Signal processing methods and systems for radio frequency signals under different operating conditions

Abstract

This invention discloses a method and system for processing radio frequency (RF) signals under different operating conditions. The invention relates to the technical field of RF signals. Based on the attenuation parameters of the RF signal and the signal propagation items of each sub-signal propagation region, the attenuation control curve of the RF signal is determined. Targeted amplification is performed on the RF signal before it is submerged by noise to dynamically maintain the signal propagation level. During the signal processing, signal compensation content of the RF signal is output. A corresponding signal list is constructed based on the signal compensation content and the corresponding signal content. The propagation-processing items of the RF signal are determined based on the signal influencing factors in the signal list and the signal load of the RF signal. This achieves a holistic consideration of the signal influencing factors in the signal list and the signal load of the RF signal, improving the accuracy of the RF signal propagation-processing items.

Description

Technical Field

[0001] This invention relates to the field of radio frequency signals, and more particularly to a signal processing method and system for radio frequency signals under different operating conditions. Background Technology

[0002] With the development of technology, radio frequency (RF) signals refer to electromagnetic wave signals with frequencies ranging from 3kHz to 300GHz. Simply put, they are high-frequency electromagnetic oscillations used in radio technology for transmitting information. RF signals themselves typically do not directly carry the voice or video we want to hear or see; instead, they act as a "carrier wave." We modulate useful information (such as sound, data, and images) onto this high-frequency signal (by changing its amplitude, frequency, or phase) and then transmit it. In existing technologies, RF signals propagate through a transmitter. During propagation, the RF signal undergoes different conditions and follows a single propagation logic, affecting the accuracy of the signal propagation level and resulting in lower precision in the RF signal propagation-processing process. Summary of the Invention

[0003] The purpose of this invention is to overcome the shortcomings of the prior art. This invention provides a signal processing method and system for radio frequency signals under different operating conditions.

[0004] This invention provides a method for processing radio frequency signals under different operating conditions, including:

[0005] Mark the sub-signal propagation areas in the signal propagation path of the radio frequency signal, and determine the corresponding operating scenario based on the combination of operating parameters of each sub-signal propagation area;

[0006] Based on the detection of various working conditions, the working condition changes of the radio frequency signal are determined. Under the combination of multiple factors such as the working condition changes and the signal propagation parameters of the radio frequency signal, the signal propagation items of each sub-signal propagation area are determined.

[0007] Based on the attenuation parameters of the radio frequency signal and the signal propagation items of each sub-signal propagation region, the attenuation control curve of the radio frequency signal is determined, and targeted amplification is performed before the radio frequency signal is submerged by noise in order to dynamically maintain the signal propagation level of the radio frequency signal.

[0008] The lost data of the radio frequency signal is marked, and the corresponding signal processing logic is determined in combination with the signal propagation level of the radio frequency signal. During the signal processing of the radio frequency signal, the signal compensation content of the radio frequency signal is output.

[0009] Construct a corresponding signal list based on the signal compensation content and the corresponding signal content of the radio frequency signal. Determine the propagation-processing items of the radio frequency signal based on the signal influencing factors in the signal list and the signal load of the radio frequency signal.

[0010] This invention provides a signal processing system for radio frequency signals under different operating conditions, which is applied to the aforementioned signal processing method for radio frequency signals under different operating conditions.

[0011] Compared with the prior art, the beneficial effects of the present invention are:

[0012] (1) Mark the sub-signal propagation areas in the signal propagation path of the radio frequency signal, and determine the corresponding operating conditions based on the combination of operating condition parameters of each sub-signal propagation area; determine the operating condition changes of the radio frequency signal based on the detection of each operating condition scenario, and determine the signal propagation items of each sub-signal propagation area under the combination of multiple factors of the operating condition changes and the signal propagation parameters of the radio frequency signal; determine the attenuation control curve of the radio frequency signal based on the attenuation parameters of the radio frequency signal and the signal propagation items of each sub-signal propagation area, and perform targeted amplification before the radio frequency signal is submerged by noise, so as to dynamically maintain the signal propagation level of the radio frequency signal. The operating condition changes of the radio frequency signal are introduced, and the signal propagation items of each sub-signal propagation area are controlled to adaptively control the radio frequency signal under different operating conditions, and dynamically maintain the signal propagation level of the radio frequency signal in combination with the attenuation control curve of the radio frequency signal, thereby improving the accuracy of the signal propagation level of the radio frequency signal.

[0013] (2) Mark the packet loss data of the radio frequency signal and determine the corresponding signal processing logic in combination with the signal propagation level of the radio frequency signal. In the signal processing process of the radio frequency signal, output the signal compensation content of the radio frequency signal. Construct the corresponding signal list according to the signal compensation content of the radio frequency signal and the corresponding signal content. Determine the propagation-processing items of the radio frequency signal according to the signal influencing factors in the signal list and the signal load of the radio frequency signal. This realizes the signal compensation of the radio frequency signal in response to different working conditions, ensures the accuracy of the signal compensation content of the radio frequency signal, realizes the overall consideration of the signal influencing factors in the signal list and the signal load of the radio frequency signal, and improves the accuracy of the propagation-processing items of the radio frequency signal. Attached Figure Description

[0014] Figure 1 This is a flowchart illustrating the signal processing method for radio frequency signals under different operating conditions in embodiments of the present invention.

[0015] Figure 2 This is a flowchart illustrating step S11 of the radio frequency signal signal processing method under different operating conditions in an embodiment of the present invention.

[0016] Figure 3 This is a flowchart illustrating step S12 in the signal processing method for radio frequency signals under different operating conditions in this embodiment of the invention.

[0017] Figure 4 This is a flowchart illustrating step S13 of the radio frequency signal signal processing method under different operating conditions in the embodiments of the present invention.

[0018] Figure 5 This is a flowchart illustrating step S14 of the radio frequency signal signal processing method under different operating conditions in an embodiment of the present invention.

[0019] Figure 6 This is a flowchart illustrating step S15 of the radio frequency signal signal processing method under different operating conditions in an embodiment of the present invention. Detailed Implementation

[0020] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention.

[0021] Please see Figures 1 to 6 A signal processing method for radio frequency (RF) signals under different operating conditions, applied to RF signal scenarios; the signal processing method for RF signals under different operating conditions includes:

[0022] Step S11: Mark each sub-signal propagation region in the signal propagation path of the radio frequency signal, and determine the corresponding operating scenario based on the combination of operating parameters of each sub-signal propagation region;

[0023] Step S12: Determine the changes in the operating conditions of the radio frequency signal based on the detection of each operating condition scenario, and determine the signal propagation items of each sub-signal propagation area under the combination of multiple factors of the changes in the operating conditions and the signal propagation parameters of the radio frequency signal.

[0024] Step S13: Determine the attenuation control curve of the radio frequency signal based on the attenuation parameters of the radio frequency signal and the signal propagation items of each sub-signal propagation region, and perform targeted amplification before the radio frequency signal is submerged by noise in order to dynamically maintain the signal propagation level of the radio frequency signal.

[0025] Step S14: Mark the packet loss data of the radio frequency signal, and determine the corresponding signal processing logic in combination with the signal propagation level of the radio frequency signal. During the signal processing of the radio frequency signal, output the signal compensation content of the radio frequency signal.

