Optical system temperature field acquisition method and system

Abstract

The present invention discloses a method and system for acquiring the temperature field of an optical system. The method comprises: acquiring temperature data of a preset position of the optical system during a temperature change process, obtaining a temperature sequence for the current period, wherein the temperature sequence for the current period is formed by arranging the temperature data of the preset position within the current period at the same time as the process progresses; inputting the temperature sequence for the current period into a temperature prediction model, outputting a temperature sequence for the next period, wherein the temperature sequence for the next period is formed by arranging the temperature data of the preset position within the next period at the same time as the process progresses; and obtaining the temperature field of the optical system within the next period based on the temperature sequence for the next period. Obtaining the temperature prediction model comprises: acquiring temperature data of a preset position of the optical system during a temperature change process, obtaining training data including an input temperature sequence and an output temperature sequence, and using the training data to train the temperature prediction model. The present invention can predict the temperature field of the optical system, thereby enabling monitoring of the temperature condition of the optical system.

Description

Technical Field

[0001] The present invention relates to the field of optical systems, and in particular to a method and system for acquiring a temperature field of an optical system. Background Art

[0002] The design and research of infrared optical systems in cryogenic environments are becoming increasingly important in fields such as space exploration and astronomical observation. Whether in space or on the ground, multi-physics loads such as temperature, vibration, and gravity can impact the optical performance of optical systems. Temperature is the most significant external factor affecting cryogenic infrared optical systems, primarily due to temperature gradients that generate significant deformation and thermal stress between optical components and mechanical structures, potentially causing damage. Therefore, predicting and monitoring optical system temperature changes in real time, as well as predicting and analyzing various optical system performance, are key to improving their efficiency and lifespan. Summary of the Invention

[0003] Based on the above, the purpose of the present invention is to provide a method and system for obtaining the temperature field of an optical system, which can predict the temperature field of the optical system, enable monitoring the temperature conditions of the optical system, and have reference significance for predicting and analyzing various performances of the optical system.

[0004] To achieve the above object, the present invention provides the following technical solutions:

[0005] A method for acquiring a temperature field of an optical system, comprising:

[0006] During the temperature change process, temperature data of a preset position of the optical system is acquired to obtain a temperature sequence of the current period, wherein the temperature sequence of the current period is formed by arranging the temperature data of the preset position in the current period as the process progresses;

[0007] Inputting the temperature sequence of the current period into a temperature prediction model, the temperature prediction model outputs a temperature sequence of a subsequent period, the temperature sequence of the subsequent period being formed by arranging the temperature data of the preset position in the subsequent period predicted as the process progresses, the subsequent period being the period after the current period;

[0008] obtaining a temperature field of the optical system in the latter period according to the temperature sequence of the latter period, wherein the temperature field of the optical system includes temperature data of the preset position of the optical system and temperature data of other positions of the optical system except the preset position;

[0009] Obtaining the temperature prediction model includes:

[0010] During the temperature change process, the temperature data of the preset position of the optical system is obtained, and training data is obtained based on the obtained temperature data. The training data includes an input temperature sequence and an output temperature sequence. The input temperature sequence is formed by arranging the temperature data of the preset position in the previous time period at the same time as the process progresses, and the output temperature sequence is formed by arranging the temperature data of the preset position in the next time period at the same time as the process progresses. The training data is used to train the constructed temperature prediction model to obtain the temperature prediction model.

[0011] Optionally, during the temperature change process, acquiring temperature data of the preset position of the optical system, and obtaining training data according to the acquired temperature data includes:

[0012] During the cooling process, obtaining temperature data of the preset position of the optical system, and obtaining the training data according to the obtained temperature data;

[0013] Alternatively, in a process of returning to a higher temperature after cooling, temperature data of the preset position of the optical system is obtained, and the training data is obtained according to the obtained temperature data;

[0014] Alternatively, during the cooling process and during the warming process after cooling, the temperature data of the preset position of the optical system is respectively acquired, and the training data is acquired according to the acquired temperature data.

[0015] Optionally, obtaining training data according to the obtained temperature data includes:

[0016] The obtained temperature data is processed into the input temperature sequence and the output temperature sequence through the time window. The input temperature sequence is expressed as , the output temperature sequence is expressed as , represents a time window of length m starting from time t, express Then a time window of length n is generated. Represents X t is a vector formed by the temperature data obtained at i preset positions.

[0017] Optionally, using the training data to train the constructed temperature prediction model includes:

[0018] Establishing a loss function, wherein the loss function describes the loss of the predicted value of the output temperature sequence relative to the true value, and differentiating the loss function with respect to the temperature prediction model parameters to obtain a gradient function of the loss function derived with respect to the temperature prediction model parameters;

[0019] In any iteration, the current temperature prediction model parameter value is substituted into the gradient function to obtain the current gradient value, and the first-order moment estimate is updated according to the current first-order moment estimate and the current gradient value, the second-order moment estimate is updated according to the current second-order moment estimate and the current gradient value, and the temperature prediction model parameter value is updated according to the updated first-order moment estimate and the updated second-order moment estimate, and the next iteration is performed with the updated temperature prediction model parameter value;

[0020] When an iteration stopping condition is met, the iteration is stopped, and the temperature prediction model is obtained according to the temperature prediction model parameter values ​​obtained in the last iteration;

[0021] Among them, the first-order moment estimate represents the estimate of the first-order moment of the gradient of the loss function derived from the temperature prediction model parameters, and the second-order moment estimate represents the estimate of the second-order moment of the gradient of the loss function derived from the temperature prediction model parameters.

[0022] Optionally, updating the first-order moment estimate according to the current first-order moment estimate and the current gradient value includes:

[0023] Obtain a first product of a first decay rate and the current first-order moment estimate, obtain a second product of a difference between 1 and the first decay rate and the current gradient value, and use the sum of the first product and the second product as an updated first-order moment estimate;

[0024] Updating the second-order moment estimate based on the current second-order moment estimate and the current gradient value includes:

[0025] Obtain a third product of the second attenuation rate and the current second-order moment estimate, obtain a fourth product of the difference between 1 and the second attenuation rate and the square of the current gradient value, and use the sum of the third product and the fourth product as the updated second-order moment estimate.

