Geomagnetic sequence matching method

Abstract

The invention relates to the technical field of geomagnetic positioning, and discloses a geomagnetic sequence matching method, which comprises the following steps of: firstly, constructing a sequence library by using a geomagnetic reference map, and receiving real-time geomagnetic data for matching; secondly, evaluating the sequence similarity through a particle swarm algorithm in combination with a multi-dimensional dynamic time planning algorithm, and introducing a Meltgov criterion and a roulette optimization search; then, the speed and the position of the particles are continuously adjusted through the self-adaptive inertia weight; and in each iteration, the fitness of each particle is calculated, and the individual optimal position and the global optimal position of the particle are updated. When the maximum number of iterations or the fitness threshold value of the particles is reached, iteration is terminated, the optimal matching position is output, and finally the final position of the user is calculated through the geomagnetic sequence. According to the technical scheme, the optimal alignment path of the two sequences in the time dimension can be found by using the particle swarm and the simulated annealing algorithm without increasing the calculation amount, the matching precision is improved, and the calculation time is shortened.

Description

Technical Field

[0001] The present invention relates to the technical field of geomagnetic positioning, and in particular to a geomagnetic sequence matching method based on particle swarm optimization and multi-dimensional dynamic time programming algorithm. Background Art

[0002] With the acceleration of urbanization, people's demand for high-precision positioning services is growing. In outdoor environments, the use of various technologies such as the Beidou satellite navigation system can achieve high-precision and real-time positioning effects. In indoor environments such as large shopping malls and office buildings, the accuracy of GPS positioning is easily affected by factors such as building obstruction and cannot meet the requirements of many practical applications. At present, WiFi, Bluetooth, etc. have been widely used. However, due to the presence of a large number of obstacles in indoor environments, radio waves will attenuate during indoor transmission, which in turn affects the accuracy of the received signal. In addition, this method also has problems such as configuring specific equipment, adding additional hardware, and regular maintenance.

[0003] Geomagnetic positioning measures the geomagnetic field data on the moving carrier and matches it with the pre-made geomagnetic map to determine the real-time position of the carrier. Because the geomagnetic field is extremely stable, it will not change unless ferromagnetic materials are added. It does not rely on external signals and does not require additional hardware deployment. It is suitable for complex environments such as urban canyons and indoors, and has low cost and easy maintenance. At the same time, geomagnetic positioning has the advantages of high precision and strong anti-interference, and can provide stable and reliable navigation services for aircraft, vehicles, etc. Summary of the invention

[0004] In view of this, the purpose of the present invention is to provide a geomagnetic sequence matching method, which can use particle swarm and simulated annealing algorithm to find the optimal alignment path of two sequences in the time dimension without increasing the amount of calculation, thereby improving the matching accuracy and shortening the calculation time.

[0005] In order to achieve the above-mentioned invention object, the technical solution adopted is as follows:

[0006] A geomagnetic sequence matching method comprises the following steps:

[0007] S1. First, the geomagnetic reference map is cropped to obtain the geomagnetic sequence library. When the geomagnetic positioning system receives a positioning request, it receives the current geomagnetic data and uses it as the target sequence to be matched. The particle swarm algorithm is used to generate the initial particle swarm, and the starting position and speed of each particle are randomly initialized, and the relevant initialization parameters are set;

[0008] S2. Use the multi-dimensional DTW algorithm to calculate the similarity of the geomagnetic sequence and set it as the objective function. Construct a distance matrix based on the five-dimensional features, find the shortest path to measure the similarity, and use it as the PSO-SA algorithm evaluation criterion. Judge the validity of the particle by the first single-point geomagnetic Euclidean distance, calculate the initial population fitness, select the minimum DTW value as the global optimal position, and initialize the individual and global optimal positions.

[0009] S3, update the position and speed of the particle according to the optimal fitness value of the historical population and the adaptive inertia weight, calculate the fitness value of the updated population, and retain it as the subsequent update direction. If the population generates a better individual during the iteration, it will be updated;

[0010] S4. Use the Metropolis criterion to calculate the probability of each particle accepting a new solution at the current temperature. If the new solution is better than the current solution, the new solution is accepted. Otherwise, the Metropolis criterion is used to accept the new solution. After that, the temperature drops, the cooling coefficient is updated, and the roulette wheel is used to redetermine the global optimal solution, and the particle swarm is continuously optimized in iterations.

