Signal virtual block coverage processing method and system for communication control

Abstract

The application provides a signal virtual block coverage processing method and system for communication control, and relates to the technical field of signal processing. The method comprises the following steps: constructing a signal arrival phase evolution model before a communication control device enters a standby state; dividing a physical coverage area into multiple signal virtual coverage blocks; after entering the standby state, closing a continuous full-frequency scanning mechanism, enabling a micro-power energy gating skeleton channel to open a short-time micro-activation time window before a predicted phase anchor point arrives to execute a double-condition trigger judgment; and activating a complete receiver mechanism only when the conditions are met. The application solves the technical problem that, in the prior art, because the communication control system simply prolongs the signal search interval in the standby state to reduce power consumption, signal reception has a large delay or even misses, and a sudden signal cannot be captured in time and accurately, further affecting communication reliability. The application realizes zero-miss and low-delay signal capture while maintaining micro-power standby, and improves communication reliability.

Description

Technical Field

[0001] This application relates to the field of signal processing technology, specifically to a method and system for processing virtual block overlays of signals for communication control. Background Technology

[0002] Currently, the signal monitoring mechanism of communication control systems in standby mode generally adopts a strategy of extending the search interval to reduce power consumption. That is, the signal scanning frequency is reduced during periods of no service interaction, so that the communication control system is in a low-power sleep mode most of the time. However, this passive monitoring strategy based on a fixed period has inherent defects. When a sudden signal arrives within the sleep window, it cannot be detected and responded to in time, resulting in signal reception delays or even complete omissions. This is especially true in application scenarios where the channel environment changes dynamically or services are triggered suddenly, which seriously affects the reliability and real-time performance of communication.

[0003] In summary, existing technologies suffer from the technical problem that, due to the communication control system simply extending the signal search interval in standby mode to reduce power consumption, signal reception is subject to significant delays or even omissions, making it impossible to capture sudden signals in a timely and accurate manner, which further affects communication reliability. Summary of the Invention

[0004] The purpose of this application is to provide a signal virtual block coverage processing method and system for communication control, in order to solve the technical problem in the prior art that, due to the communication control system simply extending the signal search interval in the standby state to reduce power consumption, there is a large delay or even omission in signal reception, making it impossible to capture sudden signals in a timely and accurate manner, which further affects the reliability of communication.

[0005] To achieve the above objectives, this application provides a method and system for processing signal virtual block overlay for communication control.

[0006] In a first aspect, this application provides a signal virtual block coverage processing method for communication control. This method is implemented through a signal virtual block coverage processing system for communication control. The method includes: before the communication control device enters standby mode, collecting historical signal arrival time sequences, channel active duty cycle distribution, and service trigger spatial distribution data; constructing a signal arrival phase evolution model; and forming a block-time coupling matrix characterizing the signal arrival probability in different spatial regions. Based on the block-time coupling matrix, the physical coverage area is divided into multiple signal virtual coverage blocks, and each signal virtual coverage block is assigned an independent set of phase anchor points and phase uncertainty parameters. A low-power energy-gated skeleton channel is constructed. After entering standby mode, the continuous full-frequency scanning mechanism is disabled, and a short-term micro-activation time window is opened within a preset time window before the arrival of the time phase anchor point of the corresponding virtual coverage block. A dual-condition trigger determination is performed in conjunction with the low-power energy-gated skeleton channel. If the dual-condition trigger determination passes, the complete signal reception mechanism of the corresponding virtual coverage block is activated, and signal reception is performed.

[0007] Optionally, the collected data undergoes denoising, normalization, and time synchronization processing to construct a unified data representation set. Using this data representation set, each spatial region, each time slice, and channel state is mapped to a multi-dimensional state vector. The dimensions of the multi-dimensional state vector include signal arrival time, signal strength estimation, channel activity duty cycle, and service trigger probability. The multi-dimensional state vector is statistically distributed, and the distribution density and covariance matrix of each dimension are calculated. Based on the distribution density and covariance matrix, a multi-dimensional phase evolution function is used to map historical signal arrival time and strength information to the phase evolution curve of each spatial region. A signal arrival phase evolution model is constructed based on the phase evolution curve.

[0008] Optionally, the multidimensional phase evolution function includes the following computational terms: an amplitude adjustment term, used to calculate the amplitude factor of the signal arrival phase change based on the distribution density of the multidimensional state vector in each spatial region; a directional coupling term, used to calculate the phase drift direction and drift rate using the covariance matrix between multiple dimensions, mapping the correlation between dimensions to the gradient direction of the multidimensional phase evolution function; a historical inertia term, used to form an inertial trend based on the weighted accumulation of phase changes in historical time series; and a disturbance correction term, used to correct short-term abnormal deviations by combining signal strength estimation and channel active duty cycle.

[0009] Optionally, the physical coverage area is processed into a spatial grid to form an initial set of sub-regions; for each initial sub-region, the signal arrival probability and signal arrival phase evolution model of the corresponding time slice in the block-time coupling matrix are used to calculate the signal activity index of the corresponding initial sub-region in the future time window; the signal activity index is used to merge or subdivide adjacent initial sub-regions to form multiple virtual signal coverage blocks.

[0010] Optionally, after disabling the continuous full-frequency scanning mechanism, the low-power energy-gated skeleton channel is activated to perform low-resolution energy projection processing on the spectrum to be monitored using frequency domain compression mapping, generating a low-dimensional projection vector characterizing the probability distribution of signal presence. Based on the low-dimensional projection vector, a continuously updated signal presence probability field is constructed, and prediction parameters characterizing the disturbance trend and phase drift trend are output. Within a short-term micro-activation time window before the arrival of the time phase anchor point of the corresponding virtual coverage block, a phase compression calibration mechanism is enabled to perform local high-resolution sampling correction on the signal presence probability field. When the high-resolution sampling correction result and the prediction trend of the prediction parameters meet the consistency judgment condition, the dual-condition trigger judgment passes.

[0011] Optionally, the signal existence probability field is also used to drive the spatial-temporal structure linkage reconstruction of the virtual signal coverage block, including: calculating the probability density distribution gradient and active centroid position within each virtual coverage block based on the continuously updated signal existence probability field; determining the block drift amount using the probability density distribution gradient and active centroid position, wherein the block drift amount includes the drift direction and drift amplitude; when the drift amplitude exceeds a preset structure adjustment threshold, performing an overall translation or boundary morphology reconstruction on the virtual signal coverage block according to the block drift amount; after the block completes the spatial position adjustment, recalculating the corresponding time phase anchor point set and short-term micro-activation time window parameters based on the adjusted block spatial coordinates.

[0012] Optionally, if the dual-condition triggering determination fails, the corresponding signal virtual coverage block is kept in a low-power active state in the low-power energy-gated skeleton channel, and a low-resolution frequency domain scan is periodically performed.

[0013] Optionally, the signal reception status is determined, and a signal reception dataset is established; the signal reception dataset is used to identify reception anomalies and establish a reception anomaly warning system.

[0014] Optionally, after signal reception is completed, the signal arrival phase evolution model is updated according to the offset between the actual signal arrival time and the predicted phase anchor point; the updated signal arrival phase evolution model is used to perform block reconstruction on multiple virtual signal coverage blocks, and the next round of standby state trigger determination and signal reception optimization is performed based on the block reconstruction results.

[0015] Secondly, this application also provides a signal virtual block coverage processing system for communication control, used to execute the signal virtual block coverage processing method for communication control as described in the first aspect, wherein the signal virtual block coverage processing system for communication control includes: a signal prediction module, used to collect historical signaling arrival time series, channel active duty cycle distribution, and service triggering spatial distribution data before the communication control device enters standby mode, construct a signal arrival phase evolution model, and form a block-time coupling matrix characterizing the signal arrival probability of different spatial regions; and a region division module, used to divide the region based on the block-time coupling matrix. The array divides the physical coverage area into multiple virtual signal coverage blocks and assigns an independent set of phase anchor points and phase uncertainty parameters to each virtual signal coverage block. The low-power skeleton and dual-condition judgment module are used to construct the low-power energy-gated skeleton channel. After entering the standby state, the continuous full-frequency scanning mechanism is turned off, and a short-term micro-activation time window is opened within a preset time window before the time phase anchor point of the corresponding virtual coverage block is reached. Combined with the low-power energy-gated skeleton channel, dual-condition trigger judgment is performed. The signal receiving module is used to activate the complete signal receiving mechanism of the corresponding virtual coverage block and perform signal reception if the dual-condition trigger judgment is passed.

[0016] One or more technical solutions provided in this application have at least the following technical effects or advantages: by collecting historical signal arrival time series to construct a signal arrival phase evolution model and dividing the signal virtual coverage block, the continuous full-frequency scanning mechanism is turned off and the low-power energy-gated skeleton channel is enabled during standby, and a short-time micro-activation time window is opened to perform dual-condition triggering judgment within a certain period before the predicted phase anchor point arrives. The complete reception mechanism is activated only when the conditions are met, so as to achieve zero signal loss and low-latency acquisition while maintaining low-power standby, and greatly improve communication reliability. Attached Figure Description

[0017] Figure 1 This is a flowchart illustrating the signal virtual block overlay processing method for communication control used in this application.

[0018] Figure 2 This is a schematic diagram of the signal virtual block overlay processing system used for communication control in this application.

