CN116774124A - OPM debugging and calibrating method and device - Google Patents
OPM debugging and calibrating method and device Download PDFInfo
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- G—PHYSICS
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- G—PHYSICS
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Abstract
A method of debug calibration of an OPM, the method comprising the steps of: step 1, acquiring current working parameters and environment parameters of a target OPM; step 2, calling an optimal sensitivity model, and calculating target sensitivity according to the environmental parameters; step 3, calculating a sensitivity deviation value according to the current working parameter and the target sensitivity; step 4, if the sensitivity deviation value does not exceed a preset deviation threshold value, parameter adjustment is not performed; if the sensitivity deviation value exceeds the preset deviation threshold, the parameter calculation module calls the optimal parameter model to generate a target parameter combination according to the environment parameter; step 5, corresponding parameters of a target OPM are adjusted according to the current working parameters and the target parameter combination; by means of the method, debugging and calibration of the OPM under complex working conditions can be automatically achieved.
Description
Technical Field
The application relates to the technical field of magnetoencephalography, in particular to a method and a device for debugging and calibrating an OPM.
Technical Field
The current OPM relies on the coupling of multiple parameters, including driving and detecting the current and temperature of the laser, detecting the detuning (wavelength) of the laser, the temperature of the air chamber, etc., which need to be manually adjusted during the outgoing debugging of the OPM to be combined into an optimized parameter state. In addition, when the OPM operates in different magnetic field environments, the optimal parameters thereof will change due to the interaction of atoms caused by the external magnetic field, and the OPM needs to be manually readjusted to be in the optimal sensitivity state according to the different magnetic field environments.
At present, the debugging and calibration of the OPM under the complex working condition still depends on a manual adjustment mode, the efficiency is low, the accuracy of the debugging and calibration is relatively poor, and the requirement of quick debugging and calibration cannot be met.
Disclosure of Invention
Aiming at the limitation of the limitation, the application provides a method and a device for debugging and calibrating OPM, by means of the method, the debugging and calibration of OPM under complex working conditions can be automatically realized, and the debugging and calibration speed is high and the efficiency is high.
In order to achieve the above purpose, the present application adopts the following technical scheme:
a debugging and calibrating method of OPM is characterized in that,
the method comprises the following steps:
step 1, acquiring current working parameters and environment parameters of a target OPM;
step 2, calling an optimal sensitivity model, and calculating target sensitivity according to the environmental parameters; the target sensitivity is the optimal sensitivity under the environmental parameters;
the optimal sensitivity model is used for calculating optimal sensitivity according to the environmental parameters of the target OPM to obtain target sensitivity;
step 3, calculating a sensitivity deviation value according to the current working parameter and the target sensitivity;
step 4, if the sensitivity deviation value does not exceed a preset deviation threshold value, parameter adjustment is not performed; if the sensitivity deviation value exceeds the preset deviation threshold, the parameter calculation module calls the optimal parameter model to generate a target parameter combination according to the environment parameter;
the optimal parameter model is used for calculating a target parameter combination under the target sensitivity according to the environmental parameters of the target OPM;
and 5, adjusting corresponding parameters of the target OPM according to the current working parameters and the target parameter combination.
Compared with the prior art, the application has the following advantages:
(1) The debugging and calibration of the OPM under complex working conditions can be automatically realized by means of an automatic method;
(2) The OPM quick adjustment and calibration can be realized by means of the optimal sensitivity model and the optimal parameter model, and the working efficiency is high.
The foregoing description is only an overview of the present application, and is intended to be implemented in accordance with the teachings of the present application, as well as the preferred embodiments thereof, together with the following detailed description of the application, given by way of illustration only, together with the accompanying drawings.
Drawings
Fig. 1 is a block diagram of an OPM parameter debugging and calibrating apparatus according to an embodiment of the present application.
Fig. 2 is a flowchart of an OPM debug calibration method according to an embodiment of the present application.
FIG. 3 is a flow chart of an optimal sensitivity model and an optimal parameter model construction method according to an embodiment of the present application.
