Intelligent quality control and standardized management system for tumor markers

Abstract

The invention relates to the technical field of quality control of in-vitro diagnostic equipment, and discloses an intelligent quality control and standardized management system for tumor markers, which comprises a feature acquisition module and a feature weighting module, which are used for acquiring time sequence signals of a physical sensor and outputting a specific weighted feature matrix; the baseline modeling module is used for carrying out dimensionality reduction decomposition on the feature matrix in the calibration stage, and constructing and solidifying a principal component baseline space model; the real-time monitoring module is used for orthogonally projecting the matrix to a baseline model to evaluate the stability of the microenvironment and outputting an interception signal, a score and a residual vector when the matrix exceeds the limit; the dynamic reconstruction module is used for reversely reconstructing an actual deviation matrix of a physical channel based on the vector and generating dynamic curve parameters by combining Jacobian matrix mapping; and the self-adaptive solution and traceability module executes self-adaptive concentration solution, extracts the deviation matrix to analyze the relative error contribution degree, and outputs a hardware diagnosis log. According to the invention, microenvironment abnormity monitoring, dynamic compensation solution and precise traceability of hardware failure are realized.

Description

Technical Field

[0001] This invention relates to the field of quality control technology for in vitro diagnostic equipment, specifically to an intelligent quality control and standardization management system for tumor markers. Background Technology

[0002] In the field of in vitro diagnostics, the quantitative detection of tumor markers places extremely high demands on the physical stability of analytical instruments and the consistency of multi-channel signals. Current detection equipment primarily relies on periodic quality control testing or fixed threshold judgment logic to monitor instrument status. This monitoring method typically has a long lag and struggles to identify transient measurement anomalies caused by fluctuations in the physical microenvironment during the detection process in real time. Due to a lack of deep feature extraction capabilities from the underlying sensor time-series signals, existing systems cannot accurately distinguish between random noise and systematic baseline shifts when faced with complex physical noise interference, thus limiting the sensitivity and accuracy of microenvironment stability assessment.

[0003] Furthermore, when slight temperature drift or optical path aging in the system's physical channels causes signal deviations, existing technologies typically employ forced interception of results or manual recalibration by shutting down the system. Such solutions lack a mechanism for online dynamic reconstruction of biological calculation parameters based on real-time physical disturbances. They cannot adaptively correct the standard curve using algorithms when the system experiences compensable deviations, leading to decreased measurement accuracy during transitional phases of environmental fluctuations and increased unnecessary downtime for maintenance.

[0004] Meanwhile, existing hardware fault diagnosis mechanisms are mostly limited to monitoring the on / off status of individual modules, failing to quantify the contribution of relative errors in the final concentration calculation. When underlying hardware performance degrades or irreversible physical failures occur, the system struggles to accurately isolate and locate the specific sensor channels causing the errors. This leads to a heavy reliance on manual troubleshooting during hardware maintenance, increasing the time cost of tracing equipment faults and failing to meet the industry demands for standardized management and precise operation and maintenance in tumor marker detection. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention provides an intelligent quality control and standardization management system for tumor markers, thus solving the problems.

[0006] To achieve the above objectives, the present invention provides an intelligent quality control and standardization management system for tumor markers, comprising:

[0007] The feature acquisition module acquires the timing signals of the physical sensors of the analyzer's underlying microcontroller, constructs and outputs the original feature matrix;

[0008] The feature weighting module receives the original feature matrix, calls a preset target marker sensitivity weighting matrix according to the tumor marker detection items, performs linear weighting transformation, and outputs a specific weighted feature matrix.

[0009] The baseline modeling module performs dimensionality reduction decomposition on the specific weighted feature matrix obtained in the calibration stage, and constructs and solidifies the principal component baseline space model. The solidified principal component baseline space model encapsulates basic statistical parameters such as the mean vector, standard deviation vector, and orthogonal loading matrix calculated and cached in the calibration stage, which are then called by subsequent modules of the system.

[0010] In the sample testing phase, the real-time monitoring module first expands and splices the two-dimensional specific weighted feature matrix output by the feature weighting module in dimensional order to construct a one-dimensional flattened specific weighted feature vector. Then, the flattened specific weighted feature vector is orthogonally projected onto the principal component baseline space model to evaluate the microenvironment stability. When the evaluation result exceeds the limit, the module outputs an interception signal, the principal component score vector, and the model external residual vector.

[0011] The dynamic reconstruction module, based on the intercepted signal, uses the principal component score vector and the model external residual vector to inversely reconstruct the actual deviation matrix of the physical channel, and combines the biochemical parameter perturbation Jacobian matrix mapping to generate dynamic curve parameters; the dynamic curve parameters include independent sub-parameters for controlling the four-parameter logistic regression equation, specifically including dynamic bottom parameters, dynamic top parameters, dynamic inflection point concentration parameters, and dynamic slope factors.

[0012] The adaptive solution and tracing module substitutes the original emission signal into the dynamic curve parameters to perform adaptive concentration solution and outputs the final state adaptive solution concentration. It also extracts the relative error contribution of the actual deviation matrix of the physical channel and outputs the hardware tracing, maintenance and diagnostic log.

[0013] Preferably, the prior construction process of the target biomarker sensitivity weighting matrix called by the feature weighting module includes: constructing a multiple linear regression equation with physical channel perturbation as the independent variable and emission signal deviation as the dependent variable; introducing a ridge regression mechanism when solving the partial regression coefficients and adding an L2 regularization penalty term to the loss function; using the reference values ​​of the basic measurement range of each physical channel to eliminate the dimensions of the partial regression coefficients, and introducing a preset minimum normal number in the normalized division operation to prevent division by zero anomalies and calculation overflow; mapping the calculated specificity weight coefficients to the diagonal elements of a diagonal matrix to complete the prior construction, and in the target biomarker sensitivity weighting matrix structure, all off-diagonal elements are forcibly set to zero.

[0014] In one specific embodiment, the process of the baseline modeling module performing dimensionality reduction decomposition on the specific weighted feature matrix during the calibration stage includes: obtaining a set of calibrator-specific weighted feature matrices generated by continuous testing during the calibration stage, stacking them row-wise to construct a calibration feature matrix; calculating the mean vector and standard deviation vector in the column direction, and performing zero-mean, unit-variance standardization on the calibration feature matrix, introducing a constant in the standardized division operation to prevent system-level interruption caused by division-to-zero anomalies, and caching the calculated mean vector and standard deviation vector; using principal component analysis algorithm to perform dimensionality reduction decomposition on the standardized calibration feature matrix, indirectly solving it by calculating a small-dimensional inner product matrix, and obtaining the eigenvectors of the original high-dimensional covariance matrix through inverse mapping; setting a cumulative variance contribution rate threshold to truncate the principal components corresponding to low eigenvalues, defining the effective dimension of the principal component space, and extracting eigenvectors to form an orthogonal loading matrix.

[0015] Preferably, the process of the baseline modeling module solidifying the principal component baseline space model includes: projecting the standardized calibration feature matrix onto a low-dimensional space to generate a principal component score matrix; calculating the product of the transpose of the principal component score matrix and the principal component score matrix to obtain the principal component score covariance matrix; forcibly clamping the diagonal elements of the principal component score covariance matrix below a preset lower limit to a preset lower limit value; and uniformly writing the mean vector, standard deviation vector, orthogonal loading matrix, and the clamped principal component score covariance matrix into a non-volatile memory for static solidification to constitute the principal component baseline space model.

[0016] In one specific embodiment, the calculation logic of the real-time monitoring module performing orthogonal projection operation to evaluate the stability of the microenvironment is as follows: the flattened specific weighted feature vector of the test sample is subjected to orthogonal projection operation onto a preset orthogonal load matrix to extract the projection coordinates of the current detected microenvironment in the main physical causal direction of the system, and the principal component score vector is generated; the Hotelling statistic is calculated based on the extracted principal component score vector, and the variance weight of each dimension is performed using the inverse matrix of the fixed principal component score covariance matrix; before performing the inversion, the diagonal matrix bias is forcibly introduced by superimposing the product of the regularization bias constant and the unit diagonal matrix to eliminate the divergence of the inversion caused by small singular values.

[0017] Preferably, the determination mechanism for the real-time monitoring module to output an interception signal when the limit is exceeded includes: reconstructing the feature vector based on the orthogonal complement space decomposition principle to remove known information and extract the model exterior residual vector; calculating the product of the model exterior residual vector and its transpose matrix to obtain the squared prediction error statistic; introducing a multi-dimensional exponentially weighted moving average time-series smoothing mechanism in the determination of the output result to attenuate transient noise using historical state data; when the smoothed state variable exceeds the preset control limit and the state remains stable for multiple consecutive detection cycles, determining that the current test microenvironment has generated an irreversible physical deviation and outputting the interception signal.

[0018] In one specific embodiment, the specific execution steps of the dynamic reconstruction module in reverse reconstructing the actual deviation matrix of the physical channel include: setting a dynamic residual activation bias coefficient based on the squared prediction error statistic; calculating the product of the principal component score vector and the transpose of the orthogonal loading matrix; adding the product of the residual activation bias coefficient and the model-outside residual vector; performing inverse projection reconstruction of the standardized space to generate a standardized comprehensive deviation vector; calling the standard deviation vector cached by the principal component baseline space model to perform inverse scaling of the physical dimensions on the standardized comprehensive deviation vector to obtain a dimensionless absolute deviation vector; performing inverse spatial dimension reconstruction on the dimensionless absolute deviation vector and arranging it into a weighted state deviation matrix; multiplying the weighted state deviation matrix with the inverse of the target weighted diagonal matrix that is forcibly introduced with a regularization smoothing constant; and performing inverse decoupling calculation to obtain the actual deviation matrix of the physical channel.

[0019] Preferably, the step of generating dynamic curve parameters by combining the dynamic reconstruction module with the biochemical parameter perturbation Jacobian matrix mapping includes: expanding and reorganizing the actual deviation matrix of the physical channel into a flattened physical deviation feature vector, and performing linear neighborhood truncation to force abnormal over-limit elements to the upper limit boundary; performing matrix mapping multiplication on the flattened and linearly truncated physical deviation feature vector and the transpose of the biochemical parameter perturbation Jacobian matrix to calculate and generate a curve parameter compensation vector; performing physical extreme value limiting determination on the generated curve parameter compensation vector, extracting the basic regression equation parameters of the current reagent batch, performing an algebraic superposition operation with the limited curve parameter compensation vector, and generating the dynamic curve parameters including dynamic bottom parameters, dynamic top parameters, dynamic inflection point concentration parameters, and dynamic slope factor parameters.

