79 results about "Engineering optimization" patented technology

The invention discloses a large model prompt project optimization system and method fusing a domain knowledge graph. The system comprises an analysis module, a template generation engine module, a large model interaction interface module, a feedback analysis module and an optimization strategy module. And the analysis module forms a constraint coding signal containing an entity attribute incidence matrix. The template generation engine module forms an enhanced prompt text stream with a reservoir physical property parameter slot; the large model interaction interface module receives the enhanced prompt text stream and generates a question and answer response data stream containing geological terminologies; the feedback analysis module forms a feedback signal containing semantic deviation measurement through a semantic error vector calculation algorithm; and the optimization strategy module forms a parameter optimization instruction signal and transmits the parameter optimization instruction signal to the analysis module to complete iterative updating of the constraint condition. According to the large model prompt project optimization system fusing the domain knowledge graph, the problem of low answer accuracy of a large model in the oil-gas exploration field due to lack of professional constraints can be solved.

The invention discloses a multi-agent cooperative cue word engineering optimization method, system and device and a medium, and relates to the technical field of cue word engineering, the method comprises the following steps: after initially receiving a user input task, starting a cue word optimization process; an initial cue word template is created through a cue word generation agent, historical optimal templates are retrieved from a knowledge base for fusion, and candidate cue word templates are generated. And the analysis agent inputs the candidate template into the second large language model to generate a candidate answer. And the evaluation agent performs multi-dimensional evaluation on the answers and gives scores and defect analysis. And according to an analysis result, updating the cue word to generate a candidate template of the intelligent agent. And arranging the intelligent agent to cyclically execute the steps until the stop condition is met, and outputting the optimal cue word template. And finally, storing the optimal template into a knowledge base and establishing a semantic index to support subsequent retrieval reuse. According to the method, manual intervention is not needed, and a complete automatic process of generating, evaluating and optimizing the cue word is realized.

The invention discloses an engineering optimization method based on an improved particle swarm algorithm, and the method comprises the steps: constructing a particle swarm optimization model according to an engineering problem mathematical model, and initializing the position and speed of particles; then, calculating a fitness value index and a diversity index of the particles according to the model, and constructing convergence and diversity data according to the fitness value index and the diversity index; the two kinds of data are used for updating the initial positions and speeds of the particles, and the middle adjustment positions and speeds are obtained. Then, on the basis of the particle swarm optimization model and the intermediate adjustment position, the particle individuals and the global position are updated, and individual convergence and global convergence positions are obtained; and calculating a particle swarm output result according to convergence and diversity data and individual and global convergence positions. And finally, obtaining an optimal solution of the engineering problem according to an output result. According to the embodiment of the invention, two kinds of data are constructed by taking population diversity and fitness value as evaluation criteria, the population position and speed are updated, the optimal solution can be found more quickly and accurately, and the engineering optimization problem is efficiently solved.

The invention provides a multi-objective optimization method and system for material layout beside a production line based on evolutionary optimization, and relates to the technical field of intelligent manufacturing and industrial engineering optimization. The method comprises the following steps: firstly, acquiring basic information data required by material layout optimization beside a production line, and carrying out data preprocessing and standardization; then establishing a multi-objective optimization mathematical model for layout of materials beside the production line, and determining an objective function and constraint conditions; aiming at the material layout multi-objective optimization mathematical model beside the production line, adopting a heuristic initialization strategy and a dynamic Thompson sampling mechanism to improve the MOEA / D algorithm to obtain a heuristic initialization MOEA / D algorithm; and finally, based on a heuristic initialization MOEA / D algorithm, solving the multi-objective optimization mathematical model for the layout of the materials beside the production line, and obtaining a Pareto optimal solution set through iterative optimization, namely obtaining the optimal layout of the materials beside the production line. According to the method, the reasonability and efficiency of material arrangement can be effectively improved.

The invention discloses a multi-strategy improved fox-monkey optimization algorithm for global optimization and application thereof, and belongs to the technical field of optimization algorithms, the method comprises the following steps: firstly, using Chebyshev chaotic mapping to initialize a population so as to improve the diversity and coverage range of the initial population; secondly, a difference population evolution strategy is introduced in the iteration process, and the algorithm is prevented from falling into local optimum in the later period; and finally, introducing a crisscross strategy to enhance global search capability and maintain population diversity. 20 benchmark test functions and a part of complex functions in a cec2017 function set are adopted for a simulation experiment, and the experiment result shows that the optimization precision, the convergence speed and the stability of the improved algorithm are obviously improved. The improved algorithm is applied to the engineering optimization problem, compared with an original algorithm, the convergence precision of the algorithm in three actual engineering problems is improved by 19.56%, 19.18% and 6.7% respectively, the standard deviation is reduced by 91.44%, 27.56% and 92.19% respectively, and the feasibility of the improved algorithm is further verified.

