Touch control processing system and control method of centralized control type touch all-in-one machine

Abstract

The invention relates to the technical field of touch control of touch all-in-one machines, in particular to a touch control processing system of a centralized control type touch all-in-one machine and a control method, and aims to solve the problems that in the prior art, touch events and track characteristics cannot be accurately fused, gesture types cannot be accurately recognized, and the touch control efficiency is high. Operation modules cannot be accurately divided, behavior dynamics cannot be described, and closed-loop analysis from touch input to behavior intention is difficult to realize; a gesture behavior analysis module fuses a touch event and track features, constructs a multi-dimensional vector, recognizes a gesture category through a regression model, aligns an interaction and physiological sequence, calculates a Pearson's correlation coefficient, combines a filtering threshold value to generate an emotion trend, and utilizes a dynamic time warping matching template and a clustering division operation mode. And calculating an evolution rate to describe behavior dynamics, outputting structured data, and realizing closed-loop analysis from touch input to behavior intention.

Description

TECHNICAL FIELD

[0001] The present application relates to the touch control technology field of touch all-in-one machine, more particularly to a touch control processing system and operation method of a control integrated touch all-in-one machine. BACKGROUND

[0002] Although the touch all-in-one machine is widely used in the fields of conferences, teaching and training, the traditional device needs to be long-pressed or exited to call out the control area when in full screen, which is cumbersome to operate, the control area occupies the screen when not in full screen and affects the display effect, and lacks personalized and quick functions, cannot establish gesture mapping according to user habits, needs to remember fixed operation steps, and leads to inconvenience and poor experience.

[0003] The patent application with publication number CN116909465A discloses an interactive system and method of an intelligent touch all-in-one machine, obtains abnormal touch states and normal touch states through processing of a touch information processing module, then obtains the operation of a user in the normal touch state through an interface information judgment module, obtains the correlation value between keywords, then obtains abnormal operation states and normal operation states according to the correlation value, prevents children or others from mis-touching the touch all-in-one machine; then a touch screen operation monitoring module obtains the control state of the target touch machine at this time, when the control state is a blank state, first recognizes the face in front of the screen and generates return confirmation information, which is transmitted to the corresponding user, a touch interface control module controls the touch interface of the target touch machine according to the abnormal touch signal and the return main control interface signal, prevents the target touch machine from staying in the search interface for a long time without being used, and causes traffic loss;

[0004] However, the above-mentioned reference patent automatically triggers the return of the main interface through blank operation, face change recognition of abnormal touch and user switching, prevents mis-touch and traffic waste, realizes intelligent simplification of the interface, but cannot accurately integrate touch events and track features, cannot accurately identify gesture categories, cannot accurately divide operation modules and depict behavior dynamics, and is difficult to realize closed-loop analysis from touch input to behavior intention; at the same time, cannot accurately integrate user behavior, system load and environmental parameters to realize dynamic resource regulation, cannot automatically switch performance and energy saving modes according to the load state, cannot combine power consumption rules to close-loop regulate resource allocation and task priority, and reduces system stability and response efficiency.

[0005] Therefore, we propose a touch control processing system and operation method of a control integrated touch all-in-one machine for the above-mentioned problems. SUMMARY

[0006] The application aims to provide a touch control processing system and a control method of a centralized touch all-in-one machine, solve the problem that the prior art cannot accurately fuse touch events and track features, cannot accurately identify gesture categories, cannot accurately divide operation modules and depict behavior dynamics, and cannot realize closed-loop analysis from touch input to behavior intention; meanwhile, the prior art cannot accurately fuse user behavior, system load and environmental parameters to realize dynamic resource regulation, cannot automatically switch performance and energy-saving modes according to load states, cannot adjust resource allocation and task priority in a closed loop according to power consumption rules, and reduces system stability and response efficiency.

[0007] The application aims to provide a touch control processing system and a control method of a centralized touch all-in-one machine, solve the problem that the prior art cannot accurately fuse touch events and track features, cannot accurately identify gesture categories, cannot accurately divide operation modules and depict behavior dynamics, and cannot realize closed-loop analysis from touch input to behavior intention; meanwhile, the prior art cannot accurately fuse user behavior, system load and environmental parameters to realize dynamic resource regulation, cannot automatically switch performance and energy-saving modes according to load states, cannot adjust resource allocation and task priority in a closed loop according to power consumption rules, and reduces system stability and response efficiency.

[0008] The touch control processing system of the centralized touch all-in-one machine is integrated in a centralized touch management platform and comprises:

[0009] The touch control processing system of the centralized touch all-in-one machine is integrated in a centralized touch management platform and comprises:

[0010] The gesture behavior analysis module performs multi-dimensional behavior modeling based on the standardized touch event data and the two-dimensional track enhanced features derived from the continuous touch coordinate sequence, associates the interaction action timing and the physiological state change, identifies the gesture structure, depicts the emotional state trend, analyzes the operation behavior mode, and outputs the gesture category, the emotional state level and the behavior mode.

[0011] The touch feedback regulation module is used for dynamically adjusting the screen brightness and color temperature, the haptic vibration waveform and intensity, the system sound rhythm and volume according to the interaction situation, combining the gesture category, the emotional state level, the environmental light intensity, the environmental temperature and the environmental humidity, and generating a multi-modal feedback output adapted to the current operation state.

[0012] The resource scheduling management module is used for dynamically regulating the computing resource allocation, the power management mode and the task execution priority according to the user behavior mode, the device power consumption level, the environmental temperature and humidity conditions and the system load state.

