A kind of intelligent monitoring method, device and system for PCR detection

Abstract

The present invention relates to the field of data processing technology, and more specifically, to an intelligent monitoring method, device and system for PCR detection, wherein the method comprises: collecting fluorescence signal intensity data during the PCR reaction process at a predetermined frequency; preprocessing the fluorescence signal intensity data; obtaining a first abnormality degree according to the preprocessed fluorescence signal intensity data; calculating the trend abnormality factor of each data point according to the position coordinates of each data point of the fluorescence signal intensity data; calculating the deviation degree of the fluorescence signal intensity data through standard fluorescence signal intensity data; updating the first abnormality degree through the trend abnormality factor and the deviation degree of the fluorescence signal intensity data to obtain a second abnormality degree; judging whether an alarm is needed according to the second abnormality degree of each data point. The present invention updates the abnormality degree of each PCR data point by analyzing the trend of the fluorescence signal intensity data, so that the abnormality degree of the data point is more accurate.

Description

Technical Field

[0001] The present invention generally relates to the field of data processing technology. More specifically, the present invention relates to an intelligent monitoring method, device and system for PCR detection. Background Art

[0002] PCR technology has a wide range of application value in clinical diagnosis. For example, PCR technology can be used to detect pathogenic microorganisms such as bacteria, viruses and parasites, as well as detect genetic diseases and cancer-related gene mutations. By using PCR technology, a large number of DNA fragments can be obtained in a short period of time and qualitative or quantitative analysis can be performed, thus providing strong support for early diagnosis and treatment of diseases.

[0003] The LOF algorithm can detect anomalies based on the relative density of local data. Therefore, the existing technology usually uses the LOF algorithm to detect the data generated by the PCR reaction, and then determines whether the PCR reaction is abnormal. However, the change trend of various data during the PCR reaction is uncertain. In addition to the fluctuation of the data itself, the normal changes in the data itself may also be regarded as abnormal fluctuations, resulting in misjudgment of normal data and failure in monitoring the abnormal state of the PCR reaction process. Summary of the invention

[0004] In order to solve the problem that the abnormal detection of fluorescence signal intensity data during the PCR reaction is easily affected by the change trend of the data itself and leads to false detection, the present invention proposes an intelligent monitoring method, device and system for PCR detection. To this end, the present invention provides solutions in the following aspects.

[0005] In a first aspect, an intelligent monitoring method for PCR detection includes: collecting fluorescence signal intensity data during a PCR reaction process at a predetermined frequency; preprocessing the fluorescence signal intensity data, wherein the fluorescence signal intensity data is time series data; obtaining a first abnormality degree according to the preprocessed fluorescence signal intensity data through a LOF algorithm, wherein the first abnormality degree of the fluorescence signal intensity data is L i , i represents the index of the data point in the fluorescence signal intensity data; calculates the trend abnormality factor of each data point according to the position coordinates of each data point in the fluorescence signal intensity data, wherein the trend abnormality factor of the fluorescence signal intensity data is U i ; Calculate the degree of deviation of the fluorescence signal intensity data by the standard fluorescence signal intensity data, wherein the degree of deviation of the fluorescence signal intensity data is V i; The first abnormality degree is updated by the abnormal trend factor and the deviation degree of the fluorescence signal intensity data to obtain a second abnormality degree, wherein the second abnormality degree of the fluorescence signal intensity data is: A i : A i =U i × V i × L i In response to the number of data points whose second abnormality degree is greater than the abnormality degree threshold being greater than or equal to the quantity threshold, the PCR reaction is recorded as an abnormal reaction and an alarm signal is sent to the experimenter.

[0006] In one embodiment, calculating the trend abnormality factor of each data point according to the position coordinates of each data point of the fluorescence signal intensity data includes: calculating the corresponding slope change degree according to the first R data points closest to each data point in time series, wherein the slope change degree of the fluorescence signal intensity data S i for:

[0007]

[0008] D i,j is the first point in a data point set including the fluorescence signal intensity data and the first R fluorescence signal intensity data points closest to it in time series. j data points, D i,j-1 is the first j-1 data points, and is the difference in the fluorescence signal intensity of two adjacent data in the data point set, max is the maximum value function, and min is the minimum value function; the trend abnormality factor of each data point is calculated according to the degree of change of the slope of each data point, wherein the trend abnormality factor of the fluorescence signal intensity data U i for:

[0009]

[0010] D i+1 and D i is the fluorescence signal intensity data separated by one time unit, D i and D i-1 is the fluorescence signal intensity data at one time unit interval.

