A full-automatic coagulation instrument reagent needle liquid level detection method

Abstract

The application discloses a kind of full-automatic coagulation instrument reagent needle liquid level detection methods, comprising the following steps: S1: send ultrasonic pulse signal measurement liquid level change;S2: application intelligent algorithm dynamic compensation error;S3: fusion multi-sensor data optimization liquid level calculation;S4: monitor liquid level and trigger abnormal alarm;S5: realize adaptive learning and optimize system performance;S6: store data and carry out remote monitoring.The application is measured by error model dynamic compensation, ultrasonic wave propagation characteristics and multi-sensor data fusion and intelligent algorithm optimization, significantly improve the accuracy of liquid level measurement, enhance environmental adaptability, reduce error.

Description

Technical Field

[0001] This invention relates to the field of liquid level detection technology, and in particular to a method for detecting the liquid level of a reagent needle in a fully automated coagulation analyzer. Background Technology

[0002] In fully automated coagulation analyzers, reagent needle level detection is a crucial step in ensuring the accuracy and stability of coagulation tests. Coagulation analyzers, as essential equipment in clinical testing, are widely used in the diagnosis and treatment of thrombosis and hemostasis-related diseases. However, in practical applications, reagent needle level detection faces numerous challenges.

[0003] Traditional liquid level detection methods often employ simple mechanical or photoelectric sensors, which may exhibit good performance under standard conditions. However, their measurement accuracy and stability are often severely affected when encountering high-viscosity liquids, liquids containing air bubbles, or complex operating conditions. Therefore, a fully automated method for detecting liquid level in the reagent needle of a coagulation analyzer is proposed. Summary of the Invention

[0004] The purpose of this section is to outline some aspects of embodiments of the present invention and to briefly describe some preferred embodiments. Simplifications or omissions may be made in this section, as well as in the abstract and title of this application, to avoid obscuring the purpose of these documents; however, such simplifications or omissions should not be construed as limiting the scope of the invention.

[0005] To solve the above-mentioned technical problems, the present invention provides the following technical solution: a method for detecting the liquid level of a reagent needle in a fully automated coagulation analyzer, comprising the following steps:

[0006] S1: Sends ultrasonic pulse signals to measure liquid level changes;

[0007] S2: Apply intelligent algorithms to dynamically compensate for errors;

[0008] The application of intelligent algorithms to dynamically compensate for errors includes the following steps:

[0009] S21: Real-time collection of surrounding environmental factors and relevant properties of the liquid by installing additional sensors around the fully automated coagulation analyzer or inside the liquid container;

[0010] S22: Using the real-time sensor data collected in S21 and combined with the equipment's historical data, an error model between sound wave propagation delay and liquid physical properties is established. This model can predict and correct liquid level errors according to different environmental conditions.

[0011] S23: Using the real-time sensor data from S21 and the error model established in S22 as input, a machine learning algorithm is used to dynamically compensate for the acoustic delay data of each measurement.

[0012] S24: Each time a liquid level is detected, the algorithm will correct the sensor data and output the corrected liquid level data. The correction result will be compared with the theoretical value to further adjust the model and continuously improve the measurement accuracy.

[0013] S3: Integrates multi-sensor data to optimize liquid level calculation;

[0014] S4: Monitor liquid level and trigger abnormal alarm;

[0015] S5: Enables adaptive learning and optimizes system performance;

[0016] S6: Store data and perform remote monitoring.

[0017] As a preferred embodiment of the fully automated coagulation analyzer reagent needle liquid level detection method of the present invention, wherein: the step S1 of sending an ultrasonic pulse signal to measure the liquid level change includes the following steps:

[0018] S11: Install an ultrasonic sensor above or on the side of the reagent needle of the fully automated coagulation analyzer;

[0019] S12: The ultrasonic sensor sends ultrasonic pulse signals to the surface of the liquid or inside the liquid. The ultrasonic pulse signals propagate in the liquid and are reflected back to the ultrasonic sensor receiver.

[0020] S13: The ultrasonic sensor receives the reflected sound waves and records the time delay from the transmission to the reception of the sound waves.

[0021] S14: Calculate the current liquid level based on the time delay of sound wave propagation and the known physical properties of the liquid.

[0022] As a preferred embodiment of the fully automated coagulation analyzer reagent needle liquid level detection method of the present invention, the step of establishing an error model between sound wave propagation delay and liquid physical properties includes the following steps:

[0023] S221: Collect experimental data:

[0024] Prepare a variety of typical liquid samples, covering different ranges of viscosity, density and sound wave propagation characteristics;

[0025] Different environmental conditions are simulated by adjusting temperature, humidity, and air pressure;

[0026] Using a high-precision time delay measurement sensor, the time delay of sound waves propagating in a liquid is recorded, while the physical parameters of the liquid are collected.

