CN117686570B - Data detection method, system and medium prepared by graphene field effect tube sensor - Google Patents

Abstract

The invention provides a data detection method, a system and a medium for graphene field effect transistor sensor preparation, wherein the method comprises the following steps: preparing a graphene field effect transistor sensor of a solution system as a reference sensor; preparing a corresponding hydrogel graphene field effect transistor sensor; detecting channel currents of the hydrogel graphene field effect transistor sensors respectively; respectively acquiring corresponding hydrogel system parameter data of each hydrogel graphene field effect tube sensor, carrying out deep learning by combining corresponding channel currents, and predicting channel currents of the graphene field effect tube sensors prepared by other hydrogel systems; and comparing portability among the hydrogel systems, comparing differences between channel currents corresponding to the hydrogel systems and channel currents of the reference sensor, and selecting an optimal hydrogel system through a screening algorithm. According to the invention, the influence of different hydrogels on channel current is analyzed through data detection, and an excellent sensing platform is established for food quality safety detection.

Description

Data detection method, system and medium prepared by graphene field effect tube sensor

Technical Field

The present invention relates to the field of electrochemical sensing technology, and in particular to a data detection method, system and medium for preparing a graphene field effect tube sensor.

Background technique

Graphene is mainly composed of carbon atoms connected by hybridization. As an excellent nanomaterial, it has many advantages, such as high specific surface area and good performance. Original graphene can be used as a zero-gap semiconductor or zero-overlap semimetal, and exhibits a bipolar electric field effect because both holes and electrons can act as mobile carriers. Since 2004, when a stable single-layer graphene was isolated under ambient conditions for the first time, great progress has been made in the development of various graphene-based sensing parts.

Electrolyte gated graphene field effect transistor (SGGT) is a new electrochemical sensing platform that has emerged in recent years. Compared with other field effect transistors, SGGT is the most promising real-time, high-efficiency, high-throughput biosensor. Depending on the detected substance, the application of electrolyte gated graphene field effect transistor in the field of biosensors mainly includes: ion sensors, small molecule sensors, protein sensors, DNA sensors, bacteria sensors, cell sensors, and in vivo detection sensors.

SGGT-based ion sensors mainly use the interaction between ions and graphene because the device works in solution. In recent years, various SGGT-based ion sensors, including Na+, K+, Ca2+, Mg2+, Hg2+, Pb2+, etc., have attracted widespread attention.

The electrolyte-gated graphene field-effect transistor integrates the sensor and the amplifier, providing an excellent biosensing platform for the rapid detection of trace targets. At present, SGGT has been used in the field of chemical biosensors: the detection of pH and the presence of ions in liquids, as well as the detection of histamine, dopamine, glucose, DNA and even cells in organisms. In these practices, SGGT has shown advantages such as rapidity, real-time, high sensitivity and high throughput.

At present, SGGT mostly uses a solution system as an electrolyte exchange medium, so that two double electron layers are formed at the gate/electrolyte and electrolyte/channel interfaces, which are equivalent to two capacitors. This also makes the entire system in use, even a small change in gate voltage can cause a large change in channel current. However, the electrolyte solution system makes SGGT less portable in practical applications such as food quality and safety testing, which seriously limits the practicality of SGGT in practical applications such as food quality and safety testing.

Summary of the invention

In order to solve at least one of the above technical problems, the present invention proposes a data detection method, system and medium for preparing a graphene field effect tube sensor, using graphene as a channel layer to prepare an SGGT device, using hydrogel to encapsulate the SGGT to connect the gate and the graphene channel, and through data detection and analysis of the influence of different hydrogel systems and parameters on the SGGT channel current signal, a hydrogel gate-controlled graphene field effect sensor is constructed and its performance is optimized, and an excellent sensing platform is established for the rapid detection of trace food hazards, which greatly improves the portability of SGGT in the practical application of food quality and safety detection, so that SGGT can maintain good performance in practical applications and even in harsh scenarios. At the same time, the present invention analyzes the influence of different hydrogel systems and parameters on the SGGT channel current signal through data detection, thereby selecting the most suitable hydrogel system, providing important methods and technologies for the rapid detection of trace food hazards.

The first aspect of the present invention provides a data detection method for preparing a graphene field effect transistor sensor, the method comprising:

A graphene field effect transistor sensor of a solution system is prepared as a reference sensor, and an SGGT channel current signal of the reference sensor is detected as first current data;

Selecting a variety of hydrogel systems with representative characteristics from the hydrogel system library, and preparing corresponding hydrogel graphene field effect transistor sensors respectively;

For the prepared multiple hydrogel graphene field effect transistor sensors, the SGGT channel current signal of each hydrogel graphene field effect transistor sensor is detected as the second current data;

Based on the prepared multiple hydrogel graphene field effect transistor sensors, the corresponding hydrogel system parameter data of each hydrogel graphene field effect transistor sensor is obtained respectively, and deep learning is performed in combination with the corresponding second current data to predict the SGGT channel current signal of the graphene field effect transistor sensors prepared by other hydrogel systems in the hydrogel system library as the third current data;

The portable performance data of each hydrogel system in the hydrogel system library are compared and analyzed, and the difference between the second current data or the third current data corresponding to each hydrogel system and the first current data of the reference sensor is compared and analyzed, and the best hydrogel system is selected through a preset screening algorithm.

In this scheme, the corresponding hydrogel graphene field effect tube sensor is prepared, specifically including:

Preparation of SGGT devices:

Substrate preparation: A glass sheet is used as the substrate of the SGGT device, and the glass substrate is sequentially immersed in acetone, isopropanol, ethanol and deionized water for ultrasonic cleaning;

To make the electrode, the glass substrate is first dried with high-purity nitrogen, then adhered to the mask with high-temperature tape, and the patterned source, drain and gate are deposited by magnetron sputtering;

Transferring graphene, spin-coating a polymethyl methacrylate film on the graphene, and then transferring the graphene spin-coated with the polymethyl methacrylate film to a channel region formed by a source electrode and a drain electrode;

Annealing, annealing the SGGT device on a heating table for a predetermined time;

Dissolving the polymethyl methacrylate film, and immersing the annealed SGGT device in acetone to remove the polymethyl methacrylate film;

Packaged SGGT devices:

A hydrogel system is selected, a hydrogel solution of corresponding concentration is prepared, and the prepared SGGT device is immersed in the solution in a specific mold;

The hydrogel system is formed by cooling or adding a cross-linking agent, thereby achieving encapsulation of the SGGT device through the hydrogel system.

