317 results about "Combinatorial optimization" patented technology

The invention relates to the field of underground powerhouse construction risk identification, and provides an underground powerhouse construction risk identification and disposal method, system, device and medium, and the method comprises the steps: collecting multi-source heterogeneous data in real time, obtaining historical risk case data, and carrying out the preprocessing to obtain structured time-space correlation data; constructing a multi-dimensional analysis model based on a parallel computing algorithm, and performing multi-scale risk analysis on the structured time-space associated data to obtain multi-level risk feature data; performing risk feature recognition through a multi-modal machine learning model to obtain risk quantitative indexes, and performing recognition based on a fuzzy comprehensive evaluation algorithm to obtain construction risk levels; and matching emergency strategies of construction risk levels, carrying out parameter expansion through a combinatorial optimization algorithm, generating a plurality of candidate disposal schemes, carrying out weight calculation and sorting on the candidate disposal schemes by adopting a multi-criterion evaluation model, and outputting an optimal disposal scheme. According to the invention, efficient identification and accurate emergency decision-making of the construction risk of the underground powerhouse are realized.

The invention discloses a traffic infrastructure full life cycle carbon emission assessment method based on a knowledge graph. The method comprises the steps that knowledge is acquired through multi-source data acquisition, entities, attributes and relations related to carbon emission are defined, and knowledge graph ontology design and graph construction are carried out; establishing a carbon emission evaluation model based on an artificial neural network on a framework of the knowledge graph through data preprocessing, multi-layer perceptron network structure design and model training; and in combination with the carbon emission evaluation result and the optimization knowledge in the knowledge graph, generating a specific optimization strategy, and performing combinatorial optimization of the optimization strategy to formulate a recommendable optimization scheme for execution of an optimization decision. According to the scheme provided by the invention, carbon emission can be accurately quantified, a scientific and reasonable optimization strategy is generated, dynamic adjustment is supported, and a systematic and intelligent solution is provided for green and low-carbon development of traffic infrastructures and other engineering projects.

The invention relates to the field of computer industry, and discloses an integrated computer software and hardware combined sales optimization system and method, and the method comprises the steps: extracting software and hardware sales records, customer transaction behaviors and version updating tracks, and constructing a fusion data set of heterogeneous sales data; performing association weight analysis on the fused data set, and extracting strategy sensitive points in software and hardware linkage transaction in combination with a customer behavior intention model and product life cycle characteristics; constructing a product combination feasible region in a specific sales window by adopting a combination optimization graph search algorithm based on the tag nested structure; mapping the candidate combined product list to a historical sales behavior map, and extracting dynamic feedback modes of a combined strategy under different sales paths by introducing a price elastic response model and market disturbance fluctuation characteristics; and carrying out iterative adjustment and node intervention on the strategy path based on the high-sensitivity area. The method has the advantage of improving the scientificity of sales strategy formulation.

A system and method include reformulating an optimization program as a Quadratic Unconstrained Binary Optimization (QUBO) model and in a single iteration, solving the optimization program by inputting the QUBO model into a quantum computing solver, instructing the quantum computing solver to generate a plurality of solutions to the optimization program based on the QUBO model, receiving the plurality of solutions from the quantum computing solver, inputting each of the plurality of solutions into a nonquantum computing solver, wherein the nonquantum computing solver uses each of the plurality of solutions as a starting point to continue solving the optimization program, and outputting an optimal solution to the optimization program from the nonquantum computing solver.

The invention relates to the technical field of unmanned aerial vehicle signal transmission, and discloses an unmanned aerial vehicle signal transmission rate detection method and system.The method comprises the steps that a compressed sampling matrix is constructed through quantum random projection to reduce the signal dimension, and a quantum entanglement gate is driven based on attitude parameters to generate a dynamic sparse basis matrix; a federated learning framework is adopted to be fused with multi-node data optimization base parameters, rate detection is converted into combinatorial optimization solution in combination with a quantum annealing algorithm, and finally real-time adjustment of a modulation mode and transmitting power is achieved through closed-loop control. The innovation is embodied in a quantum compression sampling-federated optimization-annealing detection cross-domain cooperation mechanism, the defects of a traditional method in dynamic adaptability, calculation efficiency and anti-interference performance are overcome, and the method has the technical advantages of sparse representation space dynamic adaptation, quantum parallel acceleration optimization, multi-target parameter balance and the like. And the communication rate detection precision and link reliability of the unmanned aerial vehicle in a complex electromagnetic environment are remarkably improved.

