Magnetic resonance rapid quantum measurement method and system

Abstract

The invention relates to a magnetic resonance rapid quantum measurement method and system, and the method comprises the following steps: obtaining historical data, including ODMR spectrum data generated in a historical measurement process, and measurement object feature data and measurement instrument feature data corresponding to the historical measurement process; preprocessing the historical data to form training data, and training a neural network model through the training data, so that the trained neural network model can distinguish a plurality of frequency band nodes of the ODMR spectrum generated in the measurement process; obtaining feature data of a current to-be-measured object and feature data of a current measuring instrument, and inputting the feature data to the trained neural network model to obtain a plurality of frequency band nodes; the method comprises the following steps: generating microwaves with corresponding frequencies through a measuring instrument to act on diamond, respectively measuring the fluorescence intensity emitted by the color center of the diamond at each frequency, fitting a curve of a current ODMR spectrum through a curve fitting mode, and inverting an ODMR spectrum distribution function to obtain the magnetic field intensity of a to-be-measured object.

Description

Technical Field

[0001] The invention relates to a magnetic resonance fast quantum measurement method and system, belonging to the technical field of quantum magnetic field measurement. Background Art

[0002] To detect the resonance frequency or zero-field splitting of the ground state energy level of the NV color center, the continuous optical detection magnetic resonance (cw-ODMR) technique is widely used. This technique usually involves sweeping the microwave frequency over a certain range to measure the difference between the trough and the center frequency.

[0003] In the measurement time of the swept frequency method, the microwave modulation time is relatively long, reaching the order of seconds. In view of the different requirements for magnetic measurement sampling rate in different application scenarios, the current sampling rate of the swept frequency method can only reach a few hertz, which greatly limits its scope of application. Summary of the invention

[0004] In order to solve the above problems existing in the prior art, the present invention proposes a magnetic resonance fast quantum measurement method and system.

[0005] The technical solution of the present invention is as follows:

[0006] A magnetic resonance fast quantum measurement method comprises the following steps:

[0007] Acquiring historical data, including ODMR spectrum data generated during historical measurement processes, and characteristic data of measurement objects and characteristic data of measurement instruments corresponding to the historical measurement processes;

[0008] Preprocessing the historical data to form training data, and training the neural network model through the training data, so that the trained neural network model can distinguish multiple frequency band nodes of the ODMR spectrum generated in the measurement process according to the characteristic data of the measurement object and the characteristic data of the measurement instrument;

[0009] Wherein, the frequency band nodes are the connection points between the frequency windows where the curve in the ODMR spectrum undergoes significant changes;

[0010] Acquire characteristic data of the current object to be measured and characteristic data of the current measuring instrument, and input them into the trained neural network model to obtain multiple frequency band nodes;

[0011] Corresponding to the frequencies of multiple frequency band nodes, microwaves of corresponding frequencies are generated by measuring instruments to act on diamond, and the fluorescence intensity emitted by the diamond color center at each frequency is measured respectively. The curve of the current ODMR spectrum is fitted by curve fitting using the multiple fluorescence intensity data obtained by measurement and the corresponding frequencies, and the ODMR spectrum distribution function is inverted to obtain the center frequency of the ODMR spectrum from the ODMR spectrum distribution function;

[0012] The magnetic field strength of the object to be measured is obtained according to the center frequency of the ODMR spectrum.

[0013] As a preferred implementation, the step of preprocessing the historical data to form training data includes:

[0014] Setting a slope change threshold, and traversing the ODMR spectrum curve generated in each historical measurement process according to the slope change threshold;

[0015] For each ODMR spectrum curve, two adjacent points are taken as a point group, and the slope ratio of the point with a larger slope to the point with a smaller slope in each point group is calculated, and the point group with a slope ratio greater than the slope threshold is marked;

[0016] According to all the marked point groups, the significant change segments in the ODMR spectrum curve are determined, and the middle points of each curve change segment are marked as frequency band nodes;

[0017] The number of frequency band nodes of each ODMR spectrum curve is recorded, and the frequency value of each frequency band node is extracted.

