A production system and method for a high-sensitivity anti-JO-1 antibody CBA detection kit

Abstract

The present invention provides a production system and method for a high-sensitivity CBA detection kit for anti-JO-1 antibody. The system includes an antigen preparation module, a magnetic bead coupling module, a microsphere preparation module, a quality monitoring module, an assembly module, and a control and management module. The antigen preparation module is used to prepare JO-1 antigen raw materials. The magnetic bead coupling module is used to generate antigen-coated magnetic microspheres. The microsphere preparation module is used to make the magnetic microspheres into solid freeze-dried microspheres and pre-package them into a microfluidic substrate. The quality monitoring module is used to implement multi-level quality inspection and monitoring during the production process. The assembly module is used to complete the final assembly process. The control and management module is used to coordinate the operation of each module in the system. Through an intelligent production system and data-driven optimization, the present invention integrates multi-level quality inspection, dynamic parameter optimization, and closed-loop control to ensure the high sensitivity, high stability, and batch consistency of the kit, and improve production efficiency and detection reliability.

Description

Technical Field

[0001] The present invention relates to the technical field of biomedical detection systems, and particularly to a production system and method for a high-sensitivity anti-JO-1 antibody CBA detection kit. Background Art

[0002] Autoimmune diseases, such as polymyositis and dermatomyositis, usually rely on the detection of specific autoantibodies in clinical diagnosis. Among them, anti-JO-1 antibody, as an important serological marker for polymyositis, has high clinical value. At present, conventional antibody detection methods have problems such as limited sensitivity, unstable specificity, and complex operation, making it difficult to meet the needs of accurate detection. With the development of cell genome analysis technology, magnetic microsphere technology, and microfluidic detection, new detection methods have gradually improved the sensitivity, stability, and automation of detection. However, in the industrial production process of the kit, technical challenges such as magnetic bead coupling efficiency, stability of freeze-dried microspheres, batch-to-batch consistency, and optimization of process parameters still exist. Therefore, there is an urgent need for an efficient, stable, and intelligent production system to improve the accuracy and batch stability of anti-JO-1 antibody detection and promote its wide application in the fields of clinical detection and disease monitoring.

[0003] Referring to the relevant publicly disclosed technical solutions, the solution with the publication number CN102621308A proposes a colloidal gold chromatography anti-Jo-1 antibody detection test strip and its preparation method. The colloidal gold chromatography anti-Jo-1 antibody detection test strip includes a sample pad, a conjugate pad, a nitrocellulose-coated membrane, and an absorbent pad. The sample pad, conjugate pad, nitrocellulose-coated membrane, and absorbent pad are sequentially pasted on the bottom plate from one side of the bottom plate to the other side of the bottom plate. The conjugate pad is coated with gold-labeled antibody a and gold-labeled antibody b; the nitrocellulose-coated membrane is provided with a test line and a quality control line. The test line is coated with Jo-1 antigen protein, and the quality control line is coated with gold-labeled antibody c. This solution adopts an indirect immunoassay method, introduces Jo-1 antigen protein, and optimizes the process of the conjugate pad and the sample pad to achieve high-sensitivity, high-specificity, and high-accuracy detection performance of anti-JO-1 antibody, providing a reference basis for the auxiliary diagnosis of dermatomyositis / polymyositis. However, this solution relies on the colloidal gold chromatography method, the detection sensitivity is limited, and the process optimization mainly focuses on the conjugate pad and the sample pad, without involving magnetic microsphere coupling, microfluidic technology, and intelligent quality monitoring, making it difficult to ensure batch stability and consistency of automated production. Summary of the Invention

[0004] The purpose of the present invention is to propose a production system and method for a high-sensitivity anti-JO-1 antibody CBA detection kit in view of the current deficiencies.

