Quantum sensing technology-oriented high-precision temperature field measurement method

Abstract

A quantum sensing technology-oriented high-precision temperature field measurement method is favorable for guaranteeing the realization of a high-precision uniform temperature field, is also a necessary condition for realizing long-term stable work of an alkali metal gas chamber, and comprises the following steps: optimizing the layout of platinum resistors based on a greedy algorithm of global temperature field RMSE minimization; based on heat source data, a heat conduction theory and a finite difference method, obtaining prior data of an alkali metal air chamber temperature field evolved along with time; obtaining a primary function of a temperature field by using the prior data based on data-driven intrinsic orthogonal decomposition; real-time synchronous temperature measurement is realized by using a multi-channel time synchronous temperature measurement system; by using the measurement data, the RMSE of the temperature measurement point is optimized based on the least square method to obtain a basis coefficient, and high-precision online reconstruction of the temperature field is realized.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of quantum sensing, and in particular to a high-precision temperature field measurement method for quantum sensing technology. BACKGROUND

[0002] With the continuous progress of science and technology, quantum sensing technology has emerged and shown great application potential. Quantum sensing technology utilizes the interaction of atoms with physical quantities such as light, heat, and magnetism, and takes advantage of its low noise and high precision to make significant progress in many fields. For example, in the field of inertial navigation, an atomic gyroscope provides high-precision positioning and navigation capabilities by accurately measuring the angular velocity information of the carrier. The Spin Exchange Relaxation Free (SERF) gyroscope based on thermal atomic spins has shown great potential for this technology. The key to the SERF gyroscope lies in the spin exchange relaxation characteristics in the alkali metal cell. The temperature uniformity inside the cell is crucial to the stability of the atomic density, the scale factor of the instrument, and the stability of the zero bias. In order to improve the temperature field uniformity of the cell, high-precision temperature field measurement must be achieved first. Measuring high-precision temperature fields is a common requirement for quantum sensors, which can provide a foundation for improving the accuracy of quantum sensors. Therefore, a high-precision temperature field measurement method is needed to achieve high-precision measurement of the temperature field in the alkali metal cell region.

[0003] In current technology, temperature field measurement is still in its infancy. Temperature field reconstruction, as a means of measuring temperature field, is currently mostly used to reconstruct temperature field using sensor measurement data, which has low precision and cannot meet the needs of quantum sensing technology for high-precision temperature field. Existing patent documents, such as CN202411755547 (an acoustic temperature field reconstruction method based on least squares and radial basis functions), use sensor measurement data to calculate a temperature matrix through least squares and perform fitting through radial basis function interpolation to reconstruct the temperature field. If temperature field reconstruction relies solely on sensor measurement data, it lacks prior physical knowledge constraints, and if measurement data is abnormal or unevenly distributed, it can easily lead to reconstruction results that violate physical laws and reduce the accuracy of temperature field reconstruction. At the same time, the accuracy of temperature field reconstruction depends on the number of sensors, and the excessive increase in the number of sensors due to sensor errors can greatly affect the accuracy of temperature field reconstruction.

[0004] The retrieved patent document CN202410397048 (a temperature field reconstruction method and device based on maximum likelihood estimation, apparatus, and medium) uses maximum likelihood estimation to calculate temperature field basis coefficients. However, the maximum likelihood method is highly dependent on the noise model and requires a long calculation time to improve accuracy.

[0005] Therefore, the prior art urgently needs a high-precision temperature field measurement method for quantum sensing technology. The present application provides a high-precision temperature field measurement method for quantum sensing technology, which uses heat source information to calculate the temperature field as prior data, and combines the collected high-precision data to realize the reconstruction of the temperature field. This method has the advantages of high precision and high real-time performance, and can accurately measure the temperature field, providing strong support for the performance improvement of the SERF atomic gyroscope. SUMMARY

[0006] The present application provides a high-precision temperature field measurement method for quantum sensing technology, which combines heat source information to calculate the temperature field as prior data through heat conduction and finite difference theory. The method uses snapshot data to obtain the basis function of the temperature field based on data-driven intrinsic orthogonal decomposition, uses the least squares method to obtain the basis coefficient based on the measurement data, and uses the basis function and the basis coefficient to realize the reconstruction of the temperature field. The method uses a greedy algorithm based on global temperature field RMSE minimization combined with the temperature field reconstruction method to optimize the spatial layout and number of temperature sensors in the temperature field, and improves the temperature field reconstruction accuracy. The method realizes real-time synchronous measurement of temperature based on a high-precision multi-channel time-synchronous temperature measurement system, realizes online reconstruction of the temperature field of the alkali metal gas chamber, and realizes high-precision temperature field measurement.

[0007] The technical solution of the present application is as follows:

[0008] A high-precision temperature field measurement method for quantum sensing technology, characterized in that it comprises the following steps:

[0009] Step 1: combining heat source information to calculate the temperature field as prior data through heat conduction and finite difference theory;

[0010] Step 2: using the prior data to obtain the basis function of the temperature field by intrinsic orthogonal decomposition, using the sensor measurement data to obtain the basis coefficient by the least squares method, and using the linear combination of the basis function and the basis coefficient to realize the reconstruction of the temperature field;

[0011] Step 3: combining the greedy algorithm based on global temperature field RMSE minimization with the temperature field reconstruction algorithm to optimize the spatial layout and number of sensors in the temperature field;

[0012] Step 4: combining the spatial layout of the sensors in the temperature field, and using the temperature data of the high-precision multi-channel time-synchronous temperature measurement system to realize the online reconstruction of the temperature field of the alkali metal gas chamber.