[0018] As a preferred implementation, the neural network model includes a first network model and a second network model;

[0019] The training process of the first network model is:

[0020] Taking the measured object characteristic data and the measuring instrument characteristic data of the historical measurement process as the input of the first network model, taking the number of frequency band nodes as the label, and iteratively training the first network model according to the loss between the predicted number of frequency band nodes output by the first network model and the label;

[0021] The training process of the second network model is:

[0022] The measurement object characteristic data, measurement instrument characteristic data, and the number of frequency band nodes in the historical measurement process are used as inputs of the second network model, and the array composed of the frequency values ​​of each frequency band node is used as a label. The second network model is iteratively trained according to the loss between the prediction array output by the second network model and the label.

[0023] As a preferred implementation, the measurement object characteristic data includes the type of the measurement object;

[0024] The characteristic data of the measuring device include optical path laser intensity, polarization state, and operating frequency.

[0025] On the other hand, the present invention also provides a magnetic resonance fast quantum measurement system, comprising:

[0026] A data acquisition module, used to acquire historical data, including ODMR spectrum data generated in the historical measurement process, and characteristic data of the measurement object and characteristic data of the measurement instrument corresponding to the historical measurement process;

[0027] A model training module is used to pre-process historical data to form training data, and train a neural network model through the training data, so that the trained neural network model can distinguish multiple frequency band nodes of the ODMR spectrum generated during the measurement process according to the characteristic data of the measurement object and the characteristic data of the measurement instrument;

[0028] Wherein, the frequency band nodes are the connection points between the frequency windows where the curve in the ODMR spectrum undergoes significant changes;

[0029] A frequency band distinguishing module is used to obtain characteristic data of the current object to be measured and characteristic data of the current measuring instrument, and input them into the trained neural network model to obtain multiple frequency band nodes;

[0030] The characteristic parameter measurement module is used to correspond to the frequencies of multiple frequency band nodes, generate microwaves of corresponding frequencies through a measuring instrument to act on the diamond, measure the fluorescence intensity emitted by the diamond color center at each frequency, and use the multiple fluorescence intensity data obtained by measurement and the corresponding frequencies to fit the curve of the current ODMR spectrum through curve fitting, and invert the ODMR spectrum distribution function to obtain the center frequency of the ODMR spectrum from the ODMR spectrum distribution function;

[0031] The magnetic field calculation module is used to obtain the magnetic field strength of the object to be measured according to the central frequency of the ODMR spectrum.

[0032] As a preferred implementation, the step of preprocessing the historical data to form training data includes:

[0033] Setting a slope change threshold, and traversing the ODMR spectrum curve generated in each historical measurement process according to the slope change threshold;

[0034] For each ODMR spectrum curve, two adjacent points are taken as a point group, and the slope ratio of the point with a larger slope to the point with a smaller slope in each point group is calculated, and the point group with a slope ratio greater than the slope threshold is marked;

[0035] According to all the marked point groups, the significant change segments in the ODMR spectrum curve are determined, and the middle points of each curve change segment are marked as frequency band nodes;

[0036] The number of frequency band nodes of each ODMR spectrum curve is recorded, and the frequency value of each frequency band node is extracted.

[0037] As a preferred implementation, the neural network model includes a first network model and a second network model;

[0038] The training process of the first network model is:

[0039] Taking the measured object characteristic data and the measuring instrument characteristic data of the historical measurement process as the input of the first network model, taking the number of frequency band nodes as the label, and iteratively training the first network model according to the loss between the predicted number of frequency band nodes output by the first network model and the label;

[0040] The training process of the second network model is:

[0041] The measurement object characteristic data, measurement instrument characteristic data, and the number of frequency band nodes in the historical measurement process are used as inputs of the second network model, and the array composed of the frequency values ​​of each frequency band node is used as a label. The second network model is iteratively trained according to the loss between the prediction array output by the second network model and the label.

[0042] As a preferred implementation, the measurement object characteristic data includes the type of the measurement object;

[0043] The characteristic data of the measuring device include optical path laser intensity, polarization state, and operating frequency.

[0044] On the other hand, the present invention further proposes an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the method described in any embodiment of the present invention when executing the program.

[0045] In yet another aspect, the present invention further provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method described in any embodiment of the present invention.

[0046] Additional aspects and advantages of the present invention will be set forth in the following description, and some of them will be apparent from the description, or may be understood by practicing the present invention. In addition, the various aspects and advantages of the present invention may be realized and obtained by the method steps and combinations particularly pointed out in the appended claims. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 The figure is a schematic diagram of the method flow of the first embodiment of the present invention. DETAILED DESCRIPTION

[0048] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0049] It should be understood that the step numbers used in this document are only for convenience of description and are not intended to limit the order in which the steps are executed.

