High-sensitivity anti-JO-1 antibody CBA detection kit production system and method

Abstract

The invention provides a high-sensitivity anti-JO-1 antibody CBA detection kit production system and method. The system comprises 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 for preparing a JO-1 antigen material; the magnetic bead coupling module is used for generating antigen-coated magnetic microspheres; the microsphere preparation module is used for preparing magnetic microspheres into solid freeze-dried microspheres and pre-packaging the solid freeze-dried microspheres to a microfluidic substrate; the quality monitoring module is used for performing multi-stage quality inspection monitoring on the production process; the assembling module is used for completing the final assembling process. The control management module is used for coordinating the operation of each module in the system; through an intelligent production system and data-driven optimization, multi-stage quality inspection, dynamic parameter optimization and closed-loop control are integrated, high sensitivity, high stability and batch consistency of the kit are ensured, and production efficiency and detection reliability are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of biomedical detection systems, and in particular 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 of polymyositis, has high clinical value. At present, conventional antibody detection methods have problems such as limited sensitivity, unstable specificity, and complex operation, which can hardly 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 test kit, it still faces technical challenges such as magnetic bead coupling efficiency, freeze-dried microsphere stability, batch consistency and process parameter optimization. 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 widespread application in clinical testing and disease monitoring.

[0003] According to the related disclosed technical solutions, the solution with publication number CN102621308A proposes a colloidal gold chromatography anti-Jo-1 antibody detection test strip and a preparation method thereof. The colloidal gold chromatography anti-Jo-1 antibody detection test strip comprises a sample pad, a binding pad, a nitrocellulose coating membrane, and a water-absorbing pad. The sample pad, the binding pad, the nitrocellulose coating membrane, and the water-absorbing pad are sequentially attached to the bottom plate from one side of the bottom plate to the other side of the bottom plate, and the binding pad is coated with gold-labeled antibody a and gold-labeled antibody b; a detection line and a quality control line are arranged on the nitrocellulose coating membrane, and the detection line is coated with Jo-1 antigen protein, the quality control line is coated with gold-labeled antibody c; this scheme adopts indirect immunoassay, introduces Jo-1 antigen protein, and optimizes the process of binding pad and sample pad to achieve high sensitivity, high specificity and high accuracy detection performance of anti-Jo-1 antibody, providing a reference basis for auxiliary diagnosis of dermatomyositis / polymyositis; however, this scheme relies on colloidal gold chromatography, with limited detection sensitivity, and the process optimization is mainly focused on the binding pad and sample pad, without involving magnetic microsphere coupling, microfluidics 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 address the current deficiencies and propose a highly sensitive anti-JO-1 antibody CBA detection kit production system and method.

[0005] The present invention adopts the following technical solution: A high-sensitivity anti-JO-1 antibody CBA detection kit production system, the system comprises 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 material; the magnetic bead coupling module is used to covalently couple purified antigens using carboxylated nano magnetic beads to generate antigen-coated magnetic microspheres; the microsphere preparation module is used to mix the coupled magnetic microspheres with chemiluminescent reaction auxiliary reagents, adopt vacuum freezing spray technology to prepare solid freeze-dried microspheres and pre-package them into microfluidic substrates; the quality monitoring module is used to perform multi-level quality inspection and monitoring on the magnetic bead coupling uniformity and the freeze-dried microsphere integrity through AI vision and fluorescence detection technology, and feed back monitoring information to the control management module; the assembly module is used to assemble the freeze-dried microsphere pre-packaged microfluidic substrate that has passed the quality inspection with the microfluidic substrate with integrated temperature control and magnetic separation to form a final detection chip, which is transported to a packaging line after a sealing test; the control management module is used to coordinate the operation of each module in the system, and in combination with the feedback of the quality monitoring module, issue early warning reminders for abnormal situations in production.

[0006] 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 the carboxyl groups for covalent coupling with antigens; the antigen fixation unit is used to precisely control the pH and temperature so that the antigens are covalently bound to the surface of the activated magnetic beads under optimal reaction conditions to generate antigen-coated magnetic microspheres.