In this scheme, the SGGT channel current signal of the graphene field effect transistor sensor prepared by other hydrogel systems in the hydrogel system library is predicted as the third current data, specifically including:

Based on the prepared hydrogel system parameter data and the corresponding second current data of the multiple hydrogel graphene field effect transistor sensors, deep learning is performed to analyze the corresponding relationship between the hydrogel system parameter data and the second current data;

Based on the corresponding relationship, a channel current prediction model based on a hydrogel graphene field effect transistor sensor is constructed;

The parameter data of other hydrogel systems in the hydrogel system library are obtained, and the corresponding SGGT channel current signal, i.e., the third current data, is predicted by the channel current prediction model.

In this solution, after the corresponding SGGT channel current signal is predicted by the channel current prediction model, the method further includes:

Based on a plurality of hydrogel systems with representative characteristics, characteristic calculation is performed on parameter data of each hydrogel system with representative characteristics to obtain a first characteristic value;

Acquiring parameter data of the hydrogel system to be predicted, and performing characteristic calculation to obtain a second characteristic value;

The second characteristic value of the hydrogel system to be predicted is compared with the first characteristic value of each hydrogel system with representative characteristics in the hydrogel system library, and a first difference between the two is calculated;

Determine whether the first difference is greater than a first preset threshold value, if so, add the corresponding hydrogel system with representative characteristics to the correction database, otherwise discard it;

Based on each hydrogel system with representative characteristics in the revised database, the corresponding parameter data is input into the channel current prediction model for prediction, so as to obtain the predicted channel current data corresponding to each hydrogel system with representative characteristics;

Based on each hydrogel system with representative characteristics in the correction database, a difference calculation is performed between the second current data actually detected and the corresponding predicted channel current data to obtain a second difference value;

Calculate the average value of the second difference values corresponding to all hydrogel systems with representative characteristics in the correction database to obtain a correction value;

The correction value is added to the predicted corresponding SGGT channel current signal to obtain a corrected SGGT channel current signal.

In this scheme, the optimal hydrogel system is selected through a preset screening algorithm, specifically including:

Calculating the approximation between the second current data or the third current data of each hydrogel system in the hydrogel system library and the first current data of the reference sensor;

The approximation corresponding to each hydrogel system in the hydrogel system library is compared with the approximations corresponding to other hydrogel systems one by one;

If the approximation of the former is better than that of the latter, then the channel current evaluation value of the former is increased by 1, otherwise, it is increased by 0;

Compare the portable performance data of each hydrogel system in the hydrogel system library with the portable performance data of other hydrogel systems one by one;

If the portability performance data of the former is better than that of the latter, then the portability evaluation value of the former is increased by 1, otherwise, it is increased by 0;

After all the hydrogel systems in the hydrogel system library have completed the pairwise comparison evaluation, they are ranked based on the channel current evaluation value and portability evaluation value of each hydrogel system;

The best hydrogel system was screened out according to the score ranking.

In this scheme, the scoring is performed based on the channel current evaluation value and portability evaluation value of each hydrogel system, including:

Obtain the influence weights of the channel current evaluation value and the portability evaluation value on the screening of the hydrogel system respectively;

Based on each hydrogel system in the hydrogel system library, the channel current evaluation value is multiplied by the corresponding influence weight to obtain a first product, and the portability evaluation value is multiplied by the corresponding influence weight to obtain a second product;

Based on each hydrogel system in the hydrogel system library, the first product is added to the second product to obtain a weighted integral of each hydrogel system;

The hydrogel systems are sorted according to their weighted scores.

The second aspect of the present invention further provides a data detection system prepared by a graphene field effect transistor sensor, comprising a memory and a processor, wherein the memory comprises a data detection method program prepared by a graphene field effect transistor sensor, and when the data detection method program prepared by the graphene field effect transistor sensor is executed by the processor, the following steps are implemented:

A graphene field effect transistor sensor of a solution system is prepared as a reference sensor, and an SGGT channel current signal of the reference sensor is detected as first current data;

Selecting a variety of hydrogel systems with representative characteristics from the hydrogel system library, and preparing corresponding hydrogel graphene field effect transistor sensors respectively;

For the prepared multiple hydrogel graphene field effect transistor sensors, the SGGT channel current signal of each hydrogel graphene field effect transistor sensor is detected as the second current data;

Based on the prepared multiple hydrogel graphene field effect transistor sensors, the corresponding hydrogel system parameter data of each hydrogel graphene field effect transistor sensor is obtained respectively, and deep learning is performed in combination with the corresponding second current data to predict the SGGT channel current signal of the graphene field effect transistor sensors prepared by other hydrogel systems in the hydrogel system library as the third current data;

The portable performance data of each hydrogel system in the hydrogel system library are compared and analyzed, and the difference between the second current data or the third current data corresponding to each hydrogel system and the first current data of the reference sensor is compared and analyzed, and the best hydrogel system is selected through a preset screening algorithm.

In this scheme, the SGGT channel current signal of the graphene field effect transistor sensor prepared by other hydrogel systems in the hydrogel system library is predicted as the third current data, specifically including:

Based on the prepared hydrogel system parameter data and the corresponding second current data of the multiple hydrogel graphene field effect transistor sensors, deep learning is performed to analyze the corresponding relationship between the hydrogel system parameter data and the second current data;

Based on the corresponding relationship, a channel current prediction model based on a hydrogel graphene field effect transistor sensor is constructed;

The parameter data of other hydrogel systems in the hydrogel system library are obtained, and the corresponding SGGT channel current signal, i.e., the third current data, is predicted by the channel current prediction model.

