Integrated observation system of non-magnetic theodolite and quantum magnetic field strength meter

Abstract

This invention relates to the field of seismic magnetic field measurement technology, specifically to an integrated observation system combining a non-magnetic theodolite and a quantum magnetic field strength meter. The system includes a non-magnetic observation module, a quantum magnetic field measurement module, a data integration module, a magnetic field analysis module, and an observation result reporting module. This invention generates an observation set by acquiring azimuth data from the non-magnetic theodolite, calculates magnetic induction intensity by combining this data with quantum magnetometer signals, integrates data sources to construct an observation group, analyzes data fluctuation trends and generates a report, and finally compares the data with historical records to output a complete magnetic field observation result report. By integrating a non-magnetic theodolite and a quantum magnetometer, this invention corrects azimuth angles and aligns data to address traditional measurement biases. It optimizes synchronization through time index matching and generates accurate reports based on frequency and fluctuation trend analysis. This approach avoids the data limitations of a single device and significantly improves earthquake monitoring accuracy, comprehensive analysis, and early warning capabilities.

Description

Technical Field

[0001] This invention relates to the field of seismic magnetic field measurement technology, and in particular to an integrated observation system of a non-magnetic theodolite and a quantum magnetic field strength meter. Background Technology

[0002] The field of seismic magnetic field measurement technology encompasses magnetic field measurement equipment and methods used for seismic exploration and analysis. In this field, changes in the magnetic field are closely related to seismic activity; therefore, magnetic field measurement is widely used in earthquake precursor monitoring and underground activity research. The core content involves monitoring changes in the underground magnetic field using equipment such as magnetometers and magnetic field strength meters, thereby providing a basis for earthquake activity prediction. Key technical aspects include magnetic field signal acquisition, data processing, and the identification and analysis of anomalous magnetic field changes, with the goal of improving the accuracy and real-time performance of seismic activity monitoring.

[0003] The integrated observation system combining a non-magnetic theodolite and a quantum magnetic field strength meter refers to an observation system that integrates a non-magnetic theodolite and a quantum magnetic field strength meter. It aims to perform high-precision real-time observation and measurement of seismic magnetic fields. Specifically, the non-magnetic theodolite is used to determine the measurement location and direction, while the quantum magnetic field strength meter is used to measure the magnetic field strength. The combination of the two achieves precise positioning and intensity measurement of the magnetic field. This system overcomes the limitations of traditional magnetic field measurement equipment in terms of accuracy and stability. By integrating quantum magnetic field measurement technology with a non-magnetic theodolite, it can provide efficient and accurate seismic magnetic field data, supporting earthquake monitoring and early warning.

[0004] Current technologies for seismic magnetic field monitoring rely on traditional equipment, which suffers from problems such as unstable accuracy, data interference, and error accumulation. Traditional equipment has limited measurement angle accuracy and cannot guarantee equipment stability under environmental changes, leading to signal interference and data inconsistencies. During real-time monitoring, the lack of effective real-time correction and data fusion prevents the timely capture of subtle changes in the magnetic field, affecting monitoring accuracy. Furthermore, current technologies cannot simultaneously process data from different devices, failing to provide more comprehensive and accurate magnetic field measurement results, resulting in a delayed response to earthquake precursors and making it difficult to meet high-precision requirements. Summary of the Invention

[0005] The purpose of this invention is to overcome the shortcomings of existing technologies and propose an integrated observation system of a non-magnetic theodolite and a quantum magnetic field strength meter.

[0006] To achieve the above objectives, the present invention employs the following technical solution: an integrated observation system combining a non-magnetic theodolite and a quantum magnetic field strength meter, the system comprising:

[0007] The non-magnetic observation module acquires real-time azimuth data and sensor output signals from the non-magnetic theodolite, judges signal stability to identify offset anomalies, corrects the observation angle based on the data offset angle, and generates a non-magnetic observation dataset.

[0008] The quantum magnetic field measurement module aligns the non-magnetic observation dataset with the acquired quantum magnetic field strength meter output signal, extracts the amplitude and frequency values ​​within the continuous signal segment, calculates the magnetic induction intensity at each observation point, and correlates each set of measurement data with the observation time to generate quantum magnetic field measurement results.

[0009] The data integration module calls the non-magnetic observation dataset and the quantum magnetic field measurement results to construct an observation data group, determines the correspondence between the record items of the two data sources in terms of time index, calculates the offset of the record values ​​of different data sources at the same time point, and generates integrated magnetic field observation data.

[0010] The magnetic field analysis module calls the integrated magnetic field observation data to process the observation frequency, constructs a time-series fluctuation diagram based on the continuous change range of the measured values, obtains the fluctuation trend range by calculating the change rate of each measuring point, analyzes the distribution characteristics of the amplitude range and frequency nodes within the range and marks the start and end points, and finally outputs a magnetic field fluctuation trend report.

[0011] As a further aspect of the present invention, the non-magnetic observation dataset specifically includes real-time azimuth data, sensor output signals, coordinate reference frames, offset anomalies, observation angle correction values, and azimuth angle correction data; the quantum magnetic field measurement results specifically include magnetic induction intensity, amplitude and frequency values, amplitude waveform variation patterns, measurement data, time index parameters, and observation time; the integrated magnetic field observation data specifically includes time node recorded values, observation data groups, time index matching, measurement value offset, and integrated values; the magnetic field fluctuation trend report specifically includes a time-series fluctuation graph, fluctuation trend interval, amplitude segment, frequency node distribution characteristics, and interval start and end points.

[0012] As a further embodiment of the present invention, the non-magnetic observation module includes a coordinate frame construction submodule, a signal offset quantization submodule, and an azimuth angle correction and processing submodule;

[0013] The coordinate frame construction submodule acquires the real-time azimuth data and sensor output signals of the non-magnetic theodolite, constructs a three-dimensional coordinate reference frame based on the instrument's built-in reference direction and geographic coordinate parameters, calculates the set of projection components of the sensor output signal within the frame at each moment, and establishes the initial signal vector value.

[0014] The signal offset quantization submodule compares the continuous change of the initial signal vector value in the time series with the signal stability reference value, filters out abnormal signal points whose fluctuation amplitude exceeds the preset offset threshold, and calculates the offset angle corresponding to the abnormal signal point to obtain the azimuth offset angle.

