Virtual remote lever and touch-based pressure sensing fusion control method and device

Abstract

The application discloses a virtual remote rod and touch-based pressure sensing fusion control method and device, and relates to the technical field of intelligent control. The method comprises the following steps: receiving initial touch data collected by a touch device, wherein the initial touch data comprises touch point coordinates, touch pressure values and touch time lengths; generating a touch trajectory according to the touch point coordinates; calculating a pressure change rate of a touch behavior according to the touch pressure values and the touch time lengths; mapping the touch trajectory and the pressure change rate to obtain a dynamic feature vector of the touch behavior; classifying the dynamic feature vector to determine an operation intention of the touch behavior; generating a corresponding control instruction according to the operation intention; and sending the control instruction to a target device. Through the above method, the problem of insufficient dynamic response capability of the touch control mode based on fixed pressure threshold determination is solved, thereby improving the interactive experience of users in different scenarios.

Description

Technical Field

[0001] This application relates to the field of intelligent control technology, and in particular to a pressure sensing fusion control method and device based on virtual joystick and touch control. Background Technology

[0002] Touch technology is increasingly used in games and interactive devices, and pressure-sensitive control methods are gradually becoming an important means of improving user experience. Especially in mobile terminals and virtual reality scenarios, pressure-sensitive technology can achieve precise control of game characters, skills, or interfaces by detecting the pressure values ​​of user operations. Currently, common technical solutions typically construct a three-axis virtual joystick, using the coordinates of the screen touch point and the touch pressure value as input variables to achieve multi-dimensional control of game character behavior. Specifically, this method assigns touch pressure to the Z-axis, combined with offsets on the X and Y axes, further enriching the character control functions. However, existing technical solutions mainly rely on fixed pressure thresholds, which limits their dynamic response to pressure changes and shows certain shortcomings in adapting to the personalized needs of different users or in different scenarios.

[0003] The above content is only used to help understand the technical solution of the present invention and does not represent an admission that the above content is prior art. Summary of the Invention

[0004] The main objective of this application is to provide a pressure sensing fusion control method and device based on virtual joystick and touch control, which aims to solve the technical problem that the existing touch control method based on fixed pressure threshold judgment has insufficient dynamic response capability to pressure changes.

[0005] To achieve the above objectives, this application provides a pressure-sensing fusion control method based on a virtual joystick and touch control, the method comprising:

[0006] Receive initial touch data collected by the touch device, the initial touch data including touch point coordinates, touch pressure value and touch duration;

[0007] A touch trajectory is generated based on the coordinates of the touch point, and the pressure change rate of the touch behavior is calculated based on the touch pressure value and the touch duration.

[0008] The touch trajectory is mapped to the pressure change rate to obtain the dynamic feature vector of the touch behavior;

[0009] The dynamic feature vectors are classified to determine the operational intent of the touch behavior;

[0010] Generate corresponding control commands based on the stated operational intent, and send the control commands to the target device.

[0011] In one embodiment, the step of generating a touch trajectory based on the touch point coordinates and calculating the pressure change rate of the touch behavior based on the touch pressure value and the touch duration includes:

[0012] The time series of the touch point coordinates is extracted, and the missing coordinate data is supplemented by a linear interpolation algorithm to form a complete touch trajectory;

[0013] The pressure difference between two adjacent touch points is calculated based on the touch pressure value, and the pressure difference is divided by the corresponding time interval to obtain the local pressure change rate.

[0014] A weight value is determined based on the touch duration, and a weighted average is performed on multiple local pressure change rates based on the weight value to obtain the global pressure change rate.

[0015] In one embodiment, the step of mapping the touch trajectory to the pressure change rate to obtain the dynamic feature vector of the touch behavior includes:

[0016] The touch trajectory is divided into several sub-trajectory segments, and each sub-trajectory segment corresponds to a time window;

[0017] Within each time window, the orientation angle and curvature of the touch trajectory are calculated, and the orientation angle, curvature, and pressure change rate are combined to obtain a sub-feature vector;

[0018] All sub-feature vectors are concatenated in chronological order to form a complete dynamic feature vector of the touch behavior.

[0019] In one embodiment, the step of calculating the orientation angle and curvature of the touch trajectory within each time window, and combining the orientation angle, the curvature, and the pressure change rate to obtain a sub-feature vector includes:

[0020] Within each time window, a sequence of touch point coordinates is determined, the sequence of touch point coordinates including the coordinates of the touch point, the touch time, and the touch pressure;

[0021] Within each time window, the coordinates of the touch points within the time window are smoothed and sampled at equal intervals to generate equally spaced sampling points;

[0022] Perform a difference operation on the equally spaced sampling points to determine the first-order directional derivative of the equally spaced sampling points, and extract the principal direction, angular direction and rate of change of the direction angle based on the first-order directional derivative;

[0023] A discrete sequence is generated by selecting three consecutive equally spaced sampling points, and the average curvature, maximum curvature, and inflection point density are calculated based on the discrete sequence.

[0024] Linear regression analysis is performed on the touch point coordinate sequence to determine the correspondence between time and pressure, and the pressure change rate and pressure fluctuation are determined based on the correspondence between time and pressure.

[0025] The main direction, the angular direction, the rate of change of direction, the average curvature, the maximum curvature, the inflection point density, the rate of change of pressure, and the pressure fluctuation are combined to obtain a sub-feature vector.

[0026] In one embodiment, the step of classifying the dynamic feature vector to determine the operational intent of the touch behavior includes:

[0027] Extract dynamic feature vectors of historical touch behaviors and operation intent labels corresponding to the historical touch behaviors from a preset training dataset;

[0028] A classification model is generated by training the dynamic feature vectors of the historical touch behaviors and the operation intention labels based on the support vector machine algorithm.

[0029] The dynamic feature vector of the current touch behavior is input into the classification model, and the corresponding operation intention is output.

[0030] In one embodiment, the step of training the dynamic feature vectors of the historical touch behavior based on the support vector machine algorithm to generate a classification model includes:

[0031] The dynamic feature vectors of the historical touch behaviors are standardized and preprocessed, and the preprocessed dataset is divided into a training set and a validation set.