In this solution, after the corresponding SGGT channel current signal is predicted by the channel current prediction model, the data detection method program prepared by the graphene field effect transistor sensor is further implemented when the processor executes the following steps:

Based on a plurality of hydrogel systems with representative characteristics, characteristic calculation is performed on parameter data of each hydrogel system with representative characteristics to obtain a first characteristic value;

Acquiring parameter data of the hydrogel system to be predicted, and performing characteristic calculation to obtain a second characteristic value;

The second characteristic value of the hydrogel system to be predicted is compared with the first characteristic value of each hydrogel system with representative characteristics in the hydrogel system library, and a first difference between the two is calculated;

Determine whether the first difference is greater than a first preset threshold value, if so, add the corresponding hydrogel system with representative characteristics to the correction database, otherwise discard it;

Based on each hydrogel system with representative characteristics in the revised database, the corresponding parameter data is input into the channel current prediction model for prediction, so as to obtain the predicted channel current data corresponding to each hydrogel system with representative characteristics;

Based on each hydrogel system with representative characteristics in the correction database, a difference calculation is performed between the second current data actually detected and the corresponding predicted channel current data to obtain a second difference value;

Calculate the average value of the second difference values corresponding to all hydrogel systems with representative characteristics in the correction database to obtain a correction value;

The correction value is added to the predicted corresponding SGGT channel current signal to obtain a corrected SGGT channel current signal.

The third aspect of the present invention also proposes a computer-readable storage medium, which includes a data detection method program for preparing a graphene field effect transistor sensor. When the data detection method program for preparing a graphene field effect transistor sensor is executed by a processor, the steps of the data detection method for preparing a graphene field effect transistor sensor as described above are implemented.

The present invention uses hydrogel to encapsulate SGGT to connect the gate and graphene channel, and analyzes the influence of different hydrogel systems and parameters on the SGGT channel current signal through data detection, constructs a hydrogel gate-controlled graphene field effect sensor and optimizes its performance, establishes an excellent sensing platform for the rapid detection of trace food hazards, and greatly improves the portability of SGGT in the practical application of food quality and safety detection.

Additional aspects and advantages of the present invention will be given in part in the following description and, in part, will be obvious from the following description or learned through practice of the present invention.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG1 shows a flow chart of a data detection method for preparing a graphene field effect tube sensor according to the present invention;

FIG2 shows a structural diagram of an SGGT device according to a specific embodiment of the present invention;

FIG3 shows a flow chart of SGGT channel current signal prediction corresponding to other hydrogel systems according to a specific embodiment of the present invention;

FIG4 shows a block diagram of a data detection system prepared by a graphene field effect transistor sensor according to the present invention.

Main numbers: 10-hydrogel; 20-SGGT; 30-graphene channel layer.

Detailed ways

In order to more clearly understand the above-mentioned purpose, features and advantages of the present invention, the present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be noted that the embodiments of the present application and the features in the embodiments can be combined with each other without conflict.

In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Therefore, the protection scope of the present invention is not limited to the specific embodiments disclosed below.

FIG1 shows a flow chart of a data detection method for preparing a graphene field effect transistor sensor according to the present invention.

As shown in FIG1 , the first aspect of the present invention provides a data detection method for preparing a graphene field effect transistor sensor, the method comprising:

S102, preparing a graphene field effect transistor sensor of a solution system as a reference sensor, and detecting an SGGT channel current signal of the reference sensor as first current data;

S104, selecting a plurality of hydrogel systems with representative characteristics from a hydrogel system library, and preparing corresponding hydrogel graphene field effect transistor sensors respectively;

S106, for the prepared multiple hydrogel graphene field effect transistor sensors, respectively detect the SGGT channel current signal of each hydrogel graphene field effect transistor sensor as second current data;

S108, based on the prepared multiple hydrogel graphene field effect transistor sensors, respectively obtain the corresponding hydrogel system parameter data of each hydrogel graphene field effect transistor sensor, and perform deep learning in combination with the corresponding second current data to predict the SGGT channel current signal of the graphene field effect transistor sensors prepared by other hydrogel systems in the hydrogel system library as the third current data;

S110, comparing and analyzing the portable performance data of each hydrogel system in the hydrogel system library, and comparing and analyzing the difference between the second current data or the third current data corresponding to each hydrogel system and the first current data of the reference sensor, and selecting the best hydrogel system through a preset screening algorithm.

It should be noted that the hydrogel system with representative characteristics refers to a hydrogel system that represents a certain category of hydrogel system. Generally, the hydrogel system can include polysaccharides, polypeptides, acrylic acid and its derivatives, etc. The hydrogel system of polysaccharides further includes starch, cellulose, alginate, hyaluronic acid, chitosan, etc. For this type of hydrogel system, starch is a hydrogel system with representative characteristics. Polypeptides further include collagen, poly-L-lysine, poly-L-glutamic acid, etc. For this type of hydrogel system, collagen is a hydrogel system with representative characteristics. The hydrogel system of acrylic acid and its derivatives further includes polyacrylic acid, polymethacrylic acid, polyacrylamide, poly-N-polyacrylamide, etc. For this type of hydrogel system, polyacrylic acid is a hydrogel system with representative characteristics.

It should be noted that the hydrogel system parameter data refers to the physical and chemical parameter data of the hydrogel, such as ionic conductivity data, swelling data, temperature sensitivity data, pH sensitivity data, and adhesion data. It can be understood that the SGGT channel current signals of the hydrogel graphene field effect transistor sensors prepared by two hydrogel systems with the same or similar parameter data are the same or similar.

It should be noted that the present invention uses graphene as the channel layer to prepare an electrolyte gate-controlled graphene field effect transistor (SGGT) device, uses hydrogel to encapsulate the SGGT to connect the gate and the graphene channel, and analyzes the influence of different hydrogel systems and parameters on the SGGT channel current signal through data detection, constructs a hydrogel gate-controlled graphene field effect sensor and optimizes its performance, establishes an excellent sensing platform for the rapid detection of trace food hazards, greatly improves the portability of SGGT in the practical application of food quality and safety detection, and enables SGGT to maintain good performance in practical applications and even in harsh scenarios. At the same time, the present invention analyzes the influence of different hydrogel systems and parameters on the SGGT channel current signal through data detection, thereby selecting the most suitable hydrogel system, providing important methods and technologies for the rapid detection of trace food hazards.

It should be noted that, as shown in FIG2 , the SGGT20 is encapsulated by the hydrogel 10, and the SGGT20 includes three electrodes, namely, a source electrode S, a gate electrode G, and a drain electrode D, wherein the channel current signal is a current signal between the source electrode S and the drain electrode D. The source electrode S and the drain electrode D form a channel region, and the channel region is coated with a graphene channel layer 30 .