[0013] Step 1 includes the following expressions:

[0014]

[0015] Where ρ is the fluid density, cp is the constant pressure specific heat capacity of the fluid, c denotes the specific heat capacity of the fluid, p denotes the constant pressure condition, is the partial derivative of the fluid temperature T with respect to time t, v is the fluid velocity field, is the vector differential operator, k denotes the thermal conductivity of the fluid, x is the spatial coordinate of the fluid at different locations, Ω is the fluid domain, rad is the heat absorbed by the fluid domain by radiation, x is the spatial coordinate of the fluid at different locations, Ω is the fluid domain, is the boundary temperature T s is the partial derivative with respect to the normal direction, h is the convective heat transfer coefficient, T ∞ is the ambient temperature, ε is the surface emissivity of the gas chamber wall, σ is the Stefan-Boltzmann constant, Γ is the boundary of the fluid domain, T(x, 0) is the temperature value at the initial time x location, T0(x) is a function of the temperature value at different locations at the initial time.

[0016] The following expressions are included in Step 1:

[0017]

[0018] where Ra is the Rayleigh number, g is the acceleration due to gravity, β is the thermal expansion coefficient of the fluid, ΔT is the temperature difference between the wall surface and the ambient fluid, L is the characteristic length, v is the kinematic viscosity of the fluid, α is the thermal diffusivity of the fluid,

[0019] The following expressions are included in Step 1:

[0020]

[0021] where ρ is the fluid density, c p is the constant pressure specific heat capacity of the fluid, c denotes the specific heat capacity of the fluid, p denotes the constant pressure condition, is the partial derivative of the fluid temperature T with respect to time t, is the vector differential operator, k denotes the thermal conductivity of the fluid, x is the spatial coordinate of the fluid at different locations, Ω is the fluid domain, is the boundary temperature T s is the partial derivative with respect to the normal direction, h is the convective heat transfer coefficient, T ∞ is the ambient temperature, ε is the surface emissivity of the gas chamber wall, σ is the Stefan-Boltzmann constant, Γ is the boundary of the fluid domain, T(x, 0) is the temperature value at the initial time x location, T0(x) is a function of the temperature value at different locations at the initial time.

[0022] The following expressions are included in Step 2:

[0023]

[0024] The expression is a temperature field snapshot matrix expression of fluid temperature T, elements T(x1, t1) in the matrix are temperature field prior data of the first position x1 at the first time t1, T(x1, t n ) are temperature field prior data of the first position x1 at the nth time t n , n is a positive integer, T(x m , t1) are temperature field prior data of the mth position x m at the first time t1, m is a grid number of the gas chamber cross section division, m=a*b, a is a row number of the gas chamber cross section division, b is a column number of the gas chamber cross section division, a and b are both positive integers, T(x m , t n ) are temperature field prior data of the mth position x m at the nth time t n .

[0025] The following expression is included in step 2:

[0026]

[0027] RA=λA,

[0028]

[0029] Where T(x i ) is a temperature average value of the ith position x i at all times, i is a positive integer, n is a number of sampling times, j is a number of sampling time serial numbers, T(x i , t j ) is a temperature value of the ith position x i at the jth sampling time t j , is a temperature fluctuation value of the ith position x i at the ith sampling time t i , T(x i , t i ) is a temperature value of the ith position x i at the ith sampling time t i , is a fluctuation matrix, elements in the matrix are a temperature fluctuation value of the 1st position x1 at the 1st sampling time t1, is a temperature fluctuation value of the 1st position x1 at the nth sampling time t n , is a temperature fluctuation value of the mth position x m at the 1st sampling time t1, is a temperature fluctuation value of the mth position x m at the nth sampling time t ntemperature fluctuation value, R is correlation matrix, A is a characteristic vector, λ is an eigenvalue of R, ψ j (x) is the jth basis function, j is the eigenvalue sequence number, λ j is the jth eigenvalue, A j is the jth characteristic vector.

[0030] The following expression is included in step 2:

[0031]

[0032] Wherein α is the base coefficient, indicating the weight of the basis function in the reconstructed temperature field, is a real number field, r is a positive integer, indicating the number of basis functions, ψ s is the value matrix of the given r basis functions at the sensor position, ψ s α is the reconstructed value of the temperature field at the sensor position, y is the temperature value measured by the sensor, indicates the reconstructed temperature field, ψ(x) is the basis function corresponding to all eigenvalues.

[0033] The following expression is included in step 3:

[0034]

[0035] Wherein avgRMSE(SUj) represents the root mean square error of the temperature field of the sensor index matrix S at all times when the jth sensor Uj is added, n is the number of samples, t is the sample time sequence number, m is the number of grid division of the chamber cross section, i is the grid position sequence number, is the reconstructed temperature value of the jth sensor at the ith grid position at sampling time t, y i (t) is the temperature value of the temperature field at the ith grid position at sampling time t.

