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

Abstract

The present application relates to the technical field of signal processing, and specifically provides a full-quantization spectrum feature statistical method, system, device and storage medium, comprising: 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 a preset rule; if the signal intensity exceeds a current noise base value, determining a frequency point range of the current sampling signal by extracting a center frequency point and an effective bandwidth thereof, and recording the mapped intensity level of all frequency point units in the range once; if the signal intensity does not exceed the current noise base value, recording the mapped intensity level of a frequency point unit in the frequency point unit sequence of the current sampling signal once; and counting all involved frequency point units in the present spectrum recording process, and the occurrence number of each intensity level recorded on each frequency point unit. The present application solves the problem of false high background noise caused by the traditional peak value maintaining mode.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of signal processing, and particularly relates to a full-quantization spectrum feature statistical method, system, device and storage medium. BACKGROUND

[0002] The current mainstream spectrum recording scheme generally adopts a working mode of periodic scanning combined with peak hold. The typical implementation process is as follows: the device performs sequential traversal scanning on the target frequency band at a fixed time period (typical value: 100 ms, 500 ms or 1 s). In each scanning period, the system divides the frequency band into discrete frequency point units according to a preset resolution bandwidth (RBW) (such as 10 kHz or 100 kHz). For each frequency point unit, the spectrum analysis obtains an instantaneous signal amplitude sample sequence of the frequency point unit. The number of sampling points m is determined by the scanning speed and the RBW. The peak value is recorded. In each scanning period, the system only records the maximum amplitude value (peak value) of the signal on each frequency point unit, and ignores the average value, minimum value and time domain fluctuation characteristics of the signal in the period. For example, if a frequency point unit appears an occasional pulse of 10 us in duration and-50 dBm in amplitude once in a scanning period, and the rest of the time is background noise of-90 dBm, the system only records the peak value of-50 dBm.

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

[0004] In view of the above problems of the prior art, the application provides a full-quantization spectrum feature statistical method, system, device and storage medium to solve the above technical problems.

[0005] In a first aspect, the application provides a full-quantization spectrum feature statistical method, comprising:

[0006] discretizing the current sampling signal frequency band into a continuous frequency point unit sequence according to a preset rule, and mapping the signal intensity value to an intensity level according to a preset rule;

[0007] If the signal intensity exceeds the current noise floor value, the frequency range of the current sampling signal is determined by extracting the center frequency point and the effective bandwidth, and the intensity level mapped by all frequency point units in the range is recorded once;

[0008] If the signal intensity does not exceed the current noise floor value, the intensity level mapped by the frequency point unit in the frequency point unit sequence of the current sampling signal is recorded once;

[0009] Counting the number of times each intensity level appears in each frequency bin unit involved in the spectrum recording process;

[0010] Generating a two-dimensional histogram matrix for each frequency bin unit, with the horizontal coordinate representing all intensity levels recorded by the frequency bin unit and the vertical coordinate representing the number of times each intensity level appears in the frequency bin unit.

[0011] In an optional embodiment, the current sampling signal frequency band is discretized into a continuous sequence of frequency bin units according to a preset rule, including:

[0012] Obtaining the resolution bandwidth value of the device;

[0013] Determining the start frequency and end frequency of the target frequency band;

[0014] Based on the resolution bandwidth value and the start frequency and end frequency, the target frequency band is divided into a plurality of frequency bin units arranged in ascending order;

[0015] Wherein, the center frequency of each frequency bin unit is determined by the start frequency plus an integer multiple of the resolution bandwidth value, and the center frequency interval of adjacent frequency bin units is equal to the resolution bandwidth value;

[0016] The plurality of frequency bin units constitute a frequency bin unit sequence.

[0017] In an optional embodiment, the signal intensity value is mapped to an intensity level according to a preset rule, including:

[0018] Setting the dynamic range of signal intensity, the dynamic range having a minimum intensity value and a maximum intensity value;

[0019] Dividing the dynamic range into a preset number of discrete intensity levels, each intensity level corresponding to a unique intensity interval;

[0020] According to the pre-set mapping rule, the sampled signal intensity value is mapped to the corresponding intensity level;

[0021] Wherein, the mapping rule is to determine the intensity interval to which the sampled signal intensity value belongs, and the intensity level corresponding to the intensity interval is output as the mapping result.

