CN116976218B - Multi-magnetic dipole inversion method and device and electronic equipment - Google Patents
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Abstract
The invention provides a multi-magnetic dipole inversion method, a device and electronic equipment, wherein the method comprises the following steps: according to the quantity of the social learning factors, constructing a sub-particle swarm for each social learning factor, and determining the degree of movement of an individual along a global optimal value by the social learning factors; based on a particle swarm optimization algorithm, each sub-particle swarm independently carries out an optimization process according to an adaptability function and a speed position updating mode; screening out sub-particle swarms meeting screening conditions after optimizing each sub-particle swarm; and continuing the optimizing process of the selected sub-particle swarm, and selecting the overall optimal value after optimizing as a final optimizing result. The invention can better reflect the magnetic characteristics of the object to be measured, and obtain the inversion magnetic dipoles with high quantity, high accuracy, high success rate and strong robustness, thereby providing convenience for the development of magnetic compensation work and the optimization of satellite layout and providing a finer magnetic source model for the numerical simulation verification of the magnetic model of the magnetic sensitive area and the device.
Description
Technical Field
The invention mainly relates to the technical field of spacecraft magnetic testing, in particular to a multi-magnetic dipole inversion method, a multi-magnetic dipole inversion device and electronic equipment.
Background
In order to meet the functional requirements of satellites, a certain amount of magnetic materials are needed to be used for the spacecraft, and magnetic fields generated by the materials can act together with geomagnetic or interstellar magnetic fields to generate interference moment to influence the attitude of the satellites on one hand, and interference to magnetosensitive devices (scientific magnetometers, inertial sensors and the like) can be generated on the other hand. Therefore, accurate magnetic characteristic measurement is required to be carried out on the spacecraft so as to optimize magnetic field modeling, simulation, verification and satellite design of the region where the magnetosensitive device is located.
Currently, indirect methods are often used to calculate the magnetic moment. The indirect method is to measure the magnetic field distribution around a spacecraft or a single machine (hereinafter referred to as an object to be measured), and then calculate the magnetic moment of the object to be measured through a mathematical analysis method and an optimization algorithm, such as a magnetic dipole method, a spherical surface mapping method, an equatorial mapping method, a dynamic loop method, a near-field multi-magnetic dipole method and the like.
When the far-field magnetic dipole method is used for measuring the magnetic field, the influence of random errors on the magnetic moment calculation result is large, and the wrong result can seriously interfere with the normal operation of a scientific satellite. In addition, the magnetic dipole method, the spherical surface mapping method, and the equatorial mapping method can calculate only a single magnetic moment, which each treat the object to be measured as one dipole, but in reality, a plurality of dipoles may exist in one object to be measured, so that the above methods cannot accurately reflect the magnetic characteristics of the object to be measured. The method for calculating the multi-magnetic dipole based on the dynamic loop method can only invert 3 equivalent magnetic dipole moments, and the method for inverting the multi-magnetic dipole based on the optimization algorithm can only invert a small number of magnetic moments in an object to be measured, namely invert 1 to 3 magnetic dipole moments. Especially when facing a single machine in a working state, the number of magnetic dipole moments may become large, and the methods cannot accurately invert the actual magnetic characteristics of the object to be measured, so that the practical engineering significance has a certain limitation.
In addition, as the size, the direction and the position of the multi-magnetic dipole moment in the object to be measured have stronger uncertainty, the multi-magnetic dipole moment is difficult to be suitable for the multi-magnetic dipole moment with stronger uncertainty by using a classical or improved optimization algorithm, and the success rate of inversion is low. It can be seen how to accurately invert the magnetic characteristics of a spacecraft or a single machine is one of the problems to be solved in the field of spacecraft magnetic characteristics inversion.
Disclosure of Invention
The invention aims to solve the technical problem of providing a multi-magnetic dipole inversion method, a multi-magnetic dipole inversion device and electronic equipment, which can better reflect the magnetic characteristics of an object to be measured, and obtain inversion magnetic dipoles with high quantity, high accuracy, high success rate and strong robustness.
In order to solve the above technical problems, in a first aspect, the present invention provides a multi-magnetic dipole inversion method, including: constructing a sub-particle swarm for each social learning factor according to the quantity of the social learning factors, wherein the social learning factors determine the degree of movement of an individual along a global optimal value; based on a particle swarm optimization algorithm, each particle swarm independently carries out an optimizing process according to an adaptability function and a speed position updating mode; screening out the particle swarm meeting the screening condition after optimizing each particle swarm; and continuing the optimizing process of the selected sub-particle swarm, and selecting the global optimal value after optimizing as a final optimizing result.
Optionally, before each sub-particle group performs the optimizing process, the method further includes: and initializing parameters of the particle swarm algorithm, including setting an inertia factor, an individual learning factor and/or a social learning factor, wherein the inertia factor determines the degree of movement of the individual along the current direction, and the individual learning factor determines the degree of movement of the individual along the direction of the optimal value of the individual.
Optionally, the step of independently performing the optimizing process according to the fitness function and the speed position updating mode of each sub-particle group further comprises: and the fitness function of each sub-particle swarm is the same and/or the speed and position updating mode is the same.
Optionally, the fitness function is a sum of squares of differences between measured and calculated values of the magnetic dipoles.
