613 results about "Solver" patented technology

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.

The invention provides a plane layout optimization method and device based on CAD drawing automatic identification, relates to the field of plane layout optimization, and solves the technical problems that only non-overlapping constraints among equipment are considered, actual space obstacles cannot be automatically avoided, and a generated layout scheme cannot be implemented. The method comprises the following steps: extracting barrier data in a layer; and through a parameter configuration mechanism, generating an equipment parameter information structural body containing the mapping relationship between the equipment and the process. And establishing a continuous optimization model containing an IPOPT solver, and obtaining an initial layout solution containing the relative orientation of each device. And based on the relative orientation of each device in the initial layout solution, introducing an obstacle rejection constraint and a device non-overlapping constraint to construct a discrete optimization model, obtaining the center position coordinates of each device through a Gurobi solver, and integrating a dynamic adjustment mechanism and an interlayer relationship optimization mechanism. And generating a new layout map for visual output. The method and the device are used in a plane layout optimization process.

The present disclosure provides a computer-implemented system for automated augmentation of differential equation models. The system stores differential equations representing mechanistic behavior of physical or computational processes and constructs a hybrid computational solver by embedding a trainable universal approximator with adjustable parameters into the differential equations, where approximator outputs augment time derivatives during numerical integration. An iterative training process adjusts parameters through numerical integration, monitors integration failures, assigns infinite penalty values to loss functions when failures occur, and computes gradients using automatic differentiation otherwise. The system computes sensitivity metrics via Jacobian matrix evaluation, classifies input / output subsets as significant based on threshold-exceeding sensitivity metrics, generates a reduced approximator operating on classified subsets, and replaces the universal approximator with the reduced version to create an optimized solver.

The ELM-based frequency safety constraint linearization optimization scheduling method comprises the following steps: establishing a fire-wind-light multi-energy complementary frequency dynamic response model, and deducing a time domain analytical expression of a frequency safety index by taking a frequency maximum change rate and a frequency maximum deviation as the frequency safety index of a system; considering a frequency lowest point security constraint and a frequency maximum change rate security constraint, and establishing a frequency security constraint-containing optimization scheduling model; a maximum bearable disturbance power linearization mapping method based on ELM is provided, nonlinear frequency security constraints are converted into a series of linear constraint sets, and linearization processing of the frequency security constraints is achieved; and piecewise linearization is carried out on quadratic terms in the established objective function containing the frequency security constraint optimization scheduling model, the overall model is converted into a mixed integer linear model, and a Gurobi solver is used to solve the linearly processed model. The scheduling method can ensure that the system frequency is safe and stable, so that the established optimal scheduling model gives consideration to both economy and frequency safety.

The invention discloses a multi-degree-of-freedom mechanical arm control method based on quantum particle swarm optimization of a migration strategy. The method comprises the steps that system modeling is conducted; mPC to-be-adjusted parameter definition and constraint processing are carried out; adopting, adopting, and introducing penalty terms to construct a composite fitness function; updating particle positions by adopting a basic or enhanced quantum updating mode; triggering conditions are judged in a group diversity measurement mode, and when the conditions are met, a migration strategy and dynamic subgroup division are executed; the global optimal solution and the fitness value are loaded into a real-time MPC controller, and online adjustment is carried out; a prediction equation is constructed, tracking errors and energy consumption optimization are converted into a standard quadratic programming problem, and the solving precision is dynamically adjusted in combination with Cholesky pre-decomposition, a structured sparse solver and a warm-start and early stop strategy; and a closed-loop adaptive control system is constructed. According to the method, the precision, robustness and real-time performance of trajectory tracking control of the mechanical arm can be effectively improved.

The invention discloses a quantum bit mapping optimization method for hardware guidance and fidelity perception and a related device, and belongs to the field of quantum computing, and the method comprises the steps: defining each node in a hardware coupling graph of a target quantum processor as an initial community; evaluating all adjacent community pairs according to a reward function defined based on modularity and fidelity of candidate communities, selecting an optimal community pair for fusion, and taking the community with the largest current scale as an optimal candidate area; iteratively executing a community fusion step until a converged single community is obtained; and selecting an optimal candidate region with the physical quantum bit number greater than or equal to the logic quantum bit number of the quantum circuit to be mapped and the minimum scale, and outputting a mapping sub-graph. According to the method, through a recursive community fusion strategy, the operation range of the solver is restrained in a subspace with the scale greatly reduced and the quality better, the problem of combinatorial explosion caused by global search is fundamentally avoided, and the calculation overhead is greatly reduced.

