of the present disclosure, the screening unit includes:

[0043] The conversion module is used to convert the direction of the coordinate system of the target trajectory and the coordinates of the predicted trajectory points to obtain the converted data;

[0044] The screening module is used to screen the converted data according to the attribute information of the display screen and the prediction period to obtain the target reporting point data.

[0045] In some embodiments of the present disclosure, the conversion module is further used to:

[0046] Perform a second adjustment on the direction of the coordinate system of the target trajectory to adjust it to the direction before the first adjustment, and the second adjustment is the reverse process of the first adjustment;

[0047] According to the nearest visible coordinates of the target trajectory and the coordinates of the predicted trajectory points, determine the coordinates corresponding to the target reporting point data to obtain the converted data.

[0048] In some embodiments of the present disclosure, the screening module is further used to:

[0049] Calculate the deviation between the predicted trajectory points and the latest reporting point of the target trajectory according to the attribute information of the predicted trajectory points;

[0050] Input the deviation and the historical trajectory into a preset recognition model to output the unavailable predicted trajectory points within the prediction period;

[0051] Screen the number of predicted trajectory points according to the attribute information of the display screen to obtain the target reporting point data.

[0052] In some embodiments of the present disclosure, the adjustment module is further used to:

[0053] Judge whether the direction of the coordinate system of the target trajectory is consistent with the direction of the coordinate system of the device screen;

[0054] If not, adjust the direction of the coordinate system of the target trajectory according to the direction of the coordinate system of the device screen.

[0055] In some embodiments of the present disclosure, a training unit is further included;

[0056] The training unit is used to:

[0057] Obtain the trajectory features for training, and obtain a preset number of prediction deviation sequences within each training prediction period;

[0058] Divide the deviation labels of the predicted deviation sequence;

[0059] Generate eigenvalues based on a preset algorithm according to the training trajectory features and the predicted deviation sequence carrying deviation categories;

[0060] Input the eigenvalues into the reporting point prediction model for training to obtain a trained reporting point prediction model.

[0061] An embodiment of the third aspect of the present disclosure provides an electronic device, including: at least one processor; and a memory communicatively connected to the at least one processor; wherein, the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the method described in the embodiment of the first aspect of the present disclosure.

[0062] An embodiment of the fourth aspect of the present disclosure provides a non-transitory computer-readable storage medium storing computer instructions, wherein the computer instructions are used to cause a computer to execute the method described in the embodiment of the first aspect of the present disclosure.

[0063] An embodiment of the fifth aspect of the present disclosure provides a chip, the chip includes one or more interfaces and one or more processors; the interfaces are used to receive signals from the memory of the electronic device and send signals to the processors, and the signals include computer instructions stored in the memory. When the processors execute the computer instructions, the electronic device is enabled to execute the method described in the embodiment of the first aspect of the present disclosure.

[0064] In summary, according to the data display method proposed by the present disclosure, the method includes obtaining predicted reporting point data corresponding to a target trajectory output by a reporting point prediction model; wherein, the reporting point prediction model is used to perform data prediction from at least one dimension among the device direction dimension, data integrity dimension, trajectory bending dimension, trajectory position dimension, and trajectory repetition dimension; screening the predicted reporting point data according to the attribute information of the display screen and the prediction period to obtain target reporting point data; and drawing the target reporting point data along the target trajectory. In the present disclosure, the reporting point prediction model performs reporting point data prediction from the device direction dimension, data integrity dimension, trajectory bending dimension, trajectory position dimension, and trajectory repetition dimension, improving the accuracy of trajectory prediction. In addition, by combining the screen attribute information, dynamically adjusting the prediction period and screening the predicted reporting point data, the smoothness and continuity of the trajectory can be improved, as well as the overall follow-up experience.

[0065] It should be understood that the above general description and subsequent detailed description are only exemplary and explanatory, and cannot limit the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0066] The accompanying drawings here are incorporated into the specification and form a part of this specification, showing embodiments consistent with the present disclosure, and are used together with the specification to explain the principles of the present disclosure, and do not constitute an improper limitation to the present disclosure.

[0067] Figure 1 A flowchart of a data display method provided for an embodiment of the present disclosure;

[0068] Figure 2 A flowchart of a data display method provided for an embodiment of the present disclosure;

[0069] Figure 3 A flowchart of a data display method provided for an embodiment of the present disclosure;

[0070] Figure 4 A flowchart of a data display method provided for an embodiment of the present disclosure;

[0071] Figure 5 A schematic structural diagram of a data display device provided for an embodiment of the present disclosure;

[0072] Figure 6 A schematic structural diagram of another data display device provided for an embodiment of the present disclosure;

[0073] Figure 7 A schematic structural diagram of an electronic device provided for an embodiment of the present disclosure;

[0074] Figure 8 A schematic structural diagram of a chip provided for an embodiment of the present disclosure. Detailed implementation manners

[0075] The embodiments of the present disclosure will be described in detail below. The examples of the embodiments are shown in the accompanying drawings, in which the same or similar reference numerals denote the same or similar elements or elements with the same or similar functions from beginning to end. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to explain the present disclosure, and should not be construed as a limitation to the present disclosure.

[0076] With the popularization of intelligent terminal devices and the development of the data processing field, users have put forward higher requirements for the quality of the content displayed on the terminal. As an important direction in the field of display technology for touch input processing of terminals, when the terminal outputs touch content to the user, the display effect can be enhanced by predicting the handwriting. However, in the related technologies, large prediction errors in predicting the handwriting, inaccurate estimation of the number of prediction cycles, and insufficient adaptation to the writing scenarios will all lead to a large deviation between the predicted trajectory and the real trajectory, resulting in poor followability of writing. Therefore, a data display method with a stronger followability experience is needed.

[0077] Therefore, to solve the problems existing in the related art, the present disclosure proposes a data display method, which includes obtaining predicted reporting point data corresponding to a target trajectory output by a reporting point prediction model; wherein, the reporting point prediction model is used to perform data prediction from at least one dimension among the device direction dimension, data integrity dimension, trajectory bending dimension, trajectory position dimension, and trajectory repetition dimension; screening the predicted reporting point data according to the attribute information of the display screen and the prediction period to obtain target reporting point data; and drawing the target reporting point data along the target trajectory.

