A paper-based gene chip spotting method, device, storage medium and equipment

Abstract

The application provides a paper-based gene chip spotting method, device, storage medium and equipment. The method is applied to an inkjet spotting system. The inkjet spotting system obtains incoming quality control data of a paper-based gene chip to be spotted from a manufacturing execution system, extracts target chip features from the incoming quality control data, inputs the target chip features into a pre-trained parameter generation model, predicts optimal spotting parameters from the chip features by the parameter generation model, and then controls an inkjet printhead to spot the paper-based gene chip according to the spotting parameters generated by the parameter generation model. In this way, the AI technology is used to dynamically adjust the spotting parameters according to the chip characteristics, improve the spotting quality, reduce the need for manual intervention, and thus improve the work efficiency.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of gene chip, in particular to a paper-based gene chip spotting method, device, storage medium and equipment. BACKGROUND

[0002] As a high-throughput DNA analysis and detection means, gene chip has a wide range of applications in gene expression, disease diagnosis, drug screening and other fields. The spotting process is a key step in the gene chip experiment, which directly affects the signal intensity and data quality of the chip. The paper-based gene chip is made of paper-based materials. The characteristics of the paper-based gene chip, such as uneven thickness and aperture difference, can easily limit the spotting accuracy. The traditional solution is mainly to adjust the control parameters manually to cope with this problem. However, this method highly depends on the experience of the operator, and in most cases, the spotting quality and work efficiency are low. SUMMARY

[0003] The purpose of the present application is to provide a paper-based gene chip spotting method, device, storage medium and equipment, which aims to solve the problem of high dependence on operator experience, low spotting quality and work efficiency in the related art paper-based gene chip spotting method.

[0004] In a first aspect, the present application provides a paper-based gene chip spotting method applied to an inkjet spotting system. The method comprises: obtaining incoming quality control data of a paper-based gene chip to be spotted from a manufacturing execution system, and extracting target chip features from the incoming quality control data; processing the target chip features through a pre-trained parameter generation model to generate target spotting parameters; the parameter generation model learns to predict the best spotting parameters according to the input chip features in the training process; and controlling the inkjet print head of the inkjet spotting system to spot the paper-based gene chip according to the target spotting parameters.

[0005] In the above implementation process, a paper-based gene chip spotting method applied to an inkjet spotting system is provided. In this method, the incoming quality control data of a paper-based gene chip to be spotted is obtained from a manufacturing execution system, and the target chip features are extracted from the incoming quality control data. The target chip features are input into a pre-trained parameter generation model, and the parameter generation model predicts the best spotting parameters according to the chip features. Then, the inkjet print head is controlled to spot the paper-based gene chip according to the spotting parameters generated by the parameter generation model. In this way, the AI technology is used to dynamically adjust the spotting parameters according to the chip characteristics, improve the spotting quality, reduce the need for manual intervention, and thus improve the work efficiency.

[0006] Further, in some examples, before the target chip feature is extracted from the incoming quality control data, the incoming quality control data is subjected to cleaning and standardization processing.

[0007] In the above implementation process, after the IQC data is obtained from the MES system, the IQC data is preprocessed, including data cleaning and standardization processing, which can improve the data quality and thus improve the accuracy of subsequent model analysis.

[0008] Further, in some examples, the target chip feature includes at least one of porosity, pore size, surface roughness, and droplet amplification coefficient; and the spotting parameter includes driving voltage, droplet volume, and ejection frequency.

[0009] In the above implementation process, optional items of key features characterizing the properties of the paper-based gene chip are provided, at least one of porosity, pore size, surface roughness, and droplet amplification coefficient is selected as the target chip feature, and the target spotting parameter is generated, which can improve the spotting effect; meanwhile, optional items of important control parameters of the inkjet print head are provided, and the driving voltage, droplet volume, and ejection frequency of the inkjet print head are dynamically adjusted according to the properties of the paper-based gene chip during spotting, which can ensure that the volume and shape of each ejected droplet meet the requirements.

[0010] Further, in some examples, the parameter generation model includes a decision tree model; the decision tree model is used to predict a droplet driving waveform mode according to the input chip feature; and the droplet driving waveform mode corresponds to a set of spotting parameters.

[0011] In the above implementation process, the decision tree model is used to predict the optimal spotting parameter according to the chip properties, which can effectively improve the spotting quality.

[0012] Further, in some examples, the control of the inkjet print head of the inkjet spotting system on the paper-based gene chip according to the target spotting parameter includes: transmitting the target spotting parameter to a data filtering module of the inkjet spotting system, so that the data filtering module converts the target spotting parameter into a waveform signal recognizable by the inkjet print head of the inkjet spotting system, and then transmits the waveform signal to the inkjet print head, so that the inkjet print head spots the paper-based gene chip according to the waveform signal.

