Projector remote control system based on internet of things

Abstract

The application discloses a projector remote control system based on Internet of Things, and relates to the technical field of Internet of Things remote control, and comprises a time-frequency audit baseline module, a coupling feature analysis module, a cause-effect tracking calibration module, a multi-time scale registration module and a regulation and control choreography module; the time-frequency audit baseline module establishes a cross-device time-frequency audit baseline, collects the video sampling rate, frame phase sequence and power waveform change of multiple projectors under a unified time reference, generates a global coupling tensor and constructs a drift atlas, and is used for forming a system-level synchronous reference benchmark; through cross-device time-frequency audit, coupling feature analysis, cause-effect tracking and multi-time scale registration, the application dynamically regulates and controls frame synchronization and energy release, and realizes the collaborative optimization of multiple projectors in terms of picture consistency, electromagnetic interference suppression and remote communication stability.

Description

Technical Field

[0001] This invention relates to the field of Internet of Things (IoT) remote control technology, and more specifically to an IoT-based remote control system for projectors. Background Technology

[0002] The IoT-based remote control system for projectors refers to a comprehensive intelligent system that utilizes IoT communication technology to remotely monitor, control, and manage projector equipment. This system integrates communication modules, sensing units, and control execution units at the projector end, enabling the projector to connect to the network and upload operating status, environmental parameters, and usage information in real time. A device management and data processing center is established on a cloud platform to store, analyze, and distribute operational data from each projector. Users can remotely perform operations such as power on / off, signal source switching, brightness adjustment, and fault diagnosis via mobile terminals, computers, or the control center. This system overcomes the limitations of traditional projectors requiring manual on-site operation, achieving centralized management and intelligent operation and maintenance across regions and devices. It is widely applicable to various scenarios such as education, conferences, and exhibitions, effectively improving equipment utilization efficiency and the level of intelligent operation and maintenance management.

[0003] The existing technology has the following shortcomings:

[0004] In existing technologies, when multiple projectors perform dynamic image synthesis, they typically rely on cloud-based scheduling signals for synchronized control. However, due to slight differences in video signal sampling rates, clock references, and data transmission delays among the various terminal devices, subsynchronous states can easily arise during multi-node collaboration. This subsynchronous state causes periodic frequency shifts between different projectors, leading to resonance superposition during video signal sampling. This results in misaligned image refresh rates, uneven brightness variations, and significantly increased flicker frequency. More seriously, during this process, the internal drive circuits and power modulation units of each projector are in a high-frequency switching state, causing local electromagnetic field energy coupling and resonance, which in turn generates high-frequency electromagnetic interference signals. These interference signals can propagate through power lines, communication links, or wireless signal channels, affecting the communication stability of other control terminals, causing delays, false triggers, or interruptions in remote control commands, and in severe cases, potentially leading to system-wide communication paralysis.

[0005] 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

[0006] The purpose of this invention is to provide an Internet of Things-based remote control system for projectors to solve the problems mentioned in the background art.

[0007] To achieve the above objectives, the present invention provides the following technical solution: a remote control system for a projector based on the Internet of Things, comprising a time-frequency audit baseline module, a coupling feature analysis module, a causal tracking calibration module, a multi-timescale registration module, and a control and choreography module:

[0008] The time-frequency audit baseline module establishes a cross-device time-frequency audit baseline. Under a unified time reference, it collects video sampling rate, frame phase sequence, and power waveform changes from multiple projectors, generates a global coupling tensor, and constructs a drift spectrum to form a system-level synchronization reference.

[0009] The coupling feature analysis module analyzes the phase shift trajectory in the global coupling tensor based on the drift spectrum, extracts the subsynchronous resonant core and beat frequency corridor, calibrates potential interference channels based on energy accumulation distribution, generates a list of dangerous windows, and provides input parameters for dynamic tracking.

[0010] The causal tracking calibration module establishes a causal tracking chain based on the danger window list, records the entire process from cloud scheduling to the response of each projector terminal, calculates the arrival misalignment quantization value and interference source influence factor, and generates a cross-device calibration priority map to provide a basis for subsequent time-frequency registration.

[0011] The multi-time registration module constructs an adaptive multi-time registration stack based on the cross-device correction priority map, performs phase remapping on frame lock edges, performs micro-shifting on the video sampling threshold, and dynamically cancels power ripple to generate a stable candidate set and provide real-time feedback signals.

[0012] The choreography control module executes discrete time slot drift choreography based on a stable candidate set. Within the danger window, it sequentially triggers micro-periodic misalignment formation, alternating silent frames, energy ladder release, and carrier gating rotation. It uses feedback closed-loop dynamic sequence rearrangement to achieve video synchronization stabilization and communication interference isolation.

[0013] Preferably, the drift map construction process is as follows:

[0014] Establish a cross-device time-frequency audit baseline, and collect video sampling rate, frame phase sequence and power waveform changes from multiple projectors under a unified time reference;

[0015] Construct a high-dimensional data structure with a unified time series axis, device node axis, and signal type axis, and perform linked encoding and normalization on the three types of signals collected;

[0016] Based on the constructed 3D data structure, the frame edge displacement trajectory is extracted and a dynamic drift map is generated to map the phase change trend of each projector in a continuous time period.

