Aerial magnetic survey compensation method, system and device based on magnetic sensing chip network

Abstract

The invention discloses an aeromagnetic measurement compensation method, system and device based on a magnetic sensing chip network, and belongs to the technical field of aeromagnetic compensation and aeromagnetic measurement. The method comprises the following steps: acquiring flight attitude data of an aircraft and geomagnetic field observation values acquired by all magnetic sensing chip nodes in real time in a detection flight process of the aircraft; inputting the flight attitude data into a three-dimensional interference field prediction module of a pre-trained aviation magnetic measurement compensation model, and outputting a magnetic interference prediction value at each magnetic sensing chip node under the current flight attitude; inputting the geomagnetic field observation value and the magnetic interference prediction value at each magnetic sensing chip node into a real-time compensation calculation module of an aviation magnetic survey compensation model, and outputting a geomagnetic field detection result obtained by current detection of the aircraft; and the real-time compensation calculation module is used for determining a geomagnetic field detection result according to the geomagnetic field observation values and the magnetic interference prediction values at all the magnetic sensing chip nodes and the weight coefficient corresponding to each magnetic sensing chip node.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of airborne magnetic compensation and airborne magnetic surveying technology, and in particular to an airborne magnetic survey compensation method, system and device based on a magnetic sensor chip network. BACKGROUND

[0002] In today's growing attention to airborne geophysical exploration, high-precision airborne magnetic survey technology has become an important support for identifying underground mineral resources, mapping geological structures and detecting underwater / underground magnetic targets. Airborne magnetic survey is a geophysical exploration method that measures the geomagnetic field strength and gradient by mounting a magnetometer system on an aircraft.

[0003] Current airborne magnetic survey schemes typically use a single magnetometer (such as a fluxgate magnetometer or a proton magnetometer) installed on the tail or wingtip of an aircraft to measure geomagnetic field anomalies, enabling wide-range, rapid scanning and effective coverage of a wide area. Compared to ground magnetic survey, airborne magnetic survey has a clear advantage in precision, with positioning errors controlled at the meter level, and has the characteristics of fast response, wide coverage and no interference from terrain. However, airborne magnetic survey also has limitations, as it is susceptible to aircraft magnetic interference (such as electromagnetic fields generated by engines and electronic equipment), which can degrade detection accuracy. With increasing global demand for resource exploration precision, there is an urgent need for a solution that can effectively suppress aircraft magnetic interference and extract weak magnetic anomaly signals. Airborne magnetic compensation is a key technology for eliminating aircraft magnetic field interference in airborne magnetic survey.

[0004] Current airborne magnetic compensation schemes rely on single magnetometer point measurements and linear models, which have poor adaptability to nonlinear or spatially distributed interference, leading to error accumulation during dynamic flight and low compensation accuracy. To achieve high precision, aircraft often require structural modifications such as the addition of tail rods or pod hardware modifications to physically isolate and reduce interference, which results in high deployment complexity for current airborne magnetic compensation schemes and increases air resistance, flight risk and cost. Therefore, it is particularly necessary to provide a more optimal airborne magnetic compensation scheme. SUMMARY

[0005] The present application provides an airborne magnetic survey compensation method, system and device based on a magnetic sensor chip network, which can solve the problem of high deployment complexity and poor compensation effect of current airborne magnetic compensation schemes.

[0006] To solve the above technical problems, the present application is implemented as follows: In a first aspect, embodiments of this application provide an airborne magnetic survey compensation method based on a magnetic sensor chip network, comprising: acquiring, in real time, the flight attitude data of the aircraft and geomagnetic field observations collected by all magnetic sensor chip nodes during the aircraft's reconnaissance flight; wherein, a magnetic sensor chip network is deployed on the aircraft, the magnetic sensor chip network being composed of magnetic sensor chip nodes deployed at target locations inside and on the surface of the aircraft; inputting the flight attitude data into a three-dimensional interference field prediction module of a pre-trained airborne magnetic survey compensation model, and outputting the magnetic interference prediction at each of the magnetic sensor chip nodes under the current flight attitude. The measured values ​​are input into the real-time compensation calculation module of the airborne magnetic measurement compensation model, along with the observed geomagnetic field values ​​and the predicted magnetic interference values ​​at each of the magnetic sensor chip nodes. The real-time compensation calculation module then outputs the geomagnetic field detection results obtained by the aircraft. Specifically, the real-time compensation calculation module determines the estimated geomagnetic field value at each magnetic sensor chip node based on the observed geomagnetic field values ​​and the predicted magnetic interference values. It then determines the geomagnetic field detection results based on the estimated geomagnetic field values ​​at all magnetic sensor chip nodes and the weighting coefficients corresponding to each magnetic sensor chip node. Secondly, embodiments of this application provide an airborne magnetic measurement compensation system based on a magnetic sensor chip network, including a magnetic sensor chip network, an attitude measurement unit, a data acquisition and processing unit, and a compensation calculation unit. The magnetic sensor chip network consists of magnetic sensor chip nodes deployed at target locations inside and on the surface of the aircraft. The magnetic sensor chip network is used to acquire geomagnetic field observation values ​​through each magnetic sensor chip node. The attitude measurement unit is used to measure the flight attitude data of the aircraft. The data acquisition and processing unit is used to acquire the flight attitude data and the geomagnetic field observation values ​​acquired by all magnetic sensor chip nodes in real time during the aircraft's detection flight, and to send the flight attitude data and the geomagnetic field observation values ​​acquired by all magnetic sensor chip nodes to the compensation calculation unit. The compensation... The calculation unit is used to input the flight attitude data into the three-dimensional interference field prediction module of the pre-trained airborne magnetic measurement compensation model, and output the magnetic interference prediction value at each of the magnetic sensor chip nodes under the current flight attitude; input the geomagnetic field observation value and the magnetic interference prediction value at each of the magnetic sensor chip nodes into the real-time compensation calculation module of the airborne magnetic measurement compensation model, and output the geomagnetic field detection result currently obtained by the aircraft; wherein, the real-time compensation calculation module is used to determine the geomagnetic field estimate value at each of the magnetic sensor chip nodes based on the geomagnetic field observation value and the magnetic interference prediction value at the magnetic sensor chip node, and determine the geomagnetic field detection result based on the geomagnetic field estimate values ​​at all magnetic sensor chip nodes and the weight coefficient corresponding to each magnetic sensor chip node.

[0007] Thirdly, embodiments of this application provide an airborne magnetic measurement compensation device based on a magnetic sensor chip network, comprising: a first acquisition module, used to acquire in real time the flight attitude data of the aircraft and the geomagnetic field observation values ​​collected by all magnetic sensor chip nodes during the aircraft's detection flight; wherein, a magnetic sensor chip network is deployed on the aircraft, and the magnetic sensor chip network is composed of magnetic sensor chip nodes deployed at target locations inside and on the surface of the aircraft; and a first model processing module, used to input the flight attitude data into a three-dimensional interference field prediction module of a pre-trained airborne magnetic measurement compensation model, and output the data at each of the magnetic sensor chip nodes under the current flight attitude. The first module is a magnetic interference prediction value; the second module is a model processing module, used to input the observed geomagnetic field value and the predicted magnetic interference value at each of the magnetic sensor chip nodes into the real-time compensation calculation module of the airborne magnetic measurement compensation model, and output the geomagnetic field detection result currently obtained by the aircraft; wherein, the real-time compensation calculation module is used to determine the geomagnetic field estimate value at each of the magnetic sensor chip nodes based on the observed geomagnetic field value and the predicted magnetic interference value at the magnetic sensor chip node, and to determine the geomagnetic field detection result based on the geomagnetic field estimate values ​​at all magnetic sensor chip nodes and the weight coefficient corresponding to each of the magnetic sensor chip nodes.

[0008] Fourthly, embodiments of this application provide an electronic device including a processor; and a memory arranged to store computer-executable instructions configured to be executed by the processor to implement the steps of the aero-magnetic measurement compensation method based on a magnetic sensor chip network as described in the first aspect.

[0009] Fifthly, embodiments of this application provide a computer-readable storage medium for storing computer-executable instructions, which, when executed by a processor, implement the steps of the aerospace magnetic measurement compensation method based on a magnetic sensor chip network as described in the first aspect.

[0010] In a sixth aspect, embodiments of this application provide a computer program product, which includes a computer program that, when executed by a processor, implements the steps of the aerospace magnetic measurement compensation method based on a magnetic sensor chip network as described in the first aspect.