[0036] The high-precision multi-channel time-synchronous temperature measurement system in step 4 includes an MCU module, a power module, a time synchronization module, a sampling module and a communication module. The MCU module uses a single-chip microcomputer as the main control chip, which is used to manage sensor data acquisition and communicate with the host computer; the power module is used to power each module to ensure normal operation of the system; the time synchronization module provides high-precision synchronous clock for the analog-to-digital conversion chip through a high-precision clock source and a clock fan-out chip; the sampling module uses a combination of platinum resistance and analog-to-digital conversion chips to realize temperature acquisition through platinum resistance temperature sensing; the communication module is used for data exchange between the host computer and the MCU module; by collecting real-time synchronous temperature data and uploading it to the host computer, measurement data is provided for temperature field reconstruction, and real-time reconstruction of the temperature field is realized by real-time calculation of the base coefficient.

[0037] The technical effect of the present application is as follows: the high-precision temperature field measurement method for quantum sensing technology is beneficial to guarantee the realization of high-precision uniform temperature field, and is also a necessary condition for realizing long-term stable operation of an alkali metal gas chamber, and the method comprises the following steps: a greedy algorithm based on minimization of global temperature field RMSE is used to optimize the layout of platinum resistors; prior data of temperature field evolution of the alkali metal gas chamber with time is obtained based on heat source data, heat conduction theory and finite difference method; the base function of the temperature field is obtained based on data-driven intrinsic orthogonal decomposition by using the prior data; real-time synchronous measurement of the temperature is realized by using a multi-channel time synchronization temperature measurement system; and the base coefficient is obtained by using the measurement data and based on least square method optimization of the RMSE of the temperature measurement point, so as to realize high-precision online reconstruction of the temperature field.

[0038] Compared with the prior art, the present application has the following beneficial effects:

[0039] 1. The present application proposes a high-precision temperature field method for quantum sensing technology based on a multi-channel time synchronization temperature measurement system, which calculates the base function by using prior temperature field data and calculates the base coefficient by using sensor measurement data, so as to realize reconstruction of the temperature field.

[0040] 2. The temperature field is calculated as prior data by combining heat source information, heat conduction equation and finite difference theory, so as to improve the measurement accuracy of the temperature field.

[0041] 3. According to the greedy algorithm based on minimization of global temperature field RMSE, the local optimal temperature measurement point layout and number can be selected, the RMSE of the global temperature field is reduced, online reconstruction based on the multi-channel time synchronization temperature measurement system can utilize the accurate data of the platinum resistance temperature measurement, and the temperature measurement accuracy is effectively improved, and both of them can effectively improve the measurement accuracy of the temperature field.

[0042] 4. The method realizes high-precision temperature field measurement for quantum sensing technology, which is beneficial to optimize the performance of the gas chamber, and further improves the overall accuracy and working stability of the SERF atomic inertial gyroscope, and provides a solid technical guarantee for realizing higher-precision inertial measurement. BRIEF DESCRIPTION OF DRAWINGS

[0043] Figure 1 is a flowchart of the high-precision temperature field measurement method for quantum sensing technology. Figure 1The method comprises the following steps: step 1, combining heat source information, calculating a temperature field by heat conduction and finite difference theory, and obtaining temperature field prior data, wherein the temperature field prior data is related to a heat source, a heat conduction equation-model basis, and a finite difference method-numerical method; step 2, using the temperature field prior data to obtain temperature field base functions by eigenvalue orthogonal decomposition, using measured data to obtain temperature field base coefficients by a least square method, and realizing temperature field reconstruction by linear combination of the temperature field base functions and the temperature field base coefficients, wherein the temperature field reconstruction is related to base functions-temperature field modal decomposition and base coefficients-temperature field modal recombination; step 3, combining a global temperature field RMSE minimization greedy algorithm and the temperature field reconstruction algorithm, optimizing the spatial layout and the number of sensors in the temperature field, and the RMSE is a root mean square error; and step 4, combining the spatial layout of the sensors in the temperature field, and using data of a high-precision multi-channel time-synchronous temperature measurement system to realize online reconstruction of the temperature field of the alkali metal gas chamber.

[0044] Figure 2 is a temperature field reconstruction flowchart related to a high-precision temperature field measurement method for quantum sensing technology. Figure 2 The method comprises the following steps: step 1, obtaining temperature field snapshot data, including prior data and sensor data; step 2, calculating base functions by eigenvalue orthogonal decomposition according to the prior data, and calculating base coefficients by a least square method according to the sensor data; and step 3, calculating a reconstructed temperature field by using the base functions and the base coefficients.

[0045] Figure 3 is a hardware composition schematic diagram of online reconstruction of the temperature field of the alkali metal gas chamber based on the multi-channel time-synchronous temperature measurement system. Figure 3 The method comprises the following steps: n analog-to-digital conversion chips ADC (ADC-1, ADC-2,..., ADC-n, n is a positive integer) are arranged, each analog-to-digital conversion chip ADC is configured with a high-precision clock source and a clock fan-out chip, the n analog-to-digital conversion chips ADC are controlled through respective control pins, input ends of each analog-to-digital conversion chip ADC are connected with respective platinum resistance temperature sensors (PT1000-1, PT1000-2,..., PT1000-n), the n analog-to-digital conversion chips ADC are respectively connected with a single-chip microcomputer, the single-chip microcomputer is connected with an upper computer, the upper computer receives data transmitted from the single-chip microcomputer for temperature field reconstruction, and high-precision real-time reconstruction of the temperature field is realized by real-time calculation of base coefficients. DETAILED DESCRIPTION

[0046] The application will be described below in combination with the drawings ( Figures 1-3 ) and examples.