[0032] The regularization parameter, kernel parameter, and convergence threshold of the Gaussian kernel function are initialized to obtain the initialized Gaussian kernel;

[0033] The initial Gaussian kernel is iteratively solved using the sequence minimum optimization algorithm to obtain the Lagrange multipliers. The training set is then calculated based on the Lagrange multipliers to determine the support vectors and decision functions.

[0034] The support vectors and decision function are validated based on the validation set to determine the model error. When the model error is less than a preset error threshold, a classification model is generated based on the support vectors and the decision function.

[0035] In one embodiment, the step of generating a corresponding control command based on the operation intention and sending the control command to the target device includes:

[0036] The target control command is obtained by searching a preset mapping table according to the operation intention. The mapping table stores the correspondence between different operation intentions and control commands.

[0037] The target control command is encoded into binary format and sent to the target device through the communication interface.

[0038] In one embodiment, the step of searching a preset mapping table according to the operation intention to obtain the target control command includes:

[0039] Determine the intent flag information based on the stated operational intent;

[0040] Matching factors are found based on the intent flag information. The matching factors are unique and have a one-to-one correspondence with the mapping table. The intent flag information and the matching factors are uniquely paired.

[0041] When the intent flag information successfully matches the matching factor, the control instruction corresponding to the matching factor is determined as the target control instruction.

[0042] In one embodiment, the step of receiving initial touch data collected by the touch device further includes:

[0043] The screen resolution and touch sensitivity parameters of the touch device are detected, and the sampling frequency of the touch point coordinates is adjusted according to the screen resolution and touch sensitivity parameters.

[0044] The operating mode of the touch data acquisition module is set according to the sampling frequency to ensure the synchronous acquisition of touch point coordinates, touch pressure value and touch duration.

[0045] Furthermore, to achieve the above objectives, this application also proposes a pressure sensing fusion control device based on a virtual joystick and touch control, the pressure sensing fusion control device based on a virtual joystick and touch control comprising:

[0046] The feature acquisition module is used to receive initial touch data collected by the touch device, the initial touch data including touch point coordinates, touch pressure value and touch duration;

[0047] The feature extraction module is used to generate a touch trajectory based on the touch point coordinates and to calculate the pressure change rate of the touch behavior based on the touch pressure value and the touch duration.

[0048] The feature mapping module is used to map the touch trajectory to the pressure change rate to obtain the dynamic feature vector of the touch behavior;

[0049] An intent classification module is used to classify the dynamic feature vector and determine the operation intent of the touch behavior;

[0050] The instruction generation module is used to generate corresponding control instructions based on the operation intention and send the control instructions to the target device.

[0051] Furthermore, to achieve the above objectives, this application also proposes a pressure sensing fusion control device based on virtual joystick and touch control. The pressure sensing fusion control device based on virtual joystick and touch control includes: a memory, a processor, and a computer program stored in the memory and executable on the processor. The computer program is configured to implement the steps of the pressure sensing fusion control method based on virtual joystick and touch control as described above.

[0052] In addition, to achieve the above objectives, the present invention also proposes a storage medium, which is a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, it implements the steps of the pressure sensing fusion control method based on virtual joystick and touch as described above.

[0053] In addition, to achieve the above objectives, this application also provides a computer program product, which includes a computer program that, when executed by a processor, implements the steps of the pressure-sensing fusion control method based on virtual joystick and touch as described above.

[0054] This application provides a pressure-sensing fusion control method based on virtual joysticks and touch control. It receives initial touch data collected by a touch device, including touch point coordinates, touch pressure value, and touch duration. A touch trajectory is generated based on the touch point coordinates. The pressure change rate of the touch behavior is calculated based on the touch pressure value and touch duration. The touch trajectory is mapped to the pressure change rate to obtain a dynamic feature vector of the touch behavior. The dynamic feature vector is classified to determine the operation intention of the touch behavior. A corresponding control command is generated based on the operation intention and sent to the target device. This method solves the problem of insufficient dynamic response capability to pressure changes in touch control methods based on fixed pressure thresholds, thereby improving the user's interactive experience in different scenarios. Attached Figure Description

[0055] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0056] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0057] Figure 1 This is a flowchart illustrating an embodiment of the pressure sensing fusion control method based on virtual joystick and touch control in this application.

[0058] Figure 2 This is a schematic diagram of data synchronization acquisition from an embodiment of the pressure sensing fusion control method based on virtual joystick and touch control in this application;

[0059] Figure 3 This is a flowchart illustrating the dynamic feature construction of an embodiment of the pressure sensing fusion control method based on virtual joystick and touch control in this application.

[0060] Figure 4 This is a classification diagram of the operation intent of an embodiment of the pressure sensing fusion control method based on virtual joystick and touch control in this application;

[0061] Figure 5 This is a schematic diagram of the module structure of the pressure sensing fusion control device based on virtual joystick and touch control according to an embodiment of this application;

[0062] Figure 6 This is a schematic diagram of the device structure of the hardware operating environment involved in the pressure sensing fusion control method based on virtual joystick and touch control in the embodiments of this application.

[0063] The realization of the purpose, functional features and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0064] It should be understood that the specific embodiments described herein are merely illustrative of the technical solutions of this application and are not intended to limit this application.

[0065] To better understand the technical solution of this application, a detailed description will be provided below in conjunction with the accompanying drawings and specific implementation methods.

[0066] The main solution of this application embodiment is as follows: receiving initial touch data collected by a touch device, the initial touch data including touch point coordinates, touch pressure value, and touch duration; generating a touch trajectory based on the touch point coordinates, and calculating the pressure change rate of the touch behavior based on the touch pressure value and the touch duration; mapping the touch trajectory to the pressure change rate to obtain a dynamic feature vector of the touch behavior; classifying the dynamic feature vector to determine the operation intention of the touch behavior; generating a corresponding control command based on the operation intention, and sending the control command to the target device.