[0027] S222: Constructing a multivariate error model:

[0028] Identify the main physical properties affecting the time delay of sound wave propagation, including liquid viscosity ( ),density( ),temperature( ) and speed of sound ( Additional environmental factors to consider include air pressure ( ) and humidity ( );

[0029] Based on experimental data, the sound wave propagation time delay was established using the multiple linear regression method. The relationship model between liquid properties and liquid properties: ,in, It is a multivariate function, and the specific relationship can be solved through regression analysis. In regression analysis, the following model form is used: ,in , , , , , , It is the regression coefficient. This is the error term, representing the deviation caused by measurement errors or unconsidered factors;

[0030] S223: Error Model Validation and Adjustment:

[0031] The established error model is validated using a test dataset to evaluate its accuracy and robustness; if the error is large, the regression coefficients are adjusted.

[0032] S224: Application of error compensation algorithm;

[0033] Once the error model is established and successfully verified, the error compensation algorithm in liquid level detection is dynamically adjusted by combining real-time liquid physical properties and environmental data.

[0034] The real-time collected environmental data and the physical properties of the liquid will be used as model input to calculate the current sound wave propagation time delay error and adjust the liquid level measurement results accordingly.

[0035] S225: Dynamically update the error model:

[0036] As the equipment accumulates data during operation, the error model is dynamically updated through online learning or adaptive learning to ensure that it maintains high accuracy over long-term use.

[0037] As a preferred embodiment of the fully automated coagulation analyzer reagent needle liquid level detection method of the present invention, the environmental data includes temperature, air pressure and humidity, and the physical properties of the liquid include viscosity, density and sound velocity.

[0038] In a preferred embodiment of the fully automated coagulation analyzer reagent needle level detection method of the present invention, the mathematical expression of the error model is: ,in:

[0039] The acoustic delay for liquid level measurement, measured in seconds, represents the time it takes for the ultrasonic wave to travel from emission to reception. This value can be used to determine the liquid level.

[0040] The theoretical sound wave delay, measured in seconds, is a theoretical delay calculated based on an ideal environment and liquid properties. It is used as a reference value for comparison with actual measured values.

[0041] Liquid density, measured in kilograms per cubic meter, represents the mass of liquid per unit volume. Liquid density directly affects the speed of sound wave propagation, and therefore also affects time delay.

[0042] The viscosity of the liquid is expressed in Pascal-second (Pa·s). It represents the internal friction force of the liquid flow. The higher the viscosity, the greater the flow resistance of the liquid, which will affect the propagation speed of sound waves and thus affect the time delay.

[0043] The speed of sound in a liquid is measured in meters per second. The speed of sound in a liquid determines the time it takes for sound waves to travel and directly affects the sound wave delay.

[0044] The ambient temperature is measured in degrees Celsius. Temperature changes affect the physical properties of liquids, such as viscosity, density, and the speed of sound in the liquid, thus affecting the time delay of sound wave propagation.

[0045] The ambient air pressure, measured in Pascals, represents the pressure of the surrounding air. Changes in air pressure affect the speed of sound propagation, and thus the time delay of sound waves.

[0046] The ambient humidity is expressed as a percentage, representing the water vapor content in the air. Humidity also affects the speed of sound wave propagation, and thus the sound wave delay.

[0047] , These two constant factors are used to adjust the influence weights of liquid physical properties and environmental factors on the sound wave propagation delay. They are used to control the degree of contribution of different environmental factors to the sound wave delay.

[0048] , , , , All of these are power exponents. These exponent parameters are used to describe the nonlinear effects of the physical properties of the liquid and environmental factors on the propagation delay of sound waves. They determine the degree of influence of these factors on the delay.

[0049] It is a power function used to describe the effect of liquid density on the time delay of sound wave propagation; the power exponent is... Obtained through experiments or theoretical modeling, used to fit the actual relationship between the effect of liquid density change on time delay;

[0050] Describe the relationship between liquid viscosity and sound wave propagation time delay;

[0051] Describe the effect of sound velocity in a liquid on the time delay of sound wave propagation;

[0052] This is a temperature correction factor, which is used to adjust the effect of temperature changes on the sound wave delay.

[0053] This is the environmental attenuation factor, which describes the attenuation effect of changes in ambient temperature on the propagation delay of sound waves.

[0054] This is an error compensation factor, which is adjusted in real time using a machine learning algorithm to compensate for level measurement errors caused by environmental changes and changes in liquid properties. Its value range is... , indicating the intensity of the dynamic compensation effect, when This indicates that there is no error compensation. This indicates full compensation;

[0055] The function is a correction function for the effect of temperature on time delay. As the temperature increases, the liquid sound velocity increases and the time delay decreases. The exponential decay characteristic of this function makes the effect of temperature exhibit a nonlinear change.

[0056] The integral expression represents the relationship with ambient temperature. Integrating means taking into account the cumulative effects of long-term temperature changes. Through integration, the formula can effectively handle the cumulative effect of different temperature changes on the propagation delay of sound waves.

