Full-quantization spectrum feature statistical method, system and device and storage medium

Abstract

The invention relates to the technical field of signal processing, and particularly provides a full-quantization spectrum characteristic statistical method, system and device and a storage medium, and the method comprises the steps: discretizing a current sampling signal frequency band into a continuous frequency point unit sequence according to a preset rule, and mapping a signal intensity value into an intensity level according to the preset rule; if the signal intensity exceeds the current noise base value, determining the frequency point range of the current sampling signal by extracting the center frequency point and the effective bandwidth of the current sampling signal, and recording the intensity level mapped for all frequency point units in the range once; if the signal intensity does not exceed the current noise base value, recording the intensity grade of mapping for one time for a frequency point unit in the frequency point unit sequence of the current sampling signal; and counting all involved frequency point units in the frequency spectrum recording process and the occurrence frequency of each intensity level recorded on each frequency point unit. According to the invention, the problem of virtual high background noise caused by a traditional peak value holding mode is solved.

Description

Technical Field

[0001] This invention belongs to the field of signal processing technology, specifically relating to a method, system, device, and storage medium for full-quantization spectral feature statistics. Background Technology

[0002] Current mainstream spectrum recording schemes generally adopt a working mode of periodic scanning combined with peak hold. The typical implementation process is as follows: The device performs a sequential traversal scan of the target frequency band at fixed time periods (typical values: 100ms, 500ms, or 1s). Within each scan period, the system divides the frequency band into discrete frequency units according to a preset resolution bandwidth (RBW) (e.g., 10 kHz, 100 kHz). For each frequency unit, spectral analysis obtains the instantaneous signal amplitude sampling sequence. The number of sampling points m is determined by the scan speed and RBW, with peak recording being dominant. Within each scan period, the system only records the maximum amplitude value (peak value) of the signal at each frequency unit, ignoring the average value, minimum value, and time-domain fluctuation characteristics of the signal within that period. For example, if a frequency unit experiences a single, 10μs-long, -50dBm pulse during a scan period, while the background noise is -90dBm for the rest of the time, the system only records the -50dBm peak value.

[0003] This approach incorrectly treats the peak value of the transient pulse (-50dBm) as the "normal" background level of the frequency unit, resulting in an overestimation of the background noise in the spectrum recording. Summary of the Invention

[0004] In view of the above-mentioned shortcomings of the prior art, the present invention provides a method, system, device and storage medium for full-quantization spectrum feature statistics to solve the above-mentioned technical problems.

[0005] In a first aspect, the present invention provides a fully quantized spectral feature statistical method, comprising: The current sampled signal frequency band is discretized into a continuous sequence of frequency point units according to a preset rule, and the signal strength value is mapped to an intensity level according to a preset rule; If the signal strength exceeds the current noise floor value, the frequency range is determined by extracting the center frequency and effective bandwidth of the current sampled signal, and the strength level of the mapped frequency unit within this range is recorded once. If the signal strength does not exceed the current noise floor value, then the frequency point unit in the frequency point unit sequence of the current sampled signal records the intensity level of its mapping once. The frequency units involved in this spectrum recording process were counted, as well as the number of times each intensity level was recorded on each frequency unit. A two-dimensional histogram matrix is ​​generated for each frequency unit. The horizontal axis represents all intensity level types recorded in that frequency unit, and the vertical axis represents the number of times the corresponding intensity level appears in that frequency unit.

[0006] In an optional implementation, the current sampled signal frequency band is discretized into a continuous sequence of frequency point units according to a preset rule, including: Obtain the device's resolution bandwidth value; Determine the start and end frequencies of the target frequency band; Based on the resolution bandwidth value and the start frequency and end frequency, the target frequency band is divided into multiple frequency point units arranged in ascending order; The center frequency of each frequency unit is determined by the starting frequency plus an integer multiple of the resolution bandwidth value, and the center frequency interval between adjacent frequency units is equal to the resolution bandwidth value. Multiple frequency units constitute a frequency unit sequence.

