An aerial geomagnetic multi-parameter intelligent measurement method, device and medium

Abstract

The application discloses an aviation geomagnetic multi-parameter intelligent measurement method and device and a medium, and belongs to the technical field of aviation geomagnetic measurement. The application predicts the prediction geomagnetic intensity data and the uncertainty of the detection points which have not been detected in the to-be-detected area by a prediction model to calculate the selected probability corresponding to each detection point, selects the target detection point to be measured for the aircraft according to the selected probability, collects multiple parameters of the target detection point through an aeromagnetic module in the detection process, thereby obtaining the actual geomagnetic intensity data corresponding to the target detection point, and updates the prediction geomagnetic intensity data and the uncertainty corresponding to the detection points adjacent to the target detection point which have not been detected, and the process of "selection-collection-update-probability calculation" is circularly executed until the preset termination condition is met. Therefore, the dense measurement of the geomagnetic abnormal area is realized on the premise that the to-be-detected area is fully covered, and the reliability of the measurement data is finally improved.

Description

Technical Field

[0001] This invention relates to the field of airborne geomagnetic measurement technology, and in particular to an intelligent method, device and medium for measuring multiple parameters of airborne geomagnetism. Background Technology

[0002] Airborne geomagnetic surveying technology, as an important means of basic geological surveys and mineral resource exploration, has the advantages of low cost, wide coverage area and minimal impact from harsh geological environments. It has been widely used in geodynamics research, geological structure analysis and mineral resource exploration.

[0003] Currently, airborne geomagnetic surveys typically use fixed-wing aircraft, helicopters, or unmanned aerial vehicles as measurement platforms. However, in existing airborne geomagnetic surveying technologies, route planning generally adopts a method of uniformly laying out survey lines, conducting equal-density measurements with the same line spacing and point spacing throughout the entire area to be tested. This makes it difficult to ensure comprehensive coverage of the area while carrying out precise measurements in geomagnetic anomaly areas, resulting in low reliability of the measurement data.

[0004] Therefore, a new airborne geomagnetic measurement method is needed to improve the reliability of measurement data. Summary of the Invention

[0005] This invention provides an intelligent method, device, and medium for multi-parameter airborne geomagnetic measurement, which is used to perform fine measurements on geomagnetic anomaly areas by conducting comprehensive coverage measurements of the area to be detected, thereby enabling the measured data to record more details of the geomagnetic anomaly areas and ultimately improving the reliability of the measurement data.

[0006] To address the aforementioned problems, the first aspect of this invention discloses an intelligent method for multi-parameter airborne geomagnetic measurement, the method comprising: Step S1: Select the target detection point of the aircraft in the preset candidate detection point set through the probability selector in the preset prediction model. The candidate detection point set is the set of all undetected detection points in the area to be detected. The prediction model is used to obtain the predicted geomagnetic intensity data and uncertainty corresponding to each detection point in the candidate detection point set. The uncertainty is used to measure the accuracy of the predicted geomagnetic intensity data. Step S2: Control the aircraft to move to the target detection point, and obtain the actual geomagnetic intensity data corresponding to the target detection point based on the initial multi-parameter set corresponding to the target detection point measured by the aeromagnetic module installed on the aircraft. Step S3: Based on the actual geomagnetic intensity data corresponding to the target detection point, update the predicted geomagnetic intensity data and uncertainty of each detection point adjacent to the target detection point in the candidate detection point set; Step S4: Based on the predicted geomagnetic intensity data and uncertainty corresponding to each detection point in the candidate detection point set, obtain the selection probability corresponding to each detection point in the candidate detection point set. The selection probability is jointly determined by the degree of deviation between the predicted geomagnetic intensity data corresponding to the detection point and the normal background field value data of the area to be detected, as well as the uncertainty corresponding to the detection point. Step S5: Repeat steps S1 to S4 until the preset termination condition is met, thus completing the geomagnetic detection of the area to be detected.

[0007] As an optional implementation, in the first aspect of the present invention, obtaining the actual geomagnetic intensity data corresponding to the target detection point based on the initial multi-parameter set corresponding to the target detection point measured by the aeromagnetic module installed on the aircraft includes: The initial multi-parameter set corresponding to the target detection point collected by the aeromagnetic module is read. Each data in the initial multi-parameter set is accompanied by a timestamp indicating the collection time of the data. The initial multi-parameter set includes initial geomagnetic intensity data, spatial position data, and attitude data. Create a multi-threaded program to pass all the data in the initial multi-parameter set into an asynchronous writer. The asynchronous writer is used to associate and integrate the initial geomagnetic intensity data, the spatial location data, and the attitude data to obtain the target multi-parameter set corresponding to the target detection point. Based on the target multi-parameter set, the actual geomagnetic intensity data corresponding to the target detection point is obtained.

[0008] As an optional implementation, in the first aspect of the present invention, satisfying the preset termination condition includes: The first condition must be met, or the second and third conditions must be met simultaneously; in: The first condition includes: the estimated remaining flight time of the aircraft is lower than a safe time threshold; The second condition includes: obtaining multiple abnormal detection points from the candidate detection point set, wherein the deviation between the predicted geomagnetic intensity data of the abnormal detection points and the normal background field value data is greater than a preset abnormal threshold; and the uncertainty corresponding to each abnormal detection point is less than a preset accuracy threshold. The third condition includes: the ratio of the area of ​​the circumscribed convex hull formed by all the actually measured detection points to the total area of ​​the area to be detected is greater than a preset coverage threshold, wherein the circumscribed convex hull is the smallest convex polygon that contains all the actually measured detection points.

[0009] As an optional implementation, in the first aspect of the present invention, step S3 includes: For each of the candidate detection point sets that is adjacent to the target detection point: Based on the spatial location data corresponding to the detection point and the target detection point, the target spatial similarity corresponding to the detection point is obtained. The target spatial similarity is used to measure the proximity between the detection point and the target detection point. Update the predicted geomagnetic intensity data corresponding to the detection point based on the target spatial similarity and the actual geomagnetic intensity data corresponding to the target detection point. The uncertainty corresponding to the detection point is updated based on the target spatial similarity and the preset measurement error variance of the aeromagnetic module.

[0010] As an optional implementation, in the first aspect of the present invention, the aeromagnetic module stores a preset set of induction points, the set of induction points consisting of multiple induction points, each of the induction points having corresponding position coordinate data and actual geomagnetic intensity data; The prediction model calculates the predicted geomagnetic intensity data and uncertainty of each candidate detection point in the candidate detection point set based on the induced point set.

[0011] As an optional implementation, in the first aspect of the present invention, obtaining the predicted geomagnetic intensity data and uncertainty corresponding to each detection point in the candidate detection point set based on the location coordinate data and actual geomagnetic intensity data corresponding to each induced point in the induced point set includes: For each detection point in the candidate detection point set: Based on the spatial location data of the detection point and each of the induced points in the set of induced points, the induced spatial similarity between the detection point and each induced point in the set of induced points is obtained. The induced spatial similarity is used to measure the proximity between the detection point and an induced point in the set of induced points. Based on the actual geomagnetic intensity data corresponding to each induced point in the set of induced points, the similarity of the detection point to the induced space corresponding to each induced point in the set of induced points, and the measurement error variance of the preset aeromagnetic module, the predicted geomagnetic intensity data corresponding to the detection point is obtained. The uncertainty corresponding to the detection point is obtained based on the similarity of the detection point to the induced space corresponding to each induced point in the set of induced points, the preset prior variance, and the preset measurement error variance of the aeromagnetic module.