[0007] The microsphere preparation module includes a reagent premixing unit and a freeze-drying packaging unit; the reagent premixing unit is used to mix the chemiluminescent reaction auxiliary reagents in proportion, and the freeze-drying packaging unit is used to convert the mixed reagents into solid microspheres through vacuum freezing spray and pre-package them into the microfluidic chip reaction chamber.

[0008] 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 the magnetic microsphere image 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.

[0009] Furthermore, the control management module includes a process parameter acquisition unit, a monitoring information receiving unit, an analysis and early warning unit, and a dynamic optimization unit; the process parameter acquisition unit is used to acquire the process parameters of each module in the system in real time; the monitoring information receiving unit is used to receive real-time monitoring information from the quality monitoring module; the analysis and early warning unit issues early warning reminders for abnormal situations in production based on real-time monitoring information analysis; the dynamic optimization unit is used to optimize the process parameters of subsequent systems in combination with the historical operating conditions of the system.

[0010] Furthermore, the analysis and early warning unit includes a data preprocessing subunit, an immediate abnormality reminder unit and a trend prediction and reminder unit; the data preprocessing subunit is used to clean, normalize and extract features of the data of each dimension in the monitoring information to generate standardized indicator features; the immediate abnormality reminder unit is used to issue an early warning for immediate abnormalities in combination with real-time indicator feature analysis, and the trend prediction and reminder unit is used to perform trend analysis and prediction in combination with the indicator features of multiple consecutive batches, identify potential risks of decreased production quality and issue early warnings of predicted abnormalities.

[0011] Furthermore, the instant abnormality reminder unit compares the indicator feature with a preset indicator feature threshold range, and when a certain indicator feature exceeds its corresponding indicator feature threshold range, it issues an early warning reminder of the instant abnormality to the user.

[0012] Furthermore, the trend prediction reminder unit sets a continuous time window and obtains the indicator characteristic value sequence within the time window, and calculates the indicator stability parameter of each indicator feature based on the indicator characteristic value sequence. When the indicator stability parameter of a certain indicator feature exceeds its corresponding preset warning range, a warning reminder of the prediction abnormality is issued to the user.

[0013] Furthermore, the specific calculation method of the indicator stability parameter is as follows: For a certain indicator feature: in, is the indicator stability parameter, is the total number of indicator feature values ​​in the time window, The time window The deviation change rate of the characteristic value of each indicator, It is the time sequence number of the data in the time window. The closer the acquisition time of the indicator characteristic value is to the current time, the The larger the value of; is the preset attenuation coefficient, which is used to control the attenuation speed of the weight and is set by pre-experimentation; satisfy: ; in, The time window The deviation between the characteristic value of an indicator and the preset optimal characteristic value of the indicator; The time window The deviation between the characteristic value of an indicator and the preset optimal characteristic value of the indicator; It is the time difference between two adjacent samplings of the indicator feature.

[0014] Furthermore, the dynamic optimization unit establishes a parameter optimization model by combining the system's historical process parameters with their corresponding monitoring information, thereby completing the intelligent optimization of the production process parameters.

[0015] A method for producing a high-sensitivity anti-JO-1 antibody CBA detection kit is applied to a high-sensitivity anti-JO-1 antibody CBA detection kit production system, and the method comprises the following steps: S11: Antigen preparation: JO-1 antigen is prepared by genetic engineering to ensure its high specificity and activity; S12: Magnetic bead coupling: Carboxylated nanomagnetic beads were used to fix the JO-1 antigen to the surface of the magnetic beads by covalent coupling to generate antigen-coated magnetic microspheres; S13: Microsphere preparation: The magnetic microspheres were mixed with chemiluminescent reaction auxiliary reagents by vacuum freeze spray technology and prepared into solid freeze-dried microspheres, which were then pre-packaged into microfluidic substrates; S14: Quality monitoring: AI visual inspection, fluorescence detection and 3D imaging technology are used to conduct multi-level quality inspection and monitoring of magnetic bead coupling uniformity, freeze-dried microsphere integrity and fluorescence signal intensity; S15: Assemble the freeze-dried microsphere microfluidic substrate that has passed the quality inspection with the microfluidic substrate with integrated temperature control and magnetic separation functions to form the final detection chip, and enter the packaging stage after the sealing test.