[0013] In the above implementation process, the data filtering module is separated, the target spotting parameter generated by AI is transmitted to the data filtering module, and the data filtering module converts the target spotting parameter into a waveform signal recognizable by the inkjet print head, which can reduce the coupling degree of the print head and the system, reduce the complexity of system integration and maintenance, and thus reduce the development and maintenance costs.

[0014] Further, in some examples, the process of converting the target spotting parameters into a waveform signal recognizable by the inkjet print head of the inkjet spotting system by the data filtering module includes: loading a configuration file of the inkjet print head; the configuration file records the waveform parameters supported by the inkjet print head and the hardware limitations; dynamically loading the corresponding driving module of the inkjet print head according to the configuration file; retrieving the corresponding waveform parameters of the target spotting parameters in the configuration file, and verifying the retrieved waveform parameters; when the waveform parameters pass the verification, calling the driving module to generate a waveform signal.

[0015] In the above implementation process, one specific way of providing a data filtering module for parameter conversion is to add a configuration file and a driving module by using a plug-in driving architecture, so that the core code does not need to be modified, and new print heads can be quickly adapted by using the combination of the configuration file and the driving plug-in, thereby being applicable to a production environment where multiple types of print heads coexist.

[0016] Further, in some examples, the method further includes: monitoring the ejection state of each droplet in real time; the ejection state includes droplet shape, volume and position; if the monitoring result shows that there is an abnormal ejection state, output an alarm signal.

[0017] In the above implementation process, the quality of the gene chip spotting process is monitored, the ejection state of each droplet is monitored in real time, including droplet shape, volume and position, and if an abnormal ejection state is monitored, an alarm signal is immediately sent out, so that the operator can take corresponding processing measures in a timely manner to improve the spotting quality.

[0018] Further, in some examples, the method further includes: associating and recording the target chip features, the target spotting parameters and the monitoring results, and uploading the recorded data to the manufacturing execution system.

[0019] In the above implementation process, the system can record the ejection data of each droplet, such as position, volume and shape, in combination with the chip features and spotting parameters corresponding to the current spotting process, which is convenient for subsequent analysis and optimization. These data can also be uploaded to the MES system for production management and quality traceability.

[0020] Further, in some examples, the method further includes: adding qualified data in the recorded data to the training set of the parameter generation model; periodically retraining the parameter generation model using the latest accumulated training set.

[0021] In the implementation process, for the parameter generation model, an incremental learning method can be used for online learning. When a new point sampling task is completed and feedback data is obtained, qualified data in the data is added to the training set, and the model is retrained regularly using the latest accumulated training set to capture the latest pattern changes. In this way, the model performance is improved, which is beneficial to improve the point sampling quality.

[0022] In a second aspect, the paper-based gene chip point sampling device provided by the application is applied to an inkjet point sampling system; the device comprises: an acquisition module configured to acquire incoming quality control data of a paper-based gene chip to be sampled from a manufacturing execution system and extract target chip features from the incoming quality control data; a generation module configured to process the target chip features by using a pre-trained parameter generation model to generate target point sampling parameters; the parameter generation model learns, in a training process, to predict optimal point sampling parameters according to input chip features; and a control module configured to control an inkjet printhead of the inkjet point sampling system to sample the paper-based gene chip according to the target point sampling parameters.

[0023] In a third aspect, the electronic device provided by the application comprises a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor implements the steps of the method according to any one of the first aspect when executing the computer program.

[0024] In a fourth aspect, the computer readable storage medium provided by the application stores instructions, and when the instructions are executed on a computer, the computer executes the method according to any one of the first aspect.

[0025] In a fifth aspect, the computer program product provided by the application, when executed on a computer, causes the computer to execute the method according to any one of the first aspect.

[0026] Other features and advantages of the application will be described in the following description, or can be learned or determined from the description without any doubt, or can be known by implementing the above-mentioned technology disclosed in the application.

[0027] In order to make the above-mentioned purposes, features and advantages of the application more obvious and easy to understand, the following preferred embodiments are specifically described, and the accompanying drawings are described in detail as follows. BRIEF DESCRIPTION OF DRAWINGS

[0028] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following will briefly introduce the drawings needed to be used in the embodiments of the present application. It should be understood that the following drawings only show some of the embodiments of the present application, and therefore should not be regarded as a limitation to the scope. For those skilled in the art, other related drawings can also be obtained without creative labor on the basis of these drawings.

