A method and system for optimizing anti-interference performance of a touch screen

Abstract

The application relates to the technical field, and particularly provides a touch screen anti-interference performance optimization method and system, which comprises the following steps: acquiring an electromagnetic signal of an environment where a touch screen is located and an operator's touch signal; acquiring an environmental interference feature according to the electromagnetic signal, acquiring a touch feature according to the touch signal, and identifying a touch gesture type according to the touch feature; determining a current operation situation according to the environmental interference feature, the touch feature and the touch gesture type, wherein the current operation situation can reflect an interference state of the current environment and an operator's touch intention; and adjusting a signal processing parameter of the touch screen according to the current operation situation; the method can effectively solve the problems of touch signal misjudgment, omission and inaccurate gesture recognition caused by the fact that the prior art cannot simultaneously consider environmental noise, operator physiological state change and operation intention.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field, and particularly relates to a touch screen anti-interference performance optimization method and system. BACKGROUND

[0002] In the central control room of large industrial facilities, operators rely on industrial-grade touch screens as the core human-machine interface to monitor and control various production processes. These touch screens are usually large in size and provide rich graphical displays for presenting device operating parameters, alarm information, and operation instruction inputs in real time.

[0003] Although the control room environment is cleaner than the production workshop, it and its surroundings are still filled with various electromagnetic interference sources. For example, the frequent switching of relays in the control cabinet, the high-frequency harmonics generated by frequency converters and servo drives, the signal crosstalk of data communication lines, and the transient pulses of high-voltage power lines, all of which will couple to the capacitive sensing array of the touch screen in the form of broadband noise, continuously affecting its detection sensitivity to small changes in electric field. In such an environment, operators need to maintain high levels of concentration for a long time and frequently perform detailed touch operations. For example, accurately adjusting the temperature set point of a reaction kettle, fine-tuning the opening of a valve to control the flow, or confirming and clearing a specified system alarm. These operations require high precision and good stability of touch. However, when electromagnetic interference persists, the signal detected by the touch screen not only contains the effective electric field signal generated by the operator's finger or stylus, but also superimposes environmental noise. When the strength of the noise signal is close to that of the effective touch signal, the system will have difficulty in accurately distinguishing, which may lead to misjudgment or missed judgment of the touch signal.

[0004] Further, in a work shift lasting several hours or even tens of hours, operators inevitably experience physiological state changes. This change may manifest as a slight decline in the ability of fine motor movements of the fingers, for example, the force, contact area, or sliding speed during touch may change subtly. A clear and forceful click in a state of wakefulness may be manifested as a slightly longer contact time, slightly lighter pressure, or even a slightly trembling touch in a state of physiological change.

[0005] Current industrial touch screen anti-interference algorithms usually adopt the strategy of setting signal detection threshold or basic time domain filtering. These methods often only consider the suppression of environmental noise in design, and fail to fully identify and adapt to the attenuation or change of effective touch signal characteristics caused by the physiological state changes of the operator. When the anti-interference threshold is set too high, these "weakened" but essentially effective touch signals caused by physiological state changes may be incorrectly filtered out by the system as noise, resulting in the operator's perception of the touch screen as "unresponsive" or "insensitive". Conversely, if the threshold is set too low to ensure sensitivity, environmental noise is easily misidentified as an effective touch signal, resulting in "ghost points" or irregular cursor drift, which may trigger device malfunctions.

[0006] More complex and critical is that in the field of industrial control, some important operations or safety functions need to be triggered by specified touch gestures, such as double finger simultaneous press confirmation designed to prevent misoperation, long press on a specified area to unlock control authority, or specified trajectory sliding to execute emergency stop. The recognition of these various gestures relies on the accurate analysis of the changes in time series and spatial distribution of multiple touch points by the touch screen controller. When electromagnetic interference exists, it not only affects the stability of the signal strength of a single touch point, but also may induce false signals or noise on multiple adjacent electrodes of the touch screen, making it difficult to distinguish the boundaries or interfering with the smoothness of the gesture trajectory, so that the system cannot correctly identify the expected gesture, affecting the stability and safety of important operations, and even possibly delaying the response time in emergency situations.

[0007] Therefore, the current anti-interference optimization method of industrial touch screen, even with certain self-adjusting ability, still focuses on the overall detection and suppression of environmental noise. They generally lack the ability to instantly perceive "operator touch characteristics" and the detailed recognition and optimization mechanism of "specified gesture patterns". In other words, the system cannot analyze the current environmental interference characteristics in real time, further analyze and understand the current touch habits of the operator, and adjust its anti-interference strategy when a potential important gesture is recognized. For example, when the operator is trying to perform a long press gesture, the system should be able to temporarily adjust its signal processing logic, increase the recognition weight of sustained stable signals, and reduce the sensitivity to transient noise, or use multiple pattern recognition algorithms to distinguish between effective gestures and false signals caused by noise. This lack results in a significant decrease in the usability and operation stability of the touch screen in important operation scenarios or when the operator is in a physiological state change, significantly increasing the risk of misoperation, especially in industrial control environments where human-machine interaction precision and stability are highly required. SUMMARY

[0008] The application aims to provide a method and system for optimizing anti-interference performance of a touch screen, which can effectively solve the problems of inaccurate touch signal judgment, missed judgment and gesture recognition caused by the fact that the prior art cannot simultaneously consider environmental noise, operator physiological state change and operation intention.

[0009] In a first aspect, the application provides a method for optimizing anti-interference performance of a touch screen, which comprises the following steps:

[0010] S1, acquiring electromagnetic signals of an environment where the touch screen is located and touch signals of an operator;

[0011] S2, acquiring environmental interference features according to the electromagnetic signals, acquiring touch features according to the touch signals, and identifying a touch gesture type according to the touch features;

[0012] S3, determining a current operation situation according to the environmental interference features, the touch features and the touch gesture type, the current operation situation being capable of reflecting an interference state of the current environment and a touch intention of the operator;

[0013] S4, adjusting signal processing parameters of the touch screen according to the current operation situation.

[0014] In a second aspect, the application further provides a system for optimizing anti-interference performance of a touch screen, which comprises:

[0015] A signal acquisition module, configured to acquire electromagnetic signals of an environment where the touch screen is located and touch signals of an operator;

[0016] A signal processing module, configured to acquire environmental interference features according to the electromagnetic signals, acquire touch features according to the touch signals, and identify a touch gesture type according to the touch features;

[0017] An operation situation confirmation module, configured to determine a current operation situation according to the environmental interference features, the touch features and the touch gesture type, the current operation situation being capable of reflecting an interference state of the current environment and a touch intention of the operator;

[0018] A parameter adjustment module, configured to adjust signal processing parameters of the touch screen according to the current operation situation.

[0019] As can be seen from the above, the method and system for optimizing anti-interference performance of a touch screen provided by the application can effectively solve the problems of inaccurate touch signal judgment, missed judgment and gesture recognition caused by the fact that the prior art cannot simultaneously consider environmental noise, operator physiological state change and operation intention, by comprehensively analyzing environmental interference features, operator touch features and a touch gesture type to determine a current operation situation, and by adaptively adjusting signal processing parameters of the touch screen according to the situation, so as to effectively improve anti-interference performance and operation stability of the touch screen in a complex industrial environment. BRIEF DESCRIPTION OF DRAWINGS

[0020] Figure 1 A flow chart of a method for optimizing anti-interference performance of a touch screen is provided in the embodiments of the present application.

[0021] Figure 2 A structural schematic diagram of a system for optimizing anti-interference performance of a touch screen is provided in the embodiments of the present application.