[0005] The present invention adopts the following technical solutions:

[0006] A production system for a high-sensitivity anti-JO-1 antibody CBA detection kit, the system includes an antigen preparation module, a magnetic bead coupling module, a microsphere preparation module, a quality monitoring module, an assembly module and a control management module; the antigen preparation module is used to prepare JO-1 antigen raw materials; the magnetic bead coupling module is used to covalently couple purified antigen with carboxylated nanomagnetic beads to generate antigen-coated magnetic microspheres; the microsphere preparation module is used to mix the coupled magnetic microspheres with chemiluminescence reaction auxiliary reagents, and use vacuum freeze spraying technology to make solid freeze-dried microspheres and pre-encapsulate them into a microfluidic substrate; the quality monitoring module is used to perform multi-level quality inspection and monitoring on the magnetic bead coupling uniformity and the integrity of the freeze-dried microspheres through AI vision and fluorescence detection technology, and feedback the monitoring information to the control management module; the assembly module is used to assemble the pre-encapsulated microfluidic substrate of the qualified freeze-dried microspheres with the microfluidic substrate integrated with temperature control and magnetic separation to form a final detection chip, and after passing the sealing test, it is transported to the packaging line; the control management module is used to coordinate the operation of each module in the system, and combined with the feedback of the quality monitoring module, issue a warning reminder for abnormal situations in production.

[0007] The magnetic bead coupling module includes a magnetic bead activation unit and an antigen fixation unit; the magnetic bead activation unit is used to activate the carboxylated nanomagnetic beads to expose carboxyl groups for covalent coupling of antigens; the antigen fixation unit is used to covalently bind the antigen to the surface of the activated magnetic beads under optimal reaction conditions by precisely controlling the PH and temperature, generating antigen-coated magnetic microspheres.

[0008] The microsphere preparation module includes a reagent premixing unit and a freeze-drying and encapsulation unit; the reagent premixing unit is used to mix the chemiluminescence reaction auxiliary reagents in proportion, and the freeze-drying and encapsulation unit is used to convert the mixed reagents into solid microspheres by vacuum freeze spraying and pre-encapsulate them into the reaction cavity of the microfluidic chip.

[0009] Further, the quality monitoring module includes a magnetic bead coupling quality inspection unit and a freeze-dried microsphere quality inspection unit; the magnetic bead coupling quality inspection unit is used to obtain magnetic microsphere images through a high-resolution microscope, and obtain the particle size distribution and antigen coating uniformity of the magnetic microspheres through a convolutional neural network; the freeze-dried microsphere quality inspection unit is used to obtain the structural integrity and fluorescence intensity of the freeze-dried microspheres through three-dimensional imaging and fluorescence detection technology.

[0010] Further, the control management module includes a process parameter acquisition unit, a monitoring information receiving unit, an analysis and warning unit and a dynamic optimization unit; the process parameter acquisition unit is used to collect the process parameters of each module in the system in real time; the monitoring information receiving unit is used to receive the real-time monitoring information of the quality monitoring module; the analysis and warning unit analyzes based on the real-time monitoring information and issues a warning reminder for abnormal situations in production; the dynamic optimization unit is used to optimize the process parameters of the subsequent system in combination with the historical operation status of the system.

[0011] Further, the analysis and early warning unit includes a data preprocessing subunit, an immediate anomaly reminder unit, and a trend prediction reminder unit; the data preprocessing subunit is used to clean, normalize, and extract feature processing on each dimension data in the monitoring information to generate standardized index features; the immediate anomaly reminder unit is used to issue a warning for an immediate anomaly by combining the analysis of real-time index features, and the trend prediction reminder unit is used to perform trend analysis and prediction by combining index features of multiple consecutive batches, identify potential risks of production quality decline, and issue a warning for a predicted anomaly.

[0012] Further, the immediate anomaly reminder unit compares the index features with a preset index feature threshold range. When a certain index feature exceeds its corresponding index feature threshold range, a warning reminder for the immediate anomaly is sent to the user.

[0013] Further, the trend prediction reminder unit sets a continuous time window, obtains the sequence of index feature values within the time window, calculates the index stability parameter of each index feature based on the sequence of index feature values. When the index stability parameter of a certain index feature exceeds its corresponding preset warning range, a warning reminder for the predicted anomaly is sent to the user.

[0014] Further, the specific calculation method of the index stability parameter is as follows:

[0015] For a certain index feature:

[0016]

[0017] Where, is the index stability parameter, is the total number of index feature values within the time window, is the deviation change rate of the th index feature value within the time window, is the time serial number of the data within the time window, and the closer the acquisition time of the index feature value is to the current time the larger the value; is a preset attenuation coefficient, used to control the attenuation speed of the weight, and is set through pre-experiments; for it satisfies:

[0018] ;

[0019] Where, is the deviation between the th index feature value within the time window and the preset optimal index feature value; is the The deviation between the index eigenvalue and the preset optimal index eigenvalue; is the time difference between two adjacent samplings of the index feature.