[0019] Figure labeling: Signal prediction module 11, Region division module 12, Low power skeleton and dual-condition determination module 13, Signal receiving module 14. Detailed Implementation

[0020] This application provides a signal virtual block coverage processing method and system for communication control, solving the technical problem in the prior art where the communication control system simply extends the signal search interval to reduce power consumption in standby mode, resulting in significant delays or even omissions in signal reception, making it impossible to capture sudden signals in a timely and accurate manner, and further affecting communication reliability. By collecting historical signal arrival time series to construct a signal arrival phase evolution model and dividing the signal into virtual coverage blocks, the application disables the continuous full-frequency scanning mechanism and enables the low-power energy-gated skeleton channel in standby mode. Furthermore, a short-term micro-activation time window is opened to perform dual-condition triggering judgment a period before the predicted phase anchor point arrives. The complete reception mechanism is activated only when the conditions are met, achieving zero signal omission and low-latency acquisition while maintaining low-power standby, significantly improving communication reliability.

[0021] The technical solutions of this application will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. It should be understood that this application is not limited to the exemplary embodiments described herein. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application. It should also be noted that, for ease of description, only the parts related to this application are shown in the accompanying drawings, not all of them.

[0022] Example 1, please refer to the appendix. Figure 1 This application provides a method for processing signal virtual block overlay for communication control, wherein the method is executed by a system for processing signal virtual block overlay for communication control, and the method specifically includes the following steps:

[0023] Before the communication control device enters standby mode, historical signaling arrival time series, channel active duty cycle distribution and service triggering spatial distribution data are collected to construct a signal arrival phase evolution model and form a block-time coupling matrix that characterizes the signal arrival probability in different spatial regions.

[0024] Furthermore, this application also includes the following steps: performing denoising, normalization, and time synchronization processing on the collected data to construct a unified data representation set; using the data representation set to map each spatial region, each time slice, and channel state into a multi-dimensional state vector, wherein the dimensions of the multi-dimensional state vector include signal arrival time, signal strength estimation, channel activity duty cycle, and service trigger probability; performing statistical distribution on the multi-dimensional state vector and calculating the distribution density and covariance matrix of each dimension; based on the distribution density and covariance matrix, using a multi-dimensional phase evolution function to map historical signal arrival time and strength information onto the phase evolution curve of each spatial region; and constructing a signal arrival phase evolution model based on the phase evolution curve.

[0025] Specifically, before the communication control device enters standby mode, it initiates a data acquisition and learning phase, continuously recording the specific timestamp of each signal arrival to form a signaling arrival time sequence; it also statistically analyzes the proportion of channel occupancy time in different time periods or frequency bands to form a channel active duty cycle distribution; simultaneously, combined with positioning functions, it records the spatial location of the terminal equipment triggering the communication service, forming service trigger spatial distribution data. The historical signaling arrival time sequence is a data sequence formed by the specific moments recorded by the communication control system each time a signal is successfully received over a past period, reflecting the regularity of signals in the time dimension. The channel active duty cycle distribution is the distribution of the proportion of time the communication channel is busy within a unit of time across different time periods or regions, reflecting the busyness and regularity of channel usage. The service trigger spatial distribution data records the physical location information of the terminal equipment triggering the communication service, revealing the spatial distribution pattern of communication events.

[0026] After obtaining this raw data, data preprocessing is required, including data cleaning, normalization, and time synchronization. This removes interference or noise from historical data and filters out abnormal signaling records caused by electromagnetic interference or occasional hardware failures, ensuring the quality of the signal data. Since data from different sources is heterogeneous, normalization is used to eliminate calculation biases caused by different units such as time, duty cycle, and probability values, scaling all data to a preset numerical range, such as between zero and one. Because data may come from different acquisition modules, precise time stamps are added to all data using a high-precision system clock as a reference, ensuring they are aligned on the same time axis and achieving time synchronization, thus obtaining a unified data representation set. Noise removal filters out erroneous data caused by interference or anomalies; normalization maps data with different dimensions to the same numerical range, eliminating the influence of dimensions; and time synchronization ensures that data from different data sources or different sampling times are aligned to a unified time axis. For example, 100,000 historical signaling records were collected through a wireless sensor network within a factory, forming spatial distribution data of service triggers. Meanwhile, it was recorded that the assembly line channel activity duty cycle reached as high as 70% from 9:00 AM to 10:00 AM daily, while it was only 10% during other times. All signal arrival times were accurate to the millisecond level. 120 abnormal signaling messages generated during equipment restarts were filtered out. All data, including raw arrival times such as 09:03:15.200, 70% duty cycle, and probability value of 0.8, were uniformly mapped to the range of 0 to 1 using a maximum-minimum normalization method. Finally, using a network time protocol server as a benchmark, the timestamp error of all data was calibrated to within 1ms, forming a unified data set.

[0027] The data representation set is structured according to a predefined spatial grid and time slices. The entire coverage area is divided into small squares, and time is divided into consecutive time periods. For each spatial grid within each time slice, all data is combined into a multi-dimensional state vector. This vector contains multiple dimensions, including the average arrival time, average signal strength, statistically calculated channel activity duty cycle, and service trigger probability calculated based on historical occurrences for that grid within that time slice. In simpler terms, the dimensions include the specific time of signal arrival, signal strength, channel activity level, and the probability of triggering a service. For example, each workshop can be defined as a spatial area, and a 24-hour day can be divided into 30-minute time slices. Taking the time slot from 9:00 to 9:30 AM every day in Workshop C as an example, the historical average arrival time of the signal in this workshop during this period is 09:15:01, the average received signal strength is -85dBm, the channel active duty cycle is 65%, and the service trigger probability calculated based on the number of historical triggers is 0.95. Together, these constitute the multidimensional state vector of Workshop C in this time slot.

[0028] Statistical distribution analysis of a multidimensional state vector involves first calculating the distribution density of each dimension. For example, data in the signal arrival time dimension is likely to be concentrated around a certain time point, forming a distribution peak. Simultaneously, the covariance matrix between different dimensions is calculated to quantify their correlation. For instance, when the signal arrival time deviates from the average, the signal strength often weakens; this negative correlation is recorded in the covariance matrix. The distribution density describes the central tendency of data values ​​in each feature dimension, such as the likely time point at which the signal arrives. The covariance matrix quantifies the mutual influence between different feature dimensions, such as whether the signal strength weakens when the signal arrival time is late.

[0029] The pre-defined multidimensional phase evolution function is invoked, receiving three inputs: first, the distribution density, used to determine the amplitude factor of signal changes, i.e., the range of fluctuations in signal arrival time; second, the covariance matrix, used to determine the directional coupling term of signal changes, i.e., how the mutual influence between different feature dimensions will cause the phase anchor point to drift, and what the drift rate is; and third, the original historical signal arrival time and intensity information, used to construct the historical inertia term, i.e., the continued influence of past trends on future predictions, and the disturbance correction term, used to handle small deviations caused by short-term emergencies.

[0030] For each spatial region, the multidimensional phase evolution function begins independent computation. Taking a specific region as an example, the multidimensional phase evolution function determines the baseline amplitude of phase change based on the distribution density of that region, i.e., the possible fluctuation range of signal arrival time under normal circumstances. Using the correlation information recorded in the covariance matrix, it calculates the direction and speed at which the phase anchor point should drift at the current moment. For example, if the covariance matrix shows that the signal strength usually weakens when the arrival time is later, the function will appropriately adjust the time point when predicting subsequent phase anchor points to compensate for this trend. Next, a historical inertia term is introduced, which weights and accumulates the actual signal arrival times of the past few cycles into the current prediction to ensure the smoothness and continuity of the prediction curve. At the same time, combined with real-time inputs such as signal strength and channel activity, a perturbation correction term is used to fine-tune short-term abnormal deviations. After the above series of complex calculations, the multidimensional phase evolution function finally generates a smooth, time-varying curve for this spatial region, i.e., the phase evolution curve. Each point on this curve corresponds to a predicted value for a future time, indicating that a signal is expected to arrive in that region at that time. After performing the above calculations on all preset spatial regions, a set of phase evolution curves will be obtained. Organizing these curves according to their spatial location to form a complete database or lookup table will construct the final signal arrival phase evolution model. As long as the current time and target spatial region are input, the model can quickly output the phase anchor point where the next signal is most likely to arrive in that region, as well as the uncertainty parameters associated with the prediction.

[0031] For example, taking workshop C as an example, the signal arrival time follows a normal distribution with a mean of 09:15 and a standard deviation of 180s; the signal strength follows a normal distribution with a mean of -85dBm and a standard deviation of 5dBm. The covariance coefficient between signal arrival time and signal strength is -0.6, indicating a moderate negative correlation between the two; the covariance coefficient between signal arrival time and channel active duty cycle is 0.3, indicating a weak positive correlation. A multidimensional phase evolution function is used to generate the phase evolution curve for the morning period in workshop C. At 08:00 AM, based on the historical inertia term, the next phase anchor point is predicted to be 08:22, with an uncertainty parameter of 150s, adjusted based on the standard deviation of the distribution density. When the actual time reaches 08:22 and no signal arrives, the directional coupling term of the function begins to take effect. The covariance matrix shows a positive correlation between time and duty cycle. Since the current duty cycle is lower than the historical average, the function determines that the phase anchor point needs to drift to the right, adjusting the next prediction anchor point to 08:47. Simultaneously, the perturbation correction term expands the uncertainty parameter to 200s to reflect the decrease in prediction reliability. At 09:00 AM, based on dense historical data, the function calculates a shorter interval between phase anchor points, predicting a high-probability signal arrival at 09:15, narrowing the uncertainty parameter to 90s, and predicting a signal strength of -84dBm. This calculation is performed every 30 minutes for workshop C throughout the 24 hours, yielding 48 prediction points. Connecting these points creates a smooth curve, the phase evolution curve for workshop C. Similarly, curves are generated for other workshops. These curves are encoded by workshop location and stored in the system's non-volatile memory, forming a lookup table. At 09:10 AM that day, the phase evolution model for workshop C was checked. The model output the next phase anchor point as 09:15, with an uncertainty parameter of 90 seconds. This means that a signal is highly likely to appear in workshop C within 90 seconds before or after 09:15. The model also predicted a signal strength of approximately -84 dBm and a channel active duty cycle of approximately 68%.