[0012] As an optional implementation, in the first aspect of the present invention, selecting the target detection point of the aircraft in a preset candidate detection point set using a probability selector in a preset prediction model includes: For each detection point in the candidate detection point set: Based on the predicted geomagnetic intensity data corresponding to the detection point and the normal background field value data, the prediction deviation data corresponding to the detection point is obtained. The prediction deviation data is used to indicate the degree of deviation between the predicted geomagnetic intensity data corresponding to the detection point and the normal background field value data. Based on the prediction deviation data and the uncertainty corresponding to the detection point, the utility value corresponding to the detection point is obtained. The utility value is used to calculate the probability that each detection point in the candidate detection point set is selected by the probability selector. Based on the utility values ​​corresponding to all detection points in the candidate detection point set, the probability of selection for each detection point in the candidate detection point set is obtained; The probability selector selects the target detection point based on the selection probability corresponding to each detection point in the candidate detection point set.

[0013] As an optional implementation, in the first aspect of the invention, the utility value is calculated according to the following formula:

[0014] in, For testing points The corresponding utility value, For testing points The corresponding predicted geomagnetic intensity, The normal background field value of the area to be detected. For testing points The corresponding uncertainty, The preset balance coefficient is used to adjust the uncertainty. In the utility value Weights in; Furthermore, the selection probability of each detection point in the candidate detection point set is calculated according to the following formula:

[0015] in, For testing points The corresponding selection probability, X is the set of candidate detection points, and T is a preset temperature coefficient used to adjust the distribution of the selection probability of all detection points in the set of candidate detection points.

[0016] A second aspect of this invention discloses an intelligent airborne geomagnetic multi-parameter measurement device, the device comprising: The selection module is used to select a target detection point of the aircraft in a preset candidate detection point set by using a probability selector in a preset prediction model. The candidate detection point set is a set of all undetected detection points in the area to be detected. The prediction model is used to obtain the predicted geomagnetic intensity data and uncertainty corresponding to each detection point in the candidate detection point set. The uncertainty is used to measure the accuracy of the predicted geomagnetic intensity data. The acquisition module is used to control the aircraft to move to the target detection point, and to obtain the actual geomagnetic intensity data corresponding to the target detection point based on the initial multi-parameter set corresponding to the target detection point measured by the aeromagnetic module installed on the aircraft. The update module is used to update the predicted geomagnetic intensity data and uncertainty of each detection point adjacent to the target detection point in the candidate detection point set according to the actual geomagnetic intensity data corresponding to the target detection point; The probability module is used to obtain the selection probability of each detection point in the candidate detection point set based on the predicted geomagnetic intensity data and uncertainty of each detection point in the candidate detection point set. The selection probability is jointly determined by the degree of deviation between the predicted geomagnetic intensity data of the detection point and the normal background field value data of the area to be detected, as well as the uncertainty of the detection point. The selection module, the acquisition module, the update module, and the probability module repeatedly perform the above operations until the preset termination condition is met, thus completing the geomagnetic detection of the area to be detected.

[0017] As an optional implementation, in a second aspect of the present invention, the acquisition module, in the process of obtaining the actual geomagnetic intensity data corresponding to the target detection point, obtains the actual geomagnetic intensity data corresponding to the target detection point based on the initial multi-parameter set corresponding to the target detection point measured by the aeromagnetic module installed on the aircraft, in a specific operation method including: The initial multi-parameter set corresponding to the target detection point collected by the aeromagnetic module is read. Each data in the initial multi-parameter set is accompanied by a timestamp indicating the collection time of the data. The initial multi-parameter set includes initial geomagnetic intensity data, spatial position data, and attitude data. Create a multi-threaded program to pass all the data in the initial multi-parameter set into an asynchronous writer. The asynchronous writer is used to associate and integrate the initial geomagnetic intensity data, the spatial location data, and the attitude data to obtain the target multi-parameter set corresponding to the target detection point. Based on the target multi-parameter set, the actual geomagnetic intensity data corresponding to the target detection point is obtained.

[0018] As an optional implementation, in a second aspect of the invention, satisfying the preset termination condition includes: The first condition must be met, or the second and third conditions must be met simultaneously; in: The first condition includes: the estimated remaining flight time of the aircraft is lower than a safe time threshold; The second condition includes: obtaining multiple abnormal detection points from the candidate detection point set, wherein the deviation between the predicted geomagnetic intensity data of the abnormal detection points and the normal background field value data is greater than a preset abnormal threshold; and the uncertainty corresponding to each abnormal detection point is less than a preset accuracy threshold. The third condition includes: the ratio of the area of ​​the circumscribed convex hull formed by all the actually measured detection points to the total area of ​​the area to be detected is greater than a preset coverage threshold, wherein the circumscribed convex hull is the smallest convex polygon that contains all the actually measured detection points.

[0019] As an optional implementation, in a second aspect of the present invention, the specific operation method by which the updating module updates the predicted geomagnetic intensity data and uncertainty corresponding to each detection point adjacent to the target detection point in the candidate detection point set based on the actual geomagnetic intensity data corresponding to the target detection point includes: For each of the candidate detection point sets that is adjacent to the target detection point: Based on the spatial location data corresponding to the detection point and the target detection point, the target spatial similarity corresponding to the detection point is obtained. The target spatial similarity is used to measure the proximity between the detection point and the target detection point. Update the predicted geomagnetic intensity data corresponding to the detection point based on the target spatial similarity and the actual geomagnetic intensity data corresponding to the target detection point. The uncertainty corresponding to the detection point is updated based on the target spatial similarity and the preset measurement error variance of the aeromagnetic module.

[0020] As an optional implementation, in a second aspect of the present invention, the aeromagnetic module stores a preset set of induction points, the set of induction points consisting of multiple induction points, each of the induction points having corresponding position coordinate data and actual geomagnetic intensity data; The prediction model calculates the predicted geomagnetic intensity data and uncertainty of each candidate detection point in the candidate detection point set based on the induced point set.

[0021] As an optional implementation, in a second aspect of the present invention, the specific operation method by which the preset prediction model in the selection module obtains the predicted geomagnetic intensity data and uncertainty corresponding to each detection point in the candidate detection point set based on the location coordinate data and actual geomagnetic intensity data corresponding to each induced point in the induced point set includes: For each detection point in the candidate detection point set: Based on the spatial location data of the detection point and each of the induced points in the set of induced points, the induced spatial similarity between the detection point and each induced point in the set of induced points is obtained. The induced spatial similarity is used to measure the proximity between the detection point and an induced point in the set of induced points. Based on the actual geomagnetic intensity data corresponding to each induced point in the set of induced points, the similarity of the detection point to the induced space corresponding to each induced point in the set of induced points, and the measurement error variance of the preset aeromagnetic module, the predicted geomagnetic intensity data corresponding to the detection point is obtained. The uncertainty corresponding to the detection point is obtained based on the similarity of the detection point to the induced space corresponding to each induced point in the set of induced points, the preset prior variance, and the preset measurement error variance of the aeromagnetic module.