[0029] Figure 1 A flow chart of a paper-based gene chip spotting method provided by the embodiments of the present application;

[0030] Figure 2 An architecture schematic diagram of a spotting system involved in a paper-based gene chip non-contact spotting scheme realized by an inkjet printhead provided by the embodiments of the present application;

[0031] Figure 3 A schematic diagram of the structure of a data Filter module provided by the embodiments of the present application;

[0032] Figure 4 A block diagram of a paper-based gene chip spotting device provided by the embodiments of the present application;

[0033] Figure 5 A structural block diagram of an electronic device provided by the embodiments of the present application. DETAILED DESCRIPTION

[0034] The technical solutions in the embodiments of the present application will be described below with reference to the drawings in the embodiments of the present application.

[0035] It should be noted that: similar reference numerals and letters represent similar items in the following drawings, and therefore, once an item is defined in one drawing, it does not need to be further defined and explained in the subsequent drawings. Meanwhile, in the description of the present application, the terms “first”, “second”, etc. are only used for distinguishing description, and cannot be understood as indicating or implying relative importance.

[0036] The paper-based gene chip is a gene chip made of paper-based materials. At present, the spotting process of the paper-based gene chip is generally realized by using the contact spotting technology of reverse spot hybridization. This technology has the advantages of relatively simple operation and low equipment cost, but its limitations and potential problems cannot be ignored. For example, the characteristics of the paper-based gene chip, such as uneven thickness and aperture difference, are easy to limit the spotting accuracy. In the implementation of this technology, the problem is mainly solved by manually adjusting the control parameters, however, this way highly depends on the experience of the operator, and is easy to introduce human error, resulting in poor spotting quality. Moreover, manual adjustment needs repeated testing and verification, leading to low work efficiency.

[0037] To solve the above problems, the paper-based gene chip spotting scheme provided in the embodiments of the present application obtains the incoming quality control data of the paper-based gene chip from the manufacturing execution system, extracts the target chip features from the incoming quality control data and inputs the AI model, predicts the optimal spotting parameters according to the chip characteristics by the AI model, and then performs the spotting operation according to the spotting parameters by the inkjet print head. In this way, the AI technology is used to dynamically adjust the spotting parameters according to the chip characteristics, improve the spotting quality, reduce the need for manual intervention, and thus improve the work efficiency.

[0038] Next, the embodiments of the present application are introduced:

[0039] As shown in Figure 1 , Figure 1 is a flowchart of a paper-based gene chip spotting method provided in the embodiments of the present application. The method is applied to an inkjet spotting system. A core component of the inkjet spotting system is an inkjet print head, which is responsible for spraying the biomolecule solution in the form of extremely small droplets onto the paper-based gene chip to realize gene chip spotting. In the embodiment scheme, the inkjet print head can be piezoelectrically driven, which can realize non-contact spotting to eliminate scratches or other forms of physical damage on the chip surface caused by physical contact, avoid cross contamination between different samples, and thus improve the accuracy and reliability of experimental results.

[0040] The method comprises:

[0041] Step 101, obtaining the incoming quality control data of the paper-based gene chip to be spotted from the manufacturing execution system, and extracting the target chip features from the incoming quality control data;

[0042] Incoming quality control (IQC) is a process in which an enterprise inspects and controls the quality of raw materials, parts or products provided by a supplier. In the embodiment scheme, the IQC data of the paper-based gene chip refers to the data collected by performing IQC on experimental materials and equipment before performing the gene chip experiment. In implementation, after the inkjet spotting system is started and initialized, it can be connected to the manufacturing execution system (MES) through a network interface to obtain the relevant IQC data of the paper-based gene chip to be spotted, and then extract the key features representing the characteristics of the paper-based gene chip, i.e., the target chip features.

[0043] In some embodiments, before extracting the target chip features from the incoming quality control data, the incoming quality control data can be subjected to cleaning and standardization processing. That is, after obtaining the IQC data from the MES system, the IQC data is preprocessed, and the preprocessing includes data cleaning and standardization processing. The data cleaning can remove duplicate data, correct errors and missing values, thereby ensuring the accuracy of the data, and the data standardization can convert data of different units, formats or ranges into a unified form, making the data more unified, comparable and easy to process. In this way, the data quality is improved, thereby improving the accuracy of subsequent model analysis.

[0044] In some embodiments, the target chip features mentioned in this step can include at least one of the following: porosity, pore size, surface roughness, droplet amplification coefficient. When spotting, these features will directly affect the deposition, diffusion and fixation effect of the droplets ejected by the inkjet print head, and thus affect the spotting accuracy, resolution and function implementation. Therefore, selecting at least one of these features as a target chip feature to generate a target spotting parameter can improve the spotting effect. Of course, in other embodiments, the target chip features can also include other features, such as chip thickness, density, etc.

