A fully automatic chemiluminescence analyzer control system for blood sample detection

Abstract

The application belongs to the technical field of blood detection control, and specifically discloses a kind of blood sample detection's full-automatic chemiluminescence analyzer control system, the system is through the regional temperature difference calculation and thermal inertia trend analysis, and reaction temperature deviation degree and the space coordinate boundary of thermal inertia imbalance region are fused and analyzed, output power adjustment instruction set for different independent heating area of reaction disc, and risk assessment is carried out based on preset temperature stability determination rule, compensation control strategy is triggered when there is risk, finally, stable temperature field control signal is generated based on compensation control strategy and power adjustment instruction set and corresponding control is carried out, effectively improve the reaction disc temperature uniformity and stability, significantly improve the precision and repeatability of chemiluminescence detection result. At the same time, it also ensures the stability of temperature regulation process, maintains the environmental stability required for long-time chemical reaction.

Description

Technical Field

[0001] This invention belongs to the field of blood testing and control technology, and more specifically, relates to a fully automated chemiluminescence analyzer control system for blood sample testing. Background Technology

[0002] Blood sample testing demands extremely high precision and repeatability of results. The incubation process is one of the core steps in chemiluminescence detection, and the stability and uniformity of its temperature control directly affect the efficiency of the antigen-antibody reaction, thus influencing the intensity and reliability of the final detection signal.

[0003] Existing technologies, such as the sample analyzer and sample transfer method disclosed in Chinese invention patent application number 202410965217.X, rely on a core control unit that coordinates the gripping unit and the sample injection unit to transfer the incubated sample to a new sample rack. This allows for automatic incubation processing, enabling the deagglomeration of agglomerated samples and improving the flexibility of the sample analyzer in terms of sample container scheduling and the efficiency of detection and analysis.

[0004] Existing technologies, such as the blood analysis system disclosed in Chinese invention patent application number 202211637150.4, mainly utilize existing detection information to directly control the slide pushing parameters, avoiding repeated testing. That is, data is obtained by scanning codes, and the control device adjusts the slide pushing and drying parameters, simplifying the process and ensuring quality.

[0005] While existing technologies have made some progress in sample and information flow scheduling and have achieved some temperature control, they do not address how to ensure temperature consistency across different locations within the incubation space, leading to variations in the chemical reaction environment for different samples. Furthermore, when the grasping unit frequently opens and closes the incubation area door or injects room-temperature samples, traditional temperature control systems exhibit slow response times, causing temperature fluctuations and affecting the reaction efficiency of the incubating samples. This makes it difficult to maintain the long-term, high-stability temperature required for chemiluminescence detection, resulting in reduced precision and repeatability of the detection results. Additionally, existing technologies only address macroscopic temperature control, failing to resolve issues related to microscopic temperature field uniformity and thermal inertia response lag. Summary of the Invention

[0006] In view of this, the present invention addresses the shortcomings of existing technologies in solving the problems of uniform temperature field and thermal inertia fluctuation in the reaction disk. Through multi-module collaborative control, high-precision temperature regulation is achieved, and a fully automated chemiluminescence analyzer control system for blood sample detection is proposed.

[0007] The objective of this invention can be achieved through the following technical solution: This invention provides a fully automated chemiluminescence analyzer control system for blood sample testing. The system includes: a temperature data acquisition module, which collects temperature readings of multiple discrete monitoring points on the reaction plate in real time, as well as the timestamps corresponding to each reading.

[0008] The reaction temperature analysis module preprocesses the temperature readings, outputs a standardized temperature field distribution dataset, performs regional temperature difference calculation and thermal inertia trend analysis, and outputs the reaction temperature deviation and thermal inertia imbalance region.

[0009] The control command generation module integrates the reaction temperature deviation with the spatial coordinate boundary of the thermal inertia imbalance region, outputs a set of power adjustment commands for different independent heating zones of the reaction disk, and performs risk assessment based on preset temperature stability judgment rules, triggering compensation control strategies when risks exist.

[0010] The temperature adjustment actuator generates a stable temperature field control signal based on the compensation control strategy and power adjustment instruction set, and drives the actuator to move.

[0011] Compared with the prior art, the beneficial effects of the present invention are as follows: (1) The present invention can accurately identify the instantaneous temperature difference in different areas of the reaction plate by calculating the regional temperature difference, which makes it easier to accurately grasp the uniformity of the temperature field and provides a reliable basis for subsequent precise control, thereby effectively improving the accuracy of temperature control.

[0012] (2) By performing thermal inertia trend analysis, this invention can identify thermal response lag areas that are difficult to detect by traditional temperature monitoring by obtaining the thermal time constant and thermal response rate of each monitoring point. In this way, it can predictively discover potential temperature instability factors and provide technical support for improving the timeliness of temperature control.

[0013] (3) This invention achieves differentiated power control of independent heating zones by integrating and analyzing information on temperature deviation and thermal inertia imbalance regions. It can simultaneously address static temperature non-uniformity and dynamic thermal response lag, significantly improving temperature control accuracy under complex operating conditions and ensuring the stability of the reaction environment. It also achieves synchronous compensation for static temperature non-uniformity and dynamic thermal response lag.

[0014] (4) The present invention performs risk assessment based on temperature stability judgment rules, and can trigger a compensation strategy in a timely manner when abnormal power adjustment is detected. This effectively prevents over-adjustment or oscillation during specific regulation, ensures the stability of the temperature regulation process, and maintains the environmental stability required for long-term chemical reactions. Attached Figure Description

[0015] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0016] Figure 1 This is a schematic diagram of the system module connections of the present invention.

[0017] Figure 2 This is a schematic diagram of the overall implementation process of the present invention.

[0018] Figure 3 This is a schematic diagram of the calculation process for the baseline reaction temperature deviation of this invention. Detailed Implementation

[0019] The technical solutions of 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.

[0020] Please see Figure 1 and Figure 2 As shown, the present invention provides a fully automated chemiluminescence analyzer control system for blood sample testing. The system includes: a temperature data acquisition module, a temperature data acquisition module, a control command generation module, and a temperature adjustment execution terminal.

[0021] In the above, the temperature data acquisition module is connected to both the temperature data acquisition module and the control command generation module, and the control command generation module is connected to the temperature adjustment execution terminal.