[0007] In an optional implementation, the signal strength value is mapped to a strength level according to a preset rule, including: Define a dynamic range for the signal strength, wherein the dynamic range has a minimum strength value and a maximum strength value; The dynamic range is divided into a preset number of discrete intensity levels, and each intensity level corresponds to a unique intensity range; According to the pre-set mapping rules, the sampled signal strength values ​​are mapped to the corresponding strength levels; The mapping rule is as follows: determine the intensity range to which the sampled signal intensity value belongs, and output the intensity level corresponding to the intensity range as the mapping result.

[0008] In an optional implementation, the frequency range of the current sampled signal is determined by extracting its center frequency and effective bandwidth, and the intensity level of its mapped frequency unit is recorded once for all frequency units within this range, including: Extract the center frequency and effective bandwidth of the currently sampled signal; Based on the center frequency and effective bandwidth, the frequency range is determined as follows: the starting frequency unit is (Fc-BW / 2), and the ending frequency unit is (Fc+BW / 2), where Fc is the center frequency and BW is the effective bandwidth. On all frequency units within the frequency range, the intensity level of one mapping is accumulated using a counter pre-configured for each frequency unit; The counter update operation is independent of the actual sampling intensity level of each frequency unit.

[0009] In an optional implementation, the center frequency and effective bandwidth of the currently sampled signal are extracted, including: The center frequency point is determined by peak detection using Fast Fourier Transform; The effective bandwidth was calculated using the -3dB power point measurement method.

[0010] In an optional implementation, recording the intensity level of a frequency unit in the frequency unit sequence of the current sampled signal when it appears once, including: Increment the counter corresponding to the current sampling intensity level only on the frequency unit in the frequency unit sequence of the current sampled signal.

[0011] In an optional embodiment, the method further comprises: The short-time energy mutation detection algorithm detects signals whose signal strength exceeds the current noise floor value.

[0012] Secondly, the present invention provides a fully quantized spectral feature statistical system, comprising: The basic processing module is used to discretize the current sampled signal frequency band into a continuous sequence of frequency point units according to preset rules, and to map the signal strength value into an intensity level according to preset rules. The bandwidth statistics module is used to determine the frequency range of the current sampled signal by extracting the center frequency and effective bandwidth of the current sampled signal if the signal strength exceeds the current noise floor value, and to record the intensity level of the mapped frequency unit for all frequency units within the range once. The background statistics module is used to record the intensity level of a frequency point unit in the frequency point unit sequence of the current sampled signal if the signal strength does not exceed the current noise floor value. The data statistics module is used to count all frequency units involved in this spectrum recording process, as well as the number of times each intensity level is recorded on each frequency unit; The chart generation module is used to generate a two-dimensional histogram matrix for each frequency unit. The horizontal axis represents all intensity level types recorded for that frequency unit, and the vertical axis represents the number of times the corresponding intensity level appears in that frequency unit.

[0013] Thirdly, a device is provided, comprising: The memory is used to store the full-quantization spectral characteristic statistical program; A processor is configured to implement the steps of the full-quantization spectrum feature statistics method provided in the first aspect when executing the full-quantization spectrum feature statistics program.

[0014] Fourthly, a computer-readable storage medium is provided, on which a full-quantization spectrum feature statistics program is stored, wherein when the full-quantization spectrum feature statistics program is executed by a processor, the full-quantization spectrum feature statistics method as provided in the first aspect is implemented.

[0015] The beneficial effects of this invention are that the fully quantized spectral characteristic statistical method, system, device, and storage medium provided by this invention, through frequency point unit-intensity two-dimensional histogram statistics and dual-path update mechanism, completely solve the problem of artificially high background noise caused by traditional peak hold mode. Its core beneficial effects include: Enhanced handling of sporadic signals: Microsecond-level signal events are accurately recorded on high percentile traces (such as P95-P100), avoiding contamination of the underlying background model.

[0016] Enhanced model robustness: Probabilistic background representation effectively distinguishes steady-state noise (concentrated in P0-P50) from transient interference (distributed in P80-P100), solving the confusion problem between the two types of signals in traditional schemes.

[0017] Supports advanced analytics applications: Automatically identifies sudden interference (Δ>10dB is judged as an occasional event) by the difference between P50 and P95 traces; achieves dynamic tracking of the noise floor based on the P10 trace; and marks high-frequency violation signals using the P99 trace. Attached Figure Description

[0018] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0019] Figure 1 is a schematic flow chart of a method according to an embodiment of the present invention.