[0045] Step 102, processing the target chip features by a pre-trained parameter generation model to generate target spotting parameters; the parameter generation model learns to predict the best spotting parameters according to the input chip features during the training process;

[0046] In this embodiment, an AI model is trained, which can generate the best spotting parameters according to the chip characteristics of the paper-based gene chip. The spotting parameters can be considered as the control parameters of the inkjet print head of the inkjet spotting system. When there is a new spotting task, the system can use the AI model to process the extracted target chip features, thereby generating target spotting parameters suitable for the paper-based gene chip to be spotted.

[0047] In some embodiments, the spotting parameters mentioned in this step can include driving voltage, droplet volume and ejection frequency. These parameters are important control parameters of the inkjet print head. When spotting, dynamically adjusting the driving voltage, droplet volume and ejection frequency of the inkjet print head according to the characteristics of the paper-based gene chip can ensure that the volume and shape of the droplets ejected each time meet the requirements.

[0048] Further, in some embodiments, the parameter generation model mentioned in this step can include a decision tree model; the decision tree model is used to predict a droplet driving waveform mode according to the input chip characteristics; the droplet driving waveform mode corresponds to a set of spotting parameters. The decision tree model is a supervised learning algorithm based on tree structure for decision-making and prediction, and its core idea is to simulate the human decision-making process, decompose complex problems into a series of simple judgments (nodes), and finally form an intuitive decision path. In implementation, a decision tree model is trained using historical data and expert knowledge, and since the output of the decision tree model is usually a single target value, different driving voltages, droplet volumes and jetting frequencies are combined into multiple droplet driving waveform modes, for example, multiple droplet driving waveform modes include fire1 mode, fire2 mode, fire3 mode, etc., wherein the fire3 mode corresponds to a droplet volume of 12pL, a driving voltage of 20V and a jetting frequency of 10kHz. In this way, based on the trained decision tree model, the best spotting parameters can be generated according to the chip characteristics. The specific model training process can refer to the introduction of the training method of the decision tree model in related technologies, and the present application will not be repeated here.

[0049] Step 103, controlling the inkjet printhead of the inkjet spotting system to spot the paper-based gene chip according to the target spotting parameters.

[0050] In the embodiment, after the AI algorithm generates the best spotting parameters, the control system of the inkjet spotting system controls the inkjet printhead to perform the spotting operation according to the spotting parameters, and the spotting parameters are dynamically optimized according to the characteristics of the paper-based gene chip to be spotted, so as to effectively improve the spotting precision and improve the spotting quality.

[0051] In some embodiments, this step can include: transmitting the target spotting parameters to the data filtering module of the inkjet spotting system, so that the data filtering module converts the target spotting parameters into a waveform signal recognizable by the inkjet printhead of the inkjet spotting system, and then transmits the waveform signal to the inkjet printhead, so that the inkjet printhead spots the paper-based gene chip according to the waveform signal. The data filtering module here can be an independent software module or a circuit board, and the control system transmits the target spotting parameters to the data filtering module, which converts the target spotting parameters into a waveform signal recognizable by the inkjet printhead, so as to ensure that the inkjet printhead can be correctly executed. In this process, by separating the data filtering module, the coupling degree between the printhead and the system can be reduced, the complexity of system integration and maintenance can be reduced, and the development and maintenance costs can be reduced.

[0052] Further, in some embodiments, the process of converting the target spotting parameters into a waveform signal recognizable by the inkjet print head of the inkjet spotting system can include: loading a configuration file of the inkjet print head; recording the waveform parameters supported by the inkjet print head and the hardware limitations in the configuration file; dynamically loading the corresponding driving module of the inkjet print head according to the configuration file; retrieving the corresponding waveform parameters of the target spotting parameters in the configuration file, and verifying the retrieved waveform parameters; and calling the driving module to generate a waveform signal when the waveform parameters pass the verification. That is, when converting parameters, the data filtering module can first load the configuration file of the inkjet print head, obtain the supported waveform parameters and hardware limitations, then dynamically load the corresponding driving module, input the AI-generated spotting parameters, retrieve the corresponding waveform parameters in the configuration file, which can include voltage and pulse width, and according to the retrieval result, the data filtering module verifies whether the voltage is within the preset voltage range and whether the pulse width is within the preset pulse width range. If the verification passes, the driving module is called to generate a waveform signal, otherwise an error is prompted to avoid damaging the print head. In this way, through the plug-in driving architecture, a new print head only needs to add a configuration file and a driving module without modifying the core code, and through the combination of the configuration file and the driving plug-in, a new model of print head can be quickly adapted, so as to be applicable to a production environment where multiple models of print heads coexist.