[0078] This solution can improve the accuracy of trajectory prediction, combine the screen attribute information, dynamically adjust the number of prediction periods, improve the smoothness and continuity of the trajectory, and enhance the overall followability experience.

[0079] The embodiments of the present disclosure are not exhaustive, but only schematic illustrations of some embodiments, and do not constitute specific limitations on the protection scope of the present disclosure. Without contradiction, each step in a certain embodiment can be implemented as an independent embodiment, and the steps can be combined arbitrarily. For example, the solution after removing some steps in a certain embodiment can also be implemented as an independent embodiment, and the order of the steps in a certain embodiment can be arbitrarily exchanged. Additionally, the optional implementation manners in a certain embodiment can be combined arbitrarily; furthermore, the embodiments can be combined arbitrarily. For example, some or all of the steps of different embodiments can be combined arbitrarily, and a certain embodiment can be combined arbitrarily with the optional implementation manners of other embodiments.

[0080] In each embodiment of the present disclosure, unless otherwise specified and there is no logical conflict, the terms and / or descriptions among the embodiments are consistent and can be referenced to each other. The technical features in different embodiments can be combined to form new embodiments according to their internal logical relationships.

[0081] The terms used in the embodiments of the present disclosure are only for the purpose of describing specific embodiments, and do not constitute a limitation on the present disclosure.

[0082] In the embodiments of the present disclosure, unless otherwise specified, elements expressed in the singular form, such as "a", "one kind", "the", "above-mentioned", "said", "aforementioned", "this", etc., can mean "one and only one", or can also mean "one or more", "at least one", etc. For example, in the case of using articles such as "a", "an", "the" in English translation, the noun after the article can be understood as a singular expression form or a plural expression form.

[0083] In some embodiments, terms such as "in response to...", "in response to determining...", "in the case of...", "when...", "while...", "if...", "if...", etc. may be interchangeable.

[0084] In some embodiments, terms such as "greater than", "greater than or equal to", "not less than", "more than", "more than or equal to", "not less than", "higher than", "higher than or equal to", "not lower than", "above", etc. may be interchangeable, and terms such as "less than", "less than or equal to", "not greater than", "less than", "less than or equal to", "not more than", "lower than", "lower than or equal to", "not higher than", "below", etc. may be interchangeable.

[0085] Prefix words such as "first", "second", etc. in the embodiments of the present disclosure are only used to distinguish different described objects, and do not limit the position, order, priority, quantity, content, etc. of the described objects. The description of the described objects refers to the description in the context of the claims or embodiments, and no redundant limitation should be formed due to the use of prefix words.

[0086] In the embodiments of the present disclosure, "a plurality of" means two or more.

[0087] In the embodiments of the present disclosure, terms such as "import", "input", "read in", etc. may be interchangeable.

[0088] In some embodiments, a device, etc. may be interpreted as physical or virtual, and its name is not limited to the name recorded in the embodiments. Terms such as "device", "equipment", "device", "circuit", "network element", "node", "function", "unit", "section", "system", "network", "chip", "chip system", "entity", "subject", etc. may be interchangeable.

[0089] In some embodiments, terms such as "terminal", "terminal device", "user equipment (UE)", "user terminal", "mobile station (MS)", "mobile terminal (MT)", subscriber station, mobile unit, subscriber unit, wireless unit, remote unit, mobile device, wireless device, wireless communication device, remote device, mobile subscriber station, access terminal, mobile terminal, wireless terminal, remote terminal, handset, user agent, mobile client, client, etc. may be used interchangeably.

[0090] Figure 1 The figure is a flowchart of a data display method provided by an embodiment of the present disclosure. This method can be applied to application scenarios such as smart terminals that support touch functions. The smart terminal can be input through a stylus, a finger, or even gaze (staring), which is not limited in the present disclosure. As Figure 1 shown, the data display method includes steps 101-104.

[0091] Step 101, obtaining predicted reporting point data corresponding to a target trajectory output by a reporting point prediction model; wherein, the reporting point prediction model is used to perform data prediction from at least one dimension among the device direction dimension, data integrity dimension, trajectory bending dimension, trajectory position dimension, and trajectory repetition dimension;

[0092] Embodiments of the present application can be applied to smart devices that support touch functions. The device can be a touch screen device with different sampling rates, and the sampling rates include but are not limited to 500Hz, 360Hz, 240Hz, etc., which are not limited in the present disclosure.

[0093] The device supports different input methods, not limited to input methods such as fingers, active pens, passive pens, resistive, capacitive, etc.

[0094] Different auxiliary sensing devices, including but not limited to watches, bracelets, etc., and different scenarios (including but not limited to Chinese, English, architecture diagrams, illustrations, etc.). When using the above devices for drawing, it does not limit the user's gender, the user's dominant hand (left or right), etc.

[0095] The method described in this disclosure is applied to predict the drawing trajectory during the drawing process in a device to achieve the followability of the drawing process. The target trajectory is the trajectory that the user is currently drawing.

[0096] The target trajectory is composed of several trajectory points, and each trajectory point contains corresponding attribute information. This attribute information includes timestamp, touch point coordinates, the orientation of the input device relative to the screen (MotionEventOrientation), the angle between the input device and the screen (MotionEventTilt), the pressure of the input device (pressure), and the orientation of the screen (ScreenOrientation). Due to the different types of input devices, some of the attribute information of these trajectory points comes from the touch screen, and some comes from the input device sensors. When processing later, the data sources are not distinguished. There is also some attribute information from the sensor data of the auxiliary sensing device, such as acceleration, temperature, capacitance, impedance, sweating rate, etc.

[0097] During the drawing process of the target trajectory, the reporting point prediction model predicts the trajectory points (reporting points) to be displayed later, and realizes the followability of the trajectory according to the predicted reporting point data.

[0098] In some embodiments, the reporting point prediction model outputs predicted reporting point data according to the above attribute information of the trajectory points. The reporting point prediction model is used to perform data prediction from at least one dimension among the device direction dimension, data integrity dimension, trajectory bending dimension, trajectory position dimension, and trajectory repetition dimension. As an implementable way of the embodiments of this disclosure, in order to achieve the accuracy of the predicted reporting point data, when predicting the reporting points, the reporting point prediction model performs the prediction of the reporting points from 5 dimensions of the device direction dimension, data integrity dimension, trajectory bending dimension, trajectory position dimension, and trajectory repetition dimension. However, the determination of the dimensions can select at least one or any combination of multiple dimensions from the 5 dimensions according to business needs or the hardware conditions of the device, and the embodiments of this disclosure do not limit it.