[0026] Optionally, updating the temperature prediction model parameter value according to the updated first-order moment estimate and the updated second-order moment estimate includes:

[0027] The updated first-order moment estimate is corrected for deviation, and the updated second-order moment estimate is corrected for deviation. The temperature prediction model parameter value is updated according to the corrected first-order moment estimate and the corrected second-order moment estimate, wherein the ratio of the updated first-order moment estimate to the difference between 1 and the first attenuation factor is the corrected first-order moment estimate, and the ratio of the updated second-order moment estimate to the difference between 1 and the second attenuation factor is the corrected second-order moment estimate.

[0028] Optionally, using the training data to train the constructed temperature prediction model includes:

[0029] A loss function is established and an iterative operation is performed based on the loss function to obtain temperature prediction model parameter values ​​that meet the conditions, so as to obtain the temperature prediction model determined by the temperature prediction model parameter values ​​that meet the conditions, wherein the loss function expresses the multi-power averaging of the difference between the true value and the predicted value of the output temperature sequence of the training data.

[0030] Optionally, the temperature prediction model includes an encoder and a decoder;

[0031] The encoder includes a plurality of parallel layers, each layer including a first network and a second network, wherein the first network is used to learn the dependency between the temperature data in the input temperature sequence and the output temperature sequence, and generate a representation expressing the correlation between the temperature data at previous and subsequent moments, and the second network is used to perform a nonlinear transformation on the representation generated by the first network;

[0032] The decoder includes an internal memory unit, an input gate, a forget gate and an output gate. The internal memory unit is used to allow sequence information to be transmitted along the internal memory unit. The forget gate is used to determine whether the sequence information transmitted along the internal memory unit is discarded. The input gate is used to determine whether the sequence information is stored in the internal memory unit. The output gate is used to determine whether the sequence information in the internal memory unit is output.

[0033] Optionally, it also includes:

[0034] Inputting the temperature sequence of the subsequent period into the temperature prediction model, the temperature prediction model outputs a temperature sequence of a period after the subsequent period, the temperature sequence of the period after the subsequent period being formed by arranging the predicted temperature data of the preset position within the period after the subsequent period as the process progresses;

[0035] According to the temperature sequence of a period after the latter period, the temperature field of the optical system in a period after the latter period is obtained.

[0036] An optical system temperature field acquisition system, comprising:

[0037] memory for storing computer programs;

[0038] A processor is configured to implement the steps of the method for acquiring the temperature field of an optical system as described in any one of the above items when executing the computer program.

[0039] As can be seen from the above technical solution, the present invention provides a method and system for acquiring the temperature field of an optical system, the method comprising: acquiring temperature data of a preset position of the optical system during a temperature change process, obtaining a temperature sequence for the current period, the temperature sequence for the current period being formed by arranging the temperature data of the preset position within the current period at the same time as the process progresses; inputting the temperature sequence for the current period into a temperature prediction model, the temperature prediction model outputting a temperature sequence for a subsequent period, the temperature sequence for the subsequent period being formed by arranging the predicted temperature data of the preset position within the subsequent period at the same time as the process progresses, the subsequent period being the period after the current period; and obtaining the temperature field of the optical system within the subsequent period based on the temperature sequence for the subsequent period. Obtaining the temperature prediction model comprises: acquiring temperature data of a preset position of the optical system during a temperature change process, obtaining training data based on the obtained temperature data, the training data comprising an input temperature sequence and an output temperature sequence, the input temperature sequence being formed by arranging the temperature data of the preset position within the previous period at the same time as the process progresses, the output temperature sequence being formed by arranging the temperature data of the preset position within the subsequent period at the same time as the process progresses, and using the training data to train the constructed temperature prediction model to obtain the temperature prediction model.

[0040] The optical system temperature field acquisition method and system of the present invention acquire temperature data at preset positions in the optical system during a temperature change process, obtaining training data including input and output temperature sequences to develop a temperature prediction model for the optical system. Based on the temperature sequence acquired from the optical system in real time during the current period, the temperature prediction model can be used to predict the temperature sequence of the optical system for the next period, and further obtain the optical system temperature field for the next period, thereby achieving prediction of the optical system temperature field. Therefore, the optical system temperature field acquisition method and system of the present invention can predict the optical system temperature field, enabling monitoring of the optical system's temperature conditions and providing valuable insights for predicting and analyzing various optical system performance characteristics. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] In order to more clearly illustrate the embodiments of the present invention 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 invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0042] Figure 1 A flow chart of a method for acquiring a temperature field of an optical system provided by one embodiment of the present invention;

[0043] Figure 2-1 A temperature data diagram obtained from an optical system in a method for obtaining a temperature field of an optical system according to an embodiment of the present invention;

[0044] Figure 2-2Schematic diagram of processing temperature data of an optical system through a time window in a method for acquiring a temperature field of an optical system according to an embodiment of the present invention;

[0045] Figure 3 Schematic diagram of a temperature prediction model in a method for acquiring a temperature field of an optical system according to an embodiment of the present invention;

[0046] Figure 4-1 A structural model of the optical system in a method for acquiring a temperature field of an optical system according to an embodiment of the present invention;

[0047] Figure 4-2 The optical system temperature field obtained in the optical system temperature field acquisition method according to an embodiment of the present invention;

[0048] Figure 5 The changes between the predicted values ​​and the actual values ​​of the temperature measurement points of the primary mirror, the secondary mirror, the tertiary mirror, and the connection platform in the cooling condition in the optical system temperature field acquisition method according to one embodiment of the present invention;

[0049] Figure 6 The changes between the predicted values ​​and the actual values ​​of the temperature measurement points of the primary mirror, secondary mirror, tertiary mirror, and connection platform in the temperature recovery condition in the optical system temperature field acquisition method according to one embodiment of the present invention;

[0050] Figure 7 This is a comparison chart of the mean absolute percentage error and root mean square error of the temperature prediction model constructed using five networks in one embodiment of the present invention.

