Method for remotely accessing an internet of things based device

Abstract

The application discloses a device remote access method based on Internet of Things and relates to the technical field of Internet of Things, and comprises the following steps: establishing a space-time interference observation baseline in a remote access process, extracting a conflict phase fingerprint, generating a phase difference spectrum and using the phase difference spectrum as a time sequence reference for interference tracking; performing anti-aliasing tracking by using the phase difference spectrum, dynamically scanning signal energy density, identifying overlapping channels and congested time slots and constructing an interference source topology structure. Through the construction of the space-time interference observation and phase difference spectrum mechanism, the combination of the anti-aliasing tracking and the topology modeling, the accurate identification of the interference source and the quantitative analysis of the risk, the dynamic optimization of the channel and time slot configuration, the improvement of the anti-interference ability and stability of the remote access and the construction of the adaptive closed-loop communication control system, the identity lock frequency, the phase conjugate suppression and the reversible reconstruction mechanism are matched.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of Internet of Things, and particularly relates to a device remote access method based on Internet of Things. BACKGROUND

[0002] Device remote access based on Internet of Things refers to connecting a clothes care machine with the Internet through Internet of Things technology, so that a user can access and control the clothes care machine at a remote location through a smart phone, a computer or the like. Specifically, the clothes care machine exchanges data with a cloud platform or a local network through a built-in sensor, a communication module (such as Wi-Fi, Bluetooth, etc.), to realize functions such as remote monitoring, parameter adjustment and fault diagnosis. The user can view the device status in real time, start or stop operation, and even adjust the device settings such as temperature, humidity, operation mode, without directly contacting the device. This remote access function improves the use convenience and intelligent level of the clothes care machine, and also provides a more personalized operation experience for the user.

[0003] The prior art has the following disadvantages:

[0004] In the prior art, with the wide application of Internet of Things technology, the situation of multiple devices accessing the same network simultaneously is increasing. However, in this environment, the communication interference problem between devices gradually appears, especially in the case of a large number of high-frequency devices. Since the signal frequencies of high-frequency devices are similar or overlapping, it is easy to cause mutual interference between devices, and then cause the problem of signal loss or data packet loss. This interference may affect the normal communication and data transmission of the device, especially in the process of remotely controlling the device or synchronizing data, the phenomenon of communication error or incomplete data transmission may occur. For Internet of Things devices such as clothes care machines, communication interference will directly cause the execution failure of remote control instructions or data synchronization abnormalities, thereby affecting the normal operation of the device, and even may cause the device to fail to complete the task within the scheduled time, thereby reducing the user experience and affecting the stability and reliability of the device.

[0005] The above information disclosed in the background section is only used to enhance the understanding of the background of the present disclosure, and therefore it can include information that does not constitute prior art known to those of ordinary skill in the art. SUMMARY

[0006] The purpose of the present application is to provide a device remote access method based on Internet of Things to solve the problems in the background.

[0007] In order to achieve the above purpose, the present application provides the following technical solution: a device remote access method based on Internet of Things, comprising the following steps:

[0008] The space-time interference observation baseline is established in the remote access process, the conflict phase fingerprint is extracted, the phase difference spectrum is generated and used as a timing reference for interference tracking;

[0009] The phase difference spectrum is used to perform anti-aliasing tracking, dynamically scan signal energy density, identify overlapping channels and congested time slots, and construct an interference source topology structure;

[0010] Based on the interference source topology structure, a risk operator matrix is constructed, the timing damage degree is calculated, and the interference disposal queue sorted by risk is output;

[0011] According to the interference disposal queue, identity lock frequency linkage is performed, channel priority is adjusted, time slot isolation window is set, high-risk channels are frozen and safe channel transmission is maintained;

[0012] Under the identity lock frequency linkage, the counterfactual replay chain is introduced, the communication track is reproduced, the phase deviation is calibrated, and the reversible time grid parameters are output;

[0013] Taking the time grid parameters as input, the Hamiltonian variation routing linkage phase conjugation suppression chain is enabled, the interference suppression mechanism is driven to operate cooperatively, the channel and time slot adaptive rearrangement is realized by combining the cross-layer majority voting weight reduction mechanism, and the dynamic and steady-state control loop construction is completed.

[0014] Preferably, the phase difference spectrum generation step is as follows:

[0015] When the clothes care machine is ready to establish a remote connection, start the interference observation initialization process, scan the working state of the high-frequency communication channel, and set the frequency reference benchmark to ensure signal frequency synchronization consistency;

[0016] Set the observation time window and sampling interval, collect signal phase, amplitude and propagation time delay data of frequency points, and record signal incident angle, time difference and intensity distribution of multiple spatial sampling points;

[0017] The data in the continuous sampling period is background noise calibrated to form an initial space-time reference table;

[0018] Feature extraction and cluster analysis are performed on the phase mutation data to generate conflict phase fingerprints, and the fingerprints are fused to form a complete phase difference spectrum, which is used as a timing reference for subsequent interference tracking.

[0019] Preferably, the interference source topology structure construction step is as follows:

[0020] Based on the phase difference spectrum, the phase change curve of the frequency slice is extracted, the frequency mutation event is identified, and the frequency-time disturbance spectrum is generated;

[0021] In the mutation frequency interval, an energy density scanning mechanism on the time axis is constructed, the energy and phase change rate are calculated, and the frequency spectrum congestion time slot is identified;

[0022] The spatial propagation direction corresponding to the frequency mutation and the energy mutation is fused to form a time-frequency-space three-dimensional path cluster;

[0023] According to the start and end points of the path, the direction consistency and the frequency coincidence relationship, an interference source topology graph is constructed, which is used as data support for subsequent interference disposal steps.

[0024] Preferably, when constructing the interference source topology graph, the start point and the end point of each interference path are used as the two end nodes of the directed edge, and a connection node is established at the path intersection position. The propagation duration, the energy intensity change interval and the spatial propagation direction are combined to label the topology edge attributes.