The embodiment of the invention provides a federated learning contribution evaluation method and device, and the method comprises the steps: carrying out the grouping of a plurality of edge computing devices, and obtaining a plurality of sub-federated learning sets; and for the target federated learning sub-set, aggregating model update information corresponding to each edge computing device in the target federated learning sub-set, and determining a collaborative contribution value of the target federated learning sub-set based on the performance index of the updated global model on the common test set. Through a first linear programming solver, according to the collaborative contribution values of the multiple federated learning sub-sets, obtaining the maximum loss value corresponding to all the federated learning sub-sets and optimizing the maximum loss value to obtain the minimized maximum loss value, and through a second linear programming solver, obtaining the maximum loss value corresponding to all the federated learning sub-sets; and according to the maximum loss value after all the sub federated learning sets are minimized and the reference contribution values corresponding to the plurality of edge computing devices, target contribution vectors corresponding to the plurality of edge computing devices are determined, and the contribution degree of each edge computing device in the training process is accurately quantified.

The invention provides a narrow gap swing laser welding pool wettability prediction method based on CFD simulation. The method comprises the following steps that an interaction platform is constructed through Python + PyQt5, and geometric modeling, UDF compiling and a material library module are integrated; the groove parameters and the swing track are dynamically loaded to a Fluent solver, and a full-coupling transient model of a steam recoil pressure-buoyancy-molten drop mass source item is established; in CFD-POST, the widest section of the molten pool is positioned on the basis of the contour surface with the volume fraction being 0.5, and Z-direction displacement-flow velocity data are extracted along the two side wall surfaces; fusing defects are predicted through double criteria of a bilateral average flow velocity difference threshold value and F test variance homogeneity; according to the method, dynamic quantitative evaluation of wetting behaviors can be achieved, the process development period is shortened, the defect detection rate is increased, and the method is suitable for aerospace thick plate welding quality control.

The invention discloses a ship viscous flow numerical simulation method based on a hybrid Cartesian grid, and relates to the technical field of ship CFD. The method comprises the steps that after a computational domain is constructed, a body-fitted grid is generated at the object plane boundary of a ship model; then, on the basis of the outermost-layer body-fitted grid, a full-thread tree is used for carrying out space division on a computational domain to generate a Cartesian grid, and the full-thread tree is output as Cartesian grid topological information matched with the data structure of the target viscous flow solver; and finally, assembling and coupling the body-fitted grid and the Cartesian grid by using an overlapping grid technology to obtain a mixed partition grid unit, and applying the mixed partition grid unit to a solving process of a target viscous flow solver. Therefore, the accuracy and robustness of viscous flow numerical simulation of the ship with the complex geometry are improved, and the method can be well applied to resistance and flow field numerical simulation scenes in underwater and water surface ship body motion states.

The invention relates to the technical field of calculation, and provides a CFD solution acceleration method and system based on parallel calculation and load dynamic balance. Physical characteristics such as a CFL number and a residual error are quantified into load indexes, so that a load balancing decision and a flow field evolution rule are tightly coupled; by adopting an asynchronous communication overlapping framework deeply coupled with a CFD solver, blocked communication waiting time is converted into effective calculation time, and communication delay is systematically hidden; based on an incremental grid migration strategy of real-time load evaluation, high-load grid blocks in a hotspot process can be intelligently identified and migrated, and differentiation and high-efficiency utilization of computing power are realized.

The invention provides a power grid traveling wave synchronous positioning method based on mixed integer convex quadratic programming, and the method comprises the steps: building a mathematical model for describing the topological structure and line length of a power grid, obtaining the fault traveling wave arrival time recorded by each monitoring point in the power grid, and building an actual measurement time difference matrix based on the fault traveling wave arrival time and the mathematical model; constructing a mixed integer convex quadratic programming model based on the actual measurement time difference matrix, calling a mixed integer programming solver to solve the constructed mixed integer convex quadratic programming model to obtain a global minimum mismatch degree, and meanwhile, obtaining a global optimal fault line, a fault position and a traveling wave velocity corresponding to the global minimum mismatch degree. And traveling wave synchronous positioning is realized. The method can improve the positioning accuracy and reliability under the condition of unknown wave velocity and complex network topology.