The invention relates to the technical field of extremely-small curved surface structures, in particular to an optimal design method, system and equipment for an extremely-small curved surface structure and a storage medium. The minimum curved surface structure optimization design method comprises the steps that a multi-objective optimization problem is determined according to design variables and optimization objectives of a three-period minimum curved surface lattice structure; constructing an approximate function relationship between the optimization target and the design variable by utilizing a self-adaptive multi-target engineering optimization method of a Kriging agent model; latin hypercube sampling and a sequential quadratic programming optimization algorithm are combined to carry out optimization design on the three-period minimal curved surface lattice structure. According to the design method, the light / bearing / wave-absorbing minimum curved surface lattice sandwich structure with good structure-performance dual characteristics can be designed, the wave-absorbing structure is filled with the minimum curved surface lattice structure in a shape follow-up mode, design and manufacturing of a wave-absorbing and bearing integrated structure are achieved, and rapid iteration and performance improvement of design of the wave-absorbing and bearing structure are supported.

The invention provides a jacket type offshore electrical platform optimization method based on a sequential quadratic programming algorithm. The jacket type offshore electrical platform optimization method comprises the following steps: S1, establishing a parameterized offshore electrical platform structure analysis model; s2, calculating an environmental load borne by the offshore electrical platform by adopting an equivalent static load method; s3, establishing an offshore electrical platform optimization model which comprises a target function and a constraint function; s4, performing optimization design by adopting a sequential quadratic specification algorithm; and S5, performing dynamic analysis on the optimal structure, judging whether the optimal structure meets a constraint condition or not under a dynamic load working condition, if so, ending optimization, and if not, manually adjusting the size of the component which does not meet the constraint until the constraint condition is met. According to the method, the sequential quadratic programming algorithm is adopted, the optimization efficiency is high, the engineering optimization requirement is met, the size optimization of the offshore structure considering the pile-soil interaction under wind, wave and flow loading can be achieved, and the cost is further reduced on the premise that the structural stability and safety are guaranteed.

The invention relates to an engineering optimization method and system based on a whale optimization algorithm, and a medium, and belongs to the technical field of intelligent optimization algorithms. Comprising the following steps: S1, initializing parameters and populations: setting a population scale, a maximum number of iterations, dimensions and upper and lower bounds of variables, generating an initial population, and initializing a global optimal solution and a current optimal solution; s2, executing an individual variation strategy: recording a global optimal individual position and a current iteration optimal individual position; s3, executing a unified search strategy; s4, executing a group communication strategy; and S5, judging whether the current number of iterations reaches the maximum number of iterations, if so, outputting a globally optimal solution, otherwise, adding 1 to the current number of iterations and returning to the step S2 to continue iteration. The algorithm provided by the invention aims to improve the global search capability and convergence precision of the algorithm and maintain the time complexity equivalent to that of the original WOA at the same time.

The invention discloses a high-speed aircraft aerodynamic configuration agile optimization method based on an artificial intelligence model. The method comprises the following steps: A1, establishing an aerodynamic force agent model; a2, performing a global optimization algorithm; a3, establishing an intelligent autonomous decision-making model; by constructing a multi-target adaptive weight distribution model, weight dynamic collaborative optimization of proxy model confidence, a constraint penalty function and target function key elements is achieved, the mathematical modeling problem of dynamic matching of weight parameters and optimization targets in the iteration process is solved, a closed-loop optimization system with the autonomous perception-decision-execution capacity is developed, and the optimization efficiency is improved. Therefore, the convergence speed is improved in a complex engineering optimization scene, local optimum is avoided, and the robustness of a global optimal solution is ensured; and A4 pneumatic optimization iteration: based on the proxy model for predicting indexes such as the lift-drag ratio, performing global optimization of design parameters by using an artificial intelligence optimization algorithm, performing verification through CFD, and after verification, putting a CFD calculation result into a training set of the model to re-train the model and continuing optimization until optimization design is converged.