[0011] In one embodiment, calculating the degree of deviation of the fluorescence signal intensity data by using the standard fluorescence signal intensity data includes: obtaining the standard fluorescence signal intensity data; obtaining the historical data of the PCR reaction to obtain the historical fluorescence signal intensity data; averaging the difference between the fluorescence signal intensity of the historical fluorescence signal intensity data and the fluorescence signal intensity of the corresponding data point of the standard fluorescence signal intensity data to obtain the theoretical deviation of the fluorescence signal intensity data. ; Calculate the degree of deviation of the fluorescence signal intensity data by the standard fluorescence signal intensity data, wherein the degree of deviation of the fluorescence signal intensity data V i for:

[0012]

[0013] i It can also be the index of the data point in the fluorescence signal intensity data. D m The fluorescence signal intensity data m The fluorescence signal intensity of each data point is F m The fluorescence signal intensity data m The fluorescence signal intensity of each data point is is the theoretical deviation of the fluorescence signal intensity data.

[0014] In one embodiment, a first degree of abnormality is obtained based on the preprocessed fluorescence signal intensity data through the LOF algorithm, including: obtaining the K data points that are closest in time to each data point in the fluorescence signal intensity data, where the K data points are the neighboring data corresponding to each data point; calculating the inverse of the average reachable distance from each data point to its corresponding neighboring data to obtain the first local reachable density of each data point; obtaining the average value of the second local reachable density of the neighboring data corresponding to each data point; dividing the first local reachable density of each data point by the average value of the corresponding second local reachable density to obtain the first degree of abnormality of each data point.

[0015] In one embodiment, preprocessing the fluorescence signal intensity data includes: performing noise filtering and background correction on the fluorescence intensity data to obtain noise-reduced fluorescence intensity data.

[0016] In one embodiment, an intelligent monitoring method for PCR detection also includes: plotting the noise-reduced fluorescence signal intensity data at each moment in a coordinate system, connecting each data point to obtain a fluorescence combustion curve, and the fluorescence combustion curve is used to display the trend of the fluorescence signal intensity, wherein the horizontal axis of the coordinate system is time, and the vertical axis is the fluorescence signal intensity.

[0017] In one embodiment, a smart monitoring method for PCR detection further includes: in response to the second abnormality level of a predetermined number of consecutive data being greater than the abnormality level threshold, sending an alarm message to an experimenter.

[0018] In one embodiment, a smart monitoring method for PCR detection further includes: obtaining historical PCR reaction data, and obtaining the abnormality threshold through the historical PCR reaction data.

[0019] In a second aspect, an intelligent monitoring device for PCR detection includes: a processor configured to execute program instructions; and a memory configured to store the program instructions, wherein when the program instructions are loaded and executed by the processor, the processor executes an intelligent monitoring method for PCR detection as described in the first aspect above.

[0020] In the third aspect, an intelligent monitoring system for PCR detection includes: a PCR instrument, which is an instrument for amplifying specific DNA and monitors the generation of PCR products by detecting the intensity of the fluorescence signal, wherein the more PCR products there are, the higher the fluorescence intensity; the PCR instrument is connected to the intelligent monitoring device for PCR detection as described in the second aspect above.