[0051] The reference numerals in the drawings of the specification include:

[0052] 100-connection platform, 101-primary mirror, 102-secondary mirror, 103-tertiary mirror. DETAILED DESCRIPTION

[0053] In order to enable those skilled in the art to better understand the technical solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts should fall within the scope of protection of the present invention.

[0054] This embodiment provides a method for obtaining the temperature field of an optical system. Figure 1 , Figure 1 A flow chart of a method for acquiring a temperature field of an optical system provided in one embodiment includes the following steps:

[0055] S11: During the temperature change process, temperature data of a preset position of the optical system is acquired to obtain a temperature sequence of the current period, wherein the temperature sequence of the current period is formed by arranging the temperature data of the preset position in the current period as the process progresses.

[0056] For any preset position of the optical system, the temperature data of the preset position of the optical system is obtained. The temperature sequence of the current period is formed by arranging the temperature data of the preset position of the optical system at the time of each process in the current period.

[0057] S12: Input the temperature sequence of the current time period into a temperature prediction model, and the temperature prediction model outputs a temperature sequence of a subsequent time period, wherein the temperature sequence of the subsequent time period is formed by arranging the temperature data of the preset position in the predicted subsequent time period as the process progresses, and the subsequent time period is the time period after the current time period.

[0058] The temperature sequence of the subsequent period is formed by the predicted temperature data of the preset position of the optical system at the time of each process in the subsequent period, which is arranged along with the time of the process.

[0059] S13: Obtaining a temperature field of the optical system in the latter period according to the temperature sequence of the latter period, wherein the temperature field of the optical system includes temperature data of the preset position of the optical system and temperature data of other positions of the optical system except the preset position.

[0060] The temperature sequence of the subsequent period includes the predicted temperature data of the preset position of the optical system. For any process moment in the subsequent period, the temperature field of the optical system at the time of the process can be obtained based on the temperature data of the preset position of the optical system at the time of the process.

[0061] Accordingly, obtaining the temperature prediction model includes the following process: during the temperature change process, obtaining the temperature data of the preset position of the optical system, obtaining training data based on the obtained temperature data, the training data including an input temperature sequence and an output temperature sequence, the input temperature sequence is formed by arranging the temperature data of the preset position in the previous time period as the process progresses, and the output temperature sequence is formed by arranging the temperature data of the preset position in the next time period as the process progresses, and the constructed temperature prediction model is trained using the training data to obtain the temperature prediction model.

[0062] As the temperature change process proceeds, temperature data is collected at a preset position of the optical system, and the temperature data corresponds to the time of the process.

[0063] A set of training data includes an input temperature sequence and an output temperature sequence. The subsequent period corresponding to the output temperature sequence is the period after the previous period corresponding to the input temperature sequence. The input temperature sequence is formed by arranging the temperature data acquired at the preset position of the optical system at various process times during the previous period over the time interval. The output temperature sequence is formed by arranging the temperature data acquired at the preset position of the optical system at various process times during the subsequent period over the time interval over the time interval.

[0064] The optical system temperature field acquisition method of this embodiment acquires temperature data at preset positions in the optical system during a temperature change process, obtaining training data including input and output temperature sequences to develop a temperature prediction model for the optical system. Based on the temperature sequence acquired from the optical system in real time during the current period, the temperature prediction model can be used to predict the temperature sequence of the optical system for the next period, further obtaining the optical system temperature field for the next period, thereby achieving prediction of the optical system temperature field. Therefore, the optical system temperature field acquisition method of this embodiment can predict the optical system temperature field, enabling monitoring of the optical system's temperature conditions and providing valuable insights for predicting and analyzing various optical system performance characteristics.

[0065] In some embodiments, the temperature change process is a process in which the temperature changes in a single trend, that is, the temperature change process is a cooling process, or the temperature change process is a heating process. For some optical systems, it is necessary to perform a low-temperature environment test on the optical system, and the test process includes a cooling test process and a warming test process after cooling. In view of this, in some embodiments, the process of obtaining a temperature prediction model may include: during the cooling process, obtaining the temperature data of the preset position of the optical system, and obtaining training data based on the obtained temperature data. In some embodiments, the process of obtaining a temperature prediction model may include: during the warming process after cooling, obtaining the temperature data of the preset position of the optical system, and obtaining training data based on the obtained temperature data.

[0066] In some embodiments, the process of obtaining a temperature prediction model may include: obtaining temperature data of the preset position of the optical system during a cooling process and a heating process after cooling, and obtaining training data based on the obtained temperature data. The temperature data obtained from the optical system can be divided into cooling conditions and heating conditions, which can be expressed as:

[0067] t≤t(T min ), cooling condition;

[0068] t>t(T min ), reheating condition;

[0069] Among them, t represents the time corresponding to the temperature data, T minIndicates the lowest temperature reached by the optical system.

[0070] In some embodiments, a temperature sensor may be provided at a preset position of the optical system to collect temperature data. For example, the temperature sensor continuously collects temperature data during a cooling test and continuously collects temperature data during a reheating test.

[0071] In some embodiments, obtaining training data based on the obtained temperature data includes: processing the obtained temperature data into the input temperature sequence and the output temperature sequence through a time window, wherein the input temperature sequence is represented as , the output temperature sequence is expressed as , represents a time window of length m starting from time t, express Then a time window of length n is generated. Represents X t is a vector formed by temperature data obtained at i preset positions, where i is a positive integer greater than or equal to 1.

[0072] In this embodiment, there is no limitation on the length m of the input temperature sequence and the length n of the output temperature sequence. In practical applications, they can be set according to the requirements for temperature prediction accuracy or the computational efficiency of the temperature prediction model. Figure 2-1 and Figure 2-2 , Figure 2-1 FIG. 1 is a diagram of temperature data obtained from an optical system in a method for obtaining a temperature field of an optical system according to an embodiment of the present invention. Figure 2-2 This is a schematic diagram of processing optical system temperature data using a time window in a method for acquiring an optical system temperature field according to one embodiment. As shown, the optical system undergoes a cooling process followed by a heating process over time, resulting in temperature data for both cooling and heating conditions. The time window is slid along the time axis, and the temperature data arranged over time is processed to obtain sets of input and output temperature sequences.

