Method, device and equipment for optimizing substrate structure of fiber grating sensor and medium

Abstract

The invention discloses a substrate structure optimization method and device of a fiber grating sensor, equipment and a medium, and relates to the technical field of artificial intelligence, and the method comprises the steps: building a three-dimensional model comprising a fiber grating sensor substrate, configuring the three-dimensional model, and obtaining a target three-dimensional model; correcting the stress-life curve and the average stress to calculate the fatigue life of the base; establishing an orthogonal test scheme of each sensor based on the substrate structure parameter, the average stress and the substrate fatigue life of the target fiber grating sensor, performing modeling and finite element calculation on the orthogonal test scheme of each sensor to obtain corresponding average strain and substrate fatigue life values, and fitting a response surface model; and performing accuracy evaluation on the response surface model, if the evaluation is passed, performing multi-target optimization and iterative solution on the response surface model to obtain a Pareto solution, and screening out a target Pareto solution, so that the measurement precision and reliability of the hanging load sensor can be improved, drilling parameters can be accurately measured, and the service life of the sensor under harsh conditions is prolonged.

Description

Technical Field

[0001] The present invention relates to the field of artificial intelligence technology, and in particular to a method, device, equipment and medium for optimizing the substrate structure of a fiber grating sensor. Background Art

[0002] Domestic oil and gas exploration and development continues to expand into deep and ultra-deep formations. Ultra-deep well drilling is characterized by long drilling cycles, complex geological conditions, and uncertain drilling conditions, placing higher demands on sensor performance. Research has shown that existing drill string weight indicators can display erratic readings during the drill string hoisting and lowering process, particularly in deep and ultra-deep wells. This phenomenon makes it difficult for drillers to accurately grasp critical drilling information such as drill string weight and weight on bit, leading to drilling accidents and compromising the efficiency and safety of drilling operations. Furthermore, drilling operations such as drilling, circulating drilling fluid, and connecting single rods generate vibrations and alternating loads, which accelerate fatigue in the weight sensor structure and reduce its service life. Furthermore, the long drilling cycles and high loads associated with deep wells (10,000 meters) require sensors with high measurement accuracy to capture the changing drilling parameters under complex conditions and long service life to ensure continuous drilling operations.

[0003] As can be seen from the above, how to improve the measurement accuracy and reliability of the suspended load sensor, achieve accurate measurement of drilling parameters, and increase the service life of the sensor under harsh conditions are problems to be solved in this field. Summary of the Invention

[0004] In view of this, the present invention aims to provide a method, device, equipment, and medium for optimizing the substrate structure of a fiber Bragg grating sensor. This method can improve the measurement accuracy and reliability of the suspended load sensor, enable precise measurement of drilling parameters, and extend the service life of the sensor under harsh conditions. The specific solution is as follows:

[0005] In a first aspect, the present application discloses a method for optimizing the substrate structure of a fiber Bragg grating sensor, comprising:

[0006] Establishing a three-dimensional model including a fiber Bragg grating sensor substrate, and configuring the three-dimensional model to obtain a target three-dimensional model; the configuration includes material property configuration and parameter interaction configuration;

[0007] Correcting the stress-life curve and the average stress of the target three-dimensional model to obtain the corrected stress-life curve and the average stress, and calculating the fatigue life of the substrate using the corrected stress-life curve and the average stress;

[0008] establishing orthogonal test schemes for each sensor based on the substrate structural parameters, the average stress, and the substrate fatigue life of the target fiber grating sensor in the target three-dimensional model, performing modeling and finite element calculation on each of the orthogonal test schemes for the sensor, obtaining corresponding average strain and substrate fatigue life values, and fitting a response surface model using the average strain and substrate fatigue life values;

[0009] performing an accuracy evaluation on the response surface model; if the accuracy evaluation passes, performing multi-objective optimization and iterative solution on the response surface model to obtain a Pareto solution, and screening a target Pareto solution from the Pareto solutions;

[0010] The target Pareto solution is used to optimize the substrate structural parameters of the target fiber grating sensor.

[0011] Optionally, the step of establishing a three-dimensional model including a fiber Bragg grating sensor substrate and configuring the three-dimensional model to obtain a target three-dimensional model includes:

[0012] Using preset three-dimensional mechanical design software to establish a three-dimensional model including a fiber Bragg grating sensor substrate, epoxy resin, and a fiber Bragg grating; the preset three-dimensional mechanical design software includes Solidworks;

[0013] Utilize preset engineering simulation finite element software to configure properties and parameter interactions for the three-dimensional model to obtain a target three-dimensional model; the preset engineering simulation finite element software includes Abaqus.

[0014] Optionally, the step of correcting the stress-life curve and the average stress of the target three-dimensional model to obtain the corrected stress-life curve and the average stress, and calculating the fatigue life of the substrate using the corrected stress-life curve and the average stress includes:

[0015] The stress-life curve of the target three-dimensional model is corrected using a preset comprehensive influence coefficient to obtain the corrected stress-life curve; the preset comprehensive influence coefficient includes a stress concentration factor, a geometric size factor, a surface quality factor, and a surface strengthening factor;

[0016] Correcting the average stress of the target three-dimensional model using the Goodman curve to obtain the corrected average stress;

[0017] The modified stress-life curve and the average stress are calculated based on a substrate fatigue life calculation formula in fatigue analysis software to obtain substrate fatigue life; the fatigue analysis software includes Fe-safe; the substrate fatigue life calculation formula is:

[0018] ;

[0019] in, is the stress amplitude, is the mean stress value, is the fatigue strength under symmetrical cycles, is the tensile limit.

[0020] Optionally, the establishing of an orthogonal test scheme for each sensor based on the substrate structural parameters of the target fiber Bragg grating sensor in the target three-dimensional model, the average stress, and the substrate fatigue life includes:

[0021] The substrate structural parameters of the target fiber Bragg grating sensor in the target three-dimensional model are used as optimization parameters, the average stress and the substrate fatigue life are used as optimization indicators, and the response surface optimization method is used to establish orthogonal test plans for each sensor.

