Component parameter determination method and device, computer equipment and storage medium

Abstract

The invention relates to a component parameter determination method and device, computer equipment and a storage medium. The method comprises the following steps: acquiring at least one component preparation parameter of a to-be-prepared component, and simulating a quality evaluation index and a corrosion performance index when the to-be-prepared component is used under each component preparation parameter; according to the quality evaluation index and the corrosion performance index, target preparation parameters of the to-be-prepared component are determined; acquiring at least one member size parameter allowed to be used when the member preparation parameters are adopted to prepare the to-be-prepared member, and simulating bearing performance indexes and anti-seismic performance indexes corresponding to the to-be-prepared members using different member size parameters under different temperature conditions; and selecting a target size parameter from the at least one component size parameter according to each bearing performance index and each anti-seismic performance index. By adopting the method, a globally optimal solution can be efficiently explored and found in a wide parameter space by using a relatively low calculation amount, and the safety and reliability of the structure are ensured.

Description

Technical Field

[0001] This application relates to the field of computer technology, and particularly to a method and device for determining component parameters, a computer device, and a storage medium. Background Art

[0002] In the context of the continuous growth of global energy demand, the construction and optimization of power transmission networks are becoming increasingly important, especially in coastal areas. Due to the concentration of economic activities and special geographical and climatic conditions in these areas, higher requirements are imposed on the performance and durability of power engineering structures. The characteristics of the coastal climate environment are high humidity, high salinity, and strong sea winds. Under the combined action of these factors, the reinforced concrete in traditional power engineering structures is prone to corrosion and damage, thereby affecting the stability and service life of the structure.

[0003] The corrosion problem is one of the main challenges faced in coastal structural engineering. When the steel bars in reinforced concrete come into contact with salt-containing water vapor, they are prone to electrochemical reactions and form rust, which not only reduces the strength of the steel bars but also increases the risk of cracks in the concrete, further leading to a decline in the overall durability of the structure. Traditional solutions such as coating protection and outsourcing anti-corrosion materials have certain effects, but most of them are costly and complex to maintain, and it is difficult to meet the requirements of long-term use in actual projects. At the same time, with the depletion of river sand resources, using seawater and sea sand to make concrete has become an effective way, which not only alleviates the shortage of river sand resources but also helps the construction of large-scale infrastructure in coastal areas. However, seawater and sea sand concrete face challenges in terms of steel bar corrosion and long-term durability. In particular, the corrosion of steel bars by chloride ions seriously affects the service life of concrete. As a new type of composite material, fiber steel bars improve their performance by wrapping a layer of fiber material outside the traditional steel bars. This layer of fiber forms an effective protection barrier for the inner steel bars, significantly enhancing their ability to resist chloride ion corrosion. In this way, fiber steel bars not only increase the tensile strength and toughness of the steel bars but also significantly enhance their corrosion resistance. This makes the combination of fiber steel bars and seawater and sea sand concrete have a very broad application prospect in the coastal environment. Research shows that the fiber coating of steel bars can effectively block chloride ions in seawater, thereby reducing the rust of steel bars and extending the service life of the structure.

[0004] In traditional technologies, a frame structure topology model with the goal of minimizing the structural modal compliance under seismic loads and meeting the specified volume requirements is usually constructed by considering the influence of the quality of structural branches and distributed inertial forces. At the same time, variable constraint limit measures and the moving asymptote optimization algorithm are combined to solve the topology optimization of frame structures with high seismic performance requirements.

[0005] Although this method can meet the requirements of high seismic performance, it also increases the computational complexity. Especially in the case of complex structures and large-scale design domains, the computational burden during the iterative process is relatively heavy. Therefore, there is a problem of heavy computational burden. Summary of the Invention

[0006] Based on this, in view of the above technical problems, it is necessary to provide a method, device, computer device, and storage medium for determining component parameters that can reduce the computational burden.

[0007] In a first aspect, the present application provides a method for determining component parameters, including:

[0008] Obtain at least one component preparation parameter of the component to be prepared, as well as the quality evaluation index and corrosion performance index when simulating the use of the component to be prepared under each component preparation parameter;

[0009] Determine the target preparation parameter of the component to be prepared according to the quality evaluation index and the corrosion performance index;

[0010] Obtain at least one component size parameter allowed when preparing the component to be prepared using the component preparation parameter, as well as the bearing performance index and seismic performance index corresponding to the component to be prepared when simulating the use of different component size parameters under different temperature conditions;

[0011] Select the target size parameter from at least one component size parameter according to each bearing performance index and each seismic performance index.

[0012] In one embodiment, determining the target preparation parameter of the component to be prepared according to the quality evaluation index and the corrosion performance index includes:

[0013] According to the first fitness of the component to be prepared under each component preparation parameter, select a preset number of component preparation parameters to construct a component preparation parameter set; wherein, the first fitness is the weighted sum value between the quality evaluation index, the corrosion performance index of the component to be prepared under the component preparation parameter, and the corresponding preset index weight;

[0014] For each iteration process, select the component preparation parameter corresponding to the maximum first fitness from the component preparation parameter set as the reference preparation parameter;

[0015] Perform crossover and mutation processing on the reference preparation parameter to obtain a mutated preparation parameter;

[0016] Add the mutated preparation parameter to the component preparation parameter set to update the component preparation parameter set;

[0017] Take the component preparation parameter corresponding to the maximum first fitness in the component preparation parameter set under the last iteration process as the target preparation parameter.

[0018] In one embodiment, obtaining the quality evaluation index and corrosion performance index when simulating the use of the component to be prepared under each component preparation parameter includes:

[0019] Input each component preparation parameter into the index prediction model to obtain the quality evaluation index and corrosion performance index of the component to be prepared under the corresponding component preparation parameter;

[0020] Among them, the index prediction model is trained based on the sample preparation parameter and the quality evaluation index and corrosion performance index corresponding to the sample component under the sample preparation parameter; the quality evaluation index of the sample component under the sample preparation parameter is the quality evaluation index obtained by conducting a drawing experiment and a shear experiment on the sample component under the sample preparation parameter in the laboratory; the corrosion performance index of the sample component under the sample preparation parameter is the corrosion performance index obtained by placing the sample component under the sample preparation parameter in a marine atmospheric environment for corrosion testing.

[0021] In one embodiment, selecting the target dimension parameter from at least one component dimension parameter according to each load-bearing performance index and each seismic performance index includes:

[0022] For each component to be prepared under each component dimension parameter in each temperature environment, take the weighted sum value between the load-bearing performance index and seismic performance index of the component to be prepared and the corresponding preset performance weight as the second fitness of the component to be prepared;

[0023] For each temperature environment, select the component dimension parameter corresponding to the maximum second fitness as the reference dimension parameter, and construct a reference dimension parameter set according to each reference dimension parameter;

[0024] Select two reference dimension parameters in the temperature environments of adjacent temperatures from the reference dimension parameter set in turn, and determine whether to update the reference dimension parameter set according to the magnitude relationship between the second fitness values corresponding to the two reference dimension parameters;

[0025] Select the component dimension parameter corresponding to the maximum second fitness from the updated reference dimension parameter set as the target dimension parameter.