[0015] The azimuth angle correction and processing submodule performs point-by-point compensation and correction on the original observation angle sequence based on the obtained azimuth offset angle. It calls the time series information to match and integrate the corrected azimuth angle data with the corresponding timestamps, and arranges them in a fixed format to generate a non-magnetic observation dataset.

[0016] As a further aspect of the present invention, the quantum magnetic field measurement module includes a data alignment and fusion submodule, a signal feature extraction submodule, and a magnetic induction intensity calculation submodule;

[0017] The data alignment and fusion submodule aligns the non-magnetic observation dataset with the acquired quantum magnetic field strength meter output signal, calls the timestamps in their respective data streams, calculates the time difference between each strength meter signal point and all observation data points, selects the observation point with the smallest time difference for pairing, and removes paired data with time differences exceeding the synchronization threshold to establish a synchronization signal sequence.

[0018] The signal feature extraction submodule, based on the synchronization signal sequence, sets a fixed-length sliding window within a continuous signal segment, extracts the peak and valley values ​​of the signal within each window to calculate the amplitude, and counts the number of times the signal period occurs to obtain the frequency. The amplitude and frequency values ​​of all windows are then summarized to obtain a set of signal feature parameters.

[0019] The magnetic induction intensity calculation submodule calls the amplitude and frequency values ​​from the signal characteristic parameter set, calibrates the amplitude of each observation point according to the preset conversion coefficient, calculates the magnetic induction intensity value, and associates each set of magnetic induction intensity values ​​with the observation time by combining the time index parameter to generate quantum magnetic field measurement results.

[0020] As a further aspect of the present invention, the data integration module includes an observation data grouping submodule, a time index matching submodule, and an integrated numerical construction submodule;

[0021] The observation data grouping submodule classifies the recorded values ​​from the two data sources based on the common time nodes of the non-magnetic observation dataset and the quantum magnetic field measurement results, and combines all the recorded items belonging to the same time point into a data unit to establish a time-series data node set.

[0022] The time index matching submodule, for the time series data node set, determines whether the time indices of record items from different data sources in each data unit are completely consistent, assigns a synchronization matching identifier to the record items with consistent indices and extracts them to obtain synchronized observation data pairs;

[0023] The integrated numerical construction submodule extracts the non-magnetic observation value and quantum magnetic field measurement value of each pair of data in the synchronous observation data pair, calculates the difference between the non-magnetic observation value and the quantum magnetic field measurement value as the offset, adds the offset value to the preset benchmark measurement value to form the integrated value, and generates integrated magnetic field observation data.

[0024] As a further embodiment of the present invention, the magnetic field analysis module includes a fluctuation trend interval calculation submodule, a key node distribution identification submodule, and a fluctuation trend report generation submodule;

[0025] The fluctuation trend interval calculation submodule calls the integrated magnetic field observation data to perform observation frequency processing, constructs a time series fluctuation diagram based on the continuous change segment of the measured value, calculates the ratio of the difference of the measured value at adjacent time nodes of each measuring point to the time interval as the rate of change, extracts the time range of the rate of change exceeding the preset fluctuation threshold, and establishes a set of magnetic field change segments.

[0026] The key node distribution identification submodule extracts amplitude and frequency nodes within the corresponding segments from the integrated magnetic field observation data based on the magnetic field change segment set. It calculates the maximum and minimum amplitude values ​​within each segment as amplitude boundaries, identifies nodes where the frequency value changes direction, marks amplitude boundary points and frequency inflection points, and obtains key feature node parameters.

[0027] The fluctuation trend report generation submodule arranges the amplitude boundary points and frequency inflection points identified in the key feature node parameters in chronological order, combines the start and end time information of the magnetic field change segment set, organizes the various parameters into structured entries with time indexes, and generates a magnetic field fluctuation trend report.

[0028] As a further aspect of the present invention, the system further includes:

[0029] The observation results report module calls the magnetic field fluctuation trend report and historical observation records to construct a magnetic field change segment comparison table, determines the range of measurement values ​​for different observation days within the same period and extracts the difference distribution points, obtains the change difference interval by calculating the difference gradient, and generates a distribution list by classifying by time, and outputs a complete magnetic field observation results report.

[0030] The magnetic field observation results report specifically includes a comparison table of magnetic field change sections, a distribution point of difference, a distribution list of change difference intervals, and a distribution list of time categories.

[0031] As a further embodiment of the present invention, the observation result reporting module includes a segment difference comparison submodule, a change gradient quantization submodule, and an observation report generation submodule;

[0032] The segment difference comparison submodule calls the magnetic field fluctuation trend report and the acquired historical observation records, constructs a magnetic field change segment comparison table based on the timestamp, determines the range of measurement values ​​for different observation days within the same period, extracts the numerical difference between the current measurement value and the historical record value at each time point, and establishes a periodic difference scatter set.

[0033] The gradient quantization submodule calculates the ratio of the numerical change of adjacent scatter points to the time interval based on the periodic difference scatter point set as the difference gradient, compares the difference gradient with the preset difference benchmark value, filters and integrates continuous time periods in which the gradient exceeds the benchmark value, and obtains the gradient over-limit interval.

[0034] The observation report generation submodule calls the gradient over-limit interval and extracts the difference distribution points in the corresponding segment from the period difference scatter point set. It then classifies and combines the difference distribution points and the corresponding difference gradient data into structured entries according to time order to generate a complete magnetic field observation result report.

[0035] Compared with the prior art, the advantages and positive effects of the present invention are as follows:

[0036] This invention significantly improves the accuracy and real-time performance of seismic magnetic field measurements by integrating and calibrating data from a non-magnetic theodolite and a quantum magnetic field strength meter. Combining time series data with azimuth correction and alignment with quantum magnetic field data resolves the deviation issues inherent in traditional measuring equipment under conditions of observation angle error and unstable magnetic field signals. Data integration and time index matching optimize the relationship between different measurements, ensuring data synchronization and improving the reliability of magnetic field measurements. Based on frequency analysis and fluctuation trend processing, more accurate reports on magnetic field changes are provided, enhancing the accuracy and predictive capability of seismic activity monitoring. This technical solution effectively avoids the limitations of data acquisition from a single device, improves the comprehensive analysis capabilities of magnetic field data, and supports more efficient earthquake monitoring and early warning. Attached Figure Description

[0037] Figure 1 This is a system flowchart of the present invention;

[0038] Figure 2 This is a flowchart illustrating the acquisition process of the non-magnetic observation module of the present invention.