[0011] S5, determining the speed and position of the particle after the current iteration. If the particle speed exceeds the speed interval, the particle speed is taken as the speed interval boundary, and the position of the particle is judged. If the particle position exceeds the position interval, the particle is marked as invalid.

[0012] S6. Update the particle position again by selecting the optimal solution, and update the particle again by selecting the optimal solution, and optimize it, and compare it with the historical best fitness of each particle. If it is lower than its historical best fitness, replace its historical best fitness with the updated particle position, otherwise, it will not be changed.

[0013] S7, determine the iteration termination condition, the maximum number of iterations is reached or the absolute error of the minimum fitness value of the two iterations is less than the DTW distance threshold and the number of continuous iterations is greater than the distance threshold to terminate the iteration number, otherwise return to step S3 iteration;

[0014] S8. The iteration stops and the optimal solution, i.e. the final coordinates of the user, is obtained.

[0015] As a further improvement of the present invention, the step S1 specifically includes the following steps:

[0016] S1-1. Use Kriging interpolation method to make geomagnetic reference map, split the sequence in geomagnetic reference map in the way of sliding window, set the window length a1, a1 is a positive integer, and obtain a geomagnetic database MagArray = {Q l ,Q2,Q3,...,Q l}, and take the coordinates corresponding to the last geomagnetic point in the sequence as the position label of the sequence;

[0017] S1-2, in the range of geomagnetic fingerprint map (B min ,B max ) randomly generates N particles, and takes the first geomagnetic point of the geomagnetic sequence as a particle. After initialization, the initial position of the particle is x i and speed v i , set the initial temperature T and the cooling coefficient β.

[0018] As a further improvement of the present invention, step S2 specifically includes the following steps:

[0019] S2-1. Use the multi-dimensional DTW algorithm to calculate the similarity of geomagnetic sequences: The calculation process is mainly divided into two steps. The first step is to calculate the distance between the multi-dimensional features of each element in the two geomagnetic sequences to form a distance matrix. The second step is to calculate the distance matrix of the two geomagnetic sequences and find a path with the shortest cumulative distance from the (1,1) position of the distance matrix to the (n,q) position.

[0020] S2-2, use the first single point geomagnetism of the geomagnetic sequence to be located to judge the validity of the particle: perform Euclidean distance judgment on the first point in the geomagnetic sequence found by the particle and the first point in the geomagnetic sequence to be located. If the obtained value is greater than the threshold α1, it is marked as invalid;

[0021] S2-3, through fitness(x i )=-f(x i )=-D(n,q) calculate the fitness value of the initial population, compare and get the minimum fitness value, and set the corresponding individual as the optimal individual, i.e., p besti .

[0022] As a further improvement of the present invention, step S3 specifically includes the following steps:

[0023] S3-1. Adaptively update the inertia weight by combining the temperature and DTW distance in the simulated annealing algorithm, using the formula Adaptively update the velocity of particles in the particle swarm and use x i (t+1)=x i (t)+v i (t+1) Update the particle position;

[0024] S3-2, evaluate the current position of each particle and calculate the fitness value. If the fitness value of the current position of the particle is better than the fitness value of its individual optimal position, update the individual optimal position; if the fitness value of the individual optimal position of the particle is better than the fitness value of the current global optimal position, update the global optimal position.

[0025] As a further improvement of the present invention, step S4 specifically includes the following steps:

[0026] S4-1, using the Metropolis sampling criterion as the basis for the optimal selection of individuals, by comparing the current fitness value of the particle with the historical fitness value, the current value is better than p besti If it is better, the current value is set to p besti ; The current value is worse than p besti When the probability is calculated by the Metropolis criterion formula, the probability value of the current generation of the particle is used to decide whether to accept the current value as p besti ;

[0027] S4-2. Compare the fitness value of the current particle with the global optimal solution g best , if the current particle fitness value is better than g best , then update g best is the current particle fitness value, otherwise it will not be updated;

[0028] S4-3, if the current state has not reached the cooling state, continue to cool down, set T = T × β, and go to step S4-1 until the annealing operation is completed;

[0029] S4-4. Use the roulette method to redetermine the global optimal solution and continue to optimize the particle swarm during iteration.

[0030] As a further improvement of the present invention, step S5 specifically includes the following steps:

[0031] S5-1, determine the particle swarm update speed of the current iteration number, and perform speed out-of-bounds operation, that is, if the particle speed exceeds the speed interval (v min ,v max ), then the particle velocity is taken as the velocity interval boundary;

[0032] S5-2, the particle position is judged to determine whether the current particle exceeds the position interval. If the particle exceeds the position interval, the particle is marked as invalid. Otherwise, the particle is retained and updated.