[0017] Based on the dynamic drift map, the synchronization dependency and drift risk distribution between devices are derived, a phase deviation evolution chain is established, and a calibration reference list is formed.

[0018] Preferably, the steps for generating the danger window list are as follows:

[0019] Based on the drift map analysis, the phase shift trajectory in the global coupled tensor is analyzed, frame phase change features are extracted, and a preliminary set of suspected trajectories is marked.

[0020] A cross-device trajectory overlap map is constructed based on a set of suspected trajectories, and the subsynchronous resonance core and beat frequency corridor are extracted.

[0021] By integrating the current pulse overlap region and the ripple signal concentration band in the power waveform, a time-frequency energy concentration map is constructed and the propagation path of the interference signal is derived, forming an interference channel mapping list;

[0022] Based on the interference channel mapping list, key interference time windows are identified, categorized and grouped to form a list of dangerous windows for subsequent dynamic tracking and control of inputs.

[0023] Preferably, the steps for generating the cross-device calibration priority map are as follows:

[0024] Based on the danger window list, the entire process from cloud-based scheduling instructions to terminal responses is traced back to construct a time sequence chain for multiple projectors;

[0025] Analyze the equipment's response offset within each danger window, generate an equipment response offset map, and extract a response stability score;

[0026] The response time difference between equipment pairs is extracted based on the equipment response offset spectrum, and the interference source influence factor matrix is ​​formed by combining the phase offset trend and the power disturbance factor.

[0027] Based on the response offset spectrum and the interference source influence factor matrix, a cross-device calibration priority map is constructed, and the calibration order for multi-time standard registration is output.

[0028] Preferably, when constructing a cross-device calibration priority map, dynamic calibration weights are assigned based on the degree of response misalignment, power disturbance amplitude, and instruction decoding stability of the devices in the high-energy coupling region, and the registration order is arranged from high to low weights.

[0029] Preferably, the stable candidate set generation steps are as follows:

[0030] The target device is selected based on the cross-device correction priority map, and phase remapping is performed on the frame lock edge to complete the edge alignment.

[0031] After completing edge alignment, a slight translation is performed on the video sampling threshold to optimize the sampling trigger timing;

[0032] Based on the completed sampling adjustment, dynamic cancellation is performed on the power output behavior to reduce spectral coupling interference;

[0033] After registration is completed, a stability candidate set is constructed and run tests are conducted to lock in the parameter combination;

[0034] The candidate set test results are fed back to the control center to update the calibration priority map and optimize the registration strategy for the next round.

[0035] Preferably, the phase remapping of the frame lock edge is calculated by referring to the phase difference between the historical frame offset data and the synchronization center position of the target frame, and the frame start point is adjusted to the zero-crossing point or stable level edge segment.

[0036] Preferably, discrete time slot drift is performed based on a stable candidate set, and micro-periodic misalignment formation, alternating silent frames, energy ladder mitigation, and carrier gating rotation are triggered sequentially within the danger window. The following steps are used to dynamically rearrange the sequence adjustment using a feedback closed loop:

[0037] Based on the stable candidate set, a micro-periodic misalignment plan is set within the danger window, and the frame start delay is executed sequentially;

[0038] Alternating silent frames are introduced on the basis of misalignment to reduce high-frequency resonance caused by continuous operation;

[0039] Performing energy tiered release operations mitigates power surges by increasing power in stages.

[0040] Implementing carrier selection rotation reduces channel conflict by adjusting the order of frequency band usage.

[0041] The execution results of all strategies are fed back to the control node to construct a feedback closed loop, dynamically rearrange the sequence, and update the control parameters for the next round.

[0042] The technical effects and advantages provided by the present invention in the above technical solution are as follows:

[0043] This invention establishes a cross-device time-frequency audit baseline, comprehensively collects and models the signal behavior characteristics of each device, and achieves coordinated reference of frame phase, video sampling, and power waveform. By analyzing coupling characteristics in the drift spectrum and identifying interference paths, it enables early warning and precise location of potential subsynchronous resonance and beat frequency interference problems. Based on this, it constructs a causal tracking chain and response offset model, realizing quantitative analysis of multi-device behavior chains and scientific prioritization of corrections. Furthermore, it completes three-dimensional coordinated correction of frame lock edges, sampling thresholds, and power ripple through a multi-timescale registration strategy, forming a stable set of parameter candidates. Finally, it dynamically controls interference behavior through time slot misalignment choreography, silent frame injection, energy mitigation, and carrier rotation, achieving frame refresh peak staggering, power peak decoupling, and communication channel collision avoidance, comprehensively improving the overall performance of multi-projector systems in terms of image synchronization consistency, electromagnetic interference control, and remote communication reliability. Attached Figure Description

[0044] 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.

