Constant temperature control method, system and equipment applied to PCR (Polymerase Chain Reaction) detector

Abstract

The invention relates to the technical field of constant temperature control, in particular to a constant temperature control method, system and equipment applied to a PCR detector, and the method comprises the following steps: collecting temperature data of any test solution at each collection moment; taking the duration required by each circulation in the PCR detection process as a period, and obtaining each constant-temperature interval and each non-constant-temperature interval in each period; determining a fluctuation value of the temperature data in each constant temperature interval; determining the smoothness of the temperature data in each non-constant-temperature interval based on the gentle degree of the change of the temperature data in each non-constant-temperature interval; determining an abnormal factor of each period based on the difference of the fluctuation values in the corresponding constant-temperature interval between each period and the adjacent period and the difference of the smoothness in the corresponding non-constant-temperature interval in combination with the similarity degree of the temperature data in the corresponding constant-temperature interval; and determining optimized proportional parameters of the PID controller, and controlling the temperature of the test solution. The invention aims to improve the constant temperature control accuracy of the PCR detector.

Description

Technical Field

[0001] The present application relates to the field of constant temperature control technology, and specifically to a constant temperature control method, system and equipment applied to a PCR detector. Background Art

[0002] PCR detector, or polymerase chain reaction nucleic acid detector, is an instrument that uses polymerase chain reaction (PCR) technology to amplify specific DNA and is widely used in the fields of biological science research and genetic diagnosis.

[0003] In the PCR detection process, accurate temperature control is crucial. If there is a large error in temperature control, it may lead to nonspecific amplification or erroneous amplification results, resulting in false positives and false negatives. PCR detectors generally maintain a constant temperature through a PID controller. However, PID controllers usually use fixed parameters for temperature control, without considering the impact of ambient temperature changes around the PCR detector, heat conduction between adjacent PCR reaction tubes, and uneven heating of the heating device, resulting in low accuracy of constant temperature control of the PCR detector. Summary of the invention

[0004] In view of the above, it is necessary to provide a constant temperature control method, system and device for PCR detector, which improves the accuracy of constant temperature control of PCR detector compared with the constant temperature control method of traditional PCR detector: In a first aspect, an embodiment of the present application provides a constant temperature control method applied to a PCR detector, the method comprising the following steps: Collect the temperature data of any test solution at each collection time; The time required for each cycle in the PCR detection process is taken as a cycle, and based on the change of temperature data in each cycle, each constant temperature interval and each non-constant temperature interval in each cycle is obtained; Based on the distribution of differences between all extreme values ​​of the temperature data in each constant temperature interval and the preset temperature, and the variation range of the temperature data in each constant temperature interval, determining the fluctuation value of the temperature data in each constant temperature interval; Determine the smoothness of the temperature data in each non-constant temperature interval based on the degree of smoothness of the temperature data change in each non-constant temperature interval; Based on the difference in the fluctuation value in the corresponding constant temperature interval between each cycle and its adjacent cycle and the difference in the smoothness in the corresponding non-constant temperature interval, and combined with the similarity of the temperature data in the corresponding constant temperature interval, determine the abnormal factor of each cycle; Combined with the abnormal factors of the current cycle and the preset initial proportional term parameters, the optimized proportional term parameters of the PID controller are determined to control the temperature of any test solution.

[0005] In one embodiment, the acquisition process of each constant temperature interval and each non-constant temperature interval is: Arrange the temperature data of all collected moments in any period in time sequence to form a temperature sequence; The mutation point detection algorithm is used to obtain the mutation points in the temperature sequence, and the temperature sequence is divided into temperature subsequences according to the mutation points; Arrange all the temperature subsequences from small to large in terms of their discreteness, and use the time intervals of the temperature subsequences corresponding to the first three discretenesses as the constant temperature intervals, which correspond to the denaturation, annealing and extension reaction stages respectively in time sequence; The time intervals where the remaining temperature subsequences are located are regarded as non-constant temperature intervals.

[0006] In one embodiment, the fluctuation value is determined as follows: For any temperature subsequence in a constant temperature interval, calculate the difference between the preset temperature of the temperature subsequence corresponding to the reaction stage and each extreme value in the temperature subsequence; Based on the number of consecutive positive or negative data in all the difference values ​​corresponding to the temperature subsequence, a jump value of the extreme value in the temperature subsequence is obtained; The trend test algorithm is used to obtain the trend slope of the temperature data between any two adjacent extreme values ​​in the temperature subsequence, and the difference between any two adjacent extreme values ​​in the temperature subsequence is calculated, which is recorded as the extreme value difference; Calculate the product of the absolute value of the trend slope and the corresponding extreme value difference, and calculate the cumulative value of the product between all any two adjacent extreme values ​​in the temperature subsequence; The fluctuation value of the temperature data within any constant temperature interval is a fusion result of the jump value and the accumulated value.

