1434 results about "Solver" patented technology

The invention discloses a multi-modal visual arrangement recommendation method, belongs to the technical field of artificial intelligence and data visualization crossing, and realizes visual arrangement recommendation based on multi-modal input analysis, a dynamic mixed recommendation model and an intelligent optimization algorithm. Comprising the following steps: multi-modal intention analysis: realizing intelligent analysis of multi-modal input through combined use of a base model and a fine tuning model, realizing high-precision intention classification in combination with a pre-training language model and a domain adaptation fine tuning technology, and triggering dynamic prompt word recommendation; performing intelligent layout generation: performing global optimization of component space allocation by adopting a genetic algorithm, performing business rule adaptation by combining a constraint solver, and modeling an interaction relationship between components by utilizing a graph neural network; and dynamic mixed recommendation: constructing a three-level recommendation architecture including collaborative filtering, content matching and reinforcement learning. According to the method, a closed-loop recommendation process of user intention-intelligent recommendation-feedback optimization is realized, and the intelligent level of visual arrangement and the user experience are remarkably improved.

A source-grid-load-hydrogen storage multi-stage planning method and system considering flexible resources, relating to the technical field of source-grid-load-hydrogen storage system planning. The method comprises: collecting source-grid-load-hydrogen storage data for data preprocessing; constructing a flexibility supply and demand characteristic model and a source-grid-load-hydrogen storage multi-stage dynamic planning model; calling a solver to solve the source-grid-load-hydrogen storage multi-stage dynamic planning model to obtain an optimal solution; and outputting a multi-stage source-grid-load-hydrogen storage investment result, a multi-stage source-grid-load-hydrogen storage operation policy, and a multi-stage flexibility supply evaluation result within a planning period. Using the minimization of investment costs, operation costs, and insufficient flexibility penalty costs within the whole planning period as target functions, various constraints such as a new energy permeability constraint and a load loss rate constraint are introduced, a dynamic planning method is proposed, and a source-grid-load-hydrogen storage multi-stage planning solution that has sufficiently economical planning operation and is sufficiently flexible is obtained. According to a processing method based on piecewise linearization, the model is simplified, and the computation speed is increased.

The invention provides a security data penetration test method. The method comprises the steps of pre-establishing a multi-thread application program race condition vulnerability feature library, performing static analysis on source codes of a target application program, identifying potential race condition vulnerability points, and obtaining a preliminary vulnerability candidate set; for monitored suspected race condition events, abstracting event context information into constraint conditions, exploring an input combination of suspected static conditions by using a constraint solver, and determining a minimum input set of stable triggering race conditions; aiming at memory models and synchronous primitives of different programming languages, obtaining a race condition inverse mode, and extracting universal utilization primitives covering various race condition vulnerabilities; in the penetration test process, static analysis, dynamic monitoring and symbolic execution are applied to mine race condition vulnerabilities of a target application system, the availability of the vulnerabilities is verified, and evaluation of system security is assisted to be completed.

In one embodiment, a method herein comprises: inputting, by a device, an input prompt to a first large language model to generate an output; computing, by the device, a reward metric in part by using a solver to process the output; tuning, by the device and based on the reward metric, a second large language model configured to correct errors of the first large language model using reinforcement learning; and using, by the device, the second large language model to correct an error of the first large language model.

The invention discloses a hierarchical optimization scheduling method for deep peak regulation of thermal power generating units, and the method comprises the following steps: system layering: dividing a power system into a plurality of subsystems, each subsystem comprising a thermal power generating unit cluster and differentiated load demands; subsystem-level prediction: generating a basic scheduling plan; dynamic preference-driven multi-target collaborative optimization: receiving multi-target data from each subsystem, carrying out multi-target collaborative optimization, modeling three targets of a power grid company, an environmental protection department and a terminal user as game participants, quantifying the priority of each party by adopting a fuzzy membership function, generating a dynamic game solution through a Nash equilibrium solver, and carrying out multi-target collaborative optimization; screening out a Pareto optimal solution giving consideration to interests of multiple parties; performing online verification on the dynamic security domain; virtual synchronous machine cooperative support: simulating operation characteristics of a synchronous generator; and global coordination and iterative optimization: uploading the optimal scheduling scheme of each subsystem to a scheduling center through a message queue telemetry transmission protocol, and performing global constraint verification.

