Non-magnetic ultralow-temperature vacuum system and method based on quantum sensing closed-loop control

Abstract

The invention discloses a non-magnetic ultralow-temperature vacuum system and method based on quantum sensing closed-loop control, and relates to the technical field of monitoring analysis, and the method comprises the steps: carrying out the real-time endogenous magnetic field noise monitoring of the non-magnetic ultralow-temperature vacuum system, collecting the real-time endogenous magnetic field noise spectrum data, and carrying out the real-time endogenous magnetic field noise spectrum data; according to the real-time endogenous magnetic field noise spectrum data, association relationship mining between a magnetic field noise frequency component and a quantum bit decoherence rate is carried out to obtain magnetic field-decoherence association characteristic data, and a multi-physics coupling suppression control strategy is generated based on the magnetic field-decoherence association characteristic data. And according to the multi-physical field coupling suppression control strategy data, adaptive adjustment of the cooling power of the cryogenic refrigerator and active damping control parameter optimization of the vibration suppression device are carried out, and environment optimization control instruction data are generated. The method has the effect of improving the stability of the non-magnetic ultralow-temperature vacuum environment.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of monitoring analysis, in particular to a non-magnetic ultra-low temperature vacuum system and method based on quantum sensing closed-loop control. BACKGROUND

[0002] In the field of quantum technology, maintaining a non-magnetic ultra-low temperature vacuum environment is the key to ensuring the long coherence time of solid-state quantum bits. The existing technology usually uses a split refrigerator and a flexible cold conducting component to suppress mechanical vibration transmission, and relies on accelerometers and temperature sensors for monitoring and control. However, this method has fundamental limitations: mechanical vibration and thermal fluctuations can couple to produce weak but sufficient induced magnetic fields inside the system through the magnetostriction effect of materials or structural displacement currents. This "endogenous" magnetic field noise from the system's own dynamic processes cannot be directly detected by traditional sensors, resulting in a blind spot in the existing feedback control strategy based on vibration and temperature signals, which cannot specifically suppress the physical root cause of quantum decoherence. Therefore, it is difficult to achieve a truly stable "non-magnetic" environment, and there is room for improvement. SUMMARY

[0003] In view of the deficiencies of the prior art, the present application provides a non-magnetic ultra-low temperature vacuum system and method based on quantum sensing closed-loop control.

[0004] In a first aspect, the present application provides a non-magnetic ultra-low temperature vacuum system based on quantum sensing closed-loop control, comprising: An acquisition and analysis module is configured to obtain a system structure diagram of a non-magnetic ultra-low temperature vacuum system, ultra-low temperature refrigerator operating parameters, vibration suppression device configuration parameters, and quantum bit long coherence time reference. Finite element analysis of mechanical vibration transmission path and thermal fluctuation distribution is performed based on the system structure diagram, thereby obtaining vibration transmission path characteristic data and thermal fluctuation distribution characteristic data. An optimization and deployment module is configured to calculate the coupling field strength of the magnetostriction effect induced magnetic field and the structural displacement current induced magnetic field based on the vibration transmission path characteristic data and the thermal fluctuation distribution characteristic data, obtain endogenous magnetic field noise field strength prediction data, select the type of quantum sensing probe based on the endogenous magnetic field noise field strength prediction data, and optimize the spatial layout to generate quantum sensing probe optimization and deployment data. A relationship mining module is configured to configure a quantum sensor array in the non-magnetic ultra-low temperature vacuum system based on the quantum sensing probe optimization and deployment data, and to perform real-time endogenous magnetic field noise monitoring on the non-magnetic ultra-low temperature vacuum system. Real-time endogenous magnetic field noise spectrum data is obtained by acquisition. The correlation between the magnetic field noise frequency component and the quantum bit decoherence rate is mined based on the real-time endogenous magnetic field noise spectrum data, and magnetic field-decoherence correlation characteristic data is obtained. The adjustment control module is used for generating a multi-physical field coupling suppression control strategy based on the magnetic field-decoherence correlation feature data, obtaining multi-physical field coupling suppression control strategy data, and performing adaptive adjustment of the cooling power of the ultra-low temperature refrigerator and optimization of the active damping control parameters of the vibration suppression device according to the multi-physical field coupling suppression control strategy data to generate environment optimization control instruction data; and performing quantum sensing feedback closed-loop control of the non-magnetic ultra-low temperature vacuum environment through the environment optimization control instruction data, and adjusting the working state of the ultra-low temperature refrigerator and the vibration suppression device in real time, so as to obtain an optimized non-magnetic ultra-low temperature vacuum environment.

[0005] Preferably, the process of obtaining the vibration transmission path feature data and the thermal fluctuation distribution feature data specifically comprises: obtaining a system structure diagram of the non-magnetic ultra-low temperature vacuum system, ultra-low temperature refrigerator working parameters, vibration suppression device configuration parameters, and quantum bit long coherence time reference; performing structure vectorization processing on the system structure diagram to generate a system structure vector diagram; extracting a mechanical vibration transmission path and identifying a thermal fluctuation distribution region from the system structure vector diagram to obtain vibration transmission path data and thermal fluctuation distribution data; performing finite element analysis based on the vibration transmission path data, the thermal fluctuation distribution data, the ultra-low temperature refrigerator working parameters, the vibration suppression device configuration parameters, and the quantum bit long coherence time reference, thereby obtaining the vibration transmission path feature data and the thermal fluctuation distribution feature data.

[0006] Preferably, the process of performing finite element analysis based on the vibration transmission path data, the thermal fluctuation distribution data, the ultra-low temperature refrigerator working parameters, the vibration suppression device configuration parameters, and the quantum bit long coherence time reference, thereby obtaining the vibration transmission path feature data and the thermal fluctuation distribution feature data, specifically comprises: constructing a three-dimensional finite element model of the non-magnetic ultra-low temperature vacuum system based on the vibration transmission path data, the thermal fluctuation distribution data, the ultra-low temperature refrigerator working parameters, and the vibration suppression device configuration parameters, and further generating finite element model data; applying multi-physical field loads to the finite element model data, thereby obtaining loaded finite element model data; performing structure dynamics modal solving based on the loaded finite element model data, extracting resonance frequencies, vibration mode displacements, and energy transmission functions under each modal, and obtaining structure vibration modal data; performing transient thermal-mechanical coupling field solving based on the loaded finite element model data, calculating temperature field, stress field, and deformation field distributions under the joint action of thermal loads and vibration loads, and obtaining thermal-mechanical coupling field data; Coherence time sensitive feature filtering is performed on the structural vibration mode data and the thermo-mechanical coupling field data based on the quantum bit long coherence time reference, so as to obtain vibration transmission path feature data and thermal fluctuation distribution feature data.

[0007] Preferably, based on the endogenous magnetic field noise field strength prediction data, the type selection and spatial layout optimization of the quantum sensing probe are performed, and quantum sensing probe optimization deployment data is generated, specifically including: Extract the vibration frequency spectrum in the vibration transmission path feature data and the temperature gradient distribution in the thermal fluctuation distribution feature data; According to the vibration frequency spectrum and the temperature gradient distribution, magnetostrictive effect induced magnetic field strength calculation and structural displacement current induced magnetic field strength calculation are performed respectively, to obtain magnetostrictive induced magnetic field data and displacement current induced magnetic field data; The magnetostrictive induced magnetic field data and the displacement current induced magnetic field data are coupled field strength superposition calculation, to generate endogenous magnetic field noise field strength prediction data; Based on the endogenous magnetic field noise field strength prediction data, the type selection and spatial layout optimization of the quantum sensing probe are performed, and quantum sensing probe optimization deployment data is generated.

[0008] Preferably, according to the vibration frequency spectrum and the temperature gradient distribution, magnetostrictive effect induced magnetic field strength calculation and structural displacement current induced magnetic field strength calculation are performed respectively, to obtain magnetostrictive induced magnetic field data and displacement current induced magnetic field data, specifically including: According to the vibration frequency spectrum, material magnetostrictive coefficient matching is performed to obtain magnetostrictive coefficient matching data; Based on the magnetostrictive coefficient matching data and the temperature gradient distribution, vibration induced magnetic field strength integral operation is performed to generate vibration induced magnetic field strength data; According to the temperature gradient distribution, structural displacement current density calculation is performed to obtain displacement current density data; Based on the displacement current density data, displacement current excitation magnetic field strength vector synthesis is performed to generate displacement current excitation magnetic field strength data; The vibration induced magnetic field strength data and the displacement current excitation magnetic field strength data are coupled field strength superposition calculation, to generate magnetostrictive induced magnetic field data and displacement current induced magnetic field data.

