An optimized identification method based on geomagnetic anomaly data

Abstract

The application discloses an optimized identification method based on geomagnetic anomaly data, and particularly relates to the field of geomagnetic data processing, which is realized by comprehensively analyzing a sampling frequency oscillation index and a correction overall anomaly index to obtain an anomaly coefficient, comparing the anomaly coefficient with upper and lower threshold values of the anomaly, and identifying a use state of a geomagnetic sensor. Under a low-level geomagnetic signal, a monitoring data set is constructed, a dispersion index and a concentration variation index are comprehensively considered to evaluate the anomaly of the data, an anomaly coefficient under a corresponding state signal is introduced, a warning degree index is generated, and the warning degree index is compared with a preset warning threshold value to generate a warning signal. The application is helpful for more accurately identifying abnormal data, reducing uncertainty, and improving the identification effect of abnormal geomagnetic data.

Description

Technical Field

[0001] This invention relates to the field of geomagnetic data processing, and more specifically, to an optimized identification method based on geomagnetic anomaly data. Background Technology

[0002] The Earth's magnetic field is a global geomagnetic physical field, composed of the superposition of various magnetic field components generated by magnetic rocks within the Earth and electric currents distributed throughout the Earth's interior and exterior. Geomagnetism is an observation-based discipline. Geomagnetic observation signals contain extremely rich information about the Earth-Sun space. Geomagnetic observation data can directly or indirectly serve research on the basic and changing magnetic fields of the Earth, data exchange for other magnetic measurement methods, space weather research, earthquake prediction and forecasting research, navigation, mineral exploration, and other research. Geomagnetic observation data not only serves geomagnetism itself but is also an indispensable national strategic resource. After years of construction and upgrading of geomagnetic stations, my country now has more than 150 digital geomagnetic stations, accumulating a large amount of high-precision observation data.

[0003] However, with the rapid development of the national economy and the acceleration of urbanization, the large-scale construction and use of national infrastructure such as railways, highways, rail transit, power grids, and infrastructure projects have brought many geomagnetic stations with relatively good observation environments and relatively small electromagnetic backgrounds closer and closer to these external interference sources. Geomagnetic station observations have been affected by various interference sources to varying degrees. Furthermore, as the sensors at geomagnetic stations age, they also affect the observation results to some extent. The superposition of multiple factors has affected the quality of observation data, and thus affected related research work that relies on geomagnetic data.

[0004] To address the aforementioned problems, a technical solution is provided. Summary of the Invention

[0005] To overcome the aforementioned deficiencies of the prior art, embodiments of the present invention provide an anomaly coefficient obtained by comprehensively analyzing the sampling frequency oscillation index and the overall anomaly correction index. This coefficient is then compared with upper and lower anomaly thresholds to identify the operational status of the geomagnetic sensor. Under low-level geomagnetic signals, a monitoring dataset is constructed, and the anomaly of the data is assessed by comprehensively considering the deviation index and the concentration variation index. An anomaly coefficient corresponding to the state signal is introduced to generate a warning level index, which is then compared with a preset warning threshold to generate a warning signal. This helps to more accurately identify abnormal data, reduce uncertainty, and improve the monitoring accuracy of the geomagnetic sensor, thereby solving the problems mentioned in the background art.

[0006] To achieve the above objectives, the present invention provides the following technical solution:

[0007] Step S1: Collect the sampling frequency signal and calibration completion signal when the geomagnetic sensor is in use. The sampling frequency signal includes the sampling frequency oscillation index, and the calibration completion signal includes the overall calibration anomaly index.

[0008] Step S2: Construct an analysis model by sampling frequency oscillation index and correction overall anomaly degree index, generate anomaly coefficients, and further analyze the anomaly coefficients to obtain state signals, which include high-level signals, low-level signals and normal signals.

[0009] Step S3: After acquiring the low-level signal, geomagnetic data is monitored and recorded multiple times within a unit time to construct a monitoring data set. The offset information and discrete information of the monitoring data set are obtained. The offset information includes the deviation index, and the discrete information includes the concentration variation index. The offset index, concentration variation index and anomaly coefficient are comprehensively processed to obtain the warning level index.

