Method and system for biological detection based on channel response regulation

Abstract

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, comprising the following steps: based on a detection channel, analyzing sample signal intensity and background noise, judging the relationship between standard deviation and fluctuation interval, correcting threshold parameters and screening gain ratio, combining marker signals and environmental parameters for weighted normalization, performing interval judgment and risk classification, and obtaining risk level distribution data.In the present application, through accurate analysis of real-time fluctuation characteristics of detection signals, combined with signal intensity and noise dynamic characteristics, channel parameters are corrected step by step and multi-stage gain screening is performed, environmental acquisition elements and sample correlation factors are introduced for multi-dimensional weight distribution, and factors such as signal fluctuation, environmental change and sample source are comprehensively considered for full-process data fusion processing, which improves the adaptive distinguishing ability of abnormal signals and boundary states, and effectively enhances the automatic recognition level of biomarker signal differences in complex sample environments.

Description

Technical Field

[0001] This invention relates to the field of biological detection technology, and in particular to biological detection methods and systems based on channel response regulation. Background Technology

[0002] Biological detection mainly involves the detection and analysis of various components in biological samples, such as proteins, nucleic acids, metabolites, and cells. Through various methods such as physical, chemical, immunological, and molecular methods, it can identify and quantify sample states, disease biomarkers, or environmental factors. It is widely used in medical diagnosis, food safety, environmental monitoring, and life science research. Traditional biological detection methods rely on optical colorimetry, colorimetry, chemiluminescence, enzyme-linked reactions, nucleic acid hybridization, electrochemical sensing, etc., and use standard curve comparison or manual experience analysis to quantitatively or qualitatively determine the target substances in the sample. It usually uses instruments and equipment such as colorimeters, enzyme-linked immunosorbent assay (ELISA) readers, fluorescence detectors, and electrochemical workstations to complete the detection and determination of target analytes.

[0003] Traditional detection methods often employ inherent signal processing procedures, lacking dynamic correction capabilities in parameter settings and judgment modes. When faced with signal fluctuations or environmental changes, they are prone to insufficient response to abnormal signals. Detection results rely on manual parameter settings, making it difficult to cope with diverse detection scenarios. Changes in environmental noise blur the signal discrimination boundary, reducing the detection discrimination of some samples. It is difficult to accurately identify the weak responses of target molecules in complex backgrounds, resulting in unclear risk stratification of different types of samples and difficulty in achieving stable output under conditions of high-throughput processing and heterogeneous samples. Summary of the Invention

[0004] To address the technical problems existing in the prior art, embodiments of the present invention provide a biological detection method and system based on channel response modulation. The technical solution is as follows:

[0005] On the one hand, a biological detection method based on channel response regulation is provided, including the following steps:

[0006] 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 the signal intensity of each channel with the fluctuation range of the background noise, identify abnormal channels, and obtain the distribution characteristics of abnormal channels;

[0007] S2: Based on the abnormal channel distribution characteristics, determine the standard deviation distribution of the high-throughput cell detection platform channels, compare the relationship between the standard deviation and the upper and lower bounds of the historical fluctuation range, and adjust the response threshold parameters for channels that exceed the upper and lower bounds respectively to obtain the channel threshold adjustment coefficient;

[0008] S3: Based on the channel threshold adjustment coefficient, compare it item by item with the real-time signal strength of the detection channel, analyze the state of signals below the threshold parameter, increase the gain step by step, record the noise amplitude and signal strength, calculate the gain change ratio, and obtain the signal enhancement amplitude distribution.

[0009] S4: Based on the signal enhancement amplitude distribution, combined with the channel threshold adjustment coefficient and the nucleic acid biomarker signal of the high-throughput protein detection unit, the acquisition location, acquisition duration, ambient temperature and humidity and biomarker signal are weighted and normalized to determine the role of the parameters in the weight allocation, and the biomarker weighted characteristic group is obtained.

[0010] 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 coefficient number, 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 allocation weight, weight normalization coefficient, and parameter group classification.

[0011] On the other hand, the specific steps for obtaining the abnormal channel distribution characteristics are as follows:

[0012] S101: Based on the detection channel, analyze the real-time signal strength and background noise baseline collected in the current batch. By comparing the standard deviation of the signal strength of each channel with the corresponding noise fluctuation range, construct the pairing information of standard deviation and noise fluctuation for each channel to obtain the channel fluctuation range group.

[0013] S102: Based on the channel fluctuation interval group, determine the relationship between the standard deviation of each channel and the noise fluctuation interval, classify the channels above the fluctuation interval as abnormal channels, classify the channels below the fluctuation interval as signal stable channels, and classify the remaining channels as normal types to obtain the channel response distribution label;

[0014] S103: Based on the channel response distribution labels, summarize the corresponding numbers and statuses of various channels, and gather the associated attributes of abnormal channel types through sorting and grouping to obtain the abnormal channel distribution characteristics.

[0015] On the other hand, the specific steps for obtaining the channel threshold adjustment coefficient are as follows:

[0016] 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, determine the differences in signal fluctuation range between channels, and match the channels with deviations in fluctuation trend with the channel identifiers to obtain the standard deviation difference characteristics.

[0017] S202: Based on the standard deviation difference characteristics, compare the distribution position of the standard deviation of each channel in the channel set, determine whether it is located in the boundary region of the fluctuation sequence, and identify the channels located at the boundary as difference response types to obtain the boundary response category group;

[0018] S203: Based on the boundary response category group, optimize the response threshold parameter configuration of each response type channel, and integrate the channel adjustment results by recording the optimization direction and parameter correction content to obtain the channel threshold adjustment coefficient.

[0019] On the other hand, the specific steps for obtaining the signal enhancement amplitude distribution are as follows:

[0020] S301: Based on the channel threshold adjustment coefficient, compare the real-time signal strength of each detection channel with the corresponding threshold configuration, and locate the set of channels that need to be adjusted by filtering channels whose signal strength is lower than the threshold range, and establish a gain adjustment identification group;

[0021] S302: Based on the gain adjustment identifier group, the signal gain is gradually increased for each channel in a step manner. After each gain adjustment, the signal strength change amplitude and the corresponding noise change amplitude are recorded synchronously. The corresponding gain state data is constructed according to the time series structure to obtain the gain change trend set.

[0022] 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 in conjunction with the direction of noise amplitude change to perform linkage screening. Combine the channel identifiers that meet the screening conditions with the signal change range to obtain the signal enhancement amplitude distribution.