The invention provides a thermal-mechanical coupling topological optimization method for a thin-wall non-uniform filling structure, and the method comprises the steps: 1, building a finite element model according to design requirements, and determining a non-design domain and a design domain; 2, initializing a design variable, and then obtaining a local volume fraction polymerization field through one-step filtering; 3, describing the distribution of a material in a design domain; 4, thermal coupling finite element calculation is carried out, and a structural displacement field U is solved; step 5, performing corresponding sensitivity analysis; 6, submitting the sensitivity to a moving asymptote algorithm solver to update an overall topology field and a dot matrix density field; and step 7, checking whether an iteration stop condition is met or not. According to the invention, the lightweight design of the power battery module bracket structure facing the thermal elasticity problem is realized; according to the power battery module support with the thin-wall non-uniform filling structure characteristic, the structural rigidity is improved, and meanwhile, the good heat dissipation performance is shown.

The invention discloses an electric power system climbing demand evaluation and optimization method considering load side resources, and the method comprises the steps: providing a calculation method for maximum upward and downward climbing demands in different time periods, including a calculation method for deterministic and uncertain climbing demands, in order to effectively cope with the fluctuation and uncertainty of loads or new energy; a climbing demand optimization model which takes minimization of the uncertainty climbing demand as an objective function and takes constraint conditions such as load adjusting quantity and demand response into consideration is provided; a gradeability optimization model which takes minimization of comprehensive consumables of the power system as an optimization target and considers constraints such as node balance and line power flow is provided; modeling is carried out on AMPL software, a solver GUROBI is called for solving, and an electric power system climbing demand evaluation and optimization scheme is obtained. The comprehensive operation consumables of the power system can be effectively reduced, and the net load peak load shifting effect on the power system is achieved, so that the operation reliability of the power system is improved, and the dispatching pressure of the power system is relieved.

The invention discloses a three-dimensional unstructured grid adaptive refinement method and system based on machine learning, and belongs to the field of machine learning, partial differential equation solving and computational fluid mechanics simulation. Residual errors of a fluid control equation are used as a novel error indicator and a refinement criterion, imprecise flow field data and the fluid control equation are obtained by fusing coarse grids of simulation flow through a physical information neural network, and the total residual errors of the equations are conveniently calculated by using automatic differentiation after training is completed. Therefore, the coarse grid units with relatively high residual errors can be adaptively marked and refined. By matching an h-refinement scheme, Delaunay tetrahedron subdivision is executed after vertexes are strategically inserted to maintain the quality of the refined grid. According to the method, any numerical solver can be flexibly matched to carry out grid adaptive refinement, so that various typical flow problems can be solved with high precision. The method achieves better balance between calculation precision and the number of grids, and has the advantages of being simple, convenient, high in compatibility and universality and the like.

The invention provides a parallel training method, device and equipment based on adaptive re-calculation and load division and a computer readable medium. The method comprises the following steps: firstly, converting a dynamic calculation graph in a deep learning framework into a static calculation graph data structure; then, the calculation sequence is divided, and calculation sub-graphs are generated; constructing a mixed integer programming model according to the calculation time of the operator, the transfer cost of the operator and the constraint condition; then, solving the scheduling optimization problem of the operator by using a solver to generate a video memory optimization strategy; aiming at the generated video memory optimization strategy, evaluating the execution time of each calculation sub-graph, and calculating to obtain a load balancing rate; and finally, according to a calculation result of the load balancing rate, determining a calculation sub-graph which is finally divided, and performing parallel training by utilizing the calculation sub-graph which is finally divided. Through the method provided by the invention, the final operator execution sequence can be found, so that the peak video memory consumption during the operation of the computational graph is within a given limit, and the training time of the neural network model is minimized.

The invention discloses a mold and formwork design system based on virtual simulation, and particularly relates to the field of mold design, the mold and formwork design system comprises a multi-mode perception fusion module, a digital twin modeling module, a multi-physics field coupling simulation module, a hybrid intelligent optimization module, a digital twin closed loop verification module and a data center module, and each module forms a design closed loop through a data center. The multi-modal sensing fusion module adaptively collects and fuses multi-source signals to generate high-credibility data; the digital twin modeling module constructs and iteratively corrects a model based on the data; the multi-physics field coupling simulation module realizes multi-solver co-simulation under a dynamic boundary; the hybrid intelligent optimization module accelerates to generate an optimal solution through secondary optimization and an agent model; the digital twin closed-loop verification module detects defects and generates a correction instruction; the data center module is responsible for data management, cross-module scheduling and precision control; the system improves the design precision and efficiency of the mold frame, reduces the physical mold testing cost, and is suitable for a high-precision mold development scene.

