Biological detection method and system based on channel response regulation and control

Abstract

The invention relates to the technical field of biological detection, in particular to a biological detection method and system based on channel response regulation, and the method comprises the following steps: based on a detection channel, analyzing sample signal intensity and background noise, judging a relation between a standard deviation and a fluctuation interval, correcting a threshold parameter, and screening a gain ratio; and performing interval judgment and risk classification in combination with marker signals and environmental parameter weighted normalization to obtain risk level distribution data. According to the method, the real-time fluctuation characteristics of the detection signals are accurately analyzed, the signal intensity and the noise dynamic characteristics are combined, channel parameters are corrected step by step, multi-stage gain screening is executed, and environment acquisition elements and sample correlation factors are introduced to carry out multi-dimensional weight distribution; factors such as signal fluctuation, environment change and sample sources are integrated to realize whole-course data fusion processing, the adaptive distinguishing capability of abnormal signals and boundary states is improved, and the automatic identification level of biomarker signal differences in a complex sample environment is effectively enhanced.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of biological detection, in particular to a biological detection method and system based on channel response regulation. BACKGROUND

[0002] Biological detection mainly involves the detection and analysis of various components such as proteins, nucleic acids, metabolites and cells in biological samples, and through physical, chemical, immune, molecular and other methods, the state of the sample, disease markers or environmental factors are identified and quantified, which is widely used in medical diagnosis, food safety, environmental monitoring and life science research, etc. Among them, the traditional biological detection method refers to relying on optical color development, colorimetry, chemiluminescence, enzyme-linked reaction, nucleic acid hybridization, electrochemical sensing, etc. Through standard curve comparison or manual experience analysis, the target substance in the sample is quantitatively or qualitatively judged, and instruments and equipment such as colorimeters, enzyme markers, fluorescence detectors and electrochemical workstations are usually used to complete the detection and judgment of the target analyte.

[0003] Traditional detection methods mostly use inherent signal processing procedures, and the parameter setting and judgment mode lacks dynamic correction ability. When facing signal fluctuations or environmental changes, it is easy to have insufficient response to abnormal signals. The detection result depends on manual parameter setting, and it is difficult to cope with diversified detection scenarios. Environmental noise changes lead to blurred signal discrimination boundaries, and the detection discrimination of some samples decreases. The weak response of target molecules in a complex background is difficult to accurately identify, resulting in unclear risk stratification of different types of samples, and it is difficult to balance high-throughput processing and stable output under heterogeneous sample conditions. SUMMARY

[0004] In order to solve the technical problems existing in the prior art, the embodiments of the present application provide a biological detection method and system based on channel response regulation. The technical solution is as follows: On the one hand, a biological detection method based on channel response regulation is provided, comprising the following steps: S1: Based on the detection channel, analyze the real-time signal intensity of the current batch of samples, collect the background noise baseline, measure the response stability of the target molecule, compare the standard deviation of each channel signal intensity with the background noise fluctuation range, identify the abnormal channel, and obtain the abnormal channel distribution characteristics; S2: Based on the abnormal channel distribution characteristics, judge the standard deviation distribution of the high-throughput cell detection platform channel, compare the upper and lower boundaries of the standard deviation and the historical fluctuation interval, adjust the response threshold parameters for the channels exceeding the upper and lower boundaries respectively, and obtain the channel threshold adjustment coefficient; S3: Based on the channel threshold adjustment coefficient, the real-time signal intensity of each detection channel is compared one by one, the signal below the threshold parameter is analyzed for its state, the gain is increased step by step, the noise amplitude and signal intensity are recorded, the gain change ratio is calculated, and the signal enhancement amplitude distribution is obtained. S4: Based on the signal enhancement amplitude distribution, the channel threshold adjustment coefficient and the nucleic acid marker signal of the high-throughput protein detection unit are combined, the collection position, collection time length, environmental temperature and humidity and marker signal are weighted and normalized, the role of the parameters in the weight distribution is judged, and the marker weighted characteristic group is obtained.

[0005] On the other hand, the abnormal channel distribution characteristics include abnormal channel number, abnormal distribution range, and abnormal type, the channel threshold adjustment coefficient includes adjustment amount, correction direction, and channel grouping identifier, the signal enhancement amplitude distribution includes enhancement amplitude category, amplitude distribution interval, and signal change level, and the marker weighted characteristic group includes marker distribution weight, weight normalization coefficient, and parameter group classification.

[0006] On the other hand, the acquisition step of the abnormal channel distribution characteristics is specifically: S101: Based on the detection channel, the real-time signal intensity collected in the current batch is analyzed with the background noise baseline, the standard deviation of each channel signal intensity is compared with the corresponding noise fluctuation interval, the paired information of standard deviation and noise fluctuation is constructed channel by channel, and the channel fluctuation interval group is obtained. S102: Based on the channel fluctuation interval group, the relationship between the standard deviation of each channel and the noise fluctuation interval is judged, the channels higher than the fluctuation interval are divided into abnormal channels, the channels lower than the fluctuation interval are divided into signal stable channels, and the remaining channels are classified as regular type, and the channel response distribution label is obtained. S103: According to the channel response distribution label, the corresponding number and state of each type of channel are summarized, the associated attributes of the abnormal type channel are gathered through sorting and grouping, and the abnormal channel distribution characteristics are obtained.

[0007] On the other hand, the acquisition step of the channel threshold adjustment coefficient is specifically: S201: Based on the abnormal channel distribution characteristics, the signal intensity standard deviation distribution of each detection channel of the high-throughput cell detection platform is compared, the difference of signal fluctuation range between channels is judged, the channels with fluctuation trend deviation are corresponded with channel identifier, and the standard deviation difference characteristics are obtained. S202: Based on the standard deviation difference characteristics, the distribution position of the standard deviation of each channel in the channel set is compared, whether it is located in the boundary region of the fluctuation sequence is judged, and the channels in the boundary are respectively identified as difference response type, and the boundary response category group is obtained. S203: Based on the boundary response category group, the response threshold parameter configuration of each response type channel is optimized respectively, the adjustment results of the channels are integrated by recording the optimization direction and parameter correction content, and a channel threshold adjustment coefficient is obtained.

[0008] On the other hand, the acquisition step of the signal enhancement amplitude distribution is specifically: S301: Based on the channel threshold adjustment coefficient, the real-time signal intensity of each detection channel is compared with the corresponding threshold configuration, the channel set that needs to perform gain adjustment is located by screening the channels with signal intensity lower than the threshold range, and a gain adjustment identification group is established; S302: Based on the gain adjustment identification group, the signal gain is increased in a step-by-step manner for each channel, the signal intensity change amplitude and the corresponding noise change amplitude are recorded synchronously after each gain adjustment, the corresponding gain state data is constructed according to the time sequence structure, and a gain change trend set is obtained; S303: According to the gain change trend set, the signal change ratio of each channel before and after continuous gain change is calculated, and the ratio is linked with the change direction of the noise amplitude to perform screening, the channel identification that meets the screening condition is combined with the signal change range to obtain the signal enhancement amplitude distribution.

