Structural optimization method of container-modified mobile laboratory based on finite element analysis

Abstract

The present invention belongs to the technical field of structural optimization of mobile laboratories. The present invention discloses a structural optimization method for a container-modified mobile laboratory based on finite element analysis, comprising: S1, acquiring basic information of the container, performing finite element analysis on the container based on the basic information of the container and obtaining finite element analysis results; S2, dividing the container into regions according to the finite element analysis results, and then obtaining weak areas and non-weak areas of the container; S3, acquiring functional information of the mobile laboratory, determining the number of openings and the positions of the openings in the non-weak areas based on the functional information, and then obtaining n opening strategies; the present invention can divide the weak areas and non-weak areas of the container, and provides a basis for formulating opening strategies; by comprehensively considering the thermal environment performance, airflow distribution performance and stress distribution performance of each opening strategy through an improved genetic algorithm, it is possible to easily select the optimal opening strategy that meets the target performance threshold.

Description

Technical Field

[0001] The invention relates to the technical field of mobile laboratory structure optimization, and more specifically to a structure optimization method for a container-modified mobile laboratory based on finite element analysis. Background Art

[0002] With the rapid development of modern construction technology, containers, as a form of light steel structure construction, have been widely used in many fields due to their advantages such as quick construction, convenient mobility, environmental protection and energy saving. In particular, converting containers into mobile laboratories not only meets the flexibility requirements of scientific research and testing work, but also greatly improves the efficiency of equipment deployment and transfer. However, in the process of converting containers into mobile laboratories, it is inevitable to open door openings and window openings on the box body to meet the functional requirements. This operation directly weakens the original corrugated sheet skin effect of the container, which in turn has a significant impact on its mechanical properties.

[0003] However, existing methods for container hole design mostly rely on empirical formulas or simplified models. These methods have obvious limitations when comprehensively considering various performance values. It is difficult to select the optimal hole location and ensure that the container-type mobile laboratory after the hole is opened can simultaneously achieve multiple target performance thresholds.

[0004] In view of this, the present invention proposes a structural optimization method of a container-converted mobile laboratory based on finite element analysis to solve the above problems. Summary of the invention

[0005] In order to overcome the above-mentioned defects of the prior art and to achieve the above-mentioned purpose, the present invention provides the following technical solution: a structural optimization method of a container-converted mobile laboratory based on finite element analysis, comprising:

[0006] S1. Obtain basic information of the container, perform finite element analysis on the container based on the basic information of the container and obtain finite element analysis results;

[0007] S2. Divide the container into regions according to the finite element analysis results, and then obtain the weak regions and non-weak regions of the container;

[0008] S3, obtaining functional information of the mobile laboratory, determining the number and positions of openings in the non-weak areas based on the functional information, and then obtaining n opening strategies;

[0009] S4, setting an improved genetic algorithm, and obtaining the optimal hole opening strategy among n hole opening strategies through the improved genetic algorithm;

[0010] S5. Preset the target performance threshold and determine whether the performance values ​​of the optimal hole opening strategy reach the target performance threshold. If so, the optimal hole opening strategy is used as the final optimization result. If not, the optimal hole opening strategy is optimized until it reaches the target performance threshold.

[0011] Furthermore, the step of obtaining basic information of the container, performing finite element analysis on the container based on the basic information of the container and obtaining finite element analysis results includes:

[0012] The basic information of the container includes specifications, material properties and structural composition;

[0013] The analysis type of the finite element analysis is determined according to the basic information of the container and the load conditions are set, a finite element analysis model of the container is established and meshing is performed, load conditions and constraint conditions are applied to the finite element analysis model, and finite element calculation is performed to obtain the finite element analysis results of the container, wherein the finite element analysis results include stress distribution and deformation, wherein the stress distribution and deformation are respectively displayed as stress cloud maps and displacement cloud maps.

[0014] Furthermore, the step of dividing the container into regions according to the finite element analysis results to obtain weak regions and non-weak regions of the container includes:

[0015] A first measurement grid and a second measurement grid are respectively set on the stress cloud map and the displacement cloud map, and the color value of each intersection point on the first measurement grid and the gray value of the corresponding intersection point on the second measurement grid are respectively obtained;

[0016] A mapping relationship between color value and stress in a stress cloud map is established to obtain a first mapping relationship, and a color threshold is preset based on the first mapping relationship; a mapping relationship between gray value and displacement in a displacement cloud map is established to obtain a second mapping relationship, and a gray threshold is preset based on the second mapping relationship; an area formed by intersection points reaching the color threshold in the stress cloud map and an area formed by intersection points reaching the gray threshold in the displacement cloud map are recorded as a first area and a second area, respectively;

[0017] Establishing an influence relationship between stress and displacement, and setting a first weight of stress and a second weight of displacement respectively based on the influence relationship;

[0018] Multiply the color value of each intersection point by its corresponding first weight, and multiply the gray value of the corresponding intersection point by its corresponding second weight, and then add the two products to obtain the final evaluation value of each intersection point;

[0019] A weak threshold is preset, and an area formed by the intersection of the final evaluation value reaching either the color threshold or the gray threshold is taken as the third area;

[0020] The first area, the second area and the third area are added together to obtain the weak area of ​​the container, and the other areas of the container except the weak area are regarded as non-weak areas;

[0021] The coordinates of the intersection of the first measurement network and the second measurement network at the same position of the container correspond to each other, and the intersection is the point where the grid lines of the first measurement network and the second measurement network intersect.

