Method and system for evaluating anomalies of the magnetic field of a drone
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- NAVAL UNIV OF ENG PLA
- Filing Date
- 2026-03-19
- Publication Date
- 2026-06-26
Smart Images
Figure CN122283548A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of magnetic field anomaly assessment technology, and in particular to a method and system for assessing anomalies in the magnetic field of an unmanned aerial vehicle (UAV). Background Technology
[0002] Accurate modeling and assessment of the UAV's own magnetic field are crucial prerequisites for improving the effectiveness of its magnetic detection missions. This relies heavily on the inversion calculation of the magnitude and position of the magnetic moment of the UAV's own magnetic dipoles. Existing technologies have significant shortcomings in this scenario: they lack constraint strategies tailored to the distribution characteristics of the UAV's own magnetic sources (such as built-in electronic components and mounted equipment), further exacerbating the uncertainty of the inversion results. Simultaneously, traditional techniques rely on random initialization of the population for global search, resulting in slow algorithm convergence and a high likelihood of invalid solutions. Consequently, the accuracy of UAV magnetic field simulation is low (magnetic moment inversion errors are generally between 5% and 8%), failing to accurately support magnetic anomaly value assessment.
[0003] Therefore, how to improve the certainty of the inversion results and thus achieve accurate assessment of magnetic anomalies has become an urgent problem to be solved.
[0004] The above content is only used to help understand the technical solution of the present invention and does not represent an admission that the above content is prior art. Summary of the Invention
[0005] The main objective of this invention is to provide a method and system for anomaly assessment of the magnetic field of an unmanned aerial vehicle (UAV), aiming to improve the certainty of inversion results and thus achieve accurate assessment of magnetic anomalies.
[0006] To achieve the above objectives, the present invention provides a method for anomaly assessment of the magnetic field of a UAV, the method comprising: S1 collects magnetic field measurement data of the UAV through a three-axis fluxgate sensor array, and predicts the number of magnetic dipoles and spatial orientation boundary based on the UAV structural design information; S2, construct magnetic dipole range constraints based on the number of magnetic dipoles and the spatial orientation boundary; S3, Based on the magnetic dipole range constraint, the optimal magnetic dipole parameter set is obtained through a genetic algorithm; S4, embed the optimal magnetic dipole parameter set into the magnetic field calculation model, and obtain the magnetic field intensity distribution cloud map and the closed surface with equal magnetic induction intensity of the UAV's own magnetic field under a specific scenario through the magnetic field calculation model; S5. Based on the magnetic field intensity distribution cloud map and the closed surface of the isomagnetic induction intensity, an anomaly assessment is performed on the UAV's own magnetic field.
[0007] Optionally, S3 includes: S3.1, The magnetic dipole range constraint is embedded in the genetic algorithm to initialize multiple sets of magnetic dipole parameters; S3.2 Calculate the theoretical magnetic field strength of each magnetic dipole based on the initialized multiple sets of magnetic dipole parameters and the measured coordinates of the measurement point; S3.3, based on the theoretical and measured magnetic field strength of each magnetic dipole, the fitness deviation and total fitness deviation of each magnetic dipole are obtained through the fitness function; S3.4, Based on the fitness deviation of each magnetic dipole, multiple magnetic dipoles are divided into a set of high-quality magnetic dipoles and a set of magnetic dipoles to be optimized; S3.5, perform crossover and mutation processing on the magnetic dipole subset to be optimized to obtain the optimized magnetic dipole subset; S3.6, determine whether the preset iteration stop condition is met based on the total fitness deviation and the current iteration number; S3.7, If so, then the high-quality magnetic dipole subset and the optimized magnetic dipole subset shall be taken as the optimal magnetic dipole parameter set; S3.8 If not, calculate the fitness deviation of each magnetic dipole and the total fitness deviation based on the high-quality magnetic dipole subset and the optimized magnetic dipole subset, and return to S3.4 until the preset stopping condition is met.