[0011] In this embodiment, during the aircraft's detection flight, flight attitude data and geomagnetic field observations collected by all magnetic sensor chip nodes are acquired in real time. The aircraft is equipped with a magnetic sensor chip network, consisting of magnetic sensor chip nodes deployed at target locations inside and on the surface of the aircraft. Flight attitude data is input into the three-dimensional interference field prediction module of a pre-trained airborne magnetic survey compensation model to output the predicted magnetic interference value at each magnetic sensor chip node under the current flight attitude. The observed geomagnetic field value and the predicted magnetic interference value at each magnetic sensor chip node are then input into the real-time compensation calculation module of the airborne magnetic survey compensation model to output the geomagnetic field detection result obtained by the aircraft. The real-time compensation calculation module determines the estimated geomagnetic field value at each magnetic sensor chip node based on the observed and predicted geomagnetic field values, and then determines the geomagnetic field detection result based on the estimated geomagnetic field values ​​at all magnetic sensor chip nodes and the weight coefficients corresponding to each magnetic sensor chip node. By deploying a dense network of multiple magnetic sensor chip nodes at key locations inside and on the surface of the aircraft, and pre-training an airborne magnetic measurement compensation model, three-dimensional field perception of magnetic interference generated by the aircraft can be achieved. This allows for the individual cancellation of local magnetic field distortions caused by interference sources at different parts of the aircraft, yielding geomagnetic field estimates. Furthermore, by weighted fusion of the geomagnetic field estimates from all magnetic sensor chip nodes, the final geomagnetic field detection result is obtained. This elevates airborne magnetic compensation from "point" compensation to "field" compensation, achieving higher compensation accuracy compared to "point" compensation schemes. Specifically, this technology possesses the ability to suppress the failure of individual magnetic sensor chip nodes or avoid the influence of transient noise. It exhibits inherent robustness to continuous changes in flight attitude and the non-stationary characteristics of interference sources. The compensation effect is not significantly deteriorated by aircraft maneuvers, maintaining stable high performance and geomagnetic field detection accuracy in complex real-world flight environments, thus realizing a more reliable airborne magnetic compensation scheme. Furthermore, the deployment of magnetic sensor chip nodes does not alter the aerodynamic shape of the aircraft. This allows the technology to achieve high-precision geomagnetic field detection capabilities comparable to current external hardware compensation schemes without requiring any external modifications to the aircraft. This significantly reduces the complexity, cost, and safety risks of deploying aeromagnetic compensation schemes, enabling high-precision airborne magnetic surveying technology to be more flexibly applied to various small and medium-sized aircraft. Clearly, this technology effectively compensates for the insufficient compensation accuracy of point-based compensation schemes and the high deployment complexity of external hardware compensation schemes, improving geomagnetic field detection efficiency and robustness, and providing an efficient solution for fields such as resource exploration, basic geophysical research, geomagnetic field mapping, and environmental monitoring.

[0012] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and do not limit this application. Attached Figure Description

[0013] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0014] Figure 1 A flowchart illustrating an exemplary embodiment of the present application of an airborne magnetic measurement compensation method based on a magnetic sensor chip network is shown. Figure 2 This illustration shows a schematic diagram of the placement of a magnetic sensing chip node provided in an exemplary embodiment of this application; Figure 3 This illustration shows a schematic diagram of the training process of an airborne magnetic measurement compensation model provided in an exemplary embodiment of this application; Figure 4 This illustration shows a schematic diagram of the implementation principle of the training process of the airborne magnetic measurement compensation model provided in an exemplary embodiment of this application; Figure 5 This illustration shows a schematic diagram of the sample data acquisition process provided in an exemplary embodiment of this application; Figure 6 A schematic block diagram of an airborne magnetic measurement compensation system based on a magnetic sensor chip network, provided in an exemplary embodiment of this application, is shown. Figure 7 This illustration shows a schematic diagram of the structure of a magnetic sensing chip node provided in an exemplary embodiment of this application; Figure 8 A schematic block diagram of an airborne magnetic measurement compensation system based on a magnetic sensor chip network, provided in another exemplary embodiment of this application, is shown. Figure 9 This illustration shows a schematic diagram of the structure of an airborne magnetic measurement compensation device based on a magnetic sensor chip network, provided in an exemplary embodiment of this application. Figure 10 A schematic diagram of the structure of an electronic device provided in an exemplary embodiment of this application is shown. Detailed Implementation

[0015] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0016] Figure 1This application illustrates an exemplary embodiment of an airborne magnetic measurement compensation method based on a magnetic sensor chip network. This method can be executed by an electronic device, which may include a terminal device and / or a network-side device. In other words, the method can be executed by software or hardware installed in the electronic device, and the method may include the following steps: S102 acquires the flight attitude data of the aircraft and the geomagnetic field observation values ​​collected by all magnetic sensor chip nodes in real time during the aircraft's exploration flight.

[0017] Here, "aircraft" refers to any device capable of flight, encompassing a broader range including drones, airplanes, and helicopters. Its core lies in its system capable of generating lift and propulsion. The reconnaissance flight involves real-time detection of the Earth's magnetic field along a predetermined survey line. In this embodiment, a magnetic sensor chip network is deployed on the aircraft, consisting of magnetic sensor chip nodes deployed at target locations inside and on the surface of the aircraft.

[0018] In one possible implementation, the magnetic sensing chip nodes are deployed as follows: Figure 2 As shown, 8 to 16 magnetic sensor chip nodes can be scientifically deployed near the nose, wings, engine nacelles, and tail of the aircraft to form a magnetic sensor chip network, thereby achieving three-dimensional field perception of magnetic interference on the aircraft. Magnetic interference on an aircraft refers to magnetic fields generated by the aircraft's own equipment, structure, and materials that contaminate geomagnetic field measurements. Three-dimensional field perception (3D Field Sensing) refers to acquiring the distribution and gradient information of physical fields (such as magnetic fields) in three-dimensional space through a spatially distributed array of sensors, rather than being limited to measurements at a single point.

[0019] In practice, the flight attitude data of the aircraft can be obtained by an attitude measurement unit deployed inside the aircraft. The attitude measurement unit can be an Inertial Measurement Unit (IMU), a core sensor used to measure the three-dimensional motion attitude of an object. The flight attitude data can be the three-axis attitude angles (roll angles) of the aircraft provided by the IMU. , looking up and heading ).

[0020] In practical implementation, during the aircraft's reconnaissance flight, magnetic field data frames (i.e., geomagnetic field observations) from all magnetic sensor chip nodes can be collected synchronously, and the aircraft's three-axis attitude angles, the current geographical location, and the Coordinated Universal Time (UTC) timestamp provided by the IMU can be recorded. The current geographical location can include precise latitude, longitude, and altitude provided by the Global Positioning System (GPS). Then, the aircraft's three-axis attitude angles at the current geographical location and the geomagnetic field observations from all magnetic sensor chip nodes are time-aligned according to the UTC timestamp. Subsequently, the time-aligned data is organized into a standard format, such as organizing the flight attitude data into flight attitude vectors. The geomagnetic field observations from all magnetic sensor chip nodes are organized into an observation vector set. ,in, For the first N The geomagnetic field observations are collected by individual magnetic sensor chip nodes. By performing preprocessing operations such as time alignment and standard formatting on the collected data in real time during the aircraft's probe flight, high-quality real-time input can be provided for subsequent airborne magnetic measurement compensation processes.

[0021] S104 inputs the flight attitude data into the three-dimensional interference field prediction module of the pre-trained airborne magnetic measurement compensation model, and outputs the magnetic interference prediction value at each magnetic sensor chip node under the current flight attitude.

[0022] In practical implementation, the flight attitude data input to the three-dimensional interference field prediction module can be a flight attitude vector. The three-dimensional interference field prediction module can complete forward inference within milliseconds, calculate the predicted magnetic interference value at each magnetic sensor chip node under the current flight attitude, and obtain the set of magnetic interference prediction vectors. ,in, For the first N Predicted magnetic interference values ​​at each magnetic sensing chip node.

[0023] S106 inputs the geomagnetic field observation value and magnetic interference prediction value at each magnetic sensor chip node into the real-time compensation calculation module of the airborne magnetic measurement compensation model, and outputs the geomagnetic field detection results obtained by the aircraft at present.

[0024] The real-time compensation calculation module is used to determine the geomagnetic field estimate of each magnetic sensor chip node based on the observed geomagnetic field value and the predicted magnetic interference value at that magnetic sensor chip node. Then, based on the geomagnetic field estimate values ​​of all magnetic sensor chip nodes and the weight coefficient corresponding to each magnetic sensor chip node, the current geomagnetic field detection result obtained by the spacecraft is determined.

