143results about How to "Improve solution efficiency" patented technology

The invention discloses an unmanned aerial vehicle cluster task allocation method based on a large language model optimization genetic algorithm, and belongs to the field of computers. The method comprises the following steps: setting a specific chromosome coding mode; generating a multi-constraint initial population; calculating fitness to quantify the advantages and disadvantages of individual genes of the population; when the optimal individual meets the requirement or the maximum iteration round is reached, ending; retaining the optimal individual as a filial generation; generating a batch of new filial generation individuals by the large language model, and fusing the new filial generation individuals with the current filial generation population; calling an optimized large language model to analyze individual chromosome semantics, and outputting an evolutionary potential score; obtaining an individual comprehensive selection probability by integrating the fitness and the evolution potential score, and executing a selection operation; and selecting individuals based on the individual comprehensive selection probability to carry out crossover and mutation operation to generate offspring. The large language model is embedded into the core link of the genetic algorithm, and the algorithm efficiency is improved by improving the population diversity of the genetic algorithm in the unmanned aerial vehicle cluster task allocation scene.

The invention discloses an industrial robot motion pose error calibration method based on constraint solution. The industrial robot motion pose error calibration method comprises the steps that S1, joint angles, tail end coordinates and temperature data of multiple measurement points are collected; s2, correcting the kinematic model by using the thermal elongation and constructing a parameter identification model; s3, constructing a mixed integer nonlinear constraint pool according to mechanical limitation; s4, introducing a semi-definite programming slack variable to convert the non-convex constraint into a linear matrix inequality; s5, combining a primal-dual interior point method and an alternating direction multiplier method to iteratively solve the optimal kinematics parameter correction; s6, introducing a square root information filtering improvement mechanism to correct a state vector square root factor, and calculating an updated nominal kinematics parameter; and S7, based on the condition number of the error Jacobian matrix, judging observability and repeatedly calibrating. According to the method, the problem of precision reduction caused by thermal errors is solved, and the identification precision, the numerical stability and the self-adaptive calibration capability of the kinematics parameters of the robot are improved.

The application discloses a point spread function calculation method and device, electronic equipment and medium. The method can include: establishing an initial point spread function; setting a constraint parameter; obtaining a final point spread function according to the initial point spread function and the constraint parameter and calculating the solution. The application realizes fast solution of the point spread function based on a local neighborhood and a high-frequency approximation, better serves the properties of the Hessian matrix and the high-dimensional forward research of seismic data, provides a new technical idea for high-precision and efficient high-resolution processing of seismic data in the future, and provides strong technical support for actual production and application.

This invention relates to a method, system, equipment, and storage medium for the operation and maintenance scheduling of offshore wind farms. The method includes acquiring basic scheduling data, which is relevant data used for scheduling maintenance of offshore wind turbines; determining multiple initial solutions based on preset initial solution construction rules and the basic scheduling data; determining a first optimal solution among the multiple initial solutions based on a preset solution model and the multiple initial solutions; optimizing the first optimal solution according to a preset first optimization rule to generate a second optimal solution; and optimizing the second optimal solution based on a preset second optimization rule to determine a target optimal solution. This invention improves the efficiency of solving the operation and maintenance scheduling model.

The present application relates to the field of intelligent traffic parking scheduling, and particularly relates to a parking resource comprehensive scheduling strategy optimization method and system based on multi-region cooperation, which specifically as follows: collecting multi-region and multi-type parking resources, commuting parking demand, illegal parking risk and road network operation data according to scheduling period, constructing parking cooperative monitoring sample through standardization processing; calculating time-sharing sharing potential index, underground priority access intensity and road overflow risk elasticity index, constructing hierarchical directed ant colony search graph with heuristic information written; introducing capacity constraint self-repairing mechanism and congestion degree pheromone updating mechanism, solving Pareto solution set through multi-objective ant colony optimization, screening optimal compromise scheme combining fuzzy membership and dynamic preference weight, outputting parking guidance instruction and realizing rolling closed-loop iteration. The present application can realize time-sharing cooperative deployment of parking resources in residential and office areas, balance various parking load, and improve overall utilization efficiency of parking resources.