[0023] On the other hand, the steps for obtaining the weighted characteristic group of the markers are as follows:

[0024] S401: Based on the signal enhancement amplitude distribution, the channel threshold adjustment coefficient is numbered and paired with the nucleic acid biomarker signals collected by each detection channel, and the collection location, ambient temperature and ambient humidity parameters are organized to construct multi-parameter combination data for each detection channel to obtain an environmental parameter set;

[0025] S402: Based on the set of environmental parameters, compare the distribution of each parameter in the detection channel, determine the participation ratio of the acquisition location, ambient temperature, ambient humidity and marker signal in the combined data, and establish a weight allocation structure by statistically configuring the weight of the difference parameters.

[0026] S403: According to the weight allocation structure, normalization processing is performed on the environmental parameters and marker signals, 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 to obtain the marker weighted characteristic group.

[0027] On the other hand, the method also includes:

[0028] S5: Based on the weighted characteristic group of the markers, the marker signals are merged with the weight configuration ratio respectively. The merged result is used to determine the risk interval with the upper and lower limits of the risk interval. The risk level distribution data is obtained by classifying and organizing the data according to the interval determination category.

[0029] The risk level distribution data includes level labels, judgment intervals, and classification indexes.

[0030] On the other hand, the specific steps for obtaining the risk level distribution data are as follows:

[0031] S501: Based on the weighted characteristic group of the markers, calculate the proportional merging result of the marker signal and the corresponding weight configuration in each channel, analyze the distribution of the proportional merging data in the channel, and determine the interval position corresponding to each channel according to the upper and lower boundaries of the risk interval to obtain the signal risk interval category.

[0032] S502: Based on the aforementioned signal risk interval categories, classify and organize the data according to the interval judgment results, record the channel number and signal merging result corresponding to each category, and output the distribution of each category as structured data to obtain risk level distribution data.

[0033] On the other hand, the detection channel refers to the pathway in the biological detection device used to independently collect and output detection signals. 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 exhibiting abnormal fluctuations or noise characteristics. The collection position refers to the geographical location information of the sample or the spatial location information on the sample plate.

[0034] On the other hand, a channel-response-based biological detection system is provided, which is applied to channel-response-based biological detection methods, including:

[0035] The signal fluctuation recognition 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 stability of the target molecule response, compares the standard deviation of the signal intensity of each channel with the corresponding background noise fluctuation range, and identifies abnormal channels by judging the relationship between the standard deviation and the fluctuation range, thus obtaining the distribution characteristics of abnormal channels.

[0036] Based on the abnormal channel distribution characteristics, the threshold parameter correction module determines the current standard deviation distribution of the high-throughput cell detection platform channels, compares the correspondence between the standard deviation and the upper and lower bounds of the historical fluctuation range, directly adjusts the response threshold parameter for channels above the upper bound, and directly reduces the response threshold parameter for channels below the lower bound. After the adjustment is completed, the channel parameter configuration is entered to obtain the channel threshold adjustment coefficient.

[0037] The gain dynamic screening module compares the channel threshold adjustment coefficient with the real-time signal strength of each detection channel item by item. For signals below the threshold parameter, it analyzes their current state and increases the gain in a step-by-step manner. After each gain adjustment, it records the noise amplitude and signal strength, calculates the ratio before and after the gain change, and filters the channels whose ratio meets the conditions to obtain the signal enhancement amplitude distribution.

[0038] Based on the signal enhancement amplitude distribution, the multi-parameter weighting and normalization module combines the channel threshold adjustment coefficient with the nucleic acid biomarker signal of the high-throughput protein detection unit to perform weighting and normalization on the acquisition location, acquisition duration, ambient temperature and humidity and biomarker signal, and determines the role of each parameter in the weighting allocation to obtain the biomarker weighted characteristic group.

[0039] The risk level discrimination module, based on the weighted characteristic group of the markers, proportionally merges the marker signals with the weight configuration, makes interval judgments with the upper and lower limits of the risk interval, classifies and organizes the data according to the category of the interval judgment, and outputs the corresponding category to obtain the risk level distribution data.

[0040] The beneficial effects of the technical solutions provided in the embodiments of the present invention include at least the following:

[0041] By accurately analyzing the real-time fluctuation characteristics of the detection signal, combining the dynamic characteristics of signal strength and noise, the channel parameters are corrected step by step and multi-stage gain screening is performed. Environmental acquisition elements and sample correlation factors are introduced for multi-dimensional weight allocation. By comprehensively considering factors such as signal fluctuation, environmental changes and sample sources, the data fusion processing is achieved throughout the process, improving the adaptive differentiation capability of abnormal signals and boundary states. This effectively enhances the automatic identification level of biomarker signal differences in complex sample environments. The data processing workflow further expands the stratification of risk levels and realizes accurate classification and determination of biological detection in diverse scenarios. Attached Figure Description

[0042] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0043] Figure 1 This is a flowchart of the main steps of the present invention;

[0044] Figure 2 This is a flowchart of steps S1 of the present invention;

[0045] Figure 3 This is a flowchart of steps S2 of the present invention;

[0046] Figure 4 This is a flowchart of steps S3 of the present invention;

[0047] Figure 5 This is a flowchart of step S4 of the present invention;

[0048] Figure 6 This is a flowchart of steps S5 of the present invention;

[0049] Figure 7 This is a system block diagram of the present invention. Detailed Implementation

[0050] The technical solution of the present invention will now be described with reference to the accompanying drawings.

[0051] In embodiments of the present invention, words such as "exemplarily," "for example," etc., are used to indicate that something is an example, illustration, or description. Any embodiment or design described as "exemplary" in the present invention should not be construed as being more preferred or advantageous than other embodiments or designs. Specifically, the use of the word "exemplary" is intended to present the concept in a concrete manner. Furthermore, in embodiments of the present invention, the meaning expressed by "and / or" can be both, or either one.

[0052] In the embodiments of this invention, the terms "image" and "picture" may sometimes be used interchangeably. It should be noted that, without emphasizing the distinction between them, they convey the same meaning. Similarly, the terms "of," "corresponding (relevant)," and "corresponding" may sometimes be used interchangeably. It should be noted that, without emphasizing the distinction between them, they convey the same meaning.

[0053] In this embodiment of the invention, sometimes a subscript such as W1 may be written in a non-subscript form such as W1. When the difference is not emphasized, the meaning they express is the same.

[0054] To make the technical problems, technical solutions and advantages of the present invention clearer, a detailed description will be given below in conjunction with the accompanying drawings and specific embodiments.

[0055] This invention provides a biological detection method based on channel response modulation, such as... Figure 1 As shown, it includes the following steps:

[0056] S1: Based on the detection channel, analyze the real-time signal intensity of the current batch of samples, combine the collected background noise baseline, measure the stability of the target molecule response, compare the standard deviation of the signal intensity of each channel with the corresponding background noise fluctuation range, identify abnormal channels by judging the relationship between the standard deviation and the fluctuation range, and obtain the distribution characteristics of abnormal channels.