[0026] In one embodiment, determining whether to update the reference dimension parameter set according to the magnitude relationship between the second fitness values corresponding to the two reference dimension parameters includes:

[0027] Take the reference dimension parameter corresponding to the temperature environment with a higher temperature as the first dimension parameter, and take the reference dimension parameter corresponding to the temperature environment with a lower temperature as the second dimension parameter;

[0028] When the first size parameter is greater than the second size parameter, determine the retention probability of the second size parameter according to the quotient of the fitness difference and the temperature value of the temperature environment corresponding to the first size parameter; the fitness difference is the difference between the second fitness corresponding to the first size parameter and the second fitness corresponding to the second size parameter.

[0029] When the retention probability is lower than the preset probability threshold, delete the second size parameter to update the reference size parameter set.

[0030] In one embodiment, obtaining the bearing performance index and the seismic performance index corresponding to the to-be-prepared component when simulating the use of different component size parameters under different temperature conditions includes:

[0031] Input the component size parameters in different temperature environments into the performance prediction model to obtain the bearing performance index and the seismic performance index of the to-be-prepared component under the component size parameters in the corresponding temperature environment.

[0032] Among them, the performance prediction model is trained based on the sample size parameters corresponding to different temperature environments and the bearing performance index and the seismic performance index corresponding to the sample components under the sample size parameters in the corresponding temperature environment; the bearing performance index of the sample components under the sample size parameters in different temperature environments is the bearing performance index obtained by conducting a bearing test on the sample components under the sample size parameters in the corresponding temperature environment; the seismic performance index of the sample components under the sample size parameters in different temperature environments is the seismic performance index obtained by conducting a seismic test on the sample components under the sample size parameters in the corresponding temperature environment.

[0033] In a second aspect, the present application also provides a component parameter determination device, including:

[0034] A first parameter acquisition module, configured to acquire at least one component preparation parameter of the to-be-prepared component, and the quality evaluation index and the corrosion performance index when simulating the use of the to-be-prepared component under each component preparation parameter.

[0035] A first target determination module, configured to determine the target preparation parameter of the to-be-prepared component according to the quality evaluation index and the corrosion performance index.

[0036] A second parameter acquisition module, configured to acquire at least one component size parameter allowed to be used when preparing the to-be-prepared component using the component preparation parameter, and the bearing performance index and the seismic performance index corresponding to the to-be-prepared component when simulating the use of different component size parameters under different temperature conditions.

[0037] A second target determination module, configured to select the target size parameter from at least one component size parameter according to each bearing performance index and each seismic performance index.

[0038] In a third aspect, the present application further provides a computer device, including a memory and a processor, where the memory stores a computer program, and when the processor executes the computer program, the following steps are implemented:

[0039] Obtain at least one component preparation parameter of the component to be prepared, as well as the quality evaluation index and corrosion performance index when the component to be prepared is simulated for use under each component preparation parameter;

[0040] Determine the target preparation parameter of the component to be prepared according to the quality evaluation index and the corrosion performance index;

[0041] Obtain at least one component size parameter that is allowed to be used when preparing the component to be prepared with the component preparation parameter, as well as the load-bearing performance index and seismic performance index corresponding to the component to be prepared with different component size parameters simulated for use under different temperature conditions;

[0042] Select the target size parameter from at least one component size parameter according to each load-bearing performance index and each seismic performance index.

[0043] In a fourth aspect, the present application further provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the following steps are implemented:

[0044] Obtain at least one component preparation parameter of the component to be prepared, as well as the quality evaluation index and corrosion performance index when the component to be prepared is simulated for use under each component preparation parameter;

[0045] Determine the target preparation parameter of the component to be prepared according to the quality evaluation index and the corrosion performance index;

[0046] Obtain at least one component size parameter that is allowed to be used when preparing the component to be prepared with the component preparation parameter, as well as the load-bearing performance index and seismic performance index corresponding to the component to be prepared with different component size parameters simulated for use under different temperature conditions;

[0047] Select the target size parameter from at least one component size parameter according to each load-bearing performance index and each seismic performance index.

[0048] In a fifth aspect, the present application further provides a computer program product, including a computer program, and when the computer program is executed by a processor, the following steps are implemented:

[0049] Obtain at least one component preparation parameter of the component to be prepared, as well as the quality evaluation index and corrosion performance index when the component to be prepared is simulated for use under each component preparation parameter;

[0050] Determine the target preparation parameter of the component to be prepared according to the quality evaluation index and the corrosion performance index;

[0051] Obtain at least one component size parameter that is allowed to be used when preparing a component to be prepared using component preparation parameters, and the bearing performance index and seismic performance index corresponding to the component to be prepared when simulating the use of different component size parameters under different temperature conditions;

[0052] Select a target size parameter from at least one component size parameter according to each bearing performance index and each seismic performance index.

[0053] The above-mentioned component parameter determination method, device, computer device and storage medium obtain at least one component preparation parameter of the component to be prepared, as well as the quality evaluation index and corrosion performance index when simulating the use of the component to be prepared under each component preparation parameter; determine the target preparation parameter of the component to be prepared according to the quality evaluation index and the corrosion performance index; obtain at least one component size parameter that is allowed to be used when preparing the component to be prepared using the component preparation parameter, and the bearing performance index and seismic performance index corresponding to the component to be prepared when simulating the use of different component size parameters under different temperature conditions; select a target size parameter from at least one component size parameter according to each bearing performance index and each seismic performance index. This application uses the quality evaluation index, corrosion performance index, bearing performance index and seismic performance index as evaluation criteria, effectively improving the various performances of the structure in the marine atmospheric environment, extending the service life of the structure, and at the same time capturing the complex relationship between the performance of the preparation material and the size parameter, being able to use a relatively low amount of calculation to efficiently explore and find the global optimal solution in a wide parameter space, ensuring the safety and reliability of the structure. Brief Description of the Drawings

[0054] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the following will briefly introduce the drawings required for use in the description of the embodiments or related technologies. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained according to these drawings.

[0055] Figure 1 It is an application environment diagram of a component parameter determination method provided in this embodiment;

[0056] Figure 2 It is a flowchart of the first component parameter determination method provided in this embodiment;

[0057] Figure 3 It is a schematic diagram of a load-slip curve provided in this embodiment;

[0058] Figure 4 It is a schematic diagram of a hysteresis curve provided in this embodiment;

[0059] Figure 5 Schematic diagram of a stiffness degradation curve provided in this embodiment;

[0060] Figure 6 Schematic diagram of a failure mode provided in this embodiment;

[0061] Figure 7 Schematic diagram of a skeleton curve provided in this embodiment;

[0062] Figure 8 Structural block diagram of a component parameter determination device provided in this embodiment;

[0063] Figure 9 Internal structure diagram of a computer device provided in this embodiment. Detailed implementation manners

[0064] In order to make the objectives, technical solutions, and advantages of this application clearer, the following further describes this application in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not used to limit this application.