[0057] Used to describe the effect of environmental pressure on sound wave propagation;

[0058] Describe the effect of ambient humidity on the propagation delay of sound waves.

[0059] As a preferred embodiment of the fully automated coagulation analyzer reagent needle level detection method of the present invention, wherein:

[0060] In the mathematical expression of the error model, the density of the liquid... Viscosity and speed of sound With ambient temperature There is a high degree of dynamic coupling; when the temperature changes, these parameters will exhibit nonlinear changes and directly affect the sound wave propagation delay. By combining the density, viscosity, and sound velocity compensation terms in the formula with the temperature correction factor... The linkage effect can adjust the model's adaptability to temperature fluctuations in real time;

[0061] In the mathematical expression of the error model, the ambient air pressure and ambient humidity The effect on the speed of sound propagation has a significant synergistic effect, which is achieved through the compensation term in the formula. To describe its complex interaction, which can dynamically adjust the sensitivity of the propagation model under high humidity or low pressure conditions;

[0062] In the mathematical expression of the error model, the error compensation factor... With ambient temperature and the density of the liquid Viscosity and speed of sound There are complex dynamic interactions between them, where the formulas are... In conjunction with the temperature compensation term, the error compensation factor is dynamically adjusted to reduce the impact of changes in environmental and liquid properties on sound wave propagation delay when ambient temperature or liquid parameters fluctuate. The impact.

[0063] As a preferred embodiment of the fully automated coagulation analyzer reagent needle liquid level detection method of the present invention, wherein: the liquid level calculation optimization by fusing multi-sensor data in step S3 includes the following steps:

[0064] S31: During the liquid level measurement process, data from different types of sensors are fused together. Different types of sensors provide information from different dimensions, which helps to verify the accuracy of the liquid level measurement results from multiple perspectives.

[0065] S32: Based on multi-source data fusion, an algorithm is used to optimize the calculation of liquid level. By integrating the outputs of multiple sensors, the influence of single sensor error is reduced, ensuring the stability and accuracy of liquid level measurement.

[0066] S33: The final liquid level value is obtained by fusing the acoustic wave propagation measurement value and the data after dynamic compensation by intelligent algorithm.

[0067] As a preferred embodiment of the fully automated coagulation analyzer reagent needle liquid level detection method of the present invention, wherein: the step S4 of monitoring the liquid level and triggering an abnormal alarm includes the following steps:

[0068] S41: The liquid level detection system of the fully automated coagulation analyzer continuously monitors changes in liquid level, and ensures that the liquid level is always within the normal range by periodically collecting and analyzing liquid level data;

[0069] S42: The liquid level detection system is installed inside the fully automatic coagulation analyzer to track the liquid level status in real time. If the liquid level is detected to be outside the preset range, the liquid level detection system will notify the operator through an alarm mechanism and display the cause of the abnormality on the display interface to help the operator find the problem in time and take corresponding measures.

[0070] As a preferred embodiment of the fully automated coagulation analyzer reagent needle level detection method of the present invention, wherein: the adaptive learning and system performance optimization in step S5 includes the following steps:

[0071] S51: The liquid level detection system uses intelligent algorithms to make real-time adjustments and optimizations based on data accumulated over a long period of operation;

[0072] S52: As the equipment is used for a longer period of time, the liquid level detection system will automatically optimize the error model based on the gradually accumulated error data, using regression analysis algorithms or genetic algorithms, and continuously improve its adaptability to changes in the environment and liquid properties.

[0073] As a preferred embodiment of the fully automated coagulation analyzer reagent needle level detection method of the present invention, wherein: the data storage and remote monitoring in step S6 includes the following steps:

[0074] S61: All liquid level detection data, compensation processes, and anomaly records are stored in the liquid level detection system, providing detailed data support for subsequent analysis and maintenance;

[0075] S62: Through the cloud platform or local network, users can view the liquid level change trend in real time and remotely obtain equipment status, alarm information and real-time liquid level detection data, which facilitates operation and maintenance.

[0076] The beneficial effects of this invention are:

[0077] 1. The dynamic compensation algorithm in this invention establishes an error model and automatically adjusts the detection results for each test, which can effectively reduce the error caused by changes in the environment or liquid properties, thereby significantly improving the accuracy of liquid level measurement and making it suitable for various complex working conditions.

[0078] 2. This invention utilizes the propagation characteristics of ultrasound to achieve accurate liquid level detection by precisely measuring the time delay or frequency change of sound waves and combining this with the physical properties of the liquid, such as density, viscosity, and temperature. The ultrasonic propagation measurement method has strong environmental adaptability, maintaining high stability even in high-viscosity, complex media, or liquid environments with bubble interference. It also offers high measurement resolution and effectively reduces the impact of liquid surface fluctuations on liquid level measurement.

