Medical centrifuge temperature calibration method and device based on quantum annealing particle filter technology

Abstract

The invention relates to a medical centrifuge temperature calibration method and device based on quantum annealing particle filtering. An infrared detector, a wireless sensor and an optical fiber sensor are used for measuring the internal temperature of the centrifugal machine, and a Triple-Collocation method is used for fusing temperature data measured by different sensors into a unified standard. Temperature measurement is optimized through a quantum annealing particle filtering algorithm, and the temperature measurement accuracy is improved by combining the global optimization capability of quantum annealing and the recursive Bayesian estimation characteristic of particle filtering. And finally, precise temperature control is realized by utilizing fuzzy PID control. Compared with the prior art, non-contact temperature monitoring is achieved, the response speed and precision of temperature monitoring are improved, it is ensured that the centrifugal process is conducted at the stable temperature, the accuracy of biological sample treatment and the reliability of experimental results are improved, and an efficient and safe temperature management solution is provided for a medical laboratory.

Description

Technical Field

[0001] The present invention belongs to the fields of medical equipment, temperature control and quantum computing applications, and discloses a medical centrifuge temperature calibration method and device based on quantum annealing particle filtering technology. Background Art

[0002] Medical centrifuges, as indispensable equipment in the medical field, are widely used in the separation and purification of biological samples. Their core function is to generate centrifugal force through high-speed rotation, effectively separating materials of varying densities. However, precise control of the centrifuge's internal temperature during operation is crucial to ensuring separation efficiency and sample quality. Even slight temperature fluctuations can cause denaturation or loss of activity in biological samples, thereby impacting the accuracy and reliability of experimental results. Traditional methods for calibrating medical centrifuge temperatures primarily rely on contact temperature sensors, such as thermocouples or RTDs. While these methods offer a certain level of temperature measurement accuracy, they have significant limitations. First, contact sensors require direct contact with the object being measured, which can interfere with the temperature distribution within the centrifuge and potentially contaminate or damage the sample. Second, contact sensors have a slow response speed, making real-time, dynamic temperature monitoring and calibration difficult. Furthermore, with the continuous advancement of centrifuge technology, their operating speed and complexity are increasing, making traditional contact temperature calibration methods unable to meet the high-precision and efficient temperature control requirements of modern medical laboratories. Therefore, developing an innovative, non-contact temperature calibration technology to accurately monitor and calibrate the internal temperature of medical centrifuges in real time has become a critical issue in the current medical device field. This will not only help improve the accuracy of biological sample processing and the reliability of experimental results, but also provide a more efficient and safe temperature management solution for medical laboratories. Summary of the Invention

[0003] Purpose of the invention: In response to the problems pointed out in the background technology, the present invention discloses a medical centrifuge temperature calibration method and device based on quantum annealing particle filtering technology, which adopts quantum annealing particle filtering technology to achieve precise control and calibration of the internal temperature of the centrifuge.

[0004] Technical solution: The present invention discloses a method for calibrating the temperature of a medical centrifuge based on quantum annealing particle filtering, comprising the following steps:

[0005] Step (1) Use infrared detector, wireless sensor and optical fiber sensor to measure the internal temperature of the centrifuge a 、T b 、T c ;

[0006] Step (2) uses the Triple-Collocation method to convert the temperature T detected by the infrared detector into a , the temperature T measured by the wireless sensor b and the temperature T detected by the fiber optic probe c Fusion into a unified temperature standard T total ;

[0007] Step (3) using the quantum annealing particle filter (QAPF) algorithm to optimize the centrifuge temperature. The quantum annealing particle filter algorithm combines quantum annealing and particle filtering to improve the temperature measurement accuracy by optimizing the Hamiltonian. The potential energy term guides the particles to approach the target temperature, and the kinetic energy term maintains particle diversity and avoids local optimality.

[0008] Step (4) uses fuzzy PID control to accurately control the centrifuge temperature. The input variable of the fuzzy controller is the deviation e(t) between the set temperature and the actual temperature, and the control output is u(t). The control output u(t) is applied to the actuator to adjust the heating or cooling equipment.

