A method for temperature compensation of quartz flexible meter

Abstract

The present application discloses a quartz flexible meter temperature compensation method, belonging to the field of quartz flexible meter temperature compensation technology, comprising: constructing a temperature compensation model that simultaneously considers the influence of temperature, temperature change rate, and temperature spatial gradient in the sensitive axis direction; using a super Latin square generational elimination particle swarm algorithm to identify the coefficients of the temperature compensation model, and based on the coefficients, obtaining a quartz flexible meter temperature compensation model that considers the temperature spatial gradient in the sensitive axis direction; and determining the compensation result of the quartz flexible meter temperature using the temperature compensation model based on real-time temperature measurement data of the quartz flexible meter in the sensitive axis direction. This method solves the problem of poor accuracy of traditional temperature compensation models and compensation methods, improves the accuracy and efficiency of determining temperature compensation results, and greatly improves the measurement accuracy and speed of quartz flexible meters on large space carriers.

Description

Technical Field

[0001] The present application relates to the technical field of quartz flexible gauge temperature compensation, and in particular to a quartz flexible gauge temperature compensation method. Background Art

[0002] A quartz flexible gauge is a key sensitive device for the navigation and guidance of large space vehicles. It is mainly composed of a pendulum, a differential capacitance sensor, a torquer, a magnet, a yoke, and a housing. The measurement accuracy of a quartz flexible gauge is significantly affected by the ambient temperature. To ensure the navigation and guidance accuracy of large space vehicles, it is usually necessary to configure a temperature control system for it during actual use to ensure that it operates in a relatively stable ambient temperature. Therefore, large space vehicles using quartz flexible gauges usually need to undergo a certain period of heating before launch. Although this ensures the navigation and guidance accuracy of the large space vehicle, it also prolongs the launch preparation time of the large space vehicle and affects its rapid response capability.

[0003] Temperature error compensation technology has been proposed and is gaining widespread application. This technology first establishes a temperature error model for a quartz flexible gauge based on experimental data. Then, it compensates the gauge's output in real time based on the monitored ambient temperature to ensure navigation accuracy across the entire temperature range. Compared to traditional temperature control methods, this technology avoids structural redundancy, saves heating time, and, after compensation, still meets navigation accuracy requirements. It has garnered widespread attention.

[0004] However, most of the temperature error compensation models currently established only consider the time-varying influence of temperature, and do not adequately consider the spatial temperature gradient along the sensitive axis of the quartz flexible meter. Influenced by the characteristics of the working environment of the quartz flexible meter, there is often a temperature difference on both sides of the sensitive axis of the quartz pendulum in some typical conditions. This leads to poor accuracy of traditional temperature compensation models and compensation methods. Summary of the Invention

[0005] In response to the above-mentioned deficiencies in the prior art, the present application provides a quartz flexible meter temperature compensation method, which constructs a temperature compensation model based on the spatial temperature gradient in the sensitive axis direction of the quartz flexible meter, solves the problem of poor accuracy of traditional temperature compensation models and compensation methods, and improves the measurement accuracy and speed of quartz flexible meters on large space carriers.

[0006] In order to achieve the above-mentioned invention objectives, the technical solutions adopted in this application are:

[0007] The present application provides a quartz flexible surface temperature compensation method, comprising:

[0008] S1: Construct a temperature compensation model that considers the effects of temperature, temperature change rate, and temperature spatial gradient along the sensitive axis.

[0009] S2: using a super Latin square generational elimination particle swarm algorithm to identify the coefficients of the temperature compensation model, and based on the coefficients, obtaining a quartz flexible surface temperature compensation model that takes into account the temperature spatial gradient in the sensitive axis direction;

[0010] S3: Determine a compensation result of the quartz flexible meter temperature according to the real-time temperature measurement data of the quartz flexible meter in the sensitive axis direction and using the quartz flexible meter temperature compensation model.