[0010] Step S4: Compare the warning level index and the warning threshold, and determine whether to generate a warning signal based on the comparison result.

[0011] In a preferred embodiment, the logic for obtaining the sampling frequency oscillation index is as follows:

[0012] Step S1-001: Obtain multiple sampling frequency data within the most recent unit time period to ensure that the sampling interval of the sampling frequency data is fixed;

[0013] Step S1-002: Convert the sampling frequency data into a rate of return, expressed as: ;

[0014] In the formula, Indicates a point in time rate of return, Indicates a point in time The sampling frequency value, Indicates a point in time The sampling frequency value at that time;

[0015] Rate of return is the rate of change of frequency between adjacent time points;

[0016] Step S1-003, the expression for the sampling frequency oscillation index is: ;

[0017] In the formula, This indicates the sampling frequency oscillation index. This represents the total number of points in time. Indicates the sequence number of a specific time point. This represents the average of all returns.

[0018] In a preferred embodiment, the logic for obtaining the overall anomaly index is as follows:

[0019] Step S2-001: Within the most recent unit of time, acquire the data on the calibration completion time of the geomagnetic sensor, and construct a calibration data set, wherein... This represents the time data for each correction within the correction dataset;

[0020] Step S2-002: Calculate the covariance matrix of the corrected data, expressed as: ;

[0021] In the formula, These represent the row and column indices in the covariance matrix, respectively. Indicates the first Data points, These represent the average values ​​of the corrected data for the rows and columns, respectively.

[0022] Step S2-003: For each corrected data point, calculate the deviation vector between it and the data mean, expressed as: ;

[0023] In the formula, This represents the deviation vector of each corrected data point relative to the mean vector of the dataset;

[0024] Step S2-004: For each correction data point, calculate the multidimensional correlation index of the correction data, expressed as: ;

[0025] In the formula, It is the multidimensional correlation index of the corrected data for each data point, used to represent the degree of deviation of each corrected data point relative to the overall dataset. Denotes the inverse of the covariance matrix. Representing vectors Transpose of;

[0026] Step S2-005, standardize the multidimensional correlation index of the corrected data, the expression is: ;

[0027] In the formula, This represents the multidimensional correlation index of standardized corrected data. This represents the standard deviation of the multidimensional correlation index of standardized corrected data;

[0028] Step S2-006: Calculate the overall anomaly index, the expression of which is: ;

[0029] In the formula, This indicates the overall abnormality index after correction. This indicates that the multidimensional correlation index of the corrected data for each corrected data point is greater than or equal to an outlier threshold, used to identify outliers. This condition determines which data points are considered outliers. This is an accumulator, initialized to 0. This represents the standard deviation of the multidimensional correlation index of standardized corrected data. This indicates the percentage of abnormal data identified.

[0030] In a preferred embodiment, after obtaining the anomaly coefficient, the anomaly coefficient is compared with the upper and lower thresholds of the anomaly limit, respectively.

[0031] If the anomaly coefficient is greater than or equal to the upper threshold of the anomaly, a higher-level signal is generated.

[0032] If the abnormality coefficient is less than the upper limit threshold of abnormality and greater than or equal to the lower limit threshold of abnormality, a low-level signal is generated.

[0033] If the abnormality coefficient is less than the lower limit threshold of abnormality, a normal signal is generated.

[0034] In a preferred embodiment, step S3 specifically includes the following:

[0035] The logic for obtaining the deviation index is as follows:

[0036] Step S3-001: After confirming the acquisition of a low-level signal, under the same monitoring environment, use a geomagnetic sensor in that signal state to collect geomagnetic data and store the geomagnetic data points as... ,in Indicates the number of geomagnetic data points;

[0037] Step S3-002, calculate the deviation index, the expression is: ;

[0038] In the formula, It is the deviation index. Indicates the sequence number of the geomagnetic data point. This represents the mean of the geomagnetic data points.