[0065] The component parameter determination method provided in the embodiments of this application can be applied to an application environment as Figure 1 shown. Among them, the terminal 102 communicates with the server 104 through a network. The data storage system can store the data that the server 104 needs to process. The data storage system can be integrated on the server 104, or can be placed in the cloud or other network servers. The computer device obtains at least one component preparation parameter of the component to be prepared, as well as the quality evaluation index and corrosion performance index when simulating the use of the component to be prepared under each component preparation parameter; according to the quality evaluation index and corrosion performance index, determines the target preparation parameter of the component to be prepared; obtains at least one component size parameter that is allowed to be used when preparing the component to be prepared using the component preparation parameter, as well as the bearing performance index and seismic performance index corresponding to the component to be prepared when simulating the use of different component size parameters under different temperature conditions; according to each bearing performance index and each seismic performance index, selects the target size parameter from at least one component size parameter. Among them, the computer device can be either a terminal or a server. The terminal 102 can be, but is not limited to, various personal computers, laptop computers, smart phones, tablet computers, Internet of Things devices, and portable wearable devices. The Internet of Things devices can be smart speakers, smart TVs, smart air conditioners, smart vehicle-mounted devices, etc. The portable wearable devices can be smart watches, smart bracelets, head-mounted devices, etc. The server 104 can be implemented by an independent server or a server cluster composed of multiple servers.

[0066] In an exemplary embodiment, as Figure 2As shown, a method for determining component parameters is provided. Taking the computer device applied in Figure 1 as an example for illustration, it includes the following steps 201 to 204. Among them:

[0067] Step 201: Obtain at least one component preparation parameter of the component to be prepared, as well as the quality evaluation index and corrosion performance index when the component to be prepared is simulated for use under each component preparation parameter.

[0068] Specifically, the diameter value range of the fiber reinforced bar is 9 mm - 15 mm, the outer layer fiber content value range is 0.3% - 1.7%, the concrete mix ratio of the seawater sea sand concrete includes water-cement ratio, cement dosage, and sea sand ratio. The value range of the water-cement ratio is 0.3 - 0.6, the value range of the cement dosage is 300 - 400 kg per cubic meter, the sea sand ratio is the weight ratio of the sea sand usage in the total aggregate, and its value range is 18% - 45%.

[0069] It should be noted that these parameters directly affect the strength, durability, and crack resistance of the concrete, thus determining the overall bearing capacity and service life of the structure. For example, the diameter and outer layer fiber content of the fiber reinforced bar affect the tensile and shear strength of the concrete, while the water-cement ratio is crucial for the workability and strength development of the concrete. Appropriate cement dosage not only affects the strength of the concrete but also relates to its impermeability and durability; while the reasonable selection of the sea sand ratio helps to optimize the salt corrosion resistance of the concrete, especially when used in marine environments. Therefore, reasonably setting these value ranges can not only improve the performance of the concrete but also ensure its safety and economy in practical applications, providing an important basis for relevant engineering design and construction.

[0070] Step 202: Determine the target preparation parameters of the component to be prepared according to the quality evaluation index and corrosion performance index.

[0071] Specifically, obtain the maximum slip amount, maximum displacement amount, shear strength, and bond strength of the component to be prepared under each component preparation parameter, and generate a quality evaluation index for evaluating the reliability of the sample structure.

[0072] It should be noted that data such as the maximum bond force, shear force, maximum displacement amount, and maximum slip amount are key indicators for measuring the reliability of the sample structure. By calculating the bond strength and shear strength and performing normalization processing, it can ensure that each variable is compared under the same dimension, thus avoiding the unbalanced influence caused by different units and orders of magnitude. The finally generated quality evaluation index provides a quick identification and evaluation of the safety and reliability of the structure to ensure its durability and effectiveness in practical applications.

[0073] Step 203: Obtain at least one component size parameter that is allowed to be used when preparing a component to be prepared using component preparation parameters, and the corresponding bearing performance index and seismic performance index of the component to be prepared when simulating the use of different component size parameters under different temperature conditions.

[0074] Specifically, obtain at least one component size parameter that is allowed to be used when preparing a component to be prepared using component preparation parameters, and the corresponding bearing performance index and seismic performance index of the component to be prepared when simulating the use of different component size parameters under different temperature conditions. The advantage of this setting is that by systematically evaluating the bearing capacity and seismic capacity of concrete beam components, the safety and reliability of the structure during actual use can be ensured. Moreover, through the calculation of the bearing performance index and seismic performance index, quantitative performance indicators are provided for engineers, enabling them to intuitively understand the performance of components under extreme loads and seismic actions. This not only helps optimize the design and material selection, improving the overall performance of the structure, but also provides a scientific basis for subsequent maintenance and safety assessment.

[0075] Step 204: Select a target size parameter from at least one component size parameter according to each bearing performance index and each seismic performance index.

[0076] For the above component parameter determination method, obtain at least one component preparation parameter of the component to be prepared, and the quality evaluation index and corrosion performance index when simulating the use of the component to be prepared under each component preparation parameter; determine the target preparation parameter of the component to be prepared according to the quality evaluation index and corrosion performance index; obtain at least one component size parameter that is allowed to be used when preparing a component to be prepared using component preparation parameters, and the corresponding bearing performance index and seismic performance index of the component to be prepared when simulating the use of different component size parameters under different temperature conditions; select a target size parameter from at least one component size parameter according to each bearing performance index and each seismic performance index. This application uses the quality evaluation index, corrosion performance index, bearing performance index, and seismic performance index as evaluation criteria, effectively improving various performances of the structure in the marine atmospheric environment, extending the service life of the structure, while capturing the complex relationship between the performance of the preparation material and the size parameter, being able to use a relatively low amount of calculation, efficiently explore and find the global optimal solution in a wide parameter space, and ensuring the safety and reliability of the structure.

[0077] In one embodiment, obtaining the quality evaluation index and corrosion performance index when simulating the use of the component to be prepared under each component preparation parameter includes: inputting each component preparation parameter into the index prediction model to obtain the quality evaluation index and corrosion performance index of the component to be prepared under the corresponding component preparation parameter; wherein, the index prediction model is trained based on the sample preparation parameter and the quality evaluation index and corrosion performance index corresponding to the sample component under the sample preparation parameter; the quality evaluation index of the sample component under the sample preparation parameter is the quality evaluation index obtained by performing a pull-out test and a shear test on the sample component under the sample preparation parameter in the laboratory; the corrosion performance index of the sample component under the sample preparation parameter is the corrosion performance index obtained by placing the sample component under the sample preparation parameter in a marine atmospheric environment for a corrosion test.