[0009] Furthermore, the step (2) is specifically as follows:

[0010] Assume three sets of independent centrifuge temperature data and the actual centrifuge temperature T t There is a linear relationship:

[0011]

[0012] Where a a 、a b 、a c Represents the temperature measurement data sets of three centrifuges relative to the true value T t Added deviation coefficient; b a 、b b 、b c Represents the temperature measurement data sets of three centrifuges relative to the true value T t Multiplication of the deviation coefficient; g a 、g b 、g c Represents the temperature measurement data sets of three centrifuges relative to the true value T t Error;

[0013] To eliminate the true value, divide both sides of the equation by b i ,have to:

[0014]

[0015] Where, T i * =(T j- a j) / b i , g i * =g j / b i ;

[0016] Multiply the three equations above by two and take the mean to get the variance of each temperature:

[0017]

[0018] Where, express and The time series mean of the product of ;

[0019] Set a a =0;b a =1; record T′ i =T i -T i * , we get the following equation:

[0020]

[0021] Error variance σ i * , the calculation formula of i=a, b, c is as follows:

[0022]

[0023] Where, <T′ a 2 > refers to T′ a 2 The time series mean of ; a T′ b > refers to T′ a and T′ b The time series mean of ;

[0024] Original weighting coefficient w i The calculation formula is as follows:

[0025]

[0026] The three sets of data are fused into a unified centrifuge temperature data set T using the weighted average method. total :

[0027]

[0028] Furthermore, the step (3) uses the quantum annealing particle filter QAPF algorithm to optimize the centrifuge temperature as follows:

[0029] S1: Initialize the particle swarm, determine the number of particles N, and then initialize the state x of each particle i (0), represents the local value of the temperature field, where i = 1, 2, ..., N; initialize the weight of each particle Make the initial probabilities of all particles equal:

[0030]

[0031] S2: Particle weight update: predict the next state of each particle based on the state transition function, then use the observed data to update the particle weight, and finally normalize the weight;

[0032] The formula for predicting the next state of a particle is as follows:

[0033] x i (t+1)=f(x i (t),u(t))+v i (t)

[0034] Where f(·) is the state transfer function, u(t) is the control input, and v i (t) is the process noise;

[0035] The particle weight update formula is as follows:

[0036]

[0037] Where p(·) is the observation likelihood function and z(t+1) is the observation value;

[0038] The normalized particle weight formula is as follows:

[0039]

[0040] S3: Quantum annealing optimization, constructing the Hamiltonian H, which includes a potential energy term related to temperature measurement errors and a kinetic energy term that promotes particle diversity. The particle state is iteratively updated through the quantum annealing algorithm, and the quantum tunneling effect is used to help particles escape from the local optimal solution:

[0041]

[0042] Where Δx ij =x i -x j represents the difference between particles i and j;

[0043] S4: Particle update, combining the quantum annealing results to update the particle state, and then resample according to the particle weight, removing particles with smaller weights and copying particles with larger weights:

[0044] x i(t+1)=x i (t)+Δx i (t)

[0045] Where Δx i (t) is the random perturbation affected by the Hamiltonian;

[0046] S5: Calculate the temperature estimate based on the particle state and weight, evaluate the error between the temperature estimate and the true value, and optimize the particle state and weight through multiple iterations:

[0047]

[0048] Where, is the particle weight; x i (t) is the particle state, that is, the local value of the temperature field.

[0049] Furthermore, the step (4) is specifically as follows:

[0050] Set the target temperature T of the centrifuge according to actual needs set , fuzzy PID control is used to achieve precise control of the centrifuge temperature. The input variable of the fuzzy controller is the deviation e(t) between the set temperature and the actual temperature. The calculation formula is:

[0051]

[0052] Another input variable is the error change rate Δe(t), which is calculated as:

[0053]

[0054] Map the input variables e(t) and Δe(t) to fuzzy sets, which are "negative large" NB, "negative medium" NM, "negative small" NS, "zero" ZO, "positive small" PS, "positive medium" PM, and "positive large" PB;

[0055] The triangle membership function is defined as:

[0056]

[0057] Where x is the input variable; a is the left endpoint of the membership function, that is, the left vertex of the triangle; b is the midpoint of the membership function, that is, the highest point of the triangle; c is the right endpoint of the membership function, that is, the right vertex of the triangle;

[0058] The fuzzy rules are established as follows:

[0059] If e(t) is "positive" and Δe(t) is "positive", then ΔKp is "positive";

[0060] If e(t) is "zero" and Δe(t) is "zero", then ΔKp is "zero";

[0061] According to fuzzy rules, PID parameters are adjusted dynamically:

[0062] K p (t) = K p (t-1)+ΔK p

[0063] K i (t) = K i (t-1)+ΔK i

[0064] K d (t) = K d (t-1)+ΔK d

[0065] Where K p (t) refers to the proportional gain of the controller at time t; K i (t) refers to the integral gain of the controller at time t; K d (t) refers to the differential gain of the controller at time t;

[0066] The control output u(t) is:

[0067]

[0068] The control output u(t) is applied to the actuator to regulate the heating or cooling equipment.