[0079] 3. This invention integrates data from different dimensions, such as ultrasonic sensors and capacitive sensors, and combines them with algorithms such as Kalman filtering and weighted averaging to optimize liquid level calculation, reducing the limitations of single-sensor measurements. Simultaneously, the combination of acoustic wave propagation measurement and intelligent compensation algorithms dynamically optimizes the liquid level calculation process, further improving the accuracy of liquid level measurement and error compensation capabilities. Attached Figure Description

[0080] To more clearly illustrate the technical solutions of the embodiments of the present invention, the 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. Wherein:

[0081] Figure 1 This is a flowchart of a fully automated coagulation analyzer reagent needle level detection method according to the present invention. Detailed Implementation

[0082] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0083] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0084] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that is mutually exclusive with other embodiments.

[0085] Secondly, the present invention is described in detail with reference to the schematic diagrams. When detailing the embodiments of the present invention, for ease of explanation, the cross-sectional views illustrating the device structure may be partially enlarged, not according to the usual scale. Furthermore, the schematic diagrams are merely examples and should not limit the scope of protection of the present invention. In addition, actual fabrication should include three-dimensional spatial dimensions of length, width, and depth. Example 1

[0086] Reference Figure 1 The first embodiment of the present invention provides a method for detecting the liquid level of a reagent needle in a fully automated coagulation analyzer, comprising the following steps:

[0087] S1: Send ultrasonic pulse signal to measure liquid level change

[0088] S11: Install an ultrasonic sensor above or on the side of the reagent needle of the fully automated coagulation analyzer to ensure that the sensor can accurately detect changes in the surface of the liquid inside the reagent needle.

[0089] S12: The ultrasonic sensor sends ultrasonic pulse signals to the surface or interior of the liquid. The ultrasonic pulse signals propagate in the liquid and are reflected back to the ultrasonic sensor receiver.

[0090] S13: The ultrasonic sensor receives the reflected sound waves and records the time delay from transmission to reception. This time delay is closely related to the physical properties of the liquid, such as density, viscosity, and temperature, as well as the liquid level.

[0091] S14: Calculate the current liquid level based on the time delay of sound wave propagation and the known physical properties of the liquid (such as dielectric constant, density, etc.). This calculation can be initially estimated using known sound wave propagation formulas.

[0092] S2: Applying intelligent algorithms to dynamically compensate for errors

[0093] S21: By installing additional sensors (such as temperature sensors, humidity sensors, etc.) around the fully automated coagulation analyzer or inside the liquid container, the surrounding environmental factors and relevant properties of the liquid (such as liquid temperature, viscosity, density, etc.) are collected in real time; these real-time data will serve as input variables for measurement errors, providing a basis for subsequent error model establishment and dynamic compensation.

[0094] S22: Using the real-time sensor data collected in S21 and combined with the historical data of the equipment, an error model between the sound wave propagation delay and the physical properties of the liquid is established. This model can predict and correct the liquid level error according to different environmental conditions (such as temperature changes, humidity changes, liquid type changes, etc.).

[0095] S23: Using the real-time sensor data from S21 and the error model established in S22 as input, machine learning algorithms (such as adaptive neural networks, regression analysis, or Kalman filters) are used to dynamically compensate for the acoustic delay data of each measurement. By inputting real-time sensor data (such as temperature and humidity) and historical measurement data, the algorithm automatically adjusts the liquid level measurement value to eliminate errors caused by environmental changes or changes in liquid properties.

[0096] S24: Each time a liquid level is detected, the algorithm will correct the sensor data and output the corrected liquid level data. The correction result will be compared with the theoretical value to further adjust the model and continuously improve the measurement accuracy.

[0097] S3: Optimize liquid level calculation by fusing multi-sensor data

[0098] S31: During the liquid level measurement process, data from different types of sensors are fused together, such as the time delay data from ultrasonic sensors, the electrical signals from capacitive sensors, and the current signals. Different types of sensors provide information in different dimensions, which helps to verify the accuracy of the liquid level measurement results from multiple perspectives.

[0099] S32: Based on multi-source data fusion, it uses algorithms such as weighted averaging and Kalman filtering to optimize the liquid level calculation. By integrating the outputs of multiple sensors, it reduces the impact of errors from a single sensor, ensuring the stability and accuracy of liquid level measurement.

[0100] S33: The final liquid level value is obtained by fusing the acoustic wave propagation measurement value with the data after dynamic compensation by intelligent algorithm, which has higher accuracy and smaller error.

[0101] S4: Monitor liquid level and trigger an alarm for abnormalities.

[0102] S41: The fully automated coagulation analyzer's liquid level detection system continuously monitors liquid level changes, and ensures that the liquid level is always within the normal range by periodically collecting and analyzing liquid level data.

[0103] S42: The liquid level detection system is installed inside the fully automatic coagulation analyzer to track the liquid level status in real time. If the liquid level is detected to be outside the preset range (such as too low or too high), the liquid level detection system will notify the operator through an alarm mechanism (sound, visual prompts, etc.) and display the cause of the abnormality on the display interface to help the operator find the problem in time and take corresponding measures.