[0011] Furthermore, in S1, the temperature compensation model is:

[0012]

[0013] in, The error is caused by the temperature change on the A side of the quartz gauge. It is the temperature measurement value of the internal side A of the quartz flex gauge; is the fifth-order coefficient of the temperature on side A; is the fourth-order coefficient of the temperature on side A; is the cubic coefficient of the temperature on side A; is the quadratic coefficient of the temperature on side A; is the linear coefficient of the temperature on side A; is the linear coefficient of the temperature change rate on side A; is the quadratic coefficient of the temperature change rate on side A, is a constant coefficient; It is the error caused by the quartz flexibility plus the temperature change on the B side of the table; It is the temperature measurement value of the B side inside the quartz flex gauge; is the fifth-order coefficient of the temperature on the B side; is the fourth-order coefficient of the temperature on the B side; is the cubic coefficient of the temperature on the B side; is the quadratic coefficient of the temperature on the B side; is the linear coefficient of the temperature on the B side; is the linear coefficient of the temperature change rate on the B side; is the quadratic coefficient of the temperature change rate on the B side, is a constant coefficient; It is a function used to describe the temperature change on the A side of the quartz flexure gauge; It is a function used to describe the temperature change on the B side of the quartz flexure. is the temperature measurement time; 、 Indicates the temperature change rate of the A side temperature and the B side temperature, 、 Indicates the temperature change acceleration rate of the A side temperature and the B side temperature, is the total error.

[0014] Furthermore, the S2 specifically includes:

[0015] S201: Initialize the population;

[0016] S202: Construct fitness function:

[0017]

[0018] in, To minimize the cumulative sum of squares of temperature errors, is the sum of squares of temperature errors, E(t) is the temperature error compensation term, is the true temperature, is the temperature obtained from the test, This is the first problem to be solved in the compensation model. coefficients, 、 For the The lower and upper limits of the search range for each parameter, For time;

[0019] S203: Calculating the fitness values of all random particles in the population using the fitness function, taking the individual fitness extreme value of each particle in the population as the individual optimal solution, and determining the current global optimal solution from all the individual optimal solutions;

[0020] S204: Compare the current global optimal solution with the historical global optimal solution to obtain a comparison result; if the current global optimal solution is better than the historical global optimal solution, update the particle position and velocity, calculate the fitness of the updated particle, determine the individual optimal solution and the current global optimal solution, where the updated particle position and velocity are:

[0021]

[0022]

[0023] in, is the inertia weight, 、 is the acceleration constant, 、 is a random number in [0,1], For particles In the Generation, the The individual extreme value of dimension, For particles In the Generation, the The global extreme value of dimension, For particles In the Generation, the The location of the dimension, For particles In the Generation, the The speed of dimension, For particles In the Generation, the The location of the dimension, For particles In the Generation, the Dimensional speed;

[0024] S205: When the smaller fitness value of all random particles in the population satisfies: , reduce the population size until the termination condition is met, where, is the minimum fitness value of the fitness function of the first generation of particles, For the y The fitness minimum value of the fitness function of the generation particle, is a constant;

[0025] S206: Using the particle parameters corresponding to the global optimal solution as coefficients of the temperature compensation model;

[0026] S207: According to the coefficients, a quartz flexibility plus surface temperature compensation model is obtained that takes into account the temperature spatial gradient in the sensitive axis direction.

[0027] Furthermore, the initialization of the population in S201 specifically includes:

[0028] S2011: Initialize population parameters, including: initialization iteration number, population size, particle dimension, variables per dimension The search interval, fitness function, variable value range, and particle swarm;

[0029] S2012: The initial sample population is sampled using the super Latin square method to obtain uniformly distributed random initial particles.

[0030] Furthermore, the S2012 uses the super Latin square method to sample the initial sample population to obtain uniformly distributed random initial particles, specifically including:

[0031] S20121: Determine the initial particle swarm size H;

[0032] S20122: Each dimension variable The search interval Divide into H equally spaced intervals, forming H hypercubes;

[0033] S20123: Based on the initial particle swarm size H and the equally spaced intervals, generate an H×16 matrix A, where each column of the matrix A is a random full permutation of the sequence {1, 2, …, H};

[0034] S20124: When only one small hypercube is selected in each row of the matrix A, a particle is generated in each small hypercube. The coordinates of the jth particle in the 16-dimensional space are ( ), among which Dimension The particles are:

[0035]

[0036] Where 1≤ ≤16, 1≤ ≤ , is the initial subgroup size, 、 For each dimension variable The endpoints of the interval, For each subinterval length, For the The random displacement of particles in the subinterval, for The matrix, For the Dimension particles;

[0037] S20125: Each particle constitutes an initial particle.