[0025] Preferably, the interference disposal queue output step is as follows:

[0026] Based on the interference source topology structure, the duration, the frequency bandwidth, the spatial propagation distance, the path intersection number and the repeated appearance frequency of the interference path are extracted, and a risk operator matrix is formed by normalization;

[0027] The risk operator matrix is matched with the time sequence stage sensitivity model of the remote control instruction to construct the time sequence damage degree score of each path in the remote connection, instruction transmission, state synchronization and feedback response stage;

[0028] According to the score results, the interference disposal queue is output from high to low, and the freeze frequency band, the delay window and the power adjustment strategy are set according to the path characteristics to realize the scheduling priority division.

[0029] Preferably, the identity frequency locking linkage step is as follows:

[0030] According to the risk score, the interference duration, the frequency range and the signal intensity change rate of the interference path in the interference disposal queue, a high-risk frequency channel set is extracted and a communication identity frequency use permission table is established, and temporary blocking is performed on the high-risk channel;

[0031] The channel priority is set and the time slot isolation window is divided, the continuous available time period is allocated to the high-priority channel, and part of the isolation window is configured for the medium-priority channel;

[0032] The whole communication time period of the high-risk channel is frozen, and the performance of the continuously activated channel is evaluated and the scheduling priority thereof is dynamically adjusted.

[0033] Preferably, the reversible time grid parameter output step is as follows:

[0034] The historical data of the high-risk channel in the communication cycle before freezing is extracted to construct a communication event sequence containing the transmission phase, the reception phase, the frequency offset and the abnormal marker;

[0035] An anti-fact replay chain is established based on the communication event sequence, the phase disturbance cause is analyzed in a backtracking manner, and a phase calibration parameter set is output;

[0036] A reversible time grid structure is constructed according to the phase calibration parameter set, and a time grid parameter group including a time calibration value, a phase offset vector and a scheduling delay correction value is output.

[0037] Preferably, the time grid parameters are used as input to enable a Hamiltonian variation routing linkage phase conjugate suppression chain, drive the interference suppression mechanism to operate cooperatively, and realize adaptive rearrangement of channels and time slots by combining a cross-layer majority voting weight reduction mechanism as follows:

[0038] A communication routing graph is constructed based on the reversible time grid parameters, and a Hamiltonian variation path strategy is applied to determine a master communication path;

[0039] A phase conjugate suppression operation is performed on the master communication path to eliminate phase mutation faults between adjacent time grids;

[0040] Energy return and synchronization bias injection processes are jointly performed to construct a signal energy feedback path and offset correct the communication time axis;

[0041] Topology reversible reconstruction is performed according to path operation characteristics to complete scheduling dispersion of high-load paths and optimal channel rearrangement;

[0042] A majority voting weight reduction control is implemented in combination with communication task levels to finally adjust channel and time slot resource allocation results, and realize a dynamic scheduling closed loop.

[0043] In the above technical solutions, the present application provides technical effects and advantages:

[0044] The present application realizes accurate extraction of high-frequency signal conflict characteristics by introducing a space-time interference observation baseline and a phase difference spectrum, locates and restores the interference source structure by combining anti-aliasing tracking and topology modeling capabilities, and then uses a risk operator matrix to quantitatively analyze the degree of interference influence, and constructs an intervention path sorted by risk. On this basis, the communication path is dynamically adjusted in real time by means of identity frequency locking linkage and phase conjugate suppression chain, and the energy return mechanism and time grid calibration operation, effectively alleviating problems such as signal congestion, transmission jump and synchronization offset. At the same time, the introduction of topology reversible reconstruction and cross-layer majority voting weight reduction mechanism ensures the optimal allocation and stable operation of channel and time slot resources in complex environments. In summary, the scheme not only improves the anti-interference ability and control accuracy of the clothes care machine remote access, but also builds a dynamic communication stabilization mechanism with self-sensing, self-adjusting and self-closed loop capabilities at the system level. BRIEF DESCRIPTION OF DRAWINGS

[0045] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings needed in the embodiments will be briefly introduced as follows. Obviously, the drawings in the following description only represent some embodiments described in the present application, and other drawings can also be obtained by those skilled in the art based on these drawings.

[0046] Figure 1 The method flow chart of the device remote access method based on the Internet of Things of the present application. DETAILED DESCRIPTION

[0047] Example implementations will now be described more fully with reference to the accompanying drawings. Example implementations may, however, be implemented in many different forms and should not be construed as limited to the examples set forth herein; rather, these example implementations are provided so that this disclosure will be thorough and complete, and will fully convey the gist of each example to those skilled in the art.

[0048] The present application provides a device remote access method based on the Internet of Things as shown in Figure 1 The method flow chart of the device remote access method based on the Internet of Things of the present application.

[0049] In the remote access process of the clothes care machine, a space-time interference observation baseline is established, a conflict phase fingerprint is extracted through a high-frequency communication data stream, a complete phase difference spectrum is generated, and the phase difference spectrum is used as a timing reference for interference tracking;

[0050] In order to solve the problems of high-frequency signal interference, phase overlap and data transmission distortion caused by multi-device concurrent communication in the remote access process of the clothes care machine, a method for establishing a space-time interference observation baseline is proposed, which is used to realize interference capture, interference characterization and phase tracking calibration in the communication process. The method synchronously observes the time domain and spatial domain signals in the high-frequency communication environment, extracts conflict phase characteristics, and generates a traceable phase difference spectrum to support subsequent interference tracking and stable control of remote communication. The specific steps are as follows:

[0051] When the clothes care machine is ready to establish a remote connection, the interference observation initialization process is started, and the working state of the high-frequency communication channel is fully scanned. By setting high-precision frequency reference baselines at the communication transmitting end and receiving end respectively, the frequency deviation of the signal source is ensured to be less than 10⁻ 6orders of magnitude, so that the subsequent phase observations have time synchronization consistency. On this basis, the length of the observation time window is defined, for example, 100 milliseconds is an observation period, and 10 microseconds is a sampling interval, and the instantaneous signal phase, amplitude and signal propagation delay data of all frequency points are collected within the period. To realize the interference recognition in the spatial dimension, the multi-point sampling antenna array is used to synchronously collect high-frequency signal data at different positions of the garment care machine, and each sampling point records the signal incident angle, wave front arrival time difference and received intensity distribution, thereby forming a three-dimensional space-time data set. By preliminarily comparing the data of the continuous sampling period, the background noise baseline and the normal communication signal characteristics are calibrated, and an initial space-time reference table for interference observation is constructed, which provides an accurate comparison template for subsequent phase conflict detection.