[0026] Step S15: Construct a corresponding signal list based on the signal compensation content and the corresponding signal content of the radio frequency signal, and determine the propagation-processing items of the radio frequency signal based on the signal influencing factors in the signal list and the signal load of the radio frequency signal.

[0027] refer to Figure 2In step S11, the specific steps are as follows:

[0028] S111: Radio frequency signal propagation between the transmitter and receiver, the radio frequency signal propagation path is determined based on the tracing of the radio frequency signal, multiple sub-signal propagation nodes are determined based on the identification of the signal propagation path, and the corresponding sub-signal propagation area is determined based on the combination of the node position of each sub-signal propagation node and the features of the surrounding obstacles. The boundary of the sub-signal propagation area is determined by the change of obstacle material and physical space geometry.

[0029] S112: For each sub-signal propagation area, multiple operating parameters are collected in real time. Based on the multiple operating parameters and their corresponding priority, a combination of operating parameters is determined. This combination of operating parameters must at least cover background noise and dielectric loss factor. At the same time, the radio frequency signal forms an operating condition classifier under the construction of a deep neural network. The combination of operating parameters for each sub-signal propagation area is mapped to the operating condition classifier to output the corresponding operating condition scenario.

[0030] In the embodiments of this application, radio frequency (RF) signals are propagated between the transmitting end and the receiving end. The RF signal propagation path is determined based on the tracing of the RF signal. Multiple sub-signal propagation nodes are determined based on the identification of the signal propagation path. The corresponding sub-signal propagation area is determined based on the synthesis of the node position of each sub-signal propagation node and the features of surrounding obstacles. The boundary of the sub-signal propagation area is determined by the changes in obstacle material and physical space geometry. This approach takes into account the overall consideration of the synthesis of the node position of each sub-signal propagation node and the features of surrounding obstacles, ensuring the accuracy of the corresponding sub-signal propagation area.

[0031] At this point, the system transmits known probe signals through the transmitter, and the receiver uses channel estimation algorithms (such as least squares (LS) or minimum mean square error (MMSE)) to analyze the multipath components. By analyzing the arrival time and angle of each path, the system reconstructs the spatial geometric trajectory of the signal from the transmitter to the receiver. On the reconstructed trajectory, the system does not sample uniformly, but inserts logical nodes according to signal feature abrupt change points (such as reflection points and diffraction points). These nodes not only contain three-dimensional spatial coordinate information, but also bind the signal phase and initial amplitude information at that point.

[0032] Based on known digital terrain models or environmental perception data (such as SLAM maps), the system maps the propagation path to the physical environment; through ray tracing technology, it identifies the physical entity attributes around each node; each sub-signal propagation node is assigned an "influence domain". This influence domain is not a simple circular coverage, but is synthesized by Boolean operations based on the Fresnel zone principle and the geometry of obstacles around the node (such as wall edges and equipment corners) to determine the main spatial sector responsible for signal transmission by the node.

[0033] When the propagation path passes through the interface of different media (e.g., from air into a non-metallic barrier, or from an insulating layer to a metal surface), the propagation mode of the electromagnetic wave changes due to the abrupt change in dielectric constant and conductivity (e.g., total internal reflection or strong attenuation absorption occurs), and the system uses this material abrupt change surface as the hard boundary of the region.

[0034] When the propagation path encounters obvious geometric obstructions (such as corners or walls), causing the direct path to be cut off and diffraction or scattering must be relied upon, the geometric contour of the physical space constitutes the soft boundary of the region; the final boundary of the sub-signal propagation region is a closed spatial polyhedron generated by the above-mentioned material abrupt change surface and physical geometric contour through topological intersection operation.

[0035] Specifically, a signal transmitter is deployed at the entrance of the utility tunnel to send a probe signal with known parameters, such as a frequency of 2.4 GHz and a power of 10 dBm. Multiple signal receivers are deployed inside the utility tunnel, for example, one receiver every 50 meters, to receive the probe signal and the sensor feedback signal. The receivers use the least squares (LS) channel estimation algorithm to analyze the multipath components and obtain the arrival time and angle information of each path. Combining the digital terrain model and SLAM map of the utility tunnel, the arrival time and angle information of each path are mapped into three-dimensional space to reconstruct the spatial geometric trajectory of the signal from the transmitter to the receiver.

[0036] On the reconstructed trajectory, logical nodes are inserted based on signal characteristic abrupt change points (such as reflection points and diffraction points), for example, at the corners of pipe corridors or near power lines. Based on the Fresnel zone principle, Boolean operations are performed to synthesize the signal by combining the geometry of obstacles around the nodes (such as wall edges and equipment corners) to determine the main spatial sector responsible for signal transmission for each node. For example, the coverage area of ​​each node can be set as a circular area with a radius of 20 meters.

[0037] The interior of the utility tunnel contains various materials, such as concrete, metal, and plastic. When the propagation path passes through the interface of different materials, the propagation mode of electromagnetic waves will change. For example, near a metal pipe, the signal will be reflected and attenuated. The system uses the material transition surface as the hard boundary of the area, for example, dividing the area within 10 meters around the metal pipe into a sub-signal propagation area.

[0038] The interior of the utility tunnel contains various geometric obstructions, such as walls and equipment. When the propagation path encounters these obstructions, the direct path will be cut off, and the signal will rely on diffraction or scattering to propagate. The geometric contour of the physical space constitutes the soft boundary of the area. For example, the area within 5 meters behind the wall can be divided into a sub-signal propagation area.

[0039] By performing topological intersection operations, the aforementioned material abrupt change surfaces and physical geometric contours are used to generate closed spatial polyhedra, which serve as the boundaries of the sub-signal propagation regions. For example, the area within 10 meters around the metal pipe and the area within 5 meters behind the wall are respectively divided into two sub-signal propagation regions.

[0040] Furthermore, for each sub-signal propagation region, multiple operating parameters are collected in real time. Based on these multiple operating parameters and their corresponding priorities, a combination of operating parameters is determined. This combination of operating parameters at least covers background noise and dielectric loss factor. Simultaneously, the radio frequency signal forms an operating condition classifier through the construction of a deep neural network. The operating condition parameter combination of each sub-signal propagation region is mapped to the operating condition classifier to output the corresponding operating condition scenario. This approach takes into account the overall consideration of multiple operating parameters and their corresponding priorities, ensuring the accuracy of the operating condition parameter combination.

[0041] At this point, the system deploys highly sensitive sensing probes or uses physical layer signaling to extract parameters in real time for each sub-signal propagation area divided by S111. The collected indicators include not only signal amplitude fluctuations in the time domain but also spectral characteristics in the frequency domain. Simultaneously, the system pre-sets a dynamic weighting algorithm, assigning different priorities to parameters based on their contribution to signal attenuation. For example, in environments with strong electromagnetic interference, the priority of background noise is increased; while in long-distance transmission scenarios, the priority of the dielectric loss factor dominates. Based on the weighted calculation results, the system outputs a multi-dimensional "operating condition parameter combination," which is a feature vector that can characterize the current physical state of the environment.