[0013] As a preferred embodiment of the application, the process that the gesture behavior analysis module performs multi-dimensional behavior modeling based on the standardized touch event data and the two-dimensional track enhanced features derived from the continuous touch coordinate sequence comprises:

[0014] Acquire standardized touch event data, extract continuous touch coordinate sequences from the standardized touch event data, calculate the sum of the ratio of displacement to time interval between adjacent touch points as the sum velocity, calculate the ratio of the difference of continuous sum velocity to the corresponding time interval as the acceleration, accumulate the Euclidean distance between adjacent points to obtain the trajectory length, calculate the angle between continuous velocity vectors, and take the number of direction changes per unit time as the direction change rate.

[0015] The first and second derivatives are obtained by differentiating the trajectory values. The touch duration is obtained by calculating the difference between the start and end times of the touch. The rate of change of the contact area is calculated. The resultant velocity, acceleration, trajectory length, rate of change of direction, curvature, touch duration, rate of change of contact area, and initial touch speed are combined to generate a two-dimensional trajectory enhancement feature set.

[0016] Touch duration, initial contact speed, total trajectory length, average speed, maximum acceleration, curvature variance, area change rate, and skin temperature change trend are selected from standardized data and enhanced features and arranged in a fixed order to form a multidimensional behavioral feature vector;

[0017] Load preset integer behavior category labels, use feature vectors as input and labels as output to build a multiple linear regression model. When running, receive new touch input, execute the same process to generate feature vectors, input them into the model, output the predicted behavior category and return it.

[0018] In a preferred embodiment of the present invention, the process by which the gesture behavior analysis module performs interaction action timing and physiological state correlation modeling, identifies gesture structure, and characterizes emotional state trends includes:

[0019] Acquire synchronous user interaction behavior data and physiological state data, organize the interaction behavior data by time to generate an interaction behavior feature sequence, organize the physiological state data by time to generate a physiological state monitoring data sequence, calculate the Pearson correlation coefficient between the two sequences, and determine that there is a significant correlation between the interaction behavior and the physiological state when the absolute value of the correlation coefficient exceeds a preset threshold.

[0020] Extract the total displacement, trajectory closure, principal direction angle, number of curvature extrema points, and inflection point density of the touch trajectory. Use the dynamic time warping algorithm to match the input trajectory with predefined gesture templates, select the template corresponding to the minimum distance, and determine the gesture category as single-point touch.

[0021] Collect time series of emotion intensity, apply moving average filtering to generate a smoothed emotion intensity series, and compare the smoothed value with a preset interval: if the smoothed value is within interval one, output the emotion state label "calm", if the smoothed value is within interval two, output the emotion state label "concerned", if the smoothed value is within interval three, output the emotion state label "tense". Arrange the continuous labels by time to generate an emotion trend sequence, and output the significant correlation judgment result, gesture category, and emotion trend sequence.

[0022] In a preferred embodiment of the present invention, the process by which the gesture behavior analysis module parses the operation behavior pattern and outputs the gesture category, emotional state level, and behavior pattern includes:

[0023] Obtain a multidimensional behavioral feature vector sequence, segment it according to a fixed duration, and assemble the vector segments into a behavioral feature matrix in chronological order. Set the number of clusters to 5, using the first 5 vectors as the initial centroids, and repeat the process a fixed number of times. Calculate the Euclidean distance from each vector to the centroid, assign it to the nearest cluster, update the centroid to the cluster mean, and map the 5 clustering results to: quick browsing, fine-tuning, accidental touch probing, continuous operation, and pause observation. Calculate the behavioral pattern evolution rate, obtain scalar values, and generate structured output data with the following fields in order: user unique identifier, data processing start time, data processing end time, gesture category, emotional state level, operation behavior pattern, touch duration, initial contact speed, total trajectory length, average speed, maximum acceleration, curvature variance, contact area change rate, emotional trend sequence, heart rate sequence, skin conductance sequence, interaction-physiological correlation coefficient, and behavioral pattern evolution rate. Return the output data.

[0024] In a preferred embodiment of the present invention, the process by which the touch feedback control module dynamically adjusts the screen brightness and color temperature, tactile vibration waveform and intensity, and system sound effect rhythm and volume according to the interaction context includes:

[0025] The system acquires ambient light intensity, emotional state intensity, gesture type, gesture speed, and touch pressure value. It adjusts the screen brightness, weights the emotional state intensity to generate an emotional contribution value, weights the light intensity to generate an environmental contribution value, adds the environmental contribution value and the emotional contribution value to the constant offset in the configuration table to obtain the screen brightness output value, applies boundary constraints to the output value, and generates the final brightness value.

[0026] Adjust the screen color temperature, combine the light intensity and emotional state intensity into a two-dimensional parameter, look up the two-dimensional lookup table defined in the system parameter configuration table, output the screen color temperature value, adjust the tactile vibration intensity, add the touch pressure value and gesture speed weighted together to generate gesture force, add the gesture force and emotional state intensity weighted together to obtain the tactile vibration intensity, apply boundary constraints to the intensity to generate the final vibration intensity value.

[0027] Adjust the haptic waveform, find the corresponding waveform template identifier according to the gesture type, load the pre-stored waveform data, generate the vibration drive signal, adjust the sound effect rhythm, apply a proportional coefficient to the gesture speed, add an offset, and output the final rhythm value after constraint. Adjust the sound effect volume, apply a proportional coefficient to the emotional state intensity, add an offset, and output the final volume value after constraint. Output the final brightness value, screen color temperature value, final vibration intensity value, vibration drive signal, final rhythm value, and final volume value.

[0028] In a preferred embodiment of the present invention, the process by which the touch feedback control module adjusts and generates a multimodal feedback output adapted to the current operation state based on gesture type, emotional state level, ambient light intensity, ambient temperature, and ambient humidity includes:

[0029] The system acquires the gesture category, emotional state level, ambient light intensity, ambient temperature, and ambient humidity. It generates the main regulation result based on the gesture category and emotional state level, and calculates the environmental regulation value by weighting the ambient temperature, ambient humidity, and light intensity. The main regulation result is added to the environmental regulation value, and the final tactile vibration intensity value is generated after constraints.