[0022] The reference signs: 1, signal acquisition module; 2, signal processing module; 3, operation scenario confirmation module; 4, parameter adjustment module. DETAILED DESCRIPTION

[0023] The technical solutions in the embodiments of the present application will be clearly and completely described in the embodiments of the present application combined with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments of the present application. The components of the embodiments of the present application described and shown in the accompanying drawings can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present application provided in the accompanying drawings is not intended to limit the scope of the claimed present application, but only represents selected embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present application.

[0024] It should be noted that: similar reference signs and letters represent similar items in the following drawings, therefore, once an item is defined in one drawing, it does not need to be further defined and explained in subsequent drawings. Meanwhile, in the description of the present application, the terms "first", "second", etc. are only used to distinguish the description, and cannot be understood as indicating or implying relative importance.

[0025] In a first aspect, as shown in the drawings, the present application provides a method for optimizing anti-interference performance of a touch screen, which comprises the following steps: Figure 1

[0026] S1, acquiring electromagnetic signals of an environment where the touch screen is located and touch signals of an operator;

[0027] S2, acquiring environmental interference features according to the electromagnetic signals, acquiring touch features according to the touch signals, and identifying a touch gesture type according to the touch features;

[0028] S3, determining a current operation scenario according to the environmental interference features, the touch features and the touch gesture type, the current operation scenario being capable of reflecting an interference state of the current environment and a touch intention of the operator;

[0029] S4, adjusting signal processing parameters of the touch screen according to the current operation scenario.

[0030] ​The electromagnetic signal refers to various electromagnetic waves or electric field changes existing in the working environment of the touch screen. The embodiment can acquire the electromagnetic signal by using an electromagnetic sensor, a spectrum analyzer or a special noise detection circuit. The embodiment can acquire interference information in the environment by acquiring the electromagnetic signal, so as to provide original data for subsequent environment interference feature extraction. The touch signal refers to electric field or capacitance changes generated when the operator interacts with the touch screen. The embodiment can acquire the touch signal by using a sensing array of the touch screen, a touch controller or a signal acquisition circuit (for example, by using an X-Y electrode array of a capacitive touch screen or a pressure sensing layer of a resistive touch screen). The embodiment can capture the touch behavior of the operator by acquiring the touch signal, so as to provide basic data for subsequent touch feature extraction and gesture recognition. The environment interference feature refers to quantified information reflecting the nature of environmental noise extracted from the electromagnetic signal. The embodiment can acquire the environment interference feature by using a signal processing algorithm, spectrum analysis or statistical analysis method, for example, by acquiring the environment interference feature by Fourier transform analysis of frequency components, calculation of signal intensity root mean square value or statistical interference duration. The embodiment can identify and quantify the degree and type of influence of the current environment on the performance of the touch screen by acquiring the environment interference feature, so as to distinguish between effective touch signals and noise. The touch feature refers to quantified information reflecting details of the touch behavior of the operator extracted from the touch signal. The embodiment can acquire the touch signal by using a signal processing algorithm, pattern recognition technology or machine learning model, for example, by acquiring the touch feature by analyzing the coordinates of the touch point, the change of the contact area, the value of the touch pressure, the duration of the touch and the geometric shape of the touch trajectory. The main purpose is to capture subtle changes in the touch of the operator (including differences in touch behavior caused by physiological state changes), so as to provide a basis for subsequent operation situation judgment. The touch gesture type refers to a specific interactive action intended by the operator identified according to the touch feature. The embodiment can identify the touch gesture type by using a pattern recognition algorithm, a machine learning classifier or a preset rule base, for example, the embodiment can identify the touch gesture type by identifying click, swipe or long press and the like by using a support vector machine. The embodiment can understand the explicit operation intention of the operator by identifying the touch gesture type, so as to adopt differentiated signal processing strategies in different operation situations. The current operation situation refers to the real-time working state of the touch screen determined by comprehensively considering the environment interference feature, the touch feature and the touch gesture type. The embodiment can acquire the current operation situation by using a decision logic, a fuzzy inference system or a neural network model, for example, by obtaining the current operation situation by weighted fusion of the environment interference level, the touch behavior mode and the gesture intention. The current operation situation can comprehensively reflect the interference condition of the current environment and the touch intention of the operator, so as to provide a decision basis for adaptive parameter adjustment of the touch screen.The signal processing parameters refer to key configuration items that affect the processing and interpretation of the original signals by the touch screen. The embodiment can use software configuration, firmware update or hardware register setting to adjust the signal processing parameters. The embodiment is equivalent to dynamically optimizing the recognition ability of the touch screen for valid touch signals and the suppression ability of the touch screen for interference signals according to the current operation context, thereby improving the overall anti-interference performance.

[0031] The core innovation of the present application is that by comprehensively analyzing the environmental interference features, operator touch features and touch gesture types to determine the current operation context and adaptively adjusting the signal processing parameters of the touch screen according to the context, the problem of touch signal misjudgment, missed judgment and inaccurate gesture recognition caused by the fact that the prior art cannot simultaneously consider environmental noise, operator physiological state changes and operation intention is effectively solved, thereby effectively improving the touch screen anti-interference performance and operation stability in a complex industrial environment.

[0032] Specifically, the method optimizes the touch screen anti-interference performance through a series of steps working in coordination. First, the system acquires the electromagnetic signals of the environment where the touch screen is located and the touch signals of the operator. These two original signals are the basis for all subsequent analyses to ensure comprehensive perception of external interference and internal interaction. Then, the original signals are processed and features are extracted: the electromagnetic signals are used to obtain environmental interference features to quantify the nature of environmental noise; the touch signals are used to obtain touch features to capture the details of the operator's touch behavior, and the touch features are used to identify the operator's touch gesture type to understand the operator's specific operation intention. Subsequently, the system comprehensively analyzes the extracted environmental interference features, touch features and touch gesture types to determine the current specific operation context. The operation context is a multi-dimensional information set that not only reflects the interference state of the current environment, but more importantly, it can deeply understand the touch intention of the operator and determine whether there is a subtle change in the touch signal caused by the physiological state change of the operator. This contextual understanding enables the system to distinguish between real noise and valid but feature-changing touch signals caused by physiological factors. Finally, based on the accurate judgment of the current operation context, the system dynamically adjusts the signal processing parameters of the touch screen. For example, when a high-intensity interference is identified but at the same time the operator is performing a key gesture, the system can temporarily adjust the signal processing parameters (such as increasing the recognition weight of the gesture features or using a more robust filtering strategy) to ensure the accuracy of the key operation. This adaptive adjustment capability enables the touch screen to dynamically optimize its anti-interference performance according to the real-time changes in the environment and the operator's state, thereby improving the operation stability and accuracy in a complex industrial environment. The entire process forms a closed loop from data acquisition to context judgment to parameter adjustment, realizing intelligent adaptive optimization of the touch screen anti-interference performance.

