Control method of waterproof touch type LED driving controller

Abstract

The invention relates to the field of LED driving controllers, in particular to a control method of a waterproof touch type LED driving controller, which automatically identifies environmental conditions and user preferences through an AI algorithm, dynamically adjusts sensor parameters, realizes self-adaptive configuration, intelligently distinguishes real touch control and false triggering through charge detection, capacitive sensing and weak magnetic field change, and improves the control accuracy of the waterproof touch type LED driving controller. A user operation intention such as light touch, long press and sliding is deeply analyzed in combination with machine learning, after a control strategy is dynamically generated, LED brightness and color temperature are automatically adjusted and a command is executed according to the user intention and environmental parameters, the control strategy is optimized through a reinforcement learning algorithm, meanwhile, a health monitoring function is integrated, a user operation mode and environmental data are analyzed, and the user experience is improved. The health state of the equipment is intelligently judged, potential faults are warned in advance, and stable operation of the equipment and user experience are ensured.

Description

Technical Field

[0001] The present application relates to the field of LED drive controllers, and in particular to a control method for a waterproof touch-type LED drive controller. Background Art

[0002] In the traditional LED driver controller design, the mechanical control mode is widely used, which usually requires opening a hole in the shell and installing a mechanical switch button. In the traditional mechanical control method, the waterproof performance of the equipment has a significant impact on the control method, especially in LED driver controllers involving outdoor or high humidity environments.

[0003] Traditional mechanical LED drive controllers use physical buttons or knobs, which are directly connected to the internal circuit and lack effective waterproof design. The presence of moisture will change the conductivity and capacitance characteristics of the circuit, causing interference in the transmission of control signals, signal distortion, and affecting the accuracy and stability of control. Summary of the invention

[0004] In order to improve the control stability of an LED driving controller, the present application provides a control method of a waterproof touch-type LED driving controller.

[0005] In a first aspect, the present application provides a control method of a waterproof touch-type LED driving controller, comprising the following steps: S1: Use AI algorithms for initialization settings, automatically identify environmental conditions and user preferences, dynamically adjust sensor sensitivity and response time, and achieve adaptive configuration; S2: Based on the charge level generated between the operator's finger and the touch button pad, the LED driver controller is activated, and the real touch and false trigger are intelligently distinguished by combining charge detection, capacitance sensing and weak magnetic field changes; S3: Use machine learning algorithms to deeply analyze sensor signals and identify user operation intentions, such as distinguishing between different operations such as light touch, long press, and sliding; S4: Dynamically generate control strategies based on user intent and environmental parameters, including automatic adjustment of LED brightness and color temperature based on time, light intensity, and user preferences; S5: After the controller executes the command, it collects user feedback and environmental data and continuously optimizes the control strategy through reinforcement learning algorithms; S6: Integrated health monitoring function, by analyzing user operation patterns and environmental data, intelligently judge the health status of the device and warn of potential failures in advance.

[0006] Optionally, the initialization setting using an AI algorithm to automatically identify environmental conditions and user preferences, dynamically adjust the sensitivity and response time of the sensor, and achieve adaptive configuration includes the following steps: S11: Use AI algorithms to analyze environmental changes in real time, dynamically adjust sensor parameters, and achieve adaptive configuration; S12: Analyze user historical operation data through deep learning models, predict user behavior, and adjust controller settings in advance; S13: The controller is automatically calibrated through a self-learning algorithm when used for the first time; S14: Intelligently integrate configuration parameters based on environmental conditions and user preferences.

[0007] Optionally, based on the charge level generated between the operator's finger and the touch key pad, the LED drive controller is activated, and charge detection, capacitance sensing and weak magnetic field changes are combined to intelligently distinguish between real touch and false triggering, including the following steps: S21: Uses deep neural network technology to deeply fuse multimodal signals such as charge detection, capacitance sensing, and weak magnetic field changes; S22: Adaptively adjust the signal processing algorithm according to the environmental noise level to suppress noise interference; S23: Through machine learning algorithms, it can intelligently identify touch modes, such as light touch, long press, and slide, to achieve diversified and intelligent touch operations; S24: Based on touch pattern recognition, intelligently generate touch events and optimize signal processing flow.

[0008] Optionally, the method of using a machine learning algorithm to deeply analyze the sensor signal and identify the user's operation intention, such as distinguishing different operations such as light touch, long press, and sliding, includes the following steps: S31: Uses natural language processing technology to deeply understand the user's operation intention and realize touch interaction; S32: Predict the user's possible intention through user behavior analysis, prepare control strategies in advance, and optimize the touch response process; S33: When the user issues multiple operation instructions at the same time, intelligent fusion processing is performed to ensure the continuity and consistency of touch operations; S34: Intelligently generate touch feedback according to user intention, including tactile feedback, sound feedback, and visual feedback, to enhance the user's touch experience.