[0067] Currently, touch technology is increasingly widely used in games and interactive devices, and pressure-sensitive control methods are gradually becoming an important means of improving user experience. Especially in mobile terminals and virtual reality scenarios, pressure-sensitive technology can achieve precise control of game characters, skills, or interfaces by detecting the pressure values ​​of user operations. Commonly used solutions typically construct a three-axis virtual joystick, using the coordinates of the screen touch point and the touch pressure value as input variables to achieve multi-dimensional control of game character behavior. Specifically, this method assigns touch pressure to the Z-axis, combined with offsets on the X and Y axes, further enriching the character control functions. However, existing solutions mainly rely on fixed pressure thresholds, which limits their dynamic response to pressure changes and shows certain shortcomings in adapting to the personalized needs of different users or in different scenarios.

[0068] This application provides a solution that receives initial touch data collected by a touch device, including touch point coordinates, touch pressure value, and touch duration. A touch trajectory is generated based on the touch point coordinates. The pressure change rate of the touch behavior is calculated based on the touch pressure value and touch duration. The touch trajectory is mapped to the pressure change rate to obtain a dynamic feature vector of the touch behavior. The dynamic feature vector is classified to determine the operation intent of the touch behavior. A corresponding control command is generated based on the operation intent and sent to the target device. This method solves the problem of insufficient dynamic response capability to pressure changes in touch control methods based on fixed pressure thresholds, thereby improving the user's interactive experience in different scenarios.

[0069] It should be noted that the executing entity in this embodiment can be a computing service device with data processing, network communication, and program execution functions, such as a tablet computer, personal computer, or mobile phone, or an electronic device capable of performing the above functions, such as a pressure-sensing fusion control device based on a virtual joystick and touch control, etc. This embodiment does not specifically limit it. The following uses a pressure-sensing fusion control device based on a virtual joystick and touch control as an example to describe this embodiment and the following embodiments.

[0070] This application provides a pressure-sensing fusion control method based on virtual joystick and touch control, referring to... Figure 1 , Figure 1 This is a flowchart illustrating the first embodiment of the pressure sensing fusion control method based on virtual joystick and touch control in this application.

[0071] In this embodiment, the pressure sensing fusion control method based on virtual joystick and touch control includes steps S10~S40:

[0072] Step S10: Receive initial touch data collected by the touch device, wherein the initial touch data includes touch point coordinates, touch pressure value, and touch duration;

[0073] It should be noted that a touch device refers to an electronic device or apparatus that can be input and operated by touch. When a touch device responds to a touch action, it can generate touch data, which includes touch point coordinates, touch pressure value, and touch duration.

[0074] In practical implementation, touch point coordinates are determined by sensors or electrodes on the touchscreen to pinpoint the precise location of the touch, typically expressed as horizontal (X-axis) and vertical (Y-axis) coordinates. For example, on a 1080p resolution touchscreen phone screen, when a user taps the screen with their finger, the touch device detects the X and Y coordinates of the point of contact between the finger and the screen, such as (540, 960). These coordinates can be used to locate the area of ​​the screen where the touch occurred. The pressure applied during touch can also be used as a control parameter. The touch pressure value can be determined by sensors sensing different levels of pressure applied to the screen, quantifying the force. Touch duration measures the duration of a touch operation. Touch devices can have built-in timers. The start time is defined as the time when a touch action occurs and the corresponding touch signal is generated at the touch point on the touch surface, and the end time is defined as the time the touch is removed from the touch point. The time between the start and end times is considered the touch duration.

[0075] In one feasible implementation, the step of receiving the initial touch data collected by the touch device further includes:

[0076] The screen resolution and touch sensitivity parameters of the touch device are detected, and the sampling frequency of the touch point coordinates is adjusted according to the screen resolution and touch sensitivity parameters.

[0077] The operating mode of the touch data acquisition module is set according to the sampling frequency to ensure the synchronous acquisition of touch point coordinates, touch pressure value and touch duration.

[0078] It's important to note that screen resolution refers to the panel size of the touchscreen, which can be considered as the coordinate system for touch points on the touch device screen, allowing the touch position to be determined by a range of row and column numbers. Touch sensitivity, on the other hand, determines how sensitive the device is to touch input; it is directly related to the timeliness of the response to touch events.

[0079] In practical implementation, the screen resolution of a touch device can be obtained through the device's screen driver or screen configuration file, for example, expressed as the number of horizontal and vertical pixels, such as 1920×1080 pixels. Higher resolution results in richer screen detail, requiring more precise touch point coordinate sampling to ensure operational accuracy. Touch sensitivity parameters can be obtained using built-in touch sensitivity testing tools. These parameters reflect the device's sensitivity to touch operations, including its sensitivity to touch pressure, touch area, and response speed to touch point movement. Devices with high sensitivity can more accurately identify slight touch actions and subtle changes in touch position.

[0080] Generally, higher screen resolution and touch sensitivity require a higher sampling frequency to ensure accurate tracking of touch point position changes and prevent missed touch locations or inaccurate position information due to insufficient sampling. Adjusting the sampling frequency of touch point coordinates based on screen resolution and touch sensitivity parameters can be achieved through pixel density. and minimum detectable displacement Determine the sampling frequency .

[0081]

[0082] Reference Figure 2 , Figure 2 This is a schematic diagram of synchronous data acquisition. The working mode corresponds to the sampling frequency. For example, the working mode can be divided into single-cycle mode according to the sampling frequency. ), pipeline mode ( ), and supersampling mode ( To ensure synchronized data collection, timestamps can be standardized and aligned. The specific formula is as follows:

[0083]

[0084] in, For the alignment time, The master clock reference time It is a clock cycle counter. This refers to the hardware clock frequency.

[0085] Once the timestamps are consistent, the touch point coordinates, touch pressure values, and touch duration can be collected through the coordinate acquisition channel, pressure acquisition channel, and time timing channel, respectively, and these data can be packaged together.

[0086] Step S20: Generate a touch trajectory based on the touch point coordinates, and calculate the pressure change rate of the touch behavior based on the touch pressure value and the touch duration;

[0087] It should be noted that the touch trajectory is the path formed by the user's continuous movement on the touchscreen, consisting of the touch point coordinates in a time series. Composition. The pressure change rate represents the intensity of change in touch pressure per unit time, reflecting the user's operational intent.