The invention discloses a polyurethane permeation grouting diffusion simulation method, device and equipment and a medium, and the method comprises the steps: building a geometric model of permeation grouting diffusion of polyurethane in a to-be-injected porous medium according to the physical attribute data of the to-be-injected porous medium; establishing a physical field of the geometric model by adopting a Richardz equation and a porous medium dilute material transfer equation, and adjusting the geometric model according to material attribute parameters of the geometric model obtained by the physical field; setting a boundary condition and an initial value of the adjusted geometric model, and performing grid division on the adjusted geometric model based on a local refinement method to obtain a permeation grouting diffusion simulation model; and solving the permeation grouting diffusion simulation model according to the transient solver, and completing simulation of permeation grouting diffusion of polyurethane in the porous medium. According to the method provided by the invention, the modeling accuracy of the slurry diffusion process is improved.

The invention discloses a riverway three-dimensional water flow numerical simulation method, and relates to the technical field of intelligent water conservancy simulation, and the method comprises the steps: generating a longitudinal long and narrow hexahedral mesh of a main riverway in a C gradient direction through employing a mucous network growth algorithm, and outputting a hexahedral mesh with a shear layer identifier; turbulence viscosity coefficient distribution is dynamically initialized through a DDPG reinforcement learning model, and turbulence parameters are output; inputting the hexahedral mesh with the shear layer identifier and turbulence parameters into a coupling solver, performing parallel numerical solution, and outputting transient flow field data; a lambda 2 criterion is adopted to identify a vortex core area and calculate a vorticity modulus, a transverse circulation is defined, an on-way distribution cloud picture is generated, an immersive VR scene is constructed through a Unity 3D engine, and a three-dimensional visual water flow simulation scene with a physical feature label is output. Through fusion of lambda2 criterion vortex identification and a Unity 3D visualization technology, a complete solution with both calculation precision and engineering practicability is provided for river flow simulation.

The invention discloses a power distribution network-shared energy storage-multi-microgrid optimization scheduling method based on a multi-agent game. The method specifically comprises the following steps: constructing a multi-microgrid shared energy storage multi-target optimization configuration model considering energy storage investment cost and load fluctuation; solving the multi-microgrid shared energy storage multi-target optimization configuration model by adopting a multi-target particle swarm algorithm, and optimizing to obtain charging and discharging power of energy storage to each microgrid; establishing a one-master and two-slave game scheduling model taking the power distribution network as a master and multiple microgrids and shared energy storage as followers; and solving the one-master two-slave game scheduling model by adopting a genetic algorithm nested solver to obtain an optimal scheduling scheme. By constructing a one-master two-slave multi-agent game scheduling framework, cooperation and coordination among the power distribution network, the shared energy storage and the multiple microgrids are facilitated, coordination of three-party scheduling is realized, balanced distribution of benefits of the power distribution network, the shared energy storage and the multiple microgrids is further realized, and the overall benefit is improved.

The invention discloses a method for evaluating the bearing time of an internal fault of large oil-filled main equipment, and belongs to the technical field of relay protection of oil-filled main equipment of a power system. Obtaining a plurality of fault currents in the internal fault current range of the oil-filled main equipment; establishing a three-dimensional geometric simulation model and a grid model of the oil charge main equipment; calling a transient pressure-based solver, simulating a turbulence phenomenon and an oil pressure rise phenomenon of the oil-filled main equipment under a fault current condition, judging whether the oil-filled main equipment is broken or not, and obtaining allowable fault bearing time of the oil-filled main equipment under different fault currents; and establishing a neural network model for mapping the fault current and the allowable fault withstanding time through a convolutional layer, a pooling layer and a full connection layer in the convolutional neural network, so as to evaluate the allowable fault withstanding time of the oil-filled main equipment. And the fracture and failure phenomena of the oil-filled main equipment in a high-stress state are accurately captured, so that a quantitative method which is more accurate than a traditional empirical method is provided for evaluating the fault bearing time of the oil-filled main equipment.

The invention provides a software demand defect detection method based on a large language model and a satisfiability problem solver, and the method comprises the steps: constructing a domain knowledge base, guiding the large language model to extract key elements in a demand text through combining with a cue word project, and recognizing imperfect and ambiguous defects; and meanwhile, the demand is converted into a logic expression, and logic inconsistency is detected by using an SAT solver. According to the method, incremental updating of the knowledge base is supported, efficient retrieval and comparison are achieved through vectorization and Hash coding technologies, experiments show that the method is remarkably superior to a traditional technology in demand detection in the fields of finance and the like, defects can be accurately positioned, correction suggestions can be provided, and the efficiency and accuracy of demand analysis are effectively improved.