This specification pertains to the field of refining and chemical production processing technology, and particularly relates to a method and apparatus for optimizing refining and chemical production plans for a target secondary processing unit. The method includes: obtaining parameters of the secondary processing unit under a refining and chemical production optimization scenario; constructing a nonlinear constraint equation for product output based on the parameters of the secondary processing unit; performing an equivalent transformation on the nonlinear constraint equation to obtain an equivalent transformation equation; extracting the nonlinear constraint terms and performing a Taylor expansion; transforming the Taylor expansion formula into a linear approximation equation based on the proportion of the feed material to the output material of the blending tank, the feed material quantity to the blending tank, and the feed property values ​​of the blending tank, according to the property balance constraints of the blending tank; and then substituting these into the nonlinear constraint equation for product output, thereby transforming the nonlinear constraint equation for product output into a linear approximation equation containing only material information variables. This method effectively ensures model convergence and shortens the solution time.

The invention relates to the field of quantum calculation and combinatorial optimization solution, and provides a measurement resource adaptive allocation and risk control recursive dimension reduction method and system based on a parameterized quantum circuit, and a medium. The classical controller issues a circuit control and read-out instruction to the quantum processing unit, executes calculation base projection read-out sampling, counts and estimates diagonal Pauli observable measurement and a tensor product term thereof, and generates point fixation and edge contraction candidate actions. Constructing a lower confidence boundary based on observation uncertainty and dynamically distributing sampling times; forming classical confidence in combination with the flipping cost, performing weighted fusion on the classical confidence and the quantum confidence according to uncertainty to obtain a score, and calculating the risk; selecting low-risk actions in batches under conflict constraints, performing elimination, updating coupling and an external field, and executing amplitude cutting, re-calibration and rarefaction; and iterating to a scale solvable solution and backfilling an output solution.

The invention discloses a heuristic-based iterative legalization method and system for an analog circuit layout, and the method comprises the steps: dividing layout groups according to a special structure and an array module in a layout based on the initial layout of the layout containing overlapped devices or modules; constructing a constraint graph for describing the relative position relationship between the layout groups, and solving symmetric constraint conflicts in the layout so as to ensure that all symmetric constraints are met; performing simulation processing on a non-overlapping constraint, a minimum area constraint and a minimum spacing constraint through a code simulation solver to generate an initial legal layout; and iteratively adjusting the constraint graph based on a heuristic method, identifying a suspicious group and adjusting the constraint edge of the suspicious group, and re-verifying and repairing the symmetric constraint after each adjustment until the layout area converges. According to the method, the symmetric constraint is synchronously verified and repaired in each iterative optimization, so that the layout area is minimized on the premise of ensuring the layout legality, and the solving efficiency is greatly improved at the same time.

This invention provides a spacecraft escort control method based on zero-sum differential games, belonging to the field of aerospace technology. The control method includes the following steps: obtaining spacecraft escort problem parameters for both the pursuing and defending satellites; establishing a spacecraft escort model based on zero-sum differential games and inputting initial parameters into the model; transforming the spacecraft escort model into a two-point boundary value problem model; using a genetic algorithm to solve the two-point boundary value problem model to obtain the state trajectory within the game time, thus completing the spacecraft escort control. This spacecraft escort control method can effectively solve the spacecraft escort problem under continuous thrust, and is an effective extension of existing continuous thrust escort problem models and methods. The model employs a differential game method described by a zero-sum cost function, thus simplifying the solution of the difficult-to-solve weighted performance index escort problem and improving the solution efficiency of the spacecraft escort game problem.

The automobile front door cover size detection device of the present disclosure comprises a part storage table for placing parts, a part grabbing hand for grabbing parts, a support component for fixing and clamping parts, a detection system comprising a data acquisition element, a data calculation system and a data display system, the data acquisition element is used for acquiring part data, the data calculation system is used for determining part size change value according to part data, and the data display system is used for outputting part size change value; a collaborative robot is used for moving the part grabbing hand, and the collaborative robot is used for moving the data acquisition element; a control system is in communication connection with the part grabbing hand, the detection system and the collaborative robot, the control system is used for outputting detection instructions, the detection instructions comprise gripping instructions, part moving instructions and scanning instructions, the part moving instructions are used for controlling the collaborative robot to move the parts through the part grabbing hand, and the scanning instructions are used for controlling the collaborative robot to move the data acquisition element to scan the parts to acquire part data.