[0007] In one embodiment, the jump value is the maximum value of the number of data.

[0008] In one embodiment, the smoothness is determined as follows: Obtain the fitting curve of all temperature data in any non-constant temperature interval; The average of the absolute values ​​of the curvature of the fitting curve at all temperature data is taken as the smoothness of the temperature data in any non-constant temperature interval.

[0009] In one embodiment, the abnormal factor is determined as follows: Arrange the fluctuation values ​​in all constant temperature intervals and the smoothness in all non-constant temperature intervals in each cycle in time sequence to form a temperature state characteristic sequence; Calculate the average of the absolute values ​​of the similarities of the temperature subsequences of all corresponding constant temperature intervals between each cycle and its adjacent previous cycle; Based on the distance between the temperature state characteristic sequence of each cycle and its adjacent previous cycle, and the average value, the abnormal factor of each cycle is obtained.

[0010] In one embodiment, the expression of the abnormal factor is: ; In the formula, represents the abnormal factor of the ith period; Represents the distance between the temperature state characteristic sequence of the ith cycle and its adjacent previous cycle; It represents the average value of the absolute value of the similarity of the temperature subsequences of all corresponding constant temperature intervals between the ith cycle and its adjacent previous cycle; γ represents a preset value greater than 0.

[0011] In one embodiment, the expression of the optimized proportional term parameter is: ; In the formula, Represents the optimized proportional term parameter; L represents the preset initial proportional term parameter; H represents the abnormal factor of the current cycle; Norm() represents the normalization function; T represents a preset value greater than 0.

[0012] In a second aspect, the present application also provides a constant temperature control system for a PCR detector, the system comprising: The temperature data acquisition module is used to collect the temperature data of any test solution at each acquisition time; The temperature data analysis module is used to take the time required for each cycle in the PCR detection process as a cycle, and based on the change of temperature data in each cycle, obtain each constant temperature interval and each non-constant temperature interval in each cycle; Based on the distribution of differences between all extreme values ​​of the temperature data in each constant temperature interval and the preset temperature, and the variation range of the temperature data in each constant temperature interval, determining the fluctuation value of the temperature data in each constant temperature interval; Determine the smoothness of the temperature data in each non-constant temperature interval based on the degree of smoothness of the temperature data change in each non-constant temperature interval; Based on the difference in the fluctuation value in the corresponding constant temperature interval between each cycle and its adjacent cycle and the difference in the smoothness in the corresponding non-constant temperature interval, and combined with the similarity of the temperature data in the corresponding constant temperature interval, determine the abnormal factor of each cycle; The constant temperature control module is used to determine the optimized proportional term parameters of the PID controller in combination with the abnormal factors of the current cycle and the preset initial proportional term parameters, so as to control the temperature of any test solution.

[0013] In the third aspect, an embodiment of the present application also provides a constant temperature control device applied to a PCR detector, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, wherein when the processor executes the computer program, the steps of any one of the above-mentioned constant temperature control methods applied to a PCR detector are implemented.

[0014] This application has at least the following beneficial effects: The present application obtains the constant temperature interval and non-constant temperature interval during the PCR detection process, so as to perform a detailed analysis of the temperature change characteristics during the PCR detection process; by analyzing the temperature jump and change amplitude in the constant temperature interval during the PCR detection, the fluctuation value of the temperature data in the constant temperature interval is determined, and the change of the actual temperature relative to the preset temperature is fully considered, thereby improving the accuracy of the temperature fluctuation analysis in the constant temperature interval; Furthermore, the instantaneous temperature change degree in the non-constant temperature interval, that is, the heating interval and the cooling interval, is analyzed to obtain the smoothness of the temperature in the non-constant temperature interval; the fluctuation value and smoothness can reflect the changing characteristics of the test solution temperature in different reaction stages, and then according to the differences in the test solution temperature change characteristics between different cycles in the PCR detection process and the similarity of the temperature, the abnormal factors of each cycle are determined, reflecting the degree to which the test solution temperature in each cycle in the PCR detection process is affected by the changes in the ambient temperature around the PCR detector, the heat conduction between adjacent PCR reaction tubes, and the uneven heating of the heating device; and the parameters of the PID controller are optimized according to the abnormal factors to improve the accuracy of the constant temperature control of the PCR detector. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present application or the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0016] Figure 1 A flowchart of the steps of a constant temperature control method applied to a PCR detector provided in one embodiment of the present application; Figure 2 It is a partial schematic diagram of a PCR detector; Figure 3 It is a schematic diagram of the acquisition process of the constant temperature interval and the non-constant temperature interval; Figure 4 Schematic diagram of the process of determining the fluctuation value; Figure 5 Schematic diagram of the process of determining abnormal factors; Figure 6This is a comparison chart of the test solution temperature before and after the proportional term parameter optimization. DETAILED DESCRIPTION