[0099] Step 102: Screen the predicted reporting point data according to the attribute information of the display screen and the prediction period to obtain the target reporting point data.

[0100] The purpose of step 102 is to limit the quantity of the predicted reporting point data output by the reporting point prediction model to improve the accuracy of the predicted reporting point data.

[0101] In step 101, the prediction model outputs the predicted values (predicted reporting point data) for the next N prediction cycles. According to the attribute information of the screen, such as the sampling rate and the refresh rate, the number of predicted points is further controlled. Exemplarily, when the screen refresh rate is 120 Hz and the desired number of predicted frames is 3 frames. When the sampling rate is 500 Hz, the number of points is no more than 3 * 1000 ms / 120 Hz * 500 Hz = 12.5; when the sampling rate is 240 Hz, the number of points is no more than 3 / 120 Hz * 240 Hz = 6. The above examples are only for illustrative purposes and are not limitations on specific values.

[0102] To avoid the leading or lagging of the predicted reporting point data, the embodiment of the present disclosure makes the model independent of the specific time interval size according to the reporting points in the predicted specified cycle (prediction cycle).

[0103] Step 103, draw the target reporting point data along the target trajectory.

[0104] The target reporting point data obtained after screening through 102 is drawn on the display screen of the device to achieve the followability of the drawing. The implementation method of the drawing can adopt but is not limited to any implementation method in the related art, and the embodiment of the present disclosure will not elaborate herein.

[0105] According to the data display method proposed by the present disclosure, the method includes obtaining the predicted reporting point data corresponding to the target trajectory output by the reporting point prediction model; wherein, the reporting point prediction model is used to perform data prediction from at least one dimension of the device direction dimension, data integrity dimension, trajectory bending dimension, trajectory position dimension, and trajectory repetition dimension; screening the predicted reporting point data according to the attribute information of the display screen and the prediction cycle to obtain the target reporting point data; and drawing the target reporting point data along the target trajectory. In the present disclosure, the reporting point prediction model performs reporting point data prediction from the device direction dimension, data integrity dimension, trajectory bending dimension, trajectory position dimension, and trajectory repetition dimension, which improves the accuracy of trajectory prediction. In addition, by combining the screen attribute information, dynamically adjusting the prediction cycle and screening the predicted reporting point data, the smoothness and continuity of the trajectory can be improved, as well as the overall followability experience.

[0106] Figure 2 Further shows a flowchart of a data display method proposed by the present disclosure. Based on Figure 1 the embodiments shown, step 101 is further explained as follows Figure 2 and may include the following steps:

[0107] Step 201, perform a first adjustment on the direction of the coordinate system of the target trajectory to make it consistent with the direction of the coordinate system of the device screen.

[0108] This step corresponds to the dimension of the device direction in the reporting point prediction model.

[0109] Adjust the x and y axis coordinates according to the screen orientation to align with the user's writing, so as to distinguish horizontal and vertical stroke features.

[0110] In some embodiments of the present disclosure, first determine whether the direction of the coordinate system of the target trajectory is consistent with the direction of the coordinate system of the device screen. If it is determined that they are inconsistent, then adjust the direction of the coordinate system of the target trajectory according to the direction of the coordinate system of the device screen. If it is determined that they are consistent, then this step is not executed.

[0111] The adjustment of the coordinate system direction is divided into the following scenarios:

[0112] If the device screen orientation is ORIENTATION_PORTRAIT (portrait), then this step is not executed.

[0113] If the device screen orientation is Orientation_PortraitUpsideDown (portrait, upside down), the x and y coordinates of all trajectories are rotated 180 degrees around the center point of the screen.

[0114] If the device screen orientation is Orientation_LandscapeLeft (screen rotated 90 degrees to the left), the x and y coordinates of all trajectories are rotated counterclockwise 90 degrees around the center point of the screen.

[0115] If the device screen orientation is Orientation_LandscapeRight (screen rotated 90 degrees to the right), the x and y coordinates of all trajectories are rotated clockwise 90 degrees around the center point of the screen.

[0116] In other cases (unknown direction, screen facing up or down flat), process according to the most recent known direction.

[0117] Among the collected features, except for the x and y coordinates, the remaining attribute information has nothing to do with the device screen orientation and does not need to be processed. For example, the attribute information orientation rotates with the screen orientation; the attribute information tilt only relates to the vertical axis of the screen and has nothing to do with rotation; the attribute information pressure also has nothing to do with the screen orientation; the auxiliary sensor input also has nothing to do with the screen.

[0118] Step 202, perform interpolation calculation on the predicted trajectory points according to the preset sampling interval and the attribute information of the predicted trajectory points to obtain the first data.

[0119] This step corresponds to the data integrity dimension of the reporting point prediction model.

[0120] By means of interpolation calculation, the time interval of the historical trajectory is made more stable, so that the reporting points at a specified time in the related technology are changed to the reporting points of the prediction period (preset sampling interval) in the embodiments of the present disclosure, making the model independent of the specific time interval size and avoiding the lead or lag of the prediction result.

[0121] In some embodiments, the average sampling interval T is estimated according to the sampling rate. If the time stamp interval D of the predicted trajectory point satisfies mT <= D < nT (m and n are positive integers, m > 1, for example, m = 2 and n = 5 can be taken), interpolation calculation is performed; the number of interpolations is D / / T - 1, where / / represents integer division (for example, when m = 2 and n = 5, the number of interpolations is 1 to 3); when the number of interpolations is small, linear interpolation can be used, and when the number is large, methods such as Lagrange interpolation and Taylor expansion fitting interpolation can be used; the attribute information of the interpolation includes all model input features.

[0122] It should be noted that the part where D >= nT will not be interpolated to avoid excessive continuous interpolation data affecting the model output effect.

[0123] Step 203, calculate the curvature of the target trajectory according to the first trajectory point at the current moment and the second trajectory points at historical moments within the preset sampling interval.

