A mobile communication signal strength acquisition and processing system

Abstract

The application relates to the technical field of signal monitoring, in particular to a mobile communication signal strength acquisition and processing system, which comprises a signal analysis module, an extreme value extraction module, a time sequence discrimination module, an interval superposition module and a state output module. In the application, the signal time continuity is constrained by combining continuous sampling interval control and time sequencing, the local fluctuation is inhibited while the overall trend is retained by the synergistic effect of amplitude smoothing and abnormal replacement, the transmission state recognition precision is improved by introducing the time sequence characteristics to depict the extreme value screening and change interval calculation, the interval division and superposition judgment enhance the multi-state distinguishing ability, the joint output forms a structured result, the real-time analysis stability is enhanced, the complex scene adaptation ability is improved, the network evaluation basis integrity is enhanced, the operation and maintenance decision support reliability is improved, the monitoring feedback timeliness is enhanced, and the data utilization depth is improved.

Description

Technical Field

[0001] This invention relates to the field of signal monitoring technology, and in particular to a mobile communication signal strength acquisition and processing system. Background Technology

[0002] The field of signal monitoring technology primarily involves the detection and evaluation of signal strength, quality, and coverage in wireless communication networks. It encompasses a range of core aspects, including signal measurement, monitoring, analysis, and optimization, and is widely applied in the planning, optimization, maintenance, and fault diagnosis of mobile communication networks. The core objective is to evaluate network performance and provide effective decision support by collecting and analyzing wireless signal parameters such as signal strength, signal-to-noise ratio, and interference, thereby ensuring the stability and quality of wireless communication networks.

[0003] One traditional mobile communication signal strength acquisition and processing system involves collecting signal strength data from the mobile communication network using acquisition devices and transmitting it to a processing system for analysis and processing. Traditional signal acquisition and processing methods typically employ a combination of base stations and terminal equipment to automatically collect signal strength data, which is then analyzed, statistically analyzed, and reported using specific processing programs. This system usually uses preset signal strength thresholds for judgment and employs processing algorithms to identify signal quality problems, generating relevant data reports to provide a reference for subsequent network optimization and maintenance.

[0004] Existing technologies mainly rely on independent acquisition devices and basic processing programs. The sampling interval is affected by environmental fluctuations and is difficult to maintain stability. The judgment of signal time continuity is insufficient. Fixed threshold processing ignores local fluctuation characteristics. Data analysis focuses on statistical summarization and lacks temporal correlation. Dynamic changes are difficult to accurately characterize. Real-time monitoring capabilities are limited. Results feedback is delayed. The basis for network optimization is not complete. Signal quality assessment depends on single-point samples. Coverage status judgment has bias. Multi-scenario adaptability is insufficient. The reliability of operation and maintenance decision support is reduced. Fault location accuracy is limited. Long-term trend analysis is not stable enough. Summary of the Invention

[0005] The purpose of this invention is to overcome the shortcomings of existing technologies and propose a mobile communication signal strength acquisition and processing system.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: a mobile communication signal strength acquisition and processing system includes:

[0007] The signal analysis module collects the received signal strength at continuous times, controls a constant sampling interval, analyzes the signal strength time series and smooths the amplitude, generates a smoothed signal strength time series and transmits it to the extreme value extraction module.

[0008] The extreme value extraction module reads the signal intensity of multiple sampling points in the smooth signal intensity time series, filters the sampling point time that meets the extreme value judgment condition, forms a key time series and calculates the difference between adjacent time positions, generates a change interval sequence and transmits it to the time series discrimination module.

[0009] The timing discrimination module calculates the fluctuation characteristic intensity based on the changing interval sequence, analyzes the propagation timing state of the current signal strength acquisition process, generates timing state results, and transmits them to the interval overlay module.

[0010] The interval overlay module divides time intervals based on the smoothed signal intensity time series and the time series state results, compares the signal intensity within the intervals and determines the interval overlay state, generates an overlay state set and transmits it to the state output module.

[0011] The status output module combines the smoothed signal intensity time series, the time-series status result, and the superimposed status set to perform state-differentiated output of the signal intensity values, generating signal intensity processing results.

[0012] As a further embodiment of the present invention, the smoothed signal intensity time series includes a sampling time index and a smoothed signal intensity; the variation interval sequence includes key sampling time points, adjacent time differences, and interval distribution sequences; the time series state results include fluctuation intensity levels and state classification codes; the superimposed state set includes interval superposition identifiers, interval continuity relationship identifiers, and interval combination state codes; and the signal intensity processing results include state-distinguished signal intensity values, state-corresponding time series, and intensity-state mapping sets.

[0013] As a further aspect of the present invention, the signal analysis module includes:

[0014] The signal acquisition submodule acquires the received signal strength values ​​and sampling timestamps corresponding to continuous sampling moments in the mobile communication environment, detects the interval between multiple sampling timestamps and applies constant sampling interval control, discards signal strength values ​​that do not meet the interval requirements, and generates a sampling signal strength sequence.

[0015] The timing construction submodule, based on the sampled signal strength sequence, sorts the time according to the corresponding sampling timestamp, determines the temporal continuity between adjacent signal strength values, and combines and arranges the signal strength values ​​in continuous intervals to obtain the signal strength time sequence.

[0016] The amplitude smoothing submodule calls the signal strength time series, performs amplitude smoothing processing on the difference in signal strength amplitude between adjacent sampling points, performs replacement calculation on abnormal fluctuation values ​​according to the set amplitude change threshold, keeps the time order unchanged, and outputs the processed sequence to generate a smoothed signal strength time series.

[0017] As a further aspect of the present invention, the replacement operation of abnormal fluctuation values ​​based on a set amplitude change threshold includes: when the absolute value of the difference in signal intensity amplitude between adjacent sampling points exceeds the amplitude change threshold, the arithmetic mean of the signal intensity values ​​of adjacent sampling points is used to replace the abnormal fluctuation values, wherein the amplitude change threshold is in decibels and its value ranges from 3 to 10.

[0018] As a further aspect of the present invention, the extreme value extraction module includes:

[0019] The extreme value determination submodule acquires the signal intensity of multiple sampling points in the smoothed signal intensity time series, compares the signal intensity of adjacent sampling points, filters the current sampling point value to simultaneously exceed or fail to reach the values ​​of the adjacent sampling points before and after it, and generates a peak-valley time point set.

[0020] The time point assembly submodule, based on the peak and valley time point set, sorts the data according to the order of sampling time, judges the continuity of adjacent time points, removes duplicate time markers and retains feasible time positions, and establishes key time series.

[0021] The interval calculation submodule calls the key time series, calculates the time difference between adjacent time positions in the series, reads the timestamp values ​​before and after and performs the difference operation, records the results of multiple adjacent time differences and arranges them in the original order to generate a change interval sequence.