[0039] In a preferred embodiment, the logic for obtaining the central variation index is as follows:

[0040] Step S3-011: Sort the geomagnetic data in ascending order;

[0041] Step S3-012: Obtain the 25th percentile in the geomagnetic dataset, representing the lower quarter of the data. The expression is: ;

[0042] This represents the 25th percentile in the geomagnetic dataset.

[0043] If the number of geomagnetic data points is odd, then the 25th percentile in the geomagnetic dataset is the smaller middle value;

[0044] Step S3-013: Obtain the 75th percentile in the geomagnetic dataset, representing the upper quarter of the geomagnetic data. The expression is: ;

[0045] This represents the 75th percentile in the geomagnetic dataset.

[0046] If the number of geomagnetic data points is odd, then the 75th percentile in the geomagnetic dataset is the larger of the middle values;

[0047] Step S3-014, calculate the central variation index, the expression is: ;

[0048] In the formula, This represents the dispersion of the middle 50% range of geomagnetic data, that is, the distance between the 75th percentile and the 25th percentile of the geomagnetic dataset.

[0049] In a preferred embodiment, the dispersion index, the central tendency index, and the anomaly coefficient are combined to obtain the alert level index, expressed as: ;

[0050] In the formula, Indicating the level of alertness, It is the outlier coefficient. Indicates the upper limit threshold of abnormality. Indicates the deviation index. These represent the 25th percentile, 75th percentile, and central tendency variability index in the geomagnetic dataset, respectively. This represents the adjustment factor, used for correction.

[0051] In a preferred embodiment, after obtaining the warning level index, the warning level index is compared with the warning threshold. If the warning level index is greater than or equal to the warning threshold, a warning signal is generated; otherwise, if the warning level index is less than the warning threshold, no signal is generated.

[0052] The technical effects and advantages of the optimized identification method based on geomagnetic anomaly data of this invention are as follows:

[0053] 1. By collecting the sampling frequency oscillation index and the overall correction anomaly index during the use of the geomagnetic sensor, the sampling frequency oscillation index and the overall correction anomaly index are comprehensively processed to obtain the anomaly coefficient. The usage status of the geomagnetic sensor is analyzed by the anomaly coefficient, and the anomaly coefficient is compared with the upper and lower thresholds of the anomaly. Based on the comparison results, high-level signals, low-level signals and normal signals are generated, thereby realizing the analysis of the usage status of the geomagnetic sensor. This helps to identify sensor anomalies in a timely manner, distinguish the degree of anomaly, make maintenance and management more targeted, and reduce the uncertainty in monitoring.

[0054] 2. After acquiring low-level geomagnetic signals, a monitoring dataset is constructed through multiple monitoring and recording processes. Anomalies are comprehensively assessed by considering deviation index and central tendency index, and anomaly coefficients under corresponding signal states are introduced. This information, along with the anomaly coefficients, is processed to generate a warning level index. The warning level index is compared with a preset warning threshold to determine whether to generate a warning signal. This primarily involves identifying geomagnetic anomalies based on the deviation of the monitored geomagnetic data and the direction of data outliers. Anomaly coefficients reflecting the usage status of the geomagnetic sensor under corresponding states are introduced for intervention, resulting in a warning level index that better reflects the monitoring situation. This allows for more comprehensive and accurate identification of abnormal data, precise generation of warning signals, and effective identification of anomalous data. Attached Figure Description

[0055] Figure 1 This is a flowchart illustrating an optimized identification method based on geomagnetic anomaly data according to the present invention. Detailed Implementation

[0056] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0057] Example 1

[0058] Figure 1 This invention presents an optimized identification method based on geomagnetic anomaly data, which includes the following steps:

[0059] Step S1: Collect the sampling frequency signal and calibration completion signal when the geomagnetic sensor is in use. The sampling frequency signal includes the sampling frequency oscillation index, and the calibration completion signal includes the overall calibration anomaly index.

[0060] Step S2: Construct an analysis model by sampling frequency oscillation index and correction overall anomaly degree index, generate anomaly coefficients, and further analyze the anomaly coefficients to obtain state signals, which include high-level signals, low-level signals and normal signals.