[0042] To ensure the accuracy of the classification, the combination of operating parameters must include two core physical quantities: background noise: usually refers to the total noise power spectral density after the superposition of thermal noise and electromagnetic interference (EMI) in the industrial environment, which directly affects the signal-to-noise ratio (SNR); dielectric loss factor: characterizes the rate of energy loss when electromagnetic waves propagate in a specific medium, which is directly related to the imaginary part of the dielectric constant of the medium and determines the amplitude attenuation slope of the signal.

[0043] By utilizing deep neural networks (DNNs), such as variants of multilayer perceptrons (MLPs) or convolutional neural networks (CNNs), a nonlinear classification model is constructed. This network receives a "combination of operating condition parameters" through the input layer, undergoes nonlinear transformations (activation function processing) through multiple hidden layers, and maps to a predefined operating condition category space at the output layer.

[0044] The real-time collected operating condition parameters are combined into a vector and input into the trained DNN model. The network calculates the similarity probability between the input vector and the center point of various operating conditions through forward propagation. Finally, the label with the highest probability is output through the Softmax layer, which determines the specific "operating condition scenario" (such as strong multipath fading scenario, Gaussian white noise dominant scenario, dynamic occlusion scenario, etc.) of the current propagation area of ​​the sub-signal.

[0045] Specifically, the system is processing a B-radio frequency signal in a sub-signal propagation area near the "substation entrance" in the utility tunnel. The system detects an abnormal increase in the spectral noise floor in this area, and the signal attenuates drastically when passing through the metal shielding door. The dynamic weighting algorithm determines that "background noise" and "dielectric loss factor" are both key indicators, and generates a feature vector containing high noise power spectral density and high dielectric loss value.

[0046] The feature vector is input into the DNN model. After analysis, the model finds that the feature vector highly matches the center point of the "strong electromagnetic interference plus high attenuation blockage" category in the training data. The system outputs the operating scenario of the area as "strong electromagnetic interference and high attenuation coupling scenario". This accurate scenario label (rather than just "poor signal") will guide subsequent steps (such as S12 and S13) to formulate targeted strategies, such as activating a stronger anti-interference filtering algorithm or adjusting the compensation curve of automatic gain control (AGC), so as to ensure that the monitoring data can still be reliably transmitted in this harsh environment.

[0047] refer to Figure 3 In step S12, the specific steps are as follows:

[0048] S121: Acquire a detection mechanism based on sliding window covariance analysis, and trigger the change of operating parameters of the radio frequency signal in each sub-signal propagation area along the detection mechanism, and mark the corresponding change coefficient. If the change coefficient exceeds the preset change coefficient, the change of the operating parameter is determined as the operating condition change content. At this time, the change details of the radio frequency signal in the preset channel are presented in the detection mechanism.

[0049] S122: Combine the changes in the operating conditions with the signal propagation parameters of the radio frequency signal, and construct a corresponding multi-factor decision model during the combination process. Based on the multi-factor decision model, trigger dynamic decision-making for each sub-signal propagation region and generate signal propagation items for each sub-signal propagation region to clarify the target signal-to-noise ratio and the maximum allowable bit error rate of the signal propagation item under the corresponding operating conditions.

[0050] In the embodiments of this application, a detection mechanism based on sliding window covariance analysis is used to collect data. The changes in operating parameters of the radio frequency signal in each sub-signal propagation region are triggered along the detection mechanism, and the corresponding change coefficients are marked. If the change coefficient exceeds the preset change coefficient, the change in the operating parameter is determined as the operating condition change content. At this time, the change details of the radio frequency signal in the preset channel are presented in the detection mechanism, thus introducing the change details of the radio frequency signal in the preset channel in the detection mechanism.

[0051] At this point, the system sets a fixed-length time window (e.g., containing N sampling points), which slides forward point by point on the data stream over time. Within each window, the system calculates the covariance matrix or variance value of key operating parameters of the RF signal (such as Received Signal Strength Indication (RSSI), Channel Impulse Response (CIR), Signal-to-Noise Ratio (SNR), etc.). Covariance is used to measure the consistency within parameters and the linear correlation between parameters. In a stable channel, the covariance is small. However, when the environment changes abruptly, the statistical distribution characteristics of the data change drastically, resulting in a significant increase in the covariance value.

[0052] As the window slides, the detection mechanism continuously monitors the parameter status within each sub-signal propagation area. Once the signal characteristics within the window fluctuate, the calculation process is triggered. The system quantifies the calculated covariance value into a scalar index, namely the "variance coefficient," which reflects the instability or drastic change of the signal within the current window. At the same time, the system not only records the value but also reconstructs the signal's response characteristics in the preset channel within the detection mechanism, capturing the specific form of change, such as step abrupt change, impulse interference, or gradual drift.

[0053] The system compares the real-time calculated change coefficient with the preset "preset change coefficient" (i.e., the threshold for stable system operation). If the change coefficient exceeds the preset threshold, it means that the current signal fluctuation has exceeded the tolerance range of normal system operation or the correction capability of the linear equalizer. At this time, the system officially confirms this event as "operating condition change content" and records the timestamp of the trigger time and the corresponding parameter characteristics as the input source for subsequent decision-making.

[0054] Specifically, after the B radio frequency signal (the feedback signal from the IoT-based environmental monitoring and safety sensor) completes the classification of the operating conditions of each sub-signal propagation area (such as "strong multipath fading scenario" or "strong electromagnetic interference scenario") in step S112, the system sets a fixed-length time window (e.g., containing N sampling points) for each sub-signal propagation area (e.g., "dense power pipeline area" or "T-bend area") within the utility tunnel. This window is not static, but slides forward point by point on the time axis as the B radio frequency signal data stream arrives, forming a continuous monitoring stream of the signal.

[0055] Within each sliding window, the system performs high-frequency sampling and calculation of key operating parameters of the B radio frequency signal, including Received Signal Strength Indication (RSSI), Signal-to-Noise Ratio (SNR), and Channel Impulse Response (CIR); the system calculates the covariance matrix or variance value of these parameters within the current window.

[0056] Covariance is used to measure the consistency within parameters and the linear correlation between parameters. In a relatively closed environment like a utility tunnel, if the channel state is stable (i.e., there are no sudden changes in environmental interference), the calculated covariance value is small and the data distribution is compact. However, when a sudden situation occurs in the utility tunnel, such as the sudden start-up of high-power equipment or the movement of inspection robots causing drastic changes in multipath effects, the statistical distribution characteristics of the data will change drastically, resulting in a significant increase in the covariance value.

[0057] As the window continues to slide, once the detection mechanism detects fluctuations in the signal characteristics within the window, it immediately triggers the calculation process. The system quantifies the calculated covariance value into a scalar index, namely the "variance coefficient," which accurately reflects the instability or drastic change of the B radio frequency signal within the current time window.

[0058] The system not only records numerical values, but also reconstructs the signal response characteristics in the preset channel within the detection mechanism, thereby accurately capturing the specific form of change. In the pipe gallery environment, this may manifest as: step change: for example, when the sensor passes through a metal shielding door, the signal strength drops sharply; pulse interference: for example, due to the operation of a high-voltage switch, a strong electromagnetic pulse noise occurs momentarily; gradual drift: for example, as the humidity in the pipe gallery increases, the signal amplitude slowly decays due to dielectric loss.