[0022] As a further aspect of the present invention, the timing discrimination module includes:

[0023] The fluctuation calculation submodule, based on the change interval sequence, obtains multiple adjacent interval time values, performs difference calculation on continuous interval values, reads and accumulates the offset amplitude between adjacent time differences, summarizes the proportion relationship of the accumulated multiple values, and generates the interval fluctuation amount.

[0024] The intensity mapping submodule collects the signal intensity values ​​during the synchronous time period of the corresponding signal intensity acquisition process, reads the signal intensity changes at multiple sampling points, weights the interval fluctuations and the signal intensity changes, and calculates the fluctuation characteristic intensity.

[0025] The state discrimination submodule calls the wave characteristic intensity, collects the corresponding propagation time sequence state, compares and judges the positional relationship of the intensity value within the state identifier distribution interval, completes the state classification output based on the value falling within the interval range, and generates the time sequence state result.

[0026] As a further aspect of the present invention, the calculation of the wave characteristic intensity is performed using the following formula:

[0027] ;

[0028] in, This represents the intensity of the fluctuation characteristics within the m-th synchronization time period. Represents the sequence number of multiple sampling points. This represents the total number of sampling points within the synchronization time period. This represents the change in signal strength at the j-th sampling point within the m-th synchronization time period. This represents the interval fluctuation corresponding to the j-th sampling point within the m-th synchronization time period. This represents the arithmetic mean of the changes in signal intensity at all sampling points within the m-th synchronization time period, with the subscript m indicating the synchronization time period.

[0029] As a further aspect of the present invention, the interval overlay module includes:

[0030] The interval division submodule obtains continuous sampled values ​​of signal strength based on the smoothed signal strength time series and the time sequence state results, reads the corresponding sample point set for a fixed time span, divides the continuous intervals according to the time sequence, and generates a time interval set.

[0031] The amplitude difference calculation submodule obtains the signal strength sampling values ​​in multiple intervals based on the time interval set, reads the maximum and minimum values ​​of the intervals, calculates the difference of the signal strength in the same interval, summarizes the corresponding difference results of multiple intervals, and obtains the interval amplitude difference set.

[0032] The superposition determination submodule calls the interval amplitude difference set and the time series state result to obtain the adjacent interval amplitude difference sequence, analyzes the interval change trend of the adjacent difference value change direction and change amplitude, calculates the superposition trend, and generates the superposition state set.

[0033] As a further aspect of the present invention, the status output module includes:

[0034] The intensity alignment submodule obtains the continuous sampled signal intensity based on the smoothed signal intensity time series, reads the corresponding time-series state result index position according to the sampling time sequence, performs signal intensity and state label alignment processing according to the consistency of time identifier, and generates an intensity state alignment value set.

[0035] The state joint submodule calls the intensity state alignment value set and the superimposed state set to obtain the signal strength, time sequence state identifier and interval superimposed identifier at the same time position, and performs a joint judgment on the combination relationship of multiple state identifiers to obtain a joint state coding sequence.

[0036] The result output submodule obtains the signal strength value distribution of multiple codes according to the joint state coding sequence, distinguishes the state of the coding sequence, summarizes the signal strength value arrangement results under different states, and generates the signal strength processing result.

[0037] As a further aspect of the present invention, the signal strength and status label alignment process includes: performing sliding window processing on the smoothed signal strength time series, calculating the average value of the signal strength within each window, and performing time series alignment based on the average value and the corresponding time series status label.

[0038] Compared with the prior art, the advantages and positive effects of the present invention are as follows:

[0039] In this invention, by combining continuous sampling interval control with time sorting, the continuity of signal time is constrained, amplitude smoothing and abnormal replacement work synergistically to suppress local fluctuations while preserving the overall trend, extreme value screening and change interval calculation introduce temporal feature characterization, improve the accuracy of propagation state identification, enhance the ability to distinguish multiple states through interval division and superposition judgment, form structured results through joint output, enhance the stability of real-time analysis, improve the adaptability to complex scenarios, enhance the integrity of network evaluation basis, improve the reliability of operation and maintenance decision support, enhance the timeliness of monitoring feedback, and improve the depth of data utilization. Attached Figure Description

[0040] Figure 1 This is a system flowchart of the present invention;

[0041] Figure 2 This is a flowchart of the signal analysis module of the present invention;

[0042] Figure 3 This is a flowchart of the extreme value extraction module of the present invention;

[0043] Figure 4 This is a flowchart of the timing discrimination module of the present invention;

[0044] Figure 5 This is a flowchart of the interval overlay module of the present invention;

[0045] Figure 6 This is a flowchart of the status output module of the present invention. Detailed Implementation

[0046] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0047] Please see Figure 1 A mobile communication signal strength acquisition and processing system includes:

[0048] The signal analysis module collects the received signal strength at continuous times, controls a constant sampling interval, analyzes the signal strength time series and smooths the amplitude, generates a smoothed signal strength time series and transmits it to the extreme value extraction module.

[0049] The extreme value extraction module reads the signal intensity of multiple sampling points in the smooth signal intensity time series, filters the sampling point time that meets the extreme value judgment condition, forms a key time series and calculates the difference between adjacent time positions, generates a change interval sequence and transmits it to the time series discrimination module.

[0050] The timing discrimination module calculates the intensity of wave characteristics based on the changing interval sequence, analyzes the propagation timing state of the current signal strength acquisition process, generates timing state results, and transmits them to the interval overlay module.

[0051] The interval overlay module divides time intervals based on the smoothed signal strength time series and time-series state results, compares the signal strength within the intervals and determines the interval overlay state, generates an overlay state set and transmits it to the state output module.

[0052] The status output module combines the smoothed signal strength time series, time-series status results, and superimposed status set to distinguish the status of the signal strength values ​​and generate the signal strength processing results.

[0053] The smoothed signal intensity time series includes the sampling time index and the smoothed signal intensity. The variation interval sequence includes key sampling time points, adjacent time differences, and interval distribution sequences. The time series state results include fluctuation intensity levels and state classification codes. The superimposed state set includes interval superposition identifiers, interval continuity relationship identifiers, and interval combination state codes. The signal intensity processing results include state-distinguished signal intensity values, state-corresponding time series, and intensity-state mapping sets.

[0054] Please see Figure 2 The specific steps for obtaining the signal analysis module are as follows:

[0055] The signal acquisition submodule acquires the received signal strength values ​​and sampling timestamps corresponding to continuous sampling moments in the mobile communication environment, detects the interval between multiple sampling timestamps and applies constant sampling interval control, discards signal strength values ​​that do not meet the interval requirements, and generates a sampling signal strength sequence.