[0017] In the description of the embodiments of the present application, words such as "exemplary", "or", "for example" and the like are used to indicate examples, illustrations or descriptions. Any embodiment or design described as "exemplary" or "for example" in the embodiments of the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "exemplary", "or", "for example" and the like is intended to present related concepts in a concrete manner.

[0018] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art in the present application. The terms used in the present application are only for the purpose of describing specific embodiments and are not intended to limit the present application. It should be understood that, unless otherwise specified, " / " means or.

[0019] It should also be noted that the terms "first" and "second" in the present application are used to distinguish similar objects rather than to describe a specific order or sequence.

[0020] The specific scheme of the constant temperature control method, system and equipment for PCR detector provided by the present application is described in detail below with reference to the accompanying drawings.

[0021] See also Figure 1 , which shows a flow chart of the steps of a constant temperature control method applied to a PCR detector provided by an embodiment of the present application, the method comprising the following steps: Step S1, collecting temperature data of any test solution at each collection time.

[0022] Constant temperature control is one of the core functions of PCR detectors, which directly affects the efficiency of polymerase chain reaction and the accuracy of results. The working principle of PCR detectors is based on three basic reaction steps, namely denaturation, annealing and extension. These three steps are cyclically performed to achieve DNA amplification. Since the temperature required for each reaction step is different, different reaction modules are set in the PCR detector to perform these three steps respectively. A partial schematic diagram of PCR detector is shown below. Figure 2 shown.

[0023] In the figure, 1 and 4 are denaturation reaction modules, 2 and 5 are annealing reaction modules, 3 and 6 are extension reaction modules, 7 and 8 are fluorescence detection holes, and 9 is a heating base. A copper heating block and a temperature control circuit are installed under the heating base, and the temperature control circuit is used to stabilize each reaction module at a set temperature. A rotating platform that can accommodate multiple PCR reaction tubes is placed above the heating base.

[0024] The temperature data of the test solution in each PCR reaction tube is collected by a temperature sensor.

[0025] In this embodiment, the time interval for collecting temperature data is 0.01 seconds. The value of the time interval is preset manually and can be set by the implementer. This application does not impose any special restrictions.

[0026] Step S2, obtaining the constant temperature interval and the non-constant temperature interval in the PCR detection process, determining the fluctuation value of the temperature data in the constant temperature interval, determining the smoothness of the temperature data in the non-constant temperature interval, and then determining the abnormal factors of each cycle.

[0027] For any test solution, the PCR detection process needs to go through the three stages of denaturation, annealing and extension for multiple cycles, and temperature control is crucial in these three stages. For example, in the denaturation stage, if the temperature is too high, it will affect the activity of the polymerase and reduce the amplification efficiency of DNA. If the temperature is too low, it will lead to insufficient denaturation and may cause false negatives. In the annealing stage, if the temperature is too high or too low, it will affect the specificity of the PCR reaction. Therefore, it is necessary to ensure that the temperature of each stage in each cycle is in a stable state as much as possible. Therefore, the PCR detector must meet the requirements of high temperature control accuracy, small overshoot and short adjustment time.

[0028] Step 2.1, taking the time required for each cycle in the PCR detection process as a cycle, based on the change of temperature data in each cycle, obtaining each constant temperature interval and each non-constant temperature interval in each cycle.

[0029] The time required for each cycle in the PCR detection process is considered as a cycle, that is, the time required to go through the three stages of denaturation, annealing and extension is considered as a cycle, and the temperature data characteristics within each cycle are analyzed. In the DNA denaturation stage, the temperature needs to be constant at 95 degrees Celsius, the temperature in the annealing stage needs to be constant at 55 degrees Celsius, and the temperature in the extension stage needs to be constant at 72 degrees Celsius. Therefore, there are rapid mutations in the temperature data between different stages.

