Design method and system, equipment, medium for reinforced slope with prefabricated cable anchor frame beam

Abstract

The present invention relates to the technical field of engineering data processing. Specifically, it relates to a design method and system, equipment, and medium for strengthening slopes with prefabricated cable anchor frame beams. The neural network is optimized by improving the butterfly optimization algorithm. Based on the butterfly optimization algorithm, improvements are made to make the distribution of the butterfly population more uniform. The global search is carried out by introducing a selection factor, the mutation is dynamically adjusted, and the adaptive weight strategy is used to optimize the butterfly population. The optimal butterfly population is found, which is the corresponding hidden layer parameters and learning rate parameters of the neural network. The safest design scheme for the prefabricated frame beam slope is output through the optimal hidden layer parameters and learning rate parameters of the neural network. The more accurate prediction of the design scheme for the prefabricated frame beam slope is realized. The optimized model can better utilize complex and diverse slope data information, improve the robustness and accuracy of the prediction model, and provide important technical support for engineering decision-making.

Description

Technical Field

[0001] The present invention relates to the technical field of engineering data processing, and more particularly to a design method and system, equipment, and medium for reinforcing slopes with prefabricated cable anchor frame beams. Background Art

[0002] With the continuous progress and development of social economy, more and more infrastructure construction projects have emerged. The articulated prefabricated cable anchor frame beam has the characteristics of flexible support. Through the hoop action of the frame beam and the active tension of the cable anchor, the deformation of the slope body can be effectively restricted. The cable anchor frame beam support system can be applied to various rock masses and soil bodies, and can effectively reduce the excavation of fractured soil bodies during the construction process, thus accelerating the construction progress. Compared with traditional passive protection, the cable anchor frame beam structure actively restricts the displacement of the soil body by improving the mechanical properties of the soil body, achieving the overall stability of the slope body. As an important engineering project in the process of infrastructure construction, how to ensure the stability of the slope body during the construction and use processes has become an issue that must be solved in the process of infrastructure construction. At present, the commonly used support forms can meet the basic requirements of current slope support projects, but there are still problems to varying degrees, such as high technical requirements at the construction site, complex and cumbersome processes, environmental pollution, and impact on the later environmental restoration. Using traditional slope support technologies often causes an extension of the construction period due to a large amount of concrete pouring work on site, and causes damage to the local environment.

[0003] The prefabricated concrete structure has the advantages of low cost, easy construction, and shortening of the construction period. However, due to its relatively short application time, the current theoretical research content is relatively small, and the theoretical research lags behind the engineering practice. The frame design mainly relies on experience at present, which is likely to cause design deficiencies or waste, and may indirectly lead to major engineering accidents such as slope instability.

[0004] Currently, when designing prefabricated frame beam slopes, it is greatly affected by human factors. The number of parameters considered and the mutual coupling of various parameters increase the difficulty of manual operation, increasing the probability of structural failure. Therefore, in order to prevent disasters, manual design will make a conservative estimate of the design, resulting in a great waste of materials and economic costs, and invisibly increasing the construction cost and time cost. At the same time, most of the current frame beams are constructed by on-site casting, which lengthens the construction period, cannot meet the requirements of rapid construction, and there are problems such as poor on-site environment, ineffective maintenance, and the possibility of landslide accidents during the construction process.

[0005] For the traditional LSTM model, there are many hyperparameters, which are difficult to select manually, and the training process is very slow, having a great impact on the performance loss. Summary of the Invention

[0006] The object of the present invention is to provide a design method, system, equipment and medium for strengthening slopes with prefabricated anchor cable frame beams to solve the problems in the above-mentioned prior art.

[0007] The present invention is realized through the following technical solutions:

[0008] In a first aspect, the present invention provides a design method for strengthening slopes with prefabricated anchor cable frame beams, including:

[0009] Step S1: Obtain the design data and historical monitoring data of the prefabricated frame beam slope, integrate them into a data sample, and classify and divide the data sample to obtain a training set and a test set;

[0010] Step S2: Send the training set and the test set to a preset neural network for training and verification, and optimize the preset neural network through a preset improved butterfly optimization algorithm to obtain a prediction model for the optimized design scheme of the prefabricated frame beam slope;

[0011] Step S3: Send the historical monitoring data of the prefabricated frame beam slope to the prediction model of the optimized design scheme of the prefabricated frame beam slope to predict the design scheme of the prefabricated frame beam slope.

[0012] Preferably, the step S2 includes:

[0013] Step S21: Send the training set to a preset neural network for training to obtain a trained neural network;

[0014] Step S22: Optimize the hidden layer parameters and learning rate parameters of the trained neural network based on the improved butterfly optimization algorithm to obtain optimized hidden layer parameters and learning rate parameters.

[0015] Step S23: Assign the optimized hidden layer parameters and learning rate parameters to the trained neural network to obtain an assigned neural network;

[0016] Step S24: Send the training set and the test set to the assigned neural network for training and verification to obtain a prediction model for the optimized design scheme of the prefabricated frame beam slope.