[0016] Beneficial effects achieved by the present invention: The present invention integrates magnetic bead coupling, vacuum freezing spray, AI quality monitoring and microfluidic technology through intelligent production system and data-driven optimization to ensure the high sensitivity, high stability and batch consistency of the anti-JO-1 antibody CBA detection kit; through AI visual detection, fluorescence detection and three-dimensional imaging technology, multi-level quality control of magnetic bead coupling uniformity and freeze-dried microsphere integrity is achieved to ensure consistent product quality; by establishing a parameter optimization model, the process parameters are intelligently optimized and dynamically adjusted, thereby improving the stability and accuracy of the production process. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The present invention can be further understood from the following description in conjunction with the accompanying drawings. The components in the figures are not necessarily drawn to scale, but the emphasis is placed on illustrating the principles of the embodiments. In different views, the same reference numerals designate corresponding parts.

[0018] Figure 1 It is a schematic diagram of the overall module of the present invention.

[0019] Figure 2 This is a schematic diagram of the production method of the high-sensitivity anti-JO-1 antibody CBA detection kit of the present invention.

[0020] Figure 3 A schematic diagram of a flow chart for establishing a parameter optimization model of the present invention. DETAILED DESCRIPTION

[0021] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is 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, other systems, methods and / or features of the present embodiment will become apparent after reviewing the following detailed description; 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 apparent from the following detailed description.

[0022] The same or similar numbers in the drawings of the embodiments of the present invention correspond to the same or similar parts; in the description of the present invention, it should be understood that if the terms "upper", "lower", "left", "right" and the like indicate directions or positional relationships based on the directions or positional relationships shown in the drawings, it is 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 direction, be constructed and operated in a specific direction. Therefore, the terms describing the positional relationship in the drawings are only used for illustrative purposes and cannot be understood as limitations on this patent. For ordinary technicians in this field, the specific meanings of the above terms can be understood according to specific circumstances.

[0023] Embodiment 1:

[0024] like Figure 1As shown, this embodiment provides a high-sensitivity anti-JO-1 antibody CBA detection kit production system, 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 material; the magnetic bead coupling module is used to use carboxylated nano magnetic beads to covalently couple purified antigens to generate antigen-coated magnetic microspheres; the microsphere preparation module is used to mix the coupled magnetic microspheres with chemiluminescent reaction auxiliary reagents, and use vacuum freezing spray technology to make solid freeze-dried microspheres and pre-package them into microfluidic substrates; the quality monitoring module is used to perform multi-level quality inspection and monitoring of the uniformity of magnetic bead coupling and the integrity of freeze-dried microspheres through AI vision and fluorescence detection technology, and feedback monitoring information to the control management module; the assembly module is used to assemble the freeze-dried microsphere pre-packaged microfluidic substrate that has passed the quality inspection with the microfluidic substrate with integrated temperature control and magnetic separation to form a final detection chip, which is transported to the packaging line after the sealing test; 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 an early warning reminder for abnormal conditions in production; The magnetic bead coupling module includes a magnetic bead activation unit and an antigen fixing unit; the magnetic bead activation unit is used to activate the carboxylated nano magnetic beads to expose the carboxyl groups for covalently coupling with antigens; the antigen fixing unit is used to precisely control the pH and temperature so that the antigens are covalently bound to the surface of the activated magnetic beads under optimal reaction conditions to generate antigen-coated magnetic microspheres; The microsphere preparation module includes a reagent premixing unit and a freeze-drying packaging unit; the reagent premixing unit is used to mix the chemiluminescent reaction auxiliary reagents in proportion, and the freeze-drying packaging unit is used to convert the mixed reagents into solid microspheres by vacuum freezing spray, and pre-package them into the microfluidic chip reaction chamber; 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 the magnetic microsphere image through a high-resolution microscope, and obtain the particle size distribution and antigen coating uniformity of the magnetic microsphere 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; Furthermore, the auxiliary reagents for the chemiluminescent reaction include but are not limited to quantum dot-labeled secondary antibodies, chemiluminescent substrates, blocking solutions and other reagents; Furthermore, the control management module includes a process parameter acquisition unit, a monitoring information receiving unit, an analysis and early 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 early warning unit issues early warning reminders for abnormal situations in production based on the real-time monitoring information analysis; 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; Further, the process parameters collected by the process parameter collection 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 chemiluminescent reaction auxiliary reagents in the microsphere preparation module, and operation parameters of the equipment in the system; Furthermore, the analysis and early warning unit includes a data preprocessing subunit, an immediate abnormality reminder unit and a trend prediction reminder unit; the data preprocessing subunit is used to clean, normalize and extract features of the data of each dimension in the monitoring information to generate standardized indicator features; the immediate abnormality reminder unit is used to issue an early warning for the immediate abnormality in combination with the real-time indicator feature analysis, and the trend prediction reminder unit is used to perform trend analysis and prediction in combination with the indicator features of multiple consecutive batches, identify the potential risk of production quality decline and issue an early warning of the predicted abnormality; Furthermore, the instant abnormality reminder unit compares the indicator feature with a preset indicator feature threshold range, and when a certain indicator feature exceeds its corresponding indicator feature threshold range, it issues an early warning reminder of the instant abnormality to the user; Furthermore, the trend prediction reminder unit sets a continuous time window and obtains the indicator characteristic value sequence within the time window, calculates the indicator stability parameter of each indicator feature based on the indicator characteristic value sequence, and when the indicator stability parameter of a certain indicator feature exceeds its corresponding preset warning range, a warning reminder of the forecast abnormality is issued to the user; Furthermore, the specific calculation method of the indicator stability parameter is as follows: For a certain indicator feature: in, is the indicator stability parameter, is the total number of indicator feature values ​​in the time window, The time window The deviation change rate of the characteristic value of each indicator, It is the time sequence number of the data in the time window. The closer the acquisition time of the indicator characteristic value is to the current time, the The larger the value of; is the preset attenuation coefficient, which is used to control the attenuation speed of the weight and is set by pre-experimentation; satisfy: ; in, The time window The deviation between the characteristic value of an indicator and the preset optimal characteristic value of the indicator; The time window The deviation between the characteristic value of an indicator and the preset optimal characteristic value of the indicator; is the time difference between two adjacent samplings of the indicator feature; like Figure 2 As shown, this embodiment provides a method for producing a high-sensitivity anti-JO-1 antibody CBA detection kit, the method comprising the following steps: S11: Antigen preparation: JO-1 antigen is prepared by genetic engineering to ensure its high specificity and activity; S12: Magnetic bead coupling: Carboxylated nanomagnetic beads were used to fix the JO-1 antigen to the surface of the magnetic beads by covalent coupling to generate antigen-coated magnetic microspheres; S13: Microsphere preparation: The magnetic microspheres were mixed with chemiluminescent reaction auxiliary reagents by vacuum freeze spray technology and prepared into solid freeze-dried microspheres, which were then pre-packaged into microfluidic substrates; S14: Quality monitoring: AI visual inspection, fluorescence detection and 3D imaging technology are used to conduct multi-level quality inspection and monitoring of magnetic bead coupling uniformity, freeze-dried microsphere integrity and fluorescence signal intensity; S15: Assemble the freeze-dried microsphere microfluidic substrate that has passed the quality inspection with the microfluidic substrate with integrated temperature control and magnetic separation functions to form the final detection chip, and enter the packaging stage after the sealing test.