The invention discloses a CFD-based liquefied natural gas after-condenser structure optimization method and device. The method comprises the steps that an initial model is established based on an assembly drawing, and geometric pretreatment is completed; extracting a fluid channel, segmenting and selecting a computational domain, and carrying out local encryption on a key region; generating a polyhedral grid by using a grid division tool, and completing grid independence verification; importing the polyhedral mesh into a solver, and carrying out model setting; boundary and wall surface conditions are set, and solver parameters are configured; setting an initial flow field through a hybrid initialization method, and establishing a flow monitoring window and a temperature monitoring window to adjust simulation until convergence; analyzing the temperature cloud picture and the flow vector diagram, and adjusting the structure of the baffle plate; the tower diameter and the height of the filler layer are adjusted according to a physical quantity field change curve of the filler layer, and the performance of the recondenser is continuously improved. The problem of how to deeply study the flow heat transfer rule in the after-condenser and obtain the flow field distribution characteristics is solved, so that the working performance of the after-condenser is improved.

The invention discloses a full-reactor-core parallel fluid-solid coupling simulation method and system. The system comprises a global collaborative coupling simulation scheduling module, a hierarchical index forest management module, a high-performance parallel solver module and an intelligent parameter transmission module. Wherein the global collaborative coupling simulation scheduling module is used for being responsible for overall process control, time stepping, solver scheduling, convergence judgment and inter-module communication triggering; the hierarchical index forest management module is used for being responsible for distributed parallel construction and efficient dynamic updating of CLGIF and providing rapid geometric query service; the high-performance parallel solver module is used for embedding or tightly integrating numerical solvers of fluid and solid physical fields and applying a dynamic parallel multi-solver optimization module to maximize the calculation performance and expansibility of the high-performance parallel solver module; and the intelligent parameter transmission module is used for executing physical quantity exchange on a fluid-solid interface, the core of the intelligent parameter transmission module is a GME-driven region sensing RBF mapping engine, and the transmission precision and efficiency are ensured.

The invention discloses a visual setting calculation system and method, and relates to the technical field of power grid setting calculation, and the method comprises the following steps: obtaining operation data of a target system, extracting feature data, and constructing a knowledge graph of entities and relationships, the feature data including control data; constructing a system structure diagram, and generating a semantic enhancement diagram in combination with semantic information in the knowledge graph; inputting the semantic enhancement graph into a pre-trained graph neural network model, performing node feature aggregation and edge weight learning in combination with a prior rule of a knowledge graph, and outputting sensitivity scores of all control data nodes on performance indexes; according to the sensitivity score, constructing a boundary constraint condition, and generating a control data setting candidate solution based on a CSP solver; performing parameter response simulation verification on the control data setting candidate solution, and screening out an optimal control data setting candidate solution; according to the application, accurate sensitivity scoring can be realized by fusing the knowledge graph, the semantic enhancement graph and the graph neural network.

The invention discloses a path planning method for a quad-rotor unmanned aerial vehicle. The method comprises the following steps: acquiring a voxel map through a visual sensor of a quadrotor unmanned aerial vehicle, establishing a Voronoi map according to the voxel map, and solving three-dimensional points of the Voronoi map near an obstacle in the voxel map through a quadratic programming solver to obtain a plurality of path points; updating the plurality of path points through the ESDF gradient field to obtain a front-end path point set; establishing a multi-order B spline curve according to the front-end path point set; extracting control points according to a preset constraint condition through a B spline curve to obtain a plurality of control point groups, and aggregating the plurality of control point groups to obtain an aggregated control point group; performing path search on the aggregation control point group based on an A * algorithm to obtain an initial path trajectory; and optimizing the initial path trajectory through a preset rule to obtain a final path trajectory. Compared with the prior art, the path planning trajectory of the quad-rotor unmanned aerial vehicle provided by the invention is higher in quality and efficiency.