[0069] The present invention also discloses a medical centrifuge temperature calibration device based on quantum annealing particle filtering, comprising:

[0070] The temperature detection module includes an infrared detector, a wireless sensor, and an optical fiber sensor, which are used to measure the internal temperature T of the centrifuge. a 、T b 、T c ;

[0071] The temperature fusion module is used to use the Triple-Collocation method to convert the temperature T detected by the infrared detector into a , the temperature T measured by the wireless sensor b and the temperature T detected by the fiber optic probe c Fusion into a unified temperature standard T total ;

[0072] The temperature optimization module is used to optimize the centrifuge temperature using the quantum annealing particle filter (QAPF) algorithm. The quantum annealing particle filter algorithm combines quantum annealing and particle filtering to improve temperature measurement accuracy by optimizing the Hamiltonian. The potential energy term guides particles to approach the target temperature, while the kinetic energy term maintains particle diversity and avoids local optimality.

[0073] The temperature control module is used to precisely control the centrifuge temperature using fuzzy PID control. The input variable of the fuzzy controller is the deviation e(t) between the set temperature and the actual temperature, and the control output is u(t). The control output u(t) is applied to the actuator to adjust the heating or cooling equipment.

[0074] Beneficial effects:

[0075] 1. The present invention adopts a non-contact temperature monitoring method, which avoids the interference of traditional contact temperature measurement means on the temperature field distribution inside the centrifuge, and eliminates the risk of contamination or damage to the sample.

[0076] 2. High-precision control: Through quantum annealing particle filtering technology, the device can accurately control and calibrate the internal temperature of the centrifuge, significantly improving the response speed and accuracy of temperature monitoring, and ensuring that the centrifugation process is carried out under stable temperature conditions.

[0077] 3. Real-time dynamic calibration: The device can monitor and adjust the temperature in real time to adapt to dynamic changes during centrifuge operation, ensuring the accuracy and stability of temperature control, thereby improving the accuracy of biological sample processing and the reliability of experimental results.

[0078] 4. Enhanced system robustness: By integrating multiple sensor data and adopting quantum annealing particle filtering technology, the system's robustness to environmental changes and equipment noise is enhanced, improving the overall performance and reliability of the temperature control system. BRIEF DESCRIPTION OF THE DRAWINGS

[0079] Figure 1 This is a technical block diagram of a medical low-temperature centrifuge;

[0080] Figure 2 This is the flow chart of the quantum annealing particle filter (QAPF) technology;

[0081] Figure 3 The error comparison between the traditional temperature measurement method and the error of the present invention is shown in FIG.

[0082] Figure 4 The figure shows the response time comparison between the traditional temperature measurement method and the temperature measurement method of the present invention.

[0083] The present invention will be further described below in conjunction with the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solutions of the present invention and are not intended to limit the scope of protection of the present invention. DETAILED DESCRIPTION

[0084] The present invention discloses a method and device for calibrating the temperature of a medical centrifuge based on quantum annealing particle filtering, comprising the following steps:

[0085] The temperature detection module includes an infrared detector, a wireless sensor, and an optical fiber sensor, which are used to measure the internal temperature T of the centrifuge. a 、T b 、T c .

[0086] The temperature fusion module is used to use the Triple-Collocation method to convert the temperature T detected by the infrared detector into a , the temperature T measured by the wireless sensor b and the temperature T detected by the fiber optic probe c Fusion into a unified temperature standard T total .