[0045] Figure 1 This is a schematic diagram of the modules of the Internet of Things-based remote control system for projectors according to the present invention. Detailed Implementation

[0046] 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.

[0047] This invention provides, for example Figure 1 The IoT-based remote control system for the projector shown includes a time-frequency audit baseline module, a coupling feature analysis module, a causal tracking calibration module, a multi-timescale registration module, and a control and choreography module.

[0048] The time-frequency audit baseline module establishes a cross-device time-frequency audit baseline. Under a unified time reference, it collects video sampling rate, frame phase sequence, and power waveform changes from multiple projectors, generates a global coupling tensor, and constructs a drift spectrum to form a system-level synchronization reference.

[0049] To achieve video sampling synchronization and interference suppression across multiple projectors, a cross-device time-frequency audit baseline must first be established to provide a unified reference and dynamic calibration basis for subsequent image refresh consistency and timing coordination. The specific steps are as follows:

[0050] Several representative projector devices in the deployment environment were selected as audit targets and connected to a unified clock source. This clock source provides a unified time reference signal to each projector via a wired synchronization signal or a master-slave distribution method based on a network time protocol. After ensuring that all projectors receive a stable synchronization reference, a dedicated acquisition device was used to measure the video signal sampling rate of each projector in its current operating state. Simultaneously, the refresh rate and frame boundary timing of the video frames were periodically recorded, and the acquisition range was further expanded to whole-frame phase sequence data to obtain inter-frame delay distribution, intra-frame phase drift patterns, and edge transition characteristics. Furthermore, for the waveform behavior at the projector power supply end, high-precision voltage and current sensors were used to acquire the output power waveform characteristics in real time, including power ripple frequency, envelope peak distribution, rise rate, periodic ripple waveform morphology, and amplitude variation trend, ensuring complete time-domain and amplitude-domain information on the overall behavior of the photoelectric conversion drive state.

[0051] Based on the collected raw data, three key signals from each projector—video sampling frequency, frame phase sequence, and power waveform variation—are uniformly encoded in a high-dimensional manner. The sampling information is constructed into a tensor structure with clear physical meaning along the time sequence axis, device node axis, and signal type axis. This tensor structure is no longer a traditional planar data stacking, but rather a linked spatial relationship expression method strictly established according to three-dimensional coordinate relationships. After normalizing the data points on each time axis within the tensor, minute offset behaviors between devices under the same time reference are identified, allowing for the location and expression of temporal drift at the frame, subframe, and sub-sampling levels. Sliding analysis is performed on adjacent time windows for each data point to construct a coupled expression space for video frame phase overlap, power fluctuation coordination, and sampling rate stability among devices. This encodes both the horizontal and vertical cross-characteristics between data points into the structure, laying a foundation for subsequent feature trajectory analysis.

[0052] Based on the constructed coupled structure, further trajectory extraction and drift mapping of global data are performed. Using frame edge markers under a unified time reference as reference anchors, the variation curves of inter-frame edge displacement of each projector within a continuous time period are analyzed. A labeling system is established for signal points with obvious jitter, periodic jumps, and intermittent misalignment characteristics, and their temporal evolution trajectories are extracted to draw phase drift curve maps between devices. Using this map as a reference, a synchronization offset visualization of the device cluster in the time dimension is generated. Through interpolation expansion and feature point fitting, discrete drift behavior is transformed into a continuous manifold expression, thereby establishing a three-dimensional dynamic drift map of time-phase-energy, further capturing the behavior of interference signal sources strongly correlated with power fluctuations during frame phase transitions. This stage not only completes the map representation of drift behavior but also provides a clear temporal reference and error distribution model for the next step of interference area identification and synchronization calibration.

[0053] By utilizing the generated global drift map, the time reference consistency among all projectors is systematically examined. Based on the high-density offset paths and repetitive interference trends presented in the map, the strength of the cooperative relationship and potential resonance trends among the devices are deduced. By analyzing the offset correlation between adjacent frame edges on the time axis, a synchronization dependency hierarchy among the devices is further established. On this basis, a phase deviation evolution chain is constructed for each pair of devices, identifying device combinations prone to frame misalignment coupling. Based on phase jump frequency, power waveform overlap area, and frame refresh cycle conflict bandwidth, high-risk drift periods and highly coupled device pairs are defined, forming a reference list for subsequent calibration. This process not only achieves a comprehensive understanding of the internal time-frequency state of the multi-projector system but also establishes a detailed, specific, and quantifiable analytical foundation for further cooperative registration and active interference suppression operations.

[0054] The coupling feature analysis module analyzes the phase shift trajectory in the global coupling tensor based on the drift spectrum, extracts the subsynchronous resonant core and beat frequency corridor, calibrates potential interference channels based on energy accumulation distribution, generates a list of dangerous windows, and provides input parameters for dynamic tracking.