[0022] Optionally, the numerically fitting a response surface model using the average strain and the substrate fatigue life includes:

[0023] The average strain and the substrate fatigue life are numerically fitted using a quadratic polynomial to obtain a response surface model; the response surface model includes a substrate fatigue life response surface model and an average strain response surface model; the expression of the response surface model is:

[0024] ;

[0025] Where n is the number of variables, is the offset term, is a linear offset, is the second-order offset term, is the cross coefficient, y is the optimization target, and x is the optimization parameter.

[0026] Optionally, the performing accuracy assessment on the response surface model includes:

[0027] The multiple correlation coefficient, the modified multiple correlation coefficient, the difference value between the data, and the difference index calculated based on the difference value are used as accuracy evaluation indicators, and the accuracy of the response surface model is evaluated using the variance analysis method.

[0028] Optionally, performing multi-objective optimization and iterative solution on the response surface model to obtain a Pareto solution, and screening a target Pareto solution from the Pareto solutions includes:

[0029] The response surface model is multi-objective optimized and iteratively solved using a Latin hypercube sampling optimization algorithm and a preset multi-objective genetic algorithm code to obtain a Pareto solution; the multi-objective genetic algorithm code includes an optimized NSGA-II algorithm code;

[0030] The target Pareto solution is selected from the Pareto solutions by using the entropy weight method and the approximate ideal solution sorting method.

[0031] In a second aspect, the present application discloses a substrate structure optimization device for a fiber Bragg grating sensor, comprising:

[0032] A model building module is used to establish a three-dimensional model including a fiber Bragg grating sensor substrate and configure the three-dimensional model to obtain a target three-dimensional model; the configuration includes material property configuration and parameter interaction configuration;

[0033] a correction module, configured to correct the stress-life curve and the average stress of the target three-dimensional model to obtain the corrected stress-life curve and the average stress, and calculate the fatigue life of the substrate using the corrected stress-life curve and the average stress;

[0034] a response surface model fitting module, configured to establish an orthogonal test scheme for each sensor based on the substrate structural parameters, the average stress, and the substrate fatigue life of the target fiber Bragg grating sensor in the target three-dimensional model, perform modeling and finite element calculation on each of the orthogonal test schemes for the sensor, obtain corresponding average strain and substrate fatigue life values, and fit a response surface model using the average strain and substrate fatigue life values;

[0035] A solution module is used to evaluate the accuracy of the response surface model. If the accuracy evaluation is passed, the response surface model is optimized and iteratively solved to obtain a Pareto solution, and a target Pareto solution is selected from the Pareto solutions.

[0036] An optimization module is used to optimize the substrate structure parameters of the target fiber grating sensor using the target Pareto solution.

[0037] In a third aspect, the present application discloses an electronic device, comprising:

[0038] Memory, used to store computer programs;

[0039] The processor is used to execute the computer program to implement the aforementioned method for optimizing the substrate structure of the fiber grating sensor.

[0040] In a fourth aspect, the present application discloses a computer storage medium for storing a computer program; wherein, when the computer program is executed by a processor, the steps of the aforementioned method for optimizing the substrate structure of a fiber grating sensor are implemented.

[0041] It can be seen that the present application provides a method for optimizing the substrate structure of a fiber Bragg grating sensor, comprising establishing a three-dimensional model including a fiber Bragg grating sensor substrate, and configuring the three-dimensional model to obtain a target three-dimensional model; the configuration includes material property configuration and parameter interaction configuration; correcting the stress-life curve and average stress of the target three-dimensional model to obtain the corrected stress-life curve and the average stress, and calculating the substrate fatigue life using the corrected stress-life curve and the average stress; establishing orthogonal test schemes for each sensor based on the substrate structure parameters, the average stress, and the substrate fatigue life of the target fiber Bragg grating sensor in the target three-dimensional model, modeling and finite element calculation are performed on each of the orthogonal test schemes of the sensor to obtain corresponding average strain and substrate fatigue life values, and fitting a response surface model using the average strain and the substrate fatigue life values; performing an accuracy evaluation on the response surface model, and if the accuracy evaluation passes, performing multi-objective optimization and iterative solution on the response surface model to obtain a Pareto solution, screening a target Pareto solution from the Pareto solution; and optimizing the substrate structure parameters of the target fiber Bragg grating sensor using the target Pareto solution. The present application establishes a three-dimensional model including a fiber Bragg grating sensor substrate, configures the three-dimensional model, obtains a target three-dimensional model, modifies the stress-life curve and average stress of the target three-dimensional model, and calculates the fatigue life of the substrate using the modified stress-life curve and average stress. The present application proposes to introduce the sensitivity-substrate fatigue life multi-objective collaborative optimization into the fiber Bragg grating sensor substrate, establishes an orthogonal test scheme for each sensor based on the substrate structural parameters, the average stress, and the substrate fatigue life of the target fiber Bragg grating sensor in the target three-dimensional model, and then performs modeling and finite element calculation on each sensor orthogonal test scheme respectively. , the corresponding average strain and base fatigue life values are obtained, the average strain and the base fatigue life values are used to fit the response surface model, and orthogonal experiments and response surface modeling are used to construct an explicit mathematical model of optimization variables and output responses to provide theoretical support for optimization. After constructing the response surface model, the accuracy of the response surface model is evaluated. If the accuracy evaluation passes, the response surface model is multi-objective optimized and iteratively solved to obtain a Pareto solution. The target Pareto solution is screened out from the Pareto solution, which can improve the measurement accuracy and reliability of the suspended weight sensor, achieve accurate measurement of drilling parameters, and increase the service life of the sensor under harsh conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] 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 merely embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying any creative work.