[0061] Step S3: After acquiring the low-level signal, geomagnetic data is monitored and recorded multiple times within a unit time to construct a monitoring data set. The offset information and discrete information of the monitoring data set are obtained. The offset information includes the deviation index, and the discrete information includes the concentration variation index. The offset index, concentration variation index and anomaly coefficient are comprehensively processed to obtain the warning level index.

[0062] Step S4: Compare the warning level index and the warning threshold, and determine whether to generate a warning signal based on the comparison result.

[0063] Step S1 specifically includes the following:

[0064] Analyzing changes in sampling frequency is of significant practical importance for assessing the operational status of geomagnetic sensors. Changes in sampling frequency reflect the stability of the sensor. Changes in the sampling frequency suggest problems with the sensor's control circuitry or sensing elements, or interference from the external environment. Therefore, monitoring and analyzing changes in sampling frequency helps in real-time detection of potential problems and allows for timely maintenance or repair measures to ensure the normal operation of the geomagnetic sensor.

[0065] Furthermore, variations in sampling frequency can easily affect the quality and reliability of geomagnetic data. Some applications require high-precision and stable geomagnetic data, such as navigation and earthquake monitoring. Unstable sampling frequencies lead to inaccurate data, thus impacting the effectiveness of these critical applications. Therefore, analyzing variations in sampling frequency allows for the timely identification of potential problems, ensuring that the data output from geomagnetic sensors maintains high reliability and accuracy across various application scenarios.

[0066] In summary, analyzing changes in sampling frequency helps monitor the state of geomagnetic sensors, ensure data quality, and promptly identify and resolve problems to meet the needs of various practical applications. This is crucial for ensuring the stability and reliability of geomagnetic sensors.

[0067] The logic for obtaining the sampling frequency oscillation index is as follows:

[0068] Step S1-001: Obtain multiple sampling frequency data within the most recent unit time period to ensure that the sampling interval of the sampling frequency data is fixed;

[0069] Step S1-002: Convert the sampling frequency data into a rate of return, expressed as: ;

[0070] In the formula, Indicates a point in time rate of return, Indicates a point in time The sampling frequency value, Indicates a point in time The sampling frequency value at that time;

[0071] Rate of return is the rate of change of frequency between adjacent time points;

[0072] Step S1-003, the expression for the sampling frequency oscillation index is: ;

[0073] In the formula, This indicates the sampling frequency oscillation index. This represents the total number of points in time. Indicates the sequence number of a specific time point. This represents the average of all returns.

[0074] The sampling frequency oscillation index is used to represent the fluctuation of the sampling frequency of a geomagnetic sensor and to reflect the operational stability of the sensor's control circuit. A large sampling frequency oscillation index means that the sampling frequency of the geomagnetic sensor fluctuates significantly and is unstable over a period of time. This may be an indication of a problem with the control circuit, indicating that the controllability of the geomagnetic sensor is decreasing and its stability is poor. A smaller sampling frequency oscillation index indicates that the sampling frequency of the geomagnetic sensor is relatively stable, which is a sign of normal operation of the control circuit and indicates that the stability of the geomagnetic sensor is good.

[0075] Obtaining changes in the calibration completion time of geomagnetic sensors is of significant practical importance for analyzing their operational status. Variations in calibration completion time can reflect the stability of the geomagnetic sensor and identify potential problems.

[0076] Significant changes or fluctuations in calibration completion time may indicate an anomaly or instability in the internal control and calibration system of the geomagnetic sensor. Calibration completion time should ideally be relatively stable, as it is crucial for the sensor's calibration and accuracy. Therefore, unstable calibration times suggest increased instability in the geomagnetic sensor, requiring further inspection and maintenance.

[0077] Furthermore, variations in calibration completion time can affect the quality and accuracy of geomagnetic data. Unstable calibration times can lead to data inaccuracies, impacting the effectiveness of various applications such as navigation, earthquake monitoring, and resource exploration. Therefore, monitoring and analyzing variations in calibration completion time allows for the timely detection of potential problems and the implementation of necessary maintenance and repair measures to ensure the normal operation of geomagnetic sensors and the quality of data.