[0032] The signal arrival phase evolution model is a predictive model built on historical communication data. Its core function is to predict the optimal expected time point for future signal arrival, i.e., the phase anchor point, for each spatial region. The signal arrival phase evolution model learns the signal arrival patterns for each spatial region based on historical data, and can predict the most likely phase anchor point for the next signal arrival based on the current time, i.e., the optimal expected reception time point, and attaches an uncertainty parameter to each prediction.

[0033] For each spatial grid and each time slice, the historical number of signal arrivals within that time slice is directly counted and divided by the total number of historical days or the total number of samples to obtain a probability value between zero and one. For example, if a grid has detected a signal on 25 out of the past 30 days within the time slice from 9:00 AM to 9:30 AM, then the probability value for that cell is 25 divided by 30, approximately equal to 0.83. Filling the probability values ​​of all grids and all time slices into a two-dimensional table forms the block-time coupling matrix, which shows the temporal and spatial distribution of the signal. The block-time coupling matrix is ​​a two-dimensional mathematical table where rows represent different spatial regions (blocks), columns represent consecutive time slices, and the value of each cell in the matrix represents the probability of signal arrival within the corresponding spatial region and time slice.

[0034] The block-time coupling matrix can be dynamically updated by statistically analyzing the actual prediction results of the phase evolution model, making it more accurately reflect the current signal distribution patterns. Simultaneously, the block-time coupling matrix is ​​not a one-way information receiver; it also provides guidance for optimizing the phase evolution model. When historical data for a spatial region is sparse, the probability values ​​in the coupling matrix can serve as a reference for the initial parameters of the phase evolution model. For example, if the coupling matrix shows that the probability of a certain region is extremely low in a certain time slice, the prediction interval for that time slice can be appropriately increased to reduce unnecessary calculations. If the predictions of the phase evolution model deviate significantly from the long-term statistical probabilities in the coupling matrix—for example, the model predicts a high probability for a certain period, but the coupling matrix shows an extremely low historical probability—an anomaly detection mechanism is triggered to check for environmental changes or model drift. The signal arrival phase evolution model is the data source and update basis for the coupling matrix, while the block-time coupling matrix is ​​the probabilistic statistical projection of the signal arrival phase evolution model on a macroscopic spatiotemporal scale. Simply put, by statistically analyzing all the prediction results output by the signal arrival phase evolution model over a period of time, the block-time coupling matrix can be formed and continuously updated.

[0035] By constructing a block-time coupling matrix, the arrival probability of signals in different time periods and regions is predicted, thereby reducing signal reception delay. The phase anchor point and uncertainty parameters output by the phase evolution model are the direct basis for subsequent short-time micro-activation time window settings; the probability values ​​in the block-time coupling matrix are the core input for subsequently dividing the physical area into virtual signal coverage blocks. Without these two fundamental tools, the entire solution cannot be implemented. By constructing the signal arrival phase evolution model and the block-time coupling matrix, the communication control system no longer merely reacts to the arriving signal, but can prepare in advance based on model predictions, waking up at the most precise moment with the lowest power consumption to receive the signal.

[0036] Furthermore, this application also includes the following steps: the multidimensional phase evolution function includes the following calculation terms: amplitude adjustment term, used to calculate the amplitude factor of the signal arrival phase change based on the distribution density of the multidimensional state vector in each spatial region; directional coupling term, used to calculate the phase drift direction and drift rate using the covariance matrix between multiple dimensions, mapping the correlation between dimensions to the gradient direction of the multidimensional phase evolution function; historical inertia term, used to form an inertial trend based on the weighted accumulation of phase changes in historical time series; and disturbance correction term, used to correct short-term abnormal deviations by combining signal strength estimation and channel active duty cycle.

[0037] Specifically, the multidimensional phase evolution function (MEC) is the core algorithm for constructing a signal arrival phase evolution model. It is a composite function composed of multiple specially designed computational terms. The inputs include statistical data and real-time monitoring data for each spatial region, and the output is the future phase evolution curve for that region. The MEC includes an amplitude adjustment term, a directional coupling term, a historical inertia term, and a disturbance correction term. The amplitude adjustment term determines the basic amplitude of the signal arrival time variation. Based on the distribution density (standard deviation) of the multidimensional state vector for each spatial region, it calculates the inherent fluctuation range of the signal arrival time. Simply put, it determines how far the signal might deviate from the average time under normal circumstances. The directional coupling term handles the mutual influence between different feature dimensions. Using the correlation information recorded in the covariance matrix, it calculates the direction and speed in which the phase anchor point should drift at the current moment. In simple terms, it determines how the signal arrival time will change when a factor changes. The historical inertia term is responsible for maintaining the continuity and smoothness of the prediction. It uses a weighted average of the actual signal arrival times over a period of time, with higher weights for recent data and lower weights for older data, forming an inertial trend and preventing drastic jumps in the predicted value. The disturbance correction term handles abnormal deviations caused by short-term emergencies. It combines the currently monitored real-time signal strength estimate and channel active duty cycle with the historical average. When a significant deviation is found, the prediction is fine-tuned. Simply put, it can be understood as temporarily adjusting the prediction when the environment suddenly changes.

[0038] When it is necessary to predict the next phase anchor point for a spatial region, the multidimensional phase evolution function is initiated. The amplitude adjustment term obtains the standard deviation of the signal arrival time from the distribution density of the region, multiplies it by a preset adjustment coefficient, and calculates the basic phase change amplitude, which represents the inherent fluctuation range of the signal arrival time in the region and serves as the benchmark scale for the entire prediction. For example, if the standard deviation of the signal arrival time in a region is 180s and the adjustment coefficient is set to 1.0, then the basic amplitude output by the amplitude adjustment term is 180s, meaning that under normal circumstances, the signal may arrive within a range of 180s before or after the average time.

[0039] The system acquires real-time monitoring data, including the current channel active duty cycle and the current monitored signal strength. These real-time values ​​are compared with historical averages to calculate the deviation in each dimension. The covariance matrix for that region is then used to perform calculations with the deviation vector, yielding a comprehensive drift amount and direction. This is used to capture the interactions between different factors. The covariance matrix quantifies whether the arrival time will be advanced or delayed when the signal strength increases. The directional coupling term determines the tangent direction of the phase evolution curve at that moment—that is, the direction of the phase anchor drift—by calculating the product of the covariance matrix and the current deviation vector. For example, if the covariance matrix shows a positive correlation between signal arrival time and channel active duty cycle, and the current duty cycle is significantly higher than the historical average, the directional coupling term will determine that the phase anchor should drift to the right, i.e., the time will be delayed. The magnitude of the drift is determined by both the magnitude of the deviation and the strength of the correlation.

[0040] After the amplitude adjustment term and direction coupling term determine the basic prediction, the historical inertia term is introduced for smoothing. The historical inertia term maintains a historical buffer of length L, recording the L most recent actual signal arrival times. These historical times are weighted and averaged, with weights allocated according to the time decay principle, with the most recent arrival time having the highest weight and the furthest having the lowest weight. The calculated weighted average represents the inertial trend of recent signal arrivals. Superimposing this value into the current prediction can prevent the prediction value from jumping drastically due to a single anomaly, maintaining the smoothness and continuity of the phase evolution curve.

[0041] The system continuously monitors current signal strength and channel duty cycle. When their deviation from historical averages exceeds a preset threshold, it is considered a short-term anomaly. For example, a sudden spike in the current channel duty cycle may indicate an impending surge in traffic, in which case the disturbance correction term will advance the phase anchor point appropriately. Conversely, a persistently weak signal strength may mean the terminal is moving away, in which case the phase anchor point will be delayed appropriately. The adjustment range of the disturbance correction term is typically small, intended only to address short-term fluctuations and avoid overreaction.

[0042] After the amplitude adjustment term, directional coupling term, historical inertia term, and disturbance correction term are applied sequentially, the final output is the predicted next phase anchor point for that spatial region at the current moment. The output uncertainty parameter is dynamically adjusted based on the current level of prediction uncertainty. For example, if the drift calculated by the directional coupling term is large, it indicates drastic changes in the current environment, reducing prediction reliability, and the uncertainty parameter will increase accordingly; conversely, if the historical inertia term shows a high degree of agreement between recent predictions and actual conditions, the uncertainty parameter will decrease.

[0043] Based on the block-time coupling matrix, the physical coverage area is divided into multiple virtual signal coverage blocks, and each virtual signal coverage block is assigned an independent set of phase anchor points and phase uncertainty parameters.

[0044] Furthermore, this application also includes the following steps: processing the physical coverage area into a spatial grid to form an initial set of sub-regions; for each initial sub-region, using the signal arrival probability and signal arrival phase evolution model of the corresponding time slice in the block-time coupling matrix, calculating the signal activity index of the corresponding initial sub-region in the future time window; using the signal activity index to merge or subdivide adjacent initial sub-regions to form multiple virtual signal coverage blocks.

[0045] Specifically, the physical coverage area is spatially gridded according to a preset size to form an initial set of sub-regions. For example, an industrial park might be divided into tens of thousands of 50m x 50m grids, each with a unique number. The choice of these grid sizes requires a balance between spatial resolution and computational complexity: grids that are too small will lead to a dramatic increase in computation, while grids that are too large will obscure the details of signal distribution. The physical coverage area is the actual spatial region covered by the communication and control system, typically the area where the equipment can receive signals.