[0055] After constructing the global drift map, it is necessary to conduct in-depth analysis of the temporal shift trends presented in the map to extract key interference feature regions, thereby identifying potential resonance sources and interference paths, and forming a reference list for subsequent dynamic control. The specific steps are as follows:

[0056] The generated drift map is processed by unfolding it trajectory by trajectory. All frame phase offset curves under a unified time reference are selected as the analysis objects. The device number, frame number, phase change amplitude, and evolution direction corresponding to each curve are grouped and reconstructed as complete trajectory metadata. Subsequently, using the time series as the basic axis, the phase jump rate and frequency perturbation density of all trajectories in adjacent time periods are calculated, and trajectory sequences exhibiting periodic fluctuations or approximate modulation changes are analyzed. After identifying time periods with regular overlap and large amplitude fluctuations in the trajectories, these sequences are further marked as a preliminary set of suspected resonant trajectories, and local feature extraction is performed, mainly including indicators such as maximum phase jump rate, oscillation period amplitude, peak jump density, and minimum amplitude-frequency boundary, to preliminarily screen candidate trajectories that may cause subsynchronous phenomena during multi-device frame refresh.

[0057] Based on the suspected trajectory set, a cross-device trajectory overlap map is constructed. Through time-segment window mapping and edge alignment rules, regions exhibiting resonance or synchronous beat frequency characteristics in the frame phase trajectories of different devices are identified. On the time reference, the trajectories of multiple devices are located to exhibit near-synchronous phase shifts within short time scales. The consistency of trajectory amplitude direction and frequency modulation trends within these time periods are jointly evaluated to extract the signal strength evolution pattern of overlapping intervals. In this process, regions with high frequency amplitude coupling and high overlap density of transition edges in the signal waveform are defined as resonance core regions, denoted as subsynchronous resonance cores. Simultaneously, for device pairs with similar but not completely overlapping trajectory frequencies, the maximum envelope region of the frequency interference band is extracted by calculating its beat frequency period distribution and interference waveform overlap. A beat frequency corridor is formed using the dynamic width of this region on the time reference as a scale. The interactive frequency band region identified between the boundary of the resonance core and the beat frequency corridor reflects the long-term periodic modulation overlap phenomenon caused by slight sampling rate differences between different devices, representing a concentrated manifestation of potential interference paths.

[0058] Based on the extracted subsynchronous resonant core and beat frequency corridor, and by fusing the current pulse overlap region and ripple signal concentration band extracted from the power waveform, a time-frequency energy accumulation map is constructed. In this map, by comparing the overlap between the video signal phase drift spectrum and the power signal fluctuation trend map, regions exhibiting both high-amplitude phase jumps and power disturbance peaks within the same time window are identified and marked as high-coupling energy accumulation regions. These regions often possess the dual characteristics of frame synchronization edge mismatch and high-frequency power interference superposition, making them the main potential sources of electromagnetic interference propagation and frame refresh misalignment. Further analysis of the spatial propagation paths of these high-energy accumulation regions, combined with the physical wiring of the communication link and the power circuit topology, derives the propagation direction and interference intensity evolution trend of potential interference signals. Based on this, an interference channel mapping list is constructed, and the risk level order of interference paths is determined according to the number of overlaps of interference channels, the loop closure degree of the signal propagation path, and the local energy transition index.

[0059] Based on the interference channel mapping, a comprehensive analysis is performed on all high-risk paths, frequency resonant bands, beat frequency modulation regions, and energy coupling peak regions. The time periods exhibiting the most concentrated interference characteristics, the most severe amplitude fluctuations, and the strongest propagation directionality in the time series are selected and marked as critical interference time windows. These time windows are categorized and grouped to form a complete list of hazardous windows. Each hazardous window includes start and end times, corresponding device combination numbers, interference intensity levels, waveform modulation characteristics, and propagation path information. This list not only describes the specific temporal and spatial location of potential interference occurrences but also provides quantitative indicators for subsequent dynamic tracking and priority assessment. After establishing the hazardous window list, a clear data input and decision-making basis can be provided for operations such as cross-device feedback path tracking, frame refresh registration and control, and adaptive suppression of high-frequency interference, enabling prior identification and real-time management of interference risks in complex multi-device collaborative environments.

[0060] The causal tracking calibration module establishes a causal tracking chain based on the danger window list, records the entire process from cloud scheduling to the response of each projector terminal, calculates the arrival misalignment quantization value and interference source influence factor, and generates a cross-device calibration priority map to provide a basis for subsequent time-frequency registration.

[0061] After generating the list of danger windows, it is necessary to analyze the event chain within each interference window step by step, identify the causal relationship from the scheduling source to the terminal response, and form a quantified interference impact map to provide an intervention basis for subsequent multi-device synchronization registration. The specific steps are as follows:

[0062] For each interference time period marked in the danger window list, the content, trigger time, target device number, and command type of the scheduling instructions issued from the cloud within that time period are traced back one by one. Using the timestamp of the scheduling instruction as a reference point, the communication time of receiving the instruction, the signal parsing start time, the command response start time, and the actual action completion time are extracted from each projector within the corresponding time period, and a complete time sequence chain is established. Each time node is calibrated using the physical layer clock reference, and the processing latency of each device between the network transmission layer, command parsing layer, and driver execution layer is independently measured to ensure accurate reconstruction of the actual propagation path and response structure in the scheduling chain. After constructing the event chain for all devices, a difference analysis is performed chain by chain to clearly identify the action lag time, start offset magnitude, and response completion time misalignment range of each device under the same scheduling event. Based on this, the degree of response difference between each pair of devices under the same scheduling event is calculated.