[0008] Optionally, S3.2 includes: S3.2.1, Determine the magnetic moment of each magnetic dipole and the predicted coordinates of the measurement point based on the initialized multiple sets of magnetic dipole parameters; S3.2.2 Calculate the theoretical magnetic field strength of each magnetic dipole based on the magnetic moment corresponding to each magnetic dipole, the predicted coordinates of the measurement point, and the measured coordinates of the measurement point.
[0009] Optionally, the fitness function is:
[0010]
[0011] In the formula, For the fitness deviation of magnetic dipole k, Let be the measured magnetic field strength of magnetic dipole k. Let be the theoretical strength of the magnetic field of magnetic dipole k. The total fitness bias, denoted as the number of magnetic dipoles.
[0012] Optionally, obtaining the magnetic field intensity distribution cloud map and the closed surface with equal magnetic induction intensity of the UAV's own magnetic field under a specific scenario through the magnetic field calculation model includes: Input the UAV cross-sectional distance and grid accuracy information into the magnetic field calculation model; The resultant magnetic field strength of each grid point is calculated using a multi-magnetic dipole superposition algorithm based on the UAV cross-sectional distance and the grid accuracy information. Based on the resultant magnetic field strength at each grid point, a contour map of the magnetic field strength distribution in the plane is generated by fitting contour lines. A closed surface with equal magnetic induction intensity is generated based on the target magnetic induction intensity and the resultant magnetic field intensity of each grid point.
[0013] Optionally, the step of generating a closed surface with equal magnetic induction intensity based on the target magnetic induction intensity and the resultant magnetic field intensity at each grid point includes: A preset spatial range is determined based on the drone's coordinates, and spatial sampling is performed within the preset spatial range, selecting multiple discrete sampling points; The resultant magnetic field strength at each discrete sampling point is calculated using the multi-magnetic dipole superposition algorithm. The target sampling point is selected from each discrete sampling point based on the target magnetic induction intensity and the resultant magnetic field intensity of each discrete sampling point; The target sampling points are fitted using a surface fitting algorithm to generate closed surfaces with equal magnetic induction intensity.
[0014] Optionally, S5 includes: S5.1, Based on the magnetic field intensity distribution cloud map and the closed surface with equal magnetic induction intensity, perform a full-domain three-dimensional model of the UAV's own magnetic field to obtain the magnetic field intensity difference distribution cloud map and magnetic field vector field; S5.2, anomaly assessment of the UAV's own magnetic field is performed based on the closed surface of the equal magnetic induction intensity, the cloud map of the magnetic field intensity difference distribution, and the magnetic field vector field.
[0015] Furthermore, to achieve the above objectives, the present invention also proposes an anomaly assessment system for the self-magnetic field of a UAV, the system comprising: The determination module is used to collect magnetic field measurement data of the UAV through a three-axis fluxgate sensor array, and to predict the number of magnetic dipoles and spatial orientation boundaries based on the UAV's structural design information; The construction module is used to construct the magnetic dipole range constraint based on the number of magnetic dipoles and the spatial orientation boundary; The calculation module is used to obtain the optimal magnetic dipole parameter set through a genetic algorithm based on the magnetic dipole range constraint; The calculation module is also used to embed the optimal magnetic dipole parameter set into the magnetic field calculation model, and obtain the magnetic field intensity distribution cloud map and the closed surface with equal magnetic induction intensity of the UAV’s own magnetic field under a specific scenario through the magnetic field calculation model. The evaluation module is used to perform anomaly assessment of the UAV's own magnetic field based on the magnetic field intensity distribution cloud map and the closed surface of the isomagnetic induction intensity.
[0016] Furthermore, to achieve the above objectives, the present invention also proposes an anomaly assessment device for the self-magnetic field of a drone, the device comprising: a memory, a processor, and an anomaly assessment program for the self-magnetic field of a drone stored in the memory and executable on the processor, the anomaly assessment program for the self-magnetic field of a drone being configured to implement the steps of the anomaly assessment method for the self-magnetic field of a drone as described above.
[0017] In addition, to achieve the above objectives, the present invention also proposes a storage medium storing an anomaly assessment program for the UAV's own magnetic field, wherein when the UAV's own magnetic field anomaly assessment program is executed by a processor, the steps of the UAV's own magnetic field anomaly assessment method as described above are implemented.