[0053] In order to enrich and enhance the functions of the inkjet spotting system, the present application also provides the following improvements:

[0054] In some embodiments, real-time monitoring of the ejection state of each droplet can also be included; the ejection state includes droplet shape, volume and position; and if the monitoring result shows that there is an abnormal ejection state, an alarm signal is output. That is, the inkjet spotting system can integrate a camera to monitor the quality of the gene chip spotting process. In implementation, the inkjet spotting system can monitor the ejection state of each droplet in real time, including droplet shape, volume and position, where the droplet shape can be represented by circularity, aspect ratio, etc., the droplet shape can be estimated by image analysis, and the droplet position can be represented by X and Y coordinates. If an abnormal ejection state is detected, such as droplet deviation from the target position or irregular droplet shape, the control system immediately issues an alarm signal. In this way, the operator can take corresponding processing measures in a timely manner to improve the spotting quality.

[0055] Further, in some embodiments, the method can further include: recording the target chip feature, the target spotting parameter and the monitoring result after association, and uploading the recorded data to the manufacturing execution system. That is, the system can record the ejection data of each droplet, such as position, volume and shape, in combination with the chip feature and the spotting parameter corresponding to the current spotting process, for subsequent analysis and optimization. These data can also be uploaded to the MES system for production management and quality traceability.

[0056] In addition, in some embodiments, the method can further include: adding qualified data in the recorded data to the training set of the parameter generation model; and periodically retraining the parameter generation model using the latest accumulated training set. That is, for the parameter generation model, an incremental learning method can be used for online learning. Whenever a new spotting task is completed and feedback data is obtained, qualified data in these data is immediately added to the training set, and the model is retrained comprehensively using the latest accumulated training set to capture the latest pattern changes. In this way, the model performance is improved, which is beneficial to improve the spotting quality.

[0057] The embodiments of the present application provide a paper-based gene chip spotting method applied to an inkjet spotting system. In the method, incoming quality control data of a paper-based gene chip to be spotted is obtained from a manufacturing execution system, target chip features are extracted from the incoming quality control data, the target chip features are input into a pre-trained parameter generation model, the parameter generation model predicts optimal spotting parameters according to the chip features, and then the spotting parameters generated by the parameter generation model are used to control the inkjet print head to spot the paper-based gene chip. In this way, AI technology is used to dynamically adjust the spotting parameters according to the chip characteristics, improve the spotting quality, reduce the need for manual intervention, and thus improve the work efficiency.

[0058] In order to make a more detailed description of the scheme of the present application, a specific embodiment is introduced as follows:

[0059] The present embodiment provides a non-contact spotting scheme for paper-based gene chips based on an inkjet print head. The architecture of the spotting system involved in the scheme is as shown in Figure 2As shown, the spotting system includes an inkjet printhead 21, an ink supply system 22, a motion control platform 23, a control system 24, and a data filter module 25, wherein the MES system 26 and the AI module 27 are integrated in the control system 24. Among them, the inkjet printhead 21 adopts piezoelectric drive to realize non-contact spotting; the ink supply system 22 is built-in with a circulating liquid supply device to ensure the stability of the solution. In the preparation of the sample stage, different concentrations of DNA probe solutions need to be prepared and loaded into the ink supply system 22; the motion control platform 23 is a high-precision XY motion platform that can accurately control the spotting position. In this embodiment, the experimental equipment also includes a paper-based gene chip, which is a carrier for spotting.

[0060] The workflow of this scheme includes:

[0061] S201, system startup and initialization;

[0062] Specifically, the user starts the entire device through the control panel or remote control system, and the device self-checking program starts to check the status of each subsystem, such as the inkjet printhead 21, the ink supply system 22, the motion control platform 23, etc., to ensure that all components are working properly;

[0063] In the initialization stage, the inkjet printhead 21 is automatically cleaned and calibrated to ensure that each nozzle is unobstructed and the initial voltage waveform parameters are adjusted; the ink supply system 22 is started, and the built-in pump starts to operate to ensure that the biomolecule solution is uniformly distributed in the liquid storage cavity and impurities are removed through the filter; the motion control platform is zeroed, and the servo motor and encoder are calibrated to ensure the accuracy of subsequent motion control;

[0064] S202, obtain IQC data, AI identify and generate spotting parameters;

[0065] Specifically, the system connects to the MES system 26 through the network interface to obtain the relevant IQC data of the paper-based gene chip to be spotted. In one experiment, the paper-based gene chip has the following characteristics: high porosity of 65%; large pore size of 8 μm; low surface roughness of 2 μm; and droplet amplification coefficient of 5;

[0066] Based on the above IQC data, the AI module 27 performs the following processing: cleaning and standardizing the IQC data; extracting key features such as porosity, pore size, surface roughness, and droplet amplification coefficient from the preprocessed IQC data; processing the extracted key features based on the trained decision tree model to generate target spotting parameters; wherein the decision tree model is trained using historical data and expert knowledge, which can predict the best spotting parameters based on the characteristics of the chip; in this way, the AI module 27 can determine whether to increase the droplet volume to ensure sufficient coverage range according to the porosity, pore size, surface roughness, and droplet amplification coefficient of the chip;

[0067] The output result of the decision tree model can be a droplet driving waveform mode, which can correspond to a set of dotting parameters. For example, if the droplet driving waveform mode output by the decision tree model is fire3 mode, it means that the droplet volume is 12 pL, the driving voltage is 20 V, and the ejection frequency is 10 kHz.