[0022] Furthermore, the influence relationship between stress and displacement is established, and the first weight of stress and the second weight of displacement are set based on the influence relationship as follows:

[0023] Obtaining color values ​​and grayscale values ​​of corresponding intersection points in the first measurement grid and the second measurement grid;

[0024] Establish the influence relationship between each color value and gray value according to the color value and gray value of the corresponding intersection point;

[0025] The first weight and the second weight are respectively preset according to the influence relationship.

[0026] Furthermore, the step of obtaining functional information of the mobile laboratory, determining the number and positions of openings in the non-weak areas based on the functional information, and then obtaining n opening strategies includes:

[0027] The functional information includes ventilation function, heat dissipation function and safety function corresponding to the mobile laboratory after functional division;

[0028] Set the number of holes in the non-weak area of ​​the container according to the functional information;

[0029] Obtain all the opening positions of the non-weak areas on the container, preset combination conditions, and arrange and combine all the opening positions according to the combination conditions and the number of openings, thereby obtaining n opening strategies, each of which includes different opening positions corresponding to the same number of openings;

[0030] The combination condition is: after functional zoning, the maximum number of openings corresponding to each area is preset, and the number of openings in each area must be less than the corresponding maximum number of openings.

[0031] Furthermore, the step of setting an improved genetic algorithm and obtaining the optimal hole opening strategy among n hole opening strategies by using the improved genetic algorithm includes:

[0032] Initialize the population, take n hole-opening strategies as the initial population, take each hole-opening strategy as an individual, and take the hole-opening position corresponding to each hole-opening strategy as the gene that constitutes the individual;

[0033] Define the fitness function, evaluate each individual through the fitness function, and obtain the fitness value of each individual;

[0034] The individuals that enter the next generation population in the initial population are determined by combining the elite selection method with the tournament selection method.

[0035] Among them, first, select M' individuals with the highest fitness from the initial population, and the selection ratio is K'%. For the remaining individuals in the initial population, randomly select J' fixed number of individuals from the remaining initial population to form a tournament group, compare the fitness values ​​of these individuals, and then select the individual with the highest fitness as the winner. Repeat this process until N' individuals are selected to fill the next generation population after removing the elite individuals; Among them, M' and J' are both integers greater than 1, K' is a constant belonging to the interval (0,100); N' is a preset quantity threshold; its value is an integer greater than 1;

[0036] Perform crossover and mutation operations on the individuals entering the next generation population to obtain offspring individuals;

[0037] Repeat the above selection, crossover and mutation operations to generate a new next-generation population, and recalculate the fitness value of each individual in the new next-generation population until the preset iteration condition is reached, output the optimal individual, and use this optimal individual as the optimal hole opening strategy.

[0038] Furthermore, the fitness function is:

[0039] ,

[0040] In the formula, For individuals The fitness of For individuals Thermal environment performance value, Is an individual The air flow distribution performance value, Is an individual The stress distribution performance value, They are the weights corresponding to the thermal environment performance value, air flow distribution performance value and stress distribution performance value, and they satisfy ;

[0041] The formula for the thermal environment performance value is: ,

[0042] In the formula, is the individual heat dissipation intensity value, is the total heat generated by the individual, To balance the heat dissipation intensity value and the weighted performance value of temperature uniformity, For the The temperature at the measuring point, is the average temperature of all measurement points, is the number of measurement points;

[0043] The formula for the air flow distribution performance value is: ,

[0044] In the formula, is the standard deviation of the individual airflow field, is the preset individual maximum airflow field standard deviation value, is the maximum airflow value of the airflow field, is the minimum airflow value of the airflow field, is a small positive number;

[0045] The formula for the stress distribution performance value is: ,

[0046] In the formula, is the average value of individual stress, is the standard deviation of the individual stresses, is the maximum value of individual stress, is the minimum value of individual stress, is the weight factor.

[0047] Further, the crossover operation includes:

[0048] The initial population is stratified according to the fitness values ​​from high to low to obtain the first stratum, the second stratum and the third stratum;

[0049] The probability ratios of the genes in the first layer, the second layer, and the third layer are calculated respectively; then, in each layer, the genes are sorted from high to low according to their probability ratios, thereby obtaining the first probability sequence, the second probability sequence, and the third probability sequence respectively;

[0050] In the first probability sequence, the second probability sequence, and the third probability sequence, the genes ranked in the first W' position are defined as dominant genes, the genes ranked in the last position and then the first Z position are defined as inferior genes, and the genes ranked between the W' position and the Z position are defined as stable genes; wherein W' and Z are both integers greater than 1;

[0051] The crossover ratios of dominant genes, stable genes and inferior genes are preset, and the crossover ratios are R1, R2 and R3, wherein R1+R2+R3=1, R1>R2>R3, and the crossover operation is performed in an alternating manner using single-point crossover and multi-point crossover;

[0052] The mutation operation is:

[0053] Select individuals from different strata, ensure that the selected individuals contain dominant genes, stable genes, and inferior genes, and select at least one individual from each stratum;

[0054] For individuals containing dominant genes, the mutation rate of P1 is used, for individuals containing stable genes, the mutation rate of P2 is used, and for individuals containing disadvantageous genes, the mutation rate of P3 is used, among which P1+P2+P3=1, P3>P2>P1. The new individuals generated after the crossover operation and mutation operation are evaluated for fitness, and whether to retain the new individuals in the next generation population is selected based on the fitness evaluation.