[0020] Figure 2 This is another illustrative flowchart of a method according to an embodiment of the present invention.

[0021] Figure 3 FIG. 4 is a schematic block diagram of a system according to an embodiment of the present invention.

[0022] Figure 4 This is a schematic diagram of the structure of a device provided in an embodiment of the present invention. Detailed Implementation

[0023] In order to enable those skilled in the art to better understand the technical solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts should fall within the scope of protection of the present invention.

[0024] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art of the present invention. The terms used in this specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention.

[0025] The full quantization spectrum feature statistics method provided in this embodiment of the invention is executed by a computer device, and correspondingly, the full quantization spectrum feature statistics system runs in the computer device.

[0026] Figure 1 This is a schematic flowchart illustrating a method according to an embodiment of the present invention. Wherein, Figure 1 The executing entity can be a fully quantized spectral characteristic statistical system. Depending on different requirements, the order of the steps in this flowchart can be changed, and some can be omitted.

[0027] like Figure 1 As shown, the method includes: S1. Discretize the current sampled signal frequency band into a continuous sequence of frequency point units according to a preset rule, and map the signal strength value into an intensity level according to a preset rule; S2. If the signal strength exceeds the current noise floor value, the frequency range is determined by extracting the center frequency and effective bandwidth of the current sampled signal, and the strength level of the mapped frequency unit within this range is recorded once. S3. If the signal strength does not exceed the current noise floor value, then the frequency point unit in the frequency point unit sequence of the current sampled signal records the intensity level of its mapping once. S4. Count all frequency units involved in this spectrum recording process, and the number of times each intensity level is recorded on each frequency unit; S5. Generate a two-dimensional histogram matrix for each frequency unit. The horizontal axis represents all intensity level types recorded in that frequency unit, and the vertical axis represents the number of times the corresponding intensity level appears in that frequency unit.

[0028] In one embodiment of the present invention, based on step S1, the following will provide a possible embodiment and describe its specific implementation in a non-limiting manner.

[0029] S101. Frequency band segmentation.

[0030] Using the sampled signal's frequency band as the target frequency band, the primary task before segmenting the target frequency band into frequency units is to accurately obtain the device's resolution bandwidth (RBW). This parameter is typically obtained through the device's hardware interface, such as GPIB, USB, or Ethernet, by sending a specific query command, such as the SCPI standard command ":SENSe:BANDwidth:RESolution?". The obtained value must then be corrected in real-time by the device's internal calibration module to eliminate measurement deviations caused by factors such as temperature changes and device aging. For devices that support dynamic resolution bandwidth configuration, the bandwidth parameter in the current operating mode must be locked using a preset command before obtaining this parameter to ensure consistency in subsequent segmentation calculations.

[0031] Next, when determining the start and end frequencies of the target frequency band, the specific application scenario must be considered. In spectrum monitoring scenarios, the frequency band range can be set directly by user input or automatically identified by the active signal range from the spectrum scanning module. In communication testing scenarios, however, precise configuration is required based on relevant communication standards, such as LTE frequency band allocation and 5G NR frequency range. The numerical accuracy must be controlled within one-tenth of the device's minimum frequency step to prevent signal leakage at frequency unit boundaries.

[0032] Frequency point cell segmentation based on the above parameters must follow the following mathematical logic and operational procedures: First, calculate the total bandwidth of the target frequency band, which is the difference between the ending frequency and the starting frequency. If this total bandwidth is less than the resolution bandwidth, then the frequency band is directly treated as a single frequency unit, and the center frequency of this frequency unit is the average of the starting frequency and the ending frequency. When the total bandwidth is greater than or equal to the resolution bandwidth value, the number of frequency point units should be calculated by rounding up, that is, the result of dividing the total bandwidth by the resolution bandwidth value should be rounded up. This is done to ensure that the coverage of all frequency point units can completely cover the target frequency band. The center frequency of the i-th frequency point unit (i starts from 0 and increases sequentially until the number of frequency point units decreases by one) is equal to the starting frequency plus the product of i and the resolution bandwidth value. To verify the integrity of the frequency unit sequence, the center frequency of the last frequency unit plus half of the resolution bandwidth value must be greater than or equal to the termination frequency of the target frequency band. In other words, the upper boundary of the last frequency unit must be able to cover the termination frequency of the target frequency band.