[0039] Figure 3 This is a flowchart illustrating the acquisition process of the quantum magnetic field measurement module of the present invention.

[0040] Figure 4 This is a flowchart illustrating the data acquisition process of the data integration module of the present invention.

[0041] Figure 5 This is a flowchart illustrating the acquisition process of the magnetic field analysis module of the present invention.

[0042] Figure 6This is a flowchart of the observation result reporting module of the present invention. Detailed Implementation

[0043] The technical solution of the present invention will now be described with reference to the accompanying drawings.

[0044] In embodiments of the present invention, words such as "exemplarily," "for example," etc., are used to indicate that something is an example, illustration, or description. Any embodiment or design described as "exemplary" in the present invention should not be construed as being more preferred or advantageous than other embodiments or designs. Specifically, the use of the word "exemplary" is intended to present the concept in a concrete manner. Furthermore, in embodiments of the present invention, the meaning expressed by "and / or" can be both, or either one.

[0045] In the embodiments of this invention, the terms "image" and "picture" may sometimes be used interchangeably. It should be noted that, without emphasizing the distinction between them, they convey the same meaning. Similarly, the terms "of," "corresponding (relevant)," and "corresponding" may sometimes be used interchangeably. It should be noted that, without emphasizing the distinction between them, they convey the same meaning.

[0046] In this embodiment of the invention, sometimes a subscript such as W1 may be written in a non-subscript form such as W1. When the difference is not emphasized, the meaning they express is the same.

[0047] To make the technical problems, technical solutions and advantages of the present invention clearer, a detailed description will be given below in conjunction with the accompanying drawings and specific embodiments.

[0048] Please see Figure 1 This invention provides a technical solution: an integrated observation system combining a non-magnetic theodolite and a quantum magnetic field strength meter, the system comprising:

[0049] The non-magnetic observation module acquires real-time azimuth data and sensor output signals from the non-magnetic theodolite, constructs a coordinate reference frame based on the instrument's built-in reference direction and geographic coordinate parameters, judges signal stability to identify offset anomalies, corrects the observation angle based on the data offset angle, and completes the azimuth correction data processing by combining time series data to generate a non-magnetic observation dataset.

[0050] The quantum magnetic field measurement module aligns the non-magnetic observation dataset with the acquired quantum magnetic field strength meter output signal, extracts the amplitude and frequency values ​​within the continuous signal segment, calculates the magnetic induction intensity at each observation point based on the amplitude waveform variation law, and associates each set of measurement data with the observation time by combining the time index parameter to generate quantum magnetic field measurement results.

[0051] The data integration module calls the non-magnetic observation dataset and the quantum magnetic field measurement results, constructs the observation data group with the recorded values ​​at each time node, determines the correspondence between the record items of the two data sources on the time index and synchronously identifies the match, calculates the offset between different measurement values ​​at the same time point to construct the integrated value, and generates integrated magnetic field observation data.

[0052] The magnetic field analysis module calls the integrated magnetic field observation data to process the observation frequency, constructs a time series fluctuation map based on the continuous change range of the measured values, obtains the fluctuation trend range by calculating the change rate of each measuring point, analyzes the distribution characteristics of the amplitude range and frequency nodes within the range and marks the start and end points, and finally outputs a magnetic field fluctuation trend report.

[0053] The observation results report module calls the magnetic field fluctuation trend report and historical observation records to construct a comparison table of magnetic field change segments, determines the range of measurement values ​​for different observation days within the same period and extracts the difference distribution points, obtains the change difference range by calculating the difference gradient, and generates a distribution list by time classification, outputting a complete magnetic field observation results report.

[0054] The non-magnetic observation dataset specifically includes real-time azimuth data, sensor output signals, coordinate reference frames, offset anomalies, observation angle correction values, and azimuth angle correction data; the quantum magnetic field measurement results specifically include magnetic induction intensity, amplitude and frequency values, amplitude waveform variation patterns, measurement data, time index parameters, and observation time; the integrated magnetic field observation data specifically includes time node recorded values, observation data groups, time index matching, measurement value offset, and integrated values; the magnetic field fluctuation trend report specifically includes time-series fluctuation graphs, fluctuation trend intervals, amplitude segments, frequency node distribution characteristics, and interval start and end points; the magnetic field observation result report specifically includes a magnetic field change segment comparison table, difference distribution points, change difference intervals, and a time classification distribution list.

[0055] Please see Figure 2 The non-magnetic observation module includes a coordinate frame construction submodule, a signal offset quantization submodule, and an azimuth correction and processing submodule.

[0056] The coordinate frame construction submodule acquires the real-time azimuth data and sensor output signals of the non-magnetic theodolite, constructs a three-dimensional coordinate reference frame based on the instrument's built-in reference direction and geographic coordinate parameters, calculates the set of projection components of the sensor output signal within the frame at each moment, and establishes the initial signal vector value.

[0057] To acquire real-time azimuth data and sensor output signals from a non-magnetic theodolite, the following steps are taken: The non-magnetic theodolite is set up at a predetermined observation point. The geographical coordinates of this observation point are set as 116°23′17″E, 39°54′27″N, with an altitude of 52 meters. The instrument's built-in reference direction is set with true north as the azimuth angle. At timestamp T0=10:00:00.000, the real-time azimuth data collected by the non-magnetic theodolite is the azimuth angle. degrees, altitude angle At the same time, the internal magnetoresistive sensor outputs a signal of... Subsequently, a three-dimensional coordinate reference frame was constructed based on the geographic coordinate parameters, with the measurement point as the origin (0,0,0). The east direction was defined as the positive X-axis, the north direction as the positive Y-axis, and the vertically upward direction as the positive Z-axis. The set of projection components of the sensor output signal at time T0 within the frame was calculated. The calculation process is as follows:

[0058] X-axis component ;

[0059] Y-axis component ;

[0060] Z-axis component = ;

[0061] The azimuth angle collected at timestamp T1=10:00:01.000 degrees, altitude angle Degree, sensor output signal U1 And calculate its projection components. = , = , = By doing so, data from 100 consecutive time points are collected to establish the initial signal vector value.