[0020] Furthermore, the dynamic optimization unit establishes a parameter optimization model by combining the system historical process parameters and their corresponding monitoring information, so as to complete the intelligent optimization of the production process parameters.

[0021] A production method of a high-sensitivity anti-JO-1 antibody CBA detection kit, which is applied to a production system of a high-sensitivity anti-JO-1 antibody CBA detection kit. The method includes the following steps:

[0022] S11: Antigen preparation: Prepare JO-1 antigen through genetic engineering to ensure its high specificity and high activity;

[0023] S12: Magnetic bead coupling: Use carboxylated nanomagnetic beads to immobilize JO-1 antigen on the surface of the magnetic beads by covalent coupling to generate antigen-coated magnetic microspheres;

[0024] S13: Microsphere preparation: Mix magnetic microspheres with chemiluminescence reaction auxiliary reagents through vacuum freeze spraying technology, and prepare them into solid freeze-dried microspheres, and then pre-encapsulate them into a microfluidic substrate;

[0025] S14: Quality monitoring: Use AI vision detection, fluorescence detection and three-dimensional imaging technology to perform multi-level quality inspection and monitoring on the magnetic bead coupling uniformity, freeze-dried microsphere integrity and fluorescence signal intensity;

[0026] S15: Assemble the qualified freeze-dried microsphere microfluidic substrate with the microfluidic substrate with integrated temperature control and magnetic separation functions to form the final detection chip, and enter the packaging link after passing the sealing test.

[0027] The beneficial effects obtained by the present invention:

[0028] Through the intelligent production system and data-driven optimization, the present invention integrates magnetic bead coupling, vacuum freeze spraying, AI quality monitoring and microfluidic technology to ensure the high sensitivity, high stability and batch consistency of the anti-JO-1 antibody CBA detection kit; through AI vision detection, fluorescence detection and three-dimensional imaging technology, multi-level quality control of magnetic bead coupling uniformity and freeze-dried microsphere integrity is realized to ensure consistent product quality; by establishing a parameter optimization model to intelligently optimize and dynamically adjust process parameters, the stability and accuracy of the production process are improved. Description of the Drawings

[0029] The present invention can be further understood from the following description in conjunction with the accompanying drawings. The components in the drawings are not necessarily drawn to scale, but the emphasis is on showing the principles of the embodiments. In different views, the same reference numerals designate corresponding parts.

[0030] Figure 1 Schematic diagram of the overall module of the present invention.

[0031] Figure 2 Schematic diagram of the production method of the high-sensitivity anti-JO-1 antibody CBA detection kit of the present invention.

[0032] Figure 3 Schematic diagram of the process for establishing the parameter optimization model of the present invention. Detailed implementation manners

[0033] In order to make the objectives, technical solutions and advantages of the present invention clearer and more understandable, the present invention will be further described in detail below in conjunction with its embodiments; it should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention; for those skilled in the art, after referring to the following detailed description, other systems, methods and / or features of this embodiment will become obvious; it is intended that all such additional systems, methods, features and advantages are included in this specification; included within the scope of the present invention and protected by the appended claims; additional features of the disclosed embodiments are described in the following detailed description, and these features will be obvious according to the following detailed description.

[0034] In the drawings of the embodiments of the present invention, the same or similar reference numerals correspond to the same or similar components; in the description of the present invention, it should be understood that if there are terms such as "upper", "lower", "left", "right", etc. indicating the orientation or positional relationship, they are based on the orientation or positional relationship shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or component referred to must have a specific orientation, be constructed and operated in a specific orientation, so the terms describing the positional relationship in the drawings are only for illustrative purposes and cannot be understood as a limitation of this patent. For those of ordinary skill in the art, the specific meanings of the above terms can be understood according to specific circumstances.