[0033] Through the above steps, adaptive segmentation of the target frequency band based on resolution bandwidth can be achieved. The generated continuous frequency point unit sequence can not only meet the measurement accuracy requirements of the equipment, but also ensure that the target frequency band is covered without omission, providing a reliable frequency point unit basis for subsequent spectrum analysis, signal detection and other operations.

[0034] S102. Intensity Mapping.

[0035] The setting of the signal strength dynamic range needs to be considered in conjunction with the application scenario and equipment performance. The minimum strength value is usually determined by averaging multiple air interface measurements based on the noise floor detectable by the equipment. The maximum strength value is constrained by the upper limit of the equipment's linear operating range to avoid measurement distortion due to signal saturation. A certain margin should be reserved for the upper and lower limits of the dynamic range, generally extended by 5%-10% from the actual measurement extreme values ​​to cope with sudden signal fluctuations. For example, in wireless communication testing, the dynamic range may be set to -110dBm to -30dBm, covering the range from the terminal's receiving sensitivity to the strong signal blocking threshold.

[0036] When dividing the dynamic range into a preset number of discrete intensity levels, the principle of equal interval division should be followed to simplify the mapping logic. The number of levels should be determined comprehensively based on signal resolution requirements and data processing efficiency. If fine differentiation of weak signals is required, a higher level division can be used in the low-intensity range; if the focus is on changes in strong signals, the level density can be increased in the high-intensity range. Each intensity level corresponds to a unique continuous intensity range, and the range boundaries need to be accurately calculated and fixed. For example, when the dynamic range is 80dB and divided into 16 levels, each level corresponds to a 5dB range, and the boundary values ​​of adjacent ranges are defined in a left-closed, right-open form to avoid overlap or omission.

[0037] The execution of mapping rules requires real-time judgment through hardware logic or software algorithms. For each sampled value, it is first checked whether it is within the set dynamic range; if it exceeds the range, it must be marked as out of range. If it is within the range, the sampled value is compared with the boundary values ​​of each level interval to determine the interval to which it belongs and output the corresponding level identifier. The level identifier is usually in integer encoding form, for example, 0 represents the lowest level and N-1 represents the highest level (N is the total number of levels). To improve mapping efficiency, a binary search algorithm can be used to shorten the interval matching time, especially in scenarios with a large number of levels, which can significantly reduce processing latency.

[0038] In one embodiment of the present invention, based on step S2, the following will provide a possible embodiment and describe its specific implementation in a non-limiting manner.

[0039] Signals whose intensity exceeds the current noise floor value are detected using a short-time energy mutation detection algorithm. The noise floor value is adaptively estimated using a sliding window. A window length is set, and the current noise floor value is the sum of the mean and standard deviation of the signal intensity of all sampling points within that window length, where the mean and standard deviation represent the average level and dispersion of the signal intensity within the window, respectively. The short-time energy mutation detection is implemented by calculating the difference between the signal strength and the noise floor value. When the strength of the currently sampled signal is greater than the noise floor value, a valid signal is determined to exist, thus triggering the subsequent feature extraction process; otherwise, noise statistics are performed.

[0040] If the signal strength of the currently sampled signal exceeds the current noise floor value, then execute: S201. Extract the center frequency and effective bandwidth of the currently sampled signal.

[0041] The center frequency is determined using Fast Fourier Transform (FFT) peak detection. An FFT of a certain length is performed on the current sampled signal to obtain the corresponding spectrum. The frequency corresponding to the maximum value in this spectrum is then searched; this frequency is the center frequency. To improve detection accuracy, cubic interpolation is used to fit the spectral points near the peak, thus more accurately determining the location of the center frequency.

[0042] The effective bandwidth is calculated using the -3dB power point measurement method. In the obtained spectrum, the left and right boundary frequencies corresponding to when the signal power drops to half of the peak power (i.e., -3dB) are found. The difference between these two boundary frequencies is the effective bandwidth. If an asymmetric spectrum is encountered, the half-power points on both the left and right sides are calculated separately, and the difference between them is taken as the effective bandwidth.