[0017] Preferably, the step S22 includes:

[0018] Step S221: Initialize the hidden layer parameters and learning rate parameters in the preset neural network, and set the parameters of the preset improved butterfly optimization algorithm. The parameters include various parameters of the butterfly population, including the initial position of each butterfly, the number of butterfly populations, the maximum number of iterations, the dimension of the space where it is located, the sensory modality, the power exponent, and the dynamic conversion probability;

[0019] Step S222: Randomly generate a butterfly population with a population size of a preset number, uniformly distribute the butterfly population based on the ICMIC chaos mapping method, and change the initial positions of the butterfly population to obtain the optimized positions of the butterfly population;

[0020] Step S223: Screen the butterfly population using differential mutation, crossover, and selection strategies to obtain the screened excellent butterfly population;

[0021] Step S224: Calculate the fitness values of the screened excellent butterfly population, and introduce a selection factor for global search and dynamically adjust mutation for local search based on the calculated fitness values and random numbers and dynamic conversion probabilities until the number of iterations reaches the maximum number of iterations, to obtain the optimal hidden layer parameters and learning rate parameters.

[0022] Preferably, the step S224 includes:

[0023] Step S2241: Calculate the fitness values of the screened excellent butterfly population based on a preset fitness function, sort the calculated fitness values of the butterfly population, and select the optimal fitness value of the butterfly population;

[0024] Step S2242: Generate a random number to judge the relationship with the preset dynamic conversion probability to make a strategy selection, and introduce a selection factor for global search and dynamically adjust mutation for local search to update the butterfly positions;

[0025] Step S2243: Check whether the end condition is satisfied. If it is satisfied, that is, the maximum number of iterations T is reached, the algorithm ends, and the best solution found, that is, the corresponding optimized hidden layer parameters and learning rate parameters, are output; otherwise, return to step S2242 to continue the next round of iteration.

[0026] Preferably, the updating of the butterfly positions includes:

[0027]

[0028]

[0029] In the formula, represents the number of butterflies in the population; represents the stimulation factor of the th butterfly in the improved butterfly optimization algorithm, is the probability that the th butterfly moves towards the best butterfly in the previous iteration, , , respectively represent the th generation of the butterfly population at the Only, the Only, the corresponding position of the butterfly, is the generation of the butterfly population corresponding position of the is a random number within [0, 1]; represents the best position of the butterfly population, represents the set dynamic conversion probability; represents the scent perception intensity of the represents the current iteration number; represents the preset number of the butterfly population; is a constant, is the generation of the probability that the

[0030] Preferably, the step S24 includes:

[0031] Step S241, sending the hidden layer parameters and the learning rate parameters to the neural network to construct a forward calculation formula, and obtaining the forward calculation formula of the neural network after assignment;

[0032] Step S242, sending the training set to the neural network after assignment for training again, wherein the output of the neural network after assignment at a preset moment is calculated through the forward calculation formula, and the loss value at the preset moment is calculated based on the output;

[0033] Step S243, updating the loss value at the preset moment, and predicting the data at each moment to obtain the output result corresponding to the historical monitoring data in the neural network.

[0034] Preferably, the construction of the forward calculation formula includes:

[0035]

[0036] In the formula, is the forget gate of the neural network, is the recurrent weight of the input gate, is the recurrent weight of the forget gate, is the recurrent weight of the output gate, is the recurrent weight of the cell state, is the sigmoid function, is the short-term memory of the neural network at moment, is the short-term memory of the neural network at moment, is the sequence of operating parameters input at time is the bias term of the input gate, is the bias term of the forget gate, is the bias term of the output gate, is the bias term of the cell state, is the input gate, is the state of the memory cell at time is the intermediate value of the updated memory cell state, and Tanh is the hyperbolic tangent activation function; is the state of the memory cell at time is the output gate.

[0037] In a second aspect, the present invention provides an assembled anchor cable frame beam reinforced slope design system, including:

[0038] A data collection module, configured to obtain assembled frame beam slope design data and historical monitoring data, integrate them into data samples, and classify the data samples;

[0039] A model construction module, configured to send the training set and test set to a preset neural network for training and verification, and optimize the preset neural network through a preset improved butterfly optimization algorithm to obtain a prediction model for the optimized assembled frame beam slope design scheme;

[0040] A scheme output module, configured to send the historical monitoring data of the assembled frame beam slope to the prediction model of the optimized assembled frame beam slope design scheme to predict the assembled frame beam slope design scheme;

[0041] A main control module, connected to the data collection module, the model construction module, and the scheme output module, for executing the above-mentioned method for designing an assembled anchor cable frame beam reinforced slope.

[0042] In a third aspect, an electronic device includes a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the processor implements the above-mentioned method for designing an assembled anchor cable frame beam reinforced slope when executing the computer program.

[0043] In a fourth aspect, a computer-readable storage medium is characterized in that a computer program is stored on the computer-readable storage medium, and the computer program implements the above-mentioned method for designing an assembled anchor cable frame beam reinforced slope when executed by a processor.