[0030] Generate screen brightness value, screen color temperature value, tactile waveform identifier, sound effect rhythm value, and sound effect volume value; generate display drive signal and send it to the display screen; generate vibration drive signal and send it to the vibration motor; generate audio drive signal and send it to the audio playback unit.

[0031] Generate a structured data packet containing timestamps, gestures, physiological information, environmental parameters, and feedback parameters, and send the structured data packet.

[0032] In a preferred embodiment of the present invention, the process by which the resource scheduling and management module analyzes user behavior patterns and manages device power consumption levels includes:

[0033] Obtain the timestamp, task type, execution duration, and resource consumption data of user operations, generate a user behavior sequence in chronological order, process the behavior sequence using a preset prediction model, output the future task weights and resource requirements, calculate the system load based on the future task weights and resource requirements, and obtain the future load value.

[0034] Taking task weight, resource requirements, and current time as input, query the priority mapping rules in the parameter table, sort the task queue according to priority, record the task execution sequence within a continuous time window, and statistically analyze the frequency, time period, and resource mode of task types to generate the probability distribution of task occurrence.

[0035] At the target time point, generate a set of possible tasks based on the probability distribution, preload their resources, query the frequency mapping rules of the parameter table with the load value to generate the target frequency, and query the voltage-frequency rules with the target frequency to generate the target voltage.

[0036] Before starting the task, read the task type, query the power consumption configuration rules in the parameter table, generate power consumption parameters, set the hardware status, monitor the input device event interval, and if the interval is greater than the sleep threshold, set the frequency to the mode A frequency and the number of cores to the mode A core number. If the interval is greater than the deep sleep threshold, set the number of cores to 0 and the peripheral power supply to the mode B state.

[0037] In a preferred embodiment of the present invention, the process by which the resource scheduling and management module adjusts environmental temperature and humidity conditions and system load status, and dynamically regulates computing resource allocation, power management mode, and task execution priority includes:

[0038] Get the device temperature and the upper limit of the temperature defined in the parameter table. If the temperature exceeds the upper limit, set the processor target frequency to the mode C frequency, limit the number of parallel tasks to the mode C task number, and apply the mode C task control strategy. Get the ambient relative humidity and the upper limit of the humidity defined in the parameter table. If the humidity exceeds the upper limit, set the peripheral enable state to the mode D state, limit the processor maximum frequency to the mode D frequency, and apply the mode D data write strategy.

[0039] Get the current system load value, get the idle state threshold and saturation state threshold defined in the parameter table. If the current load value is less than the idle state threshold, perform idle state control. If the current load value is greater than the saturation state threshold, perform saturation state control.

[0040] Query the power consumption control rules in the parameter table, generate a strategy, and adjust the allocation of computing resources, power management mode, and task execution priority according to the strategy to achieve dynamic control.

[0041] The touch control method for an integrated touch screen all-in-one machine includes the following steps:

[0042] Step 1: Collect raw touch perception data during the user's interaction with the touch screen, preprocess the collected raw touch perception data, and output standardized touch event data including continuous touch coordinate sequence, contact area, capacitance change, signal quality level, touch duration, first contact speed, skin temperature, and contact surface humidity.

[0043] Step 2: Based on standardized touch event data and two-dimensional trajectory enhancement features derived from continuous touch coordinate sequences, perform multi-dimensional behavior modeling, associate the timing of interactive actions with changes in physiological state, identify gesture structure, characterize emotional state trends, analyze operation behavior patterns, and output gesture category, emotional state level, and behavior pattern.

[0044] Step 3: Based on the interaction context, dynamically adjust the screen brightness and color temperature, tactile vibration waveform and intensity, system sound effect rhythm and volume, and combine gesture type, emotional state level, ambient light intensity, ambient temperature and humidity to generate multimodal feedback output that is adapted to the current operation state.

[0045] Step 4: Dynamically adjust the allocation of computing resources, power management mode, and task execution priority based on user behavior patterns, device power consumption levels, ambient temperature and humidity conditions, and system load status.

[0046] Compared with the prior art, the advantages of this invention are:

[0047] (1) In this invention, the gesture behavior analysis module integrates touch events and trajectory features to construct a multi-dimensional vector, identifies gesture categories through a regression model, aligns interactions with physiological sequences, calculates Pearson correlation coefficients and generates emotional trends by combining filtering thresholds, uses dynamic time warping to match templates, clusters and divides operation modes, calculates evolution rate to characterize behavioral dynamics, outputs structured data, and realizes closed-loop analysis from touch input to behavioral intent.

[0048] (2) In this invention, the resource scheduling and management module integrates user behavior, system load and environmental parameters to achieve dynamic resource regulation. It automatically switches between performance and energy-saving modes according to the load status, optimizes frequency, number of cores and memory bandwidth, monitors temperature and humidity in real time, triggers abnormal protection strategies, and combines power consumption rules to adjust resource allocation and task priority in a closed loop to ensure a balance between performance and energy efficiency and improve system stability and response efficiency. Attached Figure Description

[0049] Figure 1 This is a system block diagram of Embodiment 1 of the present invention;

[0050] Figure 2 This is a system block diagram of Embodiment 2 of the present invention;

[0051] Figure 3 This is a flowchart illustrating the touch control method for a touch-screen all-in-one machine in this invention. Detailed Implementation

[0052] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0053] Example 1: As Figure 1As shown, the touch control processing system of the integrated touch screen all-in-one machine proposed in this invention is integrated into a centralized touch management platform, including:

[0054] The touch sensing processing module is used to collect raw touch sensing data during user interaction with the touch screen, including raw code values ​​of the sensing channel, ambient light intensity, ambient temperature, ambient humidity, skin temperature, contact surface humidity, power supply voltage, and screen surface temperature. It preprocesses the collected raw touch sensing data, including time synchronization alignment (timestamp alignment of multi-source sensor data), bad pixel removal (filtering out raw code values ​​with abnormal jumps), baseline correction (dynamically compensating for zero-point drift of the sensing channel and generating capacitance change), bandpass filtering (suppressing noise and retaining effective touch signal frequency bands), coordinate calculation (calculating touch coordinates and contact area based on filtered sensing channel data), and coordinate smoothing (smoothing the touch coordinate sequence to optimize trajectory continuity). The output includes standardized touch event data containing continuous touch coordinate sequences, contact area, capacitance change, signal quality level, touch duration, first contact speed, skin temperature, and contact surface humidity.

[0055] By fusing environmental and physiological parameters through a touch sensing processing module, multi-source interference is suppressed. The signal robustness and weak touch detection rate are improved through bad pixel removal, bandpass filtering, and dynamic baseline correction. Touch coordinates are calculated and smoothed to ensure trajectory accuracy. Multi-dimensional data is synchronized and aligned in time, and standardized events containing coordinates, area, capacitance change, signal quality, duration, speed, temperature, and humidity are output, which can be directly used downstream. The front end completes preprocessing and feature extraction, reducing the load on the main processor and improving system response efficiency.

[0056] The gesture behavior analysis module, based on standardized touch event data and two-dimensional trajectory enhancement features (such as curvature and acceleration vectors) derived from continuous touch coordinate sequences, performs multi-dimensional behavior modeling, associates the timing of interactive actions with changes in physiological state, identifies gesture structure, characterizes emotional state trends, analyzes operational behavior patterns, and outputs gesture category, emotional state level, and behavior pattern.

[0057] The gesture behavior analysis module performs multi-dimensional behavior modeling based on standardized touch event data and enhanced two-dimensional trajectory features derived from continuous touch coordinate sequences. The process includes:

[0058] Acquire standardized touch event data, extract continuous touch coordinate sequences from the standardized touch event data, calculate the sum of the ratio of displacement to time interval between adjacent touch points as the sum velocity, calculate the ratio of the difference of continuous sum velocity to the corresponding time interval as the acceleration, accumulate the Euclidean distance between adjacent points to obtain the trajectory length, calculate the angle between continuous velocity vectors, and take the number of direction changes per unit time as the direction change rate.

[0059] The first and second derivatives are obtained by differentiating the trajectory values. The curvature is calculated by subtracting the absolute value of the product of the first derivative of x and the second derivative of y from the product of the first derivative of y and the second derivative of x, and the denominator is the square of the first derivative of x plus the square of the first derivative of y to the power of 1.5. The touch duration is obtained by calculating the difference between the start and end times of the touch. The rate of change of the contact area is calculated. The resultant velocity, acceleration, trajectory length, rate of change of direction, curvature, touch duration, rate of change of contact area, and initial touch speed are combined to generate a two-dimensional trajectory enhancement feature set.

[0060] Touch duration, initial contact speed, total trajectory length, average speed, maximum acceleration, curvature variance, area change rate, and skin temperature change trend are selected from standardized data and enhanced features and arranged in a fixed order to form a multidimensional behavioral feature vector;

[0061] Load preset integer behavior category labels, use feature vectors as input and labels as output to build a multiple linear regression model. The model is a weighted sum of constant terms and each feature. Solve the parameters using the least squares method and save them. When running, receive new touch input, execute the same process to generate feature vectors, input them into the model, output the predicted behavior category and return it.

[0062] The gesture behavior analysis module performs modeling of the correlation between the timing of interactive actions and physiological states, identifies gesture structures, and characterizes emotional state trends. This process includes:

[0063] Simultaneously acquire user interaction behavior data and physiological state data. Interaction behavior data includes touch coordinates, contact area, timestamp, and pressure value. Physiological state data includes heart rate, skin conductance, respiratory rate, and skin temperature. Organize the interaction behavior data by time to generate an interaction behavior feature sequence. Organize the physiological state data by time to generate a physiological state monitoring data sequence. Calculate the Pearson correlation coefficient between the two sequences using the following formula:

[0064] Where x i Let s represent the interaction feature value of the i-th sampling point. i This represents the physiological index value at the i-th sampling point. This represents the arithmetic mean of all sampled points in sequence X. Let ρ represent the arithmetic mean of all sampled points in sequence S, where n represents the total number of sampled points in sequences X and S, and ρ represents the arithmetic mean of all sampled points in sequence X and S. X,S The Pearson correlation coefficient ρ represents the correlation coefficient between sequences X and S. X,S The value is a real number, ranging from [-1, 1]. When the absolute value of the correlation coefficient exceeds the preset threshold, it is determined that the interactive behavior and the physiological state are significantly related.

[0065] Extract the total displacement, trajectory closure, principal direction angle, number of curvature extrema points, and inflection point density of the touch trajectory. Use the dynamic time warping algorithm to match the input trajectory with predefined gesture templates, select the template corresponding to the minimum distance, and determine the gesture category as single-point touch.