[0038] Furthermore, the termination conditions are: the current global optimal solution of the fitness of particles in the population reaches a preset value; the number of algorithm cycles reaches a preset value.

[0039] The beneficial effects of this application are:

[0040] This application is based on a temperature compensation model that takes into account the influence of the spatial gradient of the temperature in the sensitive axis direction, and takes into account the temperature difference on both sides of the sensitive axis direction of the quartz pendulum of the meter under some typical conditions, thereby improving the accuracy and precision of the model. In addition, the coefficients of the temperature compensation model are identified by the super Latin square generational elimination particle swarm algorithm, which avoids the calculation from falling into the local optimum during the coefficient identification process and improves the calculation efficiency. In addition, combined with the test data of the temperature cycle change in the sensitive axis direction of the quartz flexible meter, a high-precision temperature compensation model of the quartz flexible meter of a large space vehicle can be obtained, which can greatly improve the measurement accuracy and measurement speed of the quartz flexible meter of a large space vehicle, and promote the improvement of the flight trajectory and attitude control capability of the large space vehicle. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other embodiments can also be obtained based on these drawings.

[0042] Figure 1 This is a flow chart of a quartz flexible meter temperature compensation method provided in an embodiment of the present application.

[0043] Figure 2 This is a layout diagram of a temperature measurement sensor in the sensitive axis direction of a quartz flexible surface-added temperature measuring device for a large space vehicle provided in an embodiment of the present application.

[0044] Figure 3 A schematic diagram of temperature compensation implementation of a quartz flexible surface for a large space vehicle provided in an embodiment of the present application. DETAILED DESCRIPTION

[0045] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field based on this application are within the scope of protection of this application.

[0046] The present application provides a method for temperature compensation of a quartz flexible meter. The method can be applied to any large space carrier quartz flexible meter. The method can be found in Figure 1 , Figure 1 The figure shows a flow chart of a quartz flexible meter temperature compensation method provided by an embodiment of the present application, including:

[0047] S1: Construct a temperature compensation model that simultaneously considers the effects of temperature, temperature change rate, and spatial temperature gradient along the sensitive axis.

[0048] Furthermore, the temperature compensation model is:

[0049]

[0050] in, The error is caused by the temperature change on the A side of the quartz gauge. It is the temperature measurement value of the internal side A of the quartz flex gauge; is the fifth-order coefficient of the temperature on side A; is the fourth-order coefficient of the temperature on side A; is the cubic coefficient of the temperature on side A; is the quadratic coefficient of the temperature on side A; is the linear coefficient of the temperature on side A; is the linear coefficient of the temperature change rate on side A; is the quadratic coefficient of the temperature change rate on side A, is a constant coefficient; It is the error caused by the quartz flexibility plus the temperature change on the B side of the table; It is the temperature measurement value of the B side inside the quartz flex gauge; is the fifth-order coefficient of the temperature on the B side; is the fourth-order coefficient of the temperature on the B side; is the cubic coefficient of the temperature on the B side; is the quadratic coefficient of the temperature on the B side; is the linear coefficient of the temperature on the B side; is the linear coefficient of the temperature change rate on the B side; is the quadratic coefficient of the temperature change rate on the B side, is a constant coefficient; It is a function used to describe the temperature change on the A side of the quartz flexure gauge; It is a function used to describe the temperature change on the B side of the quartz flexure. is the temperature measurement time; 、 Indicates the temperature change rate of the A side temperature and the B side temperature, 、 Indicates the temperature change acceleration rate of the A side temperature and the B side temperature, is the total error.

[0051] In a possible embodiment, the model simultaneously considers the influence of temperature, temperature change rate and temperature spatial gradient in the sensitive axis direction on the test error of the quartz flexible plus meter. A fifth-order model was constructed to precisely characterize the effect of the temperature on the test error of the quartz flexible meter. A two-order model was constructed for the temperature change rate on the A side of the quartz flexible meter's sensitive axis to characterize the effect of the temperature change rate on the test error of the quartz flexible meter. Similarly, a two-order model was constructed for the temperature parameter on the B side of the quartz flexible meter's sensitive axis. A fifth-order model was constructed to finely characterize the influence of the B-side temperature on the test error of the quartz flexible gauge. A two-order model was constructed for the temperature change rate of the B-side in the sensitive axis direction of the quartz flexible gauge to characterize the influence of the temperature change rate of the B-side in the sensitive axis direction of the quartz flexible gauge on the test error of the quartz flexible gauge.