[0057] S2: Based on the abnormal channel distribution characteristics, determine the current standard deviation distribution of the high-throughput cell detection platform channels, compare the correspondence between the standard deviation and the upper and lower bounds of the historical fluctuation range, directly adjust the response threshold parameters for channels above the upper bound, and directly reduce the response threshold parameters for channels below the lower bound. After the adjustment is completed, enter the channel parameter configuration to obtain the channel threshold adjustment coefficient.

[0058] S3: Based on the channel threshold adjustment coefficient, compare it with the real-time signal strength of each detection channel item by item. For signals below the threshold parameter, analyze their current state and increase the gain in a step-by-step manner. After each gain adjustment, record the noise amplitude and signal strength, calculate the ratio before and after the gain change, and filter the channels with the ratio that meet the conditions to obtain the signal enhancement amplitude distribution.

[0059] S4: Based on the signal enhancement amplitude distribution, combined with the channel threshold adjustment coefficient and the nucleic acid biomarker signal of the high-throughput protein detection unit, weighting and normalization are performed on the acquisition location, acquisition duration, ambient temperature and humidity and biomarker signal to determine the role of each parameter in the weight allocation and obtain the biomarker weighted characteristic group.

[0060] S5: Based on the weighted characteristic group of markers, the marker signals are proportionally merged with the weight configuration. The merged result is used to make interval judgments with the upper and lower limits of the risk interval. The interval judgments are then classified and organized, and the corresponding categories are output to obtain the risk level distribution data.

[0061] The abnormal channel distribution characteristics include abnormal channel number, abnormal distribution range, and abnormal type; the channel threshold adjustment coefficient includes the number of adjustment coefficients, correction direction, and channel grouping identifier; the signal enhancement amplitude distribution includes enhancement amplitude category, amplitude distribution range, and signal change level; the marker weighted characteristic group includes marker allocation weight, weight normalization coefficient, and parameter group classification; and the risk level distribution data includes level label, judgment range, and classification index.

[0062] In S1, a detection channel refers to a pathway in a biological detection device used for independently acquiring and outputting detection signals. Each detection channel can correspond to the signal acquisition of one sample or a group of samples. A batch sample refers to the set of samples acquired and detected in the same detection round or the same processing procedure. Real-time signal intensity refers to the intensity value of the instantaneous electronic or optical signal generated by the device for the target molecule of the sample during the detection process. The 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. The target molecule refers to the biomolecule (such as a specific protein, nucleic acid, antibody, etc.) designated as the analyte in this round of detection. The standard deviation of signal intensity refers to the standard deviation of the detection signal intensity of the same channel in the batch of samples, used to measure the fluctuation of the detection signal. The background noise fluctuation range refers to the measurement of the fluctuation amplitude of the background noise baseline over a period of time, reflecting the range of noise level changes. The fluctuation range refers to the upper and lower limits set to determine whether the signal intensity fluctuation is reasonable. An abnormal channel refers to a detection channel whose detection signal exhibits abnormal fluctuations or noise characteristics and requires further attention or processing.

[0063] In S2, the high-throughput cell detection platform refers to an automated cell analysis system capable of simultaneously detecting a large number of samples, widely used for the efficient detection of molecular, protein, or cell signals; the historical fluctuation range refers to the upper and lower limits of the normal fluctuation of a specific parameter (such as the signal standard deviation) statistically derived from long-term accumulated data, used to determine whether the current parameter is abnormal; the correspondence refers to the relative position of the current parameter with the historical range (such as higher than, lower than, or within the range); the upper and lower bounds refer to the maximum (upper bound) and minimum (lower bound) boundaries of the historical fluctuation range; the response threshold parameter refers to the threshold point set for the response to the detection signal (for example, only when the signal is higher than the threshold is the detection considered positive), used for signal screening and judgment; the channel parameter configuration refers to the configuration file or table of relevant parameters set and recorded for each detection channel, used for subsequent signal processing and traceability.

[0064] In S3, analyzing the current state refers to evaluating the state of detection signals below the threshold parameter, including signal amplitude, stability, and their relationship with noise; incremental gain in step mode refers to gradually increasing the signal amplification factor of the detection channel according to the set step size to enhance weak signals and facilitate subsequent detection; the ratio before and after gain change refers to the change in the ratio of signal strength to noise amplitude before and after gain increase, used to evaluate the signal quality after gain adjustment; channels that meet the ratio criteria refer to the detection channels that meet the requirements after signal amplification by comparing the ratio with the set standard.

[0065] In S4, the high-throughput protein detection unit refers to an automated device or module that can achieve efficient parallel detection of protein molecules, enabling the simultaneous detection of multiple biomarkers; nucleic acid biomarker signals refer to signals collected in the test sample targeting specific nucleic acid molecules (such as DNA and RNA fragments), used for biometric analysis or disease determination; collection location refers to the geographical location information of the collected sample or the spatial location information on the sample plate; collection duration refers to the length of time from the start of sample collection to the completion of signal reading; the role in weight allocation refers to the weight or influence of participating factors such as environmental parameters and biomarker signals in the comprehensive risk assessment, which is often achieved through weighting coefficients.

[0066] In S5, 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; proportional merging refers to linear weighting or weighted superposition of each marker signal according to the weight configuration to obtain a comprehensive score or signal value; risk interval refers to the risk judgment boundary interval set according to historical data or standard specifications, used to classify the level of the result; interval judgment refers to comparing the result after proportional merging with the upper and lower boundaries 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, danger, etc.) corresponding to the comparison result.

[0067] like Figure 2 As shown, the specific steps for obtaining the distribution characteristics of abnormal channels are as follows:

[0068] S101: Based on the detection channel, analyze the real-time signal strength and background noise baseline collected in the current batch. By comparing the standard deviation of the signal strength of each channel with the corresponding noise fluctuation range, construct the pairing information of standard deviation and noise fluctuation for each channel to obtain the channel fluctuation range group.