[0059] The system will strictly compare the real-time calculated "variation coefficient" with the preset "preset variation coefficient" (i.e. the threshold for stable system operation). If the variation coefficient exceeds the preset threshold, it means that the fluctuation of the current B radio frequency signal has exceeded the tolerance range of normal system operation or the correction capability of the linear equalizer. The system will officially confirm the change of the operating condition parameter as the "operating condition change content" and accurately record the timestamp of the trigger time and the corresponding parameter characteristics. This confirmation provides a key input source for the multi-factor decision model in the subsequent S122 step, enabling the system to respond in a timely manner to specific sudden changes in operating conditions (such as sudden interference or path obstruction) in the pipe gallery.

[0060] Furthermore, the changes in the operating conditions and the signal propagation parameters of the radio frequency signal are combined with multiple factors, and a corresponding multi-factor decision model is constructed during the combination process. Based on the multi-factor decision model, dynamic decision-making for each sub-signal propagation region is triggered, and signal propagation items for each sub-signal propagation region are generated to clarify the target signal-to-noise ratio and the maximum allowable bit error rate of the signal propagation item under the corresponding operating conditions. This introduces the method of clarifying the target signal-to-noise ratio and the maximum allowable bit error rate of the signal propagation item under the corresponding operating conditions.

[0061] At this point, the system performs feature-level splicing and normalization processing on the "operating condition change content" extracted from S121 (such as non-stationary features such as channel time-domain fading depth, multipath delay spread, and interference pulse spectral density) and the current "signal propagation parameters" of the B radio frequency signal (such as modulation and demodulation order M-QAM, carrier spacing, forward error correction coding rate, and transmit power spectral density).

[0062] This combination is not a simple numerical superposition, but rather the establishment of a correlation mapping between physical environment characteristics and link layer parameters; for example, when the "fast fading" characteristic is identified, the "symbol period" and "cyclic prefix length" in the correlation parameters are used to assess whether the current configuration is sufficient to resist the time-selective fading of the channel.

[0063] Based on the fused multidimensional feature vectors, the system constructs a multi-factor decision model, which is usually based on the hierarchical analysis method (AHP) or a trained random forest / deep reinforcement learning network. Its core is an optimization function containing multiple constraints (such as power consumption limit, spectrum mask, maximum latency).

[0064] The model scores the status of each sub-signal propagation area in real time. When the fused feature vector shows that the current link parameters cannot maintain the preset quality of service (QoS), or when the channel margin is too large and causes resource waste, the model triggers dynamic decision logic. This logic calculates the optimal control strategy based on the weight distribution of the input vector, deciding whether to improve robustness (such as down-order modulation) or improve throughput (such as up-order modulation).

[0065] Based on the results of dynamic decision-making, the system generates a specific "signal propagation project" for each sub-signal propagation area. This is not just a status label, but a set of instructions containing physical layer execution logic. In this signal propagation project, two core performance indicators must be quantified and defined as hard constraints for the subsequent S13 and S14 steps: target signal-to-noise ratio (SNR): the minimum SNR threshold that the demodulator input needs to achieve under the current operating conditions; maximum bit error rate (BER): the highest BER that the current service type (such as control signaling or video stream) can tolerate. These two indicators directly determine the selection boundary of the adaptive coding modulation (AMC) strategy.

[0066] Specifically, the changes in operating conditions are as follows: Step S121 detects a decrease in signal propagation quality in the area near the metal tube, with the signal-to-noise ratio (SNR) falling below a preset threshold; Step S121 analysis reveals that electromagnetic interference near the metal tube is the main cause of the signal quality degradation; Meanwhile, the signal propagation parameters are: modulation method: QPSK; encoding method: convolutional coding, code rate 1 / 2; transmit power: 5dBm; receive sensitivity: -90dBm.

[0067] The "signal quality degradation in the area near the metal tube" detected in step S121 is combined with the signal propagation parameters collected in step S122; a multi-factor decision model is constructed, such as based on the analytic hierarchy process (AHP) or random forest algorithm, taking into account the following factors: electromagnetic interference intensity; signal propagation distance; receiver sensitivity; service type (e.g., sensor data transmission); power consumption limit; based on the multi-factor decision model, the signal propagation parameters in the area near the metal tube are determined as follows: target signal-to-noise ratio: 10dB; modulation method: downgraded to BPSK; coding method: convolutional coding, code rate 1 / 2; transmit power: maintained at 5dBm; at the same time, due to the strong electromagnetic interference near the metal tube, in order to maintain signal reliability, the modulation order needs to be reduced from QPSK to BPSK to reduce the signal-to-noise ratio requirement.

[0068] refer to Figure 4 In step S13, the specific steps are as follows:

[0069] S131: Based on the attenuation monitoring of the radio frequency signal, the corresponding attenuation parameters are determined, and each attenuation parameter is matched to the corresponding sub-signal propagation area. In each sub-signal propagation area, the attenuation control curve of the radio frequency signal is determined based on the attenuation parameter, the corresponding signal propagation item, and the attenuation threshold range of the radio frequency signal. The attenuation control curve not only describes the current attenuation state, but also predicts the attenuation trend in the short term.

[0070] S132: Based on the identification of the attenuation control curve, determine the corresponding attenuation risk area, and construct the corresponding signal-to-noise ratio edge monitoring in the attenuation risk area to output the critical point where the radio frequency signal is about to be submerged by noise but has not yet been completely submerged;

[0071] S133: Based on this critical point, targeted gain amplification control is triggered. At this time, dynamic compensation in the frequency domain is performed based on the signal propagation parameters to dynamically maintain the signal propagation level of the RF signal with minimal power consumption.

[0072] In the embodiments of this application, the corresponding attenuation parameters are determined based on the attenuation monitoring of the radio frequency signal, and each attenuation parameter is matched to the corresponding sub-signal propagation region. In each sub-signal propagation region, the attenuation control curve of the radio frequency signal is determined based on the attenuation parameter, the corresponding signal propagation item, and the attenuation threshold range of the radio frequency signal. The attenuation control curve not only describes the current attenuation state, but also predicts the attenuation trend in the short term. It takes into account the overall consideration of the attenuation parameter, the corresponding signal propagation item, and the attenuation threshold range of the radio frequency signal, thus ensuring the accuracy of the attenuation control curve of the radio frequency signal.

[0073] At this point, the system measures the pilot or reference signal using the physical layer channel estimation algorithm, and analyzes the path loss, the fluctuation of shadow fading speed, and the amplitude and phase distortion caused by multipath effects. These quantitative indicators are defined as "attenuation parameters". Using the spatial index of the sub-signal propagation area divided in S111, the attenuation parameters collected in real time are mapped back to the corresponding area one by one. This step ensures that the attenuation data has spatial attributes and can distinguish whether it is a local degradation in a specific area (such as a corner or a shading area) or a general attenuation of the entire link.