[0009] On the other hand, the acquisition step of the marker weighting characteristic group is specifically: S401: Based on the signal enhancement amplitude distribution, the channel threshold adjustment coefficient is numbered and paired with the nucleic acid marker signal collected by each detection channel, the collection position, environmental temperature and environmental humidity parameters are arranged, the multi-parameter combined data of each detection channel is constructed, and an environmental parameter set is obtained; S402: Based on the environmental parameter set, the distribution of each parameter in the detection channel is compared, the participation proportion of the collection position, environmental temperature, environmental humidity and marker signal in the combined data is judged, and the weight allocation structure is established by statistically weighting the difference parameters; S403: According to the weight allocation structure, the environmental parameters and the marker signal are normalized, the influence of each parameter is superimposed according to the weighted allocation principle, and the effect of the weight allocation result in the channel is judged to obtain the marker weighting characteristic group.

[0010] On the other hand, the method further comprises: S5: Based on the marker weighting characteristic group, the marker signal is combined with the weight configuration proportion respectively, the combination result is interval judged with the upper and lower limits of the risk interval, the risk grade distribution data is obtained by classifying and arranging according to the interval judgment category. The risk grade distribution data includes grade label, judgment interval and classification index.

[0011] In another aspect, the risk level distribution data acquisition step is specifically: S501: Based on the marker weighted characteristic group, the ratio of the marker signal in each channel to the corresponding weight configuration is calculated, the distribution of the ratio combination data in the channel is analyzed, and the interval position corresponding to each channel is judged according to the upper and lower boundaries of the risk interval, and the signal risk interval category is obtained; S502: Based on the signal risk interval category, the classification and arrangement are carried out according to the interval judgment result, the channel number and signal combination result corresponding to each category are recorded, and the distribution of each category is output in a structured data, and the risk level distribution data is obtained.

[0012] In another aspect, the detection channel refers to the path in the biological detection device for independently collecting and outputting the detection signal. Each detection channel can correspond to the signal collection of one sample or a group of samples. The abnormal channel refers to the detection signal showing abnormal fluctuation or noise characteristics. The collection position refers to the geographical position information or the spatial position information on the sample plate.

[0013] In another aspect, a biological detection system based on channel response regulation is provided. The system is applied to a biological detection method based on channel response regulation, and includes: The signal fluctuation identification module analyzes the real-time signal intensity of the current batch of samples based on the detection channel, determines the response stability of the target molecule in combination with the collected background noise baseline, compares the standard deviation of the signal intensity of each channel with the corresponding background noise fluctuation range, identifies the abnormal channel by judging the relationship between the standard deviation and the fluctuation interval, and obtains the abnormal channel distribution characteristics; The threshold parameter correction module judges the current standard deviation distribution of the high-throughput cell detection platform channel based on the abnormal channel distribution characteristics, compares the corresponding relationship between the standard deviation and the upper and lower boundaries of the historical fluctuation interval, directly adjusts the response threshold parameter for the channels higher than the upper boundary, and directly reduces the response threshold parameter for the channels lower than the lower boundary. After the adjustment is completed, the channel parameter configuration is recorded, and the channel threshold adjustment coefficient is obtained; The gain dynamic screening module compares each detection channel real-time signal intensity based on the channel threshold adjustment coefficient, analyzes the current state of the signal lower than the threshold parameter, and increases the gain in a step-by-step manner. The noise amplitude and signal intensity are recorded after each gain adjustment, the ratio before and after the gain change is calculated, the channels with a ratio meeting the conditions are screened, and the signal enhancement amplitude distribution is obtained. The multi-parameter weight normalization module executes weighting and normalization on the collection position, collection time length, environmental temperature and humidity, and marker signal based on the signal enhancement amplitude distribution, the channel threshold adjustment coefficient, and the nucleic acid marker signal of the high-throughput protein detection unit, judges the role of each parameter in weight distribution, and obtains the marker weighted characteristic group. The risk grade discrimination module proportionally combines the marker signals with the weight configuration based on the marker weighted characteristic group, interval judges the combination result with the upper and lower limits of the risk interval, classifies and arranges according to the category of the interval judgment, and outputs the corresponding classification to obtain risk grade distribution data.

[0014] The technical scheme provided by the embodiment of the application has at least the following beneficial effects: Through the accurate analysis of the real-time fluctuation characteristics of the detection signal, the signal intensity and the noise dynamic characteristics are combined, the channel parameters are corrected step by step, and multi-stage gain screening is performed, the environmental collection elements and sample correlation factors are introduced for multi-dimensional weight distribution, the factors such as signal fluctuation, environmental change and sample source are comprehensively considered for full-process data fusion processing, the adaptive distinguishing ability for abnormal signals and boundary states is improved, the automatic recognition level for biomarker signal differences in a complex sample environment is effectively enhanced, the data processing flow further expands the hierarchical and detailed degree of the risk grade, and accurate grading judgment of biological detection in diversified scenes is realized. BRIEF DESCRIPTION OF DRAWINGS

[0015] In order to more clearly illustrate the technical scheme in the embodiments of the application, the drawings needed in the embodiment description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the application, and other drawings can be obtained by those skilled in the art without creative labor.

[0016] Figure 1 It is the main step flowchart of the application; Figure 2 It is the step flowchart of S1 of the application; Figure 3 It is the step flowchart of S2 of the application; Figure 4 It is the step flowchart of S3 of the application; Figure 5 It is the step flowchart of S4 of the application; Figure 6 It is the step flowchart of S5 of the application; Figure 7 It is the system block diagram of the application. DETAILED DESCRIPTION

[0017] The technical scheme in the application will be described below with reference to the drawings.

[0018] In the embodiments of the present application, the words such as "exemplary", "for example", etc. are used to represent an example, illustration, or description. Any embodiment or design scheme described as "exemplary" in the present application should not be interpreted as more preferred or more advantageous than other embodiments or design schemes. Rather, the word "exemplary" is intended to present the concept in a specific manner. In addition, in the embodiments of the present application, the meaning expressed by "and / or" can be both, or can be one of the two.

[0019] In the embodiments of the present application, "image" and "picture" can be used interchangeably at times, and it should be pointed out that the meanings expressed are consistent when the distinction is not emphasized. "Of", "corresponding" and "relevant" can be used interchangeably at times, and it should be pointed out that the meanings expressed are consistent when the distinction is not emphasized.

[0020] In the embodiments of the present application, sometimes the subscript such as W1 can be written in the form of non-subscript such as W1, and the meanings expressed are consistent when the distinction is not emphasized.

[0021] In order to make the technical problems, technical schemes and advantages to be solved by the present application more clear, specific embodiments will be described in detail below with reference to the drawings.