[0043] Figure 1 This is a flow chart of a method for optimizing the substrate structure of a fiber Bragg grating sensor disclosed in this application;

[0044] Figure 2 This is an example diagram of a fiber Bragg grating sensor structure disclosed in this application;

[0045] Figure 3 A base load and constraint distribution diagram disclosed in this application;

[0046] Figure 4 This is a base diagram of an I-shaped sensor disclosed in this application;

[0047] Figure 5 This is an example diagram of optimization parameter selection disclosed in this application;

[0048] Figure 6 A model diagram of the response surface of the spacing, slot width and average strain of a serpentine mechanism disclosed in this application;

[0049] Figure 7 This is a response surface model diagram of the spacing, groove width and substrate fatigue life of a serpentine mechanism disclosed in this application;

[0050] Figure 8 A response surface model diagram of the spacing, middle piece length and average strain of a serpentine mechanism disclosed in this application;

[0051] Figure 9 This is a response surface model diagram of the spacing of the serpentine mechanism, the length of the middle piece and the fatigue life of the substrate disclosed in this application;

[0052] Figure 10 A response surface model diagram of groove width, middle piece length and average strain disclosed in this application;

[0053] Figure 11 A response surface model diagram of groove width, middle piece length and substrate fatigue life disclosed in this application;

[0054] Figure 12 A Pareto optimal frontier solution set diagram disclosed in this application;

[0055] Figure 13 This is a flow chart of the NSGA-Ⅱ multi-objective optimization algorithm disclosed in this application;

[0056] Figure 14 A specific flow chart for optimizing the substrate structure of a fiber Bragg grating sensor disclosed in this application;

[0057] Figure 15 This is a schematic structural diagram of a substrate structure optimization device for a fiber Bragg grating sensor disclosed in this application;

[0058] Figure 16 This is a structural diagram of an electronic device provided in this application. DETAILED DESCRIPTION

[0059] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. 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 are within the scope of protection of the present invention.

[0060] Domestic oil and gas exploration and development continues to expand into deep and ultra-deep formations. Ultra-deep well drilling is characterized by long drilling cycles, complex geological conditions, and uncertain drilling conditions, placing higher demands on sensor performance. Research has shown that existing drill string weight indicators can display erratic readings during the drill string hoisting and lowering process, particularly in deep and ultra-deep wells. This phenomenon makes it difficult for drillers to accurately grasp critical drilling information such as drill string weight and weight on bit, leading to drilling accidents and compromising the efficiency and safety of drilling operations. Furthermore, drilling operations such as drilling, circulating drilling fluid, and connecting single rods generate vibrations and alternating loads, which accelerate fatigue in the weight sensor structure and reduce its service life. Furthermore, the long drilling cycles and high loads associated with deep wells (10,000 meters) require sensors with high measurement accuracy to capture the changing drilling parameters under complex conditions and long service life to ensure continuous drilling operations. As can be seen from the above, how to improve the measurement accuracy and reliability of the suspended load sensor, achieve accurate measurement of drilling parameters, and increase the service life of the sensor under harsh conditions are problems to be solved in this field.

[0061] See also Figure 1 As shown, the embodiment of the present invention discloses a method for optimizing the substrate structure of a fiber Bragg grating sensor, which may specifically include:

[0062] Step S11: establishing a three-dimensional model including a fiber Bragg grating sensor substrate, and configuring the three-dimensional model to obtain a target three-dimensional model; the configuration includes material property configuration and parameter interaction configuration.

[0063] In this embodiment, a three-dimensional model including a fiber Bragg grating sensor substrate, epoxy resin, and a fiber Bragg grating is established using preset three-dimensional mechanical design software; the preset three-dimensional mechanical design software includes Solidworks; and preset engineering simulation finite element software is used to configure properties and parameter interactions for the three-dimensional model to obtain a target three-dimensional model; the preset engineering simulation finite element software includes Abaqus.

[0064] The principle of the fiber Bragg grating sensor proposed in this application is as follows: The wavelength drift of the fiber Bragg grating sensor is mainly related to strain and temperature. The specific formula is:

[0065] ;

[0066] in, is the change in the central wavelength of the fiber Bragg grating, is the effective elastic-optical coefficient, and are the thermal expansion coefficient and the thermo-optical coefficient.

[0067] Under constant temperature conditions, the wavelength drift of the fiber Bragg grating sensor is Mainly depends on its axial strain By improving the strain response of the fiber Bragg grating, the wavelength drift can be enhanced, thereby improving the detection sensitivity of the sensor.

[0068] The process of establishing a three-dimensional model including a fiber Bragg grating sensor substrate in this application is as follows: constructing a three-dimensional model of a fiber Bragg grating sensor substrate, epoxy resin, and a fiber Bragg grating. The fiber Bragg grating sensor structure is shown in FIG. Figure 2 As shown, epoxy resin is placed on both sides of the loop mechanism. To simulate the actual force on the sensor, a fixed constraint is applied to one end of the sensor and a displacement constraint is applied to the other end, with a size of 9.25× mm, that is, the strain of the substrate is 2500 ; limit the displacement and rotation of the base y and z axes, the base load and constraint distribution are as follows Figure 3 shown.

[0069] Step S12: correcting the stress-life curve and the average stress of the target three-dimensional model to obtain the corrected stress-life curve and the average stress, and calculating the substrate fatigue life using the corrected stress-life curve and the average stress.

[0070] In this embodiment, the stress-life curve of the target three-dimensional model is corrected using a preset comprehensive influence coefficient to obtain the corrected stress-life curve; the preset comprehensive influence coefficient includes a stress concentration factor, a geometric dimension factor, a surface quality factor, and a surface enhancement factor; the average stress of the target three-dimensional model is corrected using a Goodman curve to obtain the corrected average stress; the corrected stress-life curve and the average stress are calculated based on a substrate fatigue life calculation formula in fatigue analysis software to obtain substrate fatigue life; the fatigue analysis software includes Fe-safe; the substrate fatigue life calculation formula is:

[0071] ;

[0072] in, is the stress amplitude, is the mean stress value, is the fatigue strength under symmetrical cycles, is the tensile limit.

[0073] This application modifies the stress-life curve. The traditional SN (fatigue-life) curve is obtained by testing standard specimens under specific stress conditions. However, the fiber Bragg grating sensor substrate structure has obvious stress concentration. In addition, the surface processing quality and size effect of the substrate also have a significant impact on the substrate fatigue life. Therefore, it is necessary to consider multiple factors to modify the substrate fatigue life curve. The calculation formula is as follows:

[0074] ;

[0075] ;

[0076] ;

[0077] in, is the comprehensive influence coefficient, is the effective stress concentration factor, is the size factor, is the surface quality coefficient, is the surface enhancement coefficient.

[0078] In some pure tensile conditions, the test piece is subjected only to unidirectional cyclic tensile stress. In this case, the effect of mean stress on the substrate fatigue life must be considered. The substrate fatigue life can be calculated using the substrate fatigue life calculation formula based on Goodman's mean stress correction theory.