[0063] The event chain analysis results are overlaid and matched with the previously generated list of danger windows to identify the device combinations with the most significant response time misalignments during the periods of highest interference risk. Using response difference curves as input, potential bottlenecks in the network transmission path of scheduling commands and key nodes causing command execution jitter are identified, such as nondeterministic response delays caused by link contention, processor waiting, and device cold start states. Based on this, a device response offset map is constructed, recording the scheduling response deviation trajectory of each device within each danger window, and then calculating its response stability score across all periods of high interference incidence. This score includes multiple factors, including but not limited to the mean response delay, maximum misalignment duration, response jitter amplitude, and the duration required to recover stability, thus forming a robust profile of each device's behavior in complex interference environments.

[0064] The offset map is further refined to the paired response relationship between each device and other devices. The relative arrival time difference and response difference sequences are extracted for each pair, and a dynamic impact weight map is constructed based on these time misalignments. This weight map uses device pairs as the basic unit, and the weight values ​​reflect the probability trend of synchronization imbalance that may occur between any two devices under multiple scheduling events. In this process, the phase offset trend extracted from the subsynchronous resonant core and beat frequency corridor, as well as the interference source diffusion direction marked in the energy accumulation map, are further combined to cross-evaluate the time misalignment value and the power disturbance factor, forming an interference source impact factor matrix. Each factor is weighted and superimposed according to its coupling strength in the time axis, power axis, and response axis, ultimately resulting in a unified ranking of all devices. The ranking criteria not only consider the device's own response delay performance but also incorporate its potential role as a resonance inducing source or interference amplifier in high-energy coupling regions, thereby achieving accurate modeling of its contribution to interference intensity.

[0065] Based on the dual analysis results of device response offset maps and interference source influence factor matrices, a cross-device correction priority map is constructed. In this priority map, each device is assigned a dynamic correction weight, and devices with higher weights are given priority in subsequent frame synchronization, phase alignment, and power coordination control processes. The priority is determined not only based on the breadth of the interference impact range but also comprehensively considers indicators such as response delay volatility, command decoding stability, and physical layer noise tolerance. When constructing the priority map, the pairing relationships between all devices are structured and grouped, constructing a hierarchical descending structure from high-interference, high-response misalignment key devices to low-sensitivity, low-misalignment devices, providing path guidance for subsequent multi-time standard registration. Through this priority structure, timing correction of key nodes can be completed preferentially within a limited time window, thereby driving the restoration of synchronization stability of the entire device network and enabling the system to have stronger dynamic response and time-frequency coordination capabilities in complex interference environments.

[0066] The multi-time registration module constructs an adaptive multi-time registration stack based on the cross-device correction priority map, performs phase remapping on frame lock edges, performs micro-shifting on the video sampling threshold, and dynamically cancels power ripple to generate a stable candidate set and provide real-time feedback signals.

[0067] After obtaining the cross-device calibration priority map, a responsive multi-timescale registration process needs to be constructed according to its weights. This process involves layer-by-layer joint alignment of frame timing, video sampling parameters, and power behavior to generate candidate synchronization combinations that meet stability requirements, which are then fed back for subsequent dynamic adjustment. The specific steps are as follows:

[0068] Based on the correction weights defined in the cross-device correction priority diagram, all projector devices are selected as target objects according to their priority level, and multi-round registration processing is carried out from high priority to low priority. Before entering each round of registration, the frame lock edge offset information of the device in the historical interference window is read, and the phase difference between its current frame synchronization reference point and the ideal reference frame is extracted. The offset trend is evaluated based on the current frame refresh frequency, edge rise time, edge plateau time period, and acceleration change curve of the jump point. Then, based on the frame edge comparison between the target device and other devices, a phase mapping table for correction is constructed, and the original frame lock point is remapped to the zero-crossing point or stable level edge segment near the synchronization center region to avoid the spread of synchronization error caused by frequent jumps. After the remapping is completed, the current frame start point of the device is synchronously adjusted to the new reference phase point to complete the first stage of edge alignment.

[0069] Entering the sampling threshold fine-tuning stage, based on the completed frame lock edge alignment, the trigger threshold distribution range during the current video signal sampling process is further analyzed, including the video brightness value jump range, color component conversion region, and gamma value abrupt change segment. By analyzing the time delay trend of its trigger response point by point and referring to the corresponding channel time misalignment data in the drift spectrum, the amplitude of the current sampling start and end points is fine-tuned. The adjustment process does not change the original sampling frequency, but by slightly shifting the boundary of the threshold trigger window, the sampling trigger time is made closer to the middle position of the ideal synchronized frame, reducing the sampling hit rate in the range of frequent edge disturbances and reducing the accumulation of sampling errors caused by trigger threshold jumps. After the adjustment is completed, a short-cycle test is performed on the new sampling threshold window to evaluate its trigger consistency and sampling stability, thereby confirming whether to enter the next stage of power behavior cancellation.

