Intelligent device adaptive control method based on Internet of Things and AI algorithm

Abstract

The invention discloses an intelligent device adaptive control method based on Internet of Things and an AI algorithm, and relates to the technical field of intelligent control and Internet of Things application, and the method comprises the following steps: S1, building a time rhythm alignment mechanism and a safety threshold limiting mechanism in an adaptive control stage of an intelligent device, performing fluctuation characteristic analysis on the electric signals acquired through the Internet of Things to generate a surge emergency scheme; and S2, setting a fluctuation capture area and a zero protection window in a signal sensing link according to the surge emergency scheme, carrying out real-time monitoring on an electric signal waveform, filtering an abnormal waveform, extracting a credible signal segment, and generating a fluctuation fingerprint record. Through time rhythm alignment and safety threshold limit, rhythm cooperation of signal acquisition and control execution is realized, and misjudgment caused by surge interference is suppressed; power and stepping amplitude are dynamically optimized in combination with fluctuation fingerprints and an instruction blocking mechanism, control self-excitation and repeated triggering are prevented, energy balance and control stability are kept, and system reliability and anti-interference performance are improved.

Description

Technical Field

[0001] This invention relates to the field of intelligent control and Internet of Things (IoT) application technology, specifically to an adaptive control method for intelligent devices based on IoT and AI algorithms. Background Technology

[0002] Intelligent device adaptive control based on IoT and AI algorithms refers to a control method that, during device operation, achieves real-time acquisition and interconnection of multi-source sensing data through a data acquisition and control system and the IoT. Artificial intelligence algorithms are then used to analyze, learn, and predict the acquired data, dynamically adjusting the device's operating strategies and control parameters. This enables the device to self-optimize and make autonomous decisions based on environmental changes, task requirements, and performance feedback. This method establishes a data closed loop between the device, the data acquisition and control system, the cloud, and edge nodes, integrating perception, analysis, and execution. This not only improves the timeliness and accuracy of control response but also endows the device with environmental perception, state understanding, and behavior optimization capabilities, thereby achieving truly intelligent operation and adaptive control.

[0003] The existing technology has the following shortcomings: In existing technologies, during the adaptive control of smart devices based on IoT and AI algorithms, the sensing channels are often exposed to complex electromagnetic and signal interference environments. When a sudden signal surge occurs, the abnormal signal can easily be mistaken by the sensors as real-state data, causing the AI ​​to judge the system as being in an emergency, thus triggering a high-frequency response mechanism. At this time, the control system will continuously perform frequent parameter adjustments and command switching, forming a self-excited cycle, causing the device to repeatedly operate under high load in a short period of time. Because the AI ​​cannot distinguish the difference between the surge interference and the real signal in time, it is prone to over-response, abnormal energy consumption, and fatigue of actuators. In severe cases, it can lead to circuit oscillation, burnout of drive units, or failure of key components, thereby affecting the overall operational stability and safety of the device.

[0004] The information disclosed in the background section is only intended to enhance the understanding of the background of this disclosure, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0005] The purpose of this invention is to provide an adaptive control method for intelligent devices based on the Internet of Things and AI algorithms, so as to solve the problems in the background art mentioned above.

[0006] To achieve the above objectives, the present invention provides the following technical solution: an adaptive control method for intelligent devices based on the Internet of Things and AI algorithms, comprising the following steps: S1, establishes a time rhythm alignment mechanism and a safety threshold limitation mechanism in the adaptive control phase of intelligent devices, performs fluctuation characteristic analysis on electrical signals collected through the Internet of Things, and generates a surge emergency plan for interference protection; S2, based on the surge emergency plan, sets up a surge capture area and a zero-point protection window in the signal sensing stage, monitors the electrical signal waveform in real time, filters abnormal waveforms and extracts reliable signal segments, and generates a surge fingerprint record for subsequent control stage calls. S3, combined with fluctuation fingerprint recording, performs response limiting operation in the control stage, sets the power buffer interval and action step amplitude according to the surge emergency plan, and generates a stable control rhythm plan to constrain the instantaneous changes in control output; S4. Based on the stable control rhythm scheme, perform time backtracking analysis on historical control commands, adjust the order of control command issuance, set delayed release strategy and feedback suppression strategy, and generate command blocking records to prevent repeated command triggering. S5, combined with instruction blocking records, re-plans the signal acquisition rhythm and execution rhythm during real-time control, and dynamically updates the power buffer space and action step amplitude according to the surge emergency plan and stable control rhythm plan, forming an adaptive control process with anti-interference capability.

[0007] Preferably, step S1 includes: By dynamically planning the time rhythm in the control process, a time rhythm alignment mechanism is established to enable the IoT acquisition end and execution end to operate under the same time reference, and to synchronously map the electrical signal time slices according to the device operation cycle and feedback cycle. After aligning the timing, the amplitude range of the electrical signal in the signal acquisition channel is limited, and a safety threshold limiting mechanism is established. The signal is divided into normal fluctuation range, overshoot critical range and surge interference range by the range limitation. After screening by the safety threshold limitation mechanism, the fluctuation characteristics of the electrical signals collected by the Internet of Things are analyzed to extract the peaks, troughs, rise time and fall time to form a set of fluctuation characteristics. After completing the fluctuation characteristic analysis, a surge emergency plan is generated based on the analysis results, and the protection delay sequence and signal amplitude buffer range are set to keep the emergency plan in coordination with the equipment operation cycle.