[0043] S202. Based on the center frequency and effective bandwidth, determine the frequency range: the starting frequency unit is (Fc-BW / 2), and the ending frequency unit is (Fc+BW / 2), where Fc is the center frequency and BW is the effective bandwidth; S203. On all frequency units within the frequency range, the intensity level of one mapping is accumulated using a counter pre-configured for each frequency unit; wherein the counter update operation is independent of the actual sampling intensity level of each frequency unit.

[0044] When the signal strength exceeds the noise floor value, the system performs a batch update on all frequency units within the index range. Each frequency unit is configured with an independent multi-dimensional counter, the dimensions of which are consistent with the number of strength levels (e.g., M levels correspond to M counting dimensions). During the update, based on the level Li mapped to the current signal strength, the Li dimension counter value of all frequency units within that range is incremented by 1. This process is strictly independent: even if a frequency unit does not detect a signal during real-time sampling, it will still be counted according to the current signal strength level as long as it is within the index range. This design aims to completely record the spectral characteristic distribution within the signal coverage area, avoiding the omission of key information due to instantaneous sampling.

[0045] In one embodiment of the present invention, based on step S3, the following will provide a possible embodiment and describe its specific implementation in a non-limiting manner.

[0046] When the signal strength does not exceed the noise floor value, the system adopts a single-point update strategy. First, the corresponding frequency unit is determined by the center frequency of the current sampled signal (achieved by matching the center frequency of the frequency unit sequence). Then, the counter corresponding to the level Lj of the current noise strength mapping in that unit is incremented by 1. Unlike the valid signal scenario, the update range here is strictly limited to the frequency unit actually occupied by the signal, and the counting logic is directly related to the noise intensity level sampled in real time to ensure the accuracy of noise statistics.

[0047] All frequency unit counters employ a double-buffering mechanism: during sampling, the current counter group receives real-time updates, while the standby counter group remains frozen to support parallel statistical query operations. Upon completion of a round of spectrum recording (e.g., reaching the preset sampling duration), the system automatically triggers a counter group switch, transferring the current counter group to standby mode and simultaneously resetting the new current counter group to avoid data overwrite conflicts. In addition, the counter is stored in 32-bit unsigned integer type, and automatically resets to zero when the count reaches the maximum value. The data continuity and traceability are ensured by using timestamps and log recording.

[0048] In one embodiment of the present invention, based on step S4, the following will provide a possible embodiment and describe its specific implementation in a non-limiting manner.

[0049] After recording is completed, all frequency units need to be screened, and only units that have had counter update records during recording (i.e., "involved frequency units") should be retained, while invalid frequency units that have not been activated should be excluded to reduce the amount of redundant data.

[0050] For each selected frequency unit, the cumulative counts of all intensity levels in its counter matrix are extracted. This data must undergo integrity verification to ensure there are no missing counts or abrupt jumps (such as counts for a single intensity level far exceeding a reasonable range). If anomalies are found, linear interpolation is used for correction to ensure the reliability of the original data.

[0051] In one embodiment of the present invention, based on step S5, a possible embodiment will be given below, and its specific implementation will be described in a non-limiting manner.

[0052] A two-dimensional histogram matrix is ​​generated for each frequency unit. The horizontal axis represents all intensity level types recorded in that frequency unit, and the vertical axis represents the number of times the corresponding intensity level appears in that frequency unit.

[0053] Each frequency unit's corresponding two-dimensional histogram matrix is ​​an independent two-dimensional data structure, its dimension determined by the number of intensity levels. The horizontal axis strictly corresponds to a preset set of intensity levels \(\{L_1,L_2,...,L_M\}\), and is arranged in ascending order of level number to ensure that the horizontal axis of the matrix for different frequency units has a unified reference standard. For example, if the intensity levels are divided into 8 levels, the horizontal axes are \(L_1\) to \(L_8\), corresponding to the signal intensity ranges from the lowest to the highest.

[0054] The vertical axis represents the frequency of occurrence of each intensity level, and its value is directly taken from the cumulative value of the corresponding level in the counter matrix of that frequency unit. The scale range of the vertical axis is dynamically adjusted according to the maximum count value of all frequency units to ensure the clarity of data visualization—when the maximum count of a frequency unit is 1000, while other units are mostly within 500, a scale range of 0 to 1000 is uniformly used.