[0070] Entering the dynamic power ripple adjustment phase, the power output behavior of the target device within its current operating cycle is remodeled. The power output curve of the device from synchronization trigger to frame refresh completion is extracted, primarily analyzing its ripple frequency, peak amplitude, trough duration, and pulsation cycle offset. Combining the previously established interference source influence factor matrix and energy concentration distribution information, it is determined whether the power disturbance characteristics of this device during critical periods overlap with the spectrum of other devices. When high-amplitude ripple interference is identified, the device's internal power modulation parameters are adjusted, such as duty cycle, conduction period, and switching frequency fine-tuning, to create a power waveform with a timing offset relative to other devices, achieving interference spectrum decoupling. During adjustment, the total power output remains constant; only the ripple shape and phase are reconstructed to avoid affecting display brightness or image quality. After dynamic cancellation, the overlap of the new ripple characteristics with interference from other devices is recorded as an important indicator for stability assessment.

[0071] After completing the three-step operations of frame lock edge remapping, sampling threshold micro-shifting, and power behavior cancellation, a stability candidate set is constructed for the current device. This candidate set contains multiple operating state samples, each corresponding to a set of parameter combinations, including the frame synchronization start point, sampling trigger threshold window, and power output waveform parameters. By conducting short-term operational tests on each sample in the candidate set, its synchronization stability performance over several consecutive frames is recorded, including inter-frame edge coincidence rate, sampling point fluctuation amplitude, and power noise synchronization index. A sliding window analysis method is used to calculate the stability score of each sample in multiple rounds of testing. The sample with the highest score in the candidate set is temporarily locked as the standard parameter set for the current device to enter the dynamic operation phase. The stability candidate set is not only used for parameter optimization of the current device but also provides phase guidance and behavior alignment references for other devices in the future.

[0072] The operational results of each device in the stability candidate set are fed back to the control center to construct a real-time registration feedback network. This feedback includes not only the selection information of the static parameter group, but also the dynamic drift trend detected during operation, interference recovery time, and the fluctuation range of behavior under the new parameter group. By aggregating the feedback data of all devices, the effectiveness of the current registration strategy in group collaboration is re-verified, and the scoring model in the correction priority graph is dynamically updated. In the next round of multi-timescale registration, priority adjustments, parameter rollbacks, or individual device readjustments can be made based on the latest feedback results, thus forming a closed-loop registration mechanism based on behavior and guided by interference trends. This provides a highly accurate dynamic support structure for ultimately achieving stable video synchronization and signal interference suppression.

[0073] The choreography control module executes discrete time slot drift choreography based on the stable candidate set. Within the danger window, it sequentially triggers micro-periodic misalignment formation, alternating silent frames, energy ladder release, and carrier gating rotation. It uses feedback closed-loop dynamic reordering of the sequence to achieve video synchronization stabilization and communication interference isolation.

[0074] After constructing a stable candidate set and confirming its excellent synchronization performance, active timing intervention is required within the danger window. This involves using staging-style time slot adjustments and energy release control to achieve the synergistic goal of stable image synchronization and suppression of electromagnetic interference. The specific steps are as follows:

[0075] Based on the high-scoring parameter groups in the stable candidate set, a micro-periodic misalignment plan is established for each projector within a specified danger window. This plan, based on a cross-device correction priority map and historical response offset trajectories, sets tiny frame refresh delay intervals for all projectors currently participating in synchronization. Building upon the original unified frame start point, a progressive delay arrangement is implemented for each device according to the smallest controllable time granularity, refining and separating the originally synchronous screen refresh behavior along the time axis. By controlling the misalignment interval to not exceed the device sampling tolerance threshold, it ensures that synchronization and continuity are maintained in the human eye's perception, while simultaneously staggering the peak drive currents of multiple devices to avoid power surges and transient electromagnetic coupling caused by simultaneous startup. This micro-periodic misalignment strategy is preferentially applied to devices with higher weights, gradually expanding from the center to the edge, ensuring initial coordination between synchronization rhythm and energy balance.

[0076] Building upon the staggered formation, to further reduce the peak frequency of frame start-ups, an alternating silent frame mechanism is introduced at key frame positions of some devices. Based on the device's location, past power ripple superposition risk, and channel interference score, frame periods with high interference sensitivity are selected. Within these periods, specific devices are scheduled to skip one frame refresh operation, i.e., not emitting visible image signals, but maintaining active control and communication links. This silent operation is only performed on high frame rate portions imperceptible to the human eye; for example, in a refresh rate of sixty frames per second, one frame is silenced to eliminate the accumulation of high-frequency resonance caused by continuous operation in the device's drive circuitry. Simultaneously, it disconnects high-frequency coupling paths, forming a current regulation band between peaks, thus mitigating electromagnetic disturbances. The selection strategy for silent frames is based on the stability performance of the candidate set, prioritizing the participation of nodes with high influence in the interference propagation path, resulting in a periodically staggered frame refresh distribution.