[0069] The system extracts real-time signal intensity data from each channel of the current batch. This signal intensity data is a sequence of continuously acquired detection values ​​within a set time period after target molecule injection. It is typically an electrical or optical signal value, for example, acquired once per second for 60 seconds, resulting in 60 detection points. Simultaneously, the system acquires the background noise baseline data for that channel under no-sample or blank conditions. This background noise baseline must be acquired before sample loading, and periodically sampled to form a noise sequence. Next, the signal intensity data for each channel is analyzed to calculate the signal fluctuation amplitude. The fluctuation amplitude represents the dispersion of all acquired values ​​in the channel, typically calculated using the standard deviation method. The system performs line operations, and then calculates the noise fluctuation range for the background noise data. The method is to extract the difference between the maximum and minimum values ​​and record this value to measure the inherent noise fluctuation of the channel. For example, the signal fluctuation value in channel 1 is 2.3 and the noise fluctuation value is 1.1, channel 2 is 1.0 and 0.8, and 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 dozens to hundreds of channels. For example, 96 channels will generate 96 sets of corresponding data. The pairing information is uniformly recorded and organized into fluctuation interval groups for further judgment of the state of each channel.

[0070] S102: Based on the channel fluctuation interval group, determine the relationship between the standard deviation of each channel and the noise fluctuation interval, classify the channels above the fluctuation interval as abnormal channels, classify the channels below the fluctuation interval as signal stable channels, and classify the remaining channels as normal types to obtain the channel response distribution label;

[0071] The signal fluctuation value and its corresponding noise fluctuation value for each channel are read sequentially and compared one by one. A fixed judgment amplitude value is set to determine whether the difference between the signal fluctuation value and the noise fluctuation value exceeds the judgment amplitude threshold. When the signal fluctuation value of a channel is significantly greater than its noise fluctuation value plus the judgment threshold, the channel is classified as an abnormal channel. When the signal fluctuation value is significantly less than the noise fluctuation value minus the judgment threshold, it is classified as a stable channel. If the fluctuation value is between the upper and lower limits, it is classified as a normal channel. In practice, the judgment amplitude threshold can be set to 0.2. Referring to the example in the previous step, the signal fluctuation of channel 1 is 2.3, and the noise fluctuation is... The sound fluctuation is 1.1, and the difference is 1.2. If it is higher than 0.2, it is judged as an abnormal channel. The signal fluctuation of channel 2 is 1.0, the noise fluctuation is 0.8, and the difference is 0.2. It is in the critical range and is judged as a normal channel. The signal fluctuation of channel 3 is 0.6, the noise fluctuation is 0.7, and the difference is -0.1. It is less than the set lower limit and is judged as a stable channel. All channels are compared and tagged in this way. The tag content is represented by letters. For example, A represents an abnormal channel, B represents a stable channel, and C represents a normal channel. Finally, each channel number is paired with its response category and recorded to form a complete channel response distribution tag for subsequent summary analysis.

[0072] S103: Based on the channel response distribution labels, summarize the corresponding numbers and statuses of various channels, and gather the associated attributes of abnormal channels through sorting and grouping to obtain the distribution characteristics of abnormal channels;

[0073] All channels are categorized and organized, and a list of channel numbers corresponding to each category is extracted. Channel numbers marked as abnormal are extracted centrally, and the relevant attribute information of each channel in the current batch is summarized one by one. The attribute information includes the number of times the signal abnormality occurred in the batch, the number of detection cycles during which the abnormality lasted, the difference between the signal fluctuation value and the noise fluctuation value of the channel, and the offset threshold used in the judgment process. By unifying the information into structured data, an abnormal feature information set for each abnormal channel can be formed. For example, channel 5 abnormally occurred 3 times, each time lasting 6 seconds, with a fluctuation difference of 1.4 and a judgment offset of 0.2. All abnormal channel information is sorted according to channel number, and then grouped according to the similarity of their signal fluctuation performance. For example, fluctuation differences greater than 1.0 are in the first group, between 0.5 and 1.0 are in the second group, and less than 0.5 are in the third group. Finally, the distribution characteristics of abnormal channels are obtained, including their numbers, abnormal attributes, and group classification information, providing input basis for subsequent signal processing and threshold adjustment.

[0074] like Figure 3 As shown, the specific steps for obtaining the channel threshold adjustment coefficient are as follows:

[0075] 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 to determine the differences in signal fluctuation range between channels, and match the channels with deviations in fluctuation trend with the channel labels to obtain the standard deviation difference characteristics.

[0076] Extract the channel numbers and their corresponding abnormal fluctuation information obtained in the previous stage, and sequentially match them with the real-time signal strength standard deviation distribution data of all channels in the current batch. For each channel, read the dispersion data of its signal strength within the complete detection period, and arrange the standard deviation values ​​of all channels in ascending order of their numbers. Then, mark the corresponding abnormal channel number in this sequence. Next, locate the standard deviation value of each abnormal channel and its relative position in the entire channel sequence, and compare it with the standard deviation distribution values ​​of its surrounding channels to calculate its offset from adjacent channels. If the standard deviation value of an abnormal channel is significantly lower than that of the preceding and following channels, then the channel is considered to have a higher offset. Significant jumps are defined as deviations from the trend channel. The criterion for this deviation is that it exceeds 20% of the average standard deviation of the channels before and after it. For example, if the standard deviation of channel 25 is 3.2, and the standard deviations of adjacent channels 24 and 26 are 2.1 and 2.3 respectively, then their average is 2.2, and the deviation value is 1.0, which exceeds the threshold of 0.44. Therefore, channel 25 is judged as a deviation channel. This process is repeated for all abnormal channels. For each deviation channel, its number, deviation direction (greater than or less than the average of adjacent channels), and deviation magnitude are recorded. The channel number and deviation status are combined to form the standard deviation difference feature.

[0077] S202: Based on the standard deviation difference characteristics, compare the distribution position of the standard deviation of each channel in the channel set, determine whether it is located in the boundary region of the fluctuation sequence, and identify the channels in the boundary as difference response types to obtain the boundary response category group;

[0078] The standard deviations 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 sequentially, and its position is determined according to the set boundary range threshold to determine whether it is on the edge. For example, the sorting sequence is divided into three segments according to percentiles, where the first 10% of channels are low boundary channels, the last 10% of channels are high boundary channels, and channels outside the two ends are considered middle region channels. For example, in 96 channels, the first 9 and last 9 channels are boundary channels. For channels in these two regions, the standard deviation difference characteristics are compared again. If it has been marked as an offset channel, it is classified as a difference response type channel, and its number and boundary type are recorded. For example, channel 7 is in the 6th position and the channel standard deviation is 0.4, which is judged as a low boundary response channel. Channel 91 is in the 92nd position and the standard deviation is 3.8, which is judged as a high boundary response channel. All channels that meet the judgment conditions are summarized, and their number, sorting position, standard deviation value and boundary type label are output to finally construct a boundary response category group.

[0079] S203: Based on the boundary response category group, optimize the response threshold parameter configuration of the corresponding channel for each response type channel. By recording the optimization direction and parameter correction content, integrate the adjustment results of the channel to obtain the channel threshold adjustment coefficient.