The invention relates to the technical field of mobile robot navigation, and discloses a robot adaptive motion control method and system, and the system comprises a step test module which is used for recording odometer data and control quantity data with a timestamp; the parameter fitting module is used for analyzing the odometer data and the control quantity data and establishing a mapping function of the control quantity and the movement speed; the delay compensation module is used for maintaining a historical control quantity queue and predicting the expected pose of the robot when the control instruction sent at the moment t is executed based on the control delay tau; the multi-objective optimization module is used for solving the optimal control quantity; and the online updating module is used for continuously collecting motion data in the motion process of the robot and dynamically updating the mapping function and the key parameters. According to the method, actual motion parameters are automatically obtained through step testing, different robot individuals and performance attenuation scenes are adapted, the problem of model mismatching is solved, and time delay compensation is controlled to reduce positioning deviation.

A transformer partial discharge positioning detection method based on ultrahigh frequency signals is characterized by comprising the following steps: S1, estimating time delay of signals obtained by an ultrahigh frequency (UHF) sensor array based on a generalized cross-correlation method; and S2, arranging and combining signals of the UHF sensor array, and solving coordinates of a positioning point based on an improved multi-moth optimization (MFO) algorithm according to the time delay obtained in the step S1. And S3, clustering the coordinates of the plurality of positioning points obtained in the step S2 based on a density clustering algorithm (DBSCAN), and selecting the geometric center position of the category with the maximum sample number as the final local discharge source coordinate.

The application relates to the technical field of artificial intelligence, and provides an interactive method, device, equipment, medium and program product of a customer service robot, the method comprising the following steps: determining a target operation process matched with a user demand indicated by a conversation context based on the conversation context; the conversation context is determined based on a user query to be replied and historical interaction data belonging to the same conversation as the user query; inputting process content corresponding to the conversation context and the target operation process into a node detection model to obtain a target process node to be executed output by the node detection model; the node detection model is used for determining a target process node most matched with the conversation context from a plurality of process nodes through semantic understanding capability; and generating reply information of the user query based on the target process node. The application solves the problem that a process execution is rigid and cannot adapt to user jump expression, realizes nonlinear and dynamic execution of an operation process, and further improves the interactive experience of the customer service robot.

A kind of aluminum electrolysis MES system real-time interface data exception feedback and dynamic re-push method, it is related to aluminum electrolysis production informatization management technical field, including steps S1, set interface data exception data rule, according to the abnormal data rule to the real-time monitoring and abnormal perception of multi-source interface data;Step S2, set exception data level, according to exception data level, MES system receives exception data and carries out hierarchical feedback;Step S3, according to the server state exception interface data dynamic re-push condition setting, MES system according to the server load state dynamically re-pushes exception data to external system etc..The present application makes up the problem that existing aluminum electrolysis MES system and external system exist in interface data interaction process in the production process, and exception feedback lag and re-push mechanism rigidification, ensure the accuracy and reliability of interface data.

The invention relates to the technical field of power grids, in particular to a distributed new energy acceptance capacity dynamic calculation method and system, and the method comprises the steps: constructing a combined small-disturbance dynamic model for distributed new energy access, and obtaining a stable constraint boundary; establishing an optimization model by taking distributed new energy acceptance capacity maximization as a target function; and solving the optimization model by adopting a self-adaptive particle swarm algorithm to obtain the maximum acceptance capacity of the distributed new energy in the current operation state of the power distribution network. According to the invention, based on the equivalent dynamic model of the network-forming inverter and the state-space equation of the power distribution network, the combined small-disturbance dynamic model is constructed and the stability analysis is carried out, the stability constraint is directly introduced into the acceptance capacity optimization, and the adaptive particle swarm algorithm is adopted for solving, so that the maximum access capacity of the distributed new energy can be accurately evaluated. Meanwhile, small-interference stability of the system and operation constraints of the power distribution network are considered, and calculation precision, efficiency and engineering application value are improved.

The application discloses a voltage stability margin calculation method considering a control mode of a photovoltaic power station, and adds modeling consideration of a reactive power-voltage control mode of the photovoltaic power station in voltage stability margin calculation. In a continuous power flow calculation process, parameterized power flow equations are obtained by extending node voltage with the fastest voltage drop. Meanwhile, in a prediction link in the continuous power flow calculation process, a hybrid prediction method is adopted. In the case that voltage margin is large, nonlinear prediction is adopted to accelerate the calculation speed. In the case that voltage stability limit is approached, linear prediction is adopted. The voltage stability margin calculation method fully considers the influence of the control mode of the large-scale photovoltaic power station on voltage stability, and improves the calculation efficiency and the calculation precision.

