WO2025213334A1 - 时间序列信号水平检测方法、装置、计算机设备、及介质 - Google Patents

时间序列信号水平检测方法、装置、计算机设备、及介质

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Publication number
WO2025213334A1
WO2025213334A1 PCT/CN2024/086643 CN2024086643W WO2025213334A1 WO 2025213334 A1 WO2025213334 A1 WO 2025213334A1 CN 2024086643 W CN2024086643 W CN 2024086643W WO 2025213334 A1 WO2025213334 A1 WO 2025213334A1
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Prior art keywords
time series
series signal
histogram
data
median
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PCT/CN2024/086643
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English (en)
French (fr)
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WO2025213334A9 (zh
Inventor
蔡志强
章文蔚
颜旭
曾涛
陈俊毅
董宇亮
黎宇翔
徐讯
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BGI Shenzhen Co Ltd
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BGI Shenzhen Co Ltd
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Priority to PCT/CN2024/086643 priority Critical patent/WO2025213334A1/zh
Publication of WO2025213334A1 publication Critical patent/WO2025213334A1/zh
Publication of WO2025213334A9 publication Critical patent/WO2025213334A9/zh
Pending legal-status Critical Current
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    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing

Definitions

  • the present application relates to the field of signal detection technology, and in particular to a method, apparatus, computer equipment, storage medium, and computer program product for detecting the level of a time series signal.
  • a time series signal is a time-dependent signal whose value changes over time. It can be either continuous or discrete. It is often used to describe events or behaviors that occur at specific points in time, such as audio signals, video signals, and sequencing signals.
  • time series data analysis approach which performs time-frequency conversion on time series data and uses statistical analysis methods to manually extract special sequence features to perform similarity calculations and threshold filtering for detection.
  • end-to-end deep learning method This method does not require manual feature extraction and directly trains a deep learning model based on the original data, which is then used to detect signal levels.
  • a time series signal level detection method comprising:
  • a level detection result of the time series signal is determined based on the median and the standard deviation.
  • acquiring the time series signal includes:
  • the above-mentioned time series signal level detection method further includes:
  • obtaining peak data in the histogram includes:
  • the histogram is smoothed to obtain peak data in the smoothed histogram.
  • smoothing the histogram includes:
  • a moving average method is used to smooth the histogram.
  • the smoothing of the histogram to obtain peak data in the smoothed histogram includes:
  • the histogram is smoothed, and peak data having a frequency greater than a preset frequency threshold in the smoothed histogram is intercepted.
  • the above-mentioned time series signal level detection method further includes:
  • determining the level detection result of the time series signal based on the median and the standard deviation includes:
  • peaks whose standard deviation is greater than a preset first constant or whose median is less than a preset second constant are eliminated to obtain candidate peaks;
  • the median maximum value is taken as the signal level of the time series signal.
  • the time-series signal comprises a nanopore sequencing sequence signal.
  • the present application also provides a time series signal level detection device.
  • the device includes:
  • Signal acquisition module used to acquire time series signals
  • a histogram construction module configured to divide the time series signal into a preset number of intervals, count the number of points in each interval, and construct a histogram based on the number of points in each interval;
  • a data extraction module is used to obtain peak data in the histogram
  • a calculation module configured to obtain the median and standard deviation of the time series data corresponding to each peak in the peak data
  • a detection module is used to determine a level detection result of the time series signal based on the median and the standard deviation.
  • the signal acquisition module is further configured to acquire an initial time series signal; and remove abnormal data from the initial time series signal to obtain a time series signal.
  • the signal acquisition module is further configured to obtain an amplitude range threshold corresponding to a normal time series signal; and based on the amplitude range threshold, remove abnormal data from the initial time series signal to obtain a time series signal.
  • the data extraction module is further configured to smooth the histogram using a moving average method based on a sliding window of a preset size.
  • the data extraction module is further used to perform smoothing on the histogram to obtain the smoothed histogram. Peak data in the figure.
  • the data extraction module is further configured to perform smoothing on the histogram, and to intercept peak data having a frequency greater than a preset frequency threshold in the smoothed histogram.
  • the calculation module is further used to filter the peak data based on a preset point threshold to obtain filtered peak data; and obtain the median and standard deviation of the time series data corresponding to each peak in the filtered peak data.
  • the detection module is also used to eliminate peaks whose standard deviation is greater than a preset first constant or whose median is less than a preset second constant based on the median and the standard deviation to obtain candidate peaks; compare the median of the time series data corresponding to the candidate peaks; and use the maximum median as the signal level of the time series signal.
  • the time-series signal comprises a nanopore sequencing sequence signal.
  • the present application provides a computer device comprising a memory and one or more processors, wherein the memory stores computer-readable instructions.
  • the computer-readable instructions are executed by one or more processors, the one or more processors execute the steps of the above-mentioned time series signal level detection method.
  • the present application provides one or more computer storage media storing computer-readable instructions.
  • the computer-readable instructions are executed by one or more processors, the one or more processors execute the steps of the above-mentioned time series signal level detection method.
  • the present application provides a computer program product, which, when executed by one or more processors, causes the one or more processors to perform the steps of the above-mentioned time series signal level detection method.
  • FIG1 is a schematic diagram of an application scenario of a time series signal level detection method according to an embodiment
  • FIG2 is a schematic flow chart of a method for detecting a time series signal level in one embodiment
  • FIG3 is a schematic flow chart of a method for detecting a time series signal level in another embodiment
  • FIG4 is a schematic diagram of signal levels in sequencing signals
  • Figure 5 is a schematic diagram of intercepted nanopore sequencing sequence data
  • FIG6 is a histogram obtained after nanopore sequencing sequence data is divided into intervals
  • FIG7 is a schematic diagram of the histogram after moving average processing
  • FIG8 is a schematic diagram of peak screening based on a preset frequency
  • FIG9 is a schematic diagram of peak screening based on a preset number of points
  • FIG10 is a schematic diagram of peak screening based on median and standard deviation
  • FIG11 is a structural block diagram of a time series signal level detection device according to an embodiment
  • FIG12 is a diagram showing the internal structure of a computer device in one embodiment.
  • references herein to "embodiments” mean that a particular feature, structure, or characteristic described in connection with the embodiments may be included in some embodiments of the present application.