[0033] As a preferred embodiment, the solution of the present application is implemented as follows: in an industrial control room, the controller of a touch screen is integrated with a signal acquisition circuit. The circuit is connected to the capacitive sensing array of the touch screen to acquire the touch signals of the operator in real time. At the same time, an independent electromagnetic sensor or an induction coil integrated in the bezel of the touch screen is used to monitor the electromagnetic signals in the environment in real time. The acquired electromagnetic signals are sent to a module composed of an analog-to-digital converter (ADC) and a digital signal processor (DSP), which runs a spectrum analysis algorithm (such as Fast Fourier Transform (FFT)) to extract the frequency, intensity and duration of environmental interference, etc. These features are quantified as a set of environmental interference parameters. At the same time, the touch signals acquired from the capacitive array of the touch screen are also sent to the DSP, which extracts the touch features such as the position, contact area, touch pressure, duration and movement trajectory of the touch point by analyzing the spatial and temporal distribution of the capacitive changes. Based on these touch features, the gesture recognition module (such as a machine learning-based classifier) inside the DSP identifies the type of touch gesture (such as single click, double click, swipe or long press) currently being performed by the operator. Subsequently, the context judgment module receives the environmental interference features, touch features and touch gesture type from the DSP, and the module is internally preset with a series of rules or trained models. The module can determine the current operating context according to the environmental interference features, touch features and touch gesture type using the preset rules or models, for example, when the environmental interference intensity is high, the touch features show that the touch pressure is low and the duration is slightly long, and the gesture type is identified as "long press confirmation", the context judgment module will determine that the current situation is "operator's critical long press under high interference". Finally, according to the current operating context output by the context judgment module, the parameter adjustment module dynamically modifies the signal processing parameters inside the touch screen controller. For example, in the above-mentioned "operator's critical long press under high interference" situation, the system can temporarily lower the signal detection threshold to improve the sensitivity to weak signals, adjust the filter coefficients to more effectively suppress interference at specific frequencies, and optimize the gesture recognition parameters, such as relaxing the pressure and duration tolerance range of the long press gesture, to ensure that even if the operator's touch force is weakened or has slight tremors, the critical long press operation can be accurately recognized. These parameter adjustments are implemented by sending configuration instructions to the touch screen controller.

[0034] In some preferred embodiments, the environmental interference features include frequency, intensity, and duration of electromagnetic interference, and the touch features include location, contact area, touch pressure, duration, and movement trajectory of the touch point. The frequency of electromagnetic interference refers to the specific oscillation rate of electromagnetic waves, which can be determined by processing the captured electromagnetic signals with Fourier transform or other spectral analysis techniques. The intensity of electromagnetic interference refers to the amplitude or power level of electromagnetic disturbance, which can be measured by quantifying the voltage, current, or power of electromagnetic signals. The duration of electromagnetic interference refers to the length of time that electromagnetic disturbance persists, which can be determined by analyzing the time-domain waveform of electromagnetic signals, identifying the start and end points of significant interference events. The location of touch point refers to the coordinates on the touch screen where contact occurs, which can be determined by analyzing the capacitance changes on the sensor grid. The contact area refers to the physical extent of the operator's finger or stylus in contact with the touch screen surface, which can be estimated by analyzing the distribution and amplitude of capacitance changes on multiple adjacent sensor electrodes. The touch pressure refers to the force exerted by the operator's finger or stylus on the touch screen, which can be directly measured with pressure-sensitive layers integrated in the touch screen. The duration refers to the length of time of a single touch event, from initial contact to release, which can be determined by timing the time between touch down and touch up events. The movement trajectory refers to the sequence of locations recorded as the touch point moves across the touch screen surface, which can be obtained by tracking the coordinates of the touch point over time.

[0035] In some preferred embodiments, the signal processing parameters include signal detection threshold, filter coefficients, and gesture recognition parameters. The signal detection threshold refers to the signal intensity limit used to distinguish valid touch signals from background noise, which can be implemented with fixed numerical values, dynamic adjustment algorithms, or probability values output by machine learning models. The filter coefficients refer to the numerical values used to control the frequency response and time response characteristics of signal filters, which can be implemented with coefficients of digital filters (e.g., low-pass, high-pass, band-pass, or notch filters), adjustment parameters of adaptive filters (e.g., Kalman filters, least mean squares algorithm filters), or spectral analysis parameters based on Fourier transform. The gesture recognition parameters refer to the configuration data used to define, match, and verify specific touch gesture patterns, which can be implemented with geometric features of gestures (e.g., trajectory length, curvature, start and end point locations), temporal features (e.g., duration, velocity, acceleration), pressure features (e.g., touch pressure range, pressure rate of change), or trained model parameters based on pattern recognition algorithms (e.g., support vector machines, neural networks, dynamic time warping).

[0036] However, in actual industrial control environment, operators need to work for a long time, and their physiological state will inevitably change, resulting in subtle attenuation or change of their touch characteristics (such as force, contact area, duration, etc.). Relying solely on the current touch characteristics to judge the touch intention of the operator, it is difficult to accurately distinguish between these valid but signal strength weakened touch signals caused by physiological state changes and environmental noise or misoperations. This may cause the system to misjudge the effective touch signal as noise and filter it out, so that the operator perceives that the touch screen is "unresponsive" or "insensitive", thereby affecting the operation accuracy and system reliability, especially in the industrial control environment with high requirements for human-computer interaction accuracy and stability.

[0037] In some preferred embodiments, step S3 comprises:

[0038] S31, obtaining historical touch signals of the operator;

[0039] S32, generating touch characteristic baseline according to the historical touch signals;

[0040] S33, obtaining touch characteristic deviation information according to the touch characteristic and the touch characteristic baseline;

[0041] S34, obtaining touch state change type according to the change trend of the touch characteristic deviation information within a preset time window, the touch state change type including instantaneous fluctuation or continuous drift;

[0042] S35, determining the current operation context according to the environmental interference characteristics, the touch characteristic, and the touch state change type.

[0043] The historical touch signal refers to a set of all raw or preliminary processed touch data generated by the operator when interacting with the touch screen in the past period of time, which can be realized by storing the touch event log in the local memory, recording the user behavior in the cloud database or continuously recording the touch sensor raw data through the data acquisition system. The touch feature baseline refers to the reference standard of the operator's touch behavior pattern in the normal physiological state by analyzing the historical touch signal of the operator, and the embodiment can realize the generation of the touch feature baseline by using the statistical average value, the median, the standard deviation or the personalized behavior model generated based on the machine learning algorithm (for example, clustering analysis, Gaussian mixture model). The touch feature deviation information refers to the difference between the current real-time acquired touch feature and the operator touch feature baseline, which can be realized by using the Euclidean distance, the Mahalanobis distance, the percentage deviation or the result based on the statistical significance test (for example, Z-score, T-test). The preset time window refers to a continuous length of time for analyzing the change trend of the touch feature deviation information, which can be realized by using a fixed length (for example, 30 seconds, 1 minute), a length dynamically adjusted based on the operation task type or a length adaptively determined according to the historical behavior pattern of the operator. The touch state change type refers to the category of the operator's physiological state or touch behavior identified according to the change trend of the touch feature deviation information in the preset time window, and the touch state change type includes instantaneous fluctuation or continuous drift, and the embodiment can realize the acquisition of the touch state change type by using the threshold judgment, the trend analysis algorithm (for example, linear regression, moving average) or the machine learning classifier (for example, support vector machine, neural network).