[0062] The signal offset quantization submodule compares the continuous change of the initial signal vector value over time with the signal stability reference value, filters out abnormal signal points whose fluctuation amplitude exceeds the preset offset threshold, and calculates the offset angle corresponding to the abnormal signal point to obtain the azimuth offset angle.

[0063] The continuous change of the initial signal vector value over time is compared with the signal stability reference value. This process first requires determining the signal stability reference value and a preset offset threshold. The signal stability reference value is determined by conducting a 30-minute static observation of a non-magnetic theodolite in a magnetically shielded room, collecting the sensor output signal, and then calculating the modulus of 12,000 signal vectors collected in the following 20 minutes, and finally taking their arithmetic mean. For example, if the calculated average modulus value is... Therefore, 2.915V is used as the signal stability reference value. The determination of the preset offset threshold is based on the standard deviation of the above 12,000 signal vector magnitudes. And using 3 times the standard deviation as the offset threshold, if calculated Then the preset offset threshold During the comparison, the initial signal vector value is retrieved, and the magnitude of the signal vector at each time step is calculated, for example... The model of time:

[0064] ;

[0065] Then calculate the absolute value of the difference between it and the benchmark value. This value is greater than the offset threshold. ,but The signal point at time 1 is identified as an abnormal signal point. Next, assuming at 10:00 AM... At time t, its signal vector magnitude is The absolute value of the difference is

[0066] This value is greater than This was also identified as an anomalous signal point, and the offset angle corresponding to this anomalous signal point was calculated. The calculation method was to... signal vector at time Compared with the previous stable moment signal vector Perform dot product and modulus operations, and obtain the spatial angle using the inverse cosine function. This angle is then projected onto the azimuth plane to obtain the azimuth offset angle. Assuming the calculation yields The azimuth offset angle is obtained from the degrees.

[0067] The azimuth angle correction and processing submodule performs point-by-point compensation and correction on the original observation angle sequence based on the acquired azimuth offset angle. It calls the time series information to match and integrate the corrected azimuth angle data with the corresponding timestamps, and arranges them in a fixed format to generate a non-magnetic observation dataset.

[0068] Based on the obtained azimuth offset angle, the original observation angle sequence is compensated and corrected point by point. This process involves retrieving... The azimuth offset angle measured at any time degrees and the original observation azimuth at that moment Since this point was determined to be an anomaly, and its offset direction was determined to be positive through the vector cross product, the original observed angle of this point was compensated and corrected. The corrected azimuth angle is... For observation angles not identified as anomalous signal points, their azimuth offset is 0 and requires no correction. For example... If time is a stable point, then its corrected azimuth angle is... Then, the time series information is retrieved, and each corrected azimuth angle data point is matched and integrated with its corresponding timestamp. For example, ... The timestamp 10:05:10.000 is associated with the corrected azimuth angle of 48.100 degrees. The timestamp 10:05:09.000 is associated with the corrected azimuth angle 46.500 degrees, and then arranged in a fixed format defined as comma-separated values ​​(CSV). Each row represents the data at a time point and contains three fields: timestamp, corrected azimuth angle, and corrected elevation angle, generating a non-magnetic observation dataset.

[0069] Table 1: Example Table of Non-Magnetic Observation Datasets

[0070] Timestamp Corrected azimuth angle (degrees) Corrected elevation angle (degrees) 10:05:08.000 46.450 31.220 10:05:09.000 46.500 31.225 10:05:10.000 48.100 31.230 10:05:11.000 48.150 31.235

[0071] As shown in Table 1, this table lists a portion of the generated non-magnetic observation dataset, where the data at time 10:05:10.000 is the result after anomaly correction.

[0072] Please see Figure 3 The quantum magnetic field measurement module includes a data alignment and fusion submodule, a signal feature extraction submodule, and a magnetic induction intensity calculation submodule.

[0073] The data alignment and fusion submodule aligns the non-magnetic observation dataset with the acquired quantum magnetic field strength meter output signal, calls the timestamps in their respective data streams, calculates the time difference between each strength meter signal point and all observation data points, selects the observation point with the smallest time difference for pairing, and removes paired data with time differences exceeding the synchronization threshold to establish a synchronization signal sequence.

[0074] To align the non-magnetic observation dataset with the acquired quantum magnetic field strength meter output signal, the synchronization threshold was first set with reference to the crystal oscillator stability and maximum drift rate of the clock modules of the two devices in the system (non-magnetic theodolite and quantum magnetic field strength meter). Through 24-hour uninterrupted synchronous operation testing, the maximum difference in timestamps between the two devices was measured to be 3 milliseconds. Therefore, the synchronization threshold was set accordingly. The time is 5 milliseconds (ms) to ensure sufficient redundancy. During alignment, a data point is read from the output signal data stream of the quantum magnetic field strength meter, for example, at the timestamp. At 10:05:09.005, the intensity meter output signal value was 55010.2 nT. Subsequently, the time difference between this signal point and all observation data points in the non-magnetic observation dataset was calculated. Specifically, the time difference was calculated. ,in The timestamp in the non-magnetic observation dataset, such as the time difference with the data points in the second row of Table 1, is... ms, the time difference with the third row of data points is For each millisecond (ms), the observation point with the smallest time difference is selected for pairing, i.e., the data point at time 10:05:09.000 is selected for pairing. Next, it is determined whether this minimum time difference of 5ms exceeds the synchronization threshold. =5ms, because The pairing is valid. If another intensity meter data point exists with a timestamp of 10:05:08:010, its time difference with the first row of data points in Table 1 is the smallest, at 10ms. The paired data will be discarded, and this process is repeated for all intensity meter data points to establish a synchronization signal sequence.

[0075] The signal feature extraction submodule sets a fixed-length sliding window within a continuous signal segment based on the synchronization signal sequence. It extracts the peak and valley values ​​of the signal within each window to calculate the amplitude and counts the number of times the signal period occurs to obtain the frequency. It then summarizes the amplitude and frequency values ​​of all windows to obtain the signal feature parameter set.