[0008] Preferably, the time rhythm alignment mechanism achieves synchronization by continuously segmenting the electrical signal input time series and dynamically mapping it according to the equipment operation cycle at the start and end points of the time slice; the safety threshold limitation mechanism activates the signal temporary storage delay strategy when the signal exceeds the safety threshold, delays the abnormal signal and marks it as a surge signal to be processed, so as to ensure the temporal continuity and amplitude stability of the signal during the fluctuation characteristic analysis stage.

[0009] Preferably, step S2 includes: Based on the risk range and safety threshold parameters defined in the surge emergency plan, a fluctuation capture area is established in the signal acquisition channel. The fluctuation monitoring segment is divided through a time rhythm alignment mechanism, and the fluctuation of the electrical signal is captured in real time. After forming a fluctuation baseline in the fluctuation capture area, a zero-point protection window is established between the amplitude buffers in the surge emergency plan. A static delay time is set for the zero-point area of ​​the electrical signal and the width of the protection window is dynamically adjusted. Under the action of the fluctuation capture area and the zero-point protection window, the electrical signal waveform is monitored in real time, and the changes of the peak, trough and zero point are continuously recorded to form a complete fluctuation trajectory. After real-time monitoring is completed, abnormal waveforms are filtered from the electrical signal and reliable signal segments are extracted. The signal segments are then arranged in time sequence to generate fluctuation fingerprint records for use in the control phase.

[0010] Preferably, during the generation of the fluctuation fingerprint record, the amplitude of the fluctuation trajectory obtained by real-time monitoring is filtered according to the safety threshold in the surge emergency plan. Abnormal fluctuations exceeding the buffer are removed, and the remaining signal segments are reconstructed in time series so that the fluctuation fingerprint record remains continuous and consistent in the time dimension and amplitude dimension, so as to ensure the stability and repeatability of the signal reference when called in the control phase.

[0011] Preferably, step S3 includes: Based on the time series and amplitude change patterns in the fluctuation fingerprint record, the initial response range of the control output is limited, and a response limiting operation is performed to maintain output continuity and security. After the response limiting operation is completed, the power buffer is set according to the surge emergency plan, and the allowable range of power change is determined by statistically analyzing the average output power of the control signal and synchronizing with the set of fluctuation characteristics. After setting the power buffer zone, the action step amplitude is set according to the surge emergency plan, so that the control response changes gradually under surge interference and forms a dynamic balance with the power buffer zone. Based on the combined effects of response limiting operation, power buffer interval and action step amplitude, a stable control rhythm scheme is generated by combining fluctuation fingerprint recording and surge emergency scheme to constrain instantaneous changes in control output.

[0012] Preferably, the generation of the stable control rhythm scheme uses the time rhythm alignment mechanism as the time reference, integrates the amplitude range determined by the response limit, the energy boundary determined by the power buffer, and the change rhythm determined by the action step amplitude, and calls the historical fluctuation characteristics in the fluctuation fingerprint record when a sudden interference occurs to dynamically correct the time step and amplitude range of the control output, so that the control process maintains time synchronization and amplitude balance.

[0013] Preferably, step S4 includes: Based on the time step and power buffer of the stable control rhythm scheme, historical control commands are analyzed by time backtracking, and the mapping relationship of control commands on the time axis is established by time sequence reconstruction. After the time backtracking analysis is completed, the control rhythm and fluctuation balance relationship defined by the stable control rhythm scheme are adjusted to ensure that the execution order of control signals is consistent with the equipment operation rhythm. After adjusting the control command issuance sequence, a delayed release strategy is set according to the stable control rhythm scheme, so that the control command enters the execution state after the delay time is reached, thus forming time coordination. After the delayed release strategy is executed, a feedback suppression strategy is set according to the stable control rhythm scheme. A command blocking record is generated by time comparison to prevent the command from being triggered repeatedly.

[0014] Preferably, the feedback suppression strategy blocks repetitive feedback by setting a time masking interval in the feedback signal channel, and generates an instruction blocking record by combining the blocked feedback signal with the corresponding control command. The instruction blocking record includes the command number, trigger time, delay duration, feedback time difference, and suppression interval information, which is used for interference pattern recognition and instruction flow path optimization in the subsequent control stage.

[0015] Preferably, step S5 includes: By combining the time-series information and feedback suppression results in the command blocking record, the signal acquisition rhythm in the real-time control process is re-planned so that the signal acquisition avoids the interference range and maintains coordination with the control rhythm. After the signal acquisition rhythm is replanned, the execution rhythm is synchronously reconstructed based on the instruction blocking record, so that the execution action corresponds to the time distribution of the acquisition process and is locked during the instruction blocking period; After the signal acquisition rhythm and execution rhythm are synchronously reconstructed, the power buffer is dynamically updated according to the surge emergency plan and the stable control rhythm plan to keep the energy output state balanced. Based on the updates within the power buffer zone, the step size of the action is dynamically adjusted according to the stable control rhythm scheme, so that the control action remains consistent and balanced in the time and amplitude dimensions.

[0016] The technical effects and advantages provided by the present invention in the above technical solution are as follows: This invention establishes a time-rhythm alignment mechanism and a safety threshold limitation mechanism during the adaptive control phase, and combines them with a surge emergency response plan to achieve full-process rhythm coordination in signal acquisition, control execution, and feedback. This ensures that signals undergo multiple layers of screening before entering the control loop, including fluctuation capture, zero-point protection, and amplitude constraints, thereby preventing the mis-acquisition and amplification of sudden interference signals. This approach maintains the temporal continuity and amplitude stability of the sensed data, effectively suppressing misjudgments caused by surge interference. It enables the equipment to maintain a stable operating rhythm and precise response control even in complex electromagnetic environments, improving the reliability and anti-interference capability of the control system.