[0044] Specifically, the present solution significantly enhances the ability to determine the current operating context by introducing a deep analysis of the operator's historical touch behavior. First, the system continuously acquires the operator's historical touch signals, which serve as the data basis for establishing a personalized touch behavior pattern. Based on these historical signals, the system generates an operator-specific touch feature baseline, which represents the operator's typical touch habits under normal physiological state. When the operator performs real-time touch operations, the system acquires the current touch features and compares them with the established touch feature baseline to obtain touch feature deviation information, which quantifies the difference between the current touch behavior and the normal pattern. Further, instead of focusing on instantaneous deviation, the system analyzes the trend of touch feature deviation information within a preset time window. Through this trend analysis, the system can distinguish between touch state change types (e.g., whether the touch state change is a transient fluctuation or a sustained drift indicating a physiological state change), enabling the system to recognize valid but weak touch signals caused by operator physiological state changes (such as fatigue), thereby avoiding confusing valid touch signals with environmental noise or misoperations. Finally, in determining the current operating context, the present solution takes into account environmental interference features, the operator's current touch features, and touch state change types. This comprehensive judgment enables the system to more accurately understand whether the current challenge is mainly from environmental interference, operator physiological state change, or a combination of the two, i.e., the present solution enables the touch screen to adapt to physiological touch changes caused by the operator due to long-term work, so that when the operator's physiological state changes, it can still accurately recognize his touch intention, thereby effectively improving the accuracy of human-computer interaction and system reliability.

[0045] As a preferred embodiment, the scheme of the present application is implemented as follows: in step S31, the touch screen controller configures a data recording module for continuously collecting detailed data of each touch event of the operator on the touch screen, which can be time-stamped and stored in the non-volatile memory of the touch screen controller to form the historical touch signals of the operator. In step S32, a baseline generation algorithm can be run periodically (e.g., every week or when the operator logs in for the first time) to analyze the stored historical touch signals, which can calculate the mean and standard deviation of the touch features (such as touch pressure, contact area) corresponding to each touch signal, and take these statistics as the touch feature baseline of the operator. In step S33, when the operator performs real-time touch operation, the system obtains the current touch features and compares them with the touch feature baseline of the operator to calculate the deviation information. In step S34, the trend analysis module continuously monitors the change of the touch feature deviation information within a preset time window (e.g., the last 30 seconds), which can use methods such as moving average or linear regression to identify trends, and if the deviation information fluctuates greatly in a short time but quickly recovers, it is judged as transient fluctuation (e.g., hand jitter); if the deviation information continuously deviates in a certain direction (e.g., the touch pressure continuously below the baseline), it is judged as continuous drift, for example, if the touch pressure of 10 consecutive touches is lower than the touch feature baseline, the system can judge that the operator is in a continuous drift state, and the operator may be in a fatigue state. Finally, in step S35, the context judgment unit determines the current operation context by combining the environmental interference features, the current touch features, and the touch state change type, for example, if the environmental interference is low, the touch feature is a slight click, and the operator is in a continuous drift state, the system can judge that the current context is "low interference - effective click of the operator in a fatigue state", thereby providing a more accurate basis for subsequent signal processing parameter adjustment.

[0046] Through the above scheme, the present application can establish an operator's personalized touch behavior baseline, and accurately identify the physiological state changes of the operator according to the deviation trend of the current touch features and the baseline. This enables the system to effectively distinguish between valid but weak touch signals caused by physiological state changes and environmental noise or misoperations, so that this embodiment can avoid filtering out the operator's valid touch signals as noise, thereby solving the problem of "no response" or "insensitivity" of the touch screen when the operator's physiological state changes, thereby effectively improving the stability and effectiveness of human-computer interaction.

[0047] However, in actual applications, the touch feature deviation information can be affected by both environmental interference and physiological factor changes of the operator. If the two influences are not distinguished, the system can not determine the real reason for the touch feature deviation, for example, mistaking the touch signal fluctuation caused by environmental interference as the physiological state change of the operator, or vice versa. This confusion can lead to inaccurate judgment of the touch state change type, which in turn affects the correct recognition of the subsequent operation context and the effective adjustment of the signal processing parameters, so that the anti-interference performance optimization effect of the touch screen is not good, and the touch problem caused by different reasons cannot be solved specifically.

[0048] In some preferred embodiments, step S34 comprises:

[0049] S341, analyzing the influence of environmental interference on the touch feature according to the environmental interference feature to obtain environmental interference influence information;

[0050] S342, adjusting the touch feature deviation according to the environmental interference influence information to obtain physiological touch feature deviation information caused by physiological factors;

[0051] S343, obtaining the touch state change type according to the change trend of the physiological touch feature deviation information in a preset time window.

[0052] The environmental interference influence information refers to the specific quantitative influence of environmental interference on the touch screen touch signal, which can be represented by signal amplitude attenuation, noise power spectral density or signal distortion. The physiological touch feature deviation information refers to the touch feature change amount caused only by the physiological factors of the operator after excluding the influence of environmental interference, which can be represented by the drift distance of the touch point position, the change amplitude of the contact area or the fluctuation range of the touch pressure.

[0053] Specifically, the operation logic of the present scheme lies in the fine identification and separation of the source of the touch screen touch signal deviation, so as to improve the accuracy of touch state judgment. First, after obtaining the touch feature and the touch feature deviation information, the system will perform step S341, which analyzes how the environmental interference feature affects the touch feature according to the environmental interference feature, and quantifies the effect, so as to obtain the environmental interference effect information. This process actively identifies and quantifies the direct effect of external electromagnetic interference on the touch screen touch signal, laying a foundation for subsequent interference stripping. Then, the system enters step S342, which adjusts the original touch feature deviation information using the environmental interference effect information. Since the original touch feature deviation information is the result of the combined action of environmental interference and operator physiological factors, by subtracting or compensating the environmental interference effect information, the system can effectively filter out the environmental interference component, thereby separating out the physiological touch feature deviation information caused only by the operator physiological factors, to ensure the purity of the subsequent judgment of the touch state change. Finally, in step S343, the system analyzes the trend of the physiological touch feature deviation information within a preset time window, and identifies different types of touch state change types by observing these trends. Through the above steps, the present scheme decomposes the complex reasons for touch feature deviation into environmental interference and physiological factors that can be independently analyzed, to reduce the interference of environmental noise on the judgment of touch state change type. The accurate acquisition of the touch state change type makes the subsequent determination of the current operation context more truly reflect the operator's touch intention and physiological state, so that the signal processing parameter adjustment of the touch screen will be more targeted and can effectively distinguish and cope with the touch problems caused by environmental interference or operator physiological changes, thereby improving the stability and reliability of the touch screen in complex industrial environments.

[0054] As a preferred embodiment, the present solution is implemented as follows: In an industrial control room environment, the touch screen system can integrate a separate electromagnetic interference sensor for real-time monitoring of electromagnetic signals in the environment. In step S341, the electromagnetic signals obtained by the sensor can be sent to a spectrum analysis module to analyze environmental interference characteristics such as frequency, intensity, and duration. The system uses a pre-constructed environmental interference model to obtain environmental interference impact information based on environmental interference characteristics. Specifically, the model is trained using pre-labeled experimental data or simulation data, and the model can map the impact of different environmental interference characteristics on touch characteristics (such as touch point position, contact area). For example, when a high-intensity electromagnetic interference of a specific frequency is detected, the model can predict that the touch point position may drift a certain distance in a certain direction or that the contact area signal may exhibit a fixed amplitude fluctuation. In step S342, the touch characteristic deviation information is compensated using the environmental interference impact information, thereby eliminating the influence of environmental interference to obtain physiological touch characteristic deviation information without environmental interference impact. Finally, in step S343, the physiological touch characteristic deviation information is continuously monitored within a preset time window. For example, the system can calculate the root mean square value or the rate of change of the deviation within the past 5 seconds. If the root mean square value exceeds a preset threshold within a short period of time, it can be judged as a transient fluctuation, which may correspond to the operator's brief hand jitter; if the rate of change of the deviation remains a non-zero value and exhibits a certain directionality for a long period of time, it can be judged as a sustained drift, which may indicate the operator's fatigue or decreased attention.