[0009] Optionally, dynamically generating a control strategy based on user intention and environmental parameters, including automatically adjusting LED brightness and color temperature based on time, light intensity and user preference, includes the following steps: S41: Intelligently identify scenarios and match the most appropriate control strategy through environmental perception and user behavior analysis; S42: Dynamically adjust control strategies based on user preferences and real-time environmental changes to provide personalized touch experience; S43: Optimize strategy parameters by evaluating the effect of control strategy in real time; S44: Based on historical data and user behavior, predict possible future control needs and generate control strategies in advance.

[0010] Optionally, after executing the command, the controller collects user feedback and environmental data, and continuously optimizes the control strategy through a reinforcement learning algorithm, including the following steps: S51: Through deep learning algorithms, in-depth analysis of user feedback is carried out to identify key factors of user satisfaction and provide data support for control strategy optimization; S52: Adaptively adjust the control strategy according to environmental changes and perform self-optimization of environmental applicability; S53: Through intelligent algorithms and automatic iterative control strategies, continuous optimization and upgrading of touch control is achieved; S54: Based on user feedback and environmental data, predict user satisfaction and take measures to improve user touch experience.

[0011] Optionally, the integrated health monitoring function analyzes user operation patterns and environmental data to intelligently determine the health status of the device and provide early warning of potential failures, including the following steps: S61: Through deep learning models, intelligently evaluate the health status of equipment, predict potential failures, and realize intelligent equipment maintenance; S62: Using machine learning algorithms to intelligently identify failure modes and issue early warnings; S63: Intelligently generate maintenance strategies and provide maintenance recommendations based on equipment health status assessment results; S64: Continuously optimize device performance through intelligent algorithms.

[0012] In a second aspect, the present application provides a control system of a waterproof touch-type LED drive controller, comprising the following modules: The environment perception and user interaction module is responsible for collecting environmental parameters and user operation data and performing preliminary processing; Intelligent control strategy generation and execution module, which generates and executes control strategies based on environmental perception and user interaction data, including brightness adjustment and color temperature adjustment; Feedback learning and self-optimization module, which is used to collect user feedback and environmental data, optimize control strategies through learning algorithms, and improve control accuracy and user satisfaction; The health monitoring and early warning module is used to continuously monitor the operating status of equipment, identify potential failure modes, evaluate the health status of equipment, and provide early warnings and maintenance recommendations.

[0013] In a third aspect, the present application provides an electronic device, which adopts the following technical solution: An electronic device comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the steps of a control method of a waterproof touch-type LED driving controller as described in any one of the above items are implemented.

[0014] In a fourth aspect, the present application provides a computer-readable storage medium, which adopts the following technical solution: A computer-readable storage medium comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the steps of a control method of a waterproof touch-type LED driving controller as described in any one of the above items are implemented.

[0015] In summary, this application includes the following beneficial technical effects: 1. The combination of charge detection technology and capacitive touch sensing principle enables the controller to identify touch operation by detecting the change of charge level between the finger and the touch key pad without direct contact with the circuit. This non-contact control method significantly improves the waterproof performance of the controller, avoiding the change of conductivity and capacitance characteristics caused by direct contact of moisture with the circuit, thereby reducing signal distortion and control instability. 2. By intelligently distinguishing between real touch and false trigger, this technical solution can effectively filter out unexpected signal interference and improve the accuracy and stability of control signals. Combined with the deep analysis of sensor signals by machine learning algorithms, the controller can more accurately identify user operation intentions, such as light touch, long press, sliding, etc., thereby achieving more sophisticated control strategies and improving user experience; 3. Use AI algorithms for initialization settings, automatically identify environmental conditions and user preferences, dynamically adjust the sensitivity and response time of sensors, and achieve adaptive configuration. In addition, by collecting user feedback and environmental data, the controller can continuously optimize the control strategy through reinforcement learning algorithms to ensure that the control performance is always maintained in the best state under different environments and usage conditions, while warning of potential failures in advance, improving the reliability and service life of the equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 It is a flow chart of a control method of a waterproof touch-type LED driving controller in an embodiment of the present application.

[0017] Figure 2 It is a flowchart of the sub-steps of step S1 in the embodiment of the present application.

[0018] Figure 3 It is a flowchart of the sub-steps of step S2 in the embodiment of the present application.

[0019] Figure 4 It is a flowchart of the sub-steps of step S3 in the embodiment of the present application.