[0020] In one specific embodiment, the adaptive solution and tracing module performs the numerical approximation process of adaptive concentration solution, which includes: receiving the original emission signal captured by the underlying photometric unit; performing boundary clamping on the original emission signal to obtain the effective emission intensity before performing nonlinear inversion; and performing anti-zero regularization on the dynamic slope factor in the dynamic curve parameters; constructing a nonlinear residual function and iterative update formula of the Newton-Raphson adaptive approximation algorithm with L2 regularization constraints; forcibly introducing a safety isolation limit before constructing the residual function to avoid division overflow of the exponent base; using the first-order local slope information of the dynamic curve at the current concentration working point to control the convergence of the iterative calculation along the error descent direction; and when the absolute difference in concentration between two adjacent iterations is less than the set tolerance band, using the converged concentration estimate as the final state adaptive solution concentration output.

[0021] Preferably, the conditions for the adaptive solution and tracing module to analyze the relative error contribution and output the hardware tracing and maintenance diagnostic log are as follows: Root mean square (RMS) calculation is performed on each column vector in the actual deviation matrix of the physical channel to extract the time-domain absolute error of each independent sensing channel; global normalization is performed on the extracted absolute errors of each channel, and a global smoothing constant is forcibly introduced into the denominator of the normalization algorithm to calculate and generate the relative error contribution of each independent physical sensing channel, which is then arranged in descending order of value; when the maximum extracted relative contribution exceeds a preset absolute sensitivity threshold, and the ratio of the maximum relative contribution to the second largest relative contribution is greater than the primary-secondary isolation threshold, and this is continuously triggered within multiple consecutive test cycles, it is determined that the specific hardware sensing channel mapped by the corresponding index has experienced an irreversible physical failure, and the hardware tracing and maintenance diagnostic log is output.

[0022] This invention provides an intelligent quality control and standardization management system for tumor markers. It has the following beneficial effects:

[0023] 1. This invention constructs a principal component baseline space model through a feature weighting module and a baseline modeling module, and orthogonally projects the feature vectors of the test samples onto the model during the real-time monitoring stage. Combined with a multi-dimensional exponentially weighted moving average time-series smoothing mechanism to process the model's external residual vectors and principal component score vectors, it can accurately assess the stability of the analyzer's detection microenvironment in complex noise, intercept abnormal test states, and avoid the output of erroneous detection results.

[0024] 2. This invention uses a dynamic reconstruction module to inversely reconstruct the score vector and residual vector output by real-time monitoring into the actual deviation matrix of the physical channel, and performs matrix mapping multiplication with the Jacobian matrix of biochemical parameter perturbation to generate dynamic curve parameters. Combined with the Newton-Raphson approximation algorithm with L2 regularization constraints in the adaptive solution and source tracing module, it can correct the standard working curve in real time when the system experiences micro-environment drift, output accurate adaptive solution concentration, and improve the anti-interference ability of the measurement results.

[0025] 3. This invention uses an adaptive solution and tracing module to perform root mean square calculation and global normalization on the actual deviation matrix of the physical channel to obtain the relative error contribution of each independent physical sensing channel. Based on this, by judging the numerical relationship between the maximum relative contribution and the preset sensitivity threshold and primary-secondary isolation threshold, the specific hardware sensing channel that has experienced irreversible physical failure can be accurately located and a diagnostic log can be output, reducing the time cost of system hardware maintenance and troubleshooting. Attached Figure Description

[0026] Figure 1 This is a system architecture diagram of the present invention;

[0027] Figure 2 This is a flowchart of the method of the present invention;

[0028] Figure 3 This is a statistical evolution diagram based on online monitoring of multivariable microenvironments according to an embodiment of the present invention;

[0029] Figure 4 This is a comparison chart of the concentration mapping of a conventional fixed curve and a dynamically reconstructed curve under abnormal operating conditions according to an embodiment of the present invention.

[0030] Figure 5 This is a histogram showing the distribution of the relative error contribution of the physical sensing channel according to an embodiment of the present invention. Detailed Implementation

[0031] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0032] See attached document Figure 1 This invention provides an intelligent quality control and standardization management system for tumor markers. The system is deployed on a host computer or independent data processing device and establishes data communication with the underlying microcontroller of the analyzer. The system includes:

[0033] The feature acquisition module is configured to extract the timing signals of the underlying hardware sensors in the preset sampling frequency band of the microcontroller through the communication interface, and aggregate them according to the time node and physical channel dimensions to construct the original feature matrix.

[0034] The feature weighting module is connected to the feature acquisition module. It internally stores sensitivity weight data corresponding to different detection items. Based on the currently executed detection item, it calls the corresponding weight data to perform a physical channel-level linear weighting transformation on the original feature matrix and outputs a specific weighted feature matrix.

[0035] The baseline modeling module, connected to the feature weighting module, receives the specific weighted feature matrix of the calibrator during the calibration stage, performs dimensionality reduction decomposition on the matrix to construct the principal component baseline space model, and calculates and stores the orthogonal loading matrix and the principal component score covariance matrix as a reference benchmark.

[0036] The real-time monitoring module is connected to the feature weighting module and the baseline modeling module respectively. During the sample testing phase, it projects the specific weighted feature matrix of the current sample onto the principal component baseline space model and calculates the Hotelling statistic and the squared prediction error statistic based on the projection results.

[0037] The parameter reconstruction module is connected to the real-time monitoring module and is configured with the corresponding biochemical parameter perturbation Jacobian matrix for the detection items. When the monitoring statistics exceed the preset control limit, the model external residual vector is extracted and inversely projected and reconstructed. The residual of the physical channel is mapped to the bias compensation amount of each parameter of the four-parameter logistic regression curve using the biochemical parameter perturbation Jacobian matrix, and the basic parameter reconstruction of the regression curve is performed.

[0038] The diagnostic module, connected to the parameter reconstruction module, obtains the measured photoelectric signal from the analyzer and substitutes it into the reconstructed four-parameter logistic regression curve to calculate the concentration measurement value. Based on the relative contribution values ​​of each physical sensor channel in the residual vector, it generates hardware traceability and maintenance instructions and outputs diagnostic logs.

[0039] See attached document Figure 2 This invention provides an intelligent quality control and standardized management method for tumor markers, comprising the following steps:

[0040] S100, within a single tumor marker detection cycle, simultaneously acquires time-series signals from multiple underlying physical sensors to construct the original feature matrix corresponding to that test cycle;

[0041] S200: Based on the current detection item, call the specific target marker sensitivity weighting matrix, and perform a linear transformation on the original feature matrix to generate a specific weighted feature matrix;

[0042] S300 performs dimensionality reduction decomposition based on the specific weighted feature matrix of the calibrator during the calibration stage, constructs the principal component baseline space model under normal equipment operation, and solidifies the orthogonal load matrix.

[0043] S400 projects the specific weighted feature matrix of the current sample onto the principal component baseline spatial model during the routine sample testing phase, and calculates the Hotelling statistic and the squared prediction error statistic in real time to determine the stability of the current reaction microenvironment.

[0044] S500: When the monitoring statistics exceed the dynamic control limit, the model external residual vector is extracted and reversed. The deviation of the physical sensing channel is comprehensively mapped into the compensation vector of each parameter of the four-parameter logistic regression curve by using the preset biochemical parameter perturbation Jacobian matrix, so as to realize the dynamic reconstruction of the curve.

[0045] The S600 performs inverse calculation on the actual luminous signal based on the reconstructed four-parameter logistic regression curve, outputs the final concentration after compensation, and analyzes the relative contribution of each physical channel in the deviation vector to locate the core hardware failure node and outputs the source diagnosis log.

[0046] The above steps will be described in detail below with reference to specific embodiments and accompanying drawings.

[0047] In this embodiment, during a single tumor marker detection cycle, the feature acquisition module simultaneously acquires time-series signals from multiple underlying physical sensors to construct the original feature matrix. Step S100 specifically includes the following sub-steps:

[0048] S101, the feature acquisition module extracts timing data from the hardware sensing channels within the analyzer's microcontroller at a preset sampling frequency band via a communication interface. As a preferred method, the hardware sensing channels specifically include the current channel of the sample application arm stepper motor, the temperature channel of the incubation chamber thermistor, the fluid pressure channel of the cleaning pump pipeline, and the dark counting channel of the photomultiplier tube. These physical parameters collectively constitute the core physical causal chain that determines the final chemiluminescence intensity. For the duration of a single detection cycle, the feature acquisition module sets a fixed sampling frequency to obtain discrete data points. Regarding the specific value of the sampling frequency, those skilled in the art can perform conventional configuration based on the Nyquist sampling theorem and the microcontroller bus bandwidth. Parameter tuning is a well-known technique in the field and will not be elaborated upon here. Based on the set sampling frequency, a single detection cycle is discretized into multiple consecutive time nodes.

[0049] S102, the feature acquisition module aligns and stitches the acquired multi-channel time-series data according to the time nodes and physical sensor channel dimensions. Considering that different underlying sensors are usually driven by independent hardware timers or asynchronous interrupts in the microcontroller, direct stitching would lead to timestamp misalignment. Based on the above hardware characteristics, the feature acquisition module performs linear interpolation or zero-order hold resampling based on the global clock before data stitching to ensure that the data of all physical sensor channels are strictly aligned at a unified discrete time node. To ensure the physical isomorphism of feature extraction, the feature acquisition module uniformly uses the synchronization edge of the core mechanical action at the system's underlying level (e.g., the sudden change in the liquid level when the sampling needle touches the liquid) as the absolute time zero point to trigger the extraction window when extracting data. After completing the time synchronization, the feature acquisition module establishes the original reaction feature matrix with the discrete time nodes as the row dimension and the physical sensor channels as the column dimension. The structural expression of the original reaction feature matrix is ​​as follows:

[0050] ;

[0051] In the formula, This is the original reaction characteristic matrix; The total number of discrete time points within a single detection cycle; This represents the total number of physical sensor channels that are being collected synchronously. In the first The first time node extracted The signal sample values ​​of each physical sensor channel, among which are positive integers and , are positive integers and .

[0052] By constructing this matrix structure, the system transforms the originally isolated and heterogeneous one-dimensional physical signals into a two-dimensional spatial feature set reflecting the dynamic evolution of the reaction, laying the data foundation for subsequent extraction of high-dimensional coupling bias. After the feature acquisition module completes data aggregation, it transmits the original reaction feature matrix to the feature weighting module to execute subsequent processing logic.