[0078] Specifically, for the pull-out test, fix the sample component in the pull-out testing machine, ensure the connection between the end of the fiber reinforced bar and the testing machine, apply a tensile force at a constant speed of 1 mm / min until the bond failure occurs between the fiber reinforced bar and the concrete, and record the maximum bond force P. max And the maximum slip δ. max , calculate the bond strength through the following formula (1-1):

[0079] (1-1)

[0080] Where τ l represents the bond strength, P max is the maximum bond force, and A is the effective area of contact between the fiber reinforced bar and the concrete.

[0081] Specifically, for the shear test, fix the sample component in the shear testing machine to evaluate the bond and shear performance between the fiber reinforced bar and the concrete. During the test, apply an increasing shear force until the bond failure or slip occurs between the fiber reinforced bar and the concrete, and record the maximum shear force V max and the maximum displacement W max during the test, calculate the shear strength through the following formula (1-2):

[0082] (1-2)

[0083] Where τ S represents the shear strength, V max is the maximum shear force, and S is the shear contact area;

[0084] Specifically, based on the maximum-minimum normalization method, data normalization is performed on the bond strength, shear strength, and the recorded maximum displacement and maximum slip of each sample. Using the normalized bond strength, shear strength, maximum slip, and maximum displacement, the quality evaluation index is generated through the following formulas (1-3):

[0085] (1-3)

[0086] where I represents the quality evaluation index, τ l ’, τ S ’, δ max ’, W max are the normalized bond strength, shear strength, maximum slip, and maximum displacement; in the above formula for calculating the quality evaluation index, the purpose of normalization is to convert each variable to the same dimension for convenient multiplication operation. Each variable may have different units and orders of magnitude, and direct multiplication may cause the influence of some variables on the result to be masked or exaggerated. Through normalization, all values are adjusted to a unified range, usually between [0,1], so that these indicators can fairly reflect in the final quality evaluation index. Moreover, the better the performance of the sample in terms of tensile pullout and shear resistance, the greater the bond strength and shear strength, and the larger the maximum slip and maximum displacement. The larger the quality evaluation index, the better the performance of the sample component in terms of anti-tensile shear.

[0087] Specifically, a corrosion test is carried out on the sample component, including: placing the sample component in a marine environment to ensure exposure to marine atmosphere, monitoring the actual environmental conditions, including temperature and humidity, and maintaining the environmental temperature range at 20-35°C and the humidity range at 80%-95% through humidity and temperature control equipment, and keeping the wind speed at 2-6 m / s; to accelerate the corrosion process, a salt spray environment simulating seawater is adopted by salt spray spraying, and regular spraying is carried out through a salt spray spraying device, with the salt spray concentration set as , the spraying frequency is set to once every 12 h, and the sample component is exposed to the salt spray environment for accelerated corrosion for 6 months; the electrochemical impedance spectroscopy test method is used to monitor the corrosion process, obtain the corrosion current density and the exposed area of the fiber reinforced bar, and calculate the corrosion rate using the following formula (1-4):

[0088] (1-4)

[0089] where v(t) represents the corrosion rate at time t during the corrosion test, t is the corrosion time variable, B is the exposed area of the fiber reinforced bar, I corr (t) is the corrosion current density at time t, and K is a constant related to the fiber reinforced bar material, with a value of 0.03.

[0090] Based on the corrosion rate obtained from electrochemical analysis, the corrosion depth of the fiber - reinforced steel bars is calculated using the following formulas (1 - 5):

[0091] (1 - 5)

[0092] Where d is the corrosion depth of the fiber - reinforced steel bars at the end of the corrosion test, and T is the duration of the corrosion test;

[0093] Based on the corrosion depth and the duration of the corrosion test, the average corrosion rate is calculated using the following formula (1 - 6), and the calculation formula is as follows:

[0094] (1 - 6)

[0095] Where v is the average corrosion rate during the corrosion test;

[0096] According to the calculated average corrosion rate and corrosion depth, the corrosion performance index is generated using the following formula (1 - 7):

[0097] (1 - 7)

[0098] Where CPI is the corrosion performance index; in the above formula for generating the corrosion performance index, it reflects the corrosion resistance of the material: the greater the corrosion depth and rate, the more serious the deterioration of the material, and the smaller the CPI value, thus effectively demonstrating the decline in corrosion resistance ability.

[0099] In this embodiment, by placing the sample components in a simulated marine environment and using methods such as salt - spray to accelerate corrosion, the durability and corrosion resistance of the steel bars under extreme conditions can be truly reflected. Monitoring the corrosion rate and corrosion depth can not only accurately evaluate the degree of material deterioration but also predict its service life and safety in actual applications. The corrosion performance index obtained through calculation provides a quantitative indicator, enabling engineers to effectively compare the corrosion resistance of different samples, and then optimize material selection and structural design. This process is crucial for ensuring the long - term stability and safety of structures in marine environments, helping to prevent potential structural failures, thereby reducing maintenance costs and extending service life, and ensuring the economy and sustainability of engineering projects.

[0100] In one embodiment, the target preparation parameters of the component to be prepared are determined according to the quality evaluation index and the corrosion performance index, including: selecting a preset number of component preparation parameters according to the first fitness of the component to be prepared under each component preparation parameter to construct a component preparation parameter set; wherein, the first fitness is the weighted sum value between the quality evaluation index, the corrosion performance index of the component to be prepared under the component preparation parameter and the corresponding preset index weight; for each iteration process, selecting the component preparation parameter corresponding to the maximum first fitness from the component preparation parameter set as the reference preparation parameter; performing crossover mutation processing on the reference preparation parameter to obtain a mutated preparation parameter; adding the mutated preparation parameter to the component preparation parameter set to update the component preparation parameter set; taking the component preparation parameter corresponding to the maximum first fitness in the component preparation parameter set in the last iteration process as the target preparation parameter.

[0101] Specifically, a multi-layer perceptron is selected as the network to construct a first-level index prediction model. The sample preparation parameter combination is used as the input, and the quality evaluation index and the corrosion performance index are used as the output labels. The output layer in the model receives the sample data of the preparation parameter combination, takes these parameters as the input features of the model, and inputs them into the hidden layer. The hidden layer performs a non-linear transformation on the input features and combines and extracts the feature parameters and sends them to the output layer. The output layer contains two neurons, corresponding to the quality evaluation index and the corrosion performance index respectively. The mean square error is selected as the loss function to quantitatively analyze the deviation between the predicted index and the true value. When the root mean square error is not higher than 0.05, it indicates that the model has converged, and it is considered that the training of the first-level index prediction model is completed; according to the quality evaluation index and the corrosion performance index, the first fitness value is calculated through the following formula (1-8):

[0102] (1-8)

[0103] Among them, S1 is the first fitness value, μ1 and μ2 are the weight ratios corresponding to the quality evaluation index and the corrosion performance index respectively, I is the quality evaluation index, CPI is the corrosion performance index, and μ2 > μ1 > 0. In the formula for calculating the first fitness value above, the weight ratio is set as μ2 > μ1 > 0 because the corrosion resistance of the material is crucial for its long-term use performance. Especially in harsh environments, corrosion may cause serious damage and performance degradation. Therefore, a higher weight μ2 is given to the corrosion performance index. However, the basic quality standard of the material is still an important part of the evaluation and still has a certain impact on the first fitness value. So, keeping μ1 positive is to ensure that the quality evaluation index I still plays a role in the calculation of the fitness value, thus avoiding overemphasis on one aspect and neglecting the overall performance. S1 is directly proportional to I and CPI. The larger the quality evaluation index I, the more stable the component structure and the stronger the anti-yielding ability. The larger the corrosion performance index CPI, the stronger the corrosion resistance of the component and the more effectively it can resist the erosion of seawater and sea breeze. Therefore, the larger the first fitness value, the more adaptable the specimen is to various extreme environments.