[0044] The technical solution of the present invention has at least the following advantages and beneficial effects:

[0045] By using the method provided by the present invention, the neural network is optimized by improving the butterfly optimization algorithm. Based on the butterfly optimization algorithm, improvements are made. First, in the initial population stage, the ICMIC chaotic mapping method is introduced to make the distribution of the butterfly population more uniform. Then, a selection factor is introduced for global search, dynamic adjustment of mutation, and an adaptive weight strategy is used to optimize the butterfly population. The optimal butterfly population is the corresponding hidden layer parameters and learning rate parameters of the neural network. The safest prefabricated frame beam slope design scheme is output through the optimal hidden layer parameters and learning rate parameters of the neural network. The more accurate prediction of the prefabricated frame beam slope design scheme is realized. The optimized model can better utilize complex and diverse slope data information, improve the robustness and accuracy of the prediction model, and provide important technical support for engineering decision-making. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for use in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention, and therefore should not be regarded as limiting the scope. For those of ordinary skill in the art, other related drawings can be obtained based on these drawings without creative efforts.

[0047] Figure 1 Schematic diagram of the model based on the butterfly optimization algorithm of the present invention;

[0048] Figure 2 Schematic diagram of the neural network process of the present invention;

[0049] Figure 3 Schematic diagram of the control process of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0050] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. Usually, the components of the embodiments of the present invention described and shown in the drawings here can be arranged and designed in various different configurations.

[0051] The terms "first", "second", etc. in the specification and claims of this application and the above drawings are used to distinguish similar objects, and do not necessarily need to be used to describe a specific order or sequence. The naming or numbering of steps that appear in this application does not mean that the steps in the method process must be executed in the time / logical order indicated by the naming or numbering. The named or numbered process steps can be changed in the execution order according to the technical objectives to be achieved, as long as the same or similar technical effects can be achieved.

[0052] Please refer to Figures 1-3 , a design method for strengthening slopes with prefabricated cable anchor frame beams provided by the present invention includes:

[0053] Step S1: Obtain the design data and historical monitoring data of the prefabricated frame beam slope, integrate them into data samples, and classify and divide the data samples;

[0054] Collect and organize the design database of the prefabricated frame beam slope, including the engineering geological conditions of slopes in different regions (including filling information, hydrogeological information, geological conditions, etc.) and the required parameters for the design of the prefabricated frame beam slope (mainly including the section form size of the beam body in the prefabricated frame beam slope, the model of the stressed steel bar, the concrete strength grade, the cost of the prefabricated frame beam slope, the displacement monitoring data of the prefabricated frame beam slope, etc.), and incorporate all the design materials of the prefabricated frame beam slope into the database.

[0055] Among them, the filling information includes the cohesion and friction angle of clay and the cohesion of sand, the hydrogeological information includes rainfall information and groundwater information, the geological conditions include the horizontal acceleration and vertical acceleration considering earthquakes and the situation without considering earthquakes, and the slope types include steep slopes, gentle slopes, and retaining walls.

[0056] After classifying and dividing all the data samples and performing standardization and normalization processing, 72% of the samples are selected as the training set and 22% of the samples are selected as the test set.

[0057] S2: Send the training set and the test set to a preset neural network for training and verification, and optimize the preset neural network through a preset improved butterfly optimization algorithm to obtain a prediction model for the optimized design scheme of the prefabricated frame beam slope;

[0058] It can be understood that in this step, the neural network is optimized through the improved butterfly optimization algorithm, which is an improvement based on the butterfly optimization algorithm. First, in the initialization population stage, the ICMIC chaos mapping method is introduced to make the distribution of the butterfly population more uniform. Then, a selection factor is introduced for global search, dynamic adjustment of mutation, and an adaptive weight strategy is used to optimize the butterfly population to find the optimal butterfly population, which is the corresponding hidden layer parameters and learning rate parameters of the neural network. The output of the optimal hidden layer parameters and learning rate parameters of the neural network is the safest design scheme of the prefabricated frame beam slope. This realizes a more accurate prediction of the design scheme of the prefabricated frame beam slope. The optimized model can better utilize the complex and diverse slope data information, improve the robustness and accuracy of the prediction model, and provide important technical support for engineering decision-making. Among them, step S2 includes step S21, step S22, step S23, and step S24.

[0059] Step S21: Send the training set to a preset neural network for training to obtain a trained neural network;

[0060] It can be understood that in this step, by training the training set, the parameters of the neural network are optimized to enable it to have high prediction ability and generalization ability. The training results can reflect the performance and effect of the model in dealing with the prediction problem of the slope design scheme, providing a basis for subsequent testing and verification.

[0061] Step S22: Optimize the hidden layer parameters and learning rate parameters of the trained neural network based on an improved butterfly optimization algorithm to obtain optimized hidden layer parameters and learning rate parameters;

[0062] It can be understood that in this step, by using the improved butterfly optimization algorithm to optimize the hidden layer parameters and learning rate of the neural network, the prediction accuracy and generalization ability of the model are significantly improved. The optimized model can more accurately predict the slope design scheme, providing more reliable support for engineering practical applications. In this step, step S22 includes step S221, step S222, step S223, and step S224.