[0022] The embodiments of the present application provide a biological detection method based on channel response regulation, as shown in the following steps: Figure 1 The embodiments of the present application provide a biological detection method based on channel response regulation, as shown in the following steps: S1: Based on the detection channel, the real-time signal intensity of the current batch of samples is analyzed, the background noise baseline collected is combined, the target molecule response stability is determined, the standard deviation of the signal intensity of each channel is compared with the corresponding background noise fluctuation range, the relationship between the standard deviation and the fluctuation interval is judged, the abnormal channel is identified, and the abnormal channel distribution characteristics are obtained; S2: Based on the abnormal channel distribution characteristics, the current standard deviation distribution of the high-throughput cell detection platform channel is judged, the corresponding relationship between the standard deviation and the upper and lower boundaries of the historical fluctuation interval is compared, the response threshold parameter is directly adjusted for the channel higher than the upper boundary, the response threshold parameter is directly reduced for the channel lower than the lower boundary, the channel parameter configuration is recorded after the adjustment is completed, and the channel threshold adjustment coefficient is obtained; S3: Based on the channel threshold adjustment coefficient, the real-time signal intensity of each detection channel is compared item by item, the current state of the signal lower than the threshold parameter is analyzed, and the gain is increased in a step-by-step manner. The noise amplitude and signal intensity are recorded after each gain adjustment, the ratio before and after the gain change is calculated, the channels with the ratio meeting the conditions are screened, and the signal enhancement amplitude distribution is obtained. S4: Based on the signal enhancement amplitude distribution, the nucleic acid marker signal of the high flux protein detection unit is combined with the channel threshold adjustment coefficient to perform weighted and normalized on the collection position, collection time length, environmental temperature and humidity, and marker signal, judge the role of each parameter in weight distribution, and obtain the marker weighted characteristic group; S5: Based on the marker weighted characteristic group, the marker signal is proportionally combined with the weight configuration, the combined result is interval judged with the upper and lower limits of the risk interval, classified and arranged according to the category of interval judgment, and the corresponding classification is output, and the risk grade distribution data is obtained.

[0023] The abnormal channel distribution characteristics include abnormal channel number, abnormal distribution range, and abnormal type. The channel threshold adjustment coefficient includes adjustment amount, correction direction, and channel grouping identifier. The signal enhancement amplitude distribution includes enhancement amplitude category, amplitude distribution interval, and signal change level. The marker weighted characteristic group includes marker distribution weight, weight normalization coefficient, and parameter group classification. The risk grade distribution data includes grade label, judgment interval, and classification index.

[0024] In S1, the detection channel refers to the path in the biological detection equipment for independently collecting and outputting the detection signal. Each detection channel can correspond to the signal collection of a sample or a group of samples. Batch sample refers to the sample set collected and detected under the same detection round or the same processing process. Real-time signal intensity refers to the intensity value of the instant electronic signal or optical signal generated by the equipment on the sample target molecule during the detection process. Background noise baseline refers to the baseline noise value recorded by the detection system when there is no effective target molecule signal input, reflecting the inherent noise level of the system. Target molecule refers to the biological molecule (such as specific protein, nucleic acid, antibody, etc.) designated as the analysis object in this round of detection. Signal intensity standard deviation refers to the standard deviation of the detection signal intensity of the same channel in the batch sample, used to measure the fluctuation of the detection signal. Background noise fluctuation range refers to the determination of the fluctuation amplitude of the background noise baseline within a period of time, reflecting the noise level change interval. Fluctuation interval refers to the upper and lower limit range set for determining whether the signal intensity fluctuation is reasonable. Abnormal channel refers to the detection channel whose detection signal shows abnormal fluctuation or noise characteristics, which needs further attention or processing.

[0025] In S2, the high-throughput cell detection platform refers to an automated cell analysis system with the ability to detect a large number of samples simultaneously, which is widely used in efficient detection of molecular, protein or cell signals; the historical fluctuation interval refers to the upper and lower limit ranges of the regular fluctuation of a certain parameter (such as signal standard deviation) statistically obtained from long-term accumulated data, which is used to determine whether the current parameter is abnormal; the corresponding relationship refers to the relative position relationship (such as higher, lower or within the interval) between the current parameter and the historical interval; the upper limit and the lower limit refer to the maximum (upper limit) and minimum (lower limit) boundaries of the historical fluctuation interval; the response threshold parameter refers to the threshold set for the detection signal response (for example, only when the signal is higher than the threshold is the detection considered positive), which is used for signal screening and judgment; the channel parameter configuration refers to the relevant parameter configuration file or table set and recorded for each detection channel, which is used for subsequent signal processing and tracing.

[0026] In S3, analyzing its current state refers to evaluating the state of the detection signal below the threshold parameter, including signal amplitude, stability and its relationship with noise; the step-by-step incremental gain refers to gradually increasing the signal amplification coefficient of the detection channel according to the set step, enhancing the weak signal for subsequent detection; the gain change ratio refers to the change in the ratio of signal intensity to noise amplitude before and after the gain is increased, which is used to evaluate the signal quality after gain adjustment; the channel with a ratio meeting the condition refers to the detection channel that meets the requirements after signal amplification by comparing the ratio with the set standard.

[0027] In S4, the high-throughput protein detection unit refers to an automated device or module that can realize efficient parallel detection of protein molecules, which can realize synchronous detection of multiple markers; the nucleic acid marker signal refers to the signal collected for specific nucleic acid molecules (such as DNA, RNA fragments) in the detection sample, which is used for biological feature analysis or disease determination; the collection location refers to the geographical location information or spatial location information on the sample plate; the collection duration refers to the time length experienced from the start of sample collection to the completion of signal reading; the role in weight allocation refers to the weight or influence of environmental parameters, marker signals and other factors in the comprehensive risk judgment, which is usually realized through a weighting coefficient.

[0028] In S5, the weight configuration refers to the weight allocation scheme of each detection parameter or signal factor in the comprehensive judgment, which is generally determined by normalization calculation and weight factor; the proportional combination refers to linear weighting or weighted superposition of each marker signal according to the weight configuration to obtain a comprehensive score or signal value; the risk interval refers to the risk judgment interval set according to historical data or standard specifications, which is used for classification of the result; the interval judgment refers to comparing the result after proportional combination with the upper and lower limits of the risk interval to determine which risk category the result belongs to; the category of interval judgment refers to the risk level category (such as safe, warning, dangerous, etc.) divided based on the comparison result.

[0029] For example,Figure 2 As shown, the step of acquiring the abnormal channel distribution characteristics specifically comprises: S101: Based on the detection channel, analyze the real-time signal intensity and background noise baseline collected under the current batch, compare the standard deviation of each channel signal intensity with the corresponding noise fluctuation interval, and construct the paired information of standard deviation and noise fluctuation for each channel, to obtain the channel fluctuation interval group; Extract the real-time signal intensity data recorded by each channel in the current batch from the device. The signal intensity data is a sequence of detection values collected continuously within a certain set time after the injection of target molecules, usually electrical or optical signal values, for example, collected once per second, and a total of 60 seconds, i.e. 60 detection points. At the same time, the background noise baseline data recorded by the channel under the condition of no sample input or blank state is obtained. The background noise baseline needs to be collected when the sample is not loaded, and periodic sampling is also used to form a noise sequence. Next, the signal fluctuation amplitude of each channel signal intensity data is calculated. The fluctuation amplitude is the degree of dispersion of all collected values in the channel, which is usually calculated using the standard deviation method. Then, the noise fluctuation range of the background noise data is calculated. The method is to extract the difference between the maximum value and the minimum value, and record the value for measuring the inherent noise fluctuation condition of the channel. For example, the signal fluctuation value of channel 1 is 2.3, the noise fluctuation is 1.1, the signal fluctuation value of channel 2 is 1.0 and 0.8, and the signal fluctuation value of channel 3 is 0.6 and 0.7. The signal fluctuation value and noise fluctuation value of each channel are combined to construct the fluctuation pairing information of the channel. In the detection system, there are usually tens to hundreds of channels, for example, 96 channels will generate 96 corresponding data groups. The pairing information is recorded and sorted as a fluctuation interval group, which is used for further judgment of the state of each channel.