[0087] Assume that there are three independent sets of centrifuge temperature data and the actual centrifuge temperature T t There is a linear relationship:

[0088]

[0089] Where a a 、a b 、a c Represents the temperature measurement data sets of three centrifuges relative to the true value T t Added deviation coefficient; b a 、b b 、b c Represents the temperature measurement data sets of three centrifuges relative to the true value T t Multiplication of the deviation coefficient; g a 、g b 、g c Represents the temperature measurement data sets of three centrifuges relative to the true value T t Error;

[0090] To eliminate the true value, divide both sides of the equation by b i ,have to:

[0091]

[0092] Where, T i * =(T j- a j ) / b i , g i * =g j / bi ;

[0093] Multiply the three equations above by two and take the mean to get the variance of each temperature:

[0094]

[0095] Where, express and The time series mean of the product of ;

[0096] Set a a =0;b a =1; record T′ i =T i -T i * , we get the following equation:

[0097]

[0098] Error variance The calculation formula for i=a, b, c is as follows:

[0099]

[0100] Where, <T′ a 2 > refers to T′ a 2 The time series mean of ; a T′ b > refers to T′ a and T′ b The time series mean of ; the original weight coefficient w i The calculation formula is as follows:

[0101]

[0102] The three sets of data are fused into a unified centrifuge temperature data set T using the weighted average method. total :

[0103]

[0104] The temperature optimization module is used to optimize the centrifuge temperature using the quantum annealing particle filter (QAPF) algorithm. The quantum annealing particle filter algorithm combines quantum annealing and particle filtering to improve temperature measurement accuracy by optimizing the Hamiltonian. The potential energy term guides particles to approach the target temperature, while the kinetic energy term maintains particle diversity and avoids local optimality.

[0105] S1: Initialize the particle swarm, determine the number of particles N, and then initialize the state x of each particle i(0), represents the local value of the temperature field, where i = 1, 2, ..., N; initialize the weight of each particle Make the initial probabilities of all particles equal:

[0106]

[0107] S2: Particle weight update: predict the next state of each particle based on the state transition function, then use the observed data to update the particle weight, and finally normalize the weight;

[0108] The formula for predicting the next state of a particle is as follows:

[0109] x i (t+1)=f(x i (t),u(t))+v i (t)

[0110] Where f(·) is the state transfer function, u(t) is the control input, and v i (t) is the process noise;

[0111] The particle weight update formula is as follows:

[0112]

[0113] Where p(·) is the observation likelihood function and z(t+1) is the observation value;

[0114] The normalized particle weight formula is as follows:

[0115]

[0116] S3: Quantum annealing optimization, constructing the Hamiltonian H, which includes a potential energy term related to temperature measurement errors and a kinetic energy term that promotes particle diversity. The particle state is iteratively updated through the quantum annealing algorithm, and the quantum tunneling effect is used to help particles escape from the local optimal solution:

[0117]

[0118] Where Δx ij =x i -x j represents the difference between particles i and j;

[0119] S4: Particle update, combining the quantum annealing results to update the particle state, and then resample according to the particle weight, removing particles with smaller weights and copying particles with larger weights:

[0120] x i (t+1)=x i (t)+Δx i (t)

[0121] Where Δx i (t) is the random perturbation affected by the Hamiltonian;

[0122] S5: Calculate the temperature estimate based on the particle state and weight, evaluate the error between the temperature estimate and the true value, and optimize the particle state and weight through multiple iterations:

[0123]

[0124] Where, is the particle weight; x i (t) is the particle state, that is, the local value of the temperature field.

[0125] The temperature control module is used to precisely control the centrifuge temperature using fuzzy PID control. The input variable of the fuzzy controller is the deviation e(t) between the set temperature and the actual temperature, and the control output is u(t). The control output u(t) is applied to the actuator to adjust the heating or cooling equipment.

[0126] Set the target temperature T of the centrifuge according to actual needs set , fuzzy PID control is used to achieve precise control of the centrifuge temperature. The input variable of the fuzzy controller is the deviation e(t) between the set temperature and the actual temperature. The calculation formula is:

[0127]

[0128] Another input variable is the error change rate Δe(t), which is calculated as:

[0129]

[0130] Map the input variables e(t) and Δe(t) to fuzzy sets, which are "negative large" NB, "negative medium" NM, "negative small" NS, "zero" ZO, "positive small" PS, "positive medium" PM, and "positive large" PB;

[0131] The triangle membership function is defined as:

[0132]

[0133] Where x is the input variable; a is the left endpoint of the membership function, that is, the left vertex of the triangle; b is the midpoint of the membership function, that is, the highest point of the triangle; c is the right endpoint of the membership function, that is, the right vertex of the triangle;

[0134] The fuzzy rules are established as follows:

[0135] If e(t) is "positive" and Δe(t) is "positive", then ΔKp is "positive";

[0136] If e(t) is "zero" and Δe(t) is "zero", then ΔKp is "zero";

[0137] According to fuzzy rules, PID parameters are adjusted dynamically:

[0138] K p (t) = K p (t-1)+ΔK p

[0139] K i (t) = K i (t-1)+ΔK i

[0140] K d (t) = K d (t-1)+ΔK d

[0141] Where K p (t) refers to the proportional gain of the controller at time t; K i (t) refers to the integral gain of the controller at time t; K d (t) refers to the differential gain of the controller at time t;

[0142] The control output u(t) is:

[0143]

[0144] The control output u(t) is applied to the actuator to regulate the heating or cooling equipment.