[0035] Embodiment 1:

[0036] As Figure 1As shown, this embodiment provides a production system for a high-sensitivity CBA detection kit for anti-JO-1 antibodies. The system includes an antigen preparation module, a magnetic bead coupling module, a microsphere preparation module, a quality monitoring module, an assembly module, and a control and management module. The antigen preparation module is used to prepare JO-1 antigen raw materials. The magnetic bead coupling module is used to covalently couple purified antigens to carboxylated nanomagnetic beads to generate antigen-coated magnetic microspheres. The microsphere preparation module is used to mix the coupled magnetic microspheres with chemiluminescence reaction auxiliary reagents, and use vacuum freeze spraying technology to make solid freeze-dried microspheres and pre-package them into a microfluidic substrate. The quality monitoring module is used to perform multi-level quality inspection and monitoring on the uniformity of magnetic bead coupling and the integrity of freeze-dried microspheres through AI vision and fluorescence detection technologies, and feedback the monitoring information to the control and management module. The assembly module is used to assemble the pre-packaged microfluidic substrate with freeze-dried microspheres that have passed quality inspection and a microfluidic substrate with integrated temperature control and magnetic separation to form a final detection chip, and send it to the packaging line after a sealing test. The control and management module is used to coordinate the operation of each module in the system, and combine the feedback of the quality monitoring module to issue early warning reminders for abnormal situations during production.

[0037] The magnetic bead coupling module includes a magnetic bead activation unit and an antigen fixation unit. The magnetic bead activation unit is used to activate carboxylated nanomagnetic beads to expose carboxyl groups for covalent coupling of antigens. The antigen fixation unit is used to covalently bind antigens to the surface of activated magnetic beads under optimal reaction conditions by precisely controlling the pH and temperature to generate antigen-coated magnetic microspheres.

[0038] The microsphere preparation module includes a reagent premixing unit and a freeze-drying and encapsulation unit. The reagent premixing unit is used to mix chemiluminescence reaction auxiliary reagents in proportion. The freeze-drying and encapsulation unit is used to convert the mixed reagents into solid microspheres by vacuum freeze spraying and pre-package them into the reaction chamber of the microfluidic chip.

[0039] Furthermore, the quality monitoring module includes a magnetic bead coupling quality inspection unit and a freeze-dried microsphere quality inspection unit. The magnetic bead coupling quality inspection unit is used to obtain images of magnetic microspheres through a high-resolution microscope and obtain the particle size distribution and antigen coating uniformity of magnetic microspheres through a convolutional neural network. The freeze-dried microsphere quality inspection unit is used to obtain the structural integrity and fluorescence intensity of freeze-dried microspheres through three-dimensional imaging and fluorescence detection technologies.

[0040] Furthermore, the chemiluminescence reaction auxiliary reagents include, but are not limited to, reagents such as quantum dot-labeled secondary antibodies, chemiluminescent substrates, and blocking solutions.

[0041] Furthermore, the control and management module includes a process parameter acquisition unit, a monitoring information receiving unit, an analysis and warning unit, and a dynamic optimization unit; the process parameter acquisition unit is used to collect the process parameters of each module in the system in real time; the monitoring information receiving unit is used to receive the real-time monitoring information of the quality monitoring module; the analysis and warning unit analyzes based on the real-time monitoring information to issue a warning reminder for abnormal situations in production; the dynamic optimization unit is used to optimize the process parameters of the subsequent system in combination with the historical operating conditions of the system;

[0042] Furthermore, the process parameters collected by the process parameter acquisition unit include, but are not limited to, reaction condition parameters, raw material state parameters, and equipment operation parameters in the magnetic bead coupling module and the microsphere preparation module, such as PH, temperature, and reaction time in the magnetic bead coupling module, freezing spray pressure, vacuum degree, ratio and mixing ratio of chemiluminescence reaction auxiliary reagents in the microsphere preparation module, and the operation parameters of the equipment in the system;

[0043] Furthermore, the analysis and warning unit includes a data preprocessing subunit, an immediate anomaly reminder unit, and a trend prediction reminder unit; the data preprocessing subunit is used to clean, normalize, and extract feature processing of the data in each dimension in the monitoring information to generate standardized index features; the immediate anomaly reminder unit is used to issue a warning for immediate anomalies by combining the analysis of real-time index features, and the trend prediction reminder unit is used to perform trend analysis and prediction by combining the index features of multiple consecutive batches, identify potential risks of production quality decline, and issue a warning for predicted anomalies;