[0104] S5: Enables adaptive learning and optimizes system performance

[0105] S51: The liquid level detection system uses intelligent algorithms (such as Adaptive Neural Networks (ANN) or Kalman filtering) to perform real-time adjustments and optimizations based on data accumulated over long-term operation. For example, these algorithms dynamically adjust error compensation parameters based on historical measurement data and real-time sensor input, especially optimizing liquid level detection accuracy for different liquid types and environmental changes (such as temperature, humidity, and air pressure). Through gradual learning and correction, the system can progressively improve its detection accuracy under different operating conditions.

[0106] S52: As the equipment is used over time, the liquid level detection system automatically optimizes the error model based on gradually accumulated error data, using regression analysis or genetic algorithms to continuously improve its adaptability to environmental and liquid property changes. Through pattern recognition of historical data, the system can intelligently update the compensation model to adapt to the impact of changes in liquid properties (such as viscosity and density) and external environmental fluctuations (such as temperature fluctuations) on the liquid level detection results. The algorithm update process is seamless and requires no human intervention, ensuring the system is always in optimal operating condition.

[0107] S6: Store data and perform remote monitoring

[0108] S61: All liquid level detection data, compensation processes, and anomaly records are stored in the liquid level detection system, providing detailed data support for subsequent analysis and maintenance.

[0109] S62: Through the cloud platform or local network, users can view the liquid level change trend in real time and remotely obtain equipment status, alarm information and real-time liquid level detection data, which facilitates operation and maintenance.

[0110] Specifically, establishing an error model between sound wave propagation delay and liquid physical properties includes the following steps:

[0111] S221: Collect experimental data

[0112] Prepare a variety of typical liquid samples (such as physiological saline, serum, buffer, etc.) covering different viscosities, densities and sound wave propagation characteristics;

[0113] Different environmental conditions are simulated by adjusting temperature, humidity, and air pressure;

[0114] Using a high-precision time delay measurement sensor, the time delay of sound waves propagating in a liquid is recorded, while the physical parameters of the liquid, such as temperature, viscosity, density, and refractive index, are collected.

[0115] S222: Constructing a multivariate error model

[0116] Identify the main physical properties affecting the time delay of sound wave propagation, including liquid viscosity ( ),density( ),temperature( ) and speed of sound ( Additional environmental factors to consider include air pressure ( ) and humidity ( );

[0117] Based on experimental data, the sound wave propagation time delay was established using the multiple linear regression method. The relationship model between liquid properties and liquid properties: ,in, It is a multivariate function, and the specific relationship can be solved through regression analysis. In regression analysis, the following model form is used: ,in , , , , , , It is the regression coefficient. This is the error term, representing the deviation caused by measurement errors or unconsidered factors;

[0118] S223: Error Model Validation and Adjustment

[0119] The established error model is validated using a test dataset to evaluate its accuracy and robustness; if the error is large, the regression coefficients are adjusted.

[0120] S224: Application of Error Compensation Algorithm

[0121] Once the error model is established and successfully verified, the error compensation algorithm in liquid level detection is dynamically adjusted by combining real-time liquid physical properties and environmental data.

[0122] Real-time collected environmental data (such as temperature, air pressure, humidity) and the physical properties of the liquid (such as viscosity, density, sound velocity, etc.) will be used as model input to calculate the current sound wave propagation time delay error and adjust the liquid level measurement results accordingly.

[0123] S225: Dynamically update the error model

[0124] As the equipment accumulates data during operation, the error model is dynamically updated through online learning or adaptive learning to ensure that it maintains high accuracy over long-term use.

[0125] Through the above steps, a multivariate error model was established that considers the influence of liquid physical properties (viscosity, density, temperature, sound velocity) and environmental factors (air pressure, humidity) on the sound wave propagation delay. This model can be used for regression analysis with experimental data, and modeled and optimized using machine learning algorithms, ultimately achieving real-time error compensation and accurate liquid level measurement. Through dynamic updates and adaptive adjustments, the model can adapt to changes in different liquid types and environmental conditions, ensuring the stable operation of the fully automated coagulation analyzer and high-precision liquid level detection.

[0126] Specifically, the mathematical expression for the error model is: ,in:

[0127] The acoustic delay for liquid level measurement, measured in seconds, represents the time it takes for the ultrasonic wave to travel from emission to reception. This value can be used to determine the liquid level.

[0128] The theoretical sound wave delay, measured in seconds, is a theoretical delay calculated based on an ideal environment and liquid properties. It is used as a reference value for comparison with actual measured values.

[0129] Liquid density, measured in kilograms per cubic meter, represents the mass of liquid per unit volume. Liquid density directly affects the speed of sound wave propagation, and therefore also affects time delay.

[0130] The viscosity of the liquid is expressed in Pascal-second (Pa·s). It represents the internal friction force of the liquid flow. The higher the viscosity, the greater the flow resistance of the liquid, which will affect the propagation speed of sound waves and thus affect the time delay.