[0050] It should be understood that the terms used in the present specification are only for the purpose of describing specific embodiments and are not intended to limit the present invention. As used in the present specification and the appended claims, unless the context clearly indicates otherwise, the singular forms "a", "an" and "the" are intended to include plural forms.

[0051] The terms “include” and “comprising” indicate the presence of described features, integers, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or combinations thereof.

[0052] The term "and / or" means and includes any and all possible combinations of one or more of the associated listed items.

[0053] Embodiment 1:

[0054] See also Figure 1 This embodiment provides a magnetic resonance fast quantum measurement method, comprising the following steps:

[0055] S100, acquiring historical data, including ODMR spectrum data generated in the historical measurement process, and characteristic data of the measurement object and characteristic data of the measurement instrument corresponding to the historical measurement process;

[0056] S200, preprocessing the historical data to form training data, and training the neural network model through the training data, so that the trained neural network model can distinguish multiple frequency band nodes of the ODMR spectrum generated in the measurement process according to the characteristic data of the measurement object and the characteristic data of the measurement instrument;

[0057] Wherein, the frequency band nodes are the connection points between the frequency windows where the curve in the ODMR spectrum undergoes significant changes;

[0058] S300, obtaining characteristic data of the current object to be measured and characteristic data of the current measuring instrument, and inputting them into the trained neural network model to obtain multiple frequency band nodes;

[0059] S400, corresponding to the frequencies of multiple frequency band nodes, using a measuring instrument to generate microwaves of corresponding frequencies to act on diamond, respectively measuring the fluorescence intensity emitted by the diamond color center at each frequency, using the multiple fluorescence intensity data obtained by measurement and the corresponding frequencies, fitting the curve of the current ODMR spectrum by curve fitting, and inverting the ODMR spectrum distribution function, and obtaining the center frequency of the ODMR spectrum from the ODMR spectrum distribution function;

[0060] S500 , obtaining the magnetic field intensity of the object to be measured according to the central frequency of the ODMR spectrum.

[0061] Based on the above implementation methods, this embodiment can obtain multiple frequency band nodes through the characteristic data of the object to be measured and the characteristic data of the current measuring instrument. The frequency band nodes are the connection points of the frequency windows where the curve in the ODMR spectrum produces significant changes, which can well characterize the changes in the ODMR spectrum curve. The ODMR spectrum curve follows statistical laws such as Gaussian distribution and Poisson distribution. Through these frequency band nodes, we can fit the ODMR spectrum curve, which only requires a few microwave modulations to complete, greatly reducing the number and time of microwave modulation, thereby significantly improving the speed of quantum magnetic field measurement.

[0062] As a preferred implementation of this embodiment, the step of preprocessing the historical data to form training data includes:

[0063] A specific slope change threshold is set, and the optical double resonance microwave (ODMR) spectrum curve generated in each historical measurement process is analyzed in detail through this threshold;

[0064] When analyzing each optical magnetic resonance (ODMR) spectrum curve, the step we take is to group any two adjacent points on the curve into a point group. Next, we will calculate the slope ratio of the points with larger slopes relative to the points with smaller slopes in this point group. Once this slope ratio exceeds our pre-set slope threshold, this specific point group will be marked for subsequent analysis and processing.

[0065] When analyzing the ODMR spectrum curve, it is first necessary to identify the significant change segments in the spectrum curve based on all the marked point groups. The significant change segments are composed of a group of continuous marked points. This process involves careful observation and analysis of the curve changes to ensure that every important turning point can be accurately captured. Once these significant change segments are identified, the next step is to accurately mark the middle points of each change segment. These middle points will be used as frequency band nodes in subsequent analysis, and they are the key to understanding the dynamic changes of the entire ODMR spectrum curve.

[0066] The number of frequency band nodes of each ODMR spectrum curve is recorded, and the frequency value of each frequency band node is extracted.