[0047] Figure 1 is a flowchart of implementation of the high-precision temperature field measurement method for quantum sensing technology. Figure 2It is a high-precision temperature field measurement method for quantum sensing technology, and the temperature field reconstruction process schematic diagram involved is shown. Figure 3 It is a hardware composition schematic diagram of alkali metal cell temperature field online reconstruction based on multi-channel time synchronization temperature measurement system. Figures 1 to 3 As shown in the figure, a high-precision temperature field measurement method for quantum sensing technology, comprising the following steps: step 1, combining heat source information, calculating temperature field as prior data through heat conduction and finite difference theory; step 2, using the prior data, using the least square method to obtain the base function of the temperature field, using the least square method to obtain the base coefficient, and using the linear combination of the base function and the base coefficient to realize the reconstruction of the temperature field; step 3, combining the greedy algorithm of global temperature field RMSE minimization and temperature field reconstruction algorithm, optimizing the spatial layout and number of sensors in the temperature field; step 4, combining the spatial layout of the sensor in the temperature field, using the temperature data of the high-precision multi-channel time synchronization temperature measurement system to realize the online reconstruction of the alkali metal cell temperature field.

[0048] Step 1 includes the following expressions:

[0049]

[0050] Where ρ is the fluid density, c p is the constant pressure specific heat capacity of the fluid, c represents the specific heat capacity of the fluid, p represents the constant pressure condition, is the partial derivative of the fluid temperature T with respect to time t, v is the fluid flow velocity field, is the vector differential operator, k represents the thermal conductivity of the fluid, Q rad is the heat absorbed by the fluid domain radiation, x is the spatial coordinate of the fluid at different positions, Ω is the fluid domain, is the boundary temperature T s derivative with respect to the normal direction, h is the convective heat transfer coefficient, T ∞ is the ambient temperature, ε is the surface emissivity of the cell wall, σ is the Stefan-Boltzmann constant, Γ is the boundary of the fluid domain, T(x,0) is the temperature value at position x at the initial time, T0(x) is a function of the temperature value at different positions at the initial time.

[0051] Step 1 includes the following expressions:

[0052]

[0053] Where Ra is the Rayleigh number, g is the acceleration of gravity, β is the thermal expansion coefficient of the fluid, ΔT is the temperature difference between the wall surface and the ambient fluid, L is the characteristic length, v is the kinematic viscosity of the fluid, and α is the thermal diffusivity of the fluid.

[0054] Step 1 includes the following expressions:

[0055]

[0056] where p is the fluid density, c p is the constant pressure specific heat capacity of the fluid, c denotes the specific heat capacity of the fluid, p denotes the constant pressure condition, is the partial derivative of the fluid temperature T with respect to time t, is the vector differential operator, k denotes the thermal conductivity of the fluid, x is the spatial coordinate of the fluid at different positions, and Ω is the fluid domain, is the boundary temperature T s , the partial derivative with respect to the normal direction, h is the convective heat transfer coefficient, T ∞ is the ambient temperature, ε is the surface emissivity of the gas chamber wall, σ is the Stefan-Boltzmann constant, Γ is the boundary of the fluid domain, T(x, 0) is the temperature value at the initial time x position, and T0(x) is a function of the temperature value at different positions at the initial time.

[0057] The following expression is included in step 2:

[0058]

[0059] The expression is a temperature field snapshot matrix expression of the fluid temperature T, the element T(x1, t1) in the matrix is the temperature field prior data of the first position x1 at the first time t1, T(x1, t n ) is the temperature field prior data of the first position x1 at the nth time t n , n is a positive integer, T(x m , t1) is the temperature field prior data of the mth position x m at the first time t1, m is the number of grid division of the gas chamber section, m = a * b, a is the number of row division of the gas chamber section, b is the number of column division of the gas chamber section, a and b are both positive integers, T(x m , t n ) is the temperature field prior data of the mth position x m at the nth time t n .

[0060] The following expression is included in step 2:

[0061]

[0062]

[0063] RA = λA,

[0064]

[0065] where T(x i ) is the temperature field prior data of the ith position x iThe average temperature over all times, where i is a positive integer, n is the number of sampling times, and j is the sampling time index, T(x) i ,t j ) is the i-th position x i At the j-th sampling time t j Temperature value, It is the i-th position x i At the i-th sampling time t i Temperature fluctuation value, T(x) i ,t i ) is the i-th position x i At the i-th sampling time t i Temperature value, It is a pulsation matrix, and the elements in the matrix are... It is the temperature fluctuation value at the first location x1 at the first sampling time t1. It is the first position x1 at the nth sampling time t n Temperature fluctuation value, It is the m-th position x m The temperature fluctuation value at the first sampling time t1, It is the m-th position x m At the nth sampling time t n The temperature fluctuation value, R is The correlation matrix, A is the eigenvector of λ, λ is the eigenvalue of R, ψ j (x) is the j-th basis function, j is the eigenvalue index, and λ j It is the j-th eigenvalue, A j It is the j-th eigenvector.