[0033] The beneficial effects of the present invention are:

[0034] 1. This invention greatly improves the accuracy and robustness of the geomagnetic positioning system by introducing a method combining particle swarm algorithm with dynamic time warping (DTW). Traditional geomagnetic positioning may produce errors due to slight changes in the environmental magnetic field, but this invention can effectively overcome these challenges through the intelligent search of particle swarm algorithm and the flexible matching of DTW, ensuring that the positioning results are more accurate and reliable, and providing users with higher quality navigation services;

[0035] 2. The present invention innovatively integrates the simulated annealing probability criterion and the multi-dimensional dynamic time planning algorithm into the inertia weight, generates an adaptively updated inertia weight formula, and effectively improves the accuracy and robustness of geomagnetic sequence matching and positioning. By finely controlling the particle motion trajectory, the best matching area is quickly locked, significantly shortening the positioning time. At the same time, the global and individual optimal solutions are updated in real time to ensure that the algorithm continues to converge to the optimal direction, providing an efficient and reliable solution for precise navigation and positioning in complex environments;

[0036] 3. The present invention innovatively integrates the global search and the local optimal mechanism in the simulated annealing algorithm. Through the simulated annealing algorithm's strategy of probabilistically accepting poor solutions, the global exploration capability of the particle swarm algorithm is enhanced, effectively avoiding premature convergence to the local optimum. At the same time, the fine search advantage of the particle swarm algorithm improves the convergence speed and optimization accuracy of the algorithm. This fusion mechanism enables the algorithm to efficiently and accurately find the global optimal solution in a wide range of solution spaces, and also makes the positioning results more stable and efficient, meeting the needs of modern navigation systems for fast response and efficient calculation. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] The drawings constituting a part of this application are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0038] Figure 1 is a schematic diagram of the geomagnetic sequence matching method of the present invention;

[0039] Figure 2 It is a schematic diagram of the overall geomagnetic positioning system. DETAILED DESCRIPTION

[0040] It should be noted that, in the absence of conflict, the embodiments and features in the embodiments of the present application can be combined with each other. The present invention will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0041] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present application.

[0042] like Figure 1 As shown, a geomagnetic sequence matching method is used for indoor positioning, comprising the following steps:

[0043] S1. First, the geomagnetic reference map is cropped to obtain the geomagnetic sequence library. When the geomagnetic positioning system receives a positioning request, it receives the current geomagnetic data and uses it as the target sequence to be matched. The particle swarm algorithm is used to generate the initial particle swarm, and the starting position and speed of each particle are randomly initialized, and the relevant initialization parameters are set;

[0044] The step S1 specifically includes the following steps:

[0045] S1-1. Use Kriging interpolation method to make geomagnetic reference map, split the sequence in geomagnetic reference map in the way of sliding window, set the window length a1, a1 is a positive integer, and obtain a geomagnetic database MagArray = {Q l ,Q2,Q3,...,Q l}, and take the coordinates corresponding to the last geomagnetic point in the sequence as the position label of the sequence;

[0046] S1-2, in the range of geomagnetic fingerprint map (B min ,B max ) randomly generates N particles, and takes the first geomagnetic point of the geomagnetic sequence as a particle. After initialization, the initial position of the particle is x i and speed v i , set the initial temperature T and the cooling coefficient β, the formula is as follows:

[0047] x i (0) = x min +(x max -x min )·rand (1) v i (0) = v min +(v max -v min )·rand (2)

[0048] S2. Use the multi-dimensional DTW algorithm to calculate the similarity of the geomagnetic sequence and set it as the objective function. Construct a distance matrix based on the five-dimensional features, find the shortest path to measure the similarity, and use it as the PSO-SA algorithm evaluation criterion. Judge the validity of the particle by the first single-point geomagnetic Euclidean distance, calculate the initial population fitness, select the minimum DTW value as the global optimal position, and initialize the individual and global optimal positions.