[0104] Specifically, an initial population is randomly generated based on the value range of the preparation parameters. Each individual represents a combination of preparation parameters. Calculate the first fitness value of each individual, use the trained first-level index prediction model to calculate the corresponding quality evaluation index and corrosion performance index, and calculate the first fitness value corresponding to this individual based on the quality evaluation index and the corrosion performance index. Sort the individuals in the population from largest to smallest according to the first fitness value, select the individuals at the forefront of the ranking as the parent generation, and generate a new generation of individuals based on the crossover and mutation operations of the genetic algorithm. Combine the new generation of individuals and the parent generation individuals to form a new population, and repeat the process of calculating the first fitness value, selection, crossover, and mutation until the predetermined number of iterations is reached. Select the individual corresponding to the largest first fitness value as the optimal preparation parameter combination.

[0105] This embodiment utilizes the non-linear transformation ability of the neural network to accurately capture the relationship between complex preparation parameters and performance indicators, thereby improving the prediction accuracy of the model. Secondly, the genetic algorithm is introduced. Through the calculation and selection of fitness values, it can effectively explore the parameter space, optimize the preparation conditions, and ensure finding the best combination of performance manifestations. In addition, the setting of weights further enhances the personalized adaptation of the model to different performance indicators, making the finally selected optimal preparation parameter combination meet the quality requirements.

[0106] In one embodiment, obtaining the bearing performance index and seismic performance index corresponding to a to-be-prepared component that simulates the use of different component size parameters under different temperature conditions includes: inputting the component size parameters in different temperature environments into a performance prediction model to obtain the bearing performance index and seismic performance index of the to-be-prepared component under the component size parameters in the corresponding temperature environment. Among them, the performance prediction model is trained based on the sample size parameters corresponding to different temperature environments and the bearing performance index and seismic performance index of the sample components under the sample size parameters in the corresponding temperature environments; the bearing performance index of the sample components under the sample size parameters in different temperature environments is the bearing performance index obtained by conducting a bearing test on the sample components under the sample size parameters in the corresponding temperature environment; the seismic performance index of the sample components under the sample size parameters in different temperature environments is the seismic performance index obtained by conducting a seismic test on the sample components under the sample size parameters in the corresponding temperature environment.

[0107] Specifically, the size parameters include the length, width, and height in a concrete beam component. Based on the optimal preparation parameter combination, concrete beam components with different size parameter combinations are prepared. A bearing test and a seismic test are conducted on the concrete beam components. In the bearing test, a gradually increasing load is applied to the beam component until the critical point of the maximum bearing load Z max is reached, and the maximum displacement L of the beam is recorded simultaneously. max In the seismic test, a simulated seismic wave is applied to the model by a shaking table to observe its dynamic response. By applying a gradually increasing static load, the deformation and failure mode of the structure are observed until the yield limit of the beam is reached, and the maximum allowable displacement of the beam is recorded; according to the recorded maximum bearing load Z max of the beam and the maximum displacement L max of the beam, and setting the allowable bearing load Z design of the beam and the allowable displacement L design of the beam, the bearing performance index and the seismic performance index are calculated and generated through the following formula (1-9):

[0108] (1-9)

[0109] where LCI is the bearing performance index, SPI is the seismic performance index, Z max , L max are respectively the maximum bearing load and the maximum displacement of the beam recorded, and Z design , L design are respectively the allowable bearing load and the maximum allowable displacement of the beam.

[0110] In one embodiment, selecting target dimension parameters from at least one component dimension parameter according to each load-bearing performance index and each seismic performance index includes: for each component to be prepared under each component dimension parameter in each temperature environment, taking the weighted sum value between the load-bearing performance index and the seismic performance index of the component to be prepared and the corresponding preset performance weight as the second fitness of the component to be prepared; for each temperature environment, selecting the component dimension parameter corresponding to the maximum second fitness as the reference dimension parameter, and constructing a reference dimension parameter set according to each reference dimension parameter; sequentially selecting two reference dimension parameters in the temperature environments of adjacent temperatures from the reference dimension parameter set, and determining whether to update the reference dimension parameter set according to the magnitude relationship between the second fitness values corresponding to the two reference dimension parameters; selecting the component dimension parameter corresponding to the maximum second fitness from the updated reference dimension parameter set as the target dimension parameter.

[0111] In one embodiment, determining whether to update the reference dimension parameter set according to the magnitude relationship between the second fitness values corresponding to the two reference dimension parameters includes: taking the reference dimension parameter corresponding to the temperature environment with a higher temperature as the first dimension parameter, and taking the reference dimension parameter corresponding to the temperature environment with a lower temperature as the second dimension parameter; in the case where the first dimension parameter is greater than the second dimension parameter, determining the retention probability of the second dimension parameter according to the quotient value between the fitness difference and the temperature value of the temperature environment corresponding to the first dimension parameter; the fitness difference is the difference between the second fitness corresponding to the first dimension parameter and the second fitness corresponding to the second dimension parameter; in the case where the retention probability is lower than the preset probability threshold, deleting the second dimension parameter to update the reference dimension parameter set.

[0112] It should be noted that an advanced convolutional neural network is used to accurately predict the load-bearing performance index and the seismic performance index of concrete beam components, so as to effectively optimize the combination of dimension parameters. By taking the dimension parameters as input features, the model can automatically extract key features and provide higher prediction accuracy. Combining the selection, crossover and mutation mechanisms of the genetic algorithm can quickly find the dimension combination with the optimal performance in a large-scale parameter space, ensuring the safety and stability of the structure.