Detailed Description
Other advantages and advantages of the present application will become apparent to those skilled in the art from the following detailed description, which, by way of illustration, is to be read in connection with certain specific embodiments, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application. For a further understanding of the present application, the present application will be described in further detail with reference to the following preferred embodiments.
The following is an explanation of terms involved in embodiments of the present application:
OPM (optical-pumped Magnetometer): an atomic magnetometer (also known as an optical pump magnetometer), is a detector for measuring weak magnetic fields; the principle of OPM is: and exciting a gas atomic system in the magnetic field to be detected by using circularly polarized light to generate a particle number difference between the energy levels of the zeeman thereof, thereby observing the magnetic resonance effect.
Hereinafter, exemplary embodiments of the present application will be described in detail with reference to the foregoing noun terminology and accompanying drawings.
An aspect of the present application is to provide an OPM parameter tuning calibration apparatus; referring to fig. 1, the OPM parameter debugging and calibrating device includes an environmental parameter detection module, a model training module, a parameter calculation module, a parameter adjustment module, a state analysis module, a data interaction module, and a database module.
The environment parameter detection module is used for acquiring environment parameters of a space where the target OPM is detected (namely environment parameters of a working environment of the target OPM), wherein the environment parameters comprise an environment magnetic field (static magnetic field), thermal magnetic noise of a shielding device, working parameters of a digital acquisition system and performance parameters of a detector laser;
the model training module is used for establishing an optimal sensitivity model and an optimal parameter model; the optimal sensitivity model can calculate optimal sensitivity according to the environmental parameters of the target OPM to obtain target sensitivity; the optimal parametric model may calculate a target parameter combination at the target sensitivity from the environmental parameters of the target OPM.
The parameter calculation module is used for calling the optimal sensitivity model to calculate target sensitivity according to the environmental parameters and calling the optimal parameter model to generate target parameter combination according to the environmental parameters.
The target sensitivity is a sensitivity index value when the target OPM reaches the optimal working state, namely the optimal sensitivity;
the target parameter combination is an operating parameter combination for the target OPM to reach the optimal sensitivity under the working environment magnetic field, and the operating parameter combination comprises one or more of atomic vapor density, laser optical power density, laser wavelength and self magnetic field compensation of the detector.
The parameter adjusting module is used for generating parameter adjusting data according to the output result (namely the target parameter combination) of the parameter analyzing module and the current working parameter of the target OPM.
The state analysis module is used for carrying out working state analysis on the target OPM and judging whether the OPM debugging and calibration are completed or not according to the working state analysis result.
The data interaction module is used for receiving OPM working parameters and environment parameters and outputting parameter adjustment control signals.
The database module consists of a parameter model database and a working parameter database; the model database is used for storing the optimal sensitivity model and the related data of the optimal parameter model, and comprises environment parameter sample data, optimal sensitivity model parameters, optimal sensitivity model sample data, optimal parameter model parameters and optimal parameter model sample data; the working parameter database is used for storing working state parameters of a plurality of OPMs.
The application further provides a debugging and calibrating method of the OPM, which is used for automatically acquiring the optimal parameters of the OPM, and outputting the optimal parameter combination and the corresponding parameter adjustment control signals under the target OPM working environment through an optimal parameter model to realize the debugging and calibration of the OPM.
Referring to fig. 2, the debugging calibration method includes the following steps:
step 1, acquiring current working parameters of a target OPM, and calling the environment parameter detection module to acquire environment parameters;
step 2, the parameter calculation module calls an optimal sensitivity model, and calculates target sensitivity according to the environmental parameters; the target sensitivity is the optimal sensitivity under the environmental parameters;
step 3, the state analysis module calculates a sensitivity deviation value according to the current working parameter and the target sensitivity;
step 4, if the sensitivity deviation value does not exceed a preset deviation threshold value, parameter adjustment is not performed; if the sensitivity deviation value exceeds the preset deviation threshold, the parameter calculation module calls the optimal parameter model to generate a target parameter combination according to the environment parameter;
step 5, the parameter adjusting module adjusts corresponding parameters of a target OPM according to the current working parameters and the target parameter combination;
step 6, the state analysis module controls the parameter adjustment process according to the working state after the target OPM parameter adjustment:
if the parameter adjustment is not completed and the adjustment times do not exceed the preset adjustment times threshold, updating the current working parameters of the target OPM and returning to the step 4;
if the parameter adjustment is not completed and the adjustment times exceeds a preset adjustment times threshold, a model training module is called to optimize the parameter model, and the step 1 is returned;
and if the parameter adjustment is completed, exiting the parameter adjustment process.