[0025] This solution implements multi-level quality inspection and monitoring during the production process, and combines monitoring information to perform real-time anomaly detection and abnormal trend prediction analysis, thereby improving the intelligent control capabilities of the production process, achieving accurate early warning of production anomalies, ensuring product quality stability and consistency, and improving the reliability and production efficiency of the test kit.

[0026] Embodiment 2:

[0027] This embodiment should be understood to include at least all the features of any of the above embodiments, and further improve upon them; The present embodiment provides a high-sensitivity anti-JO-1 antibody CBA detection kit production system, 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 material; the magnetic bead coupling module is used to covalently couple purified antigens using carboxylated nanomagnetic beads to generate antigen-coated magnetic microspheres; the microsphere preparation module is used to mix the coupled magnetic microspheres with chemiluminescent reaction auxiliary reagents, use vacuum freezing spray technology to make solid freeze-dried microspheres and pre-package them into microfluidic substrates; the quality monitoring module is used to perform multi-level quality inspection and monitoring of the magnetic bead coupling uniformity and the freeze-dried microsphere integrity through AI vision and fluorescence detection technology, and feedback monitoring information to the control management module; the assembly module is used to assemble the freeze-dried microsphere pre-packaged microfluidic substrate that has passed the quality inspection with the microfluidic substrate with integrated temperature control and magnetic separation to form a final detection chip, which is transported to the packaging line after the sealing test; 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 early warning reminders for abnormal situations in production; The control management module includes a process parameter acquisition unit, a monitoring information receiving unit, an analysis and early 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 early warning unit issues early warning reminders for abnormal situations in production based on real-time monitoring information analysis; 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; Furthermore, the dynamic optimization unit establishes a parameter optimization model by combining the system historical process parameters and the corresponding monitoring information, thereby completing the intelligent optimization of the production process parameters; Further, such as Figure 3 As shown, the parameter optimization model is specifically established in the following manner: S21: Obtaining historical process parameters of the system and their corresponding monitoring information; S22: Extracting process parameter features and quality monitoring features; the process parameter features are obtained by calculating statistical features of 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 its optimal index feature value; S23: construct a training data set using process parameter features as input variables and quality monitoring features as target variables; S24: Establish a parameter optimization model framework and train the model using a training data set to optimize model parameters; including: S241: Setting parameters to optimize the model: ; in, is the model prediction value, is the input variable, are model parameters, is the mapping function; S242: Train the model using the training data set, where the loss function of the model is: ; in, is the loss value of the loss function, is the total number of samples in the training set, For the training set The difference between the collection time of a sample and the current time, It is a time decay control parameter used to control the influence of the time of historical data on the change of model parameters, and is set through pre-experimental settings; For the The true value of the sample, that is, Quality monitoring features in samples; S243: Update optimization model parameters based on gradient descent method: in, is the learning rate, i.e., the step size for updating model parameters, which is set by prior experiments; Furthermore, some functional implementation codes of the parameter optimization model are as follows: import numpy as np import tensorflow as tf from tensorflow import keras from tensorflow.keras.models import Sequential from tensorflow.keras.layers import Dense, Input from tensorflow.keras.optimizers import Adam # Generate sample data (simulate historical process parameters and quality monitoring data) np.random.seed(42) # Assume there are 1000 historical data, each containing 10 process parameters N = 1000 # Total number of samples num_features = 10 # Number of process parameters # Generate process parameter characteristics X (mean, standard deviation, variance, etc.) X = np.random.rand(N, num_features) # Generate quality monitoring feature Y_k (true value), assuming it is some nonlinear mapping of process parameters Y_true = np.sin(np.sum(X, axis=1)) + np.random.normal(0, 0.1, N) # Calculate the time weight e^(-ρ * t_k) rho = 0.01 # Time decay control parameter t_k = np.linspace(0, 10, N) # Set the time difference (assuming the data are evenly spaced) time_weights = np.exp(-rho * t_k) # Calculate weights # Define parameter optimization model model = Sequential([ Input(shape=(num_features,)), # Input layer Dense(16, activation='relu'), # Hidden layer 1 Dense(8, activation='relu'), # Hidden layer 2 Dense(1, activation='linear') # Output layer, predict Y_pred ]) # Custom loss function, combined with time-decay weighted loss def weighted_loss(y_true, y_pred): loss = tf.reduce_sum(time_weights * tf.square(y_true - y_pred)) / tf.reduce_sum(time_weights) return loss # Compile the model model.compile(optimizer=Adam(learning_rate=0.01), loss=weighted_loss) # Train the model model.fit(X, Y_true, epochs=50, batch_size=32, verbose=1) # Test new data for prediction X_new = np.random.rand(5, num_features) # Assume there are 5 new production batches Y_pred = model.predict(X_new) # Output prediction results print("Predicted quality monitoring feature value Y_pred:", Y_pred).