Optionally, the screening the population of the sub-particles meeting the screening condition comprises: and arranging the global optimal value of each sub-particle swarm in order from small to large, and screening out a plurality of front sub-particle swarms to perform next optimization.
Optionally, the speed position updating mode includes:
Wherein V i t+1 and Representing the speed and position of the ith particle at the t+1st iteration, V i t and/>, respectivelyRespectively representing the speed and position of the ith particle at the t-th iteration, P ibest and P gbest respectively representing the individual optimum and the global optimum, r i and r g representing two random numbers equally distributed between 0 and 1, ω, c i and c g respectively representing the inertia factor, individual learning factor and social learning factor.
Optionally, the step of screening out the sub-particle swarm meeting the screening condition further comprises: setting a screening mechanism asWherein p ratio is the number of the sub-particle groups after screening, r pass is the elimination rate of the sub-particle groups,/>For the number of social learning factors.
Optionally, the termination condition of each sub-particle swarm optimization process is that the number of the optimized iterations reaches a preset number of iterations.
In a second aspect, the present invention provides a multi-magnetic dipole inversion apparatus, including a construction module, configured to construct a sub-particle swarm for each of the social learning factors according to a number of social learning factors, where the social learning factors determine a degree of movement of an individual along a global optimal value; the first optimizing module is used for independently carrying out optimizing process on each sub particle swarm according to the fitness function and the speed position updating mode based on the particle swarm optimizing algorithm; the screening module is used for screening the sub-particle swarms meeting the screening conditions after optimizing each sub-particle swarm; and the second optimizing module is used for continuing the optimizing process of the selected sub-particle swarm, and selecting the global optimal value after optimizing as a final optimizing result.
In a third aspect, the present invention provides an electronic device, comprising: a processor and a memory storing a program or instructions executable on the processor, which when executed by the processor, implement the steps of the multi-magnetic dipole inversion method according to the first aspect.
In a fourth aspect, the present invention provides a readable storage medium having stored thereon a program or instructions which when executed by a processor performs the steps of the multi-magnetic dipole inversion method according to the first aspect.
Compared with the prior art, the invention has the following advantages: firstly, constructing a sub-particle swarm for each social learning factor according to the quantity of the social learning factors, wherein the social learning factors determine the degree of the individual moving along the global optimal value; based on a particle swarm optimization algorithm, each sub-particle swarm independently carries out an optimization process according to an adaptability function and a speed position updating mode; after optimizing each sub-particle swarm, screening out the sub-particle swarm meeting the screening condition; and finally, continuing the optimizing process of the screened sub-particle swarm, and selecting the overall optimal value after optimizing as a final optimizing result, so that the magnetic characteristics of the object to be measured can be reflected better, and the inversion magnetic dipole has the advantages of large quantity, high accuracy, high success rate and strong robustness.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this specification, illustrate embodiments of the application and together with the description serve to explain the principles of the application. In the accompanying drawings:
FIG. 1 is a flow chart of a method for multi-magnetic dipole inversion according to one embodiment of the present invention;
FIG. 2 is a flow chart of a method of multi-magnetic dipole inversion according to another embodiment of the present invention;
FIG. 3 is a schematic diagram of iterative updating of each sub-population of particles in an embodiment of the present invention;
FIG. 4 is a schematic diagram of an application process of a multi-magnetic dipole inversion method according to an embodiment of the present invention;
FIG. 5 is a schematic diagram of a multi-magnetic dipole inversion apparatus according to one embodiment of the present invention;
fig. 6 is a schematic diagram of an electronic device according to an embodiment of the invention.
Detailed Description
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are used in the description of the embodiments will be briefly described below. It is apparent that the drawings in the following description are only some examples or embodiments of the present application, and it is apparent to those of ordinary skill in the art that the present application may be applied to other similar situations according to the drawings without inventive effort. Unless otherwise apparent from the context of the language or otherwise specified, like reference numerals in the figures refer to like structures or operations.
As used in the specification and in the claims, the terms "a," "an," "the," and/or "the" are not specific to a singular, but may include a plurality, unless the context clearly dictates otherwise. In general, the terms "comprises" and "comprising" merely indicate that the steps and elements are explicitly identified, and they do not constitute an exclusive list, as other steps or elements may be included in a method or apparatus.
In addition, the terms "first", "second", etc. are used to define the components, and are only for convenience of distinguishing the corresponding components, and the terms have no special meaning unless otherwise stated, and therefore should not be construed as limiting the scope of the present application. Furthermore, although terms used in the present application are selected from publicly known and commonly used terms, some terms mentioned in the present specification may be selected by the applicant at his or her discretion, the detailed meanings of which are described in relevant parts of the description herein. Furthermore, it is required that the present application is understood, not simply by the actual terms used but by the meaning of each term lying within.
A flowchart is used in the present application to describe the operations performed by a system according to embodiments of the present application. It should be understood that the preceding or following operations are not necessarily performed in order precisely. Rather, the various steps may be processed in reverse order or simultaneously. At the same time, other operations are added to or removed from these processes.