[0022] The temperature data acquisition module collects temperature readings from multiple discrete monitoring points on the reaction plate in real time, along with the timestamps corresponding to each reading.

[0023] Specifically, the process of acquiring the temperature data of the reaction disk includes: A1. Identifying heat sources and cold sources based on the structure of the reaction disk, analyzing the heat transfer path, and establishing a geometric structure model of the reaction disk.

[0024] Understandably, heat sources include, but are not limited to, Peltier elements, drive motors, and environmental radiation, while cold sources include, but are not limited to, radiators and environmental convection.

[0025] In the specific implementation process, after heat is generated from the Peltier element, it is first transferred axially to the metal substrate of the reaction plate through the thermally conductive silicone grease layer, and then conducted upwards from the substrate to the bottom of each reaction cup, forming the main axial heat conduction path. During this process, heat is simultaneously parasiticly conducted to the instrument frame through the mechanical fixing screws and support arms, forming a temperature gradient at the edge of the reaction plate and generating the main parasitic heat conduction path. The upper surface of the reaction plate undergoes natural convection heat exchange with the air inside the chamber, while the movement of the sample needles and reagent needles and the spraying operation of the cleaning station induce forced convection, causing transient temperature fluctuations in local areas and forming a composite convection path. In addition, the surface of the reaction plate also continuously undergoes radiative heat exchange with components such as the reagent chamber and the inner wall of the instrument at different temperatures, generating a radiative heat exchange path. The specific parameters of the four paths are as follows: Main axial heat conduction path: heat transfer efficiency of 85% to 92%, temperature difference from Peltier to the bottom of the reaction cup controlled within ±0.3℃.

[0026] Parasitic heat conduction path: causes the temperature of the area within a 15mm radius around the mechanical fixing point to drop by 3°C to 5°C, with a heat loss rate of 6% to 8%.

[0027] Composite convection path: The movement of the needle causes periodic fluctuations of ±0.5℃, while the spraying of the cleaning fluid causes the local temperature to drop sharply by 8℃ to 10℃ within 2 seconds.

[0028] Radiative heat transfer path: causes an overall temperature shift of 1°C to 2°C on the surface of the reaction disk, and the heat transfer accounts for 2% to 3% of the total heat flux.

[0029] A2. Mark the active temperature zone, edge effect zone, and core reaction zone on the model.

[0030] Understandably, the active temperature zone refers to the area in direct contact with the Peltier element, the edge effect zone refers to the edge area that is prone to forming temperature boundaries due to structural asymmetry, and the core reaction zone refers to the area involved in the mixing and reaction of reagents and samples.

[0031] A3. Matrix-style point placement is implemented in the core reaction zone, ring-density point placement is implemented in the edge effect zone, and point placement is implemented at the center and boundary of the active temperature zone.

[0032] In the specific deployment process, the core reaction zone uses a polar coordinate grid layout, with points evenly spaced radially and circumferentially to ensure coverage of all reaction cup orifices. The edge effect zone is centered on the reaction disk's center, with three concentric rings of monitoring points within 10mm of the edge. Adjacent rings are spaced 5mm apart, with 12 points per ring, evenly spaced at 30-degree angles, achieving high-density monitoring of temperature gradient changes. The active temperature zone has a reference point at the center of the circular area directly opposite the Peltier element, and four symmetrically distributed monitoring points at half its radius and at its maximum boundary, forming a double-layered ring monitoring system of center-transition-boundary. This deployment scheme ensures that the temperature monitoring network can comprehensively cover the critical areas of the reaction disk while accurately capturing various temperature characteristics such as axial conduction, edge attenuation, and local anomalies.

[0033] A4. Temperature readings are synchronously acquired using distributed temperature sensors at a fixed sampling frequency, and a timestamp is generated at the start point of each acquisition.

[0034] Understandably, chemiluminescence analyzers have extremely high requirements for temperature control, because the reaction temperature of reagents and samples directly affects the accuracy of the test results. For example, the reaction pan usually needs to be kept at a constant temperature, possibly 37°C to simulate human body temperature, so temperature acquisition and control are crucial.

[0035] In practice, once the temperature sensor is activated, it enters a real-time acquisition loop, periodically executing at a sampling rate of no less than 1Hz. First, a unified high-precision timestamp is generated at the start of each acquisition cycle. Then, readings from all sensors are synchronously acquired via multiple ADCs, and the readings are converted into temperature values ​​based on calibration parameters. Finally, the temperature data from all monitoring points at the same timestamp, along with the reaction plate identifier and the status identifier of any additional equipment, are packaged into a single data frame and output to the temperature data processing module for data processing and subsequent analysis.

[0036] The reaction temperature analysis module preprocesses the temperature readings, outputs a standardized temperature field distribution dataset, performs regional temperature difference calculation and thermal inertia trend analysis, and outputs the reaction temperature deviation and thermal inertia imbalance region.

[0037] Specifically, the preprocessing process includes: B1, removing outliers and aligning timestamps on the temperature readings, and recording the discrete monitoring points corresponding to the remaining temperature readings after removal as valid monitoring points.

[0038] It should be noted that if the instantaneous temperature reading at a monitoring point deviates from the average of the previous 10 cycles by more than ±2.0℃, the reading will be marked as invalid. Furthermore, for invalid readings, linear extrapolation will be performed using valid readings from the previous 3 cycles at the same monitoring point to fill the gap.

[0039] B2. Using the rotation center of the reaction disk as the center, a circular area covering the distribution range of all reaction cup holes is taken as the effective area, and a two-dimensional coordinate system of the reaction disk is established.

[0040] In practice, the effective area boundary is defined by the line connecting the center points of the outermost reaction cup orifices, excluding edge areas on the reaction plate used for mechanical fixation, transmission mechanism installation, and non-functional purposes. This effective area is the sole target area for temperature field reconstruction and data analysis, ensuring that data processing is fully focused on the critical temperature zones affecting the test results.

[0041] B3. Divide the effective area into grids according to the preset grid size.

[0042] In specific implementations, the setting of the preset grid size needs to take into account both the actual size of the reaction disk and the accuracy requirements of temperature field reconstruction.