[0028] This solution builds a data-driven parameter optimization model and combines it with a time-decayed weighted loss function to ensure that the latest data has a greater impact on parameter optimization and improve the model's adaptability to changes in production trends. This allows for accurate prediction and optimization of production process parameters, effectively improving the test kit's detection sensitivity, stability, and batch consistency, and enabling intelligent and efficient production.

[0029] The contents disclosed above are only preferred feasible embodiments of the present invention, and do not limit the protection scope of the present invention. Therefore, all equivalent technical changes made using the contents of the present invention specification and drawings are included in the protection scope of the present invention. In addition, the elements therein can be updated as technology develops.

Claims

1. A highly sensitive anti-JO-1 antibody CBA detection kit production system, 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 management module; the antigen preparation module is used to prepare JO-1 antigen material; the magnetic bead coupling module is used to covalently couple purified antigens using carboxylated nano magnetic beads to generate antigen-coated magnetic microspheres; the microsphere preparation module is used to mix the coupled magnetic microspheres with chemiluminescent reaction auxiliary reagents, and use vacuum freezing spray technology to make solid freeze-dried microspheres and pre-package them into microfluidic substrates; the quality monitoring module is used to perform multi-level quality inspection and monitoring of the magnetic bead coupling uniformity and the freeze-dried microsphere integrity through AI vision and fluorescence detection technology, and feedback monitoring information to the control management module; the assembly module is used to assemble the freeze-dried microsphere pre-packaged microfluidic substrate that has passed the quality inspection with the microfluidic substrate with integrated temperature control and magnetic separation to form a final detection chip, which is transported to the packaging line after the sealing test; the control management module is used to coordinate the operation of each module in the system, and combined with the feedback from the quality monitoring module, issue early warning reminders for abnormal situations in production; The magnetic bead coupling module includes a magnetic bead activation unit and an antigen fixing unit; the magnetic bead activation unit is used to activate the carboxylated nano magnetic beads to expose the carboxyl groups for covalently coupling with antigens; the antigen fixing unit is used to precisely control the pH and temperature so that the antigens are covalently bound to the surface of the activated magnetic beads under optimal reaction conditions to generate antigen-coated magnetic microspheres; The microsphere preparation module includes a reagent premixing unit and a freeze-drying packaging unit; the reagent premixing unit is used to mix the chemiluminescent reaction auxiliary reagents in proportion, and the freeze-drying packaging unit is used to convert the mixed reagents into solid microspheres through vacuum freezing spray and pre-package them into the microfluidic chip reaction chamber.

2. A highly sensitive anti-JO-1 antibody CBA detection kit production system 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 the magnetic microsphere image 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.

3. A highly sensitive anti-JO-1 antibody CBA detection kit production system according to claim 2, characterized in that: The control management module includes a process parameter acquisition unit, a monitoring information receiving unit, an analysis and early warning unit, and a dynamic optimization unit; The process parameter acquisition unit is used to acquire the process parameters of each module in the system in real time; The monitoring information receiving unit is used to receive real-time monitoring information from the quality monitoring module; The analysis and early warning unit issues early warning reminders for abnormal situations in production based on real-time monitoring information analysis; 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.