As an example, the sensitivity deviation value is calculated by:
s＝fs(x 1 ,x 2 ,…)-S 0
wherein S is a sensitivity deviation value, S 0 For the target sensitivity, fs () is a calculation function that calculates the current sensitivity by the current operating parameter, x i (i=1, 2, …, n) represents the relevant operating parameters of the OPM.
As an example, referring to fig. 3, the optimal sensitivity model and the optimal parameter model are constructed by:
step S1, acquiring working environment parameter data of an OPM under a plurality of working environments to obtain an environment parameter sample data set;
step S2, performing OPM working state test under a plurality of working environments to obtain optimal sensitivity and corresponding OPM working parameters under the plurality of working environments, and respectively forming an optimal sensitivity sample data set and an optimal working parameter set;
s3, training a machine learning model by using the environmental parameter sample data set and the optimal sensitivity sample data set to obtain an optimal sensitivity model; and training a machine learning model by using the environment parameter sample data set and the optimal working parameter set to obtain an optimal parameter model.
The environmental parameter sample data set, the optimal sensitivity sample data set, and the optimal operating parameter set are stored in a parameter model database of the database module when steps S1 and S2 are performed.
Upon execution of step S3 (i.e., training a machine learning model process), the parametric model database will be called by the model training module to obtain the environmental parameter sample data set, the optimal sensitivity sample data set, the optimal operating parameter set as sample data for the machine learning model.
As an embodiment, the optimal sensitivity model and the optimal parameter model in step S3 may be trained by a neural network, where the neural network includes an input layer, a hidden layer, and an output layer.
The training steps by using the neural network comprise:
(1) Performing data cleaning on the original data set and splitting the original data set into a training set and a testing set;
(2) Setting model parameters of a neural network; the model parameters comprise the number of input layers, the number of output layers, the number of neurons and the number of network layers;
(3) Model training with the training set by adopting the neural network set in the step (2), and evaluating by means of the test set;
(4) And adjusting model parameters until the recall rate and the accuracy rate meet a preset threshold value, and outputting the obtained model.
When training the optimal sensitivity model, the raw data set in (1) is composed of the environmental parameter sample data set and the optimal sensitivity sample data set, and model parameters of the neural network are set as follows: the number of input layers is set to 1, the number of output layers is set to 1, and the number of network layers is set to 3.
When training the optimal parametric model, the original data set in (1) is composed of the environmental parameter sample data set and the optimal working parameter set, and the model parameters of the neural network are set as follows: the number of input layers is set to 1, the number of output layers is set to 1, and the number of network layers is set to 3.
As an embodiment, the optimal sensitivity model and the optimal parameter model may be further obtained by training any machine learning algorithm including a K-nearest algorithm, a support vector machine algorithm, a naive bayes algorithm, and a decision tree algorithm; the machine learning algorithm is a mature technical means, and a person skilled in the art can smoothly implement the regression algorithm according to the description of the embodiment, which is not repeated here.
As one embodiment, the environmental parameter detecting module includes an environmental magnetic field detecting device, an environmental temperature monitoring device, an electronics system detecting device, a laser light intensity detecting device, and a laser wavelength detecting device.
The environment magnetic field detection device is configured around the target OPM and is used for acquiring environment magnetic field data under the working environment of the target OPM.