[0049] The step S2 specifically includes the following steps:

[0050] S2-1 uses a multi-dimensional DTW algorithm to calculate the similarity of geomagnetic sequences. The first step is to use a multi-dimensional DTW algorithm to calculate the similarity of geomagnetic sequences as a criterion for judging particle effects. The calculation formula is:

[0051] Among them, d(i,j) is the distance between the i-th element of the test sequence and the j-th element of the matching sequence in the geomagnetic library, n is the length of the test sequence, q is the length of the matching sequence in the geomagnetic library, and s is the characteristic dimension of each element in the geomagnetic sequence

[0052] The second step is to calculate the distance matrix of the two geomagnetic sequences, and then find a path with the shortest cumulative distance from the (1,1) position of the distance matrix to the (n,q) position. The cumulative distance calculation formula of the path is:

[0053] D(i,j)=d(i,j)+min{D(i-1,j),D(i-1,j-1),D(i,j-1)} (4)

[0054] Finally, D(n,q) is the cumulative distance between the two geomagnetic sequences. The smaller the value of D(n,q), the higher the similarity of the two geomagnetic sequences. The fitness in the particle swarm algorithm is expressed as the negative value of DTW, that is, fitness(x i )=-f(x i )=-D(n,q), calculate the DTW distance value after each iteration, and the minimum DTW value is taken as the global optimal position g best , the individual optimal position of each particle is initialized to its initial position, and the global optimal position is initialized to the individual optimal position with the best fitness value among all particles.

[0055] S2-2. Use the first single point of the geomagnetic sequence to be located to determine the validity of the particle. Perform a Euclidean distance judgment between the first point in the geomagnetic sequence found by the particle and the first point in the geomagnetic sequence to be located. If the value obtained is greater than the threshold α1, it is marked as invalid. The formula for checking the validity of the particle is:

[0056]

[0057] S2-3, through fitness(x i )=-f(x i )=-D(n,q) calculate the fitness value of the initial population, compare and get the minimum fitness value, and set the corresponding individual as the optimal individual, i.e., p besti .

[0058] S3, update the position and speed of the particle according to the optimal fitness value of the historical population and the adaptive inertia weight, calculate the fitness value of the updated population, and retain it as the subsequent update direction. If the population generates a better individual during the iteration, it will be updated;

[0059] The step S3 specifically includes the following steps:

[0060] S3-1. The particle speed update consists of three main parts: inertia part, individual optimal position part and global optimal position part. Combining the simulated annealing probability criterion and the matching quality between the current particle and the reference sequence, an adaptive updated inertia weight formula is designed to update the particle speed and position. The formula is as follows:

[0061]

[0062] x i (t+1)=x i (t)+v i (t+1) (6)

[0063] Where, ν i (t) is the velocity of particle i at the tth iteration; x i (t) is the position of particle i at the tth iteration; ω(i) is the inertia weight value of the population at the tth iteration; ω0 represents the initial inertia weight; D(n,q) inorm is the DTW distance of particle i at the tth iteration; Tinorm is the temperature of particle i at the tth iteration; c1 is the individual learning factor; c2 is the social learning factor; r1 and r2 are random numbers between [0,1]; p besti is the individual optimal position of particle i at the tth iteration; g best is the global optimal position of the particle at the tth iteration; x i (t) is the position of the particle at the tth iteration.

[0064] S3-2, evaluate the current position of each particle and calculate the fitness value. If the fitness value of the particle's current position is better than the fitness value of its individual optimal position, then update the individual optimal position. If the fitness value of the particle's individual optimal position is better than the fitness value of the current global optimal position, then update the global optimal position. The formula for updating the individual and global optimal positions is:

[0065]

[0066] S4. Use the Metropolis criterion to calculate the probability of each particle accepting a new solution at the current temperature. If the new solution is better than the current solution, the new solution is accepted. Otherwise, the Metropolis criterion is used to accept the new solution. After that, the temperature drops, the cooling coefficient is updated, and the roulette wheel is used to redetermine the global optimal solution, and the particle swarm is continuously optimized in iterations.

[0067] The step S4 specifically includes the following steps:

[0068] S4-1, using the Metropolis sampling criterion as the basis for the optimal selection of individuals, by comparing the current fitness value of the particle with the historical fitness value, the current value is better than p besti If it is better, the current value is set to p besti ; The current value is worse than p besti When the probability is calculated by the Metropolis criterion formula, the probability value of the current generation of the particle is used to decide whether to accept the current value as p besti , the Metropolis criterion formula is:

[0069]

[0070] S4-2. Compare the fitness value of the current particle with the global optimal solution g best , if the current particle fitness value is better than g best , then update g best is the current particle fitness value, otherwise it will not be updated;