[0074] Within each sub-signal propagation area, the system uses the matched "attenuation parameter" as the core input variable, while introducing the "signal propagation item" generated by S122 as a constraint (such as the service priority and minimum fidelity required for the area), and combining it with the system's preset "attenuation threshold range" (i.e., the receiver sensitivity baseline or the physical layer threshold specified by the communication protocol). Based on the above factors, the system constructs a continuous "attenuation control curve" in the time domain. The vertical axis of this curve represents the attenuation of signal strength or signal-to-noise ratio, and the horizontal axis represents time. It is no longer a discrete sampling point, but a smoothed continuous trajectory that can reflect the dynamic changes in signal quality.

[0075] The initial segment of the attenuation control curve accurately depicts the attenuation state of the radio frequency signal at the current moment, including the depth and frequency of instantaneous fluctuations. The system uses a time series prediction model to extrapolate and calculate the attenuation trend in the short future (such as tens to hundreds of milliseconds) based on the historical slope and curvature of the curve. This makes the control curve "forward-looking" and can show in advance whether the signal is approaching a precipitous drop, thus providing a basis for subsequent predictive compensation.

[0076] Furthermore, based on the identification of the attenuation control curve, the corresponding attenuation risk area is determined, and the corresponding signal-to-noise ratio edge monitoring is constructed in the attenuation risk area to output the critical point where the radio frequency signal is about to be submerged by noise but has not yet been completely submerged. This is compatible with the overall consideration of attenuation control curve identification and ensures the accuracy of the corresponding attenuation risk area.

[0077] At this time, the system continuously analyzes the attenuation control curve generated by S131; by comparing the predicted value or current value on the control curve with the preset "alarm threshold", it identifies the spatiotemporal interval where the signal quality is deteriorating and about to reach the edge of collapse; when the slope of the control curve shows an abnormal attenuation rate, or when the predicted trajectory will penetrate the system's minimum demodulation threshold within the set time window, the corresponding sub-signal propagation area is marked as an "attenuation risk area". This is a dynamic concept, which means that although the signal can still be connected in this area, it is in a very unstable state.

[0078] Once the system enters the attenuation risk zone, it immediately activates a high-precision signal-to-noise ratio (SNR) edge monitoring mechanism. Unlike conventional periodic measurements, edge monitoring typically uses a higher sampling frequency and may even initiate a fast Layer 1 (L1) measurement of the physical layer to reduce processing latency.

[0079] The core of monitoring is no longer the absolute average signal strength, but focuses on the instantaneous fluctuation edge of the signal-to-noise ratio; the system tracks the process of SNR approaching the lower limit in real time by calculating the Euclidean distance distribution of the received signal constellation diagram or the ratio of the reference signal received power (RSRP) to the interference power.

[0080] The system seeks a specific characteristic moment, namely the moment when the signal-to-noise ratio at the receiver has dropped to the very edge of the demodulation threshold (e.g., the EVM (error vector amplitude) is close to its limit), causing the bit error rate (BER) to start to rise exponentially, but the carrier has not yet lost synchronization, and the demodulator has not yet output a "link failure" state. This moment is defined as the critical point. Outputting this point means that the last window of opportunity when "the signal is about to be submerged by noise but has not yet been completely submerged" has been captured, which is the only effective trigger signal to trigger S133 to perform emergency repair.

[0081] Therefore, based on this critical point, targeted gain amplification control is triggered. At this time, dynamic compensation in the frequency domain is performed based on the signal propagation items to dynamically maintain the signal propagation level of the RF signal with minimal power consumption. This introduces the concept of dynamically maintaining the signal propagation level of the RF signal with minimal power consumption. At the same time, the operating condition changes of the RF signal are introduced, and the signal propagation items of each sub-signal propagation region are controlled to adaptively manage the RF signal under different operating conditions. Combined with the attenuation control curve of the RF signal, the signal propagation level of the RF signal is dynamically maintained, thereby improving the accuracy of the signal propagation level of the RF signal.

[0082] At this point, unlike traditional automatic gain control (AGC) based on hysteresis response, the triggering mechanism here is predictive and instantaneous. Once the system determines that the signal is in a critical state of "about to be submerged but not yet submerged", it immediately releases the conventional power consumption limit and sends a command to the variable gain amplifier (VGA) in the RF front end or the gain control unit in the digital domain. This is a "rescue" intervention, which aims to take advantage of the last time window before the signal is completely lost lock to forcibly increase the sensitivity of the receiving link.

[0083] The system retrieves the "signal propagation project" generated by S122 and analyzes the spectrum resource allocation defined therein. In the frequency domain, different subcarriers of the B radio frequency signal often suffer from varying degrees of attenuation or interference (i.e., frequency-selective fading). At the same time, the system does not perform overall amplification across the entire frequency band (which would lead to an overall increase in noise floor and a surge in power consumption), but instead implements dynamic compensation in the frequency domain. Specifically, the system identifies high-quality subcarriers or frequency bands that carry key information (such as pilots, control channels, and high-priority data streams) and applies targeted gain weights to these specific spectral components. For "ineffective" subcarriers that have been deeply fading or overwhelmed by strong interference, their gain is limited or even suppressed.

[0084] By selectively amplifying only the necessary spectrum segments, the increased linearity requirements and static power consumption caused by high gain across the entire frequency band are avoided. While meeting demodulation requirements, the system always seeks to minimize the gain vector magnitude, thereby achieving signal recovery with minimal hardware power consumption. At the same time, through this precise frequency domain compensation, the signal propagation level of the RF signal (such as modulation / demodulation order and link quality indication) can be dynamically locked within a preset range, preventing degradation or link interruption due to instantaneous signal-to-noise ratio fluctuations, thus ensuring high availability of communication services.

[0085] refer to Figure 5 In step S14, the specific steps are as follows:

[0086] S141: Based on the tracing of the radio frequency signal, the corresponding lost packet content is determined, and the corresponding lost packet data is matched according to the detection of the lost packet content. The sub-signal propagation area, timestamp, and corresponding working condition scenario where the lost packet occurred are marked to determine the lost packet factors of the lost packet data.

[0087] S142: Collect the signal propagation level of the radio frequency signal in the sub-signal propagation area, combine the signal propagation level and the packet loss factors of the packet loss data to construct a hierarchical compensation mechanism, and trigger the signal processing of the radio frequency signal at different levels based on the hierarchical compensation mechanism to determine the corresponding signal processing logic.

[0088] S143: Monitor the signal processing of the RF signal in real time and output multiple signal compensation features of the RF signal. Construct corresponding signal compensation content based on the multiple signal compensation features. The signal compensation content includes gain adjustment amount, phase rotation correction value or time domain equalization coefficient.

[0089] In the embodiments of this application, the corresponding packet loss content is determined based on the tracing of the radio frequency signal, the corresponding packet loss data is matched according to the detection of the packet loss content, and the sub-signal propagation area, timestamp and corresponding working condition scenario where the packet loss occurred are marked to determine the packet loss factors of the packet loss data. This approach is compatible with the overall consideration of tracing radio frequency signals and ensures the accuracy of the corresponding packet loss content.

[0090] At this point, the system establishes a full-link log tracking mechanism at the data link layer (Layer 2) and the physical layer (Layer 1); through sequence number detection, cyclic redundancy check (CRC) failure recording or acknowledgment response (ACK) timeout mechanism, it identifies lost or corrupted data units in the transmission stream.

