An integrated light source optimization method for ultra-weak magnetic field detection
By constructing a dynamic state perception matrix and optimizing the light source parameters using a hybrid intelligent decision engine, the problems of sensitivity attenuation and insufficient anti-interference ability in the detection of extremely weak magnetic fields are solved, achieving efficient system adaptation and long-term stability.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2026-02-11
- Publication Date
- 2026-05-26
AI Technical Summary
Existing technologies lack the ability to coordinate the perception of atomic states, environmental disturbances, and light source conditions in the detection of extremely weak magnetic fields. This results in decreased system sensitivity and insufficient anti-interference capabilities, low optimization efficiency, and difficulty in meeting long-term stability and sensitivity requirements.
By integrating atomic ensemble quantum response, environmental multi-physics field perturbation parameters, and historical operating data of the light source in real time, a dynamic state perception matrix is constructed. A hybrid intelligent decision engine is used to generate a collaborative optimization strategy for multi-degree-of-freedom parameters of the light source, and the light source parameters are continuously iteratively optimized through a closed-loop feedback mechanism.
This significantly enhances the system's adaptability to complex operating conditions and its anti-interference level, ensures that the adjustment of light source parameters conforms to the principle of quantum sensing, and improves the system's robustness and long-term stability.
Smart Images
Figure CN122085189A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of precision magnetic field measurement technology, specifically to an integrated light source optimization method for detecting extremely weak magnetic fields. Background Technology
[0002] The detection of extremely weak magnetic fields is one of the core technologies in the field of quantum precision measurement, and it is widely used in fields such as atomic magnetometers, magnetoencephalography (MEG), magnetocardiography (MCG), geological exploration, and basic physics research.
[0003] Existing technologies mostly employ fixed-parameter light sources or single-variable feedback adjustment strategies, lacking the ability to collaboratively perceive multi-source information such as atomic states, environmental disturbances, and light source operating conditions. The optimization process relies on manual experience to set thresholds, making it difficult to dynamically adapt to complex operating conditions, resulting in decreased sensitivity and insufficient anti-interference capabilities of the system during long-term operation.
[0004] Furthermore, traditional methods lack a collaborative decision-making mechanism that integrates physical laws and data-driven approaches, which can easily generate adjustment instructions that do not conform to the principles of quantum sensing. This results in problems such as low optimization efficiency and poor system robustness, leading to a decrease in the signal-to-noise ratio of the atomic ensemble quantum response when the light source parameters fluctuate. Consequently, the long-term stability and sensitivity of detecting extremely weak magnetic fields are difficult to meet practical needs. Summary of the Invention
[0005] The technical problem to be solved by the present invention is to overcome the above-mentioned technical defects and provide an integrated light source optimization method for detecting extremely weak magnetic fields, which can realize intelligent collaborative optimization of light source parameters and improve the robustness of the extremely weak magnetic field detection system.
[0006] To solve the above-mentioned technical problems, the technical solution provided by the present invention is: an integrated light source optimization method for detecting extremely weak magnetic fields, comprising the following steps: S1: Real-time fusion acquisition of atomic ensemble quantum response trajectory, environmental multi-physics field perturbation parameters, historical operating data of light source and external spatiotemporal reference signal; S2: Construct a dynamic state perception matrix based on the collected data in S1; S3: Based on the dynamic state perception matrix obtained in S2, a hybrid intelligent decision engine is used to generate a collaborative optimization strategy for multi-degree-of-freedom parameters of the light source. S4: Based on the obtained collaborative optimization strategy, the light source parameters are adjusted in real time by the light source controller to apply the adjusted light field to the atomic magnetometer system; S5: Real-time monitoring of the magnetic field detection performance indicators output by the system. Based on the deviation between the performance indicators and the preset sensitivity threshold, the closed-loop feedback mechanism is driven to continuously iterate and optimize the light source parameters.
[0007] Preferably, in S1, the real-time fusion acquisition is based on the raw data collected by the distributed edge sensing network, and the federated feature fusion algorithm is used to perform spatiotemporal alignment and semantic-level compression on the four types of heterogeneous data collected to generate perceptual stream data.
[0008] Preferably, the construction of the dynamic state perception matrix in S2 includes extracting the original perception stream to extract atomic ensemble quantum response features, environmental disturbance comprehensive features, light source state prediction features, and spatiotemporal synchronization features, and arranging them in a time series to form a dynamically updated high-dimensional matrix.