Example 1
The present embodiment provides a multi-magnetic dipole inversion method, referring to fig. 1, the method 100 includes: 110. constructing a sub-group of particles (sub-group) for each of the social learning factors based on the number of social learning factors, the social learning factors determining the extent to which the individual moves along the global optimum; 120. based on a particle swarm optimization algorithm, each particle swarm independently carries out an optimizing process according to an adaptability function and a speed position updating mode; 130. screening out the particle swarm meeting the screening condition after optimizing each particle swarm; 140. and continuing the optimizing process of the selected sub-particle swarm, and selecting the global optimal value after optimizing as a final optimizing result.
By adopting the method of the embodiment, on one hand, the particle swarm is continuously iterated and converged according to the particle speed and position updating mode by utilizing the characteristics of the particle swarm algorithm, and finally, the solution with the minimum fitness function is found; on the other hand, different subgroups are divided according to social learning factors to perform inversion respectively, so that the defect that a classical particle swarm algorithm is easy to fall into local optimum can be overcome, and the success rate of inversion is improved.
In this embodiment, when the number of actual magnetic dipole moments of the object to be measured is smaller than the number set in the algorithm, the method can still accurately invert the magnetic dipole moments of the object to be measured. The inversion results may be in two cases: the actual magnetic dipole moment is decomposed into two magnetic dipole moments with the same position and different sizes; and secondly, a magnetic dipole moment with extremely small magnitude exists. Typical optimization algorithms generally have the problems of faster search phase, easy sinking into local optimum and inability to jump out of the local optimum solution when inverting the multi-magnetic dipole problem. In this embodiment, the appropriate social learning factor will be searched extensively, and through a combination of different social learning factors with other factors, the set that can be successfully inverted for each set of multi-magnetic dipole targets is found. According to the embodiment, different social learning factors are set, a sub-particle swarm is built for each social learning factor, the sub-particle swarms are mutually independent, group optimization and individual optimization are calculated respectively, and speed and position updating is performed respectively until iteration times or exit conditions are met, so that the situation that a particle swarm algorithm falls into local optimization and the inversion result is poor in accuracy is avoided.
In this embodiment, each sub-particle group is first subjected to a optimizing process, then a sub-particle group meeting a screening condition is screened out from the optimized sub-particle groups, and optimizing is performed again. Therefore, the method of the embodiment adopts a layering mechanism for the sub-particle swarm, after the sub-particle swarm is iterated for a certain number of times, all the sub-particle swarms are compared, the sub-particle swarm with better current effect is screened out, and then the next round of iterative screening is carried out until the unique sub-particle swarm is selected. The layering mechanism can avoid iterative computation of all sub-particle groups, and the computation time is greatly saved.
In one example, before each sub-particle swarm performs the optimizing process, the particle swarm algorithm is first initialized with parameters including inertia factors, individual learning factors and/or social learning factors, wherein the inertia factors determine the degree of movement of the individual in the current direction, and the individual learning factors determine the degree of movement of the individual in the direction of the optimal value of the individual.
In an example, the step of independently performing the optimizing process according to the fitness function and the speed position updating manner of each sub-particle swarm may further include: the fitness function of each sub-particle swarm is the same and/or the speed position updating mode is the same.
For the problem of inverting the multi-magnetic dipole by the particle swarm algorithm, a proper fitness function needs to be constructed to calculate the fitness of each individual in the particle swarm, so as to update the position of the particle, and the square sum of the differences between the measured value and the calculated value of each measuring point can be used as the fitness function in the embodiment.
In one example, screening out a population of sub-particles that meet the screening criteria may include: and arranging the global optimal values of all the sub-particle groups in order from small to large, and screening out a plurality of previous sub-particle groups for next optimization. Exemplary, when all the sub-particle groups satisfy the iteration number, the position of each individual in each sub-particle group is saved, the global optimum value of each sub-particle group is calculated, and the sub-particle groups are arranged in order from small to large, and the previous sub-particle groups are screened outThe next round of iterative screening is carried out on each particle swarm, wherein r pass is the elimination rate of the particle swarm, and the rate of elimination of the particle swarm is/areIs the number of social learning factors.
According to the multi-magnetic dipole inversion method provided by the embodiment, from the perspective of social learning factors, sub-particle groups with different social learning factors are constructed, iteration is carried out respectively, continuous screening and elimination are carried out, and finally, a unique sub-particle group is selected to finish inversion work, so that the magnetic characteristics of an object to be detected can be reflected better, and the inversion magnetic dipole has the advantages of large quantity, high accuracy, high success rate and strong robustness.
Example two
The present embodiment provides another multi-magnetic dipole inversion method, which can be applied to multi-magnetic dipole inversion of a spacecraft stand-alone. Because the magnetic dipole moment of the single spacecraft cannot be directly and accurately measured, only the magnetic field at the measuring point around the single spacecraft can be generally measured by using the triaxial magnetometer, and then the information of the magnetic field is used for calculating or inverting the information of the multiple magnetic dipoles of the single spacecraft.
Referring to fig. 2, the multi-magnetic dipole inversion method of the present embodiment mainly includes:
201. and initializing parameters.
That is, the particle swarm algorithm is initialized by setting inertia factor omega, individual learning factor c i, iteration number gen, elimination rate r pass(0<rpass < 1) and several different social learning factorsEtc.