[0124] This step corresponds to the trajectory bending dimension of the reporting point prediction model.

[0125] In the embodiments of the present disclosure, the curvature of the target trajectory is used to measure the degree of bending of the historical curve. The smaller the curvature, the smoother the historical trajectory; the larger the curvature, the more tortuous the historical trajectory. Supplementing the curvature can significantly optimize the model for scenarios with high curvature such as curves and broken lines.

[0126] Assume that the current moment is T, and the first trajectory points at times T - 2, T - 1, and T are denoted as A, B, and C in sequence. The curvature is the reciprocal of the radius R of the circumcircle of triangle ABC. According to the curvature definition and trigonometric function formulas, we have

[0127] curv = 1 / R

[0128] 1 / R = 2sinA / (|BC|) = 2sinB / (|AC|) = 2sinC / (|AB|)

[0129] nsinB = (BA × BC) / (|BA||BC|)

[0130] curv = 1 / R = 2sinB / (|AC|) = (2BA × BC) / (n|BA||BC||AC|)

[0131] In the above formula, BA, BC, and AC are all vectors, n is a unit vector perpendicular to vectors BA and BC, and BA×BC is the cross product of vectors BA and BC. Since the cross product has a direction, the calculated curvature also has a direction, corresponding to clockwise and counterclockwise. For example, for a circle and an S-shaped curve, the curvature magnitudes may be the same, but the curvature sign of the S-shaped curve changes at the turning points. If two of the three points coincide, the curvature is recorded as 0.

[0132] Step 204: Perform a difference operation on the first data to obtain second data.

[0133] This step corresponds to the trajectory position dimension of the reporting point prediction model.

[0134] In order to make the point trajectory in the target trajectory independent of the screen drawing area, the first data is differentiated to extract features independent of position. The features for calculating the difference include x, y, orientation, tilt, pressure, and curvature.

[0135] In some embodiments, since the timestamps have been interpolated (step 202) and the time intervals are fixed, no separate difference operation is performed.

[0136] In the embodiments of the present disclosure, no rotation invariant processing is performed. The reasons are as follows: First, to distinguish the writing differences between the x and y axes; second, the orientation invariant processing has been performed in step 201.

[0137] In the embodiments of the present disclosure, the second data is not normalized after differentiation, but the original difference values are used. After normalization, the data distributions of the x and y axes are similar and their differences cannot be distinguished.

[0138] Step 205: Perform a filtering operation on the second data to obtain the predicted reporting point data.

[0139] This step corresponds to the trajectory repetition dimension of the reporting point prediction model.

[0140] In the actual application process, when there are many repeated coordinate points in the adjacent historical trajectories during drawing, the trajectory is discontinuous and jumps, making it difficult to predict. Therefore, a threshold is set (for example, the proportion of repeated points > 20%), and the second data is filtered to delete the trajectory points of the repeated coordinate points, which are not used for prediction.

[0141] After Figure 2 performing the data preprocessing in the manner shown, all the preprocessed attribute parameters are input into the reporting point prediction model to obtain the predicted reporting point data.

[0142] In some embodiments, after data preprocessing, a tensor with dimensions [None, seqLen, featureSize] is obtained. Here, None corresponds to the batchsize, which is the amount of data for training one batch. For example, it can be set to 1024 according to the GPU memory capacity and training effect. seqLen is the number of historical reported points, such as 19 points. featureSize represents the number of features. The specific features include: sensor parameter features of the screen and input device (such as x, y, orientation, tilt, pressure, etc.), input features of auxiliary sensors (such as x, y, z components of acceleration, skin humidity, impedance, etc.), and high-order features (such as curvature). It should be noted that the input parameters can adjust the selection of features according to actual needs, and the specific embodiments of the present disclosure do not limit this.

[0143] The output of the reported point prediction model is a tensor with dimensions [None, predLen, 2]. Here, None is the batchsize, as mentioned above. predLen is the number of predicted reported point data. Suppose predLen is N and the sampling interval is T, then the timestamps for predicting N reported points are the current moment + T, 2T,..., NT. For example, if predLen is taken as 12, the last dimension 2 corresponds to two features, namely the x coordinate and the y coordinate, and other features are not predicted.

[0144] In some embodiments, the reported point prediction model can be regarded as a seq2seq model combining an encoder and a decoder, where:

[0145] Encoder part: A single-layer GRU. The result after the encoder is [None, seqLen, gruDim], where gruDim is the dimension of the GRU cell. For example, it can be set to 64, and the present disclosure embodiments do not limit the dimension size.

[0146] Decoder part: Use a Multi-Layer Perception (MLP) to complete multi-step prediction at one time, rather than passing the result of the previous step to the next step like a traditional LSTM decoder, to avoid error propagation. Specifically:

[0147] Directly complete multi-step prediction through the MLP, rather than having a Dense layer at each time step, and not passing the previous predicted value to the next step to cause bias accumulation.

[0148] The hidden vector is transformed into a vector with dimensions [None, dim, seqLen] through transpose.

[0149] After being converted into a vector of [None, dim, predLen] by Dense(predLen), and then transposed back to [None, predLen, dim], it corresponds to the states of the future predLen prediction points.

[0150] The state is converted into the corresponding x and y coordinates by Dense(2). Since differencing has been done before, here it is actually the difference between the prediction point and the last visible historical point.

[0151] Figure 3 Further shows a flowchart of a data display method proposed by the present disclosure. Based on Figure 1 the shown embodiment, step 102 is further explained. Figure 3 It may include the following steps:

[0152] Step 301, convert the direction of the coordinate system of the target trajectory and the coordinates of the predicted trajectory points to obtain the converted data.

[0153] Figure 2 When performing prediction reporting point data, data preprocessing is done. Therefore, before finally determining the target reporting point data, reverse data is also required to obtain the target reporting point data.

[0154] In some embodiments, the direction of the coordinate system of the target trajectory is secondarily adjusted to the direction before performing the first adjustment. The second adjustment is the reverse process of the first adjustment. According to the nearest visible coordinates of the target trajectory and the coordinates of the predicted trajectory points, determine the coordinates corresponding to the target reporting point data to obtain the converted data.

[0155] Position-independent reverse process: Add the x and y coordinates of the last visible point of the historical trajectory (the nearest visible coordinates of the target trajectory) to the predicted trajectory point to obtain the dx and dy of the predicted trajectory point.