[0067] As a preferred implementation of this embodiment, the neural network model includes a first network model and a second network model;

[0068] The training process of the first network model is:

[0069] Taking the measured object characteristic data and the measuring instrument characteristic data of the historical measurement process as the input of the first network model, taking the number of frequency band nodes as the label, and iteratively training the first network model according to the loss between the predicted number of frequency band nodes output by the first network model and the label;

[0070] The training process of the second network model is:

[0071] The measurement object characteristic data, measurement instrument characteristic data, and the number of frequency band nodes in the historical measurement process are used as inputs of the second network model, and the array composed of the frequency values ​​of each frequency band node is used as a label. The second network model is iteratively trained according to the loss between the prediction array output by the second network model and the label.

[0072] According to the above implementation, the neural network model constructed and trained in this embodiment can first predict the number of frequency band nodes in the ODRM spectrum curve of the current object to be measured through the first network model, and then input the predicted number of frequency band nodes together with the characteristic data of the measurement object and the characteristic data of the measuring instrument into the second network model to output the frequency prediction value of each frequency band node.

[0073] As a preferred implementation of this embodiment, the described measurement object characteristic data is not limited to a single parameter, but includes information of multiple dimensions, among which the key feature of the type of measurement object is particularly emphasized, which can classify and identify different types of measurement objects.

[0074] In this embodiment, the characteristic data of the measuring instrument described covers a number of key parameters that are essential for ensuring the accuracy and reliability of the measurement. Specifically, these characteristic data include the optical path laser intensity, polarization state, and operating frequency. The optical path laser intensity refers to the intensity level of the laser beam when it propagates in the optical path during the measurement process. The polarization state describes the polarization characteristics of the laser beam. The operating frequency refers to the frequency of the laser emission of the measuring instrument when performing the measurement.

[0075] Embodiment 2:

[0076] This embodiment provides a magnetic resonance fast quantum measurement system, including:

[0077] A data acquisition module, used to acquire historical data, including ODMR spectrum data generated in the historical measurement process, and characteristic data of the measurement object and characteristic data of the measurement instrument corresponding to the historical measurement process; this module is used to implement the function of step S100 in the first embodiment, which will not be described in detail here;

[0078] A model training module is used to pre-process historical data to form training data, and train the neural network model through the training data, so that the trained neural network model can distinguish multiple frequency band nodes of the ODMR spectrum generated during the measurement process according to the characteristic data of the measurement object and the characteristic data of the measurement instrument; this module is used to implement the function of step S200 in the first embodiment, and will not be repeated here;

[0079] Wherein, the frequency band nodes are the connection points between the frequency windows where the curve in the ODMR spectrum undergoes significant changes;

[0080] A frequency band distinguishing module, used to obtain characteristic data of the current object to be measured and characteristic data of the current measuring instrument, and input them into the trained neural network model to obtain multiple frequency band nodes; this module is used to implement the function of step S300 in the first embodiment, and will not be repeated here;

[0081] The characteristic parameter measurement module is used to correspond to the frequencies of multiple frequency band nodes, generate microwaves of corresponding frequencies through a measuring instrument to act on the diamond, measure the fluorescence intensity emitted by the diamond color center at each frequency, and use the multiple fluorescence intensity data obtained by measurement and the corresponding frequencies to fit the curve of the current ODMR spectrum through curve fitting, and invert the ODMR spectrum distribution function to obtain the center frequency of the ODMR spectrum from the ODMR spectrum distribution function; this module is used to implement the function of step S400 in the first embodiment, and will not be repeated here;

[0082] The magnetic field calculation module is used to obtain the magnetic field strength of the object to be measured according to the central frequency of the ODMR spectrum; this module is used to implement the function of step S500 in the first embodiment, and will not be described in detail here.

[0083] As a preferred implementation of this embodiment, the step of preprocessing the historical data to form training data includes:

[0084] Setting a slope change threshold, and traversing the ODMR spectrum curve generated in each historical measurement process according to the slope change threshold;

[0085] For each ODMR spectrum curve, two adjacent points are taken as a point group, and the slope ratio of the point with a larger slope to the point with a smaller slope in each point group is calculated, and the point group with a slope ratio greater than the slope threshold is marked;

[0086] According to all the marked point groups, the significant change segments in the ODMR spectrum curve are determined, and the middle points of each curve change segment are marked as frequency band nodes;

[0087] The number of frequency band nodes of each ODMR spectrum curve is recorded, and the frequency value of each frequency band node is extracted.