[0030] S102: Based on the channel fluctuation interval group, judge the relationship between the standard deviation and the noise fluctuation interval of each channel, divide the channels higher than the fluctuation interval into abnormal channels, divide the channels lower than the fluctuation interval into signal stable channels, and the remaining channels are classified as conventional type, to obtain the channel response distribution label; Read the signal fluctuation value and the corresponding noise fluctuation value of each channel in turn, compare the values one by one, set a fixed judgment amplitude value, judge whether the difference between the signal fluctuation value and the noise fluctuation value exceeds the judgment amplitude threshold value, when the signal fluctuation value of a certain channel is obviously greater than the noise fluctuation value plus the judgment threshold value, the channel is divided into an abnormal type channel, when the signal fluctuation value is obviously less than the noise fluctuation value minus the judgment threshold value, it is divided into a signal stable type channel, if the fluctuation value is between the upper and lower limits, it is divided into a regular type channel, the judgment amplitude threshold value can be set to 0.2 in actual operation, combined with the example in the previous step, channel 1 signal fluctuation is 2.3, noise fluctuation is 1.1, difference is 1.2, which is higher than 0.2, that is, it is judged as an abnormal channel, channel 2 signal fluctuation is 1.0, noise fluctuation is 0.8, difference is 0.2, which is in the critical interval, it is judged as a regular channel, channel 3 signal fluctuation is 0.6, noise fluctuation is 0.7, difference is -0.1, which is less than the set lower limit, it is judged as a signal stable channel, all channels perform numerical comparison and labeling in this way, the label content is represented by letters, for example, A represents an abnormal channel, B represents a stable channel, and C represents a regular channel, finally, the channel number is matched with the response classification to form a complete channel response distribution label for subsequent summary analysis.

[0031] S103: According to the channel response distribution label, the numbers and states of various channels are summarized, the associated attributes of the abnormal type channels are gathered through sorting and grouping, and the abnormal channel distribution characteristics are obtained; All channels are classified and arranged, the channel number list corresponding to each type of label is extracted, the channel numbers marked as an abnormal type are extracted and collected, and the related attribute information of the channel under the current batch is summarized one by one. The attribute information includes the number of signal abnormalities of the channel in the batch, the number of detection periods of abnormal duration, the difference between the signal fluctuation value and the noise fluctuation value of the channel, and the offset threshold value used in the judgment process. By integrating the information into structured data, the abnormal feature information set of each abnormal channel can be formed, for example, channel 5 appears 3 times, each time lasts for 6 seconds, the fluctuation difference is 1.4, and the judgment offset is 0.2. All abnormal channel information is sorted according to the channel number, and then grouped according to the signal fluctuation performance, for example, the fluctuation difference greater than 1.0 is the first group, the fluctuation difference between 0.5 and 1.0 is the second group, and the fluctuation difference less than 0.5 is the third group. Finally, the abnormal channel distribution characteristics are obtained, including the number, abnormal attributes and grouping classification information, which provides input basis for further signal processing and threshold adjustment.

[0032] As shown in Figure 3 The acquisition step of the channel threshold adjustment coefficient is specifically: S201: Based on the abnormal channel distribution characteristics, compare the signal intensity standard deviation distribution of each detection channel of the high-throughput cell detection platform, judge the difference of signal fluctuation range among channels, correspond the channel with channel identification that appears fluctuation trend deviation, and obtain the standard deviation difference characteristics; The channel number obtained in the previous stage and its corresponding abnormal fluctuation information are extracted, and the real-time signal intensity standard deviation distribution data of all channels in the current batch are matched in turn. For each channel, read the signal intensity dispersion data in the complete detection period, arrange all channel standard deviation values in ascending order of number, mark the number of the corresponding abnormal channel in the sequence, then locate the standard deviation value of each abnormal channel and its relative position in the full channel sequence, and compare the standard deviation distribution values of the surrounding channels, calculate the offset amplitude of the adjacent channels, if the standard deviation value of a certain abnormal channel appears large jump compared with the front and rear channels, define it as fluctuation trend deviation channel, the judgment standard of the offset is more than 20% of the average value of the standard deviation of the front and rear channels, that is, more than 0.44, for example, the standard deviation of channel 25 is 3.2, the standard deviation of adjacent channels 24 and 26 is 2.1 and 2.3 respectively, the average value is 2.2, the offset value is 1.0, which exceeds the threshold value 0.44, so channel 25 is judged as fluctuation trend deviation channel, and so on. Traverse all abnormal channels, record the number, offset direction (greater or less than the average value of adjacent channels), offset amplitude value in each deviation channel, and combine the channel number with the deviation state to form the standard deviation difference characteristics.

[0033] S202: Based on the standard deviation difference characteristics, compare the distribution position of the standard deviation of each channel in the channel set, judge whether it is located in the boundary area of the fluctuation sequence, and identify the channel in the boundary as difference response type respectively to obtain the boundary response class group. The standard deviation values of all channels are extracted and sorted from low to high to construct a standard deviation sorting sequence, then the position number of each channel in the sorting sequence is read in turn, and whether its position is at the edge is judged according to the set boundary range threshold, for example, the sorting sequence is divided into three sections according to the percentile, and the channels in the first 10% position are low boundary channels, and the channels in the last 10% position are high boundary channels, and the channels outside the two ends are considered as intermediate region channels, for example, in 96 channels, the channels ranked in the first 9 and the last 9 are boundary channels, and the channels in these two regions are again matched with the standard deviation difference characteristics, if they have been marked as offset channels, they are classified as difference response type channels, and their numbers and boundary types are recorded, for example, channel 7 is at the 6th position, and the channel standard deviation is 0.4, which is judged as a low boundary response channel, and channel 91 is at the 92nd position, and the standard deviation is 3.8, which is judged as a high boundary response channel, all channels meeting the judgment condition are summarized, and the number, sorting position, standard deviation value and boundary type label of the channel are output, and finally a boundary response category group is formed.

[0034] S203: Based on the boundary response category group, the response threshold parameter configuration of each response type channel is optimized respectively, the adjustment result of the channel is integrated by recording the optimization direction and parameter correction content, and the channel threshold adjustment coefficient is obtained; The high boundary and low boundary categories are summarized respectively, the number, current standard deviation value and boundary label of each channel are read in turn, then the current response threshold value of the channel in the device parameter configuration is called, and it is judged whether adjustment is needed, the default adjustment direction of the high boundary channel is to increase the threshold value, and the default adjustment direction of the low boundary channel is to decrease the threshold value, the adjustment amplitude is calculated by referring to the proportion of the current standard deviation value of the channel deviating from the average value, for example, the current channel standard deviation is 3.8, the average standard deviation of all channels is 2.0, the deviation amplitude is 1.8, and the proportion is 90%, then the threshold adjustment amplitude is set to 20% of the initial threshold value, if the current threshold is 100, then the adjusted threshold is 120, at the same time, the adjustment operation is recorded as upward correction, and the reverse is downward correction, after all channels are executed, the number, adjustment direction, threshold value before and after adjustment, correction amplitude and boundary category of each channel are integrated, and the channel threshold adjustment coefficient is formed and output as structured data.