[0052] After establishing the initial observation baseline, the high-frequency signal data obtained by continuous sampling is subjected to conflict phase fingerprint extraction. This process first expands the signal waveform collected in each observation period into a time sequence, continuously calculates the phase change amount between adjacent sampling points, and forms a complete phase change trajectory. Subsequently, by setting a phase mutation detection threshold, for example, three times the average phase change rate as the mutation judgment standard, the time segments in which the phase mutation exceeds the threshold in a short time are detected. For each mutation segment, the corresponding frequency position, spatial azimuth and signal intensity decay rate are further analyzed to confirm that the signal belongs to a potential interference event. Then, the characteristic parameters of these mutation signals are normalized to generate independent conflict phase fingerprint data sets, each containing five parameters: time identifier, center frequency, spatial coordinates, phase jump amplitude and relative energy density. To prevent misjudgment, the phase mutation events that repeatedly appear within adjacent time windows are subjected to cluster analysis, and the pseudo-interference items caused by normal signal switching are eliminated, and only high-confidence interference fingerprints that appear multiple times and have consistent directions are retained, thereby ensuring the authenticity and reproducibility of the conflict phase fingerprints.

[0053] After obtaining multiple sets of conflict phase fingerprint, the fingerprint data is fused to generate a complete phase difference spectrum. In this process, first, all fingerprint data is arranged in time sequence, and the fingerprint signals in the same frequency range are time-aligned, so that the interference events in different time periods can be compared in a unified frequency spectrum coordinate. Then, the phase difference values between adjacent fingerprints are calculated to obtain the continuous change curve of the phase difference at each frequency point over time. By vertically stacking these curves, a phase difference distribution map covering the entire observation period can be formed. To further reflect the energy coupling characteristics of the interference signal, the phase difference value at each frequency point is multiplied by the corresponding signal energy density to obtain a phase interference intensity spectrum. This spectrum can intuitively reflect the change trend of the interference event in the three dimensions of time, frequency and energy. Subsequently, a multi-dimensional interpolation method is used to fill in the data gaps to generate a high-resolution continuous phase difference spectrum. By observing the phase offset trajectories of different frequency bands in the phase difference spectrum, the propagation path of the interference source in space and its impact on the communication signal can be identified. This spectrum not only shows the time and duration of the interference, but also characterizes the energy concentration of the interference signal and its change rate.

[0054] The generated phase difference spectrum is used as a time sequence reference for subsequent interference tracking and communication quality evaluation. When the laundry care machine communicates again during remote operation, the real-time signal is sampled at the same high frequency and phase analyzed to obtain a new phase change sequence, which is compared point by point with the previously established phase difference spectrum. If the phase trajectory of the current signal highly matches the characteristic curve of a certain segment in the phase difference spectrum, it is determined to be a known interference type, so that appropriate measures can be taken immediately, such as delaying command transmission or adjusting signal transmission angle to avoid the interference direction in the interference frequency band; if a new offset pattern is found in the current phase trajectory, it is determined to be an unknown interference event, and the characteristic parameters of the event are recorded to expand the original phase difference spectrum database. Through this dynamic comparison mechanism, continuous identification and behavior tracking of the interference source can be achieved. When a repeatedly occurring interference pattern is identified, its trigger conditions, such as the start time of other home appliance devices or the signal retransmission interval of network relay nodes, can be further analyzed to form a targeted interference avoidance strategy, ensuring stable transmission of remote control commands. Since this method is based on a high-resolution phase difference spectrum, it realizes real-time detection and correction of small phase disturbances in the communication channel, thus effectively suppressing transmission errors caused by signal superposition, reflection delay and spectrum congestion, improving the stability, response speed and anti-interference ability of the laundry care machine during remote access.

[0055] Performing anti-aliasing tracking of the entire frequency band using the phase difference spectrum, identifying overlapping channels and congested time slots by dynamically scanning signal energy density changes, and then constructing the topology of the interference source;

[0056] To realize the dynamic tracking and spatial behavior reconstruction of communication interference behavior, based on the generated phase difference spectrum, the following steps are performed to perform full-band de-aliasing analysis, identify spectral overlap and time congestion, and finally establish the topology of the interference source:

[0057] After the phase difference spectrum is constructed, all frequency slices covering the communication frequency band range are selected, with each megahertz as the division interval, and the phase change curve in each frequency slice is gradually extracted, and the phase jump point detection is performed on the adjacent frequency layers. In this detection process, based on the phase time sequence corresponding to each frequency layer, a jump identification threshold is set, for example, three times the average phase change rate of the frequency layer is used as the mutation criterion, and the corresponding relationship between the time point and the frequency point of all frequency layers is extracted according to the mutation. Subsequently, according to the distribution density of the mutation event in the frequency axis and the time axis, a frequency-time two-dimensional disturbance map is drawn, and the mutation concentrated interval is pre-marked to preliminarily demarcate the possible frequency aliasing area. This processing step is significantly different from the traditional method of estimating frequency conflict by power spectrum amplitude, but introduces the frequency phase disturbance slope as the identification basis, and realizes the microscopic identification of the interference behavior in the frequency dimension.