4. A highly sensitive anti-JO-1 antibody CBA detection kit production system according to claim 3, characterized in that: The analysis and early warning unit includes a data preprocessing subunit, an immediate abnormality reminder unit and a trend prediction and reminder unit; the data preprocessing subunit is used to clean, normalize and extract features of the data of each dimension in the monitoring information to generate standardized indicator features; the immediate abnormality reminder unit is used to issue an early warning for immediate abnormalities in combination with real-time indicator feature analysis, and the trend prediction and reminder unit is used to perform trend analysis and prediction in combination with the indicator features of multiple consecutive batches, identify potential risks of decreased production quality and issue early warnings of predicted abnormalities.

5. A highly sensitive anti-JO-1 antibody CBA detection kit production system according to claim 4, characterized in that: The instant abnormality reminder unit compares the indicator feature with a preset indicator feature threshold range, and when a certain indicator feature exceeds its corresponding indicator feature threshold range, it issues an early warning reminder of the instant abnormality to the user.

6. A highly sensitive anti-JO-1 antibody CBA detection kit production system according to claim 5, characterized in that: The trend prediction reminder unit sets a continuous time window and obtains the indicator characteristic value sequence within the time window, and calculates the indicator stability parameter of each indicator feature based on the indicator characteristic value sequence. When the indicator stability parameter of a certain indicator feature exceeds its corresponding preset warning range, a warning reminder of the prediction abnormality is issued to the user.

7. A highly sensitive anti-JO-1 antibody CBA detection kit production system according to claim 6, characterized in that: The specific calculation method of the indicator stability parameter is as follows: For a certain indicator feature: in, is the indicator stability parameter, is the total number of indicator feature values ​​in the time window, The time window The deviation change rate of the characteristic value of each indicator, It is the time sequence number of the data in the time window, and the time distance of obtaining the characteristic value of the indicator. The closer the current time is, the The larger the value of; is the preset attenuation coefficient, which is used to control the attenuation speed of the weight and is set by pre-experimentation; satisfy: ； in, The time window The deviation between the characteristic value of an indicator and the preset optimal characteristic value of the indicator; The time window The deviation between the characteristic value of an indicator and the preset optimal characteristic value of the indicator; It is the time difference between two adjacent samplings of the indicator feature.

8. A highly sensitive anti-JO-1 antibody CBA detection kit production system according to claim 7, characterized in that: The dynamic optimization unit establishes a parameter optimization model by combining the system's historical process parameters with the corresponding monitoring information, thereby completing the intelligent optimization of the production process parameters.

9. A method for producing a high-sensitivity anti-JO-1 antibody CBA detection kit, applied to a high-sensitivity anti-JO-1 antibody CBA detection kit production system according to claim 6, characterized in that: The method comprises the following steps: S11: Antigen preparation: JO-1 antigen is prepared by genetic engineering to ensure its high specificity and activity; S12: Magnetic bead coupling: Carboxylated nanomagnetic beads were used to fix the JO-1 antigen to the surface of the magnetic beads by covalent coupling to generate antigen-coated magnetic microspheres; S13: Microsphere preparation: The magnetic microspheres were mixed with chemiluminescent reaction auxiliary reagents by vacuum freeze spray technology and prepared into solid freeze-dried microspheres, which were then pre-packaged into microfluidic substrates; S14: Quality monitoring: AI visual inspection, fluorescence detection and 3D imaging technology are used to conduct multi-level quality inspection and monitoring of magnetic bead coupling uniformity, freeze-dried microsphere integrity and fluorescence signal intensity; S15: Assemble the freeze-dried microsphere microfluidic substrate that has passed the quality inspection with the microfluidic substrate with integrated temperature control and magnetic separation functions to form the final detection chip, and enter the packaging stage after the sealing test.

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