[0044] Furthermore, the immediate anomaly reminder unit compares the index features with a preset index feature threshold range, and when a certain index feature exceeds its corresponding index feature threshold range, it issues a warning reminder for the immediate anomaly to the user;

[0045] Furthermore, the trend prediction reminder unit sets a continuous time window, obtains the sequence of index feature values within the time window, calculates the index stability parameter of each index feature based on the sequence of index feature values, and when the index stability parameter of a certain index feature exceeds its corresponding preset warning range, it issues a warning reminder for the predicted anomaly to the user;

[0046] Furthermore, the specific calculation method of the index stability parameter is as follows:

[0047] For a certain index feature:

[0048]

[0049] where, is the index stability parameter, is the total number of index feature values within the time window, is the deviation change rate of the -th index eigenvalue within the time window. is the time sequence number of the data within the time window. The closer the acquisition time of the index eigenvalue is to the current time , the larger the value. is a preset attenuation coefficient used to control the attenuation speed of the weight, which is set through preliminary experiments. For , it satisfies:

[0050] ;

[0051] Among them, is the deviation between the -th index eigenvalue within the time window and the preset optimal index eigenvalue; is the deviation between the -th index eigenvalue within the time window and the preset optimal index eigenvalue; is the time difference between two adjacent samplings of the index eigenvalue;

[0052] As Figure 2 shown, this embodiment provides a production method for a high-sensitivity anti-JO-1 antibody CBA detection kit, and the method includes the following steps:

[0053] S11: Antigen preparation: Prepare the JO-1 antigen through genetic engineering to ensure its high specificity and high activity;

[0054] S12: Magnetic bead coupling: Use carboxylated nanomagnetic beads to immobilize the JO-1 antigen on the surface of the magnetic beads through covalent coupling to generate antigen-coated magnetic microspheres;

[0055] S13: Microsphere preparation: Through vacuum freeze spray technology, mix the magnetic microspheres with chemiluminescence reaction auxiliary reagents and prepare them into solid freeze-dried microspheres, and then pre-encapsulate them into a microfluidic substrate;

[0056] S14: Quality monitoring: Use AI vision detection, fluorescence detection and three-dimensional imaging technology to conduct multi-level quality inspection and monitoring on the magnetic bead coupling uniformity, freeze-dried microsphere integrity and fluorescence signal intensity;

[0057] S15: Assemble the qualified freeze-dried microsphere microfluidic substrate with the microfluidic substrate with integrated temperature control and magnetic separation functions to form a final detection chip, and enter the packaging link after passing the sealing test.

[0058] This solution implements multi-level quality inspection and monitoring during the production process, and combines the monitoring information for real-time anomaly detection and anomaly trend prediction analysis, thereby improving the intelligent control ability of the production process, achieving precise early warning of production anomalies, ensuring the quality stability and consistency of products, and enhancing the reliability and production efficiency of the kits.

[0059] Example Two:

[0060] This example should be understood as including at least all the features of any one of the foregoing examples, and further improving on this basis;

[0061] This example provides a production system for a high-sensitivity anti-JO-1 antibody CBA detection kit. The system includes an antigen preparation module, a magnetic bead coupling module, a microsphere preparation module, a quality monitoring module, an assembly module, and a control and management module. The antigen preparation module is used to prepare JO-1 antigen raw materials. The magnetic bead coupling module is used to covalently couple carboxylated nano magnetic beads with purified antigens to generate antigen-coated magnetic microspheres. The microsphere preparation module is used to mix the coupled magnetic microspheres with chemiluminescence reaction auxiliary reagents, and use vacuum freeze spray technology to make solid freeze-dried microspheres and pre-package them into a microfluidic substrate. The quality monitoring module is used to perform multi-level quality inspection and monitoring on the magnetic bead coupling uniformity and the integrity of the freeze-dried microspheres through AI vision and fluorescence detection technologies, and feedback the monitoring information to the control and management module. The assembly module is used to assemble the pre-packaged microfluidic substrate of the qualified freeze-dried microspheres with the microfluidic substrate integrated with temperature control and magnetic separation to form a final detection chip, and after passing the sealing test, it is transported to the packaging line. The control and management module is used to coordinate the operation of each module in the system, and combine the feedback of the quality monitoring module to issue a warning reminder for abnormal situations during production;