[0080] The high-boundary and low-boundary categories are processed separately. The corresponding channel number, current standard deviation, and boundary label are read sequentially. Then, the current response threshold value of that channel in the device parameter configuration is called to determine if adjustment is needed. The default adjustment direction for high-boundary channels is to increase the threshold value, and the default adjustment direction for low-boundary channels is to decrease the threshold value. The adjustment range is calculated based on the proportion of the current standard deviation of the channel deviating from the average value. For example, if the current channel standard deviation is 3.8, the average standard deviation of all channels is 2.0, the offset range is 1.8, and the proportion is 90%, then the threshold adjustment range is set to 20% of the initial threshold value. If the current threshold is 100, then the adjusted threshold is 120. This adjustment operation is recorded as an upward correction, and the reverse is a downward correction. After all channels have undergone adjustment, the channel number, adjustment direction, threshold values ​​before and after adjustment, correction range, and boundary category are integrated to form the channel threshold adjustment coefficient, which is then output as structured data.

[0081] like Figure 4 As shown, the specific steps for obtaining the signal enhancement amplitude distribution are as follows:

[0082] S301: Based on the channel threshold adjustment coefficient, compare the real-time signal strength of each detection channel with the corresponding threshold configuration, filter the channels with signal strength below the threshold range, locate the set of channels that need to be adjusted for gain, and establish a gain adjustment identification group;

[0083] Extract the response threshold value configured for each detection channel in the current batch and the corresponding real-time signal strength data. The threshold value comes from the channel threshold adjustment coefficient file output from the previous stage. Each channel configuration records the latest set threshold value, its adjustment direction, and adjustment amplitude. Then, read the average real-time signal strength collected in the current period according to the channel number, and compare the signal value with the threshold value corresponding to the channel. If the signal strength value is less than the threshold value, the channel is determined to be an under-response channel, and gain adjustment operation needs to be performed. For example, the threshold configuration for channel number A is 12. 0, 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 strength value, and difference value are recorded as the basic information before adjustment. The above comparison and judgment process is performed on each channel. The threshold value in the judgment criteria must be the latest value after the previous step 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 data structure of the list is transformed, and the adjustment status field of each channel is recorded as "needs adjustment" or "does not adjust". The output is a gain adjustment identifier group.

[0084] S302: Based on the gain adjustment identifier group, the signal gain is gradually increased for each channel in a step manner. After each gain adjustment, the signal strength change amplitude and the corresponding noise change amplitude are recorded synchronously. The corresponding gain state data are constructed according to the time series structure to obtain the gain change trend set.

[0085] Read the channel number marked "Needs Adjustment" and call the current gain value initial configuration of that channel in the gain control unit. Then set the step gain adjustment range, for example, a gain adjustment step size of 10 per round, and execute three rounds continuously. After the first round of gain increase operation, read the new signal strength value and noise amplitude value of the channel in real time, and record the signal increment and noise increment at the current gain level. Then enter the second round of gain adjustment, and continue to accumulate the gain value by 10 on the original basis, and record the new round of signal and noise values ​​again. This process continues until the maximum number of adjustments is set and then terminates. Each gain adjustment and sampling must record the timestamp and channel number, and record the time point, Gain value, signal strength, and noise amplitude constitute a complete time series data structure. For example, channel B starts with a gain of 100. After the first round of gain adjustment, the signal strength 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 status record table according to the adjustment round number. The table fields should include channel number, gain level, signal strength, noise amplitude, and timestamp. After all channels are recorded, a corresponding gain status data set is formed. The set is grouped by channel number, sorted by time, and integrated to generate a gain change trend set.

[0086] 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.

[0087] The ratio is used in conjunction with the direction of noise amplitude variation for screening, employing the following formula:

[0088] ;

[0089] 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.

[0090] 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.

[0091] 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:

[0092] ;

[0093] 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:

[0094] AU, after normalization ;

[0095] The normalization ratio for noise variation is calculated as follows:

[0096] ;

[0097] Then, the absolute difference between the signal change ratio and the noise change ratio is processed to obtain the linkage screening parameters:

[0098] ;

[0099] Based on the preset linkage filtering benchmark interval [3, 10], the result is compared with the interval. If it is determined to be greater than the upper limit, the channel number needs to be matched with the corresponding... The data are combined and categorized into the signal enhancement amplitude distribution result set, and then other channel data are further processed and filtered.

[0100] Compare with the other channel number The detection data shows that the signal strength before and after gain is 198AU and 205AU, respectively. Therefore:

[0101] AU;

[0102] AU;

[0103] The corresponding value after normalization is 0.95.

[0104] Calculate its proportion:

[0105] ;

[0106] Meanwhile, the noise level of this channel changed from 9 AU to 12 AU. AU, AU, after normalization, is 0.96. The noise change ratio is calculated as follows:

[0107] ;

[0108] Therefore, we can conclude that:

[0109] ;

[0110] If the result is below the lower limit of the screening interval by 3, it will not be included in the enhancement amplitude result set. This process will be repeated for each detection channel to complete the batch screening task.

[0111] This result indicates that the current channel number is Linked filtering parameters The value range it falls within is greater than 10. Based on the screening criteria, the range division for this parameter is as follows:

[0112] when When the signal change within the channel is weak and basically consistent with the noise change, it can be identified as an invalid response region.

[0113] when When the channel signal change is within the normal fluctuation range relative to the noise change, it can be identified as a stable detection area;

[0114] when When the signal increase is much higher than the noise disturbance, it indicates a sudden increase trend and can be identified as a significantly enhanced region.

[0115] Therefore, the numerical result falls into An interval indicates that the channel is in a state of abrupt change in both signal and noise, with a prominent signal enhancement trend. Therefore, the channel needs to be numbered according to the difference in its signal strength. The amplitude distribution of the input signal enhancement.

[0116] The formula's calculation logic is: the intensity difference before and after signal gain. Standard deviation of historical fluctuations The ratio is used to calculate the signal variation amplitude, and then the noise variation difference is calculated. Standard deviation after gain The ratio of the two values ​​determines the noise disturbance amplitude, and the absolute value of the final difference between the two values ​​is the linkage screening parameter. This is used to represent the relative change trend between signal and noise within a channel during gain adjustment. The formula normalizes the signal and noise changes according to the standard deviation at different time points, and constructs a linkage parameter using the difference. The introduction of the standard deviation parameter makes the fluctuation basis of signal and noise more comparable, avoids misleading results due to a single amplitude difference, and effectively distinguishes detection channels with different actual fluctuation trends.