[0055] Furthermore, the preset target performance threshold determines whether each performance value of the optimal hole opening strategy reaches the target performance threshold, if so, the optimal hole opening strategy is used as the final optimization result, if not, the optimal hole opening strategy is optimized until it reaches the target performance threshold, including:

[0056] Preset multiple target performance thresholds, and compare each performance value of the optimal hole opening strategy with the corresponding target performance threshold;

[0057] If all of them are achieved, the optimal hole opening strategy is taken as the final optimization result;

[0058] If not, the performance value that does not reach the target performance threshold is optimized.

[0059] Furthermore, the step of optimizing the optimal hole opening strategy until it reaches the target performance threshold comprises:

[0060] Obtaining a difference between a performance value that does not reach a target performance threshold and a target performance threshold;

[0061] Obtaining the optimization direction corresponding to the performance value, obtaining the relationship between the optimization direction and the performance value according to the linear model or the nonlinear model, and constructing a tolerance compensation model based on the relationship between the optimization direction and the performance value;

[0062] The performance value is optimized through the bearing compensation model, and a new optimal hole opening strategy is generated after the optimization is completed;

[0063] Conduct finite element analysis on the new optimal hole opening strategy to determine whether the performance values ​​of the new optimal hole opening strategy reach the target performance threshold. If so, use it as the final optimization result.

[0064] Among them, the method of obtaining the optimization direction corresponding to the performance value is: each performance value corresponds to h optimization directions, different weights are preset for the capital consumption, time consumption, and adjustment difficulty of each optimization direction, and the score of each optimization direction is obtained by weighted average, and the optimization direction corresponding to the highest score is used as the optimization direction corresponding to the performance value.

[0065] The technical effects and advantages of the structural optimization method of the container-converted mobile laboratory based on finite element analysis of the present invention are as follows:

[0066] 1. Through precise finite element analysis, the weak and non-weak areas of the container can be accurately identified, providing a solid foundation for the scientific formulation of hole opening strategies; by comprehensively considering the functional requirements of the mobile laboratory, reasonable and diverse hole opening strategies are formulated to meet the performance requirements of the mobile laboratory; the subjectivity and arbitrariness of the hole opening design and construction of the container are reduced, and the work efficiency and quality of the results are significantly improved, providing a scientific and practical solution for the hole opening design of the container mobile laboratory;

[0067] 2. By designing and improving the genetic algorithm, the thermal environment performance, airflow distribution performance and stress distribution performance are comprehensively considered to optimize the hole opening strategy of the container-type mobile laboratory, so that the optimal hole opening strategy that meets the target performance threshold can be selected; by adopting flexible crossover and mutation operations, the population diversity can be maintained and the speed of finding the optimal individual can be accelerated; by presetting multiple target performance thresholds and comparing them one by one with the performance values ​​of the optimal hole opening strategy, it is ensured that the final optimization result meets all preset requirements; for the performance values ​​that do not reach the target, the tolerance compensation model is used to make fine adjustments to ensure that the final optimization result can meet all target performance thresholds. BRIEF DESCRIPTION OF THE DRAWINGS

[0068] Figure 1 It is a schematic flow chart of the structural optimization method of a container-converted mobile laboratory based on finite element analysis of the present invention;

[0069] Figure 2 It is a schematic diagram of the process of improving the genetic algorithm in the present invention. DETAILED DESCRIPTION

[0070] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in 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 creative work are within the scope of protection of the present invention.

[0071] Example 1

[0072] See also Figure 1 As shown, the structural optimization method of the container-converted mobile laboratory based on finite element analysis in this embodiment includes:

[0073] S1. Obtain basic information of the container, perform finite element analysis on the container based on the basic information of the container and obtain finite element analysis results;

[0074] S2. Divide the container into regions according to the finite element analysis results, and then obtain the weak regions and non-weak regions of the container;

[0075] S3, obtaining functional information of the mobile laboratory, determining the number and positions of openings in the non-weak areas based on the functional information, and then obtaining n opening strategies;

[0076] The steps of obtaining basic information of the container, performing finite element analysis on the container based on the basic information of the container and obtaining finite element analysis results include:

[0077] The basic information of the container includes specifications (size, internal volume, weight, thickness, etc.), material properties (main materials, connecting materials, insulation materials, anti-corrosion layer materials, etc.) and structural composition (corner fittings, doors, ordinary doors, emergency doors, heat dissipation windows, reinforcement ribs, beams, etc.);

[0078] According to the basic information of the container, the analysis type of finite element analysis is determined and the load conditions are set. The finite element analysis model of the container is established and meshing is performed. Load conditions and constraints are applied to the finite element analysis model, and finite element calculation is performed to obtain the finite element analysis results of the container. The finite element analysis results include stress distribution and deformation. The stress distribution and deformation are displayed in the form of stress cloud maps and displacement cloud maps, respectively. The stress cloud map is a color map, and the displacement cloud map is output in grayscale when the finite element software is output.