[0030] Taking any cycle as an example, the temperature data of all collected moments in the cycle are arranged in time sequence to form a temperature sequence. The mutation point detection algorithm is used to obtain the mutation point in the temperature sequence, and the mutation point is used as the segmentation point to segment the temperature sequence into 6 temperature subsequences. The reason for obtaining 6 temperature subsequences is that there is a temperature rise or fall adjustment process before each stage, and the cycle is divided into 6 time intervals.

[0031] In this embodiment, the Bernaola Galvan segmentation algorithm is used to obtain the mutation points in the temperature sequence. As other implementation methods, on the basis of being able to obtain the mutation points in the temperature sequence, the implementer can use other existing technologies to obtain the mutation points in the temperature sequence, such as the Bayesian mutation point detection algorithm, the Mann-Kendall mutation point detection algorithm, etc., and this application does not make any special restrictions.

[0032] Calculate the degree of discreteness of each temperature subsequence, arrange the discreteness from small to large, and take the time intervals of the temperature subsequences corresponding to the first three discreteness as the constant temperature intervals, and correspond to the denaturation, annealing and extension reaction stages in time sequence. Take the time intervals of the remaining temperature subsequences as non-constant temperature intervals, including cooling intervals and heating intervals. The schematic diagram of the process of obtaining constant temperature intervals and non-constant temperature intervals is shown in the figure. Figure 3 shown.

[0033] In this embodiment, the degree of dispersion is the standard deviation. As other implementation methods, based on the degree of uneven distribution of temperature data in the measurable temperature subsequence, the implementer can use other statistics for measurement, such as variance, coefficient of variation, etc., and this application does not impose any special restrictions.

[0034] Step 2.2, based on the distribution of the differences between all extreme values ​​of the temperature data in each constant temperature interval and the preset temperature, and the variation range of the temperature data in each constant temperature interval, determine the fluctuation value of the temperature data in each constant temperature interval.

[0035] Although the test solution is theoretically kept at a constant temperature within the constant temperature range, the temperature data within the constant temperature range fluctuates due to changes in the ambient temperature around the PCR detector, heat conduction between adjacent PCR reaction tubes, and uneven heating of the heating device. Specifically, it fluctuates randomly and irregularly above and below the preset temperature.

[0036] Taking any constant temperature interval in any cycle as an example, all extreme values ​​in the temperature subsequence of any constant temperature interval are obtained, including maximum and minimum values. If the temperature is more stable during the reaction process, the obtained extreme value is closer to the preset temperature, and it appears alternately above and below the preset temperature, for example, the mth extreme value is higher than the preset temperature, the m+1th extreme value is lower than the preset temperature, and the m+2th extreme value is higher than the preset temperature. If there are multiple consecutive extreme values ​​higher or lower than the preset temperature, the temperature instability in the corresponding time period of these extreme values ​​is greater.

[0037] Based on the above characteristics, the difference between the preset temperature of the reaction stage corresponding to the temperature subsequence and each extreme value in the temperature subsequence is calculated, and the maximum number of consecutive positive or negative data in all the differences corresponding to the temperature subsequence is counted as the jump value of the extreme value in the temperature subsequence. The larger the jump value, the greater the degree to which the temperature data in the temperature subsequence deviates from the preset temperature.

[0038] Since the trend change degree of the temperature data between adjacent extreme values ​​can reflect the change speed characteristics of the temperature data in a short period of time, a trend test algorithm is used to obtain the trend slope of the temperature data between any two adjacent extreme values ​​in the temperature subsequence, calculate the difference between any two adjacent extreme values ​​in the temperature subsequence, record it as the extreme value difference, calculate the product of the absolute value of the trend slope and the corresponding extreme value difference, and calculate the cumulative value of all the products of the temperature subsequence. The larger the cumulative value, the greater the change amplitude of the temperature data in any constant temperature interval.

[0039] In this embodiment, the Theil-Sen Median slope estimation method is used to obtain the trend slope. As other implementation methods, based on the obtainable trend slope, the implementer can use other existing technologies to obtain the trend slope, such as the Mann-Kendall trend test, Sen's Slope test, etc. This application does not impose any special restrictions.