[0035] As shown in Figure 4 The signal enhancement amplitude distribution acquisition step is specifically: S301: Based on the channel threshold adjustment coefficient, compare the real-time signal intensity of each detection channel with the corresponding threshold configuration, locate the channel set that needs to perform gain adjustment by screening the channels with signal intensity lower than the threshold range, and establish a gain adjustment identification group; The response threshold value configured in the current batch of each detection channel is extracted, and the threshold value is derived from the channel threshold adjustment coefficient file output in the previous stage. Each channel configuration records the latest threshold size set for the channel and its adjustment direction and adjustment amplitude. Then, the real-time signal intensity mean value collected in the current period is read in sequence according to the channel number. The signal value is compared with the threshold value corresponding to the channel. If the signal intensity value is less than the threshold value, the channel is determined to be a response insufficient channel, and gain adjustment operation needs to be performed. For example, the threshold configuration of channel number A is 120, and the real-time signal value is 87. The signal is less than the threshold value, so it is included in the gain adjustment range. The channel number, threshold value, signal intensity value, and difference size are recorded as the basis information before adjustment. The above comparison and judgment process is performed for each channel, and the threshold value in the judgment standard must be the latest value after the previous step of correction. The initial value or default value cannot be used. After the execution is completed, all channel numbers that meet the adjustment conditions are arranged in ascending order to form a list. The list is converted to a data structure, and the adjustment state field of each channel is recorded as "adjustment required" or "no adjustment required". The output is a gain adjustment identifier group.

[0036] S302: Based on the gain adjustment identifier group, the signal gain is increased in a step-by-step manner for each channel. The signal intensity change amplitude and the corresponding noise change amplitude are recorded synchronously after each gain adjustment. The corresponding gain state data is constructed in time sequence structure to obtain the gain change trend set; The channel number marked as "adjustment required" in each of them is read and the current gain value start configuration of the channel in the gain control unit is called. Then, the step gain adjustment amplitude is set, for example, the gain adjustment step is 10 per round, and three rounds are continuously executed. After the first round of gain increase operation, the new signal intensity value and noise amplitude value of the channel are read in real time. The signal increment and noise increment under the current gain level are recorded. Then, the second round of gain adjustment is entered. The gain value is continuously added by 10 based on the original basis, and the new round of signal and noise values are recorded again. This process continues until the maximum number of adjustments is set. Each gain adjustment and sampling needs to record the time stamp and channel number. The time point, gain value, signal intensity, and noise amplitude form a complete time sequence data structure. For example, the starting gain of channel B is 100. After the first round of gain, the signal intensity increases from 87 to 95, and the noise increases from 2.1 to 2.5. After the second round, the signal increases to 105, and the noise increases to 3.2. After the third round, the signal increases to 110, and the noise increases to 4.0. All data are written into the gain state record table according to the adjustment round number. The table fields should include channel number, gain level, signal intensity, noise amplitude, and time stamp. After all channels complete the recording, the corresponding gain state data set is formed. The set is grouped by channel number and sorted by time to integrate and generate the gain change trend set.

[0037] S303: Based on the gain change trend set, calculate the signal change ratio of each channel before and after continuous gain change, and use this ratio and the direction of noise amplitude change to perform linkage screening. Combine the channel identifier that meets the screening conditions with the signal change range to obtain the signal enhancement amplitude distribution. The ratio is used in conjunction with the direction of noise amplitude variation for screening, employing the following formula: ; Calculate the signal linkage screening parameters, and combine the calculation results with the channel identifiers that meet the screening criteria and the signal variation range to obtain the signal enhancement amplitude distribution. The signal linkage filtering parameters represent channel i. Representative Channel In the The difference between the signal strength after gain adjustment and the signal strength before gain adjustment. Representative Channel In the The standard deviation of signal strength before gain adjustment Representative Channel In the The change in noise amplitude after step gain adjustment Representative Channel In the Standard deviation of signal strength after step gain adjustment.

[0038] The signal linkage screening parameter is a parameter used to comprehensively characterize the coupling relationship between the signal change ratio and the noise change direction during continuous gain adjustment of a single detection channel. It reflects the linkage between signal variability and noise variability, indicating the strength of the linkage between signal and noise within the same channel. It is subsequently used to screen which channels should be identified and participate in the combination of signal enhancement amplitude distribution.

[0039] The channel number is The signal detection data, in the first The original signal strength values ​​were collected before and after gain adjustment. The signal strength before gain adjustment was 210 AU, and the signal strength after gain adjustment was 310 AU. The difference in signal strength was obtained by subtracting the two values. AU, and thus obtain the channel in the AU. step signal standard deviation AU, after normalization, yields a normalized standard deviation of 1.00. The normalization ratio for signal variation is calculated as follows: ; The noise amplitudes before and after gain adjustment were then recorded as 12 AU and 21 AU, respectively. The difference between these values ​​yielded the change in noise amplitude. AU, the standard deviation of the signal after gain adjustment for this channel is: AU, after normalization ; The noise change normalization ratio is calculated as: ; The signal change ratio and the noise change ratio are then processed by absolute difference to obtain the linkage screening parameter: ; According to the preset linkage screening reference interval [3, 10], the result is compared with the interval, and it is determined that it is greater than the upper limit, and the channel number and the corresponding combination are included in the signal enhancement amplitude distribution result set, and then other channel data are processed and screened; The detection data of another channel number is compared, and the signal intensities before and after gain are 198 AU and 205 AU, respectively, so that: AU; AU; The corresponding normalized value is 0.95 The ratio is calculated as: ; At the same time, the noise change of this channel is 9 AU to 12 AU, AU, AU, and the normalized value is 0.96, and the noise change ratio is calculated as: ; Therefore: ; It is lower than the lower limit of the screening interval 3, and is not included in the enhancement amplitude result set. The process is repeated for each detection channel to complete the batch screening task.

[0040] The result shows that the linkage screening parameter of the current channel number is in the range of greater than 10, and combined with the screening reference setting, the interval division of the parameter is as follows: When , it indicates that the signal change in the channel is weak and consistent with the noise change, which can be identified as an invalid response area; When , it indicates that the channel signal change is within the normal fluctuation range relative to the noise change, which can be identified as a stable detection area; When , it indicates that the channel signal amplitude is much higher than the noise disturbance, and there is a sudden increase trend, which can be identified as a significant enhancement area.

[0041] Therefore, the numerical result falls into the interval, meaning that the channel belongs to the signal and noise linkage mutation state, the signal enhancement trend is prominent, and the channel number and the signal intensity difference of the signal enhancement amplitude distribution are recorded.

[0042] The formula operation logic is: the ratio of the intensity difference before and after the signal gain to the historical fluctuation standard deviation , the signal change amplitude is calculated, and then the ratio of the noise fluctuation difference to the standard deviation after the gain is calculated to form the noise disturbance amplitude, and the absolute value of the difference between the two is the linkage screening parameter , which is used to represent the relative change trend between the signal and the noise in the channel during the gain adjustment process; the formula normalizes the signal change and the noise change according to the standard deviation of different time nodes, and constructs the linkage parameter through the difference value; the introduction of the standard deviation parameter makes the fluctuation basis of the signal and the noise more comparable, avoiding the misleading of the result caused by the single amplitude difference, and effectively distinguishing the detection channels with different actual fluctuation trends.