[0071] S4-3, if the current state has not reached the cooling state, continue to cool down, set T = T × β, and go to step S4-1 until the annealing operation is completed;

[0072] S4-4, according to the above calculation of individual fitness (x i ), calculate the probability fitness(x) of each individual being selected i ), there are N individuals in the particle swarm, and their fitness are f(x1),f(x2),...,f(x N ), the selection probability p of particle i i for

[0073]

[0074] Calculate the cumulative probability of each individual, and then generate a random number r within the range. i-1 <r<p i , then individual i is selected as the individual of the next generation population, and the fitness value and optimal fitness value of the particle swarm selected by roulette are calculated. The formula for the cumulative probability is:

[0075]

[0076] S5, determining the speed and position of the particle after the current iteration. If the particle speed exceeds the speed interval, the particle speed is taken as the speed interval boundary, and the position of the particle is judged. If the particle position exceeds the position interval, the particle is marked as invalid.

[0077] The step S5 specifically includes the following steps:

[0078] S5-1, determine the particle swarm update speed of the current iteration number according to formula (6), and perform speed out-of-bounds operation, that is, if the particle speed exceeds the speed interval (v min ,v max ), then the particle velocity is taken as the velocity interval boundary;

[0079] S5-2, the particle position is judged to determine whether the current particle exceeds the position interval. If the particle exceeds the position interval, the particle is marked as invalid. Otherwise, the particle is retained and updated.

[0080] S6. Update the particle position again by selecting the optimal solution, and update the particle again by selecting the optimal solution, and optimize it, and compare it with the historical best fitness of each particle. If it is lower than its historical best fitness, replace its historical best fitness with the updated particle position, otherwise, it will not be changed.

[0081] S7, determine the iteration termination condition, the maximum number of iterations is reached or the absolute error of the minimum fitness value of the two iterations is less than the DTW distance threshold and the number of continuous iterations is greater than the distance threshold to terminate the iteration number, otherwise return to step S3 iteration;

[0082] S8. The iteration stops and the optimal solution, i.e. the final coordinates of the user, is obtained.

[0083] The present invention uses geomagnetic reference maps to construct sequence libraries, combines particle swarm algorithms with multidimensional dynamic time planning to evaluate the similarity of geomagnetic data in real time, introduces the Meltrov criterion and roulette optimization search to improve particle accuracy; adaptively adjusts particle parameters, iteratively optimizes matching positions until convergence; and then obtains accurate positioning coordinates. While ensuring the real-time performance of geomagnetic positioning, the present invention can not only improve the accuracy and robustness of positioning, but also avoid the disadvantage that the multidimensional dynamic time planning algorithm is difficult to run efficiently on large-scale geomagnetic data sets due to high computational complexity when used alone.

[0084] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, component splitting or combination, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A geomagnetic sequence matching method, characterized in that: The following steps are involved: S1. First, the geomagnetic reference map is cropped to obtain the geomagnetic sequence library. When the geomagnetic positioning system receives a positioning request, it receives the current geomagnetic data and uses it as the target sequence to be matched. The particle swarm algorithm is used to generate the initial particle swarm, and the starting position and speed of each particle are randomly initialized, and the relevant initialization parameters are set; S2. Use the multi-dimensional DTW algorithm to calculate the similarity of the geomagnetic sequence and set it as the objective function. Construct a distance matrix based on the five-dimensional features, find the shortest path to measure the similarity, and use it as the PSO-SA algorithm evaluation criterion. Judge the validity of the particle by the first single-point geomagnetic Euclidean distance, calculate the initial population fitness, select the minimum DTW value as the global optimal position, and initialize the individual and global optimal positions. S3, update the position and speed of the particle according to the optimal fitness value of the historical population and the adaptive inertia weight, calculate the fitness value of the updated population, and retain it as the subsequent update direction. If the population generates a better individual during the iteration, it will be updated; S4. Use the Metropolis criterion to calculate the probability of each particle accepting a new solution at the current temperature. If the new solution is better than the current solution, the new solution is accepted. Otherwise, the Metropolis criterion is used to accept the new solution. After that, the temperature drops, the cooling coefficient is updated, and the roulette wheel is used to redetermine the global optimal solution, and the particle swarm is continuously optimized in iterations. S5, determining the speed and position of the particle after the current iteration. If the particle speed exceeds the speed interval, the particle speed is taken as the speed interval boundary, and the position of the particle is judged. If the particle position exceeds the position interval, the particle is marked as invalid. S6. Update the particle position again by selecting the optimal solution, and update the particle again by selecting the optimal solution, and optimize it, and compare it with the historical best fitness of each particle. If it is lower than its historical best fitness, replace its historical best fitness with the updated particle position, otherwise, it will not be changed. S7, determine the iteration termination condition, the maximum number of iterations is reached or the absolute error of the minimum fitness value of the two iterations is less than the DTW distance threshold and the number of continuous iterations is greater than the distance threshold to terminate the iteration number, otherwise return to step S3 iteration; S8. The iteration stops and the optimal solution, i.e. the final coordinates of the user, is obtained.