[0040] Based on the above analysis, the fusion result of the jump value of the extreme value in the temperature subsequence and the accumulated value is taken as the fluctuation value of the temperature data in any constant temperature interval.

[0041] It should be understood that fusion refers to combining multiple independent variables in a way that enhances the overall effect, such as additive relationship, multiplicative relationship, etc. Implementers can define it according to actual conditions.

[0042] In this embodiment, the sum of the jump value of the extreme value in the temperature subsequence and the accumulated value is used as the fluctuation value of the temperature data in any constant temperature interval.

[0043] In another embodiment, the product of the jump value of the extreme value in the temperature subsequence and the accumulated value is used as the fluctuation value of the temperature data in any constant temperature interval.

[0044] It should be noted that the larger the fluctuation value, the greater the fluctuation degree of the temperature data in any constant temperature range, and the more unstable it is. The flow chart of determining the fluctuation value is as follows: Figure 4 shown.

[0045] Step 2.3, based on the degree of smoothness of the temperature data change in each non-constant temperature interval, determine the smoothness of the temperature data in each non-constant temperature interval.

[0046] The heating and cooling rates of the PCR detector have a direct impact on the results of DNA amplification. Inappropriate rates may lead to insufficient DNA melting or non-specific amplification. Therefore, the temperature data in the non-constant temperature range are analyzed as follows: For any non-constant temperature interval in any cycle, obtain a fitting curve of all temperature data in any non-constant temperature interval, and calculate the curvature of the fitting curve at each temperature data. During the heating and cooling process, the curvature can reflect the degree of sudden change in temperature, especially in the starting and ending parts. When the PCR detector is running well, the curvature of the fitting curve at all temperature data is generally small. The average of the absolute values ​​of the curvature of the fitting curve at all temperature data is taken as the smoothness of the temperature data in any non-constant temperature interval. The smaller the obtained smoothness, the more gentle the change of the temperature data in any non-constant temperature interval. Among them, the calculation of curvature is a well-known technology and will not be repeated in this application.

[0047] In this embodiment, the least squares method is used to obtain the fitting curve. As other implementation methods, on the basis of the available fitting curve, the implementer can use other existing technologies to obtain the fitting curve, such as local weighted regression, K-nearest neighbor regression, etc., and this application does not impose any special restrictions.

[0048] Step 2.4, based on the difference in the fluctuation value in the corresponding constant temperature interval between each cycle and its adjacent cycle and the difference in the smoothness in the corresponding non-constant temperature interval, and combined with the similarity of the temperature data in the corresponding constant temperature interval, determine the abnormal factor of each cycle.

[0049] During the constant temperature control process of the PCR detector, the temperature state of each cycle needs to be kept consistent as much as possible. To ensure normal DNA amplification. During the constant temperature control process, the temperature at different positions on the reaction module of the PCR detector may be different due to the uneven heating of the heating device, resulting in deviations in the DNA amplification results; and the degree of temperature fluctuation in the constant temperature interval and the rate of temperature rise and fall in the non-constant temperature interval will also be affected by the uneven heating of the device. Therefore, the fluctuation values ​​in all constant temperature intervals in any cycle and the smoothness in all non-constant temperature intervals are arranged in time sequence to form a temperature state characteristic sequence.

[0050] The distance between the characteristic sequence of the temperature state of any cycle and its adjacent previous cycle is calculated. The larger the distance, the greater the difference in the temperature state between any cycle and its adjacent previous cycle. Furthermore, in the temperature control process of the PCR detector, the stability of the temperature in the constant temperature interval is relatively more important. Therefore, the average value of the absolute value of the similarity of the temperature subsequences of all corresponding constant temperature intervals between any cycle and its adjacent previous cycle is calculated. The smaller the average value, the less similar the temperature in the constant temperature interval between the cycles.

[0051] In this embodiment, the distance between the temperature state feature sequences is the SBD (Shape-based Distance) distance. As other implementation methods, on the basis of the measurable distance between the temperature state feature sequences, the implementer may adopt other existing technologies to measure the distance between the temperature state feature sequences, such as Euclidean distance, Manhattan distance, etc., and this application does not impose any special restrictions.

[0052] In this embodiment, the similarity between temperature subsequences is the Spearman correlation coefficient. As other implementation methods, on the basis of the measurable similarity between temperature subsequences, the implementer may use other existing technologies to measure the similarity between temperature subsequences, such as the Pearson correlation coefficient, cosine similarity, etc., and this application does not impose any special restrictions.