[0088] In a specific implementation, the process of generating a touch trajectory based on the touch point coordinates and calculating the pressure change rate of the touch behavior based on the touch pressure value and touch duration can be as follows: extract the time series of the touch point coordinates and supplement the missing coordinate data through a linear interpolation algorithm to form a complete touch trajectory; calculate the pressure difference between two adjacent touch points based on the touch pressure value, and divide the pressure difference by the corresponding time interval to obtain the local pressure change rate; determine the weight value based on the touch duration, and perform a weighted average of multiple local pressure change rates based on the weight value to obtain the global pressure change rate.

[0089] When generating a touch trajectory based on touch point coordinates, the set of touch point coordinates can be determined using a time series. This includes missing frames. The missing coordinate data is supplemented using a linear interpolation formula to form a complete touch trajectory. The specific formula is as follows:

[0090]

[0091] in, This is the last valid frame of data before the missing point. This is the first frame of valid data after the missing point. For the timestamps that need to be padded, based on the sampling frequency Generate at equal intervals.

[0092] Meanwhile, when determining the rate of pressure change, the pressure difference between two adjacent touch points can be calculated using the touch pressure value. This yields the local rate of change. The formula for calculating the local rate of change is:

[0093]

[0094] in, This represents the pressure value of adjacent touch points. When... Time is considered noise and automatically filtered. Increasing pressure Pressure decreases.

[0095] After determining the local pressure change rate, the weight value can be determined based on the touch duration. The calculation formula is:

[0096]

[0097] in, For local time intervals, A smoothing factor to suppress transient mutation interference.

[0098] When calculating the global pressure change rate, a weighted average of multiple local pressure change rates can be performed based on weight values ​​to obtain the global pressure change rate. The calculation formula is:

[0099]

[0100] During weighted calculations, the weights can be dynamically optimized and updated. For example, during long-press recognition, i.e., single-point recognition... At that time, weight To eliminate interference from the static state. When the Wie mutation is enhanced, i.e., at 3 consecutive points... , The overall standard deviation is used, with a weighting increase of 50%.

[0101] Step S30: Map the touch trajectory to the pressure change rate to obtain the dynamic feature vector of the touch behavior;

[0102] It should be noted that the dynamic feature vector is a multi-dimensional representation vector formed by fusing the segmented spatiotemporal features of the touch trajectory with dynamic changes in pressure.

[0103] It is understood that the step of mapping the touch trajectory to the pressure change rate to obtain the dynamic feature vector of the touch behavior includes: dividing the touch trajectory into several sub-trajectory segments, each sub-trajectory segment corresponding to a time window; calculating the direction angle and curvature of the touch trajectory in each time window, and combining the direction angle, the curvature and the pressure change rate to obtain a sub-feature vector; and concatenating all the sub-feature vectors in chronological order to form the complete dynamic feature vector of the touch behavior.

[0104] In the specific implementation, refer to Figure 3 , Figure 3 A flowchart is constructed for dynamic features. The touch trajectory is divided based on a time window, resulting in multiple sub-trajectory segments. The total touch duration is set to... Time window length Adaptive parameters Therefore, the number of segments .For example hour, When extracting sub-trajectory segments, the first... The set of points representing the trajectory of a joke can be represented as: Therefore, in determining the direction angle of the touch trajectory... and curvature When, the calculation formulas are as follows:

[0105]

[0106]

[0107] in, The average of the coordinates of the first 3 points within the window. The average of the coordinates of the three points behind the window. For point The tangents at that point intersect. It is an arc length unit.

[0108] In determining the direction angle of the touch trajectory and curvature Then, it is combined with the pressure change rate to obtain the sub-feature vector corresponding to the sub-trajectory segment. All sub-feature vectors are concatenated in chronological order to form a complete dynamic feature vector of the touch behavior. .

[0109] In one feasible implementation, the step of calculating the orientation angle and curvature of the touch trajectory within each time window, and combining the orientation angle, the curvature, and the pressure change rate to obtain a sub-feature vector includes:

[0110] Within each time window, a sequence of touch point coordinates is determined, the sequence of touch point coordinates including the coordinates of the touch point, the touch time, and the touch pressure;

[0111] Within each time window, the coordinates of the touch points within the time window are smoothed and sampled at equal intervals to generate equally spaced sampling points;

[0112] Perform a difference operation on the equally spaced sampling points to determine the first-order directional derivative of the equally spaced sampling points, and extract the principal direction, angular direction and rate of change of the direction angle based on the first-order directional derivative;

[0113] A discrete sequence is generated by selecting three consecutive equally spaced sampling points, and the average curvature, maximum curvature, and inflection point density are calculated based on the discrete sequence.

[0114] Linear regression analysis is performed on the touch point coordinate sequence to determine the correspondence between time and pressure, and the pressure change rate and pressure fluctuation are determined based on the correspondence between time and pressure.

[0115] The main direction, the angular direction, the rate of change of direction, the average curvature, the maximum curvature, the inflection point density, the rate of change of pressure, and the pressure fluctuation are combined to obtain a sub-feature vector.

[0116] In a more detailed implementation, the step of calculating the direction angle and curvature of the touch trajectory within each time window, and combining the direction angle, curvature, and pressure change rate to obtain a sub-feature vector can be further refined and broken down. This is achieved by analyzing the input touch point coordinate sequence. Coordinate smoothing is performed, specifically using a Savitzky-Golay filter for coordinate smoothing, followed by equal-interval sampling.

[0117]

[0118] Perform a difference operation on equally spaced sampling points to determine the first-order directional derivative of each equally spaced sampling point. For the sequence of equally spaced sampling points... The first-order directional derivative is obtained by performing a difference operation on it:

[0119]

[0120] in, This represents the displacement increment in the x-direction. This represents the displacement increment in the y-direction. Instantaneous velocity This is the instantaneous direction angle.

[0121] Then calculate the principal direction of the orientation angle. , angle direction and the rate of change of direction .

[0122]

[0123]

[0124]

[0125] A discrete sequence is generated by selecting three consecutive equally spaced sampling points. The average curvature, maximum curvature, and inflection point density are then calculated from this discrete sequence. The three-point curvature formula is as follows:

[0126]

[0127] Therefore, the mean curvature can be determined. Maximum curvature and inflection point density .

[0128]

[0129]

[0130]

[0131] Linear regression analysis was performed on the touch point coordinate sequence to construct a linear regression model. Its pressure change rate Pressure fluctuation .