[0018] This invention first acquires magnetic field measurement data from a UAV using a three-axis fluxgate sensor array. Based on the UAV's structural design information, the number of magnetic dipoles and their spatial orientation boundaries are estimated. Then, a magnetic dipole range constraint is constructed based on the number of magnetic dipoles and the spatial orientation boundaries. Based on this range constraint, a genetic algorithm is used to obtain the optimal set of magnetic dipole parameters. This optimal parameter set is then embedded into a magnetic field calculation model. The model obtains a magnetic field intensity distribution cloud map and a closed surface with isomagnetic induction intensity for the UAV's own magnetic field under specific scenarios. Finally, anomaly assessment of the UAV's own magnetic field is performed based on the magnetic field intensity distribution cloud map and the closed surface. This invention improves the determinism of the inversion results by introducing a priori range constraint of magnetic dipoles into the genetic algorithm. Then, based on the optimal magnetic dipole parameters, a three-dimensional model of the UAV's own magnetic field domain is constructed using the magnetic field calculation model, meeting the requirements of high-precision magnetic detection missions for UAVs and thus achieving accurate assessment of magnetic anomalies. Attached Figure Description
[0019] Figure 1 This is a schematic diagram of the structure of an anomaly assessment device for the magnetic field of a drone in the hardware operating environment involved in the embodiments of the present invention; Figure 2 This is a flowchart illustrating the first embodiment of the method for assessing the anomalies in the magnetic field of a drone according to the present invention. Figure 3 This is a structural block diagram of the first embodiment of the UAV's own magnetic field anomaly assessment system of the present invention.
[0020] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation
[0021] It should be understood that the specific embodiments described herein are for illustrative purposes only and are not intended to limit the scope of the invention.
[0022] Reference Figure 1 , Figure 1 This is a schematic diagram of the structure of an abnormal assessment device for the magnetic field of a drone in the hardware operating environment involved in the embodiments of the present invention.
[0023] like Figure 1 As shown, the device for assessing the anomaly of the UAV's own magnetic field may include: a processor 1001, such as a central processing unit (CPU), a communication bus 1002, a user interface 1003, a network interface 1004, and a memory 1005. The communication bus 1002 is used to establish communication between these components. The user interface 1003 may include a display screen or an input unit such as a keyboard; optionally, the user interface 1003 may also include a standard wired interface or a wireless interface. The network interface 1004 may optionally include a standard wired interface or a wireless interface (such as a Wireless-Fidelity (Wi-Fi) interface). The memory 1005 may be high-speed random access memory (RAM) or stable non-volatile memory (NVM), such as a disk drive. The memory 1005 may also optionally be a storage system independent of the aforementioned processor 1001.
[0024] Those skilled in the art will understand that Figure 1 The structure shown does not constitute a limitation on the device for assessing the anomalies of the drone's own magnetic field, and may include more or fewer components than shown, or combine certain components, or have different component arrangements.
[0025] like Figure 1 As shown, the memory 1005, which serves as a storage medium, may include an operating system, a network communication module, a user interface module, and an anomaly assessment program for the UAV's own magnetic field.
[0026] exist Figure 1In the abnormal assessment device for the drone's own magnetic field shown, the network interface 1004 is mainly used for data communication with the network server; the user interface 1003 is mainly used for data interaction with the user; the processor 1001 and the memory 1005 in the abnormal assessment device for the drone's own magnetic field of the present invention can be set in the abnormal assessment device for the drone's own magnetic field. The abnormal assessment device for the drone's own magnetic field calls the abnormal assessment program for the drone's own magnetic field stored in the memory 1005 through the processor 1001 and executes the abnormal assessment method for the drone's own magnetic field provided in the embodiment of the present invention.
[0027] This invention provides a method for assessing anomalies in the magnetic field of a drone, referring to... Figure 2 , Figure 2 This is a flowchart illustrating the first embodiment of the method for assessing the anomalies in the magnetic field of a drone according to the present invention.