[0053] Based on the above analysis, based on the distance between the temperature state characteristic sequence of any cycle and its adjacent previous cycle, and the average value, the abnormal factor of any cycle is determined, and the expression is: ; In the formula, represents the abnormal factor of the ith period; Represents the distance between the temperature state characteristic sequence of the ith cycle and its adjacent previous cycle; It represents the average value of the absolute value of the similarity of the temperature subsequences of all corresponding constant temperature intervals between the ith cycle and its adjacent previous cycle; γ represents a preset value greater than 0. The value of γ is preset manually and can be set by the implementer. In this embodiment, the value of γ is 0.1.

[0054] It should be noted that: the larger the abnormal factor of the ith cycle, the greater the difference in temperature state between the ith cycle and its adjacent previous cycle, and the smaller the similarity of the temperature data in the corresponding constant temperature range, the more significant the abnormal fluctuation of the test liquid temperature in the ith cycle caused by the uneven heating of the heating device and other reasons. The flow chart of determining the abnormal factor is as follows Figure 5 shown.

[0055] Step S3, combining the abnormal factor of the current cycle and the preset initial proportional term parameter, determining the optimized proportional term parameter of the PID controller to control the temperature of any test solution.

[0056] The larger the abnormal factor of the current cycle, the more it is necessary to improve the response speed of the temperature control of the PCR detector. Specifically, the temperature control rate can be increased by increasing the proportional term parameter of the PID controller. If the abnormal factor of the current cycle is smaller, it means that the temperature of the current cycle is relatively stable, and a smaller proportional term parameter can be used for temperature control to avoid overshoot.

[0057] According to the abnormal factors, the parameters of the PID controller are optimized, and the values ​​of the integral term parameters, differential term parameters and initial proportional term parameters of the PID controller are set manually. In the first two cycles of the detection process, the integral term, differential term and initial proportional term parameters are used for constant temperature control, and parameter optimization is performed from the third cycle. Based on the abnormal factors of the current cycle, the expression for determining the optimized proportional term parameters is: ; In the formula, represents the optimized proportional term parameter; L represents the preset initial proportional term parameter; H represents the abnormal factor of the current cycle; Norm() represents the normalization function; T represents a preset value greater than 0, which is used to adjust the size of the proportional term parameter. The value of T is preset manually and can be set by the implementer. In this embodiment, the value of T is 5.

[0058] In this embodiment, the values ​​of the integral item parameter, the differential item parameter and the initial proportional item parameter are 10, 1 and 1 respectively. The values ​​of the integral item parameter, the differential item parameter and the initial proportional item parameter are preset manually and can be set by the implementer. This application does not impose any special restrictions.

[0059] In this embodiment, the tanh activation function is used to normalize the abnormal factors of the current period. As other implementation methods, on the basis of being able to realize the normalization of the abnormal factors of the current period, the implementer may use other existing technologies to normalize the abnormal factors of the current period, such as sigmoid function, decimal calibration normalization, etc., and this application does not impose any special restrictions.

[0060] The temperature of the test solution is controlled by a PID controller using the integral parameter, differential parameter and optimized proportional parameter. Figure 6 As shown, the horizontal axis in the figure is time, the unit is seconds, and the vertical axis is temperature, the unit is The three lines in the figure represent the set temperature, the temperature of the test solution before the proportional item parameter optimization, and the temperature of the test solution after the proportional item parameter optimization, respectively. Among them, the set temperature is the preset temperature of the reaction stage. It can be seen from the figure that compared with before the proportional item parameter optimization, the temperature of the test solution after the proportional item parameter optimization is more stable, and the constant temperature control effect of the PCR detector is better.

[0061] Based on the same inventive concept as the above method, the embodiment of the present application also provides a constant temperature control system applied to a PCR detector, comprising: The temperature data acquisition module is used to collect the temperature data of any test solution at each acquisition time; The temperature data analysis module is used to take the time required for each cycle in the PCR detection process as a cycle, and based on the change of temperature data in each cycle, obtain each constant temperature interval and each non-constant temperature interval in each cycle; Based on the distribution of differences between all extreme values ​​of the temperature data in each constant temperature interval and the preset temperature, and the variation range of the temperature data in each constant temperature interval, determining the fluctuation value of the temperature data in each constant temperature interval; Determine the smoothness of the temperature data in each non-constant temperature interval based on the degree of smoothness of the temperature data change in each non-constant temperature interval; Based on the difference in the fluctuation value in the corresponding constant temperature interval between each cycle and its adjacent cycle and the difference in the smoothness in the corresponding non-constant temperature interval, and combined with the similarity of the temperature data in the corresponding constant temperature interval, determine the abnormal factor of each cycle; The constant temperature control module is used to determine the optimized proportional term parameters of the PID controller in combination with the abnormal factors of the current cycle and the preset initial proportional term parameters, so as to control the temperature of any test solution.