[0132] The principal direction, angular direction, rate of change of direction, average curvature, maximum curvature, inflection point density, pressure rate of change, and pressure fluctuation are combined to obtain the sub-feature vector. .

[0133]

[0134] Step S40: Classify the dynamic feature vector to determine the operation intent of the touch behavior.

[0135] It should be noted that the operational intent refers to the system function or interaction goal that the user hopes to trigger through touch behavior; it is a semantic mapping of touch behavior. The classification process involves transforming dynamic feature vectors into executable interaction commands, with the core being the identification of the user's purpose behind the touch behavior.

[0136] Understandably, referring to Figure 4 , Figure 4 This is a classification diagram of operational intentions. Operational intentions can be divided into different levels according to typical intentions. As a feasible example, refer to Table 1 for one classification method.

[0137]

[0138] The steps of classifying the dynamic feature vectors to determine the operational intent of the touch behavior include:

[0139] Extract dynamic feature vectors of historical touch behaviors and operation intent labels corresponding to the historical touch behaviors from a preset training dataset;

[0140] The dynamic feature vectors of the historical touch behaviors are standardized and preprocessed, and the preprocessed dataset is divided into a training set and a validation set.

[0141] The regularization parameter, kernel parameter, and convergence threshold of the Gaussian kernel function are initialized to obtain the initialized Gaussian kernel;

[0142] The initial Gaussian kernel is iteratively solved using the sequence minimum optimization algorithm to obtain the Lagrange multipliers. The training set is then calculated based on the Lagrange multipliers to determine the support vectors and decision functions.

[0143] The support vectors and decision function are validated based on the validation set to determine the model error. When the model error is less than a preset error threshold, a classification model is generated based on the support vectors and the decision function.

[0144] The dynamic feature vector of the current touch behavior is input into the classification model, and the corresponding operation intention is output.

[0145] In the specific implementation, a dynamic feature vector set of historical touch behaviors is extracted. and its corresponding operation intent tag set The dynamic feature vectors of historical touch behaviors are preprocessed using standardization. The standardization process is as follows:

[0146]

[0147] in, The characteristic mean, The standard deviation is denoted as .

[0148] The preprocessed dataset was divided into two parts: 70% was used as the training set and 30% as the validation set.

[0149] The regularization parameter, kernel parameter, and convergence threshold of the Gaussian kernel function are initialized to obtain the initialized Gaussian kernel. The initialized Gaussian kernel is then iteratively solved using the sequence minimum optimization algorithm to obtain the Lagrange multipliers. These Lagrange multipliers are then used to calculate the support vectors and decision function on the training set. Iterative solution for the Lagrange multipliers:

[0150]

[0151] in, This is the Gaussian kernel function.

[0152] The support vectors and decision function are determined by calculating the Lagrange multipliers on the training set. The support vector set is as follows: The decision function is:

[0153]

[0154] The support vectors and decision functions are validated based on the validation set to determine the model error. When the model error is less than a preset error threshold, a classification model is generated based on the support vectors and the decision function. The dynamic feature vector of the current touch behavior is input into the classification model, and the corresponding operation intention is output.

[0155] Step S50: Generate a corresponding control command based on the operation intention, and send the control command to the target device.

[0156] It should be noted that control commands are control data stream information used to respond to operational intentions, and target devices refer to devices used to receive control commands and respond to them to perform corresponding control effects.

[0157] It is understood that generating corresponding control commands based on the stated operational intent and sending the control commands to the target device includes:

[0158] Determine the intent flag information based on the stated operational intent;

[0159] Matching factors are found based on the intent flag information. The matching factors are unique and have a one-to-one correspondence with the mapping table. The intent flag information and the matching factors are uniquely paired.

[0160] When the intent flag information successfully matches the matching factor, the control command corresponding to the matching factor is determined as the target control command. The mapping table stores the correspondence between different operation intents and control commands.

[0161] The target control command is encoded into binary format and sent to the target device through the communication interface.

[0162] In practical implementation, operational intentions can be realized through digital representation, using a hierarchical encoding structure:

[0163]

[0164]

[0165] For example: Two-finger zoom: Flag=2031 (Function Operation / Zoom / Normal).

[0166] Then, the intent flag information is used to find matching factors. Matching factors are unique and have a one-to-one correspondence with the mapping table. The intent flag information and the matching factor are uniquely paired. A prime number hash algorithm is used to ensure this one-to-one correspondence, while also supporting bidirectional conversion between flag information and factors. For example, if the intent flag is 1011 (click), its matching factor is: This design, due to the large range of factor values, can avoid hash collisions. The target control instructions are encoded into binary format and sent to the target device via the communication interface.

[0167] This embodiment provides a pressure-sensing fusion control method based on virtual joysticks and touch control. It receives initial touch data collected by a touch device, including touch point coordinates, touch pressure value, and touch duration. A touch trajectory is generated based on the touch point coordinates. The pressure change rate of the touch behavior is calculated based on the touch pressure value and touch duration. The touch trajectory is mapped to the pressure change rate to obtain a dynamic feature vector of the touch behavior. The dynamic feature vector is classified to determine the operation intention of the touch behavior. A corresponding control command is generated based on the operation intention and sent to the target device. This method solves the problem of insufficient dynamic response capability to pressure changes in touch control methods based on fixed pressure thresholds, thereby improving the user's interactive experience in different scenarios.

[0168] It should be noted that the above examples are only for understanding this application and do not constitute a limitation on the pressure sensing fusion control method based on virtual joystick and touch control in this application. Any simple modifications based on this technical concept are within the protection scope of this application.

[0169] This application also provides a pressure-sensing fusion control device based on virtual joystick and touch control; please refer to [reference needed]. Figure 5 The pressure-sensing fusion control device based on virtual joystick and touch control includes:

[0170] The feature acquisition module 10 is used to receive initial touch data acquired by the touch device, wherein the initial touch data includes touch point coordinates, touch pressure value and touch duration;

[0171] Feature extraction module 20 is used to generate a touch trajectory based on the coordinates of the touch point and to calculate the pressure change rate of the touch behavior based on the touch pressure value and the touch duration;

[0172] The feature mapping module 30 is used to map the touch trajectory to the pressure change rate to obtain the dynamic feature vector of the touch behavior;

[0173] The intent classification module 40 is used to classify the dynamic feature vector and determine the operation intent of the touch behavior;

[0174] The instruction generation module 50 is used to generate corresponding control instructions according to the operation intention and send the control instructions to the target device.