  • the appearance of this phrase in various places in the specification does not necessarily refer to the same embodiment, nor does it constitute an independent or alternative embodiment that is mutually exclusive of other embodiments. It is understood, both explicitly and implicitly, by those skilled in the art that the embodiments described herein may be combined with other embodiments.
  • the time series signal level detection method provided in the embodiments of the present application can be applied to the application environment of nanopore sequencing signal level detection as shown in Figure 1.
  • the single-channel nanopore sequencing device 102 and the detection terminal 104 are shown.
  • the single-channel nanopore sequencing device 102 collects the nanopore sequencing signal and transmits the nanopore sequencing signal to the detection terminal 104.
  • the detection terminal 104 obtains the nanopore sequencing signal, divides the nanopore sequencing signal into a preset number of intervals, counts the number of points in each interval, and constructs a histogram based on the number of points in each interval; smoothes the histogram to obtain peak data in the smoothed histogram; obtains the median and standard deviation of the time series data corresponding to each peak in the peak data; and determines the nanopore sequencing signal level detection result based on the median and standard deviation. Furthermore, the detection terminal 104 can push the level detection result to the user. For example, the final level detection result can be displayed on a display screen.
  • the detection terminal 104 can specifically be a PC.
  • the present application provides a time series signal level detection method, which can be applied to the detection terminal in FIG1 , and the method includes:
  • a time series signal is a collection of observation points ordered in chronological order, used to reflect the state or degree of change of an object, phenomenon, etc. over time.
  • the time series signal can be collected by an external device and then transmitted to the detection terminal.
  • the time series signal can be collected by an external device and then buffered. When the time series signal level test is required, the buffered time series signal is then transmitted to the detection terminal.
  • S200 Divide the time series signal into a preset number of intervals, count the number of points in each interval, and Construct a histogram based on the number of points between .
  • the preset number is a pre-set number, specifically a larger number, which can be set according to the needs of detection accuracy. If higher detection accuracy is required, the preset number can be selected as a larger value. If only a certain detection accuracy is required, the preset number can be selected as a normal value. Generally speaking, the preset number is 800, 1000, 1200, etc.
  • the preset number is 800, 1000, 1200, etc.
  • the number of points falling into each interval is counted, and a histogram is constructed based on the number of points in each interval. Specifically, after having the point count data for each interval, a histogram can be constructed.
  • Various drawing tools or libraries can be used to create histograms, such as Python's matplotlib library and R language's ggplot2 package. Through the histogram, you can intuitively see the distribution of the time series signal in different intervals.
  • the corresponding peak data is extracted from the histogram.
  • the histogram is a diagram similar to a rectangular box, for the subsequent accurate data processing, the histogram can be smoothed here. After smoothing, the entire graph can present a smooth waveform, and then the specific peak data is extracted from the smooth waveform. It can be understood that the peak here refers to the interval corresponding to more points in the time series signal.
  • smoothing the histogram can help eliminate peaks and grooves caused by data fluctuations or random noise, thereby more clearly revealing the main distribution trend of the data. Smoothing can be achieved through a variety of methods, including but not limited to moving average, Gaussian filtering or kernel density estimation.
  • multiple peaks may be obtained, and the median and standard deviation of the corresponding time series data are calculated for each peak. Specifically, the median and standard deviation of the corresponding time series data are calculated for each peak. If there are multiple peaks, the medians and standard deviations corresponding to the multiple peaks are calculated separately. The calculated medians and standard deviations are aggregated and organized to determine the level detection results of the time series signal in the next step.
  • S500 Determine a level detection result of the time series signal based on the median and the standard deviation.
  • the level detection result of the entire time series signal is further determined based on the median and standard deviation corresponding to each peak obtained in S400. Specifically, the target peak that ultimately meets the median and standard deviation requirements can be screened based on the median and standard deviation corresponding to each peak, and the level detection result of the time series signal can be determined based on the median corresponding to the target peak.
  • the above-described time series signal level detection method obtains a time series signal; divides the time series signal into a preset number of intervals, counts the points in each interval, and constructs a histogram based on the number of points in each interval; obtains peak data in the histogram; obtains the median and standard deviation of the time series data corresponding to each peak in the peak data; and determines the time series signal level detection result based on the median and standard deviation.
  • the entire process requires only simple data processing of the time series signal, eliminating the need for complex calculations, thus achieving efficient time series signal level detection.
  • S100 includes:
  • S140 Eliminate abnormal data in the initial time series signal to obtain a time series signal.
  • the acquired initial time series signal can be subjected to abnormal data elimination processing to ensure that a valid time series signal is obtained.
  • the abnormal data here can be incomplete data or erroneous data.
  • the abnormal data in the initial time series signal can be eliminated based on the amplitude range corresponding to the normal time series signal to obtain a time series signal.
  • the normal range of nanopore sequencing current values is generally 0 to 350. If the current value exceeds this value, it is considered an abnormal nanopore sequencing sequence signal. Therefore, for the nanopore sequencing sequence data, points less than 0 or greater than 350 can be eliminated to obtain the final valid nanopore sequencing sequence data.
  • smoothing the histogram includes:
  • the moving average method is used to smooth the histogram.
  • smoothing is performed using a sliding window of a preset size.
  • the window size can be set based on actual needs, for example, 8, 10, or 12.
  • the window size corresponds to the time period size in the moving average method.
  • the average value of the data within a single time period is calculated and used as the predicted value for that time period.
  • the simple moving average takes the average value of the data within a certain time period as the predicted value for that time period. The formula is:
  • SMA t represents the simple moving average at time point t
  • X t represents the data at time point t
  • n is the length of the time period, that is, the sliding window size.
  • smoothing the histogram and obtaining peak data in the smoothed histogram includes: smoothing the histogram and intercepting peak data in the smoothed histogram whose frequency is greater than a preset frequency threshold.
  • the preset frequency threshold is a pre-set threshold that can be set according to the actual needs. Its specific characterization requires the selection of the size of the peak. If it is necessary to filter out larger peaks, the preset frequency threshold can be set to a larger value; if it is necessary to filter out a large number of peaks (smaller peaks), the preset frequency threshold can be set to a smaller value. Specifically, the preset frequency threshold can be 1, 2, 3, etc. Generally speaking, data with larger peaks can accurately reflect the horizontal detection results of the time series signal. Therefore, in order to further reduce the amount of subsequent data processing, the peak corresponding to the preset frequency threshold is used here.