[0055] Through the above solution, the present solution can accurately distinguish the true source of touch characteristic deviation, i.e., environmental interference and operator physiological factors. Thus, the system can accurately determine the root cause of touch characteristic deviation, avoiding misjudgment of signal fluctuations caused by environmental interference as changes in the operator's physiological state, or misjudgment of the operator's valid touch signal as environmental interference. This clear distinction makes the judgment of touch state change type more accurate, thereby supporting correct identification of subsequent operation scenarios and effective adjustment of signal processing parameters. Ultimately, the anti-interference performance of the touch screen is optimized, and touch problems caused by different reasons can be targetedly addressed, improving the stability and reliability of the touch screen under complex industrial environments and changes in the operator's physiology.

[0056] In some preferred embodiments, step S343 comprises:

[0057] A1, obtaining operation task information currently executed by the industrial control system, the operation task information including task type and task priority;

[0058] A2, determining operation task risk level according to the operation task information;

[0059] A3. Determine the touch state change type judgment rule according to the operation task risk level;

[0060] A4. Obtain the touch state change type according to the change trend of the physiological touch feature offset information within the preset time window by using the touch state change type judgment rule.

[0061] The operation task information refers to the descriptive data of the specific operation task currently being executed by the industrial control system, which can be implemented in a structured data format, a task ID, or metadata associated with a specific operation process. The task type refers to the classification of the operation task, such as data browsing, parameter setting, device start / stop, alarm confirmation, or emergency operation, which can be implemented in a predefined task category code, a text label, or a function module identifier. The task priority refers to the importance or urgency of the operation task in the system, which can be implemented in a numerical level, a high / medium / low classification, or a Boolean flag. The operation task risk level refers to the potential risk degree that the operation task may cause to the industrial production process, equipment safety, or personnel safety, which can be implemented by a risk matrix evaluation, an expert system rule reasoning, or a machine learning model trained based on historical data. The touch state change type judgment rule refers to the logic or algorithm used to distinguish whether the physiological touch feature offset information belongs to transient fluctuation or sustained drift, which can correspond to different ranges of physiological touch feature offset information, a classification algorithm based on statistical characteristics, or a fuzzy logic reasoning system.

[0062] Specifically, the scheme optimizes the recognition accuracy of the operator's physiological touch state changes by introducing the criticality information of the operation task. First, the system obtains the operation task information that the industrial control system is currently executing, which includes the type and priority of the task. For example, the system can identify whether the current operation is fine adjustment of device parameters or only data viewing. Then, the system assesses and determines the risk level of the current operation task according to these operation task information, for example, tasks involving emergency shutdown or critical process parameter modification are assigned a higher risk level; while routine data monitoring is assigned a lower risk level. Then, the system adjusts the rules for judging the touch state change type according to the dynamically determined operation task risk level. This means that the threshold or algorithm for distinguishing whether the physiological touch feature deviation information is transient fluctuation or sustained drift is no longer fixed, for example, for high-risk tasks, the judgment rule becomes more stringent, even a slight physiological touch deviation may be quickly identified as an abnormal state that needs attention to ensure the accuracy and safety of the operation; for low-risk tasks, the judgment rule can be relatively lenient, allowing a certain range of physiological fluctuations not to be misjudged as abnormal. Finally, using this dynamically adjusted judgment rule according to the risk level of the task, the system can more accurately obtain the touch state change type according to the trend of the physiological touch feature deviation information within the preset time window. By associating the operator's physiological state changes with the risk level of the current task, the system can more intelligently judge the operator's true intention and state, avoiding misjudgment or missed judgment that may be caused by fixed rules in different task situations, for example, when performing high-risk tasks, even slight physiological tremors may be identified as sustained drift, triggering more aggressive signal processing parameter adjustment to compensate for the operator's physiological changes, ensuring the accuracy of the operation; while performing low-risk tasks, the same physiological fluctuations may be judged as transient fluctuations, without triggering excessive intervention, thus maintaining the sensitivity and user experience of the system. This dynamic adjustment of the judgment rule enables the system to consider the actual situation and potential risks of the operation when identifying the operator's physiological state changes, providing a more reliable basis for subsequent signal processing parameter adjustment, thereby improving the anti-interference performance, operation safety and reducing the risk of misoperation of the touch screen in complex industrial environments.

[0063] As a preferred embodiment, the scheme of the present application is implemented as follows: in an industrial control system, a touch screen is connected to a main controller responsible for executing the present method. When an operator interacts with the touch screen, the main controller first acquires the information of the current operation task being performed, for example, if the operator is operating an emergency stop button, and obtains from the task management system that the task type of the current operation task being performed is "emergency stop" and the task priority is "highest". Then, the main controller determines the risk level of the current operation task according to the acquired operation task information, by querying a preset task risk level mapping table or through a built-in risk assessment module. For example, the "emergency stop" task can be mapped to "extremely high risk level", and the "viewing historical data trend" task can be mapped to "low risk level". Then, the main controller selects or dynamically generates corresponding touch state change type judgment rules from a rule base according to the determined operation task risk level. For example, for the task of "extremely high risk level", the judgment rule can be set as: the physiological touch feature offset information exceeding 0.2 mm within 50 consecutive milliseconds is recognized as "continuous drift"; and for the task of "low risk level", the judgment rule can be set as: the physiological touch feature offset information exceeding 1.5 mm within 500 consecutive milliseconds is recognized as "continuous drift". These rules can be a set of threshold parameters or specific pattern recognition algorithm configurations. Finally, the main controller analyzes the change trend of the physiological touch feature offset information within a preset time window obtained from the previous step using the judgment rule dynamically determined according to the task risk level. For example, if the current task is "emergency stop" (extremely high risk), and a slight but continuous tremor of the operator's finger is detected in a short time, even if the amplitude is not large, it will be recognized as a "continuous drift" state according to the strict judgment rule. Conversely, if the current task is "viewing historical data" (low risk), the same slight tremor can be judged as "transient fluctuation" and does not trigger further intervention. In this way, the system can flexibly and accurately identify the operator's physiological state change type according to the importance of the actual operation task.

[0064] Through the above scheme, the present application can avoid misjudging the slight physiological fluctuation of the operator as a state requiring intervention in non-critical tasks, while in high-risk or emergency tasks, the abnormal change of the operator's physiological state can be timely and accurately identified. This improves the accuracy and timeliness of subsequent signal processing parameter adjustment, thereby enhancing the stability and safety of industrial operation, optimizing the judgment accuracy of touch state change type, and enabling the system to adapt to the criticality differences of different operation tasks in industrial control systems.

[0065] In some preferred embodiments, step S341 comprises:

[0066] B1, obtaining environmental interference distribution information of the touch screen and environmental interference sensitivity of different regions of the touch screen;

[0067] B2, determining a local influence mode of the environmental interference on the touch control characteristics of different regions of the touch screen according to the environmental interference distribution information and the environmental interference sensitivity;

[0068] B3, obtaining a local touch control interference influence amount caused by the environmental interference in different regions of the touch screen according to the environmental interference characteristics and the local influence mode;

[0069] B4, synthesizing all the local touch control interference influence amounts to obtain environmental interference influence information.