[0052] S2: using a super Latin square generational elimination particle swarm algorithm to identify the coefficients of the temperature compensation model, and based on the coefficients, obtaining a quartz flexible surface temperature compensation model that considers the temperature spatial gradient in the sensitive axis direction.

[0053] Because the temperature error compensation model constructed above contains 16 unknown parameter values and the parameter variation range is relatively wide, when using the PSO algorithm for parameter identification, a large initial population can better avoid the identification process from falling into a local optimum, but it may cause the parameter identification process to be slow; if the initial population is too small, the parameter identification process may fall into a local optimum solution. Based on this, in order to effectively improve the speed and accuracy of the temperature error compensation model parameter identification, the super Latin square generational elimination particle swarm algorithm is used to identify the coefficients of the temperature compensation model.

[0054] Furthermore, the S2 specifically includes:

[0055] S201: Initialize the population.

[0056] Furthermore, the initialization of the population in S201 specifically includes:

[0057] S2011: Initialize population parameters, including: initialization iteration number, population size, particle dimension, variables per dimension The search interval, fitness function, variable value range, and particle swarm;

[0058] S2012: The initial sample population is sampled using the super Latin square method to obtain uniformly distributed random initial particles.

[0059] Furthermore, the S2012 uses the super Latin square method to sample the initial sample population to obtain uniformly distributed random initial particles, specifically including:

[0060] S20121: Determine the initial particle swarm size H;

[0061] S20122: Each dimension variable The search interval Divide into H equally spaced intervals, forming H hypercubes;

[0062] In one possible embodiment, each dimension variable The search interval Divide into H equally spaced intervals to form H hypercubes, thus dividing the entire 16-dimensional search space into A hypercube.

[0063] S20123: Based on the initial particle swarm size H and the equally spaced intervals, generate an H×16 matrix A, where each column of the matrix A is a random full permutation of the sequence {1, 2, …, H};

[0064] S20124: When only one small hypercube is selected in each row of the matrix A, a particle is generated in each small hypercube. The coordinates of a particle in 16-dimensional space are ( ), among which Dimension The particles are:

[0065]

[0066] Where 1≤ ≤16, 1≤ ≤ , is the initial subgroup size, 、 For each dimension variable The endpoints of the interval, For each subinterval length, For the The random displacement of particles in the subinterval, for The matrix, For the Dimension particles;

[0067] S20125: Each particle constitutes an initial particle.

[0068] In one possible embodiment, the hyper Latin square method is used to sample the initial sample population to obtain uniformly distributed random initial particles. H samples can be uniformly selected in the entire search space, and the samples are randomly distributed in each hypercube, which ensures both the uniformity and randomness of the samples and avoids the parameter identification process from falling into local optimality.

[0069] S202: Construct fitness function:

[0070]

[0071] in, To minimize the cumulative sum of squares of temperature errors, is the sum of squares of temperature errors, E(t) is the temperature error compensation term, is the true temperature, is the temperature obtained from the test, This is the first problem to be solved in the compensation model. coefficients, 、 For the The lower and upper limits of the search range for each parameter, For time.

[0072] S203: Calculate the fitness values of all random particles in the population using the fitness function, take the individual fitness extreme value of each particle in the population as the individual optimal solution, and determine the current global optimal solution from all the individual optimal solutions.

[0073] In a possible embodiment, the 16 random parameters of the generated random particles are substituted into the fitness function determined in step S202. , calculate the fitness values of all random particles in the population, and determine the individual fitness extreme value of each particle in the population as the individual optimal solution, and the fitness extreme value of all particles as the global optimal solution.

[0074] S204: Compare the current global optimal solution with the historical global optimal solution to obtain a comparison result; if the current global optimal solution is better than the historical global optimal solution, update the particle position and velocity, calculate the fitness of the updated particle, determine the individual optimal solution and the current global optimal solution, where the updated particle position and velocity are:

[0075]

[0076]

[0077] in, is the inertia weight, 、 is the acceleration constant, 、 is a random number in [0,1], For particles In the Generation, the The individual extreme value of dimension, For particles In the Generation, the The global extreme value of dimension, For particles In the Generation, the The location of the dimension, For particles In the Generation, the The speed of dimension, For particles In the Generation, the The location of the dimension, For particles In the Generation, the The speed of dimension.