[0009] Preferably, according to the real-time endogenous magnetic field noise spectrum data, the correlation between the magnetic field noise frequency component and the quantum bit decoherence rate is mined to obtain magnetic field-decoherence correlation feature data, specifically including: According to the quantum sensing probe optimization deployment data, a quantum sensor array is configured in the non-magnetic ultra-low temperature vacuum system; The real-time endogenous magnetic field noise of the non-magnetic ultra-low temperature vacuum system is monitored by using the configured quantum sensor array, and real-time endogenous magnetic field noise spectrum data is collected; The real-time endogenous magnetic field noise spectrum data is decomposed in the frequency domain to obtain magnetic field noise frequency component data; The quantum bit decoherence rate data is obtained by calculating the quantum bit decoherence rate according to the magnetic field noise frequency component data; The magnetic field-decoherence correlation characteristic data is obtained by mining the correlation relationship based on the magnetic field noise frequency component data and the quantum bit decoherence rate data.

[0010] Preferably, the real-time endogenous magnetic field noise spectrum data is decomposed in the frequency domain to obtain the magnetic field noise frequency component data, which specifically includes: The real-time endogenous magnetic field noise spectrum data is Fourier transformed to obtain magnetic field noise frequency spectrum data; The main frequency component data is generated by extracting the main frequency component based on the magnetic field noise frequency spectrum data; The harmonic component data is obtained by identifying the harmonic component of the main frequency component data; The magnetic field noise frequency component data is obtained by decomposing the frequency domain according to the harmonic component data.

[0011] Preferably, the implementation process of the adjustment control module includes: The multi-physical field coupling suppression control strategy is generated based on the magnetic field-decoherence correlation characteristic data, and the generation of the strategy includes: identifying a target magnetic field frequency component that affects the quantum bit decoherence rate more than a preset threshold, and formulating a corresponding cooling power adjustment and active damping control scheme for the target magnetic field frequency component to obtain multi-physical field coupling suppression control strategy data; The cooling power adjustment rule data is generated by designing the ultra-low temperature refrigerator cooling power self-adaptive adjustment rule according to the multi-physical field coupling suppression control strategy data; The damping control parameter optimization data is generated by optimizing the active damping control parameters of the vibration suppression device based on the multi-physical field coupling suppression control strategy data; The environmental optimization control instruction data is obtained by generating the environmental optimization control instruction through the cooling power adjustment rule data and the damping control parameter optimization data; The quantum sensing feedback closed-loop control of the non-magnetic ultra-low temperature vacuum environment is performed by using the environmental optimization control instruction data, and the working state of the ultra-low temperature refrigerator and the vibration suppression device is adjusted in real time, thereby obtaining the optimized non-magnetic ultra-low temperature vacuum environment.

[0012] Preferably, the damping control parameter optimization data is generated by optimizing the active damping control parameters of the vibration suppression device based on the multi-physical field coupling suppression control strategy data, which specifically includes: According to the multi-physical field coupling suppression control strategy data, the damping coefficient of the vibration suppression device is matched with the vibration transfer function, and damping coefficient-transfer function matching data is obtained; Based on the damping coefficient-transfer function matching data, active damping control parameter optimization is performed to generate damping control parameter optimization data; The damping control parameter optimization data is simulated and verified for vibration suppression effect, so as to obtain the optimized damping control parameters.

[0013] In a second aspect, the application provides a non-magnetic ultra-low temperature vacuum method based on quantum sensing closed-loop control, comprising the following steps: Obtain the system structure diagram of the non-magnetic ultra-low temperature vacuum system, the ultra-low temperature refrigerator operating parameters, the vibration suppression device configuration parameters and the quantum bit long coherence time reference, based on the system structure diagram, perform finite element analysis on the mechanical vibration transmission path and the thermal fluctuation distribution, thereby obtaining vibration transmission path characteristic data and thermal fluctuation distribution characteristic data; According to the vibration transmission path characteristic data and the thermal fluctuation distribution characteristic data, the coupling field intensity of the magnetostrictive effect induced magnetic field and the structure displacement current induced magnetic field is calculated, the endogenous magnetic field noise field intensity prediction data is obtained, based on the endogenous magnetic field noise field intensity prediction data, the type selection and spatial layout optimization of the quantum sensing probe is performed, and the quantum sensing probe optimization deployment data is generated; According to the quantum sensing probe optimization deployment data, the quantum sensor array is configured in the non-magnetic ultra-low temperature vacuum system, and the real-time endogenous magnetic field noise of the non-magnetic ultra-low temperature vacuum system is monitored, and the real-time endogenous magnetic field noise spectrum data is collected, and the correlation between the magnetic field noise frequency component and the quantum bit decoherence rate is mined according to the real-time endogenous magnetic field noise spectrum data, and the magnetic field-decoherence correlation characteristic data is obtained; Based on the magnetic field-decoherence correlation characteristic data, the multi-physical field coupling suppression control strategy is generated, the multi-physical field coupling suppression control strategy data is obtained, the ultra-low temperature refrigerator cooling power self-adaptive adjustment and vibration suppression device active damping control parameter optimization are performed according to the multi-physical field coupling suppression control strategy data, and the environment optimization control instruction data is generated; the quantum sensing feedback closed-loop control of the non-magnetic ultra-low temperature vacuum environment is performed through the environment optimization control instruction data, the working state of the ultra-low temperature refrigerator and the vibration suppression device is adjusted in real time, and the optimized non-magnetic ultra-low temperature vacuum environment is obtained.

[0014] In summary, the application has at least one of the following beneficial technical effects: 1. The application provides a magnetic-free ultra-low temperature vacuum system based on quantum sensing closed-loop control, which monitors the endogenous magnetic field noise generated by mechanical vibration and thermal fluctuation coupling in real time through a quantum sensor array, overcomes the blind area of traditional accelerometers and temperature sensors that cannot detect such noise, can directly identify and suppress the magnetic field interference that causes quantum decoherence from the physical root, and thus significantly improves the purity of the magnetic-free environment; 2. By exploiting the correlation between the frequency components of the magnetic field noise and the decoherence rate of the quantum bit, a targeted multi-physical field coupling suppression control strategy is generated, the cooling power of the ultra-low temperature refrigerator and the damping parameters of the vibration suppression device are dynamically adjusted, adaptive closed-loop control is realized, the decoherence rate is reduced, and the quantum bit is stably operated under long coherence time, providing a more reliable environmental basis for quantum computing and quantum sensing applications; 3. Based on finite element analysis to predict the strength of endogenous magnetic field noise, and accordingly optimize the type selection and spatial deployment of quantum sensing probes, avoid the redundancy and blindness of traditional trial-and-error layout, improve the monitoring accuracy and system integration, and reduce the hardware cost and maintenance complexity. BRIEF DESCRIPTION OF DRAWINGS

[0015] In order to more clearly illustrate the technical solutions of the embodiments of the application, the following will briefly introduce the drawings needed to be used in the embodiment description. Obviously, the drawings in the following description are only some embodiments of the application, and other drawings can also be obtained by those skilled in the art without creative labor.

[0016] Figure 1 is a system schematic diagram of the magnetic-free ultra-low temperature vacuum system based on quantum sensing closed-loop control according to the embodiments of the application.

[0017] Figure 2 is a method flowchart of the magnetic-free ultra-low temperature vacuum system based on quantum sensing closed-loop control according to the embodiments of the application. DETAILED DESCRIPTION

[0018] The following will be described in detail with reference to the drawings. Figures 1-2 The application will be further described in detail.

[0019] Embodiment 1 The embodiments of the application disclose a magnetic-free ultra-low temperature vacuum system based on quantum sensing closed-loop control.