[0046] For each initial sub-region, the block-time coupling matrix and the signal arrival phase evolution model are retrieved. The signal arrival probabilities for each time slice involved in a future period are extracted from the block-time coupling matrix. These probabilities are then weighted and averaged or accumulated to obtain a basic activity score. The signal arrival phase evolution model is invoked to query the number, density, and uncertainty of each anchor point predicted for each initial sub-region in the same future time period. If multiple signal arrivals are predicted for the grid within the next hour, and the anchor points are closely spaced, it indicates high recent activity for the grid. The long-term probabilities of the block-time coupling matrix are fused with the recent predictions of the signal arrival phase evolution model, such as through weighted summation, to obtain a comprehensive signal activity index. The higher this value, the more active the grid is in the future period. The signal activity index is a comprehensive score used to quantify how active a grid is in the future period, combining the long-term probability of the grid in the block-time coupling matrix and the recent arrival event density predicted by the signal arrival phase evolution model.

[0047] Merging or subdividing adjacent initial sub-regions using signal activity indices is a similarity-based spatial clustering process. An activity threshold range is set, and the entire region is scanned. When a group of adjacent grids is found whose activity indices all fall within the same preset range, these grids are merged into a single virtual signal coverage block. Conversely, if a region initially considered a single entity exhibits significant differentiation in activity indices—for example, within a large grid where higher-resolution data reveals a large difference in activity between its east and west sides—the region is further subdivided into smaller virtual blocks. Through this adaptive merging and subdivision, each resulting virtual block exhibits similar signal arrival behavior, while different blocks show clear differences between themselves.

[0048] Multiple virtual signal coverage blocks obtained through merging or subdivision are formed by merging several adjacent grids with similar signal activity. The signal behavior within each virtual block is highly consistent, thus allowing for the allocation of uniform parameters to the entire block. Parameters required for subsequent wake-up are assigned to each newly generated virtual signal coverage block. The phase evolution model prediction results of all grids within the block are collected, and these predictions are synthesized and deduplicated to form a set of phase anchor points belonging to that block. Each anchor point represents the time when the system expects a signal to arrive within that block. Simultaneously, based on the consistency of predictions from each grid within the block and the uncertainty parameter of each anchor point itself, a comprehensive phase uncertainty parameter is calculated for each anchor point. If the predictions of all grids within the block are highly consistent, the uncertainty parameter can be set smaller, meaning a narrower micro-activation time window can be opened, thus saving power. If the prediction differences within the block are large, the uncertainty parameter needs to be appropriately increased to ensure that no signal is missed. The phase anchor set is a set of predicted time points assigned to a virtual block, representing the time when the system expects a signal to arrive in that block; the phase uncertainty parameter is a parameter associated with the phase anchors, representing the confidence range of each prediction anchor. A larger uncertainty means greater instability in signal phase prediction.

[0049] By using gridding and calculating signal activity metrics, signal reception is optimized for different areas. Areas with strong signals are merged to form larger virtual coverage blocks, while areas with weak signals are subdivided to increase signal reception accuracy. By assigning a set of phase anchor points and uncertainty parameters to each virtual coverage block, signal reception is synchronized, reducing errors caused by signal fluctuations.

[0050] A low-power energy-gated skeleton channel is constructed. After entering the standby state, the continuous full-frequency scanning mechanism is turned off. A short-term micro-activation time window is opened within a preset time window before the time phase anchor point of the corresponding virtual coverage block is reached. The low-power energy-gated skeleton channel is combined to perform dual-condition trigger judgment.

[0051] Furthermore, this application also includes the following steps: after disabling the continuous full-frequency scanning mechanism, the low-power energy-gated skeleton channel is activated, and low-resolution energy projection processing is performed on the spectrum to be monitored using frequency domain compression mapping to generate a low-dimensional projection vector characterizing the probability distribution of signal existence; based on the low-dimensional projection vector, a continuously updated signal existence probability field is constructed, and prediction parameters characterizing the disturbance trend and phase drift trend are output; within a short-term micro-activation time window before the arrival of the time phase anchor point of the corresponding virtual coverage block, a phase compression calibration mechanism is enabled to perform local high-resolution sampling correction on the signal existence probability field; when the high-resolution sampling correction result and the prediction trend of the prediction parameters meet the consistency judgment condition, the dual-condition trigger judgment passes.

[0052] Specifically, a low-power energy-gated backbone channel is constructed. This low-power signal monitoring module is a hardware and algorithm co-designed system. Its core architecture consists of four main parts: an RF front-end preprocessing unit, a frequency domain compression mapping unit, an energy integration and gating decision unit, and a timing control and parameter storage unit. These units are connected sequentially to form a closed-loop feedback mechanism. The RF front-end preprocessing unit is directly connected to the antenna and includes a broadband low-noise amplifier and a set of configurable analog bandpass filters. Its function is to perform preliminary amplification and anti-aliasing filtering on the received broadband RF signal, and then send the processed analog signal to the frequency domain compression mapping unit. The frequency domain compression mapping unit contains a multi-channel energy detector array, which divides the entire spectrum range to be monitored into several continuous frequency domain sub-bands according to a preset sub-band width. Each sub-band corresponds to an independent energy detection channel. Each channel consists of a mixer, a local oscillator, an integrator, and an analog-to-digital converter connected in series. Its working mechanism is as follows: the local oscillator of each channel downconverts the radio frequency signal of the corresponding sub-band to the baseband. The integrator accumulates the energy of the baseband signal within a fixed time period. The analog-to-digital converter converts the accumulation result into a digital energy value. Finally, the energy values ​​of all channels are combined into a low-dimensional projection vector with a dimension equal to the number of sub-bands, thereby realizing the frequency domain compression mapping that compresses the massive spectrum data of the wide frequency band into a small number of values. After receiving the low-dimensional projection vector, the energy integration and gating decision unit first stores it in a circular buffer to construct a time series. Then, it calculates the statistical characteristics of the energy of each sub-band through a sliding window, including the moving mean, variance, and energy change slope. Based on these statistical characteristics, it dynamically updates a vector field that characterizes the probability of the existence of each sub-band signal, namely the signal existence probability field. At the same time, it extracts the perturbation trend parameter that reflects the severity of energy changes and the phase drift trend parameter that reflects the tendency of the signal to shift in time from this probability field. The timing control and parameter storage unit is responsible for interacting with the system's main control chip and the phase evolution model. It obtains the set of phase anchor points and uncertainty parameters of the current virtual coverage block from the phase evolution model, and generates precise timing control signals based on this information. These signals are used to temporarily improve the working accuracy or sampling rate of the energy integration and gating decision unit within a preset micro-activation time window before the phase anchor points arrive. When the signal characteristics detected within the micro-activation time window meet the preset consistency conditions with the previously extracted disturbance trend and phase drift trend, the unit outputs a high-level wake-up signal to the system's main control, requesting the activation of the complete signal receiving path. At the same time, the result of each trigger decision (whether successful or not) is recorded and fed back to the signal arrival phase evolution model for subsequent fine-tuning and optimization of model parameters, thus forming a complete closed loop from macroscopic prediction to microscopic monitoring and feedback correction.

[0053] When the communication control system is active, the network signal and data signal search interval is shorter than when it is in standby mode. When the communication control system enters standby mode, the network signal and data signal search is delayed, and the traditional continuous full-frequency scanning mechanism is disabled to avoid continuous high-power operation. A specially designed low-power energy-gated skeleton channel is activated, operating continuously with extremely low power consumption. Its task is not precise signal decoding, but rather a coarse energy sensing of the surrounding electromagnetic environment. The continuous full-frequency scanning mechanism is the traditional receiver operating mode, where the receiver continuously scans and decodes all frequency bands, consuming high power but ensuring no signal is missed. The continuous full-frequency scanning mechanism is disabled after entering standby mode to save power.

[0054] By employing frequency domain compression mapping technology, the spectral information of a wide frequency band is compressed into a low-dimensional projection vector through energy accumulation or sub-band division. For example, the 100MHz frequency band that originally required fine sampling is now divided into 10 10MHz sub-bands. Each sub-band outputs only a value representing the total energy within that sub-band, thereby generating a 10-dimensional vector, which greatly reduces the amount of data and processing power consumption.

[0055] The low-power energy-gated skeleton channel repeatedly performs this low-resolution energy projection at a fixed period, generating a low-dimensional projection vector each time. Low-resolution energy projection performs a coarse energy detection of the spectrum, focusing only on the presence of energy, not the type of signal. The result is a low-dimensional energy distribution vector. The low-dimensional projection vector is the output of the frequency domain compression mapping, a vector containing a few values. Each value represents the sum of signal energy within the corresponding sub-band or frequency band.

[0056] These continuously generated vectors are stored in chronological order, and a dynamically updated signal existence probability field is constructed based on them. This can be understood as a dynamic heatmap depicting where the signal might be located, reflecting the probability distribution of energy disturbances in different frequency bands over a recent period. By performing time series analysis on this probability field, two key prediction parameters are extracted: those characterizing the disturbance trend and those characterizing the phase drift trend. The disturbance trend characterizes the severity and direction of energy changes; the phase drift trend prediction parameter characterizes the tendency of the signal's occurrence time to deviate, such as whether it occurs earlier or later than historical patterns.

[0057] Simultaneously, it constantly monitors the current virtual coverage block and its corresponding set of phase anchor points. When the system clock approaches a certain phase anchor point, such as within a preset time window before the anchor point is reached, a short-term micro-activation time window is triggered. Within this short-term micro-activation time window, a phase compression calibration mechanism is initiated, focusing on the local frequency band in the signal existence probability field that indicates the highest probability of a signal. This frequency band undergoes high-resolution sampling and precise energy detection. Instead of performing a coarse scan of the entire spectrum, the phase compression calibration mechanism performs high-resolution sampling and precise decoding on the local frequency band in the probability field that indicates the high probability of a signal.