[0073] In some embodiments, training the constructed temperature prediction model using training data includes:

[0074] Establishing a loss function, wherein the loss function describes the loss of the predicted value of the output temperature sequence relative to the true value, and differentiating the loss function with respect to the temperature prediction model parameters to obtain a gradient function of the loss function derived with respect to the temperature prediction model parameters;

[0075] In any iteration, the current temperature prediction model parameter value is substituted into the gradient function to obtain the current gradient value, and the first-order moment estimate is updated according to the current first-order moment estimate and the current gradient value, the second-order moment estimate is updated according to the current second-order moment estimate and the current gradient value, and the temperature prediction model parameter value is updated according to the updated first-order moment estimate and the updated second-order moment estimate, and the next iteration is performed with the updated temperature prediction model parameter value;

[0076] When an iteration stopping condition is met, the iteration is stopped, and the temperature prediction model is obtained according to the temperature prediction model parameter values ​​obtained in the last iteration;

[0077] Among them, the first-order moment estimate represents the estimate of the first-order moment of the gradient of the loss function derived from the temperature prediction model parameters, and the second-order moment estimate represents the estimate of the second-order moment of the gradient of the loss function derived from the temperature prediction model parameters.

[0078] If this is the first iteration, the current temperature prediction model parameter values ​​refer to the initial values ​​of the temperature prediction model parameters, the current first-order moment estimate value refers to the initial value of the first-order moment estimate, and the current second-order moment estimate value refers to the initial value of the second-order moment estimate. If this is any iteration after the first iteration, the current temperature prediction model parameter values ​​are the temperature prediction model parameter values ​​updated in the previous iteration, the current first-order moment estimate value refers to the first-order moment estimate value updated in the previous iteration, and the current second-order moment estimate value refers to the second-order moment estimate value updated in the previous iteration. In any iteration, the current temperature prediction model parameter values ​​are substituted into the gradient function, and the value of the gradient function is the current gradient value.

[0079] In any iteration, the first-order moment estimate is updated according to the current first-order moment estimate and the current gradient value, the second-order moment estimate is updated according to the current second-order moment estimate and the current gradient value, and the temperature prediction model parameter value is updated according to the updated first-order moment estimate and the updated second-order moment estimate. In this embodiment, the temperature prediction model parameter value is updated using the first-order moment estimate and the second-order moment estimate of the gradient derived from the loss function of the temperature prediction model parameter, and adaptive moment estimation is used to optimize and update the temperature prediction model parameters. Adaptive learning makes parameter adjustment more flexible and efficient, and can meet the need for less adjustment of the learning rate. This optimization and update method is more suitable for processing large data sets and high-dimensional spaces, and is suitable for situations where the objective function changes over time or there are many local minima.

[0080] In some embodiments, updating the first-order moment estimate based on the current first-order moment estimate and the current gradient value includes: obtaining a first product of a first decay rate and the current first-order moment estimate, obtaining a difference between 1 and the first decay rate, and a second product with the current gradient value, and taking the sum of the first product and the second product as the updated first-order moment estimate.

[0081] In some embodiments, updating the second-order moment estimate based on the current second-order moment estimate and the current gradient value includes: obtaining a third product of a second decay rate and the current second-order moment estimate, obtaining a fourth product of a difference between 1 and the second decay rate and the square of the current gradient value, and taking the sum of the third product and the fourth product as the updated second-order moment estimate.

[0082] In some embodiments, updating the temperature prediction model parameter value based on the updated first-order moment estimate and the updated second-order moment estimate includes: obtaining a first ratio of the updated first-order moment estimate to the square root of the updated second-order moment estimate, obtaining a fifth product of the learning rate and the first ratio, and using the difference between the current temperature prediction model parameter value and the fifth product as the updated temperature prediction model parameter value. Obtaining the first ratio of the ratio of the updated first-order moment estimate to the square root of the updated second-order moment estimate. Obtaining the fifth product of the product of the learning rate and the first ratio. Using the difference between the current temperature prediction model parameter value and the fifth product as the updated temperature prediction model parameter value.

[0083] For example, the process of optimizing and updating the temperature prediction model parameter values ​​can be expressed as follows:

[0084] ; (1)

[0085] ; (2)

[0086] ; (3)

[0087] ; (4)

[0088] Among them, g t Represents the gradient function of the loss function with respect to the temperature prediction model parameters, θ t Indicates the current temperature prediction model parameter value, m t Represents the updated first-order moment estimate, m t-1 Represents the current first-order moment estimate, β1 represents the first decay rate, that is, the decay rate of the first-order moment estimate, v t represents the updated second-order moment estimate, v t-1 Represents the current second-order moment estimate, β2 represents the second decay rate, that is, the decay rate of the second-order moment estimate, θt+1 represents the updated temperature prediction model parameter value, and η represents the learning rate. ε is used to avoid division by zero errors in calculations and is usually set to a very small value, such as ε ≤ 1e-5, or ε = 1e-8. The above formulas (1)-(4) can be considered as an adaptive moment estimation optimizer for optimizing and updating the temperature prediction model parameters.

[0089] m t or m t-1 It can be used to express the first-order moment estimation of the gradient of the loss function with respect to the temperature prediction model parameters in the form of momentum. t or v t-1 It can be used to express the second-order moment estimation of the gradient of the loss function with respect to the parameters of the temperature prediction model in the form of momentum.

[0090] In some embodiments, updating the temperature prediction model parameter value based on the updated first-order moment estimate and the updated second-order moment estimate includes: performing bias correction on the updated first-order moment estimate, performing bias correction on the updated second-order moment estimate, and updating the temperature prediction model parameter value based on the corrected first-order moment estimate and the corrected second-order moment estimate, wherein the ratio of the updated first-order moment estimate to the difference between 1 and the first attenuation factor is the corrected first-order moment estimate, and the ratio of the updated second-order moment estimate to the difference between 1 and the second attenuation factor is the corrected second-order moment estimate.