[0022] In an optional embodiment, the frequency range of the current sampling signal is determined by extracting the center frequency and effective bandwidth of the current sampling signal, and the mapped intensity level of all frequency bin units in the range is recorded once, including:

[0023] Extracting the center frequency and effective bandwidth of the current sampling signal;

[0024] determining a frequency point range based on the center frequency point and the effective bandwidth, wherein a start frequency point unit is (Fc-BW / 2) and an end frequency point unit is (Fc+BW / 2), Fc is the center frequency point, and BW is the effective bandwidth;

[0025] counting the strength level of the current sampling signal on all frequency point units in the frequency point range by using a counter configured for each frequency point unit in advance;

[0026] The counter updating operation is independent of the actual sampling strength level of each frequency point unit.

[0027] In an optional embodiment, the center frequency point and the effective bandwidth of the current sampling signal are extracted, including:

[0028] The center frequency point is determined by a fast Fourier transform peak detection.

[0029] The effective bandwidth is calculated by a -3dB power point measurement method.

[0030] In an optional embodiment, the frequency point unit in the frequency point unit sequence of the current sampling signal is recorded to have the strength level mapped once, including:

[0031] The counter corresponding to the current sampling strength level is only executed to have an add-one operation on the frequency point unit in the frequency point unit sequence of the current sampling signal.

[0032] In an optional embodiment, the method further includes:

[0033] The signal with a signal strength exceeding a current noise floor value is detected by a short-time energy mutation detection algorithm.

[0034] In a second aspect, the present application provides a full-quantization spectrum feature statistical system, including:

[0035] A basic processing module is configured to discretize a current sampling signal frequency band into a continuous frequency point unit sequence according to a preset rule and map a signal strength value into a strength level according to a preset rule;

[0036] A bandwidth statistical module is configured to determine a frequency point range of the current sampling signal by extracting a center frequency point and an effective bandwidth of the current sampling signal if a signal strength exceeds a current noise floor value, and record the strength level mapped once for all frequency point units in the range;

[0037] A background statistical module is configured to record the frequency point unit in the frequency point unit sequence of the current sampling signal to have the strength level mapped once if the signal strength does not exceed the current noise floor value.

[0038] A data statistics module is configured to count all frequency point units involved in the current spectrum recording process and the occurrence times of each intensity level recorded on each frequency point unit.

[0039] A graph generating module is configured to generate a two-dimensional histogram matrix for each frequency point unit, with the horizontal coordinate being all intensity level categories recorded by the frequency point unit and the vertical coordinate being the occurrence times of the corresponding intensity level on the frequency point unit.

[0040] In a third aspect, a device is provided, comprising:

[0041] A memory is configured to store a full-quantization spectrum feature statistics program.

[0042] A processor is configured to implement the steps of the full-quantization spectrum feature statistics method provided in the first aspect when the full-quantization spectrum feature statistics program is executed.

[0043] In a fourth aspect, a computer-readable storage medium is provided, and the storage medium stores a full-quantization spectrum feature statistics program. When the full-quantization spectrum feature statistics program is executed by a processor, the steps of the full-quantization spectrum feature statistics method provided in the first aspect are implemented.

[0044] The full-quantization spectrum feature statistics method, system, device and storage medium provided by the application can completely solve the problem of false high background noise caused by the traditional peak value maintenance mode through the frequency point unit-intensity two-dimensional histogram statistics and the double-path update mechanism. The core beneficial effects include:

[0045] Improved sporadic signal processing capability: microsecond-level signal events are accurately recorded on high percentile traces (such as P95-P100), avoiding pollution to the basic background model.

[0046] Enhanced model robustness: probabilistic background representation effectively distinguishes between steady-state noise (concentrated in P0-P50) and transient interference (distributed in P80-P100), solving the confusion problem of the two types of signals in the traditional solution.