[0175] In one feasible implementation, the feature extraction module 20 is further used to extract the time series of the touch point coordinates and supplement the missing coordinate data through a linear interpolation algorithm to form a complete touch trajectory.

[0176] The pressure difference between two adjacent touch points is calculated based on the touch pressure value, and the pressure difference is divided by the corresponding time interval to obtain the local pressure change rate.

[0177] A weight value is determined based on the touch duration, and a weighted average is performed on multiple local pressure change rates based on the weight value to obtain the global pressure change rate.

[0178] In one feasible implementation, the feature mapping module 30 is further configured to divide the touch trajectory into several sub-trajectory segments, each sub-trajectory segment corresponding to a time window;

[0179] Within each time window, the orientation angle and curvature of the touch trajectory are calculated, and the orientation angle, curvature, and pressure change rate are combined to obtain a sub-feature vector;

[0180] All sub-feature vectors are concatenated in chronological order to form a complete dynamic feature vector of the touch behavior.

[0181] In one feasible implementation, the feature mapping module 30 is further configured to determine a sequence of touch point coordinates within each time window, the sequence of touch point coordinates including the coordinates of the touch point, the touch time, and the touch pressure;

[0182] Within each time window, the coordinates of the touch points within the time window are smoothed and sampled at equal intervals to generate equally spaced sampling points;

[0183] Perform a difference operation on the equally spaced sampling points to determine the first-order directional derivative of the equally spaced sampling points, and extract the principal direction, angular direction and rate of change of the direction angle based on the first-order directional derivative;

[0184] A discrete sequence is generated by selecting three consecutive equally spaced sampling points, and the average curvature, maximum curvature, and inflection point density are calculated based on the discrete sequence.

[0185] Linear regression analysis is performed on the touch point coordinate sequence to determine the correspondence between time and pressure, and the pressure change rate and pressure fluctuation are determined based on the correspondence between time and pressure.

[0186] The main direction, the angular direction, the rate of change of direction, the average curvature, the maximum curvature, the inflection point density, the rate of change of pressure, and the pressure fluctuation are combined to obtain a sub-feature vector.

[0187] In one feasible implementation, the intent classification module 40 is further configured to extract dynamic feature vectors of historical touch behaviors and operation intent labels corresponding to the historical touch behaviors from a preset training dataset.

[0188] A classification model is generated by training the dynamic feature vectors of the historical touch behaviors and the operation intention labels based on the support vector machine algorithm.

[0189] The dynamic feature vector of the current touch behavior is input into the classification model, and the corresponding operation intention is output.

[0190] In one feasible implementation, the intent classification module 40 is further configured to perform standardized preprocessing on the dynamic feature vector of the historical touch behavior, and divide the preprocessed dataset into a training set and a validation set.

[0191] The regularization parameter, kernel parameter, and convergence threshold of the Gaussian kernel function are initialized to obtain the initialized Gaussian kernel;

[0192] The initial Gaussian kernel is iteratively solved using the sequence minimum optimization algorithm to obtain the Lagrange multipliers. The training set is then calculated based on the Lagrange multipliers to determine the support vectors and decision functions.

[0193] The support vectors and decision function are validated based on the validation set to determine the model error. When the model error is less than a preset error threshold, a classification model is generated based on the support vectors and the decision function.

[0194] In one feasible implementation, the instruction generation module 50 is further configured to look up a preset mapping table according to the operation intention to obtain the target control instruction, wherein the mapping table stores the correspondence between different operation intentions and control instructions.

[0195] The target control command is encoded into binary format and sent to the target device through the communication interface.

[0196] In one feasible implementation, the instruction generation module 50 is further configured to determine intent flag information based on the operation intent;

[0197] Matching factors are found based on the intent flag information. The matching factors are unique and have a one-to-one correspondence with the mapping table. The intent flag information and the matching factors are uniquely paired.

[0198] When the intent flag information successfully matches the matching factor, the control instruction corresponding to the matching factor is determined as the target control instruction.

[0199] In one feasible implementation, the feature acquisition module 10 is further configured to detect the screen resolution and touch sensitivity parameters of the touch device, and adjust the sampling frequency of the touch point coordinates according to the screen resolution and the touch sensitivity parameters.

[0200] The operating mode of the touch data acquisition module is set according to the sampling frequency to ensure the synchronous acquisition of touch point coordinates, touch pressure value and touch duration.

[0201] The pressure-sensing fusion control device based on virtual joystick and touch provided in this application, employing the pressure-sensing fusion control method based on virtual joystick and touch in the above embodiments, can solve the technical problem of insufficient dynamic response capability to pressure changes in touch control methods based on fixed pressure threshold judgment. Compared with the prior art, the beneficial effects of the pressure-sensing fusion control device based on virtual joystick and touch provided in this application are the same as those of the pressure-sensing fusion control method based on virtual joystick and touch provided in the above embodiments, and other technical features in the pressure-sensing fusion control device based on virtual joystick and touch are the same as those disclosed in the methods of the above embodiments, and will not be repeated here.

[0202] This application provides a pressure-sensing fusion control device based on virtual joystick and touch control. The pressure-sensing fusion control device based on virtual joystick and touch control includes: 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 pressure-sensing fusion control method based on virtual joystick and touch control in the above embodiment 1.

[0203] The following is for reference. Figure 6 This document illustrates a structural schematic diagram of a pressure-sensing fusion control device based on virtual joysticks and touch control, suitable for implementing embodiments of this application. The pressure-sensing fusion control device based on virtual joysticks and touch control in the embodiments of this application may include, but is not limited to, mobile terminals such as mobile phones, laptops, digital radio receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Description), PMPs (Portable Media Players), and in-vehicle terminals (e.g., in-vehicle navigation terminals), as well as fixed terminals such as digital TVs and desktop computers. Figure 6 The pressure-sensing fusion control device based on virtual joystick and touch shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of this application.