[0113] Therefore, a secondary index prediction model needs to be constructed and the second fitness value needs to be calculated. The method is as follows:

[0114] The convolutional neural network is selected as the network structure of the secondary exponential prediction model. The combined size parameters of the samples are used as input features, while the bearing performance index and the seismic performance index are used as output labels. The model structure includes an input layer for receiving the combined data of the size parameters, followed by multiple convolutional layers and pooling layers to capture the key features of the input data through feature extraction. Finally, the fully connected layer maps the extracted features to the output layer, with two neurons output, corresponding to the bearing performance index and the seismic performance index respectively. During the training process, the mean squared error is selected as the loss function, and the model ends training when the set maximum number of iterations is reached; According to the calculated bearing performance index and bearing performance index, the second fitness value is calculated through the following formula (1-10), and the formula is as follows:

[0115] (1-10)

[0116] Among them, S2 represents the second fitness value, ω1 and ω2 respectively represent the weight ratios of the corresponding indices, and ω1>ω2>0; In the above formula for calculating the second fitness value, the weight ratio is set to ω1>ω2>0 because in structural engineering, the bearing capacity is the basic requirement to ensure the safety of the structure. The concrete beam members must be able to withstand the design load without failure. Therefore, the bearing performance index should be given a higher weight in the overall performance evaluation to reflect its importance in ensuring the structural safety; The seismic capacity is also crucial, especially in earthquake-prone areas, but its main role is to ensure the safety of the structure under extreme conditions, usually evaluated on the premise that the bearing capacity is sufficient. Therefore, it is reasonable to give a relatively lower weight to the seismic performance index to avoid overemphasizing the seismic performance when the bearing capacity is insufficient; S2 is in a proportional relationship with LCI, which means that if the bearing capacity index and the seismic performance index increase, the second fitness value will also increase. Improving the bearing capacity directly enhances the overall fitness value, which reflects the improvement of the structure in terms of safety.

[0117] Specifically, set the initial temperature and the cooling rate to control the temperature change during the iteration process. Take the original dimensional parameter combination as the initial parameter combination, gradually reduce the temperature, generate a new current parameter combination at each temperature, calculate the corresponding second fitness value at present, and then evaluate the superiority and inferiority of the new combination relative to the current combination according to the acceptance criterion. Decide whether to accept the new parameter combination according to the acceptance criterion. Specifically, if the new fitness value is better than the current fitness value, directly accept the new combination. If the new fitness value is worse, the new combination can be accepted according to the probability determined by the following formula (1-11), so as to avoid falling into a local optimal solution. Set the temperature threshold. After one evaluation is completed, gradually reduce the initial temperature. When the current temperature is less than the temperature threshold, stop the iteration. During the whole process, record the optimal fitness value and the corresponding parameter combination at each step. Finally, select the parameter combination corresponding to the maximum value of the second fitness value as the optimal dimensional parameter combination.

[0118] (1-11)

[0119] Among them, φ is the acceptance probability, T is the current temperature, S2' is the newly calculated second fitness value, and S2 is the current second fitness value.

[0120] Exemplarily, such as Figure 3 As shown in the schematic diagram of the load-slip curve, in the bond-slip curve of the experimental specimen, the relationship between load and slip is revealed. It is revealed that the bond strength of the fiber reinforced seawater sea sand concrete specimen with a diameter of 2-4 mm and a length of 14 mm is the highest and the energy dissipation is the largest, which means that the structure is relatively stable and not easy to collapse under seawater corrosion.

[0121] Exemplarily, such as Figure 4 As shown in the schematic diagram of the hysteresis curve, the relationship between the load and displacement of the component is shown in the hysteresis curve of the experimental specimen during the repeated loading and unloading process. As the loading increases, the displacement of the component gradually increases, and the force does not completely return to the original state during the unloading process, forming a closed loop curve. It can be seen from the figure that the highest point of the hysteresis curve usually represents the maximum load that the component can withstand during the loading process, that is, the maximum load. This point is very important for evaluating the strength and bearing capacity of the component. And the initial linear part of the hysteresis curve will deviate from linearity at a certain point, usually called the yield point, which is the sign of the structure transitioning from the elastic stage to the plastic stage. The deformation before the yield point is elastic deformation, and plastic deformation will occur after the yield, which may lead to structural damage or failure.

[0122] Exemplarily, such as Figure 5The stiffness decay curve diagram shown in the figure mainly reflects the stiffness change of the component in the stiffness decay curve of the experimental component, especially after multiple loadings, as the material or structural damage accumulates, the stiffness of the curve gradually decreases, which is manifested as the load-displacement curve gradually becoming flat. This phenomenon is called stiffness decay. By analyzing the stiffness decay, the durability and damage development of the structure can be evaluated. By analyzing the stiffness decay curve, the influence of different design parameters such as component material, reinforcement ratio, component cross section, etc. on the seismic performance of the component can be evaluated, and how different parameters affect the stiffness decay process and seismic resistance of the structure can be understood.

[0123] For example, Figure 6 As shown in the schematic diagram of the failure mode, in the failure mode diagram of the experimental component, the stress load analysis of the experimental component is carried out based on the failure mechanism. The failure mode diagram intuitively shows the form of structural or component failure. It can be clearly seen in the figure that the connection between the fiber reinforcement and the concrete in the component is subject to a large shear force, so shear failure is prone to occur; at the end of the fiber reinforcement, it is subject to a large tensile force, so tensile failure is prone to occur, resulting in deformation of the reinforcement; different failure modes have different influencing factors and critical conditions. The failure mechanism diagram can help analyze the weaknesses and potential failure modes of the structure under specific loads.

[0124] For example, Figure 7 The skeleton curve diagram shown in the figure describes the relationship between the bearing capacity and displacement of the component during the loading process, so that the bearing capacity of the component under different loads can be clearly determined. The shape of the curve usually shows a gradually increasing force and displacement relationship until the maximum load point, and then begins to decline or presents a platform shape, and finally reaches a state of destruction. The skeleton curve shows the transition of the component from the elastic stage to the plastic stage, and the yield point marks the transition of the component from elastic deformation to plastic deformation. The maximum point of the curve is the ultimate load, which indicates the maximum force that the component can withstand under the load. If this load point is exceeded, the component may be significantly damaged or ruptured, and the skeleton curve can also reveal the deformation capacity of the component, especially the plastic deformation area of ​​the component when it is subjected to a large load. The shape of the curve can be used to judge the ductility of the component after reaching the ultimate load. Structures with good ductility usually show a longer horizontal section or a gentler decline on the skeleton curve, indicating that it can maintain a certain bearing capacity under large deformation.

[0125] The annealing algorithm in this embodiment simulates the physical annealing process, and by gradually reducing the temperature and the probability of accepting a poor solution, it effectively avoids the problem of local optimal solutions, allowing the optimization process to explore in a wider solution space, thereby finding the global optimal size parameter combination. This method not only improves the adaptability and flexibility of the model, but also can achieve an overall improvement in performance in complex multi-objective optimization tasks.

[0126] The above formulas are all dimensionless and take their numerical values for calculation. The formulas are obtained by collecting a large amount of data for software simulation to obtain a formula that is closest to the actual situation. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0127] It should be understood that although the steps in the flowcharts involved in the above-described embodiments are sequentially shown according to the indications of the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless there is a clear description in this article, the execution of these steps has no strict order limit, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-described embodiments may include multiple steps or multiple stages. These steps or stages are not necessarily executed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be executed alternately or alternately with at least a part of other steps or steps or stages in other steps.