[0058] Based on the mutation frequency interval, an energy density continuous scanning mechanism on the time axis is established. The specific method is to set a fixed time step in the identified suspicious frequency interval, for example, every 10 milliseconds is a unit scanning period, and the energy density value of the frequency point is collected in this period. The energy density is calculated by the average value of the amplitude square of the actual signal, and together with the phase change value of the frequency point, it constitutes an energy-phase data pair. In each time period, the change rate of the data pair is calculated, and through the trend line fitting of the change rate in multiple consecutive time windows, the time slots with steep energy growth and severe phase disturbance are identified. These time slots are likely to be the instantaneous spectrum congestion area caused by the resource competition of high-density communication devices. In order to ensure the accuracy of identification, overlapping sliding time window structure is used in the above operation, and there is 50% time intersection between each window, so as to realize the fine-grained coverage of high-frequency short-time interference behavior. This process breaks through the limitation of only using static power threshold to judge congestion in the prior art, and establishes a phase dynamic and energy dynamic double-index cross-validation mechanism, which greatly improves the congestion identification accuracy and time slot positioning ability.

[0059] The identified overlapping channel and congestion time slot information are fused with the spatial direction data contained in the phase difference spectrum to realize path reconstruction of the interference behavior. Specifically, the spatial incident angle information and arrival time data corresponding to each mutation point in each frequency layer in the phase difference spectrum are extracted, and the wave front time difference value obtained by the multi-point synchronous sampling antenna is combined to calculate the trajectory line of the signal in the spatial propagation direction. For each mutation frequency point, the trajectory lines appearing in the adjacent multiple time slots are compared for direction consistency, and the signal trajectory with stable path and coherent direction is selected as the main path of interference propagation. If the spatial propagation direction of a frequency point suddenly changes, for example, the propagation trend changes from east to west to north to south, it is judged that the frequency interference signal has been controlled by another interference source from one interference source, and a new path marker is started. Through the above method, the spatial propagation lines appearing on multiple frequency points are classified and aggregated to obtain multiple interference propagation path groups, forming a time-frequency-space three-dimensional path cluster as the pre-data support for constructing the interference topology structure.

[0060] After the interference path cluster is established, the interference source topology map is drawn according to the intersection points between the paths, the propagation synchronicity and the frequency coincidence. The specific operation includes: taking each interference path as a directed edge, the starting point of the path as the position where the phase disturbance first appears, and the ending point as the position where the phase disturbance ends or changes; taking the path intersection position as a connection node, and labeling the interference propagation duration, energy intensity change interval and spatial propagation direction on each connection edge. Structurally arrange all edges and nodes to obtain a complete interference source topology map. The topology map not only represents the mutual influence relationship between the interference sources in space, but also reflects the propagation sequence and duration intensity on the time axis, so that it can be used for subsequent risk calculation and interference disposal sorting. In addition, by analyzing the path density, node connection degree, propagation direction change frequency and other indicators in the topology, a high-risk interference source group and a core interference center can be identified.

[0061] According to the interference source topology structure, a risk operator matrix is constructed to quantitatively calculate the time sequence damage degree in the remote control instruction transmission process, and an interference disposal queue sorted according to the risk weight is output;

[0062] In order to realize the risk quantification and priority disposal of communication interference influence, on the basis of constructing the interference source topology structure, the risk operator matrix is constructed by the following steps to quantitatively calculate the time sequence damage degree of the remote control instruction and form an interference disposal queue with clear order:

[0063] With each identified interference path in the interference source topology as the starting point for analysis, five types of key characteristic parameters of the interference path are extracted to serve as the basic dimensions for constructing the risk operator matrix. The first type of parameter is the duration of the path, which refers to the complete time length from the first occurrence of signal interference to the termination of the last phase disturbance, recorded accurately in milliseconds; the second type of parameter is the path coverage frequency bandwidth, which represents the total range of frequency intervals occupied by the interference signal at different time periods, calculated by counting the maximum and minimum frequency point difference of the corresponding frequency layer in consecutive time slots; the third type of parameter is the spatial propagation distance, which is the maximum straight-line distance of the path in the three-dimensional coordinate space from the starting point to the ending point, calculated by positioning and conversion based on the aforementioned spatial incidence angle information and reception time difference; the fourth type of parameter is the number of path intersections, which records the number of intersections of the path with other interference paths in the time and frequency dimensions, which is used to judge the connectivity and influence range of the path in the overall interference map; and the fifth type of parameter is the frequency of repeated occurrence, which is the number of occurrences of the path within the observation period of the historical phase difference spectrum, reflecting its periodicity and predictability. The above five types of parameters are extracted and uniformly converted into dimensionless standard values, and through linear normalization processing, each dimension has the same dimension and comparison basis, and finally a five-dimensional risk operator vector matrix is formed, with each row representing an interference path and each column representing the corresponding structured interference characteristics.

[0064] With the risk operator matrix as the input basis, a phased sensitivity model of the remote control instruction life cycle is introduced to quantitatively evaluate the actual harmfulness of the interference path and form a targeted time sequence damage degree index. In specific implementation, the entire process of the remote control task of the clothes care machine is divided into four time sequence stages: remote connection establishment stage, control instruction transmission stage, device state synchronization stage, and execution feedback response stage. Each stage has independent time tolerance error, packet loss tolerance, and delay limit standards. For example, the longest interruption time allowed in the control instruction transmission stage must not exceed 20 milliseconds, the round-trip confirmation response in the state synchronization stage must be less than 5 milliseconds, and the retransmission window in the execution feedback stage must not exceed 3 times. The above standards are constructed into a task time sequence sensitivity table to establish a sensitivity weight distribution model for different communication stages. Then, the active interval of each interference path in the risk operator matrix on the time axis is compared with the time windows of each stage to determine whether it overlaps with the instruction behavior in the high sensitivity stage. If there is an overlap, the frequency span, propagation range, and path repetition probability of the path are combined to assign a phased risk score to the interference path. The weighted average value of the overlapping risks in all stages constitutes the comprehensive time sequence damage degree score of the path to the complete communication chain.