The application discloses a kind of based on manifold constraint's elastic body erosion profile multimodal self-attention fusion prediction method, comprising: data set construction and pre-processing;Build multimodal self-attention fusion network model;Based on the geometric manifold characteristics and physical evolution law of elastic body erosion profile, build the dual compound constraint loss function including data loss, geometric manifold regularization loss and physical consistency loss;Model training and hyperparameter optimization;The microstructure image and physical parameter under the working condition to be predicted are input into the trained model, and the discrete coordinate points and continuous profile curve of the elastic body erosion profile are output, and the importance of different input features to the prediction result is quantified by the integrated explainability analysis module.The application realizes the high-precision and rapid prediction of the elastic body erosion profile, and has the advantages of strong generalization ability and explainability, which provides important technical support for the completion of ammunition warhead design, protection engineering optimization and damage efficiency evaluation tasks.

The invention relates to the technical field of structural engineering optimization design and intelligent model selection, in particular to a structured generative adversarial network-based pipe truss structural design and model selection method, which comprises the following steps of: obtaining pipe truss load data and corresponding structural design schemes under different load conditions; respectively coding the load data and the design scheme to obtain a multi-dimensional dynamic load feature vector and graph structure information; inputting the two schemes into a generator, and generating a design scheme and a model selection scheme by taking the minimum steel use amount as a target under the constraint of physical information; discriminating the generation scheme and the real scheme through a discriminator, calculating an adversarial loss, and alternately training the generator and the discriminator until convergence; and finally, inputting the preset load and the structure design scheme into a convergence generator, and outputting an optimal scheme. According to the method, the end-to-end mapping from the dynamic load to the optimal design and model selection scheme is realized, the design efficiency is remarkably improved, and the structural lightweight is realized while the engineering reliability is ensured.

The invention provides a construction project cost analysis method and device, a storage medium and a program product, and relates to the technical field of construction cost analysis. Performing budget reverse decomposition operation on the current construction project by using the cloud computing service center to obtain a sub-item project parameter set, geographic position data, sub-item budget constraint conditions and a sub-item project optimization strategy; a background database is used for executing missing parameter influence operation on the subentry engineering parameter set according to the geographic position data and the material supply chain data, and a cost influence weight is obtained; key parameter locking operation is executed on the front-end application software according to the cost influence weight, so that key parameters are obtained; and utilizing a background database to generate a cost decision analysis report according to the key parameters, the sub-item engineering parameter set, the sub-item engineering optimization strategy and the sub-item budget constraint conditions. The technical problem that the parameter processing efficiency is low in the construction project cost analysis process in the prior art is solved.

The application provides an engineering optimization method based on failure state variable routing and landscape discrimination, and relates to the technical field of engineering design optimization.The application comprises the following steps: establishing an engineering design constraint optimization model; constructing a fitness evaluation function; initializing a differential evolution algorithm population and parameters; according to individual historical failure states and population evolution states, self-adaptively selecting and generating a first mutation vector from a plurality of pre-defined mutation strategies; constructing a reference direction based on excellent individuals in the population, discriminating and repairing the first mutation vector for direction consistency, and obtaining a second mutation vector; discriminating the problem form of a current search area according to the correlation between population distribution information and fitness sequences, and adjusting the search behavior accordingly; updating the population through a crossover operation; iterating until a termination condition is met, and outputting optimal engineering design parameters.The application improves the optimization precision and convergence efficiency of complex engineering design problems through failure state driven variable routing and landscape structure discrimination.

The invention relates to the technical field of engineering optimization, in particular to an accurate sloping design and earth volume dynamic optimization method oriented to electric power engineering. According to the method, the technical problem that the slope boundary of the complex terrain is not accurately determined is solved through the steps of dynamically shifting the polyline, performing multi-stage encryption intersection, performing three-dimensional topology reconstruction and the like. According to the method, a gradient trigger encryption mechanism and combined integral earthwork calculation are innovatively introduced, in the scenes such as a transformer substation side slope and a power transmission line tower footing, the slope boundary precision is improved by 300% or above, the earthwork volume calculation error is controlled within 5%, and the reliability and economical efficiency of electric power engineering design are remarkably improved.