[0062] The control and management module includes a process parameter acquisition unit, a monitoring information receiving unit, an analysis and warning unit, and a dynamic optimization unit. The process parameter acquisition unit is used to collect the process parameters of each module in the system in real time. The monitoring information receiving unit is used to receive the real-time monitoring information of the quality monitoring module. The analysis and warning unit analyzes based on the real-time monitoring information to issue a warning reminder for abnormal situations during production. The dynamic optimization unit is used to optimize the process parameters of the subsequent system in combination with the historical operating conditions of the system;

[0063] Furthermore, the dynamic optimization unit establishes a parameter optimization model by combining the historical process parameters of the system with their corresponding monitoring information, thereby completing the intelligent optimization of the production process parameters;

[0064] Furthermore, as Figure 3 shown, the parameter optimization model is specifically established through the following method:

[0065] S21: Obtain the system's historical process parameters and their corresponding monitoring information;

[0066] S22: Extract process parameter features and quality monitoring features; the process parameter features are obtained by calculating statistical features from historical process parameters; the quality monitoring features are obtained by calculating the difference between the index features corresponding to each dimension of data in the monitoring information and their optimal index feature values;

[0067] S23: Construct a training dataset with the process parameter features as input variables and the quality monitoring features as target variables;

[0068] S24: Establish a parameter optimization model framework and use the training dataset to train the model to optimize the model parameters; including:

[0069] S241: Set the parameter optimization model:

[0070] ;

[0071] Where, is the model prediction value, is the input variable, is the model parameter, is the mapping function;

[0072] S242: Use the training dataset to train the model, where the loss function of the model is:

[0073] ;

[0074] Where, is the loss value of the loss function, is the total number of samples in the training set, is the difference between the acquisition time of the th sample in the training set and the current time, is the time decay control parameter, used to control the influence of the time where the historical data is located on the change of the model parameters, set by pre-experiment; is the th sample's true value, that is, the quality monitoring feature in the th sample;

[0075] S243: Update and optimize the model parameters based on the gradient descent method:

[0076]

[0077] Where, is the learning rate, that is, the model parameter update step size, set by pre-experiment;

[0078] Furthermore, part of the functional implementation code for establishing the parameter optimization model is as follows:

[0079] import numpy as np

[0080] import tensorflow as tf

[0081] from tensorflow import keras

[0082] from tensorflow.keras.models import Sequential

[0083] from tensorflow.keras.layers import Dense, Input

[0084] from tensorflow.keras.optimizers import Adam

[0085] # Generate sample data (simulating historical process parameters and quality monitoring data)

[0086] np.random.seed(42)

[0087] # Assume there are 1000 historical data, each containing 10 process parameters

[0088] N = 1000 # Total number of samples

[0089] num_features = 10 # Number of process parameters

[0090] # Generate process parameter features X (mean, standard deviation, variance, etc.)

[0091] X = np.random.rand(N, num_features)

[0092] # Generate quality monitoring feature Y_k (true value), here assume it is a non - linear mapping of process parameters

[0093] Y_true = np.sin(np.sum(X, axis = 1))+np.random.normal(0, 0.1, N)

[0094] # Calculate time weight e^(-ρ * t_k)

[0095] rho = 0.01 # Time decay control parameter

[0096] t_k = np.linspace(0, 10, N) # Set the time difference (assuming uniform data intervals)

[0097] time_weights = np.exp(-rho * t_k) # Calculate the weights

[0098] # Define the parameter optimization model

[0099] model = Sequential(

[0100] Input(shape=(num_features,)), # Input layer

[0101] Dense(16, activation='relu'), # Hidden layer 1

[0102] Dense(8, activation='relu'), # Hidden layer 2

[0103] Dense(1, activation='linear') # Output layer, predicting Y_pred )

[0105] # Custom loss function, combining time-decayed weighted loss

[0106] def weighted_loss(y_true, y_pred):

[0107] loss = tf.reduce_sum(time_weights * tf.square(y_true - y_pred)) / tf.reduce_sum(time_weights)

[0108] return loss

[0109] # Compile the model

[0110] model.compile(optimizer=Adam(learning_rate=0.01), loss=weighted_loss)

[0111] # Train the model

[0112] model.fit(X, Y_true, epochs=50, batch_size=32, verbose=1)