[0047] Support for advanced analysis applications: automatic identification of burst interference based on the difference between P50 and P95 traces (Δ>10 dB is determined as an occasional event), dynamic tracking of noise floor based on P10 traces, and labeling of high-frequency violation signals using P99 traces. BRIEF DESCRIPTION OF DRAWINGS

[0048] In order to more clearly illustrate the technical solutions in the embodiments of the application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiment or prior art description. Obviously, for those skilled in the art, other drawings can also be obtained without creative labor based on these drawings.

[0049] Figure 1 is a schematic flow chart of a method of one embodiment of the present application.

[0050] Figure 2 is another schematic flow chart of a method of one embodiment of the present application.

[0051] Figure 3 is a schematic block diagram of a system of one embodiment of the present application.

[0052] Figure 4 is a schematic structural diagram of an apparatus provided by an embodiment of the present application. DETAILED DESCRIPTION

[0053] In order to make the technical personnel in the art better understand the technical solutions in the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, not all. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative labor should belong to the scope of protection of the present application.

[0054] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which the present application belongs. The terms used in the specification of the present application are only for the purpose of describing specific embodiments and are not intended to limit the present application.

[0055] The full-quantization spectrum feature statistical method provided by the embodiments of the present application is executed by a computer device, and accordingly, the full-quantization spectrum feature statistical system runs in the computer device.

[0056] Figure 1 is a schematic flow chart of a method of one embodiment of the present application. Wherein, Figure 1 The execution subject can be a full-quantization spectrum feature statistical system. According to different needs, the order of steps in the flow chart can be changed, and some can be omitted.

[0057] As Figure 1 shown, the method comprises:

[0058] S1. Discretize the current sampling signal frequency band into a continuous frequency point unit sequence according to a preset rule, and map the signal intensity value to an intensity level according to a preset rule;

[0059] S2. If the signal intensity exceeds the current noise floor value, determine the frequency point range of the current sampling signal by extracting the center frequency point and the effective bandwidth, and record the mapped intensity level for all frequency point units in the range once;

[0060] S3. If the signal strength does not exceed the current noise floor value, record the frequency bin in the frequency bin sequence of the current sampling signal once the intensity level mapped by it appears;

[0061] S4. Count all the frequency bins involved in the current spectrum recording process and the number of times each intensity level recorded on each frequency bin appears;

[0062] S5. Generate a two-dimensional histogram matrix for each frequency bin, with the horizontal coordinate being all the intensity levels recorded by the frequency bin and the vertical coordinate being the number of times the corresponding intensity level appears on the frequency bin.

[0063] In an embodiment of the present application, based on step S1, a possible embodiment will be given below to illustrate the specific implementation.

[0064] S101. Frequency band segmentation.

[0065] The frequency band of the sampling signal is the target frequency band. Before carrying out the frequency bin segmentation of the target frequency band, the first task is to accurately obtain the resolution bandwidth value (Resolution Band width, RBW) of the device. This parameter is usually obtained by sending a specific query command through the device's hardware interface, such as GPIB, USB, or Ethernet, etc., such as the ":SENSe:BANDwidth:RESolution?" command following the SCPI standard. The obtained value also needs to be modified in real time by the device's internal calibration module to eliminate the measurement deviation caused by factors such as temperature change and device aging. For devices that support dynamic resolution bandwidth configuration, before obtaining the parameter, the bandwidth parameter in the current working mode is locked through a preset instruction to ensure consistency in the subsequent segmentation calculation process.

[0066] Next, when determining the start frequency and end frequency of the target frequency band, the specific application scenario needs to be considered. In the spectrum monitoring scenario, the frequency band range can be directly set by user input or defined by the signal active interval automatically identified by the spectrum scanning module; while in the communication test scenario, the frequency range needs to be accurately configured according to the relevant communication standards, such as the frequency band division of LTE and the frequency range of 5GNR, etc. The numerical accuracy needs to be controlled within one-tenth of the device's minimum frequency step to prevent signal leakage at the frequency bin boundary.