[0053] In this embodiment, after constructing the original reaction feature matrix, the system needs to further eliminate the characterization differences between general physical parameters and specific biochemical reactions. Based on the above requirements, the feature weighting module executes targeted data transformation logic based on the detection items. The relevant execution process specifically includes the following sub-steps:

[0054] S201: During the factory or reagent calibration stage, the system pre-constructs a sensitivity weighted matrix of target biomarkers corresponding to different detection items and embeds it into the database within the feature weighting module. In clinical chemiluminescence immunoassay, different types of tumor biomarkers exhibit non-uniform sensitivity responses to underlying physical perturbations due to inherent differences in their molecular spatial conformation, antigen-antibody binding rate constants, and dissociation constants.

[0055] As a preferred approach, projects with smaller molecular weights and significantly thermodynamically influenced affinity are more sensitive to subtle temperature fluctuations in the incubation chamber; projects relying on prolonged washing to remove background non-specific bindings are more sensitive to subtle fluctuations in the fluid pressure and shear force of the cleaning pump pipeline. The physical significance of the target biomarker sensitivity weighting matrix lies in quantitatively transforming the fluctuation amplitude of general electromechanical sensors into a specific risk exposure index from a particular biochemical analysis perspective, thereby achieving cross-modal decoupling between the physical and biochemical domains at the algorithmic level.

[0056] In S202, researchers extracted and calculated the prior sensitivity weight data for each physical sensing channel using experimental design techniques. To obtain the fundamental parameters for targeted weighted processing, a multivariate perturbation input experiment was introduced during the system calibration phase. This was done for specific detection items. During the operation of the standard calibrator, a set of known physical perturbations at the control tolerance edge are actively applied, and the actual relative luminescence unit deviation after the perturbation is recorded. The system collects these experimental sample pairs and constructs a multiple linear regression equation with the physical channel perturbation as the independent variable and the luminescence signal deviation as the dependent variable:

[0057] ;

[0058] In the formula, For testing items Measured relative luminescence unit deviation sequence; For the first The measured sequence of physical perturbations applied to each physical sensing channel; Let be the partial regression coefficient to be solved, representing the _th _ When a unit fluctuation occurs in each physical channel, the detection item The degree of linear influence on the final luminous intensity; This represents the random residual term of the regression model; This represents the total number of physical sensing channels; For accumulation symbols, i.e., for from Accumulate to .

[0059] Considering the physical coupling between the various electromechanical components at the analyzer's core (such as the high collinearity between the heating current and the ambient temperature sequence), directly using the least squares method to solve the problem would lead to a singular design matrix. To address this matrix singularity risk, this embodiment introduces a ridge regression mechanism when solving the partial regression coefficients, adding an L2 regularization penalty term to the loss function. Mathematically, this is equivalent to adding a non-negative bias constant to the diagonal of the autocorrelation matrix of the independent variables in the regression equation. By forcibly shrinking the coefficients, it eliminates computational divergence caused by multicollinearity, ensuring... The physical rationality of the numerical values ​​and the completeness of the calculation process.

[0060] S203 extracts the partial regression coefficient sequence obtained from the regression calculation and introduces standardization and normalization logic to generate the final diagonal matrix structure. Based on the objective fact that different physical sensing channels have different dimensions, the system uses the reference values ​​of the basic measurement range of each physical channel to eliminate the dimensions of the partial regression coefficients. Let the... The basic reference value for each physical channel is Calculate the specificity weighting coefficient The normalized weight formula is as follows:

[0061] ;

[0062] In the formula, For the finalized testing items The The specific weighting coefficient of each physical sensing channel; This is the absolute value of the partial regression coefficient. The absolute value operation is used to extract the magnitude of the pure fluctuation effect without considering the positive or negative direction. The corresponding channel's nominal physical reference value; The value of the preset minimum positive number is preferably within the range of 1×10⁻⁶. -6 Up to 1×10 -8 In the above normalized division operation, a parameter is introduced. The technical objective is to forcibly prevent division by zero anomalies and computational overflow when experimental data shows that the responses of each channel are extremely weak, causing the denominator to approach zero, thus ensuring the robustness of the algorithm under extreme boundary conditions.

[0063] S204, the system maps the calculated specificity weight coefficients to the diagonal elements of a diagonal matrix, completing the prior construction of the target biomarker sensitivity weighted matrix. The formula for constructing the sensitivity weighted matrix is ​​as follows:

[0064] ;

[0065] In the formula, To correspond to the testing items The target biomarker sensitivity weighted matrix; Represents the diagonal matrix construction operation; The first one calculated by the system Each element has a specific weighting coefficient. In this matrix structure, all off-diagonal elements are forced to be zero.

[0066] The purpose of employing strictly diagonal matrices is to ensure that, based on the design principle of independent closed-loop control of the underlying electromechanical system, the intrinsic measurement errors of the sensors in each physical channel are orthogonal and without higher-order coupling. This diagonalized matrix setting reduces storage space and the time complexity of real-time matrix operations, ensuring that the system can achieve high-frequency real-time tensor operations under the limited computing power constraints of the microcontroller. The completed matrices... The categories are stored in the prior database of the weighted module for later use.

[0067] S205, the analyzer assigns a unique item identifier to the current single testing cycle by reading the RFID tags of reagent kits or the barcodes of sample tubes in the reagent compartment and combining this with the test instructions issued by the laboratory information system. During the process of the feature acquisition module constructing the original reaction feature matrix and transmitting data to the feature weighting module, the feature acquisition module encapsulates the item identifier in the communication frame header of the asynchronous data packet. This data binding mechanism between the underlying hardware and system software constitutes the pre-existing physical trigger condition for the feature weighting module to execute matrix calls. Regarding the definition of the communication frame header format and the data parsing logic of the RFID tags, those skilled in the art can perform conventional configuration according to specific industrial communication protocols. The protocol framing and parsing process is well-known technology in the field and will not be elaborated upon here.

[0068] S206, the feature weighting module continuously monitors the receiving port and, while extracting the valid raw response feature matrix, parses the accompanying communication frame header to extract the index code of the target detection item. The feature weighting module uses this index code as the key to initiate an addressing query to the built-in prior sensitivity weight database. By establishing a direct mapping relationship between the item index and the corresponding weighting matrix, the system can ensure that the underlying physical data is guided to the correct biochemical compensation calculation branch in a multi-task concurrent environment.

[0069] S207. To ensure the system's robustness when facing unknown reagent items or missing index codes, the feature weighting module is configured with state determination and degradation mapping logic when a query is triggered. The dynamic weight matrix mapping function executed by the feature weighting module is as follows:

[0070] ;

[0071] In the formula, The target weighting matrix is ​​ultimately called by the feature weighting module; This is the index encoding of the current detection item extracted from the communication frame header; This is the set of index codes for the detection items that have been fixed in the system's prior database; To correspond to the testing items The target biomarker sensitivity weighted matrix; For dimensions and A uniform unit diagonal matrix has all diagonal elements as 1 and all off-diagonal elements as 0. Represents the empty set. When the parsed item Exists in the support set Within that time, the system determines that the query has been hit and retrieves the specific... Perform the conversion.

[0072] When the system detects a newly loaded detection item whose sensitivity has not yet been calibrated, or when the item's encoding is lost due to communication interference and cannot be matched, the system determines that the target item is not within the support set. The feature weighting module automatically calls the unit diagonal matrix. As an alternative, the purpose of calling the unit diagonal matrix is ​​to degenerate the subsequent linear weighted transformation into a proportional pass-through of the original features. This maintains the consistency of the subsequent tensor calculation dimensions while cutting off the specific physical perturbation amplification mechanism, effectively avoiding system crashes caused by matrix dimension mismatch or null values.

[0073] S208, the feature weighting module performs matrix multiplication on the acquired raw response feature matrix and the target weighted matrix. In the specific underlying system processing unit, this process is instantiated as a linear algebraic operation of right multiplication of a diagonal matrix. The specific feature weighting transformation formula called by the feature weighting module is as follows:

[0074] ;

[0075] In the formula, The transformed, weighted feature matrix has the following dimensions: ; This is the original reaction feature matrix constructed in the previous stage, with dimensions of... ; The target weighted matrix is ​​retrieved from the database and mapped, with dimensions of [missing information]. .

[0076] Based on the algebraic properties of matrix multiplication, due to Since the matrix is ​​strictly diagonal, the aforementioned right multiplication operation essentially performs scalar scaling on the column vectors of the original reaction feature matrix (i.e., the discrete time-series data of each physical sensor channel) according to their corresponding weights. Through this transformation, for physical sensor channels with high sensitivity to current biochemical detection items, the data fluctuation amplitude and its implicit perturbation characteristics are significantly amplified by the weighting coefficients; for physical channels that are not sensitive to these items, their inherent acquisition noise and normal fluctuations are suppressed by the weighting coefficients. This operation extracts specific risk exposure variables from massive amounts of physical data without disrupting the original causal relationship of sensor time-series evolution.

[0077] S209, considering the potential risk of numerical boundary overflow when the underlying embedded system processes floating-point operations, the feature weighting module performs amplitude truncation processing based on physical range on all elements within the specific weighted feature matrix after completing matrix multiplication. As a preferred method, the system presets a maximum allowable upper limit and a minimum allowable lower limit for matrix element truncation. The system adjusts these thresholds based on the quantization bit width of the underlying physical sensor's analog-to-digital converter (ADC) and the extreme values ​​of physical damage to the sensor. For example, when a channel uses a 16-bit unsigned ADC for sampling, its original extreme value range is physically clamped to 0 to 65535. The system sets the minimum lower limit to the mapping result of the 00 value corresponding to that channel and sets the maximum upper limit to 1.1 times the tolerance floating value of the mapping result corresponding to the 65535 full-scale range. When an element within the matrix is ​​abnormally disturbed beyond the above-mentioned amplitude truncation range, the system forcibly clamps it to the corresponding boundary threshold. The technical purpose of setting this limiting logic is to isolate the damage to the spatial mapping model caused by hardware spikes and glitches, and to ensure the numerical stability of subsequent high-dimensional tensor calculations.

[0078] S210, to accommodate the mathematical projection requirements of the subsequent principal component baseline spatial model, the feature weighting module flattens the numerically cleaned two-dimensional specific weighted feature matrix row-wise. The one-dimensional feature vector flattening formula performed by the feature weighting module is as follows:

[0079] ;

[0080] In the formula, The flattened, specific weighted feature vector has the following dimensions: ; For specific weighted feature matrix The Middle Line number The matrix elements of the columns. Through the flattening operation, the feature weighting module separates the independent time dimensions. With physical sensing dimension Forced coupling reconstructs the physical state of the entire process in a single detection cycle into a single state point in a high-dimensional space. This high-dimensional single-point representation constitutes the standardized feedforward data form for the system to subsequently determine the deviation of multivariable state microenvironment and extract collinear residuals. After completing the above data reconstruction, the feature weighting module outputs the specific weighted feature vector to the baseline modeling module and the real-time monitoring module.