The invention discloses a simulation method and system based on multi-field coupling and dynamic grid processing, and belongs to the field of computational simulation, and the method comprises the steps: building and solving a coupling physical field model: carrying out finite element spatial discretization on a solving domain, building and solving a coupling partial differential equation set which at least comprises a temperature field, a displacement field and a stress field, and carrying out the finite element spatial discretization; obtaining numerical solutions of the physical fields in the current state; carrying out progressive unit removal based on a physical criterion; and finally, grid data structure updating and solver synchronization are executed. According to the method, the metal material can be gradually removed when the physical quantity reaches the critical condition, and the grid change is dynamically and robustly processed in the process.

The invention provides a multi-agent conflict-free path planning method and system based on an improved CBS algorithm. The method comprises the following steps: constructing an environment model; analyzing task starting points, task ending points and current state information of k intelligent agents from the received task request information, and judging whether all the intelligent agents need to re-plan paths or not by traversing task path sets of all the intelligent agents; when the path is planned again, forbidding points are added through the distance and the end point positions of the intelligent agents, a conflict-free path solver is called to obtain a path set of k intelligent agents, and the path set is cut off in space; comprehensively judging and processing the generated path; and selecting the shortest path which meets the safety requirement from the length by judging the same-direction or reverse-direction relationship between the paths, and issuing the shortest path. The method can be used in warehouse logistics and other complex environments, and the calculation speed and robustness of path planning are improved.

A mechanistic nested topological design optimization method for using Convolution-Hierarchical Deep-learning Neural Network Tensor Decomposition (C-HiDeNN-TD) comprises (1) obtaining at least one design variable and at least one design requirement; (2) decomposing the at least one design variable into at least one equation in each of at least two directions; (3) solving the at least one design variable in each of the at least two directions using a C-HiDeNN-TD solver to obtain at least one part design; and (4) outputting an optimal design based on the at least one part design when the optimal design meets the least one design requirement.

The invention discloses a unit combination scheduling method based on a constraint space-time diagram neural network and a related device. The method comprises the steps of collecting bus load data and power system load data of a power grid at each moment every day; establishing a unit combination problem by taking each day as a unit, and solving to obtain corresponding optimal unit power under constraint conditions; using a sliding window to divide the bus load and the corresponding optimal unit power according to time periods; based on the expanded training data set, taking the divided bus load as input data, taking the corresponding optimal unit power as a data label, and training a pre-established space-time diagram neural network model; and dividing bus load data needing to be predicted according to time periods, and inputting the trained time-space diagram neural network model to obtain a unit combination scheduling scheme. According to the method, the solving precision of a unit commitment scheme based on data driving is improved, the time consumption and the calculation amount for calling a solver are reduced, a linear constraint layer and a post-processing method are constructed, and the reliability of a prediction result is perfected.

A k-parallel row sorting problem solving method considering multiple channels relates to the technical field of disassembly line layout, and mainly comprises the following steps: determining an objective function, calculating channel coordinate information in a region, generating a population P1 and randomly generating a population P2 by using a greedy strategy, combining the population P1 and the population P2 into an initial population P, calculating the fitness of all solutions in the initial population, and obtaining a k-parallel row sorting problem; screening an elite solution, selecting solutions except the elite solution in the initial population based on the fitness by using a roulette mechanism to execute genetic circulation, sequentially carrying out crossover and mutation operation based on Q-learning, combining the population with the elite solution after mutation with the population P1 and the population P2 to update the population, according to the maximum number of iterations, it is judged that iterative calculation continues or a result is output; according to the variable domain genetic algorithm based on Q-learning, an optimal solution for solving kPROPP can be provided in a short time, the solving efficiency of the variable domain genetic algorithm is greatly superior to that of a conventional accurate solver, the solving effect of the variable domain genetic algorithm is superior to that of other methods, and reliable support is provided for solving the problem of parallel layout planning.

The invention provides a ship micro-grid energy management method based on hierarchical coordination of model predictive control and a chaos evolution optimization algorithm, and the method comprises the steps: constructing a hierarchical coordination control architecture which comprises an upper rolling optimization scheduling layer and a lower real-time stability control layer; the upper layer adopts a model prediction control framework, a chaos evolutionary optimization algorithm is built in to serve as a solver on the basis of load and new energy output prediction of a future time domain, and an optimal power reference instruction in the future short time domain is solved in a rolling manner by taking operation cost, energy storage aging cost and power grid stability penalty as a comprehensive target; and the lower layer executes power scheduling according to the reference instruction issued by the upper layer, absorbs and compensates high-frequency power fluctuation in real time by using a droop control quick response mechanism of the energy storage system, and maintains the stability of the power grid. According to the method, decoupling cooperation of global economic optimization and local dynamic stability of the ship microgrid can be realized, the fuel consumption is remarkably reduced, and the safety and reliability of the system under complex working conditions are improved.