  • the above-mentioned time series signal level detection method also includes: filtering the peak data based on a preset point threshold to obtain filtered peak data; and obtaining the median and standard deviation of the time series data corresponding to each peak in the filtered peak data.
  • the peak data is further filtered based on a preset point threshold.
  • the preset point threshold is a pre-set value that can be set according to actual needs, for example, 100, 200, 300, etc.
  • the number of points corresponding to each peak data is counted, and peak data with point counts exceeding the preset point threshold are filtered.
  • the median and standard deviation of the time series data corresponding to each peak in the filtered peak data are then obtained.
  • S500 includes:
  • S560 Use the maximum median as the signal level of the time series signal.
  • the peaks are screened again to finally determine the candidate peaks.
  • the screening is based on the preset first constant and the preset second constant.
  • the preset first constant and the preset second constant are constants set based on empirical values, which are used to further screen the peaks.
  • the preset first constant can be 4, 5, 6, etc.; the preset second constant can be 50, 100, 150, etc.
  • the medians of the time series data corresponding to different candidate peaks are compared, and the maximum median is selected as the signal level of the final time series signal.
  • Nanopore sequencing signals refer to the changes in the perforation current recorded when a DNA molecule passes through a nanopore. This change is mainly caused by the different currents of different bases in the nanopore.
  • the specific identification steps are as follows:
  • Step 1 Sequencing Data Collection: Collect nanopore sequencing data including signal levels and save them to a hard disk file. Signal levels refer to the median of the data above 100 before the sequencing signal drops significantly. See 4 for details.
  • Step 2 Capture nanopore sequencing sequence data, as shown in FIG5 .
  • Step 3 Eliminate points that are less than 0 or greater than 350, and divide the data into 1000 intervals from 0 to the maximum value, as shown in Figure 5.
  • the x-coordinate is the sequence number of the point. In Figure 5, the x-coordinate ranges from 0 to 10000, representing 10000 points.
  • the y-coordinate is the current value of the signal. Count the number of points in each interval to obtain the histogram of the data. The histogram is shown in Figure 6.
  • Step 4 Calculate the moving average of the data obtained in step 3.
  • the sliding window size corresponding to the moving average is 10, and the waveform shown in Figure 7 is obtained.
  • Step 5 For the data obtained in step 4, only the data with a vertical coordinate value greater than 2 (frequency) are selected to obtain four peaks as shown in Figure 8.
  • Step 6 For the four peak data obtained in step 5, only the peaks with a point count greater than 200 are selected, as shown in FIG9 .
  • Step 7 For the peak data obtained in step 6, calculate the median and standard deviation of the sequencing sequence data corresponding to each peak, as shown in Figure 10.
  • Step 8 After removing the peaks with standard deviation greater than 5 or median less than 100 in step 7, the maximum median is selected as the signal level. Specifically, as shown in Figure 10, after removing the first peak, the median corresponding to the fourth peak is 196 as the final signal level. Specifically, in Figures 4, 5, and 10 above, the x-coordinate and y-coordinate have the same meaning, the x-coordinate is the number of the point, and the y-coordinate is the current value. In Figures 6 to 9, the x-coordinate and y-coordinate have the same meaning, the x-coordinate is the number of the point, and the y-coordinate is the current value. is the current value, and the y coordinate is the frequency.
  • embodiments of the present application also provide a time series signal level detection device for implementing the aforementioned time series signal level detection method.
  • the solution provided by this device is similar to the solution described in the aforementioned method. Therefore, the specific limitations of one or more of the following embodiments of the time series signal level detection device can be found in the aforementioned limitations of the time series signal level detection method and will not be further elaborated here.
  • a time series signal level detection device comprising:
  • the signal acquisition module 100 is used to acquire a time series signal
  • a histogram construction module 200 is used to divide the time series signal into a preset number of intervals, count the number of points in each interval, and construct a histogram based on the number of points in each interval;
  • the data extraction module 300 is used to obtain peak data in the histogram
  • the calculation module 400 is used to obtain the median and standard deviation of the time series data corresponding to each peak in the peak data.
  • the detection module 500 is configured to determine a level detection result of the time series signal based on the median and the standard deviation.
  • the signal acquisition module 100 is further configured to acquire an initial time series signal; and remove abnormal data from the initial time series signal to obtain a time series signal.
  • the signal acquisition module 100 is further configured to obtain an amplitude range threshold corresponding to a normal time series signal; and based on the amplitude range threshold, remove abnormal data from the initial time series signal to obtain a time series signal.
  • the data extraction module 300 is also used to extract data from the data extraction module 300 .
  • the data extraction module 300 is also used to extract data from the data extraction module 300 .
  • the data extraction module 300 is further configured to smooth the histogram using a moving average method based on a sliding window of a preset size.
  • the data extraction module 300 is further configured to perform smoothing on the histogram, and to intercept peak data having a frequency greater than a preset frequency threshold in the smoothed histogram.
  • the calculation module 400 is further used to filter the peak data based on a preset point threshold to obtain filtered peak data; and obtain the median and standard deviation of the time series data corresponding to each peak in the filtered peak data.
  • the detection module 500 is further configured to eliminate the data with a standard deviation greater than a preset value based on the median and the standard deviation.
  • a peak whose constant or median is smaller than a preset second constant is obtained as a candidate peak; the median of the time series data corresponding to the candidate peak is compared; and the maximum median is used as the signal level of the time series signal.
  • the time-series signal comprises a nanopore sequencing sequence signal.
  • a computer device which may be a terminal, and its internal structure diagram may be shown in Figure 12.
  • the computer device includes a processor, memory, a communication interface, a display screen, and an input device connected via a system bus.
  • the processor of the computer device is used to provide computing and control capabilities.
  • the memory of the computer device includes a non-volatile storage medium and an internal memory.
  • the non-volatile storage medium stores an operating system and a computer program.
  • the internal memory provides an environment for the operation of the operating system and computer program in the non-volatile storage medium.
  • the communication interface of the computer device is used to communicate with an external terminal via wired or wireless communication, where the wireless communication may be achieved via Wi-Fi, a mobile cellular network, NFC (near field communication), or other technologies.
  • the computer program implements a method for detecting the level of a time series signal.
  • the display screen of the computer device may be a liquid crystal display or an electronic ink display screen.