[0025] In specific implementation, regarding the first i Each magnetic sensing chip node can utilize the geomagnetic field observations at that node. Subtract the predicted magnetic interference value at the node of the magnetic sensing chip. The estimated geomagnetic field value at the node of the magnetic sensing chip was obtained. Furthermore, the calculation method for the geomagnetic field estimate can also be based on... reduce The result is taken as the absolute value. The specific calculation method for the geomagnetic field estimate can be set according to application requirements, and this embodiment does not limit it.

[0026] In one possible implementation, the weight coefficient corresponding to each magnetic sensing chip node can be determined by the signal-to-noise ratio (SNR) of that magnetic sensing chip node within a preset period. The SNR of any magnetic sensing chip node is determined by the observed and actual values ​​of the geomagnetic field for that node. For example, the SNR can be the difference between the observed and actual values ​​of the geomagnetic field, or it can be the ratio between the observed and actual values ​​of the geomagnetic field; this embodiment of the application does not limit this. In one possible implementation, if the SNR is the difference between the observed and actual values ​​of the geomagnetic field, nodes with high SNR can be assigned small weights, and nodes with low SNR can be assigned large weights, to avoid the magnetic sensing chip nodes with insufficient measurement accuracy from having a significant impact on the overall fusion result.

[0027] In another implementation, the weight coefficients corresponding to each magnetic sensing chip node can be model parameters of the real-time compensation calculation module of the airborne magnetic measurement compensation model. After the airborne magnetic measurement compensation model is trained, these model parameters are determined.

[0028] Optionally, the geomagnetic field detection results currently obtained by the spacecraft. The calculation method can be: ,in, For the first i The weighting coefficients corresponding to each magnetic sensing chip node For the first i Estimated geomagnetic field values ​​at each magnetic sensor chip node.

[0029] In this embodiment, during the aircraft's detection flight, flight attitude data and geomagnetic field observations collected by all magnetic sensor chip nodes are acquired in real time. The aircraft is equipped with a magnetic sensor chip network, consisting of magnetic sensor chip nodes deployed at target locations inside and on the surface of the aircraft. Flight attitude data is input into the three-dimensional interference field prediction module of a pre-trained airborne magnetic survey compensation model to output the predicted magnetic interference value at each magnetic sensor chip node under the current flight attitude. The observed geomagnetic field value and the predicted magnetic interference value at each magnetic sensor chip node are then input into the real-time compensation calculation module of the airborne magnetic survey compensation model to output the geomagnetic field detection result obtained by the aircraft. The real-time compensation calculation module determines the estimated geomagnetic field value at each magnetic sensor chip node based on the observed and predicted geomagnetic field values, and then determines the geomagnetic field detection result based on the estimated geomagnetic field values ​​at all magnetic sensor chip nodes and the weight coefficients corresponding to each magnetic sensor chip node. By deploying a dense network of multiple magnetic sensor chip nodes at key locations inside and on the surface of the aircraft, and pre-training an airborne magnetic measurement compensation model, three-dimensional field perception of magnetic interference generated by the aircraft can be achieved. This allows for the individual cancellation of local magnetic field distortions caused by interference sources at different parts of the aircraft, yielding geomagnetic field estimates. Furthermore, by weighted fusion of the geomagnetic field estimates from all magnetic sensor chip nodes, the final geomagnetic field detection result is obtained. This elevates airborne magnetic compensation from "point" compensation to "field" compensation, achieving higher compensation accuracy compared to "point" compensation schemes. Specifically, this technology possesses the ability to suppress the failure of individual magnetic sensor chip nodes or avoid the influence of transient noise. It exhibits inherent robustness to continuous changes in flight attitude and the non-stationary characteristics of interference sources. The compensation effect is not significantly deteriorated by aircraft maneuvers, maintaining stable high performance and geomagnetic field detection accuracy in complex real-world flight environments, thus realizing a more reliable airborne magnetic compensation scheme. Furthermore, the deployment of magnetic sensor chip nodes does not alter the aerodynamic shape of the aircraft. This allows the technology to achieve high-precision geomagnetic field detection capabilities comparable to current external hardware compensation schemes without requiring any external modifications to the aircraft. This significantly reduces the complexity, cost, and safety risks of deploying aeromagnetic compensation schemes, enabling high-precision airborne magnetic surveying technology to be more flexibly applied to various small and medium-sized aircraft. Clearly, this technology effectively compensates for the insufficient compensation accuracy of point-based compensation schemes and the high deployment complexity of external hardware compensation schemes, improving geomagnetic field detection efficiency and robustness, and providing an efficient solution for fields such as resource exploration, basic geophysical research, geomagnetic field mapping, and environmental monitoring.

[0030] Specifically, by executing S102-S106, this technical solution achieves a real-time compensation process of "individualized prediction, local subtraction, and global fusion." First, the three-dimensional interference field prediction module predicts the specific magnetic interference value at each magnetic sensor chip node. Second, local interference subtraction is performed at each magnetic sensor chip node, making the compensation operation more targeted. Finally, the pure estimates of all magnetic sensor chip nodes are synthesized through adaptive weighted fusion. Compared with the "point" compensation scheme of a single magnetic sensor, this technical solution has the ability to suppress the failure of individual magnetic sensor chip nodes or avoid the influence of transient noise, and can smooth random errors, thereby optimizing the signal-to-noise ratio of the output signal (i.e., the geomagnetic field detection result) as a whole. At the same time, this technical solution has a certain degree of redundancy. Even if a single magnetic sensor chip node temporarily fails, it can still continue to work through the remaining magnetic sensor chip nodes, improving the reliability of the airborne magnetic measurement compensation scheme. Furthermore, compared to current compensation schemes that rely on linear models or specific frequency filtering, the real-time compensation process implemented by this technical solution makes full use of spatial dimension information and has inherent strong robustness to continuous changes in flight attitude and non-stationary characteristics of interference sources. It can avoid the problem of poor adaptability leading to decreased compensation effect or even failure when the aircraft is performing maneuvers such as roll and pitch, or when the frequency of interference sources changes.

[0031] In one implementation, such as Figure 3 As shown, the training process of the airborne magnetic measurement compensation model may include the following steps: S302 acquires multiple sets of sample data during the aircraft's calibration flight. Each set of sample data includes the aircraft's sample flight attitude data, sample geomagnetic field observations collected by all magnetic sensor chip nodes, and the current geographical location of the sample being detected.

[0032] In practice, aircraft calibration flight can involve maneuvering the aircraft to a known airspace with a uniform geomagnetic background, and then performing a set of standard Tolles-Lawson calibration maneuvers under the control of the autopilot, including specific maneuvers such as ±15° roll and ±10° pitch in multiple headings.

[0033] In this embodiment, the multiple sets of sample data can be data that has been preprocessed in real time during the aircraft calibration flight process, including removing outliers, smoothing and denoising, time alignment, and organizing into a standard format. The specific acquisition method of each set of sample data will be detailed in the following embodiments and will not be repeated here.

[0034] In practice, the representation of sample flight attitude data can be in the form of sample flight attitude vectors. The representation of the sample geomagnetic field observations acquired by all magnetic sensing chip nodes can be a set of sample observation vectors. .

[0035] S304: Input the sample flight attitude data into the three-dimensional interference field prediction module of the airborne magnetic measurement compensation model to be trained, and output the sample magnetic interference prediction value at each magnetic sensor chip node under the current flight attitude.

[0036] In practical implementation, the 3D interference field prediction module can be built based on the least squares regression algorithm. Alternatively, the 3D interference field prediction module can be constructed using a deep neural network, such as a fully connected network with three hidden layers. The sample flight attitude data input to the 3D interference field prediction module can be a sample flight attitude vector. The three-dimensional interference field prediction module can complete forward inference within milliseconds, calculate the sample magnetic interference prediction value at each magnetic sensor chip node under the current flight attitude, and obtain the sample magnetic interference prediction vector set. Therefore, the three-dimensional interference field prediction module can be viewed as a mapping relationship between the established flight attitude vector and the set of magnetic interference prediction vectors. Thus, based on the input flight attitude vector, the corresponding set of magnetic interference prediction vectors can be obtained.

[0037] S306 inputs the sample geomagnetic field observation value and sample magnetic interference prediction value at each magnetic sensor chip node into the real-time compensation calculation module of the airborne magnetic measurement compensation model, and outputs the sample geomagnetic field detection results corresponding to the sample geographical location.