[0088] As a preferred implementation of this embodiment, the neural network model includes a first network model and a second network model;

[0089] The training process of the first network model is:

[0090] Taking the measured object characteristic data and the measuring instrument characteristic data of the historical measurement process as the input of the first network model, taking the number of frequency band nodes as the label, and iteratively training the first network model according to the loss between the predicted number of frequency band nodes output by the first network model and the label;

[0091] The training process of the second network model is:

[0092] The measurement object characteristic data, measurement instrument characteristic data, and the number of frequency band nodes in the historical measurement process are used as inputs of the second network model, and the array composed of the frequency values ​​of each frequency band node is used as a label. The second network model is iteratively trained according to the loss between the prediction array output by the second network model and the label.

[0093] As a preferred implementation manner of this embodiment, the measurement object characteristic data includes the type of the measurement object;

[0094] The characteristic data of the measuring device include optical path laser intensity, polarization state, and operating frequency.

[0095] Embodiment three:

[0096] This embodiment provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the magnetic resonance fast quantum measurement method as described in any embodiment of the present invention is implemented.

[0097] Embodiment 4:

[0098] This embodiment provides a computer-readable storage medium on which a computer program is stored. When the program is executed by a processor, the magnetic resonance fast quantum measurement method as described in any embodiment of the present invention is implemented.

[0099] In the embodiments of the present application, "at least one" refers to one or more, and "more than one" refers to two or more. "And / or" describes the association relationship of associated objects, indicating that three relationships may exist. For example, A and / or B can represent the existence of A alone, the existence of A and B at the same time, and the existence of B alone. Among them, A and B can be singular or plural. The character " / " generally indicates that the previous and next associated objects are in an "or" relationship. "At least one of the following" and similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one of a, b and c can be represented by: a, b, c, a and b, a and c, b and c, or a and b and c, where a, b, c can be single or multiple.

[0100] Those of ordinary skill in the art will appreciate that the various units and algorithm steps described in the embodiments disclosed herein can be implemented in a combination of electronic hardware, computer software, and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0101] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0102] In several embodiments provided in the present application, if any function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (Read-Only Memory; hereinafter referred to as: ROM), random access memory (Random Access Memory; hereinafter referred to as: RAM), disk or optical disk, and other media that can store program codes.

[0103] The above descriptions are merely embodiments of the present invention and are not intended to limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made using the contents of the present invention specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A magnetic resonance fast quantum measurement method, characterized in that: The following steps are involved: Acquiring historical data, including ODMR spectrum data generated during historical measurement processes, and characteristic data of measurement objects and characteristic data of measurement instruments corresponding to the historical measurement processes; Preprocessing the historical data to form training data, and training the neural network model through the training data, so that the trained neural network model can distinguish multiple frequency band nodes of the ODMR spectrum generated in the measurement process according to the characteristic data of the measurement object and the characteristic data of the measurement instrument; Wherein, the frequency band nodes are the connection points between the frequency windows where the curve in the ODMR spectrum undergoes significant changes; Acquire characteristic data of the current object to be measured and characteristic data of the current measuring instrument, and input them into the trained neural network model to obtain multiple frequency band nodes; Corresponding to the frequencies of multiple frequency band nodes, microwaves of corresponding frequencies are generated by measuring instruments to act on diamond, and the fluorescence intensity emitted by the diamond color center at each frequency is measured respectively. The curve of the current ODMR spectrum is fitted by curve fitting using the multiple fluorescence intensity data obtained by measurement and the corresponding frequencies, and the ODMR spectrum distribution function is inverted to obtain the center frequency of the ODMR spectrum from the ODMR spectrum distribution function; The magnetic field strength of the object to be measured is obtained according to the center frequency of the ODMR spectrum.

2. A magnetic resonance fast quantum measurement method according to claim 1, characterized in that: The step of preprocessing the historical data to form training data comprises: Setting a slope change threshold, and traversing the ODMR spectrum curve generated in each historical measurement process according to the slope change threshold; For each ODMR spectrum curve, two adjacent points are taken as a point group, and the slope ratio of the point with a larger slope to the point with a smaller slope in each point group is calculated, and the point group with a slope ratio greater than the slope threshold is marked; According to all the marked point groups, the significant change segments in the ODMR spectrum curve are determined, and the middle points of each curve change segment are marked as frequency band nodes; The number of frequency band nodes of each ODMR spectrum curve is recorded, and the frequency value of each frequency band node is extracted.