[0076] Based on the synchronization signal sequence, a fixed-length sliding window is set within a continuous signal segment. The length of the sliding window is determined according to the main frequency characteristics of the signal. For example, for a diurnal stable geomagnetic field signal, historical data is analyzed using Fourier transform to determine that its main variation frequency is below 0.01Hz. To capture the waveform completely, the window length is set to 20 seconds, and the sliding step size is 1 second. Assuming that in the first window, with a start time of 10:10:00.000 and an end time of 10:10:20.000, the synchronization signal sequence contains 20 magnetic field strength data points. Within this window, the peak value of the signal is extracted to be 55015.8 nT, and the valley value is 55008.1 nT. Then, the amplitude A of this window is calculated as the difference between the peak value and the valley value, i.e. Meanwhile, by identifying the number of times the signal sequence crosses its mean or using the zero-crossing detection method, the number of times the signal completes a full cycle within 20 seconds is counted. For example, if the count reaches 0.2 cycles, the frequency f is calculated as the number of cycles divided by the window duration, i.e. Hz, then the window slides forward 1 second, becoming 10:10:01.000 to 10:10:21.000, and the above calculation process for amplitude and frequency is repeated to obtain and The amplitude and frequency values ​​calculated from all windows are summarized according to the timestamp corresponding to the center point of each window to obtain the signal characteristic parameter set.

[0077] The magnetic induction intensity calculation submodule calls the amplitude and frequency values ​​from the signal characteristic parameter set, calibrates the amplitude of each observation point according to the preset conversion coefficient, calculates the magnetic induction intensity value, and associates each set of magnetic induction intensity values ​​with the observation time by combining the time index parameter to generate quantum magnetic field measurement results.

[0078] The amplitude and frequency values ​​from the signal characteristic parameter set are used to calibrate the amplitude at each observation point according to a preset conversion coefficient. This conversion coefficient is not a fixed value, but is determined through a 24-hour synchronous observation comparison experiment between the quantum magnetic field strength meter and a higher-precision standard fluxgate magnetometer at the same location. The experimental data reveals a frequency-dependent linear relationship between the amplitude output by a specific model of strength meter and the standard magnetic induction intensity. The conversion coefficient is obtained by least squares fitting. Where f is the signal frequency, and the physical meaning of this coefficient is the calibration of the instrument's own frequency response. Now, we call the signal characteristic parameter set calculated in the previous step. Amplitude of the time window nT and frequency Hz, first calculate the conversion factor at that frequency. Then, the original amplitude within the window is calibrated, and the magnetic induction intensity value is calculated. nT (here, nT refers to the change in magnetic field corresponding to the vibration amplitude, not the absolute value), and the magnetic induction intensity values ​​of each group are associated with the observation time in combination with the time index parameter. The time index here uses the center time of the window, i.e., 10:10:10.000. The final generated record is {time:10:10:10.000, change in magnetic induction intensity:7.739nT}. This process is repeated for all data in the signal characteristic parameter set to generate quantum magnetic field measurement results.

[0079] Please see Figure 4 The data integration module includes an observation data grouping submodule, a time index matching submodule, and an integrated numerical construction submodule.

[0080] The observation data grouping submodule classifies the recorded values ​​from the two data sources based on the common time nodes of the non-magnetic observation dataset and the quantum magnetic field measurement results, and combines all records belonging to the same time point into a data unit to establish a time-series data node set.

[0081] Based on the common time nodes of the non-magnetic observation dataset and the quantum magnetic field measurement results, the recorded values ​​from the two data sources are categorized. Specifically, the system iterates through each time node in the quantum magnetic field measurement results; for example, selecting a time node... The corresponding change in magnetic flux density is 7.739 nT. Then, in the non-magnetic observation dataset, we search for data with the exact same timestamp. The record items are assumed to be {corrected azimuth: 50.210 degrees, corrected elevation: 32.850 degrees}. Then, these two record items from different data sources but at the same time point are combined into a data unit with the structure {timestamp: 10:10:10.000, azimuth: 50.210 degrees, elevation: 32.850 degrees, magnetic induction intensity change: 7.739 nT}. If no perfectly matching timestamp is found in the non-magnetic observation dataset, the data point of the quantum magnetic field measurement result will be discarded and will not participate in the grouping. This process will traverse all time points in the quantum magnetic field measurement results and create a data unit for each successfully matched node, thereby establishing a time-series data node set.

[0082] The time index matching submodule, for the time series data node set, determines whether the time indices of record items from different data sources within each data unit are completely consistent, assigns a synchronization matching identifier to the record items with consistent indices and extracts them to obtain synchronized observation data pairs;

[0083] For the time-series data node set, the system determines whether the time indices of records from different data sources within each data unit are completely consistent. This step serves as the final verification step in data fusion. The system will examine each data unit established in the previous step, extracting the timestamps of the non-magnetic observation data and the quantum magnetic field measurement results recorded within the data unit. For example, when examining a data unit, the timestamps of the non-magnetic observation data recorded within it are... The timestamp of the quantum magnetic field measurement results is The system performs a binary-level comparison to confirm that the string representations or 64-bit integer representations of the two timestamps are completely equal. If they are equal, a synchronization match flag is assigned to the data unit. This flag can be a boolean value 'True' or an integer '1'. The contents of the data unit are then extracted to form a new set, for example, {timestamp: 10:15:30.000, azimuth: 51.050 degrees, elevation: 33.100 degrees, magnetic field strength change: 8.120 nT, synchronization flag: 1}. If a slight difference exists in the time index within a data unit due to system errors, for example... and Even if they are incorrectly grouped together during grouping, the rigorous comparison in this step will identify them as inconsistent and thus not assign a synchronization match identifier. Finally, only those data units that have been successfully assigned a synchronization match identifier are extracted to obtain synchronized observation data pairs.

[0084] The integrated numerical construction submodule extracts the non-magnetic observation value and quantum magnetic field measurement value of each pair of data in the synchronous observation data pair, calculates the difference between the non-magnetic observation value and the quantum magnetic field measurement value as the offset, adds the offset value to the preset benchmark measurement value to form the integrated value, and generates integrated magnetic field observation data;

[0085] Extract the non-magnetic observation value and quantum magnetic field measurement value from each pair of synchronous observation data. Here, "non-magnetic observation value" specifically refers to the magnetic field strength value measured at the same moment by the low-precision magnetic sensor carried by the theodolite itself, rather than the angle value. Assume that in the synchronous observation data pair, at time point... The data is {quantum magnetic field measurement value} nT, non-magnetic observation value Calculate the difference between nT and nT as the offset. nT, then, this offset is added to a preset reference measurement value to form an integrated value. This preset reference measurement value is set based on the daily average value of the magnetic field strength of that day in the previous year published by the geomagnetic observatory in the observation area, as a stable reference. Assuming that the reference value is found to be nT, nT, then the integrated value at that point in time. nT, repeat this difference calculation and summation process for all synchronous observation data pairs to generate a new dataset indexed by timestamps and containing integrated values, namely integrated magnetic field observation data.