This invention discloses an adaptive switching method for rigid / non-rigid algorithms in solving system simulation models. During the system simulation model solving process, the rigidity of the ODE-IVP equations is monitored in real time, and an appropriate solution method is automatically selected based on the current rigidity value. To maximize solution efficiency, this invention does not calculate the rigidity of the current equation at every solution step. Instead, it dynamically "samples" the model according to a certain probability, ensuring that information on model rigidity changes is not lost while maintaining computational efficiency.

The present application belongs to the field of geothermal energy development and utilization, bridge engineering, and deicing and snow melting technology, and proposes a heat exchange pipe maintenance and intelligent operation and maintenance control method for an energy pile active bridge deck deicing and snow melting system, which comprises an energy pile active bridge deck deicing and snow melting federated learning operation and maintenance control model and a heat exchange pipe maintenance device based on electromagnetic induction principle; the energy pile active bridge deck deicing and snow melting federated learning operation and maintenance control model preliminarily judges and makes alarm positioning, and the heat exchange pipe maintenance device based on electromagnetic induction principle carries out fine repair work; the damaged point is repaired by automatic repair agent or the inner layer pipe is unscrewed. The heat exchange pipe maintenance device based on electromagnetic induction principle comprises a double-layer intelligent heat exchange pipe structure, a micro magneto-electric sensor, a cable storage tray, a data automatic acquisition instrument and a control end. The present application realizes fine control management and early warning of the energy pile active bridge deck deicing and snow melting system, accurately positions and repairs the damaged point, globally and accurately controls the snow melting system, and improves the deicing and snow melting efficiency.

The application discloses a kind of based on reinforcement learning and branch pricing container terminal resource integrated scheduling method, system and storage medium.The method comprises: obtaining the scheduling associated data of container terminal scheduling, and constructing the original model of describing berth, shore crane and yard joint allocation problem based on scheduling associated data;Based on the block angle structure of original model, the original model is decomposed into a main problem and multiple pricing sub-problems, and the main problem and multiple pricing sub-problems after decomposition are solved based on the branch pricing framework constructed in advance to obtain linear relaxation optimal solution and integer feasible solution;The branching process of branch pricing framework is modeled as a tree Markov decision process, a tripartite graph representing the decision state is constructed based on the linking relationship of branching variable, main problem column and constraint node, and the target scheduling scheme is determined based on the tripartite graph and reinforcement learning dynamic branching strategy.The technical effects of guaranteeing the global optimality of scheduling scheme and meeting the complex scheduling requirements of real terminal are achieved.

The invention relates to the technical field of power system safety, and provides a power distribution system safety operation optimization method and system based on low inertia. The invention discloses a power distribution system safe operation optimization method based on low inertia. The method comprises the following steps: constructing an evaluation model based on a minimum inertia demand by taking minimization of the total operation cost of a system as an optimization target; under a set constraint condition, solving the evaluation model based on the minimum inertia demand by using a grey wolf algorithm to obtain a minimum inertia demand value at each moment; in the process of searching the minimum inertia demand value at each moment, the grey wolf algorithm adopts a convergence factor to determine a search area; the convergence factor is represented as the fractional order power of the ratio of the difference value of the maximum number of iterations and the current number of iterations to the maximum number of iterations. Response reliability of the system to sudden disturbance can be improved.

This invention discloses a collaborative training method and system for autonomous driving planning and control, belonging to the field of autonomous driving technology. The method includes: acquiring the current state of the vehicle, the states of surrounding traffic participants, and reference road information; using a Transformer-based planning network to output a control sequence, which is then used to generate a planned trajectory via a vehicle kinematics model; inputting the planned trajectory into a model predictive control-based neural network controller for dynamic tracking optimization to obtain the executed control quantity and predicted vehicle dynamic state; constructing a control loss and backpropagating its gradient to the planning network; and, during training, layering constraints on the planning cost term, using safety and traffic rule-related terms as hard constraints and comfort-related terms as soft constraints, and employing a multi-stage cost scheduling strategy to adjust the constraint strength or weight. This invention achieves collaborative optimization of planning and control, improves the dynamic executability and safety compliance of the trajectory, and enhances training stability and closed-loop robustness.