[0021] The beneficial effects of the present invention are:

[0022] In order to solve the problem that during the PCR reaction, the abnormality detection of fluorescence signal intensity data is easily affected by the changing trend of the data itself and leads to false detection, the present invention analyzes the trend abnormality factor of the fluorescence signal intensity data (or the fluorescence combustion curve) and the overall deviation degree of the fluorescence signal intensity data, and corrects the abnormality degree of each data point obtained by the LOF algorithm according to the trend abnormality factor and the deviation degree, so as to obtain a more accurate abnormality degree based on the curve trend. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] By reading the following detailed description with reference to the accompanying drawings, the above and other objects, features and advantages of the exemplary embodiments of the present invention will become readily understood. In the accompanying drawings, several embodiments of the present invention are shown in an exemplary and non-limiting manner, and the same or corresponding reference numerals represent the same or corresponding parts, wherein:

[0024] Figure 1 is a flowchart of the steps of an intelligent monitoring method for PCR detection according to an embodiment of the present invention;

[0025] Figure 2 is a flowchart of step S3 according to an embodiment of the present invention;

[0026] Figure 3is a flowchart of step S4 according to an embodiment of the present invention;

[0027] Figure 4 is a flowchart of step S5 according to an embodiment of the present invention;

[0028] Figure 5 This is a block diagram of an intelligent monitoring device for PCR detection according to this embodiment.

[0029] Figure 6 It is a structural diagram of an intelligent monitoring system for PCR detection according to this embodiment. DETAILED DESCRIPTION

[0030] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are 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 those skilled in the art without creative work are within the scope of protection of the present invention.

[0031] The specific embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0032] Figure 1 It is a step diagram of an intelligent monitoring method for PCR detection according to an embodiment of the present invention.

[0033] like Figure 1 As shown, an intelligent monitoring method for PCR detection includes steps S1 to S6.

[0034] Step S1: Collecting fluorescence signal intensity data during the PCR reaction process at a predetermined frequency.

[0035] In one embodiment, during the PCR reaction, the intensity of the fluorescent signal is collected at a frequency of once per second. The fluorescent signal is used to detect the progress of the PCR reaction and the generation of PCR products: the number of PCR products continues to increase, so the intensity of the fluorescent signal also increases. Among them, the PCR product is an amplification product of a specific DNA sequence, which will emit a fluorescent signal after combining with a fluorescent marker.

[0036] Step S2: Preprocessing the fluorescence signal intensity data.

[0037] The fluorescence signal intensity data is time series data, and the fluorescence signal intensity data is recorded in a uniform time sequence. In one embodiment, the interval between two adjacent data points in the fluorescence signal intensity data is 1 second.

[0038] In particular, in PCR reaction experiments, due to interference factors such as environmental noise and instrument drift, the intensity of the detected fluorescence signal may fluctuate, making the experimental data inaccurate. In addition, there may be some background signals in the PCR reaction process, such as autofluorescence, non-specific amplification, etc. These background signals will affect the experimental results and reduce the accuracy of the data. Therefore, it is necessary to perform background correction on the fluorescence intensity data to eliminate these interference signals.

[0039] In one embodiment, the background correction program eliminates the influence of background signals by measuring the blank fluorescence intensity of the reaction tubes and water used by the quantitative PCR instrument. During the operation of the correction program, the quantitative PCR instrument continuously reads the fluorescence intensity of the background correction plate at a temperature of 60°C for 10 minutes. The average value of the collected fluorescence intensity is calculated by SDS software, and the result is saved in a correction file. In real-time PCR experiments, the correction file is called by the software to deduct the background signal from the experimental data to ensure the accuracy and reliability of the experimental results.

[0040] Furthermore, for the pre-processed fluorescence signal intensity data, a fluorescence combustion curve is drawn based on the two variables of time and fluorescence intensity. The fluorescence combustion curve is used to display the trend of the fluorescence signal intensity, and the horizontal axis of the coordinate system is time, and the vertical axis is fluorescence intensity. Based on this, the fluorescence combustion curve reflects the trend of the fluorescence signal intensity over time in the PCR reaction, and by analyzing the slope of the fluorescence combustion curve and other data, the dynamic change of the fluorescence signal intensity can be obtained, so that the abnormal degree of each data point can be updated later through the trend of each data point (trend abnormality factor). By constructing the fluorescence combustion curve, the experimenter can intuitively observe the change of the fluorescence intensity signal.