[0145] The following is an experimental verification of the above calibration method:

[0146] The traditional PT100 sensor uses a contact thermocouple sensor to convert the temperature into an electrical signal by physically contacting the sample or cavity surface. The data measured by the traditional temperature measurement method is compared with the data of the present invention. Figure 3 Error comparison shows that the temperature error measured by the traditional method is 0.5-2.0°C, with an average of 1.25°C, while the present invention compresses the error to a narrow band of 0.05-0.25°C. This shows that the anti-noise capability of the Triple-Collocation algorithm dynamically weighted high-precision fiber optic sensing can improve the temperature measurement accuracy of medical centrifuges. Figure 4A comparison of response times shows that the traditional heating response time is 40 to 45 seconds, and the cooling response time is 45 to 55 seconds, while the heating time of the present invention is controlled at 20 to 25 seconds, and the cooling response time is 22 to 27 seconds. Therefore, when the centrifuge is running at high speed, temperature correction must be completed within 5 seconds, effectively avoiding sample inactivation. The present invention reconstructs the temperature control response paradigm through quantum optimization, providing "zero delay" protection for high-speed medical centrifuges.

[0147] The above embodiments are intended only to illustrate the technical concepts and features of the present invention. Their purpose is to enable those skilled in the art to understand the contents of the present invention and implement them accordingly. They are not intended to limit the scope of protection of the present invention. Any equivalent changes or modifications made in accordance with the spirit of the present invention are intended to be covered by the scope of protection of the present invention.

Claims

1. A medical centrifuge temperature calibration method based on quantum annealing particle filtering, characterized in that: The following steps are involved: Step (1) Use infrared detector, wireless sensor and optical fiber sensor to measure the internal temperature of the centrifuge a 、T b 、T c ; Step (2) uses the Triple-Collocation method to convert the temperature T detected by the infrared detector into a , the temperature T measured by the wireless sensor b and the temperature T detected by the fiber optic probe c Fusion into a unified temperature standard T total ; Step (3) using the quantum annealing particle filter (QAPF) algorithm to optimize the centrifuge temperature. The quantum annealing particle filter algorithm combines quantum annealing and particle filtering to improve the temperature measurement accuracy by optimizing the Hamiltonian. The potential energy term guides the particles to approach the target temperature, and the kinetic energy term maintains particle diversity and avoids local optimality. Step (4) uses fuzzy PID control to accurately control the centrifuge temperature. The input variable of the fuzzy controller is the deviation e(t) between the set temperature and the actual temperature, and the control output is u(t). The control output u(t) is applied to the actuator to adjust the heating or cooling equipment.

2. The method for temperature calibration of a medical centrifuge using quantum annealing particle filtering according to claim 1, wherein: The step (2) is specifically as follows: Assume that there are three independent sets of centrifuge temperature data and the actual centrifuge temperature T t There is a linear relationship: Where a a 、a b 、a c Represents the temperature measurement data sets of three centrifuges relative to the true value T t Added deviation coefficient; b a 、b b 、b c Represents the temperature measurement data sets of three centrifuges relative to the true value T t Multiplication of the deviation coefficient; g a 、g b 、g c Represents the temperature measurement data sets of three centrifuges relative to the true value T t Error; To eliminate the true value, divide both sides of the equation by b i ,have to: Where, T i * =(T j- a j ) / b i , g i * =g j / b i ; Multiply the three equations above by two and take the mean to get the variance of each temperature: Where, express and The time series mean of the product of ; Set a a =0;b a =1; record T i ′=T i -T i * , we get the following equation: Error variance The calculation formula for i=a, b, c is as follows: Where, refers to The time series mean of a 'T b ′> refers to T a ′ and T b The time series mean of ′; Original weighting coefficient w i The calculation formula is as follows: The three sets of data are fused into a unified centrifuge temperature data set T using the weighted average method. total :