The invention discloses a VLA task space control method and system for a multi-degree-of-freedom mechanical arm, and relates to the technical field of intelligent robot control, visual information and language instructions when the mechanical arm executes a task are obtained, and a trained VLA model is adopted to obtain a target pose of an end effector in a task space; an IK solver corresponding to the mechanical arm configuration is adopted, and according to the target pose, multiple sets of joint angle solutions of all joints of the mechanical arm are obtained; selecting an optimal joint angle of each joint from the multiple groups of joint angle solutions by adopting a preset optimization criterion; and after the optimal joint angle sequence of the mechanical arm is subjected to track smoothing processing, the mechanical arm is driven to move. Mechanical arm motion control is carried out in combination with a VLA model and an IK solver, the problems that the generalization ability between different mechanical arm configurations is poor, the data collection cost is high and the model interpretability is insufficient are solved, and the generalization ability and data efficiency of mechanical arm control are improved.

There is provided a system and methods to determine an improved solution to a Mixed Integer Problem (MIP) using a quantum-assisted MIP solver. The methods are performed by a digital processor in communication with a quantum processor. Methods include: selecting at least one feasible solution determined by an MIP solver, determining a first sub-problem of the MIP based on the at least one feasible solution; casting the first sub-problem as Binary Quadratic Models (BQMs); solving the BQMs using the quantum processor to generate sample solutions; determining a second sub-problem based on at least the sample solutions, and obtaining a current solution to the MIP by evaluating the second sub-problem; and updating an incumbent solution if the current solution improves over the current incumbent solution. The quantum-assisted MIP solver uses hybrid crossover and mutation heuristics to improve the convergence time and accuracy of solutions obtained using Branch-and-Cut solvers.

The invention discloses a reaction steam turbine through-flow method and system based on variable reaction degree optimization, and belongs to the technical field of steam turbine through-flow design. The method comprises the steps that an initial geometric model of a steam turbine through-flow part and thermal boundary conditions of all stages are obtained; inputting the initial geometric model and the thermal boundary conditions into a flow field solver, and solving a flow control equation to obtain an original flow field solution; constructing a functional taking through-flow efficiency as a target, and deducing an adjoint equation corresponding to the flow control equation; solving the adjoint equation to obtain gradient information of the target functional to each level of reaction degree; inputting the gradient information into a sequential quadratic programming optimizer, and iteratively updating the reactance of each stage by combining the maximum equivalent stress upper limit of the moving blade and the lower limit of the through-flow throat area to generate a variable reactance distribution scheme; and adjusting molded line geometric parameters of each stage of stationary blade and moving blade, and outputting optimized three-dimensional molded line data and a digital model file for numerical control machining. According to the method, the in-stage flow loss is reduced, and the design robustness is enhanced.

The invention discloses a building design modeling method and system based on a parameter entity and a Boolean relationship thereof, and the method comprises the steps: converting a plurality of building components into geometric body data based on a structure shape, and abstracting a single geometric body into a whole model according to the Boolean relationship between the building components required by the design; the method specifically comprises the steps that firstly, building design related component data given by a user is preprocessed, a two-stage structure is adopted to redefine a geometry, the geometry is stored, and the Boolean relation between components is represented through a tree structure; the preprocessed data are transmitted into a grid generation solver, and a final three-dimensional model and affiliated topological information are obtained through background grid and contour surface construction, topological information integration and grid post-processing. The method can ensure that the building design modeling process has good robustness and calculation efficiency.

The invention belongs to the technical field of new energy power generation and chemical production, and provides a green power chemical system consumption capability evaluation method and related device, and the method comprises the steps: collecting new energy power scene data, and generating a time sequence scene of wind power and photovoltaic output based on a long and short term sequence generative adversarial network; constructing an operation constraint model of the energy storage power station based on a time sequence scene of wind power and photovoltaic output; on the basis of the operation constraint model of the energy storage power station, constructing an operation constraint model of the hydrogen energy storage system; an operation constraint model of an energy storage power station and an operation constraint model of a hydrogen energy storage system are integrated, new energy consumption maximization and grid power demand minimization are taken as optimization targets, a green power chemical system consumption capability evaluation model considering new energy uncertainty is constructed, the model is solved by adopting a mathematical programming solver, and evaluation indexes are output. According to the method, the uncertainty of wind and light output is quantified through new energy scene generation, and the reliability of evaluation is improved.