  • the input device of the computer device may be a touch layer covering the display screen, or may be buttons, a trackball, or a touchpad provided on the computer device housing, or may be an external keyboard, touchpad, or mouse.
  • FIG12 is merely a block diagram of a portion of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied.
  • the specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different arrangement of components.
  • a computer device comprising a memory and one or more processors, wherein the memory stores computer-readable instructions.
  • the one or more processors execute the steps of the above-mentioned time series signal level detection method.
  • the present application provides one or more computer storage media storing computer-readable instructions.
  • the computer-readable instructions are executed by one or more processors, the one or more processors execute the steps of the above-mentioned time series signal level detection method.
  • the present application provides a computer program product, which, when executed by one or more processors, enables the one or more processors to perform the steps of the above-mentioned time series signal level detection method.
  • any reference to memory, database or other media used in the embodiments provided in this application may include at least one of non-volatile and volatile memory.
  • Non-volatile memory may include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc.
  • Volatile memory may include random access memory (RAM) or external high-speed Buffer memory, etc.
  • RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).
  • SRAM static random access memory
  • DRAM dynamic random access memory
  • the database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database.
  • Non-relational databases may include, but are not limited to, distributed databases based on blockchains.
  • the processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processing unit, a digital signal processor, a programmable logic unit, a data processing logic unit based on quantum computing, etc., but are not limited to this.

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Abstract

本申请涉及一种时间序列信号水平检测方法、装置、计算机设备、存储介质和计算机程序产品，其中，方法包括：获取时间序列信号；将所述时间序列信号划分为预设数量的区间，统计每个区间的点数，并基于所述每个区间的点数构建直方图；获取直方图中的波峰数据；获取所述波峰数据中每个波峰对应的时间序列数据的中位数和标准差；及基于所述中位数和所述标准差，确定所述时间序列信号的水平检测结果。

Description

时间序列信号水平检测方法、装置、计算机设备、及介质 技术领域
本申请涉及信号检测技术领域，特别是涉及一种时间序列信号水平检测方法、装置、计算机设备、存储介质和计算机程序产品。
背景技术
时间序列信号是一种与时间有关的信号，其值随时间的变化而变化。时间序列信号可以是连续的，也可以是离散的。时间序列信号通常用于描述在特定时间点上发生的事件或行为，如音频信号、视频信号、测序信号等。
传统检测时间序列信号水平的方案主要有两种，一种是传统时序数据分析思路，对时序数据进行时频转换，通过统计分析方法，人工提取特殊序列特征等方式做相似度计算、阈值过滤进行检测；另一种是深度学习端对端的方法，此方法无需人工提取特征，直接基于原始数据训练出深度学习模型，再用此深度学习模型检测信号水平。
上述两种方式虽然都能实现基本的时间序列信号水平检测，但是其实现过程复杂，需要进行大量的数据处理，其检测效率低下。
发明内容
根据本申请的各种实施例，提供一种时间序列信号水平检测方法、装置、计算机设备、存储介质和计算机程序产品。