[0020] Referring to Figure 1 , the magnetic-free ultra-low temperature vacuum system based on quantum sensing closed-loop control comprises: The acquisition and analysis module is used for acquiring a system structure diagram of a non-magnetic ultralow-temperature vacuum system, working parameters of an ultralow-temperature refrigerator, configuration parameters of a vibration suppression device, and a quantum bit long coherence time benchmark, performing finite element analysis on a mechanical vibration transmission path and a thermal fluctuation distribution based on the system structure diagram, and thus obtaining vibration transmission path characteristic data and thermal fluctuation distribution characteristic data. An example of the process of obtaining the vibration transmission path characteristic data and the thermal fluctuation distribution characteristic data specifically includes the following steps. The system structure diagram of the non-magnetic ultralow-temperature vacuum system, the working parameters of the ultralow-temperature refrigerator, the configuration parameters of the vibration suppression device, and the quantum bit long coherence time benchmark are acquired. The system structure diagram is subjected to structure vectorization processing to generate a system structure vector diagram. The system structure vector diagram is subjected to mechanical vibration transmission path extraction and thermal fluctuation distribution region identification to obtain vibration transmission path data and thermal fluctuation distribution data. Finite element analysis is performed based on the vibration transmission path data, the thermal fluctuation distribution data, the working parameters of the ultralow-temperature refrigerator, the configuration parameters of the vibration suppression device, and the quantum bit long coherence time benchmark, and thus the vibration transmission path characteristic data and the thermal fluctuation distribution characteristic data are obtained.

[0021] Specifically, in the embodiments of the present application, after obtaining the system structure diagram of the non-magnetic ultra-low temperature vacuum system, the working parameters of the ultra-low temperature refrigerator, the configuration parameters of the vibration suppression device, and the long coherence time reference of the quantum bit, first, the system structure diagram is subjected to structure vectorization processing, the geometric contour features in the drawing are extracted through a boundary tracking algorithm, the two-dimensional drawing information is converted into a three-dimensional vector model containing point, line, and surface topological relationships, and an accurate system structure vector diagram is generated; on this basis, the system structure vector diagram is subjected to mechanical vibration transmission path extraction and thermal fluctuation distribution area identification, wherein the vibration transmission path extraction adopts a connectivity analysis method based on graph theory, a vibration transmission network is established by calculating the contact stiffness and connection relationship between components, and the main transmission path is identified in combination with the material density distribution and the structure stiffness matrix, while the thermal fluctuation distribution area identification is performed through a heat flow path analysis method, a thermal resistance network model is established according to the material thermal conductivity and the structure geometric features, the key thermal nodes such as the refrigerator cold head contact area, the radiation screen structure, and the vacuum cavity are identified, and finally the vibration transmission path data and the thermal fluctuation distribution data containing the path weight and the thermal characteristics are obtained, then based on the above data and in combination with the cooling power curve and the temperature stability index in the working parameters of the ultra-low temperature refrigerator, the damping coefficient and the stiffness characteristics in the configuration parameters of the vibration suppression device, and the vibration and thermal fluctuation tolerance threshold corresponding to the long coherence time reference of the quantum bit, finite element analysis is performed, the analysis process first establishes a three-dimensional finite element model containing material nonlinear characteristics, wherein the working parameters of the ultra-low temperature refrigerator are used to define the non-uniform thermal boundary conditions of the model, and the configuration parameters of the vibration suppression device are integrated into the system dynamics equation in the form of a damping matrix, in the loading stage, multi-point harmonic excitation is set according to the vibration transmission path data, and non-steady-state thermal load is set according to the thermal fluctuation distribution data in combination with the refrigerator parameters, by solving the coupled mechanical-thermal control equation, first, the structural dynamics modal analysis is performed to extract the resonance frequency and the corresponding vibration mode displacement distribution of the system under each modal, and at the same time, the transient thermal-mechanical coupling field analysis is performed to obtain the temperature field gradient distribution and the thermal stress field generated thereby under the action of the alternating thermal load, finally, based on the allowable limit determined by the long coherence time reference of the quantum bit, the analysis results are subjected to coherence time sensitivity characteristic filtering, specifically, the correlation strength between the vibration energy distribution of each modal and the dephasing rate is calculated by weighted integral, and the influence degree of the thermal stress field on the quantum bit energy level stability is evaluated by combining the temperature fluctuation amplitude through the regional maximum principal stress analysis, finally, the key modal characteristics whose vibration amplitude exceeds the coherence time tolerance threshold are extracted as the vibration transmission path characteristic data, and the key area characteristics whose thermal stress and temperature fluctuation both exceed the allowable range are extracted as the thermal fluctuation distribution characteristic data, thereby completing the complete conversion process from the basic parameters to the key characteristic data.

[0022] An exemplary finite element analysis is performed based on the vibration transmission path data, the thermal fluctuation distribution data, the ultra-low temperature refrigerator operating parameters, the vibration suppression device configuration parameters, and the quantum bit long coherence time reference, to obtain vibration transmission path characteristic data and thermal fluctuation distribution characteristic data, specifically including: Based on the vibration transmission path data, the thermal fluctuation distribution data, the ultra-low temperature refrigerator operating parameters, and the vibration suppression device configuration parameters, a three-dimensional finite element model of the non-magnetic ultra-low temperature vacuum system is constructed, and finite element model data is generated; The ultra-low temperature refrigerator operating parameters are used to define the low-temperature thermal boundary conditions of the model, and the vibration suppression device configuration parameters are used to define the mechanical boundary conditions and damping characteristics of the model, to generate system finite element model data; The finite element model data is subjected to multi-physical field load application, to obtain loaded finite element model data; wherein the vibration load is set according to the vibration transmission path data, and the thermal load is set according to the thermal fluctuation distribution data and the ultra-low temperature refrigerator operating parameters; Based on the loaded finite element model data, structural dynamics modal solving is performed, and resonance frequencies, vibration mode displacements, and energy transfer functions under each modal are extracted, to obtain structural vibration modal data; According to the loaded finite element model data, transient thermal-mechanical coupled field solving is performed, to calculate the temperature field, stress field, and deformation field distribution under the combined action of thermal load and vibration load, to obtain thermal-mechanical coupled field data; Based on the quantum bit long coherence time reference, the structural vibration modal data and the thermal-mechanical coupled field data are subjected to coherence time sensitivity characteristic filtering, to obtain vibration transmission path characteristic data and thermal fluctuation distribution characteristic data.

[0023] Specifically, in the embodiment of the present application, when constructing a three-dimensional finite element model of the non-magnetic ultra-low temperature vacuum system based on the vibration transmission path data, the thermal fluctuation distribution data, the ultra-low temperature refrigerator operating parameters and the vibration suppression device configuration parameters, first, a geometric model is established according to the system structure vector diagram, and the system is discretized into a finite element grid with a specific unit type through a meshing algorithm, wherein the cooling power and temperature control accuracy in the ultra-low temperature refrigerator operating parameters are converted into the thermal boundary conditions of the model, specifically, the heat flux density constraints corresponding to the actual working temperature range are applied in the refrigerator cold head contact area, while the damping coefficient and stiffness characteristics in the vibration suppression device configuration parameters are realized by creating corresponding spring-damping units and assigning them to the system connection parts to accurately simulate the mechanical boundary conditions, thereby generating the system finite element model data which fully characterizes the physical properties of the system; then the finite element model data is subjected to multi-physical field load application, wherein the vibration load is applied to the system vibration source position in the form of multi-point harmonic load according to the excitation frequency spectrum and amplitude distribution extracted from the vibration transmission path data, and the thermal load is applied to each heat exchange surface of the system in the form of a non-uniform heat flux density function combined with the temperature gradient characteristics in the thermal fluctuation distribution data and the dynamic refrigeration characteristics in the ultra-low temperature refrigerator operating parameters, forming the loaded system finite element model data containing complete mechanical-thermal boundary conditions; when solving the structural dynamics mode based on this loaded model data, the eigenvalue extraction method based on Lanczos algorithm is used to calculate the free vibration characteristics of the system under the action of external force, by solving the generalized eigenvalue problem composed of the mass matrix and the stiffness matrix of the system, the resonance frequency and the corresponding mode shape displacement distribution of each mode of the system are obtained, and at the same time, the energy transfer function is obtained by calculating the transmission path of the modal strain energy density in the system, thereby outputting the structural vibration modal data which fully reflects the dynamic characteristics of the system; then, according to the loaded system finite element model data, the transient thermal-mechanical coupled field is solved, and the direct coupling analysis method is used to solve the temperature field and displacement field control equations simultaneously, and the dynamic response process of the system under the action of vibration load and thermal load is simulated through the time stepping algorithm, wherein the calculation of the temperature field is based on the unsteady heat conduction equation and considers the nonlinear characteristics of the material thermal conductivity with temperature, and the calculation of the stress field is realized by inputting the temperature field results as thermal strain into the structural equilibrium equation, while considering the temperature dependence of the material thermal expansion coefficient and the elastic modulus, finally obtaining the temperature field distribution, stress field distribution and deformation field distribution data of the system under the action of coupled load, i.e. the thermal-mechanical coupled field data;Finally, the structural vibration modal data and the thermal-mechanical coupling field data are filtered based on the quantum bit long coherence time reference. The process first establishes a quantitative relationship model between vibration acceleration amplitude and decoherence rate according to the quantum bit decoherence mechanism, and a correlation function between temperature fluctuation amplitude and decoherence rate, then differentiates the mode shape displacement of each mode in the structural vibration modal data to obtain the acceleration distribution, and selects the mode with the maximum acceleration value exceeding the coherence time tolerance threshold as the key vibration mode. At the same time, the time domain statistical analysis of the temperature field in the thermal-mechanical coupling field data obtains the temperature fluctuation standard deviation of each node, and combined with the equivalent stress distribution in the thermal stress field, the key region where the temperature fluctuation and the thermal stress exceed the allowable range of the quantum bit stability requirement is identified. These selected key vibration mode features and thermal dynamics region features are output as vibration transmission path feature data and thermal fluctuation distribution feature data respectively, completing the complete conversion process from system basic parameters to quantum bit performance oriented key feature data.