[0070] The environmental interference distribution information refers to the intensity, frequency or type distribution of the environmental interference signals in the space on the touch screen surface or its sensing area, which can be obtained by deploying a micro sensor array to monitor the electromagnetic field intensity in real time at different positions of the touch screen, or by pre-calibrating the response characteristics of different areas under specific interference sources. The environmental interference sensitivity of different areas of the touch screen refers to the degree of response or susceptibility of different physical areas of the touch screen to specific types and intensities of environmental interference signals, which can be obtained by pre-calibrating or modeling the environmental interference sensitivity of different areas of the touch screen by applying known interference to different areas and measuring their impact on touch signals during the manufacturing or calibration stage of the touch screen. The local influence mode refers to the specific rules or models of the influence of environmental interference on touch features in specific areas of the touch screen, which can be obtained by using the environmental interference distribution information and environmental interference sensitivity as input to a pre-established mathematical model to output the expected deviation, noise superposition characteristics or signal distortion type of the touch signal in that area. The local influence mode can also be determined by looking up a pre-set interference influence lookup table according to the corresponding environmental interference (obtained from the environmental interference distribution information) and environmental interference sensitivity of different areas, for example, in the edge area of the capacitive array, electromagnetic interference causes the touch features to appear stretched and offset, while in the central area, electromagnetic interference causes the touch features to appear compressed and offset. The local touch interference influence quantity refers to the specific quantitative deviation or distortion degree of the touch features caused by environmental interference in a specific local area of the touch screen, which can be obtained by inputting the real-time environmental interference characteristics into the local influence mode for calculation or inquiry. The comprehensive local touch interference influence quantity refers to the aggregation or weighted processing of the local touch interference influence quantities of different areas of the touch screen to obtain a quantity representing the influence of overall environmental interference on the touch features, which can be achieved by spatially weighted averaging or selective accumulation based on the touch point position. The environmental interference influence information of the embodiment can reflect the distortion rules produced by electromagnetic interference in different areas of the touch screen, so when using the environmental interference influence information to compensate for the touch feature deviation information, the embodiment can compensate for each sampling point in the original touch feature deviation information according to the distortion rules corresponding to its position: for example, a sampling point produces a 1.2 millimeter nonlinear offset in the X-axis direction due to electromagnetic interference, accompanied by a 0.8 millimeter jitter deviation in the Y-axis direction, and the processing unit will correct the deviation in the two axes respectively to obtain the physiological touch feature deviation information excluding the influence of environmental interference.

[0071] Specifically, the present scheme aims to accurately quantify the influence of environmental interference on the touch screen touch features in a more refined manner, so as to more accurately identify the touch offset caused by physiological factors. First, the method obtains the environmental interference distribution information of the touch screen and the environmental interference sensitivity of different regions of the touch screen, laying the foundation for subsequent accurate analysis. The environmental interference distribution information reflects the specific distribution of interference in the space of the touch screen, for example, which regions are strong in interference and which regions are weak in interference; while the environmental interference sensitivity of different regions of the touch screen considers the difference in response to interference caused by the design or physical characteristics of the touch screen itself. That is, the scheme is equivalent to recognizing that environmental interference does not uniformly act on the entire touch screen, and different parts of the touch screen may respond differently to the same interference, so the scheme can avoid the simplified processing of regarding the entire screen as uniformly disturbed by combining the two types of information, thereby enabling regional identification of interference effects. On this basis, according to the obtained environmental interference distribution information and environmental interference sensitivity, the system further determines the local influence mode of environmental interference on the touch features of different regions of the touch screen. This process converts static distribution and sensitivity information into dynamic, regional influence rules, enabling the system to understand how a specific region of the touch screen will be specifically affected by touch signals under specific interference conditions, such as signal strength attenuation or noise superposition mode, thereby providing a basis for subsequent quantitative analysis. Subsequently, according to the current environmental interference characteristics and the determined local influence mode, the system obtains the local touch interference influence amount caused by environmental interference in different regions of the touch screen. The environmental interference characteristics here are real-time, dynamic interference data, combined with the local influence mode, the system can calculate the specific touch interference influence amount of each local region of the touch screen under the current interference characteristics at the current time, so that the evaluation of interference influence moves from a macroscopic to a more realistic situation. Finally, by integrating all local touch interference influence amounts, the system obtains the final environmental interference influence amount. This local-to-global calculation method ensures the accuracy of the environmental interference influence amount. It is precisely due to this refined environmental interference influence amount calculation that in the subsequent steps, the part caused by environmental interference can be more effectively stripped from the touch feature deviation amount, and thus the physiological touch feature deviation information caused by the physiological factors of the operator can be more accurately identified.

[0072] As a preferred embodiment, the scheme of the present application is implemented as follows: first, the electromagnetic sensor array integrated in the touch screen is used to obtain the environmental interference distribution information of the touch screen to obtain an electromagnetic interference intensity distribution map, and at the same time, the inherent sensitivity of different regions of the touch screen to various interferences (environmental interference sensitivity) is obtained based on the environmental interference sensitivity matrix calibrated before the touch screen leaves the factory or at the time of installation. These data are aggregated into a real-time. Next, a pre-trained neural network model is used to determine the local influence mode of environmental interference on the touch control characteristics of different regions of the touch screen according to the real-time obtained environmental interference distribution information and environmental interference sensitivity matrix. The model takes the real-time interference distribution map and the sensitivity matrix as input, and takes the local influence mode of the touch control characteristics of different regions of the touch screen as output. For example, when the system detects that there is a high-intensity 10 kHz high-frequency interference on the right edge of the screen, and the sensitivity of this region to 10 kHz interference is high, the neural network model will output a local influence mode function (local influence mode) indicating that the contact area at the touch point may be reduced on average and the touch pressure fluctuation is increased. Subsequently, the current environmental interference characteristics are input into the local influence mode function to obtain the local touch control interference influence amount. Finally, a weighted average method is used to synthesize all the local touch control interference influence amounts to obtain the final environmental interference influence amount. This embodiment can allocate weights based on the importance of each region in the industrial control interface, for example, the weight of the emergency stop button region is 0.3, and the weight of the ordinary data display region is 0.05.

[0073] Through the above technical scheme, the present application can accurately reflect the actual influence of environmental interference on the specific touch control region of the touch screen, so as to accurately strip the influence of environmental interference when adjusting the touch control characteristic deviation in the subsequent, and further improve the judgment accuracy of the physiological state change of the operator.

[0074] In some preferred embodiments, step S4 comprises:

[0075] S41, determining a preliminary parameter adjustment strategy according to the current operation context;

[0076] S42, obtaining operation task information currently executed by the industrial control system, the operation task information comprising a task type and a task priority;

[0077] S43, adjusting the preliminary parameter adjustment strategy according to the operation task information to obtain a signal processing parameter adjustment strategy;

[0078] S44, adjusting the signal processing parameter of the touch screen according to the signal processing parameter adjustment strategy.

[0079] The preliminary parameter adjustment strategy refers to a signal processing parameter adjustment scheme determined preliminarily according to the current operation context, which can be implemented by using a preset rule library, a machine learning model, or a fuzzy logic system. The signal processing parameter adjustment strategy refers to a scheme for guiding the specific adjustment of the touch screen signal processing parameters after being corrected by the operation task information. This embodiment can use a parameter adjustment rule set, a dynamic weight distribution algorithm, or an adaptive control algorithm to adjust the preliminary parameter adjustment strategy according to the operation task information.