This invention provides a method, device, and storage medium for multi-UAV collaborative four-dimensional trajectory planning, relating to the field of multi-UAV collaborative technology. Addressing complex three-dimensional terrain, spatiotemporal uncertainties, and the demands of multi-UAV collaboration, a four-dimensional executable trajectory planning framework is proposed: It employs a two-layer safety assessment mechanism combining conditional continuous collision verification and conservative propulsion CCD, introducing scenario-based risk measurement and importance sampling to achieve tail risk control; it establishes dynamic executable constraints including velocity, acceleration, lateral acceleration, climb rate, and continuous energy consumption models; it achieves multi-UAV spatiotemporal collaboration and micro-time scheduling through a unified time grid; and it proposes an optimization algorithm, employing a spherical manifold incremental encoding and distribution estimation framework, combined with progressive homotopy constraints, feasible solution consensus guidance, and tail risk-driven sampling mechanisms, to solve the trajectory planning problem under high-dimensional, strongly constrained conditions.

The invention discloses a neural network proxy model-based power distribution area collaborative planning method and system. The method comprises the following steps of: constructing a power distribution area flexible interconnection and energy storage collaborative planning double-layer optimization model; candidate addressing and sizing schemes meeting constraint conditions of the double-layer optimization model are randomly generated, optimization calculation of the operation model is executed on each candidate addressing and sizing scheme, the real total life cycle cost corresponding to each candidate scheme is obtained, and a training sample set is formed; performing supervised training on a preset neural network agent model; and generating and iteratively updating the addressing and sizing scheme, evaluating the predicted total cost of the addressing and sizing scheme by using the neural network agent model, optimizing the addressing and sizing scheme according to the predicted total cost until a convergence condition is met, and outputting a final target addressing and sizing scheme and a corresponding cooperative operation strategy. The problems of'regulation-building-transportation 'disjunction and low calculation efficiency in traditional planning are effectively solved, and collaborative improvement of safety and solving efficiency is achieved.

The application belongs to the technical field of workshop scheduling, and particularly discloses a kind of inconsistent batch quantity flow flexible job shop scheduling method and system, it includes: for inconsistent batch quantity flow flexible job shop scheduling problem, with sub-batch quantity as one of decision variables to construct mixed integer linear programming model;Intelligent optimization algorithm is used to solve mixed integer linear programming model iteratively, in the iteration process, in the intermediate solution generated in each iteration, select part of elite solution, fix the machine allocation of each sub-batch in elite solution and the value of processing order variable of each sub-batch on machine, with the sub-batch quantity in elite solution as optimization variable, construct batch sub-problem model;Sub-problem model is solved, so that elite solution is optimized, and the intermediate solution after processing continues to the next round of iteration.The application can efficiently solve inconsistent batch quantity flow flexible job shop scheduling problem, and realize the improvement of processing efficiency and equipment utilization.

The application discloses a rigid chemical ODE implicit solving method based on a neural network approximate Jacobian, and belongs to the cross technical field of combustion numerical simulation and artificial intelligence. The method obtains a system state vector corresponding to a rigid chemical ODE at a current time step; inputs the system state vector into a trained neural network model to obtain an approximate Jacobian matrix through the neural network model; calculates a residual vector based on the system state vector, and calculates a Newton iteration increment by using the approximate Jacobian matrix and the residual vector; updates the system state vector based on the Newton iteration increment, and judges whether the Newton iteration converges or not based on a residual norm corresponding to the updated system state vector; if the Newton iteration converges, the solving of the rigid chemical ODE at the current time step is completed, and the next time step is advanced; if the Newton iteration does not converge, the updated system state vector is returned to continue iteration. The application can improve the solving efficiency of the rigid chemical ODE under the premise of ensuring the accuracy.

The application discloses a heterogeneous energy collaborative prediction and scheduling method considering source-load low-carbon characteristics complementation and matching, relates to the technical field of power system source-load prediction and low-carbon scheduling, and comprises the following steps: based on Laguerre polynomials, pseudo-inverse learning and integrated learning, robust local mean decomposition (RLMD) and weighted permutation entropy (WPE), a wind power (WP), photovoltaic power (PVP) and power load (PL) prediction model is constructed, which takes into account the prediction accuracy and stability; a HECM mathematical model is constructed, the HECM mathematical model framework comprises an integrated flexible operation mode of carbon capture power plant (IFCCPP), a wind farm (WF), a photovoltaic power station (PP), a concentrated solar power (CSP), a load active demand response (LADR), a ladder carbon trading mechanism (LCTM), an objective function and related constraints; in the source-load prediction, the two targets of accuracy and stability are considered simultaneously, a compromise solution is selected in the Pareto frontier through the MORUN algorithm, and the wind power, photovoltaic power and power load prediction results are more suitable for the power system scheduling scene with high robustness requirements.