[0024] The optimization deployment module is used to calculate the coupling field intensity of magnetostrictive effect induced magnetic field and structure displacement current induced magnetic field according to the vibration transmission path feature data and the thermal fluctuation distribution feature data, to obtain the endogenous magnetic field noise field intensity prediction data; based on the endogenous magnetic field noise field intensity prediction data, the type selection and spatial layout optimization of the quantum sensing probe are carried out, to generate quantum sensing probe optimization deployment data; For example, based on the endogenous magnetic field noise field intensity prediction data, the type selection and spatial layout optimization of the quantum sensing probe are carried out, to generate quantum sensing probe optimization deployment data, which specifically includes: Extract the vibration frequency spectrum in the vibration transmission path feature data and the temperature gradient distribution in the thermal fluctuation distribution feature data; According to the vibration frequency spectrum and the temperature gradient distribution, the magnetostrictive effect induced magnetic field intensity calculation and the structure displacement current induced magnetic field intensity calculation are carried out respectively, to obtain the magnetostrictive effect induced magnetic field data and the structure displacement current induced magnetic field data; The magnetostrictive effect induced magnetic field data and the structure displacement current induced magnetic field data are subjected to coupling field intensity superposition calculation to generate endogenous magnetic field noise field intensity prediction data; Based on the endogenous magnetic field noise field intensity prediction data, the type selection and spatial layout optimization of the quantum sensing probe are carried out, to generate quantum sensing probe optimization deployment data.

[0025] Specifically, in the embodiments of the present application, when the type selection and spatial layout optimization of the quantum sensing probe are performed based on the endogenous magnetic field noise field strength prediction data, first, the vibration frequency spectrum containing the resonance frequencies of each order and the corresponding amplitudes is extracted from the vibration transmission path characteristic data, and the temperature gradient distribution reflecting the temperature spatial variation rate is extracted from the thermal fluctuation distribution characteristic data; then, the magnetostrictive effect induced magnetic field strength calculation is performed according to the vibration frequency spectrum, the above process is performed by establishing the constitutive relationship model between the vibration frequency and the material magnetostrictive coefficient, the strain-magnetic field coupling equation is used to convert the mechanical vibration energy into magnetic energy, the magnetic field strength distribution under each frequency component is calculated and the magnetostrictive induced magnetic field data is generated, and the structure displacement current induced magnetic field strength calculation is performed according to the temperature gradient distribution, the displacement current term related to the temperature field in the Maxwell equation set is solved, combined with the variation characteristics of the material dielectric constant with temperature, the induced magnetic field distribution caused by thermal fluctuations is calculated and the displacement current induced magnetic field data is generated; then, the magnetostrictive induced magnetic field data and the displacement current induced magnetic field data are coupled and the field strength is calculated, the vector synthesis method is used to superimpose the magnetic fields generated by the two physical mechanisms in space, the modulus and direction angle of the two magnetic field vectors at each space point are calculated to obtain the synthesized magnetic field strength, and the phase interference effect between different frequency components is considered, and finally the endogenous magnetic field noise field strength prediction data reflecting the spatial distribution and spectral characteristics of the system overall endogenous magnetic field noise is generated; on this basis, the type selection of the quantum sensing probe is performed, according to the magnitude of the magnetic field strength and the frequency characteristics in the endogenous magnetic field noise field strength prediction data, the sensitivity-frequency response matching principle is used for sensor selection, for the low frequency noise component below 10 kHz, atomic magnetometer is preferred, for the high frequency component, nitrogen vacancy color center sensor is selected, and the sensor array size to be used is determined according to the degree of non-uniformity of the magnetic field spatial distribution; finally, the spatial layout optimization is performed, based on the magnetic field gradient distribution characteristics in the endogenous magnetic field noise field strength prediction data, the optimization arrangement method based on genetic algorithm is used, the maximum magnetic field monitoring coverage and the minimum monitoring blind area are taken as the target, the best installation position and orientation angle of each probe are determined through iterative calculation, and the system structure constraint and electromagnetic compatibility requirement are considered to ensure that the probe arrangement meets the monitoring requirements and does not interfere with the normal operation of the system, and finally the quantum sensing probe optimization deployment data containing the sensor type, number, spatial coordinates and installation parameters is generated.

[0026] For example, the magnetostrictive effect induced magnetic field strength calculation and the structure displacement current induced magnetic field strength calculation are performed according to the vibration frequency spectrum and the temperature gradient distribution respectively, and the magnetostrictive induced magnetic field data and the displacement current induced magnetic field data are obtained, which specifically includes: The material magnetostrictive coefficient matching is performed according to the vibration frequency spectrum, and the magnetostrictive coefficient matching data is obtained; The vibration-induced magnetic field strength data is generated based on the magnetostrictive coefficient matching data and the temperature gradient distribution by integral operation of the vibration-induced magnetic field strength; The displacement current density data is obtained by calculating the structural displacement current density according to the temperature gradient distribution; The displacement current excitation magnetic field strength data is generated by synthesizing the displacement current excitation magnetic field strength vector based on the displacement current density data; The magnetostrictive-induced magnetic field data and the displacement current-induced magnetic field data are generated by coupling field strength superposition calculation on the vibration-induced magnetic field strength data and the displacement current excitation magnetic field strength data.

[0027] Specifically, in the embodiment of the present application, when the magnetostrictive effect induced magnetic field strength calculation and the structural displacement current induced magnetic field strength calculation are performed according to the vibration frequency spectrum and the temperature gradient distribution respectively, first, the material magnetostriction coefficient matching is performed according to the vibration frequency spectrum, the corresponding relationship between the material database and the vibration frequency is established, the frequency-material characteristic response function is used to associate and map the vibration energy at different frequencies with the magnetostriction coefficient of the material, wherein the strain sensitivity factor corresponding to each frequency component is calculated, and the saturation magnetostriction coefficient and the magnetoelastic coupling coefficient of the material are combined to obtain the magnetostriction coefficient distribution data varying with frequency; based on the magnetostriction coefficient matching data and the temperature gradient distribution, the vibration induced magnetic field strength integral operation is performed, the strain field generated by mechanical vibration is converted into an equivalent magnetic field source through the magnetostrictive constitutive equation by constructing a strain-magnetic field conversion model, the volume integral method is used to calculate the magnetic field distribution in the whole structure space, wherein the influence of the temperature gradient on the magnetostrictive properties of the material is considered, the magnetostriction coefficient is adjusted in real time by introducing a temperature correction factor, and finally the vibration induced magnetic field strength data reflecting the spatial distribution of the vibration induced magnetic field are generated; at the same time, the structural displacement current density calculation is performed according to the temperature gradient distribution, based on the displacement current theory in the Maxwell equation set, the charge redistribution effect caused by the temperature field change is solved to calculate the polarization current density caused by thermal fluctuations, wherein the thermal-electric coupling analysis method is used, the quantitative relationship model between the temperature gradient and the displacement current density is established by combining the relationship between the dielectric constant of the material and the temperature, and the displacement current density distribution data of each point in space are obtained; based on the displacement current density data, the displacement current excitation magnetic field strength vector synthesis is performed, the induced magnetic field generated by the displacement current is calculated through the Ampere loop law, the integral form of the Biot-Savart law is used to perform vector superposition on the displacement current contribution of each point in space, while considering the interference effect of the magnetic field generated by the current elements at different positions in space, the overall displacement current excitation magnetic field strength data are obtained by calculating the magnetic field vector sum generated by each current element; finally, the vibration induced magnetic field strength data and the displacement current excitation magnetic field strength data are coupled and field strength superposition calculation is performed, the spatial vector synthesis method is used to uniformly process the magnetic fields generated by the two physical mechanisms, the module length and the direction angle of the two magnetic field vectors at each point in space are calculated, the phase matching relationship between different frequency components is considered, the field strength superposition algorithm in complex form is used to complete the fusion calculation of the two magnetic fields, wherein the interference effect of the two magnetic field sources is evaluated by introducing the coherence analysis, and finally the complete magnetostrictive induced magnetic field data and the displacement current induced magnetic field data are generated, which provide accurate magnetic field distribution information for subsequent quantum sensing probe optimization and deployment.