[0065] After obtaining the time sequence damage scores of all interference paths, the scores are sorted from high to low, and a risk priority ranked interference disposal queue is output, which is used as a key input parameter in the remote control task scheduling process. The specific processing flow is to first set the communication resource limit conditions of the current control task, including the number of currently adjustable channel priorities, the number of signal retransmissions that can be allocated, and the number of time slot rearrangement windows, and then select the path group with high ranking and interference path characteristics meeting the intervention conditions in the risk score table for priority marking. For example, if a path has the characteristics of short-time high-intensity burst, controllable frequency concentration distribution, and single propagation direction, it is marked as a path that can be intervened preferentially. For each path to be disposed, a disposal suggestion is output, including the freezing frequency range, the delay scheduling window length, the signal forwarding angle, and the communication carrier power adjustment ratio, etc. All the paths to be disposed form a linear queue in the order of the scores, and are assigned corresponding priority labels in the scheduling engine for real-time scheduling call in the execution process. In this way, the risk assessment results are truly converted into executable control queues, enabling the remote control task to have dynamic scheduling, priority response, and fine control capabilities in a complex interference environment.

[0066] Identity lock frequency linkage is performed with reference to the interference disposal queue, a time slot isolation window is dynamically adjusted by adjusting the channel priority, a freezing operation is performed on the high-risk channel, and the continuous transmission of the safe channel is maintained;

[0067] In response to the high-risk interference paths marked in the output interference disposal queue, specific communication resource scheduling is implemented, and the channel access and use time sequence are fine controlled through the identity lock frequency linkage strategy, including the following steps:

[0068] According to the risk score, interference duration, frequency range, and signal intensity change rate of each interference path in the interference disposal queue, the most significant frequency channel set is extracted, the binding relationship between the set and the current communication identity is identified, and the lock frequency strategy is implemented on the communication identity. The lock frequency strategy takes the communication identity as the execution subject, that is, the unique identity tag currently executing the remote control task, establishes a frequency use permission table, and marks the access state of the high-risk channel in the table. For channels with a risk score higher than a preset threshold and an overlap probability with the communication task on the time axis of more than 70%, the access state is immediately updated to temporary lockout, and the current communication identity is prohibited from using the channel resource. To improve the dynamic response capability of the lock frequency control, a freeze observation window field is added to the channel access permission table, that is, a feedback evaluation of the communication performance of the channel in a short period before actual lockout is allowed, and if it is found that the channel state fluctuates less, temporary delay of lockout execution is allowed to prevent false killing of active channels from causing control interruption.

[0069] After the implementation of the frequency locking strategy is completed, the channel priority reordering mechanism is started immediately, and the time slot isolation window is dynamically set for all communication channels in combination with the interference strength and channel reliability data to perform timing scheduling optimization. In specific operation, first, the channel priority is divided into five levels, which are marked as first preferred, second backup, third observation, fourth restriction, and fifth frozen from high to low, and the channels that can be scheduled by the current task are assigned one by one. In the priority assignment process, referring to the determination result of the frequency locking strategy in the previous step, the channels that have been marked as temporary lock are directly set to the fifth frozen and do not participate in communication scheduling; the channels that have shown high signal stability, low energy disturbance, and low delay feedback rate in the past 5 cycles are set to the first preferred, and the maximum continuous available window is recorded. Subsequently, time period division is established along the communication time axis, time slot intervals are set every 20 milliseconds, time slot mapping tables are established on each channel, three states of available, backup, and isolation are marked for each time slot, and the channel priority is converted into a time slot usage probability coefficient. For channels in the second and third states, the last 5 milliseconds in each time slot are set as isolation windows, and even if the channel is activated, part of the unused time slots will still be reserved for avoiding burst interference.

[0070] After completing channel sorting and time slot configuration, the freezing and continuous activation operations are performed on all channels, that is, high-risk channels are locked, and low-risk channels are maintained for priority scheduling, forming a communication stability guarantee mechanism. First, all channels in the fifth frozen state are traversed, and it is checked whether they are in the current communication task allocation list; if so, a communication switching instruction is immediately issued to guide the task to migrate to a first or second channel, and the response time and data loss rate in the switching process are recorded as reference factors for the next cycle. The frozen channels are time-closed, that is, marked as unavailable in their corresponding entire time axis intervals, and the state is updated synchronously in the access permission table. At the same time, the dynamic maintenance mechanism of continuously activated channels is started. For first preferred channels, after completing a complete communication round (i.e., sending a control instruction, receiving a device response, and completing state confirmation), three core operating parameters of the channel are immediately collected: signal average strength, round-trip delay time, and response integrity ratio. If all three parameters are better than the set threshold, the channel is given additional priority points in the next scheduling cycle, and the time width of its allocated time slot is expanded from 20 milliseconds to 25 milliseconds. The channel freezing and maintenance mechanism is repeatedly executed to achieve a dynamic evolution structure in which high-risk channels are quickly isolated and low-risk channels are continuously activated in an interference-intensive environment. The mechanism deeply integrates the interference behavior recognition result and the communication resource scheduling behavior, which not only guarantees transmission intervention in high-risk areas, but also realizes gain amplification processing of high-quality communication channels.

[0071] Under the identity lock frequency linkage mechanism, the counterfactual replay chain is introduced to reproduce high-risk communication trajectories, calibrate phase deviations in the communication process, and output reversible time grid parameters to provide accurate correction factors for subsequent channel reconstruction.