[0156] Direction-independent reverse process: Rotate in the opposite direction according to the current device screen direction during data preprocessing.

[0157] Exemplarily, when the operation during preprocessing is to rotate 90 degrees clockwise around the center point of the screen, then during the execution of the conversion, it is necessary to rotate 90 degrees counterclockwise around the center point of the screen; the above is an exemplary example and is not limited in the embodiments of the present disclosure.

[0158] Step 302, screen the converted data according to the attribute information of the display screen and the prediction period to obtain the target reporting point data.

[0159] The purpose of this step is to limit the number of prediction reporting point data.

[0160] When performing the steps, the following implementation methods can be adopted but are not limited to, for example: calculating the deviation between the predicted trajectory points and the latest reported point of the target trajectory according to the attribute information of the predicted trajectory points, inputting the deviation and the historical trajectory into a preset recognition model, outputting the unavailable predicted trajectory points within the prediction period, and screening the number of the predicted trajectory points according to the attribute information of the display screen to obtain the target reported point data.

[0161] In some embodiments, according to the attribute information of the latest predicted trajectory points, calculate the deviation between the latest reported point of the target trajectory, that is, update the prediction deviation at the previous moment, input the calculated deviation and the historical trajectory features into a discriminant model to determine which periods of predicted trajectory points are not used, and further control the number of predicted trajectory points according to the sampling rate and refresh rate of the display screen (the attribute information of the display screen). For example, the screen refresh rate is 120Hz, and the expected number of predicted frames is 3 frames. When the sampling rate is 500Hz, the number of points is no more than 3 * 1000ms / 120Hz * 500Hz = 12.5; when the sampling rate is 240Hz, the number of points is no more than 3 / 120Hz * 240Hz = 6.

[0162] Figure 4 Further, a flowchart of a data display method proposed by the present disclosure is shown. Based on Figure 1 the reported point prediction model shown, the training process of the reported point prediction model is as Figure 4 shown and includes the following steps:

[0163] Step 401, obtain the training trajectory features and obtain a preset number of prediction deviation sequences within each training prediction period.

[0164] Before the reported point prediction model predicts N predicted reported point data each time, obtain the training trajectory features at the current moment, and the training trajectory features can be historical trajectory features.

[0165] Obtain the prediction period (such as N periods), and the prediction deviation sequences of the nearest preset number (such as 10 times) in each period. The prediction deviation sequences are measured by two indicators: the prediction leading distance and the prediction direction deviation.

[0166] In some embodiments, denote the last visible trajectory point as LP, the predicted trajectory point as PP, and the true trajectory point as TP.

[0167] Prediction leading distance: distance(PP, LP) - distance(TP, LP), where the predicted point is farther than the true point is positive, and closer than the true point is negative;

[0168] Prediction direction deviation: the included angle between LP to PP and TP.

[0169] When calculating the weights of the predicted leading distance deviation and the predicted direction deviation, the predicted leading distance deviation can be calculated in, but not limited to, the way of Euclidean distance, and the predicted direction deviation is not limited to 1 - cos(A). Since both the predicted value and the true value have been differenced, in fact, they are the vectors from the predicted point and the true point to the last visible point respectively, and then they can be substituted into the cosine formula of the vector angle.

[0170] It should be noted that the longer the prediction period, the greater the deviation. Optionally, in order to prevent the model from sacrificing the prediction accuracy of short periods and improve the average prediction accuracy, different weights are added to different prediction periods. The closer the period is, the higher the weight.

[0171] Step 402: Divide the deviation labels of the predicted deviation sequence.

[0172] Based on the predicted deviation sequence obtained in step 401, alignment and labeling are performed. When the predicted leading distance is greater than the distance threshold, the predicted trajectory is ahead of the true trajectory, and the experience is poor and it should not be predicted, and the deviation label is configured as 0; when the predicted direction deviation is greater than the angle threshold, the predicted trajectory and the true trajectory are in different directions (the angle deviation is large), that is, it should not be predicted, and the deviation label is configured as 0. Except for the above two scenarios, prediction is required in other cases, and the deviation label is configured as 1.

[0173] Step 403: Generate eigenvalues based on the preset algorithm according to the training trajectory features and the predicted deviation sequence with deviation categories.

[0174] In some embodiments, through the feature engineering framework, a large number of eigenvalues are generated from the training trajectory features and the predicted deviation sequence with deviation categories (including leading distance and direction).

[0175] In some embodiments, the top K eigenvalues can also be selected according to the correlation coefficient.

[0176] Step 404: Input the eigenvalues into the reporting point prediction model for training to obtain a trained reporting point prediction model.

[0177] During the training process, classification models such as decision trees, random forests, GBDT, and SVM can be used for binary classification. As a feasible solution, the model based on decision trees in the embodiments of the present disclosure has advantages in the conditional judgment logic of converting to code. However, for the convenience of conversion, the maximum tree depth needs to be limited, such as 5. From the trained decision tree nodes, the corresponding key features and discriminant conditions are obtained. These features and discriminant conditions will be converted into if-else code discriminant conditions during use. The specific discriminant conditions include but are not limited to:

[0178] a. Trajectory linearity

[0179] b. Historical maximum prediction deviation, quartiles, sum of squares, and other distribution characteristics of the 1st, 2nd, 3rd, and 4th points

[0180] c. Linearity, rate of change, covariance, etc. of pressure

[0181] d. Quartile and other distribution characteristics of curvature

[0182] In some embodiments, theoretically, a discrimination model can be established separately for each prediction cycle. However, the farther the actual prediction time is, the greater the deviation. Therefore, a discrimination model is established for several of the largest cycles.

[0183] In summary, the method described in this disclosure can achieve the following beneficial effects:

[0184] 1. Improve the accuracy of trajectory prediction.

[0185] Through data processing and feature augmentation: The deviations of T3 (6ms), T7 (14ms), and T10 (20ms) are as follows, in pixels

[0186] Table 1

[0187] Prediction period T3 T7 T10 ours 0.49 1.35 2.31 Do not perform data integrity processing and directly use differencing 0.55 1.48 2.47 No high-order features 0.52 1.44 2.41 Do not perform screen orientation independence processing 0.52 1.43 2.45

[0188] As can be seen from Table 1, by using data completeness, high-order features, and screen direction-independent processing, the improvements are approximately 8.8%, 6.2%, and 5.6% respectively.