[0043] A preferred embodiment is to use the center-to-center spacing of the reaction cup apertures as a reference. For example, when the reaction cup apertures are distributed circumferentially and the included angle between the centers of adjacent apertures is 6 degrees and the radial spacing is 4 mm, the grid size can be set to a sector-shaped grid with a circumferential angle of 6 degrees and a radial spacing of 4 mm. This setting ensures that the area where each reaction cup aperture is located can be completely covered by an independent grid, thereby accurately reflecting the temperature conditions of each potential reaction location and providing a structurally suitable and resolution-appropriate spatial basis for subsequent temperature interpolation and analysis.

[0044] B4. Based on the coordinates and temperature readings of all valid monitoring points, calculate the temperature values ​​of the grid nodes using the inverse distance weighted interpolation algorithm to generate an absolute temperature field matrix. At the same time, convert the temperature values ​​into deviation values ​​relative to a preset reference temperature to generate a temperature deviation field matrix.

[0045] Understandably, the inverse distance weighted interpolation algorithm is an existing algorithm. In its specific implementation, the method for calculating the temperature value of each grid node based on the coordinates and temperature readings of all effective monitoring points using the inverse distance weighted interpolation algorithm is as follows: Select a grid node to be calculated, calculate the Euclidean distance from the grid node to each effective monitoring point, and assign a weight to each effective monitoring point according to the principle of inverse distance weighting. This weight is inversely proportional to the p-th power of the distance, where p is usually taken as 2. The interpolated temperature of the grid node is obtained by weighted averaging of the temperature readings of all effective monitoring points and their corresponding weights. By traversing all grid nodes and repeating the above process, a continuous absolute temperature field matrix covering the entire effective area can be generated. This matrix accurately reflects the temperature distribution in the two-dimensional plane of the reaction disk, laying a data foundation for subsequent temperature uniformity analysis and precise temperature control.

[0046] In chemiluminescence analysis, the preset reference temperature of the reaction disk is usually a constant value, such as 37°C. By calculating the temperature difference, the focus of data analysis can be shifted from how high the temperature is to how much it deviates from the set value, making temperature fluctuations and anomalies readily apparent. Furthermore, the dynamic range of the deviation is typically much smaller than the absolute temperature value.

[0047] B5. Package the absolute temperature field matrix, temperature deviation field matrix, and timestamp into a standardized dataset for output.

[0048] Specifically, the process of calculating the regional temperature difference includes: C1, extracting the temperature field matrix from the standardized dataset.

[0049] C2. Scan the temperature values ​​of the grid nodes, identify the instantaneous highest and lowest temperatures and their coordinates, and calculate the maximum transient temperature difference.

[0050] C3. Based on the reaction kinetics requirements of blood test reagents, retrieve the allowable temperature difference threshold and deviation safety tolerance threshold of the core reaction zone, and calculate the baseline reaction temperature deviation by combining the two.

[0051] The specific implementation process for retrieving the allowable temperature difference threshold and deviation safety tolerance threshold of the core reaction zone of the reaction disk is as follows: First, establish a reagent temperature characteristic database, which stores the activation energy corresponding to different detection items. Optimal temperature range, reaction rate constant Key kinetic parameters, such as temperature sensitivity coefficient and reaction rate tolerance, are retrieved by the item number when a user selects a specific detection item. The temperature sensitivity coefficient is calculated based on the Arrhenius equation, and the parameters are then determined according to the allowable deviation of the reaction rate. The requirement of ≤5% is achieved through the formula. The allowable temperature difference threshold was calculated. The temperature is 0.1-0.3℃. To account for the allowable deviation in reaction rate, Let be the ideal gas constant. The optimal reaction temperature is typically 37°C, which translates to 310.15 K in absolute temperature.

[0052] in, As an indicator to measure the absolute sensitivity of a reaction rate to temperature changes, it represents the total thermal driving potential or temperature sensitivity of a chemical reaction at a specific temperature. The greater the absolute change in reaction rate caused by a one-unit change in temperature, the higher the rate of change. This represents the change in the reaction rate constant when the temperature changes slightly.

[0053] At the optimal reaction temperature, thermal inertia or energy benchmark is a measure of molecular thermal motion energy and ambient temperature. The larger this value, the stronger the thermal disturbance in the environment, and the more the reaction rate change effect produced by the same thermal driving potential will be diluted.

[0054] This represents the ratio of thermal driving potential to the thermal background, used to standardize the total thermal driving potential relative to the thermal background of its operating environment, thereby obtaining a true and comparable temperature sensitivity index. This standardization process allows us to transform abstract biochemical reaction kinetic requirements into specific and operable engineering control parameters.

[0055] Simultaneously, based on the safety requirement that the reagent's protein denaturation critical temperature T be ±3-5℃ of the optimal reaction temperature, and considering the instrument's temperature control accuracy... It is 0.2℃, according to The principle is to set a safety tolerance threshold for deviation. The threshold values ​​are 0.5-0.8℃. Finally, these threshold parameters are loaded into the real-time monitoring system as a benchmark for temperature quality assessment.

[0056] Further, please refer to Figure 3 As shown, the calculation of the baseline reaction temperature deviation includes: C3-1, real-time monitoring of the maximum transient temperature difference, instantaneous highest temperature, and instantaneous lowest temperature during the sample addition and incubation period.

[0057] C3-2. Calculate the total number of sampling periods and the maximum number of continuous sampling periods when the maximum transient temperature difference exceeds the allowable temperature difference threshold. Then, calculate the ratio of these two numbers to the total number of sampling periods to obtain the transient total over-limit sampling period ratio and the transient continuous over-limit sampling period ratio.

[0058] Understandably, the transient total out-of-limit sampling period ratio reflects the proportion of the total duration of temperature uniformity runaway in the entire reaction phase. It measures the prevalence of temperature non-uniformity problems. The transient sustained out-of-limit sampling period ratio reflects the proportion of the duration of the most severe single event of temperature uniformity runaway. It measures the ability to recover from disturbances. Sustained out-of-limit indicates an inherent defect in the system that is difficult to correct quickly. Both reflect the temperature uniformity situation.