[0063] Step S221: Initialize the hidden layer parameters and learning rate parameters in the preset neural network, and set the parameters of the preset improved butterfly optimization algorithm. The parameters include Algorithm Step1: Set various parameters of the butterfly population, including the initial position of each butterfly, the number of butterfly populations, the maximum number of iterations, the dimension of the space where it is located, the sensory modality, the power exponent, and the dynamic transition probability

[0064] Step S222: Randomly generate a butterfly population with a preset population size, uniformly distribute the butterfly population based on the ICMIC chaos mapping method, and change the initial position of the butterfly population to obtain the optimized position of the butterfly population;

[0065] Specifically, the ICMIC chaos mapping is described as follows:

[0066]

[0067] In the formula, is the control parameter, with a value range of (0, +∞), and the range of its chaotic orbit state value is (-1, 1). is the initial position of the butterfly population, represents the position after the update of the butterfly population;

[0068] The expression of the changed population distribution is described as:

[0069]

[0070] In the formula, Indicates the distribution of the butterfly population after improvement by the ICMIC chaotic mapping, Indicates the position of the improved butterfly population.

[0071] Step S223: Screen the butterfly population using differential mutation, crossover, and selection strategies to obtain the screened excellent butterfly population;

[0072] It can be understood that when improving the butterfly optimization algorithm in this step, during each iteration process of the butterfly population, the position and fitness value of the current butterfly population will be calculated. Although the algorithm itself will update the butterfly population, there will inevitably be inferior individuals. If we can perform excellent selection on these iterated butterfly populations, theoretically, the speed of searching for the optimal value can be improved. Inspired by the genetic algorithm, it is proposed to introduce differential mutation, crossover, and selection strategies before calculating the position and fitness of the next butterfly population to obtain the screened excellent butterfly population;

[0073] By optimizing the neural network parameters using the improved butterfly optimization algorithm, the neural network can obtain a better fitting effect on the training data. By gradually optimizing the butterfly population, the optimization process becomes more concentrated and efficient, thereby obtaining the optimal butterfly population information. The neural network parameters corresponding to these optimal position information can significantly improve the prediction ability and generalization ability of the model, providing reliable optimization parameters for the subsequent steps.

[0074] Its three main stages are specifically described as follows:

[0075] Step1: Mutation

[0076] Generate a mutant difference vector according to each mutant vector;

[0077]

[0078] In the formula, is the th mutant vector of the th iteration, ,, are three random positions in the butterfly population; represents the maximum number of iterations; represents the current iteration number;

[0079] , , The random indices are randomly selected integers, and ;

[0080] is a real - valued scaling factor, with a value in the range of [0, 2], which controls the amplitude of differential mutation; represents the mutation rate;

[0081] Step2: Crossover

[0082] After the previous stage is completed, by performing a crossover operation on the perturbation parameter vector, the perturbation parameters are made diverse, and thus a trial vector is generated. The specific description is as follows:

[0083]

[0084] wherein, is a random crossover parameter, with a value range in [0.8, 1], is a random number between [0, 1]; represents the trial vector; represents the target vector; represents the target vector for the next - generation iteration;

[0085] Step3: Selection

[0086] After the initial butterfly population goes through the mutation and crossover stages, using the greedy principle, the trial vector is compared with the target vector to determine whether it can become the individual gene iteration individual for the next generation. If the fitness value of the experimental vector is less than the target vector , then is set to , otherwise, the old value is retained .

[0087] Its expression description is:

[0088]

[0089] wherein, represents the experimental vector; represents the target vector; represents the target vector for the next - generation iteration;

[0090] Step S224: Calculate the fitness value of the selected excellent butterfly population, and based on the calculated fitness value, random number, and dynamic conversion probability, introduce a selection factor for global search and dynamically adjust the mutation for local search until the number of iterations reaches the maximum number of iterations, obtaining the optimal hidden - layer parameters and learning - rate parameters.

[0091] It can be understood that this step includes step S2241, step S2242, and step S2243.

[0092] Step S2241: Calculate the fitness values of the selected excellent butterfly population based on a preset fitness function, sort the calculated fitness values of the butterfly population, and select the optimal fitness value of the butterfly population.

[0093] Among them, the preset fitness function is as follows:

[0094]

[0095] Among them, represents the position of the butterfly population at the th iteration; represents the output result of the neural network model, represents the expected value.

[0096] It can be understood that the corresponding optimal position can be obtained through the optimal fitness value of the butterfly population;

[0097] Step S2242: Generate a random number to judge the relationship with the preset dynamic conversion probability to make a strategy selection, and introduce a selection factor to perform global search and dynamically adjust mutation to perform local search to update the butterfly position.

[0098] Among them, the formula for updating the position of the butterfly population is as follows;

[0099]

[0100]

[0101] It can be understood that each butterfly emits a certain amount of fragrance, and the fragrance will spread within the area where it is located and be perceived by other butterflies. The fragrance released by a butterfly is related to its fitness, which means that when the position of a butterfly changes, its fitness will also change accordingly. When a butterfly detects that another butterfly releases a stronger fragrance in the area, it will move in that direction. This stage is called global search. In addition, there is another situation. When a butterfly cannot detect a fragrance stronger than itself, it will move freely. This stage is called the local search stage. The update formula is based on the generated random number and the set dynamic conversion probability to make a strategy selection. If , then execute the global search strategy. Otherwise, execute the local search strategy, and introduce a selection factor to perform global search and dynamically adjust mutation for local search; during the global search process of the butterfly optimization algorithm. In order to improve the convergence speed of the algorithm while ensuring the diversity of the population, a selection factor in the genetic algorithm is introduced to replace the random number 。Using dynamic adjustment of mutation for local search can avoid premature convergence. In the early stage of local search, it can jump out of the local optimum and find the optimal solution; in the later stage of iteration, when the convergence requirement is met, the mutation factor approaches 0, making the search stable;