[0066] Collect time series of emotion intensity, apply moving average filtering with a fixed window length, output the arithmetic mean within the window to generate a smoothed emotion intensity sequence, compare the smoothed value with a preset interval: if the smoothed value is within interval one, output the emotion state label "calm", if the smoothed value is within interval two, output the emotion state label "concerned", if the smoothed value is within interval three, output the emotion state label "tense", arrange the consecutive labels by time to generate an emotion trend sequence, and output the significant correlation judgment result, gesture category, and emotion trend sequence;

[0067] The process by which the gesture behavior analysis module parses operational behavior patterns and outputs gesture categories, emotional state levels, and behavioral patterns includes:

[0068] Obtain a multi-dimensional behavioral feature vector sequence, including touch duration, initial contact speed, total trajectory length, average speed, maximum acceleration, curvature variance, and contact area change rate. Divide the vectors into segments with a fixed duration, and arrange these segments into a behavioral feature matrix in chronological order. Set the number of clusters to 5, using the first 5 vectors as initial centroids. Repeat this process a fixed number of times: calculate the Euclidean distance from each vector to the centroid, assign it to the nearest cluster, and update the centroid to the cluster mean. Map the 5 clustering results to: quick browsing, fine-tuning, accidental touch probing, continuous operation, and pause observation. Calculate the behavioral pattern evolution rate using the following formula:

[0069] Where v p f represents the rate of evolution of behavioral patterns. i f represents the behavioral feature vector for the i-th time period. i+1 This represents the behavioral feature vector for the (i+1)th time period, N represents the total number of behavioral feature vectors within the time window, and ||·|| represents the Euclidean distance between vectors. A scalar value is calculated to generate structured output data. The fields are as follows: user unique identifier, data processing start time, data processing end time, gesture category, emotional state level, operation behavior pattern, touch duration, initial contact speed, total trajectory length, average speed, maximum acceleration, curvature variance, contact area change rate, emotional trend sequence, heart rate sequence, skin conductance sequence, interaction-physiological correlation coefficient, and behavioral pattern evolution rate. The output data is then returned.

[0070] By fusing touch events and trajectory enhancement features through a gesture behavior analysis module, a multi-dimensional behavior vector is constructed. Gesture category recognition is achieved through a regression model. Interactions and physiological sequences are aligned, Pearson correlation coefficients are calculated to quantify the behavior-physiological association, and emotional trends are generated by combining filtering and threshold judgment. Gesture templates are matched using trajectory features and dynamic time warping. Operation patterns are divided by clustering, and the evolution rate is calculated to characterize behavioral dynamics. Structured data containing gestures, emotions, behavioral patterns, and multi-dimensional indicators is output to support upper-level decision-making and achieve closed-loop analysis from touch input to behavioral intent.

[0071] The touch feedback control module is used to dynamically adjust the screen brightness and color temperature, tactile vibration waveform and intensity, and system sound effect rhythm and volume according to the interaction context. It combines gesture type, emotional state level, ambient light intensity, ambient temperature and ambient humidity to generate multimodal feedback output that is adapted to the current operation state.

[0072] The process by which the touch feedback control module dynamically adjusts screen brightness and color temperature, tactile vibration waveform and intensity, and system sound effect rhythm and volume based on the interaction context includes:

[0073] The system acquires ambient light intensity, emotional state intensity, gesture type, gesture speed, and touch pressure value. It adjusts the screen brightness, weights the emotional state intensity to generate an emotional contribution value, weights the light intensity to generate an environmental contribution value, adds the environmental contribution value and the emotional contribution value to the constant offset in the configuration table to obtain the screen brightness output value, applies boundary constraints to the output value, and generates the final brightness value.

[0074] Adjust the screen color temperature, combine the light intensity and emotional state intensity into a two-dimensional parameter, look up the two-dimensional lookup table defined in the system parameter configuration table, output the screen color temperature value, adjust the tactile vibration intensity, add the touch pressure value and gesture speed weighted together to generate gesture force, add the gesture force and emotional state intensity weighted together to obtain the tactile vibration intensity, apply boundary constraints to the intensity to generate the final vibration intensity value.

[0075] Adjust the haptic waveform, find the corresponding waveform template identifier according to the gesture type, load the pre-stored waveform data (including amplitude sequence, frequency parameters, duration, and shape encoding), generate a vibration drive signal, adjust the sound effect rhythm, apply a proportional coefficient to the gesture speed, add an offset, constrain and output the final rhythm value, adjust the sound effect volume, apply a proportional coefficient to the emotional state intensity, add an offset, constrain and output the final volume value, output the final brightness value, screen color temperature value, final vibration intensity value, vibration drive signal, final rhythm value, and final volume value;

[0076] The process by which the touch feedback control module adjusts and generates multimodal feedback output adapted to the current operation state based on gesture type, emotional state level, ambient light intensity, ambient temperature, and ambient humidity includes:

[0077] The system acquires the gesture category, emotional state level, ambient light intensity, ambient temperature, and ambient humidity. It generates the main regulation result based on the gesture category and emotional state level, and calculates the environmental regulation value by weighting the ambient temperature, ambient humidity, and light intensity. The main regulation result is added to the environmental regulation value, and the final tactile vibration intensity value is generated after constraints.

[0078] Generate screen brightness value, screen color temperature value, tactile waveform identifier, sound effect rhythm value, and sound effect volume value; generate display drive signal and send it to the display screen; generate vibration drive signal and send it to the vibration motor; generate audio drive signal and send it to the audio playback unit; the signal sending period is taken from the system parameter configuration table.

[0079] Generate a structured data packet containing timestamps, gestures, physiological parameters, environmental parameters, and feedback parameters. The field order and type are taken from the system parameter configuration table. All fields are required. Send the structured data packet.

[0080] The touch feedback control module integrates gesture, emotion, and environmental parameters to dynamically adjust multimodal feedback: brightness and color temperature are generated based on light and emotion weighting and lookup tables, respectively; tactile intensity is combined with gesture strength, emotional state, and environmental influence; waveforms are matched with preset templates according to gesture type; and sound effect rhythm and volume are associated with gesture speed and emotional intensity. All parameters are constrained to generate driving signals, outputting structured data packets with complete fields and time alignment, achieving context-aware and personalized immersive interactive experience.