[0055] In one embodiment, a frequency point unit-intensity two-dimensional histogram is used as the basic data structure to achieve fully quantized spectral feature statistics: Frequency dimension: The target frequency band is discretized according to the device resolution bandwidth (RBW) to form a continuous sequence of frequency point units (e.g., generating 50,000 frequency point units in 100kHz steps in the 1-6GHz band). Intensity dimension: The dynamic range of the received signal (typically -120dBm to -20dBm) is linearly mapped to 101 discrete intensity levels to achieve fine-grained quantization of signal amplitude. Statistical mechanism: Each frequency point unit maintains an independent intensity distribution counter, forming a two-dimensional histogram matrix. This matrix optimizes memory usage through sparse storage compression technology, retaining only non-zero counting units.

[0056] Real-time signal processing flow as follows Figure 2 As shown, it includes the following steps: (1) Signal detection and feature extraction.

[0057] When a signal with an amplitude exceeding the dynamic noise floor appears in the real-time spectrum stream (triggered by a short-time energy mutation detection algorithm), the system executes: Center frequency location: Determine the signal center frequency (Fc) based on Fast Fourier Transform (FFT) peak detection; Bandwidth measurement: Calculate the effective bandwidth (BW) of the signal; Intensity quantization: Maps the signal amplitude to a preset 101-level intensity scale.

[0058] (2) Bandwidth-related statistics update.

[0059] For detected valid signals, the system automatically extends their energy distribution to the physically covered frequency band: Calculate the frequency range of the actual impact of the signal: [Fc-BW / 2, Fc+BW / 2]; In all discrete frequency point units within this frequency band, the count value of the current signal strength level is synchronously increased; Technical advantages: Eliminates signal energy dispersion errors caused by resolution bandwidth limitations in traditional solutions, ensuring the statistical integrity of broadband signals.

[0060] (3) Background noise is counted independently.

[0061] When no signal detection is triggered, the system only updates the counter corresponding to the intensity level within the current sampling frequency unit to maintain the purity of the background noise statistics.

[0062] (4) Background trace generation algorithm.

[0063] After recording is complete, the system performs offline statistical analysis: Cumulative probability calculation: For each frequency unit, the cumulative distribution function is calculated based on the intensity distribution histogram to construct the mapping relationship between the intensity value and its occurrence probability.

[0064] Percentile trace extraction: Traverse 101 percentiles from 0% to 100% (step size 1%), find the intensity threshold that satisfies the target percentile probability in each frequency unit, generate a continuous intensity trace that runs through the entire frequency band, and form the core output of the background model.

[0065] In some embodiments, the full-quantization spectral characteristic statistical system may include multiple functional modules composed of computer program segments. The computer programs for each program segment in the full-quantization spectral characteristic statistical system may be stored in the memory of a computer device and executed by at least one processor to perform (see details). Figure 1 (Description) Functionality of full-quantization spectral feature statistics.

[0066] In this embodiment, the full-quantization spectral feature statistics system can be divided into multiple functional modules according to its functions, such as... Figure 3 As shown. The module referred to in this invention is a series of computer program segments that can be executed by at least one processor and perform a fixed function, and is stored in memory. In this embodiment, the functions of each module will be described in detail in subsequent embodiments.

[0067] The frequency unit segmentation module is used to discretize the target frequency band into a continuous sequence of frequency units; The intensity monitoring module is used to sample the signal intensity of each frequency unit and map the signal intensity to an intensity level according to a pre-set mapping rule; The bandwidth statistics module is used to extract the center frequency and effective bandwidth of frequency point units whose intensity level exceeds the preset base level threshold, determine the frequency point range based on the center frequency and effective bandwidth, and increment the counter of the current intensity level of all frequency point units within the frequency point range by 1. The background statistics module is used to update the counter of the current intensity level of a frequency unit only for the frequency unit whose intensity level does not exceed the preset base level threshold. The data analysis module is used to generate a continuous intensity trace that runs through the target frequency band based on the number of times the intensity level appears as recorded by the counters of each frequency unit.