[0028] In this embodiment, the method for assessing the anomalies in the UAV's own magnetic field includes the following steps: S1 collects magnetic field measurement data of the UAV through a three-axis fluxgate sensor array, and predicts the number of magnetic dipoles and spatial orientation boundaries based on the UAV's structural design information.
[0029] It is easy to understand that the execution subject of this embodiment can be an anomaly assessment system of the UAV's own magnetic field with functions such as data processing, network communication and program operation, or other computer devices with similar functions. This embodiment does not limit it.
[0030] It should be noted that the UAV magnetic field measurement data includes the three-dimensional components of the magnetic dipole and the measured coordinates of the measurement point of the magnetic dipole.
[0031] In the specific implementation, it is necessary to pre-determine the number of magnetic sources (i.e. hardware modules) of the UAV. Since each magnetic source includes at least one magnetic dipole, the number of magnetic dipoles (i.e. the number of magnetic dipoles included in all magnetic sources) and spatial orientation boundaries (i.e. the access boundaries of magnetic dipoles) should be planned and input in advance based on the UAV structural design information (such as electronic component layout, motor, and mounting position).
[0032] S2, construct magnetic dipole range constraints based on the number of magnetic dipoles and the spatial orientation boundary.
[0033] Assuming the number of magnetic dipoles is N, and the spatial orientation boundary is set as follows: The range constraint of the magnetic dipole is that the number of magnetic dipoles N must be satisfied, and the orientation boundary of the magnetic dipoles is... .
[0034] S3, Based on the range constraint of the magnetic dipole, the optimal set of magnetic dipole parameters is obtained by genetic algorithm.
[0035] Further, in step S3.1, the range constraint of the magnetic dipole is embedded into the genetic algorithm to initialize multiple sets of magnetic dipole parameters; in step S3.2, the theoretical magnetic field strength of each magnetic dipole is calculated based on the initialized multiple sets of magnetic dipole parameters and the measured coordinates of the measurement points; in step S3.3, the fitness deviation of each magnetic dipole and the total fitness deviation are obtained through the fitness function based on the theoretical and measured magnetic field strengths of each magnetic dipole; in step S3.4, the multiple magnetic dipoles are divided into a set of high-quality magnetic dipoles and a set of magnetic dipoles to be optimized based on the fitness deviations of each magnetic dipole. S3.5, perform crossover and mutation processing on the magnetic dipole subset to be optimized to obtain the optimized magnetic dipole subset; S3.6, determine whether the preset iteration stop condition is met based on the total fitness deviation and the current iteration number; S3.7, if yes, then take the high-quality magnetic dipole subset and the optimized magnetic dipole subset as the optimal magnetic dipole parameter set; S3.8, if no, then calculate the fitness deviation and total fitness deviation of each magnetic dipole based on the high-quality magnetic dipole subset and the optimized magnetic dipole subset, and return to S3.4, until the preset iteration stop condition is met.
[0036] It should be noted that each set of magnetic dipole parameters includes the magnetic moment corresponding to the magnetic dipole and the predicted coordinates of the measurement point. The preset limit-down condition is that the total fitness deviation is less than a preset threshold (e.g., 5*10). -9 Stop iterating when the maximum number of iterations is reached.
[0037] In the specific implementation, the range constraint of the magnetic dipole is embedded in the genetic algorithm. During the population initialization stage, N sets of magnetic dipole parameters are randomly generated within the range constraint of the magnetic dipole. The theoretical magnetic field strength of each magnetic dipole is calculated based on the magnetic moment corresponding to each magnetic dipole, the predicted coordinates of the measurement point, and the measured coordinates of the measurement point.
[0038] The theoretical formula for calculating the magnetic field strength generated by a magnetic dipole is as follows: Let the predicted coordinates of the i-th measurement point be... The measured coordinates of the measurement point are :
[0039]
[0040]
[0041] In the formula, Let X, Y, and Z be the magnetic moments of the i-th magnetic dipole in the X, Y, and Z directions, respectively. , This represents the theoretical strength of the magnetic field.