[0056] Through an independent logic unit configured in the baseband processing chip of the mobile communication terminal, its input is directly connected to the physical layer register of the RF receiver front-end for real-time capture of the air interface signal status. It integrates a high-sensitivity receiver interface and a high-precision clock synchronization circuit. During execution, the receiver interface reads the Received Signal Strength Indicator (RSSI) value from the physical layer register at millisecond-level polling cycles. This value quantifies the power level of the current communication link and is typically derived from the Automatic Gain Control (AGC) feedback value after the Low Noise Amplifier (LNA), with a dynamic range limited by hardware to -120dBm to -40dBm. Simultaneously, the clock synchronization circuit, based on a local temperature-controlled crystal oscillator or Precision Time Protocol (PTP) data packets sent from the network side, timestamps each read RSSI value with microsecond-level precision. The data then flows into the interval control logic circuit, which is preset with a constant sampling interval reference value, for example, set to 20ms. The internal time difference comparator calculates the time difference between the timestamp of the latest data frame in the current buffer and the timestamp of the previous valid data frame in real time. A sample point is considered valid and written to a first-level FIFO (First-In-First-Out) buffer only if the time difference falls strictly within a tolerance range centered at 20ms and with a positive or negative deviation of no more than 2ms. If the time difference is less than 18ms, the logic circuit determines it as oversampling and discards the current data to save storage resources; if the time difference is greater than 22ms, an alarm is triggered, indicating that channel congestion is expected. Through this hardware-level forced gating, the output sampled signal strength sequence exhibits high uniformity on the time axis, effectively filtering out non-stationary jitter caused by operation task scheduling delays, and providing a standardized timing reference for subsequent processing.

[0057] The timing construction submodule, based on the sampled signal strength sequence, sorts the time according to the corresponding sampling timestamp, determines the temporal continuity between adjacent signal strength values, and combines and arranges the signal strength values ​​in continuous intervals to obtain the signal strength time series.

[0058] Based on the generated sampled signal strength sequence, the built-in dual-port RAM (Random Access Memory) employs hardware sorting logic based on timestamp keys to physically remap the incoming sampled signal strength sequence. This unit rearranges data frames that are expected to be out of order due to multi-threaded concurrent writes, ensuring that all signal strength values ​​are stored continuously in strict chronological order in the storage space, according to the magnitude of the timestamp values. After physical sorting, the data stream enters the continuity discrimination logic circuit. The core of this circuit is a sliding window detector configured with a time continuity threshold parameter. This threshold is set based on the maximum tolerable packet loss rate, for example, 45ms (slightly greater than twice the sampling interval). The detector compares the timestamp difference between the signal strength values ​​in adjacent storage addresses one by one. When the difference is less than or equal to 45ms, the logic circuit determines that the signal remains continuous in the time domain and maintains the current data stream combination; once the difference exceeds 45ms, the circuit immediately triggers a segmentation mechanism, inserting a specific segmentation mask (such as a Hex code with all "1s") into the data stream to physically cut the long signal sequence into several independent continuous sub-intervals. Subsequently, the combination and arrangement unit calls the DMA (Direct Memory Access) controller to extract and encapsulate the signal strength values ​​within each consecutive interval based on the segmentation mask, generating a structured signal strength time series data packet. The header of this data packet contains the start time and duration of the interval, effectively isolating blank noise in non-continuous time periods and ensuring that subsequent analysis is performed only on data clusters that are tightly coupled in the time dimension.

[0059] The amplitude smoothing submodule calls the signal strength time series to perform amplitude smoothing on the difference in signal strength amplitude between adjacent sampling points. It performs replacement calculations on abnormal fluctuation values ​​according to the set amplitude change threshold, keeps the time order unchanged, and outputs the processed sequence to generate a smoothed signal strength time series.

[0060] The generated signal strength time series data packet is invoked. This submodule integrates an abnormal fluctuation detection unit, a linear interpolator, and a digital moving average filter, aiming to eliminate sudden amplitude noise caused by fast fading or multipath effects in the wireless channel. First, the signal strength time series is accessed, and its internal register stores an amplitude variation threshold. This threshold is set based on the statistical distribution of a large amount of measured road test data. For example, by analyzing 5000 signal samples in an urban canyon environment, the standard deviation of signal strength between adjacent 20ms sampling points is calculated, and three times the standard deviation (e.g., 8dB) is used as the baseline for judging anomalies. When the absolute value of the signal strength difference between two adjacent sampling points exceeds 8dB, the hardware comparator outputs a high level, activating the anomaly handling process. At this time, the linear interpolator automatically reads the normal values ​​of the previous and next moments of the anomaly point, calculates the arithmetic mean of the two values ​​using an adder and a shift register, and writes this calculation result as a substitute value to the storage address of the current anomaly point, thereby smoothing out spikes while preserving the time sequence. The cleaned data then flows into a digital moving average filter, which is configured with a convolution window of length 5. This window performs a weighted average of five consecutive signal strength values. This operation is executed in a pipeline using multiply-accumulate units, outputting a smoothed signal strength time series. Taking a set of actual monitoring data as an example: at consecutive sampling timestamps of 2000ms, 2020ms, 2040ms, 2060ms, and 2080ms, the corresponding original signal strength values ​​are -90dBm, -88dBm, -65dBm, -90dBm, and -91dBm, respectively. The value of -65dBm at the third sampling point (2040ms) represents a sudden change of 23dB compared to the previous time point, exceeding the set 8dB threshold. This is automatically identified as an anomaly, and the average of the preceding and following values ​​(-88dBm and -90dBm) is calculated to be -89dBm and used as a replacement. The filter then further processed the signal, and the final smoothed sequence values ​​were -90.0 dBm, -88.0 dBm, -89.0 dBm, -89.6 dBm, and -90.2 dBm. Experimental data show that, with these parameter settings, the root mean square error of the signal sequence can be reduced from 15.2 to 2.8, effectively improving the robustness of the subsequent positioning algorithm in complex electromagnetic environments.

[0061] Please see Figure 3 The specific steps for obtaining the extreme value extraction module are as follows:

[0062] The extreme value determination submodule obtains the signal intensity of multiple sampling points in the smooth signal intensity time series, compares the signal intensity of adjacent sampling points, filters the current sampling point value to be simultaneously greater than or less than the values ​​of the adjacent sampling points, and generates a peak-valley time point set.