The environment temperature monitoring device is used for acquiring thermomagnetic noise of the shielding device;
the electronic system detection device is used for obtaining the working parameters of the digital acquisition system;
the laser light intensity detection device is used for obtaining the light intensity parameter of the detector laser;
the laser wavelength detection device is used for obtaining the wavelength parameters of the detector laser.
As an embodiment, the current operation parameter is an operation state parameter of the current state of the target OPM, and the operation state parameter includes at least one type of parameter of atomic vapor density, laser optical power density, laser wavelength, and magnetic field compensation of the detector itself.
As one example, the methods of the present application may be implemented in software and/or a combination of software and hardware, e.g., using an Application Specific Integrated Circuit (ASIC), a general purpose computer, or any other similar hardware device.
The method of the present application may be implemented in the form of a software program that is executable by a processor to perform the steps or functions described above. Likewise, the software programs (including associated data structures) may be stored on a computer readable recording medium, such as RAM memory, magnetic or optical drive or diskette and the like.
In addition, some steps or functions of the methods described herein may be implemented in hardware, for example, as circuitry that cooperates with the processor to perform various steps or functions.
Furthermore, parts of the methods of the present application may be applied as a computer program product, such as computer program instructions, which when executed by a computer, may invoke or provide the methods and/or solutions according to the present application by way of operation of the computer. Program instructions for invoking the methods of the application may be stored in fixed or removable recording media and/or transmitted via a data stream in a broadcast or other signal bearing medium and/or stored within a working memory of a computer device operating according to the program instructions.
As an embodiment, the present application also provides an apparatus comprising a memory for storing computer program instructions and a processor for executing the program instructions, wherein the computer program instructions, when executed by the processor, trigger the apparatus to run a method and/or a solution according to the previous embodiments.
It should be noted that, for simplicity of description, the foregoing method embodiments are all described as a series of acts, but it should be understood by those skilled in the art that the present application is not limited by the order of acts described, as some steps may be performed in other orders or concurrently in accordance with the present application. Further, those skilled in the art will also appreciate that the embodiments described in the specification are alternative embodiments, and that the acts and modules referred to are not necessarily required for the present application.
Finally, it is pointed out that in the present document relational terms such as first and second, and the like, are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or terminal that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or terminal. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article or terminal device comprising the element.
The present application is not limited to the above-mentioned embodiments, but is intended to be limited to the following embodiments, and any modifications, equivalents and modifications can be made to the above-mentioned embodiments without departing from the scope of the application.
Claims (9)
1. A debugging and calibrating method of OPM is characterized in that,
the method comprises the following steps:
step 1, acquiring current working parameters and environment parameters of a target OPM;
step 2, calling an optimal sensitivity model, and calculating target sensitivity according to the environmental parameters; the target sensitivity is the optimal sensitivity under the environmental parameters;
the optimal sensitivity model is used for calculating optimal sensitivity according to the environmental parameters of the target OPM to obtain target sensitivity;
step 3, calculating a sensitivity deviation value according to the current working parameter and the target sensitivity;
step 4, if the sensitivity deviation value does not exceed a preset deviation threshold value, parameter adjustment is not performed; if the sensitivity deviation value exceeds the preset deviation threshold, the parameter calculation module calls the optimal parameter model to generate a target parameter combination according to the environment parameter;
the optimal parameter model is used for calculating a target parameter combination under the target sensitivity according to the environmental parameters of the target OPM;
and 5, adjusting corresponding parameters of the target OPM according to the current working parameters and the target parameter combination.
2. The method of claim 1, wherein the step of determining the position of the substrate comprises,
the method further comprises the steps of:
step 6, controlling a parameter adjusting process according to the working state after the target OPM parameter adjustment:
if the parameter adjustment is not completed and the adjustment times do not exceed the preset adjustment times threshold, updating the current working parameters of the target OPM and returning to the step 4;
if the parameter adjustment is not completed and the adjustment times exceeds a preset adjustment times threshold, a model training module is called to optimize the parameter model, and the step 1 is returned;
and if the parameter adjustment is completed, exiting the parameter adjustment process.