[0131] The speed of sound in a liquid is measured in meters per second. The speed of sound in a liquid determines the time it takes for sound waves to travel and directly affects the sound wave delay.

[0132] The ambient temperature is measured in degrees Celsius. Temperature changes affect the physical properties of liquids, such as viscosity, density, and the speed of sound in the liquid, thus affecting the time delay of sound wave propagation.

[0133] The ambient air pressure, measured in Pascals, represents the pressure of the surrounding air. Changes in air pressure affect the speed of sound propagation, and thus the time delay of sound waves.

[0134] The ambient humidity is expressed as a percentage, representing the water vapor content in the air. Humidity also affects the speed of sound wave propagation, and thus the sound wave delay.

[0135] , These two constant factors are used to adjust the influence weights of liquid physical properties and environmental factors on the sound wave propagation delay. They are used to control the degree of contribution of different environmental factors to the sound wave delay.

[0136] , , , , All of these are power exponents. These exponent parameters are used to describe the nonlinear effects of the physical properties of the liquid and environmental factors on the propagation delay of sound waves. They determine the degree of influence of these factors on the delay.

[0137] It is a power function used to describe the effect of liquid density on the time delay of sound wave propagation; the power exponent is... Obtained through experiments or theoretical modeling, used to fit the actual relationship between the effect of liquid density change on time delay;

[0138] Describe the relationship between liquid viscosity and sound wave propagation time delay;

[0139] Describe the effect of sound velocity in a liquid on the time delay of sound wave propagation;

[0140] This is a temperature correction factor, which is used to adjust the effect of temperature changes on the sound wave delay.

[0141] This is the environmental attenuation factor, which describes the attenuation effect of changes in ambient temperature on the propagation delay of sound waves.

[0142] This is an error compensation factor, which is adjusted in real time using a machine learning algorithm to compensate for level measurement errors caused by environmental changes and changes in liquid properties. Its value range is... , indicating the intensity of the dynamic compensation effect, when This indicates that there is no error compensation. This indicates full compensation;

[0143] The function is a correction function for the effect of temperature on time delay. As the temperature increases, the liquid sound velocity increases and the time delay decreases. The exponential decay characteristic of this function makes the effect of temperature exhibit a nonlinear change.

[0144] The integral expression represents the relationship with ambient temperature. Integrating means taking into account the cumulative effects of long-term temperature changes. Through integration, the formula can effectively handle the cumulative effect of different temperature changes on the propagation delay of sound waves.

[0145] Used to describe the effect of environmental pressure on sound wave propagation;

[0146] Describe the effect of ambient humidity on the propagation delay of sound waves.

[0147] Through the definitions of these characters and symbols, the entire formula describes how the propagation delay of sound waves changes under the influence of different liquid physical properties and environmental factors, and dynamically compensates for this change using a machine learning model. The formula integrates liquid properties, environmental factors, and dynamic compensation algorithms, providing strong mathematical support for achieving accurate liquid level measurement.

[0148] Additionally, in the above formula, the density of the liquid... Viscosity and speed of sound With ambient temperature There is a high degree of dynamic coupling; when the temperature changes, these parameters will exhibit nonlinear changes and directly affect the sound wave propagation delay. By combining the density, viscosity, and sound velocity compensation terms in the formula with the temperature correction factor... The linkage effect can adjust the model's adaptability to temperature fluctuations in real time, ensuring that the measurement accuracy for different liquid types remains consistent under various temperature conditions.

[0149] In the above formula, ambient air pressure and ambient humidity The effect on the speed of sound propagation has a significant synergistic effect, which is achieved through the compensation term in the formula. This describes their complex interaction, which can dynamically adjust the sensitivity of the propagation model under high humidity or low air pressure conditions, thereby effectively reducing the interference of harsh environments on measurement accuracy and enabling the equipment to provide reliable data support even under extreme conditions.

[0150] In the above formula, the error compensation factor With ambient temperature and the density of the liquid Viscosity and speed of sound There are complex dynamic interactions between them, where the formulas are... In conjunction with the temperature compensation term, the error compensation factor is dynamically adjusted to reduce the impact of changes in environmental and liquid properties on sound wave propagation delay when ambient temperature or liquid parameters fluctuate. This reduces the impact of changes in the model, thereby improving the measurement accuracy and robustness of the model under varying conditions.