3. A magnetic resonance fast quantum measurement method according to claim 2, characterized in that: The neural network model includes a first network model and a second network model; The training process of the first network model is: Taking the measured object characteristic data and the measuring instrument characteristic data of the historical measurement process as the input of the first network model, taking the number of frequency band nodes as the label, and iteratively training the first network model according to the loss between the predicted number of frequency band nodes output by the first network model and the label; The training process of the second network model is: The measurement object characteristic data, measurement instrument characteristic data, and the number of frequency band nodes in the historical measurement process are used as inputs of the second network model, and the array composed of the frequency values ​​of each frequency band node is used as a label. The second network model is iteratively trained according to the loss between the prediction array output by the second network model and the label.

4. A magnetic resonance fast quantum measurement method according to claim 1, characterized in that: The measurement object characteristic data includes the type of the measurement object; The characteristic data of the measuring device include optical path laser intensity, polarization state, and operating frequency.

5. A magnetic resonance fast quantum measurement system, characterized in that: include: A data acquisition module, used to acquire historical data, including ODMR spectrum data generated in the historical measurement process, and characteristic data of the measurement object and characteristic data of the measurement instrument corresponding to the historical measurement process; A model training module is used to pre-process historical data to form training data, and train a neural network model through the training data, so that the trained neural network model can distinguish multiple frequency band nodes of the ODMR spectrum generated during the measurement process according to the characteristic data of the measurement object and the characteristic data of the measurement instrument; Wherein, the frequency band nodes are the connection points between the frequency windows where the curve in the ODMR spectrum undergoes significant changes; A frequency band distinguishing module is used to obtain characteristic data of the current object to be measured and characteristic data of the current measuring instrument, and input them into the trained neural network model to obtain multiple frequency band nodes; The characteristic parameter measurement module is used to correspond to the frequencies of multiple frequency band nodes, generate microwaves of corresponding frequencies through a measuring instrument to act on the diamond, measure the fluorescence intensity emitted by the diamond color center at each frequency, and use the multiple fluorescence intensity data obtained by measurement and the corresponding frequencies to fit the curve of the current ODMR spectrum through curve fitting, and invert the ODMR spectrum distribution function to obtain the center frequency of the ODMR spectrum from the ODMR spectrum distribution function; The magnetic field calculation module is used to obtain the magnetic field strength of the object to be measured according to the central frequency of the ODMR spectrum.

6. A magnetic resonance fast quantum measurement system according to claim 5, characterized in that: The step of preprocessing the historical data to form training data comprises: Setting a slope change threshold, and traversing the ODMR spectrum curve generated in each historical measurement process according to the slope change threshold; For each ODMR spectrum curve, two adjacent points are taken as a point group, and the slope ratio of the point with a larger slope to the point with a smaller slope in each point group is calculated, and the point group with a slope ratio greater than the slope threshold is marked; According to all the marked point groups, the significant change segments in the ODMR spectrum curve are determined, and the middle points of each curve change segment are marked as frequency band nodes; The number of frequency band nodes of each ODMR spectrum curve is recorded, and the frequency value of each frequency band node is extracted.

7. A magnetic resonance fast quantum measurement system according to claim 6, characterized in that: The neural network model includes a first network model and a second network model; The training process of the first network model is: Taking the measured object characteristic data and the measuring instrument characteristic data of the historical measurement process as the input of the first network model, taking the number of frequency band nodes as the label, and iteratively training the first network model according to the loss between the predicted number of frequency band nodes output by the first network model and the label; The training process of the second network model is: The measurement object characteristic data, measurement instrument characteristic data, and the number of frequency band nodes in the historical measurement process are used as inputs of the second network model, and the array composed of the frequency values ​​of each frequency band node is used as a label. The second network model is iteratively trained according to the loss between the prediction array output by the second network model and the label.

8. The magnetic resonance fast quantum measurement system according to claim 5, characterized in that: The measurement object characteristic data includes the type of the measurement object; The characteristic data of the measuring device include optical path laser intensity, polarization state, and operating frequency.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, the magnetic resonance fast quantum measurement method according to any one of claims 1 to 4 is implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the magnetic resonance fast quantum measurement method according to any one of claims 1 to 4 is implemented.

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