[0079] The steps of dividing the container into regions according to the finite element analysis results and then obtaining the weak regions and non-weak regions of the container include:

[0080] A first measurement grid and a second measurement grid are respectively set on the stress cloud map and the displacement cloud map, and the color value of each intersection point on the first measurement grid and the gray value of the corresponding intersection point on the second measurement grid are respectively obtained;

[0081] A mapping relationship between color value and stress in a stress cloud map is established to obtain a first mapping relationship, and a color threshold is preset based on the first mapping relationship; a mapping relationship between gray value and displacement in a displacement cloud map is established to obtain a second mapping relationship, and a gray threshold is preset based on the second mapping relationship; an area formed by intersection points reaching the color threshold in the stress cloud map and an area formed by intersection points reaching the gray threshold in the displacement cloud map are recorded as a first area and a second area, respectively;

[0082] Establishing an influence relationship between stress and displacement, and setting a first weight of stress and a second weight of displacement respectively based on the influence relationship;

[0083] Multiply the color value of each intersection point by its corresponding first weight, and multiply the gray value of the corresponding intersection point by its corresponding second weight, and then add the two products to obtain the final evaluation value of each intersection point;

[0084] A weak threshold is preset, and the area formed by the intersection of the final evaluation value reaching the color threshold or the gray threshold is used as the third area. The weak threshold is set by relevant personnel according to actual conditions;

[0085] The first area, the second area and the third area are added together (including overlapping weak areas) to obtain the weak area of ​​the container, and the other areas of the container except the weak area are regarded as non-weak areas;

[0086] The coordinates of the intersection of the first measurement network and the second measurement network at the same position of the container correspond to each other, and the intersection is the point where the grid lines of the first measurement network and the second measurement network intersect.

[0087] The influence relationship between stress and displacement is established, and the first weight of stress and the second weight of displacement are set based on the influence relationship as follows:

[0088] Obtaining color values ​​and grayscale values ​​of corresponding intersection points in the first measurement grid and the second measurement grid;

[0089] Preset the first weight and the second weight respectively according to the influence relationship;

[0090] According to the color value and gray value of the corresponding intersection point, the influence relationship between each color value and gray value is established. Then, the first weight is , the second weight is For example, the values ​​of several intersection points are in the form of (stress value, displacement value): (6, 3), and the influence relationship between stress value and displacement value is x / y=2, x is the stress value, y is the displacement value, and the final value corresponding to each intersection point reaches one of the stress values ​​or displacement values ​​corresponding to the color threshold or grayscale threshold as the intersection point constituting the weak area.

[0091] It should be noted that through finite element analysis, the stress distribution and deformation of the container under load conditions can be accurately obtained, providing an accurate data basis for the subsequent regional division and opening strategy formulation; regional division based on the finite element analysis results can accurately identify the weak and non-weak areas of the container, which helps to avoid unnecessary damage to the container structure during the opening process.

[0092] The steps of obtaining functional information of the mobile laboratory, determining the number and positions of openings in the non-weak areas based on the functional information, and then obtaining n opening strategies include:

[0093] Functional information includes ventilation function, heat dissipation function and safety function of the mobile laboratory after functional division;

[0094] Set the number of holes in the non-weak area of ​​the container according to the functional information;

[0095] Obtain all the opening positions of the non-weak areas on the container, preset combination conditions, and arrange and combine all the opening positions according to the combination conditions and the number of openings, thereby obtaining n opening strategies. Each opening strategy includes different opening positions corresponding to the same number of openings. The n opening strategies are set by relevant personnel according to actual conditions.

[0096] The combination condition is: after the functional division, the maximum number of openings corresponding to each area is preset, and the number of openings in each area must be less than the corresponding maximum number of openings. The maximum number of openings is generally set by relevant personnel according to actual conditions;

[0097] It should be noted that by comprehensively considering the key functional information of the mobile laboratory such as ventilation, heat dissipation and safety functions, the number and location of openings in non-weak areas can be determined scientifically and reasonably; by pre-setting combination conditions, all possible opening positions are arranged and combined to generate a variety of opening strategies, which not only meets functional requirements but also ensures the integrity and safety of the structure; it can reduce the blindness and arbitrariness of the design, improve the scientificity and practicality of the design, and provide strong support for the design of the opening strategy of the container-type mobile laboratory.

[0098] In this embodiment, through precise finite element analysis, the weak and non-weak areas of the container can be accurately identified, providing a solid foundation for the scientific formulation of hole opening strategies; by comprehensively considering the functional requirements of the mobile laboratory, reasonable and diverse hole opening strategies are formulated to meet the performance requirements of the mobile laboratory; the subjectivity and arbitrariness of the hole opening design and construction of the container are reduced, which significantly improves the work efficiency and the quality of the results, and provides a scientific and practical solution for the hole opening design of the container-type mobile laboratory.