[0128] Based on the same inventive concept, the embodiments of the present application further provide a component parameter determination device for implementing the above-mentioned component parameter determination method. The implementation solutions provided by this device to solve problems are similar to the implementation solutions described in the above method. Therefore, the specific limitations in one or more embodiments of the component parameter determination device provided below can refer to the limitations on the component parameter determination method in the above text, and will not be repeated here.

[0129] In an exemplary embodiment, as Figure 8 shown, a component parameter determination device is provided, including: a first parameter acquisition module 10, a first target determination module 11, a second parameter acquisition module 12, and a second target determination module 13, where:

[0130] The first parameter acquisition module 10 is used to acquire at least one component preparation parameter of the component to be prepared, as well as the quality evaluation index and corrosion performance index when the component to be prepared is simulated for use under each component preparation parameter;

[0131] The first target determination module 11 is used to determine the target preparation parameters of the component to be prepared according to the quality evaluation index and the corrosion performance index;

[0132] The second parameter acquisition module 12 is used to acquire at least one component size parameter that is allowed to be used when preparing the component to be prepared using the component preparation parameter, as well as the bearing performance index and seismic performance index corresponding to the component to be prepared when different component size parameters are simulated for use under different temperature conditions;

[0133] The second target determination module 13 is configured to select target dimension parameters from at least one component dimension parameter according to each load-bearing performance index and each seismic performance index.

[0134] In one embodiment, the first target determination module 11 is further configured to select a preset number of component preparation parameters according to the first fitness of the component to be prepared under each component preparation parameter, and construct a component preparation parameter set; wherein, the first fitness is the weighted sum value between the quality evaluation index, the corrosion performance index of the component to be prepared under the component preparation parameter and the corresponding preset index weight; for each iteration process, select the component preparation parameter corresponding to the maximum first fitness from the component preparation parameter set as the reference preparation parameter; perform crossover mutation processing on the reference preparation parameter to obtain a mutated preparation parameter; add the mutated preparation parameter to the component preparation parameter set to update the component preparation parameter set; use the component preparation parameter corresponding to the maximum first fitness in the component preparation parameter set in the last iteration process as the target preparation parameter.

[0135] In one embodiment, the first parameter acquisition module 10 is further configured to input each component preparation parameter into an index prediction model to obtain the quality evaluation index and the corrosion performance index of the component to be prepared under the corresponding component preparation parameter; wherein, the index prediction model is trained based on the sample preparation parameter and the quality evaluation index and the corrosion performance index corresponding to the sample component under the sample preparation parameter; the quality evaluation index of the sample component under the sample preparation parameter is the quality evaluation index obtained by performing a pull-out test and a shear test on the sample component under the sample preparation parameter in the laboratory; the corrosion performance index of the sample component under the sample preparation parameter is the corrosion performance index obtained by placing the sample component under the sample preparation parameter in a marine atmospheric environment for a corrosion test.

[0136] In one embodiment, the second target determination module 13 is further configured to use the weighted sum value between the load-bearing performance index and the seismic performance index of the component to be prepared and the corresponding preset performance weight as the second fitness of the component to be prepared for each component dimension parameter of each temperature environment; for each temperature environment, select the component dimension parameter corresponding to the maximum second fitness as the reference dimension parameter, and construct a reference dimension parameter set according to each reference dimension parameter; sequentially select two reference dimension parameters in the temperature environments of adjacent temperatures from the reference dimension parameter set, and determine whether to update the reference dimension parameter set according to the magnitude relationship between the second fitness corresponding to the two reference dimension parameters; select the component dimension parameter corresponding to the maximum second fitness from the updated reference dimension parameter set as the target dimension parameter.

[0137] In one embodiment, the second target determination module 13 is further configured to use the reference dimension parameter corresponding to the temperature environment with a higher temperature as the first dimension parameter, and use the reference dimension parameter corresponding to the temperature environment with a lower temperature as the second dimension parameter; in the case where the first dimension parameter is greater than the second dimension parameter, determine the retention probability of the second dimension parameter according to the quotient of the fitness difference and the temperature value of the temperature environment corresponding to the first dimension parameter; the fitness difference is the difference between the second fitness corresponding to the first dimension parameter and the second fitness corresponding to the second dimension parameter; in the case where the retention probability is lower than the preset probability threshold, delete the second dimension parameter to update the reference dimension parameter set.

[0138] In one embodiment, the second parameter acquisition module 12 is further configured to input the component dimension parameters in different temperature environments into the performance prediction model to obtain the bearing performance index and the seismic performance index of the component to be prepared under the component dimension parameters in the corresponding temperature environment; wherein, the performance prediction model is trained based on the sample dimension parameters corresponding to different temperature environments and the bearing performance index and the seismic performance index of the sample components under the sample dimension parameters in the corresponding temperature environments; the bearing performance index of the sample components under the sample dimension parameters in different temperature environments is the bearing performance index obtained by performing a bearing test on the sample components under the sample dimension parameters in the corresponding temperature environment; the seismic performance index of the sample components under the sample dimension parameters in different temperature environments is the seismic performance index obtained by performing a seismic test on the sample components under the sample dimension parameters in the corresponding temperature environment.

[0139] Each module in the above component parameter determination device can be implemented in whole or in part by software, hardware, and their combination. The above modules can be embedded in the processor in the computer device in the form of hardware or be independent of the processor, or can be stored in the memory in the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

[0140] In an exemplary embodiment, a computer device is provided. The computer device can be a terminal, and its internal structure diagram can be as Figure 9As shown in the figure. The computer device includes a processor, a memory, an input / output interface, a communication interface, a display unit, and an input device. Among them, the processor, the memory, and the input / output interface are connected through a system bus, and the communication interface, the display unit, and the input device are connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The input / output interface of the computer device is used to exchange information between the processor and external devices. The communication interface of the computer device is used to communicate with external terminals in a wired or wireless manner, and the wireless manner can be implemented through WIFI, a mobile cellular network, NFC (Near Field Communication), or other technologies. When the computer program is executed by the processor, it implements a method for determining component parameters. The display unit of the computer device is used to form a visually visible picture, which can be a display screen, a projection device, or a virtual reality imaging device. The display screen can be a liquid crystal display screen or an electronic ink display screen. The input device of the computer device can be a touch layer covering the display screen, or a button, a trackball, or a touchpad provided on the outer shell of the computer device, or an external keyboard, touchpad, or mouse, etc.

[0141] Those skilled in the art can understand that Figure 9 the structure shown in the figure is only a block diagram of some structures related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine some components, or have different component arrangements.

[0142] In one embodiment, a computer device is further provided, including a memory and a processor. A computer program is stored in the memory, and when the processor executes the computer program, the steps in the above method embodiments are implemented.

[0143] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored, and when the computer program is executed by the processor, the steps in the above method embodiments are implemented.

[0144] In one embodiment, a computer program product is provided, including a computer program, and when the computer program is executed by the processor, the steps in the above method embodiments are implemented.

[0145] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use, and processing of relevant data need to comply with relevant regulations.