[0022] As an optional implementation, in a second aspect of the present invention, the selection module selects a specific operation method for choosing a target detection point of the aircraft in a preset candidate detection point set through a probability selector in a preset prediction model, including: For each detection point in the candidate detection point set: Based on the predicted geomagnetic intensity data corresponding to the detection point and the normal background field value data, the prediction deviation data corresponding to the detection point is obtained. The prediction deviation data is used to indicate the degree of deviation between the predicted geomagnetic intensity data corresponding to the detection point and the normal background field value data. Based on the prediction deviation data and the uncertainty corresponding to the detection point, the utility value corresponding to the detection point is obtained. The utility value is used to calculate the probability that each detection point in the candidate detection point set is selected by the probability selector. Based on the utility values ​​corresponding to all detection points in the candidate detection point set, the probability of selection for each detection point in the candidate detection point set is obtained; The probability selector selects the target detection point based on the selection probability corresponding to each detection point in the candidate detection point set.

[0023] As an optional implementation, in the second aspect of the present invention, the specific operation method for calculating the utility value and the selection probability corresponding to each detection point in the candidate detection point set in the selection module includes: The utility value is calculated according to the following formula:

[0024] in, For testing points The corresponding utility value, For testing points The corresponding predicted geomagnetic intensity, The normal background field value of the area to be detected. For testing points The corresponding uncertainty, The preset balance coefficient is used to adjust the uncertainty. In the utility value Weights in; Furthermore, the selection probability of each detection point in the candidate detection point set is calculated according to the following formula:

[0025] in, For testing points The corresponding selection probability, X is the set of candidate detection points, and T is a preset temperature coefficient used to adjust the distribution of the selection probability of all detection points in the set of candidate detection points.

[0026] The third aspect of the present invention discloses a computer storage medium storing computer instructions, which, when executed by a processor, implement the intelligent measurement method for multiple parameters of airborne geomagnetism as disclosed in the first aspect of the present invention.

[0027] Implementing this invention has the following beneficial effects: This invention selects the target detection point to be measured by the aircraft through a probability selector in the prediction model, performs multi-parameter measurements on the target detection point to obtain the corresponding actual geomagnetic intensity data, and updates the predicted geomagnetic intensity data and uncertainty of the adjacent undetected points based on the actual geomagnetic intensity data of the target detection point. Then, based on the predicted geomagnetic intensity data and uncertainty of all undetected points, the probability of each undetected point being selected by the probability selector is obtained. The process of "selection-acquisition-update-probability calculation" is executed cyclically until the preset termination condition is met.

[0028] This process involves the probability selector automatically directing measurement resources to unmeasured areas with high uncertainty or abnormal geomagnetic intensity in each iteration. This ensures that, while providing full coverage of the area to be detected, undetected points with potential geomagnetic anomalies continuously receive a high probability of being selected due to the neighbor point update mechanism. This naturally leads to dense measurements of geomagnetic anomaly areas during the measurement process, while avoiding unnecessary dense measurements in areas with normal geomagnetic intensity, thereby improving the reliability of the measurement data.

[0029] Furthermore, compared to methods that divide the area to be detected into regions and select different measurement densities, this invention can achieve dense measurement of geomagnetic anomaly areas without dividing the area to be detected into regions, reducing data redundancy caused by unreasonable region division, thereby improving the detection efficiency of the area to be detected. Attached Figure Description

[0030] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0031] Figure 1 This is a flowchart illustrating an intelligent measurement method for multiple parameters of airborne geomagnetism disclosed in an embodiment of the present invention. Figure 2 This is a schematic diagram of the structure of an intelligent airborne geomagnetic multi-parameter measurement device disclosed in an embodiment of the present invention; Figure 3 This is a schematic diagram of the structure of an intelligent airborne geomagnetic multi-parameter measurement system disclosed in an embodiment of the present invention. Detailed Implementation

[0032] To better understand and implement this invention, the technical solutions in the embodiments of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this invention, and not all of them. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention.

[0033] It should be noted that, unless otherwise expressly specified and limited, the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish different objects, not to describe a specific order. The terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0034] Example 1 In existing airborne geomagnetic surveying technologies, flight route planning generally adopts a uniform layout of survey lines, performing equal-density measurements with the same line and point spacing throughout the entire area to be measured. This embodiment further reveals that although this method can achieve full regional coverage, it has significant shortcomings: on the one hand, geomagnetic anomaly areas (such as mineralized zones, fault zones, etc.) require a high measurement density to capture the fine variations in the geomagnetic field, and the existing uniform survey line density is often insufficient to characterize the details of the anomalies; on the other hand, geomagnetically normal areas do not require such a high measurement density, and uniform measurement results in an unnecessary waste of flight time and data processing costs.

[0035] Based on the above findings, this invention discloses an intelligent method for multi-parameter measurement of airborne geomagnetism. Please refer to [link to relevant documentation]. Figure 1 The method may include: Step S1: Select the target detection point of the aircraft in the preset candidate detection point set through the probability selector in the preset prediction model.

[0036] Among them, the candidate detection point set is the set of all undetected detection points in the area to be detected. The prediction model is used to obtain the predicted geomagnetic intensity data and uncertainty for each detection point in the candidate detection point set. The uncertainty is used to measure the accuracy of the predicted geomagnetic intensity data. The prediction model can employ a sparse Gaussian process regression model, which uses a pre-defined set of induced points to sparsely approximate the covariance matrix, thereby reducing the inference computation load of the aircraft's onboard edge computing equipment. For each detection point in the candidate detection point set, the prediction model can output the predicted geomagnetic intensity and uncertainty corresponding to that detection point. Alternatively, other types of probabilistic prediction models, such as Kriging models or Bayesian neural network models, can also be used, as long as the prediction model can output the predicted geomagnetic intensity and uncertainty for each detection point in the candidate detection point set. Those skilled in the art can choose the appropriate model based on actual accuracy and onboard computing power limitations.

[0037] The candidate detection point set can be obtained by dividing the area to be detected into a dense grid. Specifically, the area to be detected is divided into multiple grid points according to a preset grid spacing, and each grid point constitutes a candidate detection point set. The candidate detection point set can also be generated by random sampling or adaptive point layout based on terrain features, as long as it can cover the area to be detected and meet the detection accuracy requirements. This embodiment does not impose any restrictions on this.

[0038] Uncertainty measures the accuracy of predicting the geomagnetic intensity corresponding to a detection point in the candidate detection point set. A higher value indicates lower accuracy, while a lower value indicates higher accuracy. Uncertainty can be obtained in various ways, such as by the dispersion of multiple predictions of the same detection point by the prediction model, the dispersion of multiple predicted geomagnetic intensity data obtained from multiple forward propagations of the detection point, or by the spatial distance between the measured detection points and the current detection point; a greater distance results in a higher uncertainty. Those skilled in the art can choose an appropriate implementation method based on the type of prediction model and accuracy requirements, and this embodiment does not impose any limitations on this.

[0039] Step S2: Control the aircraft to move to the target detection point, and obtain the actual geomagnetic intensity data corresponding to the target detection point based on the initial multi-parameter set corresponding to the target detection point measured by the aeromagnetic module set on the aircraft.