[0062] Based on the same inventive concept as the above method, an embodiment of the present application also provides a constant temperature control device applied to a PCR detector, including a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, the steps of any one of the above-mentioned constant temperature control methods applied to a PCR detector are implemented.

[0063] In summary, the present application obtains the constant temperature interval and non-constant temperature interval in the PCR detection process, so as to perform a detailed analysis of the temperature change characteristics in the PCR detection process; by analyzing the temperature jump and change amplitude in the constant temperature interval in the PCR detection, the fluctuation value of the temperature data in the constant temperature interval is determined, and the change of the actual temperature relative to the preset temperature is fully considered, thereby improving the accuracy of the temperature fluctuation analysis in the constant temperature interval;

[0064] Furthermore, the instantaneous temperature change degree in the non-constant temperature interval, that is, the heating interval and the cooling interval, is analyzed to obtain the smoothness of the temperature in the non-constant temperature interval; the fluctuation value and smoothness can reflect the changing characteristics of the test solution temperature in different reaction stages, and then according to the differences in the test solution temperature change characteristics between different cycles in the PCR detection process and the similarity of the temperature, the abnormal factors of each cycle are determined, reflecting the degree to which the test solution temperature in each cycle in the PCR detection process is affected by the changes in the ambient temperature around the PCR detector, the heat conduction between adjacent PCR reaction tubes, and the uneven heating of the heating device; and the parameters of the PID controller are optimized according to the abnormal factors to improve the accuracy of the constant temperature control of the PCR detector.

[0065] The flowchart and block diagram in the accompanying drawings show the possible architecture, function and operation of the system, method and computer program product according to the embodiment of the present disclosure. In this regard, each box in the flowchart or block diagram can represent a module, a program segment or a part of the code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. In some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two consecutive boxes can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, which can depend on the functions involved. In the description corresponding to the flowchart and the block diagram in the accompanying drawings, the operations or steps corresponding to different boxes can also occur in a different order from the order disclosed in the description, and sometimes there is no specific order between different operations or steps. For example, two consecutive operations or steps can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, which can depend on the functions involved. Each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, may be implemented by a dedicated hardware-based system that performs the specified functions or actions, or may be implemented by a combination of dedicated hardware and computer instructions.

[0066] It is obvious to those skilled in the art that the present application is not limited to the details of the above exemplary embodiments, and that the present application can be implemented in other specific forms without departing from the basic features of the present application. Therefore, no matter from which point of view, the above embodiments of the present application should be regarded as exemplary and non-restrictive.

Claims

1. A constant temperature control method for a PCR detector, characterized in that: The method comprises the following steps: Collect the temperature data of any test solution at each collection time; The time required for each cycle in the PCR detection process is taken as a cycle, and based on the change of temperature data in each cycle, each constant temperature interval and each non-constant temperature interval in each cycle is obtained; Based on the distribution of differences between all extreme values ​​of the temperature data in each constant temperature interval and the preset temperature, and the variation range of the temperature data in each constant temperature interval, determining the fluctuation value of the temperature data in each constant temperature interval; Determine the smoothness of the temperature data in each non-constant temperature interval based on the degree of smoothness of the temperature data change in each non-constant temperature interval; Based on the difference in the fluctuation value in the corresponding constant temperature interval between each cycle and its adjacent cycle and the difference in the smoothness in the corresponding non-constant temperature interval, and combined with the similarity of the temperature data in the corresponding constant temperature interval, determine the abnormal factor of each cycle; Combined with the abnormal factors of the current cycle and the preset initial proportional term parameters, the optimized proportional term parameters of the PID controller are determined to control the temperature of any test solution.

2. The constant temperature control method for a PCR detector according to claim 1, characterized in that: The acquisition process of each constant temperature interval and each non-constant temperature interval is as follows: Arrange the temperature data of all collected moments in any period in time sequence to form a temperature sequence; The mutation point detection algorithm is used to obtain the mutation points in the temperature sequence, and the temperature sequence is divided into temperature subsequences according to the mutation points; Arrange all the temperature subsequences from small to large in terms of their discreteness, and use the time intervals of the temperature subsequences corresponding to the first three discretenesses as the constant temperature intervals, which correspond to the denaturation, annealing and extension reaction stages respectively in time sequence; The time intervals where the remaining temperature subsequences are located are regarded as non-constant temperature intervals.