[0204] like Figure 6As shown, the pressure-sensing fusion control device based on virtual joystick and touch control may include a processing unit 1001 (e.g., a central processing unit, a graphics processor, etc.), which can perform various appropriate actions and processes according to programs stored in ROM (Read Only Memory) 1002 or programs loaded from storage device 1003 into RAM (Random Access Memory) 1004. RAM 1004 also stores various programs and data required for the operation of the pressure-sensing fusion control device based on virtual joystick and touch control. The processing unit 1001, ROM 1002, and RAM 1004 are interconnected via bus 1005. Input / output (I / O) interface 1006 is also connected to the bus. Typically, the following systems can be connected to I / O interface 1006: input devices 1007 including, for example, touchscreens, touchpads, keyboards, mice, image sensors, microphones, accelerometers, gyroscopes, etc.; output devices 1008 including, for example, LCDs (Liquid Crystal Displays), speakers, vibrators, etc.; storage devices 1003 including, for example, magnetic tapes, hard disks, etc.; and communication devices 1009. Communication device 1009 allows the pressure-sensing fusion control device based on virtual joysticks and touch controls to wirelessly or wiredly communicate with other devices to exchange data. Although the figure shows a pressure-sensing fusion control device based on virtual joysticks and touch controls with various systems, it should be understood that it is not required to implement or possess all the systems shown. More or fewer systems can be implemented alternatively.

[0205] Specifically, according to the embodiments disclosed in this application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments disclosed in this application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device, or installed from storage device 1003, or installed from ROM 1002. When the computer program is executed by processing device 1001, it performs the functions defined in the methods of the embodiments disclosed in this application.

[0206] The pressure-sensing fusion control device based on virtual joysticks and touch control provided in this application, employing the pressure-sensing fusion control method based on virtual joysticks and touch control described in the above embodiments, can solve the technical problems of pressure-sensing fusion control based on virtual joysticks and touch control. Compared with the prior art, the beneficial effects of the pressure-sensing fusion control device based on virtual joysticks and touch control provided in this application are the same as the beneficial effects of the pressure-sensing fusion control method based on virtual joysticks and touch control provided in the above embodiments, and other technical features in this pressure-sensing fusion control device based on virtual joysticks and touch control are the same as those disclosed in the previous embodiment method, and will not be repeated here.

[0207] It should be understood that the various parts disclosed in this application can be implemented using hardware, software, firmware, or a combination thereof. In the description of the above embodiments, specific features, structures, materials, or characteristics can be combined in any suitable manner in one or more embodiments or examples.

[0208] The above are merely specific embodiments of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0209] This application provides a computer-readable storage medium having computer-readable program instructions (i.e., a computer program) stored thereon, the computer-readable program instructions being used to execute the pressure-sensing fusion control method based on virtual joystick and touch control in the above embodiments.

[0210] The computer-readable storage medium provided in this application may be, for example, a USB flash drive, but is not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to: electrical connections having one or more wires, portable computer disks, hard disks, RAM (Random Access Memory), ROM (Read Only Memory), EPROM (Erasable Programmable Read Only Memory or Flash Memory), optical fibers, CD-ROM (CD-Read Only Memory), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, system, or device. The program code contained on the computer-readable storage medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination thereof.

[0211] The aforementioned computer-readable storage medium may be included in a pressure-sensing fusion control device based on virtual joysticks and touch; or it may exist independently and not be assembled into a pressure-sensing fusion control device based on virtual joysticks and touch.

[0212] The aforementioned computer-readable storage medium carries one or more programs. When these programs are executed by a pressure-sensing fusion control device based on a virtual joystick and touch, the pressure-sensing fusion control device based on a virtual joystick and touch performs the following actions: receiving initial touch data collected by a touch device, the initial touch data including touch point coordinates, touch pressure value, and touch duration; generating a touch trajectory based on the touch point coordinates; calculating the pressure change rate of the touch behavior based on the touch pressure value and the touch duration; mapping the touch trajectory to the pressure change rate to obtain a dynamic feature vector of the touch behavior; classifying the dynamic feature vector to determine the operation intention of the touch behavior; generating a corresponding control command based on the operation intention; and sending the control command to the target device.

[0213] Computer program code for performing the operations of this application can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, as well as conventional procedural programming languages ​​such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including LAN (Local Area Network) or WAN (Wide Area Network)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0214] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0215] The modules described in the embodiments of this application can be implemented in software or hardware. The names of the modules do not necessarily limit the functionality of the unit itself.

[0216] The readable storage medium provided in this application is a computer-readable storage medium that stores computer-readable program instructions (i.e., a computer program) for executing the aforementioned pressure-sensing fusion control method based on virtual joysticks and touch, thereby solving the technical problem of pressure-sensing fusion control based on virtual joysticks and touch. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided in this application are the same as those of the pressure-sensing fusion control method based on virtual joysticks and touch provided in the above embodiments, and will not be repeated here.

[0217] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the pressure-sensing fusion control method based on virtual joystick and touch as described above.

[0218] The computer program product provided in this application can solve the technical problem of pressure sensing fusion control based on virtual joystick and touch. Compared with the prior art, the beneficial effects of the computer program product provided in this application are the same as those of the pressure sensing fusion control method based on virtual joystick and touch provided in the above embodiments, and will not be repeated here.

[0219] The above are only some embodiments of this application and do not limit the patent scope of this application. All equivalent structural transformations made under the technical concept of this application and using the contents of the specification and drawings of this application, or direct / indirect applications in other related technical fields, are included in the patent protection scope of this application.

Claims

1. A pressure-sensing fusion control method based on virtual joystick and touch control, characterized in that, The pressure sensing fusion control method based on virtual joystick and touch control includes: Receive initial touch data collected by the touch device, the initial touch data including touch point coordinates, touch pressure value and touch duration; A touch trajectory is generated based on the coordinates of the touch point, and the pressure change rate of the touch behavior is calculated based on the touch pressure value and the touch duration. The touch trajectory is mapped to the pressure change rate to obtain the dynamic feature vector of the touch behavior. The dynamic feature vector is a multi-dimensional representation vector formed by fusing the segmented spatiotemporal features of the touch trajectory with the dynamic change of pressure. The dynamic feature vectors are classified to determine the operational intent of the touch behavior; Generate corresponding control commands based on the operational intent, and send the control commands to the target device; The step of mapping the touch trajectory to the pressure change rate to obtain the dynamic feature vector of the touch behavior includes: The touch trajectory is divided into several sub-trajectory segments, and each sub-trajectory segment corresponds to a time window; Within each time window, the orientation angle and curvature of the touch trajectory are calculated, and the orientation angle, curvature, and pressure change rate are combined to obtain a sub-feature vector; All sub-feature vectors are concatenated in chronological order to form a complete dynamic feature vector of the touch behavior.