[0017] This invention establishes a dynamic limiting and timing self-adjustment mechanism for control output by combining fluctuation fingerprint recording and command blocking recording, achieving real-time optimization of power buffer intervals and action step amplitude. The delayed release and feedback suppression strategies for control commands enable the execution process to have adaptive buffering and rhythm recovery capabilities under disturbance conditions, thereby avoiding self-excited loops or repeated triggering problems in the control system. This approach allows the equipment to maintain a balanced energy distribution and stable control output during operation, reducing fatigue of actuators and energy consumption fluctuations, and improving overall operational safety and continuous stability. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this invention. For those skilled in the art, other drawings can be obtained based on these drawings.

[0019] Figure 1 This is a flowchart of the adaptive control method for intelligent devices based on IoT and AI algorithms according to the present invention. Detailed Implementation

[0020] Exemplary embodiments will now be described more fully with reference to the accompanying drawings. However, these exemplary embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, they are provided so that the description of this disclosure will be more complete and fully convey the concept of the exemplary embodiments to those skilled in the art.

[0021] This invention provides, for example Figure 1 The adaptive control method for smart devices based on IoT and AI algorithms, as shown, includes the following steps: S1, establishes a time rhythm alignment mechanism and a safety threshold limitation mechanism in the adaptive control phase of intelligent devices, performs fluctuation characteristic analysis on electrical signals collected through the Internet of Things, and generates a surge emergency plan for interference protection; After the intelligent device enters the adaptive control phase, a time rhythm alignment mechanism is established by dynamically planning the time rhythm during the control process, ensuring that the IoT acquisition end and execution end operate under the same time reference. Specifically, by continuously segmenting the time series of electrical signal input from the acquisition end, the time slices acquired from different signal sources are synchronized with the time reference, forming a unified time coordinate system before the electrical signals enter the control channel. During this process, the start and end points of each time slice are dynamically mapped according to the device's operating cycle and feedback cycle, ensuring that the acquisition rhythm, signal transmission rhythm, and execution rhythm maintain a relatively consistent time interval relationship within the same control cycle. In this way, the acquired data stream possesses temporal continuity and rhythmic coordination when entering the adaptive analysis phase, providing a stable time basis for subsequent fluctuation characteristic analysis.

[0022] After completing the time rhythm alignment, the amplitude range of the electrical signal in the signal acquisition channel is multi-layered and a safety threshold limiting mechanism is established. By limiting the fluctuation range, rise rate, fall rate, and duration of the electrical signal, the signal is divided into a normal fluctuation range, an overshoot critical range, and a surge interference range. To prevent abnormal signals from directly entering the adaptive control loop, a dynamic safety threshold is set at the signal input. When the instantaneous amplitude of the electrical signal exceeds the safety threshold, a signal temporary storage delay strategy is activated, subjecting the signal to a brief time delay to maintain a static state and prevent erroneous transmission. During the delay, based on the time slice sequence of the time rhythm alignment mechanism, the amplitude difference analysis is performed on the change trend of the electrical signal within the preceding and following time slices. If the signal cannot recover to within the safety threshold within the delay time, it is marked as a surge signal to be processed. In this way, the safety threshold limiting mechanism can form a front-end protection barrier for the input signal, preventing abnormal surge signals from directly affecting AI control judgment.

[0023] After the safety threshold limiting mechanism completes signal screening, the electrical signals acquired through the Internet of Things (IoT) undergo fluctuation characteristic analysis. At this stage, based on the time series provided by the time rhythm alignment mechanism and the signal markers formed by the safety threshold limiting mechanism, continuous fluctuation characteristics of the electrical signal waveform are identified. By extracting the peaks, troughs, rise times, fall times, and waveform periodic characteristics of the signal in different time slices, a fluctuation characteristic set is formed. Each fluctuation characteristic set corresponds to a time rhythm window, used to characterize the stability and variation pattern of the signal within that time interval. To ensure that the fluctuation characteristic analysis is integrated with the equipment control process, the amplitude variation trend of the acquired signal is time-coupled with the equipment operating status parameters, so that the fluctuation characteristics not only reflect the instantaneous changes of the signal but also the correspondence between the signal and the equipment operating rhythm. In this way, a unified correlation between signal characteristics and equipment control rhythm is achieved in the fluctuation characteristic analysis stage, providing a dual basis of time and amplitude for subsequent surge identification and emergency response plan generation.

[0024] After completing the fluctuation characteristic analysis, a surge emergency plan for interference protection is generated based on the analysis results. The surge emergency plan is based on a time rhythm alignment mechanism, a signal safety threshold limitation mechanism, and the fluctuation characteristic analysis results, and plans emergency responses for time intervals where surge risks may exist. Specifically, firstly, surge risk intervals are determined based on the time slices marked as surge signals to be processed in the fluctuation characteristic set. Within the surge risk interval, a protection delay sequence is set, causing control commands to be temporarily suspended during this sequence to prevent accidental triggering of responses due to surge signals. Secondly, a signal amplitude buffer range is set within each surge risk interval to ensure stable output of the control system within this range, avoiding continuous fluctuations in control parameters caused by surge signals. During the generation of the emergency plan, the time base in the time rhythm alignment mechanism is also mapped to the equipment control rhythm, ensuring that the emergency plan is coordinated with the equipment operating cycle. Finally, the generated surge emergency plan serves as a pre-emptive protection reference for the control phase, providing a dynamic parameter basis for subsequent fluctuation capture, amplitude limiting control, and command scheduling.