[0113] # Predict using new data

[0114] X_new = np.random.rand(5, num_features) # Assume there are 5 new production batches

[0115] Y_pred = model.predict(X_new)

[0116] # Output the prediction results

[0117] print("Predicted quality monitoring eigenvalue Y_pred:", Y_pred)。

[0118] This solution constructs a data-driven parameter optimization model and combines a time-decaying weighted loss function to ensure that the latest data has a greater impact on parameter optimization, improving the model's adaptability to production trend changes; thus accurately predicting and optimizing production process parameters, effectively enhancing the detection sensitivity, stability, and batch consistency of the kit, and achieving intelligent and efficient production.

[0119] The content disclosed above is only the preferred feasible embodiment of the present invention, and does not limit the protection scope of the present invention. Therefore, all equivalent technical changes made by using the content of the specification and drawings of the present invention are included in the protection scope of the present invention. In addition, the elements therein can be updated with the development of technology.

Claims

1. A production system for a high-sensitivity anti-JO-1 antibody CBA detection kit, characterized in that, The system includes an antigen preparation module, a magnetic bead coupling module, a microsphere preparation module, a quality monitoring module, an assembly module, and a control and management module; the antigen preparation module is used to prepare JO-1 antigen raw materials; the magnetic bead coupling module is used to covalently couple carboxylated nanomagnetic beads with purified antigens to generate magnetic microspheres coated with JO-1 antigens; the microsphere preparation module is used to mix the coupled magnetic microspheres with chemiluminescence reaction auxiliary reagents, and use vacuum freeze spraying technology to make solid freeze-dried microspheres and pre-package them into a microfluidic substrate; the quality monitoring module is used to perform multi-level quality inspection and monitoring on the uniformity of magnetic bead coupling and the integrity of freeze-dried microspheres through AI vision and fluorescence detection technologies, and feedback the monitoring information to the control and management module; the assembly module is used to assemble the pre-packaged microfluidic substrate of the qualified freeze-dried microspheres with the microfluidic substrate integrated with temperature control and magnetic separation to form a final detection chip, and after passing the sealing test, it is transported to the packaging line; the control and management module is used to coordinate the operation of each module in the system, and combined with the feedback of the quality monitoring module, issue a warning reminder for abnormal situations in production; The magnetic bead coupling module includes a magnetic bead activation unit and an antigen fixation unit; the magnetic bead activation unit is used to activate carboxylated nanomagnetic beads to expose carboxyl groups for covalently coupling antigens; the antigen fixation unit is used to covalently bind antigens to the surface of activated magnetic beads under optimal reaction conditions by precisely controlling pH and temperature, generating magnetic microspheres coated with JO-1 antigen; the microsphere preparation module includes a reagent premixing unit and a freeze-drying encapsulation unit; the reagent premixing unit is used to mix the coupled magnetic microspheres and chemiluminescence reaction auxiliary reagents in proportion, and the freeze-drying encapsulation unit is used to convert the mixed reagents into solid microspheres by vacuum freeze spraying and pre-encapsulate them into the reaction chamber of the microfluidic chip; the control and management module includes a dynamic optimization unit; the dynamic optimization unit is used to optimize the process parameters of the subsequent system by combining the historical operating conditions of the system, including establishing a parameter optimization model by combining the historical process parameters of the system and their corresponding monitoring information, so as to complete the intelligent optimization of the production process parameters: Set the parameter optimization model: ; is the model predicted value, is the input variable, is the model parameter, is the mapping function; Use the training data set to train the model, where the loss function of the model is: ; is the loss value of the loss function, is the total number of samples in the training set, is the difference between the acquisition time of the th sample in the training set and the current time, is the time decay control parameter, which is used to control the influence of the time where the historical data is located on the change of the model parameters and is set by pre-experiment; is the quality monitoring feature of the th sample; Update and optimize the model parameters based on the gradient descent method: ; where, is the model parameter update step size, which is set by pre-experiment.