[0038] In practical implementation, for each magnetic sensing chip node, the estimated value of the sample geomagnetic field at that node can be determined based on the observed sample geomagnetic field value and the predicted sample magnetic interference value. Then, based on the estimated sample geomagnetic field values ​​at all magnetic sensing chip nodes and the weighting coefficient corresponding to each magnetic sensing chip node, the sample geomagnetic field detection result corresponding to the sample's geographical location can be determined. In this embodiment, the specific implementation process for determining the sample geomagnetic field detection result can refer to the specific implementation process for determining the geomagnetic field detection result in S106 above, and will not be repeated here.

[0039] S308. Based on the geomagnetic field label value corresponding to the sample's geographical location and the sample's geomagnetic field detection results, determine the predicted loss of the airborne magnetic survey compensation model.

[0040] The geomagnetic field label value is the actual measured value of the geomagnetic field at the sample's geographical location. It is understood that the sample's geographical location has a uniform and known geomagnetic field background; therefore, those skilled in the art can obtain the geomagnetic field label value for that location without any difficulty. Thus, during the calibration flight, the geomagnetic field label value for each detected sample geographical location is known.

[0041] S310. Based on the predicted loss, update the model parameters of the airborne magnetic measurement compensation model to obtain the trained airborne magnetic measurement compensation model.

[0042] In practical implementation, the model parameters of the airborne magnetic field compensation model can be iteratively adjusted based on the predicted loss until the iteration termination condition is met, at which point training stops, resulting in the trained airborne magnetic field compensation model. The model parameters can include the first parameter of the three-dimensional interference field prediction module, the second parameter of the real-time compensation calculation module, and the third parameter of the airborne magnetic field compensation model. The first parameter can be a coefficient matrix, network weights, etc.; the second parameter can be a coefficient matrix, weight coefficients corresponding to each magnetic sensor chip node, etc.; and the third parameter can be other parameters of the airborne magnetic field compensation model. The iteration termination condition can include convergence of the predicted loss and / or, the number of iterations reaching a preset number.

[0043] In one possible implementation, the prediction loss is the residual standard deviation. Based on this, if the airborne magnetic survey compensation model is cross-validated to ensure that the residual standard deviation is less than the standard, then the iteration termination condition can be determined to be met. At this point, training is stopped, and the trained airborne magnetic survey compensation model is obtained. Furthermore, in specific implementations, other suitable loss functions can be selected as the prediction loss of the airborne magnetic survey compensation model according to application requirements; this embodiment does not limit this.

[0044] For example, such as Figure 4 As shown, the implementation principle of the training process of the airborne magnetic measurement compensation model described in S302-S310 above is illustrated.

[0045] In this embodiment, pre-training the airborne magnetic measurement compensation model provides an accurate and usable model for the airborne magnetic measurement compensation process, facilitating the precise and efficient implementation of the airborne magnetic measurement compensation scheme. Specifically, by deploying a dense network of multiple magnetic sensor chip nodes at key locations inside and on the surface of the aircraft, three-dimensional field perception of magnetic interference generated by the aircraft can be achieved. The three-dimensional interference field prediction module, built based on the data collected by this dense network, can accurately characterize the details of how the interference magnetic field changes with spatial location. Compared to current compensation methods that rely on magnetic field measurements at single or a few points on the aircraft, simplifying complex interfering magnetic fields into a single point source model, this new technology fundamentally elevates aeromagnetic compensation from a "point" to a "field" approach. This allows interference sources in different parts of the aircraft (such as engine and wing currents) to be detected, enabling the individual cancellation of local magnetic field distortions caused by these sources. This leads to higher compensation accuracy, particularly in extracting subtle magnetic anomalies masked by strong electromagnetic interference. It achieves high-precision compensation based on three-dimensional field perception, overcoming the limitations of "point" compensation schemes that cannot detect non-uniform distributions of interfering magnetic fields (such as spatial gradients) within the aircraft's spatial structure. This results in a theoretical blind spot in the "point" compensation scheme, leading to a low upper limit for compensation accuracy.

[0046] In one implementation, such as Figure 5 As shown, during the aircraft calibration flight, multiple sets of sample data are acquired (i.e., S302), which can be executed as follows: S3022-S3028: S3022: During the calibration flight of the aircraft, sampling data at the current geographical location is acquired according to the preset sampling frequency.

[0047] The preset sampling frequency can be set according to specific application requirements, such as 10Hz (Hertz). The sampling data includes sample geomagnetic field observations collected by all magnetic sensor chip nodes on the aircraft, sample flight attitude data of the aircraft, the geographical location of the currently detected sample, and the UTC timestamp. Sample flight attitude data can be the aircraft's three-axis attitude angles (roll angles) provided by the IMU. , looking up and heading The geographical location of the sample being detected can be the precise latitude, longitude, altitude, and other information provided by GPS.

[0048] S3024, Perform data preprocessing on the sampled data to obtain preprocessed sampled data. Data preprocessing may include outlier removal and / or noise reduction.

[0049] Alternatively, data preprocessing operations can be performed on a ground workstation or an onboard high-performance computer. When removing outliers, a 3D model can be used. Guidelines. 3 The criterion is a statistical method that determines the normal range by calculating the mean and standard deviation of data. Data exceeding this range are considered outliers and removed. Noise reduction can be achieved through smoothing using a Butterworth low-pass digital filter.

[0050] S3026, Based on the UTC timestamp, perform time alignment processing on the preprocessed sampled data to obtain time-aligned sampled data.

[0051] In practice, this step can organize the sampled data into multiple data entries, each corresponding to a UTC timestamp. This data entry (i.e., the sampled data after time alignment) includes the sample geomagnetic field observations collected by all magnetic sensor chip nodes, the sample flight attitude data of the aircraft, and the geographical location of the currently detected sample.

[0052] S3028: For the time-aligned sampled data corresponding to each sampling moment, a set of sample data is formed based on the sample geomagnetic field observation values ​​collected by all magnetic sensor chip nodes on the aircraft, the sample flight attitude data of the aircraft, and the geographical location of the currently detected sample.

[0053] Since multiple time-aligned sampling data have already been obtained in S3026, in this step, for each time-aligned sampling data, a set of sample data can be formed based on the sample geomagnetic field observation values ​​collected by all magnetic sensor chip nodes on the aircraft, the sample flight attitude data of the aircraft, and the geographical location of the currently detected sample.

[0054] Optionally, for each time-aligned sampled data, before forming a corresponding set of sample data, the time-aligned sampled data can be organized into a standard format, such as organizing the sample flight attitude data into sample flight attitude vectors. The sample geomagnetic field observations collected by all magnetic sensor chip nodes are organized into a sample observation vector set. This allows for the formation of a set of sample data based on the processed data, which helps improve the quality of the training data.

[0055] In this embodiment, by performing data preprocessing on the sampling data obtained during the aircraft calibration flight, such as removing outliers, denoising, and time alignment, it is beneficial to obtain clean training data, thereby making the training results of the airborne magnetic measurement compensation model more accurate.

[0056] In one implementation, the prediction loss of the airborne magnetic survey compensation model can be the residual standard deviation. In this embodiment, the prediction loss of the airborne magnetic survey compensation model is determined based on the geomagnetic field label value corresponding to the sample geographical location and the sample geomagnetic field detection results (i.e., S308). This can be performed by: calculating the residual between the geomagnetic field label value corresponding to the sample geographical location and the sample geomagnetic field detection results; and then, based on the residuals calculated at all sample geographical locations during the aircraft calibration flight, determining the residual standard deviation of the airborne magnetic survey compensation model.

[0057] The specific calculation methods for residuals and residual standard deviations can be found in relevant technologies and will not be elaborated upon in this embodiment. In this embodiment, by clarifying how to calculate the prediction loss as the residual standard deviation in the airborne magnetic survey compensation model, it is beneficial for the accurate implementation of model training. Furthermore, since the residual standard deviation measures the dispersion between the model's predicted values ​​and the actual values, a smaller value indicates a better model fit and more reliable prediction results. Therefore, using the residual standard deviation as the prediction loss of the airborne magnetic survey compensation model is beneficial for training a model with better prediction accuracy.

[0058] In one implementation, during the flight of the spacecraft, the current geographical location and UTC timestamp can be obtained in real time. By binding the geomagnetic field detection results obtained by the spacecraft with the current geographical location and UTC timestamp, a geomagnetic field detection record with spatiotemporal information can be obtained. The geomagnetic field detection record is used for geomagnetic field mapping.

[0059] In practice, geomagnetic field measurement records with spatiotemporal information can be written in real time to an onboard shockproof solid-state drive in standard ASCII text format or a custom binary format, or transmitted to a ground station via a satellite data link. This ensures that the output data from airborne magnetic surveys can be directly used for scientific analysis applications such as geomagnetic field mapping and geophysical inversion. The American Standard Code for Information Interchange (ASCII) is a universal information exchange standard based on the Latin alphabet.