[0079] Step S13: Based on the substrate structural parameters, the average stress, and the substrate fatigue life of the target fiber grating sensor in the target three-dimensional model, an orthogonal test scheme for each sensor is established, and each orthogonal test scheme of the sensor is modeled and finite element calculated to obtain the corresponding average strain and substrate fatigue life values, and the response surface model is fitted using the average strain and the substrate fatigue life values.

[0080] In this embodiment, the substrate structural parameters of the target fiber Bragg grating sensor in the target three-dimensional model are used as optimization parameters, the average stress and the substrate fatigue life are used as optimization indicators, and the response surface optimization method is used to establish each sensor orthogonal test scheme. Each sensor orthogonal test scheme is modeled and finite element calculated to obtain the corresponding average strain and substrate fatigue life values. The average strain and substrate fatigue life values are fitted using a quadratic polynomial to obtain a response surface model; the response surface model includes a substrate fatigue life response surface model and an average strain response surface model; the expression of the response surface model is:

[0081] ;

[0082] Where n is the number of variables, is the offset term, is a linear offset, is the second-order offset term, is the cross coefficient, y is the optimization target, and x is the optimization parameter.

[0083] In this embodiment, the principle of selecting the optimized parameters is as follows: In view of the complex force characteristics of the ring-shaped mechanism, this application uses the simplified "I"-shaped model to theoretically analyze the strain transfer mechanism of the substrate. Figure 4 As shown. By abstracting the sensor structure into a variable cross-section beam with characteristic dimensions, a quantitative correlation between the local strain of the fiber Bragg grating region and the substrate strain is established. According to the deformation coordination principle, it can be obtained that:

[0084] ;

[0085] in, is the overall elongation, is the width extension, is the elongation in the narrow area;

[0086] ;

[0087] Since the elastic modulus of different regions is the same, the internal force balance shows that:

[0088] ;

[0089] We can get:

[0090] ;

[0091] The internal force F is solved as:

[0092] ;

[0093] Calculate the relationship between the strain area and the overall strain of the fiber Bragg grating:

[0094] .

[0095] Theoretical analysis shows that due to the cross-sectional area of the middle region ( ) is smaller than the area at both ends ( ), resulting in the strain in the middle area of the sensor being greater than the overall strain, causing a strain amplification effect in the middle area of the sensor, thereby achieving the sensitivity enhancement characteristics of the sensor. When the hollow area of the middle structure is increased, the strain transfer efficiency can be further improved by reducing the effective bearing area, but it will lead to local stress concentration, affecting the fatigue life of the sensor substrate. Therefore, the design of the middle area of the substrate directly affects two performance indicators of the sensor: strain sensitivity enhancement multiple and substrate fatigue life. Based on the above analysis, the spacing of the serpentine mechanism is selected. , groove width , middleware length As optimization parameters, the optimization parameter selection example is as follows Figure 5 As shown in Figure 1, the average strain of the fiber Bragg grating sensor and the fatigue life of the substrate are taken as optimization targets. The range of the selected parameters is shown in Table 1:

[0096] Table 1

[0097]

[0098] This application employed the BBD (Box-Behnken Design) experimental design method to design an orthogonal experimental scheme for substrate optimization. Based on this scheme, different structural models were generated in SolidWorks, and the average strain values of the fiber Bragg grating (FBG) were obtained using Abaqus. Simultaneously, substrate fatigue life prediction was performed in conjunction with Fe-Safe, and the results were added to the orthogonal experimental scheme table. Finally, a polynomial response surface was fitted to provide a theoretical basis for multi-objective optimization.

[0099] According to the material properties and the substrate fatigue life curve, the average strain of the fiber Bragg grating and the substrate fatigue life are calculated, and the data are entered into the orthogonal experiment table. The orthogonal experiment table and calculation results are shown in Table 2:

[0100] Table 2

[0101]

[0102] The obtained spacing of the loop mechanism , groove width Compared with the mean strain response surface model Figure 6 As shown, the spacing of the return mechanism , groove width The response surface model of substrate fatigue life is as follows Figure 7 As shown, the spacing of the return mechanism , middleware length Compared with the mean strain response surface model Figure 8 As shown, the spacing of the return mechanism , middleware length The response surface model of substrate fatigue life is as follows Figure 9 As shown, the slot width , middleware length Compared with the mean strain response surface model Figure 10 As shown, the slot width , middleware length The response surface model of substrate fatigue life is as follows Figure 11 As shown. Using the polynomial fitting response surface model, the polynomial fitting equation is as follows:

[0103] ;

[0104] .

[0105] Step S14: performing an accuracy evaluation on the response surface model. If the accuracy evaluation passes, performing multi-objective optimization and iterative solution on the response surface model to obtain a Pareto solution, and screening out a target Pareto solution from the Pareto solutions.

[0106] In this embodiment, the complex correlation coefficient, the corrected complex correlation coefficient, the difference value between the data, and the difference index calculated based on the difference value are used as accuracy evaluation indicators, and the variance analysis method is used to perform accuracy evaluation on the response surface model. If the accuracy evaluation passes, the Latin hypercube sampling optimization algorithm and the preset multi-objective genetic algorithm code are used to perform multi-objective optimization and iterative solution on the response surface model to obtain a Pareto solution; the multi-objective genetic algorithm code includes the optimized NSGA-Ⅱ algorithm code; the entropy weight method and the approximate ideal solution sorting method are used to screen out the target Pareto solution from the Pareto solution.

[0107] To analyze whether the response surface model is accurate and effective, this application systematically verified and evaluated the response surface model using the ANOVA (analysis of variance) method. R-Squared (multiple correlation coefficient), modified Adj-R-Squared (modified multiple correlation coefficient), and F-value are used to determine whether there are significant differences between groups of data. The P-value is a significance index calculated based on the F-value and is an important indicator for evaluating the goodness of fit of the model. Its value range is 0 to 1, and the closer it is to 1, the better the linear fit of the model. The model evaluation is shown in Table 3:

[0108] Table 3

[0109]

[0110] This application uses Latin hypercube sampling to optimize the performance of the algorithm. Latin hypercube sampling is a stratified sampling technique that can efficiently generate uniformly distributed sample points in multidimensional designs. Each time, the sample space is divided into N intervals, and then a point is randomly selected in each dimension to ensure the uniform distribution of sample points in the parameter space, thereby improving sampling efficiency and accuracy. This method is applied to the population initialization stage and replication, crossover and mutation operations of the NSGA-II algorithm to effectively enhance population diversity. The optimized NSGA-Ⅱ algorithm code is written in Matlab software, and the optimization model is solved to obtain the Pareto optimal frontier solution set. The Pareto optimal frontier solution set is as follows: Figure 12 As shown in , the Pareto frontier consists of a series of non-dominated solutions, each of which corresponds to a set of optimized sensor performance indicators and represents the optimal trade-off between the two objective functions. The NSGA-Ⅱ multi-objective optimization algorithm process is as follows Figure 13 shown.