The invention relates to the field of combinatorial optimization and artificial intelligence, and discloses a group logistics transportation scheduling method and system based on a role interaction graph neural network, and the method comprises the following steps: S1, dividing agent nodes and position nodes, and generating initial features; s2, iteratively updating node embedding by using a multi-channel attention mechanism of a graph neural network; s3, generating a delivery point distribution probability based on node embedding, and determining an initial distribution scheme; s4, local redistribution optimization is performed on the delivery points with low confidence distribution; and S5, performing parallel path planning on the optimal scheme, and outputting a result for reinforcement learning feedback. In the invention, through modeling of a graph neural network multi-channel attention mechanism on a complex interaction relationship and a synergistic effect of local redistribution optimization and parallel path planning, a second-level generation of a high-quality scheduling scheme is realized, cross-scale scene migration of the model is achieved, and group logistics transportation scheduling efficiency and robustness are improved.

The invention relates to the technical field of biological breeding, in particular to a rice hybrid combination optimization method based on an adaptive algorithm, and aims to solve the problems that existing rice breeding is poor in adaptability, multi-objective optimization is difficult and artificial experience is limited. The method is characterized in that a data integration module, a dynamic target module, a parent library module, a genetic prediction module, a self-adaptive optimization module (including a genetic algorithm and reinforcement learning), a simulation evaluation module and a recommendation feedback module are constructed, and precise prediction and dynamic optimization of hybrid combination are achieved. By adopting the scheme, the limitation of artificial experience is effectively overcome, the breeding decision is changed from static experience to dynamic data drive, and the breeding efficiency, accuracy and adaptability are remarkably improved.

The embodiment of the invention discloses a quantum computer, a method and a related device for solving a combinatorial optimization problem, and the quantum computer comprises a measurement and control all-in-one machine which is configured to generate a control signal based on a constraint condition, the constraint condition is used for representing the constraint when a plurality of elements are combined; the quantum chip comprises at least one group of quantum bits, and the number of the quantum bits is the same as that of the elements; and the interface is connected with the measurement and control all-in-one machine and the quantum chip and is configured to control the quantum chip based on the control signal so as to generate an optimal combination of elements represented by the ground state of the group of quantum bits. By adopting the embodiment of the invention, the requirement of quantum bits can be effectively reduced, the method is suitable for a real quantum computer, and a practical and feasible quantum circuit can be operated to solve a combinatorial optimization problem.

The invention discloses a quantum Isin model construction method for a security constraint unit commitment optimization problem, and relates to the field of quantum computation.The quantum Isin model construction method comprises the steps that a security constraint unit commitment optimization model is constructed, and parameters of a mixed integer programming problem are obtained; using a Benders decomposition method to decompose a mixed integer programming problem into a main problem and a sub-problem; substituting the optimal binary solution to solve the sub-problem to obtain a new cut plane and expand a cut plane set; constructing a compact high-dimensional quadratic function fitting cutting plane set; solving a positive semidefinite programming problem to obtain a high-dimensional quadratic function parameter; converting a quadratic unconstrained binary optimization model constructed based on a high-dimensional quadratic function into an Isin model; solving the Isin model to obtain a quantum bit state, and solving an optimal binary solution; and substituting the optimal binary solution into the above steps for iterative solution. According to the invention, the problem of huge consumption of quantum bit resources in the prior art is solved, especially the problem of difficulty in processing NP with complex constraints and more variables is solved.

The invention provides a GPU-based parallel tetrahedral mesh optimization method, system and device and a medium, and relates to the technical field of numerical simulation. Comprising the steps of generating a triangular mesh covering a surface for a three-dimensional geometric model; generating an initial tetrahedral mesh based on the triangular mesh; performing a round of point smoothing operation on each tetrahedron by using a plurality of GPU threads; carrying out iterative circulation of the combination optimization operation, and simultaneously carrying out a round of point smoothing, edge deletion and multi-surface deletion operation on a plurality of initial tetrahedrons by utilizing a plurality of GPU threads; and when the edge deletion operation and the multi-surface deletion operation cannot further improve the grid quality, simultaneously performing a round of edge contraction and edge segmentation operation on the plurality of initial tetrahedrons by utilizing the plurality of GPU threads. According to the method, the large-scale grid with reliable quality is quickly generated based on the low-quality input grid, and the grid optimization rate is improved, so that the requirement of large-scale unstructured grid optimization in a three-dimensional space is met.