[0086] Table 2: Examples of Integrated Magnetic Field Observation Data

[0087] Timestamp Quantum magnetic field measurement (nT) Non-magnetic observation (nT) Offset (nT) Integration value (nT) 10:20:00.000 55012.5 54990.2 22.3 55022.3 10:20:01.000 55012.8 54990.4 22.4 55022.4 10:20:02.000 55013.2 54990.7 22.5 55022.5 10:20:03.000 55015.0 54991.1 23.9 55023.9

[0088] As shown in Table 2, this table illustrates the generation process of integrated magnetic field observation data, where the integrated value is obtained by superimposing the dynamic changes reflected by high-precision quantum measurement results onto a stable geomagnetic background reference value.

[0089] Please see Figure 5 The magnetic field analysis module includes a fluctuation trend range calculation submodule, a key node distribution identification submodule, and a fluctuation trend report generation submodule.

[0090] The fluctuation trend interval calculation submodule calls the integrated magnetic field observation data to process the observation frequency, constructs a time series fluctuation diagram based on the continuous change segment of the measured value, calculates the ratio of the difference of the measured value at adjacent time nodes of each measuring point to the time interval as the rate of change, extracts the time range of the rate of change exceeding the preset fluctuation threshold, and establishes a set of magnetic field change segments.

[0091] The integrated magnetic field observation data is used for observation frequency processing. This processing involves constructing a time-series fluctuation plot, i.e., plotting a sequence of data points with time on the horizontal axis and the integrated value on the vertical axis. Then, the ratio of the difference in measured values ​​at adjacent time points to the time interval is calculated as the rate of change. The preset fluctuation threshold is determined by analyzing historical similar geomagnetic observation data and statistically analyzing the 95th percentile of the first-order difference of the magnetic field strength during non-magnetic storm periods. If this value is statistically obtained as 0.5 nT / s, then the preset fluctuation threshold is set. Set at 0.5 nT / s, in the specific calculation, the integrated magnetic field observation data in Table 2 is retrieved, and the rate of change between two adjacent time points, 10:20:02.000 and 10:20:03.000, is calculated. The calculated rate of change, 1.4 nT / s, is compared with the preset fluctuation threshold of 0.5 nT / s. Since... This indicates that the magnetic field changes drastically at this point. The system records this time point 10:20:03.000 and continues to calculate. When it is found that the calculated rate of change is consistently greater than 0.5 nT / s for a continuous period of time, such as from 10:20:03.000 to 10:20:15.000, the time range [10:20:03.000, 10:20:15.000] is defined as a magnetic field change segment and stored in the magnetic field change segment set.

[0092] The key node distribution identification submodule extracts amplitude and frequency nodes within the corresponding segments from the integrated magnetic field observation data based on the magnetic field change segment set. It calculates the maximum and minimum values ​​of amplitude within each segment as amplitude boundaries, identifies nodes where the frequency value changes direction, marks amplitude boundary points and frequency inflection points, and obtains key feature node parameters.

[0093] Based on the set of magnetic field change segments, amplitude and frequency nodes within the corresponding segments are extracted from the integrated magnetic field observation data. For the magnetic field change segment [10:20:03.000, 10:20:15.000] determined in the previous step, the system first retrieves all integrated values ​​within this time range from the integrated magnetic field observation data. Then, within this data subset, the maximum and minimum values ​​are found. Assuming the maximum value is 55030.1 nT (occurring at 10:20:10.000) and the minimum value is 55023.9 ​​nT (occurring at 10:20:03.000), these two values ​​are identified as the amplitude boundaries of this segment and marked as key nodes. Simultaneously, the system analyzes the frequency characteristics of the measured values ​​within this segment. Here, frequency refers to the positive or negative change in the rate of change, i.e., the change in trend. The system calculates the rate of change sequence per second, for example, ... 1.4, 1.2, 0.8, 0.2, -0.3, -0.7..., and identifies the nodes where the sign of the rate of change changes. In the above sequence, the rate of change changes from 0.2 to -0.3, indicating that the upward trend of the magnetic field changes to a downward trend. The corresponding time node (assumed to be 10:20:07.000) is identified as a frequency inflection point and marked. By traversing the entire segment, all amplitude boundary points and frequency inflection points are marked, and key feature node parameters are obtained.

[0094] The fluctuation trend report generation submodule arranges the amplitude boundary points and frequency inflection points marked in the key feature node parameters in chronological order, combines the start and end time information of the magnetic field change segment set, organizes the various parameters into structured entries with time index, and generates a magnetic field fluctuation trend report.

[0095] The amplitude boundary points and frequency inflection points identified in the key feature node parameters are arranged in chronological order. This process organizes the key nodes of multiple segments obtained in the previous step. For example, the system identifies two change segments. The first segment is [10:20:03.000, 10:20:15.000], and its key nodes include: starting point (10:20:03.000, 55023.9nT), frequency inflection point (10:20:07.000, 55028.5nT), maximum amplitude point (10:20:10.000, 55030.1nT), and ending point (10:20:15.000, 55026.4nT). The second segment is [11:30:05.000, 11:30:20.000], and its key nodes are also extracted. Subsequently, this is combined with changes in the magnetic field. The start and end time information of the segment set is used to organize the parameters into structured entries with time indexes. The format of the entries is defined as: {Segment ID:1, Start Time:10:20:03.000, End Time:10:20:15.000, Node List:[{Type:'Start Point', Time:'10:20:03.000', Value:55023.9},{Type:'Frequency Turning Point', Time:'10:20:07.000', Value:55028.5},{Type:'Maximum Amplitude', Time:'10:20:10.000', Value:55030.1},{Type:'End Point', Time:'10:20:15.000', Value:55026.4}]}. All the structured entries of the segments are arranged in chronological order to generate a magnetic field fluctuation trend report.