[0020] Figure 5 It is a flowchart of the sub-steps of step S4 in the embodiment of the present application.

[0021] Figure 6 It is a flowchart of the sub-steps of step S5 in the embodiment of the present application.

[0022] Figure 7 It is a flowchart of the sub-steps of step S6 in the embodiment of the present application. DETAILED DESCRIPTION

[0023] The following is combined with Figure 1-Figure 7 This application is described in further detail.

[0024] Reference Figure 1 The present application embodiment discloses a control method of a waterproof touch-type LED driving controller, comprising the following steps: S1: Use AI algorithms for initialization settings, automatically identify environmental conditions and user preferences, dynamically adjust sensor sensitivity and response time, and achieve adaptive configuration.

[0025] Reference Figure 2 , step S1 correspondingly includes the following sub-steps.

[0026] S11: Use AI algorithms to analyze environmental changes in real time, dynamically adjust sensor parameters, and achieve adaptive configuration.

[0027] S12: Analyze user historical operation data through deep learning models, predict user behavior, and adjust controller settings in advance.

[0028] S13: When the controller is used for the first time, it is automatically calibrated through a self-learning algorithm.

[0029] S14: Intelligently integrate configuration parameters based on environmental conditions and user preferences.

[0030] Based on the waterproof touch-type LED driving controller, the above steps S11-S14 are illustrated by way of example.

[0031] For example, in an outdoor LED display application scenario, the controller monitors the surrounding light intensity in real time through the built-in ambient light sensor. The AI ​​algorithm analyzes the trend of light changes and dynamically adjusts the sensitivity and response time of the sensor. At sunset, when the ambient light intensity drops rapidly, the AI ​​algorithm automatically increases the sensor sensitivity to ensure that touch operations can be accurately detected even in low-light environments, while adjusting the response time to meet the needs of nighttime operations, achieving adaptive configuration and ensuring stable touch response under different lighting conditions.

[0032] On the other hand, in a smart home environment, the waterproof touch-type LED driver controller analyzes the user's historical operation data through a deep learning model. For example, users usually reduce the brightness and color temperature of the living room LED lights between 10 and 11 p.m. to prepare for bedtime. Based on this pattern, the controller automatically adjusts the brightness and color temperature of the LED lights to the preset "bedtime mode" before 10 p.m. every day, without the need for manual operation by the user, providing a more personalized user experience.

[0033] Furthermore, when the user sets up the waterproof touch LED driver controller for the first time, the self-learning algorithm guides the user to perform a series of touch operations, such as light touch, long press, and slide, to collect baseline data of the sensor. The algorithm automatically calibrates the sensitivity and threshold of the sensor based on this data, ensuring that the controller can accurately identify different types of touch operations and provide a precise control experience even when used for the first time.

[0034] For another example, in an office environment, the waterproof touch LED driver controller will comprehensively consider multiple factors such as ambient light, time, and user preferences, and intelligently integrate configuration parameters. For example, when it detects that there is sufficient natural light in the office, the controller will automatically adjust the brightness of the LED light to a lower level to save energy; at the same time, according to user preferences, such as liking warm color temperature lighting, the controller will automatically adjust the color temperature of the LED light to create a work environment that is both energy-saving and in line with personal preferences.

[0035] From the above specific examples, it can be seen that the waterproof touch LED driver controller not only improves the environmental adaptability and the ability to predict user operations through AI algorithms and deep learning models, but also realizes automatic calibration and intelligent parameter fusion, significantly improving the user experience and the intelligence level of the equipment.

[0036] Replay Figure 1 , after step S1, the following steps are also included.

[0037] S2: Based on the charge level generated between the operator's finger and the touch button pad, the LED driver controller is activated, and the real touch and false trigger are intelligently distinguished by combining charge detection, capacitance sensing and weak magnetic field changes.

[0038] Reference Figure 3 , step S2 correspondingly includes the following sub-steps.

[0039] S21: Uses deep neural network technology to deeply fuse multimodal signals such as charge detection, capacitance sensing, and weak magnetic field changes.

[0040] S22: Adaptively adjust the signal processing algorithm according to the environmental noise level to suppress noise interference.

[0041] S23: Through machine learning algorithms, it can intelligently identify touch modes, such as light touch, long press, and sliding, to achieve diversified and intelligent touch operations.

[0042] S24: Based on touch pattern recognition, intelligently generate touch events and optimize signal processing flow.

[0043] The above steps S21-S24 are described with examples.