It can be understood that due to the large number of hydrogel systems in the hydrogel system library, it is impossible to enumerate and prepare all hydrogel systems one by one during the experimental operation. The present invention prepares a hydrogel graphene field effect transistor sensor by selecting a hydrogel system with representative characteristics, and detects the actual SGGT channel current signal of the hydrogel graphene field effect transistor sensor, and then performs deep learning on the experimental detection data to predict the SGGT channel current signals corresponding to other hydrogel systems in other hydrogel system libraries, thereby facilitating the rapid screening of suitable hydrogel systems.

According to an embodiment of the present invention, preparing a corresponding hydrogel graphene field effect transistor sensor specifically includes:

Preparation of SGGT devices:

Substrate preparation: A glass sheet is used as the substrate of the SGGT device, and the glass substrate is sequentially immersed in acetone, isopropanol, ethanol and deionized water for ultrasonic cleaning;

To make the electrode, the glass substrate is first dried with high-purity nitrogen, then adhered to the mask with high-temperature tape, and the patterned source, drain and gate are deposited by magnetron sputtering;

Transferring graphene, spin-coating a polymethyl methacrylate film on the graphene, and then transferring the graphene spin-coated with the polymethyl methacrylate film to a channel region formed by a source electrode and a drain electrode;

Annealing, annealing the SGGT device on a heating table for a predetermined time;

Dissolving the polymethyl methacrylate film, and immersing the annealed SGGT device in acetone to remove the polymethyl methacrylate film;

Packaged SGGT devices:

A hydrogel system is selected, a hydrogel solution of corresponding concentration is prepared, and the prepared SGGT device is immersed in the solution in a specific mold;

The hydrogel system is formed by cooling or adding a cross-linking agent, thereby achieving encapsulation of the SGGT device through the hydrogel system.

It should be noted that in the substrate preparation step, a glass cutter was used to cut a small glass piece of about 1 cm × 1 cm from a glass slide as the sensor substrate. The glass substrate was sequentially immersed in acetone, isopropanol, ethanol and deionized water for ultrasonic cleaning for 20 min.

In the graphene transfer step, in order to make the polymethyl methacrylate (PMMA) spin-coated on the graphene more uniformly, a two-step spin coating method can be used. First, a slow speed of 800 rpm for 10 s, followed by a fast speed of 2000 rpm for 20 s. Then immerse in an etching solution (CuSO4:HCl:H2O = 10 g: 50 mL: 50 mL) to etch the Cu substrate and wash it with distilled water several times, and then transfer the graphene/PMMA to the channel area.

In the annealing step, the annealing process may be performed on a heating stage at 120° C. for 15 minutes.

In the PMMA dissolving step, the annealed sensor can be immersed in acetone at 50 °C for 3 h to remove the PMMA. Later, the metal wiring that can be connected to the source, drain, and gate can be protected with a siloxane layer to avoid direct contact of the sensor with the electrolyte during the characterization process.

It can be understood that multiple tests of hydrogels of different systems and states can be conducted from the hydrogel system library to explore the effect of hydrogel on the specific channel current of SGGT, and compare it with SGGT in conventional solution systems to optimize the construction method of hydrogel gate-controlled graphene transistors.

According to a specific embodiment of the present invention, after encapsulating the SGGT device by the hydrogel system, the method further comprises:

The packaged SGGT device is placed on the packaging inspection platform, and the packaging image is collected by the CCD camera on the packaging inspection platform;

Performing image processing on the package image, and identifying defect locations in the package image using a preset detection algorithm;

Based on the defect location on the packaging image, a preset evaluation algorithm is used to evaluate whether the hydrogel system is qualified for packaging the SGGT device.

It can be understood that each position of the encapsulated image of the present invention represents each pixel.

It should be noted that the qualification of the hydrogel system for encapsulating the SGGT device is directly related to the success or failure of subsequent experiments. Therefore, after the hydrogel system is completed to encapsulate the SGGT device, the encapsulation needs to be quality inspected. The traditional method is to use manual inspection, which has low inspection efficiency, and the error of manual inspection by naked eye is large, and it is not easy to find the defective position of insufficient encapsulation. The present invention collects the encapsulation image through a CCD camera, and performs image processing on the encapsulation image, identifies the defective points in the encapsulation image through a preset detection algorithm, and then evaluates whether the encapsulation of the SGGT device by the hydrogel system is qualified through a preset evaluation algorithm, thereby quickly and accurately judging whether the encapsulation is qualified, providing favorable guarantees for subsequent experimental data detection.

According to a specific embodiment of the present invention, identifying defect points in a package image by a preset detection algorithm specifically includes:

The encapsulation structure of the hydrogel system is preset to be a rectangle, and the corresponding collected encapsulation image is also a rectangle;

Perform grayscale processing on the rectangular encapsulated image to obtain a rectangular grayscale image;

Cut the rectangular grayscale image into m equal parts vertically and then into n equal parts horizontally, so as to divide the rectangular grayscale image into m*n small images, where “*” represents the product;

Randomly swap the order of m*n small images to obtain k kinds of rectangular reference images;

Subtracting the grayscale value of the first position of the grayscale image from the grayscale value of the first position corresponding to a reference image to obtain a grayscale difference;

Determine whether the absolute value of the grayscale difference at the first position is greater than a third preset threshold value, and if so, mark the first position of the grayscale image as a possible defect, otherwise, do not mark it;

Subtracting the grayscale value of the first position of the grayscale image from the grayscale value of the first position corresponding to the remaining reference image, and determining whether to mark the first position of the grayscale image as a possible defect according to the absolute value of the grayscale difference;

Counting the total number of times the first position of the grayscale image is marked as a possible defect, and determining whether the total number is greater than a fourth preset threshold, if the first position of the grayscale image is marked as a defective position;

Subtract the grayscale values of the corresponding positions of k reference images from the grayscale values of each position of the grayscale image, and determine whether to mark each position of the grayscale image as a possible defect according to the absolute value of the grayscale difference, and determine whether to mark each position of the grayscale image as a defective position according to the total number of possible defects;

All defect positions in the grayscale image are obtained.

It can be understood that, since the grayscale image corresponds to the package image, all defect positions in the grayscale image are obtained, that is, all defect positions in the package image are obtained.

It should be noted that, usually after the encapsulation of the hydrogel system is completed, the encapsulation of most areas meets the requirements, that is, the grayscale values of most areas after image acquisition and grayscale processing are relatively close. The present invention performs grayscale processing on the encapsulated image, and divides it into m*n small images, and forms k new reference images by randomly swapping the positions of each small image. Then, the grayscale values of each position of the grayscale image are compared with the grayscale values of the corresponding positions of the k reference images, so as to determine the defect position on the grayscale image.