The invention relates to the technical field of industrial compressed air system intelligent control, in particular to a digital air compression station intelligent control method and system based on AI self-learning, and the system comprises a data collection layer, an AI prediction layer, an optimization control layer and an execution layer. The data acquisition layer realizes accurate acquisition and preprocessing of multi-dimensional data through a three-stage high-precision sensor network and an edge computing node; the AI prediction layer adopts an XGBoost and LSTM mixed model to be combined with an online learning mechanism to realize accurate prediction of the gas consumption demand and the energy efficiency ratio within 1-24 hours; the optimization control layer completes air compressor operation combination optimization and pressure dynamic control based on an improved NSGA-II algorithm and self-adaptive PID adjustment; the execution layer realizes control instruction landing through frequency converter and cluster cooperative scheduling. Meanwhile, an equipment health degree monitoring module, a multi-source data fusion correction module and a waste heat-gas consumption collaborative optimization module are innovatively introduced, and the problems that a traditional air compression station is low in energy efficiency, poor in self-adaption, insufficient in prediction precision and lack of equipment management are solved.

The invention relates to a security constraint unit commitment problem processing method and device based on time decoupling, computer equipment and a storage medium. Relates to the technical field of power systems. The method comprises the steps of determining a plurality of target coupling time points in a to-be-optimized time period of a power system through a pre-constructed comprehensive evaluation model; dividing the to-be-optimized time period into a plurality of sub time periods based on the plurality of target coupling time points; decomposing a long-time-scale security constraint unit commitment problem to obtain a sub-problem optimization model corresponding to each sub-time period; and according to each sub-problem optimization model, solving to obtain a unit commitment optimization result of the power system. By adopting the method, the constraint calculation efficiency of the unit combination in the large-scale power system can be improved.

A computing system including one or more classical binary computers coupled to one or more quantum computers. The computing system is configured to process the one or more computing tasks including at least one combinatorial optimization task using a Filtering Variational Quantum Eigensolver (F-VQE) algorithm implemented by using one or more Ansätze circuits and a cost function arrangement to generate one or more quantum circuits in the quantum computer. The computing system iteratively applies a filtering operator to a cost function arrangement to generate a corresponding filtered cost function arrangement that excludes energy states that exceed an energy threshold and uses the filtered cost function arrangement in the one or more quantum circuits to generate output results.

The invention discloses a magnesium alloy forging and pressing process parameter optimization method and system based on finite element analysis, and the method comprises the steps: collecting forging and pressing process parameters and test data of preset magnesium alloy, and carrying out the preprocessing of the forging and pressing process parameters and the test data; a forging and pressing temperature interval is obtained through the test data, a magnesium alloy finite element model is constructed based on the temperature-strain rate coupling effect, self-adaptive grids are divided, and boundary conditions are set; the forging and pressing process parameters are adjusted in the forging and pressing temperature interval, the magnesium alloy finite element model is adopted for analog simulation to obtain simulation data, and a quality evaluation function is constructed according to customer requirements and the simulation data; and a magnesium alloy forging and pressing process parameter optimization model is constructed according to the quality evaluation function, a magnesium alloy deformation mechanism is adopted to optimize the magnesium alloy forging and pressing process parameter optimization model, and a multi-objective optimization algorithm is adopted to carry out combination optimization on the second process parameters to obtain optimized combination parameters.

The invention discloses a rivet neglected loading detection method and system based on CAD digital-analog point cloud mapping, and the method comprises the steps: 1, carrying out the collection and preprocessing of an aircraft panel rivet image, and constructing a data set; 2, inputting an aircraft panel rivet image in the data set into the target detection model to obtain a rivet centroid coordinate; 3, performing coarse positioning and fine positioning on the rivet; 4, sending the fine positioning area to a detection head of the target detection model to obtain a predicted rivet neglected loading result, constructing a total loss function by using the real rivet neglected loading result and the predicted rivet neglected loading result, circulating the steps 2 to 4, and minimizing the total loss function until the total loss function is converged; and 5, detecting rivet neglected loading by using the target detection model and the semantic segmentation model on the equipment end to obtain a detection result. According to the method, deep fusion is carried out on point cloud geometric feature analysis and a combinatorial optimization method, a closed-loop coarse registration process without manual intervention is formed, and the workload of manual reference point selection is reduced.