[0081] In this embodiment, to construct a reference benchmark characterizing the normal operating state of the analyzer's physical microenvironment, the baseline modeling module executes the calibration logic of the principal component baseline space model during system calibration or reagent batch change stages. By extracting multidimensional features of the standard calibrator under ideal operating conditions, the system solidifies the electromechanical coupling state under normal operating conditions into a mathematical model. The relevant execution process specifically includes the following sub-steps:

[0082] S301, the baseline modeling module acquires the set of calibrator-specific weighted feature matrices generated by continuous testing during the calibration phase and performs data standardization preprocessing including variance scaling. When the analyzer runs the calibration program, the system typically continuously measures multiple standard calibrators of known concentrations. The baseline modeling module receives multiple flattened specific-weighted feature vectors output by the feature weighting module. Stack them row by row to construct a dimension of Scaled feature matrix ,in This represents the total number of calibrator samples that were validly tested during the calibration phase, and It must be greater than 1 to meet the degrees of freedom requirements for subsequent covariance calculations. This is to ensure the baseline model has the generalization ability to cover typical environmental drift. It includes calibrator samples obtained during the current calibration phase, and preferably integrates the feature vectors of normal quality control samples that have been verified as "fault-free" within a preset period with the clinical baseline sample set.

[0083] Considering that even after weighted mapping, different physical sensors may still exhibit inherent differences in numerical magnitude and variance scale, to prevent large-scale variables from obscuring small but crucial feature variables during dimensionality reduction, the baseline modeling module calculates the mean and standard deviation vectors along the column direction and performs zero-mean, unit-variance standardization on the calibrated feature matrix. The column mean, column standard deviation, and standardization calculation formulas called by the baseline modeling module are as follows:

[0084] ;

[0085] ;

[0086] ;

[0087] In the formula, For the scaling characteristic matrix, the first The characteristic mean of the column; For the first The sample standard deviation of the column; The first element in the original scaling feature matrix Line number Column elements; The standardized preprocessed scaling feature matrix The corresponding element in; are positive integers and ; are positive integers and ; For a preset very small positive number (e.g., a value) Introducing a constant in standardized division operations. The technical objective is to prevent system-level interruptions caused by division-by-zero anomalies when a physical sensing channel experiences hardware deadlock or signal clamping, resulting in completely constant acquired values ​​and a standard deviation approaching zero. Through the aforementioned standardization operations, the data in each dimension of the calibration feature matrix are unified to a dimensionless, identical variance scale, allowing subsequent principal component analysis to focus purely on the relative coupling relationships of physical perturbations. The baseline modeling module will calculate the mean vector... and standard deviation vector It is cached and used as a scaling and translation reference for subsequent real-time projection calculations.

[0088] In S302, the baseline modeling module uses principal component analysis (PCA) to perform dimensionality reduction decomposition on the standardized calibration feature matrix. The general principle of PCA is to map multicollinear original variables to a set of linearly orthogonal latent variables through spatial rotation transformation, thereby filtering out high-frequency random noise while extracting the dominant physical causal direction driving system fluctuations. Given the small sample size in the calibration phase... Much smaller than the flattened feature dimension Directly calculating the high-dimensional covariance matrix will lead to severe memory overflow and matrix singularity (rank deficiency) problems. Based on the aforementioned constraints of high-dimensional small-sample computation, as a preferred approach, the baseline modeling module uses singular value decomposition (SVD) or indirect methods to solve the problem by calculating the small-dimensional inner product matrix. The formulas for calculating the small-dimensional inner product matrix and its eigenvalue decomposition are as follows:

[0089] ;

[0090] ;

[0091] ;

[0092] In the formula, For dimensions only The sample inner product matrix significantly reduces the memory consumption of the microcontroller; To obtain the first One non-negative eigenvalue; For corresponding eigenvalues eigenvectors of the inner product matrix; These are the eigenvectors of the original high-dimensional covariance matrix obtained through reverse mapping; The values ​​are positive integers. The baseline modeling module arranges all the obtained eigenvalues ​​in descending order. The magnitude of the eigenvalue directly represents the amount of variance explained by the corresponding principal component vector in the original dataset.

[0093] S303, the baseline modeling module defines the effective dimensions of the principal component space based on the cumulative variance contribution rate and extracts the orthogonal loading matrix. To retain the main physical causal features of the system while filtering out high-frequency random noise, the baseline modeling module sets a cumulative variance contribution rate threshold to truncate principal components corresponding to low eigenvalues. As a preferred approach, the system presets the cumulative variance contribution rate threshold to range from 85% to 95%. This range is determined based on the fact that numerous clinical tests have shown that the preceding principal components already cover the main low-frequency drift of the core electromechanical components, while the remaining 5% to 15% variance is mostly composed of electromagnetic white noise; forcibly retaining it would lead to a decrease in the model's generalization ability. The principal component dimension truncation determination formula executed by the baseline modeling module is as follows:

[0094] ;

[0095] In the formula, The number of effective principal components retained after truncation, i.e., the actual dimension of the baseline principal component space; The first few rows after descending order A large eigenvalue; This represents all valid eigenvalues ​​obtained through the inner product matrix; a minimal positive constant is added to the denominator. Used to avoid the risk of division by zero when all eigenvalues ​​approach zero in an extremely steady state of the system; This is a preset cumulative variance contribution rate threshold. The system gradually increases... The value is determined until the above inequality is satisfied, thereby dynamically determining the dimension parameter. After determining the dimensions, the baseline modeling module extracts the pre-extraction data. The eigenvectors constitute an orthogonal loading matrix. Its dimensions are The orthogonal loading matrix mathematically defines a rotational transformation coordinate system that maps from the high-dimensional original physical space to the low-dimensional stable principal component space.

[0096] S304, the baseline modeling module calculates the principal component score matrix and principal component score covariance matrix, and solidifies the static baseline parameters. Based on the extracted orthogonal loading matrix, the baseline modeling module projects the standardized calibration feature matrix onto this low-dimensional space to generate the principal component score matrix. The principal component score calculation formula is as follows:

[0097] ;

[0098] In the formula, The principal component score matrix has the following dimensions: Each row in the matrix represents the coordinate position of a calibrator sample in the low-dimensional baseline space. Subsequently, the baseline modeling module calculates the principal component score covariance matrix based on this score matrix. Since the principal components are mutually orthogonal, this covariance matrix theoretically represents the resultant coordinates of the eigenvalues. to This is a diagonal matrix with diagonal elements. The formula for calculating the principal component score covariance matrix is ​​as follows:

[0099] ;

[0100] In the formula, Principal component score covariance matrix, its physical meaning is to define a multidimensional confidence ellipsoid composed of mutually independent principal component axes, which characterizes the inherent tolerance boundary and dispersion of the analyzer in each major physical characteristic direction under normal operating conditions. It is the transpose of the principal component score matrix; This represents the diagonal matrix construction operation.

[0101] To prevent subsequent Mahalanobis distance (Hotelling) In the calculation of the statistical measure, a numerical explosion occurred due to the inversion of the matrix. The system forcibly clamped the diagonal elements in the principal component score covariance matrix that were below a preset lower limit to that value. After completing the above calculation, the baseline modeling module will convert the mean vector... Standard deviation vector Orthogonal load matrix and principal component score covariance matrix The parameters are uniformly written into non-volatile memory for static fixation. The set of fixed parameters constitutes the principal component baseline space model characterizing the normal and healthy microenvironment of the system, and serves as the absolute reference coordinate system for the real-time monitoring module of multivariate processes in subsequent routine testing phases to perform online judgment and residual extraction.

[0102] In this embodiment, after the static parameters of the principal component baseline space model are solidified, the analyzer immediately enters the continuous testing and transfer state of routine clinical samples. To capture abnormal fluctuations that deviate from ideal operating conditions during real-time testing, the real-time monitoring module performs a real-time state-space assessment of the multivariate microenvironment for each independent testing cycle based on the solidified baseline model. The specific multivariate real-time monitoring logic of the testing microenvironment includes the following sub-steps:

[0103] S401, the real-time monitoring module receives the flattened specific weighted feature vector of the current test sample and performs an orthogonal projection operation onto the preset principal component baseline space. Before projection, based on the general technical principle of feature acquisition and multi-source heterogeneous state comparison, to ensure a strict physical match between the real-time test vector and the calibration baseline model, the real-time monitoring module uses the same absolute time zero-point logic as the calibration stage to perform time window alignment. Using this absolute zero-point as a reference, the system performs time window truncation and resampling alignment on the continuous sensor data stream of the current period, thereby generating an original weighted feature vector that is physically causally identical to the calibration model. Subsequently, the real-time monitoring module calls the column mean vector and column standard deviation vector cached in the calibration stage to perform real-time online standardization preprocessing on the time-aligned current feature vector. The online standardization calculation formula called by the real-time monitoring module is as follows:

[0104] ;

[0105] In the formula, The first of the currently acquired flattened weighted feature vectors Each feature element; and These are the solidified first and second generations in the baseline model, respectively. The mean vector and standard deviation vector corresponding to each dimension; To complete the standardized real-time feature elements; are positive integers and ; To prevent extremely small positive numbers from being divided by zero, their values ​​are preferably 1×10⁻⁶, based on the lower limit of the microprocessor's single-precision floating-point operation. -8 After standardization, the system projects the high-dimensional measured features into the low-dimensional baseline subspace. The real-time projection formula is as follows:

[0106] ;

[0107] In the formula, For dimension Online standardized feature vectors; For dimension The solidification orthogonal load matrix; The calculated dimension is 1 The principal component score vector. The technical principle of this projection operation is to use an orthogonal transformation matrix to filter out high-frequency random white noise, extract the evolution coordinates of the current detection microenvironment in the main physical causal directions of the system, and thus establish a relative reference relationship between the current working condition and the ideal calibration working condition in a low-dimensional space.