202. And constructing the same number of sub-particle groups according to the number of the social learning factors, and respectively iterating.
And calculating an fitness function value according to the fitness function by each sub-particle swarm according to the constructed sub-particle swarm, updating an individual optimal value and a global optimal value according to a speed position updating formula, and updating the individual position.
In this embodiment, assuming that n magnetic dipole moments actually exist in the object to be measured, the magnetic field generated by each magnetic dipole moment in the magnetometer p can be expressed by the formula (1):
Wherein m k＝(mk,x,mk,y,mk,z) (with the unit of Am 2) and r k＝(rk,x,rk,y,rk,z) (with the unit of m) represent the magnitude and position parameters of a magnetic dipole moment k in the object to be measured, and B p,k is the unit of T.
The magnetic field generated by the magnetometer p by all the magnetic dipole moments of the object to be measured can be expressed as
For each particle in a particle population, the size and position of n magnetic moments are contained, for a total of 6n parameters (size and position are three-dimensional data). The parameters of each particle are iteratively updated in a particle swarm algorithm. The magnetic field generated at magnetometer p can also be calculated for each particle during each iteration, expressed as
Where m k'＝(mk',x,mk',y,mk',z) (in Am 2) and r k'＝(rk',x,rk',y,rk',z) (in m) represent the magnitude and position parameters of a magnetic dipole moment k' in a particle, and B c,p is in T.
Thus, for each particle in a population, the difference between the magnetic field values and the measured values generated at each measurement point can be calculated
ε＝Bm,p-Bc,p (4)
The scalar of the sum of squares of the differences between the measured values and calculated values of each measuring point is used as a fitness function, expressed as
In this embodiment, the particle swarm optimization algorithm will calculate fitness of each particle according to the fitness function, record the global optimum of the whole population and the individual optimum of each particle, and update the speed and position according to equation (6) and equation (7).
Wherein V i t andRespectively representing the speed and position of the ith particle at the t-th iteration, P ibest and P gbest respectively representing the individual optimum and the global optimum, r i and r g representing two random numbers equally distributed between 0 and 1, ω, c i and c g respectively representing the inertia factor, individual learning factor and social learning factor. The inertia factor determines the degree to which the individual moves in the current direction, the individual learning factor determines the degree to which the individual moves in the direction of the individual's optimal value, and the social learning factor determines the degree to which the individual moves in the direction of the global optimal value.
203. Calculating global extremum (optimal value) of each current subgroup, arranging from small to large, and screening out the previous subgroupThe sub-groups continue to iterate.
When all the sub-particle groups meet the iteration times, the position of each individual in each sub-particle group is stored, the global optimal value of each sub-particle group is calculated, the sub-particle groups are arranged in the order from small to large, and the previous sub-particle group is screened out for the next iteration screening.
204. It is determined whether the number of subgroups is n e.
In order to improve the calculation efficiency, the present embodiment adopts a layering mechanism to screen sub-particle swarms (sub-swarms), the iteration number of each sub-particle swarm of each layer is gen, and the screening mechanism is
Wherein p ratio is the number of the sub-particle groups after screening, r pass is the elimination rate of the sub-particle groups,Is the number of social learning factors. In each layer of screening, after the inversion of all the sub-particle groups meets the iteration times, recording the global optimal value of each sub-particle group, sorting from small to large, screening out the previous p ratio sub-particle groups, entering the next layer of iterative inversion, and the like until n e particle groups are screened out, continuing to iterate, and finally reaching the termination condition.
205. And continuing iteration until the termination condition of the particle swarm algorithm is met.
206. And selecting a subgroup result with the minimum global extremum as output.
FIG. 3 is a schematic diagram of iterative updating of each sub-particle swarm according to an embodiment of the present invention, and referring to FIG. 3, the method mainly includes the following steps: 301. and (5) setting parameters. For example, an inertia factor, an individual learning factor, and/or a social learning factor, etc. are set. 302. Individual fitness function values are calculated. For example, the fitness function is the sum of squares of the differences between the measured and calculated values of the magnetic dipoles. 303. Updating the individual optimum value and the global optimum value. 304. Updating the individual location. 305. And judging whether the iteration times are satisfied. And 306, saving the global optimum and the location of each individual.
FIG. 4 is a schematic diagram of an application process of the multi-magnetic dipole inversion method according to an embodiment of the present invention, and referring to FIG. 4, the method mainly includes: 401. and collecting a surrounding magnetic field of the object to be measured, and recording magnetometer data and coordinates. For example, depending on the magnetic measuring apparatus, it is necessary to install three-axis magnetometers before performing magnetic field measurement, and record the number of magnetometers at the time of the test and the coordinates of each magnetometer in the magnetic measuring space. The magnetometer number is n p, the coordinate is r p＝(rp,x,rp,y,rp,z),p＝1,2,…,np, and the unit is m. After the end of the magnetic measurement, the measurement value of each magnetometer is recorded, denoted by B m,p in T. 402. The number of dipole magnetic moments is estimated in combination with the data of the object to be measured. In this embodiment, 1 to 5 magnetic dipole moments of the object to be measured are generally inverted. 403. Inverting the magnetic characteristics of the object to be detected by using a magnetic characteristic inversion method based on social learning factors for screening particle swarms. 404. And calculating the magnetic field of the inverted magnetic moment around the object to be measured and comparing the magnetic field with the measured data. 405. Verifying magnetic field indexes, proposing layout optimization suggestions and the like.