[0068] S203, dot parameter conversion, performing dotting operation;

[0069] Specifically, the AI module 27 sends the generated optimal dotting parameters to the data Filter module 25, which converts them into driving waveforms recognizable by the inkjet printhead 21. The structure of the data Filter module 25 is shown in Figure 3 The data Filter module 25 includes a configuration management submodule 31, a drive plug-in submodule 32, a parameter conversion submodule 33, and a waveform generation submodule 34, wherein:

[0070] The configuration management submodule 31 is used to load the configuration file of the inkjet printhead 21, which contains the waveform parameters supported by the printhead and the hardware limitations.

[0071] The drive plug-in submodule 32 is used to dynamically load the corresponding drive module according to the drive type in the configuration file. For example, if the inkjet printhead 21 is a Kyocera model KJ4B, and the value of the drive_type field in the configuration file is "FPGA_A", then the FPGA_Driver_A is loaded to generate a special protocol waveform signal for the Kyocera model KJ4B. If the value of the drive_type field in the configuration file is "UNIVER_I2C", then the UNIVER_I2C is loaded to generate an I2C standard communication mode to drive the printhead.

[0072] The parameter conversion submodule 33 is used to retrieve and verify the corresponding waveform parameters in the configuration file based on the dotting parameters generated by the AI module 27. The waveform parameters include voltage and pulse width. The parameter conversion submodule 33 checks whether the retrieved voltage is within the range of [10V, 30V] and whether the pulse width is within the range of [1μs, 3μs]. If so, the verification is passed.

[0073] The waveform generation submodule 34 is used to realize protocol adaptation and real-time output of the waveform signal. When the parameter verification is passed, the waveform generation submodule 34 generates the waveform signal by calling the drive module. If drive_type = UNIVER_I2C, the waveform generation submodule 34 sends the waveform signal to the inkjet print head 21 through the I2C standard communication mode. If drive_type = FPGA_A, the waveform signal is converted into a level signal recognizable by the inkjet print head 21 through the FPGA hardware module and is sent to the inkjet print head 21.

[0074] During the spotting process, the data filter module 25 issues instructions, and the piezoelectric elements of the inkjet print head 21 are deformed by electricity to extrude the liquid in the liquid storage cavity to form tiny droplets. The droplets are sprayed onto the predetermined position in a non-contact manner, avoiding mechanical damage and cross contamination. The multiple nozzles of the inkjet print head 21 work simultaneously, and the spraying time and frequency of each nozzle are independently controlled to ensure that each droplet can accurately fall on the predetermined position.

[0075] At the same time, the motion control platform 23 accurately moves the inkjet print head 21 according to the preset path, and the servo motor and the encoder feedback the position information in real time. The control system 24 dynamically adjusts the position of the inkjet print head 21 according to the feedback information to ensure high-precision spotting.

[0076] S204, quality monitoring and feedback;

[0077] Specifically, the system integrates a camera to monitor the spraying state of each droplet in real time, including the droplet shape, volume and position, to analyze the spotting effect. If abnormal conditions such as droplet deviation from the target position and irregular droplet shape are detected, the control system 24 immediately issues an alarm signal.

[0078] For each spotting task, the control system 24 automatically records the following information: droplet position, droplet volume, droplet shape, timestamp, porosity, pore size, surface roughness, droplet magnification factor, parameter settings used (such as fire3 mode), and information indicating whether the spotting effect is qualified. The control system 24 uploads these data to the MES system 26 for production management and quality traceability. After the spotting task is completed and the feedback data is obtained, the control system 24 adds the qualified data in these data to the training set, and periodically re-trains the decision tree model using the latest accumulated training set.

[0079] The embodiment has at least the following advantages: by adopting the non-contact inkjet printing head technology and combining with the intelligent control system, the precision, efficiency and reliability of the paper-based gene chip preparation can be significantly improved, the errors caused by manual operation are reduced, and the overall work efficiency is improved; the data filter module is used as an independent software module or circuit board, the coupling degree of the printing head and the system is reduced, different brands and performance of the printing head can be quickly switched, and the system versatility and development efficiency are improved.