[0091] After identifying packet loss, the system does not stop at the "packet loss" state, but combines the protocol stack header information to parse the type and payload attributes of the lost data packet (such as whether it is a retransmission packet, whether it contains critical control signaling, data packet size, etc.), thereby accurately defining the "packet loss content".

[0092] The system compares the parsed lost packet content with the data buffer at the sending end to match the specific "lost packet data" entity and determine the specific bit stream that needs to be recovered or retransmitted. In order to perform root cause analysis, the system assigns a set of spatiotemporal and environmental labels to the packet loss event: Sub-signal propagation area: records the specific spatial partition where the receiving end is located when the packet loss occurs (such as the area ID divided by S111); Timestamp: records the precise time when the packet loss occurred (usually down to the microsecond level); Operating scenario: records the scenario classification output by S112 at that time (such as "strong multipath area" or "pulse interference area").

[0093] Based on the aforementioned multidimensional markers, the system combines historical signal-to-noise ratio (SNR) curves, measured interference power spectral density (PSD), and physical layer bit error rate (BER) statistics to perform feature fusion. Through cross-comparison, the system distinguishes the underlying causes of packet loss. For example, it determines whether the loss is due to signal strength being lower than receiver sensitivity (coverage-related factors), co-channel interference causing carrier-to-noise ratio degradation (interference-related factors), or multipath delay exceeding equalizer capacity (channel dispersion factors).

[0094] Furthermore, the signal propagation level of the radio frequency signal in the sub-signal propagation area is collected, and a hierarchical compensation mechanism is constructed by combining the signal propagation level and the packet loss factors of the packet loss data. Based on the hierarchical compensation mechanism, the signal processing of the radio frequency signal at different levels is triggered to determine the corresponding signal processing logic. This approach is compatible with the overall consideration of triggering the signal processing of the radio frequency signal at different levels by the hierarchical compensation mechanism, ensuring the accuracy of the corresponding signal processing logic.

[0095] At this point, the system performs a quantitative assessment of the link status in the current sub-signal propagation area and collects the "signal propagation level". This level is usually composed of a comprehensive score of the channel quality indicator (CQI), modulation and coding strategy (MCS) index value, reference signal received power (RSRP), and signal-to-noise ratio (SNR).

[0096] The real-time acquired signal propagation level is fused with the "packet loss factor" determined in S141. For example, if the signal propagation level shows high-order modulation (such as 256-QAM) but the packet loss factor is "phase jitter", it indicates that the current system configuration is too aggressive and the anti-interference capability is insufficient. This combination is used to locate the mismatch between the current system configuration and the actual carrying capacity of the physical environment.

[0097] Based on fused information, the system constructs a layered compensation model across protocol stacks. This model is not limited to single-layer repair but defines a collaborative response strategy for the physical layer (L1), data link layer (L2), and even application layer (L7). The mechanism formulates a layered decision matrix based on the severity and type of packet loss. For example, waveform-level compensation is used for transient interference at the physical layer; protocol-level (such as ARQ / HARQ) redundancy compensation is used for continuous congestion or high bit error rate; and access control (such as switching frequency bands or reducing throughput targets) compensation is used for areas with extremely poor propagation levels.

[0098] Based on the calculation results of the hierarchical compensation mechanism, the system sends control commands to the baseband processing unit (BBU) and protocol stack of the radio transceiver to trigger signal processing actions at specific levels. The final determined "signal processing logic" is a specific execution scheme that clarifies the algorithm modules that need to be called in subsequent S143. For example, the logic is set to "activate maximum ratio combining (MRC) diversity reception" or "start the incremental redundancy mode of hybrid automatic repeat request (HARQ)", thereby providing direction for the generation of specific compensation parameters.

[0099] Therefore, the signal processing of the RF signal is monitored in real time, and multiple signal compensation features of the RF signal are output. Based on these multiple signal compensation features, corresponding signal compensation content is constructed. This signal compensation content includes gain adjustment, phase rotation correction value, or time-domain equalization coefficient, which takes into account the overall consideration of multiple signal compensation features and ensures the accuracy of the corresponding signal compensation content.

[0100] At this time, during the execution of the signal processing logic determined in S142 (such as enabling equalization, diversity, or retransmission), the system performs high-precision real-time monitoring of the radio frequency signal through the feedback loop of baseband processing; the system analyzes the intermediate signal before demodulation (such as ADC sampling data and frequency domain data after FFT) and the soft decision information after demodulation.

[0101] Based on monitoring data, the system calculates and outputs "signal compensation features". These features quantify the specific dimensions of signal impairment, such as: the deviation of the signal amplitude from the reference level (amplitude feature), the offset angle of the carrier phase from the ideal constellation point (phase feature), and the time delay and power distribution of the multipath component relative to the main path in the channel impulse response (CIR) (time domain feature).

[0102] The system inputs the extracted compensation features into the compensation algorithm engine to calculate and generate specific "signal compensation content." This is a set of precise parameters used to correct the physical layer waveform, designed to offset distortions caused by the channel environment. The compensation content mainly includes the following three core physical parameters: gain adjustment: amplitude compensation value (dB) for path loss or shadow fading; phase rotation correction value: angle calibration amount (radians or degrees) for Doppler shift, local oscillator drift, or phase noise; time-domain equalization coefficient: finite impulse response (FIR) filter tap weight coefficients (complex sequence) for inter-symbol interference (ISI) caused by multipath effects.

[0103] refer to Figure 6 In step S15, the specific steps are as follows:

[0104] S151: Associate and bind the signal compensation content of the radio frequency signal with the corresponding signal content, and construct a corresponding signal list. This signal list records the operating conditions, signal supplement content and signal propagation level experienced by each data packet in the radio frequency signal during the propagation process, and forms a closed-loop information feedback flow.

[0105] S152: In this signal list, the corresponding signal influencing factors are determined based on the anomaly detection of the signal list. At the same time, the signal load of the radio frequency signal is collected. Based on the dynamic resource scheduling of each signal influencing factor and the signal load of the radio frequency signal, the radio frequency propagation-processing project is determined. The radio frequency propagation-processing project will coordinate and adjust the transmission power, routing path and underlying signal processing parameters, and output an adaptive signal execution scheme.

[0106] In the embodiments of this application, the signal compensation content of the radio frequency signal and the corresponding signal content are associated and bound, and a corresponding signal list is constructed. The signal list records the working conditions, signal supplement content and signal propagation level experienced by each data packet in the radio frequency signal during the propagation process, and forms a closed-loop information feedback flow, thus introducing a closed-loop information feedback flow.

[0107] At this point, at the baseband processing and protocol stack interaction level, the system establishes a cross-layer mapping mechanism; the "signal compensation content" (such as specific gain value, equalizer tap coefficient, phase correction amount) calculated and applied by the physical layer (PHY) is uniquely bound to the data unit (i.e. "signal content", specifically corresponding to a certain protocol data unit PDU or data stream ID) of the upper layer (such as MAC layer or IP layer) with a unique ID.

[0108] This binding is usually achieved by adding a physical layer processing tag to the packet header or by attaching the packet sequence number to the status report at the receiving end; its core purpose is to accurately match the "transmission cost of the physical layer" with the "business object of the data layer" and to know exactly which packet has undergone what kind of physical layer intervention.