[0102] In the formula, represents the number of butterflies in the population; represents the stimulation factor of the th butterfly in the improved butterfly optimization algorithm, represents the selection factor, which is the probability that the th butterfly moves towards the best butterfly at the current iteration, , , respectively represent the positions corresponding to the th, th, th butterflies in the butterfly population at the th generation, is the position corresponding to the th butterfly in the butterfly population at the th generation; is a random number within [0, 1]; represents the best position of the butterfly population, represents the set dynamic conversion probability; represents the th butterfly's fragrance perception intensity; represents the current iteration number; represents the preset number of the butterfly population; is a constant, is the probability that the th butterfly moves towards the best butterfly at the th generation;

[0103] Step S2243, check whether the end condition is satisfied. If it is satisfied, that is, the maximum iteration number T is reached, then the algorithm ends and outputs the found best solution, namely the corresponding optimized hidden layer parameters and learning rate parameters; otherwise, return to step S2242 to continue the next round of iteration.

[0104] Step S23, assign the optimized hidden layer parameters and learning rate parameters to the trained neural network to obtain the assigned neural network;

[0105] It can be understood that in this step, the neural network is updated with the parameters optimized by using the improved butterfly optimization algorithm, enabling the model parameters to better adapt to the characteristics of the data and the prediction task. The optimized hidden layer parameters and learning rate parameters can improve the prediction accuracy and generalization ability of the model, thus more accurately predicting the precast frame beam slope design scheme.

[0106] Step S24: Send the training set and test set to the neural network after assignment for training and verification to obtain an optimized prediction model for the precast frame beam slope design scheme.

[0107] In this step, by assigning the optimized parameters to the neural network, the neural network model is in an optimal state, enabling more accurate training and prediction. When processing the prediction of the precast frame beam slope design scheme, the optimized neural network will have higher accuracy and generalization ability, providing more reliable support for engineering practical applications. In this step, step S24 includes step S241, step S242, and step S243.

[0108] Step S241: Send the hidden layer parameters and learning rate parameters to the neural network for forward calculation formula construction to obtain the forward calculation formula of the neural network after assignment;

[0109] The construction of the forward calculation formula includes:

[0110]

[0111] In the formula, is the forget gate of the neural network, is the recurrent weight of the input gate, is the recurrent weight of the forget gate, is the recurrent weight of the output gate, is the recurrent weight of the cell state, is the sigmoid function, is the neural network at the short-term memory at time is the neural network at the short-term memory at time is the input running parameter sequence at time is the bias term of the input gate, is the bias term of the forget gate, is the bias term of the output gate, is the bias term of the cell state, is the input gate, is the memory cell state at time is the median value of the updated memory cell state, and Tanh is the hyperbolic tangent activation function; is the memory cell state at time is the output gate.

[0112] Step S242: Send the training set to the assigned neural network for further training. Among them, the output of the assigned neural network at a preset time is calculated through the forward calculation formula, and the loss value at the preset time is calculated based on the output;

[0113] It can be understood that in this step, the loss value at the preset time is calculated through a preset loss value calculation formula, and the loss value calculation formula is as follows:

[0114]

[0115] Among them, represents the loss value at time represents the observed value of the training sample at time represents the output of the neural network model at time.

[0116] Step S243: Update the loss value at the preset time, and predict the data at each time to obtain the output result corresponding to the historical monitoring data in the neural network.

[0117] In this step, by updating the loss value and predicting the data at each time, the parameters of the model can be gradually optimized, so that the model can more accurately predict the design scheme of the precast frame beam slope. By predicting the historical monitoring data, the performance of the model can be verified, and valuable prediction results can be provided for actual engineering applications.

[0118] Step S3: Send the historical monitoring data of the precast frame beam slope to the optimized prediction model of the precast frame beam slope design scheme to predict the precast frame beam slope design scheme.

[0119] It can be understood that the prediction system of the precast frame beam slope design scheme can consider various parameters, has strong adaptability, fault tolerance and self-improving ability, and improves the accuracy of calculation.

[0120] In another embodiment, the sparrow optimization algorithm can also be used to initialize the position information of the sparrow population, set the maximum number of iterations, construct the fitness function, and calculate the fitness value of the sparrow through the fitness function, and select the smallest fitness value as the initial fitness of the sparrow;

[0121] Construct the sparrow discoverer function model, sparrow follower function model, and sparrow early warning function model respectively, and calculate the first new position of the sparrow discoverer, the second new position of the sparrow follower, and the third new position of the sparrow early warning function model respectively;

[0122] Update the initial fitness according to whether the fitness of the first new position is better than the initial fitness, update the fitness of the first new position according to whether the fitness of the second new position is better than the fitness of the first new position, and update the initial fitness of the second new position according to whether the fitness of the third new position is better than the initial fitness of the second new position;

[0123] Obtain the best position information of the sparrow through the updated fitness, construct a neural network, and assign values to the hidden layer parameters and learning rate parameters in the neural network based on the best sparrow position information to obtain the optimized hidden layer parameters and learning rate parameters;

[0124] It can be understood that in this step, the neural network is updated by using the parameters optimized by the moth-flame optimization algorithm, so that the model parameters can better adapt to the characteristics of the data and the prediction task. The optimized hidden layer parameters and learning rate parameters can improve the prediction accuracy and generalization ability of the model, so as to more accurately predict the design scheme of the prefabricated frame beam slope.