[0028] a relationship mining module configured to optimize deployment of the quantum sensing probe, configure a quantum sensor array in the magnetically-free ultralow-temperature vacuum system, and monitor the magnetically-free ultralow-temperature vacuum system in real time for endogenous magnetic field noise, collect endogenous magnetic field noise spectrum data, and mine a correlation between magnetic field noise frequency components and quantum bit decoherence rates based on the endogenous magnetic field noise spectrum data to obtain magnetic field-decoherence correlation characteristic data; For example, the correlation between magnetic field noise frequency components and quantum bit decoherence rates is mined based on the endogenous magnetic field noise spectrum data to obtain magnetic field-decoherence correlation characteristic data, which specifically includes: configuring the quantum sensor array in the magnetically-free ultralow-temperature vacuum system according to the quantum sensing probe optimization deployment data; monitoring the magnetically-free ultralow-temperature vacuum system in real time for endogenous magnetic field noise using the configured quantum sensor array and collecting endogenous magnetic field noise spectrum data; frequency domain decomposition of the endogenous magnetic field noise spectrum data to obtain magnetic field noise frequency component data; quantum bit decoherence rate calculation based on the magnetic field noise frequency component data to obtain quantum bit decoherence rate data; correlation mining based on the magnetic field noise frequency component data and the quantum bit decoherence rate data to obtain magnetic field-decoherence correlation characteristic data.

[0029] Specifically, in the embodiment of the present application, when the correlation between the magnetic field noise frequency component and the quantum bit decoherence rate is mined according to the real-time endogenous magnetic field noise spectrum data, first, according to the sensor type, spatial position and installation parameters determined in the quantum sensing probe optimization deployment data, the quantum sensor array is accurately arranged at the key position of the non-magnetic ultra-low temperature vacuum system, wherein atomic magnetometers are configured for low-frequency magnetic field monitoring points, and nitrogen vacancy color center sensors are configured for high-frequency magnetic field monitoring points, and the measurement accuracy of each probe is ensured through the calibration program; the quantum sensor array configured is used for continuous real-time monitoring of the system, the original magnetic field signal containing the time stamp is collected, after the baseline drift is eliminated through the signal conditioning circuit, the multi-channel synchronous sampling technology is used to obtain the time series data of the magnetic field strength in the system running process, and then the time domain signal is converted into frequency domain spectrum line through fast Fourier transform, to obtain the real-time endogenous magnetic field noise spectrum data containing each frequency component and the corresponding amplitude; the frequency domain decomposition processing is performed on the spectrum data, the significant frequency peak is screened out by setting the amplitude threshold, and the center frequency and half-width of each peak are accurately determined by using the Gaussian fitting method, and the correlation between the fundamental frequency and each harmonic is identified by using the harmonic analysis method, and finally the independent frequency component and its characteristic parameters with physical meaning are extracted, to form the structured magnetic field noise frequency component data; based on these frequency component data, the quantum bit decoherence rate is calculated, first, the decoherence theoretical model taking the magnetic field noise frequency and amplitude as variables is established, which comprehensively considers multiple decoherence mechanisms such as energy level shift, phase diffusion and energy level relaxation, the magnetic field strength of each frequency component is substituted into the corresponding term of the model, the contribution value of each frequency noise to the decoherence rate is calculated, the frequency weighting factor is introduced according to the noise non-Markov property, and finally the total decoherence rate data of the system is obtained through vector superposition; finally, the correlation relationship is mined based on the magnetic field noise frequency component data and the quantum bit decoherence rate data, a multivariate statistical method combining principal component analysis and canonical correlation analysis is used to establish a multidimensional correlation model of frequency-amplitude-decoherence rate, the key influencing frequency is determined by calculating the correlation coefficient of the amplitude of each frequency component and the decoherence rate, the contribution weight of different frequency noises to the decoherence rate is quantified by using the partial least squares regression analysis, and the frequency combination with synergistic effect is identified through clustering analysis, finally the key frequency characteristics with the greatest influence on the quantum bit performance and the quantitative relationship between the key frequency characteristics and the decoherence rate are extracted, to form the magnetic field-decoherence correlation characteristic data which can be used to guide the optimization of subsequent control strategy.

[0030] For example, the real-time endogenous magnetic field noise spectrum data is subjected to frequency domain decomposition to obtain the magnetic field noise frequency component data, which specifically includes: The real-time endogenous magnetic field noise spectrum data is subjected to Fourier transform processing to obtain the magnetic field noise frequency domain spectrum data; Extracting main frequency components based on the magnetic field noise frequency domain spectrum data to generate main frequency component data; Identifying harmonic components from the main frequency component data to obtain harmonic component data; Decomposing in frequency domain according to the harmonic component data to obtain magnetic field noise frequency component data.

[0031] Specifically, in the embodiment of the present application, when the real-time endogenous magnetic field noise spectrum data is decomposed in frequency domain, first, the Fourier transform processing is performed on the collected time domain magnetic field signal, the fast Fourier transform algorithm is used to convert the time series magnetic field strength data into frequency domain spectrum distribution, and the magnetic field noise frequency domain spectrum data containing amplitude and phase information is obtained by calculating the complex amplitude of each frequency point; On this basis, the main frequency component extraction is performed, the spectrum peak value significantly higher than the background noise is screened out by setting the amplitude threshold, the curve fitting is performed on each peak value area by using the Gaussian fitting method to accurately determine the center frequency, peak amplitude and half width, and the energy proportion of each frequency component is evaluated by calculating the ratio of peak area to total spectrum area, thereby generating main frequency component data containing main frequency characteristics and parameters; Then, the harmonic component identification is performed on the main frequency component data, the frequency relationship model of fundamental frequency and harmonic is established, the integer multiple relationship between each main frequency is calculated, the harmonic sequence is identified by using the frequency ratio analysis method, the harmonic characteristics are determined by calculating the attenuation coefficient of harmonic amplitude and fundamental amplitude, and the correctness of the harmonic relationship is verified by using the phase consistency test, and finally the harmonic component data containing the fundamental and harmonic components and their associated characteristics is obtained; Finally, the frequency domain decomposition is performed based on the harmonic component data, the independent non-harmonic frequency components are obtained by separating the identified harmonic components from the original spectrum, using the spectrum subtraction technique to eliminate the harmonic contribution, the independence of each frequency component is evaluated by calculating the coherence coefficient between the frequency components, and the peak value is extracted again to supplement the possible missing secondary frequency components, and finally the complete magnetic field noise frequency component data containing the fundamental, harmonic and independent frequency components is obtained, which records the center frequency, amplitude, phase, bandwidth and energy distribution characteristics of each frequency component in the spectrum in detail, and provides accurate frequency characteristic input for subsequent dephasing rate analysis.

[0032] The adjustment control module is used for generating a multi-physical field coupling suppression control strategy based on the magnetic field-dephasing correlation characteristic data, obtaining multi-physical field coupling suppression control strategy data, and performing adaptive adjustment of the cooling power of the ultra-low temperature refrigerator and optimization of the active damping control parameters of the vibration suppression device according to the multi-physical field coupling suppression control strategy data to generate environment optimization control instruction data; and performing quantum sensing feedback closed-loop control of the non-magnetic ultra-low temperature vacuum environment through the environment optimization control instruction data to adjust the working state of the ultra-low temperature refrigerator and the vibration suppression device in real time, so as to obtain an optimized non-magnetic ultra-low temperature vacuum environment.