[0111] The entropy weight method is an objective weight determination method based on information entropy, used in multi-indicator decision analysis. It assigns weights by calculating the degree of data dispersion for each indicator, avoiding subjective judgment of weights. The core idea is that the amount of information an indicator carries determines its importance. The calculation steps are as follows:

[0112] (1) Use the range method to standardize the Pareto matrix:

[0113] ;

[0114] (2) Normalization processing:

[0115] ;

[0116] (3) Calculate the information entropy of the indicator:

[0117] ;

[0118] (4) Determine the weight of the indicator:

[0119] ;

[0120] (5) Find the weighted matrix:

[0121] ;

[0122] The TOPSIS (Top-Inferior Solutions Distance Method) method is a multi-criteria decision-making method that comprehensively evaluates the advantages and disadvantages of each solution by calculating the Euclidean distance between each solution and the ideal solution (optimal solution) and the negative ideal solution (worst solution). The core idea is that the optimal solution should be closest to the ideal solution and farthest from the negative ideal solution. The calculation steps are as follows:

[0123] (1) Calculate the positive and negative ideal solutions of each indicator:

[0124] ;

[0125] ;

[0126] (2) Calculate the ideal solution distance of each indicator:

[0127] ;

[0128] ;

[0129] (3) Calculate relative proximity:

[0130] ;

[0131] Sorting by relative proximity, the solution with a larger comprehensive index is closer to the ideal solution and the optimization effect is more ideal.

[0132] The entropy weight method + TOPSIS method is used to screen the target Pareto solution from the Pareto solution. The entropy weight method is used to calculate the weights of the average strain and substrate life. The weights are shown in Table 4:

[0133] Table 4

[0134]

[0135] The TOPSIS method is used to calculate the relative closeness of the solutions, and the solution set is ranked according to the closeness to obtain the target Pareto solution. The solution results are shown in Table 5:

[0136] Table 5

[0137]

[0138] Step S15: Optimizing the substrate structural parameters of the target fiber Bragg grating sensor using the target Pareto solution.

[0139] To verify the optimization results, a sensor (with parameters of 1.27, 1, and 7.3) was selected and compared with the optimal ideal solution. Based on the polynomial fitting equation, the theoretical calculation of the sensor's average strain was 4817, with a strain increase rate of 5%, and a substrate life of 6.704, representing an increase rate of 11%.

[0140] Structure selection under different working conditions: This shows that this method can select corresponding sensor structure parameters according to different requirements.

[0141] Alternatively, if increasing sensor sensitivity is the only consideration, Option 1 can be selected, with a 24% strain increase, but an 86% reduction in lifespan. Option 2, if only considering a longer sensor lifespan, increases lifespan by 53%, but reduces the average strain of the fiber Bragg grating by 22%. This application allows the selection of a suitable substrate structure based on actual needs. The results of parameter selection and efficiency improvement calculations for different operating conditions are shown in Table 6:

[0142] Table 6

[0143]

[0144] The specific process of optimizing the base structure of the fiber Bragg grating sensor proposed in this application is as follows: Figure 14 As shown, specifically including:

[0145] Step 1: constructing a three-dimensional model including a fiber Bragg grating sensor substrate, epoxy resin, and a fiber Bragg grating;

[0146] Step 2: Modify the SN curve using the comprehensive influence coefficient to form the SN curve of the base part;

[0147] Step 3: The factors affecting the average strain of the sensor and the fatigue life of the substrate are theoretically analyzed. The three parameters of the meander structure, the slot width, and the length of the middle piece are used as optimization parameters, and the average strain of the fiber Bragg grating and the fatigue life of the substrate are used as optimization indicators.

[0148] Step 4: Use the BBD method to design an orthogonal test plan table, and perform modeling and finite element calculations on each set of sensor plans. Fill the corresponding average strain and substrate fatigue life values into the test table, and use a quadratic polynomial to fit the response surface model.

[0149] Step 5: Use the F value, P value, multiple correlation coefficient, and modified multiple correlation coefficient of each parameter in the variance analysis as model evaluation criteria to evaluate the accuracy of the response surface model;

[0150] Step 6: Using the population initialization and replication, crossover, and mutation parent gene operations in the Latin hypercube sampling optimization algorithm to improve population diversity, the optimized NSGA-II algorithm code was written in Matlab software to calculate the Pareto solution of the average strain of the fiber Bragg grating and the fatigue life of the substrate;

[0151] Step 7: Select the target Pareto solution closest to the ideal solution based on the entropy weight method + TOPSIS method.

[0152] The key technical points of the present invention are: for the first time, a dual-objective collaborative optimization model of fiber Bragg grating sensor substrate sensitivity and substrate fatigue life is established; the response surface method is used to accurately fit the response function of the optimization variable and the optimization target, thereby improving the calculation accuracy of the optimization project; Latin hypercube sampling is used to optimize the NSGA-Ⅱ algorithm, and the entropy weight method + TOPSIS method is used to provide an ideal solution based on data distribution, providing a theoretical reference for sensor structure optimization.

[0153] This method demonstrates remarkable effectiveness in optimizing the structure of fiber Bragg grating (FBG) sensors. By establishing a finite element model of the FBG sensor, strain and stress distributions are obtained, providing data support for the optimized design. The substrate forces are theoretically analyzed, and three variable parameters are scientifically selected as optimization parameters. A comprehensive coefficient is used to correct the life fatigue curve, making fatigue calculation data more accurate. A response surface model is constructed and fitted using polynomials. The NSGA-II algorithm is optimized using Latin hypercube sampling, effectively increasing population diversity. The entropy weight method (TOPSIS) evaluation method provides an ideal solution based on data distribution, resolving the engineering challenge of balancing sensitivity and lifespan. In sensor structure optimization tasks, this method can select the sensor substrate size based on actual requirements.