[0063] The receiver receives a smoothed time series of received signal strength. During operation, a sliding window register group is configured to store signal strength values ​​at three consecutive sampling times in parallel, labeled as the forward neighbor value, the current detected value, and the backward neighbor value, respectively. A numerical comparison logic circuit is hard-connected to this register group, performing bidirectional amplitude comparison logic in real time. This circuit has a preset set of rules: the logic circuit outputs a "peak" flag if and only if the current detected value is simultaneously and strictly greater than both the forward and backward neighbor values; conversely, if the current detected value is simultaneously less than both the forward and backward neighbor values, it outputs a "trough" flag. To filter out spurious extrema caused by quantization noise, an extremum filtering gating unit introduces a sensitivity threshold parameter. This parameter is set based on measured statistics of the receiver's thermal noise floor, for example, set to 0.5 dBm. An extremum is only considered valid if the absolute value of the difference between the current detected value and both the forward and backward neighbor values ​​exceeds this threshold. Taking a set of actual signal data as an example, the register group captures three consecutive sampling points: the value at 2020ms is -88.0dBm, the value at 2040ms is -82.5dBm, and the value at 2060ms is -86.0dBm. The comparator circuit first calculates the difference: the difference between -82.5 and -88.0 is 5.5dBm, and the difference between -82.5 and -86.0 is 3.5dBm. Since both differences are greater than the preset 0.5dBm threshold and the middle value is significantly higher than the two sides, the logic unit determines that there is a valid peak at 2040ms and writes the sampling timestamp of that moment into the peak-valley time point set buffer.

[0064] The time point assembly submodule, based on the peak and valley time point set, sorts the data according to the order of sampling time, judges the continuity of adjacent time points, removes duplicate time markers and retains feasible time positions, and establishes key time series.

[0065] Based on the generated original peak-valley time point set, a time-series reordering controller, deduplication logic, and continuity verification unit are used. The time-series reordering controller has a built-in hardware sorting acceleration engine, which uses the hardware implementation logic of the fast sorting algorithm to remap the physical addresses of the timestamp data in the cache. This engine ensures that all captured peak-valley time points are strictly arranged in ascending order of the time axis in the storage space, thereby correcting the problem of out-of-order timestamp writing caused by multi-threaded parallel processing estimation. After sorting, the data stream enters the deduplication logic. This logic unit compares the timestamp values ​​of adjacent storage units bit by bit using XOR gates. If two adjacent timestamps are found to be completely identical, they are determined to be redundant records, and the controller automatically discards the data at the latter position, retaining only the unique timestamp. Subsequently, the continuity verification unit receives the cleaned sequence. This unit is configured with a minimum feasible time interval threshold. The threshold is set based on the maximum Doppler frequency shift characteristics of the mobile terminal in the physical environment. For example, at a walking speed of 1.5 meters per second, the theoretical minimum period of fast channel fading is about 200ms. Therefore, the threshold is set to 50ms to accommodate a certain amount of measurement error. When the interval between two adjacent retained time points is less than 50ms, the verification unit determines that these two feature points are not physically independent, and retains the point with the better amplitude based on the significance of signal strength, while discarding the other point. Taking a sorted timestamp sequence as an example: 3000ms, 3050ms, 3060ms, 3200ms, and 3450ms. Logical detection reveals that the interval between 3050ms and 3060ms is only 10ms, far less than the 50ms threshold. Based on the signal strength record, the peak amplitude corresponding to 3060ms is found to be higher, therefore 3050ms is discarded, and 3060ms is retained. Simultaneously, the interval between 3200ms and 3450ms is detected to be 250ms, greater than the 50ms threshold, and is retained. The final generated key time series are 3000ms, 3060ms, 3200ms, and 3450ms.

[0066] The interval calculation submodule calls the key time series, calculates the time difference between adjacent time positions in the series, reads the timestamp values ​​before and after and performs the difference operation, records the results of multiple adjacent time differences and arranges them in the original order to generate a change interval sequence.

[0067] The key time series of the output is retrieved. In the execution logic, dual-channel read registers load adjacent time positions in the sequence sequentially using a sliding window, defining them as the preceding and following time points. The arithmetic logic unit then executes a subtraction instruction to calculate the time difference between the two. This operation supports 32-bit unsigned integer arithmetic at the hardware level, ensuring no overflow errors occur at millisecond precision. The calculated difference result is immediately sent to the serialization memory controller, which writes each calculated time difference value sequentially into the continuous address space of the output buffer according to the original chronological order, forming a sequence of varying intervals. To monitor the validity of the data, statistical analysis logic is also integrated, capable of calculating the mean and variance of the currently generated sequence in real time. For example, based on the aforementioned key time series of 3000ms, 3060ms, 3200ms, and 3450ms, the processing unit first calculates the first interval: 3060-3000=60ms; then it calculates the second interval: 3200-3060=140ms; and finally, it calculates the third interval: 3450-3200=250ms. The final generated interval sequence is 60ms, 140ms, and 250ms. This sequence directly reflects the fluctuation frequency characteristics of the received signal on the time axis. If the values ​​in this sequence show a gradually decreasing trend, it physically corresponds to the mobile terminal accelerating through a signal fading region or an increase in the density of environmental scatterers.

[0068] Please see Figure 4 The specific steps for obtaining the timing discrimination module are as follows:

[0069] The fluctuation calculation submodule obtains multiple adjacent time interval values ​​based on the changing interval sequence, performs difference calculation on the continuous interval values, reads and accumulates the offset amplitude between adjacent time differences, summarizes the proportion relationship of the accumulated multiple values, and generates the interval fluctuation amount.

[0070] Based on the generated sequence of changing intervals, a differential operation logic array, a statistical histogram construction unit, and a fluctuation quantization encoder are used. The differential operation logic array internally contains a 50-depth FIFO shift register for parallel loading of 50 consecutive interval values. Subtractors in the array perform difference operations on interval values ​​at adjacent addresses in a pipelined manner, calculating the absolute difference between the k-th and (k+1)-th intervals to obtain the offset amplitude between adjacent time differences. The resulting data stream then enters the statistical histogram construction unit, which has a pre-defined set of quantization intervals, for example, 0 to 10 milliseconds as the "perturbation zone," 10 to 50 milliseconds as the "normal zone," and above 50 milliseconds as the "abrupt zone." A hardware comparator maps each offset amplitude value to the corresponding interval and drives the corresponding counter to accumulate, forming a set of interval change amplitudes. To determine the current dominant fluctuation pattern, the logic circuit calculates the proportion of each interval's count value to the total sample size. If the proportion of the "abrupt zone" exceeds 60%, the current state is considered to be highly volatile. Subsequently, the fluctuation quantization encoder is activated. It extracts all offset amplitude values ​​within the interval with the highest proportion, calculates their arithmetic mean using an accumulator and a divider, and defines this average as the interval fluctuation. For example, in one processing cycle, the shift register captures a set of interval sequences. Calculations show that 80% of the adjacent interval differences are concentrated between 60 and 80 milliseconds, and the calculated average offset amplitude is 72 milliseconds. This value of 72 is directly latched into the output register as a quantization indicator reflecting the uncertainty of the current channel timing characteristics, providing an accurate time-domain jitter benchmark for subsequent intensity mapping.