[0189] 2. Refinely model the prediction deviation, combine the screen refresh rate and sampling rate, and dynamically adjust the number of prediction cycles to improve the smoothness and continuity of the trajectory.

[0190] 3. Only when the prediction is extremely far or the direction is inaccurate, reduce the number of prediction points to reduce false alarm errors.

[0191] 4. Improve the overall follow-up experience.

[0192] Figure 5 The following is a schematic structural diagram of a data display device 500 provided by an embodiment of the present disclosure. The data display device includes:

[0193] An acquisition unit 51, configured to acquire prediction report point data corresponding to a target trajectory output by a report point prediction model; wherein, the report point prediction model is used to perform data prediction from at least one dimension of the device direction dimension, data integrity dimension, trajectory bending dimension, trajectory position dimension, and trajectory repetition dimension;

[0194] A screening unit 52, configured to screen the prediction report point data according to the attribute information of the display screen and the prediction cycle to obtain target report point data;

[0195] A drawing unit 53 for drawing the target reported point data along the target trajectory.

[0196] According to the data display device proposed by the present disclosure, it includes obtaining the predicted reported point data corresponding to the target trajectory output by the reported point prediction model; wherein, the reported point prediction model is used to perform data prediction from at least one of the dimensions of device direction, data integrity, trajectory bending, trajectory position, and trajectory repetition; screening the predicted reported point data according to the attribute information of the display screen and the prediction period to obtain the target reported point data; and drawing the target reported point data along the target trajectory. In the present disclosure, the reported point prediction model performs reported point data prediction from the dimensions of device direction, data integrity, trajectory bending, trajectory position, and trajectory repetition, improving the accuracy of trajectory prediction. In addition, by combining the screen attribute information, dynamically adjusting the prediction period and screening the predicted reported point data, the smoothness and continuity of the trajectory can be improved, as well as the overall follow-up experience.

[0197] Further, in a possible implementation manner of this embodiment, as Figure 6 shown, the obtaining unit 51 includes:

[0198] An adjustment module 511 for performing a first adjustment on the direction of the coordinate system of the target trajectory to make it consistent with the direction of the coordinate system of the device screen;

[0199] A first calculation module 512 for performing interpolation calculation on the predicted trajectory points according to the preset sampling interval and the attribute information of the predicted trajectory points to obtain the first data;

[0200] A second calculation module 513 for calculating the curvature of the target trajectory according to the first trajectory point at the current moment and the second trajectory points at historical moments within the preset sampling interval;

[0201] A first processing module 514 for performing differential processing on the first data to obtain the second data;

[0202] A second processing module 515 for performing filtering processing on the second data to obtain the predicted reported point data.

[0203] Further, in a possible implementation manner of this embodiment, as Figure 6 shown, the screening unit 52 includes:

[0204] A conversion module 521 for converting the direction of the coordinate system of the target trajectory and the coordinates of the predicted trajectory points to obtain the converted data;

[0205] A screening module 522, configured to screen the converted data according to the attribute information of the display screen and the prediction period to obtain target reporting point data.

[0206] Further, in a possible implementation manner of this embodiment, as Figure 6 shown, the conversion module 521 is further configured to:

[0207] Perform a second adjustment on the direction of the coordinate system of the target trajectory, and adjust it to the direction before performing the first adjustment, where the second adjustment is the reverse process of the first adjustment;

[0208] Determine the coordinates corresponding to the target reporting point data according to the nearest visible coordinates of the target trajectory and the coordinates of the predicted trajectory points to obtain the converted data.

[0209] Further, in a possible implementation manner of this embodiment, as Figure 6 shown, the screening module 522 is further configured to:

[0210] Calculate the deviation between the predicted trajectory points and the latest reporting point of the target trajectory according to the attribute information of the predicted trajectory points;

[0211] Input the deviation into a preset recognition model for the historical trajectory to output the unavailable predicted trajectory points within the prediction period;

[0212] Screen the number of the predicted trajectory points according to the attribute information of the display screen to obtain the target reporting point data.

[0213] Further, in a possible implementation manner of this embodiment, as Figure 6 shown, the adjustment module 511 is further configured to:

[0214] Determine whether the direction of the coordinate system of the target trajectory is consistent with the direction of the coordinate system of the device screen;

[0215] If not, adjust the direction of the coordinate system of the target trajectory according to the direction of the coordinate system of the device screen.

[0216] Further, in a possible implementation manner of this embodiment, as Figure 6 shown, it further includes a training unit 54;

[0217] The training unit 54 is configured to:

[0218] Obtain training trajectory features and obtain a preset number of prediction deviation sequences within each training prediction period;

[0219] Divide the deviation labels of the prediction deviation sequences;

[0220] Generate a feature value based on a preset algorithm according to the training trajectory features and the predicted deviation sequence carrying deviation categories;

[0221] Input the feature value into a reporting point prediction model for training to obtain a trained reporting point prediction model.

[0222] Since the device provided in the embodiments of the present disclosure corresponds to the methods provided in the above several embodiments, the implementation manners of the methods are also applicable to the device provided in this embodiment and will not be described in detail in this embodiment.

[0223] In the above embodiments provided by the present application, the methods and devices provided by the embodiments of the present application are introduced. To implement the various functions in the methods provided by the embodiments of the present application, an electronic device may include a hardware structure and software modules, and implement the above various functions in the form of a hardware structure, a software module, or a combination of a hardware structure and a software module. A certain function among the above various functions may be executed in the form of a hardware structure, a software module, or a combination of a hardware structure and a software module.

[0224] Figure 7 FIG. 13 is a block diagram of an electronic device 1000 for implementing the above data display method according to an exemplary embodiment. For example, the electronic device 700 may be a mobile phone, a computer, a digital broadcast terminal, a messaging device, a game console, a tablet device, a medical device, a fitness device, a personal digital assistant, etc.

[0225] Refer to Figure 7 , the electronic device 700 may include one or more of the following components: a processing component 702, a memory 704, a power component 706, a multimedia component 708, an audio component 710, an input / output (I / O) interface 712, a sensor component 714, and a communication component 716.