[0117] like Figure 5 As shown, the specific steps for obtaining the weighted characteristic group of the markers are as follows:

[0118] S401: Based on the signal enhancement amplitude distribution, the channel threshold adjustment coefficient is numbered and paired with the nucleic acid biomarker signals collected by each detection channel, and the collection location, ambient temperature and ambient humidity parameters are organized to construct multi-parameter combination data for each detection channel to obtain the environmental parameter set;

[0119] Each record in the channel threshold adjustment coefficient is extracted and matched one-to-one with the nucleic acid biomarker signals 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 biomarker signal value comes from the sampled signal intensity dataset. The signal value is recorded by channel and the sampling time point is marked. After the numbering is matched, the spatial positioning information of each detection channel is retrieved, i.e., the acquisition position parameters. The acquisition position needs to be provided by the position information or coordinate information in the carrier structure. For example, if the channel number is A3 and the carrier is the 3rd column and 4th row of the 2nd board, the acquisition position can be recorded as "board 2-C4". Then, the temperature and humidity of the environment during the acquisition process of this channel are obtained sequentially. The temperature is in degrees Celsius and the humidity is in percentage. In contrast, temperature and humidity data should be provided through the environmental recording module of the detection equipment. Each sampling should be matched with a corresponding timestamp and channel number. For example, if the sampling time for channel A3 is 14:32 on August 19, 2025, the corresponding recorded temperature is 26.4 degrees Celsius and the humidity is 48.7%. The channel number, threshold adjustment value, marker signal value, sampling location, temperature, and humidity are combined into a single multi-parameter combined data record and sorted in ascending order according to the channel number. Finally, a set of multi-parameter records with a consistent structure is formed. That is, each record has a fixed structure and complete fields. Each field has uniform and consistent data precision. The threshold adjustment value is retained to one decimal place, the marker signal value is retained to two decimal places, and the temperature and humidity are each retained to one decimal place. The final output is a set of environmental parameters.

[0120] S402: Based on the set of environmental parameters, compare the distribution of each parameter in the detection channel, determine the participation ratio of the acquisition location, ambient temperature, ambient humidity and marker signal in the combined data, and establish a weight allocation structure by statistically configuring the weight of the difference parameters.

[0121] Each multi-parameter combination record in the set is expanded into independent fields, and the four parameter values ​​of acquisition location, ambient temperature, ambient humidity, and nucleic acid biomarker signal are extracted sequentially. First, the acquisition locations in all detection channels are classified and statistically analyzed, and the sample plate is divided into zones according to spatial region dimensions, such as zone A, zone B, and zone C. The number of channels contained in each zone is counted, and the proportion of the total number of channels in that zone is calculated. For example, zone A has 28 channels, accounting for 29.2% of the total 96, which is considered a high-distribution zone. Then, the temperature parameter is analyzed for numerical distribution, and temperature intervals are set, for example, each 1 degree Celsius is a level. The proportion of channels in each level is counted, and the relationship between the temperature value and the biomarker signal value in the channel is compared. If the biomarker signal is significantly higher or lower in a certain temperature range, the temperature range is recorded as an abnormal range. Then, the humidity parameter is processed, with a range of 5%. Divide the humidity range into intervals and compare the same quantity proportion with the signal response. Record the segment number of the corresponding signal deviation. Finally, compare the proportion of each parameter in all channels, the number of participating channels, and the contribution of the corresponding parameter value to the change in signal strength. If a parameter remains stable in most channels, but the deviation in some channels causes significant signal fluctuations, it is a high-weight parameter. A judgment threshold can be set so that the parameter's influence on the signal exceeds 15%, and it 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 other parameters are assigned weight values ​​according to their influence proportions. The sum of the weights of the four parameters needs to be uniformly normalized to 1.00. Assign values ​​to each parameter and record them in the weight allocation structure. The fields in the structure include parameter name, set of active channel numbers, contribution proportion, weight value, etc., and output the weight allocation structure.

[0122] S403: Based on the weight allocation structure, normalize the environmental parameters and marker signals, superimpose the influence of each parameter according to the weight allocation principle, and determine the effect of the weight allocation result in the channel to obtain the marker weighted characteristic group.

[0123] Based on the weighted allocation principle and the combined influence of each parameter, the following formula is used:

[0124] ;

[0125] Calculate the weighted average parameters The effect of the weight allocation result on the channel is determined, and the weighted characteristic group of the marker is obtained, where, Representing the The weighting coefficients of each environmental parameter in the weighting allocation Representing the Normalized values ​​of environmental parameters Representing the The weighting coefficients of each marker signal in the weighting allocation Representing the The normalized value of a marker signal, The total number of environmental parameters and marker signals;

[0126] The weighted average parameter refers to a parameter obtained by weighting environmental parameters and biomarker signals together using their respective weighting coefficients, and then averaging the results over all participating factors. It represents the normalized value of the environmental parameters. Normalized values ​​of marker signals In their respective weighting coefficients , The adjusted overall performance is used to reflect the overall level after all participating factors are weighted and superimposed in the same detection channel.

[0127] First, the environmental parameters and biomarker signal data collected in the detection system were extracted, namely, temperature 22.5℃, humidity 52%, vibration intensity 0.03g, and three sets of biomarker signal values ​​of 100, 280, and 190, representing the instantaneous photoelectric response intensity of nucleic acid biomarkers collected under different channels. For the above parameters, the normalized data were directly read based on the historical normalization strategy file, with the normalized values ​​as follows: temperature 0.5, humidity 0.55, vibration 0.5, and the normalized values ​​of the three sets of biomarker signals 0.25, 0.6, and 0.5, respectively. The corresponding weighting coefficients were given by the fitting results of multiple batches of samples collected in the system's history. The weights of the environmental parameters were set as follows: temperature... ,humidity ,shock The weighting coefficients of the marker signals are as follows: Signal 1 Signal 2 Signal 3 Then the formula is called, where The specific calculation process after substituting the data is as follows:

[0128] The weighted calculation for the first item is as follows:

[0129] ;

[0130] The second weighted calculation is as follows:

[0131] ;

[0132] The third weighted calculation is as follows:

[0133] ;

[0134] Add the three sets of results above:

[0135] ;

[0136] Divide by 6 to get:

[0137] ;

[0138] This result indicates that the weighted average parameter Located within the set reference range Based on the interval segmentation logic defined in the historical sample stability analysis, the following three segments are defined:

[0139] when When classified as a low-interference expression segment, it indicates that the overall activation level of each influencing factor in the channel is weak, and the parameter combination may fail to effectively trigger the target signal response.

[0140] when When classified as a stable expression segment, it indicates that the weighted relationship between environmental factors and biomarker signals is under standard settings, the combination configuration is reasonable, and it is suitable as a feature template;

[0141] when When this is classified as an abnormal activation segment, it indicates that some parameters are over-amplified in the combination. Due to local environmental interference or abnormal enhancement of marker signals, it is necessary to further identify the source of the high-deviation parameters.