[0125] Send the hidden layer parameters and learning rate parameters to the neural network to construct a forward calculation formula to obtain an LSTM model;

[0126] Send the training set to the assigned neural network for training again, calculate the output of the neural network at a preset time through the forward calculation formula of the neural network, and calculate the loss value at the preset time based on the output of the neural network at the preset time;

[0127] It can be understood that in this step, the loss value at the preset time is calculated through a preset loss value calculation formula, and the loss value calculation formula is as follows:

[0128]

[0129] Among them, represents the loss value at the time, represents the observed value of the training sample at the time, represents the output of the neural network model at the

[0130] Update the loss value at the preset time, and predict the data at each time to obtain the output result corresponding to the historical monitoring data in the neural network;

[0131] In this step, by updating the loss value and making predictions on the data at each moment, the parameters of the model can be gradually optimized, enabling the model to more accurately predict the design scheme of the prefabricated frame beam slope. By predicting the historical monitoring data, the performance of the model can be verified, and valuable prediction results can be provided for practical engineering applications.

[0132] Send the training set and the test set to the assigned neural network for training and verification to obtain an optimized design scheme for the prefabricated frame beam slope.

[0133] It can be understood that in this step, through repeated training and verification, it is ensured that the optimized neural network model not only performs well on the training set but also has good generalization ability on the test set. This step makes the model valuable for practical applications, capable of effectively predicting the situation of the prefabricated frame beam slope design scheme, and providing reliable data support for engineering decisions.

[0134] Among many deep learning models, RNN introduces the concept of time series. Relying on its cyclic network structure, the ability to maintain memory, and the ability to process non-linear data, it shows stronger adaptability in solving the long-term dependence problem of sequence data. However, when the number of network layers increases, RNN has problems such as gradient vanishing and gradient explosion during information feedback, and it is difficult for RNN to model large-span time series. LSTM solves the problem that RNN cannot establish a prediction model for large-span time series. The LSTM model replaces the neurons in the hidden layer of the RNN model with memory units with a "gating" mechanism, which consists of a storage unit and three logical gates: forget, input, and output. The "gating" mechanism controls the writing of new information and the forgetting of previously accumulated information, thus solving the long-term dependence problem in the cyclic network structure and avoiding gradient explosion and gradient vanishing caused by the multiplicative effect in gradient backpropagation in the cyclic network structure. And in the LSTM model, the number of hidden layers HN and the learning rate directly affect the output of the LSTM prediction model. The SSA can be used to find the optimal values of HN and . In the LSTM-SSA model, the root mean square error of the training data set is used as the fitness of the LSTM model for optimal search, and finally the optimal parameter values are found to improve the optimization of the prefabricated frame beam design scheme, reduce the error rate in complex designs, reduce the disaster incidence rate, ensure the life and property safety of relevant personnel, and improve the design accuracy and speed.

[0135] Therefore, by using the method provided by the present invention and utilizing the long short-term memory network model (LSTM), relevant parameters such as different geological environments, construction conditions, and concrete materials are used as input feature values of the neural network model. All the collected and sorted design materials and data are incorporated into the established neural network. Its samples expand as the subsequent construction data such as monitoring increases and the subsequent construction plans are continuously supplemented. When it is necessary to treat and protect the slope, by inputting the engineering area, strength parameters of rock and soil masses, other external load strength parameters, etc. into the constructed neural network, the neural network first predicts all possible precast frame beam design plans according to the content of the input layer, and searches for all design plans through the sparrow search algorithm (SSA) to obtain the optimal design plan. This design system can automatically compare the plans, select the optimal plan that meets all requirements, save costs, make more efficient use of the economy, and thus minimize the time loss caused by factors such as complex regional geological conditions as much as possible, and improve the design safety.

[0136] In an exemplary embodiment of the present invention, the initialization of the sparrow population position information includes:

[0137]

[0138] In the formula, is the initial position information of the sparrow population, is the th hidden layer parameter of the th iteration, is the learning rate parameter of the

[0139] th hidden layer of the th iteration. Among them, the maximum number of iterations , the current number of iterations , the constant and the dimension are set. In the LSTM-SSA model, the sparrow position is composed of the parameters to be optimized in the LSTM model (the hidden layer parameter is

[0140] and the learning rate parameter is

[0141]

[0142] In the formula, is the fitness value of sparrows, is the number of training sets, The output result of the th training set in the current iteration for the neural network model, is the observed value of the training set sample at moment.