[0080] Specifically, the present scheme first generates a preliminary parameter adjustment strategy according to the current operation context. This preliminary strategy has taken into account environmental interference and the operator's immediate touch intention, providing a starting point for subsequent fine-tuning. Next, the system obtains the operation task information of the industrial control system currently being executed, which includes the type and priority of the task. By understanding the type of the current operation (such as regular monitoring, parameter adjustment, or emergency shutdown) and its corresponding priority, the system can provide decision-making basis for subsequent strategy adjustment. Subsequently, the preliminary parameter adjustment strategy is corrected according to the obtained operation task information, thereby obtaining the final signal processing parameter adjustment strategy. This core optimization process enables the system to dynamically adjust parameters according to the type and priority of the task. For example, for high-priority emergency operation tasks, the system can adjust the parameters to maximize the response speed and recognition accuracy of touch, even if it means sacrificing the filtering of minor noise to some extent; for low-priority regular monitoring tasks, it may focus more on stability and false touch suppression. This dynamic adjustment based on task criticality enables the anti-interference performance of the touch screen to be optimized in multiple dimensions, dynamically adapting to the actual needs of industrial control, ensuring the accuracy and safety of operations in critical moments. Finally, the signal processing parameters of the touch screen are adjusted according to the obtained signal processing parameter adjustment strategy. This ensures that the touch screen can process signals in the most optimal way according to the current environment, operator intention, and most critical operation task requirements, effectively suppressing interference and improving touch accuracy and reliability. This method takes into account the type and priority of the operation task, enabling the anti-interference strategy of the touch screen to go beyond pure environmental and touch context perception, achieving deep adaptation to the actual operation requirements of the industrial control system, and thus providing a more reliable and safer interactive experience in complex and variable industrial environments.

[0081] As a preferred embodiment, the scheme of the present application is implemented as follows: first, a preliminary parameter adjustment strategy is generated according to the current operating context, which can be obtained from a pre-set lookup table or calculated by a rule-based engine that maps specific contexts to initial settings of signal detection thresholds, filtering coefficients (such as the cutoff frequency of a low-pass filter) and gesture recognition parameters (such as the minimum duration of a long press). At the same time, the touch screen system obtains operating task information from the task management system. After obtaining the preliminary parameter adjustment strategy and operating task information, the preliminary parameter adjustment strategy is adjusted according to the operating task information, for example, when the task type is "emergency control" and the priority is "critical", the signal detection threshold of the preliminary strategy is lowered and the filtering aggressiveness is reduced to ensure that even weak or slightly distorted touch signals can be correctly recognized; when the task type is "monitoring" and the priority is "low", the filtering strength is enhanced and the detection threshold is increased to enhance stability and prevent false touches caused by environmental noise, even if this means a slight delay in response time. Finally, the signal processing parameters of the touch screen are adjusted according to the signal processing parameter adjustment strategy. This ensures that the signal processing of the touch screen can be optimally configured according to the current environmental conditions, operator intent and most critical industrial operation requirements. Through the above scheme, the system can dynamically adjust the anti-interference performance of the touch screen according to the specific requirements of the industrial control task, which makes the touch response more accurate and reliable, thereby effectively reducing the risk of misoperation caused by insufficient anti-interference optimization, and thus improving the stability and safety of the industrial control system.

[0082] In some preferred embodiments, step S43 comprises:

[0083] S431, obtaining current industrial process parameter information and system event information, the industrial process parameter information including temperature, pressure or flow, and the system event information including alarm status or device fault code;

[0084] S432, determining the real-time criticality level of the operating task according to the industrial process parameter information, system event information and operating task information, the real-time criticality level reflecting the importance of the operating task under the current industrial process state;

[0085] S433, adjusting the preliminary parameter adjustment strategy according to the real-time criticality level.

[0086] Industrial process parameter information refers to real-time data reflecting physical or chemical quantities in industrial production processes, which can be collected by sensors, obtained through PLC or DCS data interfaces. System event information refers to signals indicating abnormalities, warnings or specific state changes occurring in industrial control systems, which can be obtained from alarm system logs, device state feedback or fault diagnosis module outputs. Real-time criticality level refers to a dynamic evaluation index measuring the importance of operational tasks under the current industrial process state, which can be calculated by rule-based reasoning engines, fuzzy logic systems or machine learning models.

[0087] Specifically, the method first acquires current industrial process parameter information and system event information, where the industrial process parameter information can include temperature, pressure or flow, and the system event information can include alarm status or device fault codes. By acquiring these key indicators reflecting the operating conditions and potential risks in the industrial field in real time, the system can establish a comprehensive perception of the current operating environment, which can indicate the complexity and risk level of the current operating environment in real time. Subsequently, the system comprehensively analyzes the real-time acquired industrial process parameter information, system event information and existing operational task information to determine the real-time criticality level of the current operational task. This real-time criticality level reflects the importance of the operational task under the current industrial process state. Unlike relying only on pre-set task types and priorities, this method closely combines the inherent importance of the task with the actual operating conditions of the current industrial process and system events. For example, a valve adjustment task with low priority under normal circumstances will have its real-time criticality level significantly increased when a critical parameter anomaly is detected with an accompanying alarm. This dynamic evaluation mechanism ensures that the system's judgment of the importance of the operational task is more comprehensive, accurate and real-time, so that it can more effectively guide subsequent parameter adjustment. Finally, the preliminary parameter adjustment strategy is further adjusted according to the determined real-time criticality level. This means that the adjustment of touch screen signal processing parameters (such as signal detection threshold, filter coefficient, gesture recognition parameter, etc.) is no longer based only on the pre-set priority of the task, but can be dynamically optimized according to the actual criticality of the task in the current industrial process. This method, based on the perception of environmental interference and operator touch intention, introduces the consideration of real-time state and events of the industrial process, so as to more accurately judge the actual importance of the operation and finely adjust the anti-interference performance of the touch screen accordingly. By dynamically evaluating the real risk of the operation, the system can ensure a more reliable and accurate human-machine interaction experience at critical moments, effectively avoiding misoperation or response delay caused by improper parameter adjustment.

[0088] As a preferred embodiment, the solution of the present application is implemented as follows: in an industrial control system, in order to obtain current industrial process parameter information and system event information, a central processing unit can continuously receive real-time data from field sensors and control devices. For example, temperature data can be obtained from a resistance temperature detector (RTD), pressure readings can be obtained from a pressure transmitter, and flow data can be obtained from a magnetic flowmeter, which are transmitted to the central processing unit through industrial Ethernet or fieldbus protocol. At the same time, system event information (such as specific alarm codes indicating over-temperature or low-pressure warnings, and device fault codes indicating motor overload or valve failure) can be sent to the central processing unit from a programmable logic controller (PLC) or a distributed control system (DCS). Next, in order to determine the real-time criticality level of the operation task, a software module running on the central processing unit can process the obtained industrial process parameter information, system event information and operation task information, which can generate a real-time criticality level using a set of predefined rules or decision matrix, for example, if an operation task involves adjusting the pump speed, and the system detects a critical pressure alarm and a "pump motor failure" event, the module can raise the real-time criticality level of the pump adjustment task from its default "regular" priority to "emergency"; if all process parameters are within the normal operating range and no system event is active, a regular data recording task will remain at a "low" real-time criticality level. Finally, the preliminary parameter adjustment strategy is adjusted according to the determined real-time criticality level. For example, if the real-time criticality level is determined to be "emergency", the system can modify the preliminary strategy (such as increasing the signal detection threshold to reduce false alarms caused by environmental noise) to prioritize accuracy and responsiveness of touch control, while activating an advanced adaptive filtering algorithm to ensure that effective but possibly weak touch signals are not missed.

[0089] Through the above solution, the present application can dynamically adapt the anti-interference performance of the touch screen to the operation requirements of the industrial control system under different real-time working conditions. This ensures that the signal processing parameters of the touch screen can be accurately adjusted when the industrial process parameters are abnormal or the system has an emergency event, thereby improving the accuracy and responsiveness of human-machine interaction. As a result, the risk of misoperation at critical moments can be effectively reduced, and the overall stability and safety of the industrial control system can be significantly enhanced, especially in industrial control environments with extremely high requirements for human-machine interaction precision and stability.