[0025] S2, based on the surge emergency plan, sets up a surge capture area and a zero-point protection window in the signal sensing stage, monitors the electrical signal waveform in real time, filters abnormal waveforms and extracts reliable signal segments, and generates a surge fingerprint record for subsequent control stage calls. After the surge emergency response plan is generated, to form a stable anti-interference structure in the signal sensing stage, a fluctuation capture area is established in the signal acquisition channel based on the risk range and safety threshold parameters defined in the surge emergency response plan. The fluctuation capture area is set up using a time rhythm alignment mechanism as the time reference. By dividing a continuous time slice into several fluctuation monitoring segments, the electrical signal is allocated to the corresponding fluctuation monitoring segment for real-time fluctuation capture during transmission. Each fluctuation monitoring segment has a protection delay sequence corresponding to the surge emergency response plan. When the electrical signal enters the fluctuation capture area, the system continuously tracks the rising edge, falling edge, and duration of the signal based on the surge risk range distribution of the previous stage. This gives the fluctuation capture area not only time-domain coverage characteristics but also amplitude tracking characteristics. Within the fluctuation capture area, each fluctuation change of the electrical signal forms a set of time-amplitude correspondences, used for subsequent determination of whether it is in a surge interference state. In this way, the fluctuation capture area completes dynamic monitoring of potential surge waveforms in the early stages of signal entry into the sensing channel, providing a continuous fluctuation baseline for the subsequent setting of the zero-point protection window.

[0026] After completing basic fluctuation tracking in the fluctuation capture area, a zero-point protection window is established based on the amplitude buffer space defined in the surge emergency plan. The zero-point protection window uses the fluctuation baseline formed in the fluctuation capture area as a reference to protectively define the zero-point region of the electrical signal within each time frame window. Specifically, a time segment between the rising and falling segments of the electrical signal waveform that crosses the zero point is selected and defined as the signal zero-point protection zone. A static delay time is set within this protection zone to temporarily suppress the transitional fluctuations of the electrical signal at the zero point, preventing misjudgment of the signal zero point due to high-frequency noise or surge interference. Simultaneously, the width of the zero-point protection window is dynamically adjusted according to the safety threshold in the surge emergency plan. When a surge risk zone is detected, the zero-point protection window range is automatically expanded to extend the buffer time of the electrical signal near the zero point. In this way, the zero-point protection window provides dual protection in both the time and amplitude dimensions, ensuring the electrical signal remains stable and continuous when crossing the zero point, thereby preventing surge signals from being misidentified as normal waveforms within the zero-point region.

[0027] The electrical signal waveform is monitored in real time through the combined action of the fluctuation capture area and the zero-point protection window. The real-time monitoring process uses a unified time reference provided by a time rhythm alignment mechanism to continuously track the peaks, troughs, and zero-point changes of the electrical signal within the fluctuation capture area. Real-time monitoring not only focuses on the overall trend of the waveform but also records minute fluctuations within different time slices, forming a continuous fluctuation trajectory. To ensure consistency between the real-time monitoring results and the surge emergency response plan's protection strategy, each fluctuation process within the fluctuation capture area is filtered according to the amplitude range set by the safety threshold limitation mechanism, eliminating abnormal fluctuation points exceeding the buffer zone. Under the action of the zero-point protection window, real-time monitoring also weights the waveform changes near the zero point, ensuring a continuous amplitude transition in this area during recording. Through this real-time monitoring method, the fluctuation characteristics of the electrical signal are completely recorded in a continuous time series, thus enabling the signal sensing process to possess both temporal continuity and fluctuation integrity.

[0028] After real-time monitoring is completed, the electrical signal, after being filtered through the fluctuation capture area and zero-point protection window, undergoes abnormal waveform filtering, and reliable signal segments are extracted to generate a fluctuation fingerprint record. Abnormal waveform filtering is based on the fluctuation trajectory obtained from real-time monitoring, separating time segments exhibiting surge characteristics from the continuous signal sequence, and extracting stable signal segments within the safety threshold range of the surge emergency response plan from the remaining signal sequence. Each reliable signal segment possesses complete peak, trough, and zero-point variation characteristics, which are continuous in time and controlled in amplitude. By arranging these reliable signal segments in time sequence, a fluctuation fingerprint record is formed. The fluctuation fingerprint record, with time rhythm as the horizontal axis and amplitude variation as the vertical axis, constitutes a fingerprint structure characterizing the stability of the electrical signal. This fingerprint structure not only preserves the dynamic fluctuation characteristics of the electrical signal in the sensing stage but also records the stable operating characteristics under the protection of the surge emergency response plan. The fluctuation fingerprint record is stored for subsequent control phase recall. When the control loop performs response limiting operation, the real-time signal can be dynamically referenced based on the time series and amplitude change pattern in the fluctuation fingerprint record, thereby maintaining the stability of the signal rhythm and the controllability of the amplitude during control execution.

[0029] S3, combined with fluctuation fingerprint recording, performs response limiting operation in the control stage, sets the power buffer interval and action step amplitude according to the surge emergency plan, and generates a stable control rhythm plan to constrain the instantaneous changes in control output; After generating the fluctuation fingerprint record, to achieve adaptive response to surge interference and dynamic constraints on control output in the control loop, the initial response range of the control output is limited based on the time series and amplitude variation patterns contained in the fluctuation fingerprint record, and a response limiting operation is performed. This operation continuously maps the amplitude variation trend of each time slice in the fluctuation fingerprint record, transforming the amplitude fluctuation range of the electrical signal in different time intervals into the output amplitude limit range of the control command. The response limiting is established based on the safety threshold in the surge emergency plan, and amplitude constraints are applied to the correspondence between the input signal and the control output to ensure that the amplitude variation of the control signal within the same time slice does not exceed the maximum safe variation in the fluctuation fingerprint record. To ensure the stability of the control output, a transition smoothing zone is also set for each time rhythm window during the response limiting process, so that the control output maintains a linear transition relationship within a continuous time period, thereby avoiding instantaneous jumps in the control output caused by surge interference. In this way, the response limiting operation can combine the protection parameters in the surge emergency plan with the characteristic data in the fluctuation fingerprint record, enabling the control process to maintain the continuity and safety of the output under surge interference.