[0068] Figure 4 The full-quantization spectral feature statistical method provided in the embodiments of this application can be applied to devices. Those skilled in the art will understand that the device structures involved in the embodiments of this invention do not constitute a limitation on the device. A device may include more or fewer components than illustrated, or combine certain components, or have different component arrangements. In the embodiments of this invention, the device includes, but is not limited to, laptop computers, desktop computers, workbenches, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The device may also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the embodiments of this application described and / or claimed herein.

[0069] The device 400 may include a processor 410, a memory 420, and a communication unit 430. These components communicate via one or more buses. Those skilled in the art will understand that the server structure shown in the figure does not constitute a limitation of the present invention. It may be a bus topology or a star topology, and may include more or fewer components than shown, or combine certain components, or have different component arrangements.

[0070] The memory 420 can be used to store execution instructions of the processor 410. The memory 420 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. When the execution instructions in the memory 420 are executed by the processor 410, the device 400 is able to perform some or all of the steps in the above method embodiments.

[0071] The processor 410 serves as the control center of the storage device, connecting various parts of the electronic device via various interfaces and lines. It executes software programs and / or modules stored in the memory 420, and calls data stored in the memory to perform various functions of the electronic device and / or process data. The processor can be composed of integrated circuits (ICs), such as a single packaged IC or multiple packaged ICs with the same or different functions connected together. For example, the processor 410 may consist only of a central processing unit (CPU). In this embodiment of the invention, the CPU may have a single processing core or include multiple processing cores.

[0072] The communication unit 430 is used to establish a communication channel, enabling the storage device to communicate with other devices. It can receive user data sent by other devices or send user data to other devices.

[0073] The present invention also provides a computer storage medium, wherein the computer storage medium may store a program that, when executed, may include some or all of the steps of each embodiment provided herein. The storage medium may be a magnetic disk, an optical disk, a read-only memory (ROM), or a random access memory (RAM).

[0074] Those skilled in the art will clearly understand that the techniques in the embodiments of the present invention can be implemented using software plus necessary general-purpose hardware platforms. Based on this understanding, the technical solutions in the embodiments of the present invention, or the parts that contribute to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium such as a USB flash drive, a portable hard drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk, or any other medium capable of storing program code. It includes several instructions to cause a computer device (which may be a personal computer, a server, or a second device, network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention.

[0075] The same or similar parts between the various embodiments in this specification can be referred to mutually. In particular, the device embodiments are basically similar to the method embodiments, so the description is relatively simple, and the relevant parts can be referred to the description in the method embodiments.

[0076] In the several embodiments provided by the present invention, it should be understood that the disclosed systems and methods can be implemented in other ways. For example, the system embodiments described above are merely illustrative. For example, the division of the modules is merely a logical function division. In actual implementation, there may be other division methods, such as multiple modules or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of systems or modules, and can be electrical, mechanical or other forms.

[0077] The modules described as separate components may or may not be physically separate, and the components shown as modules may or may not be physical modules, that is, they may be located in one place or distributed across multiple network modules. Some or all of the modules may be selected to achieve the purpose of this embodiment according to actual needs.

[0078] In addition, each functional module in each embodiment of the present invention may be integrated into one processing module, or each module may exist physically separately, or two or more modules may be integrated into one module.

[0079] Although the present invention has been described in detail with reference to the accompanying drawings and preferred embodiments, the present invention is not limited thereto. Various equivalent modifications or substitutions can be made to the embodiments of the present invention by those skilled in the art without departing from the spirit and essence of the invention, and such modifications or substitutions should all be within the scope of the present invention. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should also be covered within the protection scope of the present invention.

Claims

1. A fully quantized spectral feature statistical method, characterized in that, include: The current sampled signal frequency band is discretized into a continuous sequence of frequency point units according to a preset rule, and the signal strength value is mapped to an intensity level according to a preset rule; If the signal strength exceeds the current noise floor value, the frequency range is determined by extracting the center frequency and effective bandwidth of the current sampled signal, and the strength level of the mapped frequency unit within this range is recorded once. If the signal strength does not exceed the current noise floor value, then the frequency point unit in the frequency point unit sequence of the current sampled signal records the intensity level of its mapping once. The frequency units involved in this spectrum recording process were counted, as well as the number of times each intensity level was recorded on each frequency unit. A two-dimensional histogram matrix is ​​generated for each frequency unit. The horizontal axis represents all intensity level types recorded in that frequency unit, and the vertical axis represents the number of times the corresponding intensity level appears in that frequency unit.