[0067] Based on the above parameters, the frequency bin segmentation needs to follow the following mathematical logic and operation specifications:

[0068] First, the total bandwidth of the target frequency band is calculated, which is the difference between the end frequency and the start frequency. If the total bandwidth is less than the resolution bandwidth value, the frequency band is directly processed as a single frequency point unit, and the center frequency of the frequency point unit is the average of the start frequency and the end frequency;

[0069] When the total bandwidth is greater than or equal to the resolution bandwidth value, the number of frequency point units is calculated by rounding up, i.e. the result of the total bandwidth divided by the resolution bandwidth value is rounded up. This is done to ensure that the coverage of all frequency point units can completely contain the target frequency band;

[0070] The center frequency of the i-th frequency point unit (i starts from 0 and increases sequentially until the number of frequency point units minus one) is equal to the start frequency plus the product of i and the resolution bandwidth value;

[0071] To verify the integrity of the frequency point unit sequence, the center frequency of the last frequency point unit plus half of the resolution bandwidth value must be greater than or equal to the end frequency of the target frequency band, i.e. the upper boundary of the last frequency point unit must be able to cover the end frequency of the target frequency band.

[0072] Through the above steps, the target frequency band adaptive segmentation based on the resolution bandwidth can be realized, and the generated continuous frequency point unit sequence not only meets the measurement accuracy requirements of the device, but also ensures that the target frequency band is covered without omission, providing a reliable frequency point unit basis for subsequent spectrum analysis, signal detection, etc.

[0073] S102. Intensity mapping.

[0074] The setting of the signal strength dynamic range needs to be combined with the application scenario and device performance. The minimum intensity value is usually based on the device's detectable noise floor, and is determined by taking the average value of multiple air interface measurements; the maximum intensity value is constrained by the upper limit of the device's linear working range to avoid measurement distortion due to signal saturation. The upper and lower limits of the dynamic range need to leave a certain margin, generally expanding 5%-10% on the basis of actual measurement extremes, to cope with sudden signal fluctuations. For example, in wireless communication testing, the dynamic range may be set to -110dBm to -30dBm, covering the terminal reception sensitivity to the strong signal blocking threshold interval.

[0075] When dividing the dynamic range into a preset number of discrete intensity levels, the equal interval division principle should be followed to simplify the mapping logic, and the number of levels should be determined comprehensively according to the signal resolution requirement and data processing efficiency. If fine distinction of weak signals is required, encrypted level division can be used in the low intensity interval; if strong signal changes are concerned, the level density can be increased in the high intensity interval. Each intensity level corresponds to a unique continuous intensity interval, and the interval boundary needs to be accurately calculated and fixed. For example, when the dynamic range is 80 dB and is divided into 16 levels, each level corresponds to an interval range of 5 dB, and the boundary values of adjacent intervals are defined in the form of left closed and right open to avoid overlap or omission.

[0076] The execution of the mapping rule needs to be realized by hardware logic or software algorithm for real-time judgment. For each sample value, first check whether it is within the set dynamic range, and mark it as an out-of-range state if it is out of range; if it is within the range, then determine the interval to which it belongs and output the corresponding level identifier by comparing the sample value with the boundary values of each level interval. The level identifier usually adopts integer coding 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 the case of a large number of levels, which can significantly reduce the processing delay.

[0077] In an embodiment of the present application, based on step S2, a possible embodiment will be given below to illustrate the specific implementation scheme.

[0078] The signal intensity exceeding the current noise floor value is detected by a short-time energy mutation detection algorithm. The noise floor value is adaptively estimated by 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 sample points in the past window length, wherein the mean and standard deviation represent the average level and dispersion degree of the signal intensity in the window, respectively.

[0079] The implementation of short-time energy mutation detection is to calculate the difference between the signal intensity and the noise floor value. When the intensity of the current sampling signal is greater than the noise floor value, it is determined that there is an effective signal, which triggers the subsequent feature extraction process; otherwise, noise statistics operation is performed.

[0080] If the signal intensity of the current sampling signal exceeds the current noise floor value, the following is performed:

[0081] S201. Extract the center frequency point and effective bandwidth of the current sampling signal.

[0082] The center frequency is determined by fast Fourier transform (FFT) peak detection. A certain length of FFT transform is performed on the current sampling signal to obtain the corresponding frequency spectrum, and the frequency corresponding to the maximum value point in the frequency spectrum is searched, which is the center frequency. In order to improve the detection accuracy, a cubic interpolation method is used to fit the frequency spectrum points near the peak value, and the position of the center frequency is determined more accurately through this method.