[0041] The horizontal axis of the coordinate system of the fluorescence combustion curve is time, and the vertical axis is the fluorescence signal intensity. The fluorescence combustion curve reflects the change of the fluorescence signal intensity over time during the PCR reaction. By observing the shape of the fluorescence combustion curve, the progress of the PCR reaction, the number of target sequences in the test sample and other information can also be judged.

[0042] Step S3: Using the LOF algorithm, a first abnormality degree is obtained based on the preprocessed fluorescence signal intensity data, wherein the first abnormality degree of the fluorescence signal intensity data is L i .

[0043] Wherein, for any data point in the fluorescence signal intensity data, there is a corresponding first abnormality level, for example: i The first abnormality degree of the data point is L i , iRepresents the index of the data point in the fluorescence signal intensity data. The first abnormality degree is the calculation of the abnormality degree of the data point based only on the LOF algorithm. The LOF algorithm can compare the density of the neighboring points around the target data point with the density of the point itself to determine the abnormality degree. When the density of a data point is much lower than the density of its neighboring points, the data point is an abnormal point and its first abnormality degree is high.

[0044] In particular, since the fluorescence combustion curve composed of the fluorescence signal intensity data has a specific curve trend, it will affect the calculation of the abnormal score obtained by the LOF algorithm, thereby misjudging some normal data points as abnormal data points. Therefore, the present invention proposes to analyze the local trend of the fluorescence signal intensity data in the next step, and update the abnormal degree of the data points in the normal trend part of the curve, so as to obtain a more accurate abnormal degree.

[0045] Step S4: Calculate the trend abnormality factor of each data point according to the position coordinates of each data point of the fluorescence signal intensity data.

[0046] Among them, for the coordinates of any data point in the fluorescence signal intensity data and a predetermined number of adjacent data points, the degree of change of the slope within the local data range of the data point can be calculated, that is, the trend abnormality factor of the data point. Therefore, the trend abnormality factor can correct (update) the abnormal degree of the data point.

[0047] In particular, the correction (update) of the degree of abnormality of the data point according to the abnormal trend factor of each data point only considers whether the degree of change of the slope within the local data range of each data point is locally acceptable, and cannot explain whether the trend of the slope change conforms to the change trend of the conventional fluorescence signal intensity data (the fluorescence combustion curve formed). In addition, it should also be judged whether the fluorescence signal intensity data of the PCR reaction process is inconsistent with the normal situation based on the overall change trend of the fluorescence signal intensity data. Therefore, the present invention proposes in the next step to compare the standard fluorescence signal intensity data with the fluorescence signal intensity data obtained by detection, and calculate the degree of deviation of the fluorescence signal intensity data.

[0048] Step S5: Calculating the degree of deviation of the fluorescence signal intensity data using the standard fluorescence signal intensity data.

[0049] The fluorescence signal intensity data obtained by the test is compared with the standard fluorescence signal intensity data obtained theoretically, and the difference between the fluorescence signal levels of the corresponding data points of the two is calculated to obtain the deviation degree. Therefore, the deviation degree reflects the overall deviation between the fluorescence signal intensity data actually obtained and the standard fluorescence signal intensity data.

[0050] Step S6: updating the first abnormality degree according to the abnormal trend factor and the deviation degree of the fluorescence signal intensity data to obtain a second abnormality degree.

[0051] The second abnormality degree of the fluorescence signal intensity data is: A i : A i =U i × V i × L i . A i represents the second abnormality level (updated abnormality level) of the fluorescence signal intensity data, U i Indicates the abnormal trend factor of the fluorescence signal intensity data, V i Indicates the degree of deviation of the fluorescence signal intensity data. L i Indicates the first abnormality degree of the fluorescence signal intensity data. The first abnormality degree of the data point is updated by multiplying the trend abnormality factor and the deviation degree of each data point in the fluorescence signal intensity data with the first abnormality degree of the data point, so that the updated second abnormality degree is proportional to the trend abnormality factor and the deviation degree.

[0052] Step S7: In response to the number of data points whose second abnormality degree is greater than the abnormality degree threshold being greater than or equal to the quantity threshold, the PCR reaction is recorded as an abnormal reaction and an alarm signal is sent to the experimenter.