[0072] To further improve the accuracy and adjustability of communication scheduling in a dynamic interference environment, after completing the identity lock frequency linkage operation, the counterfactual replay chain is introduced to reproduce, trace back, and establish reversible time grid parameters for high-risk communication trajectories to support accurate execution of subsequent channel reconstruction. The specific operation includes the following steps:

[0073] Select all channels identified as high-risk and having executed the freeze operation, and extract all historical communication data of these channels within one complete communication period before freezing to construct the original data set of the counterfactual replay chain. This data set contains three types of data content: first, the signal frequency, transmission phase angle, target receiving identity, and task type at the communication transmission time; second, the actual received signal content, including the receiving time, phase offset, signal strength change rate, and frequency micro-offset; third, abnormal behavior markers appearing within the communication period, such as signal timeout, response loss, transmission interruption, or phase jump exceeding the limit. Sort the above data by time axis and fill in missing sampling points to form a time-continuous and parameter-complete communication event sequence. On this basis, construct an event chain rollback framework, starting from the communication failure point, and gradually analyze each communication behavior node to determine whether it is caused by specific interference characteristics, such as phase overlap caused by channel competition, path ambiguity caused by frequency drift, or feedback distortion caused by abnormal receiving end state. Each rollback path constitutes a counterfactual chain segment, and multiple chain segments combine to form a complete communication behavior counterfactual replay chain, which is used to reproduce the key error occurrence logic in the communication process and provide a concrete high-risk path behavior expression structure.

[0074] Based on the time sequence and phase record of each communication node in the counterfactual replay chain, the phase offset occurring in the communication process is modeled backtracking, forming a continuous profile of phase deviation, and from which a set of phase calibration parameters is constructed. The specific method is as follows: first, align the transmission phase and the receiving phase of all communication nodes on a unified time reference, compare the phase angle change value before and after each communication event, and identify the mutation jump region and the stable fluctuation region. In the mutation region, take the previous and next five time slices as the window, calculate the continuous change rate of the phase angle, and extract the average offset amplitude and offset direction. For the region where the phase fluctuates continuously but does not exceed the abnormal threshold, use the weighted moving average method to extract the trend line to form a continuous phase drift curve. Superimpose all phase offset events on the time axis of the entire communication period to obtain a phase profile covering all communication events. This graph is used to mark which time period the communication event is disturbed by the obvious phase disturbance, and is classified according to the offset trend. The classification methods include three categories: sharp mutation, slow drift and stable oscillation. Each category corresponds to a different phase calibration factor processing method. For the mutation offset, a short-period compensation strategy is adopted to superimpose the original signal offset vector in the opposite direction on the received signal for immediate correction; for the drift offset, a delay correction is adopted, which slowly corrects by dynamically adjusting the channel access time of the receiving end; for the oscillation offset, a fault-tolerant delay window and a dynamic time jump threshold are introduced to cooperate to perform anti-jitter compensation. The correction parameters output by all calibration methods are uniformly included in the phase calibration parameter set to form a multi-dimensional phase compensation vector, which provides offset input data sources for subsequent time grid reconstruction.

[0075] After obtaining the phase calibration parameter set, a reversible time grid structure is constructed based on the communication scheduling period, and a time grid parameter group containing complete timing calibration values is output. The specific construction method is to divide the entire communication execution period into time grid units based on the communication scheduling basic time slot, for example, every 20 milliseconds. For each communication event in the time grid, the corresponding phase calibration vector is called, and three types of parameter values are marked: one is the average phase error value, which is used to judge the basic stability of the time grid; the second is the maximum offset instantaneous value, which is used to evaluate the potential high mutation risk; the third is the consistency coefficient of the historical offset direction, which is used to judge whether the time grid has predictable offset characteristics. According to the judgment rules of the three types of parameters, the grid interval that needs to perform time correction is identified. For the case where the phase offset direction is consistent and the amplitude continuously rises in three or more grids, these grids are combined into a time offset cluster for unified time rearrangement. The rearrangement strategy adjusts the actual execution order of these time grids in the scheduling period to achieve time relief of the offset trend, thereby reducing the probability of forming a chain error in continuous communication. Finally, each reconstructable time grid block is output as a parameter package containing four items: start time, end time, correction offset vector, and scheduling time slot delay correction value. The parameter package will be directly transmitted to the channel reconstruction stage as the bias input of the time-frequency mapping table to realize the adaptive timing reconstruction function.

[0076] With the reversible time grid parameters as input, the Hamiltonian variational routing linkage phase conjugation suppression chain is enabled to drive the energy folding trap, synchronization bias injection, and topology reversible reconstruction mechanism to operate cooperatively, and combined with the cross-layer majority voting weight reduction mechanism to realize adaptive rearrangement of channels and time slots, and finally complete the construction of the dynamic steady-state control loop of the clothes care machine remote access.

[0077] After the generation and calibration of the reversible time grid parameters are completed, a dynamically adjustable communication scheduling and control mechanism is established to realize stable control and resource adaptive allocation of the clothes care machine during remote access in a complex interference environment. The process specifically includes the following steps:

[0078] The constructed reversible time grid parameters are taken as input information to start the communication routing planning strategy based on the Hamiltonian variation mechanism. The strategy takes the time grid as the basic routing unit, extracts the starting time, ending time, channel identifier, phase offset vector and transmission delay correction value of each grid, and maps them into a high-dimensional communication routing graph. Each node in the graph represents a communicable time slice, and the weight of the edge is evaluated by the phase stability, energy utilization and historical interference frequency. In the path planning process, the Hamiltonian path principle is applied to ensure that each grid node is visited only once in the current scheduling period, thereby avoiding repeated occupation and resource conflicts. The variation calculation method is used to select the path with the minimum overall phase disturbance and the lowest frequency conflict probability from all possible feasible path combinations as the main control communication path for subsequent control flow transmission.