[0060] Figure 6 This is a schematic block diagram of an airborne magnetic measurement compensation system based on a magnetic sensor chip network, provided in an exemplary embodiment of this application, as follows: Figure 6As shown, the airborne magnetic measurement compensation system 600 based on a magnetic sensor chip network includes a magnetic sensor chip network 610, an attitude measurement unit 620, a data acquisition and processing unit 630, and a compensation calculation unit 640. The magnetic sensor chip network consists of magnetic sensor chip nodes deployed at target locations inside and on the surface of the aircraft. It is understood that there are multiple magnetic sensor chip nodes. The magnetic sensor chip network 610 is used to acquire geomagnetic field observations through each magnetic sensor chip node.

[0061] Attitude measurement unit 620 is used to measure and obtain flight attitude data of the aircraft. For example, as shown... Figure 8 As shown, the airborne magnetic measurement compensation system based on a magnetic sensor chip network can be equipped with an IMU / GPS integrated navigation system to provide information such as flight attitude data, current geographical location, and UTC timestamp.

[0062] The data acquisition and processing unit 630 is used to acquire flight attitude data and geomagnetic field observation values ​​collected by all magnetic sensor chip nodes in real time during the aircraft's exploration flight, and send the flight attitude data and geomagnetic field observation values ​​collected by all magnetic sensor chip nodes to the compensation calculation unit.

[0063] The compensation calculation unit 640 is used to input flight attitude data into the three-dimensional interference field prediction module of a pre-trained airborne magnetic survey compensation model, and output the magnetic interference prediction value at each magnetic sensor chip node under the current flight attitude. Then, it inputs the observed geomagnetic field value and the predicted magnetic interference value at each magnetic sensor chip node into the real-time compensation calculation module of the airborne magnetic survey compensation model, and outputs the geomagnetic field detection result currently obtained by the aircraft. Specifically, the real-time compensation calculation module is used to determine the geomagnetic field estimate value at each magnetic sensor chip node based on the observed geomagnetic field value and the predicted magnetic interference value at that node. Therefore, based on the geomagnetic field estimates at all magnetic sensor chip nodes and the weight coefficient corresponding to each magnetic sensor chip node, it determines the geomagnetic field detection result currently obtained by the aircraft.

[0064] In practical implementation, to realize an airborne magnetic measurement compensation system based on a magnetic sensor chip network, hardware deployment and network construction are first required. The aim of this deployment and construction is to build a distributed sensor network capable of accurately sensing the distribution of the spatial magnetic field. Specifically, such as... Figure 2As shown, 8 to 16 magnetic sensor chip nodes can be scientifically deployed near the nose, wings, engine nacelles, and tail of the aircraft to form a magnetic sensor chip network. Each magnetic sensor chip node is an independent high-precision measurement unit, thereby achieving three-dimensional field perception of magnetic interference on the aircraft. After completing the hardware deployment and network construction, a power-on self-test can be performed to ensure that each magnetic sensor chip node is usable, thus ensuring the reliability of the airborne magnetic measurement and compensation system based on the magnetic sensor chip network.

[0065] For example, such as Figure 7 As shown, the hardware configuration of a magnetic sensing chip node can include a Tunneling Magnetoresistance (TMR) chip as the sensing core. This chip directly senses changes in the magnetic field in the environment and has high sensitivity. The TMR chip and amplifier are connected to a filter. The amplifier can be a low-noise operational amplifier, used to initially amplify the weak voltage signal output by the TMR chip by 10-100 times. The filter can be an anti-aliasing filter, used to suppress high-frequency noise. The conditioned analog signal is acquired and converted by a high-precision 16-bit analog-to-digital converter (ADC) integrated by a high-performance microcontroller (such as the ARM Cortex-M series). The microcontroller is also responsible for executing preliminary digital filtering algorithms such as moving average and packaging the three-axis magnetic field data into a standard magnetic field data frame. All magnetic sensing chip nodes are connected to the central processing unit through a highly reliable CAN bus or a Bluetooth Low Energy network using a time-division multiple access protocol, forming a star or bus topology to ensure that the data synchronization accuracy of the entire network is better than 1 millisecond. The central processing unit can be an airborne industrial computer, responsible for sending synchronous acquisition commands to all magnetic sensor chip nodes, polling and collecting magnetic field data frames, and receiving flight attitude data and the current detected geographical location from an independent high-precision IMU / GPS integrated navigation system, thereby building a complete hardware sensing platform.

[0066] In this embodiment, since all magnetic sensor chip nodes utilize miniaturized, low-power TMR chips, these chips can be entirely embedded within the aircraft. Therefore, there is no need to alter the aircraft's aerodynamic shape. This means the high performance of the airborne magnetic measurement compensation system based on the magnetic sensor chip network no longer relies on physical isolation, but is achieved through a distributed network and advanced algorithms. This allows for high-precision geomagnetic field detection capabilities comparable to current external hardware compensation schemes without requiring external modifications to the aircraft. This significantly reduces the complexity, cost, and safety risks of deploying the airborne magnetic measurement compensation system based on the magnetic sensor chip network, enabling more flexible application of high-precision airborne magnetic measurement technology to various small and medium-sized aircraft.

[0067] In addition, the sensing core of the magnetic sensing chip node can also be a chip based on the Hall effect. In specific implementation, it can be set according to the application requirements, and this embodiment does not limit it.

[0068] In one implementation, such as Figure 8 As shown, the data acquisition and processing unit 630 may include a data acquisition subunit 6310 and a data preprocessing subunit 6320. The data acquisition subunit 6310 is used to acquire flight attitude data from the attitude measurement unit 620 and geomagnetic field observations collected from all magnetic sensor chip nodes of the magnetic sensor chip network 610 in real time during the aircraft's reconnaissance flight. It then sends the flight attitude data and the geomagnetic field observations collected from all magnetic sensor chip nodes to the data preprocessing subunit 6320 for data preprocessing. The data preprocessing subunit 6320 is used to perform preprocessing operations such as time alignment and organization into a standard format on the received data, and then sends the preprocessed data to the compensation calculation unit 640.

[0069] In specific implementations, the data acquisition subunit may include a central processing unit and a CAN bus, or a central processing unit and a Bluetooth Low Energy network using a time-division multiple access protocol.

[0070] After that, as Figure 8 As shown, the compensation calculation unit 640 may include an airborne magnetic measurement compensation model with a three-dimensional interference field prediction module 6410 and a real-time compensation calculation module 6420. The three-dimensional interference field prediction module 6410 is used to determine and output the predicted magnetic interference value at each magnetic sensor chip node under the current flight attitude based on the input flight attitude data. The real-time compensation calculation module 6420 is used to determine the estimated geomagnetic field value at each magnetic sensor chip node based on the observed geomagnetic field value and the predicted magnetic interference value at that magnetic sensor chip node. Therefore, based on the estimated geomagnetic field values ​​at all magnetic sensor chip nodes and the weighting coefficient corresponding to each magnetic sensor chip node, the current geomagnetic field detection result obtained by the aircraft is determined.

[0071] In addition, such as Figure 8 As shown, the airborne magnetic field compensation system based on a magnetic sensor chip network may include a data output unit 650. In this embodiment, the IMU / GPS integrated navigation system can acquire flight attitude data, current geographical location, UTC timestamp, and other information in real time during the aircraft's detection flight. Thus, the data output unit 650 is used to bind the geomagnetic field detection results obtained by the aircraft (determined by the real-time compensation calculation module 6420) with the current geographical location and UTC timestamp to obtain a geomagnetic field detection record with spatiotemporal information. The geomagnetic field detection record can be used for geomagnetic field mapping.

[0072] In one implementation, after hardware deployment and network setup are completed, and a power-on self-test is performed, and before the aircraft's reconnaissance flight, the airborne magnetic measurement compensation system based on the magnetic sensor chip network enters a calibration phase. During this phase, the aircraft can be manipulated to perform calibration maneuvers in a specific airspace, simultaneously collecting magnetic field data frames and flight attitude data from all magnetic sensor chip nodes. Based on this data, an accurate airborne magnetic measurement compensation model is calculated offline. After the model is loaded, the airborne magnetic measurement compensation system based on the magnetic sensor chip network then enters the formal reconnaissance flight phase.