[0108] S402, the real-time monitoring module calculates the Hotelling statistic based on the extracted principal component score vector. This statistic quantifies the degree of systematic drift under the current test condition within the system's normal fluctuation tolerance space. Because Hotelling... The mathematical essence of the statistic is to calculate the Mahalanobis distance in a multidimensional space, which requires weighting the variances of each dimension using the inverse of the baseline covariance matrix. Considering that the variances of some principal components obtained under extreme steady-state conditions may approach zero, to ensure the numerical stability of this quadratic form calculation, the system forcibly introduces a diagonal matrix bias before performing the inversion. The corrected Hotelling is then performed by the real-time monitoring module. The formula for calculating the statistic is as follows:

[0109] ;

[0110] In the formula, This represents the Hotelling statistic value for the current real-time sample. The principal component score vector obtained in the preceding steps; This is the transpose of the principal component score vector; The principal component score covariance matrix is ​​fixed. It is a unit diagonal matrix of dimension A×A; This is the regularization bias constant. Based on the numerical truncation effect of practical embedded systems... The value is determined based on the microcontroller's quantization noise floor, and in this embodiment, it is preferably set to 1×10. −6 By mixing in This forces an increase in the condition number of the covariance matrix, eliminating the divergence in inversion caused by tiny singular values ​​at its source. The physical meaning of the statistics lies in the fact that they characterize the normalized spatial offset of the current physical microenvironment state point from the centroid of the baseline model, and are used to capture gradual faults that conform to the known coupling relationship within the system, such as the slow tensile wear of mechanical transmission belts and the long-term slow temperature drift of the incubation chamber.

[0111] S403, the real-time monitoring module further extracts the out-of-model residual vector and calculates the squared prediction error statistic. Based on the orthogonal complement space decomposition principle, and considering that novel fault modes may not have been exposed during the calibration phase in complex electromechanical systems, the system needs to reconstruct the feature vectors to remove known information. The formulas for extracting the out-of-model residual vector and calculating the squared prediction error statistic performed by the real-time monitoring module are as follows:

[0112] ;

[0113] ;

[0114] In the formula, For dimension The model-external residual vector represents the original feature portion that could not be explained by the principal component baseline space; This is the transpose of the orthogonal load matrix; This is the squared prediction error statistic (SPE statistic). The physical essence of this statistic is the square of the vertical Euclidean distance from the current high-dimensional state point to the low-dimensional baseline principal plane. This indicator is extremely sensitive to sudden anomalies that disrupt the inherent physical multicollinearity of the system, such as transient pressure discontinuities in the cleaning pump caused by air bubbles in the bottom pipeline, or hardware deadlocks in individual sensors. Such anomalies can cause its value to increase exponentially.

[0115] S404, the real-time monitoring module, based on pre-set dynamic control limits, performs state combination judgments and triggers subsequent feedback logic. As a preferred approach, developers set static threshold critical points within the system based on statistical confidence distributions. For Statistics and Normal fluctuation control limits for statistics can be set by those skilled in the art based on confidence levels (e.g., for medical in vitro diagnostics with extremely high safety requirements, a significance level of 100% is preferred). The conventional configuration is performed by looking up the F-distribution table and approximating the weighted chi-square distribution multinomial. The tuning of the critical values ​​of the parameters is a well-known technique in this field and will not be elaborated here.

[0116] To avoid false alarms caused by single-point spikes in the underlying analog-to-digital converter, the real-time monitoring module incorporates a multi-dimensional exponentially weighted moving average time-series smoothing mechanism in its output result determination. Compared to absolute blocking relying on a single extreme value, this mechanism utilizes historical operating condition inertia to attenuate transient noise. The Boolean decision logic for the system's trigger parameter reconstruction mechanism is as follows:

[0117] The real-time monitoring module maintains smooth state variables. and Its update logic is as follows = Among them, the decay memory coefficient The preferred value is 0.2. This applies to the first detection cycle after system initialization or recalibration. Since the real-time monitoring has not yet accumulated historical operating conditions, the system forcibly initializes the initial values ​​of the smoothed state variables. and This allows for a smooth start-up of the weighted sliding filter mechanism.

[0118] When the calculated smooth state variables Exceeding the preset control limit , or corresponding Exceeding control limits And this state is continuous Within a detection cycle (e.g., selecting) When the condition remains stable and continuous, the system determines that the current test microenvironment has experienced an irreversible physical deviation. Based on the above triggering conditions, the real-time monitoring module immediately outputs an interception signal and activates the internal dynamic parameter reconstruction and biochemiluminescence value compensation closed loop to prevent distorted physical conditions from leading to the final output of false clinical diagnostic luminescence data.

[0119] Based on the interception signal output by the real-time monitoring module, the system's underlying control flow transitions to the multi-dimensional data inversion and dynamic reconstruction stage. In this embodiment, to achieve accurate compensation of the subsequent biochemical luminescence curve, the dynamic reconstruction module 50 is configured to inversely restore the abstract high-dimensional statistical deviation characteristics to physical channel engineering deviations with absolute dimensions. Based on the general technical principles of spatial coordinate system inverse mapping and algebraic recombination, the dynamic reconstruction module 50 executes a quantitative calculation program for the physical deviation. The relevant execution process specifically includes the following sub-steps:

[0120] S501, the dynamic reconstruction module 50 extracts the out-of-model residual vector and principal component score vector that trigger the over-limit alarm cycle, and performs inverse projection reconstruction of the standardized space based on the fixed orthogonal load matrix. When the test condition deviates from the normal baseline, the deviation in space is composed of the principal component drift within the model and the abrupt change in the out-of-model residuals. The standardized space deviation reconstruction formula called by the dynamic reconstruction module 50 is as follows:

[0121] ;

[0122] In the formula, The dimensions generated after recombination are The standardized composite deviation vector; The dimension obtained by solving for the real-time monitoring module is Principal component score vectors; This is the transpose of the statically cured orthogonal load matrix, with dimensions of... ; The dimensions extracted for the preceding calculation are: The model's external residual vector; This is the preset residual activation bias coefficient.

[0123] Considering that electromagnetic interference alone often leads to high-frequency oscillations in the residual vector, as a preferred approach, the system combines the preceding squared prediction error statistic with a dynamic μ value. When the squared prediction error does not exceed the limit, A value of 0 is set to suppress background noise; when this error exceeds the limit, it is determined that a real physical fault has occurred. The value is set to 1 to fully introduce the abrupt residuals. Through this inverse projection reconstruction operation, the system mathematically adds the relative coordinates in the low-dimensional latent variable space to the unexplained orthogonal complement space features, reconstructing a dimensionless standard deviation that fully covers all physical dimensions.

[0124] S502, the dynamic reconstruction module 50 calls the scale parameters cached in the calibration stage to perform an inverse scaling operation on the standardized comprehensive deviation vector of physical dimensions. Since the preceding principal component analysis erased the absolute numerical differences between the sensors, the system needs to utilize prior variance information to recover its true physical fluctuation amplitude. The inverse scaling transformation formula performed by the dynamic reconstruction module 50 is as follows:

[0125] ;

[0126] In the formula, The first one obtained after reverse scaling The dimensionless absolute deviation value of each feature element; For the corresponding element in the standardized composite deviation vector; The first solidified part in the baseline spatial model Standard deviation vector elements of each feature dimension; are positive integers and The technical purpose of the aforementioned scalar multiplication operation is to reassign the dimensionless variance drift at the purely mathematical level to underlying physical engineering dimensions such as Celsius, Pascal, or millivolt, thereby establishing a direct physical correspondence with the underlying hardware sensors.

[0127] S503, the dynamic reconstruction module 50 performs a reverse folding of the spatial dimension of the one-dimensional absolute deviation vector, and performs inverse decoupling calculation based on the system's preset target weighted matrix to ultimately obtain the actual deviation of the physical channel. Based on the matrix rearrangement principle, the dynamic reconstruction module 50, according to the inherent addressing sequence and discrete timestamps of the underlying hardware registers, folds the one-dimensional absolute deviation vector in reverse folding. vector Rearranged according to the original time and channel order as dimensions. Weighted state deviation matrix Subsequently, the system executes the physical mapping decoupling formula as follows:

[0128] ;

[0129] In the formula, The final output is the physical channel actual deviation matrix, which contains the actual physical channel evolution timing. Its dimension is... ; The weighted state deviation matrix generated for reverse folding; The target weighted diagonal matrix dynamically invoked by the feature weighting module in the current test cycle has dimensions of ; For dimensions are the same an identity diagonal matrix; To prevent matrix singularities, a regularization smoothing constant is introduced. Considering that certain weights of some insensitive physical channels may approach zero, directly inverting the weighted matrix would cause computational divergence. Therefore, the system forcibly introduces a minimal constant. (Based on the precision of underlying floating-point operations, the preferred value is 1) This ensures the absolute invertibility and computational completeness of the inverse matrix. After eliminating artifacts amplified by the specific amplification of biochemical projects, each column vector output by this matrix multiplication operation represents the absolute physical engineering error of an independent physical sensing channel at various time sampling points in the current test cycle, deviating from the ideal calibration conditions. This provides accurate underlying physical variable inputs for the subsequent construction of Jacobian correlation partial derivatives for luminescence value compensation in the system.

[0130] S504, the dynamic reconstruction module 50 obtains the actual deviation matrix of the physical channel generated by the preceding calculation and reassembles it into a one-dimensional spatial feature vector in sequence. The index mapping of the flattening sequence must be strictly bound to the sensor memory registry sequence when the Jacobian matrix is ​​generated in the calibration stage to avoid spatiotemporal crossover misalignment. Based on the dimension alignment requirement of high-dimensional partial derivative calculation, the system will convert the dimension of... The deviation matrix is ​​expanded row-wise. The vector flattening formula called by the dynamic reconstruction module 50 is as follows:

[0131] ;

[0132] In the formula, The flattened physical deviation feature vector has a dimension of ; Representing the At the nth time sampling point The absolute engineering error derived from the inversion of each physical sensor channel, among which are positive integers and , are positive integers and , This represents the total number of discrete-time sampling points within the detection period. This represents the total number of physical sensing channels. Through this rearrangement operation, the system maps independent time series and spatial channels into a unified one-dimensional high-dimensional set of independent variables, thereby providing a standard data structure foundation for cross-modal partial derivative matrix operations.

[0133] Before performing cross-modal mapping, because the Jacobian matrix of subsequent calls depends on the local linearity assumption near the steady-state operating point, the system needs to perform linear neighborhood truncation on the physical deviation eigenvectors. The dynamic reconstruction module 50 will... Abnormal over-limit elements exceeding 5% of the full scale of the corresponding physical sensor are forcibly clamped to the upper limit boundary to prevent higher-order nonlinear errors from being erroneously amplified in matrix mapping.