[0081] Example 2: The technical solution of this embodiment of the invention differs from that of Example 1 in that:

[0082] like Figure 2 As shown, the resource scheduling and management module is used to dynamically adjust the allocation of computing resources, power management mode and task execution priority based on user behavior patterns, device power consumption levels, ambient temperature and humidity conditions and system load status.

[0083] The resource scheduling and management module's process of analyzing user behavior patterns and managing device power consumption includes:

[0084] Obtain user action timestamps, task types, execution durations, and resource usage data; generate user behavior sequences in chronological order; process the behavior sequences using a pre-defined prediction model; output future task weights and resource requirements; and calculate system load based on future task weights and resource requirements.

[0085] Where L(t) represents the system load at time t, w i (t) represents the weight of the i-th task at time t, r i (t) represents the computational resource requirement of the i-th task at time t, and n represents the total number of concurrent tasks in the system. The future load value is calculated.

[0086] Taking task weight, resource requirements, and current time as input, query the priority mapping rules in the parameter table to generate task priorities from level 1 to 5, sort the task queue according to priority, record the task execution sequence within a continuous time window, with the window length taken from the parameter table, and statistically analyze the frequency, time period, and resource mode of task types to generate the probability distribution of task occurrence.

[0087] At the target time point, generate a set of possible tasks based on the probability distribution, preload their resources, query the frequency mapping rules of the parameter table with the load value to generate the target frequency, query the voltage-frequency rules with the target frequency to generate the target voltage, set the processor frequency and voltage, and comply with hardware safety specifications.

[0088] Before the task starts, read the task type, query the power consumption configuration rules in the parameter table, generate power consumption parameters (frequency lower limit, voltage range, number of cores), set the hardware status, monitor the input device event interval, if the interval is greater than the sleep threshold, set the frequency to the mode A frequency, the number of cores to the mode A core number, if the interval is greater than the deep sleep threshold, set the number of cores to 0, and the peripheral power supply to the mode B state.

[0089] The resource scheduling and management module dynamically adjusts environmental temperature and humidity conditions and system load status, and dynamically regulates computing resource allocation, power management mode, and task execution priority. This process includes:

[0090] Get the device temperature and the upper limit of the temperature defined in the parameter table. If the temperature exceeds the upper limit, set the processor target frequency to the mode C frequency, limit the number of parallel tasks to the mode C task number, and apply the mode C task control strategy. Get the ambient relative humidity and the upper limit of the humidity defined in the parameter table. If the humidity exceeds the upper limit, set the peripheral enable state to the mode D state, limit the processor maximum frequency to the mode D frequency, and apply the mode D data write strategy.

[0091] Get the current system load value, get the idle state threshold and saturation state threshold defined in the parameter table. If the current load value is less than the idle state threshold, perform idle state control: set the processor frequency to mode E frequency, shut down inactive computing cores, and set the peripheral power supply cycle to mode E cycle. If the current load value is greater than the saturation state threshold, perform saturation state control: increase the processor frequency to the maximum allowed frequency, enable the maximum number of cores, and set the memory bandwidth allocation priority to mode F priority.

[0092] Query the power consumption control rules in the parameter table, generate a strategy, and adjust the allocation of computing resources, power management mode, and task execution priority according to the strategy to achieve dynamic control.

[0093] The resource scheduling and management module integrates user behavior patterns, system load, ambient temperature and humidity, and power consumption status to achieve multi-dimensional collaborative regulation: predicting future task load based on historical operation sequences and dynamically adjusting processor frequency and voltage to balance performance and energy consumption; improving the response efficiency of critical tasks through task priority sorting and resource preloading; automatically switching power modes based on idle and saturation thresholds to shut down inactive cores or increase performance limits to optimize energy efficiency; real-time monitoring of temperature and humidity to trigger environmental anomaly response strategies and ensure system stability; dynamically adjusting resource allocation and task priority in conjunction with power consumption control rules to form a closed-loop regulation mechanism; and outputting structured data to support traceability and optimization, achieving efficient, stable, and adaptive resource management.

[0094] Example 3: The technical solution of this embodiment of the invention differs from that of Example 1 and Example 2 in that:

[0095] like Figure 3 As shown, the touch control method of the integrated touch screen all-in-one machine includes the following steps:

[0096] Step 1: Collect raw touch perception data during the user's interaction with the touch screen, preprocess the collected raw touch perception data, and output standardized touch event data including continuous touch coordinate sequence, contact area, capacitance change, signal quality level, touch duration, first contact speed, skin temperature, and contact surface humidity.

[0097] Step 2: Based on standardized touch event data and two-dimensional trajectory enhancement features derived from continuous touch coordinate sequences, perform multi-dimensional behavior modeling, associate the timing of interactive actions with changes in physiological state, identify gesture structure, characterize emotional state trends, analyze operation behavior patterns, and output gesture category, emotional state level, and behavior pattern.

[0098] Step 3: Based on the interaction context, dynamically adjust the screen brightness and color temperature, tactile vibration waveform and intensity, system sound effect rhythm and volume, and combine gesture type, emotional state level, ambient light intensity, ambient temperature and humidity to generate multimodal feedback output that is adapted to the current operation state.

[0099] Step 4: Dynamically adjust the allocation of computing resources, power management mode, and task execution priority based on user behavior patterns, device power consumption levels, ambient temperature and humidity conditions, and system load status.

[0100] The integrated touch control device constructs a closed loop of perception-analysis-feedback-regulation: it generates standardized touch events through multi-source data preprocessing, integrates trajectory and physiological characteristics to achieve gesture recognition and emotion trend analysis, dynamically adjusts multimodal feedback in combination with interactive context, and collaboratively optimizes resource allocation and power management based on behavior patterns and system status to achieve a high-precision, personalized, and low-power intelligent touch experience.