The invention discloses a table collaborative editing method and system, and relates to the technical field of artificial intelligence, and the method comprises the following steps: during multi-user collaborative editing, receiving user editing operations, packaging each user editing operation into an independent operation sequence, summarizing all the operation sequences, and performing unified operation on the operation sequences, the operation sequence comprises content information and parameter information of user editing behaviors; performing operation conflict detection based on the operation sequence, and if an operation conflict is detected, dividing the conflict into a direct conflict, a structure conflict, a style conflict and a dependency conflict through a conflict classifier; corresponding strategies are matched and executed for different conflict types through the intelligent conflict solver, the strategies comprise the automatic merging strategy, the user selection strategy and the delay processing strategy, and the problems that in the prior art, cell merging intelligent verification cannot be achieved, and conflict solving cannot be achieved during multi-user collaborative editing are solved are solved.

The invention discloses a Taylor flow two-phase volume fraction prediction method based on CFD and machine learning, and belongs to the field of hydrodynamics. The method aims at solving the problems that traditional CFD is low in efficiency and weak in generalization, and pure machine learning lacks explanatory and physical consistency. According to the method, a mixed CFD-ML framework integrating an interFoam solver of OpenFOAM and machine learning is constructed. The method comprises the steps that firstly, a multi-working-condition calculation example is established based on OpenFOAM, and a training data set containing velocity fields Ux and Uy and a liquid phase volume fraction field alpha. Water is generated through interFoam; training a fourier neural operator (FNO)-based model by using a training solver to obtain an optimal model; and finally, loading the model by using a prediction loop solver, and quickly predicting the volume fraction field of the next time step in CFD time marching. The method improves the prediction efficiency and precision, guarantees the physical consistency, is suitable for the fields of ocean engineering, oil and gas transmission and the like, and can be expanded to a multiphase flow scene.

An artificial intelligence-based business management system for real-time decision optimization, consisting of: a data acquisition unit configured to ingest structured, semi-structured and unstructured data streams from enterprise resource planning (ERP) systems, customer relationship management (CRM) platforms, Internet of Things (IoT) devices, external market feeds and financial transaction systems, encrypting, timestamping and verifying the data prior to further processing; a graph processing unit that is communicatively connected to the said acquisition layer, wherein the unit is configured to encode heterogeneous data into dynamic graph structures comprising nodes representing business entities and edges representing transaction or relationship dependencies, wherein the unit is further configured to perform deduplication, metadata tagging and real-time data synchronization; a decision optimization unit operationally linked to the graph processing unit, wherein the decision optimization unit includes modules for reinforcement learning, modules for Bayesian optimization and multi-objective solvers configured to simulate multiple alternative decision paths and select an optimal path based on performance indicators such as cost efficiency, resource utilization, customer satisfaction and risk minimization; a real-time inference control unit with specialized hardware cores, including at least one graphics processing unit (GPU), a field-programmable gate array (FPGA) and an application-specific integrated circuit (ASIC), wherein the accelerator performs inference tasks of the decision optimization unit with a latency in the millisecond range; a diagnostic processing unit configured to generate causal diagrams, feature mapping maps, and interpretable result summaries according to the optimization outputs; and a control interface unit configured to transmit optimized decisions to process controls within the enterprise, robot actuators, planning systems or interactive dashboards, with the interface supporting bidirectional communication for higher-level interventions, error feedback and triggers for re-optimization.

The invention relates to a port micro-grid energy control strategy considering incomplete information, and the strategy is technically characterized in that the time-of-use electricity price of a power grid, the parameters of a micro-grid operator and the like are set, and a data source is determined; scene sampling is carried out, and uncertain information in micro-grid operation is converted into a computable deterministic scene; establishing a Bayesian-Stackelberg game model under incomplete information by taking a power transaction agent as a main body and a micro-grid operator as a slave body; a follower model constraint is established by considering port berth distribution, and a KKT condition is established for a follower model so that the double-layer game model can be converted into a single-layer optimization model; and solving the model by using a CPLEX solver to obtain an optimal electricity price and an energy control strategy of each scene. According to the method, the uncertainty of port micro-grid information is considered, port operation constraints, berth distribution strategies and the like are comprehensively considered, the economy of micro-grid operation is considered while the port logistics transportation efficiency is ensured, and a port micro-grid energy control strategy considering incomplete information is provided.