This invention relates to the field of energy system optimization and intelligent scheduling technology, and particularly to a multi-domain economic scheduling method and system based on game theory and generative networks. The invention includes: constructing a master-slave game framework; using a conditional generative adversarial network to generate a sequence of predicted scenarios for future time periods and selecting typical scenarios; the slaves calculating their local optimal trading volume based on time-of-use pricing and typical scenarios; calculating a global consistency variable based on the local optimal trading volumes of all slaves, and dynamically adjusting the penalty factor of the alternating direction multiplier method according to scenario deviations; updating the dual variable based on the global consistency variable until the convergence condition is met, obtaining the converged trading result; evaluating the overall system benefit based on the converged trading result; if the preset target is not achieved, adjusting the master's strategy and re-triggering the game process until the preset target is achieved, outputting the final scheduling plan. This invention significantly improves the game model's ability to handle uncertainty and achieves high accuracy.

The invention relates to a support multi-degree-of-freedom pose monitoring method and device based on machine vision, and the method comprises the steps: collecting a pier top integrated target image, carrying out the graying processing and contour extraction, and carrying out the ellipse fitting of a contour, so as to obtain geometric parameters; screening according to an ellipse inclusion relation to form an identification code unit, establishing a local rounding coordinate system, and degenerating an ellipse into a perfect circle through coordinate transformation; constructing a vector under the rounded coordinate system, calculating a signed included angle, and carrying out normalization processing; performing interval matching based on the normalized angle to decode the unique identifier, and synchronously outputting a high-precision circle center coordinate; and finally, combining at least four identification code circle center coordinates, solving a camera pose by adopting a 3D to 2D point pair projection method (PnP), aligning to a support coordinate system through calibration transformation, and outputting a six-degree-of-freedom pose parameter. Compared with the prior art, the non-contact high-precision pose monitoring is realized, and the method has submillimeter-level measurement precision and strong environmental adaptability.

The application provides an optical element surface simulation method based on a physical information neural network, relates to the technical field of element design and simulation, and comprises the following steps: constructing a feedforward neural network; generating a deformation control equation model library; constructing a three-dimensional simulation database; forming a machine learning parameter library; training a PINN deformation simulation model in combination with the deformation control equation model library, the three-dimensional simulation database and the machine learning parameter library; screening out a target PINN deformation simulation model meeting preset conditions; inputting design parameters, support structure parameters and given external load parameters of different combinations into the target PINN model to obtain key performance prediction results of the optical element; the application can not only quickly evaluate the key performance of the element under various parameter combinations, shorten the design iteration cycle and reduce the trial and error cost, but also provides direct data support for support structure optimization design, and effectively solves the bottleneck problem of the traditional method in restricting the research and development efficiency of a high-precision optical system.

This invention discloses an integrated coupling method and system for intelligent production and operation of gas storage facilities. The method includes: constructing a numerical simulation model of the gas storage reservoir: establishing a geological model of the gas storage reservoir based on dynamic production data and geological knowledge, and establishing a numerical simulation model of the gas storage reservoir reservoir; constructing a wellbore model of the gas storage reservoir: establishing a wellbore model of the gas storage reservoir based on relevant basic data of the injection and production wells; constructing a surface pipeline network model of the gas storage reservoir: establishing a surface pipeline network model of the gas storage reservoir based on relevant basic data of the surface pipeline network; and constructing an integrated coupling model of the gas storage reservoir: combining the wellbore model and the surface pipeline network model of the gas storage reservoir, and coupling the numerical simulation model of the gas storage reservoir to complete well mapping and fluid mapping, forming an integrated coupling model of the gas storage reservoir reservoir-wellbore-pipeline network. This invention considers the comprehensive production constraints from the gas reservoir, wellbore, and surface pipeline network, and is of great significance for analyzing and optimizing the injection and production systems of gas storage facilities.