[0073] During the calibration phase of training the airborne magnetic measurement compensation model, the airborne magnetic measurement compensation system based on the magnetic sensor chip network may include a model training unit. In this embodiment, the data acquisition and processing unit 630 can be used to acquire multiple sets of sample data during the aircraft calibration flight and send the sample data to the model training unit; each set of sample data includes the aircraft's sample flight attitude data, the sample geomagnetic field observation values ​​acquired by all magnetic sensor chip nodes, and the currently detected sample geographical location.

[0074] The model training unit is used to input sample flight attitude data into the three-dimensional interference field prediction module of the airborne magnetic measurement compensation model to be trained, and output the sample magnetic interference prediction value at each magnetic sensor chip node under the current flight attitude. Then, the sample geomagnetic field observation value and sample magnetic interference prediction value at each magnetic sensor chip node are input into the real-time compensation calculation module of the airborne magnetic measurement compensation model, and the sample geomagnetic field detection result corresponding to the sample geographical location is output. Then, based on the geomagnetic field label value corresponding to the sample geographical location and the sample geomagnetic field detection result, the prediction loss of the airborne magnetic measurement compensation model is determined. Furthermore, based on the prediction loss, the model parameters of the airborne magnetic measurement compensation model are updated to obtain the trained airborne magnetic measurement compensation model.

[0075] The model training unit is specifically used to acquire sampling data at the currently detected geographical location according to a preset sampling frequency during the aircraft's calibration flight. The sampling data includes sample geomagnetic field observations collected by all magnetic sensor chip nodes on the aircraft, sample flight attitude data of the aircraft, the currently detected sample geographical location, and the Coordinated Universal Time (UTC) timestamp. The sampled data undergoes preprocessing operations to obtain preprocessed sampling data, including outlier removal and / or noise reduction. Then, based on the UTC timestamp, the preprocessed sampling data is time-aligned to obtain time-aligned sampling data. Further, for each sampling moment, the time-aligned sampling data is used to form a set of sample data based on the sample geomagnetic field observations collected by all magnetic sensor chip nodes on the aircraft, the sample flight attitude data of the aircraft, and the currently detected sample geographical location.

[0076] If the predicted loss of the airborne magnetic survey compensation model is the residual standard deviation, then the predicted loss of the airborne magnetic survey compensation model can be determined based on the geomagnetic field label value corresponding to the sample geographical location and the sample geomagnetic field detection results. This can be done by: calculating the residual between the geomagnetic field label value corresponding to the sample geographical location and the sample geomagnetic field detection results, and then determining the residual standard deviation of the airborne magnetic survey compensation model based on the residuals calculated at all sample geographical locations during the aircraft calibration flight.

[0077] In this embodiment, during the aircraft's detection flight, flight attitude data and geomagnetic field observations collected by all magnetic sensor chip nodes are acquired in real time. The aircraft is equipped with a magnetic sensor chip network, consisting of magnetic sensor chip nodes deployed at target locations inside and on the surface of the aircraft. Flight attitude data is input into the three-dimensional interference field prediction module of a pre-trained airborne magnetic survey compensation model to output the predicted magnetic interference value at each magnetic sensor chip node under the current flight attitude. The observed geomagnetic field value and the predicted magnetic interference value at each magnetic sensor chip node are then input into the real-time compensation calculation module of the airborne magnetic survey compensation model to output the geomagnetic field detection result obtained by the aircraft. The real-time compensation calculation module determines the estimated geomagnetic field value at each magnetic sensor chip node based on the observed and predicted geomagnetic field values, and then determines the geomagnetic field detection result based on the estimated geomagnetic field values ​​at all magnetic sensor chip nodes and the weight coefficients corresponding to each magnetic sensor chip node. By deploying a dense network of multiple magnetic sensor chip nodes at key locations inside and on the surface of the aircraft, and pre-training an airborne magnetic measurement compensation model, three-dimensional field perception of magnetic interference generated by the aircraft can be achieved. This allows for the individual cancellation of local magnetic field distortions caused by interference sources at different parts of the aircraft, yielding geomagnetic field estimates. Furthermore, by weighted fusion of the geomagnetic field estimates from all magnetic sensor chip nodes, the final geomagnetic field detection result is obtained. This elevates airborne magnetic compensation from "point" compensation to "field" compensation, achieving higher compensation accuracy compared to "point" compensation schemes. Specifically, this technology possesses the ability to suppress the failure of individual magnetic sensor chip nodes or avoid the influence of transient noise. It exhibits inherent robustness to continuous changes in flight attitude and the non-stationary characteristics of interference sources. The compensation effect is not significantly deteriorated by aircraft maneuvers, maintaining stable high performance and geomagnetic field detection accuracy in complex real-world flight environments, thus realizing a more reliable airborne magnetic compensation scheme. Furthermore, the deployment of magnetic sensor chip nodes does not alter the aerodynamic shape of the aircraft. This allows the technology to achieve high-precision geomagnetic field detection capabilities comparable to current external hardware compensation schemes without requiring any external modifications to the aircraft. This significantly reduces the complexity, cost, and safety risks of deploying aeromagnetic compensation schemes, enabling high-precision airborne magnetic surveying technology to be more flexibly applied to various small and medium-sized aircraft. Clearly, this technology effectively compensates for the insufficient compensation accuracy of point-based compensation schemes and the high deployment complexity of external hardware compensation schemes, improving geomagnetic field detection efficiency and robustness, and providing an efficient solution for fields such as resource exploration, basic geophysical research, geomagnetic field mapping, and environmental monitoring.

[0078] The airborne magnetic measurement compensation system based on a magnetic sensor chip network provided in this application embodiment can achieve...Figures 1 to 5 The various processes implemented in the method embodiments are not described in detail here to avoid repetition.

[0079] It should be noted that the airborne magnetic measurement compensation method based on a magnetic sensor chip network provided in this application can be executed by an airborne magnetic measurement compensation device based on a magnetic sensor chip network, or by a control module within that device for executing the airborne magnetic measurement compensation method. This application uses the execution of the airborne magnetic measurement compensation method based on a magnetic sensor chip network by the airborne magnetic measurement compensation device as an example to illustrate the airborne magnetic measurement compensation device based on a magnetic sensor chip network provided in this application.

[0080] Figure 9 This is a schematic diagram of an airborne magnetic measurement compensation device based on a magnetic sensor chip network, provided in an embodiment of this application. Figure 9 As shown, the airborne magnetic measurement compensation device 900 based on a magnetic sensor chip network includes: The first acquisition module 910 is used to acquire the flight attitude data of the aircraft and the geomagnetic field observation values ​​collected by all magnetic sensor chip nodes in real time during the aircraft's detection flight. The aircraft is equipped with a magnetic sensor chip network, which consists of magnetic sensor chip nodes deployed at target locations inside and on the surface of the aircraft. The first model processing module 920 is used to input flight attitude data into the three-dimensional interference field prediction module of the pre-trained airborne magnetic measurement compensation model and output the magnetic interference prediction value at each magnetic sensor chip node under the current flight attitude. The second model processing module 930 is used to input the observed geomagnetic field value and the predicted magnetic interference value at each magnetic sensor chip node into the real-time compensation calculation module of the airborne magnetic measurement compensation model, and output the geomagnetic field detection result obtained by the aircraft at present. The real-time compensation calculation module is used to determine the geomagnetic field estimate value at each magnetic sensor chip node based on the observed geomagnetic field value and the predicted magnetic interference value at that magnetic sensor chip node, and determine the geomagnetic field detection result based on the geomagnetic field estimate values ​​at all magnetic sensor chip nodes and the weight coefficient corresponding to each magnetic sensor chip node.

[0081] In one implementation, the training process of the airborne magnetic measurement compensation model includes: During the calibration flight of the aircraft, multiple sets of sample data are acquired; each set of sample data includes the sample flight attitude data of the aircraft, the sample geomagnetic field observation values ​​collected by all magnetic sensor chip nodes, and the geographical location of the sample currently being detected; Input the sample flight attitude data into the three-dimensional interference field prediction module of the airborne magnetic measurement compensation model to be trained, and output the sample magnetic interference prediction value at each magnetic sensing chip node under the current flight attitude. The sample geomagnetic field observation value and sample magnetic interference prediction value at each magnetic sensor chip node are input into the real-time compensation calculation module of the airborne magnetic measurement compensation model, and the sample geomagnetic field detection results corresponding to the sample geographical location are output. Based on the geomagnetic field label values ​​corresponding to the sample's geographical location and the sample's geomagnetic field detection results, the predicted loss of the airborne magnetic survey compensation model is determined. Based on the predicted loss, the model parameters of the airborne magnetic survey compensation model are updated to obtain the trained airborne magnetic survey compensation model.