[0091] In this embodiment, the current first-order moment estimate is corrected according to the first attenuation factor, and the current second-order moment estimate is corrected according to the second attenuation factor. Since the first-order moment estimate and the second-order moment estimate of the gradient of the loss function derived from the temperature prediction model parameters are updated in an exponential decay manner, in the early stage of iterative optimization, the first-order moment estimate and the second-order moment estimate are relatively small. If no correction is made, the first-order moment estimate and the second-order moment estimate will always be small values, which is not conducive to model training. By correcting the deviation of the first-order moment estimate and the second-order moment estimate, it helps the optimizer to find the correct update direction more quickly, accelerate model convergence, and improve the stability of the optimization process. It can also better adapt to different learning rates, accelerate model convergence, and improve training efficiency and performance.

[0092] For example, the process of optimizing and updating the temperature prediction model parameter values ​​can be expressed as follows:

[0093] ; (1)

[0094] ; (2)

[0095] ; (3)

[0096] ; (5)

[0097] ; (6)

[0098] ; (7)

[0099] Among them, g t Represents the gradient function of the loss function with respect to the temperature prediction model parameters, θ t Indicates the current temperature prediction model parameter value, m t Represents the updated first-order moment estimate, m t-1 Represents the current first-order moment estimate, β1 represents the first decay rate, that is, the decay rate of the first-order moment estimate, v t represents the updated second-order moment estimate, v t-1 Represents the current second-order moment estimate, β2 represents the second decay rate, that is, the decay rate of the second-order moment estimate, θ t+1 represents the updated temperature prediction model parameter value, and η represents the learning rate. represents the corrected first-order moment estimate, represents the modified second-order moment estimate, β t 1 represents the first attenuation factor, that is, the attenuation factor of the first-order moment estimate, β t 2 represents the second attenuation factor, that is, the attenuation factor of the second-order moment estimate. ε is used to avoid division by zero errors in the calculation and is usually set to a very small value, such as ε ≤ 1e-5, such as ε = 1e-8. The above formulas (1)-(7) can be considered as an adaptive moment estimate optimizer used to optimize and update the temperature prediction model parameters.

[0100] In some embodiments, the initial values ​​of the temperature prediction model parameters can be randomly set. The initial value of the first-order moment estimation can be set to 0, and the initial value of the second-order moment estimation can be set to 0. The first attenuation factor β t 1 can be taken as 0.9, the second attenuation factor β t 2 can be taken as 0.99.

[0101] In some embodiments, a maximum number of iterations may be set, and the iteration stopping condition may be that the number of iterations reaches the maximum number of iterations. When the iteration is stopped, the temperature prediction model is obtained based on the temperature prediction model parameter values ​​obtained in the last iteration. In some embodiments, the iteration stopping condition may be that the prediction accuracy of the temperature prediction model meets the requirements, and illustratively, the prediction accuracy of the temperature prediction model reaches a prediction accuracy of ±1K.

[0102] In this embodiment, the loss function is not limited, as long as it can express the loss of the predicted value of the output temperature sequence relative to the true value. In some embodiments, using the training data to train the constructed temperature prediction model includes: establishing a loss function and iterating based on the loss function to obtain temperature prediction model parameter values ​​that meet the conditions, so as to obtain the temperature prediction model determined by the temperature prediction model parameter values ​​that meet the conditions, and the loss function expresses the average of the multiple powers of the difference between the true value and the predicted value of the output temperature sequence of the training data. The predicted value of the output temperature sequence refers to the output temperature sequence predicted by the temperature prediction model determined by the current temperature prediction model parameter values.

[0103] The loss function can be expressed as the square average of the difference between the true value and the predicted value of the output temperature series of the training data. That is, the loss function can use the mean squared error loss function (Mean Squared Error Loss), which can be expressed as:

[0104] ; (8)

[0105] Where N represents the total number of training data. represents the true value of the jth training data, It represents the predicted value obtained by the temperature prediction model. The number of training data samples can be adjusted appropriately according to the time situation.

[0106] In some embodiments, the temperature prediction model may include an encoder and a decoder; the encoder includes multiple parallel layers, the layers including a first network and a second network, the first network is used to learn the dependency relationship between the input temperature sequence and the output temperature sequence, and generate a representation expressing the correlation between the temperature data at previous and next moments, and the second network is used to perform a nonlinear transformation on the representation generated by the first network; the decoder includes an internal memory unit, an input gate, a forget gate and an output gate, the internal memory unit is used to allow sequence information to be transmitted along the internal memory unit, the forget gate is used to determine whether the sequence information transmitted along the internal memory unit is discarded, the input gate is used to determine whether the sequence information is stored in the internal memory unit, and the output gate is used to determine whether the sequence information in the internal memory unit is output.

[0107] The encoder consists of multiple parallel layers, each of which includes a first network and a second network. Each set of input and output temperature sequences is processed by a separate layer. The first network can be a self-attention (Transformer) network. In this self-attention network, the encoder can simultaneously consider the dependencies between all temperature data in the input and output temperature sequences, generating a representation that expresses the correlation between temperature data at previous and subsequent moments.

[0108] For example, you can refer to Figure 3 , Figure 3 This figure is a schematic diagram of a temperature prediction model in a method for acquiring a temperature field in an optical system according to one embodiment. As shown, the temperature prediction model includes an encoder and a decoder. In the encoder, Q (query), K (key), and V (value) are three key components of the self-attention mechanism. The Query (Q) matrix is ​​used to extract information about the current point of interest. In the self-attention mechanism, the Query matrix is ​​used to perform similarity calculations with all Key matrices to determine the contribution of each input element to the current output. The Key (K) matrix represents the information of each element in the input sequence and is matched with the Query matrix to calculate the correlation between the Query matrix and all Key matrices. The correlation between the Query matrix and the Key matrix is ​​then used as a weight to weight the Value matrix. The Value (V) matrix contains the actual information in the input sequence and is used in the weighting process of the self-attention mechanism. Specifically, each element in the Value matrix is ​​weighted and summed according to the correlation (weight) between its corresponding Key matrix and the Query matrix to generate the final output.

[0109] The second network can be a feed-forward neural network, into which the representations generated by the first network are passed for further nonlinear transformations. In some embodiments, the encoder layers are connected via a residual network or a layer normalization network, which can improve training stability and performance. The encoder can be considered a structure composed of multiple identical layers stacked together. This encoder allows for processing variable-length sequences and effectively captures complex relationships within the sequence.