[0079] The Hamiltonian variation mechanism is a path selection strategy combining the Hamiltonian path principle and the variation optimization method. The core idea is to take the time slice in the communication process as a node in the graph, avoid repeated occupation of communication resources by constructing a Hamiltonian path constraint (i.e., a path that each node is visited only once), and introduce a variation calculation method to continuously evaluate and minimize the overall phase disturbance, energy distribution and interference probability of the path. In this scheme, the Hamiltonian variation mechanism is used to find a path with optimal communication performance in all available time grids within a given scheduling period, which ensures the global order of communication scheduling and accurately avoids channel phase disturbance and spectrum resource conflict, thereby establishing a stable and efficient main control channel for subsequent control instruction transmission.

[0080] To address the problem of continuous time slot phase mismatch in the main control communication path, the phase conjugate suppression chain mechanism is started to construct a phase smoothing channel between adjacent time slices. This mechanism sets a differential response processing area between two adjacent time grids, identifies the location of the phase fluctuation fault by extracting the phase offset trend and average phase jump variable before and after it. At the identified jump point, a cancellation waveform with the same amplitude but opposite direction is constructed and injected into the next grid time window through a pre-set radio frequency structure, so that the signal waveform changes from sudden to continuous smooth transition on the time axis. Within three or more consecutive jump point intervals, a complete conjugate inverse phase path chain is constructed to systematically balance the phase disturbance of the entire path segment.

[0081] In the process of executing the conjugate inhibition chain, the synchronous start energy return trap and the synchronous bias injection mechanism are implemented to realize the unified control of signal energy management and time domain synchronization scheduling. The specific method is to detect the energy amplitude change of each time grid in the path, identify the power surge point, and set the point as the energy return source node, and locate the previous stable communication node as the return target. The abnormal energy is guided back to the target node through the reverse guide path, and the local field dissipation structure is used for charge absorption and suppression to prevent high energy surges from conducting indefinitely in the path and causing channel overload. On this basis, a fixed delay offset injection mechanism is established based on the two ends of the communication path, and a synchronous control signal with a small delay offset is injected into each hop grid to calibrate the time axis alignment offset caused by phase disturbance or physical distance difference in the path. The two mechanisms work together to not only slow down the burst communication link caused by instantaneous energy impact, but also dynamically adjust the synchronization of multiple signal segments in the path on the time axis to achieve high consistency of path physical modulation attributes and control timing parameters.

[0082] The topology reversible reconstruction operation is performed on the communication structure after the path energy modulation and phase smoothing processing to adapt to the needs of task density changes and path resource dynamic adjustment in complex communication environment. This operation analyzes the use frequency, average processing delay and historical packet loss record of all time grids in the master control path, and sets the nodes with high resource allocation density but low communication quality as high-load channels, and sets the nodes with low use frequency but stable feedback performance as preferred channels. Then, combined with the node connectivity and path overlap in the current network topology graph, the channel switching strategy is used to identify the channel cluster with bottleneck congestion, communication conflict or delay amplification, and the high-load channel is transferred to the edge position of the topology to disperse the communication density. Subsequently, the scheduling time slots of all newly generated path nodes are updated, and their position order in the communication cycle table is adjusted, and their phase entry conditions are calibrated combined with the synchronization bias value in the previous stage.

[0083] After the topology reconstruction is completed, the cross-layer majority voting weight reduction mechanism is executed to confirm and adjust the weight of the channel and time slot resource configuration, thereby completing the closed-loop control of the remote access communication ring. This mechanism divides all scheduling paths into multiple hierarchical groups, and each group of nodes is distributed in layers according to the communication task urgency, historical feedback stability and access priority. During scheduling, each hierarchical group independently votes to evaluate the performance of the current channel and time slot resources, including processing delay, energy stability, phase disturbance rate and other indicators. The score of each resource unit in all levels is counted and ranked in descending order. When a resource unit is judged to be underperforming in multiple levels, its priority is reduced by one level and its high-quality time period is delayed by two time slot periods; if a resource unit is rated as high performance by multiple levels, its priority is increased and its scheduling time window is expanded to realize high-frequency reuse of high-value resources.

[0084] The cross-layer majority voting down-weighting mechanism is a resource optimization strategy based on multi-dimensional scheduling layer joint evaluation, and the core is to divide all participating communication channels and time slot resources into different level groups according to task urgency, historical feedback stability and access priority, each group independently scores the performance indicators of communication resources, including processing delay, energy stability and phase disturbance rate. By initiating voting evaluation in each level, and summarizing the evaluation results of each resource unit in the majority group, it is judged whether it is stable and reliable. If the resource performs poorly in the majority group, its priority is reduced and its use time is postponed; if it is rated as a high-performance resource by multiple levels, its use priority is increased and the scheduling time window is expanded. In this scheme, the mechanism is used to confirm and dynamically optimize the entire scheduling structure after completing path construction and resource rearrangement, ensuring that high-quality resources are highly reused and low-quality resources are actively avoided during communication, thereby improving the overall stability and control accuracy of the remote access communication ring.

[0085] The present application realizes accurate extraction of high-frequency signal conflict characteristics by introducing a space-time interference observation baseline and a phase difference spectrum, combines anti-aliasing tracking and topology modeling capabilities to locate and restore the structure of the interference source, and then uses a risk operator matrix to quantitatively analyze the degree of interference influence, and accordingly constructs an intervention path sorted by risk. On this basis, through identity lock frequency linkage and phase conjugate suppression chain, etc. Real-time dynamic adjustment of the communication path, combined with energy return mechanism and time grid calibration operation, effectively alleviates problems such as signal congestion, transmission jump, synchronization offset, etc. At the same time, the introduction of topology reversible reconstruction and cross-layer majority voting down-weighting mechanism ensures the optimal configuration and stable operation of channel and time slot resources in complex environments. In summary, the scheme not only improves the anti-interference ability and control accuracy of the clothes care machine remote access, but also builds a dynamic communication stability mechanism with self-sensing, self-adjusting and self-closed loop capabilities at the system level.