[0030] After establishing basic output constraints in response to amplitude limiting, power buffers are set according to the surge emergency plan to further balance fluctuations in control output during energy transfer. The power buffers are set with reference to the output amplitude range after amplitude limiting. The allowable range of power variation is determined by statistically analyzing the average output power of the control signal within a continuous time window. Within the power buffers, instantaneous power changes in the control signal are limited to the energy buffer range specified in the surge emergency plan, thus preventing rapid increases or decreases in energy output caused by surge signals. The power buffers not only limit amplitude fluctuations in the control output but also balance the energy consumption of the equipment, enabling it to maintain a stable power output state in the presence of surge interference. To ensure consistency between the power buffers and the fluctuation fingerprint record, the time window in the fluctuation fingerprint record is synchronized with the time distribution of the power buffers during the setting process, so that each power buffer corresponds to a set of fluctuation characteristics. Thus, when a surge interference occurs, the control system can adaptively adjust through the power buffers within the time interval corresponding to the fluctuation characteristics, achieving smooth control of output energy.

[0031] After setting the power buffer intervals, to further refine the smoothness of the control rhythm, the action step size is set according to the surge emergency plan, ensuring a controllable rhythm of change in the control output during execution. The action step size refers to the smallest step unit of control signal change within a continuous control time slice. By limiting the output change step size within each time slice, the control response maintains a gradual change under surge interference. The action step size setting is based on the energy change range within the power buffer intervals and incorporates the amplitude change characteristics in the surge fingerprint record, ensuring that the stepping rhythm of the control output remains consistent with the period of the signal fluctuation characteristics. To avoid instantaneous deviations in control commands caused by surge interference, the risk time interval in the surge emergency plan is incorporated into the adjustment criteria during the action step size setting process. When a surge risk interval is detected, the action step size is automatically reduced, ensuring that the control output changes slowly during the interference period. The action step size and the power buffer interval work together to maintain a dynamic equilibrium in the control response throughout the adaptive control phase, thereby suppressing surge interference while maintaining equipment operational stability.

[0032] Based on the combined effects of response limiting, power buffer, and action step amplitude, a stable control rhythm scheme is generated by integrating fluctuation fingerprint recording and surge emergency response measures. The stable control rhythm scheme is generated using a time rhythm alignment mechanism as the time reference, unifying and integrating the amplitude range determined by response limiting, the energy boundary determined by the power buffer, and the change rhythm determined by the action step amplitude. Specifically, within each time rhythm window, the stable control rhythm scheme defines the time step, amplitude variation range, and power adjustment cycle of the control output, enabling the control output to form a periodic dynamic equilibrium structure over continuous time. When external surge interference occurs, the stable control rhythm scheme dynamically corrects the output rhythm by calling upon historical fluctuation characteristics from the fluctuation fingerprint recording, ensuring that the control process maintains time synchronization and amplitude balance even in the presence of surge interference. The stable control rhythm scheme not only constrains the instantaneous changes in the control output but also enables the control signal to have adaptive adjustment capabilities in both time and amplitude dimensions, thereby allowing the equipment to continuously maintain a stable control rhythm and power output state in complex interference environments.

[0033] S4. Based on the stable control rhythm scheme, perform time backtracking analysis on historical control commands, adjust the order of control command issuance, set delayed release strategy and feedback suppression strategy, and generate command blocking records to prevent repeated command triggering. After the stable control rhythm scheme is generated, to further ensure the continuity and safety of the control process, a time backtracking analysis is performed on historical control commands based on the time steps and power buffers set in the stable control rhythm scheme. The time backtracking analysis uses the time series of the stable control rhythm scheme as the main axis, reconstructing the timing of past control commands' issuance, execution duration, and response delays to establish a correspondence between each control command and the equipment's operating rhythm in the time dimension. During this process, historical commands are segmented according to time rhythm windows, with each segment containing a complete control input cycle and feedback response cycle. This segmentation method clearly identifies situations such as overlapping control commands, execution delays, or response conflicts during continuous operation. The time backtracking analysis also compares the energy consumption trends during the execution of each historical command based on the energy change characteristics between power buffers. When overlapping power peaks are detected within consecutive time periods, they are marked as potential command interference intervals for subsequent command adjustment and suppression strategy invocation. Through time backtracking analysis, the system establishes a complete mapping relationship of historical control commands on the time axis, providing accurate timing references for adjusting the command issuance order.

[0034] After time backtracking analysis is completed, the order of control commands is adjusted according to the control rhythm and fluctuation balance relationship defined in the stable control rhythm scheme. The adjustment process is based on the command time mapping formed by the time backtracking analysis. By reordering the order of command issuance within the same rhythm cycle, the execution order of control signals is made consistent with the stable control rhythm scheme. Specifically, within each control rhythm cycle, commands in the energy release phase within the power buffer are issued first, while commands in the energy absorption phase are delayed, thus achieving a balance between energy output and input in the control process. During the adjustment process, historical waveform characteristics in the fluctuation fingerprint record are referenced to ensure that the reordered control commands are consistent with the time characteristics of signal fluctuations, avoiding misalignment between the command issuance order and the device's response cycle. When multiple historical commands overlap within the same time slice, the adjustment mechanism offsets the issuance time according to the rhythm step amplitude set by the stable control rhythm scheme, staggering each command in time to prevent high-frequency triggering within the same time slice. In this way, the order of control command issuance is unified with the device's operating rhythm, thereby reducing command overlap and response conflicts caused by surge interference.