The invention discloses an intelligent discharging method and device based on array combinatorial optimization and a storage medium, and relates to the technical field of image processing, and the method comprises the steps: analyzing a target graphic file to extract cutting piece information in the target graphic file; classifying the cutting piece information so as to classify the outline of the flexible material and sub cutting pieces in the flexible material; constructing an initial typesetting scheme according to the flexible material contour and the sub-cutting pieces; selecting a reference typesetting scheme from the initial typesetting schemes according to a preset fitness function; performing cross processing on the reference typesetting scheme; performing variation processing on the part of the reference typesetting schemes after the cross processing; carrying out correction processing on the reference typesetting scheme which is subjected to cross processing and is not subjected to variation processing and the reference typesetting scheme which is subjected to variation processing; and taking the reference typesetting scheme with the highest fitness as a target typesetting scheme. Typesetting of the sub-cutting pieces is regarded as a combinatorial optimization problem, and the material utilization rate is improved.

The invention relates to an electric sanitation vehicle intelligent scheduling method and device and electronic equipment, and relates to the technical field of electric vehicle scheduling, abnormal data is eliminated through a multi-source data credibility verification mechanism, and a four-dimensional space-time tensor model fused with sanitation parameters is constructed to realize accurate extraction of dynamic features; breaking through the bottleneck of large-scale path combinatorial optimization based on a quantum-classical hybrid solver, and dynamically correcting an execution deviation by combining model prediction rolling optimization; and finally, synchronous scheduling of the path instruction and the charging resource is realized through vehicle-station-cloud cooperative control. Through dynamic weight calculation and a Byzantine fault-tolerant arbitration mechanism, abnormal data of the sensor are effectively eliminated, and multi-source information fusion is realized. According to the technology, the problem of path planning deviation caused by data distortion of a traditional scheduling system is solved, it is ensured that the input data of the space-time modeling and optimization model has high credibility, and therefore the reliability of the whole scheduling scheme is improved.

Provided is a quantum computing method for obtaining an optimal solution of a problem with multiple discrete variables, wherein the problem is represented by a cost function, the method comprising:—generating a graph structure from the cost function,—dividing the graph structure into at least two disjunct subgraph structures, wherein each subgraph structure comprises a subset of the multiple variables,—mapping each subgraph structure to a local cost function represented as local cost Hamiltonian,—determining, for each local cost Hamiltonian, all eigenstates corresponding to an energy below a predetermined cut off energy using a quantum processing device, wherein each variable of the subset of multiple variables is represented by a qubit of the quantum processing device,—recombining the determined eigenstates, and-approximating a ground state from the recombined eigenstates, wherein the ground state represents the optimal solution.

The invention relates to the technical field of intelligent mines, in particular to a mine combination optimization method based on QUBO. Comprising the following steps: obtaining the type, quantity, purchase cost, operation efficiency and matching relation data of mining equipment, and constructing an initial data set; based on the initial data set, establishing a mathematical model with maximized total profit as an objective function and budget constraint and equipment matching relation as constraint conditions; the objective function and the constraint condition are converted into a QUBO model, and the QUBO model is solved through a quantum computer or a simulated annealing algorithm; and a subQUBO method is adopted to perform decomposition and iterative solution on a large-scale quantum bit problem, and a dynamic optimization result under a complex combination scene is generated. According to the method, the equipment purchasing and matching scheme under the multi-constraint condition is efficiently solved through the quantum computing technology, the problems that a traditional optimization method is low in computing efficiency and high in error rate are solved, limitation of quantum hardware resources is broken through, and long-term mine profit maximization is achieved.

The invention belongs to the technical field of power systems, and relates to a security constraint unit commitment optimization acceleration method and system based on a large language model. The method comprises the steps of obtaining economic operation basic data of a power system; constructing an integer relaxation security constraint unit commitment model and solving; generating a code corresponding to the seed algorithm; evolving a neighborhood search algorithm based on a large language model to obtain an optimal neighborhood search algorithm; constructing a security constraint unit commitment model of a limited neighborhood according to an optimal neighborhood search algorithm; solving a security constraint unit commitment model of a limited neighborhood; constructing a security constraint unit commitment model; and solving the security constraint unit commitment model. According to the method, high-quality neighborhood constraints can be generated, and efficient feasible solution optimization is realized; the solving efficiency of the security constraint unit combination problem is improved, the optimization solving efficiency of the power system based on the security constraint unit combination is improved, efficient optimization operation of a high-proportion power system is supported, and safe and stable operation of the power system is guaranteed.