[0043] As shown in Figure 5 , the acquisition step of the marker weighted characteristic group is specifically: S401: Based on the signal enhancement amplitude distribution, the channel threshold adjustment coefficient is paired with the nucleic acid marker signal collected by each detection channel, the collection position, environmental temperature and environmental humidity parameters are sorted, the multi-parameter combined data of each detection channel is constructed, and the environmental parameter set is obtained. Each record in the threshold adjustment coefficient of the extraction channel is matched one by one with the nucleic acid marker signal collected in the current batch of detection channels according to the channel number. The threshold adjustment coefficient should include the channel number, adjustment direction and adjustment amplitude. The marker signal value is derived from the signal intensity data set after sampling. The signal value is recorded by channel and labeled with the sampling time point. After the number matching is completed, the spatial positioning information of each detection channel is retrieved, that is, the collection position parameter. The collection position is provided by the position information or coordinate information in the carrier plate structure. For example, the channel number is A3, the carrier plate is No. 2 plate, the 3rd column and the 4th row, and the collection position can be recorded as "plate 2-C4". Then the temperature and humidity of the environment during the collection of the channel are obtained in turn. The temperature is in Celsius and the humidity is in percentage. The temperature and humidity data should be provided by the detection equipment environment recording module. Each sampling needs to be matched with the corresponding time stamp and channel number. For example, the collection time of channel A3 is August 19, 2025, 14:32, the corresponding recorded temperature is 26.4 degrees Celsius, and the humidity is 48.7%. The channel number, threshold adjustment value, marker signal value, collection position, temperature and humidity are combined into one multi-parameter combined data record, and arranged in ascending order according to the channel number. Finally, a multi-parameter record set with consistent structure is formed, that is, each record has a fixed structure and complete fields. Each field is uniform and has uniform data precision. The threshold adjustment value retains one decimal place, the marker signal value retains two decimal places, the temperature and humidity each retain one decimal place, and finally the environmental parameter set is output.

[0044] S402: Based on the environmental parameter set, compare the distribution of each parameter in the detection channel, judge the participation proportion of the collection position, environmental temperature, environmental humidity and marker signal in the combined data, and establish a weight distribution structure by configuring the weight of the difference parameter. Each multi-parameter combination record in the set is expanded into an independent field, and the collection position, environmental temperature, environmental humidity and nucleic acid marker signal four parameter values are extracted in turn. First, the collection positions in all detection channels are classified and counted, and the sample plate partitions are divided according to the spatial region dimension, such as A area, B area, C area, the number of channels contained in each area is counted, and the proportion of the total number of channels in the area is calculated, for example, A area has 28 channels, accounting for 29.2% of the total number of 96, which is considered as a high distribution area. Then, the temperature parameter is analyzed by numerical distribution, and the temperature interval is set, for example, every 1 degree Celsius is a gear, the number proportion of channels in each gear is counted, and the relationship between the temperature value and the marker signal value in the channel is compared. If the marker signal in a certain temperature segment is significantly high or low, the temperature segment is recorded as an abnormal segment. Then, the humidity parameter is processed, the humidity interval is divided by 5% interval, and the same number proportion and signal response comparison is performed. The segment number corresponding to the signal deviation is recorded. Finally, the proportion of each parameter in all channels, the number of participating channels and the contribution of the corresponding parameter value to the signal strength are compared. If a parameter remains stable in most channels, but the deviation value of part of the channels causes significant signal fluctuation, it is a high weight parameter. The judgment threshold can be set as the signal influence of the parameter exceeding 15%, which is recorded as a high weight factor. For example, if the humidity value changes from 45% to 60% and causes the signal to drop by more than 15%, the humidity weight value is set to 0.35. The weight values of the remaining parameters are set in proportion to the influence. The total weight of the four parameters is normalized to 1.00. The weight allocation structure is assigned and recorded respectively. The structure fields include parameter name, channel number set, contribution proportion, weight value, etc. The weight allocation structure is output.

[0045] S403: According to the weight allocation structure, the environmental parameters and the marker signal are normalized, the influence of each parameter is superimposed according to the weighted allocation principle, and the effect of the weight allocation result in the channel is judged to obtain a marker weighted characteristic group; The influence of each parameter is superimposed according to the weighted allocation principle, and the formula is: ; The weighted average parameter is calculated The effect of the weight allocation result in the channel is judged to obtain a marker weighted characteristic group, wherein, represents the weight coefficient of the th environmental parameter in the weight allocation, represents the normalized value of the th environmental parameter, represents the weight coefficient of the th marker signal in the weight allocation, represents the normalized value of the th marker signal, Total number of environmental parameters and marker signals; The weighted average parameter refers to a parameter obtained by weighting and superimposing the environmental parameters and the marker signals through respective weight coefficients and taking the average of all participating factors, which represents the normalized value of the environmental parameters The normalized value of the marker signals The comprehensive performance under the respective weight coefficients 、 Adjustment, which is used to reflect the overall level of the weighted superposition of all participating factors in the same detection channel.

[0046] First, extract the collected environmental parameter and marker signal data in the detection system, which are temperature 22.5℃, humidity 52%, vibration intensity 0.03g, and three groups of marker signal values 100, 280, 190, respectively, representing the instantaneous photoelectric response intensity of nucleic acid markers collected under different channels. For the above parameters, directly read the normalized data based on the historical normalization strategy file, which are temperature normalized value 0.5, humidity normalized value 0.55, vibration normalized value 0.5, and three groups of marker signal normalized values 0.25, 0.6, 0.5, respectively. The weight coefficients are given by the system historical multi-batch sample fitting results, and the environmental parameter weights are set as follows: temperature , humidity , vibration , and the weight coefficients of the marker signals are as follows: signal 1 , signal 2 , signal 3 , then call the formula, where , and the specific calculation process after substituting the data is as follows: The first weighted calculation is: ; The second weighted calculation is: ; The third weighted calculation is: ; Add the above three results: ; Divide by 6 to get: ; The result shows that the weighted average parameter is located between the set reference interval range , according to the interval segment logic divided in the historical sample stability analysis, set as follows: When When the ratio is less than 0.5, the expression section is classified as a low-interference expression section, indicating that the overall activation degree of each influencing factor in the channel is weak, and the parameter combination fails to effectively trigger the target signal response. When the ratio is greater than 1.5, the expression section is classified as an abnormal activation section, indicating that some parameters are over-amplified in the combination, and the source of the high-bias parameters needs to be further identified due to local environmental interference or abnormal enhancement of the marker signal. When the ratio is greater than 1.5, the expression section is classified as an abnormal activation section, indicating that some parameters are over-amplified in the combination, and the source of the high-bias parameters needs to be further identified due to local environmental interference or abnormal enhancement of the marker signal. When the ratio is greater than 1.5, the expression section is classified as an abnormal activation section, indicating that some parameters are over-amplified in the combination, and the source of the high-bias parameters needs to be further identified due to local environmental interference or abnormal enhancement of the marker signal. When the ratio is greater than 1.5, the expression section is classified as an abnormal activation section, indicating that some parameters are over-amplified in the combination, and the source of the high-bias parameters needs to be further identified due to local environmental interference or abnormal enhancement of the marker signal. Therefore, the current value is in the stable expression section, indicating that the environmental state of the channel in the current batch and the expression of the marker signal are in the historical statistical stable interval, which is used for subsequent risk level distribution judgment process, and is the key intermediate result for promoting the transformation from the sub-feature to the comprehensive feature.