[0096] Please see Figure 6 The observation results reporting module includes a segment difference comparison submodule, a change gradient quantification submodule, and an observation report generation submodule;

[0097] The segment difference comparison submodule calls the magnetic field fluctuation trend report and the acquired historical observation records, constructs a magnetic field change segment comparison table based on the timestamp, determines the range of measurement values ​​for different observation days within the same period, extracts the numerical difference between the current measurement value and the historical record value at each time point, and establishes a periodic difference scatter set.

[0098] The system retrieves the magnetic field fluctuation trend report and historical observation records. These historical records are compiled from integrated magnetic field observation data generated by the same observatory within the same time period during the previous solar rotation cycle (approximately 27 days ago). Based on the timestamp in the current report, such as 10:20:10.000, the system searches the historical records for the measurement at the same timestamp of 10:20:10.000. Assuming the current measurement is... nT, historical record value If nT is used, a comparison table of magnetic field change segments is constructed. This table uses timestamps as the primary key and records the current value, historical values, and the difference between the two. At the time point 10:20:10.000, the numerical difference is recorded. For nT, the system will iterate through all the time points of the integrated magnetic field observation data for the current observation day and compare them point by point with the historical records. It will extract the numerical differences at each time point and form a pair of these differences and their corresponding timestamps as {timestamp, numerical difference}, such as {10:20:10.000,5.0nT}, {10:20:11.000,5.2nT}, ..., to form a time series and establish a periodic difference scatter set.

[0099] Table 3: Example Table of Scatter Sets for Periodic Differences

[0100] Timestamp Current integration value (nT) Historical integration value (nT) Periodic differences (nT) 10:20:10.000 55030.1 55025.1 5.0 10:20:11.000 55029.5 55024.3 5.2 10:20:12.000 55028.2 55023.5 4.7 10:20:13.000 55027.0 55023.1 3.9

[0101] As shown in Table 3, the periodic difference scatter set quantifies the changes in geomagnetic activity in the current observation period relative to the previous period.

[0102] The gradient quantization submodule calculates the ratio of the change in value of adjacent scatter points to the time interval based on the periodic difference scatter point set as the difference gradient. It then compares the difference gradient with the preset difference benchmark value, filters and integrates continuous time periods in which the gradient exceeds the benchmark value, and obtains the gradient over-limit interval.

[0103] Based on the scatter plot of periodic differences, the ratio of the numerical change of adjacent points to the time interval is calculated as the difference gradient. The benchmark value for this difference is set with reference to the evaluation criteria of geomagnetic disturbance indices (such as the Dst index), using statistical values ​​related to the rate of change of geomagnetic still-day differences as the benchmark. For example, by analyzing a large amount of still-day data, the normal fluctuation range of the periodic difference gradient is determined, and its 80th percentile of 0.5 nT / s is set as the benchmark value for the difference. In the specific calculation, the periodic difference scatter plot in Table 3 is used to calculate the gradient of the difference between two adjacent time points, 10:20:10.000 and 10:20:11.000. nT / s, this gradient value of 0.2nT / s is compared with the difference baseline value of 0.5nT / s, because This point falls within the normal fluctuation range. Next, we will calculate the gradient of the difference between 10:20:12.000 and 10:20:13.000. nT / s, its absolute value nT / s, because This point is considered a gradient overshoot point. The system will filter and integrate all continuous time periods in which the absolute value of the gradient exceeds 0.5nT / s to obtain the gradient overshoot interval.

[0104] The observation report generation submodule calls the gradient over-limit interval and extracts the difference distribution points in the corresponding segment from the period difference scatter point set. It then classifies and combines the difference distribution points and the corresponding difference gradient data into structured entries according to the time order to generate a complete magnetic field observation result report.

[0105] Assuming a gradient exceedance interval is obtained as [10:20:12.000, 10:20:18.000], the system extracts all entries with timestamps within this range from Table 3 and the subsequent periodic difference scatter plots. This includes the difference distribution points {10:20:12.000, 4.7nT}, {10:20:13.000, 3.9nT}, ... and the corresponding difference gradient data {10:20:12.000, -0. The data points are then categorized and grouped into structured entries based on the time sequence of the difference distribution points and the corresponding difference gradient data. Each entry contains a timestamp, periodic difference value, and difference gradient value. Finally, all structured entries for gradient exceedance intervals are summarized and combined with the original magnetic field fluctuation trend report to form a complete PDF or XML document, generating a complete magnetic field observation results report.

[0106] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A system for integrated observation of a non-magnetic theodolite and a quantum magnetic field intensity meter, characterized in that, The system comprises: A non-magnetic observation module acquires real-time azimuth data and sensor output signals of a non-magnetic theodolite, judges signal stability to identify offset abnormal points, corrects observation angles based on data offset angles, and generates a non-magnetic observation dataset; A quantum magnetic field measurement module performs reference alignment on the non-magnetic observation dataset and acquired quantum magnetic field intensity meter output signals, extracts amplitude and frequency values in a continuous signal segment, calculates magnetic induction intensity of each observation point, associates each set of measurement data with observation time, and generates quantum magnetic field measurement results; A data integration module calls the non-magnetic observation dataset and quantum magnetic field measurement results to construct an observation data group, judges time index correspondence of two data source records, calculates offset of different data source record values at the same time point, and generates integrated magnetic field observation data; A magnetic field analysis module calls the integrated magnetic field observation data for observation frequency processing, constructs a time sequence fluctuation graph based on a continuously changing section of measurement values, obtains a fluctuation trend interval by calculating a change rate of each measurement point, analyzes distribution characteristics of amplitude sections and frequency nodes in the interval and identifies start and end points, and finally outputs a magnetic field fluctuation trend report.

2. The system according to claim 1, wherein the non-magnetic theodolite is integrated with the quantum magnetometer. The non-magnetic observation dataset specifically includes real-time azimuth data, sensor output signals, a coordinate reference frame, offset abnormal points, observation angle correction values, and azimuth angle correction data; the quantum magnetic field measurement results specifically include magnetic induction intensity, amplitude and frequency values, amplitude waveform change rules, measurement data, time index parameters, and observation time; the integrated magnetic field observation data specifically includes time node record values, observation data groups, time index matching, measurement value offsets, and integrated values; and the magnetic field fluctuation trend report specifically includes a time sequence fluctuation graph, a fluctuation trend interval, an amplitude section, frequency node distribution characteristics, and interval start and end points.