[0053] In one embodiment, the second abnormality threshold is 1.3 and the quantity threshold is 5.

[0054] Furthermore, when the second abnormality level of a predetermined number of data obtained continuously during the PCR reaction is greater than the abnormality level threshold, an alarm signal is issued to the experimenter. In one embodiment, when 3 data are obtained continuously during the PCR reaction, an alarm signal is issued to the experimenter.

[0055] Figure 2 is a flow chart of step S3 according to an embodiment of the present invention.

[0056] like Figure 2 As shown, step S3 includes steps S301 to S304.

[0057] Step S301: obtaining K data points that are closest in time to each data point in the fluorescence signal intensity data, wherein the K data points are adjacent data corresponding to each data point.

[0058] Among them, for each data point, according to its position on the time axis, find the K data points closest to it as its neighboring data point set. Since the fluorescence signal intensity data is time series data, the selected K (neighboring distance) should be the time index, K is a preset value, and the empirical value K=5.

[0059] Step S302: Calculate the inverse of the average reachable distance from each data point to its corresponding adjacent data to obtain the first local reachable density of each data point.

[0060] The reachable distance refers to the distance between two data points, indicating the difficulty of reaching another data point from one data point. For each data point, the inverse of its average reachable distance is taken to obtain its first local reachable density.

[0061] Step S303: obtaining an average value of the second local reachable density of neighboring data corresponding to each data point.

[0062] The second local reachable density reflects the degree of connection between the data points around the data point, that is, the degree of correlation between the data points within the set of neighboring data points. Therefore, by calculating the average value of the second local reachable density of the neighboring data corresponding to each data point, the connection between the data points around the data point can be obtained, thereby providing a basis for the subsequent calculation of the first abnormality degree.

[0063] Step S304: Divide the first local reachable density of each data point by the average value of the corresponding second local reachable density to obtain a first abnormality degree of each data point.

[0064] Among them, the first abnormality degree is the LOF score, and the first abnormality degree reflects the relative abnormality degree of the data point in the entire data set. The value of the first abnormality degree can be used to determine whether a data point is an outlier (abnormal point) in the data set. When the first abnormality degree of a data point is high, it means that the connection relationship between the data point and its neighboring data points deviates greatly from the average of the entire data set, and the data point is an outlier. When the first abnormality degree of a data point is low, it means that the connection pattern of the data point meets the characteristics of the entire data set, and the data point is an outlier.

[0065] Figure 3 is a flow chart of step S4 according to an embodiment of the present invention.

[0066] like Figure 3 As shown, step S4 includes step S401 to step S402.

[0067] Step S401: Calculate the corresponding slope change degree according to the first R data points closest to each data point in time series.

[0068] Among them, the slope change degree of the fluorescence signal intensity data S i for:

[0069]

[0070] D i,j is the first point in a data point set including the fluorescence signal intensity data and the first R fluorescence signal intensity data points closest to it in time series. j data points, D i,j-1 is the first j- 1 data point, and is the difference in fluorescence signal intensity between two adjacent data in the data point set, max is the maximum value function, and min is the minimum value function. In one embodiment, the empirical value of the data range R is 10.

[0071] In particular, since two adjacent data points are only one time unit apart, the difference in fluorescence level between any two adjacent data points in the data point set can be directly used as the slope at the corresponding data point. The total slope change within the local data range of each data point is obtained, and the quotient is taken from the number of data points within the local data range minus one to obtain the average slope change within the local data range of the data point as the slope change degree of the data point. Based on this, the slope change degree of the fluorescence signal intensity data represents the average change degree of the slope at the data point, that is, the average change range. Based on this, the larger the local slope of the data point, the smaller the corresponding trend abnormality factor.

[0072] Furthermore, in order to monitor the PCR reaction in real time with real-time fluorescence signal data, it is necessary to judge the abnormal trend of the curve by the fluorescence signal level corresponding to the adjacent points before and after each data point. Since the set acquisition frequency is large (the cycle is small), the monitoring method will not lose its real-time performance due to the acquisition of the next fluorescence signal level data.