[0044] Assume that in a smart conference room, a waterproof touch LED driver controller uses deep neural network technology to deeply fuse multimodal signals such as charge detection, capacitance sensing, and weak magnetic field changes to improve the accuracy and robustness of touch recognition. When participants operate on the touch screen, the controller simultaneously collects signals of charge level, capacitance change, and weak magnetic field change, and performs comprehensive analysis through a deep neural network model. Even in the presence of environmental noise or interference in the conference room, it can accurately distinguish between real touch and false trigger, providing a stable and reliable touch experience.

[0045] Furthermore, during the use of the smart conference room, the waterproof touch LED driver controller uses an adaptive signal processing algorithm to dynamically adjust the signal processing parameters according to the ambient noise level to effectively suppress noise interference. For example, during a meeting, when the background noise generated by the air conditioner, projector and other equipment in the conference room increases, the controller will automatically enhance the signal filtering and noise suppression functions to ensure that even in a noisy environment, the touch operations of the participants can be accurately identified to avoid misoperation and improve meeting efficiency and experience.

[0046] In addition, on the touch screen of the smart conference room, the waterproof touch LED driver controller uses machine learning algorithms to intelligently identify the touch modes of participants, such as light touch, long press, and sliding, to achieve diversified and intelligent touch operations. For example, when a participant slides on the touch screen, the controller can accurately identify the direction and speed of the slide and intelligently generate corresponding control commands, such as adjusting the scrolling speed of the screen display content or switching to the next presentation, providing a more intuitive and convenient interactive experience.

[0047] In the touch screen application of the smart conference room, the waterproof touch LED driver controller can intelligently generate touch events and optimize the signal processing process based on touch pattern recognition. When participants operate on the touch screen, the controller can quickly identify the touch pattern, such as tapping the function icon on the screen, intelligently generate the "start video conference" event, and automatically connect to the predetermined video conference platform, providing a smooth meeting experience, while optimizing the signal processing process, reducing delays, and improving response speed.

[0048] Replay Figure 1 , the following steps are also included after step S2.

[0049] S3: Use machine learning algorithms to deeply analyze sensor signals and identify user operation intentions, such as distinguishing different operations such as light touch, long press, and sliding.

[0050] Reference Figure 4 , step S3 correspondingly includes the following steps.

[0051] S31: Uses natural language processing technology to conduct deep semantic understanding of user operation intentions and realize touch interaction.

[0052] S32: Through user behavior analysis, predict the user's possible intentions, prepare control strategies in advance, and optimize the touch response process.

[0053] S33: When the user issues multiple operation instructions at the same time, intelligent fusion processing is performed to ensure the continuity and consistency of touch operations.

[0054] S34: Intelligently generate touch feedback according to user intention, including tactile feedback, sound feedback, and visual feedback, to enhance the user's touch experience.

[0055] Correspondingly, the above steps S31-S34 are illustrated by way of example.

[0056] In the smart conference room, the waterproof touch LED driver controller uses natural language processing technology to deeply understand the operation instructions input by participants through voice or text. For example, when a participant says "please adjust the screen brightness to 50%", the controller can understand and execute the corresponding brightness adjustment operation, realizing natural and smooth touch interaction without the need for users to remember complex touch gestures or menu paths.

[0057] During the use of the smart conference room, the waterproof touch LED driver controller predicts the possible intentions of participants through user behavior analysis, prepares control strategies in advance, and optimizes the touch response process. For example, when participants frequently adjust the screen brightness and volume before the meeting, the controller will learn this pattern and automatically adjust to the brightness and volume settings preferred by the participants when the meeting starts again, providing a more personalized meeting experience.

[0058] In the touch screen application of the smart conference room, the waterproof touch LED driver controller can intelligently integrate and process when participants issue multiple operation instructions at the same time to ensure the continuity and consistency of touch operations. For example, when participants request to adjust the screen brightness, switch presentations, and start a video conference at the same time, the controller can intelligently sort and execute these instructions to avoid operation conflicts and ensure a smooth meeting process.

[0059] In the touch screen application of the smart conference room, the waterproof touch LED driver controller intelligently generates touch feedback according to the operation intention of the participants, including tactile feedback, sound feedback and visual feedback, to enhance the user's touch experience. For example, when a participant touches a function icon on the screen, the controller generates a slight tactile vibration, plays a confirmation sound, and displays the operation result on the screen, providing multi-sensory feedback to make the operation more intuitive and satisfying.

[0060] Replay Figure 1 , after step S3, the following steps are included.

[0061] S4: Dynamically generate control strategies based on user intention and environmental parameters, including automatic adjustment of LED brightness and color temperature based on time, light intensity, and user preferences.

[0062] Reference Figure 5 , step S4 correspondingly includes the following sub-steps.

[0063] S41: Through environmental perception and user behavior analysis, intelligent scene identification and matching of the most appropriate control strategy.