[0059] C3-3. When the difference between the instantaneous highest temperature and the reference temperature exceeds the deviation safety tolerance threshold, or when the difference between the reference temperature and the instantaneous lowest temperature exceeds the deviation safety tolerance threshold, a temperature anomaly is marked, and the instantaneous high temperature exceedance depth or instantaneous low temperature exceedance depth is recorded.

[0060] Understandably, the instantaneous high-temperature exceedance depth is obtained by subtracting the sum of the reference temperature and the deviation safety tolerance threshold from the instantaneous highest temperature. The instantaneous low-temperature exceedance depth is obtained by subtracting the sum of the instantaneous lowest temperature and the deviation safety tolerance threshold from the reference temperature.

[0061] C3-4. After the reaction phase ends, count the total number of marked anomalies for the entire phase and calculate the ratio of the total number of samplings to obtain the absolute temperature exceedance rate.

[0062] C3-5. Sum the instantaneous high temperature exceedance depth and instantaneous low temperature exceedance depth for all sampling periods to obtain the total cumulative high temperature exceedance and total cumulative low temperature exceedance. Calculate the average exceedance depth and obtain the average temperature exceedance by minimum-maximum linear normalization.

[0063] Understandably, the absolute temperature exceedance rate reflects the frequency with which the absolute temperature of the reaction disk exceeds the limit. Once triggered, it indicates that the activity of the reagent may be threatened, representing the highest level of risk. The average temperature exceedance rate further quantifies the severity of each exceedance event. It calculates the cumulative exceedance depth per sampling period over the entire reaction phase, reflecting the total amount of temperature runaway. Both reflect the extent of temperature exceedance.

[0064] C3-5. The transient total over-limit sampling period ratio, transient continuous over-limit sampling period ratio, absolute temperature over-limit rate and average temperature over-limit are linearly weighted and summed to output the reference reaction temperature deviation.

[0065] In practice, the weight settings are based on a large amount of experimental data.

[0066] For example, for highly sensitive reagents, the weight of the absolute temperature-related index is set to 0.7, the weight of the uniformity-related index is set to 0.3, the weight of the absolute temperature exceedance rate is 0.4, the weight of the average temperature exceedance rate is 0.3, and the weights of the transient total exceedance sampling period ratio and the transient continuous exceedance sampling period ratio are set to 0.15 respectively.

[0067] For moderately sensitive reagents, a balanced weighting configuration is adopted, that is, the transient total out-of-limit sampling period ratio, the transient continuous out-of-limit sampling period ratio, the absolute temperature out-of-limit rate, and the average temperature out-of-limit rate are set to 0.2, 0.2, 0.3, and 0.3 respectively.

[0068] For low-sensitivity reagents, the weight of the homogeneity index should be appropriately increased. Specifically, the transient total out-of-limit sampling period ratio, transient continuous out-of-limit sampling period ratio, absolute temperature out-of-limit rate, and average temperature out-of-limit rate should be set to 0.3, 0.3, 0.2, and 0.2, respectively.

[0069] It should be noted that different temperature anomaly patterns have varying degrees of impact on blood test results, and different reagents also differ in their temperature sensitivity. By implementing this dynamic weighting configuration tied to reagent characteristics, monitoring can be focused on the most critical risk dimensions of the current test, such as prioritizing absolute temperature safety for highly sensitive reagents. This ensures that the final calculated baseline reaction temperature deviation is no longer a simple mathematical average, but a comprehensive evaluation indicator with clear clinical guidance that accurately reflects the true risk level of the current temperature state to the results of a specific blood test.

[0070] C4. Based on the preset temperature zone boundary coordinates, extract the temperature deviation value of the corresponding grid node of each temperature zone, and calculate the average temperature deviation value of each temperature zone. Based on this, set the compensation temperature deviation degree.

[0071] Understandably, the setting of the preset temperature zone boundary coordinates is achieved by integrating the mechanical design drawings of the reaction disk and the physical layout of the Peltier elements, transforming the physical boundaries of the active temperature zone, the core reaction zone, and the edge effect zone into precise mathematical coordinates with the rotation center of the reaction disk as the origin.

[0072] Furthermore, the setting of the compensation temperature deviation includes: C4-1, calculating the absolute temperature difference between the corresponding average temperature deviation values ​​of the active temperature zone and the core reaction zone, the core reaction zone and the edge effect zone, and the active temperature zone and the edge effect zone.

[0073] C4-2. The absolute temperature difference is compared with the allowable temperature difference threshold to obtain the active-core contribution, core-edge contribution, and active-edge contribution.

[0074] C4-3. Calculate the weight of each temperature zone based on the contribution, and perform a linear weighted summation of the average temperature deviation values ​​of each temperature zone to obtain the comprehensive temperature deviation.

[0075] In practice, the sum of the active-core contribution and the active-edge contribution is used as the active temperature zone weight, the sum of the active-core contribution and the core-edge contribution is used as the core reaction zone weight, and the sum of the core-edge contribution and the active-edge contribution is used as the edge effect zone weight. All weights are processed by min-max normalization to output the weights of each temperature zone.

[0076] The linear weighted summation is an existing calculation formula and will not be shown here.

[0077] C4-4. Standardize the overall temperature deviation using the deviation safety tolerance threshold, based on the activation energy of the blood test reagent corresponding to the current test item and... Temperature coefficient: Query the pre-stored reagent characteristic database to obtain the corresponding preset correction coefficient.

[0078] Standardization is quantified by the ratio of the absolute value of the comprehensive temperature deviation to the deviation safety tolerance threshold. The correction factor typically ranges from 0 to 1 and is used to control the correction magnitude.

[0079] The reagent properties database was established by referencing the sensitivity characteristics of enzyme-catalyzed reactions in clinical biochemistry and by validation experiments using a step temperature perturbation experiment and a chemiluminescence analyzer.