[0101] The above are merely preferred embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and its improved concept, should be covered within the scope of protection of the present invention.

Claims

1. A touch control processing system of a centralized touch all-in-one machine, integrated in a centralized touch management platform, characterized in that, The method comprises the following steps: A touch sensing processing module is used to collect raw touch sensing data during user interaction with the touch screen, pre-process the collected raw touch sensing data, and output standardized touch event data containing continuous touch coordinate sequences, contact area, capacitance change, signal quality level, touch duration, first contact speed, skin temperature, and contact surface humidity; A gesture behavior analysis module is used to perform multi-dimensional behavior modeling based on the standardized touch event data and two-dimensional trajectory enhanced features derived from the continuous touch coordinate sequences, correlate interaction action timing and physiological state changes, identify gesture structure, depict emotional state trends, and analyze operation behavior patterns, and output gesture categories, emotional state levels, and behavior patterns; A touch feedback control module is used to dynamically adjust screen brightness and color temperature, haptic vibration waveform and intensity, system sound rhythm and volume according to the interaction context, and generate multi-modal feedback output adapted to the current operation state in combination with gesture categories, emotional state levels, ambient light intensity, ambient temperature, and ambient humidity; A resource scheduling management module is used to dynamically control computing resource allocation, power management mode, and task execution priority according to user behavior patterns, device power consumption levels, environmental temperature and humidity conditions, and system load state.

2. The touch control processing system of the centralized touch all-in-one machine according to claim 1, wherein, The process of multi-dimensional behavior modeling by the gesture behavior analysis module based on standardized touch event data and two-dimensional trajectory enhanced features derived from continuous touch coordinate sequences includes: Obtain standardized touch event data, extract continuous touch coordinate sequences from the standardized touch event data, calculate the ratio of displacement between adjacent touch points and time interval to obtain velocity, calculate the ratio of continuous velocity difference and corresponding time interval to obtain acceleration, accumulate the Euclidean distance between adjacent points to obtain trajectory length, and calculate the angle between continuous velocity vectors, and count the number of direction changes per unit time as the direction change rate; Differential the trajectory values to obtain first and second derivatives, calculate the difference between touch start and end times to obtain touch duration, calculate the contact area change rate, and combine the velocity, acceleration, trajectory length, direction change rate, curvature, touch duration, contact area change rate, and first touch speed to generate a two-dimensional trajectory enhanced feature set; Select touch duration, first contact speed, total trajectory length, average speed, maximum acceleration, curvature variance, area change rate, and skin temperature change trend from the standardized data and enhanced features, arrange them in a fixed order to form a multi-dimensional behavior feature vector; Load a preset integer behavior category label, use the feature vector as input and the label as output to construct a multiple linear regression model, receive new touch input during runtime, execute the same process to generate a feature vector, input the model, and output the predicted behavior category.

3. The touch control processing system of the centralized touch all-in-one machine according to claim 2, wherein, The process of the gesture behavior analysis module correlating interaction action timing and physiological state, identifying gesture structure, and depicting emotional state trends includes: Synchronize the collection of user interaction behavior data and physiological state data, organize the interaction behavior data by time to generate an interaction behavior feature sequence, organize the physiological state data by time to generate a physiological state monitoring data sequence, calculate the Pearson correlation coefficient between the two sequences, and when the absolute value of the correlation coefficient exceeds a preset threshold, determine that there is a significant correlation between the interaction behavior and the physiological state; Extract the total displacement of the touch trajectory, the trajectory closure degree, the main direction angle, the number of curvature extreme points, and the inflection point density, match the input trajectory with the predefined gesture template using the dynamic time warping algorithm, take the template corresponding to the minimum distance, and determine the gesture category as single-point touch; Collect an emotion intensity time sequence, apply a moving average filter to generate a smoothed emotion intensity sequence, compare the smoothed value with a preset interval, output the emotion state label "calm" if the smoothed value is within interval one, output the emotion state label "focus" if the smoothed value is within interval two, output the emotion state label "tense" if the smoothed value is within interval three, arrange the continuous labels by time to generate an emotion trend sequence, and output the significant correlation determination result, the gesture category, and the emotion trend sequence.

4. The touch control processing system of the centralized touch all-in-one machine according to claim 3, wherein, The process of the gesture behavior analysis module analyzing the operation behavior pattern and outputting the gesture category, the emotion state level, and the behavior pattern includes: Obtain a multi-dimensional behavior feature vector sequence, split the sequence into segments with a fixed duration, arrange the segment vectors in time sequence to form a behavior feature matrix, set the number of clusters to 5, take the first 5 vectors as initial centroids, and repeat a fixed number of times: calculate the Euclidean distance of each vector to the centroid, assign it to the nearest cluster, update the centroid to the mean value in the cluster, map the 5 clustering results to: fast browsing, fine adjustment, accidental touch exploration, continuous operation, and pause observation, calculate the behavior pattern evolution rate, obtain a scalar value, generate structured output data, and the fields are in the order of: user unique identifier, data processing start time, data processing end time, gesture category, emotion state level, operation behavior pattern, touch duration, first contact speed, total trajectory length, average speed, maximum acceleration, curvature variance, contact area change rate, emotion trend sequence, heart rate sequence, skin conductance sequence, interaction-physiological correlation coefficient, and behavior pattern evolution rate, and return the output data.