[0058] After the phase compression calibration mechanism is activated, the latest signal presence probability field is read. This probability field has been continuously updated by the low-power skeleton channel over the past few seconds to tens of seconds, and it marks the signal presence probability distribution of each frequency band and the prediction parameters extracted from this distribution, including perturbation trends and phase drift trends. The calibration mechanism focuses on one or several local frequency domain regions with the highest probability values ​​in the probability field. These regions are considered to be the hotspots where the signal is most likely to appear. Local high-resolution sampling is performed on these hotspots. The energy detection, which was originally performed by the low-power skeleton channel with a wide subband, is now refined to a fine scan with a narrower bandwidth. At the same time, the temporal resolution of sampling is also greatly improved, from once every 100ms to once every millisecond or even sub-millisecond, to obtain the precise preserved characteristics of the signal, including the precise center frequency, the precise instantaneous energy value, the energy change curve over time, and even the preliminary demodulation of the signal's modulation characteristics.

[0059] The signal center frequency detected by high-resolution sampling is compared with the expected frequency band indicated in the prediction parameters to determine whether the signal appears at the correct predicted position. If the signal appears in a completely different frequency band, it is directly determined to be inconsistent. If the frequency domain matches, a time domain trend matching judgment is performed. The slope of energy change extracted from high-resolution sampling is compared with the disturbance trend in the prediction parameters to determine whether the rising or falling trend of energy matches the predicted direction and amplitude. For example, if the prediction parameters show that the energy in this frequency band should have a rapid rising trend, while the high-resolution sampling shows that the energy is stable or even falling, it is determined to be inconsistent. The precise time point of the actual occurrence of the signal is compared with the predicted phase anchor point to calculate the time deviation and determine whether the deviation is within the allowable range indicated by the phase drift trend in the prediction parameters. If the deviation is too large and exceeds the tolerance of the drift trend, it is determined to be inconsistent. Only when all three dimensions of frequency domain matching, time domain trend matching, and phase matching pass the preset threshold conditions are the high-resolution sampling correction result and the prediction trend of the prediction parameters determined to meet the consistency judgment conditions. Since the time condition is automatically met when entering the micro-activation window, the dual-condition trigger judgment is officially passed. After the dual-condition judgment passes, the timing control unit immediately outputs a high-level wake-up signal to the system's main control chip, activating the complete signal reception mechanism of the corresponding virtual coverage block. If the judgment in either dimension fails, the dual-condition trigger judgment fails. In this case, the main system will not be woken up; instead, the low-power skeleton channel will continue to operate, waiting for the arrival of the next phase anchor point. Simultaneously, the result of this failed judgment will be recorded and fed back to the phase evolution model for subsequent parameter adjustments, such as appropriately adjusting uncertainty parameters or correcting the calculation method for phase drift trends.

[0060] Furthermore, this application also includes the following steps: the signal existence probability field is also used to drive the spatial-temporal structure linkage reconstruction of the signal virtual coverage block, including: calculating the probability density distribution gradient and active centroid position inside each virtual coverage block based on the continuously updated signal existence probability field; determining the block drift amount using the probability density distribution gradient and active centroid position, the block drift amount including the drift direction and drift amplitude; when the drift amplitude exceeds a preset structure adjustment threshold, performing an overall translation or boundary morphology reconstruction on the signal virtual coverage block according to the block drift amount; after the block completes the spatial position adjustment, recalculating the corresponding time phase anchor point set and short-term micro-activation time window parameters based on the adjusted block spatial coordinates.

[0061] Specifically, during normal operation, the low-power energy-gated skeleton channel continuously operates, constantly updating the signal presence probability field. This probability field is not only used for immediate wake-up decisions but is also fed into a background block health monitoring and reconstruction module. This module is responsible for periodically evaluating whether existing virtual coverage blocks still conform to the current actual signal distribution and triggering reconstruction when necessary. An internal health check is performed on each existing virtual coverage block, reading the probability values ​​of all spatial grids covered by the block in the latest signal presence probability field. First, the probability density distribution gradient is calculated, analyzing the changing trend of probability values ​​within the block to identify the direction and rate of the most abrupt transition from high-probability to low-probability intervals, reflecting the abrupt spatial changes in signal activity. Second, the active centroid position is calculated by weighting the coordinates of all grids within the block using the probability value of each grid as a weight, resulting in a weighted average coordinate point. This point represents the current true signal activity center of the block. The probability density distribution gradient is the direction and rate of change of the probability value within a virtual block in the signal existence probability field, where the change is most dramatic. It can be understood as the direction from which signal intensity begins to decrease significantly, or the direction in which the densest signal region is located. The active centroid position is a weighted average position, calculated using the signal existence probability of each spatial point within the virtual block as weights. It represents the center point of signal activity within that block, similar to the center of mass in physics.

[0062] Based on the probability density distribution gradient and the location of the active centroid, the block drift is calculated by comparing it with the original centroid of the block. This drift includes the drift direction and drift amplitude. The drift direction is from the original centroid to the active centroid, and the drift amplitude is the straight-line distance between the two centroids. The calculated drift amplitude is compared with a preset structural adjustment threshold. If the drift amplitude is less than the preset structural adjustment threshold, it indicates that the current block division is still reasonable, the signal activity has not significantly deviated from the original area, and the module does not make any adjustments and continues monitoring. If the drift amplitude exceeds the preset structural adjustment threshold, it indicates that the signal activity has shifted significantly, the original block division can no longer accurately cover the current signal hotspot, and the reconstruction process must be initiated. The preset structural adjustment threshold is a preset numerical threshold used to determine whether the drift amount has become large enough to trigger block reconstruction.

[0063] When the drift direction is consistent and the overall outline of the signal distribution shown by the probability density distribution gradient within the block remains basically unchanged, the overall translation mode is selected. This means that the entire virtual block is translated according to the calculated drift direction and amplitude, so that its new geometric center coincides with the active centroid, while the shape and size of the block remain unchanged. When the drift direction is chaotic or the outline of the signal distribution shown by the probability density distribution gradient has changed significantly, such as when a previously concentrated hotspot area has dispersed into multiple hotspots, the boundary morphology reconstruction mode is selected. Instead of a simple translation, the boundary of the block is redrawn based on the latest probability field, including operations such as expansion, reduction, segmentation, or merging, until the new boundary can tightly surround the current high-probability region.

[0064] After spatial relocation, whether through overall translation or boundary morphology reconstruction, the set of spatial grids it covers changes. Therefore, all associated parameters must be recalculated for this block; that is, the corresponding time phase anchor point set and short-term micro-activation time window parameters must be recalculated based on the adjusted block spatial coordinates. The signal arrival phase evolution model is re-invoked, and for all grids covered by the new block, the phase evolution predictions for these grids within future time windows are queried or recalculated. These prediction results are then synthesized and clustered to form a new set of phase anchor points. Based on the consistency of predictions for each grid within the new block and the uncertainty of each anchor point itself, the phase uncertainty parameters corresponding to each anchor point are recalculated. Based on the new phase anchor points and uncertainty parameters, the short-term micro-activation time window parameters for this block are updated, including the micro-activation window opening timing and window length for each anchor point. The adjusted virtual block, the new set of phase anchor points, and the new micro-activation time window parameters will be used for subsequent standby wake-up management. This process will continue continuously, ensuring that the virtual block partitioning always follows the dynamic changes in the actual signal distribution.

[0065] For example, suppose a visitor block in a certain park is a virtual block divided based on historical data, covering an area of ​​approximately 100m × 100m near the east gate. The geometric center coordinates of this block are 120.1234°E, 30.5678°N. A low-power skeleton channel continuously updates the signal presence probability field for the entire park. Monitoring data from the past week shows that visitor activity is gradually shifting towards the parking lot inside the east gate. The block health monitoring module periodically analyzes the visitor block, reading the probability values ​​of the 100 grids (each 10m × 10m) covered by the block in the latest probability field. Calculating the probability density distribution gradient reveals a steep probability gradient on the east side of the block, decreasing gently towards the west. When calculating the active centroid location, a weighted average is performed using the probability value of each grid, resulting in new active centroid coordinates of 120.1236°E, 30.5678°N, representing an eastward shift of approximately 30m. Comparing the active centroid (120.1236, 30.5678) with the original geometric center (120.1234, 30.5678), the calculated drift direction is due east, with a drift amplitude of 30m. The preset structural adjustment threshold is set to 20m. Since 30m > 20m, the drift amplitude exceeds the threshold, triggering the reconstruction process. Although the centroid shifts eastward, the overall outline of the signal distribution within the block remains largely unchanged, forming a circular hotspot area without any splitting or severe deformation. Therefore, a complete translation is chosen, shifting the entire East Gate visitor block 30m eastward so that its new geometric center coincides with the calculated active centroid (120.1236, 30.5678). The block's coverage area becomes a 100m × 100m region after shifting 30m eastward from its original location, maintaining the same shape and size. After the block's position is adjusted, the set of grids it covers changes. The signal arrival phase evolution model is re-invoked, and phase evolution predictions for the next 24 hours are queried for all grids covered by the new block. After collecting predictions from all grids, cluster analysis is performed, revealing that new phase anchor points are mainly concentrated in two time periods: 8:00-9:00 AM and 5:00-6:00 PM, specifically at 08:23:15, 08:47:30, 17:12:40, and 17:38:20. Simultaneously, based on the consistency of predictions across grids within the new block, the uncertainty parameters for each anchor point are calculated to be 110s, 95s, 120s, and 105s. Based on the new phase anchor points and uncertainty parameters, the short-term micro-activation time window parameters for this block are updated. For example, for the anchor point 08:23:15 with an uncertainty of 110s, the micro-activation time window is set to 55s before and 55s after the anchor point, i.e., from 08:22:20 to 08:24:10, and micro-activation monitoring is started around the anchor point during this period every morning.