[0099] Example 2

[0100] See also Figure 1 and Figure 2 As shown, the structural optimization method of the container-converted mobile laboratory based on finite element analysis in this embodiment includes:

[0101] S4, setting an improved genetic algorithm, and obtaining the optimal hole opening strategy among n hole opening strategies through the improved genetic algorithm;

[0102] S5. Preset the target performance threshold and determine whether the performance values ​​of the optimal hole opening strategy reach the target performance threshold. If so, the optimal hole opening strategy is used as the final optimization result. If not, the optimal hole opening strategy is optimized until it reaches the target performance threshold.

[0103] The steps of setting an improved genetic algorithm and obtaining the optimal hole opening strategy among n hole opening strategies by using the improved genetic algorithm include:

[0104] Initialize the population, take n hole-opening strategies as the initial population, take each hole-opening strategy as an individual, and take the hole-opening position corresponding to each hole-opening strategy as the gene that constitutes the individual;

[0105] Define the fitness function, evaluate each individual through the fitness function, and obtain the fitness value of each individual;

[0106] The individuals that enter the next generation population in the initial population are determined by combining the elite selection method with the tournament selection method.

[0107] Among them, first, select M' individuals with the highest fitness from the initial population to ensure that they can directly enter the next generation population, and the selection ratio is K'%. For the remaining individuals in the initial population, randomly select J' fixed number of individuals from the remaining initial population to form a tournament group. The values ​​of K' and J' are set by relevant personnel according to actual conditions. Compare the fitness values ​​of these individuals, and then select the individual with the highest fitness as the winner. Repeat this process until N' individuals are selected to fill the next generation population after removing the elite individuals. Among them, M' and J' are both integers greater than 1, K' is a constant belonging to the interval (0,100); N' is a preset quantity threshold; its value is an integer greater than 1; it is set by relevant personnel according to actual conditions;

[0108] Perform crossover and mutation operations on the individuals entering the next generation population to obtain offspring individuals;

[0109] Repeat the above selection, crossover and mutation operations to generate a new next-generation population, and recalculate the fitness value of each individual in the new next-generation population until one of the preset iteration conditions or the maximum number of iterations is reached, and the optimal individual is output and used as the optimal hole opening strategy. The iteration conditions or the maximum number of iterations are set by relevant personnel according to actual conditions.

[0110] The fitness function is:

[0111] ,

[0112] In the formula, For individuals The fitness of For individuals Thermal environment performance value, Is an individual The air flow distribution performance value, Is an individual The stress distribution performance value, They are the weights corresponding to the thermal environment performance value, air flow distribution performance value and stress distribution performance value, and they satisfy ;

[0113] The formula for the thermal environment performance value is: ,

[0114] In the formula, is the individual heat dissipation intensity value, which can be calculated based on the thermal conductivity of the heat dissipation material, the efficiency of the heat dissipation structure design, etc. is the total heat generated by the individual, The weighted performance value that balances the heat dissipation intensity value and the temperature uniformity distribution is usually a value between 0 and 1. For the The temperature at the measuring point, is the average temperature of all measurement points, is the number of measurement points, , is the thermal conductivity of the heat dissipation material, is the heat dissipation surface area, is the temperature gradient, or temperature difference, usually between the temperature inside the system and the external environment. Indicates the uniform temperature distribution value;

[0115] The formula for the air flow distribution performance value is: ,

[0116] In the formula, the measurement or calculation of the individual corresponds to multiple airflow measurement points in the container, is the standard deviation of the individual airflow field, is the preset individual maximum airflow field standard deviation value, is the maximum airflow value of the airflow field, is the minimum airflow value of the airflow field, It is a small positive number, which can be 0.0001 or 0.001, or can be set by relevant personnel;

[0117] The formula for the stress distribution performance value is: ,

[0118] In the formula, the measured or calculated individual corresponds to multiple stress measurement points in the container, is the average value of individual stress, is the standard deviation of the individual stresses, is the maximum value of individual stress, is the minimum value of individual stress, is the weight factor used to adjust the influence of stress range on the distribution value.

[0119] Crossover operations include;

[0120] The crossover ratio is: the initial population is stratified according to the fitness value from high to low to obtain the first stratum, the second stratum and the third stratum. The number of strata is set by relevant personnel according to the actual situation.

[0121] Obtain the probability ratio of each gene in the first layer, the second layer, and the third layer, and sort the probability ratios of the genes in each layer from high to low to obtain a first probability sequence, a second probability sequence, and a third probability sequence, respectively;

[0122] The genes in the first, second and third probability sequences that are in the first W' position are recorded as dominant genes, the genes in the last Z position are recorded as inferior genes, and the genes between W' and Z are recorded as stable genes, where W' and Z are both integers greater than 1, and the specific values ​​of W' and Z are set by relevant personnel according to actual conditions;

[0123] The crossover ratios of dominant genes, stable genes and inferior genes are preset, and the crossover ratios are R1, R2 and R3, where R1+R2+R3=1, R1>R2>R3, and the specific data of R1, R2 and R3 are set by relevant personnel according to actual conditions;

[0124] The crossover operation is performed in an alternating manner using single-point crossover and multi-point crossover, and the alternating manner is that the crossover mode between the next generation population and the previous population (the two adjacent populations) is different;

[0125] The mutation operation is:

[0126] Select individuals from different strata to ensure that the selected individuals contain dominant genes, stable genes, and inferior genes;

[0127] At least one individual was selected from each stratum to ensure diversity of variation;

[0128] For individuals containing dominant genes, the mutation rate of P1 is used; for individuals containing stable genes, the mutation rate of P2 is used; for individuals containing inferior genes, the mutation rate of P3 is used, where P1+P2+P3=1, P3>P2>P1, and the specific data of P1, P2 and P3 are set by relevant personnel according to actual conditions; the new individuals generated after the crossover operation and mutation operation are evaluated for fitness, and whether to retain the new individuals in the next generation population is selected based on the fitness evaluation.