[0035] After adjusting the control command issuance sequence, a delayed release strategy is set based on the stable control rhythm scheme to dynamically control the timing of command execution. The delayed release strategy introduces a time buffer, preventing control commands from being triggered immediately upon reaching the execution stage, but instead allowing them to enter the execution state only after a delay period. The delay time is set based on the power buffer interval and the action step amplitude. By extending the delay time within the surge interference risk range and shortening it within the stable range, adaptive adjustment of the control rhythm is achieved. During the delayed release process, the control command remains in a pending execution state within the delay time, while continuously receiving signal feedback information from the sensing stage. When the electrical signal fluctuation in the sensing stage is stable, the delayed release ends, and the command is officially issued for execution; when the electrical signal fluctuation is still within the surge interference risk range, the delay time is automatically extended to prevent unstable signals from triggering erroneous responses. Through the delayed release strategy, the control process gains a buffer phase, enabling time coordination between command execution and sensing feedback, thereby further eliminating the problem of premature command triggering or repeated execution caused by surge interference. The delayed release strategy not only ensures the smoothness of the control output, but also enables the control rhythm to maintain a predictable timing structure in complex signal fluctuation environments.

[0036] After the delayed release strategy is executed, a feedback suppression strategy is set according to the stable control rhythm scheme, and a command blocking record is generated. The feedback suppression strategy, based on the control execution result after the delayed release, identifies repetitive responses or cyclic triggering behaviors in the feedback signal by comparing the feedback signal and the sensing signal during the execution phase in time. When the feedback signal highly overlaps with the feature sequence in the historical fluctuation fingerprint record, it indicates that the feedback may be caused by the residual effect of previous commands, and the feedback suppression strategy is immediately triggered. The feedback suppression strategy sets a time masking interval in the feedback signal channel, blocking repetitive feedback within this interval, thereby preventing the control loop from generating the same control command again due to repetitive feedback. Simultaneously, the suppressed feedback signal and its associated control command are recorded as a command blocking record. The command blocking record includes the command number, trigger time, delay duration, feedback time difference, and suppression interval information for reference in subsequent control phases. The generation of the command blocking record enables the system to identify repetitive command patterns in subsequent operation and automatically optimize the command flow path under sudden interference environments. In this way, the feedback suppression strategy not only prevents the cyclic triggering of control commands, but also provides the system with a time memory mechanism for historical interference events, enabling the control process to automatically avoid identified interference patterns based on the blocking records in the subsequent adaptive phase.

[0037] S5, combined with the instruction blocking record, re-plans the signal acquisition rhythm and execution rhythm in the real-time control process, and dynamically updates the power buffer space and action step amplitude according to the surge emergency plan and stable control rhythm plan, forming an adaptive control process with anti-interference capability. After generating the command blocking record, to ensure the control process maintains stable operation and self-optimization capabilities under sudden interference, the signal acquisition rhythm in the real-time control process is replanned by combining the time series information and feedback suppression results from the command blocking record. The replanning of the signal acquisition rhythm uses the stable control rhythm scheme as the time reference, and the interference time slots identified in the command blocking record as adjustment nodes for the acquisition rhythm. At these nodes, the acquisition time interval of the sensing element is extended or shortened, allowing the signal acquisition process to avoid the identified interference intervals. When the command blocking record shows repeated feedback within a certain time slot, the acquisition rhythm is automatically extended to increase the signal acquisition interval, ensuring that subsequent acquired signals are time-staggered with the feedback channel, preventing data overlap. When the command blocking record shows a response delay within a certain time slot, the acquisition rhythm is shortened, realigning the new acquisition cycle with the control feedback cycle. In this way, the signal acquisition rhythm gains dynamic adjustment capability during real-time control, ensuring coordination between the temporal distribution of sensed data and the control rhythm, providing a time basis for subsequent execution rhythm adjustments.

[0038] After the signal acquisition rhythm is replanned, the execution rhythm is synchronously reconstructed based on the descriptions of feedback delay and repeated triggering in the command blocking record. The reconstructed execution rhythm uses the replanned signal acquisition rhythm as input, establishing a dynamic mapping relationship between the risk range of the surge emergency plan and the power buffer of the stable control rhythm plan, ensuring a one-to-one correspondence between the execution actions and the time distribution of the acquisition process. Specifically, in time slices with high surge interference risk, the execution rhythm extends the action duration and shortens the action switching interval, creating a buffered response process at the execution level, thereby mitigating the instantaneous impact of surge signals on the control output. In stable operation time slices, the execution rhythm shortens the action duration and accelerates the step response, maintaining an efficient operating rhythm and improving the overall response speed. The reconstructed execution rhythm also incorporates the command blocking periods in the command blocking record. When a time window containing a command blocking record is detected, the execution rhythm is locked within that time window, preventing new control actions from entering the execution phase and completely avoiding repeated command triggering. In this way, the signal acquisition rhythm and execution rhythm achieve bidirectional linkage, ensuring that the real-time control process remains synchronized in time and coordinated in behavior.