[0066] Virtual blocks are no longer static divisions, but can dynamically evolve according to the actual signal distribution. Without dynamic reconstruction, over time, the initial virtual block division may gradually become decoupled from the actual signal distribution, leading to inaccurate predictions, increased false wake-ups, or signal omissions. Through continuous reconstruction, it can self-correct, ensuring that the predictions of the phase evolution model, the coverage of virtual blocks, and the distribution of the actual signal always maintain a high degree of consistency.

[0067] If the dual-condition triggering judgment passes, the complete signal reception mechanism of the corresponding virtual coverage block is activated, and signal reception is performed.

[0068] Furthermore, this application also includes the following steps: if the dual-condition trigger determination fails, the corresponding signal virtual coverage block is kept in a low-power active state in the low-power energy-gated skeleton channel, and a low-resolution frequency domain scan is periodically performed.

[0069] Specifically, when the dual-condition triggering judgment passes, the timing control and parameter storage unit immediately outputs a high-level wake-up signal to the system's main control chip. Upon receiving the wake-up signal, the main control chip quickly switches the system from deep standby mode to full-function operating mode. It then powers the entire RF receiving link, starts the high-precision local oscillator, activates the wideband analog-to-digital converter, loads the baseband processing algorithm, and initializes the decoder. The entire process is typically completed in microseconds to milliseconds, ensuring that no part of the signal is missed.

[0070] Once the complete signal reception mechanism is activated, based on the precise signal characteristics obtained during the dual-condition determination process, such as the precise center frequency and signal bandwidth obtained from high-resolution sampling, the receiver is precisely tuned to the target frequency, and the complete signal reception process begins. This includes automatic gain control adjustment, carrier synchronization, symbol synchronization, channel estimation and equalization, demodulation and decoding, cyclic redundancy check or other error detection mechanisms, and finally, data payload extraction. If the signal consists of bursty short data packets, multiple data packets need to be received within a single wake-up call.

[0071] After successfully receiving and processing the signal, key information of this reception is recorded, including the actual signal arrival time, actual signal strength, and actual center frequency. This information is then sent as feedback data to the signal arrival phase evolution model for continuous model optimization and parameter fine-tuning. After all processing is complete, a reassessment is performed to determine if there are any upcoming phase anchor points to monitor. If not, the system re-enters standby mode, shutting down the entire receiving path and keeping only the low-power skeleton channel running, awaiting the next wake-up.

[0072] When the dual-condition triggering judgment fails, no wake-up operation will be performed. The timing control unit will not output a wake-up signal, and the complete receiving mechanism remains off. The original low-power energy-gated skeleton channel continues to maintain its low-power activation state, meaning only the individual units of the skeleton channel operate at minimum power. In this state, the skeleton channel continues to perform its core task, performing a low-resolution frequency domain scan of the entire monitored spectrum at a preset fixed period. Each scan generates a low-dimensional projection vector to update the signal presence probability field. Simultaneously, it continues to monitor the phase anchor point list of the current virtual coverage block, waiting for the next anchor point.

[0073] The relevant data within the micro-activation time window that failed this time is used as negative samples and fed back to the signal arrival phase evolution model. Negative sample data helps the model identify misjudgment patterns. For example, if frequent failures occur in a certain frequency band due to noise triggering, the weight of that frequency band in the prediction can be appropriately reduced, or the consistency judgment threshold can be adjusted to reduce future false triggers. After recording, no other operations are performed, and the model continues to wait for the arrival of the next phase anchor point in a low-power state. At that time, the short-term micro-activation time window and dual-condition trigger judgment process will be restarted. When the judgment passes, the complete reception mechanism is activated in the shortest possible time to ensure that the signal is received in a timely and complete manner, achieving zero omissions; when the judgment fails, the model immediately returns to a low-power state to avoid invalid wake-ups caused by misjudgments. This design makes the wake-up behavior highly accurate, and each wake-up has practical value. Regardless of whether the judgment passes or fails, the results and relevant data are recorded and fed back to the phase evolution model. Passing judgments provide positive samples to strengthen the current prediction pattern; failing judgments provide negative samples to identify misjudgment patterns and adjust parameters. This closed-loop mechanism enables the system to continuously learn and evolve, resulting in increasingly higher prediction accuracy, smaller uncertainty parameters, and lower power consumption.

[0074] Furthermore, this application also includes the following steps: performing a signal reception status determination and establishing a signal reception dataset; using the signal reception dataset to identify reception anomalies and establish a reception anomaly warning.

[0075] Specifically, the signal reception status determination process involves several checks. The physical layer checks include verifying that the received signal strength is within a preset reasonable range (e.g., -30dBm to -110dBm), that the signal-to-noise ratio meets the demodulation threshold, and that automatic gain control converges normally. The link layer checks include verifying that the cyclic redundancy check (CRC) passes, that there are no retransmission requests, and that the length of the received data packets meets expectations. The application layer checks include verifying that the decoded payload format is correct and that key fields are complete. Only when all these checks meet the preset conditions is the reception considered successful. If any check fails, the reception is classified as an abnormal reception of a different type, such as weak signal anomaly, CRC failure anomaly, or format error anomaly, depending on the specific anomaly.

[0076] After the judgment is completed, all key information received is assembled into a complete record and stored in the signal reception dataset. This record includes the following fields: unique identifier of the received event, timestamp of occurrence, virtual coverage block number, actual arrival time, predicted phase anchor time, time deviation value, actual center frequency, received signal strength indication value, signal-to-noise ratio value, decoding success flag, cyclic redundancy check result, bit error rate, data packet length, payload digest, and anomaly type code. The signal reception dataset is stored in the system's non-volatile memory in a time-series format and is updated on a rolling basis according to a first-in-first-out (FIFO) or importance-based retention strategy to control storage space usage.

[0077] Anomaly identification is performed using accumulated signal reception datasets. Threshold-based methods can be used to detect if a single indicator consistently exceeds the normal range, such as five consecutive received signal strengths below -100 dBm. Statistical methods can be used to detect significant changes in indicator distribution, such as a sudden increase in the reception failure rate from 1% to 15% within the past hour. Pattern matching methods can be used to detect known anomalous patterns, such as frequent verification failures within a specific time period potentially indicating interference. The anomaly identification module can run locally or upload the dataset to the cloud or a central server for centralized analysis. Identification results include whether anomalies were found, the type of anomaly, its severity, and possible causes.

[0078] When the anomaly detection module detects a reception anomaly, it immediately triggers the reception anomaly early warning process. The generation and output of the early warning vary depending on the anomaly level and system configuration: for low-level anomalies, such as a single weak signal reception, it may only be recorded in the local log or indicated by a specific flashing pattern on the device status indicator; for medium-level anomalies, such as multiple consecutive verification failures, an early warning message is sent to the monitoring center or the user's mobile phone via the existing communication link; for high-level anomalies, such as the complete inability to receive any signal, it triggers a device restart, switches to a backup channel, or issues an emergency alarm through other backup communication methods. The early warning information typically includes the time of the anomaly, the type of anomaly, possible causes and suggested solutions, and the affected functional areas to help maintenance personnel quickly locate and resolve the problem.

[0079] After an early warning is issued, corresponding adaptive measures are triggered based on the type of anomaly. For example, if a persistently low signal strength is identified, the phase evolution model is requested to adjust the prediction parameters for that region or increase the uncertainty; if severe interference is identified in a specific frequency band, the frequency domain compression mapping strategy of the low-power skeleton channel is adjusted to avoid the interfered frequency band. These adaptive measures further enhance the system's robustness and environmental adaptability.

[0080] The system expands the previously simple binary result of "success upon receipt" into a quantitative evaluation system encompassing multi-dimensional quality indicators, providing a comprehensive understanding of its reception health. Through continuous data analysis and anomaly identification, it detects potential problems before they escalate and issues timely warnings. Reception anomaly warnings provide maintenance personnel with clear fault location and possible cause analysis, significantly shortening troubleshooting time. Furthermore, adaptive measures automatically triggered by warnings mitigate problems before manual intervention, improving the overall reliability of the system.

[0081] Furthermore, this application also includes the following steps: after signal reception is completed, the signal arrival phase evolution model is updated according to the offset between the actual signal arrival time and the predicted phase anchor point; block reconstruction is performed on multiple virtual signal coverage blocks using the updated signal arrival phase evolution model; and the next round of standby state trigger determination and signal reception optimization is performed based on the block reconstruction results.

[0082] Specifically, after a complete signal reception process is completed, the offset between the actual signal arrival time and the predicted phase anchor point is calculated. This offset is a signed numerical value; for example, -50ms indicates that the signal arrived 50ms earlier than predicted, and +120ms indicates that the signal arrived 120ms later than predicted. This offset, along with other relevant information from this reception, is packaged and sent to the signal arrival phase evolution model.

[0083] The signal arrival phase evolution model (SAPM) initiates an update process based on feedback data, adjusting various parameters within the model to make future predictions closer to reality. Specifically, based on the magnitude and direction of the offset, the weight of historical data in the historical inertia term is adjusted, with a slight increase in the weight of recent data, allowing the SAPM to adapt to current trends. Secondly, the correlation coefficient in the covariance matrix is ​​adjusted; if multiple feedbacks show deviations in the relationship between time and intensity from the original statistics, the SAPM will fine-tune this correlation coefficient. Finally, the distribution density parameter for each spatial region is adjusted; if multiple consecutive feedbacks show the signal arriving earlier than predicted, the SAPM will slightly adjust the mean forward or appropriately adjust the standard deviation. This adjustment typically employs a recursive algorithm with a forgetting factor, where the new parameter equals the old parameter multiplied by an attenuation coefficient plus the correction value calculated from the current feedback. This ensures the SAPM can learn new trends without drastic fluctuations due to single anomalies.