[0109] Based on the binding relationship, the system dynamically constructs and maintains a structured "signal list." This list is not merely a transmission log, but a multi-dimensional feature database. For each successfully or failed data packet, the list records the following key information: Operating conditions: Environmental characteristics at the time of data packet transmission (such as Doppler frequency shift value, delay spread, and interference type identification); Signal supplementary information: Specific physical compensation parameters applied to salvage the data packet (such as specific AGC gain steps and phase rotation angles); Signal propagation level: Transmission effect evaluation (such as modulation and coding scheme MCS level, signal-to-interference-plus-noise ratio SINR, and LLR confidence level after soft demodulation).

[0110] This signal list is not statically stored, but serves as a real-time feedback data source. The system immediately feeds back the statistical characteristics in the list (such as frequent triggering of high gain compensation in a certain area, or continuous degradation of propagation level due to a certain type of operating condition) to the upper-layer resource scheduler or decision algorithm. This feedback flow enables the network side to "perceive" the actual transmission costs and difficulties at the physical layer, thereby dynamically adjusting subsequent transmission strategies (such as reducing the modulation order or avoiding the frequency band), forming a complete closed-loop control loop from "perception-compensation-recording-feedback-adjustment".

[0111] Furthermore, within this signal list, corresponding signal influencing factors are determined based on anomaly detection. Simultaneously, the signal load of the radio frequency (RF) signal is collected. Based on the dynamic resource scheduling of each signal influencing factor and the signal load of the RF signal, an RF propagation-processing project is determined. This RF propagation-processing project will comprehensively adjust the transmit power, routing path, and underlying signal processing parameters, and output an adaptive signal execution scheme. It incorporates the overall consideration of the dynamic resource scheduling of each signal influencing factor and the signal load of the RF signal, ensuring the accuracy of the RF propagation-processing project. At the same time, it realizes signal compensation for RF signals in response to different operating conditions, ensuring the accuracy of the RF signal compensation content. This achieves the overall consideration of the signal influencing factors and the signal load of the RF signal in the signal list, improving the accuracy of the RF signal propagation-processing project.

[0112] At this point, the system uses statistical analysis or machine learning algorithms to perform a deep scan of the "signal list" constructed by S151; anomaly detection focuses on the distribution of indicators that deviate from the normal baseline, such as: whether the variance of the compensation parameter is too large, whether the propagation level has been at a low level for a long time, or whether the retransmission rate under specific operating conditions has suddenly increased.

[0113] Based on the abnormal patterns, the system identifies and quantifies the fundamental "signal influencing factors," which are generally divided into three categories: spatial environment factors (such as specific multipath fading areas and shadow areas), interference factors (such as narrowband co-channel interference and impulse noise), and mobility factors (such as Doppler frequency shift exceeding the threshold and frequent switching).

[0114] The system collects queue status at the MAC and IP layers in real time to obtain the "signal load" metric, which includes the current service throughput requirements, the buffer depth of different priority service flows (such as signaling streams, video streams, and telemetry streams), and latency sensitivity requirements. The system uses "signal influencing factors" (environmental constraints) and "signal load" (service requirements) as input variables for a joint optimization problem. Through scheduling algorithms, it calculates how to optimally allocate limited time-frequency resources, power resources, and air interface resources under the current adverse factors to meet the service quality requirements of different priority loads.

[0115] The system establishes a comprehensive "RF propagation-processing project," which means that decision-making is no longer limited to single-point optimization at the physical layer, but rather a cross-layer collaborative project designed to simultaneously address the questions of "how to propagate" (physical layer), "where to propagate" (network layer), and "how much to transmit" (power control). Based on this project, the system generates a specific "adaptive signal execution scheme," which includes explicit parameter instructions: transmit power adjustment: power allocation (PA / PB) for different subcarriers or antennas; routing path decision: selection of direct links, multi-hop relays, or heterogeneous network handover; and low-level parameter reconfiguration: adjustment of modulation and coding scheme (MCS), beamforming vector, or channel state information (CSI) feedback period.

[0116] In another real-time example of this application, the signal processing system for the radio frequency signal under different operating conditions includes:

[0117] The working condition scenario module is used to mark the various sub-signal propagation areas in the signal propagation path of the radio frequency signal, and to determine the corresponding working condition scenario based on the combination of working condition parameters of each sub-signal propagation area.

[0118] The signal propagation project module is used to determine the changes in the operating conditions of radio frequency signals based on the detection of various operating scenarios. Under the combination of multiple factors such as the changes in the operating conditions and the signal propagation parameters of radio frequency signals, the signal propagation project of each sub-signal propagation area is determined.

[0119] The signal propagation level module is used to determine the attenuation control curve of the radio frequency signal based on the attenuation parameters of the radio frequency signal and the signal propagation items of each sub-signal propagation region, and to perform targeted amplification of the radio frequency signal before it is submerged by noise, so as to dynamically maintain the signal propagation level of the radio frequency signal.

[0120] The signal compensation module is used to mark the packet loss data of the radio frequency signal and determine the corresponding signal processing logic in combination with the signal propagation level of the radio frequency signal. During the signal processing of the radio frequency signal, the signal compensation content of the radio frequency signal is output.

[0121] The propagation-processing module is used to construct a corresponding signal list based on the signal compensation content and the corresponding signal content of the radio frequency signal, and to determine the propagation-processing items of the radio frequency signal based on the signal influencing factors in the signal list and the signal load of the radio frequency signal.

[0122] The technical features of the above embodiments can be combined arbitrarily. For the sake of brevity, not all combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

Claims

1. A signal processing method for radio frequency signals under different operating conditions, characterized in that, include: Mark the propagation areas of each sub-signal in the signal propagation path of the radio frequency signal, and determine the corresponding operating scenario based on the combination of operating parameters of each sub-signal propagation area. Based on the detection of various working conditions, the working condition changes of the radio frequency signal are determined. Under the combination of multiple factors such as the working condition changes and the signal propagation parameters of the radio frequency signal, the signal propagation items of each sub-signal propagation area are determined. Based on the attenuation parameters of the radio frequency signal and the signal propagation items of each sub-signal propagation region, the attenuation control curve of the radio frequency signal is determined, and targeted amplification is performed before the radio frequency signal is submerged by noise in order to dynamically maintain the signal propagation level of the radio frequency signal. The process involves marking the packet loss data of the RF signal and determining the corresponding signal processing logic based on the signal propagation level of the RF signal. During the signal processing of the RF signal, the output signal compensation content includes: determining the corresponding packet loss content based on the tracing of the RF signal; matching the corresponding packet loss data based on the detection of the packet loss content; and marking the sub-signal propagation area, timestamp, and corresponding operating scenario where the packet loss occurred to determine the packet loss factor; collecting the signal propagation level of the RF signal in the sub-signal propagation area; constructing a hierarchical compensation mechanism based on the signal propagation level and the packet loss factor of the packet loss data; triggering signal processing of the RF signal at different levels based on the hierarchical compensation mechanism to determine the corresponding signal processing logic; monitoring the signal processing process of the RF signal in real time and outputting multiple signal compensation features of the RF signal; constructing corresponding signal compensation content based on multiple signal compensation features; and defining the hierarchical compensation mechanism includes gain adjustment, phase rotation correction value, or time-domain equalization coefficient. The hierarchical compensation mechanism defines the collaborative response strategy of the physical layer, data link layer, and even application layer. Construct a corresponding signal list based on the signal compensation content and the corresponding signal content of the radio frequency signal. Determine the propagation-processing items of the radio frequency signal based on the signal influencing factors in the signal list and the signal load of the radio frequency signal.