[0071] The intensity mapping submodule collects the signal intensity values ​​during the synchronous time period of the corresponding signal intensity acquisition process, reads the signal intensity changes at multiple sampling points, weights the interval fluctuations and the signal intensity changes, and calculates the fluctuation characteristic intensity.

[0072] Based on the generated interval fluctuation, the corresponding signal strength value is read from the RF front-end register. The data is then sent to the floating-point vector arithmetic unit, which first calculates the first-order difference of the signal strength sequence to obtain the signal strength change at multiple sampling points. To eliminate dimensional differences and comprehensively evaluate the fluctuation characteristics, the arithmetic unit calls the microcode instruction set to execute a specific weighting algorithm. The fluctuation characteristic strength is calculated using the following formula: The detailed explanation of each parameter and the calculation logic in the formula is as follows: Representing the The intensity of fluctuation characteristics within a synchronization time period is a dimensionless comprehensive evaluation index used to quantify the severity of the current communication environment; subscript This is a sequence identifier for the synchronization time period, used to distinguish different processing windows. This represents the sampling point number within that time period, with values ​​ranging from 1 to... . This represents the total number of sampling points within the synchronization time period. In this embodiment, it is set to 5. The selection of this value is based on the statistical characteristics of the coherence time of the mobile channel, ensuring that the sample size is sufficient to cover a typical fast fading microcycle. Representing the Within the first synchronous time period, the first The change in signal intensity at each sampling point is obtained by subtracting the intensity value of the previous sampling point from the current sampling point intensity value, and the unit is decibels; Representing the Within the first synchronous time period, the first The interval fluctuation corresponding to each sampling point is a value derived from the preceding module and is expressed in milliseconds. Representing the The arithmetic mean of the signal intensity changes at all sampling points within a synchronous time period. Regarding the calculation logic of the formula: the first term of the numerator is calculated using the summation symbol... With absolute value symbol The combination of these factors performs a coupled weighted operation of "fluctuation-intensity". Its core purpose is to capture the malicious event of "strong temporal jitter accompanied by strong signal fading", that is, when... (Intensity mutation) and When (time abrupt change) increases simultaneously, the product term It will exhibit non-linear amplification, significantly increasing the weight of this term in the total value; the second term in the numerator utilizes the square root. The square operation calculates the unnormalized standard deviation of signal intensity variation, aiming to independently characterize the dispersion of the signal energy dimension and supplement the stable high-frequency oscillation characteristics missed by the single weighting term prediction. The denominator calculates the total intensity variation and the total time fluctuation through two summation terms, serving as a normalization factor to eliminate evaluation biases caused by differences in sampling window length or the magnitude of the base values, ensuring the final result... This has universal comparative significance. To verify the effectiveness of the formula and the rationality of the parameter settings, a calculation example is demonstrated based on a set of actual high-speed rail moving scene data. In this scenario, a total of 5 key sampling points (i.e., ...) were collected during the synchronization time period. The process for obtaining each parameter is as follows: 1. Data: RSSI values ​​were continuously read from the RF front-end register and differentially processed to obtain the following sequence: 2, 5, -1, -4, 3 (unit: decibels), 2. Data: Output in real time through the fluctuation calculation submodule, the corresponding sequence is: 10, 40, 15, 35, 20 (unit: milliseconds), 3. Calculate: the above Summing the sequence and dividing by 5, i.e. Please refer to Table 1, which details the intermediate calculation data:

[0073] Table 1. Calculation Table of Signal Fluctuation Parameters During Synchronization Time Period

[0074]

[0075] As shown in Table 1, the arithmetic unit performs the following calculations based on the formula logic: First, it calculates the first term of the numerator: the value in the "Weighted Product Absolute Value Term" column of the cumulative table, i.e. Next, calculate the second term in the numerator: sum the values ​​in the "Square Deviation Term" column to get 50, and then calculate its arithmetic square root. Next, calculate the denominator: For Summing after taking the absolute value ( ),right Summing after taking the absolute value ( Adding the two together gives Finally, the obtained sum of numerator parameters (approximately 442.07) and sum of denominator parameters (135) are substituted into the above formula to calculate the final result. 3.27. The advantage of this formula is that by introducing the interval fluctuation as a dynamic weight, the feature intensity value can keenly reflect the “drastic time-varying fading” events that occurred at sampling points 2 and 4 (the product terms are as high as 200 and 140), whereas if only traditional variance calculation is used, these transient feature predictions will be submerged by averaging.

[0076] The state discrimination submodule calls the wave characteristic intensity, collects the corresponding propagation time sequence state, compares and judges the positional relationship of the intensity value within the state identifier distribution interval, completes the state classification output based on the value falling into the interval range, and generates the time sequence state result.

[0077] The function retrieves the output fluctuation characteristic intensity value. Its internal memory contains a pre-set table of state identifier distribution intervals, which, based on extensive field test data (covering typical scenarios such as stationary, walking, and vehicle-mounted environments), assigns fluctuation characteristic intensity values. The numerical range is divided into three key intervals: values ​​between 0 and 1.5 are defined as "stable state," corresponding to low-speed or stationary scenarios; values ​​between 1.5 and 5.0 are defined as "moderate fluctuation state," corresponding to typical vehicular multipath environments; and values ​​greater than 5.0 are defined as "violent fluctuation state," corresponding to high-speed movement or strong electromagnetic interference environments. The thresholds (1.5 and 5.0) were determined through cluster analysis of over 5000 channel samples, possessing high statistical confidence. During execution, the hardware comparator receives the calculated feature strength result of 3.27 and compares it with the threshold in the lookup table. Since 3.27 is greater than 1.5 and less than 5.0, the logic circuit determines that the current signal is in a "moderate fluctuation state." This state result is then encoded into binary format (e.g., "10") and written to the status register. This result indicates that although the terminal is currently in motion and the channel exhibits some multipath effect, it has not yet reached a level that would cause frequent link disconnections. The baseband processor can therefore maintain the current modulation and demodulation strategy without needing to activate an emergency speed reduction protection mechanism.

[0078] Please see Figure 5 The specific steps for obtaining the interval overlay module are as follows:

[0079] The interval division submodule obtains continuous sampled values ​​of signal strength based on the smoothed signal strength time series and time-series state results, reads the corresponding sample point set for a fixed time span, divides the continuous intervals according to the time sequence, and generates a time interval set.