[0129] The steps of presetting a target performance threshold, judging whether each performance value of the optimal hole opening strategy (each performance value is a thermal environment performance value, an airflow distribution performance value, and a stress distribution performance value) reaches the target performance threshold, and if so, taking the optimal hole opening strategy as the final optimization result, and if not, optimizing the optimal hole opening strategy until it reaches the target performance threshold, include:

[0130] Preset multiple target performance thresholds (set by relevant personnel based on actual conditions or thermal environment performance values, airflow distribution performance values, and stress distribution performance values), and compare each performance value of the optimal hole opening strategy with the corresponding target performance threshold;

[0131] If all of them are achieved, the optimal hole opening strategy is taken as the final optimization result;

[0132] If not, the performance value that does not reach the target performance threshold is optimized.

[0133] The steps of optimizing the optimal hole opening strategy until it reaches the target performance threshold include:

[0134] Obtaining a difference between a performance value that does not reach a target performance threshold and a target performance threshold, where the difference is obtained by calculating an absolute difference;

[0135] Obtaining the optimization direction corresponding to the performance value (for example, the number, density, number of beams, etc. of stiffeners), obtaining the relationship between the optimization direction and the performance value according to a linear model or a nonlinear model, and constructing a tolerance compensation model based on the relationship between the optimization direction and the performance value. The tolerance compensation model may be a mathematical formula for expressing the relationship between the optimization direction and the performance value;

[0136] The performance value is optimized (adjusted) through the bearing compensation model. The difference is input into the mathematical expression corresponding to the bearing compensation model to obtain the corresponding adjustment amount. The value of the optimization direction is adjusted accordingly through the adjustment amount. After the optimization is completed, a new optimal opening strategy is generated;

[0137] Conduct finite element analysis on the new optimal hole opening strategy to determine whether the performance values ​​of the new optimal hole opening strategy reach the target performance threshold. If so, use it as the final optimization result.

[0138] The method of obtaining the optimization direction corresponding to the performance value is as follows: each performance value corresponds to h optimization directions, different weights are preset for the capital consumption, time consumption, and adjustment difficulty of each optimization direction, and the score of each optimization direction is obtained by weighted average, and the optimization direction corresponding to the highest score is used as the optimization direction corresponding to the performance value;

[0139] It should be noted that by setting up an improved genetic algorithm, n hole-opening strategies can be efficiently searched and evaluated, the optimal hole-opening strategy can be quickly located, and the optimization efficiency is significantly improved; the design of the crossover operation and mutation operation takes into account the pros and cons of the genes, and through different crossover ratios and mutation rates, it not only ensures the diversity of the population, but also speeds up the optimization speed and enhances the robustness of the algorithm; multiple target performance thresholds are preset, and the optimal hole-opening strategies are compared one by one to ensure that the final optimization result can meet all preset goals, thereby improving the pertinence and practicality of the design; for performance values ​​that do not reach the target performance threshold, the tolerance compensation model is used for further optimization. The steps of accurately calculating the difference, determining the optimization direction, and constructing the compensation model have achieved the fine adjustment of the performance value, ensuring that the final optimization result can meet all the target performance thresholds. The finite element analysis is introduced in the optimization process to verify the new optimal hole opening strategy, ensuring the accuracy and reliability of the optimization results, and further improving the scientificity and practicality of the design. This method provides a scientific, efficient and practical optimization method for the hole opening design of container-type mobile laboratories through the beneficial effects of efficient optimization, comprehensive performance evaluation, flexible crossover and mutation operations, goal-oriented optimization, refined optimization adjustment and finite element analysis verification.

[0140] In this embodiment, the hole opening strategy of the container-type mobile laboratory is optimized by designing an improved genetic algorithm and comprehensively considering the thermal environment performance, airflow distribution performance and stress distribution performance; by adopting flexible crossover and mutation operations, the population diversity can be maintained and the speed of finding the optimal individual can be accelerated; by presetting multiple target performance thresholds and comparing them one by one with the performance values ​​of the optimal hole opening strategy, it is ensured that the final optimization result meets all preset requirements; for the performance values ​​that do not reach the target, fine-tuning is performed by adopting a tolerance compensation model to ensure that the final optimization result can meet all target performance thresholds.