[0009] Preferably, the hybrid intelligent decision engine in S3 includes a physical law module and an intelligent learning module; The physical law module incorporates a basic model of the interaction between atoms and light fields to verify the physical rationality of the strategy; The intelligent learning module generates an initial parameter adjustment scheme as a light source parameter optimization strategy based on historical optimization data and real-time sensing information.
[0010] Preferably, the intelligent learning module continuously updates the strategy generation logic based on newly collected system response data using an online learning mechanism.
[0011] Preferably, the intelligent learning module continuously updates the strategy generation logic based on newly collected system response data using an online learning mechanism.
[0012] Preferably, the magnetic field detection performance indicators in S5 include real-time signal-to-noise ratio and system stability parameters; the closed-loop feedback mechanism includes a fast fine-tuning loop and a strategy update loop.
[0013] Preferably, the fast fine-tuning loop corrects the light source parameters in real time based on the current deviation, and the strategy update loop periodically analyzes the historical optimization effect to optimize the decision-making logic and threshold setting.
[0014] The advantages of this invention compared to the prior art are: By integrating multi-source information such as atomic ensemble response, environmental disturbance, light source status, and spatiotemporal reference, a dynamic sensing system is constructed, which significantly enhances the system's adaptability to complex working conditions and its anti-interference level. The hybrid intelligent decision engine organically combines physical law verification with intelligent learning and strategy generation. While ensuring that the adjustment plan conforms to the principles of quantum sensing and avoids ineffective operations, it continuously optimizes the quality of decisions by relying on online learning mechanisms. The closed-loop feedback employs a dual-loop collaborative mechanism of rapid fine-tuning and strategy updates to ensure the long-term stability and reliability of the system. Attached Figure Description
[0015] Figure 1 This is a flowchart illustrating an integrated light source optimization method for detecting extremely weak magnetic fields. Detailed Implementation
[0016] The present invention will now be described in further detail with reference to the accompanying drawings.
[0017] Combined with appendix Figure 1 As shown, an integrated light source optimization method for detecting extremely weak magnetic fields includes the following steps: S1: Real-time fusion acquisition of atomic ensemble quantum response trajectory, environmental multi-physics field perturbation parameters, historical operating data of light source and external spatiotemporal reference signal; S2: Construct a dynamic state perception matrix based on the collected data in S1; S3: Based on the dynamic state perception matrix obtained in S2, a hybrid intelligent decision engine is used to generate a collaborative optimization strategy for multi-degree-of-freedom parameters of the light source. S4: Based on the obtained collaborative optimization strategy, the light source parameters are adjusted in real time by the light source controller to apply the adjusted light field to the atomic magnetometer system; S5: Real-time monitoring of the magnetic field detection performance indicators output by the system. Based on the deviation between the performance indicators and the preset sensitivity threshold, the closed-loop feedback mechanism is driven to continuously iterate and optimize the light source parameters.
[0018] In specific implementation of the present invention, This includes deploying distributed edge sensing nodes at key locations in the atomic magnetometer system, such as atomic response acquisition units: acquiring real-time transmission light intensity timing signals from the atomic gas cell through the detection optical path to reflect the atomic spin precession state; Environmental sensing unit: integrates temperature sensor, triaxial accelerometer, and background magnetic field probe to collect environmental disturbance parameters; Light source monitoring unit: Reads the light source drive current, output power, and wavelength history. Spacetime reference unit: Provides a time synchronization reference through a highly stable clock module; A lightweight federated feature fusion algorithm is adopted, which uses atomic response signals as time anchors for data alignment, removes redundant information and compresses feature dimensions to generate low-latency, high-information-density structured perception stream data. By extracting features from the sensing stream data, including atomic ensemble quantum response features, environmental disturbance comprehensive features, light source state prediction features, and spatiotemporal synchronization features, the above features are organized into matrix rows according to a sliding time window to form a high-dimensional state matrix that is dynamically updated over time, thereby representing the correlation between the internal and external states of the system in real time. The hybrid intelligent decision engine is based on the use of a physical law module and an intelligent learning module. The physical law module has a built-in simplified mathematical model of atomic energy level transition and optical pumping process, and verifies the feasibility of the parameter adjustment scheme output by the intelligent learning module. The intelligent learning module uses a lightweight neural network. It takes a dynamic state perception matrix as input and outputs suggestions for adjusting light source parameters. It supports online learning and continuously updates internal parameters based on the actual optimization effect feedback from S5 to improve the quality of strategy generation. When in use, the light source controller receives the optimization strategy and drives the internal modulation element to perform adjustments, including adjusting the laser drive current to change the output light frequency and intensity, controlling the polarizer rotation mechanism to adjust the polarization direction, manipulating the spatial light modulator to change the beam shape, adjusting the pulse generator to set the output timing, and the adjusted light field precisely acts on the atomic gas cell to optimize the polarization efficiency and detection sensitivity of the atomic ensemble. The magnetic field detection performance indicators in S5 include real-time signal-to-noise ratio and system stability parameters; the closed-loop feedback mechanism includes a fast fine-tuning loop and a strategy update loop, wherein: Fast fine-tuning loop: When the signal-to-noise ratio is lower than the threshold, the light source intensity is immediately fine-tuned to compensate. Strategy update loop: Based on the set time, summarize historical optimization records, analyze the effectiveness of the strategy, dynamically adjust the physical model parameters or the training weights of the intelligent learning module, optimize the decision logic and sensitivity threshold settings, and ensure that the system maintains high sensitivity output under instantaneous disturbances and long-term drift through the collaboration of the two loops.