In this embodiment, the iteration time of the sub-particle swarm can be limited by setting the iteration times of the sub-particle swarm, after each sub-particle swarm is inverted to reach the iteration times, the global optimal value of each sub-particle swarm is calculated and ordered, and a part of sub-particle swarms with smaller global optimal values are screened out according to the size of the global optimal value, so that iteration is continued, and the like until a unique particle swarm is screened out, and inversion work is completed.
Reference may be made to the foregoing embodiments for details of other operations performed by the steps in this embodiment, which are not further described herein.
According to the multi-magnetic dipole inversion method provided by the embodiment, from the perspective of social learning factors, sub-particle groups with different social learning factors are constructed, iterative is carried out respectively, and the sub-particle groups are continuously screened and eliminated, and finally, the unique sub-particle group is selected to finish inversion work, so that the magnetic characteristics of an object to be detected can be better reflected, and the inversion magnetic dipole has the advantages of large quantity, high accuracy, high success rate and strong robustness.
The method shown in this embodiment and its advantageous effects are verified by specific examples below.
Example 1: firstly, arranging a magnetometer for a magnetic measurement device, and placing the magnetic measurement device in a zero magnetic space; then placing the object to be measured in a magnetic measurement device, synchronously measuring the magnetic field around the object to be measured for a period of time, recording magnetometer data, and taking the average magnetic field value as the data measured by each magnetometer; and then bringing the data of the magnetometer into an adaptive function of the method, and inverting the magnetic dipoles by using the method to obtain the magnitude and position parameters of a plurality of magnetic dipole moments.
In this example, the inertia factor ω and the individual learning factor c i are set to 1 and 1.5, respectively, the social learning factor is set to 0.01 to 1.5, and the interval is 0.01, for a total of 150 different social learning factors. That is, the method of this embodiment will construct 150 sub-particle swarms, each of which corresponds to a different social learning factor, respectively. The fitness function of all particle swarms is shown as a formula (5), and the speed and position updating formulas are shown as a formula (6) and a formula (7).
In order to reduce the computational overhead, in the first layer of particle swarm screening, the iteration number is set to 500, and the elimination rate is set to 80%, i.e., 120 particles are eliminated (rounded). After the iteration of all the sub-particle swarms is completed, calculating the fitness function value of the optimal individual of all the current sub-particle swarms, and arranging according to the sequence from small to large, screening out the first 30 sub-particle swarms to continue the iteration.
In order to further expand the gap between optimal individual fitness values among particle swarms, a learning factor combination which is more suitable for the current target is searched, in the second layer, the iteration times are set to 2000, the elimination rate is set to 83%, and 5 particle swarms can be selected. And 5 sub-particle groups with the smallest fitness value are screened out to continue iteration after iteration is completed, wherein the first layer is similar to the first layer, and the algorithm reaches a termination condition. And then selecting a subgroup result with the minimum global extremum as output, wherein the learning factor combination corresponding to the subgroup is considered to be the combination most suitable for solving the current multi-magnetic dipole target.
In the last iteration, in order to obtain a more accurate result as much as possible, the iteration number is not limited any more, but the optimal individual fitness function value or the maximum stagnation algebra is limited. And when the optimal individual fitness function value is smaller than 1, the iteration is exited, and inversion is completed, or when the maximum stagnation algebra, namely the algebra of the constant optimal individual fitness function value exceeds 20, judging that the optimal solution is trapped locally and cannot be found in a short time, and then the iteration process is exited, and inversion is completed.
In this example, the magnitude of the five magnetic dipole moments is limited to within + -0.5 Am 2 and the position is limited to within + -0.25 m. The specific parameters are as follows:
| M1 | M2 | M3 | M4 | M5 | |
| x/Am2 | 0.2275 | 0.1453 | 0.3777 | -0.2412 | 0.1589 |
| y/Am2 | 0.1775 | 0.2008 | -0.3672 | -0.4004 | 0.2653 |
| z/Am2 | 0.0074 | -0.2797 | 0.3667 | 0.0910 | -0.3278 |
| R1 | R2 | R3 | R4 | R5 | |
| x/m | -0.0186 | 0.2473 | 0.0391 | -0.1853 | -0.2318 |
| y/m | 0.0172 | 0.0339 | -0.0633 | 0.0581 | 0.0641 |
| z/m | -0.1923 | 0.2011 | 0.2358 | 0.0832 | -0.0861 |
Where M1 to M5 are magnitudes corresponding to five magnetic dipole moments, and R1 to R5 are coordinates corresponding to five magnetic dipole moments (hereinafter, referred to as "the same").