[0142] Therefore, the current value The fact that it is in a stable expression range indicates that the environmental state and biomarker signal expression of this channel in the current batch are in a historically stable range. This is used in the subsequent risk level distribution judgment process and is a key intermediate result that drives the transformation from individual features to comprehensive features.

[0143] like Figure 6 As shown, the specific steps for obtaining risk level distribution data are as follows:

[0144] S501: Based on the marker weighted characteristic group, calculate the proportional merging result of the marker signal and the corresponding weight configuration in each channel, analyze the distribution of the proportional merging data in the channel, and determine the interval position corresponding to each channel according to the upper and lower boundaries of the risk interval to obtain the signal risk interval category.

[0145] The raw biomarker signal value is extracted from each detection channel. This signal value is typically the signal intensity of the nucleic acid target molecule captured by the detection system per unit time, and can be set as a relative light intensity or voltage response value. Then, the weight configuration data generated in the previous stage for that channel is read sequentially. The weight configuration includes environmental temperature weight, environmental humidity weight, acquisition location weight, and the biomarker's own reference normalization factor. These weight values ​​are multiplied proportionally with the channel's nucleic acid signal value, summed, and averaged to form a proportionally merged result. For example, channel C7 has a nucleic acid signal intensity of 148.2, a temperature weight of 0.35, a humidity weight of 0.30, a location weight of 0.25, and a biomarker factor weight of 0. After merging and calculating, the proportional merged value of channel C7 is 142.85. All channels are weighted and calculated in the same way. After completion, the proportional merged values ​​are sorted in ascending order by channel number. Then, the pre-set risk interval boundary data is retrieved. The risk interval is divided into three levels: safe zone (0 to 120), warning zone (120 to 160), and danger zone (above 160). The merged result value of each channel is compared with the upper and lower boundary values ​​of the risk interval to determine its interval position. If a channel value is 108, it is classified as a safe zone; if it is 138, it is classified as a warning zone; and if it is 172, it is classified as a danger zone. After each channel is compared, its channel number and interval label are recorded. Finally, the signal risk interval category is output.

[0146] S502: Based on the signal risk interval category, classify and organize according to the interval judgment result, record the channel number and signal merging result corresponding to each category, and output the distribution of each category in structured data to obtain the risk level distribution data;

[0147] All channels are grouped according to the risk ranges labeled in the previous stage. First, extract the set of channel numbers marked "Safe" from the category labels, record their numbers and corresponding proportional merge values, and label this group as level label "L1". Then process the "Warning" labeled channels, extract all numbers marked "Warning" and their corresponding merge values, and record them as level label "L2". Finally, process all "Danger" channel numbers, record their numbers and merge values, and uniformly label them as level label "L3". Write the three types of data into structured tables. The table fields should include channel number, merged signal value, risk range label, and level label. In the example, the merged value of channel C7 is 142.85, corresponding to the "Warning" label, and the level is recorded as L2. The merged value of channel B3 is 171.4, the label is "Danger", and the level is L3. The merged value of channel A1 is 98.3, the label is "Safe", and the level is L1. After all channels are processed, output the data table according to the level label, ensuring that the structure is complete and the numbers are not omitted. Finally, output the risk level distribution data.

[0148] like Figure 7 As shown, the biological detection system based on channel response regulation includes:

[0149] The signal fluctuation recognition 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 stability of the target molecule response, compares the standard deviation of the signal intensity of each channel with the corresponding background noise fluctuation range, and identifies abnormal channels by judging the relationship between the standard deviation and the fluctuation range, thus obtaining the distribution characteristics of abnormal channels.

[0150] The threshold parameter correction module determines the current standard deviation distribution of the channels in the high-throughput cell detection platform based on the abnormal channel distribution characteristics. It compares the correspondence between the standard deviation and the upper and lower bounds of the historical fluctuation range. For channels with a standard deviation higher than the upper bound, the response threshold parameter is directly adjusted. For channels with a standard deviation lower than the lower bound, the response threshold parameter is directly reduced. After the adjustment is completed, the channel parameter configuration is entered to obtain the channel threshold adjustment coefficient.

[0151] The gain dynamic screening module compares the channel threshold adjustment coefficient with the real-time signal strength of each detection channel item by item. For signals below the threshold parameter, it analyzes their current state and increases the gain in a step-by-step manner. After each gain adjustment, it records the noise amplitude and signal strength, calculates the ratio before and after the gain change, and filters the channels whose ratio meets the conditions to obtain the signal enhancement amplitude distribution.

[0152] The multi-parameter weighting and normalization module, based on the signal enhancement amplitude distribution, combines the channel threshold adjustment coefficient with the nucleic acid biomarker signal of the high-throughput protein detection unit to perform weighting and normalization on the acquisition location, acquisition duration, ambient temperature and humidity and biomarker signal, and determines the role of each parameter in the weighting allocation to obtain the biomarker weighted characteristic group.

[0153] The risk level discrimination module is based on the weighted characteristic group of markers. It combines the marker signals with the weight configuration proportionally, and makes interval judgments with the upper and lower limits of the risk interval based on the combined result. The module then classifies and organizes the data according to the interval judgments and outputs the corresponding categories to obtain the risk level distribution data.