[0080] The verification experiments, conducted using a stepped temperature perturbation experiment and a chemiluminescence analyzer, specifically included: performing a stepped temperature perturbation experiment on each detection item, applying a step change of ±0.3℃ to ±1.0℃ from 37℃ using a high-precision thermal cycler, and simultaneously monitoring the chemiluminescence reaction rate; obtaining the activation energy based on the Arrhenius equation using the least squares method, and calculating... The temperature coefficient was determined by setting a constant temperature deviation of 0.5℃ as the test condition, based on typical temperature fluctuations that the fully automated chemiluminescence analyzer might encounter during actual operation. With a step size of 0.1℃, verification experiments were conducted sequentially on each candidate correction coefficient within the fully automated chemiluminescence analyzer. The coefficient of variation for each coefficient was recorded in real time. Based on clinical laboratory quality standards, a coefficient of variation ≤3% was considered acceptable. The coefficient with the smallest value among all acceptable coefficients was selected as the final correction coefficient, thus forming a result including the test item number, activation energy, and... A complete database of coefficients and correction coefficients.

[0081] C4-5. The product of the correction factor and the standardized result is used as the compensation temperature deviation.

[0082] It should be noted that the impact of each temperature zone on the overall temperature quality is not equal, and the risk to biochemical reactions must be measured based on a unified safety standard. By allocating weights based on the contribution of each zone, it can be ensured that areas with significant temperature differences, which may indicate specific heating efficiency or insulation performance failures, such as excessively large active-core temperature differences, are highlighted when calculating the overall temperature deviation, thus making the compensation more diagnostically targeted.

[0083] It should also be noted that standardizing the overall temperature deviation through the deviation safety tolerance threshold is to unify temperature deviations of different dimensions into a dimensionless relative risk value relative to the reagent safety boundary. This allows the correction coefficients preset for different temperature-sensitive reagents to play a role on a unified scale with clear biochemical significance, ultimately ensuring that the compensation temperature deviation can not only truly reflect the spatial temperature distribution defects of the reaction plate, but also accurately quantify its actual impact risk on the activity of specific blood test reagents.

[0084] C5. The sum of the compensated temperature deviation and the reference reaction temperature deviation is taken as the final reaction temperature deviation.

[0085] Specifically, the output of the thermal inertia imbalance region includes: D1, continuously collecting temperature data at each monitoring point at a preset sampling frequency when the reaction disk experiences a temperature step change, and outputting a temperature time series.

[0086] Specifically, the preset sampling frequency is no less than 2Hz.

[0087] D2. The temperature time series of each monitoring point is fitted by the least squares method to obtain the thermal time constant.

[0088] In practice, when performing first-order fitting of the temperature time series at each monitoring point, the temperature data at that monitoring point is continuously collected for 20 seconds at a sampling frequency of 10Hz during the system's temperature step change from 25℃ to 37℃. Then, the least squares method is used to fit the collected time-temperature data to the first-order system response model. By iteratively optimizing the fit curve to minimize the sum of squared residuals between the fitted curve and the actual data, the thermal time constant characterizing the thermal inertia at that point is finally obtained. ,in, Indicates the first Temperature values ​​at each time point This represents the initial temperature before the step change, which is 25℃. This indicates that the set temperature step change is 12℃. It is a time variable.

[0089] D3. Calculate the heating rate and cooling rate of each monitoring point, and calculate the response time required to reach the preset boundary temperature as the thermal response rate.

[0090] In a specific embodiment, the preset boundary temperature can be set to 63.2% of the target temperature, for example, the fitting result is... =3.2 seconds. This parameter represents the time required for the temperature at the monitoring point to reach 63.2% of the steady-state change, providing a quantitative basis for subsequent thermal inertia analysis.

[0091] D4. Generate thermal time constant distribution map and thermal response rate distribution map using spatial interpolation algorithm.

[0092] The spatial interpolation algorithm is an existing algorithm, and its specific generation process will not be described in detail here.

[0093] D5. Calculate the average value and standard deviation of the thermal time constant of the entire reaction disk, and identify the region where the thermal time constant deviates from at least one standard deviation as the primary thermal inertia imbalance region.

[0094] Understandably, when defining the primary thermal inertia imbalance region, a standard of at least one standard deviation from the mean is adopted for the purpose of preliminary and broad screening. This standard is relatively lenient, enabling the comprehensive capture of all regions suspected of having abnormal thermal response, avoiding the omission of any potential risks, and ensuring the comprehensiveness of the assessment scope.

[0095] D6. In the primary imbalance region, select regions whose thermal response rate is at least 1.5 standard deviations lower than the average thermal response rate as severe thermal inertia imbalance regions.

[0096] Understandably, when defining areas of severe thermal inertia imbalance, a more stringent standard of 1.5 standard deviations below the average thermal response rate is adopted for precise and critical localization. This standard aims to further filter out areas with extremely lagging thermal response performance and the most prominent problems from the initially identified suspect areas. By raising the threshold, control resources and adjustment priorities can be precisely allocated to these identified severely abnormal areas, thereby achieving focused treatment of the weakest links and improving overall control efficiency and effectiveness.

[0097] It should be noted that the specific choice of the above standard deviation should refer to thermodynamic simulation and experimental verification, and is not a fixed value.

[0098] In practice, when screening areas with severe thermal inertia imbalance, the average and standard deviation of the thermal response rate of all monitoring points in the entire reaction disk are first calculated to obtain an average heating rate of 0.5℃ per second and a standard deviation of 0.1℃ per second. Then, within the primary thermal inertia imbalance area, the average thermal response rate of each sub-region is calculated. If the average thermal response rate of a certain sub-region is lower than the overall average minus 1.5 times the standard deviation, i.e., lower than 0.35℃ per second, the region is marked as a severe thermal inertia imbalance area. This ensures that the screening is based on quantitative statistical thresholds, avoids subjective judgment, and improves the accuracy of risk identification.

[0099] D7. Perform spatial cluster analysis on severely unbalanced regions to generate and output the spatial coordinate boundaries of the thermal inertia unbalanced regions.

[0100] In practice, spatial clustering analysis can employ classic algorithms such as DBSCAN and K-means. Since spatial clustering is an existing algorithm, the specific execution process will not be described further.

[0101] The control command generation module integrates the reaction temperature deviation with the spatial coordinate boundary of the thermal inertia imbalance region, outputs a set of power adjustment commands for different independent heating zones of the reaction disk, and performs risk assessment based on preset temperature stability judgment rules, triggering a compensation control strategy when a risk exists.