[0134] S505, the dynamic reconstruction module 50, retrieves the pre-calibrated and solidified biochemical parameter perturbation Jacobian matrix from the built-in memory, which was established during the system reagent development phase. For clinical chemiluminescence immunoassay, the luminescence intensity of the standard calibrator follows a typical four-parameter logistic regression model with respect to the actual molecular concentration. This mathematical model consists of four nonlinear structural parameters, specifically including a bottom parameter characterizing the zero-concentration background signal response. Top parameters characterizing the saturation signal response at infinite concentration Characteristic parameters representing the half-effective concentration corresponding to the inflection point of the curve And the slope factor parameter characterizing the antigen-antibody binding reaction rate. When the physical microenvironment, such as incubation temperature or cleaning pressure, shifts, the four biochemical parameters mentioned above will exhibit corresponding nonlinear drift. Based on the principle of local linearization of Taylor series expansion, the Jacobian matrix of the biochemical parameter perturbation constitutes the set of multivariate partial derivatives of these four structural parameters with respect to the underlying physical variables. The Jacobian matrix structure formula called by the dynamic reconstruction module 50 is as follows:

[0135] ;

[0136] In the formula, The Jacobian matrix is ​​perturbed for biochemical parameters and has a dimension of 4. ; Represents a certain curve parameter (in ) Regarding the first Spatiotemporal physical variables The first-order partial derivative; are positive integers and The physical significance of this matrix lies in its quantitative characterization of the influence of a single physical sensor within the analyzer on the macroscopic shape of the final biochemical calibration curve when it generates a unit physical deviation at a specific moment. For the experimental determination and offline fitting of the partial derivative elements within the Jacobian matrix, those skilled in the art can perform conventional configurations based on standard orthogonal experimental design combined with the steepest descent method. The offline parameter solution process is a well-known technique in this field and will not be elaborated upon here.

[0137] S506, the dynamic reconstruction module 50 performs matrix multiplication on the flattened and linearly truncated physical deviation eigenvector and the biochemical parameter perturbation Jacobian matrix to calculate the generated curve parameter compensation vector. The cross-modal mapping multiplication formula performed by the dynamic reconstruction module 50 is as follows:

[0138] ;

[0139] In the formula, The calculated curve parameter compensation vector has the following dimensions: The vector contains The absolute compensation components in four dimensions; For a dimension of 4 The perturbation Jacobian matrix of the parameter; The transpose of the physical deviation eigenvectors has dimensions of . Through this matrix multiplication operation, the system overcomes the physical property barriers between electromechanical engineering and biochemical reactions at the algorithm level, directly projecting the subtle physical fluctuations of hundreds or thousands of underlying hardware sensors into the core algebraic compensation amount required to correct the biochemical calibration curve.

[0140] S507, to prevent extreme physical failures from causing linear extrapolation failure of the Jacobian matrix, which could lead to non-physically meaningful negative values ​​or divergence in biochemical parameters, the dynamic reconstruction module 50 performs a physical extreme value limiting determination on the generated curve parameter compensation vector. As a preferred method, the system presets the maximum allowable coefficient of variation upper limit based on the distribution range of the four parameters in historical valid batches. The compensation amount limiting truncation formula executed by the dynamic reconstruction module 50 is as follows:

[0141] ;

[0142] In the formula, This is the final single-parameter compensation output value after safety limiting; The parameter compensation components are obtained from the preliminary calculation of matrix multiplication; The corresponding standard reference values ​​for the original manufacturer's calibration of the current batch of reagents; This is the preset maximum tolerance coefficient of variation; The step function for extracting the symbol. Based on the biochemical, physicochemical stability properties of in vitro diagnostic reagents, The value is determined based on the maximum parameter drift rate of the same batch of reagents under extreme temperature and humidity testing at the factory. In this embodiment, it is preferably set to a constant within the range of 0.15 to 0.25 according to the conventional clinical biochemical tolerance band. The purpose of implementing this amplitude limiting cutoff is to maintain the monotonicity of the four-parameter logistic regression curve within the working concentration range, and to avoid system-level computational collapse caused by overcompensation leading to multiple solutions or no solution in subsequent concentration inversion.

[0143] S508, the dynamic reconstruction module 50 extracts the basic regression equation parameters for the current reagent batch and performs an algebraic superposition operation with the curve parameter compensation vector. In routine clinical chemiluminescence assays, each batch of reagents comes with a set of standard four-parameter logistic regression (4PL) basic parameters that have undergone large-scale calibration and solidification. The system obtains this set of reference parameters by reading the RFID tag or 2D barcode on the reagent kit. The dynamic superposition formula for the basic parameters called by the dynamic reconstruction module 50 is as follows:

[0144] ;

[0145] In the formula, The parameters of the dynamic curve generated after superposition and reconstruction include the bottom parameters after reconstruction. Top parameters Inflection point concentration parameters and slope factor parameters ; These are the standard reference parameters corresponding to the current reagent batch, representing the inherent biochemical response benchmark of the system under ideal calibration conditions; This is the parameter compensation component output from the preceding steps, based on the mapping of physical deviation and Jacobian matrix and after safety limiting. The physical meaning of this scalar addition operation is that it internalizes the combined effects caused by real-time mechanical wear, fluid fluctuations, and thermodynamic temperature drift into the biochemical quantitative model in the form of algebraic translation, thereby constructing a dynamic calibration curve that fits the current real electromechanical operating state.

[0146] S509, based on the dynamic curve parameters generated by reconstruction, the dynamic reconstruction module 50 constructs an inverse multidimensional dynamic reconstruction model and substitutes the original photoelectric discrete signal of the current test cycle into it to invert the true biochemical concentration value. The original relative luminous intensity output by the photomultiplier tube during the photometric stage is often inevitably affected by dark current noise and matrix effects. To ensure the mathematical completeness of the inverse mapping across the entire domain and to avoid negative domains and division-by-zero anomalies in logarithmic or fractional exponentiation operations, the dynamic reconstruction module 50 implements strict boundary clamping on the original luminous signal before performing nonlinear inversion and performs anti-division-by-zero regularization on the dynamic slope factor. The signal boundary clamping and dynamic concentration calculation formulas called by the system are as follows:

[0147] ;

[0148] ;

[0149] ;

[0150] In the formula, The original relative luminescence intensity is obtained by collecting and integrating data from the underlying photon counter during the current test cycle; The effective luminous intensity after boundary clamping; To prevent signal overflow, a minimum bias constant is optimally set based on the photoelectric conversion noise floor. ; This is the safety slope factor after zero-point avoidance processing; To prevent the lower limit of truncation when dividing the denominator of the exponent by zero, a value of 0.01 is preferred; and These are comparison functions for taking the maximum and minimum values, respectively. The final calculated output is a dynamically compensated biochemical concentration value. The technical purpose of introducing boundary and slope clamping logic is to force the maintenance of the real number validity in the computational domain when extreme physical oscillations cause the measured optical signal to fall below the theoretical bottom background value or the reconstructed slope to approach the horizontal line, thereby effectively avoiding low-level interruptions when the microprocessor performs floating-point operations.

[0151] S510, the dynamic reconstruction module 50, performs clinical efficacy interception assessment on the output dynamically compensated biochemical concentration values ​​based on multi-dimensional tolerance judgment logic. Relying solely on the local linear approximation of the underlying Jacobian matrix may output spurious mathematical fitting solutions when the system suffers severe hardware damage. To avoid the one-sided risks of single extreme value compensation, the system forcibly compares the absolute concentration shift rate before and after compensation, and combines this with a continuous multi-cycle sliding assessment mechanism. The final clinical efficacy judgment formula executed by the system is as follows:

[0152] ;

[0153] In the formula, This represents the relative clinical deviation rate. To directly use standard reference parameters Relative luminous intensity compared to the original The calculated uncompensated baseline concentration; To prevent abnormal division by zero at extremely low concentrations, a smoothing factor (usually the reagent's lower limit of detection, LoD value) is used. If and only if The system will only proceed if the total error is below the preset clinically acceptable control limit (e.g., preferably set in the range of 5% to 12% based on different specific biochemical items) and if historical data shows no unidirectional divergence trend. As a valid result, the system assesses the current biochemical project's disturbance tolerance. If the relative deviation rate is within a controllable range, the adaptive calculation and tracing module 60 is triggered. If it exceeds the limit, the system directly blocks and triggers the underlying hardware tracing of S602. Conversely, if the calculation finds that the relative drift rate exceeds the aforementioned control limit, the system will block the output of this result and send a hardware anomaly alarm to the operating terminal. For the configuration of clinically acceptable total error control limits and medical device data communication protocols for different biochemical projects, those skilled in the art can directly implement them by referring to relevant clinical laboratory standards. Parameter lookup tables and communication encapsulation are well-known technologies in this field and will not be elaborated upon here.

[0154] Based on the dynamic curve parameters and preliminary concentration inversion results output from the aforementioned stages, the system's underlying control flow transitions to a stage of precise solution for the final concentration and location and isolation of underlying electromechanical faults. In this embodiment, the adaptive solution and source tracing module 60 is configured to perform nonlinear numerical approximation and multivariate error attribution analysis. Based on the general technical principles of numerical optimization and signal energy tracking, this module executes adaptive concentration solution and hardware-level source tracing diagnostic procedures. The relevant execution process specifically includes the following sub-steps:

[0155] S601, the adaptive solution and source tracing module 60 receives the original emission signal captured by the underlying photometric unit and, in conjunction with the reconstructed dynamic regression equation, performs an adaptive iterative solution for concentration based on local gradients. Considering the strict boundary clamping of the original emission signal before nonlinear inversion in the preceding steps, while this operation avoids the microprocessor throwing mathematically unsolvable anomalies, it also rigidly truncates the emission curve's weak resolution at the extreme concentration end (i.e., the asymptote region near the bottom of the background or the top of saturation). To find the optimal signal-to-noise ratio solution in the nonlinear asymptote region, as a preferred approach, the system introduces a Newton-Raphson adaptive approximation algorithm incorporating L2 regularization constraints.

[0156] Considering that in extreme reconstruction scenarios, the dynamic inflection point concentration parameter generated by the system may approach zero and cause division overflow of the exponential base, the system forcibly introduces a safety isolation bound before constructing the residual function. The adaptive solution and source tracing module 60 constructs a clamped nonlinear residual function and the corresponding iterative update formula as follows:

[0157] ;

[0158] ;

[0159] ;

[0160] In the formula, For the first The estimated biochemical concentration for the next iteration; This refers to the safe inflection point concentration parameter after the lower limit protection. To prevent the machine from dividing by zero by extremely small positive numbers, a value of 1 × 10⁻⁶ is preferred. -5 ; For regularized nonlinear residuals after introducing physical constraints; The original relative luminous intensity without any boundary clamping treatment; , , , The four dynamic parameters generated by the aforementioned steps are respectively reconstructed. The dynamic compensation biochemical concentration value obtained by direct analytical inversion calculation in the aforementioned steps is used here as the prior structure anchor point for iterative optimization. The regularization penalty weight coefficient is used to adjust the balance between the fit of the original signal and the physical prior value. Those skilled in the art can configure its value according to the calibration signal-to-noise ratio of the underlying sensor, with a preferred range of 0.01 to 0.1. A concentration smoothing constant is used to prevent the denominator from approaching zero; For the first Biochemical concentration values ​​updated in the next iteration; Let be the first-order partial derivative of the residual function with respect to the concentration variable.