[0080] The system receives the smoothed signal strength time series and timing status results after pre-processing. The time base generator, based on the master clock, provides high-precision nanosecond-level timestamps for time-domain alignment of the input data stream. During operation, the dynamic window address mapping logic reads a preset fixed time span parameter, typically set to 100 milliseconds (ms), based on the typical broadcast interval (102.4 ms) of beacon frames in current wireless LAN protocols, ensuring that each interval theoretically contains at least one complete signaling cycle. This logic unit uses the timestamp of the first valid signal sample point as the starting anchor point, physically dividing the continuous time axis into a series of equal-length logical time slots. For each time slot, the mapping logic traverses the input time series, using a comparator circuit to determine whether the timestamp of each sample point falls within the currently defined time range. Within the interval, This represents the start timestamp of the currently processed logical time interval. For any consecutively sampled signal strength values ​​that meet the conditions, their memory addresses are indexed and aggregated, written into the corresponding object container in the segmented data cache pool, thus generating a structured set of time intervals. To ensure data integrity, an empty packet detection circuit is also integrated. If no valid sampling point is detected within a 100-millisecond span, an invalid flag is placed in the object for that interval to prevent subsequent computational errors. Taking a practical example, the input sequence contains sampling points with timestamps of 10ms, 30ms, 45ms, 110ms, and 135ms, with signal strengths of -80dBm, -82dBm, -79dBm, -75dBm, and -73dBm, respectively. With a span of 100ms, the mapping logic identifies the three points from 10ms to 45ms as belonging to the first interval, and the two points from 110ms to 135ms as belonging to the second interval. The final output is a set of time intervals containing these two independent data packets.

[0081] The amplitude difference calculation submodule obtains the signal strength sampling values ​​in multiple intervals based on the time interval set, reads the maximum and minimum values ​​of the intervals, calculates the difference of the signal strength in the same interval, and summarizes the corresponding difference results of multiple intervals to obtain the interval amplitude difference set.

[0082] The generated time interval set is invoked. At the hardware execution level, the multi-channel extreme value search register group adopts a parallel loading mechanism to quickly scan the signal strength sample value set contained in each time interval. The register group is designed with two dedicated latches for "current maximum value" and "current minimum value". As the data stream passes through, the values ​​in the latches are updated in real time by the hardware comparator until all sampling points in the interval have been traversed, thereby locking the maximum and minimum values ​​of the interval. Subsequently, the arithmetic logic unit executes a subtraction instruction, subtracting the minimum value from the locked maximum value to calculate the signal fluctuation range of the interval, i.e., the amplitude difference. This process is performed independently and repeatedly for each time interval. The difference serialization controller is responsible for rearranging the calculation results of each interval in chronological order and writing them into the output FIFO buffer to form the interval amplitude difference set. For example, for the aforementioned first interval (signal strength: -80, -82, -79), the comparison logic determines that the maximum value is -79dBm and the minimum value is -82dBm, and the ALU calculates the difference as 3dB. For the second interval (signal strength: -75, -73), the maximum value is -73dBm, the minimum value is -75dBm, and the difference is 2dB. Ultimately, the output amplitude difference set is an ordered array. This hardware architecture uses parallel extreme value search technology to control the feature extraction latency to the microsecond level, effectively shielding the interference of absolute signal strength on fluctuation analysis and retaining only the relative amplitude features that reflect the severity of rapid fading in the channel.

[0083] The superposition determination submodule calls the interval amplitude difference set and the time sequence state result to obtain the adjacent interval amplitude difference sequence, analyzes the interval change trend of the adjacent difference value change direction and change amplitude, calculates the superposition trend, and generates the superposition state set.

[0084] The core task of this algorithm, which calls the interval amplitude difference set and time-series status results, is to quantify the evolution trend of fluctuation characteristics between adjacent intervals. The trend analysis coprocessor internally maintains a sliding window to obtain the amplitude difference sequence between adjacent intervals. The floating-point vector calculation engine is responsible for performing the core mathematical operations. It calculates the superposition trend quantity according to a preset algorithm to assess the accumulation of instability in the channel environment over continuous time slices. The superposition trend quantity is calculated using the following formula: ,in, Representing the The superimposed trend volume corresponding to each basic interval; Indicates the current number The amplitude difference (unit: dB) of each interval comes directly from the calculation results of the previous module and represents the current fluctuation benchmark; Indicates the subsequent The amplitude difference between each interval is used for comparison with the current benchmark. This indicates the number of intervals involved in continuous calculations; in this embodiment, it is set to... That is, to predict two intervals in advance. and They represent the first Interval and the first The normalized time interval parameter corresponding to the interval. This parameter is obtained by counting the number of valid signal sampling points actually captured within the corresponding interval and dividing it by the theoretical maximum number of sampling points (in this example, there should theoretically be 10 points within a 100ms time window with a 10ms sampling interval). This parameter essentially reflects the "confidence" or "density" of the interval data. The closer the value is to 1, the more complete the data in that interval, and the more reliable the calculated amplitude difference. Regarding the formula's operational logic: the core of the formula contains a difference term. With weighted terms The product of the terms. The difference term directly quantifies the degree of abrupt change in the amplitude of the fluctuation (i.e., "fluctuation of fluctuation"), and extracts the change using subtraction; the weight term constructs a geometric projection factor based on the data density, and its denominator is the square root of the sum of squares. The form constitutes the Euclidean norm, serving a normalization function. Its design aims to: normalize the data if and only if both intervals being compared have high data confidence levels. When the value is large, the weight tends to be a constant, so that the difference term is completely preserved; if the data in any interval is sparse ( If the value is small (e.g., in case of packet loss), the weight will automatically decay, thus suppressing false trend misjudgments caused by insufficient data. To verify this calculation logic, a case study is conducted based on real urban road movement test data, setting the current interval... Forward prediction There are several intervals. The acquisition and calculation of each parameter are as follows: 1. Acquisition of baseline interval parameters: Interval 1 ( Within this module, the amplitude difference calculation submodule outputs... dB; the number of valid sampling points within this interval is 8 (theoretically 10), therefore the normalized time interval parameter is calculated. 2. Obtaining parameters for subsequent intervals: Interval 2 ( The amplitude difference increased dramatically to dB, with 6 effective sampling points, therefore Interval 3 ( The amplitude difference has fallen back to dB, with 9 effective sampling points, therefore The computing engine performs the following specific calculations: First step, calculate... The components at time: the difference term is The sum of squares of the weighted denominators is Taking the square root gives 1.0, therefore the weight term is... The result of this step is the absolute value. The second step is to calculate. The components at time: the difference term is The sum of squares of the weighted denominators is The square root of is approximately 1.204. The weight term is... The result of this step is the absolute value. The third step is to sum the results: add the results from the previous two steps together. The calculated result, 4.947, is compared with a preset "superposition trend threshold." This threshold is set based on the analysis of fluctuation data under 1000 stationary channels, which shows that the trend is less than 4.0 in 95% of stationary scenarios; therefore, the threshold is set to 4.0. Since the result 4.947 in this example is greater than 4.0, the logic circuit outputs a "high superposition state" flag, indicating that the current channel is undergoing a non-stationary change. This result suggests that a sudden interruption is highly likely in the subsequent communication link, requiring immediate triggering of the Adaptive Modulation and Coding (AMC) module to reduce the modulation order from 64QAM to 16QAM, sacrificing data rate for connection stability.