5. The touch control processing system of the centralized touch all-in-one machine according to claim 1, wherein, The process of the touch feedback regulation module dynamically adjusting the screen brightness and color temperature, the haptic vibration waveform and intensity, and the system sound rhythm and volume according to the interaction context includes: Obtain the ambient light intensity, emotion state intensity, gesture type, gesture speed, and touch pressure value, adjust the screen brightness, weight the emotion state intensity to generate an emotion contribution value, weight the light intensity to generate an environment contribution value, add the environment contribution value and the emotion contribution value to the constant offset in the configuration table to obtain a screen brightness output value, apply boundary constraints to the output value to generate a final brightness value, and output the final brightness value. Adjusting screen color temperature, light intensity and emotional state intensity form a two-dimensional parameter, look up in the two-dimensional look-up table defined in the system parameter configuration table, output screen color temperature value, adjust haptic vibration intensity, add gesture speed weight to touch pressure value, generate gesture strength, add gesture strength and emotional state intensity weight, get haptic vibration intensity, apply boundary constraint to intensity, generate final vibration intensity value; Adjusting haptic waveform, according to gesture type, find corresponding waveform template identifier, load pre-stored waveform data, generate vibration driving signal, adjusting sound effect rhythm, apply proportional coefficient to gesture speed, add offset, constrain and output final rhythm value, adjusting sound effect volume, apply proportional coefficient to emotional state intensity, add offset, constrain and output final volume value, output final brightness value, screen color temperature value, final vibration intensity value, vibration driving signal, final rhythm value, final volume value.

6. The touch control processing system of the centralized touch all-in-one machine according to claim 5, wherein, The process of the touch feedback regulation module adjusting and generating multi-modal feedback output adapted to the current operation state according to gesture category, emotional state level, ambient light intensity, ambient temperature, and ambient humidity includes: Obtaining gesture category, emotional state level, ambient light intensity, ambient temperature, and ambient humidity, generating main adjustment result according to gesture category and emotional state level, weighting and summing ambient temperature, ambient humidity, and light intensity to obtain environmental adjustment value, adding main adjustment result and environmental adjustment value, and generating final haptic vibration intensity value after constraint; Generating screen brightness value, screen color temperature value, haptic waveform identifier, sound effect rhythm value, and sound effect volume value, generating display driving signal and sending it to the display screen, generating vibration driving signal and sending it to the vibration motor, and generating audio driving signal and sending it to the audio playback unit; Generating a structured data packet containing timestamp, gesture, physiology, environment, and feedback parameters, and sending the structured data packet.

7. The touch control processing system of the centralized touch all-in-one machine according to claim 1, wherein, The process of the resource scheduling management module analyzing user behavior patterns and managing device power consumption levels includes: Obtaining timestamp, task type, execution duration, and resource occupation data of user operation, generating user behavior sequence in time order, processing behavior sequence using a preset prediction model, outputting future task weight and resource demand, calculating system load according to future task weight and resource demand, and calculating future load value; Taking task weight, resource demand, and current time as input, querying parameter table priority mapping rule, sorting task queue according to priority, recording task execution sequence in continuous time window, counting task type frequency, time period, and resource mode, and generating task occurrence probability distribution; At the target time point, generating a possible task set according to the probability distribution, preloading its resources, querying the parameter table frequency mapping rule with the load value to generate the target frequency, and querying the voltage-frequency rule with the target frequency to generate the target voltage; Before task starts, read task type, query parameter table power consumption configuration rule, generate power consumption parameter, set hardware state, monitor input device event interval, if interval is greater than sleep threshold, set frequency as mode A frequency, core number as mode A core number, if interval is greater than deep sleep threshold, set core number as 0, peripheral power supply as mode B state.

8. The touch control processing system of the centralized touch all-in-one machine according to claim 7, wherein, The process of the resource scheduling management module adjusting environmental temperature and humidity conditions and system load state and dynamically controlling computing resource allocation, power management mode and task execution priority comprises: Obtaining device temperature, obtaining temperature upper limit defined by parameter table, if temperature exceeds upper limit, setting processor target frequency as mode C frequency, limiting parallel task number as mode C task number, applying mode C task control strategy, obtaining environmental relative humidity, obtaining humidity upper limit defined by parameter table, if humidity exceeds upper limit, setting peripheral enable state as mode D state, limiting processor maximum frequency as mode D frequency, applying mode D data writing strategy; Obtaining current system load value, obtaining idle state threshold and saturation state threshold defined by parameter table, if current load value is less than idle state threshold, executing idle state control, if current load value is greater than saturation state threshold, executing saturation state control; Querying parameter table power consumption control rule, generating strategy, adjusting computing resource allocation, power management mode and task execution priority according to strategy, completing dynamic control.

9. The touch control method of the centralized touch all-in-one machine, executes the touch control processing system of the centralized touch all-in-one machine as claimed in claim 1, characterized in that, The method comprises the following steps: Step one: collecting original touch sensing data in the process of user interaction with the touch screen, pre-processing the collected original touch sensing data, and outputting standardized touch event data containing continuous touch coordinate sequence, contact area, capacitance change amount, signal quality level, touch duration, first contact speed, skin temperature and contact surface humidity; Step two: based on the standardized touch event data and two-dimensional trajectory enhancement features derived from the continuous touch coordinate sequence, multi-dimensional behavior modeling is performed, the interaction action timing and physiological state changes are associated, the gesture structure is recognized, the emotional state trend is depicted, and the operation behavior mode is analyzed, and the gesture category, emotional state level and behavior mode are outputted; Step three: dynamically adjusting the screen brightness and color temperature, the haptic vibration waveform and intensity, the system sound rhythm and volume according to the interaction context, combining the gesture category, emotional state level, environmental light intensity, environmental temperature and environmental humidity, generating multi-modal feedback output adapted to the current operation state; Step four: dynamically controlling computing resource allocation, power management mode and task execution priority according to user behavior mode, device power consumption level, environmental temperature and humidity conditions and system load state.

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