[0110] The internal memory unit is the core of the decoder, allowing sequence information to be transmitted along the internal memory unit. The network structure of the internal memory unit is linearly interactive, allowing sequence information to be stored in the internal memory unit for a long time. The internal memory unit, also known as the cell state, has only slight linear interactions, which allows information to be stored in the network for a long time.

[0111] The Forget Gate is used to decide which sequence information should be discarded from the internal memory unit. It generates a vector between 0 and 1 by looking at the previous hidden state and the current input, where 1 means "completely keep this information" and 0 means "completely discard this information."

[0112] The input gate is used to determine which new sequence information is stored in the internal memory unit. In some embodiments, the input gate includes a first activation function and a second activation function. The first activation function is used to determine whether the sequence information needs to be updated, and the second activation function is used to generate new candidate values ​​based on the sequence information. The first activation function can map the sequence information to a range of 0 to 1, and the second activation function can map the sequence information to a range of -1 to 1. Whether the sequence information is added to the internal memory unit is determined based on the output value of the first activation function and the output value of the second activation function. The first activation function can be a sigmoid function, and the second activation function can be a tanh function. The tanh function is used to increase nonlinearity.

[0113] The output gate determines which sequence information from the internal memory unit should be included in the updated hidden state. The output gate uses the third activation function to determine which sequence information from the internal memory unit will be output. The internal memory unit is then passed through the fourth activation function and the output of the fourth activation function is multiplied by the output of the third activation function to obtain the final output. The third activation function can map sequence information to a range of 0 to 1, while the fourth activation function can map sequence information to a range of -1 to 1. The third activation function can be a sigmoid function, and the fourth activation function can be a tanh function. The sigmoid and tanh functions are used to increase nonlinearity.

[0114] The decoder can be a Long Short-Term Memory (LSTM) network, which is a special recurrent neural network that is used to solve the gradient vanishing or gradient exploding problems encountered by recurrent neural networks when dealing with long-distance dependency problems. This goal is achieved by introducing three gate controllers (input gate, forget gate, and output gate) and internal memory units.

[0115] For example, you can refer to Figure 3 As shown, in the decoder, H´ t-1 Represents the output of the encoder and the current hidden state of the decoder H t-1 The concatenated value, H t Denotes the updated hidden state of the decoder. The decoder’s hidden state H is used to capture long-term dependencies in the sequence, and it is updated at each time step. t-1 Indicates the current cell state, C t Indicates the cell state after update. t Represents the forget gate, which determines which sequence information should be discarded from the cell state. It generates a vector between 0 and 1 by looking at the current hidden state and the current input, where 1 means "completely keep this information" and 0 means "completely discard this information".t Represents the input gate, which determines which new sequence information is stored in the cell state. t The output gate determines which parts of the cell state should be included in the next hidden state. The output gate uses the third activation function to determine which parts of the cell state will be output. The cell state is then passed through the fourth activation function and the output of the fourth activation function is multiplied by the output of the third activation function to obtain the final output.

[0116] In this embodiment, the temperature prediction model includes an encoder and a decoder. The encoder adopts a self-attention mechanism network and a feedforward neural network, and the decoder adopts a long short-term memory network. Therefore, the temperature prediction model adopts a hybrid neural network, which can improve the accuracy and prediction precision of the optical system temperature prediction.

[0117] In some embodiments, training a constructed temperature prediction model using training data to obtain a temperature prediction model may include: the training data includes test data; inputting an input temperature sequence of the test data into the obtained temperature prediction model to obtain a predicted output temperature sequence; and calculating a mean absolute percentage error (MAPE) and / or a root mean square error (RMSE) based on the actual output temperature sequence and the predicted output temperature sequence to test the obtained temperature prediction model. For example, the mean absolute percentage error (MAPE) and the root mean square error (RMSE) may be calculated according to the following formula:

[0118] ;

[0119] Among them, J represents the total number of samples in the test set, represents the true value of the test set, that is, the true output temperature sequence, Represents the predicted value, that is, the predicted output temperature series.

[0120] In some embodiments, obtaining the temperature field of the optical system in the subsequent period based on the temperature sequence of the subsequent period includes: interpolating the temperature data of positions other than the preset positions on the structural model of the optical system based on the temperature data of each preset position included in the temperature sequence of the subsequent period, to obtain the temperature field of the optical system in the subsequent period. This can be done by interpolating the temperature data of positions other than the preset positions on the structural model of the optical system based on the temperature data of the current process in the temperature sequence of the subsequent period and the temperature data of each preset position at the current process, to obtain the temperature data of the preset positions of the optical system at the current process in the subsequent period and the temperature data of positions other than the preset positions of the optical system, to obtain the temperature field of the optical system at the current process. The constructed temperature field can be considered as a digital twin model of the temperature field of the optical system.

[0121] In some embodiments, the method for obtaining a temperature field of an optical system may further include the following steps: inputting the temperature sequence of the subsequent period into the temperature prediction model, causing the temperature prediction model to output a temperature sequence for a period after the subsequent period, wherein the temperature sequence for the period after the subsequent period is formed by arranging the predicted temperature data of the preset position within the period after the subsequent period as the process progresses; and obtaining the temperature field of the optical system within the period after the subsequent period based on the temperature sequence for the period after the subsequent period. After inputting the temperature sequence of the current period obtained for the optical system into the temperature prediction model to predict the temperature sequence of the optical system for the period after the current period, i.e., the temperature sequence for the subsequent period, the predicted temperature sequence for the subsequent period may be input into the temperature prediction model to predict the temperature sequence of the optical system for the period after the subsequent period. In this manner, the temperature prediction model may be used to predict the temperature sequence of the optical system for each subsequent period after the current period. Furthermore, for each of the subsequent periods after the current period, the temperature field of the optical system for that period may be obtained based on the predicted temperature sequence for that period. This allows the temperature field to be predicted and constructed for the entire temperature change process of the optical system. This predicted and constructed temperature field can be used to pre-diagnose potential problems with the optical system during use, thereby improving its efficiency and lifespan. For any process within any time period, the temperature field of the optical system at that moment can be obtained based on the temperature data at that moment in the temperature sequence for that period.