2. The production system of a high-sensitivity anti-JO-1 antibody CBA detection kit according to claim 1, characterized in that, The quality monitoring module includes a magnetic bead coupling quality inspection unit and a freeze-dried microsphere quality inspection unit; the magnetic bead coupling quality inspection unit is used to obtain magnetic microsphere images through a high-resolution microscope, and obtain the particle size distribution and antigen coating uniformity of magnetic microspheres through a convolutional neural network; the freeze-dried microsphere quality inspection unit is used to obtain the structural integrity and fluorescence intensity of freeze-dried microspheres through three-dimensional imaging and fluorescence detection technologies.

3. A high-sensitivity anti-JO-1 antibody CBA detection kit production system according to claim 2, characterized in that, The control and management module also includes a process parameter acquisition unit, a monitoring information receiving unit, and an analysis and warning unit; The process parameter acquisition unit is used to collect the process parameters of each module in the system in real time; The monitoring information receiving unit is used to receive the real-time monitoring information of the quality monitoring module; the analysis and warning unit analyzes based on the real-time monitoring information and issues a warning reminder for abnormal situations in production.

4. A production system for a high-sensitivity anti-JO-1 antibody CBA detection kit according to claim 3, characterized in that, The analysis and warning unit includes a data preprocessing sub-unit, an immediate anomaly reminder unit, and a trend prediction reminder unit; the data preprocessing sub-unit is used to clean, normalize, and extract feature processing on the data of each dimension in the monitoring information to generate standardized index features; the immediate anomaly reminder unit is used to issue a warning for immediate anomalies in combination with the analysis of real-time index features, and the trend prediction reminder unit is used to perform trend analysis and prediction in combination with the index features of multiple consecutive batches, identify the potential risks of production quality decline, and issue a warning for predicted anomalies.

5. A production system for a highly sensitive anti-JO-1 antibody CBA detection kit according to claim 4, characterized in that, The immediate anomaly reminder unit compares the index features with the preset index feature threshold range, and when a certain index feature exceeds its corresponding index feature threshold range, it issues a warning reminder for the immediate anomaly to the user.

6. A production system for a high-sensitivity anti-JO-1 antibody CBA detection kit according to claim 5, characterized in that, The trend prediction reminder unit sets a continuous time window, obtains the sequence of index feature values within the time window, calculates the index stability parameter of each index feature based on the sequence of index feature values, and when the index stability parameter of a certain index feature exceeds its corresponding preset warning range, it issues a warning reminder for the predicted anomaly to the user.

7. A production system for a highly sensitive CBA detection kit for anti-JO-1 antibody, according to claim 6, characterized in that, The specific calculation method of the index stability parameter is as follows: For a certain index feature: Among them, is the index stability parameter, is the total number of index eigenvalues within the time window, is the deviation change rate of the -th index eigenvalue within the time window, is the time serial number of the data within the time window. The closer the acquisition time of the index eigenvalue is to the current time, the larger its value; is a preset attenuation coefficient used to control the attenuation speed of the weight, which is set through prior experiments; for it satisfies: ； wherein, is the deviation between the -th index eigenvalue within the time window and the preset optimal index eigenvalue; is the deviation between the -th index eigenvalue within the time window and the preset optimal index eigenvalue; is the time difference between two adjacent samplings of the index eigenvalue.

8. A production method of a high-sensitivity anti-JO-1 antibody CBA detection kit, which applies the production system of a high-sensitivity anti-JO-1 antibody CBA detection kit described in claim 6, and is characterized in that, The method includes the following steps: S11: Antigen preparation: Prepare the JO-1 antigen through genetic engineering to ensure its high specificity and high activity; S12: Magnetic bead coupling: Use carboxylated nanomagnetic beads to immobilize the JO-1 antigen on the surface of the magnetic beads through covalent coupling to generate antigen-coated magnetic microspheres; S13: Microsphere preparation: Mix the magnetic microspheres with chemiluminescence reaction auxiliary reagents through vacuum freeze spraying technology and prepare them into solid freeze-dried microspheres, and then pre-encapsulate them into a microfluidic substrate; S14: Quality monitoring: Use AI vision detection, fluorescence detection and three-dimensional imaging technology to conduct multi-level quality inspection and monitoring on the magnetic bead coupling uniformity, freeze-dried microsphere integrity and fluorescence signal intensity; S15: Assemble the freeze-dried microsphere microfluidic substrate qualified by quality inspection with the microfluidic substrate integrated with temperature control and magnetic separation functions to form a final detection chip, and enter the packaging link after passing the sealing test.

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