[0073] Step S402: Calculate the trend anomaly factor of each data point according to the degree of change of the slope of each data point.

[0074] Among them, the abnormal trend factor of fluorescence signal intensity data U i for:

[0075]

[0076] D i+1 and D i is the fluorescence signal intensity data separated by one time unit, D iand D i-1 The fluorescence signal intensity data is one time unit apart: D i+1 is the first time series of the fluorescence signal intensity data i +1 data point of fluorescence signal intensity, D i is the fluorescence signal intensity of the fluorescence signal intensity data, D i-1 is the first time series of the fluorescence signal intensity data i Based on this, the trend anomaly factor at each data point is the difference between the change in the slope of the fluorescence level before and after the data point and the average change range.

[0077] Figure 4 is a flow chart of step S5 according to an embodiment of the present invention.

[0078] like Figure 4 As shown, step S5 includes steps S501 to S504.

[0079] Step S501: Obtain standard fluorescence signal intensity data.

[0080] The standard fluorescence signal intensity data is: the fluorescence intensity at each time under ideal conditions. m The fluorescence signal intensity of each data point is F m .

[0081] Furthermore, the standard fluorescence combustion curve formed by the standard fluorescence signal intensity data is an S-shaped curve, so the standard fluorescence combustion curve can be obtained according to the conventional fluorescence signal change law. m The fluorescence signal level corresponding to the data point is F m .

[0082] Step S502: Obtain historical data of PCR reaction to obtain historical fluorescence signal intensity data.

[0083] In one embodiment, relevant historical data of the PCR reaction is obtained by consulting the experimental record or the experimental log.

[0084] Furthermore, the historical data can be imported into data processing software (e.g., GraphPad Prism), and the historical data can be plotted as a historical fluorescence combustion curve through the data processing software, where the horizontal axis of the historical fluorescence combustion curve is time and the vertical axis is the fluorescence signal intensity. The historical fluorescence combustion curve can show how the data generated by the PCR reaction changes over time, making it easier for experimenters to observe and verify the accuracy of the data.

[0085] Step S503: averaging the differences between the fluorescence signal intensities of the historical fluorescence signal intensity data and the fluorescence signal intensity of the corresponding data points of the standard fluorescence signal intensity data to obtain a theoretical deviation of the fluorescence signal intensity data.

[0086] In one embodiment, the data after the start of the historical data range of the PCR reaction is obtained, and the average value of the difference between the fluorescence signal level of the corresponding data point of the actual fluorescence signal intensity data and the standard fluorescence signal intensity data is obtained. The average value is the theoretical deviation of the fluorescence combustion curve. .

[0087] In another embodiment, the difference between the fluorescence signal intensity of the historical fluorescence combustion curve and the corresponding data point of the standard fluorescence combustion curve is averaged to obtain the theoretical deviation of the fluorescence combustion curve. .

[0088] Step S504: Calculate the degree of deviation of the fluorescence signal intensity data using the standard fluorescence signal intensity data.

[0089] Among them, the degree of deviation of the fluorescence signal intensity data V i for:

[0090]

[0091] D m The fluorescence signal intensity data m The fluorescence signal intensity of each data point is F m The fluorescence signal intensity data m The fluorescence signal intensity of each data point is is the theoretical deviation of the fluorescence signal intensity data. Based on this, the deviation degree of the fluorescence signal intensity data is obtained by the overall gap between the fluorescence signal intensity data and the standard fluorescence signal intensity data.

[0092] Furthermore, since the conditions of the actual reaction process cannot be completely consistent, there is a normal difference between the collected fluorescence signal intensity data and the standard fluorescence signal intensity data. Therefore, the present invention obtains the theoretical deviation based on the difference between each data point of the obtained fluorescence signal intensity data and the corresponding data point on the standard fluorescence signal intensity data. , through theoretical deviation Correct the degree of curve deviation.

[0093] Figure 5 It is a structural block diagram of an intelligent monitoring device for PCR detection according to this embodiment.