The invention discloses a thermodynamic system analysis method and system based on a physical information neural network. The method comprises the following steps: constructing a thermodynamic system mathematical model; converting a solving problem of the mathematical model of the thermodynamic system into an optimization problem taking a minimum control equation set residual error as a target function; constructing a physical information neural network; constructing a composite loss function based on the control equation set residual sum of squares, and performing end-to-end training on the physical information neural network to obtain a trained physical information neural network; taking the trained physical information neural network as an approximate solver, and for any given thermodynamic system input working condition, predicting a group of approximate solutions as initial predicted values through one-time forward propagation; and inputting the initial predicted value into a traditional numerical solver for accurate solving to obtain a to-be-solved state variable. According to the method, a feasible technical path is provided for applications with strict requirements on the analysis and calculation speed of the thermodynamic system, such as real-time simulation, online optimization and digital twinning.

The invention belongs to the technical field of CFD (computational fluid dynamics) parameter calibration, and discloses a CFD parameter adaptive calibration method and system based on measured data and a double-agent model, and the method comprises the steps: obtaining a CFD input parameter sample, inputting the CFD input parameter sample into a CFD solver, and obtaining an initial simulation result; determining an error evaluation index according to the initial simulation result based on a target actual measurement data result; constructing a double-agent model based on a Kriging model and a radial basis function neural network by taking a CFD input parameter sample as an independent variable and an error evaluation index as a dependent variable; the double-agent model is trained, the trained double-agent model takes the error evaluation index as fitness, and CFD input parameter values are obtained based on a genetic algorithm; the CFD input parameter values are input into the CFD solver for a simulation experiment, a calibrated simulation result is output, the reliability and generalization ability of prediction are improved through a double-agent model, a high-fidelity simulation result is output through the CFD solver, and the number of times of calling the CFD solver is reduced while the calibration precision is guaranteed.

The invention relates to a zero-sample speech synthesis method and device, computer equipment, a storage medium and a program product, and the method comprises the steps: obtaining a target coding feature according to a reference speech and a target text; inputting the target coding features into a stream matching model to obtain a conditional velocity field and an unconditional velocity field; inputting the target coding feature into a prior model to obtain a prior speech feature; obtaining a prior generation flow field according to the prior voice features and the standard Gaussian noise; calculating a KL divergence value between the priori generated flow field and a preset real generated flow field, and taking a moment when the KL divergence value is smaller than or equal to a preset KL divergence threshold as an initial moment of the priori generated flow field; fusing the conditional velocity field and the unconditional velocity field to obtain a fused velocity field; and inputting the fusion velocity field from the initial moment to the target moment and the priori generated flow field into an ordinary differential equation solver to obtain the target speech features, thereby improving the speech quality of the synthesized speech.

The invention discloses a civil aviation element configuration optimization method based on mixed integer dynamic programming. The method comprises the following steps: S1, constructing a Chinese civil aviation core element and carbon emission spatial-temporal characteristic database; s2, establishing a mixed integer dynamic programming model with the purpose of minimizing the total cost of the system in the programming period; and S3, establishing decision variables, constraint conditions and a fusion solution algorithm framework required by the mixed integer dynamic programming model. And S4, based on the fusion solution algorithm framework, solving the mixed integer dynamic programming model by using a mathematical programming solver integrated with a robust optimization module so as to obtain a civil aviation core element dynamic configuration scheme which is optimal in total system cost in multiple periods in the future and meets the uncertainty scene. According to the method provided by the invention, a multi-source data fusion algorithm is optimized, a carbon emission measuring and calculating method is deepened, and solver parameters are adjusted, so that the collaborative requirements of improving the operation efficiency and reducing the carbon emission from the perspective of carbon cost are met.