[0033] The implementation process of the adjustment control module includes the following steps: Based on the magnetic field-decoherence correlation characteristic data, a multi-physical field coupling suppression control strategy is generated, including: identifying target magnetic field frequency components that have an impact on the quantum bit decoherence rate exceeding a preset threshold, and formulating corresponding cooling power adjustment and active damping control schemes for the target magnetic field frequency components to obtain multi-physical field coupling suppression control strategy data; According to the multi-physical field coupling suppression control strategy data, a superconducting cryogenic refrigerator cooling power self-adaptive adjustment rule is designed to generate cooling power adjustment rule data; Based on the multi-physical field coupling suppression control strategy data, the active damping control parameters of the vibration suppression device are optimized to generate damping control parameter optimization data; Through the cooling power adjustment rule data and the damping control parameter optimization data, an environmental optimization control instruction is generated to obtain environmental optimization control instruction data; Using the environmental optimization control instruction data, a quantum sensing feedback closed-loop control of the non-magnetic ultra-low temperature vacuum environment is performed to adjust the working state of the superconducting cryogenic refrigerator and the vibration suppression device in real time, thereby obtaining an optimized non-magnetic ultra-low temperature vacuum environment.

[0034] Specifically, in the embodiment of the present application, when the multi-physical field coupling suppression control strategy is generated based on the magnetic field-decoherence correlation characteristic data, first, the quantum bit decoherence rate tolerance threshold is set to screen out the target magnetic field frequency components that have a significant impact on decoherence from the correlation characteristic data, the spectral weight analysis method is used to calculate the contribution of each frequency component to the decoherence rate, and the key frequency band that needs to be processed first is identified in combination with the frequency distribution characteristics; For these target frequency components, the corresponding cooling power adjustment scheme is developed, the frequency-temperature transfer function model is established to analyze the suppression effect of different cooling powers on the thermal fluctuations of specific frequencies, and the active damping control scheme is designed, based on the coupling relationship between vibration frequency and magnetic field noise, the damping control parameter range for the key mechanical resonance frequency is determined, and finally the multi-physical field coupling suppression control strategy data containing frequency-specific control strategy is formed; When designing the superconducting cooler cooling power self-adaptive adjustment rule according to the control strategy data, first, the dynamic response model of the cooling power and the system temperature fluctuation is established, the temperature stability time constant under different power levels is determined by analyzing the historical control data, the fuzzy control theory is used to construct the control rule table with temperature deviation and deviation rate as input and power adjustment amount as output, and the power gradual change algorithm is designed considering the heat capacity characteristics of superconducting devices to prevent temperature mutation causing secondary thermal stress, and finally the cooling power adjustment rule data with adaptive characteristics is generated; When optimizing the active damping control parameters of the vibration suppression device based on the control strategy data, first, the vibration transmission mathematical model of the mass-spring-damper system is established, the optimal damping coefficient corresponding to each order resonance frequency is determined through frequency response analysis, the damping parameter combination that makes the system quickly converge is calculated using the pole placement method, and the temperature compensation factor is introduced to real-time correct the parameters considering the influence of material stiffness change at different temperatures on damping effect, and finally the damping control parameter optimization data that can effectively suppress the vibration of target frequency is generated; When generating the environment optimization control instruction data through the cooling power adjustment rule data and the damping control parameter optimization data, first, the multi-objective collaborative control algorithm is established, and the temperature control and vibration suppression are optimized as a coupled system, the model predictive control method is used to calculate the optimal control sequence in the future period of time, the collaborative relationship between cooling power adjustment and damping parameter change is coordinated through weight allocation, and the conflict prevention logic is designed to ensure the compatibility between different control instructions, and finally the environment optimization control instruction data that can guide the operation of the refrigerator and the vibration suppression device is generated.When quantum sensing feedback closed-loop control is performed in the non-magnetic ultra-low temperature vacuum environment using the instruction data, the difference between the magnetic field noise data monitored by the quantum sensor and the target value is compared in real time, the execution strength of the control instruction is dynamically adjusted, the incremental PID control algorithm is used to realize fine adjustment of the control parameters, a control effect evaluation mechanism is established, the effectiveness of the control strategy is verified by continuously monitoring the change trend of the decoherence rate, the control parameters are updated adaptively according to the system state, a complete perception-decision-execution-feedback closed-loop control loop is formed, and finally the continuous optimization and stable maintenance of the non-magnetic ultra-low temperature vacuum environment are realized.

[0035] For example, according to the multi-physical field coupling suppression control strategy data, the cooling power self-adaptive adjustment rule of the ultra-low temperature refrigerator is designed, and the cooling power adjustment rule data is generated, which specifically includes: According to the multi-physical field coupling suppression control strategy data, the working point stability data of the ultra-low temperature refrigerator is obtained by analyzing the working point stability. Based on the working point stability data, the cooling power-temperature fluctuation correlation model data is generated by modeling the correlation between the cooling power and the temperature fluctuation. According to the cooling power-temperature fluctuation correlation model data, the cooling power adjustment rule data is generated by designing the self-adaptive adjustment rule.

[0036] Specifically, in the embodiment of the present application, when the cooling power of the ultra-low temperature refrigerator is adaptively adjusted according to the multi-physical field coupling suppression control strategy data, first, based on the target magnetic field frequency component and the corresponding thermal fluctuation characteristics determined in the control strategy data, the working point stability of the ultra-low temperature refrigerator is analyzed. By extracting the temperature fluctuation historical data of the refrigerator under different power settings, the temperature variance and fluctuation period of each working point are calculated. The sliding window statistical method is used to analyze the mean and standard deviation trend of the temperature sequence, and the dynamic stability of the working point is evaluated by combining the temperature overshoot during power switching. Finally, the working point stability data containing the stability index under each power level is obtained. Based on these stability data, the cooling power and temperature fluctuation correlation modeling is carried out. First, a nonlinear regression model is established with cooling power as independent variable and temperature fluctuation amplitude as dependent variable. By analyzing the frequency spectrum characteristics of temperature fluctuation under different power levels, the suppression effect of power change on specific frequency thermal fluctuation is determined. The correlation coefficient of power adjustment and temperature stability time is calculated by using multivariate correlation analysis method. Considering the influence of system thermal inertia and refrigeration delay, a time lag factor is introduced to build a mathematical model that can accurately reflect the power-temperature dynamic response relationship, and generate cooling power-temperature fluctuation correlation model data containing power adjustment parameters and temperature control effect. According to the correlation model data, the adaptive adjustment rule is designed. First, based on the model predictive control theory, an optimization function is constructed to minimize the temperature fluctuation. The optimal control sequence is determined by solving the constrained optimization problem of power adjustment and temperature deviation. The fuzzy logic control method is used to convert the temperature deviation and its rate of change into the language variable of power adjustment. A rule base containing multiple adjustment levels is established, and an adaptive learning mechanism is designed to dynamically update the rule parameters according to the real-time control effect. The control accuracy is improved by continuously optimizing the amplitude and frequency of power adjustment. Finally, the cooling power adjustment rule data is generated, which can automatically adjust according to the system state. This data specifies the direction, amplitude and speed of power adjustment under different temperature deviation conditions, forming a complete adaptive control strategy.

[0037] For example, based on the multi-physical field coupling suppression control strategy data, the active damping control parameter optimization of the vibration suppression device is carried out to generate damping control parameter optimization data, which specifically includes: According to the multi-physical field coupling suppression control strategy data, the damping coefficient and vibration transfer function of the vibration suppression device are matched to obtain damping coefficient-transfer function matching data. Based on the damping coefficient-transfer function matching data, the active damping control parameter optimization is carried out, which aims to suppress the mechanical vibration corresponding to the target magnetic field frequency component, and generates damping control parameter optimization data. The damping control parameter optimization data is simulated and verified for vibration suppression effect, so as to obtain the optimized damping control parameters.