[0085] Please see Figure 6 The specific steps for obtaining the status output module are as follows:

[0086] The intensity alignment submodule obtains the continuous sampled signal intensity based on the smoothed signal intensity time series, reads the corresponding time-series state result index position according to the sampling time sequence, performs signal intensity and state label alignment processing according to the consistency of time identifier, and generates an intensity state alignment value set.

[0087] The system receives unaligned, smoothed signal strength time-series data streams and timing status result data streams in parallel. At the hardware execution level, the multi-channel data synchronization interface is internally configured with two independent FIFO (First-In-First-Out) buffer queues, temporarily storing signal strength values ​​(in dBm) with high-precision timestamps and timing status tags (such as status IDs), respectively. The timestamp comparison logic circuit, as the core processing engine, employs a sliding window search mechanism. Using the timestamps in the signal strength sequence as the reference anchor point, it sets a microsecond-level time tolerance window (e.g., ±5 microseconds) and searches for the corresponding time index position in the timing status result queue. When the comparison circuit detects that two data streams have consistent timestamps at the same time point, it triggers the write enable signal of the bidirectional associative mapping memory, binding the current signal strength value with the corresponding status tag and encapsulating it into a structured data packet containing three-dimensional attributes of "time-strength-state". If timestamp mismatches or missing data are encountered, the built-in interpolation compensation circuit automatically fills the gaps based on the linear relationship between adjacent timestamps, ensuring the continuity of the output data. Taking a real-world communication scenario as an example, if the received signal strength is -65dBm at a timestamp of 100 milliseconds, and the timing state corresponding to that moment is found to be "stationary", the logic circuit will lock the two together and generate a standard element in the strength state alignment value set.

[0088] The state joint submodule calls the strength state alignment value set and the superimposed state set to obtain the signal strength, time sequence state identifier and interval superimposed identifier at the same time position. It then performs a joint judgment on the combination relationship of multiple state identifiers to obtain the joint state coding sequence.

[0089] The generated intensity state alignment value set and the parallel input superimposed state set are invoked. Multidimensional state information at the same time point is logically aggregated. During operation, based on a unified time reference, three key components at the same moment are synchronously extracted from two independent datasets: signal strength value, temporal state identifier (e.g., features characterizing the slope of signal change), and interval superposition identifier (e.g., superposition trend state characterizing the intensity of signal fluctuation). The combinational logic gate array then performs bit-level operations on these discrete state identifiers, using bit concatenation techniques to map different state features to the same binary codeword. For example, defining that the temporal state occupies the lower 2 bits and the interval superposition identifier occupies the higher 2 bits, the logic gate array uses shift operations and logical OR operations to merge "temporal state 1" (binary 01) and "superposition state 2" (binary 10) into "joint state code 1001" (decimal 9). Subsequently, this code is sent to a joint state code lookup table for verification and normalization mapping, generating the final joint state code sequence. Each code in this sequence uniquely represents the comprehensive characteristics of the signal at that moment in terms of temporal rate of change and amplitude fluctuation. Actual test data shows that when the input time point is 200 milliseconds, the timing state read is "rising state" (identifier code 1), the interval superposition identifier is "high superposition state" (identifier code 3), the logic circuit processes them together and outputs the encoded value 13 (binary 1101), which is immediately written into the joint state encoded sequence buffer.

[0090] The result output submodule obtains the numerical distribution of signal strength corresponding to multiple codes based on the joint state coding sequence, distinguishes the state of the coding sequence, summarizes the numerical arrangement of signal strength under different states, and generates the signal strength processing result.

[0091] The system acquires the joint state coding sequence and simultaneously retrieves the corresponding raw signal strength values. Based on the input joint state coding, a multi-dimensional signal strength distribution model is constructed. During execution, the controller traverses the input coding sequence and allocates corresponding independent buckets in statistical memory according to different coding values ​​(e.g., coding 1 to coding 16). For each specific coding instance, the corresponding signal strength value is projected into the corresponding bucket, and the statistical characteristics of the data in that bucket are calculated in real time, including the average signal strength, standard deviation, and peak distribution density. For example, for the bucket with a coding value of 9 (representing a high-quality channel state with "time stability and low superposition"), all signal strength samples in this state are summarized, such as -50dBm, -52dBm, and -51dBm. The average strength in this state is calculated to be -51dBm, and the distribution is extremely concentrated. Conversely, for the bucket with a coding value of 7 (representing a poor-quality channel state with "violent fluctuations and high superposition"), the average strength is found to be -70dBm, but the standard deviation is as high as 15dB, indicating that the signal is extremely unstable. Finally, these statistical results are aggregated and arranged according to the priority of the coding sequence (e.g., from high SNR to low SNR states), generating a comprehensive processing report containing the signal strength values ​​for each state. This report is output to upper-layer applications via a formatted interface, allowing network management to intuitively see that in "State A," signal strength is concentrated in the range of -50dBm to -60dBm; while in "State B," signal strength is diffused in the range of -80dBm to -95dBm. This processing result directly guides base station handover decisions and transmit power adjustment strategies.

[0092] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention in any other way. Any person skilled in the art would expect to make changes or modifications to the above-disclosed technical content to create equivalent embodiments that can be applied to other fields. However, any simple modifications, equivalent changes, and modifications made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the protection scope of the present invention.

Claims

1. A mobile communication signal strength acquisition and processing system, characterized in that, The system includes: The signal analysis module collects the received signal strength at continuous times, controls a constant sampling interval, analyzes the signal strength time series and smooths the amplitude, generates a smoothed signal strength time series and transmits it to the extreme value extraction module. The extreme value extraction module reads the signal intensity of multiple sampling points in the smooth signal intensity time series, filters the sampling point time that meets the extreme value judgment condition, forms a key time series and calculates the difference between adjacent time positions, generates a change interval sequence and transmits it to the time series discrimination module. The timing discrimination module calculates the fluctuation characteristic intensity based on the changing interval sequence, analyzes the propagation timing state of the current signal strength acquisition process, generates timing state results, and transmits them to the interval overlay module. The interval overlay module divides time intervals based on the smoothed signal intensity time series and the time series state results, compares the signal intensity within the intervals and determines the interval overlay state, generates an overlay state set and transmits it to the state output module. The status output module combines the smoothed signal intensity time series, the time-series status result, and the superimposed status set to perform state-differentiated output of the signal intensity values, generating signal intensity processing results.