[0078] S205: When the minimum fitness value of all random particles in the population satisfies: , reduce the population size until the termination condition is met, where, is the minimum fitness value of the fitness function of the first generation of particles, For the y The fitness minimum value of the fitness function of the generation particle, is a constant.

[0079] Furthermore, the termination conditions are: the current global optimal solution of the fitness of particles in the population reaches a preset value; the number of algorithm cycles reaches a preset value.

[0080] In one possible embodiment, in order to improve the parameter identification speed and avoid the particles being too dense in the late search due to the large population, when the fitness function satisfies After the requirement, the particles with the lowest fitness in the population of 5% are eliminated. When the global optimal solution of the fitness of the particles in the population reaches the preset value or the number of algorithm cycles reaches the preset value, the elimination stops.

[0081] S206: Using the particle parameters corresponding to the global optimal solution as coefficients of the temperature compensation model.

[0082] S207: According to the coefficients, a quartz flexibility plus surface temperature compensation model is obtained that takes into account the temperature spatial gradient in the sensitive axis direction.

[0083] S3: Determine a compensation result of the quartz flexible meter temperature according to the real-time temperature measurement data of the quartz flexible meter in the sensitive axis direction and using the quartz flexible meter temperature compensation model.

[0084] In one possible embodiment, the established temperature compensation model of the quartz flexible meter that takes into account the temperature spatial gradient in the sensitive axis direction is introduced into the temperature compensation model calculation unit. When used, temperature sensors are assembled in the sensitive axis direction of the quartz flexible meter according to the experimental layout plan during parameter identification to measure the real-time temperature of the quartz flexible meter on the A and B sides in the sensitive axis direction respectively. and , the temperature sensor layout scheme can be seen in Figure 2 , Figure 2 The embodiment of the present application provides a layout diagram of a temperature sensor for measuring the sensitive axis direction of a quartz flexible adding table on a large space vehicle. The temperature measurement data and the output data of the quartz flexible adding table are synchronously output to the built-in computing unit. After the built-in computing unit calculates and compensates, high-precision acceleration information is obtained and output to the flight control machine of the large space vehicle for navigation calculation and attitude control. Figure 3 As shown, Figure 3 A schematic diagram of temperature compensation implementation of a quartz flexible surface for a large space vehicle provided in an embodiment of the present application.

[0085] This application develops a high-precision temperature compensation model for quartz flexible temperature meters on large space vehicles, based on a constructed temperature compensation model for a flexible quartz meter that considers the spatial temperature gradient in the sensitive axis direction, a temperature compensation model coefficient identification method based on a super Latin square generational elimination particle swarm, and experimental data from temperature cycling tests on the sensitive axis of the quartz flexible temperature meter. This model, combined with a built-in computing unit, a scientific layout of temperature sensors in the sensitive axis direction, and the introduction of high-precision temperature measurement circuits, can greatly improve the measurement accuracy and speed of quartz flexible temperature meters on large space vehicles, promoting improvements in the flight trajectory and attitude control capabilities of large space vehicles.

[0086] Each embodiment in this specification is described in a related manner. Similar parts between the embodiments can be referred to in conjunction with each other. Each embodiment focuses on the differences from other embodiments. The above description is only a preferred embodiment of this application and is not intended to limit the scope of protection of this application. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of this application are included in the scope of protection of this application.