[0038] Specifically, in the embodiment of the present application, when the active damping control parameter optimization of the vibration suppression device is performed based on the multi-physical field coupling suppression control strategy data, first, the damping coefficient of the vibration suppression device is matched with the vibration transfer function according to the mechanical vibration characteristics corresponding to the target magnetic field frequency component determined in the control strategy data. The vibration transfer characteristics of the system at each target frequency are calculated by establishing a multi-degree-of-freedom system dynamics model containing mass, stiffness and damping, and using the frequency response function analysis method. The required damping coefficient range is determined based on the peak response amplitude and phase delay data, and the local stiffness influence of different installation positions is considered. The damping coefficient and the system vibration transfer function at the target frequency are iteratively calculated to achieve the best matching state, thereby obtaining the damping coefficient-transfer function matching data containing the frequency-specific damping parameters. When the active damping control parameter optimization is performed based on the matching data, first, an optimization model is established with vibration decay rate and system stability as multi-objective functions. The genetic algorithm is used to search for the optimal solution set in the parameter space. The decay time constant and residual vibration energy of the target frequency vibration under different damping parameter combinations are calculated to determine the optimal parameter configuration that makes the key frequency vibration quickly converge. The phase compensation mechanism is introduced to optimize the control timing considering the damper saturation characteristics and actuator response delay, to ensure effective vibration suppression at the target frequency. Finally, the damping control parameter optimization data with the minimum target magnetic field frequency corresponding mechanical vibration as the optimization objective is generated. When simulating and verifying the vibration suppression effect of the optimization data, first, a complete system simulation model containing the vibration source, transmission path and damper is constructed. The dynamic response of the system under the action of the optimized parameters is simulated by inputting the vibration excitation signal under actual working conditions using the time domain integral method. The vibration decay rate and steady-state residual vibration amplitude at each target frequency are calculated, and the parameter robustness is analyzed. The stability of the control effect under different working conditions is verified by changing the system parameters. Finally, the simulation results are compared and evaluated with the preset vibration suppression indicators. After multiple iterations and corrections, the verified optimized damping control parameters are obtained, which can effectively suppress the mechanical vibration corresponding to the target magnetic field frequency in actual application.

[0039] Embodiment 2 The application also discloses a non-magnetic ultra-low temperature vacuum method based on quantum sensing closed-loop control.

[0040] Reference Figure 2 The non-magnetic ultra-low temperature vacuum method based on quantum sensing closed-loop control comprises the following steps: A system structure diagram of a non-magnetic ultra-low temperature vacuum system, ultra-low temperature refrigerator working parameters, vibration suppression device configuration parameters and quantum bit long coherence time reference are acquired. Finite element analysis of mechanical vibration transmission paths and thermal fluctuation distribution is performed based on the system structure diagram, so as to obtain vibration transmission path characteristic data and thermal fluctuation distribution characteristic data. According to the vibration transmission path characteristic data and the thermal fluctuation distribution characteristic data, a coupling field strength calculation of magnetostrictive effect induced magnetic field and structure displacement current induced magnetic field is performed to obtain endogenous magnetic field noise field strength prediction data, and based on the endogenous magnetic field noise field strength prediction data, type selection and spatial layout optimization of the quantum sensing probe are performed to generate quantum sensing probe optimization deployment data; According to the quantum sensing probe optimization deployment data, a quantum sensor array is configured in the non-magnetic ultra-low temperature vacuum system, and real-time endogenous magnetic field noise monitoring is performed on the non-magnetic ultra-low temperature vacuum system to obtain real-time endogenous magnetic field noise spectrum data, and according to the real-time endogenous magnetic field noise spectrum data, correlation relationship mining between magnetic field noise frequency components and quantum bit decoherence rate is performed to obtain magnetic field-decoherence correlation characteristic data; Based on the magnetic field-decoherence correlation characteristic data, a multi-physical field coupling suppression control strategy is generated to obtain multi-physical field coupling suppression control strategy data, and according to the multi-physical field coupling suppression control strategy data, an ultra-low temperature refrigerator cooling power adaptive adjustment and vibration suppression device active damping control parameter optimization are performed to generate environment optimization control instruction data; through the environment optimization control instruction data, quantum sensing feedback closed-loop control of the non-magnetic ultra-low temperature vacuum environment is performed, and the working state of the ultra-low temperature refrigerator and the vibration suppression device is adjusted in real time, so as to obtain an optimized non-magnetic ultra-low temperature vacuum environment.

[0041] The above content is only an example and description of the present application, and those skilled in the art can make various modifications or supplements or use similar ways to replace the described specific embodiments, as long as they do not deviate from the concept of the present application, which shall belong to the protection scope of the present application.

[0042] In the description of the present application, the description of the terms "one embodiment", "example", "specific example" and the like means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In the present application, the illustrative description of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0043] The preferred embodiments of the present application disclosed above are only used to help explain the present application. The preferred embodiments do not describe all the details and limit the present application to the specific embodiments. Obviously, many modifications and changes can be made according to the content of the present application. The present application selects and describes these embodiments in order to better explain the principles and practical applications of the present application, so that those skilled in the art can well understand and utilize the present application.

Claims

1. A non-magnetic cryogenic vacuum system based on quantum sensing closed-loop control, characterized in that, include: The data acquisition and analysis module is used to acquire the system structure diagram of the non-magnetic cryogenic vacuum system, the operating parameters of the cryogenic refrigerator, the configuration parameters of the vibration suppression device, and the long coherence time reference of the quantum bit. Based on the system structure diagram, it performs finite element analysis of the mechanical vibration transmission path and thermal fluctuation distribution to obtain vibration transmission path characteristic data and thermal fluctuation distribution characteristic data. The optimization deployment module is used to calculate the coupled field strength of the magnetic field induced by the magnetostrictive effect and the magnetic field induced by the structural displacement current based on the vibration transmission path characteristic data and thermal fluctuation distribution characteristic data, to obtain the endogenous magnetic field noise field strength prediction data, and to optimize the type and spatial layout of the quantum sensing probe based on the endogenous magnetic field noise field strength prediction data, thereby generating the quantum sensing probe optimization deployment data. The relation mining module is used to configure the quantum sensor array in the non-magnetic cryogenic vacuum system according to the quantum sensing probe optimization deployment data, and to monitor the intrinsic magnetic field noise of the non-magnetic cryogenic vacuum system in real time, collect real-time intrinsic magnetic field noise spectrum data, and mine the correlation between magnetic field noise frequency components and quantum bit decoherence rate based on the real-time intrinsic magnetic field noise spectrum data to obtain magnetic field-decoherence correlation feature data. The adjustment and control module is used to generate a multi-physics field coupling suppression control strategy based on magnetic field-decoherence correlation characteristic data, obtain multi-physics field coupling suppression control strategy data, and perform adaptive adjustment of the cooling power of the cryogenic refrigerator and optimization of the active damping control parameters of the vibration suppression device based on the multi-physics field coupling suppression control strategy data, thereby generating environmental optimization control command data. Through the environmental optimization control command data, quantum sensing feedback closed-loop control of the non-magnetic cryogenic vacuum environment is performed to adjust the working state of the cryogenic refrigerator and the vibration suppression device in real time, thereby obtaining the optimized non-magnetic cryogenic vacuum environment.

2. The non-magnetic cryogenic vacuum system based on quantum sensing closed-loop control according to claim 1, characterized in that, The process of obtaining vibration transmission path characteristic data and thermal fluctuation distribution characteristic data specifically includes: Obtain the system structure diagram of the non-magnetic cryogenic vacuum system, the operating parameters of the cryogenic refrigerator, the configuration parameters of the vibration suppression device, and the long coherence time reference of the quantum bit; Perform structural vectorization processing on the system structure diagram to generate a system structure vector diagram; Mechanical vibration transmission paths and thermal fluctuation distribution areas are extracted from the system structure vector diagram to obtain vibration transmission path data and thermal fluctuation distribution data. Finite element analysis is performed based on vibration transmission path data, thermal fluctuation distribution data, cryogenic refrigerator operating parameters, vibration suppression device configuration parameters, and quantum bit long coherence time reference to obtain vibration transmission path characteristic data and thermal fluctuation distribution characteristic data.

3. The non-magnetic cryogenic vacuum system based on quantum sensing closed-loop control according to claim 2, characterized in that, Finite element analysis was performed based on vibration transmission path data, thermal fluctuation distribution data, operating parameters of the cryogenic refrigerator, configuration parameters of the vibration suppression device, and the long coherence time reference of the quantum bit to obtain vibration transmission path characteristic data and thermal fluctuation distribution characteristic data, specifically including: Based on vibration transmission path data, thermal fluctuation distribution data, operating parameters of the cryogenic refrigerator, and configuration parameters of the vibration suppression device, a three-dimensional finite element model of the non-magnetic cryogenic vacuum system is constructed, and then finite element model data is generated. Multiphysics loads are applied to the finite element model data to obtain the loaded finite element model data. Based on the loaded finite element model data, structural dynamic modal solutions are performed, and the resonance frequencies, mode displacements, and energy transfer functions under each mode are extracted to obtain structural vibration modal data. Based on the loaded finite element model data, the transient thermo-mechanical coupled field is solved, and the temperature field, stress field and deformation field distribution under the combined action of thermal load and vibration load are calculated to obtain the thermo-mechanical coupled field data. Based on the long coherence time reference of the qubit, the vibration mode data of the structure and the thermo-mechanical coupling field data are filtered by coherence time sensitivity features to obtain vibration transmission path feature data and thermal fluctuation distribution feature data.