The invention provides a high-filling particle viscous fluid mixed heat generation and transfer multi-scale simulation method and system, and belongs to the technical field of multiphase flow numerical simulation and heat transfer and friction heat generation coupling calculation. A uniform flow-heat generation-heat transfer coupling solution is constructed under a double-fluid model and a KTGF framework; a drag force model more suitable for a dense particle system is introduced in an interphase momentum exchange link; a BVK drag force model is adopted, and dynamic calculation of an interphase exchange coefficient is realized in a solver through a user-defined function; therefore, the prediction consistency of the solid-phase volume fraction distribution and the relative slip velocity under the high filling condition is improved; particle phase dissipation heat generation, solid-liquid interface friction heat generation and particle-wall surface friction heat generation are introduced as controllable heat source items, and are coupled to a liquid phase and particle phase energy equation according to a set heat distribution coefficient, so that the problem that friction related heat sources are difficult to accurately characterize when only software built-in options are relied on is solved.

The present disclosure relates to a method and system for optimizing aerodynamic characteristics of a flow equalization air ring structure in a medium-speed coal mill based on Fluent simulation. The method comprises establishing and optimizing a model of the flow equalization air ring structure, meshing the model using Ansys ICEM, importing meshes, arranging an MRF rotating region on a dynamic ring structure, selecting an energy equation model, setting a DPM and a boundary condition, and adopting a modified mixture gas model based on empirical formulas for boundary conditions at an inlet and outlet, initializing computation information of a mesh node, setting a solver and performing iterative solving, after a computation result converges, performing post-processing on the computation result, storing the computation result, modifying structural parameters, and repeating the above steps to obtain a plurality of simulation results to obtain parameters of an optimal flow equalization air ring structure.

The invention provides a multi-solver CFD self-correction method and system based on semantic orchestration, and the method comprises the steps: carrying out the retrieval and slot filling processing of multi-modal input, and generating a simulation intention intermediate representation; carrying out structured verification, default completion and mechanism examination to obtain reachability classification; mapping is established for a CFD solver, and short-distance concurrent trial running and preferential long-distance running are carried out on an equivalent example and job description compiled into a target solver through a mixed strategy; the method comprises the following steps: collecting a structured fingerprint in a running period, generating a minimum change set only reaching a necessary field when a triggering condition is established, writing back to an intention layer, and automatically recompiling and continuing running after the intention layer is formed; and introducing a physical information neural network and a digital twinborn body to provide suggestions or approximate fields, generating reports, and performing Hash signature archiving on measurement point and section data. According to the method, the correctness and the traceability are ensured, and meanwhile, the one-time runnability, the cross-solver transportability and the engineering efficiency are remarkably improved.

The invention discloses a building volume prototype generation and optimization method and system based on visual language model self-evolution and application, and relates to the field of computer-aided building design, a building volume prototype and a multi-view rendering graph are generated through a predefined geometric atomic instruction group, an atomic instruction group-semantic expression data set is constructed, and a multi-view rendering graph is generated through a multi-view rendering graph; training to obtain a semantic-driven geometric instruction reasoning model; after a new instruction group generated through model reasoning is verified by a geometric constraint solver, the system further calls a visual language model to perform semantic consistency evaluation, samples conforming to a target are incorporated into a training set to optimize weights, multiple rounds of semantic feedback and parameter updating are achieved, and a'semantic-geometry 'closed-loop self-evolution mechanism is formed. According to the method, intelligent, semantic and evolutionary optimization of volume generation can be realized in the early stage of building design, and the design efficiency and semantic consistency are improved.

The invention discloses a quantum method and device for processing database multi-query optimization and a storage medium. The quantum method comprises the steps that database multi-query optimization problem expression is converted into a secondary unconstrained binary optimization problem; running a quantum circuit on a quantum computer, and calculating an energy value corresponding to Hamiltonian according to a test state prepared in a quantum processor; summing the energy values on a classical computer, and optimizing parameters of the quantum circuit through a classical optimizer according to a summing result; and iteratively executing the steps of quantum circuit operation, energy calculation and parameter optimization until an energy value converges to a stable value, obtaining an optimal solution of a multi-query optimization problem, and solving the quadratic unconstrained binary optimization model in a quantum-classical hybrid framework by the variable component sub-feature solver algorithm. According to the method, the quantum computer is introduced into the database optimizer, the multi-query optimization problem is solved by using a variable component subscript solicitation solver algorithm, and a problem modeling strategy is improved.