2. The mobile communication signal strength acquisition and processing system according to claim 1, characterized in that, The smoothed signal intensity time series includes a sampling time index and a smoothed signal intensity; the variation interval sequence includes key sampling time points, adjacent time differences, and interval distribution sequences; the time series state results include fluctuation intensity levels and state classification codes; the superimposed state set includes interval superposition identifiers, interval continuity relationship identifiers, and interval combination state codes; and the signal intensity processing results include state-distinguished signal intensity values, state-corresponding time series, and intensity-state mapping sets.

3. The mobile communication signal strength acquisition and processing system according to claim 1, characterized in that, The signal analysis module includes: The signal acquisition submodule acquires the received signal strength values ​​and sampling timestamps corresponding to continuous sampling moments in the mobile communication environment, detects the interval between multiple sampling timestamps and applies constant sampling interval control, discards signal strength values ​​that do not meet the interval requirements, and generates a sampling signal strength sequence. The timing construction submodule, based on the sampled signal strength sequence, sorts the time according to the corresponding sampling timestamp, determines the temporal continuity between adjacent signal strength values, and combines and arranges the signal strength values ​​in continuous intervals to obtain the signal strength time sequence. The amplitude smoothing submodule calls the signal strength time series, performs amplitude smoothing processing on the difference in signal strength amplitude between adjacent sampling points, performs replacement calculation on abnormal fluctuation values ​​according to the set amplitude change threshold, keeps the time order unchanged, and outputs the processed sequence to generate a smoothed signal strength time series.

4. The mobile communication signal strength acquisition and processing system according to claim 3, characterized in that, The replacement operation of abnormal fluctuation values ​​based on the set amplitude change threshold includes: when the absolute value of the difference in signal intensity amplitude between adjacent sampling points exceeds the amplitude change threshold, the arithmetic mean of the signal intensity values ​​of adjacent sampling points is used to replace the abnormal fluctuation value, wherein the amplitude change threshold is in decibels and its value ranges from 3 to 10.

5. The mobile communication signal strength acquisition and processing system according to claim 1, characterized in that, The extreme value extraction module includes: The extreme value determination submodule acquires the signal intensity of multiple sampling points in the smoothed signal intensity time series, compares the signal intensity of adjacent sampling points, filters the current sampling point value to simultaneously exceed or fail to reach the values ​​of the adjacent sampling points before and after it, and generates a peak-valley time point set. The time point assembly submodule, based on the peak and valley time point set, sorts the data according to the order of sampling time, judges the continuity of adjacent time points, removes duplicate time markers and retains feasible time positions, and establishes key time series. The interval calculation submodule calls the key time series, calculates the time difference between adjacent time positions in the series, reads the timestamp values ​​before and after and performs the difference operation, records the results of multiple adjacent time differences and arranges them in the original order to generate a change interval sequence.

6. The mobile communication signal strength acquisition and processing system according to claim 1, characterized in that, The timing discrimination module includes: The fluctuation calculation submodule, based on the change interval sequence, obtains multiple adjacent interval time values, performs difference calculation on continuous interval values, reads and accumulates the offset amplitude between adjacent time differences, summarizes the proportion relationship of the accumulated multiple values, and generates the interval fluctuation amount. The intensity mapping submodule collects the signal intensity values ​​during the synchronous time period of the corresponding signal intensity acquisition process, reads the signal intensity changes at multiple sampling points, weights the interval fluctuations and the signal intensity changes, and calculates the fluctuation characteristic intensity. The state discrimination submodule calls the wave characteristic intensity, collects the corresponding propagation time sequence state, compares and judges the positional relationship of the intensity value within the state identifier distribution interval, completes the state classification output based on the value falling within the interval range, and generates the time sequence state result.

7. The mobile communication signal strength acquisition and processing system according to claim 6, characterized in that, The formula for calculating the intensity of wave characteristics is as follows: ； in, This represents the intensity of the fluctuation characteristics within the m-th synchronization time period. Represents the sequence number of multiple sampling points. This represents the total number of sampling points within the synchronization time period. This represents the change in signal strength at the j-th sampling point within the m-th synchronization time period. This represents the interval fluctuation corresponding to the j-th sampling point within the m-th synchronization time period. This represents the arithmetic mean of the changes in signal intensity at all sampling points within the m-th synchronization time period, with the subscript m indicating the synchronization time period.

8. The mobile communication signal strength acquisition and processing system according to claim 1, characterized in that, The interval overlay module includes: The interval division submodule obtains continuous sampled values ​​of signal strength based on the smoothed signal strength time series and the time sequence state results, reads the corresponding sample point set for a fixed time span, divides the continuous intervals according to the time sequence, and generates a time interval set. The amplitude difference calculation submodule obtains the signal strength sampling values ​​in multiple intervals based on the time interval set, reads the maximum and minimum values ​​of the intervals, calculates the difference of the signal strength in the same interval, summarizes the corresponding difference results of multiple intervals, and obtains the interval amplitude difference set. The superposition determination submodule calls the interval amplitude difference set and the time series state result to obtain the adjacent interval amplitude difference sequence, analyzes the interval change trend of the adjacent difference value change direction and change amplitude, calculates the superposition trend, and generates the superposition state set.

9. The mobile communication signal strength acquisition and processing system according to claim 1, characterized in that, The status output module includes: The intensity alignment submodule obtains the continuous sampled signal intensity based on the smoothed signal intensity time series, reads the corresponding time-series state result index position according to the sampling time sequence, performs signal intensity and state label alignment processing according to the consistency of time identifier, and generates an intensity state alignment value set. The state joint submodule calls the intensity state alignment value set and the superimposed state set to obtain the signal strength, time sequence state identifier and interval superimposed identifier at the same time position, and performs a joint judgment on the combination relationship of multiple state identifiers to obtain a joint state coding sequence. The result output submodule obtains the signal strength value distribution of multiple codes according to the joint state coding sequence, distinguishes the state of the coding sequence, summarizes the signal strength value arrangement results under different states, and generates the signal strength processing result.

10. A mobile communication signal strength acquisition and processing system according to claim 9, characterized in that, The signal strength and status label alignment process includes: performing sliding window processing on the smoothed signal strength time series, calculating the average signal strength within each window, and aligning the average value with the corresponding time series status label.

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