2. The signal processing method for radio frequency signals under different operating conditions according to claim 1, characterized in that, The operating scenarios for each sub-signal propagation region in the signal propagation path of the marked radio frequency signal are determined based on the combination of operating parameters for each sub-signal propagation region, including: Radio frequency (RF) signals propagate between the transmitter and receiver. The RF signal propagation path is determined by tracing the RF signal. Multiple sub-signal propagation nodes are determined based on the identification of the RF signal propagation path. The corresponding sub-signal propagation area is determined by combining the node position of each sub-signal propagation node with the features of surrounding obstacles. The boundary of the sub-signal propagation area is determined by the changes in obstacle material and physical space geometry. For each sub-signal propagation region, multiple operating parameters are collected in real time. Based on the multiple operating parameters and their corresponding priorities, a combination of operating parameters is determined. This combination of operating parameters must at least cover background noise and dielectric loss factor. At the same time, the radio frequency signal forms an operating condition classifier under the construction of a deep neural network. The combination of operating parameters for each sub-signal propagation region is mapped to the operating condition classifier to output the corresponding operating condition scenario.

3. The method for processing radio frequency signals under different operating conditions according to claim 1, characterized in that, The process involves determining the changes in radio frequency (RF) signals based on detection under various operating conditions, and then determining the signal propagation parameters of each sub-signal propagation region based on a combination of these changes and the RF signal propagation parameters. This includes: A detection mechanism based on sliding window covariance analysis is used to collect data and trigger changes in operating parameters of the radio frequency signal in each sub-signal propagation region along the detection mechanism. The corresponding change coefficients are marked. If the change coefficient exceeds the preset change coefficient, the change in operating parameters is determined as the operating condition change content. At this time, the details of the change of the radio frequency signal in the preset channel are presented in the detection mechanism. The changes in the operating conditions and the signal propagation parameters of the radio frequency signal are combined with multiple factors. During the combination process, a corresponding multi-factor decision model is constructed. Based on the multi-factor decision model, dynamic decision-making for each sub-signal propagation region is triggered, and signal propagation items for each sub-signal propagation region are generated to clarify the target signal-to-noise ratio and the maximum allowable bit error rate of the signal propagation item under the corresponding operating conditions.

4. The signal processing method for radio frequency signals under different operating conditions according to claim 1, characterized in that, The attenuation control curve of the radio frequency signal is determined based on the attenuation parameters of the radio frequency signal and the signal propagation items of each sub-signal propagation region. Targeted amplification is then performed on the radio frequency signal before it is submerged by noise to dynamically maintain the signal propagation level. This includes: Attenuation parameters are determined based on the attenuation monitoring of the radio frequency signal, and each attenuation parameter is matched to the corresponding sub-signal propagation region. In each sub-signal propagation region, the attenuation control curve of the radio frequency signal is determined based on the attenuation parameter, the corresponding signal propagation item, and the attenuation threshold range of the radio frequency signal. This attenuation control curve not only describes the current attenuation state, but also predicts the attenuation trend in the near future.

5. The method for processing radio frequency signals under different operating conditions according to claim 4, characterized in that, The method of determining the attenuation control curve of the radio frequency signal based on the attenuation parameters of the radio frequency signal and the signal propagation items of each sub-signal propagation region, and performing targeted amplification of the radio frequency signal before it is overwhelmed by noise to dynamically maintain the signal propagation level of the radio frequency signal, also includes: Based on the identification of the attenuation control curve, the corresponding attenuation risk area is determined, and the corresponding signal-to-noise ratio edge monitoring is constructed in the attenuation risk area to output the critical point where the radio frequency signal is about to be submerged by noise but has not yet been completely submerged. Based on this critical point, targeted gain amplification control is triggered. At this time, dynamic compensation in the frequency domain is performed based on the signal propagation parameters to dynamically maintain the signal propagation level of the RF signal with minimal power consumption.

6. The method for processing radio frequency signals under different operating conditions according to claim 1, characterized in that, The step of constructing a corresponding signal list based on the signal compensation content and corresponding signal content of the radio frequency signal, and determining the propagation-processing items of the radio frequency signal based on the signal influencing factors and signal load of the radio frequency signal in the signal list, includes: The signal compensation content of the radio frequency signal is associated and bound with the corresponding signal content, and a corresponding signal list is constructed. This signal list records the operating conditions, signal supplement content and signal propagation level experienced by each data packet in the radio frequency signal during the propagation process, forming a closed-loop information feedback flow.

7. The signal processing method for radio frequency signals under different operating conditions according to claim 6, characterized in that, The step of constructing a corresponding signal list based on the signal compensation content and corresponding signal content of the radio frequency signal, and determining the propagation-processing items of the radio frequency signal based on the signal influencing factors in the signal list and the signal load of the radio frequency signal, further includes: In this signal list, the corresponding signal influencing factors are determined based on the anomaly detection of the signal list. At the same time, the signal load of the radio frequency signal is collected. Based on the dynamic resource scheduling of each signal influencing factor and the signal load of the radio frequency signal, the radio frequency propagation-processing project is determined. The radio frequency propagation-processing project will coordinate and adjust the transmission power, routing path and underlying signal processing parameters, and output an adaptive signal execution scheme.

8. A signal processing system for radio frequency signals under different operating conditions, characterized in that, The radio frequency signal processing system under different operating conditions is applied to the radio frequency signal processing method under different operating conditions as described in any one of claims 1-7; The signal processing system for the radio frequency signal under different operating conditions includes: The working condition scenario module is used to mark the various sub-signal propagation areas in the signal propagation path of the radio frequency signal, and to determine the corresponding working condition scenario based on the combination of working condition parameters of each sub-signal propagation area. The signal propagation project module is used to determine the changes in the operating conditions of radio frequency signals based on the detection of various operating scenarios. Under the combination of multiple factors such as the changes in the operating conditions and the signal propagation parameters of radio frequency signals, the signal propagation project of each sub-signal propagation area is determined. The signal propagation level module is used to determine the attenuation control curve of the radio frequency signal based on the attenuation parameters of the radio frequency signal and the signal propagation items of each sub-signal propagation region, and to perform targeted amplification of the radio frequency signal before it is submerged by noise, so as to dynamically maintain the signal propagation level of the radio frequency signal. The signal compensation module is used to mark the packet loss data of the radio frequency signal and determine the corresponding signal processing logic in combination with the signal propagation level of the radio frequency signal. During the signal processing of the radio frequency signal, the signal compensation content of the radio frequency signal is output. The propagation-processing module is used to construct a corresponding signal list based on the signal compensation content and the corresponding signal content of the radio frequency signal, and to determine the propagation-processing items of the radio frequency signal based on the signal influencing factors in the signal list and the signal load of the radio frequency signal.

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