The invention provides an air compressor scheduling and energy consumption optimization method and system based on a cloud platform, and a medium, and the method comprises the steps: collecting and uploading the operation parameter information of an air compressor through a sensor disposed at an air compressor terminal, obtaining a load rate through the combination of a real-time predicted target load, and obtaining the energy consumption of the air compressor through the combination of a mapping relation between the load rate and a mode; determining an operation control mode of the frequency converter so as to achieve the effects of single-machine energy saving and operation stability improvement; an air compressor health index is output based on a preset health assessment model, a load task is dynamically allocated under a constraint condition by adopting a combinatorial optimization algorithm, the equipment wear condition is balanced and controlled, and the service life of key parts is prolonged; the actual energy efficiency is evaluated through the energy-saving achievement rate, incremental learning and parameter iteration of the load prediction model and the health evaluation model are triggered, closed-loop self-optimization is achieved, and the preset accuracy is improved.

The invention discloses an AI business process automatic generation method based on AI model combinatorial optimization, and the method comprises the following steps: constructing a multi-level AI capability decoupling and reconstruction module, and carrying out the bottom-up hierarchical modeling and top-down modular decoupling, dynamic mapping of an AI atomic power layer, an AI modular production capacity layer, an AI general capability layer and an AI application business layer is realized; an elastic AI capability combinatorial optimization module is constructed, AI capability combinatorial optimization oriented to three dimensions of data, features and models is carried out based on service quality requirements, and multiplexing, combination and arrangement of AI capabilities are realized through evolutionary computation optimization driven by an agent model; and an AI business process automatic generation module is constructed, an AI service containerization deployment scheme is generated and optimized based on data-driven process mining and a hyper-heuristic algorithm, and AI business process automatic generation is realized. According to the invention, the adaptability and execution efficiency of the AI technology in a complex scene can be improved.

A computing system may be provided. The computing system may include an optical memory cavity configured to store signal pulses representing a current combination for a combinatorial optimization computation. The computing system may further include an optical processing cavity. The optical processing cavity may be configured to receive the signal pulses from the optical memory cavity, perform an all-optical 1-bit delay computation on the signal pulses to generate a feedback pulse for at least one signal pulse, and provide the feedback pulse to be coupled back to the optical memory cavity.

The invention relates to a method for achieving feeding and discharging of special-shaped parts of a coating production line and a robot system, and belongs to the technical field of industrial robots, and the method for achieving feeding and discharging of the special-shaped parts of the coating production line comprises the steps that scene point cloud of the coated special-shaped parts in three-dimensional point cloud of a material box is obtained; converting the constructed three-dimensional model of the coated special-shaped part to obtain a model point cloud of the coated special-shaped part; performing coarse registration and fine registration on the scene point cloud and the model point cloud in sequence, planning a feeding and discharging motion track of the robot by adopting a 3-5-3 combined piecewise polynomial interpolation function so as to construct a robot joint space track model, and taking motion time as an optimization variable so as to construct a robot joint space track model; the comprehensive optimization of the loading and unloading time, impact and dexterity of the robot is taken as a target function, and the particle swarm whale combinatorial optimization algorithm is adopted to optimize the trajectory parameters of the robot joint space trajectory model so as to obtain the optimal loading and unloading trajectory of the robot, so that the loading and unloading takt time of the robot is shortened, and the production efficiency is improved.

The invention relates to the technical field of credit marketing, in particular to an AI credit marketing management system based on multi-modal data fusion. Multi-source heterogeneous data containing social media, credit transactions and the like are obtained through a web crawler tool, an API interface and the like, the social media and credit transaction data are integrated to construct customer feature vectors, so that the problem of demand misjudgment caused by data islands is solved, real-time compliance constraint data are generated in combination with financial market and credit transaction data, and the real-time compliance constraint data is obtained. The method comprises the following steps: generating a personalized credit marketing strategy based on data to respond to market fluctuation by using'parameter dynamic generation + combinatorial optimization + preference weighting 'based on the data so as to meet diversified requirements of customers, finally calculating marketing accuracy by obtaining a multi-dimensional matching index and interactive feedback data, and replacing subjective judgment by adopting'historical matching + real-time prediction' three-dimensional evaluation so as to improve marketing accuracy. And comparing the marketing accuracy with a preset threshold value, and if the marketing accuracy does not reach the standard, adjusting a marketing strategy so as to realize intelligent upgrading of credit marketing.