[0146] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above methods. Among them, any reference to a memory, database, or other medium used in the embodiments provided in this application can include at least one of non-volatile and volatile memories. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc. The databases involved in the embodiments provided in this application can include at least one of relational databases and non-relational databases. Non-relational databases can include distributed databases based on blockchain, etc., and are not limited thereto. The processors involved in the embodiments provided in this application can be general-purpose processors, central processors, graphics processors, digital signal processors, programmable logic devices, data processing logics based on quantum computing, etc., and are not limited thereto.

[0147] The technical features of the above embodiments can be combined arbitrarily. For the sake of brevity of description, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered to be within the scope described in this specification.

[0148] The above-described embodiments merely represent several implementation manners of the present application. The description thereof is relatively specific and detailed, but it should not be construed as a limitation on the patent scope of the present application. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present application, several modifications and improvements can still be made, and these all fall within the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the appended claims.

Claims

1. A method for determining component parameters, characterized in that The method includes: Obtaining at least one component preparation parameter of the to-be-prepared component, as well as the quality evaluation index and corrosion performance index when the to-be-prepared component is simulated to be used under each of the component preparation parameters; Determining the target preparation parameter of the to-be-prepared component according to the quality evaluation index and the corrosion performance index; Obtaining at least one component size parameter that is allowed to be used when preparing the to-be-prepared component using the component preparation parameter, as well as the bearing performance index and seismic performance index corresponding to the to-be-prepared component with different component size parameters simulated to be used under different temperature conditions; Selecting the target size parameter from the at least one component size parameter according to each of the bearing performance indexes and each of the seismic performance indexes.

2. The method according to claim 1, wherein The determining the target preparation parameter of the to-be-prepared component according to the quality evaluation index and the corrosion performance index includes: Selecting a preset number of component preparation parameters according to the first fitness of the to-be-prepared component under each of the component preparation parameters, and constructing a component preparation parameter set; wherein, the first fitness is the weighted sum value between the quality evaluation index, the corrosion performance index of the to-be-prepared component under the component preparation parameter and the corresponding preset index weight; For each iteration process, selecting the component preparation parameter corresponding to the maximum first fitness from the component preparation parameter set as the reference preparation parameter; Performing crossover and mutation processing on the reference preparation parameter to obtain a mutated preparation parameter; Adding the mutated preparation parameter to the component preparation parameter set to update the component preparation parameter set; Taking the component preparation parameter corresponding to the maximum first fitness in the component preparation parameter set in the last iteration process as the target preparation parameter.

3. The method according to claim 1, characterized in that Obtaining the quality evaluation index and the corrosion performance index when the to-be-prepared component is simulated to be used under each of the component preparation parameters includes: Inputting each of the component preparation parameters into an index prediction model to obtain the quality evaluation index and the corrosion performance index of the to-be-prepared component under the corresponding component preparation parameter; Wherein, the index prediction model is trained based on the sample preparation parameter and the quality evaluation index and the corrosion performance index corresponding to the sample component under the sample preparation parameter; the quality evaluation index of the sample component under the sample preparation parameter is the quality evaluation index obtained by performing a pulling experiment and a shearing experiment on the sample component under the sample preparation parameter in the laboratory; the corrosion performance index of the sample component under the sample preparation parameter is the corrosion performance index obtained by placing the sample component under the sample preparation parameter in a marine atmospheric environment for corrosion test.

4. The method according to any one of claims 1-3, characterized in that, The selecting the target size parameter from the at least one component size parameter according to each of the bearing performance indexes and each of the seismic performance indexes includes: For the to-be-prepared component under each component size parameter in each temperature environment, taking the weighted sum value between the bearing performance index and the seismic performance index of the to-be-prepared component and the corresponding preset performance weight as the second fitness of the to-be-prepared component; For each temperature environment, selecting the component size parameter corresponding to the maximum second fitness as the reference size parameter, and constructing a reference size parameter set according to each reference size parameter; Two reference dimension parameters in the temperature environments of adjacent temperatures are sequentially selected from the set of reference dimension parameters, and whether to update the set of reference dimension parameters is determined according to the magnitude relationship between the second fitness values corresponding to the two reference dimension parameters; The component dimension parameter corresponding to the maximum second fitness value is selected from the updated set of reference dimension parameters as the target dimension parameter.

5. The method according to claim 4, wherein The determining whether to update the set of reference dimension parameters according to the magnitude relationship between the second fitness values corresponding to the two reference dimension parameters includes: Taking the reference dimension parameter corresponding to the temperature environment with a higher temperature as the first dimension parameter, and taking the reference dimension parameter corresponding to the temperature environment with a lower temperature as the second dimension parameter; In the case where the first dimension parameter is greater than the second dimension parameter, the retention probability of the second dimension parameter is determined according to the quotient of the fitness difference and the temperature value of the temperature environment corresponding to the first dimension parameter; the fitness difference is the difference between the second fitness value corresponding to the first dimension parameter and the second fitness value corresponding to the second dimension parameter; In the case where the retention probability is lower than the preset probability threshold, the second dimension parameter is deleted to update the set of reference dimension parameters.

6. The method according to claim 4, characterized in that Obtaining the bearing performance index and the seismic performance index corresponding to the component to be prepared when simulating the use of different component dimension parameters under different temperature conditions, including: Inputting the component dimension parameters in different temperature environments into the performance prediction model to obtain the bearing performance index and the seismic performance index of the component to be prepared under the component dimension parameters in the corresponding temperature environment; Wherein, the performance prediction model is trained based on the sample dimension parameters corresponding to different temperature environments and the bearing performance index and the seismic performance index corresponding to the sample components under the sample dimension parameters in the corresponding temperature environments; the bearing performance index of the sample components under the sample dimension parameters in different temperature environments is the bearing performance index obtained by performing a bearing test on the sample components under the sample dimension parameters in the corresponding temperature environment; the seismic performance index of the sample components under the sample dimension parameters in different temperature environments is the seismic performance index obtained by performing a seismic test on the sample components under the sample dimension parameters in the corresponding temperature environment.

7. A component parameter determination device, characterized in that, The device includes: A first parameter acquisition module, configured to acquire at least one component preparation parameter of the component to be prepared, and the quality evaluation index and the corrosion performance index when simulating the use of the component to be prepared under each of the component preparation parameters; A first target determination module, configured to determine the target preparation parameter of the component to be prepared according to the quality evaluation index and the corrosion performance index; A second parameter acquisition module, configured to acquire at least one component dimension parameter that is allowed to be used when preparing the component to be prepared by using the component preparation parameter, and the bearing performance index and the seismic performance index corresponding to the component to be prepared when simulating the use of different component dimension parameters under different temperature conditions; A second target determination module, configured to select a target dimension parameter from the at least one component dimension parameter according to each of the bearing performance indexes and each of the seismic performance indexes.

8. A computer device, comprising a memory and a processor, the memory storing a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the method according to any one of claims 1 to 6.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of the method according to any one of claims 1 to 6.

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