[0066] Step 2 includes the following expression:

[0067]

[0068] Where α is the basis coefficient, representing the weight of the basis function in the reconstructed temperature field. It is the real number field, r is a positive integer representing the number of basis functions, ψ s It is a matrix of values ​​for r basis functions at the sensor location, ψ s α is the reconstructed temperature field at the sensor location, and y is the temperature value measured by the sensor. Let ψ(x) represent the reconstructed temperature field, and let ψ(x) be the basis function corresponding to all eigenvalues.

[0069] Step 3 includes the following expression:

[0070]

[0071] Wherein avgRMSE(SUj) represents the root mean square error of the temperature field of the sensor index matrix S at all times when the jth sensor Uj is added, n is the number of sampling times, t is the sampling time sequence number, m is the number of grid division of the chamber cross section, i is the grid position sequence number, is the reconstructed temperature value of the j sensors at the i grid position at the sampling time t, y i (t) is the temperature value of the temperature field at the i grid position at the sampling time t.

[0072] The high-precision multi-channel time synchronization temperature measurement system in step 4 comprises an MCU module, a power module, a time synchronization module, a sampling module and a communication module, the MCU module adopts a single-chip microcomputer as a main control chip, is used for managing sensor data acquisition, and communicates with an upper computer; the power module is used for supplying power to each module to ensure normal work of the system; the time synchronization module provides a high-precision synchronous clock for an analog-to-digital conversion chip through a high-precision clock source and a clock fan-out chip; the sampling module adopts a combination of a platinum resistance and an analog-to-digital conversion chip to realize temperature acquisition through the platinum resistance sensitive temperature; the communication module is used for data exchange between the upper computer and the MCU module; real-time synchronous temperature data is uploaded to the upper computer through acquisition to provide measurement data for temperature field reconstruction, and real-time reconstruction of the temperature field is realized by real-time calculation of base coefficients.

[0073] The application provides a high-precision temperature field measurement method for quantum sensing technology, which is beneficial to guarantee the realization of a high-precision uniform temperature field and is also a necessary condition for realizing long-term stable operation of an alkali metal gas chamber. The method comprises the following steps: a greedy algorithm based on minimization of a global temperature field RMSE is used to optimize the layout of platinum resistors; prior data of time evolution of the temperature field of the alkali metal gas chamber is obtained based on heat source data, heat conduction theory and a finite difference method; base functions of the temperature field are obtained based on data-driven proper orthogonal decomposition using the prior data; real-time synchronous measurement of temperature is realized using a multi-channel time synchronization temperature measurement system; base coefficients are obtained by optimizing the RMSE of the temperature measurement points based on the least square method using the measurement data, and high-precision online reconstruction of the temperature field is realized.

[0074] As shown in Figure 1 A high-precision temperature field measurement method for quantum sensing technology provided by the application comprises the following steps: temperature field calculation as prior data is performed by combining heat source information and using heat conduction and finite difference theory; base functions of the temperature field are obtained by using the prior data and adopting proper orthogonal decomposition; base coefficients are obtained by using measurement data and adopting the least square method; temperature field reconstruction is realized by linear combination of the base functions and the base coefficients; a greedy algorithm based on minimization of a global temperature field RMSE is combined with a temperature field reconstruction algorithm to optimize the spatial layout and number of sensors in the temperature field; online reconstruction of the alkali metal gas chamber temperature field is realized by using a high-precision multi-channel time synchronization temperature measurement system in combination with the spatial layout of the sensors in the temperature field.

[0075] As Figure 2 shown, the temperature field reconstruction of the application includes obtaining temperature field prior data, base function and base coefficient calculation method; temperature field reconstruction calculation method.

[0076] As Figure 3 shown, the hardware composition of the alkali metal gas chamber temperature field online reconstruction based on the multi-channel time synchronization temperature measurement system mainly includes platinum resistance temperature sensor, high-precision clock source, clock fan-out chip, analog-to-digital conversion chip, single-chip microcomputer and upper computer.

[0077] Next, taking the alkali metal gas chamber temperature field reconstruction as an example for description.

[0078] A high-precision temperature field measurement method for quantum sensing technology, in step 1, the temperature field is calculated by combining heat source information through heat conduction and finite difference theory as prior data, that is, the alkali metal gas chamber temperature field snapshot data is obtained based on heat conduction and finite difference theory; when the oven is heated, the temperature field of the gas chamber can be described as:

[0079]

[0080] The first equation is for calculating the temperature values at different times and different positions in the fluid domain. Wherein, x represents the spatial coordinates of the fluid at different positions; Ω represents the fluid domain; ρ represents the fluid density; Q is the heat source; in c p , c represents the specific heat capacity of the fluid, p represents the constant pressure condition, c p represents the constant pressure specific heat capacity of the fluid; T represents the fluid temperature; represents the operator of partial derivative with respect to time t, represents the partial derivative of fluid temperature with respect to time; v represents the fluid velocity field; is a vector differential operator, T represents a vector composed of the partial derivative arrays of fluid temperature T in different coordinate axes in the Cartesian coordinate system; k represents the thermal conductivity of the fluid; Q rad represents the heat absorbed by the fluid domain radiation;

[0081] The second equation is for calculating the temperature values at different times and different positions on the boundary of the fluid domain. Γ represents the boundary of the fluid domain; T s represents the temperature value at the boundary; represents the derivative with respect to the normal direction, represents the boundary normal temperature gradient; h is the convective heat transfer coefficient, which represents the speed of convective heat transfer at the boundary; T ∞ represents the ambient temperature; ε is the surface emissivity of the gas chamber wall; σ represents the Stefan-Boltzmann constant;

[0082] The third equation gives the initial temperature values at different positions in the fluid domain. T(x, y) represents the temperature at position x at time y, so T(x, 0) represents the temperature at position x at the initial time; T0(x) represents a function of the temperature values at different positions at the initial time.