[0080] Corresponding to the embodiments of the foregoing method, the application also provides an embodiment of a paper-based gene chip spotting device and a terminal applying the same:

[0081] As shown in Figure 4 , Figure 4 is a block diagram of a paper-based gene chip spotting device provided by an embodiment of the application, and the device is applied to an inkjet spotting system; the device comprises:

[0082] An acquisition module 41 is configured to acquire incoming quality control data of a paper-based gene chip to be spotted from a manufacturing execution system, and extract target chip features from the incoming quality control data;

[0083] A generation module 42 is configured to process the target chip features by using a pre-trained parameter generation model to generate target spotting parameters; in a training process, the parameter generation model learns to predict optimal spotting parameters according to input chip features;

[0084] A control module 43 is configured to control an inkjet printing head of the inkjet spotting system to spot the paper-based gene chip according to the target spotting parameters.

[0085] The functions and effects of each module in the device are achieved in the implementation process of the corresponding steps in the above method, and will not be described here.

[0086] The application also provides an electronic device, please see Figure 5 , Figure 5 is a structural block diagram of an electronic device provided by an embodiment of the application. The electronic device can include a processor 510, a communication interface 520, a memory 530 and at least one communication bus 540. Wherein, the communication bus 540 is used to realize the direct connection communication of these components. Wherein, the communication interface 520 of the electronic device in the embodiment of the application is used to communicate with other node devices. The processor 510 can be an integrated circuit chip with signal processing capability.

[0087] The processor 510 described above can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), an off-the-shelf programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. It can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of this application. The general-purpose processor can be a microprocessor, or the processor 510 can be any conventional processor.

[0088] The memory 530 may be, but is not limited to, random access memory (RAM), read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), etc. The memory 530 stores computer-readable instructions. When these computer-readable instructions are executed by the processor 510, the electronic device can perform the aforementioned operations. Figure 1 The various steps involved in the method implementation examples.

[0089] Alternatively, the electronic device may also include a storage controller and an input / output unit.

[0090] The memory 530, storage controller, processor 510, peripheral interface, and input / output unit are electrically connected directly or indirectly to achieve data transmission or interaction. For example, these components can be electrically connected to each other through one or more communication buses 540. The processor 510 is used to execute executable modules stored in the memory 530, such as software function modules or computer programs included in electronic devices.

[0091] The input / output unit is used to provide users with the ability to create tasks and to set optional start periods or preset execution times for those tasks, thereby enabling user-server interaction. The input / output unit may be, but is not limited to, a mouse and keyboard.

[0092] Understandable. Figure 5 The structure shown is for illustrative purposes only; the electronic device may also include components that are more advanced than those shown. Figure 5 The more or fewer components shown, or having the sameFigure 5 different configurations. Figure 5 The components shown in the various embodiments can be implemented in hardware, software, or a combination thereof.

[0093] The embodiments of the present application also provide a storage medium, which stores instructions, when the instructions are run on a computer, the computer program is executed by a processor to implement the method of the method embodiments, to avoid repetition, here will not be repeated.

[0094] The present application also provides a computer program product, which, when run on a computer, causes the computer to execute the method of the method embodiments.

[0095] In several embodiments provided in the present application, it should be understood that the disclosed apparatus and method can also be implemented by other means. The apparatus embodiments described above are only schematic, for example, the flowcharts and block diagrams in the drawings show the possible implementation architectures, functions and operations of the apparatus, method and computer program product according to the embodiments of the present application. In this regard, each block in the flowcharts or block diagrams can represent a module, a program segment or a part of code, which contains one or more executable instructions for implementing the specified logical functions. It should also be noted that in some alternative implementations, the functions noted in the blocks can occur in different orders from that shown in the drawings. For example, two consecutive blocks can actually be executed substantially in parallel, and sometimes they can be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and the combination of blocks in the block diagrams and / or flowcharts, can be implemented by a dedicated hardware-based system for executing the specified functions or actions, or can be implemented by a combination of dedicated hardware and computer instructions.

[0096] In addition, each functional module in the various embodiments of the present application can be integrated together to form an independent part, or each module can exist independently, or two or more modules can be integrated to form an independent part.

[0097] If the functions are implemented in the form of software function modules and sold or used as independent products, they can be stored in a computer readable storage medium. Based on this understanding, the technical solutions of the present application essentially or the parts of the prior art that make contributions or parts of the technical solutions can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes a number of instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present application. The aforementioned storage medium includes: a U disk, a mobile hard disk, a read-only memory (ROM, Read-Only Memory), a random access memory (RAM, Random Access Memory), a magnetic disk or an optical disk, and various media that can store program codes.

[0098] The above merely provides an example of the present application and is not intended to limit the protection scope of the present application. For those skilled in the art, the present application can have various modifications and changes. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application. It should be noted that similar reference numbers and letters represent similar items in the following drawings, so once an item is defined in one drawing, it does not need to be further defined and explained in subsequent drawings.