[0047] As shown in Figure 6 , the acquisition step of the risk level distribution data is specifically: S501: Based on the marker weighted characteristic group, the ratio of the marker signal in each channel to the corresponding weight configuration is calculated, the distribution of the ratio combination data in the channel is analyzed, and the interval position corresponding to each channel is determined according to the upper and lower boundaries of the risk interval, to obtain the signal risk interval category. The original value of the marker signal is extracted from each detection channel, which is usually the signal intensity of the nucleic acid target molecules captured by the detection system per unit time, which can be set as relative light intensity or voltage response value. Then the weight configuration data generated in the previous stage of the channel is read in turn, which includes the weight of the environmental temperature, the weight of the environmental humidity, the weight of the collection position and the reference normalization factor of the marker itself. After multiplying the weight values and the nucleic acid signal values of the channel by proportion, the sum is taken and the average value is obtained to form the proportional merging result. For example, the nucleic acid signal intensity of channel C7 is 148.2, the temperature weight is 0.35, the humidity weight is 0.30, the position weight is 0.25, and the marker factor weight is 0.10. After merging calculation, the proportional merging value of channel C7 is 142.85. All channels are calculated in the same way. After completion, the proportional merging values are arranged in ascending order according to the channel number. Then the pre-set risk interval boundary data is called. The risk interval is divided into three equal interval: safe zone is 0 to 120, warning zone is 120 to 160, and dangerous zone is above 160. The merging result value of each channel is compared with the upper and lower boundary values of the risk interval to determine its interval position. If the value of a certain channel is 108, it belongs to the safe zone, if it is 138, it belongs to the warning zone, and if it is 172, it belongs to the dangerous zone. Each channel is recorded after comparison. The channel number and the interval label are finally outputted as the signal risk interval category.

[0048] S502: Based on the signal risk interval category, the classification is arranged according to the interval judgment result. The channel number and the signal merging result corresponding to each category are recorded, and the distribution of each category is outputted as structured data to obtain the risk level distribution data. All channels are grouped according to the risk interval labeled in the previous stage. First, the channel number set labeled as "safe" is extracted from the category label, and its number and corresponding proportional merging value are recorded. This group is labeled as level label "L1". Then the "warning" label channel is processed. The number and corresponding merging value of all channels labeled as warning are extracted and recorded as level label "L2". Finally, the "dangerous" channel number is processed. Its number and merging value are recorded and labeled as level label "L3". The three types of data are written into a structured table. The table fields should include channel number, merging signal value, risk interval label, and level label. In the example, the merging value of channel C7 is 142.85, corresponding to the "warning" label, and the level is L2. The merging value of channel B3 is 171.4, the label is "dangerous", and the level is L3. The merging value of channel A1 is 98.3, the label is "safe", and the level is L1. After all channels are arranged, the data table is outputted according to the level label to ensure the structure is complete and the number is not missed. Finally, the risk level distribution data is outputted.

[0049] As Figure 7The biological detection system based on channel response regulation comprises: The signal fluctuation identification module analyzes the real-time signal intensity of the current batch of samples based on the detection channel, combines the collected background noise baseline, measures the response stability of the target molecules, compares the standard deviation of the signal intensity of each channel with the corresponding background noise fluctuation range, identifies the abnormal channel by judging the relationship between the standard deviation and the fluctuation interval, and obtains the abnormal channel distribution characteristics; The threshold parameter correction module judges the current standard deviation distribution of the high-throughput cell detection platform channel based on the abnormal channel distribution characteristics, compares the corresponding relationship between the standard deviation and the upper and lower limits of the historical fluctuation interval, directly adjusts the response threshold parameter for the channels higher than the upper limit, and directly reduces the response threshold parameter for the channels lower than the lower limit, After the adjustment is completed, the channel parameter configuration is recorded, and the channel threshold adjustment coefficient is obtained; The gain dynamic screening module compares each detection channel real-time signal intensity based on the channel threshold adjustment coefficient, analyzes the current state of the signal lower than the threshold parameter, and increases the gain in a step-by-step manner, records the noise amplitude and signal intensity after each gain adjustment, calculates the ratio before and after the gain change, screens the channels that meet the ratio condition, and obtains the signal enhancement amplitude distribution; The multi-parameter weight normalization module combines the channel threshold adjustment coefficient and the nucleic acid marker signal of the high-throughput protein detection unit based on the signal enhancement amplitude distribution, performs weighting and normalization on the collection position, collection time, environmental temperature and humidity, and marker signal, judges the role of each parameter in weight distribution, and obtains the marker weighted characteristic group. The risk level discrimination module combines the marker signal with the weight configuration based on the marker weighted characteristic group, interval judges the upper and lower limits of the risk interval according to the category of interval judgment, classifies and arranges, outputs the corresponding classification, and obtains the risk level distribution data.

[0050] The above is only a specific embodiment of the present application, but the protection scope of the present application is not limited thereto, any skilled person in the art can easily think of changes or replacements within the technical range disclosed by the present application, which should be covered within the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. A method for biological detection based on channel response modulation, characterized in that, The method comprises: S1: based on the detection channel, analyzing the current batch of sample real-time signal intensity, collecting the background noise baseline, measuring the target molecule response stability, comparing the standard deviation of each channel signal intensity with the background noise fluctuation range, identifying the abnormal channel, and obtaining the abnormal channel distribution characteristics; S2: based on the abnormal channel distribution characteristics, judging the channel standard deviation distribution of the high-throughput cell detection platform, comparing the standard deviation with the upper and lower boundaries of the historical fluctuation interval, adjusting the response threshold parameters for the channels exceeding the upper and lower boundaries respectively, and obtaining the channel threshold adjustment coefficient; S3: based on the channel threshold adjustment coefficient, comparing the real-time signal intensity of the detection channel item by item, analyzing the state of the signal below the threshold parameter, stepping up the gain, recording the noise amplitude and signal intensity, calculating the gain change ratio, and obtaining the signal enhancement amplitude distribution; S4: based on the signal enhancement amplitude distribution, combining the channel threshold adjustment coefficient with the nucleic acid marker signal of the high-throughput protein detection unit, weighting and normalizing the collection position, collection time, environmental temperature and humidity, and marker signal, judging the role of the parameters in weight distribution, and obtaining the marker weighted characteristic group.

2. The method according to claim 1, wherein, The abnormal channel distribution characteristics include abnormal channel number, abnormal distribution range, and abnormal type. The channel threshold adjustment coefficient includes adjustment amount, correction direction, and channel grouping identifier. The signal enhancement amplitude distribution includes enhancement amplitude category, amplitude distribution interval, and signal change level. The marker weighted characteristic group includes marker distribution weight, weight normalization coefficient, and parameter group classification.