[0064] S42: Dynamically adjust control strategies based on user preferences and real-time environmental changes to provide a personalized touch experience.

[0065] S43: Optimize strategy parameters by evaluating the effectiveness of the control strategy in real time.

[0066] S44: Based on historical data and user behavior, predict possible future control needs and generate control strategies in advance.

[0067] Correspondingly, the above steps S41-S44 are illustrated by way of example.

[0068] In the smart conference room, the waterproof touch LED driver controller intelligently identifies the current scene through environmental sensing technologies such as light sensors, temperature sensors and sound sensors, as well as user behavior analysis. For example, when the light in the conference room dims, the controller automatically adjusts the screen brightness to adapt to the low-light environment. At the same time, according to the historical behavior data of the participants, such as preference for darker screen brightness, the controller matches the most appropriate control strategy to provide a comfortable and personalized touch experience.

[0069] Furthermore, during the use of the smart conference room, the waterproof touch LED driver controller dynamically adjusts the control strategy according to the preferences of the participants and real-time environmental changes. For example, when participants often adjust the screen brightness and volume at the beginning of a meeting, the controller will learn this preference and automatically adjust to the participant's preferred settings at the beginning of each meeting. At the same time, when the ambient noise in the conference room increases, the controller will automatically increase the volume to ensure that participants can clearly hear the meeting content, providing a more personalized touch experience.

[0070] In addition, in the touch screen application of the smart conference room, the waterproof touch LED driver controller optimizes the policy parameters by evaluating the effect of the control strategy in real time. For example, when the controller finds that participants often need to adjust the screen brightness multiple times to achieve a satisfactory effect, it will automatically adjust the sensitivity of the brightness adjustment to make the operation more precise, reduce the number of user operations, and improve the touch experience.

[0071] At the same time, the waterproof touch LED driver controller predicts possible future control needs based on historical data and user behavior and generates control strategies in advance. For example, when participants often turn off the screen 10 minutes before the end of a meeting to save energy, the controller will learn this pattern and automatically turn off the screen 10 minutes before the end of the meeting in future meetings without manual operation by the user, providing a more intelligent and energy-saving touch experience.

[0072] Replay Figure 1 , after step S4, the following steps are also included.

[0073] S5: After the controller executes the command, it collects user feedback and environmental data, and continuously optimizes the control strategy through reinforcement learning algorithms.

[0074] Reference Figure 6 , step S5 correspondingly includes the following sub-steps.

[0075] S51: Through deep learning algorithms, in-depth analysis of user feedback is carried out to identify key factors of user satisfaction and provide data support for control strategy optimization.

[0076] S52: Adaptively adjust the control strategy according to environmental changes and perform self-optimization of environmental suitability.

[0077] S53: Through intelligent algorithms and automatic iterative control strategies, continuous optimization and upgrading of touch control is achieved.

[0078] S54: Based on user feedback and environmental data, predict user satisfaction and take measures to improve user touch experience.

[0079] Correspondingly, steps S51-S54 are described by way of example below.

[0080] In the smart conference room, the waterproof touch LED driver controller uses a deep learning algorithm to conduct in-depth analysis of user feedback data to identify key factors that affect user satisfaction. For example, the controller collects feedback from participants on screen brightness, touch response speed, touch feedback, etc., analyzes the relationship between these feedbacks and user satisfaction through a deep learning model, and identifies that the smoothness of screen brightness adjustment and touch response speed are key factors affecting satisfaction. Based on these analysis results, the controller can optimize the control strategy, such as improving the brightness adjustment algorithm and increasing the touch response speed, to improve user satisfaction.

[0081] Furthermore, during the use of the smart conference room, the waterproof touch LED driver controller adaptively adjusts the control strategy according to environmental changes, such as light, temperature and noise level, and optimizes the suitability of the environment. For example, when the light in the conference room dims, the controller automatically adjusts the screen brightness to adapt to the low-light environment, and dynamically adjusts the touch response threshold according to the ambient noise level to ensure accurate recognition of touch operations in noisy environments, providing a stable and reliable touch experience.

[0082] In addition, in the touch screen application of smart conference rooms, the waterproof touch LED driver controller uses intelligent algorithms to automatically iterate the control strategy to achieve continuous optimization and upgrade of touch control. For example, the controller regularly collects user operation data, such as touch frequency, operation mode and feedback information, analyzes this data through machine learning algorithms, automatically adjusts touch sensitivity, optimizes touch mode recognition algorithms, and improves touch feedback mechanisms to achieve continuous optimization of touch control and improve device performance and user experience.