[0154] In this embodiment, a three-dimensional model including a fiber Bragg grating sensor substrate is established, and the three-dimensional model is configured to obtain a target three-dimensional model; the configuration includes material property configuration and parameter interaction configuration; the stress-life curve and average stress of the target three-dimensional model are corrected to obtain the corrected stress-life curve and average stress, and the substrate fatigue life is calculated using the corrected stress-life curve and average stress; an orthogonal test scheme for each sensor is established based on the substrate structural parameters, the average stress, and the substrate fatigue life of the target fiber Bragg grating sensor in the target three-dimensional model; each of the orthogonal test schemes for the sensor is modeled and finite element calculated to obtain corresponding average strain and substrate fatigue life values, and a response surface model is fitted using the average strain and the substrate fatigue life values; the response surface model is evaluated for accuracy, and if the accuracy evaluation passes, the response surface model is optimized and iteratively solved to obtain a Pareto solution, and a target Pareto solution is screened from the Pareto solutions; and the substrate structural parameters of the target fiber Bragg grating sensor are optimized using the target Pareto solution. The present application establishes a three-dimensional model including a fiber Bragg grating sensor substrate, configures the three-dimensional model, obtains a target three-dimensional model, modifies the stress-life curve and average stress of the target three-dimensional model, and calculates the fatigue life of the substrate using the modified stress-life curve and average stress. The present application proposes to introduce the sensitivity-substrate fatigue life multi-objective collaborative optimization into the fiber Bragg grating sensor substrate, establishes an orthogonal test scheme for each sensor based on the substrate structural parameters, the average stress, and the substrate fatigue life of the target fiber Bragg grating sensor in the target three-dimensional model, and then performs modeling and finite element calculation on each sensor orthogonal test scheme respectively. , the corresponding average strain and base fatigue life values are obtained, the average strain and the base fatigue life values are used to fit the response surface model, and orthogonal experiments and response surface modeling are used to construct an explicit mathematical model of optimization variables and output responses to provide theoretical support for optimization. After constructing the response surface model, the accuracy of the response surface model is evaluated. If the accuracy evaluation passes, the response surface model is multi-objective optimized and iteratively solved to obtain a Pareto solution. The target Pareto solution is screened out from the Pareto solution, which can improve the measurement accuracy and reliability of the suspended weight sensor, achieve accurate measurement of drilling parameters, and increase the service life of the sensor under harsh conditions.

[0155] See also Figure 15 As shown, the embodiment of the present invention discloses a substrate structure optimization device for a fiber Bragg grating sensor, which may specifically include:

[0156] The model building module 11 is used to establish a three-dimensional model including a fiber Bragg grating sensor substrate and configure the three-dimensional model to obtain a target three-dimensional model; the configuration includes material property configuration and parameter interaction configuration;

[0157] a correction module 12, configured to correct the stress-life curve and the average stress of the target three-dimensional model to obtain the corrected stress-life curve and the average stress, and calculate the fatigue life of the substrate using the corrected stress-life curve and the average stress;

[0158] a response surface model fitting module 13 for establishing an orthogonal test scheme for each sensor based on the substrate structural parameters, the average stress, and the substrate fatigue life of the target fiber Bragg grating sensor in the target three-dimensional model, performing modeling and finite element calculation on each of the orthogonal test schemes for the sensor, obtaining corresponding average strain and substrate fatigue life values, and fitting a response surface model using the average strain and substrate fatigue life values;

[0159] A solution module 14 is configured to perform an accuracy evaluation on the response surface model. If the accuracy evaluation is passed, the response surface model is optimized and iteratively solved to obtain a Pareto solution, and a target Pareto solution is selected from the Pareto solutions.

[0160] The optimization module 15 is configured to optimize the substrate structural parameters of the target fiber Bragg grating sensor using the target Pareto solution.

[0161] In this embodiment, a three-dimensional model including a fiber Bragg grating sensor substrate is established, and the three-dimensional model is configured to obtain a target three-dimensional model; the configuration includes material property configuration and parameter interaction configuration; the stress-life curve and average stress of the target three-dimensional model are corrected to obtain the corrected stress-life curve and average stress, and the substrate fatigue life is calculated using the corrected stress-life curve and average stress; an orthogonal test scheme for each sensor is established based on the substrate structural parameters, the average stress, and the substrate fatigue life of the target fiber Bragg grating sensor in the target three-dimensional model; each of the orthogonal test schemes for the sensor is modeled and finite element calculated to obtain corresponding average strain and substrate fatigue life values, and a response surface model is fitted using the average strain and the substrate fatigue life values; the response surface model is evaluated for accuracy, and if the accuracy evaluation passes, the response surface model is optimized and iteratively solved to obtain a Pareto solution, and a target Pareto solution is screened from the Pareto solutions; and the substrate structural parameters of the target fiber Bragg grating sensor are optimized using the target Pareto solution. The present application establishes a three-dimensional model including a fiber Bragg grating sensor substrate, configures the three-dimensional model, obtains a target three-dimensional model, modifies the stress-life curve and average stress of the target three-dimensional model, and calculates the fatigue life of the substrate using the modified stress-life curve and average stress. The present application proposes to introduce the sensitivity-substrate fatigue life multi-objective collaborative optimization into the fiber Bragg grating sensor substrate, establishes an orthogonal test scheme for each sensor based on the substrate structural parameters, the average stress, and the substrate fatigue life of the target fiber Bragg grating sensor in the target three-dimensional model, and then performs modeling and finite element calculation on each sensor orthogonal test scheme respectively. , the corresponding average strain and base fatigue life values are obtained, the average strain and the base fatigue life values are used to fit the response surface model, and orthogonal experiments and response surface modeling are used to construct an explicit mathematical model of optimization variables and output responses to provide theoretical support for optimization. After constructing the response surface model, the accuracy of the response surface model is evaluated. If the accuracy evaluation passes, the response surface model is multi-objective optimized and iteratively solved to obtain a Pareto solution. The target Pareto solution is screened out from the Pareto solution, which can improve the measurement accuracy and reliability of the suspended weight sensor, achieve accurate measurement of drilling parameters, and increase the service life of the sensor under harsh conditions.