[0154] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A biological detection method based on channel response modulation, characterized in that, The method includes: 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 the signal intensity of each channel with the fluctuation range of the background noise, identify abnormal channels, and obtain the distribution characteristics of abnormal channels; S2: Based on the abnormal channel distribution characteristics, determine the standard deviation distribution of the high-throughput cell detection platform channels, compare the relationship between the standard deviation and the upper and lower bounds of the historical fluctuation range, and adjust the response threshold parameters for channels that exceed the upper and lower bounds respectively to obtain the channel threshold adjustment coefficient; S3: Based on the channel threshold adjustment coefficient, compare it item by item with the real-time signal strength of the detection channel, analyze the state of signals below the threshold parameter, increase the gain step by step, record the noise amplitude and signal strength, calculate the gain change ratio, and obtain the signal enhancement amplitude distribution. The specific steps for obtaining the signal enhancement amplitude distribution are as follows: S301: Based on the channel threshold adjustment coefficient, compare the real-time signal strength of each detection channel with the corresponding threshold configuration, and locate the set of channels that need to be adjusted by filtering channels whose signal strength is lower than the threshold range, and establish a gain adjustment identification group; S302: Based on the gain adjustment identifier group, the signal gain is gradually increased for each channel in a step manner. After each gain adjustment, the signal strength change amplitude and the corresponding noise change amplitude are recorded synchronously. The corresponding gain state data is constructed according to the time series structure to obtain the gain change trend set. 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. S4: Based on the signal enhancement amplitude distribution, combined with the channel threshold adjustment coefficient and the nucleic acid biomarker signal of the high-throughput protein detection unit, the acquisition location, acquisition duration, ambient temperature and humidity and biomarker signal are weighted and normalized to determine the role of the parameters in the weight allocation and obtain the biomarker weighted characteristic group. The specific steps for obtaining the weighted characteristic set of the markers are as follows: S401: Based on the signal enhancement amplitude distribution, the channel threshold adjustment coefficient is numbered and paired with the nucleic acid biomarker signals collected by each detection channel, and the collection location, ambient temperature and ambient humidity parameters are organized to construct multi-parameter combination data for each detection channel to obtain an environmental parameter set; S402: Based on the set of environmental parameters, compare the distribution of each parameter in the detection channel, determine the participation ratio of the acquisition location, ambient temperature, ambient humidity and marker signal in the combined data, and establish a weight allocation structure by statistically configuring the weight of the difference parameters. S403: According to the weight allocation structure, normalization processing is performed on the environmental parameters and marker signals, 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 to obtain the marker weighted characteristic group.

2. The biological detection method based on channel response regulation according to claim 1, characterized in that, The abnormal channel distribution characteristics include abnormal channel number, abnormal distribution range, and abnormal type; the channel threshold adjustment coefficient includes adjustment coefficient number, 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 allocation weight, weight normalization coefficient, and parameter group classification.

3. The biological detection method based on channel response regulation according to claim 1, characterized in that, The specific steps for obtaining the abnormal channel distribution characteristics are as follows: S101: Based on the detection channel, analyze the real-time signal strength and background noise baseline collected in the current batch. By comparing the standard deviation of the signal strength of each channel with the corresponding noise fluctuation range, construct the pairing information of standard deviation and noise fluctuation for each channel to obtain the channel fluctuation range group. S102: Based on the channel fluctuation interval group, determine the relationship between the standard deviation of each channel and the noise fluctuation interval, classify the channels above the fluctuation interval as abnormal channels, classify the channels below the fluctuation interval as signal stable channels, and classify the remaining channels as normal types to obtain the channel response distribution label; S103: Based on the channel response distribution labels, summarize the corresponding numbers and statuses of various channels, and gather the associated attributes of abnormal channel types through sorting and grouping to obtain the abnormal channel distribution characteristics.

4. The biological detection method based on channel response regulation according to claim 1, characterized in that, The specific steps for obtaining the channel threshold adjustment coefficient are as follows: 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, determine the differences in signal fluctuation range between channels, and match the channels with deviations in fluctuation trend with the channel identifiers to obtain the standard deviation difference characteristics. S202: Based on the standard deviation difference characteristics, compare the distribution position of the standard deviation of each channel in the channel set, determine whether it is located in the boundary region of the fluctuation sequence, and identify the channels located at the boundary as difference response types to obtain the boundary response category group; S203: Based on the boundary response category group, optimize the response threshold parameter configuration of each response type channel, and integrate the channel adjustment results by recording the optimization direction and parameter correction content to obtain the channel threshold adjustment coefficient.

5. The biological detection method based on channel response regulation according to claim 1, characterized in that, The method further includes: S5: Based on the weighted characteristic group of the markers, the marker signals are merged with the weight configuration ratio respectively. The merged result is used to determine the risk interval with the upper and lower limits of the risk interval. The risk level distribution data is obtained by classifying and organizing the data according to the interval determination category. The risk level distribution data includes level labels, judgment intervals, and classification indexes.

6. The biological detection method based on channel response regulation according to claim 5, characterized in that, The specific steps for obtaining the risk level distribution data are as follows: S501: Based on the weighted characteristic group of the markers, calculate the proportional merging result of the marker signal and the corresponding weight configuration in each channel, analyze the distribution of the proportional merging data in the channel, and determine the interval position corresponding to each channel according to the upper and lower boundaries of the risk interval to obtain the signal risk interval category. S502: Based on the aforementioned signal risk interval categories, classify and organize the data according to the interval judgment results, record the channel number and signal merging result corresponding to each category, and output the distribution of each category as structured data to obtain risk level distribution data.

7. The biological detection method based on channel response regulation according to claim 1, characterized in that, The detection channel refers to the pathway in the biological detection device used to independently collect and output detection signals. Each detection channel corresponds to the signal collection of one sample or a group of samples. The abnormal channel refers to the detection signal exhibiting abnormal fluctuations or noise characteristics. The collection location refers to the geographical location information of the sample or the spatial location information on the sample plate.

8. A biological detection system based on channel response modulation, said system being used to implement the biological detection method based on channel response modulation as described in any one of claims 1-7, characterized in that, The system includes: The signal fluctuation recognition 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 stability of the target molecule response, compares the standard deviation of the signal intensity of each channel with the corresponding background noise fluctuation range, and identifies abnormal channels by judging the relationship between the standard deviation and the fluctuation range, thus obtaining the distribution characteristics of abnormal channels. Based on the abnormal channel distribution characteristics, the threshold parameter correction module determines the current standard deviation distribution of the high-throughput cell detection platform channels, compares the correspondence between the standard deviation and the upper and lower bounds of the historical fluctuation range, directly adjusts the response threshold parameter for channels above the upper bound, and directly reduces the response threshold parameter for channels below the lower bound. After the adjustment is completed, the channel parameter configuration is entered to obtain the channel threshold adjustment coefficient. The gain dynamic screening module compares the channel threshold adjustment coefficient with the real-time signal strength of each detection channel item by item. For signals below the threshold parameter, it analyzes their current state and increases the gain in a step-by-step manner. After each gain adjustment, it records the noise amplitude and signal strength, calculates the ratio before and after the gain change, and filters the channels whose ratio meets the conditions to obtain the signal enhancement amplitude distribution. Based on the signal enhancement amplitude distribution, the multi-parameter weighting and normalization module combines the channel threshold adjustment coefficient with the nucleic acid biomarker signal of the high-throughput protein detection unit to perform weighting and normalization on the acquisition location, acquisition duration, ambient temperature and humidity and biomarker signal, and determines the role of each parameter in the weighting allocation to obtain the biomarker weighted characteristic group. The risk level discrimination module, based on the weighted characteristic group of the markers, proportionally merges the marker signals with the weight configuration, makes interval judgments with the upper and lower limits of the risk interval, classifies and organizes the data according to the category of the interval judgment, and outputs the corresponding category to obtain the risk level distribution data.

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