[0226] The processing component 702 generally controls the overall operation of the electronic device 700, such as operations associated with display, telephone calls, data communication, camera operations, and recording operations. The processing component 702 may include one or more processors 720 to execute instructions to complete all or part of the steps of the above methods. In addition, the processing component 702 may include one or more modules to facilitate the interaction between the processing component 702 and other components. For example, the processing component 702 may include a multimedia module to facilitate the interaction between the multimedia component 708 and the processing component 702.

[0227] The memory 704 is configured to store various types of data to support the operation of the electronic device 700. Examples of such data include instructions for any application or method operating on the electronic device 700, contact data, phone book data, messages, pictures, videos, and the like. The memory 704 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk, or optical disk.

[0228] The power supply component 706 provides power to various components of the electronic device 700. The power supply component 706 may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power for the electronic device 700.

[0229] The multimedia component 708 includes a screen that provides an output interface between the electronic device 700 and the user. In some embodiments, the screen may include a liquid crystal display (LCD) and a touch panel (TP). If the screen includes a touch panel, the screen can be implemented as a touch screen to receive input signals from the user. The touch panel includes one or more touch sensors to sense touches, swipes, and gestures on the touch panel. The touch sensors can not only sense the boundaries of touch or swipe actions, but also detect the duration and pressure associated with the touch or swipe operations. In some embodiments, the multimedia component 708 includes a front camera and / or a rear camera. When the electronic device 700 is in an operating mode, such as a shooting mode or a video mode, the front camera and / or the rear camera can receive external multimedia data. Each of the front camera and the rear camera can be a fixed optical lens system or have focal length and optical zoom capabilities.

[0230] The audio component 710 is configured to output and / or input audio signals. For example, the audio component 710 includes a microphone (MIC) that is configured to receive external audio signals when the electronic device 700 is in an operating mode, such as a call mode, a recording mode, and a voice recognition mode. The received audio signals can be further stored in the memory 704 or transmitted via the communication component 716. In some embodiments, the audio component 710 further includes a speaker for outputting audio signals.

[0231] The I / O interface 712 provides an interface between the processing component 702 and a peripheral interface module, which can be a keyboard, a click wheel, buttons, etc. These buttons can include, but are not limited to: a home button, a volume button, a power button, and a lock button.

[0232] The sensor assembly 714 includes one or more sensors for providing an assessment of the status of various aspects of the electronic device 700. For example, the sensor assembly 714 can detect the on / off state of the electronic device 700, the relative positioning of components, such as the display and keypad of the electronic device 700. The sensor assembly 714 can also detect a change in the position of the electronic device 700 or a component of the electronic device 700, the presence or absence of user contact with the electronic device 700, the orientation or acceleration / deceleration of the electronic device 700, and a change in the temperature of the electronic device 700. The sensor assembly 714 can include a proximity sensor configured to detect the presence of nearby objects without any physical contact. The sensor assembly 714 can also include a light sensor, such as a CMOS or CCD image sensor, for use in imaging applications. In some embodiments, the sensor assembly 714 can also include an acceleration sensor, a gyroscope sensor, a magnetic sensor, a pressure sensor, or a temperature sensor.

[0233] The communication component 716 is configured to facilitate communication between the electronic device 700 and other devices in a wired or wireless manner. The electronic device 700 can access a wireless network based on communication standards, such as WiFi, 2G or 3G, 4G LTE, 5G NR (New Radio), or a combination thereof. In an exemplary embodiment, the communication component 716 receives a broadcast signal or broadcast-related information from an external broadcast management system via a broadcast channel. In an exemplary embodiment, the communication component 716 further includes a near field communication (NFC) module to facilitate short-range communication. For example, the NFC module can be implemented based on radio frequency identification (RFID) technology, infrared data association (IrDA) technology, ultra-wideband (UWB) technology, Bluetooth (BT) technology, and other technologies.

[0234] In an exemplary embodiment, the electronic device 700 can be implemented by one or more application specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components for performing the above-described methods.

[0235] In an exemplary embodiment, a non-transitory computer-readable storage medium including instructions, such as the memory 704 including instructions, is also provided. The above instructions can be executed by the processor 720 of the electronic device 700 to complete the above-described methods through image processing. For example, the non-transitory computer-readable storage medium can be a ROM, a random access memory (RAM), a CD-ROM, a magnetic tape, a floppy disk, and an optical data storage device, etc.

[0236] Embodiments of the present disclosure also propose a non-transitory computer-readable storage medium storing computer instructions, where the computer instructions are used to cause a computer to execute the methods described in the above embodiments of the present disclosure.

[0237] For the case where the electronic device may be a chip or a chip system, reference may be made to Figure 8 the schematic structural diagram of the chip shown. Figure 8 The chip shown includes a processor 801 and an interface 802. Among them, the number of processors 801 may be one or more, and the number of interfaces 802 may be multiple.

[0238] Optionally, the chip further includes a memory 803, and the memory 803 is used to store necessary computer programs and data.

[0239] Those skilled in the art can also understand that the various illustrative logical blocks and steps listed in the embodiments of the present application can be implemented by electronic hardware, computer software, or a combination of the two. Whether such a function is implemented by hardware or software depends on the specific application and the design requirements of the entire system. Those skilled in the art can use various methods to implement the functions for each specific application, but such implementation should not be construed as exceeding the scope protected by the embodiments of the present application.

[0240] It should be noted that the terms "first", "second", etc. in the specification and claims of the present disclosure and the above drawings are used to distinguish similar objects, and do not necessarily have to be used to describe a specific order or sequence. It should be understood that such used data may be interchanged under appropriate circumstances so that the embodiments of the present disclosure described herein can be implemented in an order other than those illustrated or described herein. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present disclosure. On the contrary, they are merely examples of devices and methods consistent with some aspects of the present disclosure as detailed in the appended claims.

[0241] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "illustrative embodiments", "examples", "specific examples", or "some examples", etc. means that the specific features, structures, materials, or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic descriptions of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described can be combined in a suitable manner in any one or more embodiments or examples.