[0082] In one implementation, during the aircraft calibration flight, multiple sets of sample data are acquired, including: During the calibration flight of the aircraft, sampling data at the current geographical location is acquired according to the preset sampling frequency. The sampling data includes the sample geomagnetic field observation values ​​collected by all magnetic sensor chip nodes on the aircraft, the sample flight attitude data of the aircraft, the current geographical location of the sample, and the Coordinated Universal Time (UTC) timestamp. Perform data preprocessing operations on the sampled data to obtain preprocessed sampled data; the data preprocessing operations include outlier removal and / or noise reduction. Based on the UTC timestamp, the preprocessed sampled data is time-aligned to obtain time-aligned sampled data. For each sampling moment, after time alignment processing of the sampled data, a set of sample data is formed based on the sample geomagnetic field observation values ​​collected by all magnetic sensor chip nodes on the aircraft, the sample flight attitude data of the aircraft, and the geographical location of the currently detected sample.

[0083] In one implementation, the prediction loss of the airborne magnetic measurement compensation model is the residual standard deviation; Based on the geomagnetic field label values ​​corresponding to the sample's geographical location and the sample's geomagnetic field detection results, the predicted loss of the airborne magnetic survey compensation model is determined, including: Calculate the residual between the geomagnetic field label value corresponding to the sample's geographical location and the sample's geomagnetic field detection results; The residual standard deviation of the airborne magnetic measurement compensation model is determined based on the residuals calculated at all sample geographical locations during the aircraft calibration flight.

[0084] In one implementation, the airborne magnetic measurement compensation device 900 based on a magnetic sensor chip network further includes: The second acquisition module is used to acquire the current geographical location and UTC timestamp in real time during the aircraft's detection flight. The binding module is used to bind the geomagnetic field detection results obtained by the spacecraft with the current geographical location and UTC timestamp to obtain geomagnetic field detection records with spatiotemporal information; the geomagnetic field detection records are used for geomagnetic field mapping.

[0085] In this embodiment, during the aircraft's detection flight, flight attitude data and geomagnetic field observations collected by all magnetic sensor chip nodes are acquired in real time. The aircraft is equipped with a magnetic sensor chip network, consisting of magnetic sensor chip nodes deployed at target locations inside and on the surface of the aircraft. Flight attitude data is input into the three-dimensional interference field prediction module of a pre-trained airborne magnetic survey compensation model to output the predicted magnetic interference value at each magnetic sensor chip node under the current flight attitude. The observed geomagnetic field value and the predicted magnetic interference value at each magnetic sensor chip node are then input into the real-time compensation calculation module of the airborne magnetic survey compensation model to output the geomagnetic field detection result obtained by the aircraft. The real-time compensation calculation module determines the estimated geomagnetic field value at each magnetic sensor chip node based on the observed and predicted geomagnetic field values, and then determines the geomagnetic field detection result based on the estimated geomagnetic field values ​​at all magnetic sensor chip nodes and the weight coefficients corresponding to each magnetic sensor chip node. By deploying a dense network of multiple magnetic sensor chip nodes at key locations inside and on the surface of the aircraft, and pre-training an airborne magnetic measurement compensation model, three-dimensional field perception of magnetic interference generated by the aircraft can be achieved. This allows for the individual cancellation of local magnetic field distortions caused by interference sources at different parts of the aircraft, yielding geomagnetic field estimates. Furthermore, by weighted fusion of the geomagnetic field estimates from all magnetic sensor chip nodes, the final geomagnetic field detection result is obtained. This elevates airborne magnetic compensation from "point" compensation to "field" compensation, achieving higher compensation accuracy compared to "point" compensation schemes. Specifically, this technology possesses the ability to suppress the failure of individual magnetic sensor chip nodes or avoid the influence of transient noise. It exhibits inherent robustness to continuous changes in flight attitude and the non-stationary characteristics of interference sources. The compensation effect is not significantly deteriorated by aircraft maneuvers, maintaining stable high performance and geomagnetic field detection accuracy in complex real-world flight environments, thus realizing a more reliable airborne magnetic compensation scheme. Furthermore, the deployment of magnetic sensor chip nodes does not alter the aerodynamic shape of the aircraft. This allows the technology to achieve high-precision geomagnetic field detection capabilities comparable to current external hardware compensation schemes without requiring any external modifications to the aircraft. This significantly reduces the complexity, cost, and safety risks of deploying aeromagnetic compensation schemes, enabling high-precision airborne magnetic surveying technology to be more flexibly applied to various small and medium-sized aircraft. Clearly, this technology effectively compensates for the insufficient compensation accuracy of point-based compensation schemes and the high deployment complexity of external hardware compensation schemes, improving geomagnetic field detection efficiency and robustness, and providing an efficient solution for fields such as resource exploration, basic geophysical research, geomagnetic field mapping, and environmental monitoring.

[0086] The airborne magnetic measurement compensation device based on a magnetic sensor chip network provided in this application embodiment can achieve...Figures 1 to 5 The various processes implemented in the method embodiments are not described in detail here to avoid repetition.

[0087] Figure 10 This diagram illustrates the hardware structure of an electronic device implementing the embodiments of this application. Referring to the diagram, at the hardware level, the electronic device 1000 includes a processor 1010, and optionally includes an internal bus 1020, a network interface 1030, and a memory 1040. The memory 1040 may include main memory 1041, such as high-speed random-access memory (RAM), and may also include non-volatile memory 1042, such as at least one disk storage device. Of course, the electronic device 1000 may also include other hardware required for other services.

[0088] The processor 1010, network interface 1030, and memory can be interconnected via an internal bus 1020. This internal bus 1020 can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. The bus can be categorized as an address bus, data bus, control bus, etc. For ease of illustration, only a single bidirectional arrow is used in this diagram, but this does not imply that there is only one bus or one type of bus.

[0089] Memory 1040 stores programs. Specifically, the program may include program code, which includes computer operation instructions. Memory 1040 may include main memory 1041 and non-volatile memory 1042, and provides instructions and data to processor 1010.

[0090] Processor 1010 reads the corresponding computer program from non-volatile memory 1042 into memory and then runs it, forming a device for locating the target user at the logical level. Processor 1010 executes the program stored in memory and specifically performs the following: Figure 1 The methods disclosed in the embodiments shown achieve the functions and beneficial effects of the methods described in the preceding method embodiments, and will not be repeated here.

[0091] The above is as stated in this application. Figure 1The methods disclosed in the illustrated embodiments can be applied to or implemented by the processor 1010. The processor 1010 may be an integrated circuit chip with signal processing capabilities. During implementation, each step of the above method can be completed by the integrated logic circuitry in the hardware of the processor 1010 or by instructions in software form. The processor 1010 can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. It can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of this application. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the methods disclosed in the embodiments of this application can be directly embodied in the execution of a hardware decoding processor, or executed by a combination of hardware and software modules in the decoding processor. The software module can reside in a mature storage medium in the field, such as random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, or registers. This storage medium is located in memory, and the processor reads information from the memory and, in conjunction with its hardware, completes the steps of the above method.

[0092] The computer device can also execute the methods described in the preceding method embodiments and achieve the functions and beneficial effects of the methods described in the preceding method embodiments, which will not be repeated here.

[0093] Of course, in addition to software implementation, the electronic device 1000 of this application does not exclude other implementation methods, such as logic devices or a combination of hardware and software, etc. In other words, the execution subject of the following processing flow is not limited to each logic unit, but can also be hardware or logic devices.

[0094] This application also proposes a computer-readable storage medium that stores one or more programs, which, when executed by an electronic device including multiple applications, cause the electronic device to perform... Figure 1 The methods disclosed in the embodiments shown achieve the functions and beneficial effects of the methods described in the preceding method embodiments, and will not be repeated here.

[0095] The computer-readable storage medium mentioned above includes read-only memory (ROM), random access memory (RAM), magnetic disk, or optical disk, etc.

[0096] Furthermore, embodiments of this application also provide a computer program product, the computer program product including a computer program stored on a non-transitory computer-readable storage medium, the computer program including program instructions, which, when executed by a computer, implement the following process: Figure 1 The methods disclosed in the embodiments shown achieve the functions and beneficial effects of the methods described in the preceding method embodiments, and will not be repeated here.

[0097] The embodiments of this application can be applied to various scenarios of electronic device collaboration or interconnection, including: collaboration and interconnection between mobile phones and laptops / tablets; collaboration and interconnection between mobile terminals and smart TVs / monitors; collaboration and interconnection between mobile phones or tablets and in-vehicle entertainment systems; collaboration and interconnection between mobile terminals and smart conferencing systems, etc. This satisfies users' diverse needs in smart home, smart office, and smart travel scenarios.