[0141] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed in the present invention can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0142] In the several embodiments provided by the present invention, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic, for example, the division of units is only one, and there may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0143] The above are only specific embodiments of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

[0144] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A structural optimization method for a container-modified mobile laboratory based on finite element analysis, characterized in that: include: S1. Obtain basic information of the container, perform finite element analysis on the container based on the basic information of the container and obtain finite element analysis results; The basic information of the container includes specifications, material properties and structural composition; S2. Divide the container into regions according to the finite element analysis results, and then obtain the weak regions and non-weak regions of the container; S3, obtaining functional information of the mobile laboratory, determining the number and location of openings in non-weak areas based on the functional information, and then obtaining n opening strategies; the functional information includes ventilation function, heat dissipation function and safety function corresponding to the mobile laboratory after functional zoning; S4, setting an improved genetic algorithm, and obtaining the optimal hole opening strategy among n hole opening strategies through the improved genetic algorithm; S5. Preset the target performance threshold and determine whether the performance values ​​of the optimal hole opening strategy reach the target performance threshold. If so, the optimal hole opening strategy is used as the final optimization result. If not, the optimal hole opening strategy is optimized until it reaches the target performance threshold.

2. The structural optimization method of container-converted mobile laboratory based on finite element analysis according to claim 1 is characterized in that: The step of obtaining basic information of the container, performing finite element analysis on the container based on the basic information of the container and obtaining finite element analysis results includes: The analysis type of the finite element analysis is determined according to the basic information of the container and the load conditions are set, a finite element analysis model of the container is established and meshing is performed, load conditions and constraint conditions are applied to the finite element analysis model, and finite element calculation is performed to obtain the finite element analysis results of the container, wherein the finite element analysis results include stress distribution and deformation, wherein the stress distribution and deformation are respectively displayed as stress cloud maps and displacement cloud maps.

3. The structural optimization method of container-converted mobile laboratory based on finite element analysis according to claim 2 is characterized in that: The step of dividing the container into regions according to the finite element analysis results, and then obtaining weak regions and non-weak regions of the container, comprises: A first measurement grid and a second measurement grid are respectively set on the stress cloud map and the displacement cloud map, and the color value of each intersection point on the first measurement grid and the gray value of the corresponding intersection point on the second measurement grid are respectively obtained; A mapping relationship between color value and stress in a stress cloud map is established to obtain a first mapping relationship, and a color threshold is preset based on the first mapping relationship; a mapping relationship between gray value and displacement in a displacement cloud map is established to obtain a second mapping relationship, and a gray threshold is preset based on the second mapping relationship; an area formed by intersection points reaching the color threshold in the stress cloud map and an area formed by intersection points reaching the gray threshold in the displacement cloud map are recorded as a first area and a second area, respectively; Establishing an influence relationship between stress and displacement, and setting a first weight of stress and a second weight of displacement respectively based on the influence relationship; Multiply the color value of each intersection point by its corresponding first weight, and multiply the gray value of the corresponding intersection point by its corresponding second weight, and then add the two products to obtain the final evaluation value of each intersection point; A weak threshold is preset, and an area formed by the intersection of the final evaluation value reaching either the color threshold or the gray threshold is taken as the third area; The first area, the second area and the third area are added together to obtain the weak area of ​​the container, and the other areas of the container except the weak area are regarded as non-weak areas; The coordinates of the intersection of the first measurement network and the second measurement network at the same position of the container correspond to each other, and the intersection is the point where the grid lines of the first measurement network and the second measurement network intersect.

4. The structural optimization method of container-converted mobile laboratory based on finite element analysis according to claim 3 is characterized in that: The method of establishing the influence relationship between stress and displacement and setting the first weight of stress and the second weight of displacement respectively based on the influence relationship is: Obtaining color values ​​and grayscale values ​​of corresponding intersection points in the first measurement grid and the second measurement grid; Establish the influence relationship between each color value and gray value according to the color value and gray value of the corresponding intersection point; The first weight and the second weight are respectively preset according to the influence relationship.

5. The structural optimization method of container-converted mobile laboratory based on finite element analysis according to claim 1 is characterized in that: The step of obtaining functional information of the mobile laboratory, determining the number of holes and the positions of the holes in the non-weak areas based on the functional information, and then obtaining n hole opening strategies includes: Set the number of holes in the non-weak area of ​​the container according to the functional information; Obtain all the opening positions of the non-weak area on the container, preset the combination conditions, and arrange and combine all the opening positions according to the combination conditions and the number of openings, thereby obtaining n opening strategies, each of which includes different opening positions corresponding to the same number of openings; The combination condition is: after functional zoning, the maximum number of openings corresponding to each area is preset, and the number of openings in each area must be less than the corresponding maximum number of openings.

6. The structural optimization method of container-converted mobile laboratory based on finite element analysis according to claim 1 is characterized in that: The step of setting an improved genetic algorithm and obtaining the optimal hole opening strategy among n hole opening strategies by using the improved genetic algorithm includes: Initialize the population, take n hole-opening strategies as the initial population, take each hole-opening strategy as an individual, and take the hole-opening position corresponding to each hole-opening strategy as the gene that constitutes the individual; Define the fitness function, evaluate each individual through the fitness function, and obtain the fitness value of each individual; The individuals that enter the next generation population in the initial population are determined by combining the elite selection method with the tournament selection method. Among them, first, select M' individuals with the highest fitness from the initial population, the selection ratio is K'%, and for the remaining individuals of the initial population, randomly select J' fixed number of individuals from the remaining initial population to form a tournament group, compare the fitness values ​​of these individuals, and then select the individual with the highest fitness as the winner, and repeat this process until N' individuals are selected to fill the next generation population after removing the elite individuals; among them, M' and J' are both integers greater than 1, K' is a constant belonging to the interval (0,100); N' is a preset quantity threshold; its value is an integer greater than 1; Perform crossover and mutation operations on the individuals entering the next generation population to obtain offspring individuals; Repeat the above selection, crossover and mutation operations to generate a new next-generation population, and recalculate the fitness value of each individual in the new next-generation population until the preset iteration condition is reached, output the optimal individual, and use this optimal individual as the optimal hole opening strategy.