3. The method for temperature calibration of a medical centrifuge using quantum annealing particle filtering according to claim 1, wherein: The step (3) uses the quantum annealing particle filter QAPF algorithm to optimize the centrifuge temperature as follows: S1: Initialize the particle swarm, determine the number of particles N, and then initialize the state x of each particle i (0), represents the local value of the temperature field, where i = 1, 2, ..., N; initialize the weight of each particle Make the initial probabilities of all particles equal: S2: Particle weight update: predict the next state of each particle based on the state transition function, then use the observed data to update the particle weight, and finally normalize the weight; The formula for predicting the next state of a particle is as follows: x i (t+1)=f(x i (t),u(t))+v i (t) Where f(·) is the state transfer function, u(t) is the control input, and v i (t) is the process noise; The particle weight update formula is as follows: Where p(·) is the observation likelihood function and z(t+1) is the observation value; The normalized particle weight formula is as follows: S3: Quantum annealing optimization, constructing the Hamiltonian H, which includes a potential energy term related to temperature measurement errors and a kinetic energy term that promotes particle diversity. The particle state is iteratively updated through the quantum annealing algorithm, and the quantum tunneling effect is used to help particles escape from the local optimal solution: Where Δx ij =x i -x j represents the difference between particles i and j; S4: Particle update, combining the quantum annealing results to update the particle state, and then resample according to the particle weight, removing particles with smaller weights and copying particles with larger weights: x i (t+1)=x i (t)+Δx i (t) Where Δx i (t) is the random perturbation affected by the Hamiltonian; S5: Calculate the temperature estimate based on the particle state and weight, evaluate the error between the temperature estimate and the true value, and optimize the particle state and weight through multiple iterations: Where, is the particle weight; x i (t) is the particle state, that is, the local value of the temperature field.

4. The method for temperature calibration of a medical centrifuge based on quantum annealing particle filtering according to claim 1, characterized in that: The step (4) is specifically as follows: Set the target temperature T of the centrifuge according to actual needs set , fuzzy PID control is used to achieve precise control of the centrifuge temperature. The input variable of the fuzzy controller is the deviation e(t) between the set temperature and the actual temperature. The calculation formula is: Another input variable is the error change rate Δe(t), which is calculated as: Map the input variables e(t) and Δe(t) to fuzzy sets, which are "negative large" NB, "negative medium" NM, "negative small" NS, "zero" ZO, "positive small" PS, "positive medium" PM, and "positive large" PB; The triangle membership function is defined as: Where x is the input variable; a is the left endpoint of the membership function, that is, the left vertex of the triangle; b is the midpoint of the membership function, that is, the highest point of the triangle; c is the right endpoint of the membership function, that is, the right vertex of the triangle; The fuzzy rules are established as follows: If e(t) is positive and Δe(t) is positive, then ΔKp is positive; If e(t) is "zero" and Δe(t) is "zero", then ΔKp is "zero"; According to fuzzy rules, PID parameters are adjusted dynamically: K p (t)=K p (t-1)+ΔK p K i (t)=K i (t-1)+ΔK i K d (t)=K d (t-1)+ΔK d Where K p (t) refers to the proportional gain of the controller at time t; K i (t) refers to the integral gain of the controller at time t; K d (t) refers to the differential gain of the controller at time t; The control output u(t) is: The control output u(t) is applied to the actuator to regulate the heating or cooling equipment.

5. A medical centrifuge temperature calibration device based on quantum annealing particle filtering, characterized in that: include: The temperature detection module includes an infrared detector, a wireless sensor, and an optical fiber sensor, which are used to measure the internal temperature T of the centrifuge. a 、T b 、T c ; The temperature fusion module is used to use the Triple-Collocation method to convert the temperature T detected by the infrared detector into a , the temperature T measured by the wireless sensor b and the temperature T detected by the fiber optic probe c Fusion into a unified temperature standard T total ; The temperature optimization module is used to optimize the centrifuge temperature using the quantum annealing particle filter (QAPF) algorithm. The quantum annealing particle filter algorithm combines quantum annealing and particle filtering to improve temperature measurement accuracy by optimizing the Hamiltonian. The potential energy term guides particles to approach the target temperature, while the kinetic energy term maintains particle diversity and avoids local optimality. The temperature control module is used to precisely control the centrifuge temperature using fuzzy PID control. The input variable of the fuzzy controller is the deviation e(t) between the set temperature and the actual temperature, and the control output is u(t). The control output u(t) is applied to the actuator to adjust the heating or cooling equipment.

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