The present invention does not need to specially prepare image templates for comparison, and since the grayscale values of various hydrogel systems are also different, if a universal comparison image template is used, it may be difficult to find the defect location. The present invention uses local materials, directly uses the experimental hydrogel system, and obtains multiple reference images through image cutting and position transformation processing. Since the multiple reference images are all from grayscale images, there is no grayscale deviation comparison phenomenon. At the same time, the method of the present invention can be applied to the detection of the packaging quality of each hydrogel system.

According to a specific embodiment of the present invention, based on the defect position on the packaging image, and using a preset evaluation algorithm, evaluating whether the hydrogel system is qualified for packaging the SGGT device specifically includes:

The encapsulation structure of the hydrogel system is preset to be a rectangle, and the corresponding collected encapsulation image is also a rectangle;

The rectangular packaging image is divided into multiple areas according to different functions, and the defect positions in different areas are preset to have different weights on the packaging quality;

Counting the total number of defective positions marked in each area of the package image;

Multiplying the total number of defect positions in each area of the package image by the corresponding influence weight to obtain a weighted defect value for each area;

Accumulating weighted defect values of all regions of the package image to obtain a total defect value of the package image;

It is determined whether the sum of the defect values of the package image is less than a fifth preset threshold value. If so, the SGGT device packaged by the hydrogel system is determined to be qualified, otherwise it is determined to be unqualified.

It should be noted that SGGT includes three electrodes, namely source S, gate G and drain D. The three electrodes are placed in the packaging box according to their respective positional relationships, and then packaged by the hydrogel system. It can be seen that the packaging conditions in different areas have different influence weights on the qualification of the SGGT device. The present invention analyzes the influence weight of each area on the packaging quality of the SGGT device, and combines the number of defective positions in each area, and then calculates the packaging quality result of the SGGT device encapsulated by the hydrogel system, thereby realizing automatic detection of the packaging quality of the hydrogel system.

FIG3 shows a flow chart of SGGT channel current signal prediction corresponding to other hydrogel systems according to a specific embodiment of the present invention.

As shown in FIG3 , the SGGT channel current signal of the graphene field effect transistor sensor prepared by other hydrogel systems in the hydrogel system library is predicted as the third current data, specifically including:

S302, performing deep learning based on the hydrogel system parameter data and the corresponding second current data of the prepared plurality of hydrogel graphene field effect transistor sensors, and analyzing the corresponding relationship between the hydrogel system parameter data and the second current data;

S304, constructing a channel current prediction model based on the hydrogel graphene field effect transistor sensor based on the corresponding relationship;

S306, obtaining parameter data of other hydrogel systems in the hydrogel system library, and predicting corresponding SGGT channel current signals, namely, third current data, through a channel current prediction model.

It should be noted that, since there are many types of hydrogel systems in the hydrogel system library, the present invention pre-selects a variety of hydrogel systems with representative characteristics for experiments and data detection, and then obtains the correspondence between the hydrogel system parameter data and the SGGT channel current signal by analyzing the detection data, and constructs a channel current prediction model based on the correspondence, and predicts the SGGT channel current signals corresponding to other hydrogel systems in the hydrogel system library through the channel current prediction model. It can be seen that the present invention does not need to conduct experiments on all hydrogel systems one by one, and can predict the SGGT channel current signals corresponding to other hydrogel systems by only conducting experiments on a limited number of hydrogel systems, which is convenient for quickly screening out suitable hydrogel systems and saving the cost of experiments.

According to an embodiment of the present invention, after the corresponding SGGT channel current signal is predicted by the channel current prediction model, the method further includes:

Based on a plurality of hydrogel systems with representative characteristics, characteristic calculation is performed on parameter data of each hydrogel system with representative characteristics to obtain a first characteristic value;

Acquiring parameter data of the hydrogel system to be predicted, and performing characteristic calculation to obtain a second characteristic value;

The second characteristic value of the hydrogel system to be predicted is compared with the first characteristic value of each hydrogel system with representative characteristics in the hydrogel system library, and a first difference between the two is calculated;

Determine whether the first difference is greater than a first preset threshold value, if so, add the corresponding hydrogel system with representative characteristics to the correction database, otherwise discard it;

Based on each hydrogel system with representative characteristics in the revised database, the corresponding parameter data is input into the channel current prediction model for prediction, so as to obtain the predicted channel current data corresponding to each hydrogel system with representative characteristics;

Based on each hydrogel system with representative characteristics in the correction database, a difference calculation is performed between the second current data actually detected and the corresponding predicted channel current data to obtain a second difference value;

Calculate the average value of the second difference values corresponding to all hydrogel systems with representative characteristics in the correction database to obtain a correction value;

The correction value is added to the predicted corresponding SGGT channel current signal to obtain a corrected SGGT channel current signal.

It should be noted that feature extraction is the process of selecting, converting and combining relevant information from raw data for subsequent processing and analysis. When the parameter data of each hydrogel system with representative characteristics is calculated to obtain the first characteristic value; or when the parameter data of the hydrogel system to be predicted is obtained and the characteristic is calculated to obtain the second characteristic value, the first characteristic value or the second characteristic value is the proportional relationship between the normalized values of the various parameter data of the hydrogel system, such as the normalized value of parameter data A1 is A2; the normalized value of parameter data B1 is B2; the normalized value of parameter data C1 is C2, then the first characteristic value or the second characteristic value is A2: B2: C2. It can be understood that since the parameter data of the hydrogel system can include multiple, such as ionic conductivity data, swelling data, temperature sensitivity data, pH sensitivity data, and adhesion data, but the units of these parameter data are not exactly the same, so it is necessary to perform normalization conversion calculation processing before the proportional relationship calculation can be performed. The normalized conversion calculation process is specifically as follows: for example, a certain parameter has a minimum value and a maximum value. If the actual parameter data falls at a point between the minimum value and the maximum value, the actual parameter data can be converted into the ratio of the distance between the point and the minimum value to the distance between the maximum value and the minimum value.