[0042] The fitness function is:
[0043]
[0044] In the formula, For the fitness deviation of magnetic dipole i, Let be the measured magnetic field strength of magnetic dipole i. Let be the theoretical magnetic field strength of magnetic dipole i. The total fitness bias, denoted as the number of magnetic dipoles.
[0045] In the specific implementation, the magnetic dipoles are sorted from high to low according to their fitness deviation. The top 30% of high-quality individuals with the highest fitness deviation are retained as high-quality magnetic dipole subsets and directly enter the next generation. The remaining individuals are treated as magnetic dipole subsets to be optimized and subjected to crossover and mutation processing to obtain the optimized magnetic dipole subsets.
[0046] Cross processing: Magnetic moment parameters are processed using arithmetic cross processing (... (For random weights), the position parameters use single-point crossover (exchange) or (Coordinates), after intersection, the constraint range is automatically checked, and parameters that exceed the limit are forcibly truncated to the boundary.
[0047] Variation handling: The variation range of the magnetic moment parameter is ≤ 5% of the current magnetic moment value. The position parameter is only finely adjusted within the azimuth boundary. After variation, out-of-bounds parameters are automatically truncated to the nearest constraint boundary.
[0048] S4. Embed the optimal magnetic dipole parameter set into the magnetic field calculation model, and obtain the magnetic field intensity distribution cloud map and the closed surface with equal magnetic induction intensity of the UAV's own magnetic field under a specific scenario through the magnetic field calculation model.
[0049] It should be noted that the output optimal magnetic dipole parameter set can be directly used for modeling the UAV's own magnetic field, for example, by combining multiple sets of parameters. By substituting the magnetic field into the magnetic field calculation model, the magnetic field strength at any position on the drone can be calculated, thereby assessing the magnetic anomaly value and providing a basis for suppressing magnetic interference and optimizing the accuracy of magnetic detection.
[0050] Furthermore, the processing method for obtaining the magnetic field intensity distribution cloud map and the closed surface with equal magnetic induction intensity of the UAV's own magnetic field in a specific scenario through the magnetic field calculation model is as follows: input the UAV's tangential distance and grid accuracy information into the magnetic field calculation model; calculate the resultant magnetic field intensity of each grid point using a multi-magnetic dipole superposition algorithm based on the UAV's tangential distance and grid accuracy information; generate a magnetic field intensity distribution cloud map in the plane by contour fitting based on the resultant magnetic field intensity of each grid point; determine a preset spatial range based on the UAV's coordinates, and perform spatial sampling within the preset spatial range to select multiple discrete sampling points; calculate the resultant magnetic field intensity of each discrete sampling point using a multi-magnetic dipole superposition algorithm; select a target sampling point from the discrete sampling points based on the target magnetic induction intensity and the resultant magnetic field intensity of each discrete sampling point; fit the target sampling point using a surface fitting algorithm (e.g., triangular mesh method) to generate a closed surface with equal magnetic induction intensity.
[0051] In the specific implementation, the optimal magnetic dipole parameters are substituted into the magnetic field calculation model to achieve accurate quantification of the magnetic field distribution for scenarios such as "planes at specific distances" and "surfaces with equal magnetic induction intensity." (1) Calculation of magnetic field distribution in a plane at a specific distance (parameters that can be input: tangent distance, mesh accuracy): Taking "a parallel plane 0.5m from the surface of the drone fuselage" as an example, perform the following operations: Scenario definition: Using the drone's center of gravity as the origin of the coordinate system, define the planar equations (e.g., ...). m, assuming the fuselage surface is (place); Mesh generation: Grid points are generated within this plane with a precision of 0.01m × 0.01m. ; For each grid point, the three-dimensional magnetic field components are calculated using the multi-magnetic dipole superposition model (i.e., the theoretical formula for calculating the magnetic field strength generated by the superposition of multiple magnetic dipoles):
[0052]
[0053] Calculate the resultant magnetic field strength at each grid point A cloud map of magnetic field intensity distribution in the plane is generated by contour fitting.