[0122] In this embodiment, there is no limitation on the preset position of the optical system, and the preset position is the temperature measurement point of the optical system. The preset position of the optical system can be, but is not limited to, the center of the mirror surface of the optical element, the edge of the mirror surface, the position on the support rod of the optical element, or the connection platform. Temperature sensors can be set at these positions to collect temperature, such as setting a temperature sensor at the center of the back surface of the optical element mirror or the edge of the back surface of the optical element mirror, setting a temperature sensor on the support rod of the optical element, and setting a temperature sensor on the connection platform. For example, reference can be made to Figure 4-1 and Figure 4-2 , Figure 4-1 is a structural model of the optical system in the method for acquiring the temperature field of an optical system according to an embodiment, Figure 4-2 The optical system temperature field obtained in the optical system temperature field acquisition method of an embodiment. Figure 4-1 As shown, the optical system in this example includes a primary mirror 101, a secondary mirror 102 and a third mirror 103, each mirror being mounted on a connecting platform 100. Figure 4-2 As shown, the optical system temperature field reflects the temperature distribution of the optical system, including the temperature distribution of the primary mirror 101 , the temperature distribution of the secondary mirror 102 , the temperature distribution of the tertiary mirror 103 and the temperature distribution of the connection platform 100 .

[0123] In one specific example, the optical system includes a primary mirror 101, a secondary mirror 102, and a tertiary mirror 103, as well as a connecting platform 100. Each of the primary mirror 101, the secondary mirror 102, and the tertiary mirror 103 includes three temperature measurement points, and the connecting platform 100 includes one temperature measurement point. The differences between the predicted and actual values ​​for the optical system's cooling condition and the predicted and actual values ​​for the warming condition are shown in Table 1. It can be seen that the temperature prediction errors for each temperature measurement point on the primary mirror 101, the secondary mirror 102, and the tertiary mirror 103 of the optical system are all less than 0.5, demonstrating the feasibility of this method. Figure 5 The changes between the predicted values ​​and the actual values ​​of the temperature measurement points of the primary mirror, secondary mirror, tertiary mirror and connection platform under the cooling condition in the optical system temperature field acquisition method of one embodiment are shown in FIG. Figure 6 The diagram shows the changes between the predicted and actual values ​​of the temperature measurement points of the primary mirror, secondary mirror, tertiary mirror, and connection platform in the rewarming condition in a method for acquiring the temperature field of an optical system according to one embodiment.

[0124] Table 1

[0125]

[0126] In addition, to verify that the temperature prediction model constructed by the hybrid neural network has the best prediction accuracy, this example uses the self-attention network (Transformer), convolutional neural network (CNN), long short-term memory network LSTM and multilayer perceptron (MLP) for comparison. The mean absolute percentage error (MAPE) and root mean square error (RMSE) of the model on the test set are used as evaluation indicators. The comparison results are shown in Table 2 and Figure 7 As shown, Figure 7 This figure compares the mean absolute percentage error and root mean square error (RMSE) of temperature prediction models constructed using five different networks in one embodiment. Hybrid represents a temperature prediction model constructed using a hybrid neural network, LSTM represents a temperature prediction model constructed using a long short-term memory network, MLP represents a temperature prediction model constructed using a multilayer perceptron, CNN represents a temperature prediction model constructed using a convolutional neural network, and Transformer represents a temperature prediction model constructed using a self-attention network. It can be seen that the temperature prediction model constructed using the hybrid neural network (Hybrid) has lower MAPE and RMSE evaluation indicators, and the model constructed using the hybrid neural network (Hybrid) achieves the best prediction results.

[0127] Table 2

[0128]

[0129] The optical system temperature field acquisition method of this embodiment can effectively predict the temperature field of the optical system throughout its life cycle, and can be used to monitor the temperature distribution of the optical system in real time at various time periods, to predict potential problems that exist during the use of the optical system, to predict potential factors that may cause damage to the optical system, and to improve the use efficiency and lifespan of the optical system.

[0130] This embodiment further provides an optical system temperature field acquisition system, including:

[0131] Memory for storing computer programs;

[0132] A processor is configured to implement the steps of the method for acquiring the temperature field of an optical system as described in any one of the above embodiments when executing the computer program.

[0133] The optical system temperature field acquisition system of this embodiment acquires temperature data at preset positions in the optical system during a temperature change process, obtaining training data including input and output temperature sequences to develop a temperature prediction model for the optical system. Based on the temperature sequence acquired in real time from the optical system during the current period, the temperature prediction model can be used to predict the temperature sequence of the optical system during the next period, further obtaining the optical system temperature field during the next period, thereby achieving prediction of the optical system temperature field. Therefore, the optical system temperature field acquisition system of this embodiment can predict the optical system temperature field, enabling monitoring of the optical system's temperature conditions and providing valuable insights for predicting and analyzing various optical system performance characteristics.

[0134] The above is a detailed introduction to the optical system temperature field acquisition method and system provided by the present invention. This article uses specific examples to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only intended to help understand the method and core concept of the present invention. It should be pointed out that for ordinary technicians in this technical field, without departing from the principles of the present invention, several improvements and modifications can be made to the present invention, and these improvements and modifications also fall within the scope of protection of the claims of the present invention.