[0083] Whether convection occurs in the fluid can be calculated by comparing the Rayleigh number Ra with the critical Rayleigh number. The critical Rayleigh number can be obtained by consulting the literature. When the Rayleigh number Ra is greater than the critical Rayleigh number, natural convection occurs in the fluid domain, otherwise, natural convection does not occur.

[0084] The Rayleigh number Ra is generally expressed as:

[0085]

[0086] where Ra represents the Rayleigh number, which is a dimensionless parameter; g represents the acceleration of gravity; β represents the thermal expansion coefficient of the fluid; ΔT represents the temperature difference between the wall surface and the ambient fluid; L represents the characteristic length, which is generally the side length or diameter of the alkali metal gas chamber (such as the diameter of a sphere, the side length of a cube, etc.); v represents the kinematic viscosity of the fluid, and α represents the thermal diffusivity of the fluid.

[0087] Taking a 10mm side length cube alkali metal gas chamber filled with neon gas, g is taken as 9.81m / s 2 , β is 1 / T, the boundary condition is lower than the heating rate 10℃ / s, ΔT is the maximum temperature difference taken as 10℃, L is the side length of the cube taken as 10mm, the kinematic viscosity v of Ne is 3.9*10 - 5 m 2 / s, the thermal diffusivity α of Ne is 5.8*10 -5 m 2 / s. By substituting the data, the Rayleigh number Ra of Ne is calculated as 1.45*10 2 , which is much smaller than the critical Rayleigh number of 1700 for natural convection, so there is no convection phenomenon in the gas chamber. At the same time, since Ne is a single-atom inert gas, it has almost no vibration and rotation energy level transition in the infrared wave band, so it basically does not absorb thermal radiation, and the thermal radiation term can be ignored.

[0088] Therefore, the above differential equation can be simplified as:

[0089]

[0090] where ρ is 0.760g / L, c p is 1.03*10 3 J / (kg*K), k is 0.0496W / (m*K), h is taken as 5W / (m 2 *K), and ε is taken as 0.9.

[0091] The above equation can be calculated by finite difference method.

[0092] In this example, the temperature field of the center section of the gas chamber is taken, the number of grid points is 121, and the number of time steps is 6001. The transient temperature field of the gas chamber at different times can be obtained by the above calculation as the prior data.

[0093] Regarding the step 2, the base functions of the temperature field are obtained by using the prior data and the eigenvalue orthogonal decomposition, the base coefficients are obtained by using the measured data and the least square method, and the reconstruction of the temperature field is realized by using the linear combination of the base functions and the base coefficients. In this example, the reconstruction method of the temperature field of the alkali metal gas chamber in the prior art is used, the base functions of the temperature field are obtained by using the prior data and the eigenvalue orthogonal decomposition, and the reconstruction of the temperature field is realized by using the base coefficients obtained by using the measured data and the least square method. Figure 2

[0094] The step 2 includes the step 201, the calculated prior data T is composed of the following snapshot matrix:

[0095]

[0096] Wherein, m is the number of points of the gas chamber, which is 121, and n is the number of sampled snapshots, which is 6001.

[0097] The step 202, the fluctuation matrix is obtained by averaging each temperature point:

[0098]

[0099] The step 203, the correlation matrix R of is calculated, and the base function ψ(x) is obtained by using the eigenvalue and eigenvector:

[0100]

[0101] RA=λA

[0102]

[0103] The step 204, the base function is used to describe the main characteristics of the system, and the energy of the first r base functions accounts for 99% of the energy of the full-order base function:

[0104]

[0105] In the above formula, the minimum r is taken to make E(r)≥99%.

[0106] The step 205, the selected base function is ψ(x)=[ψ1,…,ψ r ], and the sensor index matrix is S={s1,…,s k}, wherein s​​k s k represents the position of the kth sensor in the finite difference grid, e.g. s k = (i k, j k ), where i k and j k are the row and column indices of the kth sensor, respectively. k s k represents the position of the kth sensor in the finite difference grid, e.g. s k = (i k, j k ), where i k and j k are the row and column indices of the kth sensor, respectively. a*(u-1)+v s k represents the position of the kth sensor in the finite difference grid, e.g. s k = (i k, j k ), where i k and j k are the row and column indices of the kth sensor, respectively. s s k represents the position of the kth sensor in the finite difference grid, e.g. s k = (i k, j k ), where i k and j k are the row and column indices of the kth sensor, respectively. sensor s k represents the position of the kth sensor in the finite difference grid, e.g. s k = (i k, j k ), where i k and j k are the row and column indices of the kth sensor, respectively.