[0083] At the same time, during the use of the smart conference room, the waterproof touch LED driver controller predicts user satisfaction based on user feedback and environmental data, and takes measures to improve the user's touch experience. For example, the controller will analyze the user's touch operation data under different environmental conditions and predict user satisfaction in specific environments. For example, in a high-noise environment, it predicts that the user may be dissatisfied with the touch response speed and accuracy, so the control strategy is adjusted in advance, such as enhancing the touch signal processing algorithm, to improve the user's touch experience.

[0084] Replay Figure 1 , after step S5, the following steps are also included.

[0085] S6: Integrated health monitoring function, by analyzing user operation patterns and environmental data, intelligently judge the health status of the device and warn of potential failures in advance.

[0086] Reference Figure 7 , step S6 correspondingly includes the following steps.

[0087] S61: Through deep learning models, intelligently evaluate the health status of equipment, predict potential failures, and realize intelligent equipment maintenance.

[0088] S62: Use machine learning algorithms to intelligently identify failure modes and issue early warnings.

[0089] S63: Based on the equipment health status assessment results, intelligently generate maintenance strategies and provide maintenance recommendations.

[0090] S64: Continuously optimize device performance through intelligent algorithms.

[0091] The following examples are given for steps S61 to S64.

[0092] In the smart conference room, the waterproof touch LED driver controller uses a deep learning model to intelligently evaluate the health status of the device and predict potential failures. For example, the controller collects device operation data such as touch response time, screen brightness changes, temperature, and current consumption, and analyzes this data through a deep learning model to evaluate the health status of the device. When the model detects that the touch response time is gradually increasing or the screen brightness adjustment is abnormal, it predicts potential failures, such as aging of the touch sensor or degradation of LED lamp performance, thereby realizing intelligent equipment maintenance and taking measures in advance to avoid the impact of equipment failures on meetings.

[0093] In addition, the waterproof touch LED driver controller uses machine learning algorithms to intelligently identify fault modes and issue early warnings. For example, the controller analyzes historical fault data, trains machine learning models, and identifies specific fault modes, such as partial failure of the touch screen or frequent flashing of LED lamp beads. When the equipment operation data matches these fault modes, the controller will issue early warnings to notify maintenance personnel to conduct inspections and repairs to avoid faults and ensure the smooth progress of the meeting.

[0094] Furthermore, in the touch screen application of the smart conference room, the waterproof touch LED driver controller intelligently generates maintenance strategies and provides maintenance suggestions based on the equipment health status assessment results. For example, when the controller assesses that the sensitivity of the touch sensor has decreased, it will intelligently generate a maintenance strategy to recommend regular cleaning of the touch screen surface, checking the touch sensor connection, and replacing the touch sensor when necessary to maintain the best performance of the equipment, extend the equipment life, and reduce maintenance costs.

[0095] At the same time, during the use of the smart conference room, the waterproof touch LED driver controller continuously optimizes the device performance through intelligent algorithms. For example, the controller regularly collects device operation data, such as touch operation frequency, screen brightness adjustment times, and device temperature, and analyzes these data through intelligent algorithms to identify bottlenecks in device performance, such as slow touch response speed or uneven screen brightness adjustment. Based on these analysis results, the controller automatically adjusts the touch response algorithm, optimizes the screen brightness adjustment strategy, and improves the device heat dissipation design to continuously optimize device performance and enhance device stability and user experience.

[0096] On the other hand, this embodiment also provides a control system of a waterproof touch-type LED driving controller, comprising the following modules: The environment perception and user interaction module is responsible for collecting environmental parameters and user operation data and performing preliminary processing; Intelligent control strategy generation and execution module, which generates and executes control strategies based on environmental perception and user interaction data, including brightness adjustment and color temperature adjustment; Feedback learning and self-optimization module, which is used to collect user feedback and environmental data, optimize control strategies through learning algorithms, and improve control accuracy and user satisfaction; The health monitoring and early warning module is used to continuously monitor the operating status of equipment, identify potential failure modes, evaluate the health status of equipment, and provide early warnings and maintenance recommendations.

[0097] Therefore, the environmental perception and user interaction module first collects environmental parameters (such as light, temperature) and user operation data for preliminary processing; the intelligent control strategy generation and execution module, based on these data, intelligently generates and executes control strategies such as brightness adjustment and color temperature adjustment through deep learning and machine learning algorithms; the feedback learning and self-optimization module collects user feedback and environmental data, continuously optimizes control strategies through learning algorithms, improves control accuracy and user satisfaction; the health monitoring and early warning module continuously monitors the operating status of the equipment, intelligently identifies potential failure modes, evaluates the health status of the equipment, and issues early warnings, provides maintenance suggestions, and realizes intelligent maintenance and performance optimization of the equipment. Through the collaborative work of various modules, the entire system realizes intelligent control, adaptive optimization and health monitoring of the equipment, significantly improving the intelligence level of the equipment and user experience.