[0054] (2) Construction of iso-intensity surfaces for specific magnetic induction intensities With "target magnetic induction intensity" For example, perform the following operations: Spatial sampling: within a spatial range centered on the drone, such as Discrete sampling points are selected in steps of 0.05m; Magnetic field strength screening: For each sampling point, the resultant magnetic field strength B is calculated using a multi-magnetic dipole superposition model, and samples that meet the requirements are screened out. Points that meet the (precision threshold) are used as target sampling points; Equal intensity surface fitting: The triangular mesh method and equal surface fitting algorithm are used to fit the selected points to generate a closed surface with equal magnetic induction intensity in space (i.e., equal intensity surface).
[0055] S5. Based on the magnetic field intensity distribution cloud map and the closed surface of the isomagnetic induction intensity, an anomaly assessment is performed on the UAV's own magnetic field.
[0056] Furthermore, a three-dimensional model of the UAV's own magnetic field is performed based on the magnetic field intensity distribution cloud map and the closed surface with equal magnetic induction intensity to obtain the magnetic field intensity difference distribution cloud map and the magnetic field vector field; based on the closed surface with equal magnetic induction intensity, the magnetic field intensity difference distribution cloud map and the magnetic field vector field, anomaly assessment of the UAV's own magnetic field is performed.
[0057] In the specific implementation, based on the magnetic field data of the isostatic surface and key cross-sections (i.e., manually input by the user, such as the magnetic field strength data of the XY plane 5m below the target), a full-domain 3D model of the UAV's own magnetic field is completed, and the magnetic anomaly level assessment of the core area is carried out: Global Magnetic Field 3D Modeling: Utilizing professional visualization tools, this model integrates magnetic field data from "specific distance planes," "equal intensity surfaces," and other key cross-sections to construct a 3D model of the magnetic field surrounding the UAV. Outputs include: Magnetic Field Intensity Difference Distribution Cloud Map: Visually displays the distribution differences of magnetic field intensity in space using color gradients; and Magnetic Field Vector Field: Displays the direction and magnitude distribution of the magnetic field using arrow directions and lengths.
[0058] Anomaly analysis of the UAV's own magnetic field is performed based on the closed surface of equal magnetic induction intensity, the cloud map of magnetic field intensity difference distribution, and the magnetic field vector field. This provides a decision-making basis for the suppression of magnetic interference and the optimization of magnetic detection accuracy of the UAV, and yields an anomaly analysis report. The anomaly analysis report includes a list of optimal parameters for the magnetic dipole, visualization results, magnetic anomaly assessment results, and engineering optimization suggestions.
[0059] List of optimal parameter sets for magnetic dipoles: Defining the magnitude of the magnetic moment of each magnetic dipole. Spatial location Visualization results: magnetic field distribution cloud maps of isointense surfaces and key planes, magnetic field vector fields, etc.; magnetic anomaly assessment results: anomaly levels of each region, location and cause analysis of high anomaly regions; engineering optimization suggestions: such as providing suggestions based on inversion results to install permalloy shielding in high anomaly regions, and adjusting the layout of electronic components to keep them away from magnetic detection sensors, etc.
[0060] It should also be noted that the above steps have enabled a complete technical chain from "magnetic dipole parameter inversion" to "magnetic field scenario analysis - anomaly assessment - engineering optimization", which has significantly improved the accuracy and reliability of UAV magnetic detection missions.
[0061] In this embodiment, firstly, magnetic field measurement data of the UAV is collected using a three-axis fluxgate sensor array. Based on the UAV's structural design information, the number of magnetic dipoles and their spatial orientation boundaries are estimated. Then, a magnetic dipole range constraint is constructed based on the number of magnetic dipoles and their spatial orientation boundaries. Based on this range constraint, a genetic algorithm is used to obtain the optimal set of magnetic dipole parameters. This optimal set of parameters is then embedded into a magnetic field calculation model. The model obtains a magnetic field intensity distribution cloud map and a closed surface with equal magnetic induction intensity for the UAV's own magnetic field in a specific scenario. Finally, anomaly assessment of the UAV's own magnetic field is performed based on the magnetic field intensity distribution cloud map and the closed surface. This embodiment improves the determinism of the inversion results by introducing a priori range constraint of magnetic dipoles into the genetic algorithm. Then, based on the optimal magnetic dipole parameters, a three-dimensional model of the UAV's own magnetic field domain is constructed using the magnetic field calculation model, meeting the requirements of high-precision magnetic detection missions for UAVs and thus achieving accurate assessment of magnetic anomalies.