[0039] After the signal acquisition rhythm and execution rhythm are synchronously reconstructed, the power buffer is dynamically updated according to the surge emergency plan and the stable control rhythm plan. The dynamic update of the power buffer is based on the real-time acquired signal fluctuation characteristics and the power change trend of the execution rhythm, combined with the energy output timing information stored in the command blocking record, to adjust the upper and lower boundaries of the power buffer in real time. When the real-time acquired signal shows the system is in the energy transition phase, the power buffer automatically expands to provide a larger energy adjustment space; when the real-time acquired signal shows the system is in the stable output phase, the power buffer automatically contracts, making the power change range closer to the control target value. The update of the power buffer not only relies on the safety threshold parameters provided by the surge emergency plan but also refers to the power fluctuation period defined in the stable control rhythm plan, thus forming a flexible range that dynamically changes over time at the energy level. Through this update mechanism, the control system can dynamically adjust the power output according to the intensity and frequency of surge interference during real-time control, enabling the equipment to maintain a stable energy distribution state under different interference intensities.

[0040] Based on updates within the power buffer zone, and according to the action stepping logic in the stable control rhythm scheme, the action step amplitude is dynamically adjusted to maintain rhythmic consistency and amplitude balance during real-time control. The dynamic adjustment of the action step amplitude is predicated on real-time changes within the power buffer zone. When the power buffer zone expands, the action step amplitude increases accordingly to enhance the output capability of the control action; when the power buffer zone contracts, the action step amplitude decreases accordingly to slow down the output rate of the control action, thereby preventing over-response caused by surge interference. The adjustment process of the action step amplitude also references the command trigger delay information in the command blocking record, causing the control action to automatically reduce the step amplitude during delayed feedback to avoid oscillations caused by the superposition of continuous control signals. When the feedback suppression phase ends and the sensed signal stabilizes, the action step amplitude gradually returns to normal levels, achieving a gradual transition in control output. Ultimately, under the dual regulation of the power buffer zone and the action step amplitude, the system forms a real-time updated control rhythm, ensuring dynamic consistency between signal acquisition, control execution, and energy output in both time and amplitude dimensions.

[0041] This invention establishes a time-rhythm alignment mechanism and a safety threshold limitation mechanism during the adaptive control phase, and combines them with a surge emergency response plan to achieve full-process rhythm coordination in signal acquisition, control execution, and feedback. This ensures that signals undergo multiple layers of screening before entering the control loop, including fluctuation capture, zero-point protection, and amplitude constraints, thereby preventing the mis-acquisition and amplification of sudden interference signals. This approach maintains the temporal continuity and amplitude stability of the sensed data, effectively suppressing misjudgments caused by surge interference. It enables the equipment to maintain a stable operating rhythm and precise response control even in complex electromagnetic environments, improving the reliability and anti-interference capability of the control system.

[0042] This invention establishes a dynamic limiting and timing self-adjustment mechanism for control output by combining fluctuation fingerprint recording and command blocking recording, achieving real-time optimization of power buffer intervals and action step amplitude. The delayed release and feedback suppression strategies for control commands enable the execution process to have adaptive buffering and rhythm recovery capabilities under disturbance conditions, thereby avoiding self-excited loops or repeated triggering problems in the control system. This approach allows the equipment to maintain a balanced energy distribution and stable control output during operation, reducing fatigue of actuators and energy consumption fluctuations, and improving overall operational safety and continuous stability.

[0043] The foregoing has only described certain exemplary embodiments of the present invention by way of illustration. Undoubtedly, those skilled in the art can modify the described embodiments in various ways without departing from the spirit and scope of the present invention. Therefore, the foregoing drawings and descriptions are illustrative in nature and should not be construed as limiting the scope of protection of the claims of the present invention.

Claims

1. An adaptive control method for intelligent devices based on IoT and AI algorithms, characterized in that, Includes the following steps: S1, establishes a time rhythm alignment mechanism and a safety threshold limitation mechanism in the adaptive control phase of intelligent devices, performs fluctuation characteristic analysis on electrical signals collected through the Internet of Things, and generates a surge emergency plan; S2, based on the surge emergency plan, sets up a surge capture area and a zero-point protection window in the signal sensing stage, monitors the electrical signal waveform in real time, filters abnormal waveforms and extracts reliable signal segments, and generates a surge fingerprint record. S3 combines fluctuation fingerprint recording to perform response limiting operations in the control stage, and generates a stable control rhythm scheme by setting the power buffer interval and action step amplitude according to the surge emergency plan. S4. Based on the stable control rhythm scheme, perform time backtracking analysis on historical control commands, adjust the order of control command issuance, set delayed release strategy and feedback suppression strategy, and generate command blocking records. S5, combined with instruction blocking records, re-plans the signal acquisition rhythm and execution rhythm during real-time control, and dynamically updates the power buffer space and action step amplitude according to the surge emergency plan and stable control rhythm plan.

2. The adaptive control method for intelligent devices based on IoT and AI algorithms according to claim 1, characterized in that, Step S1 includes: By dynamically planning the time rhythm in the control process, a time rhythm alignment mechanism is established to enable the IoT acquisition end and execution end to operate under the same time reference, and to synchronously map the electrical signal time slices according to the device operation cycle and feedback cycle. After aligning the timing, the amplitude range of the electrical signal in the signal acquisition channel is limited, and a safety threshold limiting mechanism is established. The signal is divided into normal fluctuation range, overshoot critical range and surge interference range by the range limitation. After screening by the safety threshold limitation mechanism, the fluctuation characteristics of the electrical signals collected by the Internet of Things are analyzed to extract the peaks, troughs, rise time and fall time to form a set of fluctuation characteristics. After completing the fluctuation characteristic analysis, a surge emergency plan is generated based on the analysis results, and the protection delay sequence and signal amplitude buffer range are set to keep the emergency plan in coordination with the equipment operation cycle.