[0077] Building upon micro-periodic staggered formation and silent frame control, this paper further optimizes the power increment process during frame startup using an energy ladder-like gradual release strategy. The power modulation parameters of each projector during the critical frame startup phase are divided into multiple gradual transition segments, starting with a lower duty cycle and slowly increasing to the energy level corresponding to the target brightness frame by frame, avoiding the power circuit impact caused by traditional one-step activation. This segmented energy boosting strategy implements a multi-step approach based on the interference level of the device within the critical window; higher-level devices have longer ramp-up times and gentler energy increment gradients, effectively reducing the concentrated superposition of local electromagnetic interference sources. During the initial power increase of the device, the local current changes are monitored in real time, taking into account the load response characteristics and capacitor buffer charging and discharging behavior in the power circuit, and power throttling is performed before reaching the disturbance threshold, further enhancing the overall operational stability margin.

[0078] In the electromagnetic coupling control process caused by the synchronous behavior of multiple devices, a carrier selection round-robin mechanism is implemented to reduce the frequency overlap between the signal channels of each device. During the communication transmission phase of each projector, different carrier frequency bands are allocated based on the interference superposition score fed back from the previous round of operation, and the carrier activation priority is cyclically adjusted within several frame periods in a round-robin manner. This operation does not change the core format of the communication protocol itself, but controls the staggered use time window of the frequency band at the physical layer of the communication link, so that the transmission behavior of multiple devices in the same frame avoids the same carrier frequency interval, avoiding short-term collision accumulation in the wireless channel. The round-robin plan is based on the previously constructed interference channel mapping structure, and arranges the optimal frequency band switching order according to the device location, electromagnetic field overlap probability and response distortion degree, ensuring the robustness of the communication link in a high interference environment and improving the arrival rate and execution accuracy of remote control commands.

[0079] The dynamic performance information of the above four strategies, along with the candidate set operating parameters, is fed back to the central control node to construct a feedback closed-loop dynamic rearrangement sequence. After each round of operation, the frame synchronization error, communication success rate, power disturbance amplitude, and image stability index of each projector under the current strategy are collected. The micro-periodic misalignment order, silent frame insertion point, energy release gradient, and carrier gating weight for the next round are adjusted through dynamic scoring to achieve real-time adaptive optimization. This feedback closed loop performs a periodic check at the end of each danger window and updates the parameters for the next cycle strategy, enabling the entire timing control process to have continuous evolution capabilities. Through the rolling optimization of the dynamic rearrangement sequence, multiple projectors maintain frame refresh synchronization during dynamic collaboration while effectively suppressing the superposition and propagation of high-frequency interference, achieving high-precision image consistency and communication isolation stability.

[0080] This invention establishes a cross-device time-frequency audit baseline, comprehensively collects and models the signal behavior characteristics of each device, and achieves coordinated reference of frame phase, video sampling, and power waveform. By analyzing coupling characteristics in the drift spectrum and identifying interference paths, it enables early warning and precise location of potential subsynchronous resonance and beat frequency interference problems. Based on this, it constructs a causal tracking chain and response offset model, realizing quantitative analysis of multi-device behavior chains and scientific prioritization of corrections. Furthermore, it completes three-dimensional coordinated correction of frame lock edges, sampling thresholds, and power ripple through a multi-timescale registration strategy, forming a stable set of parameter candidates. Finally, it dynamically controls interference behavior through time slot misalignment choreography, silent frame injection, energy mitigation, and carrier rotation, achieving frame refresh peak staggering, power peak decoupling, and communication channel collision avoidance, comprehensively improving the overall performance of multi-projector systems in terms of image synchronization consistency, electromagnetic interference control, and remote communication reliability.

[0081] 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. A remote control system for a projector based on the Internet of Things, characterized in that, It includes a time-frequency audit baseline module, a coupling feature parsing module, a causal tracking calibration module, a multi-timescale registration module, and a control and choreography module: The time-frequency audit baseline module establishes a cross-device time-frequency audit baseline. Under a unified time reference, it collects video sampling rate, frame phase sequence, and power waveform changes from multiple projectors, generates a global coupling tensor, and constructs a drift spectrum to form a system-level synchronization reference. The coupling feature analysis module analyzes the phase shift trajectory in the global coupling tensor based on the drift spectrum, extracts the subsynchronous resonant core and beat frequency corridor, calibrates potential interference channels based on energy accumulation distribution, generates a list of dangerous windows, and provides input parameters for dynamic tracking. The causal tracking calibration module establishes a causal tracking chain based on the danger window list, records the entire process from cloud scheduling to the response of each projector terminal, calculates the arrival misalignment quantization value and interference source influence factor, and generates a cross-device calibration priority map to provide a basis for subsequent time-frequency registration. The multi-time registration module constructs an adaptive multi-time registration stack based on the cross-device correction priority map, performs phase remapping on frame lock edges, performs micro-shifting on the video sampling threshold, and dynamically cancels power ripple to generate a stable candidate set and provide real-time feedback signals. The choreography control module executes discrete time slot drift choreography based on a stable candidate set. Within the danger window, it sequentially triggers micro-periodic misalignment formation, alternating silent frames, energy ladder release, and carrier gating rotation. It uses feedback closed-loop dynamic sequence rearrangement to achieve video synchronization stabilization and communication interference isolation.