[0090] In a second aspect, as Figure 2 The present application also provides a touch screen anti-interference performance optimization system, which comprises:

[0091] A signal acquisition module 1 is configured to acquire electromagnetic signals in the environment of the touch screen and touch signals of the operator.

[0092] The signal processing module 2 is configured to acquire an environmental interference feature according to the electromagnetic signal, acquire a touch feature according to the touch signal, and identify a touch gesture type according to the touch feature;

[0093] The operation scenario confirmation module 3 is configured to determine a current operation scenario according to the environmental interference feature, the touch feature, and the touch gesture type, the current operation scenario being capable of reflecting an interference state of a current environment and a touch intention of an operator.

[0094] The parameter adjustment module 4 is configured to adjust a signal processing parameter of the touch screen according to the current operation scenario.

[0095] The touch screen anti-interference performance optimization system provided in the embodiment is used to execute the steps in the touch screen anti-interference performance optimization method provided in the first aspect, and the principle of the touch screen anti-interference performance optimization system provided in the embodiment is the same as that of the touch screen anti-interference performance optimization method provided in the first aspect, which will not be described in detail here.

[0096] As can be seen from the above, the touch screen anti-interference performance optimization method and system provided in the application effectively solve the problems of touch signal misjudgment, omission, and inaccurate gesture recognition caused by the fact that the prior art cannot simultaneously consider environmental noise, physiological state changes of an operator, and operation intention, by comprehensively analyzing the environmental interference feature, the touch feature of the operator, and the touch gesture type to determine a current operation scenario, and by adaptively adjusting a signal processing parameter of the touch screen according to the scenario, thereby effectively improving the touch screen anti-interference performance and operation stability in a complex industrial environment.

[0097] In the embodiments provided in the application, it should be understood that the disclosed devices and methods can be implemented in other manners. The above-described device embodiments are merely illustrative; for example, the division of the units is only a logical function division, and there can be another division manner in actual implementation; for example, a plurality of units or components can be combined or integrated into another robot, or some features can be ignored or not executed. In addition, the displayed or discussed mutual couplings or direct couplings or communication connections can be indirect couplings or communication connections through some communication interfaces, devices, or units, and can be electrical, mechanical, or in other forms.

[0098] In addition, each functional module in each embodiment of the application can be integrated together to form an independent part, or each module can exist independently, or two or more modules can be integrated to form an independent part.

[0099] In this document, relational terms such as first and second and the like can be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions.

[0100] The above description is merely illustrative of the application and not in limitation of the principles of the application. Numerous modifications and adaptations thereof will be readily apparent to those skilled in the art without departing from the spirit and scope of the application as defined in the following claims.

Claims

1. A method for optimizing anti-interference performance of a touch screen, characterized in that, The method comprises the following steps: S1, acquiring electromagnetic signals of an environment where the touch screen is located and touch signals of an operator; S2, acquiring environmental interference characteristics according to the electromagnetic signals, acquiring touch characteristics according to the touch signals, and identifying a touch gesture type according to the touch characteristics; S3, determining a current operation context according to the environmental interference characteristics, the touch characteristics, and the touch gesture type, the current operation context being capable of reflecting an interference state of a current environment and a touch intention of the operator; S4, adjusting signal processing parameters of the touch screen according to the current operation context.

2. The method of touch screen anti-jamming performance optimization of claim 1, wherein, The environmental interference characteristics include frequency, intensity, and duration of electromagnetic interference, and the touch characteristics include position, contact area, touch pressure, duration, and movement trajectory of a touch point.

3. The method of touch screen anti-jamming performance optimization of claim 1, wherein, The signal processing parameters include signal detection threshold, filtering coefficient, and gesture recognition parameter.

4. The method of touch screen anti-jamming performance optimization of claim 1, wherein, Step S3 comprises: S31, acquiring historical touch signals of the operator; S32, generating a touch characteristic baseline according to the historical touch signals; S33, acquiring touch characteristic deviation information according to the touch characteristics and the touch characteristic baseline; S34, acquiring a touch state change type according to a change trend of the touch characteristic deviation information within a preset time window, the touch state change type including instantaneous fluctuation or continuous drift; S35, determining a current operation context according to the environmental interference characteristics, the touch characteristics, and the touch state change type.

5. The method of touch screen anti-jamming performance optimization of claim 4, wherein, Step S34 comprises: S341, analyzing an influence amount of environmental interference on the touch characteristics according to the environmental interference characteristics to obtain environmental interference influence information; S342, adjusting the touch characteristic deviation amount according to the environmental interference influence information to obtain physiological touch characteristic deviation information caused by physiological factors; S343, acquiring a touch state change type according to a change trend of the physiological touch characteristic deviation information within a preset time window.

6. The method of touch screen anti-jamming performance optimization of claim 5, wherein, Step S343 comprises: A1, acquiring operation task information currently executed by an industrial control system, the operation task information including a task type and a task priority; A2, determining an operation task risk level according to the operation task information; A3, determining a touch state change type judgment rule according to the operation task risk level; A4, acquiring a touch state change type according to a change trend of the physiological touch characteristic deviation information within a preset time window by using the touch state change type judgment rule.

7. The method of touch screen anti-jamming performance optimization of claim 5, wherein, Step S341 comprises: B1, acquiring environmental interference distribution information of the touch screen and environmental interference sensitivity of different regions of the touch screen; B2, determining a local influence mode of environmental interference on touch characteristics of different regions of the touch screen according to the environmental interference distribution information and the environmental interference sensitivity; B3, acquiring local touch interference influence amounts caused by environmental interference in different regions of the touch screen according to the environmental interference characteristics and the local influence mode; B4, comprehensively integrating all the local touch interference influence amounts to obtain environmental interference influence information.

8. The method of touch screen anti-jamming performance optimization of claim 1, wherein, Step S4 comprises: S41, determining a preliminary parameter adjustment strategy according to the current operation context; S42, obtaining operation task information currently executed by the industrial control system, the operation task information including a task type and a task priority; S43, adjusting the preliminary parameter adjustment strategy according to the operation task information to obtain a signal processing parameter adjustment strategy; S44, adjusting the signal processing parameter of the touch screen according to the signal processing parameter adjustment strategy.

9. The method of touch screen anti-jamming performance optimization of claim 8, wherein, Step S43 includes: S431, obtaining current industrial process parameter information and system event information, the industrial process parameter information including temperature, pressure or flow, and the system event information including an alarm state or a device fault code; S432, determining a real-time criticality level of the operation task according to the industrial process parameter information, the system event information and the operation task information, the real-time criticality level reflecting an importance degree of the operation task under a current industrial process state; S433, adjusting the preliminary parameter adjustment strategy according to the real-time criticality level.

10. A touch screen anti-interference performance optimization system, characterized in that, The system includes: a signal acquisition module, configured to acquire an electromagnetic signal of an environment where the touch screen is located and a touch signal of an operator; a signal processing module, configured to acquire environment interference features according to the electromagnetic signal, acquire touch features according to the touch signal, and identify a touch gesture type according to the touch features; an operation context confirmation module, configured to determine a current operation context according to the environment interference features, the touch features and the touch gesture type, the current operation context being capable of reflecting an interference state of a current environment and a touch intention of the operator; a parameter adjustment module, configured to adjust a signal processing parameter of the touch screen according to the current operation context.

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