Claims

1. A method for acquiring the temperature field of an optical system, characterized in that: include: During the temperature change process, temperature data of a preset position of the optical system is acquired to obtain a temperature sequence of the current period, wherein the temperature sequence of the current period is formed by arranging the temperature data of the preset position in the current period as the process progresses; Input the temperature sequence of the current period into a temperature prediction model, and the temperature prediction model outputs a temperature sequence of a subsequent period, wherein the temperature sequence of the subsequent period is formed by arranging the temperature data of the preset position in the subsequent period as the process progresses, and the subsequent period is the period after the current period; Obtaining, according to the temperature sequence for the subsequent period, a temperature field of the optical system in the subsequent period, the temperature field of the optical system including temperature data of the preset position of the optical system and temperature data of other positions of the optical system except the preset position, comprising: interpolating the temperature data of other positions except the preset position on a structural model of the optical system according to the temperature data of each preset position included in the temperature sequence for the subsequent period, to obtain the temperature field of the optical system in the subsequent period; Obtaining the temperature prediction model includes: During the temperature change process, temperature data of the preset position of the optical system is obtained, and training data is obtained based on the obtained temperature data, wherein the training data includes an input temperature sequence and an output temperature sequence, wherein the input temperature sequence is formed by arranging the temperature data of the preset position in a previous time period along with the process, and the output temperature sequence is formed by arranging the temperature data of the preset position in a subsequent time period along with the process, and the constructed temperature prediction model is trained using the training data to obtain the temperature prediction model; Training the constructed temperature prediction model using the training data includes: Establishing a loss function, the loss function describing the loss of the predicted value of the output temperature sequence relative to the true value, and differentiating the loss function with respect to the temperature prediction model parameters to obtain a gradient function of the loss function derived with respect to the temperature prediction model parameters; In any iteration, the current temperature prediction model parameter value is substituted into the gradient function to obtain the current gradient value, and the first-order moment estimate is updated according to the current first-order moment estimate and the current gradient value, the second-order moment estimate is updated according to the current second-order moment estimate and the current gradient value, and the temperature prediction model parameter value is updated according to the updated first-order moment estimate and the updated second-order moment estimate, and the next iteration is performed with the updated temperature prediction model parameter value; When an iteration stopping condition is met, the iteration is stopped, and the temperature prediction model is obtained according to the temperature prediction model parameter values ​​obtained in the last iteration; Among them, the first-order moment estimate represents the estimate of the first-order moment of the gradient of the loss function derived from the temperature prediction model parameters, and the second-order moment estimate represents the estimate of the second-order moment of the gradient of the loss function derived from the temperature prediction model parameters.

2. The method for acquiring the temperature field of an optical system according to claim 1, wherein: During the temperature change process, obtaining temperature data of the preset position of the optical system, and obtaining training data according to the obtained temperature data includes: During the cooling process, obtaining temperature data of the preset position of the optical system, and obtaining the training data according to the obtained temperature data; Alternatively, in a process of returning to a temperature after cooling, temperature data of the preset position of the optical system is obtained, and the training data is obtained according to the obtained temperature data; Alternatively, during the cooling process and during the warming process after cooling, the temperature data of the preset position of the optical system is respectively acquired, and the training data is acquired according to the acquired temperature data.

3. The method for acquiring the temperature field of an optical system according to claim 1, wherein: The training data obtained based on the obtained temperature data includes: The obtained temperature data is processed into the input temperature sequence and the output temperature sequence through the time window. The input temperature sequence is expressed as , the output temperature sequence is expressed as , represents a time window of length m starting from time t, express Then a time window of length n is generated. Represents X t is a vector formed by the temperature data obtained at i preset positions.

4. The method for acquiring the temperature field of an optical system according to claim 1, wherein: Updating the first-order moment estimate based on the current first-order moment estimate and the current gradient value includes: Obtain a first product of a first decay rate and the current first-order moment estimate, obtain a second product of a difference between 1 and the first decay rate and the current gradient value, and use the sum of the first product and the second product as an updated first-order moment estimate; Updating the second-order moment estimate based on the current second-order moment estimate and the current gradient value includes: Obtain a third product of the second attenuation rate and the current second-order moment estimate, obtain a fourth product of the difference between 1 and the second attenuation rate and the square of the current gradient value, and use the sum of the third product and the fourth product as the updated second-order moment estimate.

5. The method for acquiring the temperature field of an optical system according to claim 1, wherein: Updating the temperature prediction model parameter values ​​according to the updated first-order moment estimate and the updated second-order moment estimate includes: The updated first-order moment estimate is corrected for deviation, and the updated second-order moment estimate is corrected for deviation. The temperature prediction model parameter value is updated according to the corrected first-order moment estimate and the corrected second-order moment estimate, wherein the ratio of the updated first-order moment estimate to the difference between 1 and the first attenuation factor is the corrected first-order moment estimate, and the ratio of the updated second-order moment estimate to the difference between 1 and the second attenuation factor is the corrected second-order moment estimate.

6. The method for acquiring the temperature field of an optical system according to claim 1, wherein: Training the constructed temperature prediction model using the training data includes: A loss function is established and an iterative operation is performed based on the loss function to obtain temperature prediction model parameter values ​​that meet the conditions, so as to obtain the temperature prediction model determined by the temperature prediction model parameter values ​​that meet the conditions, wherein the loss function expresses the multi-power averaging of the difference between the true value and the predicted value of the output temperature sequence of the training data.

7. The method for acquiring the temperature field of an optical system according to claim 1, wherein: The temperature prediction model includes an encoder and a decoder; The encoder includes a plurality of parallel layers, each layer including a first network and a second network, wherein the first network is used to learn the dependency between the temperature data in the input temperature sequence and the output temperature sequence, and generate a representation expressing the correlation between the temperature data at previous and subsequent moments, and the second network is used to perform a nonlinear transformation on the representation generated by the first network; The decoder includes an internal memory unit, an input gate, a forget gate and an output gate. The internal memory unit is used to allow sequence information to be transmitted along the internal memory unit. The forget gate is used to determine whether the sequence information transmitted along the internal memory unit is discarded. The input gate is used to determine whether the sequence information is stored in the internal memory unit. The output gate is used to determine whether the sequence information in the internal memory unit is output.

8. The method for acquiring the temperature field of an optical system according to any one of claims 1 to 7, characterized in that: Also includes: Inputting the temperature sequence of the subsequent period into the temperature prediction model, the temperature prediction model outputs a temperature sequence of a period after the subsequent period, the temperature sequence of the period after the subsequent period being formed by arranging the predicted temperature data of the preset position within the period after the subsequent period as the process progresses; According to the temperature sequence of a period after the latter period, the temperature field of the optical system in a period after the latter period is obtained.

9. An optical system temperature field acquisition system, characterized in that: include: Memory for storing computer programs; A processor is configured to implement the steps of the method for acquiring the temperature field of an optical system according to any one of claims 1 to 8 when executing the computer program.

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