一种时间序列信号水平检测方法，所述方法包括：
获取时间序列信号；
将所述时间序列信号划分为预设数量的区间，统计每个区间的点数，并基于所述每个区间的点数构建直方图；
获取直方图中的波峰数据；
获取所述波峰数据中每个波峰对应的时间序列数据的中位数和标准差；及
基于所述中位数和所述标准差，确定所述时间序列信号的水平检测结果。
在一些实施例中，所述获取时间序列信号包括：
获取初始时间序列信号；及
剔除所述初始时间序列信号中的异常数据，得到时间序列信号。
在一些实施例中，上述时间序列信号水平检测方法还包括：
获取正常时间序列信号对应的振幅范围阈值；及
基于所述振幅范围阈值，剔除所述初始时间序列信号中的异常数据，得到时间序列信号。
在一些实施例中，获取直方图中的波峰数据包括：
对直方图进行平滑处理，获取平滑处理后直方图中的波峰数据。
在一些实施例中，对所述直方图进行平滑处理包括：
基于预设尺寸的滑窗，采用移动平均法对所述直方图进行平滑处理。
在一些实施例中，所述对所述直方图进行平滑处理，获取平滑处理后直方图中的波峰数据包括：
对所述直方图进行平滑处理，截取平滑处理后直方图中频数大于预设频数阈值的波峰数据。
在一些实施例中，上述时间序列信号水平检测方法还包括：
基于预设点数阈值对所述波峰数据进行筛选，得到筛选后的波峰数据；及
获取所述筛选后的波峰数据中每个波峰对应的时间序列数据的中位数和标准差。
在一些实施例中，所述基于所述中位数和所述标准差，确定所述时间序列信号的水平检测结果包括：
基于所述中位数和所述标准差，剔除标准差大于预设第一常数或中位数小于预设第二常数的波峰，得到候选波峰；
对比所述候选波峰对应的时间序列数据的中位数大小；及
以中位数最大值作为所述时间序列信号的信号水平。
在一些实施例中，所述时间序列信号包括纳米孔测序序列信号。
第二方面，本申请还提供了一种时间序列信号水平检测装置。装置包括：
信号获取模块，用于获取时间序列信号；
直方图构建模块，用于将所述时间序列信号划分为预设数量的区间，统计每个区间的点数，并基于所述每个区间的点数构建直方图；
数据提取模块，用于获取直方图中的波峰数据；
计算模块，用于获取所述波峰数据中每个波峰对应的时间序列数据的中位数和标准差；及
检测模块，用于基于所述中位数和所述标准差，确定所述时间序列信号的水平检测结果。
在一些实施例中，所述信号获取模块还用于获取初始时间序列信号；及剔除所述初始时间序列信号中的异常数据，得到时间序列信号。
在一些实施例中，所述信号获取模块还用于获取正常时间序列信号对应的振幅范围阈值；及基于所述振幅范围阈值，剔除所述初始时间序列信号中的异常数据，得到时间序列信号。
在一些实施例中，所述数据提取模块还用于基于预设尺寸的滑窗，采用移动平均法对所述直方图进行平滑处理。
在一些实施例中，数据提取模块还用于对直方图进行平滑处理，获取平滑处理后直方 图中的波峰数据。
在一些实施例中，所述数据提取模块还用于对所述直方图进行平滑处理，截取平滑处理后直方图中频数大于预设频数阈值的波峰数据。
在一些实施例中，所述计算模块还用于基于预设点数阈值对所述波峰数据进行筛选，得到筛选后的波峰数据；及获取所述筛选后的波峰数据中每个波峰对应的时间序列数据的中位数和标准差。
在一些实施例中，所述检测模块还用于基于所述中位数和所述标准差，剔除标准差大于预设第一常数或中位数小于预设第二常数的波峰，得到候选波峰；对比所述候选波峰对应的时间序列数据的中位数大小；及以中位数最大值作为所述时间序列信号的信号水平。
在一些实施例中，所述时间序列信号包括纳米孔测序序列信号。
第四方面，本申请提供了一种计算机设备，包括存储器及一个或多个处理器，存储器中储存有计算机可读指令，计算机可读指令被一个或多个处理器执行时，使得一个或多个处理器执行上述时间序列信号水平检测方法的步骤。
第五方面，本申请提供了一个或多个存储有计算机可读指令的计算机存储介质，计算机可读指令被一个或多个处理器执行时，使得一个或多个处理器执行上述时间序列信号水平检测方法的步骤。
第六方面，本申请提供了一种计算机程序产品，该计算机程序被一个或多个处理器执行时，使得一个或多个处理器执行上述时间序列信号水平检测方法的步骤
上述说明仅是本申请技术方案的概述，为了能够更清楚了解本申请的技术手段，而可依照说明书的内容予以实施，并且为了让本申请的上述和其它目的、特征和优点能够更明显易懂，以下特举本申请的具体实施方式。
附图说明
为了更清楚地说明本申请实施例的技术方案，下面将对本申请实施例中所需要使用的附图作简单地介绍，显而易见地，下面所描述的附图仅仅是本申请的一些实施例，对于本领域普通技术人员来讲，在不付出创造性劳动的前提下，还可以根据附图获得其他的附图。
图1为一个实施例中时间序列信号水平检测方法的应用场景示意图；
图2为一个实施例中时间序列信号水平检测方法的流程示意图；
图3为另一个实施例中时间序列信号水平检测方法的流程示意图；
图4为测序信号中信号水平示意图；
图5为截取的纳米孔测序序列数据示意图；
图6为纳米孔测序序列数据区间划分后得到的直方图；
图7为直方图移动平均处理后的示意图；
图8为基于预设频数进行波峰筛选的示意图；
图9为基于预设点数进行波峰筛选的示意图；
图10为基于中位数和标准差进行波峰筛选的示意图；
图11为一个实施例中时间序列信号水平检测装置的结构框图；
图12为一个实施例中计算机设备的内部结构图。
具体实施方式
下面将结合附图对本申请技术方案的实施例进行详细的描述。以下实施例仅用于更加清楚地说明本申请的技术方案，因此只作为示例，而不能以此来限制本申请的保护范围。
除非另有定义，本文所使用的所有的技术和科学术语与属于本申请的技术领域的技术人员通常理解的含义相同；本文中所使用的术语只是为了描述具体的实施例的目的，不是旨在于限制本申请；本申请的说明书和权利要求书及上述附图说明中的术语“包括”和“具有”以及它们的任何变形，意图在于覆盖不排他的包含。
在本文中提及“实施例”意味着，结合实施例描述的特定特征、结构或特性可以包含在本申请的一些实施例中。在说明书中的各个位置出现该短语并不一定均是指相同的实施例，也不是与其它实施例互斥的独立的或备选的实施例。本领域技术人员显式地和隐式地理解的是，本文所描述的实施例可以与其它实施例相结合。
本申请实施例提供的时间序列信号水平检测方法，可以应用于如图1所示的纳米孔测序信号水平检测的应用环境中，单通道纳米孔测序装置102和检测终端104，单通道纳米孔测序装置102采集纳米孔测序信号，发送纳米孔测序信号至检测终端104，检测终端104获取纳米孔测序信号，将纳米孔测序信号划分为预设数量的区间，统计每个区间的点数，并基于每个区间的点数构建直方图；对直方图进行平滑处理，获取平滑处理后直方图中的波峰数据；获取波峰数据中每个波峰对应的时间序列数据的中位数和标准差；及基于中位数和标准差，确定纳米孔测序信号的水平检测结果。进一步的检测终端104可以将水平检测结果推送至用户。例如在显示屏上显示最终的水平检测结果。检测终端104具体可以为PC机。