2. The projector remote control system based on the Internet of Things according to claim 1, characterized in that, The drift map construction process is as follows: Establish a cross-device time-frequency audit baseline, and collect video sampling rate, frame phase sequence and power waveform changes from multiple projectors under a unified time reference; Construct a high-dimensional data structure with a unified time series axis, device node axis, and signal type axis, and perform linked encoding and normalization on the three types of signals collected; Based on the constructed 3D data structure, the frame edge displacement trajectory is extracted and a dynamic drift map is generated to map the phase change trend of each projector in a continuous time period. Based on the dynamic drift map, the synchronization dependency and drift risk distribution between devices are derived, a phase deviation evolution chain is established, and a calibration reference list is formed.

3. The projector remote control system based on the Internet of Things according to claim 2, characterized in that, The steps to generate a list of dangerous windows are as follows: Based on the drift map analysis, the phase shift trajectory in the global coupled tensor is analyzed, frame phase change features are extracted, and a preliminary set of suspected trajectories is marked. A cross-device trajectory overlap map is constructed based on a set of suspected trajectories, and the subsynchronous resonance core and beat frequency corridor are extracted. By integrating the current pulse overlap region and the ripple signal concentration band in the power waveform, a time-frequency energy concentration map is constructed and the propagation path of the interference signal is derived, forming an interference channel mapping list; Based on the interference channel mapping list, key interference time windows are identified, categorized and grouped to form a list of dangerous windows for subsequent dynamic tracking and control of inputs.

4. The projector remote control system based on the Internet of Things according to claim 3, characterized in that, The steps for generating a cross-device calibration priority map are as follows: Based on the danger window list, the entire process from cloud-based scheduling instructions to terminal responses is traced back to construct a time sequence chain for multiple projectors; Analyze the equipment's response offset within each danger window, generate an equipment response offset map, and extract a response stability score; The response time difference between equipment pairs is extracted based on the equipment response offset spectrum, and the interference source influence factor matrix is ​​formed by combining the phase offset trend and the power disturbance factor. Based on the response offset spectrum and the interference source influence factor matrix, a cross-device calibration priority map is constructed, and the calibration order for multi-time standard registration is output.

5. The projector remote control system based on the Internet of Things according to claim 4, characterized in that, When constructing a cross-device calibration priority map, dynamic calibration weights are assigned based on the degree of response misalignment, power disturbance amplitude, and command decoding stability of the devices in the high-energy coupling region, and the registration order is arranged from high to low weights.

6. The projector remote control system based on the Internet of Things according to claim 4, characterized in that, The steps for generating a stable candidate set are as follows: The target device is selected based on the cross-device correction priority map, and phase remapping is performed on the frame lock edge to complete the edge alignment. After completing edge alignment, a slight translation is performed on the video sampling threshold to optimize the sampling trigger timing; Based on the completed sampling adjustment, dynamic cancellation is performed on the power output behavior to reduce spectral coupling interference; After registration is completed, a stability candidate set is constructed and run tests are conducted to lock in the parameter combination; The candidate set test results are fed back to the control center to update the calibration priority map and optimize the registration strategy for the next round.

7. The projector remote control system based on the Internet of Things according to claim 6, characterized in that, The phase remapping of the frame lock edge is calculated by referencing the phase difference between the historical frame offset data and the synchronization center position of the target frame, and the frame start point is adjusted to the zero-crossing point or stable level edge segment.

8. The projector remote control system based on the Internet of Things according to claim 6, characterized in that, Based on the stable candidate set, discrete time slot drift is performed. Within the danger window, micro-periodic misalignment formation, alternating silent frames, energy ladder mitigation, and carrier gating rotation are triggered sequentially. The following steps are used to dynamically rearrange the sequence using a feedback closed loop: Based on the stable candidate set, a micro-periodic misalignment plan is set within the danger window, and the frame start delay is executed sequentially; Alternating silent frames are introduced on the basis of misalignment to reduce high-frequency resonance caused by continuous operation; Performing energy tiered release operations mitigates power surges by increasing power in stages. Implementing carrier selection rotation reduces channel conflict by adjusting the order of frequency band usage. The execution results of all strategies are fed back to the control node to construct a feedback closed loop, dynamically rearrange the sequence, and update the control parameters for the next round.

Images