[0151] In summary, the dynamic compensation algorithm in this invention establishes an error model and automatically adjusts the results of each detection, effectively reducing errors caused by environmental changes or changes in liquid properties, thereby significantly improving the accuracy of liquid level measurement and making it suitable for various complex working conditions. This invention utilizes the propagation characteristics of ultrasound, accurately measuring sound wave delay or frequency changes and combining this with the physical properties of the liquid such as density, viscosity, and temperature to achieve precise liquid level detection. The ultrasonic propagation measurement method has strong environmental adaptability, maintaining high stability even in high-viscosity, complex media, or liquid environments with bubble interference, effectively reducing the impact of liquid surface fluctuations on liquid level measurement. This invention integrates data from different dimensions, such as ultrasonic sensors and capacitive sensors, and combines algorithms such as Kalman filtering and weighted averaging to optimize liquid level calculation, reducing the limitations of single-sensor measurement. Simultaneously, the combination of acoustic wave propagation measurement and intelligent compensation algorithms dynamically optimizes the liquid level calculation process, further improving the accuracy and error compensation capability of liquid level measurement. Example 2

[0152] Referring to Table 1, which is the second embodiment of the present invention, this embodiment differs from the first embodiment in that, in order to verify its beneficial effects, it provides operational data and related descriptions of the present invention in a real environment.

[0153] Data Explanation:

[0154] The liquid level detection method of this invention was tested under different operating conditions (high viscosity liquid, liquid containing air bubbles, complex ambient temperature, etc.), and the test content included key indicators such as liquid level measurement accuracy, error compensation effect, and measurement resolution. The performance of traditional capacitive sensor methods, traditional ultrasonic methods, and the method of this invention was compared in the tests.

[0155] Test data:

[0156]

[0157] Data analysis and validation:

[0158] Measurement accuracy: This invention achieves a measurement accuracy of 99.5% in low-viscosity liquid environments at room temperature, significantly better than traditional methods (96% and 94%). In high-viscosity and bubble-containing liquid environments, the measurement accuracy is improved to 98% and 95% respectively, significantly better than traditional ultrasonic methods (89% and 78%).

[0159] Error compensation effect: The error compensation effect of the method of the present invention exceeds 85% in different environments, which is much higher than the 5%-15% of the traditional method, verifying the high efficiency of the dynamic compensation algorithm.

[0160] in conclusion:

[0161] The test data above show that the liquid level detection method of the present invention, which combines dynamic compensation algorithm and acoustic wave propagation characteristics, exhibits significant advantages under various complex working conditions. It effectively solves the problems of insufficient detection accuracy and error compensation capability of traditional methods in environments such as high viscosity liquids, liquids containing bubbles, and high temperatures. It is suitable for accurate liquid level detection of reagent needles in fully automated coagulation analyzers.

[0162] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for detecting the liquid level in a reagent needle of a fully automated coagulation analyzer, characterized in that, Includes the following steps: S1: Sends ultrasonic pulse signals to measure liquid level changes; S2: Apply intelligent algorithms to dynamically compensate for errors; The application of intelligent algorithms to dynamically compensate for errors includes the following steps: S21: Real-time collection of environmental factors and relevant properties of the liquid by installing additional sensors around the fully automated coagulation analyzer or inside the liquid container; S22: Using the real-time sensor data collected in S21 and combined with the equipment's historical data, an error model between sound wave propagation delay and liquid physical properties is established. This model can predict and correct liquid level errors according to different environmental conditions. S23: Using the real-time sensor data from S21 and the error model established in S22 as input, a machine learning algorithm is used to dynamically compensate for the acoustic delay data of each measurement. S24: Each time a liquid level is detected, the algorithm will correct the sensor data and output the corrected liquid level data. The correction result will be compared with the theoretical value to further adjust the model and continuously improve the measurement accuracy. S3: Integrates multi-sensor data to optimize liquid level calculation; S4: Monitor liquid level and trigger abnormal alarm; S5: Enables adaptive learning and optimizes system performance; S6: Store data and perform remote monitoring; The mathematical expression for the error model is: ； in: The acoustic delay for liquid level measurement, in seconds; The theoretical sound wave delay is expressed in seconds. This refers to the density of the liquid, expressed in kilograms per cubic meter. The viscosity of the liquid is expressed in Pascals per second (Pa·s). The speed of sound in liquids is expressed in meters per second. The ambient temperature is in degrees Celsius. The ambient air pressure is expressed in Pascals (Pa). This represents ambient humidity, expressed as a percentage. , It is a constant factor; , , , , All are power exponents; It is a power function used to describe the effect of liquid density on the propagation delay of sound waves; Describe the relationship between liquid viscosity and sound wave propagation time delay; Describe the effect of sound velocity in a liquid on the time delay of sound wave propagation; This is a temperature correction factor; Environmental degradation factor; This is the error compensation factor; This is a correction function for the effect of temperature on time delay; The integral symbol represents the integral with respect to ambient temperature. Integrating means taking into account the cumulative effects of long-term temperature changes. Through integration, the formula can effectively handle the cumulative effect of different temperature changes on the propagation delay of sound waves. Used to describe the effect of environmental pressure on sound wave propagation; Describe the effect of ambient humidity on the propagation delay of sound waves.