3. The method according to claim 1, wherein, The acquisition step of the abnormal channel distribution characteristics is specifically: S101: based on the detection channel, analyzing the real-time signal intensity collected under the current batch and the background noise baseline, constructing the paired information of standard deviation and noise fluctuation range for each channel by comparing the standard deviation of each channel signal intensity with the corresponding noise fluctuation interval, and obtaining the channel fluctuation interval group; S102: based on the channel fluctuation interval group, judging the relationship between the standard deviation of each channel and the noise fluctuation interval, dividing the channels higher than the fluctuation interval into abnormal channels, dividing the channels lower than the fluctuation interval into signal stable channels, and classifying the remaining channels into conventional types, and obtaining the channel response distribution label; S103: according to the channel response distribution label, summarizing the numbers and states of each type of channel, gathering the associated attributes of abnormal type channels through sorting and grouping, and obtaining the abnormal channel distribution characteristics.

4. The method of claim 1, wherein the channel response based modulation is performed by a channel response based modulation module. The acquisition step of the channel threshold adjustment coefficient is specifically: S201: based on the abnormal channel distribution characteristics, comparing the signal intensity standard deviation distribution of each detection channel of the high-throughput cell detection platform, judging the difference of signal fluctuation range among channels, corresponding the channels with fluctuation trend deviation with channel identifiers, and obtaining the standard deviation difference characteristics; S202: based on the standard deviation difference characteristics, comparing the distribution position of the standard deviation of each channel in the channel set, judging whether it is located in the boundary region of the fluctuation sequence, identifying the channels at the boundary as difference response types respectively, and obtaining the boundary response category group; S203: Based on the boundary response category group, the response threshold parameter configuration of each response type channel is optimized respectively, the adjustment results of the channels are integrated by recording the optimization direction and parameter correction content, and a channel threshold adjustment coefficient is obtained.

5. The method of claim 1, wherein the channel response based modulation is performed by a channel response based modulation module. The acquisition step of the signal enhancement amplitude distribution is specifically: S301: Based on the channel threshold adjustment coefficient, the real-time signal intensity of each detection channel is compared with the corresponding threshold configuration, the channel set that needs to perform gain adjustment is located by screening the channels with signal intensity lower than the threshold range, and a gain adjustment identification group is established; S302: Based on the gain adjustment identification group, the signal gain is increased in a step-by-step manner for each channel, the signal intensity change amplitude and the corresponding noise change amplitude are recorded synchronously after each gain adjustment, the corresponding gain state data is constructed according to the time sequence structure, and a gain change trend set is obtained; S303: According to the gain change trend set, the signal change proportion of each channel before and after continuous gain change is calculated, and the proportion is linked with the change direction of the noise amplitude to perform screening, the channel identification and signal change range that meet the screening conditions are combined, and a signal enhancement amplitude distribution is obtained.

6. The method of claim 1, wherein the channel response based modulation is performed by a channel response based modulation module. The acquisition step of the marker weighting characteristic group is specifically: S401: Based on the signal enhancement amplitude distribution, the channel threshold adjustment coefficient is paired with the nucleic acid marker signal collected by each detection channel, the collection position, environmental temperature and environmental humidity parameters are arranged, the multi-parameter combined data of each detection channel is constructed, and an environmental parameter set is obtained; S402: Based on the environmental parameter set, the distribution of each parameter in the detection channel is compared, the participation proportion of the collection position, environmental temperature, environmental humidity and marker signal in the combined data is judged, and the weight allocation structure is established by statistically configuring the weight of the difference parameter; S403: According to the weight allocation structure, the environmental parameters and the marker signal are normalized, the influence of each parameter is superimposed according to the weight allocation principle, and the effect of the weight allocation result in the channel is judged, and a marker weighting characteristic group is obtained.

7. The method of claim 1, wherein the channel response based modulation is performed by a channel response based modulation module. The method further comprises: S5: Based on the marker weighting characteristic group, the marker signal is combined with the weight configuration proportion respectively, the combination result is interval judged with the upper and lower limits of the risk interval, the risk grade distribution data is obtained by classifying and arranging according to the interval judgment category. The risk grade distribution data includes grade label, judgment interval and classification index.

8. The method according to claim 7, wherein the channel response based modulation is performed by, The acquisition step of the risk grade distribution data is specifically: S501: Based on the marker weighting characteristic group, the proportion combined result of the marker signal in each channel and the corresponding weight configuration is calculated, the distribution of the proportion combined data in the channel is analyzed, and the interval position corresponding to each channel is judged according to the upper and lower boundaries of the risk interval, and a signal risk interval category is obtained; S502: Based on the signal risk interval category, the classification arrangement is performed according to the interval judgment result, the channel number corresponding to each category and the signal combined result are recorded, and the distribution of each category is output in a structured data, and the risk grade distribution data is obtained.

9. The method of claim 1, wherein the channel response based modulation is performed by a plurality of transmitters and receivers. The detection channel refers to a path in a biological detection device for independently collecting and outputting a detection signal, each detection channel can correspond to the signal collection of a sample or a group of samples, the abnormal channel refers to a detection signal exhibiting abnormal fluctuations or noise characteristics, and the collection position refers to geographical position information or spatial position information on a sample plate.

10. A system for channel response modulation based bio-detection, the system being configured to implement a channel response modulation based bio-detection method according to any one of claims 1 to 9, characterized in that, The system comprises: The signal fluctuation identification module analyzes the real-time signal intensity of the current batch of samples based on the detection channel, combines the collected background noise baseline, measures the response stability of the target molecule, compares the standard deviation of the signal intensity of each channel with the corresponding background noise fluctuation range, identifies the abnormal channel by judging the relationship between the standard deviation and the fluctuation interval, and obtains the abnormal channel distribution characteristics; The threshold parameter correction module judges the current standard deviation distribution of the high-throughput cell detection platform channel based on the abnormal channel distribution characteristics, compares the corresponding relationship between the standard deviation and the upper and lower limits of the historical fluctuation interval, directly adjusts the response threshold parameter for the channels higher than the upper limit, and directly reduces the response threshold parameter for the channels lower than the lower limit, and records the channel parameter configuration after adjustment to obtain the channel threshold adjustment coefficient; The gain dynamic screening module compares each detection channel real-time signal intensity based on the channel threshold adjustment coefficient, analyzes the current state of the signal lower than the threshold parameter, and increases the gain in a step-by-step manner, records the noise amplitude and signal intensity after each gain adjustment, calculates the ratio before and after gain change, screens the channels with a ratio meeting the conditions, and obtains the signal enhancement amplitude distribution; The multi-parameter weight normalization module executes weighting and normalization on the collection position, collection time length, environmental temperature and humidity, and marker signal based on the signal enhancement amplitude distribution, the channel threshold adjustment coefficient, and the nucleic acid marker signal of the high-throughput protein detection unit, judges the role of each parameter in weight distribution, obtains the marker weighted characteristic group, and judges the risk interval upper and lower limits based on the marker weighted characteristic group, classifies and arranges according to the interval judgment category, and outputs the corresponding classification to obtain the risk level distribution data. ​

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