[0098] This embodiment also provides an electronic device. The electronic device includes a processor, a memory, a network interface and a database connected via a system bus. The processor of the electronic device is used to provide computing and control capabilities. The memory of the electronic device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The network interface of the electronic device is used to communicate with an external terminal via a network connection. When the computer program is executed by the processor, a control method for a waterproof touch LED drive controller is implemented: S1: Use AI algorithms for initialization settings, automatically identify environmental conditions and user preferences, dynamically adjust sensor sensitivity and response time, and achieve adaptive configuration.

[0099] S2: Based on the charge level generated between the operator's finger and the touch button pad, the LED driver controller is activated, and the real touch and false trigger are intelligently distinguished by combining charge detection, capacitance sensing and weak magnetic field changes.

[0100] S3: Use machine learning algorithms to deeply analyze sensor signals and identify user operation intentions, such as distinguishing different operations such as light touch, long press, and sliding.

[0101] S4: Dynamically generate control strategies based on user intention and environmental parameters, including automatic adjustment of LED brightness and color temperature based on time, light intensity, and user preferences.

[0102] S5: After the controller executes the command, it collects user feedback and environmental data, and continuously optimizes the control strategy through reinforcement learning algorithms.

[0103] S6: Integrated health monitoring function, by analyzing user operation patterns and environmental data, intelligently judge the health status of the device and warn of potential failures in advance.

[0104] When the computer program is executed by a processor, it can implement any control method of the waterproof touch-type LED driving controller in the above method embodiments.

[0105] This embodiment further provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the following steps are implemented: S1: Use AI algorithms for initialization settings, automatically identify environmental conditions and user preferences, dynamically adjust sensor sensitivity and response time, and achieve adaptive configuration.

[0106] S2: Based on the charge level generated between the operator's finger and the touch button pad, the LED driver controller is activated, and the real touch and false trigger are intelligently distinguished by combining charge detection, capacitance sensing and weak magnetic field changes.

[0107] S3: Use machine learning algorithms to deeply analyze sensor signals and identify user operation intentions, such as distinguishing different operations such as light touch, long press, and sliding.

[0108] S4: Dynamically generate control strategies based on user intention and environmental parameters, including automatic adjustment of LED brightness and color temperature based on time, light intensity, and user preferences.

[0109] S5: After the controller executes the command, it collects user feedback and environmental data, and continuously optimizes the control strategy through reinforcement learning algorithms.

[0110] S6: Integrated health monitoring function, by analyzing user operation patterns and environmental data, intelligently judge the health status of the device and warn of potential failures in advance.

[0111] When the computer program is executed by a processor, it can implement any control method of the waterproof touch-type LED driving controller in the above method embodiments.

[0112] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0113] Those skilled in the art will clearly understand that for the sake of convenience and brevity of description, only the division of the above-mentioned functional units and modules is used as an example. In actual applications, the above-mentioned functions can be distributed and completed by different functional units and modules as needed, that is, the internal structure of the system can be divided into different functional units or modules to complete all or part of the functions described above.

[0114] The above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application is described in detail in the above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above embodiments, or replace some of the technical features therein by equivalents. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application, and should be included in the protection scope of the present application.

Claims

1. A control method for a waterproof touch-type LED drive controller, characterized in that: The following steps are involved: S1: Use AI algorithms for initialization settings, automatically identify environmental conditions and user preferences, dynamically adjust sensor sensitivity and response time, and achieve adaptive configuration; S2: Based on the charge level generated between the operator's finger and the touch button pad, the LED driver controller is activated, and the real touch and false trigger are intelligently distinguished by combining charge detection, capacitance sensing and weak magnetic field changes; S3: Use machine learning algorithms to deeply analyze sensor signals and identify user operation intentions, such as distinguishing between different operations such as light touch, long press, and sliding; S4: Dynamically generate control strategies based on user intent and environmental parameters, including automatic adjustment of LED brightness and color temperature based on time, light intensity, and user preferences; S5: After the controller executes the command, it collects user feedback and environmental data and continuously optimizes the control strategy through reinforcement learning algorithms; S6: Integrated health monitoring function, by analyzing user operation patterns and environmental data, intelligently judge the health status of the device and warn of potential failures in advance.