3. The system of claim 1, wherein the non-magnetic theodolite is integrated with the quantum magnetometer. The non-magnetic observation module comprises a coordinate frame construction submodule, a signal offset quantization submodule, and an azimuth angle correction and arrangement submodule. The coordinate frame construction submodule acquires real-time azimuth data and sensor output signals of a non-magnetic theodolite, constructs a three-dimensional coordinate reference frame based on instrument built-in reference directions and geographic coordinate parameters, calculates a projection component set of the sensor output signals in the frame at each time, and establishes an initial signal vector value; The signal offset quantization submodule compares the continuous change of the initial signal vector value in the time sequence with a signal stability reference value, filters out abnormal signal points with fluctuation amplitudes exceeding a preset offset threshold, calculates offset angles corresponding to the abnormal signal points, and obtains azimuth offset angle values; The azimuth angle correction and arrangement submodule compensates and corrects an original observation angle sequence point by point according to the acquired azimuth offset angle values, matches and integrates the corrected azimuth angle data with corresponding time stamps by calling time sequence information, arranges them in a fixed format, and generates a non-magnetic observation dataset.

4. The system of claim 1, wherein the non-magnetic theodolite is integrated with the quantum magnetometer. The quantum magnetic field measurement module comprises a data alignment and fusion submodule, a signal feature extraction submodule, and a magnetic induction intensity calculation submodule. The data alignment and fusion sub-module performs reference alignment between the non-magnetic observation data set and the obtained quantum magnetic field strength meter output signal, calls the time stamps in respective data streams, calculates the time difference between each strength meter signal point and all observation data points, selects the observation points with the smallest time difference for pairing, eliminates the paired data with time difference exceeding the synchronization threshold, and establishes a synchronization signal sequence; The signal feature extraction sub-module sets a fixed length sliding window within a continuous signal segment according to the synchronization signal sequence, extracts the peak value and valley value of the signal in each window to calculate the amplitude, and counts the number of signal period occurrences to obtain the frequency, and the amplitudes and frequency values of all windows are summarized to obtain a signal feature parameter set; The magnetic induction strength solving sub-module calls the amplitude and frequency values in the signal feature parameter set, calibrates the amplitudes of each observation point according to a preset conversion coefficient, converts the magnetic induction strength values, associates each group of magnetic induction strength values with the observation time by combining the time index parameter, and generates a quantum magnetic field measurement result.

5. The system of claim 1, wherein the non-magnetic theodolite is integrated with the quantum magnetometer. The data integration module includes an observation data grouping sub-module, a time index matching sub-module, and an integrated value construction sub-module; The observation data grouping sub-module classifies the record values in the two data sources according to the common time nodes of the non-magnetic observation data set and the quantum magnetic field measurement result as a reference, combines all record items belonging to the same time point into a data unit, and establishes a time sequence data node set; The time index matching sub-module determines whether the time indexes of the record items from different data sources in each data unit are completely consistent, assigns a synchronous matching identifier to the index-consistent record items and extracts them, and obtains a synchronous observation data pair; The integrated value construction sub-module extracts the non-magnetic observation value and the quantum magnetic field measurement value of each pair of data in the synchronous observation data pair, calculates the difference between the non-magnetic observation value and the quantum magnetic field measurement value as an offset, adds the offset to a preset reference measurement value to form an integrated value, and generates integrated magnetic field observation data.

6. The system of claim 1, wherein the non-magnetic theodolite is integrated with the quantum magnetometer. The magnetic field analysis module includes a fluctuation trend interval calculation sub-module, a key node distribution identification sub-module, and a fluctuation trend report generation sub-module; The fluctuation trend interval calculation sub-module performs observation frequency processing on the integrated magnetic field observation data, constructs a time sequence fluctuation graph based on the continuous change section of the measurement value, calculates the ratio of the measurement value difference to the time interval at adjacent time nodes of each measurement point as the change rate, extracts the time range with the change rate exceeding the preset fluctuation threshold, and establishes a magnetic field change section set; The key node distribution identification sub-module extracts the amplitude and frequency nodes in the corresponding section from the integrated magnetic field observation data according to the magnetic field change section set, calculates the maximum and minimum values of the amplitude in each section as the amplitude boundaries, and identifies the nodes where the frequency value changes direction, marks the amplitude boundary points and frequency turning points, and obtains key feature node parameters; The fluctuation trend report generating submodule arranges the amplitude boundary points and the frequency turning points identified in the key feature node parameters in time sequence, organizes each parameter into a structured entry with time index in combination with the start and end time information of the magnetic field change section set, and generates a magnetic field fluctuation trend report.

7. The system of claim 1, wherein the non-magnetic theodolite is integrated with the quantum magnetometer. The system further comprises: The observation report module calls the magnetic field fluctuation trend report and historical observation records to construct a magnetic field change section comparison table, judges the measurement value interval range on different observation days in the same period, extracts difference distribution points, obtains change difference intervals by calculating difference gradients, and generates a distribution list classified by time to output a complete magnetic field observation result report. The magnetic field observation result report specifically refers to a magnetic field change section comparison table, difference distribution points, change difference intervals, and a distribution list classified by time.

8. The system of claim 7, wherein the non-magnetic theodolite is integrated with the quantum magnetometer. The observation report module comprises a section difference comparison submodule, a change gradient quantification submodule, and an observation report generating submodule. The section difference comparison submodule calls the magnetic field fluctuation trend report and the obtained historical observation records, constructs a magnetic field change section comparison table according to time stamps, judges the measurement value interval range on different observation days in the same period, extracts the numerical difference between the current measurement value and the historical record value at each time point, and establishes a period difference scatter point set. The change gradient quantification submodule calculates the ratio of the numerical change amount of adjacent scatter points to the time interval as a difference gradient according to the period difference scatter point set, compares the difference gradient with a preset difference reference value, selects and integrates continuous time periods with gradient exceeding the reference value, and obtains a gradient overrun interval. The observation report generating submodule calls the gradient overrun interval and extracts the difference distribution points in the corresponding section from the period difference scatter point set, classifies and combines the difference distribution points and the corresponding difference gradient data into structured entries in time sequence to generate a complete magnetic field observation result report.

Images