[0078] In summary, obtaining changes in calibration completion time helps monitor the status of geomagnetic sensors, ensure data quality, and promptly identify and resolve problems to meet the needs of various practical applications. This is crucial for ensuring the stability and reliability of geomagnetic sensors.

[0079] The logic for obtaining the overall anomaly index is as follows:

[0080] Step S2-001: Within the most recent unit of time, acquire the data on the calibration completion time of the geomagnetic sensor, and construct a calibration data set, wherein... This represents the time data for each correction within the correction dataset;

[0081] Step S2-002: Calculate the covariance matrix of the corrected data, expressed as: ;

[0082] In the formula, These represent the row and column indices in the covariance matrix, respectively. Indicates the first Data points, These represent the average values ​​of the corrected data for the rows and columns, respectively.

[0083] Step S2-003: For each corrected data point, calculate the deviation vector between it and the data mean, expressed as: ;

[0084] In the formula, This represents the deviation vector of each corrected data point relative to the mean vector of the dataset;

[0085] Step S2-004: For each correction data point, calculate the multidimensional correlation index of the correction data, expressed as: ;

[0086] In the formula, It is the multidimensional correlation index of the corrected data for each data point, used to represent the degree of deviation of each corrected data point relative to the overall dataset. Denotes the inverse of the covariance matrix. Representing vectors Transpose of;

[0087] Step S2-005, standardize the multidimensional correlation index of the corrected data, the expression is: ;

[0088] In the formula, This represents the multidimensional correlation index of standardized corrected data. This represents the standard deviation of the multidimensional correlation index of standardized corrected data;

[0089] Step S2-006: Calculate the overall anomaly index, the expression of which is: ;

[0090] In the formula, This indicates the overall abnormality index after correction. This indicates that the multidimensional correlation index of the corrected data for each corrected data point is greater than or equal to an outlier threshold, used to identify outliers. This condition determines which data points are considered outliers. This is an accumulator, initialized to 0. This represents the standard deviation of the multidimensional correlation index of standardized corrected data. This indicates the percentage of abnormal data identified.

[0091] The overall anomaly index reflects the overall anomaly situation in geomagnetic sensor calibration data. A higher overall anomaly index indicates more abnormal data or instability during the geomagnetic sensor calibration process. This can lead to inaccurate calibration results, affecting the sensor's measurement accuracy. Furthermore, it increases the instability of geomagnetic data, making it noisier and harder to interpret. This means reduced controllability of the geomagnetic sensor, less stable operation, and consequently lower data quality and reliability. Therefore, when the overall anomaly index is high, geomagnetic data is generally unsuitable for applications requiring high data accuracy and reliability.

[0092] Conversely, a lower overall anomaly index indicates fewer anomalous data points during the calibration process of the geomagnetic sensor, suggesting a relatively stable calibration process. This typically indicates that the sensor performs more reliably during calibration. This also improves the stability and reliability of geomagnetic data. Consequently, the monitoring controllability of the geomagnetic sensor is more stable, and its operational status is more consistent. This further enhances the quality and reliability of geomagnetic data, making it ideal for applications with high data quality requirements, such as those demanding high precision and reliability.

[0093] Step S2 specifically includes the following:

[0094] The anomaly coefficient is obtained by combining the sampling frequency oscillation index and the overall anomaly correction index. For example, the anomaly coefficient can be calculated using the following formula: ;

[0095] In the formula, It is the outlier coefficient. These represent the sampling frequency oscillation index and the overall anomaly correction index, respectively. These are the preset proportional coefficients for the sampling frequency oscillation index and the overall anomaly correction index, respectively. All are greater than 0.

[0096] After obtaining the anomaly coefficient, the anomaly coefficient is compared with the upper and lower thresholds of the anomaly.