[0019] The contents not described in detail in this specification are existing technologies known to those skilled in the art.
[0020] It should be noted that similar labels and letters in the following figures indicate similar items. Therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures.
[0021] The present invention and its embodiments have been described above. This description is not restrictive, and the accompanying drawings are only one embodiment of the present invention; the actual structure is not limited thereto. In conclusion, if those skilled in the art are inspired by this description and design similar structures and embodiments without departing from the spirit of the invention, such designs should fall within the protection scope of the present invention.
Claims
1. An integrated light source optimization method for detecting extremely weak magnetic fields, characterized in that: Includes the following steps: S1: Real-time fusion acquisition of atomic ensemble quantum response trajectory, environmental multi-physics field perturbation parameters, historical operating data of light source and external spatiotemporal reference signal; S2: Construct a dynamic state perception matrix based on the collected data in S1; S3: Based on the dynamic state perception matrix obtained in S2, a hybrid intelligent decision engine is used to generate a collaborative optimization strategy for multi-degree-of-freedom parameters of the light source. S4: Based on the obtained collaborative optimization strategy, the light source parameters are adjusted in real time by the light source controller to apply the adjusted light field to the atomic magnetometer system; S5: Real-time monitoring of the magnetic field detection performance indicators output by the system. Based on the deviation between the performance indicators and the preset sensitivity threshold, the closed-loop feedback mechanism is driven to continuously iterate and optimize the light source parameters.
2. The integrated light source optimization method for detecting extremely weak magnetic fields according to claim 1, characterized in that: In S1, real-time fusion acquisition is based on the raw data collected by a distributed edge sensing network. The federated feature fusion algorithm is used to perform spatiotemporal alignment and semantic-level compression on the four types of heterogeneous data collected to generate perceptual stream data.
3. The integrated light source optimization method for detecting extremely weak magnetic fields according to claim 2, characterized in that: The construction of the dynamic state perception matrix in S2 includes extracting the original perception stream, extracting the quantum response features of the atomic ensemble, the comprehensive features of environmental disturbances, the predictive features of the light source state, and the spatiotemporal synchronization features, and arranging them in a time series to form a dynamically updated high-dimensional matrix.
4. The integrated light source optimization method for detecting extremely weak magnetic fields according to claim 1, characterized in that: The hybrid intelligent decision engine in S3 includes a physical law module and an intelligent learning module. The physical law module incorporates a basic model of the interaction between atoms and light fields to verify the physical rationality of the strategy; The intelligent learning module generates an initial parameter adjustment scheme as a light source parameter optimization strategy based on historical optimization data and real-time sensing information.
5. The integrated light source optimization method for detecting extremely weak magnetic fields according to claim 4, characterized in that: The intelligent learning module continuously updates the strategy generation logic based on newly collected system response data using an online learning mechanism.
6. The integrated light source optimization method for detecting extremely weak magnetic fields according to claim 1, characterized in that: The light source parameters in S4 include the frequency, intensity, polarization direction, beam shape, and output timing of the light. The light source controller dynamically adjusts the light source parameters by regulating the internal modulation element.
7. The integrated light source optimization method for detecting extremely weak magnetic fields according to claim 1, characterized in that: The magnetic field detection performance indicators in S5 include real-time signal-to-noise ratio and system stability parameters; the closed-loop feedback mechanism includes a fast fine-tuning loop and a strategy update loop.
8. The integrated light source optimization method for detecting extremely weak magnetic fields according to claim 7, characterized in that: The rapid fine-tuning loop corrects the light source parameters in real time based on the current deviation, and the strategy update loop periodically analyzes the historical optimization effect to optimize the decision-making logic and threshold setting.