[0083] The calculation of the effective bandwidth adopts the -3dB power point measurement method. In the obtained frequency spectrum, the left and right boundary frequencies corresponding to the point at which the signal power drops to half of the peak power (that is, -3dB) are found, and the difference between the two boundary frequencies is the effective bandwidth. If the spectrum is asymmetric, the half-power points on the left and right sides are calculated respectively, and the difference between them is taken as the effective bandwidth.

[0084] S202. Based on the center frequency and the effective bandwidth, determine the frequency point range: the starting frequency point unit is (Fc-BW / 2), and the terminal frequency point unit is (Fc+BW / 2), Fc is the center frequency, and BW is the effective bandwidth;

[0085] S203. In all frequency point units in the frequency point range, the intensity level of one mapping is accumulated by using the counter configured for each frequency point unit in advance; wherein the counter updating operation is independent of the actual sampling intensity level of each frequency point unit.

[0086] When the signal intensity exceeds the noise floor value, the system performs batch update on all frequency point units in the index range. Each frequency point unit is configured with an independent multi-dimensional counter, and the dimension is consistent with the number of intensity levels (such as M levels corresponding to M counting dimensions). When updating, according to the level Li mapped by the current signal intensity, the value of the Li-dimensional counter of all frequency point units in the range is increased by 1.

[0087] This process has strict independence: even if a certain frequency point unit does not detect a signal in real-time sampling, as long as it is in the index range, it will still be counted according to the intensity level of the current signal. This design aims to record the complete spectral feature distribution within the signal coverage range and avoid missing key information due to instantaneous sampling.

[0088] In an embodiment of the present application, based on step S3, a possible embodiment will be given below to non-limitingly illustrate the specific implementation scheme.

[0089] When the signal intensity does not exceed the noise floor value, the system adopts a single-point update strategy. First, the center frequency of the current sampling signal is determined to correspond to the frequency point unit (achieved by matching the center frequency of the frequency point unit sequence), and then only the counter corresponding to the level Lj mapped by the current noise intensity in the unit is executed to increase by 1.

[0090] Unlike the effective signal scenario, the update range is strictly limited to the frequency point units actually occupied by the signal at this time, and the counting logic is directly related to the noise intensity level of real-time sampling, ensuring the accuracy of noise statistics.

[0091] All frequency point units use 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. When a round of spectrum recording is completed (such as reaching the preset sampling duration), the system automatically triggers counter group switching, converting the current counter group to standby state, while resetting the new current counter group to avoid data overwrite conflicts.

[0092] In addition, the counter uses a 32-bit unsigned integer type for storage, and automatically resets to zero when the count reaches the maximum value, ensuring data continuity and traceability through timestamp markers and log records.

[0093] In an embodiment of the present application, based on step S4, a possible embodiment will be given below to illustrate the specific implementation thereof.

[0094] After recording is completed, all frequency point units need to be screened first, only the units that have counter update records during recording (i.e. "involved frequency point units") are retained, and invalid frequency point units that are not activated are excluded to reduce the amount of redundant data.

[0095] For each screened frequency point unit, extract the cumulative number of all intensity levels in its counter matrix. These data need to be integrity checked to ensure that there is no missing or abnormal jump in counting (such as a single intensity level count far exceeding the reasonable range), and if abnormalities are found, linear interpolation method is used for correction to ensure the reliability of the original data.

[0096] In an embodiment of the present application, based on step S5, a possible embodiment will be given below to illustrate the specific implementation thereof.

[0097] A two-dimensional histogram matrix is generated for each frequency point unit, with the horizontal coordinate representing all intensity levels recorded by the frequency point unit, and the vertical coordinate representing the number of times the corresponding intensity level appears on the frequency point unit.

[0098] The two-dimensional histogram matrix corresponding to each frequency unit is an independent two-dimensional data structure, and its dimension is determined by the number of intensity levels. The abscissa strictly corresponds to the preset intensity level set \(\{L_1, L_2,..., L_M\}\), and is arranged in order from small to large according to the level number, ensuring that the matrix abscissa of different frequency units has a unified reference standard. For example, if the intensity level is divided into 8 levels, the abscissa is \(L_1\) to \(L_8\) in turn, which corresponds to the signal strength interval from the lowest to the highest.