[0097] If the anomaly coefficient is greater than or equal to the upper limit threshold of anomalies, it indicates that the data quality of the geomagnetic sensor is damaged. Immediate measures need to be taken to repair or replace the geomagnetic sensor in order to ensure reliable data acquisition and monitoring to generate high-level signals.

[0098] If the anomaly coefficient is less than the upper limit of the anomaly threshold but greater than or equal to the lower limit of the anomaly threshold, it indicates that there is a problem with the data quality of the geomagnetic sensor, but the problem is within an acceptable range and further monitoring and evaluation are needed to generate a low-level signal.

[0099] If the anomaly coefficient is less than the lower limit threshold, it indicates that the geomagnetic sensor is within a controllable range, the monitoring quality of the geomagnetic sensor is within an acceptable range, and a normal signal is generated.

[0100] This invention collects the sampling frequency oscillation index and the overall correction anomaly index during the use of a geomagnetic sensor. After comprehensive processing of the sampling frequency oscillation index and the overall correction anomaly index, anomaly coefficients are obtained. The usage status of the geomagnetic sensor is analyzed by using the anomaly coefficients. Furthermore, the anomaly coefficients are compared with the upper and lower thresholds of anomalies. Based on the comparison results, high-level signals, low-level signals, and normal signals are generated. This enables the analysis of the usage status of the geomagnetic sensor, helps to identify sensor usage problems in a timely manner, distinguish the degree of anomaly, make maintenance and management more targeted, and reduce uncertainty in monitoring.

[0101] Step S3 specifically includes the following:

[0102] The logic for obtaining the deviation index is as follows:

[0103] Step S3-001: After confirming the acquisition of a low-level signal, under the same monitoring environment, use a geomagnetic sensor in that signal state to collect geomagnetic data and store the geomagnetic data points as... ,in Indicates the number of geomagnetic data points;

[0104] Step S3-002, calculate the deviation index, the expression is: ;

[0105] In the formula, It is the deviation index. Indicates the sequence number of the geomagnetic data point. This represents the mean of the geomagnetic data points.

[0106] The deviation index is used to represent the degree of deviation of geomagnetic data points from the mean. The larger the deviation index value of a geomagnetic data point, the more significantly the data point deviates from the mean of the dataset, that is, the data point is far from the center of the dataset, indicating that the data point has a higher value and is larger relative to the dataset. The smaller the deviation index value of a geomagnetic data point, the more significantly the data point deviates from the mean of the dataset, that is, the data point is far from the center of the dataset, indicating that the data point has a lower value and is smaller relative to the dataset.

[0107] The logic for obtaining the central variation index is as follows:

[0108] Step S3-011: Sort the geomagnetic data in ascending order;

[0109] Step S3-012: Obtain the 25th percentile in the geomagnetic dataset, representing the lower quarter of the data. The expression is: ;

[0110] This represents the 25th percentile in the geomagnetic dataset.

[0111] If the number of geomagnetic data points is odd, then the 25th percentile in the geomagnetic dataset is the smaller middle value;

[0112] Step S3-013: Obtain the 75th percentile in the geomagnetic dataset, representing the upper quarter of the geomagnetic data. The expression is: ;

[0113] This represents the 75th percentile in the geomagnetic dataset.

[0114] If the number of geomagnetic data points is odd, then the 75th percentile in the geomagnetic dataset is the larger of the middle values;

[0115] Step S3-014, calculate the central variation index, the expression is: ;

[0116] In the formula, This represents the dispersion of the middle 50% range of geomagnetic data, i.e., the distance between the 75th percentile and the 25th percentile of the geomagnetic dataset.

[0117] The variability index is used to identify the dispersion of a granular dataset of geomagnetic data. It identifies the variability of a central portion of the geomagnetic dataset based on the quantiles of the geomagnetic data, including the first and third quartiles.

[0118] A larger concentration variation index indicates a wider range of geomagnetic data, more dispersed data, and greater fluctuations in the values ​​of geomagnetic data points within this range, meaning higher data dispersion. Conversely, a smaller concentration variation index indicates a narrower range of geomagnetic data, more compact data, and smaller fluctuations in the values ​​of geomagnetic data points within this range, meaning lower data dispersion.