[0094] The present invention also provides an intelligent monitoring device for PCR detection. Figure 5 As shown, the device includes a processor and a memory, wherein the memory stores computer program instructions, and when the computer program instructions are executed by the processor, an intelligent monitoring method for PCR detection according to the first aspect of the present invention is implemented.

[0095] The device also includes other components well known to those skilled in the art, such as a communication bus and a communication interface, and their configuration and functions are consistent with those in the art, so they will not be described in detail here.

[0096] In the present invention, the aforementioned memory may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, apparatus or device. For example, a computer-readable storage medium may be any appropriate magnetic storage medium or magneto-optical storage medium, such as a resistive random access memory RRAM (Resistive Random Access Memory), a dynamic random access memory DRAM (Dynamic Random Access Memory), a static random access memory SRAM (Static Random-Access Memory), an enhanced dynamic random access memory EDRAM (Enhanced Dynamic Random Access Memory), a high-bandwidth memory HBM (High-Bandwidth Memory), a hybrid memory cube HMC (Hybrid Memory Cube), etc., or any other medium that can be used to store the required information and can be accessed by an application, a module, or both. Any such computer storage medium may be part of a device or accessible or connectable to a device. Any application or module described in the present invention may be implemented using computer-readable / executable instructions that may be stored or otherwise maintained by such a computer-readable medium.

[0097] Figure 6It is a structural diagram of an intelligent monitoring system for PCR detection according to this embodiment.

[0098] like Figure 6 As shown, an intelligent monitoring system for PCR detection includes: a PCR instrument, which is an instrument for amplifying specific DNA and monitors the generation of PCR products by detecting the intensity of the fluorescence signal, wherein the more PCR products there are, the higher the fluorescence intensity; the PCR instrument is connected to the intelligent monitoring device for PCR detection as described in the second aspect of the present invention.

[0099] Among them, PCR is a molecular biology technique that can amplify DNA fragments in vitro and increase their number exponentially. PCR technology is widely used in gene detection, disease diagnosis, DNA cloning, genomics research and other fields. PCR instrument is an instrument specially used for polymerase chain reaction (PCR) experiments. PCR instrument usually consists of heating or cooling device, fluorescence detection unit, temperature control unit, computer control unit, etc. In the PCR reaction, the heating or cooling device of the PCR instrument can quickly adjust the temperature of the reaction system to achieve different steps of the PCR cycle, such as denaturation, annealing and extension. The fluorescence detection unit can detect the fluorescence signal of DNA during the PCR reaction, which is used for data analysis of quantitative PCR experiments. The temperature control unit can accurately control the temperature of the reaction system to ensure the accuracy and repeatability of the PCR reaction. The computer control unit can perform programmed operation and data acquisition, processing and analysis on the PCR instrument.

[0100] Furthermore, the intelligent monitoring device for PCR detection can be a PC terminal. The intelligent monitoring device for PCR detection is connected to a PCR instrument via a data line, so that the two can perform data transmission.

[0101] In the description of this specification, "plurality" or "several" means at least two, such as two, three or more, etc., unless otherwise clearly and specifically defined.

[0102] Although this specification has shown and described a number of embodiments of the present invention, it will be apparent to those skilled in the art that such embodiments are provided by way of example only. Those skilled in the art will conceive of many modifications, changes and alternatives without departing from the ideas and spirit of the present invention. It should be understood that in the practice of the present invention, various alternatives to the embodiments of the present invention described herein may be employed.