The application provides an Abaqus multivariable and multi-target fast optimization method and system based on a Bipod flexible support structure, and includes: five modules of Main_Optimizing.py, Sub_Parameter_Sensitivity_Analysis.py, Sub_Parameter_Range.py, Sub_Model_Bulider.py and Sub_Job_Submit.py work cooperatively, functions of coordinating a global process, identifying key design variables, determining an optimal search space, automatically generating a parameterized finite element model and realizing batch simulation calculation are realized, and therefore a finite element oriented multivariable and multi-target fast optimization is realized. The application can effectively coordinate conflicts among optimization targets, obtain a satisfactory parameter solution in a short time, solve problems of low efficiency of artificial experience parameter optimization and time-consuming and laborious large batch manual modeling, and is helpful to meet urgent needs of modern engineering fields for efficient and accurate structure optimization design.

The invention relates to a credible AI system solution based on double-domain modeling and quantum computing, and aims to solve the defects of the existing AI system in the aspects of cross-culture adaptability, physical correctness and decision real-time performance. Through deep fusion of modern mathematical tools (such as tensor analysis and normative field theory), quantum computing thought (such as advanced path optimization algorithm) and engineering optimization technology, a unified framework capable of processing subjective cognition and objective laws at the same time is constructed. The method specifically comprises the following steps: 1, realizing unification of cultural dynamics and physical law stability through a dual-domain modeling framework of a cognitive dependent domain (CDR) and a non-cognitive domain (NCR); 2, a hypergraph-tensor hybrid architecture which is used for representing a multivariate cultural relationship and verifying a kinematics equation; 3, an advanced computing architecture provides high-performance hardware support; and 4, a multi-mode verification system is adopted, so that the reliability and the safety of the system are ensured. The method is suitable for multiple fields of automatic driving, industrial control, financial risk control and the like, and shows strong universality and commercial potential.

A reliability design optimization method suitable for separating impact components of a shedding connector belongs to the technical field of engineering optimization. In order to solve the problems that traditional reliability optimization design is highly dependent on physical test, and the test cycle is long and the cost is high, the present application proposes a spatial self-adaptive sampling strategy for Kriging surrogate model, which combines model prediction uncertainty and gradient information, dynamically identifies active constraints, thereby effectively reducing misclassification and redundant sampling; in view of the different response characteristics of the objective function and the constraint function, an anisotropic sampling space is constructed for each function, guiding the sample to focus on the area with higher optimization potential; based on the maximum chord deviation criterion, the sampling points are placed in the nonlinear significant area, so as to improve the accuracy of the local surrogate model and accelerate the convergence speed, thereby realizing reliability design optimization.

The invention discloses a method and equipment for improving engineering problem modeling quality and a storage medium, and belongs to the field of engineering optimization problem modeling by a large language model. The method comprises the following steps: step 1, organizing a series of engineering optimization problems according to hierarchical classification and complexity, and constructing a tree structure knowledge base; step 2, receiving natural language description of a target engineering problem to be modeled, performing recursive search on the tree structure knowledge base in the step 1, and identifying modeling sub-problems corresponding to nodes related to the target engineering problem until the most matched and most specific modeling sub-problem is found; 3, retrieving an advanced modeling thought corresponding to the modeling sub-problem in the step 2, and combining the advanced modeling thought with the description of the target engineering problem to generate a global modeling thought; and 4, automatically generating an optimization model and solver codes of the target engineering problem by using the large language model according to the global modeling thought in the step 3. According to the method, the automatic modeling quality of solving a complex engineering optimization problem by using a large language model can be improved.

The invention relates to the technical field of unreal engines, and particularly discloses an unreal engine engineering optimization packaging method suitable for a localized platform, which is used for carrying out localized platform adaptation and compatibility analysis on an unreal engine, carrying out performance optimization and completing operation system adaptation. Installing and configuring a necessary SDK (Software Development Kit), ensuring that a tool chain is compatible with a platform, and setting a construction environment; constructing and packaging the project into a format required by the target platform, and generating an executable file; testing items on the target platform, debugging specific problems of the platform, and carrying out performance testing and pressure testing; the project is published to the target platform, and the project is successfully deployed and stably runs on the target platform. According to the method, by carrying out localized platform adaptation, compatibility analysis and performance optimization on the unreal engine, the potential of localized platform hardware is fully excavated, the rendering performance is remarkably improved, the rendering efficiency is greatly improved compared with that before optimization, and smoother visual experience can be provided for a user.