[0143] In an exemplary embodiment of the present invention, the sparrow finder function model includes:

[0144]

[0145] wherein, is the sparrow position information of the th iteration calculated by the sparrow finder function model, is the maximum number of iterations, is the current iteration number, is the learning rate, is a random number obeying the normal distribution, is a unit vector, is the warning value of the sparrow population position, is the safety threshold of the sparrow population position.

[0146] Among them, when it indicates that the warning value is less than the safety value, there is no predator in the foraging environment at this time, and the finder can perform extensive search operations; when it means that some sparrows in the population have found a predator and issued a warning to other sparrows in the population, and all sparrows need to fly to a safe area to forage

[0147] Secondly, the sparrow follower function model includes:

[0148]

[0149] wherein, is the sparrow position information of the th iteration calculated by the sparrow follower function model, is the optimal position found by the finder in the th iteration, is a 1*d matrix whose elements are randomly assigned 1 or -1, and d is a natural number, is a natural number.

[0150] Among them, when is satisfied, when it indicates that the th joiner has not obtained food and is in a hungry state. At this time, it needs to fly to other places to forage to obtain more energy.

[0151] Secondly, the sparrow warning function model includes:

[0152]

[0153] In the formula, is the sparrow position information at the -th iteration calculated by the sparrow early warning function model, is the global optimal position at the -th iteration, is a random number obeying the standard normal distribution used as the step size control parameter, is the sparrow position information at the -th iteration, is the fitness value of the current sparrow individual, is the global best fitness value, is a random number in [0, 1], is the global worst position at the -th iteration, is the global worst fitness value, is the tolerance value, is the number of iterations, is the initialization parameter after Logistic chaotic mapping iteration.

[0154] Among them, when it means that the sparrow is at the edge of the population and is extremely vulnerable to attacks by predators. When it means that the sparrows in the middle of the population are also in danger, and at this time they need to approach other sparrows to reduce the risk of being preyed on. At the same time, in order to improve the convergence speed and accuracy of SSA, and are corrected.

[0155] An exemplary implementation of the present invention for constructing a forward calculation formula includes:

[0156]

[0157] In the formula, is the forgetting gate of the neural network, is the recurrent weight of the input gate, is the recurrent weight of the forgetting gate, is the recurrent weight of the output gate, is the recurrent weight of the cell state, is the sigmoid function, is the short-term memory of the neural network at the moment, is the short-term memory of the neural network at the moment, is the moment input operation parameter sequence, is the bias term of the input gate, is the bias term of the forget gate, is the bias term of the output gate, is the bias term of the cell state, is the input gate, is the memory cell state at time is the intermediate value of the updated memory cell state, and Tanh is the hyperbolic tangent activation function; is the memory cell state at time is the output gate.

[0158] In this embodiment, the current design calculation and scheme comparison of the prefabricated frame beam slope are still in the stage of manual control. From design to construction completion, the time cost will greatly increase the possibility of disasters. The prefabricated frame beam slope design based on the improved SSA-LSTM neural network has a high operation speed, reduces the time cost, and simultaneously reduces the performance loss.

[0159] For complex mountainous areas, the large number of parameters considered in the prefabricated frame beam slope design greatly increases the error rate of manual operation. The prefabricated frame beam slope design based on the SSA-LSTM neural network can consider multiple parameters, has strong adaptability, fault tolerance ability and self-improving ability, and improves the design accuracy;

[0160] Due to the difficulty in obtaining relevant engineering data and the inability to provide accurate references for the project, in order to prevent disasters, the design of artificial prefabricated frame beams is too conservative and it is difficult to design a very optimized design scheme, wasting a lot of manpower and material resources. The prefabricated frame beam slope design based on the SSA-LSTM neural network can automatically search for prefabricated frame beam slope design schemes that meet any requirements according to user needs, reducing the labor cost of design and construction.

[0161] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. This computer software product is stored in a storage medium and includes several instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods in various embodiments of the present invention. The foregoing storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), magnetic disks, or optical discs that can store program codes.

[0162] The above are only the preferred embodiments of the present invention and are not intended to limit the present invention. For those skilled in the art, the present invention can have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. An assembly-type anchor cable frame beam slope reinforcement design method, characterized in that Including: Step S1: Obtain the design data and historical monitoring data of the prefabricated frame beam slope, integrate them into data samples, and classify the data samples to obtain a training set and a test set; Step S2: Send the training set and the test set to a preset neural network for training and verification, and optimize the preset neural network by a preset improved butterfly optimization algorithm to obtain a prediction model for the optimized design scheme of the prefabricated frame beam slope; Step S3: Send the historical monitoring data of the prefabricated frame beam slope to the prediction model for the optimized design scheme of the prefabricated frame beam slope to predict the design scheme of the prefabricated frame beam slope; The said Step S2 includes: Step S21: Send the training set to a preset neural network for training to obtain a trained neural network; Step S22: Optimize the hidden layer parameters and learning rate parameters of the trained neural network based on the improved butterfly optimization algorithm to obtain optimized hidden layer parameters and learning rate parameters; Step S23: Assign the optimized hidden layer parameters and learning rate parameters to the trained neural network to obtain an assigned neural network; Step S24: Send the training set and the test set to the assigned neural network for training and verification to obtain a prediction model for the optimized design scheme of the prefabricated frame beam slope; The said Step S22 includes: Step S221: Initialize the hidden layer parameters and learning rate parameters in the preset neural network, and set the parameters of the preset improved butterfly optimization algorithm. The parameters include the initial position of each butterfly, the number of butterfly populations, the maximum number of iterations, the dimension of the space where it is located, the sensory modality, the power exponent, and the dynamic conversion probability; Step S222: Randomly generate a butterfly population with a preset population size, uniformly distribute the butterfly population based on the ICMIC chaos mapping method, and change the initial position of the butterfly population to obtain an optimized butterfly population position; Step S223: Screen the butterfly population by using differential mutation, crossover, and selection strategies to obtain a screened excellent butterfly population; Step S224: Calculate the fitness value of the screened excellent butterfly population, and perform global search by introducing a selection factor based on the calculated fitness value and the relationship between the random number and the dynamic conversion probability, and perform local search by dynamically adjusting the mutation until the number of iterations reaches the maximum number of iterations to obtain the optimal hidden layer parameters and learning rate parameters.