Claims

1. A quartz flexible surface temperature compensation method, characterized in that: include: S1: Construct a temperature compensation model that considers the effects of temperature, temperature change rate, and temperature spatial gradient along the sensitive axis. S2: using a super Latin square generational elimination particle swarm algorithm to identify the coefficients of the temperature compensation model, and based on the coefficients, obtaining a quartz flexible surface temperature compensation model that takes into account the temperature spatial gradient in the sensitive axis direction; S3: Determine a compensation result of the quartz flexible meter temperature using the quartz flexible meter temperature compensation model according to the real-time temperature measurement data of the quartz flexible meter in the sensitive axis direction; In S1, the temperature compensation model is: in, The error is caused by the temperature change on the A side of the quartz gauge. It is the temperature measurement value of the internal side A of the quartz flex gauge; is the fifth-order coefficient of the temperature on side A; is the fourth-order coefficient of the temperature on side A; is the cubic coefficient of the temperature on side A; is the quadratic coefficient of the temperature on side A; is the linear coefficient of the temperature on side A; is the linear coefficient of the temperature change rate on side A; is the quadratic coefficient of the temperature change rate on side A, is a constant coefficient; It is the error caused by the quartz flexibility plus the temperature change on the B side of the table; It is the temperature measurement value of the B side inside the quartz flex gauge; is the fifth-order coefficient of the temperature on the B side; is the fourth-order coefficient of the temperature on the B side; is the cubic coefficient of the temperature on the B side; is the quadratic coefficient of the temperature on the B side; is the linear coefficient of the temperature on the B side; is the linear coefficient of the temperature change rate on the B side; is the quadratic coefficient of the temperature change rate on the B side, is a constant coefficient; It is a function used to describe the temperature change on the A side of the quartz flexure gauge; It is a function used to describe the temperature change on the B side of the quartz flexure. is the temperature measurement time; 、 Indicates the temperature change rate of the A side temperature and the B side temperature, 、 Indicates the temperature change acceleration rate of the A side temperature and the B side temperature, is the total error; Said S2 specifically includes: S201: Initialize the population; S202: Construct fitness function: in, To minimize the cumulative sum of squares of temperature errors, is the sum of squares of temperature errors, E(t) is the temperature error compensation term, is the true temperature, is the temperature obtained from the test, is the coefficient to be solved in the temperature compensation model, and is the coefficient The lower and upper bounds of the search interval; S203: Calculating the fitness values of all random initial particles in the initialized population using the fitness function, taking the individual fitness extreme value of each particle in the initialized population as the individual optimal solution, and determining the current global optimal solution from all the individual optimal solutions; S204: Compare the current global optimal solution with the historical global optimal solution to obtain a comparison result; if the current global optimal solution is better than the historical global optimal solution, update the particle position and velocity, calculate the fitness of the updated particle, determine the individual optimal solution and the current global optimal solution, where the updated particle position and velocity are: in, is the inertia weight, 、 is the acceleration constant, 、 is a random number in [0,1], For particles In the Generation, the The individual extreme value of dimension, For the Generation, the All particles The global extreme value of For particles In the Generation, the The location of the dimension, For particles In the Generation, the The speed of dimension, For particles In the Generation, The location of the dimension, For particles In the Generation, the Dimensional speed; S205: When the fitness minimum values of all random initial particles in the initialized population meet the following conditions: , reduce the size of the initial population until the termination condition is met, where is the minimum fitness value of the fitness function of the first generation of particles, For the y The fitness minimum value of the fitness function of the generation particle, is a constant; S206: Using the particle parameters corresponding to the global optimal solution as coefficients of the temperature compensation model; S207: According to the coefficients, a quartz flexibility plus surface temperature compensation model is obtained that takes into account the temperature spatial gradient in the sensitive axis direction.

2. The quartz flexible surface temperature compensation method according to claim 1, characterized in that: Initializing the population in S201 specifically includes: S2011: Initialize population parameters, including: initialization iteration number, population size, particle dimension, and coefficient of each dimension The search interval, fitness function, variable value range and particle swarm; S2012: Use the Super Latin Square method to sample the initial population and obtain uniformly distributed random initial particles.

3. The quartz flexible surface temperature compensation method according to claim 2, characterized in that: In S2012, the super Latin square method is used to sample the initial population to obtain uniformly distributed random initial particles, specifically including: S20121: Determine the random initial particle swarm size H; S20122: Each dimension coefficient The search interval Divide into H equally spaced intervals, forming Hypercubes; S20123: Based on the random initial particle swarm size H and the equally spaced intervals, generate an H×16 matrix A, where each column of the matrix A is a random full permutation of the sequence {1, 2, …, H}; S20124: When only one small hypercube is selected in each row of the matrix A, a particle is generated in each small hypercube. j The coordinates of a particle in 16-dimensional space are ( ), among which Dimension The particles are: Where 1≤ ≤16, 1≤ ≤ , For each subinterval length, For the The random displacement of particles in the subinterval, for The matrix, For the Dimension particles; S20125: Each particle is composed of uniformly distributed random initial particles.

4. The quartz flexible surface temperature compensation method according to claim 1, characterized in that: The termination conditions are: the current global optimal solution of the particle fitness in the population reaches a preset value; the number of algorithm cycles reaches a preset value.

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