[0098] In summary, the above description is merely a preferred embodiment of this application and does not limit the scope of protection of this application. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of this application.

[0099] The systems, devices, modules, or units described in the above embodiments can be implemented by computer chips or entities, or by products with certain functions. A typical implementation device is a computer. Specifically, a computer can be, for example, a personal computer, laptop computer, cellular phone, camera phone, smartphone, personal digital assistant, media player, navigation device, email device, game console, tablet computer, wearable device, or any combination of these devices.

[0100] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information by any method or technology. Information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can store information accessible to a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0101] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0102] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to interchangeably. Each embodiment focuses on describing the differences from other embodiments. In particular, the system embodiments are basically similar to the method embodiments, so the description is relatively simple; relevant parts can be referred to the descriptions in the method embodiments.

Claims

1. An airborne magnetic measurement compensation method based on a magnetic sensor chip network, characterized in that, include: During the spacecraft's exploration flight, the flight attitude data of the spacecraft and the geomagnetic field observation values ​​collected by all magnetic sensor chip nodes are acquired in real time; wherein, the spacecraft is equipped with a magnetic sensor chip network, which is composed of magnetic sensor chip nodes deployed at target locations inside and on the surface of the spacecraft. The flight attitude data is input into the three-dimensional interference field prediction module of the pre-trained airborne magnetic measurement compensation model, and the magnetic interference prediction value at each magnetic sensing chip node under the current flight attitude is output. The observed geomagnetic field value and the predicted magnetic interference value at each of the magnetic sensor chip nodes are input into the real-time compensation calculation module of the airborne magnetic measurement compensation model, and the geomagnetic field detection result currently obtained by the aircraft is output. The real-time compensation calculation module is used to determine the estimated geomagnetic field value at each magnetic sensor chip node based on the observed geomagnetic field value and the predicted magnetic interference value, and to determine the geomagnetic field detection result based on the estimated geomagnetic field values ​​at all magnetic sensor chip nodes and the weighting coefficient corresponding to each magnetic sensor chip node.

2. The method according to claim 1, characterized in that, The training process of the airborne magnetic measurement compensation model includes: During the calibration flight of the aircraft, multiple sets of sample data are acquired; each set of sample data includes the sample flight attitude data of the aircraft, the sample geomagnetic field observation values ​​collected by all magnetic sensor chip nodes, and the current geographical location of the sample. The sample flight attitude data is input into the three-dimensional interference field prediction module of the airborne magnetic measurement compensation model to be trained, and the sample magnetic interference prediction value at each magnetic sensing chip node under the current flight attitude is output. The sample geomagnetic field observation value and the sample magnetic interference prediction value at each of the magnetic sensing chip nodes are input into the real-time compensation calculation module of the airborne magnetic measurement compensation model, and the sample geomagnetic field detection result corresponding to the sample geographical location is output. Based on the geomagnetic field label value corresponding to the geographical location of the sample and the geomagnetic field detection results of the sample, the predicted loss of the airborne magnetic measurement compensation model is determined; Based on the predicted loss, the model parameters of the airborne magnetic measurement compensation model are updated to obtain the trained airborne magnetic measurement compensation model.

3. The method according to claim 2, characterized in that, During the aircraft calibration flight, multiple sets of sample data are acquired, including: During the calibration flight of the aircraft, sampling data at the currently detected geographical location is acquired at a preset sampling frequency; the sampling data includes sample geomagnetic field observation values ​​collected by all magnetic sensor chip nodes on the aircraft, sample flight attitude data of the aircraft, the geographical location of the currently detected sample, and the Coordinated Universal Time (UTC) timestamp. The sampled data is preprocessed to obtain preprocessed sampled data; the preprocessing operation includes outlier removal and / or noise reduction. Based on the UTC timestamp, the preprocessed sampled data is time-aligned to obtain time-aligned sampled data. For each sampling moment, the time-aligned sampling data is used to form a set of sample data based on the sample geomagnetic field observation values ​​collected by all magnetic sensor chip nodes on the aircraft, the sample flight attitude data of the aircraft, and the geographical location of the currently detected sample.

4. The method according to claim 3, characterized in that, The predicted loss of the airborne magnetic measurement compensation model is the residual standard deviation; The step of determining the prediction loss of the airborne magnetic survey compensation model based on the geomagnetic field label value corresponding to the geographical location of the sample and the geomagnetic field detection results of the sample includes: Calculate the residual between the geomagnetic field label value corresponding to the geographical location of the sample and the geomagnetic field detection result of the sample; The residual standard deviation of the airborne magnetic measurement compensation model is determined based on the residuals calculated at all sample geographical locations during the calibration flight of the aircraft.

5. The method according to claim 1, characterized in that, The method further includes: During the probe flight, the current geographical location and UTC timestamp are acquired in real time. The geomagnetic field detection results obtained by the spacecraft are bound with the current geographical location and the UTC timestamp to obtain a geomagnetic field detection record with spatiotemporal information; the geomagnetic field detection record is used for geomagnetic field mapping.

6. An airborne magnetic measurement compensation system based on a magnetic sensor chip network, characterized in that, It includes a magnetic sensor chip network, an attitude measurement unit, a data acquisition and processing unit, and a compensation calculation unit; the magnetic sensor chip network consists of magnetic sensor chip nodes deployed at target locations inside and on the surface of the aircraft; wherein... The magnetic sensor chip network is used to collect geomagnetic field observations through each magnetic sensor chip node; The attitude measurement unit is used to measure and obtain the flight attitude data of the aircraft; The data acquisition and processing unit is used to acquire the flight attitude data and the geomagnetic field observation values ​​collected by all magnetic sensor chip nodes in real time during the flight of the aircraft, and send the flight attitude data and the geomagnetic field observation values ​​collected by all magnetic sensor chip nodes to the compensation calculation unit. The compensation calculation unit is used to input the flight attitude data into the three-dimensional interference field prediction module of the pre-trained airborne magnetic measurement compensation model, and output the magnetic interference prediction value at each of the magnetic sensor chip nodes under the current flight attitude; input the geomagnetic field observation value and the magnetic interference prediction value at each of the magnetic sensor chip nodes into the real-time compensation calculation module of the airborne magnetic measurement compensation model, and output the geomagnetic field detection result currently obtained by the aircraft; wherein, the real-time compensation calculation module is used to determine the geomagnetic field estimate value at each of the magnetic sensor chip nodes based on the geomagnetic field observation value and the magnetic interference prediction value at the magnetic sensor chip node, and determine the geomagnetic field detection result based on the geomagnetic field estimate values ​​at all magnetic sensor chip nodes and the weight coefficient corresponding to each magnetic sensor chip node.

7. An airborne magnetic measurement compensation device based on a magnetic sensor chip network, characterized in that, include: The first acquisition module is used to acquire, in real time, the flight attitude data of the aircraft and the geomagnetic field observation values ​​collected by all magnetic sensor chip nodes during the aircraft's detection flight; wherein, the aircraft is equipped with a magnetic sensor chip network, which is composed of magnetic sensor chip nodes deployed at target locations inside and on the surface of the aircraft. The first model processing module is used to input the flight attitude data into the three-dimensional interference field prediction module of the pre-trained airborne magnetic measurement compensation model, and output the magnetic interference prediction value at each magnetic sensing chip node under the current flight attitude. The second model processing module is used to input the observed geomagnetic field value and the predicted magnetic interference value at each of the magnetic sensor chip nodes into the real-time compensation calculation module of the airborne magnetic measurement compensation model, and output the geomagnetic field detection result currently obtained by the aircraft; wherein, the real-time compensation calculation module is used to determine the geomagnetic field estimate value at each of the magnetic sensor chip nodes based on the observed geomagnetic field value and the predicted magnetic interference value at the magnetic sensor chip node, and determine the geomagnetic field detection result based on the geomagnetic field estimate values ​​at all magnetic sensor chip nodes and the weight coefficient corresponding to each of the magnetic sensor chip nodes.

8. An electronic device, characterized in that, include: processor; And a memory arranged to store computer-executable instructions configured to be executed by the processor to implement the airborne magnetic measurement compensation method based on a magnetic sensor chip network as described in any one of claims 1-5.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium is used to store computer-executable instructions, which, when executed by a processor, implement the airborne magnetic measurement compensation method based on a magnetic sensor chip network as described in any one of claims 1-5.

10. A computer program product, characterized in that, It includes a computer program that, when executed by a processor, implements the airborne magnetic measurement compensation method based on a magnetic sensor chip network as described in any one of claims 1-5.

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