[0040] Each data point in the initial multi-parameter set can be accompanied by a timestamp indicating the acquisition time of the data. The initial multi-parameter set can include the initial geomagnetic intensity data, spatial location data, and attitude data corresponding to the target detection point.

[0041] The aeromagnetic module is a measurement module installed on the aircraft to collect geomagnetic intensity data and related auxiliary data during flight. Specifically, the aeromagnetic module includes: a measurement module, including a magnetometer, laser altimeter, tilt sensor, and positioning receiver, used to collect geomagnetic intensity data with timestamps, spatial position data, and aircraft attitude data, respectively; a storage module, used to store the collected data and model parameters; a communication module, used to enable data interaction between the aeromagnetic module and the ground station to help eliminate daily variation errors in the measured geomagnetic intensity data; and an asynchronous writer, used to asynchronously write the collected data to the communication log or application log in the storage module.

[0042] Step S3: Based on the actual geomagnetic intensity data corresponding to the target detection point, update the predicted geomagnetic intensity data and uncertainty of each detection point adjacent to the target detection point in the candidate detection point set.

[0043] Among them, adjacent detection points can refer to detection points whose spatial distance from the target detection point is less than a preset distance threshold, or detection points whose target spatial similarity calculated by the prediction model is greater than a preset adjacent similarity threshold.

[0044] Step S4: Based on the predicted geomagnetic intensity data and uncertainty corresponding to each detection point in the candidate detection point set, obtain the selection probability corresponding to each detection point in the candidate detection point set.

[0045] The probability of being selected is determined by the degree of deviation between the predicted geomagnetic intensity data corresponding to the detection point and the normal background field value data of the area to be detected, as well as the uncertainty corresponding to the detection point. In this context, the selection probability of each detection point in the candidate detection point set is the probability that the detection point is selected by the probability selector. Normal background field data can be theoretical geomagnetic field values ​​calculated using the International Geomagnetic Reference Field (IGRF) model based on the geographical location of the area to be detected; or it can be prior values ​​obtained from historical geomagnetic data of the area to be detected. Those skilled in the art can select and adjust these values ​​according to the actual data acquisition conditions, as long as they can represent the geomagnetic field reference level of the area to be detected when there are no local magnetic anomalies. This embodiment does not impose any limitations on this.

[0046] Step S5: Repeat steps S1 to S4 until the preset termination condition is met, and complete the geomagnetic detection of the area to be detected.

[0047] The termination condition is a pre-set condition used to instruct the aircraft to stop the geomagnetic detection mission in the area to be detected. This could be terminating the detection when the aircraft's remaining battery power falls below a preset battery threshold, when the aircraft's flight time exceeds a preset maximum single detection time, or when the amount of data collected by the aeromagnetic module in the aircraft exceeds the amount of data required for the detection mission. Those skilled in the art can choose an appropriate termination condition based on actual mission requirements; this embodiment does not impose any restrictions on this.

[0048] In an optional embodiment, the preset termination condition includes: The first condition must be met, or the second and third conditions must be met simultaneously; in: The first condition includes: the estimated remaining flight time of the aircraft is lower than the safe time threshold; The second condition includes: obtaining multiple abnormal detection points from the candidate detection point set; the deviation between the predicted geomagnetic intensity data of the abnormal detection points and the normal background field value data is greater than the preset abnormal threshold; and the uncertainty corresponding to each abnormal detection point is less than the preset accuracy threshold. The third condition includes: the ratio of the area of ​​the circumscribed convex hull formed by all the actual measured detection points to the total area of ​​the area to be detected is greater than the preset coverage threshold, and the circumscribed convex hull is the smallest convex polygon that contains all the actual measured detection points.

[0049] It should be noted that the estimated remaining flight time of the aircraft in this embodiment refers to the predicted time that the aircraft can continue to fly safely under the current flight status and environmental conditions; the safe time threshold is a preset time threshold used to determine whether the current aircraft can return safely.

[0050] As can be seen, in this embodiment of the invention, the probability selector in the prediction model selects the target detection point to be measured by the aircraft, performs multi-parameter measurement on the target detection point to obtain the corresponding actual geomagnetic intensity data, and updates the predicted geomagnetic intensity data and uncertainty of the adjacent undetected points according to the actual geomagnetic intensity data of the target detection point. Then, based on the predicted geomagnetic intensity data and uncertainty of all undetected points, the probability of each undetected point being selected by the probability selector is obtained. The process of "selection - acquisition - update - probability calculation" is executed cyclically until the preset termination condition is met.

[0051] This process involves the probability selector automatically directing measurement resources to unmeasured areas with high uncertainty or abnormal geomagnetic intensity in each iteration. This ensures that, while providing full coverage of the area to be detected, undetected points with potential geomagnetic anomalies continuously receive a high probability of being selected due to the neighbor point update mechanism. This naturally leads to dense measurements of geomagnetic anomaly areas during the measurement process, while avoiding unnecessary dense measurements in areas with normal geomagnetic intensity, thereby improving the reliability of the measurement data.

[0052] Furthermore, compared to methods that divide the area to be detected into regions and select different measurement densities, this invention can achieve dense measurement of geomagnetic anomaly areas without dividing the area to be detected into regions, reducing data redundancy caused by unreasonable region division, thereby improving the detection efficiency of the area to be detected.

[0053] However, this invention further reveals that the aeromagnetic module collects multiple parameters (such as initial geomagnetic intensity data, spatial position data, and flight attitude data) through multiple sensors with different sampling rates. The significant differences in the collection frequencies of these parameters make it difficult to accurately align the data in the time dimension. Simultaneously, the asynchronous writer has a limited receiving rate, requiring the collected multi-parameter data to be buffered before being aligned by the asynchronous writer and written to the communication log or application log. This process further exacerbates the time-synchronization problem of multi-source data, leading to potential issues such as data mismatch and poor timeliness when performing subsequent fusion analysis and model inference based on the multi-parameter set. Therefore, based on the above findings, this invention makes the following optimizations: In another optional embodiment, step S2 obtains the actual geomagnetic intensity data corresponding to the target detection point based on the initial multi-parameter set measured by the aeromagnetic module installed on the aircraft, including: Read the initial multi-parameter set corresponding to the target detection point collected by the aeromagnetic module.

[0054] Each data point in the initial multi-parameter set is accompanied by a timestamp indicating the time of data acquisition. The initial multi-parameter set includes initial geomagnetic intensity data, spatial location data, and attitude data.

[0055] The initial geomagnetic intensity data can be either the geomagnetic intensity value at the target detection point or the geomagnetic intensity component at the target detection point.

[0056] Spatial location data can be the latitude and longitude coordinates of the target detection point, or the latitude, longitude and elevation coordinates of the target detection point, or any form of location information based on a geographic coordinate system or a projected coordinate system. As long as it can uniquely identify the location of the target detection point in space, this embodiment does not impose any restrictions on it.

[0057] Attitude data can be the roll angle, pitch angle, and yaw angle of the aircraft, or it can be the quaternion attitude data of the aircraft, or it can be the attitude representation in the form of a rotation matrix or a direction cosine matrix. As long as it can identify the spatial attitude of the aircraft at the target detection point, this embodiment does not impose any restrictions on it.