3. The constant temperature control method for PCR detector according to claim 2, characterized in that: The process of determining the fluctuation value is as follows: For any temperature subsequence in a constant temperature interval, calculate the difference between the preset temperature of the temperature subsequence corresponding to the reaction stage and each extreme value in the temperature subsequence; Based on the number of consecutive positive or negative data in all the difference values ​​corresponding to the temperature subsequence, a jump value of the extreme value in the temperature subsequence is obtained; The trend test algorithm is used to obtain the trend slope of the temperature data between any two adjacent extreme values ​​in the temperature subsequence, and the difference between any two adjacent extreme values ​​in the temperature subsequence is calculated, which is recorded as the extreme value difference; Calculate the product of the absolute value of the trend slope and the corresponding extreme value difference, and calculate the cumulative value of the product between all any two adjacent extreme values ​​in the temperature subsequence; The fluctuation value of the temperature data within any constant temperature interval is a fusion result of the jump value and the accumulated value.

4. The constant temperature control method for PCR detector according to claim 3, characterized in that: The jump value is the maximum value of the number of data.

5. The constant temperature control method for PCR detector according to claim 1, characterized in that: The smoothness is determined as follows: Obtain the fitting curve of all temperature data in any non-constant temperature interval; The average of the absolute values ​​of the curvature of the fitting curve at all temperature data is taken as the smoothness of the temperature data in any non-constant temperature interval.

6. The constant temperature control method for PCR detector according to claim 2, characterized in that: The determination process of the abnormal factor is: Arrange the fluctuation values ​​in all constant temperature intervals and the smoothness in all non-constant temperature intervals in each cycle in time sequence to form a temperature state characteristic sequence; Calculate the average of the absolute values ​​of the similarities of the temperature subsequences of all corresponding constant temperature intervals between each cycle and its adjacent previous cycle; Based on the distance between the temperature state characteristic sequence of each cycle and its adjacent previous cycle, and the average value, the abnormal factor of each cycle is obtained.

7. The constant temperature control method for PCR detector according to claim 6, characterized in that: The expression of the abnormal factor is: ; In the formula, represents the abnormal factor of the ith period; Represents the distance between the temperature state characteristic sequence of the ith cycle and its adjacent previous cycle; It represents the average value of the absolute value of the similarity of the temperature subsequences of all corresponding constant temperature intervals between the ith cycle and its adjacent previous cycle; γ represents a preset value greater than 0.

8. The constant temperature control method for PCR detector according to claim 1, characterized in that: The expression of the optimized proportional term parameter is: ; In the formula, Represents the optimized proportional term parameter; L represents the preset initial proportional term parameter; H represents the abnormal factor of the current cycle; Norm() represents the normalization function; T represents a preset value greater than 0.

9. A constant temperature control system applied to a PCR detector, characterized in that: The system comprises: The temperature data acquisition module is used to collect the temperature data of any test solution at each acquisition time; The temperature data analysis module is used to take the time required for each cycle in the PCR detection process as a cycle, and based on the change of temperature data in each cycle, obtain each constant temperature interval and each non-constant temperature interval in each cycle; Based on the distribution of differences between all extreme values ​​of the temperature data in each constant temperature interval and the preset temperature, and the variation range of the temperature data in each constant temperature interval, determining the fluctuation value of the temperature data in each constant temperature interval; Determine the smoothness of the temperature data in each non-constant temperature interval based on the degree of smoothness of the temperature data change in each non-constant temperature interval; Based on the difference in the fluctuation value in the corresponding constant temperature interval between each cycle and its adjacent cycle and the difference in the smoothness in the corresponding non-constant temperature interval, and combined with the similarity of the temperature data in the corresponding constant temperature interval, determine the abnormal factor of each cycle; The constant temperature control module is used to determine the optimized proportional term parameters of the PID controller in combination with the abnormal factors of the current cycle and the preset initial proportional term parameters, so as to control the temperature of any test solution.

10. A constant temperature control device for a PCR detector, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that: When the processor executes the computer program, the steps of the constant temperature control method applied to a PCR detector as described in any one of claims 1 to 8 are implemented.

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