[0242] Any process or method description represented in a flowchart or otherwise described herein can be understood to represent a module, segment, or portion of code including one or more executable instructions for implementing a specific logical function or process. The scope of the preferred embodiments of the present invention includes additional implementations, where functions may be performed in an order not shown or discussed, including in a substantially simultaneous manner according to the functions involved or in a reverse order, which should be understood by those skilled in the art to which the embodiments of the present invention pertain.

[0243] The logic and / or steps represented in a flowchart or otherwise described herein, for example, can be considered a sequenced list of executable instructions for implementing a logical function and can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, a system including a processing module, or other systems that can fetch and execute instructions from the instruction execution system, apparatus, or device. For the purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by or in connection with the instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of the computer-readable medium include the following: an electrical connection having one or more wires (control method), a portable computer diskette (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). Additionally, the computer-readable medium can even be paper or other suitable medium on which a program can be printed, as the program can be obtained, for example, electronically by optically scanning the paper or other medium, followed by editing, interpretation, or otherwise appropriate processing if necessary, and then stored in a computer memory.

[0244] It should be understood that various parts of the embodiments of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, any one or a combination of the following techniques well known in the art can be used: discrete logic circuits having logic gate circuits for implementing logical functions on data signals, application specific integrated circuits having appropriate combinational logic gate circuits, programmable gate arrays (PGAs), field programmable gate arrays (FPGAs), and the like.

[0245] Those of ordinary skill in the art can understand that all or part of the steps carried out in implementing the above-described embodiment methods can be completed by a program instructing relevant hardware. The program can be stored in a computer-readable storage medium. When the program is executed, it includes one or a combination of the steps of the method embodiment.

[0246] In addition, in each of the embodiments of the present invention, each functional unit can be integrated in a processing module, or each unit can exist physically alone, or two or more units can be integrated in one module. The above-mentioned integrated module can be implemented in the form of hardware or in the form of a software functional module. When the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium. The storage medium mentioned above can be a read-only memory, a magnetic disk, an optical disk, or the like.

[0247] Although the embodiments of the present invention have been shown and described above, it can be understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those of ordinary skill in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of the present invention.

Claims

1. A data display method, characterized in that: The method comprises: Obtain predicted point reporting data corresponding to the target trajectory output by the point reporting prediction model; wherein the point reporting prediction model is used to predict data from at least one of the device direction dimension, data integrity dimension, trajectory bending dimension, trajectory position dimension, and trajectory repetition dimension; The predicted reporting point data is screened according to the attribute information of the display screen and the prediction period to obtain the target reporting point data; The target point reporting data is drawn along the target trajectory.

2. The method according to claim 1, characterized in that The step of obtaining the predicted point reporting data corresponding to the target trajectory output by the point reporting prediction model includes: Performing a first adjustment on the direction of the coordinate system of the target trajectory to be consistent with the direction of the coordinate system of the device screen; Performing interpolation calculation on the predicted trajectory points according to the preset sampling interval and the attribute information of the predicted trajectory points to obtain first data; Calculating the curvature of the target trajectory according to the first trajectory point at the current moment and the second trajectory point at the historical moment within the preset sampling interval; Performing differential processing on the first data to obtain second data; The second data is filtered to obtain the predicted reporting point data.

3. The method according to claim 2, characterized in that The step of filtering the predicted reporting point data according to the attribute information of the display screen and the prediction period to obtain the target reporting point data includes: Converting the direction of the coordinate system of the target trajectory and the coordinates of the predicted trajectory points to obtain converted data; The converted data is screened according to the attribute information of the display screen and the prediction period to obtain target reporting point data.

4. The method according to claim 3, characterized in that: The direction of the coordinate system of the target trajectory and the coordinates of the trajectory points are converted to obtain the converted data, which includes: Performing a second adjustment on the direction of the coordinate system of the target trajectory to the direction before the first adjustment, wherein the second adjustment is a reverse process of the first adjustment; According to the nearest visible coordinates of the target trajectory and the coordinates of the predicted trajectory points, the coordinates corresponding to the target point reporting data are determined to obtain the converted data.

5. The method according to claim 3, characterized in that: The step of filtering the converted data according to the attribute information of the display screen and the prediction period to obtain target reporting point data includes: Calculate the deviation between the predicted trajectory point and the latest reported point of the target trajectory according to the attribute information of the predicted trajectory point; Input the deviation and the historical trajectory into a preset recognition model, and output unavailable predicted trajectory points within the prediction period; The number of predicted trajectory points is screened according to the attribute information of the display screen to obtain the target point reporting data.

6. The method according to claim 2, characterized in that The first adjusting the direction of the coordinate system of the target track to be consistent with the direction of the coordinate system of the device screen includes: Determining whether the direction of the coordinate system of the target trajectory is consistent with the direction of the coordinate system of the device screen; If they are inconsistent, the direction of the coordinate system of the target track is adjusted according to the direction of the coordinate system of the device screen.

7. The method according to any one of claims 1 to 6, characterized in that The training method of the report point prediction model includes: Obtaining trajectory features for training, and obtaining a preset number of prediction deviation sequences in each training prediction cycle; Deviation labels for classifying the prediction deviation sequence; Generate a feature value based on the training trajectory features and the predicted deviation sequence carrying the deviation category based on a preset algorithm; The characteristic value is input into the reporting point prediction model for training to obtain a trained reporting point prediction model.

8. A data display device, characterized in that: The device comprises: An acquisition unit is used to acquire predicted point reporting data corresponding to the target trajectory output by the point reporting prediction model; wherein the point reporting prediction model is used to predict data from at least one of the device direction dimension, data integrity dimension, trajectory bending dimension, trajectory position dimension, and trajectory repetition dimension; A screening unit, used for screening the predicted reporting point data according to the attribute information of the display screen and the prediction period to obtain the target reporting point data; A drawing unit is used to draw the target point data along the target trajectory.

9. An electronic device, characterized in that: include: at least one processor; as well as a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method according to any one of claims 1 to 7.

10. A non-transitory computer-readable storage medium storing computer instructions, characterized in that: The computer instructions are used to cause the computer to execute the method according to any one of claims 1-7.

11. A chip, characterized in that: It comprises one or more interfaces and one or more processors; the interface is used to receive a signal from a memory of an electronic device and send the signal to the processor, the signal includes a computer instruction stored in the memory, and when the processor executes the computer instruction, the electronic device executes the method described in any one of claims 1 to 7.

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