[0102] Specifically, the fusion analysis includes: Y1, recording the geometric boundary coordinates of the independent heating zones.

[0103] Y2. Based on the spatial coordinate boundary of the thermal inertia imbalance region, establish a spatial mapping relationship with the independent heating zone, and calculate the thermal inertia influence factor of each independent heating zone.

[0104] The calculation of the thermal inertia influence factor includes: Y2-1, identifying the percentage of overlapping area between each independent heating zone and the thermal inertia imbalance zone, and the shortest distance from the center of each independent heating zone to the boundary of the nearest thermal inertia imbalance zone.

[0105] The definition of the independent heating control zone is based on the physical partitioning of the Peltier element array integrated on the reaction plate substrate. Each partition has an independent drive circuit and temperature feedback. That is, according to the physical layout of the independent temperature control elements integrated on the reaction plate substrate, the physical partition where each independent temperature control element is located is taken as the independent heating zone.

[0106] Y2-2. Calculate the distribution influence factor based on the shortest distance, where the distribution influence factor is the complement of the ratio of the shortest distance to the radius of the reaction disk.

[0107] Y2-3. Calculate the relative deviation and coefficient of variation of the thermal time constant and thermal response rate for each independent heating region, perform minimum-maximum normalization, and obtain the comprehensive imbalance by linear weighted summation.

[0108] In the weighting example, the relative deviation of the thermal time constant directly reflects the degree of core thermal inertia deviation, so its weight is set to 0.40. The coefficient of variation of the thermal time constant is used to characterize the internal stability of the region, so its weight is set to 0.25. The relative deviation of the thermal response rate reflects dynamic response anomalies, so its weight is set to 0.25. The coefficient of variation of the thermal response rate reflects transient response consistency, so its weight is set to 0.10. This weighting scheme is based on the analysis of the thermal inertia imbalance mechanism. By prioritizing steady-state characteristics while taking into account dynamic characteristics, it ensures that the comprehensive imbalance degree can accurately characterize the core risk of thermal inertia anomalies.

[0109] Y2-4. Calculate the thermal inertia influence factor based on the overlap area ratio, distribution influence factor, and comprehensive imbalance degree.

[0110] In practice, the thermal inertia influence factor is calculated using a linear weighted quantization formula. This value comprehensively reflects the degree of combined impact of thermal inertia imbalance on the heating zone in terms of spatial coverage, proximity, and severity.

[0111] In the weighting example, since the degree of physical coverage directly determines the thermal conduction efficiency, the weight of the overlapping area ratio can be set to 0.5. Considering the attenuation effect of spatial proximity on thermal interference, the weight of the distribution influence factor can be set to 0.3. The weight of the comprehensive imbalance can be regarded as a correction term for the severity, and the weight can be set to 0.2. This allocation strategy ensures that the thermal inertia influence factor is more in line with the actual thermodynamic process by strengthening the spatial physical correlation and supplementing it with imbalance calibration.

[0112] Y3. The proportion of the thermal inertia influence factor to the sum of the thermal inertia influence factors of all independent heating zones is used as the influence weight.

[0113] Y4. Distribute the reaction temperature deviation to each independent heating zone according to the influence weight, match the power adjustment amount based on the reaction temperature deviation of each zone, and match the reference power adjustment amount for independent heating zones in non-thermal inertia imbalance areas, and integrate the set of output power adjustment commands.

[0114] In practice, when the allocated temperature deviation is within a very small dead zone, the reference power adjustment for the heating zone is zero. This design aims to avoid responding to negligible temperature fluctuations and prevent frequent power adjustments.

[0115] When the temperature deviation exceeds the dead zone but is within the linear zone, the reference power adjustment is proportional to the portion of the deviation that exceeds the dead zone, and the magnitude of the proportion is determined by the preset proportional coefficient of the heating zone.

[0116] When the temperature deviation increases further and enters the saturation region, the growth rate of the reference power adjustment will slow down. A smaller saturation coefficient will be used for calculation to prevent excessive power output from causing system instability or overshoot.

[0117] The dead zone ranges from [0, 0.15], the linear zone ranges from (0.15, 0.5], and the saturation zone ranges from (0.5, 1]. The dead zone value is based on the accuracy requirements of chemiluminescence immunoassay. The linear zone should cover the main working range, and the saturation zone corresponds to safety protection and nonlinear compensation. Within this range, the power growth rate needs to be limited to prevent overshoot. The saturation coefficient ranges from 0.3 to 0.6, and is specifically selected based on the ratio of temperature deviation to the upper limit of the saturation zone. The higher the ratio, the smaller the value.

[0118] Specifically, the temperature stability determination rules include: calculating the power adjustment difference between adjacent independent heating zones, the sum of squares of the power adjustment of all heating zones, and the ratio of the power adjustment amplitude of the independent heating zone corresponding to the thermal inertia imbalance area to the thermal inertia influence factor, as each stability evaluation index.

[0119] It also obtains the current power command set, and determines that there is a risk when a certain stability evaluation index exceeds its preset threshold or when the power adjustment direction of any heating zone is repeatedly reversed in a continuous cycle and the reversal frequency exceeds the preset oscillation frequency threshold.

[0120] It should be noted that when the absolute value of the difference in power adjustment between any two adjacent heating zones exceeds the gradient safety threshold set based on the system's thermodynamic characteristics, a risk of thermal stress concentration is identified. When the sum of the squares exceeds the stability boundary threshold set based on the system's heat capacity, an overall stability risk is identified. When the ratio of the sum to the thermal inertia influence factor exceeds the matching threshold based on thermal shock effect analysis, a thermal shock risk is identified. When the power adjustment direction of any heating zone is repeatedly reversed within a continuous period, and the reversal frequency exceeds the oscillation frequency threshold based on control system stability analysis, a control oscillation risk is identified.