[0099] The ordinate represents the number of occurrences of each intensity level, and its value is directly taken from the cumulative value of the corresponding level in the frequency unit counter matrix. The scale range of the ordinate 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 certain frequency unit is 1000, and the counts of other units are mostly within 500, a scale range of 0 to 1000 is used.

[0100] In one embodiment, a frequency unit-intensity two-dimensional histogram is used as the basic data structure to realize full-quantization spectrum feature statistics: frequency dimension: discretize and segment the target frequency band according to the device resolution bandwidth (RBW) to form a continuous frequency unit sequence (for example: 50,000 frequency units are generated with a step of 100 kHz in the 1-6 GHz frequency band). Intensity dimension: linearly map the dynamic range of the received signal (typical value: -120 dBm to -20 dBm) to 101 discrete intensity levels to realize fine classification and quantization of signal amplitude. Statistical mechanism: each frequency unit maintains an independent intensity distribution counter to form a two-dimensional histogram matrix. The matrix is optimized for memory occupation through sparse storage compression technology, and only non-zero count units are retained.

[0101] The real-time signal processing flow is shown in Figure 2 , which includes the following steps:

[0102] (1) Signal detection and feature extraction.

[0103] 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 performs:

[0104] Center frequency positioning: determine the signal center frequency (Fc) based on fast Fourier transform (FFT) peak detection;

[0105] Bandwidth measurement: calculate the signal effective bandwidth (BW);

[0106] Intensity quantization: map the signal amplitude to the preset 101-level intensity scale.

[0107] (2) Bandwidth association statistical update.

[0108] For the detected valid signal, the system automatically expands its energy distribution to the physical coverage frequency band:

[0109] Calculate the frequency range actually affected by the signal: [Fc-BW / 2, Fc+BW / 2];

[0110] In all discrete frequency point units within this frequency band, the count value of the current signal strength level is increased synchronously;

[0111] Technical advantages: eliminate the signal energy dispersion error caused by the resolution bandwidth limitation in traditional solutions, and ensure the statistical integrity of wideband signals.

[0112] (3) Background noise independent statistics.

[0113] When signal detection is not triggered, the system only updates the corresponding strength level counter within the current sampling frequency point unit to maintain the purity of background noise statistics.

[0114] (4) Background trace generation algorithm.

[0115] After recording is completed, the system performs offline statistical analysis:

[0116] Cumulative probability calculation: for each frequency point unit, calculate the cumulative distribution function based on the strength distribution histogram to construct the mapping relationship between strength value and its occurrence probability.

[0117] Percentile trace extraction: traverse 101 percentile points (step size 1%) from 0% to 100%, find the strength threshold that meets the target percentile probability in each frequency point unit, generate a continuous intensity trace that runs through the entire frequency band, and form the core output of the background model.

[0118] In some embodiments, the full-quantization spectrum feature statistical system can include a plurality of functional modules composed of computer program segments. The computer programs of each program segment in the full-quantization spectrum feature statistical system can be stored in the memory of the computer device and executed by at least one processor to perform the functions of full-quantization spectrum feature statistics (see Figure 1 Description).

[0119] In this embodiment, the full-quantization spectrum feature statistical system can be divided into a plurality of functional modules according to the functions it performs, as shown in Figure 3 The module referred to by the present application refers to a series of computer program segments that can be executed by at least one processor and can complete a fixed function, which are stored in the memory. In this embodiment, the functions of each module will be described in detail in subsequent embodiments.

[0120] Frequency point unit segmentation module, for discretizing and segmenting the target frequency band into a continuous sequence of frequency point units;

[0121] an intensity monitoring module, configured to sample signal intensity of each frequency point unit, and map the signal intensity to an intensity level according to a preset mapping rule;

[0122] a bandwidth statistics module, configured to, for a frequency point unit whose intensity level exceeds a preset base level threshold, extract a center frequency point and an effective bandwidth thereof, determine a frequency point range according to the center frequency point and the effective bandwidth, and add 1 to a current intensity level counter of all frequency point units in the frequency point range;

[0123] a background statistics module, configured to, for a frequency point unit whose intensity level does not exceed the preset base level threshold, only update a current intensity level counter of the frequency point unit;

[0124] a data analysis module, configured to generate a continuous intensity trace line throughout a target frequency band according to the intensity level occurrence times recorded by the counters of the frequency point units.