The invention provides an interpretable knowledge mining system, method and device and a medium, and belongs to the technical field of interpretable artificial intelligence and intelligent data mining. The system comprises a data processing module, an algorithm and intelligent interpretation module, a visual analysis module and an operation management module. An explicit mathematical model which is simple in structure and easy to understand is generated through a multi-branch shallow symbol feature learning tree structure, a dynamic branch expansion strategy, LLM guide initialization and a staged grey wolf optimization selection strategy; and an LLM and retrieval enhancement generation (RAG) mechanism is introduced to perform automatic semantic analysis and intelligent interpretation on the generated model. According to the interpretable knowledge mining system, method and equipment and the medium, the technical threshold of interpretable modeling is effectively reduced, meanwhile, the prediction performance, the structure interpretability and the knowledge discovery efficiency of the model are improved, and the interpretable knowledge mining system, method and equipment and the medium are suitable for engineering optimization, scientific modeling and cross-domain data analysis.

The invention discloses a water engineering multi-objective scheduling method and system, and belongs to the technical field of water engineering optimization scheduling, and the method comprises the steps: S1, building a water engineering group optimization scheduling model with the maximum power generation amount, the maximum sand discharge amount and the minimum ecological water deficit amount as objective functions based on the basic information data of water engineering, meanwhile, constraint conditions are set for the water engineering group optimization scheduling model; s2, based on the water engineering group optimization scheduling model, a double-population optimization mechanism is adopted to solve a water engineering optimal scheduling scheme; s3, outputting an optimal scheduling scheme; according to the water engineering multi-target scheduling method and system provided by the invention, the relationship among power generation, desilting and ecology of the water engineering can be coordinated, a water engineering group scheduling scheme set can be quickly given, the benefits of the water engineering can be played to the greatest extent, and the method and the system have the advantages of few calculation parameters, high convergence speed, high global optimization capability, difficulty in falling into local optimum and the like; and a more effective technical method is provided for solving a complex water engineering multi-target engineering problem.

The invention relates to the technical field of engineering optimization, in particular to an intelligent engineering scheme optimization method and system based on AI generative design, and the method comprises the steps: obtaining a historical engineering design scheme and engineering design feedback after the scheme demand information sent by a user terminal is detected, performing optimization processing on the historical engineering design schemes according to the scheme demand information and the engineering design feedback to obtain an optimized engineering design scheme set, sending the engineering design scheme set to the user terminal, and receiving a scheme check report sent by the user terminal, judging whether the project design scheme selected by the user terminal exists in the scheme check report, if yes, defining the project design scheme selected by the user terminal as a target project design scheme, and after it is detected that the target project design scheme is started and implemented, carrying out real-time potential scheme optimization on the target project design scheme, and controlling and displaying the optimized target engineering design scheme. The engineering implementation efficiency is improved.

This invention relates to an engineering optimization method, system, and medium based on the Whale Algorithm (WOA), belonging to the field of intelligent optimization algorithm technology. The method includes the following steps: S1: Initialize parameters and population: Set the population size, maximum number of iterations, dimension, and upper and lower bounds of variables; generate an initial population and initialize the global optimum and the current optimum; S2: Execute individual mutation strategy: Record the position of the global optimum and the position of the current iteration's optimum; S3: Execute a unified search strategy; S4: Execute a group communication strategy; S5: Determine if the current iteration count has reached the maximum number of iterations. If so, output the global optimum; otherwise, increment the current iteration count and return to step S2 to continue iteration. The algorithm proposed in this invention aims to improve the global search capability and convergence accuracy of the algorithm while maintaining a time complexity comparable to the original WOA.

The invention discloses an efficient data extraction method based on a large language model, and relates to the technical field of data extraction.The efficient data extraction method comprises the steps that S1, an automatic information extraction framework is constructed, S2, an engineering optimization strategy is prompted, S3, multi-model interaction and data set construction are conducted, and S4, machine learning verification is conducted. Full-process automation of literature processing is realized to extract key data; a progressive prompt strategy is adopted, a specific prompt chain is constructed, and the large language model is guided to be accurately output; selecting a multi-modal large language model, respectively extracting parameter data by utilizing sensitivity differences of the multi-modal large language model to parameters, and constructing a high-quality domain data set; performing data preprocessing and feature engineering screening based on the data set, comparing model prediction with real experimental data, and verifying the validity of the data set and the reliability of the model; according to the method, the efficiency and quality of data extraction and processing are improved, and an efficient data solution is provided for related fields.