[0084] After the signal arrives and the phase evolution model is updated, a further evaluation is conducted to determine whether the update is significant enough to trigger block reconstruction. The decision-making process for block reconstruction typically involves initiating the reconstruction process if the prediction parameters of multiple grids within a virtual block change significantly, or if the prediction consistency between different grids changes. The updated phase evolution model output is used instead of the real-time monitored signal presence probability field. The reconstruction result may be a fine-tuning of the existing block boundaries, splitting a large block into several smaller blocks, or merging several smaller blocks into a large block, depending on the consistency of the updated model predictions.

[0085] After block reconstruction is complete, each affected virtual overlay block will receive a new set of parameters, including an updated set of phase anchor points and phase uncertainty parameters. These new parameters will be directly applied to subsequent standby state trigger determination processes. For example, if a block's phase anchor points become more accurate and its uncertainty parameters decrease after updating, the micro-activation time window for that block can be set narrower, thereby further saving power. If a block's prediction mode undergoes a fundamental change, the block's monitoring strategy will be adjusted, such as increasing the frequency of micro-activations or expanding the monitoring band.

[0086] With the updated model and block parameters, it re-enters standby mode, awaiting the arrival of the next phase anchor point. As this cycle continues, it becomes increasingly adapted to the current environment, resulting in more accurate predictions, lower power consumption, and higher reliability. For example, the phase evolution model of a certain block P originally predicted the next anchor point to be 09:15:10.000, with an uncertainty parameter of 5 seconds. The actual reception time of the first reception and feedback was 09:15:09.952, with a calculated offset of -48 milliseconds, 48 ​​milliseconds earlier. This -48 millisecond feedback is input into the recursive algorithm. Assuming the historical inertia weight is 0.7 and the current feedback weight is 0.3, the model fine-tunes the mean in the direction of advancement, such as adjusting the new predicted mean to 09:15:09.985, which is 15 milliseconds earlier than before. Simultaneously, based on the magnitude of this offset, the model slightly adjusts the time-duty cycle correlation coefficient in the covariance matrix, because the duty cycle when the signal occurred earlier was also slightly higher than the historical mean, strengthening the positive correlation. The actual arrival time of the second reception and feedback was 09:38:29.200, with a predicted anchor point of 09:38:30.000 and an offset of -800 milliseconds, 0.8 seconds earlier than expected. This significant deviation alerted the model to a possible change in the environment. The model increased the weight of recent predictions, further adjusting the mean towards earlier predictions, while temporarily increasing the uncertainty parameter from 5s to 7s to reflect the decrease in prediction confidence. The updated prediction parameters of multiple grids within block P were evaluated. It was found that several grids on the eastern side of block B3 generally had large recent prediction deviations and reduced consistency among them; while the grids on the western side remained highly consistent. Boundary morphology reconstruction was performed on block P: the four grids on the eastern side with large and inconsistent deviations were separated from block P to form a new small virtual block, named the P East Extension. The original block P was then reduced to the remaining grids on the western side. The newly generated P-east extension region requires a separate set of phase anchor points. The updated phase evolution model is then called, and predictions are recalculated for these four grids, resulting in new anchor point sets of 09:14:30, 09:37:45, and 09:51:20, along with corresponding uncertainty parameters of 6 seconds, 8 seconds, and 7 seconds. The original P-block, due to reduced grid size and improved consistency, retains the same anchor point set, but its uncertainty parameter shrinks from 5 seconds to 4 seconds because it is more internally pure. In subsequent operation, the P-east extension region, with its unique signal characteristics, receives independent micro-activation management, avoiding false wake-ups caused by internal inconsistencies. The original P-block, with its reduced uncertainty parameter and narrower micro-activation time window, experiences further power consumption reduction. The overall prediction accuracy and energy efficiency of the system are improved.

[0087] When environmental changes cause signal behavior to diverge in a certain area, the block reconstruction mechanism can promptly split this large block into finer smaller blocks, avoiding performance degradation due to internal inconsistencies. When the behavior of multiple smaller blocks becomes similar, they can also be merged to reduce management overhead. This dynamic adaptation ensures that the virtual block partitioning is always optimal. Even if the environment changes drastically, it can gradually adapt to the new environment through continuous feedback learning and block reconstruction, unlike static solutions that suffer from a sharp performance decline due to environmental drift.

[0088] In summary, the signal virtual block coverage processing method for communication control provided in this application has the following technical effects: by collecting historical signal arrival time series to construct a signal arrival phase evolution model and dividing the signal virtual coverage block, the continuous full-frequency scanning mechanism is turned off and the low-power energy-gated skeleton channel is enabled during standby, and a short-term micro-activation time window is opened to perform dual-condition triggering judgment within a certain period before the predicted phase anchor point arrives. The complete reception mechanism is activated only when the conditions are met, so as to achieve zero signal loss and low-latency acquisition while maintaining low-power standby, and greatly improve communication reliability.

[0089] Example 2: Based on the same inventive concept as the signal virtual block overlay processing method for communication control in Example 1, this application also provides a signal virtual block overlay processing system for communication control. Please refer to the appendix. Figure 2 The signal virtual block overlay processing system for communication control includes:

[0090] The signal prediction module 11 is used to collect historical signal arrival time series, channel active duty cycle distribution, and service trigger spatial distribution data before the communication control device enters standby mode, construct a signal arrival phase evolution model, and form a block-time coupling matrix characterizing the signal arrival probability in different spatial regions. The region division module 12 is used to divide the physical coverage area into multiple virtual signal coverage blocks based on the block-time coupling matrix, and assign an independent set of phase anchor points and phase uncertainty parameters to each virtual signal coverage block. The low-power skeleton and dual-condition judgment module 13 is used to construct a low-power energy-gated skeleton channel. After entering standby mode, it disables the continuous full-frequency scanning mechanism, opens a short-term micro-activation time window within a preset time window before the arrival of the time phase anchor point of the corresponding virtual coverage block, and performs dual-condition trigger judgment in conjunction with the low-power energy-gated skeleton channel. The signal receiving module 14 is used to activate the complete signal receiving mechanism of the corresponding virtual coverage block and perform signal reception if the dual-condition trigger judgment passes.

[0091] Furthermore, the signal prediction module 11 in the signal virtual block coverage processing system for communication control is also used for: performing denoising, normalization, and time synchronization processing on the collected data to construct a unified data representation set; using the data representation set to map each spatial region, each time slice, and channel state into a multi-dimensional state vector, wherein the dimensions of the multi-dimensional state vector include signal arrival time, signal strength estimation, channel activity duty cycle, and service trigger probability; performing statistical distribution on the multi-dimensional state vector and calculating the distribution density and covariance matrix of each dimension; based on the distribution density and covariance matrix, using a multi-dimensional phase evolution function to map historical signal arrival time and strength information to the phase evolution curve of each spatial region; and constructing a signal arrival phase evolution model based on the phase evolution curve.

[0092] Furthermore, the signal prediction module 11 in the signal virtual block coverage processing system for communication control is also used for: the multidimensional phase evolution function includes the following calculation terms: amplitude adjustment term, used to calculate the amplitude factor of the signal arrival phase change based on the distribution density of the multidimensional state vector of each spatial region; directional coupling term, used to calculate the phase drift direction and drift rate using the covariance matrix between multiple dimensions, and map the correlation between dimensions to the gradient direction of the multidimensional phase evolution function; historical inertia term, used to form an inertial trend based on the weighted accumulation of phase changes in historical time series; and disturbance correction term, used to correct short-term abnormal deviations by combining signal strength estimation and channel active duty cycle.

[0093] Furthermore, the region division module 12 in the signal virtual block coverage processing system for communication control is also used to: process the physical coverage area according to spatial gridding to form an initial set of sub-regions; for each initial sub-region, calculate the signal activity index of the corresponding initial sub-region in the future time window using the signal arrival probability and signal arrival phase evolution model of the corresponding time slice in the block-time coupling matrix; and use the signal activity index to merge or subdivide adjacent initial sub-regions to form multiple signal virtual coverage blocks.

[0094] Furthermore, the low-power skeleton and dual-condition judgment module 13 in the signal virtual block coverage processing system for communication control are also used to: after disabling the continuous full-frequency scanning mechanism, activate the low-power energy-gated skeleton channel, perform low-resolution energy projection processing on the spectrum to be monitored in a frequency domain compression mapping manner, and generate a low-dimensional projection vector characterizing the probability distribution of signal existence; construct a continuously updated signal existence probability field based on the low-dimensional projection vector, and output prediction parameters characterizing the disturbance trend and phase drift trend; within a short-time micro-activation time window before the arrival of the time phase anchor point of the corresponding virtual coverage block, enable the phase compression calibration mechanism to perform local high-resolution sampling correction on the signal existence probability field; when the high-resolution sampling correction result and the prediction trend of the prediction parameters meet the consistency judgment condition, the dual-condition trigger judgment passes.

[0095] Furthermore, the low-power skeleton and dual-condition determination module 13 in the signal virtual block coverage processing system for communication control are also used for: the signal existence probability field is also used to drive the spatial-temporal structure linkage reconstruction of the signal virtual coverage block, including: calculating the probability density distribution gradient and active centroid position inside each virtual coverage block based on the continuously updated signal existence probability field; determining the block drift amount using the probability density distribution gradient and active centroid position, the block drift amount including the drift direction and drift amplitude; when the drift amplitude exceeds the preset structure adjustment threshold, performing overall translation or boundary morphology reconstruction on the signal virtual coverage block according to the block drift amount; after the block completes the spatial position adjustment, recalculating the corresponding time phase anchor point set and short-time micro-activation time window parameters according to the adjusted block spatial coordinates.