如图2所示，本申请提供一种时间序列信号水平检测方法，可以应用于图1中的检测终端，方法包括：
S100：获取时间序列信号。
时间序列信号是一系列的观测点按照时间顺序排序的集合，用于反映某一事物、现象等随时间的变化状态或程度。在这里，可以由外部设备采集时间序列信号，然后将再将采集到的时间序列信号发送至检测终端。或者，可以先由外部设备采集时间序列信号，然后将采集到的时间序列信号缓存，在需要进行时间序列信号水平检测时，再将缓存的时间序列信号发送至检测终端。
S200：将时间序列信号划分为预设数量的区间，统计每个区间的点数，并基于每个区 间的点数构建直方图。
预设数量是预先设定的数量，其具体是一个较大的数量，其具体可以根据检测精度的需要设定，若需要较高的检测精度，则预设数量可以选择一个较大的值，若只需一定的检测精度，则预设数量可以选择一个正常的值，一般来说，预设数量为800、1000、1200等。在将时间序列信号按照每个时间点获得的电流值划分为预设数量的区间过程中，可以按照等间距划分的方式来进行，在划分之后统计落入每个区间内的点数，并且基于每个区间的点数构建直方图。具体来说，有了每个区间的点数数据后，就可以构建直方图了，具体可以使用各种绘图工具或库来创建直方图，如Python的matplotlib库、R语言的ggplot2包等。通过直方图，可以直观地看到时间序列信号在不同区间的分布情况。
S300：获取直方图中的波峰数据。
从直方图中来提取对应的波峰数据。另外，由于直方图是一种类似矩形方框的示图，为了后续准确的数据处理，在这里可以对直方图进行平滑处理，在平滑处理之后，整个图形可以呈现出平滑的波形，再从该平滑的波形中提取出具体的波峰数据。可以理解的是，在这里的波峰是指时间序列信号中较多点对应的区间。具体来说，对直方图进行平滑处理可以帮助消除由于数据波动或随机噪声造成的尖峰和凹槽，从而更清晰地揭示数据的主体分布趋势。平滑处理可以通过多种方法实现，包括但不限于移动平均、高斯滤波或核密度估计等。
S400：获取波峰数据中每个波峰对应的时间序列数据的中位数和标准差。
在S300可能得到多个波峰，针对每个波峰数据计算对应的时间序列数据的中位数和标准差。具体来说，针对每个波峰计算其对应的时间序列数据的中位数和标准差，若有多个波峰，分别计算多个波峰对应的中位数和标注差，针对计算得到的中位数和标准差进行归集整理，以便下一步确定出时间序列信号的水平检测结果。
S500：基于中位数和标准差，确定时间序列信号的水平检测结果。
针对S400得到的每个波峰对应的中位数和标准差进一步确定出整个时间序列信号的水平检测结果。具体来说，可以先基于每个波峰对应的中位数和标准差筛选出最终符合中位数以及标准差要求的目标波峰，再基于目标波峰对应的中位数确定出时间序列信号的水平检测结果。
上述时间序列信号水平检测方法，获取时间序列信号；将时间序列信号划分为预设数量的区间，统计每个区间的点数，并基于每个区间的点数构建直方图；获取直方图中的波峰数据；获取波峰数据中每个波峰对应的时间序列数据的中位数和标准差；及基于中位数和标准差，确定时间序列信号的水平检测结果。整个过程中，只需要对时间序列信号进行简单的数据处理，无需采取复杂的计算过程，其可以实现高效的时间序列信号水平检测。
如图3所示，在一些实施例中，S100包括：
S120：获取初始时间序列信号；及
S140：剔除初始时间序列信号中的异常数据，得到时间序列信号。
在获取时间序列信号时可以针对获取到的初始时间序列信号进行异常数据剔除处理，以确保得到有效的时间序列信号。具体来说，这里的异常数据可以是不完整数据或者错误的数据。更进一步来说，可以基于正常时间序列信号对应的振幅范围来剔除初始时间序列信号中的异常数据，得到时间序列信号。以纳米孔测序序列数据为例，一般纳米孔测序电流值正常范围是0到350，超过这个数值即认定为异常的纳米孔测序序列信号，因此，针对纳米孔测序序列数据可以剔除小于0或大于350的点，得到最终有效的纳米孔测序序列数据。
在一些实施例中，对直方图进行平滑处理包括：
基于预设尺寸的滑窗，采用移动平均法对直方图进行平滑处理。
在本实施例中，平滑处理采取基于预设尺寸的滑窗方式进行。滑窗的尺寸可以根据实际情况的需要设定，例如可以为8、10、12等。滑窗大小对应的是移动平均法中对应的时间段大小，针对单个时间段内的数据，计算其平均值，将该平均值作为这个时间段的预测值。具体来说，简单移动平均是将某一时间段内的数据取平均值作为这个时间段的预测值，公式为：
其中，SMAt表示时间点t的简单移动平均值，Xt表示时间点t的数据，n为时间段的长度，即滑窗大小。
在一些实施例中，对直方图进行平滑处理，获取平滑处理后直方图中的波峰数据包括：对直方图进行平滑处理，截取平滑处理后直方图中频数大于预设频数阈值的波峰数据。
预设频数阈值是预先设定的阈值，其可以根据实际情况的需要进行设定。其具体表征需要选择波峰的大小，若需要筛选出较大的波峰，则预设频数阈值可以设置为较大的值；若需要筛选出较多数量的波峰(波峰较小)，则预设频数阈值可以设置为较小的值。具体来说，预设频数阈值可以为1、2、3等值。一般来说，波峰较大的数据才能准确体现出时间序列信号的水平检测结果，因此，为了进一步减小后续数据处理量，在这里基于预设频数阈值对应的波峰。
在一些实施例中，上述时间序列信号水平检测方法还包括：基于预设点数阈值对波峰数据进行筛选，得到筛选后的波峰数据；及获取筛选后的波峰数据中每个波峰对应的时间序列数据的中位数和标准差。
在本实施例中，基于预设点数阈值对波峰数据进一步筛选。具体来说，预设点数阈值是预先设定的值，其可以根据实际情况的需要进行设定，例如其可以为100、200、300等。针对每个波峰数据对应的点数进行统计，筛选点数超过预设点数阈值的波峰数据，然后再获取筛选后的波峰数据中每个波峰对应的时间序列数据的中位数和标准差。
如图3所示，在一些实施例中，S500包括：
S520：基于中位数和标准差，剔除标准差大于预设第一常数或中位数小于预设第二常数的波峰，得到候选波峰。
S540：对比候选波峰对应的时间序列数据的中位数大小；及
S560：以中位数最大值作为时间序列信号的信号水平。