[0058] Create multiple threads and pass all the data from the initial multi-parameter set to the asynchronous writer.

[0059] The asynchronous writer is used to associate and integrate initial geomagnetic intensity data with spatial location and attitude data to obtain a target multi-parameter set corresponding to the target detection point. The asynchronous writer is a data processing and persistence module used to associate and integrate the data in the initial target parameter set and then store it in the communication log or application log. Due to the limited processing capacity of the asynchronous writer, the collected initial multi-parameter set is temporarily stored in a cache queue. The asynchronous writer reads the data in the initial multi-parameter set sequentially from the cache and performs time alignment based on the timestamp corresponding to each data point. It associates the spatial location and attitude data corresponding to the initial geomagnetic data collected at the same time, and corrects the initial geomagnetic data based on the timestamp and corresponding attitude data. Finally, it associates the processed initial geomagnetic data, spatial location data, attitude data, and corresponding time to integrate them into the target multi-parameter set. After integration, the target multi-parameter set is written to the communication log or application log to achieve persistent data storage.

[0060] Based on the target multi-parameter set, the actual geomagnetic intensity data corresponding to the target detection point is obtained.

[0061] If the initial geomagnetic data in the target multi-parameter set corresponding to the target detection point is the geomagnetic intensity value at the target detection point, then the actual geomagnetic intensity data corresponding to the target detection point can be obtained directly. If the initial geomagnetic data is the geomagnetic intensity component at the target detection point, then the actual geomagnetic intensity component at the target detection point can be calculated based on the geomagnetic intensity component at the target detection point. The specific calculation process is as follows:

[0062] in, The actual geomagnetic intensity component at the target detection point. , , These represent the components of the Earth's magnetic field on the three orthogonal axes.

[0063] As can be seen, the embodiments of the present invention, through the cache queue mechanism of the asynchronous writer, ensure the alignment and integrity of multi-parameter data in the time dimension, providing a reliable data foundation for subsequent model inference and anomaly detection.

[0064] In yet another optional embodiment, step S3 includes: For each candidate detection point set, the target detection point is... Adjacent detection points : According to this testing point With target detection point The corresponding spatial location data are used to obtain the detection point. Corresponding target space similarity .

[0065] Among them, target space similarity Used to measure this detection point With target detection point The degree of proximity; Among them, target space similarity The specific calculation process is as follows:

[0066] It should be noted that, It is a natural exponential function. This is the square of the Euclidean distance between the detection point and the target detection point. The preset length scale is used to control the rate at which the spatial similarity of the target decreases with distance.

[0067] Based on target space similarity With target detection point Corresponding actual geomagnetic intensity data Update the detection point Corresponding predicted geomagnetic intensity data; Among them, updating the testing point The specific method for predicting geomagnetic intensity data is as follows:

[0068] It should be noted that, For point Previous predicted geomagnetic intensity data For point Updated predicted geomagnetic intensity data, For target detection points Its own prior variance is usually a constant (e.g., 1). This represents the pre-defined variance of the aeromagnetic system measurement error.

[0069] Based on target space similarity Measurement error variance compared to the preset aeromagnetic module Update the detection point The corresponding uncertainty.

[0070] Among them, updating the testing point The specific method for determining the corresponding uncertainty is as follows:

[0071] It should be noted that, For the updated uncertainty, The uncertainty before the update, The pre-defined variance of the aeromagnetic system measurement error. For target detection points Its own prior variance is usually a constant (e.g., 1).

[0072] In another optional embodiment, the aeromagnetic module stores a preset set of induction points, which consists of multiple induction points, each of which has a corresponding location coordinate data core and actual geomagnetic intensity data. The prediction model calculates the predicted geomagnetic intensity data and uncertainty for each candidate detection point in the candidate detection point set based on the set of induced points.

[0073] In another optional embodiment, based on the location coordinate data and actual geomagnetic intensity data corresponding to each induced point in the induced point set, the predicted geomagnetic intensity data and uncertainty corresponding to each detection point in the candidate detection point set are obtained, including: For each detection point in the candidate detection point set: According to this testing point The detection point is obtained by combining the spatial location data of each induced point in the induced point set. Similarity between the induced space and the induced space corresponding to each induced point in the set of induced points.

[0074] Induced spatial similarity is used to measure the similarity between the detected point and an induced point in the set of induced points. The degree of proximity; Among them, testing points With the induction point Induced spatial similarity between The specific calculation process is as follows:

[0075] It should be noted that, It is a natural exponential function. It is the square of the Euclidean distance between the detection point and the inducer point in the candidate detection point set. The preset length scale is used to control the rate at which the spatial similarity of the target decreases with distance.

[0076] Based on the actual geomagnetic intensity data corresponding to each induced point in the induced point set, the similarity of the detection point to the induced space corresponding to each induced point in the induced point set, and the measurement error variance of the preset aeromagnetic module... The predicted geomagnetic intensity data corresponding to this detection point is obtained. .

[0077] Among them, the detection point was obtained The specific method for predicting geomagnetic intensity data is as follows:

[0078] It should be noted that, For testing points The transpose of the spatial similarity vector (m-dimensional, vertical) between all induced points. Let be the covariance matrix between the induced points. It is the identity matrix. The inverse matrix of the induced point covariance matrix after adding systematic errors. The above calculation process aims to obtain the detection points in the candidate detection point set by weighted summation of the actual geomagnetic intensity data values ​​corresponding to all induced points, forming an m-dimensional, horizontal vector of actual geomagnetic intensity data values ​​for all induced points. The predicted geomagnetic intensity data, the weight of the actual geomagnetic intensity data of each induced point is determined by the candidate point. With the testing point Spatial similarity between The decision was made regarding the candidate site. With the testing point The closer the distance between them, the greater the weight, and vice versa.

[0079] According to this testing point Similarity to the induced space corresponding to each induced point in the set of induced points, and the preset prior variance. and the measurement error variance of the preset aeromagnetic module The detection point was obtained. Corresponding uncertainty .

[0080] Among them, the detection point was obtained Corresponding uncertainty The specific method is as follows:

[0081] It should be noted that, For this testing site Its prior variance is usually a constant (e.g., 1). For testing points The transpose of the spatial similarity vector (m-dimensional, vertically arranged) between all induced points. Let be the covariance matrix between the induced points. It is an identity matrix.

[0082] In another optional embodiment, a target detection point for the aircraft in a preset candidate detection point set is selected by a probability selector in a preset prediction model, including: For each detection point in the candidate detection point set: Based on the predicted geomagnetic intensity data and the normal background field value data corresponding to the detection point, the prediction deviation data corresponding to the detection point is obtained.

[0083] Among them, the prediction deviation data is used to represent the degree of deviation between the predicted geomagnetic intensity data and the normal background field value data corresponding to the detection point.

[0084] Based on the predicted deviation data and the uncertainty corresponding to the detection point, the utility value corresponding to the detection point is obtained.

[0085] The utility value is used to calculate the probability that each detection point in the candidate detection point set is selected by the probability selector; Based on the utility values ​​corresponding to all detection points in the candidate detection point set, the probability of selection for each detection point in the candidate detection point set is obtained; The probability selector selects the target detection point based on the selection probability of each detection point in the candidate detection point set.