[0107]

[0108] s k represents the position of the kth sensor in the finite difference grid, e.g. s k = (i k, j k ), where i k and j k are the row and column indices of the kth sensor, respectively. sensor s k represents the position of the kth sensor in the finite difference grid, e.g. s k = (i k, j k ), where i k and j k are the row and column indices of the kth sensor, respectively. s k represents the position of the kth sensor in the finite difference grid, e.g. s k = (i k, j k ), where i k and j k are the row and column indices of the kth sensor, respectively. j s k represents the position of the kth sensor in the finite difference grid, e.g. s k = (i k, j k ), where i k and j k are the row and column indices of the kth sensor, respectively.

[0109] s k represents the position of the kth sensor in the finite difference grid, e.g. s k = (i k, j k ), where i k and j k are the row and column indices of the kth sensor, respectively. sensor s k represents the position of the kth sensor in the finite difference grid, e.g. s k = (i k, j k ), where i k and j k are the row and column indices of the kth sensor, respectively.

[0110]

[0111] s k represents the position of the kth sensor in the finite difference grid, e.g. s k = (i k, j k ), where i k and j k are the row and column indices of the kth sensor, respectively. s s k represents the position of the kth sensor in the finite difference grid, e.g. s k = (i k, j k ), where i k and j k are the row and column indices of the kth sensor, respectively.

[0112]

[0113] s k represents the position of the kth sensor in the finite difference grid, e.g. s k = (i k, j k ), where i k and j k are the row and column indices of the kth sensor, respectively.

[0114] s k represents the position of the kth sensor in the finite difference grid, e.g. s k = (i k, j k ), where i k and j k are the row and column indices of the kth sensor, respectively.

[0115] s k represents the position of the kth sensor in the finite difference grid, e.g. s k = (i k, j k ), where i k and j k are the row and column indices of the kth sensor, respectively.

[0116]

[0117] Wherein, avgRMSE(SUj) represents the sensor index matrix S, the root mean square error of the temperature field at all times when j sensors are added, represents the temperature value reconstructed by the sensor at position j, i, t, y i (t) represents the temperature value of the temperature field at i, t.

[0118] After adding sensors at different positions, the avgRMSE will continue to decrease as the number increases, and the optimal solution of the number of sensors is obtained according to the actual needs. The representation method of the temperature measuring point position is: from the first row and the first column of the square, the number 1 is represented, and the number (n-1)*11+n is represented in the nth row and the nth column.

[0119] Regarding the combination of the sensor in the temperature field in step 4, the data of the high-precision multi-channel time synchronization temperature measurement system is used to realize the online reconstruction of the alkali gas chamber temperature field, and the specific conditions are as follows:

[0120] According to the reconstruction method of the alkali gas chamber temperature field in step 2, the present example uses the hardware as shown in Figure 3 The hardware is composed of five parts of MCU module, power module, time synchronization module, sampling module and communication module. The MCU module samples 32 single-chip microcomputers as the main control chip, manages the sensor data acquisition, and communicates with the upper computer; the power module supplies power for the other four modules to ensure the normal work of the system; the time synchronization module provides high-precision synchronous clock for the analog-to-digital conversion chip through a high-precision clock source and a clock fan-out chip; the sampling module is composed of a platinum resistance and an analog-to-digital conversion chip, and realizes temperature acquisition through the platinum resistance sensitive temperature; the communication module is used for data exchange between the upper computer and the MCU. Collect real-time synchronous temperature data and upload to the upper computer to provide accurate data for temperature field reconstruction, and realize high-precision real-time reconstruction of the temperature field by real-time calculation of the base coefficient.

[0121] The contents not described in detail in the specification of the present application belong to the prior art known to those skilled in the art. It is indicated here that the above description is helpful for those skilled in the art to understand the present application, but does not limit the protection scope of the present application. Any equivalent replacement, modification, improvement and / or deletion of the above description without departing from the essential content of the present application falls within the protection scope of the present application.

Claims

1. A high-precision temperature field measurement method for quantum sensing technology, characterized in that, Includes the following steps: Step 1: Combine the heat source information with the calculation of the temperature field using heat conduction and finite difference theory as prior data; Step 2: Using the prior data, the basis functions of the temperature field are obtained by intrinsic orthogonal decomposition. The basis coefficients are obtained by the least squares method using the sensor measurement data. The temperature field is reconstructed by the linear combination of the basis functions and basis coefficients. Step 3: Combine the greedy algorithm for minimizing the global temperature field RMSE with the temperature field reconstruction algorithm to optimize the spatial layout and number of sensors in the temperature field. Step 4: Combining the spatial layout of the sensors in the temperature field, the temperature data from a high-precision multi-channel time-synchronous temperature measurement system is used to realize the online reconstruction of the temperature field of the alkali metal gas chamber.

2. The high-precision temperature field measurement method for quantum sensing technology according to claim 1, characterized in that, Step 1 includes the following expression: Where ρ is the fluid density, and c p It is the constant-pressure specific heat capacity of the fluid, where c represents the specific heat capacity of the fluid and p represents the constant-pressure condition. It is the partial derivative of fluid temperature T with respect to time t, and v is the fluid velocity field. It is a vector differential operator, where k represents the thermal conductivity of the fluid, and Q... rad Ω represents the heat absorbed by the fluid domain through radiation, x is the spatial coordinate of the fluid at different locations, and Ω is the fluid domain. It is the boundary temperature T s The partial derivative with respect to the normal direction, h, is the convective heat transfer coefficient, T. ∞ ε is the ambient temperature, σ is the surface emissivity of the chamber wall, Γ is the Stefan-Boltzmann constant, T(x,0) is the temperature value at position x at the initial time, and T0(x) is a function of the temperature values ​​at different positions at the initial time.