The invention discloses a rock-fill dam deformation digital twin construction method based on generative AI, and the core is that a conditional denoising diffusion probability model is adopted, a conditional sampling mechanism is constructed through classifier-free guidance and continuous conditional vectors, and finite element simulation data, monitoring data and operation data are efficiently fused. And further combining a residual neural network ResNet-18 and a K-means clustering method to carry out finite element data unsupervised classification, introducing combinatorial optimization, and identifying a finite element data category which is most matched with monitoring data, so that the condition-guided deformation field is generated. The framework is applied and verified on the highest two-estuary rockfill dam (303 meters) in the current built world. The result shows that the rockfill dam deformation digital twinborn body construction method based on the generative AI can efficiently reconstruct rockfill dam deformation, has high precision and real-time performance, remarkably improves the global deformation thorough sensing ability of the rockfill dam, and provides key technical support for safe operation of the rockfill dam.

The invention concerns searching or comparing points, based on travel of entities among some of the points within a transportation system. Embodiments include a real estate search engine, where a family can search or compare homes and schools, based on commute durations, considering that an adult wants to walk a child to a stop of a school bus. Embodiments also include approaches for solving an underlying optimization problem based on: an enumeration search, a tree search, a gradient descent search, and a branch-and-bound search. An embodiment scales a solution, by decomposing the optimization problem into independent subproblems, using a combinatorial optimization algorithm, that is applied to a certain travel graph. Scaling is also facilitated by a sparsification approach, that generalizes a routing method prevalent in prior art. Some of the approaches are part of a live real estate search engine available to users in South Korea and Japan.

The invention belongs to the technical field of power system coordinated optimization, and relates to a light quantum acceleration-based multi-microgrid active power distribution network coordinated optimization method, which comprises the following steps of: 1, constructing a light quantum acceleration-based multi-microgrid active power distribution network coordinated optimization framework according to an optimization target and an actual condition; 2, constructing a light quantum acceleration collaborative optimization model for the multi-microgrid active power distribution network; 3, discretizing the multi-microgrid active power distribution network collaborative operation model, and then establishing a quantum interpretable model on the basis; 4, performing problem decoupling aiming at the discretization model, using transmission line power deviation as a coupling relation of an upper main body and a lower main body, and then providing a precision adjustment method for a precision problem of a light quantum computer so as to identify an adaptive solver of the problem; according to the method, the modeling flexibility of mixed integer optimization is reserved, and the potential parallelism and acceleration capability of quantum calculation in processing large-scale combination optimization problems are fully exerted.

The invention provides a unit commitment optimization method based on solution space expansion and space-time diagram modeling. A space enhancement strategy and a diagram neural network modeling technology are innovatively combined. According to the method, disturbance optimization and diversity constraints are introduced, a training data set containing rich approximate optimal solutions is constructed, and the generalization ability of the model is remarkably improved. Meanwhile, the unit commitment problem is converted into a space-time dependency graph structure, the sequential relation and structural constraint between variables are effectively extracted through a graph neural network, and deep modeling and prediction of the optimization problem are achieved. Furthermore, by designing a double-order constraint repair mechanism, feasibility correction and fine adjustment are carried out on a prediction result, and the feasibility and scheduling precision of a solution are guaranteed. The finally constructed scheduling framework has good real-time performance and deployability, and can provide efficient and accurate combined scheduling decision support for a large-scale power system.

This invention discloses a reinforcement learning-based optimization method for the construction process of cable-stayed tension structures, comprising the following steps: S1, randomly sorting the forming process sequence of the cable-stayed structure multiple times to generate a sample space matrix; S2, constructing a combined optimization model for the forming process of the cable-stayed structure based on reinforcement learning, and inputting the sample space matrix into the combined optimization model; S3, determining the range of various hyperparameters in the training of the combined optimization model, and finding the optimal training hyperparameters using the controlled variable method; S4, solving the sample space matrix using the model with the optimal training hyperparameters, and outputting the optimal forming process sequence. This invention, based on the Q-learning algorithm, explores the trade-off between the steel brace installation sequence and key mechanical performance characteristics during the forming process of the cable-stayed structure. The optimized steel brace installation sequence can reduce the maximum steel brace stress and the maximum cable lifting force, providing convenience for the selection of tooling in the actual forming process.