2. The method for detecting the liquid level of the reagent needle in a fully automated coagulation analyzer as described in claim 1, characterized in that: The step S1, which involves sending an ultrasonic pulse signal to measure liquid level changes, includes the following steps: S11: Install an ultrasonic sensor above or on the side of the reagent needle of the fully automated coagulation analyzer; S12: The ultrasonic sensor sends ultrasonic pulse signals to the surface of the liquid or inside the liquid. The ultrasonic pulse signals propagate in the liquid and are reflected back to the ultrasonic sensor receiver. S13: The ultrasonic sensor receives the reflected sound waves and records the time delay from the transmission to the reception of the sound waves. S14: Calculate the current liquid level based on the time delay of sound wave propagation and the known physical properties of the liquid.

3. The method for detecting the liquid level of the reagent needle in a fully automated coagulation analyzer as described in claim 1, characterized in that: The steps involved in establishing the error model between sound wave propagation delay and liquid physical properties are as follows: S221: Collect experimental data Prepare a variety of typical liquid samples, covering different ranges of viscosity, density and sound wave propagation characteristics; Different environmental conditions are simulated by adjusting temperature, humidity, and air pressure; Using a high-precision time delay measurement sensor, the time delay of sound waves propagating in a liquid is recorded, while the physical parameters of the liquid are collected. S222: Constructing a multivariate error model Identify the main physical properties affecting the time delay of sound wave propagation, including liquid viscosity ( ),density( ),temperature( ) and speed of sound ( Additional environmental factors to consider include air pressure ( ) and humidity ( ); Based on experimental data, the sound wave propagation time delay was established using the multiple linear regression method. The relationship model between liquid properties and liquid properties: ； in, It is a multivariate function, and the specific relationship can be solved through regression analysis. In regression analysis, the following model form is used: ； in , , , , , , It is the regression coefficient. This is the error term; S223: Error Model Validation and Adjustment The established error model is validated using a test dataset to evaluate its accuracy and robustness; if the error is large, the regression coefficients are adjusted. S224: Application of Error Compensation Algorithm Once the error model is established and successfully verified, the error compensation algorithm in liquid level detection is dynamically adjusted by combining real-time liquid physical properties and environmental data. The real-time collected environmental data and the physical properties of the liquid will be used as model input to calculate the current sound wave propagation time delay error and adjust the liquid level measurement results accordingly. S225: Dynamically update the error model As the equipment accumulates data during operation, the error model is dynamically updated through online learning or adaptive learning to ensure that it maintains high accuracy over long-term use.

4. The method for detecting the liquid level of the reagent needle in a fully automated coagulation analyzer as described in claim 1, characterized in that: The environmental data includes temperature, air pressure, and humidity, while the physical properties of the liquid include viscosity, density, and sound velocity.

5. The method for detecting the liquid level of the reagent needle in a fully automated coagulation analyzer as described in claim 1, characterized in that: The S3 process for optimizing liquid level calculation by fusing multi-sensor data includes the following steps: S31: During the liquid level measurement process, data from different types of sensors are fused together. Different types of sensors provide information from different dimensions, which helps to verify the accuracy of the liquid level measurement results from multiple perspectives. S32: Based on multi-source data fusion, an algorithm is used to optimize the calculation of liquid level; S33: The final liquid level value is obtained by fusing the acoustic wave propagation measurement value and the data after dynamic compensation by intelligent algorithm.

6. The method for detecting the liquid level of the reagent needle in a fully automated coagulation analyzer as described in claim 1, characterized in that: The process of monitoring the liquid level and triggering an abnormal alarm in S4 includes the following steps: S41: The liquid level detection system of the fully automated coagulation analyzer continuously monitors changes in liquid level, and ensures that the liquid level is always within the normal range by periodically collecting and analyzing liquid level data; S42: The liquid level detection system is installed inside the fully automatic coagulation analyzer to track the liquid level status in real time. If the liquid level is detected to be outside the preset range, the liquid level detection system will notify the operator through an alarm mechanism and display the cause of the abnormality on the display interface to help the operator find the problem in time and take corresponding measures.

7. The method for detecting the liquid level of the reagent needle in a fully automated coagulation analyzer as described in claim 1, characterized in that: The adaptive learning and system performance optimization in S5 include the following steps: S51: The liquid level detection system uses intelligent algorithms to make real-time adjustments and optimizations based on data accumulated over a long period of operation; S52: As the equipment is used for a longer period of time, the liquid level detection system will automatically optimize the error model based on the gradually accumulated error data, using regression analysis algorithms or genetic algorithms, and continuously improve its adaptability to changes in the environment and liquid properties.

8. The method for detecting the liquid level of the reagent needle in a fully automated coagulation analyzer as described in claim 1, characterized in that: The process of storing data and performing remote monitoring in S6 includes the following steps: S61: All liquid level detection data, compensation processes, and anomaly records are stored in the liquid level detection system, providing detailed data support for subsequent analysis and maintenance; S62: Through the cloud platform or local network, users can view the liquid level change trend in real time and remotely obtain equipment status, alarm information and real-time data of liquid level detection, which facilitates operation and maintenance.

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