[0086] In yet another optional embodiment, the utility value is calculated according to the following formula:

[0087] in, For testing points The corresponding utility value, For testing points The corresponding predicted geomagnetic intensity, This represents the normal background field value of the area to be detected. For testing points The corresponding uncertainty, These are preset balance coefficients used to adjust the uncertainty. In utility value Weights in; Furthermore, the probability of selection for each detection point in the candidate detection point set is calculated according to the following formula:

[0088] in, For testing points The corresponding selection probability, X is the set of candidate detection points, and T is a preset temperature coefficient used to adjust the distribution of the selection probability of all detection points in the candidate detection point set.

[0089] Example 2 This invention discloses an intelligent airborne geomagnetic multi-parameter measurement device, such as... Figure 2 As shown, the device includes: The selection module is used to select the target detection point of the aircraft from the preset candidate detection point set by using the probability selector in the preset prediction model.

[0090] The candidate detection point set is the set of all undetected detection points in the area to be detected. The prediction model is used to obtain the predicted geomagnetic intensity data and uncertainty for each detection point in the candidate detection point set. The uncertainty is used to measure the accuracy of the predicted geomagnetic intensity data.

[0091] The prediction model employs a sparse Gaussian process regression model, which uses a pre-defined set of induced points to sparsely approximate the covariance matrix, thereby reducing the inference computation load of the aircraft's onboard edge computing equipment. For each detection point in the candidate detection point set, the prediction model can output the predicted geomagnetic intensity and uncertainty corresponding to that detection point.

[0092] The candidate detection point set can be obtained by dividing the area to be detected into a dense grid. Specifically, the area to be detected is divided into multiple grid points according to a preset grid spacing, and each grid point constitutes the candidate detection point set.

[0093] Uncertainty measures the accuracy of the prediction of the geomagnetic intensity corresponding to a detection point in the candidate detection point set. The larger the value, the lower the accuracy of the prediction of the geomagnetic intensity corresponding to that detection point, and the smaller the value, the higher the accuracy of the prediction of the geomagnetic intensity corresponding to that detection point.

[0094] The data acquisition module is used to control the aircraft to move to the target detection point. Based on the initial multi-parameter set corresponding to the target detection point measured by the aeromagnetic module set on the aircraft, the actual geomagnetic intensity data corresponding to the target detection point is obtained.

[0095] Each data point in the initial multi-parameter set is accompanied by a timestamp indicating the acquisition time of the data. The initial multi-parameter set includes the initial geomagnetic intensity data, spatial location data, and attitude data corresponding to the target detection point.

[0096] The aeromagnetic module is a measurement module installed on the aircraft to collect geomagnetic intensity data and related auxiliary data during flight. Specifically, the aeromagnetic module includes: multiple sensors for collecting geomagnetic intensity data with timestamps, spatial position data, and aircraft attitude data; a storage module for storing the collected data and model parameters; a communication module for enabling data exchange between the aeromagnetic module and the ground station to help eliminate daily variation errors in the measured geomagnetic intensity data; and an asynchronous writer for asynchronously writing the collected data to the communication log or application log in the storage module.

[0097] The update module is used to update the predicted geomagnetic intensity data and uncertainty of each detection point adjacent to the target detection point in the candidate detection point set, based on the actual geomagnetic intensity data corresponding to the target detection point.

[0098] Among them, adjacent detection points can refer to detection points whose spatial distance from the target detection point is less than a preset distance threshold, or detection points whose target spatial similarity calculated by the prediction model is greater than a preset adjacent similarity threshold.

[0099] The probability module is used to obtain the selection probability of each detection point in the candidate detection point set based on the predicted geomagnetic intensity data and uncertainty corresponding to each detection point in the candidate detection point set.

[0100] The probability of being selected is determined by the degree of deviation between the predicted geomagnetic intensity data corresponding to the detection point and the normal background field value data of the area to be detected, as well as the uncertainty corresponding to the detection point.

[0101] The selection probability of each detection point in the candidate detection point set is the probability that the detection point is selected by the probability selector. Normal background field data represents the geomagnetic field reference level of the area to be detected when there are no local magnetic anomalies.

[0102] The above operations are repeated for the selection module, acquisition module, update module, and probability module until the preset termination condition is met, thus completing the geomagnetic detection of the area to be detected.

[0103] In an optional embodiment, during the process of obtaining the actual geomagnetic intensity data corresponding to the target detection point, the acquisition module obtains the specific operation method of the actual geomagnetic intensity data corresponding to the target detection point based on the initial multi-parameter set corresponding to the target detection point measured by the aeromagnetic module installed on the aircraft, including: Read the initial multi-parameter set corresponding to the target detection point collected by the aeromagnetic module.

[0104] Each data point in the initial multi-parameter set is accompanied by a timestamp indicating the time of data acquisition. The initial multi-parameter set includes initial geomagnetic intensity data, spatial location data, and attitude data.

[0105] The initial geomagnetic intensity data can be either the geomagnetic intensity value at the target detection point or the geomagnetic intensity component at the target detection point.

[0106] Spatial location data consists of the latitude and longitude coordinates of the target detection point.

[0107] Attitude data include the aircraft's roll angle, pitch angle, and yaw angle.

[0108] A multi-threaded process is created to pass all data from the initial multi-parameter set into an asynchronous writer. The asynchronous writer integrates the initial geomagnetic intensity data with spatial location and attitude data to obtain the target multi-parameter set corresponding to the target detection point. The asynchronous writer is a data processing and persistence module, used to process and integrate the data in the initial target parameter set before storing it in the communication log or application log. Due to the limited processing capacity of the asynchronous writer, the collected initial multi-parameter set is temporarily stored in a cache queue. The asynchronous writer reads the data from the initial multi-parameter set sequentially from the cache, aligns it according to the timestamp of each data point, associates the spatial location and attitude data corresponding to the initial geomagnetic data collected at the same time, and corrects the initial geomagnetic data based on the timestamp and corresponding attitude data. Finally, the processed initial geomagnetic data, spatial location data, attitude data, and corresponding time are associated and integrated into the target multi-parameter set. After integration, the target multi-parameter set is written to the communication log or application log, achieving persistent data storage.

[0109] Based on the target multi-parameter set, the actual geomagnetic intensity data corresponding to the target detection point is obtained.

[0110] If the initial geomagnetic data in the target multi-parameter set corresponding to the target detection point is the geomagnetic intensity value at the target detection point, then the actual geomagnetic intensity data corresponding to the target detection point can be obtained directly. If the initial geomagnetic data is the geomagnetic intensity component at the target detection point, then the actual geomagnetic intensity component at the target detection point can be calculated based on the geomagnetic intensity component at the target detection point. The specific calculation process is as follows:

[0111] in, The actual geomagnetic intensity component at the target detection point. , , These represent the components of the Earth's magnetic field on the three orthogonal axes.

[0112] In another alternative embodiment, a preset termination condition is met, including: The first condition must be met, or the second and third conditions must be met simultaneously; in: The first condition includes: the estimated remaining flight time of the aircraft is lower than the safe time threshold; The second condition includes: obtaining multiple abnormal detection points from the candidate detection point set; the deviation between the predicted geomagnetic intensity data of the abnormal detection points and the normal background field value data is greater than the preset abnormal threshold; and the uncertainty corresponding to each abnormal detection point is less than the preset accuracy threshold. The third condition includes: the ratio of the area of ​​the circumscribed convex hull formed by all the actual measured detection points to the total area of ​​the area to be detected is greater than the preset coverage threshold, and the circumscribed convex hull is the smallest convex polygon that contains all the actual measured detection points.