[0125] Figure 4 The full-quantitative spectrum feature statistics method provided by the embodiments of the present application can be applied to a device. Those skilled in the art can understand that the device structure involved in the embodiments of the present application does not constitute a limitation on the device, and the device can include more or fewer components than the illustration, or combine certain components, or different component arrangements. In the embodiments of the present application, the device includes but is not limited to a laptop computer, a desktop computer, a workstation, a personal digital assistant, a server, a blade server, a mainframe computer, and other suitable computers. The device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, 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 the present application described and / or claimed herein.

[0126] The device 400 can include a processor 410, a memory 420, and a communication unit 430. These components communicate through one or more buses. Those skilled in the art can understand that the structure of the server shown in the figure does not constitute a limitation on the present application, and it can be a bus structure or a star structure, and can include more or fewer components than the illustration, or combine certain components, or different component arrangements.

[0127] The memory 420 can be used to store the execution instructions of the processor 410, and 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 can perform part or all of the steps in the following method embodiments.

[0128] The processor 410 is the control center of the storage device, which connects various parts of the entire electronic device through various interfaces and lines, and performs various functions of the electronic device and / or processes data by running or executing software programs and / or modules stored in the memory 420 and calling data stored in the memory. The processor can be composed of an integrated circuit (IC), for example, it can be composed of a single packaged IC, or it can be composed of multiple packaged ICs with the same function or different functions connected. For example, the processor 410 can only include a central processing unit (CPU). In the embodiments of the present application, the CPU can be a single operation core or can include multiple operation cores.

[0129] The communication unit 430 is used to establish a communication channel, so that the storage device can communicate with other devices. It receives user data sent by other devices or sends user data to other devices.

[0130] The present application also provides a computer storage medium, wherein the computer storage medium can store a program, and the program can include part or all of the steps in the embodiments provided by the present application when executed. The storage medium can be a magnetic disk, an optical disk, a read-only memory (ROM) or a random access memory (RAM), etc.

[0131] Those skilled in the art can clearly understand that the technical solutions in the embodiments of the present application can be implemented by means of software plus necessary universal hardware platforms. Based on such an understanding, the technical solutions in the embodiments of the present application can be embodied in the form of a software product, which is stored in a storage medium, such as a USB flash disk, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and the like, and includes a plurality of instructions for causing a computer device (which can be a personal computer, a server, or a second device, a network device, or the like) to execute all or part of the steps of the methods described in the embodiments of the present application.

[0132] In the present specification, the same or similar parts among various embodiments can be referred to each other. In particular, for the device embodiments, since they are basically similar to the method embodiments, the description is relatively simple, and the relevant parts can be referred to the description in the method embodiments.

[0133] In the several embodiments provided in the present application, it should be understood that the disclosed system and method can be implemented in other ways. For example, the system embodiments described above are merely schematic. For example, the division of the modules is merely a logical function division. In actual implementation, another division manner can be used. For example, a plurality of modules or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the coupling or direct coupling or communication connection between the displayed or discussed modules can be indirect coupling or communication connection through some interfaces. The coupling or communication connection can be electrical, mechanical or in other forms.

[0134] The modules described as separate components can or can not be physically separate, and the components displayed as modules can or can not be physical modules, i.e., can be located in one place or distributed on a plurality of network modules. Some or all of the modules can be selected according to actual needs to achieve the purpose of the embodiments.

[0135] In addition, each functional module in the various embodiments of the present application can be integrated into a processing module, or each module can exist physically independently, or two or more modules can be integrated into one module.

[0136] Although the present application has been described in detail with reference to the preferred embodiments, it should be understood that the application is not limited to those preferred embodiments. Various modifications and equivalents can be made by those skilled in the art without departing from the spirit and scope of the application. Any and all modifications and equivalents are intended to be included within the scope of the present application.

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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