4. The non-magnetic cryogenic vacuum system based on quantum sensing closed-loop control according to claim 1, characterized in that, Based on the predicted field strength of endogenous magnetic field noise, the selection of quantum sensing probe types and spatial layout optimization are performed, generating optimized deployment data for quantum sensing probes, specifically including: Extract the vibration frequency spectrum from the vibration transmission path feature data and the temperature gradient distribution from the thermal fluctuation distribution feature data; Based on the vibration frequency spectrum and temperature gradient distribution, the magnetic field strength induced by the magnetostrictive effect and the magnetic field strength induced by the structural displacement current are calculated respectively, and the data of the magnetostrictive induced magnetic field and the data of the displacement current induced magnetic field are obtained. Coupled field strength superposition calculation is performed on magnetostrictive induced magnetic field data and displacement current induced magnetic field data to generate endogenous magnetic field noise field strength prediction data. Based on the predicted data of endogenous magnetic field noise field strength, the type of quantum sensing probe is selected and the spatial layout is optimized to generate optimized deployment data of quantum sensing probe.

5. The non-magnetic cryogenic vacuum system based on quantum sensing closed-loop control according to claim 4, characterized in that, Based on the vibration frequency spectrum and temperature gradient distribution, the magnetic field strength induced by the magnetostrictive effect and the magnetic field strength induced by the structural displacement current are calculated respectively, yielding data on the magnetostrictive induced magnetic field and the displacement current induced magnetic field, specifically including: The magnetostriction coefficient of the material is matched based on the vibration frequency spectrum to obtain the magnetostriction coefficient matching data. Vibration induced magnetic field strength data is generated by integral calculation of vibration induced magnetic field strength based on magnetostriction coefficient matching data and temperature gradient distribution. The displacement current density of the structure is calculated based on the temperature gradient distribution to obtain displacement current density data. Based on displacement current density data, displacement current excitation magnetic field strength vector synthesis is performed to generate displacement current excitation magnetic field strength data. The vibration-induced magnetic field strength data and the displacement current excitation magnetic field strength data are coupled and superimposed to generate magnetostrictive induced magnetic field data and displacement current induced magnetic field data.

6. The non-magnetic cryogenic vacuum system based on quantum sensing closed-loop control according to claim 1, characterized in that, Based on real-time endogenous magnetic field noise spectrum data, the correlation between magnetic field noise frequency components and qubit decoherence rate is mined to obtain magnetic field-decoherence correlation feature data, specifically including: Configure the quantum sensor array in the non-magnetic cryogenic vacuum system according to the quantum sensing probe optimization deployment data; The configured quantum sensor array was used to monitor the intrinsic magnetic field noise of the non-magnetic cryogenic vacuum system in real time, and the real-time intrinsic magnetic field noise spectrum data was collected. Frequency domain decomposition was performed on the real-time endogenous magnetic field noise spectrum data to obtain the magnetic field noise frequency component data. The decoherence rate of qubits is calculated based on the frequency component data of magnetic field noise to obtain the decoherence rate data of qubits; Correlation mining was performed based on magnetic field noise frequency component data and qubit decoherence rate data to obtain magnetic field-decoherence correlation feature data.

7. The non-magnetic cryogenic vacuum system based on quantum sensing closed-loop control according to claim 6, characterized in that, Frequency domain decomposition was performed on the real-time endogenous magnetic field noise spectrum data to obtain the magnetic field noise frequency component data, specifically including: Fourier transform was performed on the real-time endogenous magnetic field noise spectrum data to obtain the magnetic field noise frequency domain spectrum data. The main frequency components are extracted from the magnetic field noise frequency domain spectrum data to generate main frequency component data. Harmonic component identification is performed on the main frequency component data to obtain harmonic component data; Frequency domain decomposition is performed on the harmonic component data to obtain the magnetic field noise frequency component data.

8. The non-magnetic cryogenic vacuum system based on quantum sensing closed-loop control according to claim 1, characterized in that, The implementation process of the adjustment and control module specifically includes: The generation of a multi-physics coupling suppression control strategy is based on magnetic field-decoherence correlation feature data. The generation of the strategy includes: identifying the target magnetic field frequency component that affects the decoherence rate of the qubit by more than a preset threshold, and formulating a corresponding cooling power adjustment and active damping control scheme for the target magnetic field frequency component to obtain multi-physics coupling suppression control strategy data. Based on the multiphysics field coupling suppression control strategy data, an adaptive adjustment rule for the cooling power of the cryogenic refrigerator is designed, and cooling power adjustment rule data is generated. Based on the multi-physics field coupled suppression control strategy data, the active damping control parameters of the vibration suppression device are optimized to generate damping control parameter optimization data; Environmental optimization control commands are generated by using cooling power adjustment rule data and damping control parameter optimization data to obtain environmental optimization control command data; Quantum sensing feedback closed-loop control of a non-magnetic cryogenic vacuum environment is achieved by utilizing environmental optimization control command data to adjust the working status of the cryogenic refrigerator and vibration suppression device in real time, thereby obtaining an optimized non-magnetic cryogenic vacuum environment.

9. The non-magnetic cryogenic vacuum system based on quantum sensing closed-loop control according to claim 8, characterized in that, Based on multi-physics coupled suppression control strategy data, the active damping control parameters of the vibration suppression device are optimized, generating optimized damping control parameter data, specifically including: Based on the multiphysics field coupling suppression control strategy data, the damping coefficient of the vibration suppression device is matched with the vibration transfer function to obtain the damping coefficient-transfer function matching data. Active damping control parameters are optimized based on damping coefficient-transfer function matching data to generate optimized damping control parameters. The vibration suppression effect of the optimized damping control parameters is simulated and verified to obtain the optimized damping control parameters.

10. A non-magnetic cryogenic vacuum method based on quantum sensing closed-loop control, applied to the non-magnetic cryogenic vacuum system based on quantum sensing closed-loop control as described in any one of claims 1-9, characterized in that, Includes the following steps: Obtain the system structure diagram of the non-magnetic cryogenic vacuum system, the operating parameters of the cryogenic refrigerator, the configuration parameters of the vibration suppression device, and the long coherence time reference of the quantum bit. Based on the system structure diagram, perform finite element analysis of the mechanical vibration transmission path and thermal fluctuation distribution to obtain vibration transmission path characteristic data and thermal fluctuation distribution characteristic data. Based on the vibration transmission path characteristic data and thermal fluctuation distribution characteristic data, the coupled field strength of the magnetic field induced by the magnetostrictive effect and the magnetic field induced by the structural displacement current is calculated to obtain the predicted data of the endogenous magnetic field noise field strength. Based on the predicted data of the endogenous magnetic field noise field strength, the type of quantum sensing probe is selected and the spatial layout is optimized to generate the optimized deployment data of the quantum sensing probe. Based on the optimized deployment data of the quantum sensing probe, a quantum sensor array is configured in the non-magnetic cryogenic vacuum system, and the intrinsic magnetic field noise of the non-magnetic cryogenic vacuum system is monitored in real time. Real-time intrinsic magnetic field noise spectrum data is collected, and the correlation between magnetic field noise frequency components and quantum bit decoherence rate is mined based on the real-time intrinsic magnetic field noise spectrum data to obtain magnetic field-decoherence correlation feature data. Multi-physics coupling suppression control strategy is generated based on magnetic field-decoherence correlation characteristic data, resulting in multi-physics coupling suppression control strategy data. Based on the multi-physics coupling suppression control strategy data, the cooling power of the cryogenic refrigerator is adaptively adjusted and the active damping control parameters of the vibration suppression device are optimized, generating environmental optimization control command data. Quantum sensing feedback closed-loop control of the non-magnetic cryogenic vacuum environment is then performed using the environmental optimization control command data to adjust the working state of the cryogenic refrigerator and the vibration suppression device in real time, thereby obtaining the optimized non-magnetic cryogenic vacuum environment.

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