The invention relates to a photoresist spin-coating process simulation method and computer equipment, and the method comprises the steps: building a fan-shaped simulation grid based on the parameter information of a target wafer and the position information of a photoresist injection port; the fan-shaped simulation grid is a fan-shaped space area in the columnar space; the columnar space is a space area defined by space extension by taking the surface of the target wafer as the bottom surface and taking the plane where the photoresist injection port is located as the top surface; establishing a two-phase flow fluid mathematical model; through the target solver, the two-phase fluid mathematical model is iteratively solved according to the boundary condition of the fan-shaped simulation grid, the fluid attribute corresponding to the two-phase fluid and the initial condition of process simulation, and the photoresist spin-coating process simulation result is determined, so that the photoresist spin-coating process simulation efficiency is improved, and the photoresist spin-coating process simulation efficiency is improved. The problems of high cost and time consumption required by a traditional simulation method are avoided, and adjustment and optimization of process parameters are more efficient and flexible.

The invention relates to the technical field of low-altitude equipment, in particular to a low-altitude resource intelligent scheduling method and system based on deep learning, and the method comprises the steps: collecting the real-time state and network load data of a low-altitude flight equipment group, and constructing a dynamic operation data set; generating an operation mode feature set through multi-dimensional airspace situation awareness and analysis, and performing sparse clustering division based on the feature set to form a network resource demand priority mapping table; traversing the mapping table to dynamically calculate the resource demand, determining a multi-dimensional weight coefficient, and performing high-dimensional feature dimension reduction and optimization through a mixed integer nonlinear programming solver to obtain a resource demand feature vector; constructing a resource scheduling strategy optimization model by adopting a deep reinforcement learning algorithm based on the vector; inputting real-time data into the model to execute a resource scheduling decision, and outputting a dynamic allocation strategy; simulation deduction and compliance verification are carried out on the strategy in the digital twin simulation platform, and cooperative intelligent scheduling of communication, calculation and spectrum resources is achieved.

The invention relates to the technical field of computer-aided engineering, in particular to a nonlinear efficient iterative solving method for thermal strain load, which is used for solving an obtained finite element equation by using a modified Newton-Raphson method and comprises the following steps of: firstly, establishing a geometric model to be analyzed and performing grid division; the method comprises the following steps: firstly, setting a model to be analyzed, then setting boundary conditions and temperature field change conditions of the model to be analyzed, finally, inputting the conditions into an algorithm solver, solving a nonlinear equation set, iteratively inputting a load continuously according to a modified Newton-Raphson method in the solving process, calculating node displacement until convergence, and solving stress-strain conditions in the welding process according to a material constitutive relation. In the solving process, by judging the size of the two norms of the displacement increment obtained in the adjacent increment step heuristic stage, the convergence of nonlinear solving of the structure is enhanced through the calculation mode, and the solving speed of the nonlinear problem in the thermal elastic-plastic constitutive structure is increased.

According to the incremental defect detection method and system based on the function abstract, a function abstract design step, an SEG-based path condition storage method and a virtual node-based side effect abstract technology are adopted, a complete function call graph is constructed and stored as the function abstract, and the analysis precision and expandability are effectively improved; in the code change detection step, aiming at the differential detection requirement of the program, efficient identification of program version change is realized through an IR structured comparison method; an increment influence analysis step: based on the difference information, identifying an influence range brought by the change, and ensuring high efficiency and accuracy of increment analysis; and an incremental analysis step: recovering a path condition based on the behavior of the function abstract, recovering the complete program of the influenced program, designing a defect detection algorithm based on an SMT solver and the persistent function abstract, and realizing automatic detection and verification of potential defects of the program.

The invention provides a linear integer programming method for constructing a minimum core particle library in integrated chip design, which comprises the following steps of: respectively generating an interval matrix A based on interval attribute mapping and generating a category matrix B based on category attribute mapping by judging whether data in the core particle library belongs to an interval type or not, and further constructing and generating a matrix C for representing mixed attributes; based on the matrix C, obtaining a constraint coefficient matrix, a constant vector corresponding to the constraint condition and an optimization coefficient matrix generated through optimization through the constraint condition of the orthogonal characteristic corresponding to each interval type in the core particle; therefore, based on a linear integer programming model, an integer programming solver is called to obtain a minimum core particle library.