3. The adaptive control method for intelligent devices based on IoT and AI algorithms according to claim 2, characterized in that, The time rhythm alignment mechanism achieves synchronization by continuously segmenting the input electrical signal time series and dynamically mapping it at the start and end points of the time slice according to the equipment operating cycle; the safety threshold limitation mechanism activates the signal temporary storage delay strategy when the signal exceeds the safety threshold, delays the abnormal signal, and marks it as a surge signal to be processed.

4. The adaptive control method for intelligent devices based on IoT and AI algorithms according to claim 2, characterized in that, Step S2 includes: Based on the risk range and safety threshold parameters defined in the surge emergency plan, a fluctuation capture area is established in the signal acquisition channel. The fluctuation monitoring segment is divided through a time rhythm alignment mechanism, and the fluctuation of the electrical signal is captured in real time. After forming a fluctuation baseline in the fluctuation capture area, a zero-point protection window is established between the amplitude buffers in the surge emergency plan. A static delay time is set for the zero-point area of ​​the electrical signal and the width of the protection window is dynamically adjusted. Under the action of the fluctuation capture area and the zero-point protection window, the electrical signal waveform is monitored in real time, and the changes of the peak, trough and zero point are continuously recorded to form a complete fluctuation trajectory. After real-time monitoring is completed, abnormal waveforms are filtered from the electrical signal and reliable signal segments are extracted. The signal segments are then arranged in time sequence to generate fluctuation fingerprint records for use in the control phase.

5. The adaptive control method for intelligent devices based on IoT and AI algorithms according to claim 4, characterized in that, During the generation of fluctuation fingerprint records, the amplitude of fluctuation trajectories obtained from real-time monitoring is filtered according to the safety threshold in the surge emergency plan. Abnormal fluctuations exceeding the buffer are removed, and the remaining signal segments are reconstructed in time series to ensure that the fluctuation fingerprint records remain continuous and consistent in both time and amplitude dimensions.

6. The adaptive control method for intelligent devices based on IoT and AI algorithms according to claim 4, characterized in that, Step S3 includes: Based on the time series and amplitude change patterns in the fluctuation fingerprint record, the initial response range of the control output is limited, and a response limiting operation is performed to maintain output continuity and security. After the response limiting operation is completed, the power buffer is set according to the surge emergency plan, and the allowable range of power change is determined by statistically analyzing the average output power of the control signal and synchronizing with the set of fluctuation characteristics. After setting the power buffer zone, the action step amplitude is set according to the surge emergency plan, so that the control response changes gradually under surge interference and forms a dynamic balance with the power buffer zone. Based on the combined effects of response limiting operation, power buffer interval and action step amplitude, a stable control rhythm scheme is generated by combining fluctuation fingerprint recording and surge emergency scheme to constrain instantaneous changes in control output.

7. The adaptive control method for intelligent devices based on IoT and AI algorithms according to claim 6, characterized in that, The generation of the stable control rhythm scheme uses the time rhythm alignment mechanism as the time reference. It integrates the amplitude range determined by the response limit, the energy boundary determined by the power buffer, and the change rhythm determined by the action step amplitude. When a sudden disturbance occurs, it calls the historical fluctuation characteristics in the fluctuation fingerprint record to dynamically correct the time step and amplitude range of the control output, so that the control process maintains time synchronization and amplitude balance.

8. The adaptive control method for intelligent devices based on IoT and AI algorithms according to claim 6, characterized in that, Step S4 includes: Based on the time step and power buffer of the stable control rhythm scheme, historical control commands are analyzed by time backtracking, and the mapping relationship of control commands on the time axis is established by time sequence reconstruction. After the time backtracking analysis is completed, the control rhythm and fluctuation balance relationship defined by the stable control rhythm scheme are adjusted to ensure that the execution order of control signals is consistent with the equipment operation rhythm. After adjusting the control command issuance sequence, a delayed release strategy is set according to the stable control rhythm scheme, so that the control command enters the execution state after the delay time is reached, thus forming time coordination. After the delayed release strategy is executed, a feedback suppression strategy is set according to the stable control rhythm scheme. A command blocking record is generated by time comparison to prevent the command from being triggered repeatedly.

9. The adaptive control method for intelligent devices based on IoT and AI algorithms according to claim 8, characterized in that, The feedback suppression strategy blocks repetitive feedback by setting a time masking interval in the feedback signal channel, and generates an instruction blocking record by combining the blocked feedback signal with the corresponding control command. The instruction blocking record includes the command number, trigger time, delay duration, feedback time difference, and suppression interval information.

10. The adaptive control method for intelligent devices based on IoT and AI algorithms according to claim 8, characterized in that, Step S5 includes: By combining the time-series information and feedback suppression results in the command blocking record, the signal acquisition rhythm in the real-time control process is re-planned so that the signal acquisition avoids the interference range and maintains coordination with the control rhythm. After the signal acquisition rhythm is replanned, the execution rhythm is synchronously reconstructed based on the instruction blocking record, so that the execution action corresponds to the time distribution of the acquisition process and is locked during the instruction blocking period; After the signal acquisition rhythm and execution rhythm are synchronously reconstructed, the power buffer is dynamically updated according to the surge emergency plan and the stable control rhythm plan to keep the energy output state balanced. Based on the updates within the power buffer zone, the step size of the action is dynamically adjusted according to the stable control rhythm scheme, so that the control action remains consistent and balanced in the time and amplitude dimensions.

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