2. The method as described in claim 1, characterized in that, The steps of generating a touch trajectory based on the touch point coordinates and calculating the pressure change rate of the touch behavior based on the touch pressure value and the touch duration include: The time series of the touch point coordinates is extracted, and the missing coordinate data is supplemented by a linear interpolation algorithm to form a complete touch trajectory; The pressure difference between two adjacent touch points is calculated based on the touch pressure value, and the pressure difference is divided by the corresponding time interval to obtain the local pressure change rate. A weight value is determined based on the touch duration, and a weighted average is performed on multiple local pressure change rates based on the weight value to obtain the global pressure change rate.

3. The method as described in claim 1, characterized in that, The step of calculating the orientation angle and curvature of the touch trajectory within each time window, and combining the orientation angle, the curvature, and the pressure change rate to obtain a sub-feature vector includes: Within each time window, a sequence of touch point coordinates is determined, the sequence of touch point coordinates including the coordinates of the touch point, the touch time, and the touch pressure; Within each time window, the coordinates of the touch points within the time window are smoothed and sampled at equal intervals to generate equally spaced sampling points; Perform a difference operation on the equally spaced sampling points to determine the first-order directional derivative of the equally spaced sampling points, and extract the principal direction of the direction angle, the angular direction, and the rate of change of the direction angle based on the first-order directional derivative. A discrete sequence is generated by selecting three consecutive equally spaced sampling points, and the average curvature, maximum curvature, and inflection point density are calculated based on the discrete sequence. Linear regression analysis is performed on the touch point coordinate sequence to determine the correspondence between time and pressure, and the pressure change rate and pressure fluctuation are determined based on the correspondence between time and pressure. The main direction, the angular direction, the rate of change of direction, the average curvature, the maximum curvature, the inflection point density, the rate of change of pressure, and the pressure fluctuation are combined to obtain a sub-feature vector.

4. The method as described in claim 1, characterized in that, The step of classifying the dynamic feature vector to determine the operation intent of the touch behavior includes: Extract dynamic feature vectors of historical touch behaviors and operation intent labels corresponding to the historical touch behaviors from a preset training dataset; A classification model is generated by training the dynamic feature vectors of the historical touch behaviors and the operation intention labels based on the support vector machine algorithm. The dynamic feature vector of the current touch behavior is input into the classification model, and the corresponding operation intention is output.

5. The method as described in claim 4, characterized in that, The step of training the dynamic feature vectors of the historical touch behavior based on the support vector machine algorithm to generate a classification model includes: The dynamic feature vectors of the historical touch behaviors are standardized and preprocessed, and the preprocessed dataset is divided into a training set and a validation set. The regularization parameter, kernel parameter, and convergence threshold of the Gaussian kernel function are initialized to obtain the initialized Gaussian kernel; The initial Gaussian kernel is iteratively solved using the sequence minimum optimization algorithm to obtain the Lagrange multipliers. The training set is then calculated based on the Lagrange multipliers to determine the support vectors and decision functions. The support vectors and decision function are validated based on the validation set to determine the model error. When the model error is less than a preset error threshold, a classification model is generated based on the support vectors and the decision function.

6. The method as described in claim 1, characterized in that, The step of generating a corresponding control command based on the operation intention and sending the control command to the target device includes: The target control command is obtained by searching a preset mapping table according to the operation intention. The mapping table stores the correspondence between different operation intentions and control commands. The target control command is encoded into binary format and sent to the target device through the communication interface.

7. The method as described in claim 6, characterized in that, The step of searching a preset mapping table to obtain the target control command according to the operation intention includes: Determine the intent flag information based on the stated operational intent; Matching factors are found based on the intent flag information. The matching factors are unique and have a one-to-one correspondence with the mapping table. The intent flag information and the matching factors are uniquely paired. When the intent flag information successfully matches the matching factor, the control instruction corresponding to the matching factor is determined as the target control instruction.

8. The method as described in claim 1, characterized in that, The step of receiving the initial touch data collected by the touch device also includes: The screen resolution and touch sensitivity parameters of the touch device are detected, and the sampling frequency of the touch point coordinates is adjusted according to the screen resolution and touch sensitivity parameters. The operating mode of the touch data acquisition module is set according to the sampling frequency to ensure the synchronous acquisition of touch point coordinates, touch pressure value and touch duration.

9. A pressure-sensing fusion control device based on virtual joystick and touch control, characterized in that, The pressure-sensing fusion control device based on virtual joystick and touch control includes: The feature acquisition module is used to receive initial touch data collected by the touch device, the initial touch data including touch point coordinates, touch pressure value and touch duration; The feature extraction module is used to generate a touch trajectory based on the touch point coordinates and to calculate the pressure change rate of the touch behavior based on the touch pressure value and the touch duration. The feature mapping module is used to map the touch trajectory to the pressure change rate to obtain the dynamic feature vector of the touch behavior. The dynamic feature vector is a multi-dimensional representation vector formed by fusing the segmented spatiotemporal features of the touch trajectory with the dynamic change of pressure. An intent classification module is used to classify the dynamic feature vector and determine the operation intent of the touch behavior; The instruction generation module is used to generate corresponding control instructions based on the operation intention and send the control instructions to the target device; The step of mapping the touch trajectory to the pressure change rate to obtain the dynamic feature vector of the touch behavior includes: The touch trajectory is divided into several sub-trajectory segments, and each sub-trajectory segment corresponds to a time window; Within each time window, the orientation angle and curvature of the touch trajectory are calculated, and the orientation angle, curvature, and pressure change rate are combined to obtain a sub-feature vector; All sub-feature vectors are concatenated in chronological order to form a complete dynamic feature vector of the touch behavior.

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