Inversion of the above magnetic moment was performed using the method of this example, with the following results (the result retained four-bit decimal):
| M1 | M2 | M3 | M4 | M5 | |
| x/Am2 | 0.2276 | 0.1454 | 0.3775 | -0.2413 | 0.1590 |
| y/Am2 | 0.1775 | 0.2008 | -0.3672 | -0.4004 | 0.2653 |
| z/Am2 | 0.0075 | -0.2797 | 0.3667 | 0.0909 | -0.3279 |
| R1 | R2 | R3 | R4 | R5 | |
| x/m | -0.0186 | 0.2473 | 0.0391 | -0.1853 | -0.2318 |
| y/m | 0.0172 | 0.0339 | -0.0633 | 0.0581 | 0.0641 |
| z/m | -0.1923 | 0.2011 | 0.2358 | 0.0839 | -0.0861 |
the difference between the inversion result and the true value is as follows (result retained four-bit decimal):
| M1 | M2 | M3 | M4 | M5 | |
| x/Am2 | 5.4942×10-5 | -5.2041×10-5 | -9.2653×10-5 | -5.7583×10-5 | 0.0002 |
| y/Am2 | -1.3296×10-5 | -1.1064×10-5 | -2.1149×10-5 | 1.0064×10-5 | -3.4247×10-5 |
| z/Am2 | 8.0058×10-5 | -8.5646×10-6 | 5.4903×10-5 | -6.8177×10-5 | -3.3860×10-5 |
| R1 | R2 | R3 | R4 | R5 | |
| x/m | 1.6847×10-5 | 7.4740×10-6 | 3.4806×10-6 | 1.0640×10-5 | -2.1392×10-5 |
| y/m | -7.2250×10-6 | -1.0822×10-6 | 6.7058×10-6 | 1.4328×10-5 | 8.3482×10-6 |
| z/m | 1.5235×10-5 | 5.2194×10-6 | -7.6487×10-6 | 2.3953×10-5 | 3.3118×10-5 |
The results show that the magnetic moment deviation is less than 0.005Am 2, the position deviation is less than 0.01m, and the inversion is successful.
Example 2: to verify the robustness of the present embodiment method against the fact that the number of actual magnetic moments is smaller than the number of estimated magnetic moments, in this example, the number of actual magnetic moments is set to 3, the number of inverted magnetic moments is set to 5, and the other settings are the same as in example 1.
In this example, the specific parameters of the three target magnetic dipole moments are as follows:
| M1 | M2 | M3 | |
| x/Am2 | 0.0102 | -0.2454 | -0.3925 |
| y/Am2 | 0.0582 | -0.4440 | 0.3239 |
| z/Am2 | -0.1463 | -0.3325 | -0.4317 |
| R1 | R2 | R3 | |
| x/m | 0.1327 | -0.0866 | -0.1840 |
| y/m | -0.0995 | 0.1163 | -0.2094 |
| z/m | -0.1812 | 0.1234 | 0.0974 |
Inversion of the above magnetic moment was performed using the method of this example, with the following results (the result retained four-bit decimal):
| M1 | M2 | M3 | M4 | M5 | |
| x/Am2 | 0.0102 | -0.2453 | -0.0393 | -0.3924 | 0.0392 |
| y/Am2 | 0.0582 | -0.4440 | 0.0533 | 0.3239 | -0.0532 |
| z/Am2 | -0.1463 | -0.3324 | -0.2060 | -0.4316 | 0.2059 |
| R1 | R2 | R3 | R4 | R5 | |
| x/m | 0.1327 | -0.0866 | -0.0439 | -0.1840 | -0.0437 |
| y/m | -0.0995 | 0.1163 | -0.0392 | -0.2094 | -0.0392 |
| z/m | -0.1812 | 0.1234 | 0.1950 | 0.0974 | 0.1951 |
In the inversion result, the third magnetic dipole moment and the fifth magnetic dipole moment have the same position but opposite magnitudes, so that magnetic fields generated by the third magnetic dipole moment and the fifth magnetic dipole moment can be mutually offset, and the other three magnetic dipole moments are inversion results of corresponding target magnetic moments. The remaining three magnetic moments are differenced from the true value, resulting in the following (the result retains four decimal places):
The results show that the magnetic moment deviation is less than 0.005Am 2, the position deviation is less than 0.01m, and the inversion is successful.
Example 3: in order to verify that the inversion success rate can be greatly improved by using less time, in the method, the method is compared with a standard particle swarm algorithm and a method for removing a layering mechanism, and inversion is performed on 85 groups of target magnetic moments (five magnetic dipole moments per group), so that the success rate and the average time cost are comprehensively compared.
In order to meet the practical significance of engineering, when the deviation of the magnetic moment is smaller than 0.005Am 2 and the deviation of the position is smaller than 0.01m, the inversion is judged to be successful.
The results were as follows:
| Success rate | Average time/s | |
| Standard particle swarm algorithm | 15.3％ | 226.40 |
| AWPSO | 28.0％ | 423.22 |
| APSO | 7.3％ | 132.53 |
| Method for removing layering mechanism | 92.9％ | 18413.38 |
| The method | 80.0％ | 308.91 |
The results show that compared with the standard particle swarm algorithm, the success rate of the method of the embodiment is improved by 64.7%, and the average time is increased by 36.44%; compared with the former method, the latter method has high success rate, but the average time cost is increased by 59.61 times, the time for averagely inverting a group of magnetic moments is 5.11 hours, and the calculation efficiency is too low for satellites with dozens or even hundreds of satellites, so that the actual test requirement cannot be met, and the layering mechanism is required to be used for screening the sub-particle swarm, thereby improving the calculation efficiency.