[0161] To prevent the division operation from diverging due to a derivative of zero, a small regularization constant can be preferably set by those skilled in the art based on the precision limitations of the microprocessor. ; For sign functions, paired with absolute value maximization functions Together, they ensure that when the first-order partial derivatives in the saturation asymptote region are extremely close to zero, the compensation term will not change the original search direction of the gradient, and will provide an absolutely safe bottom line against division by zero. To prevent the physical concentration from having meaningless negative values, the lower limit cutoff parameter is usually set to 0; This is the function for finding the maximum value.

[0162] The physical significance of this iterative algorithm lies in utilizing the first-order local slope information of the dynamic curve at the current concentration working point to guide the solution trajectory to converge along the direction of the fastest error reduction, thereby approximating the true biochemical molecule concentration in complex and noisy environments. The system uses the direct analytical inversion results as the initial values ​​for iteration. When the absolute difference in concentration between two adjacent iterations is less than the set tolerance band (e.g., 1), When the maximum number of iterations is reached, the adaptive solution and source tracing module 60 will eventually converge. As the final state adaptive solution concentration output.

[0163] S602, after completing the adaptive concentration calculation, for detection cycles determined to be out of limit or triggering the compensation reconstruction mechanism, the adaptive calculation and tracing module 60 immediately extracts the actual deviation matrix of the physical channel generated in the aforementioned steps to perform absolute error extraction of the independent sensing channel. Based on the principle of discrete-time series energy integration, the system performs root mean square (RMS) calculation on each column vector in the high-dimensional matrix. The channel absolute error calculation formula called by the adaptive calculation and tracing module 60 is as follows:

[0164] ;

[0165] In the formula, For the first The absolute time-domain fluctuation energy of each physical sensing channel during the entire current test cycle; This represents the total number of discrete time sampling points after time-series alignment and truncation within a single detection period. It is a unique index number for the physical channel, and ; In the actual deviation matrix of the physical channel, the first... At the 1st time point The purely physical engineering deviation value of each physical channel. The technical purpose of this calculation step is to compress the complex waveform characteristics of high-frequency oscillations or low-frequency drifts of each sensor in the time domain into a non-directional scalar energy value, thereby quantifying the physical destructive force of different underlying components on the system's deviation from the baseline operating condition.

[0166] S603, the adaptive solution and tracing module 60 performs global normalization on the extracted absolute errors of each channel, calculating the relative error contribution of each independent channel to the current comprehensive fault characterization. To establish a comparable evaluation benchmark in a multi-source heterogeneous sensor network, the system performs proportional partitioning based on the global total error energy distribution. The relative contribution normalization formula called by the adaptive solution and tracing module 60 is as follows:

[0167] ;

[0168] In the formula, For the first The relative error contribution of each physical sensing channel is strictly mapped to the interval [0, 1]. The total number of valid physical sensing channels configured for the system; the denominator includes This is a preset global smoothing constant. Considering that when the system is in an extremely ideal steady state, the absolute errors of all channels may simultaneously approach the microcontroller's quantization noise floor, and direct summation might result in a division by zero, a constant is forcibly introduced. (preferred value) This ensures the mathematical completeness of the normalization algorithm. Through this step, the system transforms the underlying engineering errors into intuitive percentage weights, clarifying the responsibility of each hardware component in the current macroscopic deviation event.

[0169] S604, the adaptive solution and tracing module 60, based on the calculated relative error contribution sequence, executes hardware-level isolation and maintenance log output logic based on multi-dimensional dynamic weighted judgment. To avoid false fault assertions caused by accidental physical collisions, as a preferred judgment mechanism, the system will use the sequence... Sort by numerical value in descending order and extract the largest relative contribution. Relative contribution of the second largest The adaptive solution and tracing module 60 executes joint decision-making logic, with decision conditions including both magnitude exceeding limits and spatial isolation. Based on the probability distribution model of common fault trees in in vitro diagnostic pipelines, the system presets an absolute sensitivity threshold. (Preferred configuration is in the range of 0.30 to 0.45) and primary / secondary isolation threshold. (The preferred configuration is 2.5).

[0170] When the judgment Beyond And ratio structure Greater than And the above state is continuous Test cycles (preferred configuration) When the system maintains a stable triggering state, the system asserts at the software level that a dominant and irreversible physical failure has occurred in the specific hardware sensing channel mapped by the corresponding index (e.g., the special heating resistance wire of the incubation tray is broken, or the fluid valve of the cleaning needle is stuck).

[0171] Based on this hardware failure assertion, the adaptive solution and tracing module 60 sends a formatted device maintenance log to the main control computing unit via the underlying communication bus. This maintenance log output format is mandated to include: an absolute timestamp of the anomaly occurrence based on the ISO 8601 standard (an international standard for date and time representation), a unique globally unified identifier (UUID) for the failed hardware, the calculated relative contribution value, the identification code of the associated biochemical test item, and pre-compiled terminal maintenance suggestions. This log information is pushed to the operating interface terminal and the cloud server via an encrypted communication port, providing on-site engineers with accurate guidance based on a data evidence chain for targeted troubleshooting and hardware replacement.

[0172] To further disclose the working logic and technical effects of the present invention in real complex electromechanical systems, the following detailed explanation of the complete execution process of the aforementioned steps and the results of the control experiment is provided in conjunction with specific clinical alpha-fetoprotein detection application scenarios and corresponding experimental verification drawings.

[0173] In this embodiment, the target biomarker is configured as alpha-fetoprotein (AFP). As a routine screening indicator for hepatocellular carcinoma, its clinical decision threshold concentration is typically strictly defined as 20.0 ng / mL. During the routine environmental calibration phase after the analyzer is started, the baseline modeling module successfully extracted and solidified a principal component baseline space model containing core hardware channels such as sample loading arm current, incubation chamber temperature, cleaning pump pressure, and dark count background, based on multiple batches of standard calibrators under ideal operating conditions. The system presets a cumulative variance contribution rate threshold cutoff dimension of 3 and sets the Hotelling threshold based on the chi-square distribution. Statistics and Squared Prediction Error Steady-state control limits for statistics.

[0174] After entering the continuous high-throughput clinical sample testing phase, to simulate deep-seated intermittent failures, extremely small air bubbles were artificially injected into the bottom water path of the system, and software intervention was used to induce a slow negative temperature drift of 0.3 degrees Celsius in the thermistor of the incubation chamber. While such minor disturbances typically do not trigger hardware-level alarms in a single hardware threshold monitoring system, they constitute a negative feedback loop in the physical causal chain, resulting in incomplete cleaning and a decrease in the enzyme-catalyzed reaction rate.

[0175] Based on the aforementioned abnormal operating conditions, the feature acquisition module extracts multidimensional physical sensor data at a sampling rate of 100Hz within the detection period, and uses the liquid contact level pulse of the sampling needle as the absolute time zero point for strict alignment. Subsequently, the feature weighting module combines the extracted high-dimensional time-series data sequence with the AFP-specific weighting matrix pre-stored in the prior database. Since the AFP item is moderately to highly sensitive to washing efficiency and linearly dependent on temperature drift, this weighting matrix significantly amplifies the characteristic variances of the pressure and temperature channels.

[0176] The real-time monitoring module standardizes the flattened, specific weighted feature vectors online and projects them into a fixed orthogonal load space. As the testing cycle progresses, the evolution trajectory of the statistics is captured in real time. For example... Figure 3 As shown in the figure, the light gray curve with small dots represents the raw data extracted in each detection cycle. Statistic ( The distribution, after the abnormal injection point (65th cycle) where microbubbles trigger noncollinear rupture, exhibits a dramatic jump accompanied by high-frequency fluid pressure oscillations; the thick black solid line in the figure represents the smoothed state variables after processing by the exponentially weighted moving average time-series smoothing mechanism (with a decay memory coefficient configured to 0.2). ).like Figure 3 As indicated by the "continuous limit-over-limit triggering reconstruction point" marked in the middle, the smoothed state variable, after successfully buffering and filtering out transient spikes and glitches, truly reflects the irreversible drift of the microenvironment and stably exceeds the preset control limit set by the system within three consecutive detection cycles. Figure 3 Thick black dashed line Based on this intuitive over-limit assertion, the system determines that the current test microenvironment has undergone irreversible physical deviation, immediately blocks the transmission of abnormal luminescence results to the Laboratory Information System (LIS) at the software level, and simultaneously activates the parameter reconstruction and source tracing solution closed loop.

[0177] For intercepted anomaly detection cycles, the parameter reconstruction module quickly extracts the physical bias vector generated by the model's external residuals. Based on the right-multiplication algebraic mapping of this vector with the Jacobian sensitivity matrix bound to the reagent kit, the system accurately deduces the quantitative destructive force of underlying physical perturbations on biochemiluminescence properties. The parameter reconstruction module dynamically updates the parameters of the ideal four-parameter logistic regression benchmark model. Combined with the appendix... Figure 4As can be seen, the dynamic reconstruction curve (solid black line) shifts significantly to the lower right compared to the traditional fixed standard curve (dark gray dashed line). Specifically, the upper limit parameter of the luminescence response in the Y-axis (luminescence signal intensity) direction exhibits a 5.2% attenuation (manifested as a decrease in the height of the upper asymptote of the reconstruction curve), thus offsetting the insufficient luminescence noise caused by the reduction in enzyme activity; while in the X-axis (concentration) direction, the inflection point concentration parameter of the curve shifts to the right. Subsequently, this module substitutes the original luminescence signal with severe noise into the preceding direct analytical inversion formula to obtain a preliminary prior structure anchor concentration.

[0178] Next, the solution and diagnostic module removes the forced signal truncation, inputs the noisy original emission signal into the residual function, executes Newton-Raphson numerical approximation logic with L2 regularization constraints, and outputs the final state adaptive correction concentration after four iterations. For example... Figure 4 Abnormal luminous signal captured by the horizontal black dotted line ( As shown in the figure, if a simple algebraic mapping is performed using a traditional fixed curve, the result will be 17.6 ng / mL, as indicated by the white dot in the figure (which will lead to a false negative diagnosis for a sample with a true concentration of 21.5 ng / mL). However, the adaptive iterative algorithm executed by the diagnostic module in this invention performs optimization with regularization penalty in the nonlinear asymptote region of the dynamically reconstructed curve, and finally converges to the adaptive final state concentration of 21.3 ng / mL marked by the black square in the figure. Figure 4 This invention demonstrates, in a clear and rigorous manner, how to dynamically transfer a fixed luminescence reference to real-world conditions using a high-dimensional microenvironment matrix, thereby successfully salvaging the fault-tolerant compensation trajectory of the actual concentration.