3. The high-precision temperature field measurement method for quantum sensing technology according to claim 1, characterized in that, Step 1 includes the following expression: Where Ra is the Rayleigh number, g is the gravitational acceleration, β is the coefficient of thermal expansion of the fluid, ΔT is the temperature difference between the wall and the ambient fluid, L is the characteristic length, v is the kinematic viscosity of the fluid, and α is the thermal diffusivity of the fluid.

4. The high-precision temperature field measurement method for quantum sensing technology according to claim 1, characterized in that, Step 1 includes the following expression: Where ρ is the fluid density, and c p It is the constant-pressure specific heat capacity of the fluid, where c represents the specific heat capacity of the fluid and p represents the constant-pressure condition. It is the partial derivative of fluid temperature T with respect to time t. It is a vector differential operator, where k represents the thermal conductivity of the fluid, x is the spatial coordinate of the fluid at different locations, and Ω is the fluid domain. It is the boundary temperature T s The partial derivative with respect to the normal direction, h, is the convective heat transfer coefficient, T. ∞ ε is the ambient temperature, σ is the surface emissivity of the chamber wall, Γ is the Stefan-Boltzmann constant, T(x,0) is the temperature value at position x at the initial time, and T0(x) is a function of the temperature values ​​at different positions at the initial time.

5. The high-precision temperature field measurement method for quantum sensing technology according to claim 1, characterized in that, Step 2 includes the following expression: This expression is a snapshot matrix expression for the temperature field of the fluid temperature T. The element T(x1,t1) in the matrix represents the prior temperature field data at position x1 at time t1. n () is the first position x1 at time n t n Prior temperature field data, where n is a positive integer, T(x) m ,t1) is the m-th position x m Given the prior temperature field data at time t1, the number of grids in the m-cell cross-section is given by m = a * b, where a is the number of rows and b is the number of columns in the cross-section, and both a and b are positive integers. T(x m ,t n ) is the m-th position x m At time n t n Prior temperature field data.

6. The high-precision temperature field measurement method for quantum sensing technology according to claim 1, characterized in that, Step 2 includes the following expression: RA = λA, Where T(x) i ) is the i-th position x i The average temperature over all times, where i is a positive integer, n is the number of sampling times, and j is the sampling time index, T(x) i ,t j ) is the i-th position x i At the j-th sampling time t j Temperature value, It is the i-th position x i At the i-th sampling time t i Temperature fluctuation value, T(x) i ,t i ) is the i-th position x i At the i-th sampling time t i Temperature value, It is a pulsation matrix, and the elements in the matrix are... It is the temperature fluctuation value at the first location x1 at the first sampling time t1. It is the first position x1 at the nth sampling time t n Temperature fluctuation value, It is the m-th position x m The temperature fluctuation value at the first sampling time t1, It is the m-th position x m At the nth sampling time t n The temperature fluctuation value, R is The correlation matrix, A is the eigenvector of λ, λ is the eigenvalue of R, ψ j (x) is the j-th basis function, j is the eigenvalue index, and λ j It is the j-th eigenvalue, A j It is the j-th eigenvector.

7. The high-precision temperature field measurement method for quantum sensing technology according to claim 1, characterized in that, Step 2 includes the following expression: Where α is the basis coefficient, representing the weight of the basis function in the reconstructed temperature field. It is the real number field, r is a positive integer representing the number of basis functions, ψ s It is a matrix of values ​​for r basis functions at the sensor location, ψ s α is the reconstructed temperature field at the sensor location, and y is the temperature value measured by the sensor. Let ψ(x) represent the reconstructed temperature field, and let ψ(x) be the basis function corresponding to all eigenvalues.

8. The high-precision temperature field measurement method for quantum sensing technology according to claim 1, characterized in that, Step 3 includes the following expression: Where avgRMSE(SUj) represents the root mean square error of the temperature field at all times when the j-th sensor Uj is added to the sensor index matrix S, n is the number of sampling times, t is the sampling time sequence, m is the number of grids in the gas chamber cross-section, and i is the grid position sequence. y is the reconstructed temperature value at the i-th grid position of j sensors at sampling time t. i (t) is the temperature value of the temperature field at the i-th grid position at sampling time t.

9. The high-precision temperature field measurement method for quantum sensing technology according to claim 1, characterized in that, The high-precision multi-channel time-synchronized temperature measurement system in step 4 includes an MCU module, a power supply module, a time synchronization module, a sampling module, and a communication module. The MCU module uses a microcontroller as the main control chip to manage sensor data acquisition and communicate with the host computer. The power supply module supplies power to each module to ensure normal system operation. The time synchronization module provides a high-precision synchronous clock to the analog-to-digital converter chip through a high-precision clock source and a clock fan-out chip. The sampling module uses a combination of platinum resistance thermometer and analog-to-digital converter chip to acquire temperature data by sensing the temperature through the platinum resistance thermometer. The communication module is used for data exchange between the host computer and the MCU module. By collecting real-time synchronized temperature data and uploading it to the host computer, measurement data is provided for temperature field reconstruction, and the temperature field is reconstructed in real time by calculating the basic coefficients.

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