[0162] In some specific embodiments, the model building module 11 may specifically include:

[0163] A three-dimensional model building module, used to build a three-dimensional model including a fiber Bragg grating sensor substrate, epoxy resin, and a fiber Bragg grating using a preset three-dimensional mechanical design software; the preset three-dimensional mechanical design software includes Solidworks;

[0164] The configuration module is used to use preset engineering simulation finite element software to configure the properties and interaction between parameters of the three-dimensional model to obtain a target three-dimensional model; the preset engineering simulation finite element software includes Abaqus.

[0165] In some specific embodiments, the correction module 12 may specifically include:

[0166] a curve correction module, configured to correct the stress-life curve of the target three-dimensional model using a preset comprehensive influence coefficient to obtain the corrected stress-life curve; the preset comprehensive influence coefficient includes a stress concentration factor, a geometric dimension factor, a surface quality factor, and a surface strengthening factor;

[0167] a mean stress correction module, configured to correct the mean stress of the target three-dimensional model using a Goodman curve to obtain the corrected mean stress;

[0168] A substrate fatigue life calculation module is used to calculate the modified stress-life curve and the average stress based on the substrate fatigue life calculation formula in fatigue analysis software to obtain substrate fatigue life; the fatigue analysis software includes Fe-safe; the substrate fatigue life calculation formula is:

[0169] ;

[0170] in, is the stress amplitude, is the mean stress value, is the fatigue strength under symmetrical cycles, is the tensile limit.

[0171] In some specific embodiments, the response surface model fitting module 13 may specifically include:

[0172] The test plan generation module is used to use the substrate structural parameters of the target fiber Bragg grating sensor in the target three-dimensional model as optimization parameters, the average stress and the substrate fatigue life as optimization indicators, and adopt the response surface optimization method to establish the orthogonal test plan for each sensor.

[0173] In some specific embodiments, the response surface model fitting module 13 may specifically include:

[0174] A response surface model generation module is used to numerically fit the average strain and the substrate fatigue life using a quadratic polynomial to obtain a response surface model; the response surface model includes a substrate fatigue life response surface model and an average strain response surface model; the expression of the response surface model is:

[0175] ;

[0176] Where n is the number of variables, is the offset term, is a linear offset, is the second-order offset term, is the cross coefficient, y is the optimization target, and x is the optimization parameter.

[0177] In some specific embodiments, the solution module 14 may specifically include:

[0178] The accuracy evaluation module is used to use the multiple correlation coefficient, the modified multiple correlation coefficient, the difference value between the data, and the difference index calculated based on the difference value as accuracy evaluation indicators, and use the variance analysis method to evaluate the accuracy of the response surface model.

[0179] In some specific embodiments, the solution module 14 may specifically include:

[0180] A multi-objective optimization and iterative solution module is used to perform multi-objective optimization and iterative solution on the response surface model using a Latin hypercube sampling optimization algorithm and a preset multi-objective genetic algorithm code to obtain a Pareto solution; the multi-objective genetic algorithm code includes an optimized NSGA-II algorithm code;

[0181] The screening module is used to screen out the target Pareto solution from the Pareto solutions by using the entropy weight method and the approximate ideal solution sorting method.

[0182] Figure 16 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application. The electronic device 20 may specifically include: at least one processor 21, at least one memory 22, a power supply 23, a communication interface 24, an input / output interface 25, and a communication bus 26. The memory 22 is used to store a computer program, which is loaded and executed by the processor 21 to implement the relevant steps of the fiber Bragg grating sensor substrate structure optimization method performed by the electronic device as disclosed in any of the aforementioned embodiments.

[0183] In this embodiment, the power supply 23 is used to provide operating voltage for each hardware device on the electronic device 20; the communication interface 24 can create a data transmission channel between the electronic device 20 and the external device. The communication protocol it follows is any communication protocol that can be applied to the technical solution of this application and is not specifically limited here; the input and output interface 25 is used to obtain external input data or output data to the outside world. Its specific interface type can be selected according to specific application needs and is not specifically limited here.

[0184] In addition, the memory 22, as a carrier for resource storage, can be a read-only memory, random access memory, disk or CD, etc. The resources stored thereon include an operating system 221, a computer program 222 and data 223, etc. The storage method can be temporary storage or permanent storage.

[0185] The operating system 221 is used to manage and control the hardware devices and computer program 222 on the electronic device 20, enabling the processor 21 to calculate and process data 223 in the memory 22. The operating system 221 can be Windows, Unix, Linux, or other operating systems. In addition to including computer programs capable of implementing the fiber Bragg grating sensor substrate structure optimization method performed by the electronic device 20 as disclosed in any of the aforementioned embodiments, the computer program 222 may further include computer programs capable of performing other specific tasks. The data 223 may include data transmitted from external devices to the fiber Bragg grating sensor substrate structure optimization device, as well as data collected by its own input / output interface 25.

[0186] The steps of the methods or algorithms described in conjunction with the embodiments disclosed herein may be implemented directly using hardware, a software module executed by a processor, or a combination of the two. The software module may be placed in random access memory (RAM), internal memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, a hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art.

[0187] Furthermore, an embodiment of the present application also discloses a computer-readable storage medium, in which a computer program is stored. When the computer program is loaded and executed by a processor, the steps of the base structure optimization method of the fiber grating sensor disclosed in any of the aforementioned embodiments are implemented.

[0188] Finally, it should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or device comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or elements inherent to such process, method, article, or device. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of additional identical elements in the process, method, article, or device comprising the element.

[0189] The above is a detailed introduction to the base structure optimization method, device, equipment and storage medium of a fiber Bragg grating sensor provided by the present invention. Specific examples are used herein to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core idea. At the same time, for those skilled in the art, according to the ideas of the present invention, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as a limitation on the present invention.