[0119] Step S4 specifically includes the following:

[0120] The alert level index is obtained by comprehensively processing the deviation index, the central variation index, and the anomaly coefficient. The expression is as follows: ;

[0121] In the formula, Indicating the level of alertness, Indicates the upper limit threshold of abnormality. Indicates the deviation index. These represent the 25th percentile, 75th percentile, and central tendency variability index in the geomagnetic dataset, respectively. This represents the central tendency variation index. This represents the adjustment factor, used for correction.

[0122] The alert level index is used to indicate the degree of abnormality in abnormal geomagnetic data, and is used to help identify abnormal geomagnetic data more quickly and efficiently.

[0123] After obtaining the warning level index, the warning level index is compared with the warning threshold. If the warning level index is greater than or equal to the warning threshold, it indicates that the detected geomagnetic data is highly likely to be abnormal and is difficult to use as a basis for decision-making, with little supporting significance, and a warning signal is generated. Conversely, if the warning level index is lower than the warning threshold, it indicates that the detected geomagnetic anomalies are less likely to be abnormal and have greater significance in supporting decision-making, and no signal is generated.

[0124] This invention acquires low-level geomagnetic signals and constructs a monitoring dataset through multiple monitoring and recording processes. It comprehensively assesses data anomalies by considering deviation index and central tendency index, and introduces anomaly coefficients under corresponding state signals. These information and anomaly coefficients are then processed to generate a warning level index. By comparing this index with a preset warning threshold, a warning signal is generated. The invention primarily identifies geomagnetic data based on deviations and outlier directions, and intervenes by introducing anomaly coefficients reflecting the usage status of the geomagnetic sensor under corresponding states. This results in a warning level index that more closely matches the monitoring situation, enabling more comprehensive and accurate identification of abnormal data and precise generation of warning signals, thus facilitating effective identification of anomalous data.

[0125] The above formulas are all dimensionless calculations. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters and thresholds in the formulas are set by those skilled in the art according to the actual situation.

[0126] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, as a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more sets of available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. The semiconductor medium can be a solid-state drive.

[0127] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0128] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0129] In addition, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0130] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0131] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0132] In conclusion, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. An optimized identification method based on geomagnetic anomaly data, characterized in that, Includes the following steps: Step S1: Collect the sampling frequency signal and calibration completion signal when the geomagnetic sensor is in use. The sampling frequency signal includes the sampling frequency oscillation index, and the calibration completion signal includes the overall calibration anomaly index. Step S2 involves constructing an analysis model using the sampling frequency oscillation index and the overall anomaly correction index, generating anomaly coefficients, and further analyzing these coefficients to obtain state signals. These state signals include high-level signals, low-level signals, and normal signals. The logic for obtaining the sampling frequency oscillation index is as follows: Step S1-001: Obtain multiple sampling frequency data within the most recent unit time period to ensure that the sampling interval of the sampling frequency data is fixed; Step S1-002, converting the sampling frequency data into a return rate ; and denote the sampling frequency values at the time points and respectively; Rate of return is the rate of change of frequency between adjacent time points; Step S1-003, the expression of the sampling frequency oscillation index is: ; wherein, denotes the total number of time points, denotes the serial number of the time point, denotes the mean of all return rates; The logic for obtaining the overall anomaly index is as follows: Step S2-001: Within the most recent unit of time, acquire the data on the calibration completion time of the geomagnetic sensor to form a calibration data set, wherein... This represents the time data for each correction within the correction dataset; Step S2-002: Calculate the covariance matrix of the corrected data, expressed as: ; In the formula, These represent the row and column indices in the covariance matrix, respectively. Indicates the first Data points, These represent the average values ​​of the corrected data for the rows and columns, respectively. Step S2-003, for each correction data point, calculate the deviation vector between it and the data average, expressed as: ; Step S2-004: For each correction data point, calculate the multidimensional correlation index of the correction data, expressed as: In the formula, Used to indicate the degree of deviation of each corrected data point from the overall dataset. Denotes the inverse matrix of the covariance matrix. Representing vectors transpose; Step S2-005, standardize the multidimensional correlation index of the corrected data, the expression is: ; In the formula, This represents the standard deviation of the multidimensional correlation index of standardized corrected data; Step S2-006: Calculate the overall anomaly index, the expression of which is: ； In the formula, Indicates inspection Whether the value is greater than or equal to an anomaly threshold is used to identify anomalous data. This condition determines which data points are considered outliers. This is an accumulator, initialized to 0. express standard deviation This indicates the percentage of identified abnormal data; Step S3: After acquiring the low-level signal, geomagnetic data is monitored and recorded multiple times within a unit time to construct a monitoring data set. The offset information and discrete information of the monitoring data set are obtained. The offset information includes the deviation index, and the discrete information includes the concentration variation index. The deviation index, concentration variation index and anomaly coefficient are comprehensively processed to obtain the warning level index. Step S4: Compare the warning level index and the warning threshold, and determine whether to generate a warning signal based on the comparison result.