2. The method according to claim 1, characterized in that, The current sampled signal frequency band is discretized into a continuous sequence of frequency point units according to a preset rule, including: Obtain the device's resolution bandwidth value; Determine the start and end frequencies of the target frequency band; Based on the resolution bandwidth value and the start frequency and end frequency, the target frequency band is divided into multiple frequency point units arranged in ascending order; The center frequency of each frequency unit is determined by the starting frequency plus an integer multiple of the resolution bandwidth value, and the center frequency interval between adjacent frequency units is equal to the resolution bandwidth value. Multiple frequency units constitute a frequency unit sequence.

3. The method according to claim 1, characterized in that, Map signal strength values ​​to strength levels according to preset rules, including: Define a dynamic range for the signal strength, wherein the dynamic range has a minimum strength value and a maximum strength value; The dynamic range is divided into a preset number of discrete intensity levels, and each intensity level corresponds to a unique intensity range; According to the pre-set mapping rules, the sampled signal strength values ​​are mapped to the corresponding strength levels; The mapping rule is as follows: determine the intensity range to which the sampled signal intensity value belongs, and output the intensity level corresponding to the intensity range as the mapping result.

4. The method according to claim 1, characterized in that, The frequency range of the current sampled signal is determined by extracting its center frequency and effective bandwidth. The intensity level of the mapped frequency unit within this range is recorded once, including: Extract the center frequency and effective bandwidth of the currently sampled signal; Based on the center frequency and effective bandwidth, the frequency range is determined as follows: the starting frequency unit is (Fc-BW / 2), and the ending frequency unit is (Fc+BW / 2), where Fc is the center frequency and BW is the effective bandwidth. On all frequency units within the frequency range, the intensity level of one mapping is accumulated using a counter pre-configured for each frequency unit; The counter update operation is independent of the actual sampling intensity level of each frequency unit.

5. The method according to claim 4, characterized in that, Extract the center frequency and effective bandwidth of the currently sampled signal, including: The center frequency point is determined by peak detection using Fast Fourier Transform; The effective bandwidth was calculated using the -3dB power point measurement method.

6. The method according to claim 1, characterized in that, The frequency unit in the frequency unit sequence of the current sampled signal records the intensity level of its mapped occurrence once, including: Increment the counter corresponding to the current sampling intensity level only on the frequency unit in the frequency unit sequence of the current sampled signal.

7. The method according to claim 1, characterized in that, The method further includes: The short-time energy mutation detection algorithm detects signals whose signal strength exceeds the current noise floor value.

8. A fully quantized spectral characteristic statistical system, characterized in that, include: The basic processing module is used to discretize the current sampled signal frequency band into a continuous sequence of frequency point units according to preset rules, and to map the signal strength value into an intensity level according to preset rules. The bandwidth statistics module is used to determine the frequency range of the current sampled signal by extracting the center frequency and effective bandwidth of the current sampled signal if the signal strength exceeds the current noise floor value, and to record the intensity level of the mapped frequency unit for all frequency units within the range once. The background statistics module is used to record the intensity level of a frequency point unit in the frequency point unit sequence of the current sampled signal if the signal strength does not exceed the current noise floor value. The data statistics module is used to count all frequency units involved in this spectrum recording process, as well as the number of times each intensity level is recorded on each frequency unit; The chart generation module is used to generate a two-dimensional histogram matrix for each frequency unit. The horizontal axis represents all intensity level types recorded for that frequency unit, and the vertical axis represents the number of times the corresponding intensity level appears in that frequency unit.

9. A fully quantized spectral characteristic statistical device, characterized in that, include: The memory is used to store the full-quantization spectral characteristic statistical program; A processor is configured to implement the steps of the full-quantization spectrum feature statistics method as described in any one of claims 1-7 when executing the full-quantization spectrum feature statistics program.

10. A computer-readable storage medium storing a computer program, characterized in that, The readable storage medium stores a fully quantized spectrum feature statistics program, which, when executed by a processor, implements the steps of the fully quantized spectrum feature statistics method as described in any one of claims 1-7.

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