The multi-magnetic dipole inversion method provided by the embodiment can invert 1 to 5 magnetic dipole moments of the object to be measured, can reflect the magnetic characteristics of the object to be measured more accurately, can invert enough magnetic dipole moments when facing a single machine in a working state, and has engineering practical significance. The method can also calculate the surrounding magnetic field of the object to be measured more accurately, help to establish a satellite whole-satellite magnetic field model more accurately, verify whether the magnetic field in the satellite or the magnetically sensitive area meets the index, and provide positive help to the satellite structure layout.
Example III
FIG. 5 is a schematic diagram of a multi-magnetic dipole inversion apparatus according to one embodiment of the present invention, wherein the apparatus 500 mainly comprises:
A construction module 501 is configured to construct a sub-particle swarm for each of the social learning factors according to the number of social learning factors, where the social learning factors determine a degree to which an individual moves along a global optimal value.
In one example, before each sub-particle swarm performs the optimization process, the particle swarm algorithm is initialized with parameters including an inertia factor, an individual learning factor, and/or a social learning factor, wherein the inertia factor determines a degree of movement of the individual in a current direction, and the individual learning factor determines a degree of movement of the individual in a direction of an individual optimal value.
The first optimizing module 502 is configured to perform an optimizing process on each sub-particle swarm independently according to an fitness function and a speed position updating manner based on a particle swarm optimization algorithm.
In one example, the fitness function and/or the velocity location update manner are the same for each sub-population of particles.
In one example, the fitness function is the sum of squares of the differences between the measured and calculated values of the magnetic dipoles.
In one example, the speed location update approach includes:
Wherein V i t+1 and Representing the speed and position of the ith particle at the t+1st iteration, V i t and/>, respectivelyRespectively representing the speed and position of the ith particle at the t-th iteration, P ibest and P gbest respectively representing the individual optimum and the global optimum, r i and r g representing two random numbers equally distributed between 0 and 1, ω, c i and c g respectively representing the inertia factor, individual learning factor and social learning factor.
In one example, the termination condition of each sub-particle swarm optimization process is that the number of iterations of the optimization reaches a predetermined number of iterations.
And a screening module 503, configured to screen the sub-particle swarm that meets the screening condition after optimizing each sub-particle swarm.
In one example, screening out a population of sub-particles that meet the screening criteria may include: and arranging the global optimal values of all the sub-particle groups in order from small to large, and screening out a plurality of previous sub-particle groups for next optimization.
In one example, the screening mechanism is set toWherein p ratio is the number of the selected sub-particles, r pass is the elimination rate of the sub-particles,/>Is the number of social learning factors.
And the second optimizing module 504 is configured to continue the optimizing process for the selected sub-particle swarm, and select the global optimal value after optimizing as a final optimizing result.
Reference may be made to the foregoing embodiments for details of other operations performed by the modules in this embodiment, which are not further described herein.
According to the multi-magnetic dipole inversion device provided by the embodiment, from the perspective of social learning factors, sub-particle groups with different social learning factors are constructed, are respectively iterated, are continuously screened and eliminated, and finally, the unique sub-particle group is selected to finish inversion work, so that the magnetic characteristics of an object to be detected can be better reflected, and the inversion magnetic dipole has the advantages of more quantity, high accuracy, high success rate and strong robustness.
The application also provides an electronic device, comprising: a memory for storing programs or instructions executable by the processor; and a processor, configured to execute the program or instructions to implement each process of the embodiment of the multi-magnetic dipole inversion method, and achieve the same technical effects, so that repetition is avoided, and details are not repeated here.
Fig. 6 is a schematic diagram of an electronic device according to an embodiment of the invention. The electronic device 600 may include an internal communication bus 601, a Processor (Processor) 602, a Read Only Memory (ROM) 603, a Random Access Memory (RAM) 604, and a communication port 605. When applied to a personal computer, the electronic device 600 may also include a hard disk 606. The internal communication bus 601 may enable data communication among the components of the electronic device 600. The processor 602 may make the determination and issue the prompt. In some implementations, the processor 602 may be comprised of one or more processors. The communication port 605 may enable the electronic device 600 to communicate data with the outside. In some implementations, the electronic device 600 may send and receive information and data from a network through the communication port 605. The electronic device 600 may also include various forms of program storage elements and data storage elements such as hard disk 606, read Only Memory (ROM) 603 and Random Access Memory (RAM) 604, capable of storing various data files for computer processing and/or communication, as well as possible programs or instructions for execution by the processor 602. The results processed by the processor 602 are communicated to the user device via the communication port 605 for display on a user interface.
The multi-magnetic dipole inversion method described above may be implemented as a computer program stored on the hard disk 606 and executed by the processor 602 to implement any of the multi-magnetic dipole inversion methods of the present application.
The embodiment of the application also provides a readable storage medium, and the readable storage medium stores a program or an instruction, which when executed by a processor, realizes each process of the above-mentioned embodiment of the multi-magnetic dipole inversion method, and can achieve the same technical effect, so that repetition is avoided, and no description is repeated here.