2. The optimized identification method based on geomagnetic anomaly data according to claim 1, characterized in that: After obtaining the anomaly coefficient, the anomaly coefficient is compared with the upper and lower thresholds of the anomaly. If the anomaly coefficient is greater than or equal to the upper threshold of the anomaly, a higher-level signal is generated. If the abnormality coefficient is less than the upper limit threshold of abnormality and greater than or equal to the lower limit threshold of abnormality, a low-level signal is generated. If the abnormality coefficient is less than the lower limit threshold of abnormality, a normal signal is generated.

3. The optimized identification method based on geomagnetic anomaly data according to claim 2, characterized in that: Step S3 specifically includes the following: The logic for obtaining the deviation index is as follows: Step S3-001: After confirming the acquisition of a low-level signal, under the same monitoring environment, use a geomagnetic sensor in that signal state to collect geomagnetic data and store the geomagnetic data points as... ,in Indicates the number of geomagnetic data points; Step S3-002, calculate the deviation index, the expression is: ; In the formula, It is the deviation index. Indicates the sequence number of the geomagnetic data point. This represents the mean of the geomagnetic data points.

4. The optimized identification method based on geomagnetic anomaly data according to claim 3, characterized in that: The logic for obtaining the central variation index is as follows: Step S3-011: Sort the geomagnetic data in ascending order; Step S3-012: Obtain the 25th percentile in the geomagnetic dataset, representing the lower quarter of the data. The expression is: ; This represents the 25th percentile in the geomagnetic dataset. If the number of geomagnetic data points is odd, then the 25th percentile in the geomagnetic dataset is the smaller middle value; Step S3-013: Obtain the 75th percentile in the geomagnetic dataset, representing the upper quarter of the geomagnetic data. The expression is: ; This represents the 75th percentile in the geomagnetic dataset. If the number of geomagnetic data points is odd, then the 75th percentile in the geomagnetic dataset is the larger of the middle values; Step S3-014, calculate the central variation index, the expression is: ; In the formula, This represents the dispersion of the middle 50% range of geomagnetic data, that is, the distance between the 75th percentile and the 25th percentile of the geomagnetic dataset.

5. The optimized identification method based on geomagnetic anomaly data according to claim 4, characterized in that: The alert level index is obtained by comprehensively processing the deviation index, the central variation index, and the anomaly coefficient. The expression is as follows: ； In the formula, Represents the anomaly coefficient. Indicating the level of alertness, Indicates the upper limit threshold of abnormality. Indicates the deviation index. These represent the 25th percentile, 75th percentile, and central tendency variability index in the geomagnetic dataset, respectively. This represents the adjustment factor, used for correction.

6. The optimized identification method based on geomagnetic anomaly data according to claim 5, characterized in that: After obtaining the alert level index, the alert level index is compared with the alert threshold. If the alert level index is greater than or equal to the alert threshold, an alert signal is generated; otherwise, if the alert level index is less than the alert threshold, no signal is generated.

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