[0099] The above merely provides an example of the present application and is not intended to limit the protection scope of the present application. For those skilled in the art, the present application can have various modifications and changes. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application. It should be noted that similar reference numbers and letters represent similar items in the following drawings, so once an item is defined in one drawing, it does not need to be further defined and explained in subsequent drawings.

[0100] It should be noted that, in this document, relational terms such as first and second, and the like can be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises", "comprising", or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but can include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without limitation, an element preceded by "comprises... a" does not, without more constraints, foreclose the existence of additional identical elements in the process, method, article, or apparatus that comprises the element.

Claims

1. A paper-based gene chip spotting method, characterized by, The method is applied to an inkjet spotting system, and comprises the following steps: Obtaining incoming quality control data of a paper-based gene chip to be spotted from a manufacturing execution system, and extracting target chip features from the incoming quality control data; Processing the target chip features by using a pre-trained parameter generation model to generate target spotting parameters; in the training process of the parameter generation model, the parameter generation model learns to predict optimal spotting parameters according to input chip features; Controlling an inkjet print head of the inkjet spotting system to spot the paper-based gene chip according to the target spotting parameters; The step of controlling the inkjet print head of the inkjet spotting system to spot the paper-based gene chip according to the target spotting parameters comprises the following steps: Transferring the target spotting parameters to a data filtering module of the inkjet spotting system, so that the data filtering module converts the target spotting parameters into a waveform signal recognizable by the inkjet print head of the inkjet spotting system, and then transfers the waveform signal to the inkjet print head, so that the inkjet print head spots the paper-based gene chip according to the waveform signal.

2. The method of claim 1, wherein, Before the step of extracting the target chip features from the incoming quality control data, the method further comprises the following steps: Cleaning and standardizing the incoming quality control data.

3. The method of claim 1, wherein, The target chip features comprise at least one of the following: Porosity, pore size, surface roughness, and droplet amplification coefficient; The spotting parameters comprise driving voltage, droplet volume, and ejection frequency.

4. The method of claim 3, wherein, The parameter generation model comprises a decision tree model, which is used to predict a droplet driving waveform mode according to input chip features; the droplet driving waveform mode corresponds to a set of spotting parameters.

5. The method of claim 3, wherein, The process of converting the target spotting parameters into a waveform signal recognizable by the inkjet print head of the inkjet spotting system by using the data filtering module comprises the following steps: Loading a configuration file of the inkjet print head; the configuration file records waveform parameters supported by the inkjet print head and hardware limitations; Dynamically loading a driving module corresponding to the inkjet print head according to the configuration file; Retrieving waveform parameters corresponding to the target spotting parameters in the configuration file, and verifying the retrieved waveform parameters; When the waveform parameters pass the verification, calling the driving module to generate a waveform signal.

6. The method of claim 1, wherein, The method further comprises the following steps: Monitoring the ejection state of each droplet in real time; the ejection state comprises droplet shape, volume, and position; If the monitoring result shows that there is an abnormal ejection state, outputting an alarm signal.

7. The method of claim 6, wherein, The method further comprises the following steps: Associating and recording the target chip features, the target spotting parameters, and the monitoring result, and uploading the recorded data to the manufacturing execution system.

8. The method of claim 7, wherein, The method further comprises the following steps: Adding qualified data in the recorded data to a training set of the parameter generation model; Periodically retraining the parameter generation model by using the latest accumulated training set.

9. A paper-based gene chip spotting device, characterized by, The device is applied to an inkjet spotting system, and comprises the following components: An obtaining module, configured to obtain incoming quality control data of a paper-based gene chip to be spotted from a manufacturing execution system, and extract target chip features from the incoming quality control data; The generating module is configured to generate a target point sample parameter by processing the target chip feature through a pre-trained parameter generation model, and the parameter generation model learns to predict an optimal point sample parameter according to an input chip feature in a training process. The control module is configured to control an inkjet print head of the inkjet point sample system to perform point sample on the paper-based gene chip according to the target point sample parameter. The control module is specifically configured to: deliver the target point sample parameter to a data filtering module of the inkjet point sample system, so that the data filtering module converts the target point sample parameter into a waveform signal recognizable by the inkjet print head of the inkjet point sample system, and then delivers the waveform signal to the inkjet print head, so that the inkjet print head performs point sample on the paper-based gene chip according to the waveform signal.

10. A computer-readable storage medium, characterized in that, A computer program is stored on the computer readable medium, and the computer program is executed by a processor to implement the method according to any one of claims 1 to 8.

11. An electronic device, comprising: A computer program product includes a processor, a memory, and a computer program stored on the memory and executable on the processor, wherein the processor implements the method according to any one of claims 1 to 8 when executing the computer program.

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