[0179] After completing the targeted concentration rescue, the solution diagnostic module calculated the relative error contribution of each hardware channel to this comprehensive deviation event based on the time-domain absolute energy integral and global normalization logic. (Combined with the appendix...) Figure 5 The histogram showing the relative error contribution distribution of the physical sensing channels demonstrates how the system accurately quantifies the fault weights of each hardware dimension. For example... Figure 5 As shown in the figures above each column and the specific values ​​marked thereon, the relative error contribution of the cleaning pump pressure channel (dark gray dominant column) suddenly increases to 0.68, far exceeding the absolute sensitivity threshold indicated in the figure (black dashed line). ).at the same time, Figure 5 The data clearly shows the significant step difference between the channels with the largest and second largest contributions (incubation chamber temperature), with the ratio structure far exceeding the system's preset primary-secondary isolation threshold. ).based on Figure 5The rigorous spatial isolation verification presented by the solution diagnostic module accurately located the dominant failed hardware and output a structured maintenance log containing a specific ISO8601 absolute timestamp and the cleaning pump UUID to the cloud maintenance server, guiding the on-site engineer to accurately replace the faulty fluid valve.

Claims

1. An intelligent quality control and standardization management system for tumor markers, characterized in that, include: The feature acquisition module acquires the timing signals of the physical sensors of the analyzer's underlying microcontroller, constructs and outputs the original feature matrix; The feature weighting module receives the original feature matrix, calls a preset target marker sensitivity weighting matrix according to the tumor marker detection items, performs linear weighting transformation, and outputs a specific weighted feature matrix. The baseline modeling module performs dimensionality reduction decomposition on the specific weighted feature matrix mentioned in the calibration stage, and constructs and solidifies the principal component baseline space model; The real-time monitoring module performs an orthogonal projection operation on the specific weighted feature matrix onto the principal component baseline spatial model during the sample testing phase to evaluate the microenvironment stability, and outputs an interception signal, principal component score vector and model out-of-model residual vector when the limit is exceeded. The dynamic reconstruction module, based on the intercepted signal, uses the principal component score vector and the model external residual vector to inversely reconstruct the actual deviation matrix of the physical channel, and combines the biochemical parameter perturbation Jacobian matrix mapping to generate dynamic curve parameters; The adaptive solution and tracing module substitutes the original emission signal into the dynamic curve parameters to perform adaptive concentration solution and outputs the final state adaptive solution concentration. It also extracts the relative error contribution of the actual deviation matrix of the physical channel and outputs the hardware tracing, maintenance and diagnostic log.

2. The intelligent quality control and standardization management system for tumor markers according to claim 1, characterized in that, The prior construction process of the feature weighting module calling the target marker sensitivity weighting matrix includes: A multiple linear regression equation was constructed with physical channel perturbation as the independent variable and emission signal deviation as the dependent variable. A ridge regression mechanism is introduced when solving for partial regression coefficients, and an L2 regularization penalty term is added to the loss function; The partial regression coefficients are dimensionless by using the reference values ​​of the basic measurement range of each physical channel. A preset minimum normal number is introduced in the normalized division operation to prevent division by zero anomalies and calculation overflow. The calculated specificity weight coefficients are mapped to the diagonal elements of a diagonal matrix to complete the prior construction. In the target marker sensitivity weighted matrix structure, all off-diagonal elements are forcibly set to zero.

3. The intelligent quality control and standardization management system for tumor markers according to claim 1, characterized in that, The process by which the baseline modeling module performs dimensionality reduction decomposition on the specific weighted feature matrix during the calibration phase includes: Obtain the set of calibrator-specific weighted feature matrices generated by continuous testing during the calibration phase of the system, stack them row by row, and construct the calibration feature matrix. The mean vector and standard deviation vector are calculated in the column direction, and the scaling feature matrix is ​​standardized with zero mean and unit variance. A constant is introduced in the standardized division operation to prevent the system-level interruption caused by division by zero. The calculated mean vector and standard deviation vector are cached. Principal component analysis algorithm is used to reduce the dimension of the standardized scaling feature matrix. The solution is obtained indirectly by calculating the small-dimensional inner product matrix. The eigenvectors of the original high-dimensional covariance matrix are obtained by inverse mapping. A cumulative variance contribution rate threshold is set to truncate principal components corresponding to low eigenvalues, define the effective dimension of the principal component space, and extract eigenvectors to form an orthogonal loading matrix.

4. The intelligent quality control and standardization management system for tumor markers according to claim 1, characterized in that, The process of solidifying the principal component baseline space model by the baseline modeling module includes: The standardized specific weighted feature matrix is ​​projected onto a low-dimensional space to generate a principal component score matrix. The product of the transpose of the principal component score matrix and the principal component score matrix is ​​calculated to obtain the principal component score covariance matrix. Force the diagonal elements in the principal component score covariance matrix that are below a preset lower limit to be clamped to the preset lower limit value; The mean vector, standard deviation vector, orthogonal loading matrix, and the principal component score covariance matrix after clamping are uniformly written into a non-volatile memory for static fixation, thus forming the principal component baseline space model.

5. The intelligent quality control and standardization management system for tumor markers according to claim 1, characterized in that, The calculation logic for the real-time monitoring module to perform orthogonal projection operations to evaluate the stability of the microenvironment is as follows: The flattened specific weighted feature vector of the test sample is orthogonally projected onto a preset orthogonal load matrix to extract the evolution coordinates of the current detection microenvironment in the main physical causal direction of the system, and the principal component score vector is generated. The Hotelling statistic is calculated based on the extracted principal component score vectors, and the variance weights of each dimension are performed using the inverse matrix of the fixed principal component score covariance matrix. Before performing the inversion, a diagonal matrix bias is forcibly introduced by mixing in the product of the regularization bias constant and the unit diagonal matrix, thereby eliminating the divergence in the inversion caused by small singular values.

6. The intelligent quality control and standardization management system for tumor markers according to claim 1, characterized in that, The mechanism for determining when the real-time monitoring module outputs an interception signal after exceeding the limit includes: Based on the principle of orthogonal complement space decomposition, the feature vector is reconstructed to remove known information, and the external residual vector of the model is extracted. Calculate the product of the model's external residual vector and its transpose matrix to obtain the squared prediction error statistic; In the determination of the output results, a time-series smoothing mechanism based on multi-dimensional exponential weighted moving average is introduced to attenuate transient noise by utilizing the inertia of historical operating conditions. When the smoothed state variable exceeds the preset control limit and the state remains stable for multiple consecutive detection cycles, it is determined that the current test microenvironment has caused an irreversible physical deviation, and the interception signal is output.

7. The intelligent quality control and standardization management system for tumor markers according to claim 1, characterized in that, The specific execution procedure for the dynamic reconstruction module to reverse reconstruct the actual deviation matrix of the physical channel includes: By combining the squared prediction error statistic to set a dynamic residual activation bias coefficient, the product of the principal component score vector and the transpose of the orthogonal loading matrix is ​​calculated, and the product of the residual activation bias coefficient and the out-of-model residual vector is added. Then, the inverse projection of the standardized space is performed to reorganize and generate a standardized comprehensive bias vector. The standard deviation vector cached by the principal component baseline spatial model is called to perform a reverse scaling operation of physical dimensions on the standardized comprehensive deviation vector to obtain a dimensionless absolute deviation vector. The dimensionless absolute deviation vector is then rearranged by reverse folding of spatial dimensions to form a weighted state deviation matrix. The weighted state deviation matrix is ​​multiplied by the inverse of the target weighted diagonal matrix, which has been forcibly introduced with a regularization smoothing constant, and the reverse decoupling solution is performed to obtain the actual deviation matrix of the physical channel.

8. The intelligent quality control and standardization management system for tumor markers according to claim 1, characterized in that, The steps of generating dynamic curve parameters by combining biochemical parameter perturbation Jacobian matrix mapping in the dynamic reconstruction module include: The actual deviation matrix of the physical channel is expanded and reorganized into a flattened physical deviation feature vector by rows, and linear neighborhood truncation is performed to force abnormal over-limit elements to the upper limit boundary. The flattened and linearly truncated physical deviation eigenvector is multiplied by the transpose of the biochemical parameter perturbation Jacobian matrix through a cross-modal mapping to generate the curve parameter compensation vector. The generated curve parameter compensation vector is subjected to physical extreme value limiting determination. The basic regression equation parameters of the current reagent batch are extracted and algebraically superimposed with the limited curve parameter compensation vector to generate the dynamic curve parameters, which include dynamic bottom parameters, dynamic top parameters, dynamic inflection point concentration parameters, and dynamic slope factor parameters.

9. The intelligent quality control and standardization management system for tumor markers according to claim 1, characterized in that, The numerical approximation process of adaptive concentration calculation performed by the adaptive solution and source tracing module includes: The system receives the original emission signal captured by the underlying photometric unit, performs boundary clamping on the original emission signal to obtain the effective emission intensity before performing nonlinear inversion, and performs zero-prevention regularization on the dynamic slope factor in the dynamic curve parameters. Construct the nonlinear residual function and iterative update formula of the Newton-Raphson adaptive approximation algorithm with L2 regularization constraints, and forcibly introduce a safety isolation bound before constructing the residual function to avoid division overflow of the exponent base. By utilizing the first-order local slope information of the dynamic curve at the current concentration working point, the solution trajectory is guided to converge along the direction of the fastest error reduction. When the absolute difference in concentration between two adjacent iterations is less than the set tolerance band, the converged concentration estimate is used as the final state adaptive solution concentration output.

10. The intelligent quality control and standardization management system for tumor markers according to claim 1, characterized in that, The conditions for the adaptive solution and tracing module to analyze the relative error contribution and output the hardware tracing maintenance diagnostic log are as follows: The root mean square of each column vector in the actual deviation matrix of the physical channel is calculated to extract the time-domain absolute error of each independent sensing channel. The absolute errors of each extracted channel are normalized globally. A global smoothing constant is forcibly introduced into the denominator of the normalization algorithm. The relative error contribution of each independent physical sensing channel is calculated and arranged in descending order of numerical value. When the extracted maximum relative contribution exceeds the preset absolute sensitivity threshold, and the ratio of the maximum relative contribution to the second largest relative contribution is greater than the primary-secondary isolation threshold, and remains stably triggered over multiple consecutive test cycles, it is asserted that the specific hardware sensing channel mapped by the corresponding index has experienced an irreversible physical failure, and the hardware traceability maintenance diagnostic log is output.

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