Claims

1. An intelligent monitoring method for PCR detection, characterized in that: include: Collecting fluorescence signal intensity data during the PCR reaction process at a predetermined frequency; Preprocessing the fluorescence signal intensity data, wherein the fluorescence signal intensity data is time series data; The first abnormality degree is obtained according to the preprocessed fluorescence signal intensity data by using the LOF algorithm, wherein the first abnormality degree of the fluorescence signal intensity data is L i , i Represents the index of the data point in the fluorescence signal intensity data; Calculating the trend abnormality factor of each data point according to the position coordinates of each data point of the fluorescence signal intensity data, including: calculating the corresponding slope change degree according to the first R data points closest to each data point in time series, the slope change degree of the fluorescence signal intensity data S i for: ; D i,j is the first point in a data point set including the fluorescence signal intensity data and the first R fluorescence signal intensity data points closest to it in time series. j data points, D i,j-1 is the first j-1 data points, and is the difference in the fluorescence signal intensity of two adjacent data in the data point set, max is the maximum value function, and min is the minimum value function; the trend abnormality factor of each data point is calculated according to the degree of change of the slope of each data point, and the trend abnormality factor of the fluorescence signal intensity data is U i , satisfying the following relationship: ; D i+1 and D i is the fluorescence signal intensity data separated by one time unit, D i and D i-1 is the fluorescence signal intensity data separated by one time unit; Calculating the degree of deviation of the fluorescence signal intensity data by using the standard fluorescence signal intensity data includes: obtaining the standard fluorescence signal intensity data; obtaining the historical data of the PCR reaction to obtain the historical fluorescence signal intensity data; averaging the difference between the fluorescence signal intensity of the historical fluorescence signal intensity data and the fluorescence signal intensity of the corresponding data point of the standard fluorescence signal intensity data to obtain the theoretical deviation of the fluorescence signal intensity data ; The degree of deviation of the fluorescence signal intensity data is V i , satisfying the following relationship: ; D m is the actual fluorescence signal intensity data m The fluorescence signal intensity of each data point is F m is the standard fluorescence signal intensity data m The fluorescence signal intensity of each data point; The first abnormality level is updated by the abnormal trend factor and the deviation degree of the fluorescence signal intensity data to obtain a second abnormality level, wherein the second abnormality level of the fluorescence signal intensity data is: A i : A i =U i × V i × L i ; In response to the number of data points whose second abnormality degree is greater than the abnormality degree threshold being greater than or equal to the quantity threshold, the PCR reaction is recorded as an abnormal reaction, and an alarm signal is sent to the experimenter.

2. The intelligent monitoring method for PCR detection according to claim 1, characterized in that: The first abnormality degree is obtained according to the preprocessed fluorescence signal intensity data through the LOF algorithm, including: Obtaining K data points that are closest in time to each data point in the fluorescence signal intensity data, wherein the K data points are neighboring data corresponding to each data point; Calculate the inverse of the average reachable distance from each data point to its corresponding neighboring data to obtain the first local reachable density of each data point; Obtaining the average value of the second local reachable density of the neighboring data corresponding to each data point; The first local reachable density of each data point is divided by the average value of the corresponding second local reachable density to obtain the first abnormality degree of each data point.

3. The intelligent monitoring method for PCR detection according to claim 1, characterized in that: Preprocessing the fluorescence signal intensity data includes: performing noise filtering and background correction on the fluorescence intensity data to obtain noise-reduced fluorescence intensity data.

4. The intelligent monitoring method for PCR detection according to claim 3, characterized in that: Also includes: The denoised fluorescence signal intensity data at each moment is plotted in a coordinate system, and each data point is connected to obtain a fluorescence combustion curve, which is used to display the trend of the fluorescence signal intensity, wherein the horizontal axis of the coordinate system is time and the vertical axis is the fluorescence signal intensity.

5. The intelligent monitoring method for PCR detection according to claim 1, characterized in that: Also includes: In response to the second abnormality level of a predetermined number of consecutive data being greater than the abnormality level threshold, an alarm message is issued to the experimenter.

6. The intelligent monitoring method for PCR detection according to claim 5, characterized in that: Also includes: Historical PCR reaction data is obtained, and the abnormality threshold is obtained through the historical PCR reaction data.

7. An intelligent monitoring device for PCR detection, characterized in that: include: a processor configured to execute program instructions; A memory configured to store the program instructions, which, when loaded and executed by the processor, enables the processor to execute an intelligent monitoring method for PCR detection according to any one of claims 1 to 6.

8. An intelligent monitoring system for PCR detection, characterized in that: include: PCR instrument, which is an instrument used to amplify specific DNA and monitor the generation of PCR products by detecting the intensity of the fluorescent signal, wherein the more PCR products there are, the higher the fluorescence intensity; The PCR instrument is connected to the intelligent monitoring device for PCR detection as described in claim 7.

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