It will be apparent to those skilled in the art that the foregoing disclosure is by way of example only and is not intended to be limiting. Although not explicitly described herein, various modifications, improvements and adaptations of the application may occur to one skilled in the art. Such modifications, improvements, and modifications are intended to be suggested within the present disclosure, and therefore, such modifications, improvements, and adaptations are intended to be within the spirit and scope of the exemplary embodiments of the present disclosure.
Meanwhile, the present application uses specific words to describe embodiments of the present application. Reference to "one embodiment," "an embodiment," and/or "some embodiments" means that a particular feature, structure, or characteristic is associated with at least one embodiment of the application. Thus, it should be emphasized and should be appreciated that two or more references to "an embodiment" or "one embodiment" or "an alternative embodiment" in various positions in this specification are not necessarily referring to the same embodiment. Furthermore, certain features, structures, or characteristics of one or more embodiments of the application may be combined as suitable.
Similarly, it should be noted that in order to simplify the description of the present disclosure and thereby aid in understanding one or more inventive embodiments, various features are sometimes grouped together in a single embodiment, figure, or description thereof. This method of disclosure does not imply that the subject application requires more features than are set forth in the claims. Indeed, less than all of the features of a single embodiment disclosed above.
While the application has been described with reference to the specific embodiments presently, it will be appreciated by those skilled in the art that the foregoing embodiments are merely illustrative of the application, and various equivalent changes and substitutions may be made without departing from the spirit of the application, and therefore, all changes and modifications to the embodiments are intended to be within the scope of the appended claims.
Claims (9)
1. A method of multi-magnetic dipole inversion comprising:
constructing a sub-particle swarm for each social learning factor according to the quantity of the social learning factors, wherein the social learning factors determine the degree of movement of an individual along a global optimal value;
Based on a particle swarm optimization algorithm, each particle swarm independently carries out an optimizing process according to an adaptability function and a speed position updating mode; the fitness function is the sum of squares of the differences between the measured and calculated values of the magnetic dipoles;
Screening out the particle swarm meeting the screening condition after optimizing each particle swarm; screening out the sub-particle swarm meeting the screening conditions comprises the following steps: the global optimal value of each sub-particle swarm is arranged in sequence from small to large, and a plurality of front sub-particle swarms are screened out for next optimization;
And continuing the optimizing process of the selected sub-particle swarm, and selecting the global optimal value after optimizing as a final optimizing result.
2. The method of multiple magnetic dipole inversion according to claim 1, further comprising, prior to said optimizing each of said sub-particle groups:
and initializing parameters of the particle swarm algorithm, including setting an inertia factor, an individual learning factor and/or a social learning factor, wherein the inertia factor determines the degree of movement of the individual along the current direction, and the individual learning factor determines the degree of movement of the individual along the direction of the optimal value of the individual.
3. The method of multi-magnetic dipole inversion according to claim 2, wherein said step of independently optimizing each of said sub-groups according to said fitness function and said speed position update further comprises:
and the fitness function of each sub-particle swarm is the same and/or the speed and position updating mode is the same.
4. The method of multiple magnetic dipole inversion according to claim 1, wherein said means for velocity location update comprises:
Wherein V i t+1 and Representing the speed and position of the ith particle at the t+1st iteration, V i t and/>, respectivelyRespectively representing the speed and position of the ith particle at the t-th iteration, P ibest and P gbest respectively representing the individual optimum and the global optimum, r i and r g representing two random numbers equally distributed between 0 and 1, ω, c i and c g respectively representing the inertia factor, individual learning factor and social learning factor.
5. The method of multi-magnetic dipole inversion according to claim 1, wherein said step of screening out said sub-population of particles meeting said screening criteria further comprises:
Setting a screening mechanism as Wherein p ratio is the number of the sub-particle groups after screening, r pass is the elimination rate of the sub-particle groups,/>For the number of social learning factors.
6. The method of multi-magnetic dipole inversion according to claim 1, wherein each of said sub-particle swarm optimization is terminated under the condition that the number of iterations of the optimization reaches a predetermined number of iterations.
7. A multi-magnetic dipole inversion device is characterized by comprising,
The construction module is used for constructing a sub-particle swarm for each social learning factor according to the quantity of the social learning factors, and the social learning factors determine the degree of movement of an individual along a global optimal value;
the first optimizing module is used for independently carrying out optimizing process on each sub particle swarm according to the fitness function and the speed position updating mode based on the particle swarm optimizing algorithm; the fitness function is the sum of squares of the differences between the measured and calculated values of the magnetic dipoles;
The screening module is used for screening the sub-particle swarms meeting the screening conditions after optimizing each sub-particle swarm; screening out the sub-particle swarm meeting the screening conditions comprises the following steps: the global optimal value of each sub-particle swarm is arranged in sequence from small to large, and a plurality of front sub-particle swarms are screened out for next optimization;
And the second optimizing module is used for continuing the optimizing process of the selected sub-particle swarm, and selecting the global optimal value after optimizing as a final optimizing result.
8. An electronic device, comprising: a processor and a memory storing a program or instructions executable on the processor, which when executed by the processor, implement the steps of the multiple magnetic dipole inversion method according to any of claims 1-6.
9. A readable storage medium having stored thereon a program or instructions which when executed by a processor performs the steps of the multiple magnetic dipole inversion method according to any of claims 1-6.
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