It should be noted that, limited by the number of training data, when the training data is limited, it is easy to cause the current value predicted by the channel current prediction model to have a certain deviation. Considering that the deviation of the approximate hydrogel system is roughly the same, the present invention performs characteristic calculation on the parameter data of the hydrogel system, and then selects a hydrogel system similar to the hydrogel system to be predicted from multiple hydrogel systems with representative characteristics, and adds it to the correction database. Then, the second current data of each hydrogel system with representative characteristics in the correction database is differentially analyzed with the corresponding predicted channel current data to obtain a correction value. Since the hydrogel system to be predicted has similar characteristics to the hydrogel system in the correction database, it can be used as a correction value of the hydrogel system to be predicted. Based on the correction value, the corresponding SGGT channel current signal can be corrected to obtain a more accurate SGGT channel current signal, which is convenient for the subsequent accurate comparison and screening of the hydrogel system.

According to an embodiment of the present invention, the optimal hydrogel system is selected by a preset screening algorithm, specifically including:

Calculating the approximation between the second current data or the third current data of each hydrogel system in the hydrogel system library and the first current data of the reference sensor;

The approximation corresponding to each hydrogel system in the hydrogel system library is compared with the approximations corresponding to other hydrogel systems one by one;

If the approximation of the former is better than that of the latter, then the channel current evaluation value of the former is increased by 1, otherwise, it is increased by 0;

Compare the portable performance data of each hydrogel system in the hydrogel system library with the portable performance data of other hydrogel systems one by one;

If the portability performance data of the former is better than that of the latter, then the portability evaluation value of the former is increased by 1, otherwise, it is increased by 0;

After all the hydrogel systems in the hydrogel system library have completed the pairwise comparison evaluation, they are ranked based on the channel current evaluation value and portability evaluation value of each hydrogel system;

The best hydrogel system was screened out according to the score ranking.

It should be noted that the traditional screening method is mostly through artificially setting the reference value. When it is greater than the reference value, it is selected, or each hydrogel system is scored separately according to the preset scoring criteria, and the overall global consideration is not taken into account, resulting in a large sorting error. The present invention compares the hydrogel systems in the hydrogel system library in pairs, without fixed scoring standards and reference values. When all hydrogel systems are compared, it means that each hydrogel system is compared with other hydrogel systems in pairs. In this way, each hydrogel system participates in the integral evaluation of other hydrogel systems, and the final integral ranking is the integral ranking result after the comprehensive hydrogel system library. The sorting is more accurate, which is conducive to the screening of the best hydrogel system.

According to an embodiment of the present invention, the scoring is performed based on the channel current evaluation value and the portability evaluation value of each hydrogel system, specifically including:

Obtain the influence weights of the channel current evaluation value and the portability evaluation value on the screening of the hydrogel system respectively;

Based on each hydrogel system in the hydrogel system library, the channel current evaluation value is multiplied by the corresponding influence weight to obtain a first product, and the portability evaluation value is multiplied by the corresponding influence weight to obtain a second product;

Based on each hydrogel system in the hydrogel system library, the first product is added to the second product to obtain a weighted integral of each hydrogel system;

The hydrogel systems are sorted according to their weighted scores.

It should be noted that the weights of the current evaluation value and the portability evaluation value on the screening may be different. Specifically, for the current evaluation value, it reflects the difference between the channel current signal of the hydrogel system and the channel current signal of the solution system reference sensor. The smaller the difference, the more suitable the corresponding hydrogel system. As for the portability evaluation value, it reflects the portability of different hydrogel systems in their respective gel states. For example, some hydrogel systems are more stable in the gel state and are not easily deformed by extrusion, while some hydrogel systems are easily deformed by extrusion. In the screening process, hydrogel systems that are not easily deformed are more suitable. Based on the influence of the above-mentioned two dimensions of current evaluation value and portability evaluation value, the corresponding weights are combined to calculate the weighted integral, and the hydrogel systems are sorted according to the weighted integrals, which is conducive to selecting a more suitable hydrogel system.

FIG. 4 shows a block diagram of a data detection system prepared by a graphene field effect transistor sensor.

As shown in FIG4 , the second aspect of the present invention further proposes a data detection system 4 prepared by a graphene field effect transistor sensor, comprising a memory 41 and a processor 42, wherein the memory comprises a data detection method program prepared by a graphene field effect transistor sensor, and when the data detection method program prepared by the graphene field effect transistor sensor is executed by the processor, the following steps are implemented:

A graphene field effect transistor sensor of a solution system is prepared as a reference sensor, and an SGGT channel current signal of the reference sensor is detected as first current data;

Selecting a variety of hydrogel systems with representative characteristics from the hydrogel system library, and preparing corresponding hydrogel graphene field effect transistor sensors respectively;

For the prepared multiple hydrogel graphene field effect transistor sensors, the SGGT channel current signal of each hydrogel graphene field effect transistor sensor is detected as the second current data;

Based on the prepared multiple hydrogel graphene field effect transistor sensors, the corresponding hydrogel system parameter data of each hydrogel graphene field effect transistor sensor is obtained respectively, and deep learning is performed in combination with the corresponding second current data to predict the SGGT channel current signal of the graphene field effect transistor sensors prepared by other hydrogel systems in the hydrogel system library as the third current data;

The portable performance data of each hydrogel system in the hydrogel system library are compared and analyzed, and the difference between the second current data or the third current data corresponding to each hydrogel system and the first current data of the reference sensor is compared and analyzed, and the best hydrogel system is selected through a preset screening algorithm.

According to an embodiment of the present invention, predicting the SGGT channel current signal of the graphene field effect transistor sensor prepared by other hydrogel systems in the hydrogel system library as the third current data specifically includes:

Based on the prepared hydrogel system parameter data and the corresponding second current data of the multiple hydrogel graphene field effect transistor sensors, deep learning is performed to analyze the corresponding relationship between the hydrogel system parameter data and the second current data;

Based on the corresponding relationship, a channel current prediction model based on a hydrogel graphene field effect transistor sensor is constructed;

The parameter data of other hydrogel systems in the hydrogel system library are obtained, and the corresponding SGGT channel current signal, i.e., the third current data, is predicted by the channel current prediction model.