Claims

1. A method for optimizing the substrate structure of a fiber Bragg grating sensor, characterized in that: include: Establishing a three-dimensional model including a fiber Bragg grating sensor substrate, and configuring the three-dimensional model to obtain a target three-dimensional model; the configuration includes material property configuration and parameter interaction configuration; Correcting the stress-life curve and the average stress of the target three-dimensional model to obtain the corrected stress-life curve and the average stress, and calculating the fatigue life of the substrate using the corrected stress-life curve and the average stress; establishing orthogonal test schemes for each sensor based on the substrate structural parameters, the average stress, and the substrate fatigue life of the target fiber grating sensor in the target three-dimensional model, performing modeling and finite element calculation on each of the orthogonal test schemes for the sensor, obtaining corresponding average strain and substrate fatigue life values, and fitting a response surface model using the average strain and substrate fatigue life values; performing an accuracy evaluation on the response surface model; if the accuracy evaluation passes, performing multi-objective optimization and iterative solution on the response surface model to obtain a Pareto solution, and screening a target Pareto solution from the Pareto solutions; The target Pareto solution is used to optimize the substrate structural parameters of the target fiber grating sensor.

2. The method for optimizing the substrate structure of a fiber Bragg grating sensor according to claim 1, wherein: The step of establishing a three-dimensional model including a fiber Bragg grating sensor substrate and configuring the three-dimensional model to obtain a target three-dimensional model includes: Using preset three-dimensional mechanical design software to establish a three-dimensional model including a fiber Bragg grating sensor substrate, epoxy resin, and a fiber Bragg grating; the preset three-dimensional mechanical design software includes Solidworks; Utilize preset engineering simulation finite element software to configure properties and parameter interactions for the three-dimensional model to obtain a target three-dimensional model; the preset engineering simulation finite element software includes Abaqus.

3. The method for optimizing the substrate structure of a fiber Bragg grating sensor according to claim 1, wherein: The step of correcting the stress-life curve and the average stress of the target three-dimensional model to obtain the corrected stress-life curve and the average stress, and calculating the fatigue life of the substrate using the corrected stress-life curve and the average stress, comprises: The stress-life curve of the target three-dimensional model is corrected using a preset comprehensive influence coefficient to obtain the corrected stress-life curve; the preset comprehensive influence coefficient includes a stress concentration factor, a geometric size factor, a surface quality factor, and a surface strengthening factor; Correcting the average stress of the target three-dimensional model using the Goodman curve to obtain the corrected average stress; The modified stress-life curve and the average stress are calculated based on a substrate fatigue life calculation formula in fatigue analysis software to obtain substrate fatigue life; the fatigue analysis software includes Fe-safe; the substrate fatigue life calculation formula is: ； in, is the stress amplitude, is the mean stress value, is the fatigue strength under symmetrical cycles, is the tensile limit.

4. The method for optimizing the substrate structure of a fiber Bragg grating sensor according to claim 1, wherein: The orthogonal test scheme for each sensor is established based on the substrate structural parameters of the target fiber Bragg grating sensor in the target three-dimensional model, the average stress, and the substrate fatigue life, including: The substrate structural parameters of the target fiber Bragg grating sensor in the target three-dimensional model are used as optimization parameters, the average stress and the substrate fatigue life are used as optimization indicators, and the response surface optimization method is used to establish orthogonal test plans for each sensor.

5. The method for optimizing the substrate structure of a fiber Bragg grating sensor according to claim 1, wherein: The method of numerically fitting a response surface model using the average strain and the substrate fatigue life includes: The average strain and the substrate fatigue life are numerically fitted using a quadratic polynomial to obtain a response surface model; the response surface model includes a substrate fatigue life response surface model and an average strain response surface model; the expression of the response surface model is: ； Where n is the number of variables, is the offset term, is a linear offset, is the second-order offset term, is the cross coefficient, y is the optimization target, and x is the optimization parameter.

6. The method for optimizing the substrate structure of a fiber Bragg grating sensor according to claim 1, wherein: The accuracy evaluation of the response surface model comprises: The multiple correlation coefficient, the modified multiple correlation coefficient, the difference value between the data, and the difference index calculated based on the difference value are used as accuracy evaluation indicators, and the accuracy of the response surface model is evaluated using the variance analysis method.

7. The method for optimizing the substrate structure of a fiber Bragg grating sensor according to any one of claims 1 to 6, characterized in that: The multi-objective optimization and iterative solution of the response surface model are performed to obtain a Pareto solution, and the target Pareto solution is selected from the Pareto solution, including: The response surface model is multi-objective optimized and iteratively solved using a Latin hypercube sampling optimization algorithm and a preset multi-objective genetic algorithm code to obtain a Pareto solution; the multi-objective genetic algorithm code includes an optimized NSGA-II algorithm code; The target Pareto solution is selected from the Pareto solutions by using the entropy weight method and the approximate ideal solution sorting method.

8. A device for optimizing the substrate structure of a fiber Bragg grating sensor, characterized in that: include: A model building module is used to establish a three-dimensional model including a fiber Bragg grating sensor substrate and configure the three-dimensional model to obtain a target three-dimensional model; the configuration includes material property configuration and parameter interaction configuration; a correction module, configured to correct the stress-life curve and the average stress of the target three-dimensional model to obtain the corrected stress-life curve and the average stress, and calculate the fatigue life of the substrate using the corrected stress-life curve and the average stress; a response surface model fitting module, configured to establish an orthogonal test scheme for each sensor based on the substrate structural parameters, the average stress, and the substrate fatigue life of the target fiber Bragg grating sensor in the target three-dimensional model, perform modeling and finite element calculation on each of the orthogonal test schemes for the sensor, obtain corresponding average strain and substrate fatigue life values, and fit a response surface model using the average strain and substrate fatigue life values; A solution module is used to evaluate the accuracy of the response surface model. If the accuracy evaluation is passed, the response surface model is optimized and iteratively solved to obtain a Pareto solution, and a target Pareto solution is selected from the Pareto solutions. An optimization module is used to optimize the substrate structure parameters of the target fiber grating sensor using the target Pareto solution.

9. An electronic device, characterized in that: include: Memory, used to store computer programs; A processor, configured to execute the computer program to implement the method for optimizing the substrate structure of a fiber Bragg grating sensor according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that Used to store a computer program; wherein, when the computer program is executed by a processor, the substrate structure optimization method of the fiber Bragg grating sensor according to any one of claims 1 to 7 is implemented.

Images