The present application belongs to the field of marine renewable energy and floating structure engineering optimization design, and discloses a tension leg type floating wind turbine multi-parameter automatic collaborative optimization design method and system. The method realizes the integrated efficient optimization of the floating body and the anchoring system by fusing parameterized geometric modeling, open source frequency domain hydrodynamic calculation, fast load evaluation and genetic algorithm. The present application realizes efficient automatic design, avoids the unstable interface problem, and the process is reproducible and expandable. The present application can optimize in a larger design space, obtain a comprehensive design scheme with better performance matching of the floating body and the anchoring system and lower cost of the whole system, effectively balance efficiency and reliability, and stably handle complex constraints based on the genetic algorithm adaptive optimization mechanism of multi-constraint punishment.

The invention discloses an analysis method and device for transverse vibration of a variable-parameter drill column, and relates to the technical field of variable-parameter structural dynamics, and the method comprises the steps: obtaining drilling tool combination information of the drill column along the well depth, constructing a structure and mechanical parameter function changing along the well depth according to the information, and building a transverse vibration control equation and boundary conditions; performing spatial discretization on the control equation, constructing a correlation matrix, solving a generalized characteristic value to obtain a vibration characteristic parameter, and further constructing a generalized integral transformation relation; and establishing and solving a modal kinetic equation in combination with external excitation, recovering a result to a physical space to obtain a vibration response, and outputting an engineering decision basis through analysis. According to the method, the technical problems that the vibration analysis result is inaccurate and the calculation efficiency is insufficient due to the fact that an existing method is difficult to adapt to the change of the structure and mechanical properties of the drill string and the influence of a complex environment are solved, and the technical effects of accurately obtaining vibration related responses and key indexes and providing reliable support for engineering optimization and adjustment are achieved.

The application discloses a kind of arithmetic optimization algorithm based on multi-strategy improvement engineering optimization method, steps are as follows: first, engineering problem is formalized, and the parameter of input multi-strategy improved arithmetic optimization algorithm is determined.Second, compare r1 using sine factor improvement with mathematical optimizer acceleration function MOA, reconstruct the search phase of arithmetic optimization algorithm, to balance the proportion of exploration phase and development phase in arithmetic optimization algorithm.Then, the golden sine strategy is used to guide individual to approach optimal solution, to realize algorithm fine search.Using Runge-Kutta algorithm enhanced solution quality ESQ mechanism further improves the quality of current optimal solution, makes it jump out of local optimum, searches to optimal solution.The algorithm is further applied to benchmark test function and engineering optimization problem instance, and the test results show that the improved algorithm has strong search ability, high convergence precision, fast convergence speed, and good effect for the engineering optimization problem.

The present invention discloses an engineering optimization method based on a partitioned hybrid surrogate model, and its steps include: Step S100: Obtain an initial design sample according to the engineering optimization objective; Step S200: Obtain an initial hybrid surrogate model; Step S300: Based on the initial hybrid surrogate model, perform preliminary multi-objective optimization to obtain an optimal solution set P; Step S400: Utilize the continuously updated optimal solution set P, and set the best hybrid surrogate model obtained thereby as the final hybrid surrogate model. The present invention has the advantages of simple principle, convenient operation, wider application range, capable of reducing the calculation cost, and improving the optimization efficiency, etc.

The invention belongs to the technical field of oil and gas field development, and provides a model library construction method based on digital twin injection-production engineering. Comprising the steps of integrating an injection-production engineering physical entity model, reconstructing a physical three-dimensional model of a physical entity, defining attributes of the physical three-dimensional model, embedding a formula algorithm, developing an interface, realizing data interaction and visual modeling, and displaying a production state. Boundary conditions are defined based on the physical three-dimensional model, simulation software is imported to form a simulation model, the accuracy of the simulation model is verified through comparison with actual data, and an interface is constructed for a user to operate and test the injection-production process; packaging to form a directly called engineering application component, a feature engineering component and a tool component supporting knowledge reuse; and integrating a plurality of injection-production engineering optimization algorithms by utilizing an algorithm interface, and constructing a water injection optimization and oil production quantity dynamic regulation and control model in combination with empirical rules and optimal practices of an injection-production process. According to the invention, an injection-production engineering digital twinborn model library which can realize efficient modeling, accurate simulation and optimization and a flexible and extensible system is established.