According to an embodiment of the present invention, after the corresponding SGGT channel current signal is predicted by the channel current prediction model, the data detection method program prepared by the graphene field effect transistor sensor is further implemented when the processor executes the following steps:

Based on a plurality of hydrogel systems with representative characteristics, characteristic calculation is performed on parameter data of each hydrogel system with representative characteristics to obtain a first characteristic value;

Acquiring parameter data of the hydrogel system to be predicted, and performing characteristic calculation to obtain a second characteristic value;

The second characteristic value of the hydrogel system to be predicted is compared with the first characteristic value of each hydrogel system with representative characteristics in the hydrogel system library, and a first difference between the two is calculated;

Determine whether the first difference is greater than a first preset threshold value, if so, add the corresponding hydrogel system with representative characteristics to the correction database, otherwise discard it;

Based on each hydrogel system with representative characteristics in the revised database, the corresponding parameter data is input into the channel current prediction model for prediction, so as to obtain the predicted channel current data corresponding to each hydrogel system with representative characteristics;

Based on each hydrogel system with representative characteristics in the correction database, a difference calculation is performed between the second current data actually detected and the corresponding predicted channel current data to obtain a second difference value;

Calculate the average value of the second difference values corresponding to all hydrogel systems with representative characteristics in the correction database to obtain a correction value;

The correction value is added to the predicted corresponding SGGT channel current signal to obtain a corrected SGGT channel current signal.

The third aspect of the present invention also proposes a computer-readable storage medium, which includes a data detection method program for preparing a graphene field effect transistor sensor. When the data detection method program for preparing a graphene field effect transistor sensor is executed by a processor, the steps of the data detection method for preparing a graphene field effect transistor sensor as described above are implemented.

The present invention uses graphene as a channel layer to prepare an SGGT device, uses hydrogel to encapsulate the SGGT to connect the gate and the graphene channel, and analyzes the influence of different hydrogel systems and parameters on the SGGT channel current signal through data detection, constructs a hydrogel gate-controlled graphene field effect sensor and optimizes its performance, establishes an excellent sensing platform for the rapid detection of trace food hazards, greatly improves the portability of SGGT in the practical application of food quality and safety detection, and enables SGGT to maintain good performance in practical applications and even in harsh scenarios. At the same time, the present invention analyzes the influence of different hydrogel systems and parameters on the SGGT channel current signal through data detection, thereby selecting the most suitable hydrogel system, providing important methods and technologies for the rapid detection of trace food hazards.

In the several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as: multiple units or components can be combined, or can be integrated into another system, or some features can be ignored, or not executed. In addition, the coupling, direct coupling, or communication connection between the components shown or discussed can be through some interfaces, and the indirect coupling or communication connection of the devices or units can be electrical, mechanical or other forms.

The units described above as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units; they may be located in one place or distributed on multiple network units; some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

In addition, all functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may be separately used as a unit, or two or more units may be integrated into one unit; the above-mentioned integrated units may be implemented in the form of hardware or in the form of hardware plus software functional units.

A person skilled in the art can understand that: all or part of the steps of implementing the above method embodiment can be completed by hardware related to program instructions, and the aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it executes the steps of the above method embodiment; and the aforementioned storage medium includes: mobile storage devices, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), disks or optical disks, etc. Various media that can store program codes.

Alternatively, if the above-mentioned integrated unit of the present invention is implemented in the form of a software function module and sold or used as an independent product, it can also be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the embodiment of the present invention can be essentially or partly reflected in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the methods described in each embodiment of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as mobile storage devices, ROM, RAM, disks or optical disks.

The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art who is familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims (6)