2. A geomagnetic sequence matching method according to claim 1, characterized in that: The step S1 specifically includes the following steps: S1-1. Use Kriging interpolation method to make geomagnetic reference map, split the sequence in geomagnetic reference map in the way of sliding window, set the window length a1, a1 is a positive integer, and obtain a geomagnetic database MagArray = {Q l ,Q2,Q3,...,Q l }, and take the coordinates corresponding to the last geomagnetic point in the sequence as the position label of the sequence; S1-2, in the range of geomagnetic fingerprint map (B min ,B max ) randomly generates N particles, and takes the first geomagnetic point of the geomagnetic sequence as a particle. After initialization, the initial position of the particle is x i and speed v i , set the initial temperature T and the cooling coefficient β.

3. A geomagnetic sequence matching method according to claim 1, characterized in that: The step S2 specifically includes the following steps: S2-1. Use the multi-dimensional DTW algorithm to calculate the similarity of geomagnetic sequences: The calculation process is mainly divided into two steps. The first step is to calculate the distance between the multi-dimensional features of each element in the two geomagnetic sequences to form a distance matrix. The second step is to calculate the distance matrix of the two geomagnetic sequences and find a path with the shortest cumulative distance from the (1,1) position of the distance matrix to the (n,q) position. S2-2, use the first single point geomagnetism of the geomagnetic sequence to be located to judge the validity of the particle: perform Euclidean distance judgment on the first point in the geomagnetic sequence found by the particle and the first point in the geomagnetic sequence to be located. If the obtained value is greater than the threshold α1, it is marked as invalid; S2-3, through fitness(x i )=-f(x i )=-D(n,q) calculate the fitness value of the initial population, compare and get the minimum fitness value, and set the corresponding individual as the optimal individual, i.e., p besti .

4. The geomagnetic sequence matching method according to claim 1, characterized in that: The step S3 specifically includes the following steps: S3-1. Adaptively update the inertia weight by combining the temperature and DTW distance in the simulated annealing algorithm, using the formula Adaptively update the velocity of particles in the particle swarm and use x i (t+1)=x i (t)+v i (t+1) Update the particle position; S3-2, evaluate the current position of each particle and calculate the fitness value. If the fitness value of the current position of the particle is better than the fitness value of its individual optimal position, update the individual optimal position; if the fitness value of the individual optimal position of the particle is better than the fitness value of the current global optimal position, update the global optimal position.

5. The geomagnetic sequence matching method according to claim 1, characterized in that: The step S4 specifically includes the following steps: S4-1, using the Metropolis sampling criterion as the basis for the optimal selection of individuals, by comparing the current fitness value of the particle with the historical fitness value, the current value is better than p besti If it is better, the current value is set to p besti ; The current value is worse than p besti When the probability is calculated by the Metropolis criterion formula, the probability value of the current generation of the particle is used to decide whether to accept the current value as p besti ; S4-2. Compare the fitness value of the current particle with the global optimal solution g best , if the current particle fitness value is better than g best , then update g best is the current particle fitness value, otherwise it will not be updated; S4-3, if the current state has not reached the cooling state, continue to cool down, set T = T × β, and go to step S4-1 until the annealing operation is completed; S4-4. Use the roulette method to redetermine the global optimal solution and continue to optimize the particle swarm during iteration.

6. A geomagnetic sequence matching method according to claim 1, characterized in that: The step S5 specifically includes the following steps: S5-1, determine the particle swarm update speed of the current iteration number, and perform speed out-of-bounds operation, that is, if the particle speed exceeds the speed interval (v min ,v max ), then the particle velocity is taken as the velocity interval boundary; S5-2, the particle position is judged to determine whether the current particle exceeds the position interval. If the particle exceeds the position interval, the particle is marked as invalid. Otherwise, the particle is retained and updated.

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