基于每个波峰对应的中位数和标准差，再次对波峰进行筛选，最终确定得到的候选波峰。具体来说，这里是基于预设第一常数和预设第二常数来进行筛选的，预设第一常数和预设第二常数是基于经验值设定的常数，其用于对波峰的进一步筛选。具体来说，预设第一常数可以为4、5、6等；预设第二常数可以为50、100、150等。针对再次筛选后得到的候选波峰，对比不同候选波峰对应的时间序列数据的中位数大小，选择中位数最大值作为最终的时间序列信号的信号水平。
为详细说明本申请时间序列信号水平检测方法的技术方案及其效果，下面将以纳米孔测序信号水平检测为应用实例展开描述。
纳米孔测序信号指的是DAN分子穿过纳米孔时记录的穿孔电流变化，这种变化主要是由不同的碱基在纳米孔中电流不同导致的。具体识别步骤如下：
步骤1：测序数据采集：采集包含信号水平的纳米孔测序序列数据，并保存到硬盘文件，信号水平指的是测序信号大幅下降前的那一段且数值是100以上的数据的中位数。具体如下4所示。
步骤2：截取纳米孔测序序列数据，其具体如图5所示。
步骤3：剔除小于0或大于350的点，从0到此数据最大值等距划分1000个区间，如图5所示，x坐标是点的序号，图5中x坐标0到10000，代表有10000个点，y坐标是信号的电流值；统计每个区间的点数，得出此数据的直方图，直方图具体如图6所示。
步骤4：对步骤3得到的数据计算移动平均，移动平均对应的滑窗尺寸大小为10，得到如图7所示的波形。
步骤5：对步骤4得到的数据只取纵坐标值大于2(频数)的数据，得到4个如图8所示的波峰。
步骤6：对步骤5得到的4个波峰数据只取点数大于200的波峰，具体如图9所示。
步骤7：对步骤6得到的波峰数据，计算每个波峰对应的测序序列数据的中位数和标准差，具体如图10所示。
步骤8：剔除步骤7中标准差大于5或者中位数小于100的波峰后，选择中位数最大值就是信号水平。具体来说，如图10所示，剔除第一个峰值后，以第四个峰对应的中位数196作为最终的信号水平。具体来说，在上述图4、图5和图10中的x坐标和y坐标含义相同，x坐标为点的序号，y坐标为电流值。在图6～图9中，x坐标和y坐标含义相同，x坐标 为电流值，y坐标为频数。
应该理解的是，虽然如上的各实施例所涉及的流程图中的各个步骤按照箭头的指示依次显示，但是这些步骤并不是必然按照箭头指示的顺序依次执行。除非本文中有明确的说明，这些步骤的执行并没有严格的顺序限制，这些步骤可以以其它的顺序执行。而且，如上的各实施例所涉及的流程图中的至少一部分步骤可以包括多个步骤或者多个阶段，这些步骤或者阶段并不必然是在同一时刻执行完成，而是可以在不同的时刻执行，这些步骤或者阶段的执行顺序也不必然是依次进行，而是可以与其它步骤或者其它步骤中的步骤或者阶段的至少一部分轮流或者交替地执行。
基于同样的发明构思，本申请实施例还提供了一种用于实现上述所涉及的时间序列信号水平检测方法的时间序列信号水平检测装置。该装置所提供的解决问题的实现方案与上述方法中所记载的实现方案相似，故下面所提供的一个或多个时间序列信号水平检测装置实施例中的具体限定可以参见上文中对于时间序列信号水平检测方法的限定，在此不再赘述。
在一个实施例中，如图11所示，提供了一种时间序列信号水平检测装置，包括：
信号获取模块100，用于获取时间序列信号；
直方图构建模块200，用于将时间序列信号划分为预设数量的区间，统计每个区间的点数，并基于每个区间的点数构建直方图；
数据提取模块300，用于获取直方图中的波峰数据；
计算模块400，用于获取波峰数据中每个波峰对应的时间序列数据的中位数和标准差；及。
检测模块500，用于基于中位数和标准差，确定时间序列信号的水平检测结果。
在一些实施例中，信号获取模块100还用于获取初始时间序列信号；及剔除初始时间序列信号中的异常数据，得到时间序列信号。
在一些实施例中，信号获取模块100还用于获取正常时间序列信号对应的振幅范围阈值；及基于振幅范围阈值，剔除初始时间序列信号中的异常数据，得到时间序列信号。
在一些实施例中，数据提取模块300还用于
在一些实施例中，数据提取模块300还用于基于预设尺寸的滑窗，采用移动平均法对直方图进行平滑处理。
在一些实施例中，数据提取模块300还用于对直方图进行平滑处理，截取平滑处理后直方图中频数大于预设频数阈值的波峰数据。
在一些实施例中，计算模块400还用于基于预设点数阈值对波峰数据进行筛选，得到筛选后的波峰数据；及获取筛选后的波峰数据中每个波峰对应的时间序列数据的中位数和标准差。
在一些实施例中，检测模块500还用于基于中位数和标准差，剔除标准差大于预设第 一常数或中位数小于预设第二常数的波峰，得到候选波峰；对比候选波峰对应的时间序列数据的中位数大小；及以中位数最大值作为时间序列信号的信号水平。
在一些实施例中，时间序列信号包括纳米孔测序序列信号。
在一个实施例中，提供了一种计算机设备，该计算机设备可以是终端，其内部结构图可以如图12所示。该计算机设备包括通过系统总线连接的处理器、存储器、通信接口、显示屏和输入装置。其中，该计算机设备的处理器用于提供计算和控制能力。该计算机设备的存储器包括非易失性存储介质、内存储器。该非易失性存储介质存储有操作系统和计算机程序。该内存储器为非易失性存储介质中的操作系统和计算机程序的运行提供环境。该计算机设备的通信接口用于与外部的终端进行有线或无线方式的通信，无线方式可通过WIFI、移动蜂窝网络、NFC(近场通信)或其他技术实现。该计算机程序被处理器执行时以实现一种时间序列信号水平检测方法。该计算机设备的显示屏可以是液晶显示屏或者电子墨水显示屏，该计算机设备的输入装置可以是显示屏上覆盖的触摸层，也可以是计算机设备外壳上设置的按键、轨迹球或触控板，还可以是外接的键盘、触控板或鼠标等。
本领域技术人员可以理解，图12中示出的结构，仅仅是与本申请方案相关的部分结构的框图，并不构成对本申请方案所应用于其上的计算机设备的限定，具体的计算机设备可以包括比图中所示更多或更少的部件，或者组合某些部件，或者具有不同的部件布置。
在一些实施例中，提供一种计算机设备，包括存储器及一个或多个处理器，存储器中储存有计算机可读指令，计算机可读指令被一个或多个处理器执行时，使得一个或多个处理器执行上述时间序列信号水平检测方法的步骤。
在一些实施例中，本申请提供了一个或多个存储有计算机可读指令的计算机存储介质，计算机可读指令被一个或多个处理器执行时，使得一个或多个处理器执行上述时间序列信号水平检测方法的步骤。
在一些实施例中，本申请提供了一种计算机程序产品，该计算机程序被一个或多个处理器执行时，使得一个或多个处理器执行上述时间序列信号水平检测方法的步骤。