[0113] In another optional embodiment, the updating module updates the predicted geomagnetic intensity data and uncertainty of each detection point adjacent to the target detection point in the candidate detection point set based on the actual geomagnetic intensity data corresponding to the target detection point, including: For each candidate detection point set containing detection points adjacent to the target detection point: Based on the spatial location data corresponding to the detection point and the target detection point, the target spatial similarity corresponding to the detection point is obtained. The target spatial similarity is used to measure the proximity between the detection point and the target detection point.

[0114] Based on the target spatial similarity and the actual geomagnetic intensity data corresponding to the target detection point, update the predicted geomagnetic intensity data corresponding to that detection point.

[0115] The uncertainty corresponding to the detection point is updated based on the similarity of the target space and the pre-set measurement error variance of the aeromagnetic module.

[0116] In another optional embodiment, the aeromagnetic module stores a preset set of induction points, which consists of multiple induction points, each of which has specific operation methods for corresponding position coordinate data and actual geomagnetic intensity data. The prediction model calculates the predicted geomagnetic intensity data and uncertainty for each candidate detection point in the candidate detection point set based on the set of induced points.

[0117] In another optional embodiment, the preset prediction model in the selection module obtains the predicted geomagnetic intensity data and uncertainty for each detection point in the candidate detection point set based on the location coordinate data and actual geomagnetic intensity data corresponding to each induced point in the induced point set, including: For each detection point in the candidate detection point set: Based on the spatial location data of the detection point and each induced point in the set of induced points, the induced spatial similarity between the detection point and each induced point in the set of induced points is obtained.

[0118] Among them, the induced spatial similarity is used to measure the proximity of the detection point to an induced point in the set of induced points.

[0119] Based on the actual geomagnetic intensity data corresponding to each induced point in the induced point set, the similarity of the detection point to the induced space corresponding to each induced point in the induced point set, and the measurement error variance of the preset aeromagnetic module, the predicted geomagnetic intensity data corresponding to the detection point is obtained.

[0120] The uncertainty corresponding to the detection point is obtained based on the similarity of the induced space between the detection point and each induced point in the set of induced points, the preset prior variance, and the preset measurement error variance of the aeromagnetic module.

[0121] In another optional embodiment, the selection module selects the specific operation method of the aircraft in the preset candidate detection point set through a probability selector in the preset prediction model, including: For each detection point in the candidate detection point set: Based on the predicted geomagnetic intensity data and the normal background field value data corresponding to the detection point, the prediction deviation data corresponding to the detection point is obtained.

[0122] Among them, the prediction deviation data is used to represent the degree of deviation between the predicted geomagnetic intensity data and the normal background field value data corresponding to the detection point.

[0123] Based on the predicted deviation data and the uncertainty corresponding to the detection point, the utility value corresponding to the detection point is obtained.

[0124] The utility value is used to calculate the probability that each detection point in the candidate detection point set will be selected by the probability selector.

[0125] Based on the utility values ​​corresponding to all detection points in the candidate detection point set, the probability of selection for each detection point in the candidate detection point set is obtained.

[0126] The probability selector selects the target detection point based on the selection probability of each detection point in the candidate detection point set.

[0127] In yet another optional embodiment, the specific operation of the selection module in calculating the utility value and the probability of selection for each detection point in the candidate detection point set includes: The utility value is calculated using the following formula:

[0128] in, For testing points The corresponding utility value, For testing points The corresponding predicted geomagnetic intensity, This represents the normal background field value of the area to be detected. For testing points The corresponding uncertainty, These are preset balance coefficients used to adjust the uncertainty. In utility value The weights in the equation.

[0129] Furthermore, the probability of selection for each detection point in the candidate detection point set is calculated according to the following formula:

[0130] in, For testing points The corresponding selection probability, X is the set of candidate detection points, and T is a preset temperature coefficient used to adjust the distribution of the selection probability of all detection points in the candidate detection point set.

[0131] Example 3 Please see Figure 3 , Figure 3 This is a schematic diagram of the structure of an intelligent airborne geomagnetic multi-parameter measurement system disclosed in an embodiment of the present invention. Figure 3 As shown, the device including memory and processor may include: Memory containing executable program code; A processor coupled to memory; The processor calls the executable program code stored in the memory to execute some or all of the steps in any of the intelligent measurement methods based on multiple parameters of airborne geomagnetism in Embodiment 1 of the present invention.

[0132] Example 4 This invention discloses a computer storage medium storing computer instructions. When these computer instructions are executed by a processor, they implement some or all of the steps in any of the intelligent airborne geomagnetic multi-parameter measurement methods in Embodiment 1 of this invention.

[0133] The device embodiments described above are merely illustrative. The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical modules; that is, they may be located in one place or distributed across multiple network modules. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0134] Through the detailed description of the above embodiments, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, including read-only memory (ROM), random access memory (RAM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), one-time programmable read-only memory (OTPROM), electrically-Erasable Programmable Read-Only Memory (EEPROM), compact disc read-only memory (CD-ROM) or other optical disc storage, disk storage, magnetic tape storage, or any other computer-readable medium that can be used to carry or store data.

[0135] The present invention has provided a detailed description of an intelligent measurement method and device for multiple parameters of airborne geomagnetism disclosed in the embodiments of the present invention. Specific embodiments have been used to illustrate the principles and implementation of the present invention. However, the above preferred embodiments are not intended to limit the present invention. The description of the above embodiments is only for the purpose of helping to understand the method and core ideas of the present invention. At the same time, for those skilled in the art, based on the ideas of the present invention, there will be changes in the specific implementation and application scope without departing from the spirit and scope of the present invention. Therefore, the scope of protection of the present invention shall be determined by the scope defined in the claims.

Claims

1. A method for intelligent measurement of multiple parameters of airborne geomagnetism, characterized in that, The method includes: S1. Using the probability selector in the preset prediction model, select the target detection point of the aircraft in the preset candidate detection point set. The candidate detection point set is the set of all undetected detection points in the area to be detected. The prediction model is used to obtain the predicted geomagnetic intensity data and uncertainty corresponding to each detection point in the candidate detection point set. The uncertainty is used to measure the accuracy of the predicted geomagnetic intensity data. S2. Control the aircraft to move to the target detection point, and obtain the actual geomagnetic intensity data corresponding to the target detection point based on the initial multi-parameter set corresponding to the target detection point measured by the aeromagnetic module installed on the aircraft. S3. Based on the actual geomagnetic intensity data corresponding to the target detection point, update the predicted geomagnetic intensity data and uncertainty of each detection point adjacent to the target detection point in the candidate detection point set; S4. Based on the predicted geomagnetic intensity data and uncertainty corresponding to each detection point in the candidate detection point set, the selection probability corresponding to each detection point in the candidate detection point set is obtained. The selection probability is jointly determined by the degree of deviation between the predicted geomagnetic intensity data corresponding to the detection point and the normal background field value data of the area to be detected, as well as the uncertainty corresponding to the detection point. S5. Repeat steps S1 to S4 until the preset termination condition is met, and complete the geomagnetic detection of the area to be detected.