2. The design method of a prefabricated cable anchor frame beam for slope reinforcement according to claim 1, characterized in that, The said Step S224 includes: Step S2241: Calculate the fitness value of the screened excellent butterfly population based on a preset fitness function, sort the calculated fitness values of the butterfly population, and select the optimal fitness value of the butterfly population; Step S2242: Generate a random number to judge its relationship with the preset dynamic conversion probability and make a strategy selection, introduce a selection factor to perform global search, and dynamically adjust the mutation to perform local search to update the butterfly position; Step S2243: Check whether the end condition is met. If it is met, that is, the maximum number of iterations T is reached, the algorithm ends and outputs the best solution found, where the best solution is the optimized hidden layer parameters and learning rate parameters; otherwise, return to step S2242 to continue the next round of iteration.

3. The design method of a prefabricated cable anchor frame beam for reinforcing a slope according to claim 2, characterized in that, The updating of the butterfly position includes: Wherein, represents the number of butterflies in the population; represents the stimulation factor of the -th butterfly in the improved butterfly optimization algorithm, represents the selection factor, which is the probability that the -th butterfly moves towards the best butterfly at the current iteration, , , respectively represent the corresponding positions of the -th, -th, -th, -th butterflies in the butterfly population at the -th generation, is the corresponding position of the -th butterfly in the butterfly population at the -th generation; is a random number within [0, 1]; represents the best position of the butterfly population, represents the set dynamic transition probability; represents the fragrance perception intensity of the -th butterfly; represents the current iteration number; is a constant, is the probability that the -th butterfly moves towards the best butterfly at the -th generation.

4. A design method for strengthening a slope with an assembled cable anchor frame beam according to claim 3, characterized in that, The step S24 includes: Step S241: Send the hidden layer parameters and learning rate parameters to the neural network for forward calculation formula construction to obtain the forward calculation formula of the assigned neural network. Step S242: Send the training set to the assigned neural network for training again. Among them, the output of the assigned neural network at a preset moment is calculated through the forward calculation formula, and the loss value at the preset moment is calculated based on the output. Step S243: Update the loss value at the preset moment and predict the data at each moment to obtain the output result corresponding to the historical monitoring data in the neural network.

5. A design method for strengthening a slope with an assembled cable anchor frame beam according to claim 4, characterized in that The construction of the forward calculation formula includes: Wherein, is the forget gate of the neural network, is the recurrent weight of the input gate, is the recurrent weight of the forget gate, is the recurrent weight of the output gate, is the recurrent weight of the cell state, is the sigmoid function, is the short-term memory of the neural network at time, is the short-term memory of the neural network at time, is the input operation parameter sequence at time, is the bias term of the input gate, is the bias term of the forget gate, is the bias term of the output gate, is the bias term of the cell state, is the input gate, is the memory cell state at time, is the intermediate value of the updated memory cell state, and Tanh is the hyperbolic tangent activation function; is the memory cell state at time, is the output gate.

6. An assembled anchor cable frame beam reinforced slope design system, characterized in that, including: A data collection module, configured to obtain the prefabricated frame beam slope design data and historical monitoring data, integrate them into data samples, and classify and divide the data samples to obtain a training set and a test set. A model construction module, configured to send the training set and the test set to a preset neural network for training and verification, and optimize the preset neural network through a preset improved butterfly optimization algorithm to obtain a prediction model for the optimized prefabricated frame beam slope design scheme. A scheme output module, configured to send the historical monitoring data of the prefabricated frame beam slope to the prediction model of the optimized prefabricated frame beam slope design scheme to predict the prefabricated frame beam slope design scheme. A main control module, connected to the data collection module, the model construction module, and the scheme output module, is used to execute a method for designing a reinforced slope of a prefabricated cable anchor frame beam according to any one of claims 1-5.

7. An electronic device, comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements a method for designing a reinforced slope of a prefabricated cable anchor frame beam according to any one of claims 1-5.

8. A computer-readable storage medium, characterized in that, A computer program is stored on the computer-readable storage medium, and when the computer program is executed by the processor, it implements a method for designing a reinforced slope of a prefabricated cable anchor frame beam according to any one of claims 1-5.

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