[0121] For example, the gradient safety threshold is determined by combining thermal stress simulation and material testing. A safety factor is set based on the material's yield strength, and the minimum value between the simulated critical gradient and the experimentally measured elastic deformation limit gradient is taken as the threshold. The stability boundary threshold is determined by combining system heat capacity calculation and dynamic response testing. A theoretical model is established based on the law of conservation of energy, and the average value between the theoretically calculated value and the experimentally observed stability critical value is taken as the threshold. The matching threshold is determined by combining thermal shock theoretical analysis and reagent activity experiments. Considering both the thermal shock effect and reagent stability requirements, the larger value between the theoretical critical value and the experimental safety boundary is taken as the threshold. The oscillation frequency threshold is determined by combining actual operation monitoring. The theoretical value is derived based on phase margin requirements, and combined with long-term operational data statistical analysis, the smaller value between the theoretical cutoff frequency and the measured resonant frequency is taken as the threshold.

[0122] Furthermore, a preset compensation control strategy is triggered. The specific implementation process of this strategy is as follows: First, the corresponding compensation mechanism is activated according to the risk type. For the risk of thermal stress concentration, a power gradient smoothing algorithm is used to insert transitional power values ​​between adjacent heating zones and extend the time window of power changes to reduce instantaneous thermal stress. For the risk of control oscillation, the system switches to inertial damping mode, introduces a low-pass filter in the power adjustment command, and temporarily reduces the proportional gain of the control loop to suppress oscillation. For the risk of thermal shock, power ramp control is activated, decomposing the originally single-step power adjustment into multiple progressive adjustment commands with smaller step sizes, and dynamically setting the ramp time according to the severity of thermal inertia. For the risk of overall stability, global power limiting is implemented. Without changing the power adjustment ratio of each heating zone, all adjustment amounts are compressed at a uniform ratio to ensure that the total power disturbance is within the stability boundary.

[0123] It is important to note that during the execution of all compensation strategies, the relevant parameter indicators that lead to the above-mentioned risks will be continuously monitored until the risks are no longer triggered, and then the system will gradually return to the normal control mode. This ensures that the instrument always operates safely and stably while maintaining the control effect.

[0124] The temperature adjustment execution terminal generates a stable temperature field control signal based on a compensation control strategy and a power adjustment instruction set, and drives the actuator to operate.

[0125] In practice, the temperature adjustment execution terminal receives a set of power adjustment commands and compensation control strategies from the control command generation module. Subsequently, the signal processing unit within the terminal converts the digital power commands for each independent heating zone into corresponding pulse-width modulation (PWM) signals using a preset modulation algorithm. These signals are then amplified by the drive circuit and transmitted to the actuators located in each independent heating zone. Based on the received control signals, the actuators adjust their output power, thereby achieving dynamic and targeted control of the reaction disk's temperature field, ultimately achieving the goal of a stable temperature field.

[0126] The above content is merely an example and illustration of the concept of the present invention. Those skilled in the art can make various modifications or additions to the specific embodiments described, or use similar methods to replace them, as long as they do not deviate from the concept of the invention or exceed the scope defined by the present invention, and all such modifications and additions should fall within the protection scope of the present invention.

Claims

1. A fully automated chemiluminescence analyzer control system for blood sample testing, characterized in that, The system includes: The temperature data acquisition module collects temperature readings from multiple discrete monitoring points on the reaction plate in real time, as well as the timestamps corresponding to each reading. The reaction temperature analysis module preprocesses the temperature readings, outputs a standardized temperature field distribution dataset, performs regional temperature difference calculation and thermal inertia trend analysis, and outputs the reaction temperature deviation and thermal inertia imbalance region. The control command generation module integrates the reaction temperature deviation with the spatial coordinate boundary of the thermal inertia imbalance area, outputs a set of power adjustment commands for different independent heating zones of the reaction disk, and performs risk assessment based on preset temperature stability judgment rules, triggering compensation control strategies when risks exist. The temperature stability determination rules include: Calculate the power adjustment difference between adjacent independent heating zones, the sum of squares of the power adjustment of all heating zones, and the ratio of the power adjustment amplitude of the independent heating zone corresponding to the thermal inertia imbalance area to the thermal inertia influence factor, as each stability evaluation index. It also obtains the current power command set. When a certain stability evaluation index exceeds its preset threshold or when the power adjustment direction of any heating zone is repeatedly reversed in a continuous cycle and the reversal frequency exceeds the preset oscillation frequency threshold, it is determined that there is a risk. When the absolute value of the difference in power adjustment between any two adjacent heating zones exceeds the gradient safety threshold set based on the system's thermodynamic characteristics, a risk of thermal stress concentration is identified; when the sum of the squares exceeds the stability boundary threshold set based on the system's heat capacity, an overall stability risk is identified; when the ratio of the sum to the thermal inertia influence factor exceeds the matching threshold based on thermal shock effect analysis, a thermal shock risk is identified; when the power adjustment direction of any heating zone is detected to repeatedly reverse within a continuous period, and the reversal frequency exceeds the oscillation frequency threshold based on control system stability analysis, a control oscillation risk is identified. The temperature adjustment actuator generates a stable temperature field control signal based on the compensation control strategy and power adjustment instruction set, and drives the actuator to move.

2. The fully automated chemiluminescence analyzer control system for blood sample testing as described in claim 1, characterized in that: The process of acquiring the temperature data of the reaction disk includes: Based on the structure of the reaction disk, heat and cold sources are identified, heat transfer paths are analyzed, and a geometric model of the reaction disk is established. Mark the active temperature zone, edge effect zone, and core reaction zone on the model; Matrix-style deployment of points is carried out in the core reaction zone, ring-density deployment of points is carried out in the edge effect zone, and points are deployed at the center and boundary of the active temperature zone. Temperature readings are synchronously acquired using distributed temperature sensors at a fixed sampling frequency, and a timestamp is generated at the start point of each acquisition.

3. The fully automated chemiluminescence analyzer control system for blood sample testing as described in claim 1, characterized in that: The specific process of the preprocessing includes: Outlier removal and timestamp alignment are performed on temperature readings, and the discrete monitoring points corresponding to the remaining temperature readings after removal are recorded as valid monitoring points. With the center of rotation of the reaction disk as the center, the circular area covering the distribution range of all reaction cup holes is taken as the effective area, and a two-dimensional coordinate system of the reaction disk is established. The effective area is divided into grids according to the preset grid size; Based on the coordinates and temperature readings of all valid monitoring points, the temperature values ​​of the grid nodes are calculated using an inverse distance weighted interpolation algorithm to generate an absolute temperature field matrix. At the same time, the temperature values ​​are converted into deviation values ​​relative to a preset reference temperature to generate a temperature deviation field matrix. The absolute temperature field matrix, temperature deviation field matrix, and timestamps are packaged into a standardized dataset for output.