[0062] Reference Figure 3 , Figure 3 This is a structural block diagram of the first embodiment of the UAV's own magnetic field anomaly assessment system of the present invention.
[0063] like Figure 3 As shown, the anomaly assessment system for the self-magnetic field of a UAV proposed in this embodiment of the invention includes: The determination module 3001 is used to collect magnetic field measurement data of the UAV through a three-axis fluxgate sensor array, and to predict the number of magnetic dipoles and spatial orientation boundaries based on the UAV structural design information. Construction module 3002 is used to construct magnetic dipole range constraints based on the number of magnetic dipoles and the spatial orientation boundary; Calculation module 3003 is used to obtain the optimal magnetic dipole parameter set through a genetic algorithm based on the magnetic dipole range constraint; The calculation module 3003 is also used to embed the optimal magnetic dipole parameter set into the magnetic field calculation model, and obtain the magnetic field intensity distribution cloud map and the closed surface with equal magnetic induction intensity of the UAV’s own magnetic field under a specific scenario through the magnetic field calculation model. The evaluation module 3004 is used to perform anomaly evaluation of the UAV's own magnetic field based on the magnetic field intensity distribution cloud map and the closed surface of the isomagnetic induction intensity.
[0064] Other embodiments or specific implementations of the abnormal magnetic field assessment system for UAVs of the present invention can be found in the above-described method embodiments, and will not be repeated here.
[0065] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or system 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 system. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or system that includes that element.
[0066] The sequence numbers of the above embodiments of the present invention are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.
[0067] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as read-only memory / random access memory, magnetic disk, optical disk) and includes several instructions to cause a terminal device (which may be a mobile phone, computer, server, or network device, etc.) to execute the methods described in the various embodiments of the present invention.
[0068] The above are merely preferred embodiments of the present invention and do not limit the scope of the patent. Any equivalent structural or procedural transformations made based on the description and drawings of the present invention, or direct or indirect applications in other related technical fields, are similarly included within the scope of patent protection of the present invention.
Claims
1. A method for assessing anomalies in the magnetic field of a UAV, characterized in that, The method includes the following steps: S1 collects magnetic field measurement data of the UAV through a three-axis fluxgate sensor array, and predicts the number of magnetic dipoles and spatial orientation boundary based on the UAV structural design information; S2, construct magnetic dipole range constraints based on the number of magnetic dipoles and the spatial orientation boundary; S3, Based on the magnetic dipole range constraint, the optimal magnetic dipole parameter set is obtained through a genetic algorithm; S4, embed the optimal magnetic dipole parameter set into the magnetic field calculation model, and obtain the magnetic field intensity distribution cloud map and the closed surface with equal magnetic induction intensity of the UAV's own magnetic field under a specific scenario through the magnetic field calculation model; S5. Based on the magnetic field intensity distribution cloud map and the closed surface of the isomagnetic induction intensity, an anomaly assessment is performed on the UAV's own magnetic field.
2. The method as described in claim 1, characterized in that, The S3 includes: S3.1, The magnetic dipole range constraint is embedded in the genetic algorithm to initialize multiple sets of magnetic dipole parameters; S3.2 Calculate the theoretical magnetic field strength of each magnetic dipole based on the initialized multiple sets of magnetic dipole parameters and the measured coordinates of the measurement point; S3.3, based on the theoretical and measured magnetic field strength of each magnetic dipole, the fitness deviation and total fitness deviation of each magnetic dipole are obtained through the fitness function; S3.4, Based on the fitness deviation of each magnetic dipole, multiple magnetic dipoles are divided into a set of high-quality magnetic dipoles and a set of magnetic dipoles to be optimized; S3.5, perform crossover and mutation processing on the magnetic dipole subset to be optimized to obtain the optimized magnetic dipole subset; S3.6, determine whether the preset iteration stop condition is met based on the total fitness deviation and the current iteration number; S3.7, If so, then the high-quality magnetic dipole subset and the optimized magnetic dipole subset shall be taken as the optimal magnetic dipole parameter set; S3.8 If not, calculate the fitness deviation of each magnetic dipole and the total fitness deviation based on the high-quality magnetic dipole subset and the optimized magnetic dipole subset, and return to S3.4 until the preset stopping condition is met.