2. The intelligent measurement method for multiple parameters of airborne geomagnetism according to claim 1, characterized in that, The process of obtaining the actual geomagnetic intensity data corresponding to the target detection point based on the initial multi-parameter set measured by the aeromagnetic module installed on the aircraft includes: The initial multi-parameter set corresponding to the target detection point collected by the aeromagnetic module is read. Each data in the initial multi-parameter set is accompanied by a timestamp indicating the collection time of the data. The initial multi-parameter set includes initial geomagnetic intensity data, spatial position data, and attitude data. Create a multi-threaded program to pass all the data in the initial multi-parameter set into an asynchronous writer. The asynchronous writer is used to associate and integrate the initial geomagnetic intensity data, the spatial location data, and the attitude data to obtain the target multi-parameter set corresponding to the target detection point. Based on the target multi-parameter set, the actual geomagnetic intensity data corresponding to the target detection point is obtained.

3. The intelligent measurement method for multiple parameters of airborne geomagnetism according to claim 1, characterized in that, The preset termination conditions include: The first condition must be met, or the second and third conditions must be met simultaneously; in: The first condition includes: the estimated remaining flight time of the aircraft is lower than a safe time threshold; The second condition includes: obtaining multiple abnormal detection points from the candidate detection point set, wherein the deviation between the predicted geomagnetic intensity data of the abnormal detection points and the normal background field value data is greater than a preset abnormal threshold; and the uncertainty corresponding to each abnormal detection point is less than a preset accuracy threshold. The third condition includes: the ratio of the area of ​​the circumscribed convex hull formed by all the actually measured detection points to the total area of ​​the area to be detected is greater than a preset coverage threshold, wherein the circumscribed convex hull is the smallest convex polygon that contains all the actually measured detection points.

4. The intelligent measurement method for multiple parameters of airborne geomagnetism according to claim 1, characterized in that, Step S3 includes: For each of the candidate detection point sets that is adjacent to the target detection point: Based on the spatial location data corresponding to the detection point and the target detection point, the target spatial similarity corresponding to the detection point is obtained. The target spatial similarity is used to measure the proximity between the detection point and the target detection point. Update the predicted geomagnetic intensity data corresponding to the detection point based on the target spatial similarity and the actual geomagnetic intensity data corresponding to the target detection point. The uncertainty corresponding to the detection point is updated based on the target spatial similarity and the preset measurement error variance of the aeromagnetic module.

5. The intelligent measurement method for multiple parameters of airborne geomagnetism according to claim 1, characterized in that, The aeromagnetic module stores a preset set of induction points, which consists of multiple induction points, each of which has corresponding location coordinate data and actual geomagnetic intensity data. The prediction model calculates the predicted geomagnetic intensity data and uncertainty of each candidate detection point in the candidate detection point set based on the induced point set.

6. The intelligent measurement method for multiple parameters of airborne geomagnetism according to claim 5, characterized in that, The step of obtaining the predicted geomagnetic intensity data and uncertainty for each detection point in the candidate detection point set based on the location coordinate data and actual geomagnetic intensity data corresponding to each induced point in the induced point set includes: For each detection point in the candidate detection point set: Based on the spatial location data of the detection point and each of the induced points in the set of induced points, the induced spatial similarity between the detection point and each induced point in the set of induced points is obtained. The induced spatial similarity is used to measure the proximity between the detection point and an induced point in the set of induced points. Based on the actual geomagnetic intensity data corresponding to each induced point in the set of induced points, the similarity of the detection point to the induced space corresponding to each induced point in the set of induced points, and the measurement error variance of the preset aeromagnetic module, the predicted geomagnetic intensity data corresponding to the detection point is obtained. The uncertainty corresponding to the detection point is obtained based on the similarity of the detection point to the induced space corresponding to each induced point in the set of induced points, the preset prior variance, and the preset measurement error variance of the aeromagnetic module.

7. The intelligent measurement method for multiple parameters of airborne geomagnetism according to claim 1, characterized in that, The step of selecting a target detection point for the aircraft from a preset set of candidate detection points using a probability selector in a preset prediction model includes: For each detection point in the candidate detection point set: Based on the predicted geomagnetic intensity data corresponding to the detection point and the normal background field value data, the prediction deviation data corresponding to the detection point is obtained. The prediction deviation data is used to indicate the degree of deviation between the predicted geomagnetic intensity data corresponding to the detection point and the normal background field value data. Based on the prediction deviation data and the uncertainty corresponding to the detection point, the utility value corresponding to the detection point is obtained. The utility value is used to calculate the probability that each detection point in the candidate detection point set is selected by the probability selector. Based on the utility values ​​corresponding to all detection points in the candidate detection point set, the probability of selection for each detection point in the candidate detection point set is obtained; The probability selector selects the target detection point based on the selection probability of each detection point in the candidate detection point set.

8. The intelligent measurement method for multiple parameters of airborne geomagnetism according to claim 7, characterized in that, The utility value is calculated according to the following formula: in, For testing points The corresponding utility value, For testing points The corresponding predicted geomagnetic intensity, This represents the normal background field value of the area to be detected. For testing points The corresponding uncertainty, The preset balance coefficient is used to adjust the uncertainty. In the utility value Weights in; Furthermore, the selection probability of each detection point in the candidate detection point set is calculated according to the following formula: in, For testing points The corresponding selection probability, X is the set of candidate detection points, and T is a preset temperature coefficient used to adjust the distribution of the selection probability of all detection points in the set of candidate detection points.

9. An intelligent airborne geomagnetic multi-parameter measurement device, characterized in that, The device includes: The selection module is used to select a target detection point of the aircraft in a preset candidate detection point set by using a probability selector in a preset prediction model. The candidate detection point set is a set of all undetected detection points in the area to be detected. The prediction model is used to obtain the predicted geomagnetic intensity data and uncertainty corresponding to each detection point in the candidate detection point set. The uncertainty is used to measure the accuracy of the predicted geomagnetic intensity data. The acquisition module is used to control the aircraft to move to the target detection point, and obtain the actual geomagnetic intensity data corresponding to the target detection point based on the initial multi-parameter set measured by the aeromagnetic module installed on the aircraft. The update module is used to update the predicted geomagnetic intensity data and uncertainty of each detection point in the candidate detection point set that is adjacent to the target detection point, based on the actual geomagnetic intensity data corresponding to the target detection point. The probability module is used to obtain the selection probability of each detection point in the candidate detection point set based on the predicted geomagnetic intensity data and uncertainty of each detection point in the candidate detection point set. The selection probability is jointly determined by the degree of deviation between the predicted geomagnetic intensity data of the detection point and the normal background field value data of the area to be detected, as well as the uncertainty of the detection point. The selection module, the acquisition module, the update module, and the probability module repeatedly perform the above operations until the preset termination condition is met, thus completing the geomagnetic detection of the area to be detected.

10. A computer storage medium, characterized in that, The computer storage medium stores computer instructions, which, when executed by a processor, implement the intelligent measurement method for multiple parameters of airborne geomagnetism as described in any one of claims 1-8.

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