本领域普通技术人员可以理解实现上述实施例方法中的全部或部分流程，是可以通过计算机程序来指令相关的硬件来完成，计算机程序可存储于一非易失性计算机可读取存储介质中，该计算机程序在执行时，可包括如上述各方法的实施例的流程。其中，本申请所提供的各实施例中所使用的对存储器、数据库或其它介质的任何引用，均可包括非易失性和易失性存储器中的至少一种。非易失性存储器可包括只读存储器(Read-Only Memory，ROM)、磁带、软盘、闪存、光存储器、高密度嵌入式非易失性存储器、阻变存储器(ReRAM)、磁变存储器(Magnetoresistive Random Access Memory，MRAM)、铁电存储器(Ferroelectric Random Access Memory，FRAM)、相变存储器(Phase Change Memory，PCM)、石墨烯存储器等。易失性存储器可包括随机存取存储器(Random Access Memory，RAM)或外部高速 缓冲存储器等。作为说明而非局限，RAM可以是多种形式，比如静态随机存取存储器(Static Random Access Memory，SRAM)或动态随机存取存储器(Dynamic Random Access Memory，DRAM)等。本申请所提供的各实施例中所涉及的数据库可包括关系型数据库和非关系型数据库中至少一种。非关系型数据库可包括基于区块链的分布式数据库等，不限于此。本申请所提供的各实施例中所涉及的处理器可为通用处理器、中央处理器、图形处理器、数字信号处理器、可编程逻辑器、基于量子计算的数据处理逻辑器等，不限于此。
以上实施例的各技术特征可以进行任意的组合，为使描述简洁，未对上述实施例中的各个技术特征所有可能的组合都进行描述，然而，只要这些技术特征的组合不存在矛盾，都应当认为是本说明书记载的范围。
以上实施例仅表达了本申请的几种实施方式，其描述较为具体和详细，但并不能因此而理解为对本申请专利范围的限制。应当指出的是，对于本领域的普通技术人员来说，在不脱离本申请构思的前提下，还可以做出当干变形和改进，这些都属于本申请的保护范围。因此，本申请的保护范围应以所附权利要求为准。

Claims (20)

  1. 一种时间序列信号水平检测方法，所述方法包括：
    获取时间序列信号；
    将所述时间序列信号划分为预设数量的区间，统计每个区间的点数，并基于所述每个区间的点数构建直方图；
    获取所述直方图中的波峰数据；
    获取所述波峰数据中每个波峰对应的时间序列数据的中位数和标准差；及
    基于所述中位数和所述标准差，确定所述时间序列信号的水平检测结果。
  2. 根据权利要求1所述的方法，所述获取时间序列信号包括：
    获取初始时间序列信号；及
    剔除所述初始时间序列信号中的异常数据，得到时间序列信号。
  3. 根据权利要求2所述的方法，还包括：
    获取正常时间序列信号对应的振幅范围阈值；及
    基于所述振幅范围阈值，剔除所述初始时间序列信号中的异常数据，得到时间序列信号。
  4. 根据权利要求1所述的方法，获取所述直方图中的波峰数据包括：
    对所述直方图进行平滑处理，获取平滑处理后直方图中的波峰数据。
  5. 根据权利要求4所述的方法，对所述直方图进行平滑处理包括：
    基于预设尺寸的滑窗，采用移动平均法对所述直方图进行平滑处理。
  6. 根据权利要求4所述的方法，所述对所述直方图进行平滑处理，获取平滑处理后直方图中的波峰数据包括：
    对所述直方图进行平滑处理，截取平滑处理后直方图中频数大于预设频数阈值的波峰数据。
  7. 根据权利要求6所述的方法，所述方法还包括：
    基于预设点数阈值对所述波峰数据进行筛选，得到筛选后的波峰数据；及
    获取所述筛选后的波峰数据中每个波峰对应的时间序列数据的中位数和标准差。
  8. 根据权利要求1所述的方法，所述基于所述中位数和所述标准差，确定所述时间序列信号的水平检测结果包括：
    基于所述中位数和所述标准差，剔除标准差大于预设第一常数或中位数小于预设第二常数的波峰，得到候选波峰；
    对比所述候选波峰对应的时间序列数据的中位数大小；及
    以中位数最大值作为所述时间序列信号的信号水平。
  9. 根据权利要求1所述的方法，所述时间序列信号包括纳米孔测序序列信号。
  10. 一种时间序列信号水平检测装置，所述装置包括：
    信号获取模块，用于获取时间序列信号；
    直方图构建模块，用于将所述时间序列信号划分为预设数量的区间，统计每个区间的点数，并基于所述每个区间的点数构建直方图；
    数据提取模块，用于获取所述直方图中的波峰数据；
    计算模块，用于获取所述波峰数据中每个波峰对应的时间序列数据的中位数和标准差；及
    检测模块，用于基于所述中位数和所述标准差，确定所述时间序列信号的水平检测结果。
  11. 根据权利要求10所述的装置，所述信号获取模块还用于获取初始时间序列信号；及剔除所述初始时间序列信号中的异常数据，得到时间序列信号。
  12. 根据权利要求11所述的装置，所述信号获取模块还用于获取正常时间序列信号对应的振幅范围阈值；及基于所述振幅范围阈值，剔除所述初始时间序列信号中的异常数据，得到时间序列信号。
  13. 根据权利要求10所述的装置，所述数据提取模块还用于对所述直方图进行平滑处理，获取平滑处理后直方图中的波峰数据。
  14. 根据权利要求10所述的装置，所述数据提取模块还用于基于预设尺寸的滑窗，采用移动平均法对所述直方图进行平滑处理。
  15. 根据权利要求10所述的装置，所述数据提取模块还用于对所述直方图进行平滑处理，截取平滑处理后直方图中频数大于预设频数阈值的波峰数据。
  16. 根据权利要求15所述的装置，所述计算模块还用于基于预设点数阈值对所述波峰数据进行筛选，得到筛选后的波峰数据；及获取所述筛选后的波峰数据中每个波峰对应的时间序列数据的中位数和标准差。
  17. 根据权利要求10所述的装置，所述检测模块还用于基于所述中位数和所述标准差，剔除标准差大于预设第一常数或中位数小于预设第二常数的波峰，得到候选波峰；对比所述候选波峰对应的时间序列数据的中位数大小；及以中位数最大值作为所述时间序列信号的信号水平。
  18. 根据权利要求10所述的装置，所述时间序列信号包括纳米孔测序序列信号。
  19. 一种计算机设备，包括存储器及一个或多个处理器，所述存储器中储存有计算机可读指令，所述计算机可读指令被所述一个或多个处理器执行时，使得所述一个或多个处理器执行权利要求1至9中任一项所述的方法的步骤。
  20. 一个或多个存储有计算机可读指令的计算机存储介质，所述计算机可读指令被一个或多个处理器执行时，使得所述一个或多个处理器执行权利要求1至9中任一项所述的方法的步骤。
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