7. The structural optimization method of container-converted mobile laboratory based on finite element analysis according to claim 6 is characterized in that: The fitness function is: F(i)=ω T ×T(i)+ω A ×A(i)+ω S ×S(i), In the formula, F(i) is the fitness of individual i, T(i) is the thermal environment performance value of individual i, A(i) is the airflow distribution performance value of individual i, S(i) is the stress distribution performance value of individual i, ω T ,ω A ,ω S are the weights corresponding to the thermal environment performance value, air flow distribution performance value and stress distribution performance value, and satisfy ω T +ω A +ω S =1; The formula for the thermal environment performance value is: In the formula, Q 散 is the individual heat dissipation intensity value, Q 总 is the total heat generated by the individual, α is the weighted performance value that weighs the heat dissipation intensity value and the temperature uniform distribution, W j is the temperature at the jth measurement point, is the average temperature of all measurement points, u is the number of measurement points; The formula for the air flow distribution performance value is: In the formula, σA is the standard deviation of the individual airflow field, σAmax is the preset individual maximum airflow field standard deviation value, Amax is the maximum airflow value of the airflow field, Amin is the minimum airflow value of the airflow field, and δ is a small positive number; The formula for the stress distribution performance value is: In the formula, is the average value of individual stress, σS is the standard deviation of individual stress, Smax is the maximum value of individual stress, Smin is the minimum value of individual stress, and β is the weight factor.

8. The structural optimization method of container-converted mobile laboratory based on finite element analysis according to claim 7 is characterized in that: The crossover operation includes: The initial population is stratified according to the fitness values ​​from high to low to obtain the first stratum, the second stratum and the third stratum; For the genes in the first, second, and third strata, their probability ratios were calculated respectively; Then, in each layer, the genes are sorted from high to low according to their probability ratios, thereby obtaining the first probability sequence, the second probability sequence, and the third probability sequence respectively; In the first probability sequence, the second probability sequence, and the third probability sequence, the genes ranked in the first W' position are defined as dominant genes, the genes ranked in the last position and then the first Z position are defined as inferior genes, and the genes ranked between the W' position and the Z position are defined as stable genes; wherein W' and Z are both integers greater than 1; Preset the crossover ratios of dominant genes, stable genes and inferior genes, the crossover ratios are R1, R2 and R3, wherein R1+R2+R3=1, R1>R2>R3, and perform the crossover operation in an alternating manner of single-point crossover and multi-point crossover; The mutation operation is: Select individuals from different strata, ensure that the selected individuals contain dominant genes, stable genes, and inferior genes, and select at least one individual from each stratum; For individuals containing dominant genes, the mutation rate of P1 is used, for individuals containing stable genes, the mutation rate of P2 is used, and for individuals containing disadvantageous genes, the mutation rate of P3 is used, among which P1+P2+P3=1, P3>P2>P1. The new individuals generated after the crossover operation and mutation operation are evaluated for fitness, and whether to retain the new individuals in the next generation population is selected based on the fitness evaluation.

9. The structural optimization method of container-converted mobile laboratory based on finite element analysis according to claim 1 is characterized in that: The preset target performance threshold is used to determine whether each performance value of the optimal hole opening strategy reaches the target performance threshold. If so, the optimal hole opening strategy is used as the final optimization result. If not, the optimal hole opening strategy is optimized until it reaches the target performance threshold, including: Preset multiple target performance thresholds, and compare each performance value of the optimal hole opening strategy with the corresponding target performance threshold; If all of them are achieved, the optimal hole opening strategy is taken as the final optimization result; If not, the performance value that does not reach the target performance threshold is optimized.

10. The structural optimization method of container-converted mobile laboratory based on finite element analysis according to claim 9 is characterized in that: The step of optimizing the optimal hole opening strategy until it reaches the target performance threshold comprises: Obtaining a difference between a performance value that does not reach a target performance threshold and a target performance threshold; Obtaining the optimization direction corresponding to the performance value, obtaining the relationship between the optimization direction and the performance value according to the linear model or the nonlinear model, and constructing a tolerance compensation model based on the relationship between the optimization direction and the performance value; The performance value is optimized through the bearing compensation model, and a new optimal hole opening strategy is generated after the optimization is completed; Conduct finite element analysis on the new optimal hole opening strategy to determine whether the performance values ​​of the new optimal hole opening strategy reach the target performance threshold. If so, use it as the final optimization result. Among them, the method of obtaining the optimization direction corresponding to the performance value is: each performance value corresponds to h optimization directions, different weights are preset for the capital consumption, time consumption, and adjustment difficulty of each optimization direction, and the score of each optimization direction is obtained by weighted average, and the optimization direction corresponding to the highest score is used as the optimization direction corresponding to the performance value.

Images