[0086] The above only describes certain exemplary embodiments of the present application by way of illustration, without doubt, for those skilled in the art, the described embodiments can be modified in various ways without departing from the spirit and scope of the present application. Therefore, the above figures and description are illustrative in nature and should not be construed as limiting the scope of the claims of the present application.

Claims

1. A method for remote device access based on the Internet of Things, characterized in that, The method comprises the following steps: Establishing a space-time interference observation baseline during the remote access process, extracting conflict phase fingerprints, generating a phase difference spectrum, and using it as a timing reference for interference tracking; Using the phase difference spectrum to perform anti-aliasing tracking, dynamically scanning signal energy density, identifying overlapping channels and congested time slots, and constructing an interference source topology structure; Based on the interference source topology structure, a risk operator matrix is constructed, the timing damage degree is calculated, and the interference handling queue sorted by risk is output; According to the interference handling queue, identity lock frequency linkage is performed, channel priority is adjusted, time slot isolation windows are set, high-risk channels are frozen, and safe channel transmission is maintained; Under the identity lock frequency linkage, a counterfactual playback chain is introduced, the communication trajectory is reproduced, the phase deviation is calibrated, and the reversible time grid parameters are output; The identity lock frequency linkage steps are as follows: According to the risk score, interference duration, frequency range, and signal strength change rate of the interference path in the interference handling queue, a high-risk frequency channel set is extracted, a communication identity frequency usage authority table is established, and temporary blocking is performed on the high-risk channels; Set the channel priority and divide the time slot isolation window, allocate continuous available time period to high priority channel, and configure part of the isolation window to the medium priority channel; Freeze all communication time periods of high-risk channels, and dynamically adjust the scheduling priority of continuously activated channels based on performance evaluation; Take the reversible time grid parameters as input, enable the Hamiltonian variation routing linkage phase conjugation suppression chain, drive the interference suppression mechanism to operate cooperatively, realize channel and time slot adaptive rearrangement combined with cross-layer majority voting weight reduction mechanism, and complete the dynamic and steady-state control loop construction.

2. The method of claim 1, wherein, The phase difference spectrum generation steps are as follows: When the garment care machine is preparing to establish a remote connection, start the interference observation initialization process, scan the working state of the high-frequency communication channel, and set the frequency reference benchmark to ensure signal frequency synchronization consistency; Set the observation time window and sampling interval, collect signal phase, amplitude, and propagation time delay data at frequency points, and record signal incident angle, time difference, and intensity distribution at multiple spatial sampling points; Calibrate the data in the continuous sampling period to the background noise to form an initial space-time reference table; Extract features and perform cluster analysis on the phase mutation data to generate conflict phase fingerprints, and fuse the fingerprint information to form a complete phase difference spectrum, which is used as a timing reference for subsequent interference tracking. 3.The method of claim 2, wherein, The interference source topology structure construction steps are as follows: Based on the phase difference spectrum, extract the phase change curve of the frequency slice, identify the frequency mutation event, and generate a frequency-time disturbance spectrum; Construct an energy density scanning mechanism on the time axis in the mutation frequency interval, calculate the energy and phase change rate, and identify the frequency spectrum congestion time slot; Fuse the spatial propagation direction corresponding to the frequency mutation and energy mutation to form a time-frequency-space three-dimensional path cluster; According to the start and end points, direction continuity, and frequency coincidence relationship of the path, an interference source topology graph is constructed, which is used as data support for subsequent interference handling steps.

4. The method of claim 3, wherein, When constructing the interference source topology graph, the start and end points of each interference path are used as the two end nodes of the directed edge, and the connection nodes are established at the path intersection positions. The propagation duration, energy intensity change interval, and spatial propagation direction are used to label the topology edge attributes.

5. The method of claim 3, wherein, The interference handling queue output step is as follows: Based on the interference source topology, the duration, frequency bandwidth, spatial propagation distance, path intersection number and repetition frequency of the interference path are extracted, normalized and formed into a risk operator matrix; The risk operator matrix is matched with the time sequence stage sensitivity model of the remote control instruction to build the time sequence damage score of each path in the remote connection, instruction transmission, state synchronization and feedback response stage; According to the scoring results, the interference handling queue is output from high to low, and the freezing frequency band, delay window and power adjustment strategy are set according to the path characteristics to realize the scheduling priority division. 6.The method of claim 1, wherein, The reversible time grid parameter output step is as follows: Extract the historical data of high-risk channels in the communication cycle before freezing to build a communication event sequence containing transmission phase, reception phase, frequency offset and abnormal marker; Based on the communication event sequence, an anti-fact replay chain is established to analyze the phase disturbance reason and output the phase calibration parameter set; According to the phase calibration parameter set, the reversible time grid structure is constructed, and the time grid parameter group containing the time calibration value, phase offset vector and scheduling delay correction value is output.

7. The method of claim 6, wherein, With the reversible time grid parameters as input, the Hamiltonian variation routing linkage phase conjugation suppression chain is enabled to drive the interference suppression mechanism to operate cooperatively, and the cross-layer majority voting weight reduction mechanism is combined to realize the adaptive rearrangement of channels and time slots as follows: Based on the reversible time grid parameters, a communication routing graph is constructed, and the Hamiltonian variation path strategy is applied to determine the master communication path; Performing phase conjugation suppression operation on the master communication path can eliminate the phase mutation fault between adjacent time grids; Jointly performing energy return and synchronization bias injection processing, a signal energy feedback path is constructed and the communication time axis is offset corrected; According to the path running characteristics, the topology is reconstructed reversibly to complete the scheduling dispersion of high-load paths and the rearrangement of preferred channels; Combined with the communication task level, the majority voting weight reduction control is implemented to finally adjust the channel and time slot resource configuration results, realizing the dynamic scheduling closed loop.

Images