3. The method of claim 1, wherein the step of determining the position of the substrate comprises,
the optimal sensitivity model and the optimal parameter model are constructed by:
step S1, acquiring working environment parameter data of an OPM under a plurality of working environments to obtain an environment parameter sample data set;
step S2, performing OPM working state test under a plurality of working environments to obtain optimal sensitivity and corresponding OPM working parameters under the plurality of working environments, and respectively forming an optimal sensitivity sample data set and an optimal working parameter set;
s3, training a machine learning model by using the environmental parameter sample data set and the optimal sensitivity sample data set to obtain an optimal sensitivity model; and training a machine learning model by using the environment parameter sample data set and the optimal working parameter set to obtain an optimal parameter model.
4. The method of claim 3, wherein the step of,
the environmental parameter sample data set, the optimal sensitivity sample data set, and the optimal operating parameter set are stored in a parameter model database of the database module when steps S1 and S2 are performed.
Upon execution of step S3, the parametric model database will be called by the model training module to obtain the environmental parameter sample data set, the optimal sensitivity sample data set, the optimal working parameter set as sample data for the machine learning model.
5. The method of claim 3, wherein the step of,
the optimal sensitivity model and the optimal parameter model in step S3 may be trained by a neural network, and the training using the neural network includes:
(1) Performing data cleaning on the original data set and splitting the original data set into a training set and a testing set;
(2) Setting model parameters of a neural network; the model parameters comprise the number of input layers, the number of output layers, the number of neurons and the number of network layers;
(3) Model training with the training set by adopting the neural network set in the step (2), and evaluating by means of the test set;
(4) And adjusting model parameters until the recall rate and the accuracy rate meet a preset threshold value, and outputting the obtained model.
6. The sensitivity deviation value is calculated by:
s＝fs(x 1 ,x 2 ,…)-S 0
wherein S is a sensitivity deviation value, S 0 For the target sensitivity, fs () is a calculation function that calculates the current sensitivity by the current operating parameter, x i (i=1, 2, …, n) represents the relevant operating parameters of the OPM.
7. A debug and calibration device of an OPM is characterized in that,
the device comprises an environment parameter detection module, a model training module, a parameter calculation module, a parameter adjustment module and a state analysis module;
the environment parameter detection module is used for acquiring environment parameters of a space where the target OPM is detected;
the model training module is used for establishing an optimal sensitivity model and an optimal parameter model;
the parameter calculation module is used for calling the optimal sensitivity model to calculate target sensitivity according to the environmental parameters and calling the optimal parameter model to generate a target parameter combination according to the environmental parameters;
the target sensitivity is a sensitivity index value when the target OPM reaches an optimal working state;
the target parameter combination is a working parameter combination that the target OPM reaches the optimal sensitivity under the working environment magnetic field;
the parameter adjusting module is used for generating parameter adjusting data according to the target parameter set and the current working parameters of the target OPM;
the state analysis module is used for carrying out working state analysis on the target OPM and judging whether the OPM debugging and calibration are completed or not according to the working state analysis result.
8. The method of claim 8, wherein the step of determining the position of the first electrode is performed,
the environmental parameters comprise an environmental magnetic field, thermal magnetic noise of a shielding device, working parameters of a digital acquisition system and performance parameters of a detector laser;
the working parameter combination comprises one or more of atomic vapor density, laser optical power density, laser wavelength and magnetic field compensation of the detector.
9. The method of claim 1, wherein the step of determining the position of the substrate comprises,
the environment parameter detection module comprises an environment magnetic field detection device, an environment temperature monitoring device, an electronics system detection device, a laser light intensity detection device and a laser wavelength detection device;
the environment magnetic field detection device is used for acquiring environment magnetic field data under the target OPM working environment;
the environment temperature monitoring device is used for acquiring thermomagnetic noise of the shielding device;
the electronic system detection device is used for obtaining the working parameters of the digital acquisition system;
the laser light intensity detection device is used for obtaining the light intensity parameter of the detector laser;
the laser wavelength detection device is used for obtaining the wavelength parameters of the detector laser.
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