1. A data detection method for preparing a graphene field effect tube sensor, characterized in that the method comprises: A graphene field effect transistor sensor of a solution system is prepared as a reference sensor, and an SGGT channel current signal of the reference sensor is detected as first current data; Selecting a variety of hydrogel systems with representative characteristics from the hydrogel system library, and preparing corresponding hydrogel graphene field effect transistor sensors respectively; For the prepared multiple hydrogel graphene field effect transistor sensors, the SGGT channel current signal of each hydrogel graphene field effect transistor sensor is detected as the second current data; Based on the prepared multiple hydrogel graphene field effect transistor sensors, the corresponding hydrogel system parameter data of each hydrogel graphene field effect transistor sensor is obtained respectively, and deep learning is performed in combination with the corresponding second current data to predict the SGGT channel current signal of the graphene field effect transistor sensors prepared by other hydrogel systems in the hydrogel system library as the third current data; Comparing and analyzing the portable performance data of each hydrogel system in the hydrogel system library, and comparing and analyzing the difference between the second current data or the third current data corresponding to each hydrogel system and the first current data of the reference sensor, and selecting the best hydrogel system through a preset screening algorithm; Predicting the SGGT channel current signal of the graphene field effect transistor sensor prepared by other hydrogel systems in the hydrogel system library as the third current data specifically includes: Based on the prepared hydrogel system parameter data and the corresponding second current data of the multiple hydrogel graphene field effect transistor sensors, deep learning is performed to analyze the corresponding relationship between the hydrogel system parameter data and the second current data; Based on the corresponding relationship, a channel current prediction model based on a hydrogel graphene field effect transistor sensor is constructed; Obtain parameter data of other hydrogel systems in the hydrogel system library, and predict the corresponding SGGT channel current signal through the channel current prediction model; Based on a plurality of hydrogel systems with representative characteristics, characteristic calculation is performed on parameter data of each hydrogel system with representative characteristics to obtain a first characteristic value; Acquiring parameter data of the hydrogel system to be predicted, and performing characteristic calculation to obtain a second characteristic value; The second characteristic value of the hydrogel system to be predicted is compared with the first characteristic value of each hydrogel system with representative characteristics in the hydrogel system library, and a first difference between the two is calculated; Determine whether the first difference is greater than a first preset threshold value, if so, add the corresponding hydrogel system with representative characteristics to the correction database, otherwise discard it; Based on each hydrogel system with representative characteristics in the revised database, the corresponding parameter data is input into the channel current prediction model for prediction, so as to obtain the predicted channel current data corresponding to each hydrogel system with representative characteristics; Based on each hydrogel system with representative characteristics in the correction database, a difference calculation is performed between the second current data actually detected and the corresponding predicted channel current data to obtain a second difference value; Calculate the average value of the second difference values corresponding to all hydrogel systems with representative characteristics in the correction database to obtain a correction value; The correction value is added to the predicted corresponding SGGT channel current signal to obtain a corrected SGGT channel current signal. 2. The data detection method for preparing a graphene field effect transistor sensor according to claim 1, characterized in that preparing the corresponding hydrogel graphene field effect transistor sensor specifically comprises: Preparation of SGGT devices: Substrate preparation: A glass sheet is used as the substrate of the SGGT device, and the glass substrate is sequentially immersed in acetone, isopropanol, ethanol and deionized water for ultrasonic cleaning; To make the electrode, the glass substrate is first dried with high-purity nitrogen, then adhered to the mask with high-temperature tape, and the patterned source, drain and gate are deposited by magnetron sputtering; Transferring graphene, spin-coating a polymethyl methacrylate film on the graphene, and then transferring the graphene spin-coated with the polymethyl methacrylate film to a channel region formed by a source electrode and a drain electrode; Annealing, annealing the SGGT device on a heating table for a predetermined time; Dissolving the polymethyl methacrylate film, and immersing the annealed SGGT device in acetone to remove the polymethyl methacrylate film; Packaged SGGT devices: A hydrogel system is selected, a hydrogel solution of corresponding concentration is prepared, and the prepared SGGT device is immersed in the solution in a specific mold; The hydrogel system is formed by cooling or adding a cross-linking agent, thereby achieving encapsulation of the SGGT device through the hydrogel system. 3. The data detection method for preparing a graphene field effect tube sensor according to claim 1, characterized in that the optimal hydrogel system is selected by a preset screening algorithm, specifically comprising: Calculating the approximation between the second current data or the third current data of each hydrogel system in the hydrogel system library and the first current data of the reference sensor; The approximation corresponding to each hydrogel system in the hydrogel system library is compared with the approximations corresponding to other hydrogel systems one by one; If the approximation of the former is better than that of the latter, then the channel current evaluation value of the former is increased by 1, otherwise, it is increased by 0; Compare the portable performance data of each hydrogel system in the hydrogel system library with the portable performance data of other hydrogel systems one by one; If the portability performance data of the former is better than that of the latter, then the portability evaluation value of the former is increased by 1, otherwise, it is increased by 0; After all the hydrogel systems in the hydrogel system library have completed the pairwise comparison evaluation, they are ranked based on the channel current evaluation value and portability evaluation value of each hydrogel system; The best hydrogel system was screened out according to the score ranking. 4. The data detection method for preparing a graphene field effect transistor sensor according to claim 3 is characterized in that the integral ranking is performed based on the channel current evaluation value and the portability evaluation value of each hydrogel system, specifically comprising: Obtain the influence weights of the channel current evaluation value and the portability evaluation value on the screening of the hydrogel system respectively; Based on each hydrogel system in the hydrogel system library, the channel current evaluation value is multiplied by the corresponding influence weight to obtain a first product, and the portability evaluation value is multiplied by the corresponding influence weight to obtain a second product; Based on each hydrogel system in the hydrogel system library, the first product is added to the second product to obtain a weighted integral of each hydrogel system; The hydrogel systems are sorted according to their weighted scores. 5. A data detection system, suitable for use in the preparation process of a graphene field effect transistor sensor, characterized in that it comprises a memory and a processor, wherein the memory comprises a data detection method program for preparing a graphene field effect transistor sensor, and when the data detection method program for preparing a graphene field effect transistor sensor is executed by the processor, the following steps are implemented: A graphene field effect transistor sensor of a solution system is prepared as a reference sensor, and an SGGT channel current signal of the reference sensor is detected as first current data; Selecting a variety of hydrogel systems with representative characteristics from the hydrogel system library, and preparing corresponding hydrogel graphene field effect transistor sensors respectively; For the prepared multiple hydrogel graphene field effect transistor sensors, the SGGT channel current signal of each hydrogel graphene field effect transistor sensor is detected as the second current data; Based on the prepared multiple hydrogel graphene field effect transistor sensors, the corresponding hydrogel system parameter data of each hydrogel graphene field effect transistor sensor is obtained respectively, and deep learning is performed in combination with the corresponding second current data to predict the SGGT channel current signal of the graphene field effect transistor sensors prepared by other hydrogel systems in the hydrogel system library as the third current data; Comparing and analyzing the portable performance data of each hydrogel system in the hydrogel system library, and comparing and analyzing the difference between the second current data or the third current data corresponding to each hydrogel system and the first current data of the reference sensor, and selecting the best hydrogel system through a preset screening algorithm; Predicting the SGGT channel current signal of the graphene field effect transistor sensor prepared by other hydrogel systems in the hydrogel system library as the third current data specifically includes: Based on the prepared hydrogel system parameter data and the corresponding second current data of the multiple hydrogel graphene field effect transistor sensors, deep learning is performed to analyze the corresponding relationship between the hydrogel system parameter data and the second current data; Based on the corresponding relationship, a channel current prediction model based on a hydrogel graphene field effect transistor sensor is constructed; Obtain parameter data of other hydrogel systems in the hydrogel system library, and predict the corresponding SGGT channel current signal through the channel current prediction model; Based on a plurality of hydrogel systems with representative characteristics, characteristic calculation is performed on parameter data of each hydrogel system with representative characteristics to obtain a first characteristic value; Acquiring parameter data of the hydrogel system to be predicted, and performing characteristic calculation to obtain a second characteristic value; The second characteristic value of the hydrogel system to be predicted is compared with the first characteristic value of each hydrogel system with representative characteristics in the hydrogel system library, and a first difference between the two is calculated; Determine whether the first difference is greater than a first preset threshold value, if so, add the corresponding hydrogel system with representative characteristics to the correction database, otherwise discard it; Based on each hydrogel system with representative characteristics in the revised database, the corresponding parameter data is input into the channel current prediction model for prediction, so as to obtain the predicted channel current data corresponding to each hydrogel system with representative characteristics; Based on each hydrogel system with representative characteristics in the correction database, a difference calculation is performed between the second current data actually detected and the corresponding predicted channel current data to obtain a second difference value; Calculate the average value of the second difference values corresponding to all hydrogel systems with representative characteristics in the correction database to obtain a correction value; The correction value is added to the predicted corresponding SGGT channel current signal to obtain a corrected SGGT channel current signal. 6. A computer-readable storage medium, characterized in that the computer-readable storage medium includes a data detection method program for preparing a graphene field effect transistor sensor, and when the data detection method program for preparing a graphene field effect transistor sensor is executed by a processor, the steps of the data detection method for preparing a graphene field effect transistor sensor as described in any one of claims 1 to 4 are implemented.