4. The fully automated chemiluminescence analyzer control system for blood sample testing as described in claim 3, characterized in that: The process of calculating the regional temperature difference includes: Extract the temperature field matrix from the standardized dataset; Scan the temperature values ​​of the grid nodes, identify the instantaneous highest and lowest temperatures and their coordinates, and calculate the maximum transient temperature difference; Based on the reaction kinetics requirements of blood test reagents, the allowable temperature difference threshold and deviation safety tolerance threshold of the core reaction zone are retrieved, and the baseline reaction temperature deviation is calculated by combining the two. Based on the preset temperature zone boundary coordinates, the temperature deviation value of the corresponding grid node of each temperature zone is extracted, and the average temperature deviation value of each temperature zone is calculated. Based on this, the compensation temperature deviation degree is set. The sum of the compensated temperature deviation and the baseline reaction temperature deviation is taken as the final reaction temperature deviation.

5. The fully automated chemiluminescence analyzer control system for blood sample testing as described in claim 4, characterized in that: The calculated baseline reaction temperature deviation includes: During the period from sample addition to the end of incubation, the maximum transient temperature difference, instantaneous highest temperature, and instantaneous lowest temperature are monitored in real time. The total number of sampling periods and the maximum number of continuous sampling periods exceeding the allowable temperature difference threshold are counted, and the ratios of these two values ​​to the total number of sampling periods are calculated to obtain the transient total exceedance sampling period ratio and the transient continuous exceedance sampling period ratio. When the difference between the instantaneous highest temperature and the reference temperature exceeds the deviation safety tolerance threshold, or when the difference between the reference temperature and the instantaneous lowest temperature exceeds the deviation safety tolerance threshold, a temperature anomaly is marked, and the instantaneous high temperature exceedance depth or instantaneous low temperature exceedance depth is calculated. After the reaction phase is completed, the total number of marked anomalies corresponding to the entire phase is counted, and the ratio of the anomalies to the total number of samplings is calculated to obtain the absolute temperature exceedance rate. The instantaneous high temperature exceedance depth and instantaneous low temperature exceedance depth of all sampling periods are summed to obtain the total cumulative high temperature exceedance and total cumulative low temperature exceedance. The average exceedance depth is calculated and normalized to obtain the average temperature exceedance. The transient total over-limit sampling period ratio, transient continuous over-limit sampling period ratio, absolute temperature over-limit rate, and average temperature over-limit are linearly weighted and summed to output the baseline reaction temperature deviation.

6. The fully automated chemiluminescence analyzer control system for blood sample testing as described in claim 4, characterized in that: The setting of the compensation temperature deviation includes: Calculate the absolute temperature difference between the active temperature zone and the core reaction zone, the core reaction zone and the edge effect zone, and the active temperature zone and the edge effect zone, corresponding to the average temperature deviation values. The absolute temperature difference is compared with the allowable temperature difference threshold to obtain the active-core contribution, core-edge contribution, and active-edge contribution. The weight of each temperature zone is calculated based on the contribution, and the average temperature deviation of each temperature zone is weighted and summed to obtain the comprehensive temperature deviation. The overall temperature deviation is standardized by using a deviation safety tolerance threshold, based on the activation energy of the blood test reagent corresponding to the current test item and... Temperature coefficient: Query the pre-stored reagent property database to obtain the corresponding preset correction coefficient; The product of the correction factor and the standardized result is used as the compensation temperature deviation.

7. The fully automated chemiluminescence analyzer control system for blood sample testing as described in claim 1, characterized in that: The output of the thermal inertia imbalance region includes: When the reaction plate undergoes a temperature step change, temperature data at each monitoring point is continuously collected at a preset sampling frequency, and a temperature time series is output. The temperature time series of each monitoring point is fitted using the least squares method to obtain the thermal time constant. Calculate the heating rate and cooling rate at each monitoring point, and statistically determine the response time required to reach the preset boundary temperature as the thermal response rate. The thermal time constant distribution map and the thermal response rate distribution map are generated using a spatial interpolation algorithm; Calculate the average and standard deviation of the thermal time constant of the entire reaction disk, and identify the region where the thermal time constant deviates from at least one standard deviation as the primary thermal inertia imbalance region; Regions with thermal response rates at least 1.5 standard deviations below the average thermal response rate are selected from the primary imbalance regions as regions of severe thermal inertia imbalance. Spatial clustering analysis is performed on severely imbalanced regions to generate and output the spatial coordinate boundaries of the thermal inertia imbalance regions.

8. The fully automated chemiluminescence analyzer control system for blood sample testing as described in claim 7, characterized in that: The fusion analysis includes: Record the geometric boundary coordinates of the independent heating zone; Based on the spatial coordinate boundary of the thermal inertia imbalance region, a spatial mapping relationship with the independent heating zone is established, and the thermal inertia influence factor of each independent heating zone is calculated. The proportion of the thermal inertia influence factor to the sum of the thermal inertia influence factors of all independent heating zones is used as the influence weight; The reaction temperature deviation is allocated to each independent heating zone according to its influence weight. The power adjustment amount is matched based on the reaction temperature deviation of each zone. At the same time, the reference power adjustment amount is matched for independent heating zones in non-thermal inertia imbalance areas, and the set of output power adjustment commands is integrated.

9. The fully automated chemiluminescence analyzer control system for blood sample testing as described in claim 8, characterized in that: The calculation of the thermal inertia influence factor includes: Identify the percentage of overlapping area between each independent heating zone and the thermal inertia imbalance zone, as well as the shortest distance from the center of each independent heating zone to the boundary of the nearest thermal inertia imbalance zone. Calculate the distribution influence factor based on the shortest distance; Calculate the relative deviation and coefficient of variation of the thermal time constant and thermal response rate for each independent heating zone, perform minimum-maximum normalization, and obtain the overall imbalance by linear weighted summation; The thermal inertia influence factor is calculated based on the overlap area ratio, distribution influence factor, and comprehensive imbalance degree.

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