[0096] Furthermore, the signal receiving module 14 in the signal virtual block coverage processing system for communication control is also used to: if the dual-condition trigger determination fails, maintain the corresponding signal virtual coverage block in the low-power energy gating skeleton channel in a low-power activation state, and periodically perform low-resolution frequency domain scanning.

[0097] Furthermore, the signal receiving module 14 in the signal virtual block overlay processing system for communication control is also used to: perform a status determination of signal reception and establish a signal reception dataset; use the signal reception dataset to identify reception anomalies and establish a reception anomaly warning.

[0098] Furthermore, the signal receiving module 14 in the signal virtual block coverage processing system for communication control is also used to: after the signal is received, update the signal arrival phase evolution model according to the offset between the actual signal arrival time and the predicted phase anchor point; use the updated signal arrival phase evolution model to perform block reconstruction on multiple signal virtual coverage blocks; and perform the next round of standby state trigger determination and signal reception optimization according to the block reconstruction result.

[0099] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. The signal virtual block overlay processing method and specific examples for communication control in the foregoing embodiment one are also applicable to the signal virtual block overlay processing system for communication control in this embodiment. Through the foregoing detailed description of the signal virtual block overlay processing method for communication control, those skilled in the art can clearly understand the signal virtual block overlay processing system for communication control in this embodiment. Therefore, for the sake of brevity, it will not be described in detail here.

[0100] The above description of the disclosed embodiments enables those skilled in the art to make or use this application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this application. Therefore, this application is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

[0101] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the spirit and scope of this application. Therefore, if such modifications and variations fall within the scope of this application and its equivalents, this application also intends to include such modifications and variations.

Claims

1. A method for processing signal virtual block overlay for communication control, characterized in that, include: Before the communication control device enters standby mode, historical signaling arrival time series, channel active duty cycle distribution and service triggering spatial distribution data are collected to construct a signal arrival phase evolution model and form a block-time coupling matrix that characterizes the signal arrival probability in different spatial regions. Based on the block-time coupling matrix, the physical coverage area is divided into multiple virtual signal coverage blocks, and an independent set of phase anchor points and phase uncertainty parameters are assigned to each virtual signal coverage block. Each anchor point in the set of phase anchor points represents the time when the system expects a signal to arrive in that block. The phase uncertainty parameter is a parameter that is associated with the phase anchor point and represents the confidence range of each prediction anchor point. A low-power energy-gated skeleton channel is constructed. After entering the standby state, the continuous full-frequency scanning mechanism is turned off. A short-term micro-activation time window is opened within a preset time window before the time phase anchor point of the corresponding virtual coverage block is reached. The low-power energy-gated skeleton channel is combined to perform dual-condition trigger judgment. If the dual-condition triggering judgment passes, the complete signal reception mechanism of the corresponding virtual coverage block is activated, and signal reception is performed. The dual-condition triggering decision is executed in conjunction with the low-power energy-gated skeleton channel, including: After disabling the continuous full-frequency scanning mechanism, the low-power energy-gated skeleton channel is activated to perform low-resolution energy projection processing on the spectrum to be monitored using frequency domain compression mapping, generating a low-dimensional projection vector characterizing the probability distribution of signal existence. A continuously updated signal existence probability field is constructed based on the low-dimensional projection vector, and prediction parameters characterizing the perturbation trend and phase drift trend are output. Within a short micro-activation time window before the arrival of the time phase anchor point of the corresponding virtual coverage block, the phase compression calibration mechanism is activated to perform local high-resolution sampling correction on the signal existence probability field. If the high-resolution sampling correction result and the prediction trend of the prediction parameters meet the consistency judgment condition, then the dual-condition trigger judgment is passed.

2. The signal virtual block overlay processing method for communication control as described in claim 1, characterized in that, The signal existence probability field is also used to drive the spatial-temporal structure linkage reconstruction of the signal virtual coverage block, including: Based on the continuously updated signal existence probability field, the probability density distribution gradient and active centroid location within each virtual coverage block are calculated. The block drift amount is determined using the probability density distribution gradient and the location of the active centroid, and the block drift amount includes the drift direction and drift amplitude; When the drift amplitude exceeds the preset structural adjustment threshold, the signal virtual coverage block is shifted as a whole or its boundary shape is reconstructed according to the block drift amount. After the block's spatial location is adjusted, the corresponding time phase anchor point set and short-term micro-activation time window parameters are recalculated based on the adjusted block spatial coordinates.

3. The signal virtual block overlay processing method for communication control as described in claim 1, characterized in that, Constructing a signal arrival phase evolution model, including: The collected data will undergo noise reduction, normalization, and time synchronization processing to construct a unified data representation set; The data representation set is used to map each spatial region, each time slice, and channel state into a multi-dimensional state vector. The dimensions of the multi-dimensional state vector include signal arrival time, signal strength estimation, channel activity duty cycle, and service trigger probability. The multidimensional state vector is statistically distributed, and the distribution density and covariance matrix of each dimension are calculated. Based on the distribution density and covariance matrix, a multidimensional phase evolution function is used to map the arrival time and intensity information of historical signals to the phase evolution curve of each spatial region. A signal arrival phase evolution model is constructed based on the phase evolution curve.

4. The signal virtual block overlay processing method for communication control as described in claim 3, characterized in that, The multidimensional phase evolution function includes the following computational terms: An amplitude adjustment term is used to calculate the amplitude factor of the signal arrival phase change based on the distribution density of the multidimensional state vector in each spatial region, wherein the amplitude factor is the fluctuation range of the signal arrival time. The directional coupling term is used to calculate the phase drift direction and drift rate using the covariance matrix between multiple dimensions, mapping the correlation between dimensions to the gradient direction of the multidimensional phase evolution function; Historical inertia term is used to accumulate phase changes in historical time series to form an inertial trend; The disturbance correction term is used to correct short-term abnormal deviations by combining signal strength estimation with channel active duty cycle.

5. The signal virtual block overlay processing method for communication control as described in claim 1, characterized in that, Based on the block-time coupling matrix, the physical coverage area is divided into multiple virtual signal coverage blocks, including: The physical coverage area is processed into a spatial grid to form an initial set of sub-regions; For each initial sub-region, the signal arrival probability and signal arrival phase evolution model of the corresponding time slice in the block-time coupling matrix are used to calculate the signal activity index of the corresponding initial sub-region in the future time window; The signal activity index is used to merge or subdivide adjacent initial sub-regions to form multiple virtual signal coverage blocks.

6. The signal virtual block overlay processing method for communication control as described in claim 1, characterized in that, If the dual-condition triggering judgment fails, the corresponding signal virtual coverage block remains in a low-power active state in the low-power energy-gated skeleton channel, and a low-resolution frequency domain scan is periodically performed.

7. The signal virtual block overlay processing method for communication control as described in claim 1, characterized in that, Performing signal reception also includes: Perform status determination for signal reception and establish a signal reception dataset; The aforementioned signal reception dataset is used to identify reception anomalies and establish a reception anomaly early warning system.

8. The signal virtual block overlay processing method for communication control as described in claim 1, characterized in that, Performing signal reception also includes: After signal reception is completed, the signal arrival phase evolution model is updated according to the offset between the actual signal arrival time and the predicted phase anchor point. The updated signal arrival phase evolution model is used to perform block reconstruction on multiple virtual signal coverage blocks. Based on the block reconstruction results, the next round of standby state trigger determination and signal reception optimization is performed.

9. A signal virtual block overlay processing system for communication control, characterized in that, The step of implementing the signal virtual block overlay processing method for communication control according to any one of claims 1 to 8, wherein the signal virtual block overlay processing system for communication control comprises: The signal prediction module is used to collect historical signal arrival time series, channel active duty cycle distribution and service triggering spatial distribution data before the communication control device enters standby mode, construct a signal arrival phase evolution model, and form a block-time coupling matrix that characterizes the signal arrival probability in different spatial regions. The region division module is used to divide the physical coverage area into multiple virtual signal coverage blocks based on the block-time coupling matrix, and to assign an independent set of phase anchor points and phase uncertainty parameters to each virtual signal coverage block. Each anchor point in the set of phase anchor points represents the time when the system expects a signal to arrive in the block. The phase uncertainty parameter is a parameter that is associated with the phase anchor point and represents the confidence range of each prediction anchor point. The low-power skeleton and dual-condition judgment module are used to construct the low-power energy-gated skeleton channel. After entering the standby state, the continuous full-frequency scanning mechanism is turned off. A short-term micro-activation time window is opened within a preset time window before the time phase anchor point of the corresponding virtual coverage block is reached. The dual-condition trigger judgment is performed in combination with the low-power energy-gated skeleton channel. The signal receiving module is used to activate the complete signal receiving mechanism of the corresponding virtual coverage block and perform signal receiving if the dual-condition triggering judgment passes. The low-power skeleton and dual-condition judgment module are also used to: after disabling the continuous full-frequency scanning mechanism, activate the low-power energy-gated skeleton channel, perform low-resolution energy projection processing on the spectrum to be monitored using frequency domain compression mapping, and generate a low-dimensional projection vector characterizing the probability distribution of signal existence; construct a continuously updated signal existence probability field based on the low-dimensional projection vector, and output prediction parameters characterizing the disturbance trend and phase drift trend; within a short-term micro-activation time window before the arrival of the time phase anchor point of the corresponding virtual coverage block, enable the phase compression calibration mechanism to perform local high-resolution sampling correction on the signal existence probability field; when the high-resolution sampling correction result and the prediction trend of the prediction parameters meet the consistency judgment condition, the dual-condition trigger judgment passes.

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