3. The method as described in claim 2, characterized in that, S3.2 includes: S3.2.1, Determine the magnetic moment of each magnetic dipole and the predicted coordinates of the measurement point based on the initialized multiple sets of magnetic dipole parameters; S3.2.2 Calculate the theoretical magnetic field strength of each magnetic dipole based on the magnetic moment corresponding to each magnetic dipole, the predicted coordinates of the measurement point, and the measured coordinates of the measurement point.
4. The method as described in claim 2, characterized in that, The fitness function is: In the formula, For the fitness deviation of magnetic dipole k, Let be the measured magnetic field strength of magnetic dipole k. Let be the theoretical strength of the magnetic field of magnetic dipole k. The total fitness bias, denoted as the number of magnetic dipoles.
5. The method as described in claim 1, characterized in that, The process of obtaining the magnetic field intensity distribution cloud map and the closed surface with equal magnetic induction intensity of the UAV's own magnetic field in a specific scenario through the magnetic field calculation model includes: Input the UAV cross-sectional distance and grid accuracy information into the magnetic field calculation model; The resultant magnetic field strength of each grid point is calculated using a multi-magnetic dipole superposition algorithm based on the UAV cross-sectional distance and the grid accuracy information. Based on the resultant magnetic field strength at each grid point, a contour map of the magnetic field strength distribution in the plane is generated by fitting contour lines. A closed surface with equal magnetic induction intensity is generated based on the target magnetic induction intensity and the resultant magnetic field intensity of each grid point.
6. The method as described in claim 5, characterized in that, The method of generating a closed surface with equal magnetic induction intensity based on the target magnetic induction intensity and the resultant magnetic field intensity of each grid point includes: A preset spatial range is determined based on the drone's coordinates, and spatial sampling is performed within the preset spatial range, selecting multiple discrete sampling points; The resultant magnetic field strength at each discrete sampling point is calculated using the multi-magnetic dipole superposition algorithm. The target sampling point is selected from each discrete sampling point based on the target magnetic induction intensity and the resultant magnetic field intensity of each discrete sampling point; The target sampling points are fitted using a surface fitting algorithm to generate closed surfaces with equal magnetic induction intensity.
7. The method as described in claim 1, characterized in that, The S5 includes: S5.1, Based on the magnetic field intensity distribution cloud map and the closed surface with equal magnetic induction intensity, perform a full-domain three-dimensional model of the UAV's own magnetic field to obtain the magnetic field intensity difference distribution cloud map and magnetic field vector field; S5.2, anomaly assessment of the UAV's own magnetic field is performed based on the closed surface of the equal magnetic induction intensity, the cloud map of the magnetic field intensity difference distribution, and the magnetic field vector field.
8. A system for assessing anomalies in the magnetic field of a drone, characterized in that, The system includes: The determination module is used to collect magnetic field measurement data of the UAV through a three-axis fluxgate sensor array, and to predict the number of magnetic dipoles and spatial orientation boundaries based on the UAV's structural design information; The construction module is used to construct the magnetic dipole range constraint based on the number of magnetic dipoles and the spatial orientation boundary; The calculation module is used to obtain the optimal magnetic dipole parameter set through a genetic algorithm based on the magnetic dipole range constraint; The calculation module is also used to embed the optimal magnetic dipole parameter set into the magnetic field calculation model, and obtain the magnetic field intensity distribution cloud map and the closed surface with equal magnetic induction intensity of the UAV’s own magnetic field under a specific scenario through the magnetic field calculation model. The evaluation module is used to perform anomaly assessment of the UAV's own magnetic field based on the magnetic field intensity distribution cloud map and the closed surface of the isomagnetic induction intensity.