2. The control method of a waterproof touch-type LED driving controller according to claim 1, characterized in that: The method of using an AI algorithm to perform initialization settings, automatically identifying environmental conditions and user preferences, dynamically adjusting the sensitivity and response time of the sensor, and realizing adaptive configuration includes the following steps: S11: Use AI algorithms to analyze environmental changes in real time, dynamically adjust sensor parameters, and achieve adaptive configuration; S12: Analyze user historical operation data through deep learning models, predict user behavior, and adjust controller settings in advance; S13: The controller is automatically calibrated through a self-learning algorithm when used for the first time; S14: Intelligently integrate configuration parameters based on environmental conditions and user preferences.

3. The control method of a waterproof touch-type LED driving controller according to claim 1, characterized in that: The method activates the LED driving controller based on the charge level generated between the operator's finger and the touch key pad, combines charge detection, capacitance sensing and weak magnetic field changes, and intelligently distinguishes between real touch and false triggering, including the following steps: S21: Uses deep neural network technology to deeply fuse multimodal signals such as charge detection, capacitance sensing, and weak magnetic field changes; S22: Adaptively adjust the signal processing algorithm according to the environmental noise level to suppress noise interference; S23: Through machine learning algorithms, it can intelligently identify touch modes, such as light touch, long press, and slide, to achieve diversified and intelligent touch operations; S24: Based on touch pattern recognition, intelligently generate touch events and optimize signal processing flow.

4. The control method of a waterproof touch-type LED driving controller according to claim 1, characterized in that: The method of using a machine learning algorithm to deeply analyze sensor signals and identify user operation intentions, such as distinguishing different operations such as light touch, long press, and sliding, includes the following steps: S31: Uses natural language processing technology to deeply understand the user's operation intention and realize touch interaction; S32: Predict the user's possible intention through user behavior analysis, prepare control strategies in advance, and optimize the touch response process; S33: When the user issues multiple operation instructions at the same time, intelligent fusion processing is performed to ensure the continuity and consistency of touch operations; S34: Intelligently generate touch feedback according to user intention, including tactile feedback, sound feedback, and visual feedback, to enhance the user's touch experience.

5. The control method of a waterproof touch-type LED driving controller according to claim 1, characterized in that: The control strategy is dynamically generated according to the user intention and environmental parameters, including automatically adjusting the LED brightness and color temperature according to time, light intensity and user preference, including the following steps: S41: Intelligently identify scenarios and match the most appropriate control strategy through environmental perception and user behavior analysis; S42: Dynamically adjust control strategies based on user preferences and real-time environmental changes to provide personalized touch experience; S43: Optimize strategy parameters by evaluating the effect of control strategy in real time; S44: Based on historical data and user behavior, predict possible future control needs and generate control strategies in advance.

6. The control method of a waterproof touch-type LED driving controller according to claim 1, characterized in that: After the controller executes the command, it collects user feedback and environmental data, and continuously optimizes the control strategy through the reinforcement learning algorithm, including the following steps: S51: Through deep learning algorithms, in-depth analysis of user feedback is carried out to identify key factors of user satisfaction and provide data support for control strategy optimization; S52: Adaptively adjust the control strategy according to environmental changes and perform self-optimization of environmental applicability; S53: Through intelligent algorithms and automatic iterative control strategies, continuous optimization and upgrading of touch control is achieved; S54: Based on user feedback and environmental data, predict user satisfaction and take measures to improve user touch experience.

7. The control method of a waterproof touch-type LED driving controller according to claim 1, characterized in that: The integrated health monitoring function analyzes user operation patterns and environmental data to intelligently determine the health status of the device and provide early warning of potential failures, including the following steps: S61: Through deep learning models, intelligently evaluate the health status of equipment, predict potential failures, and realize intelligent equipment maintenance; S62: Using machine learning algorithms to intelligently identify failure modes and issue early warnings; S63: Intelligently generate maintenance strategies and provide maintenance recommendations based on equipment health status assessment results; S64: Continuously optimize device performance through intelligent algorithms.

8. A control system for a waterproof touch-type LED driver controller, characterized in that: Includes the following modules: The environment perception and user interaction module is responsible for collecting environmental parameters and user operation data and performing preliminary processing; Intelligent control strategy generation and execution module, which generates and executes control strategies based on environmental perception and user interaction data, including brightness adjustment and color temperature adjustment; Feedback learning and self-optimization module, which is used to collect user feedback and environmental data, optimize control strategies through learning algorithms, and improve control accuracy and user satisfaction; The health monitoring and early warning module is used to continuously monitor the operating status of equipment, identify potential failure modes, evaluate the health status of equipment, and provide early warnings and maintenance recommendations.

9. An electronic device, characterized in that: It comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the steps of the control method of a waterproof touch-type LED driving controller as described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the control method of the waterproof touch-type LED drive controller according to any one of claims 1 to 7 are implemented.

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