292 results about "Iteration process" patented technology

The invention relates to an intelligent agent system optimization method and device based on intelligent fault analysis and cross-generation knowledge inheritance, and belongs to the technical field of artificial intelligence. According to the method, interaction abnormal signals are captured in real time by deploying a lightweight log probe, and a tool benefit prediction model based on reinforcement learning is constructed to automatically generate an improvement proposal when the failure rate exceeds a threshold value; an agent genealogy map is established to realize automatic inheritance of a new agent on core memory and abandonment of failure knowledge, and a disastrous forgetting blocker is deployed to dynamically extract a functional module from a genealogy to deal with key capability degradation. Aiming at the problems of fault response lag, knowledge inheritance fracture, key capability degradation and the like in an intelligent agent system iteration process, the invention creatively provides a cooperation mechanism of an intelligent fault analysis layer and a cross-generation knowledge inheritance network, and the fault self-healing capability, version stability and service continuity guarantee level of the system are remarkably improved.

The invention belongs to the technical field of communication, and particularly relates to a multiplication active code decoding method based on an SO-ORBGRAND decoder. The SO-ORBGRAND decoder outputs soft information by calculating a correct posterior probability of a conjecture code word sequence, and high-performance decoding is realized through soft information exchange and optimization processing between the row decoder and the column decoder. In the iteration process, a method for judging the validity of the code word through the check matrix is designed by utilizing the polarization characteristic of the multiplication active code, so that the iteration is supported to jump out in advance to reduce the complexity. The invention provides a multiplication active code decoding algorithm based on an SO-ORBGRAND decoder, and compared with various non-system multiplication active code decoding algorithms and the performance of traditional long-code polarization codes, the method has remarkable advantages in error correction performance; a method for judging the validity of the code word through the check matrix by utilizing the polarization characteristic design of the multiplication positive code is provided, the complexity is reduced while the judgment success rate is improved, and the implementation overhead is reduced.

The invention relates to the technical field of intelligent manufacturing, and discloses a multi-objective optimization method and system for an aviation airborne product, so as to improve the overall performance of the aviation airborne product. The method comprises the steps of obtaining an initial population based on a series of initial model files of a target aviation airborne product, and determining a constraint condition and a target function of the target aviation airborne product; the constraint condition comprises the range of each iteration parameter; in each iteration process of obtaining the global optimal Pareto frontier according to the constraint condition, performing non-dominated sorting and congestion degree calculation on the tth generation of population through an algorithm, selecting individuals with excellent performance from a sorting result, and applying gradient-guided Gaussian disturbance to generate a (t + 1) th generation of new solution; the tth generation of population comprises cross progenies of the (t-1) th generation of population; and when the target iteration termination condition is met, iteration is terminated, and the global optimal Pareto front is obtained.

The invention relates to a toxic text collection method and system based on retrieval enhancement generation, and the method comprises the steps: firstly obtaining target text data through a crawling platform, manually constructing a small-scale initial data set through a cold start mode, and injecting the initial data set into a knowledge base as basic data; and performing semantic retrieval on each batch of target texts, obtaining the first k texts with semantic similarity from the knowledge base, reasoning by adopting a plurality of large language models through thinking chain reasoning in combination with the texts, generating a toxic label, and labeling the target texts to be labeled in the current batch. And injecting the labeled target text into a knowledge base, continuously collecting and labeling data in an iteration mode, and continuously optimizing the performance of a retriever through an incremental learning mechanism in the iteration process so as to realize toxic text collection. According to the method, large-scale, fine-grained and high-consistency toxic text tagging corpora can be efficiently accumulated, and the problems of high tagging cost, inconsistent quality and poor expansibility in traditional toxic text data collection are remarkably relieved.

The invention discloses a layered LDPC (Low Density Parity Check Code) decoding method based on syndrome feedback self-adaption, and aims to solve the problem of poor decoding performance caused by fixed correction factors and poor adaptability to different check matrixes and channel conditions in the existing LDPC decoding algorithm. The method comprises the following steps of: firstly, dividing rows of a check matrix into a plurality of independent layers by adopting a hierarchical scheduling architecture so as to accelerate propagation and convergence of decoding information; secondly, in each iteration process of hierarchical decoding, a self-adaptive mechanism based on syndrome feedback is used, and a core offset factor beta in an offset minimum sum algorithm is adjusted in real time; the mechanism intelligently increases or decreases the offset factor by diagnosing the current decoding state (i.e., the number of unsatisfied check equations) to help the decoding process jump out of local optimum and stably converge. A simulation result shows that compared with a traditional offset minimum sum algorithm, the decoding performance is effectively improved and the bit error rate is reduced on the premise that the complexity is not obviously increased, and particularly, the performance gain is obvious in a medium-high signal-to-noise ratio region, and the robustness is higher.

The invention relates to the technical field of computers, and discloses an information source coding matrix data processing method based on a modified conjugate gradient algorithm. According to the method, sparsity constraint and a fixed-point quantization mechanism are fused in iterative optimization, and a sparse fixed-point coding matrix meeting hardware deployment requirements is directly generated through gradient shielding, correction conjugate parameter calculation and Armijo criterion line search and synchronous execution of hard threshold sparseness and fixed-point quantization after each update. According to the method, the sparsity constraint and the fixed-point quantization mechanism are embedded in the iteration process of the modified conjugate gradient algorithm, so that end-to-end alignment of coding matrix optimization and hardware deployment requirements is realized.

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.

Some aspects utilize an iterative process to synthesis a logic network within an ASIC using lookup table (LUT) optimization techniques. The logic network may be represented by an And-Inverter graph (AIG), as described in more detail below. On each iteration, the current AIG representation of the logic network is mapped to a network of k-LUTs. A k-LUT is a lookup table that can represent any function of k variables. This network may then be improved using LUT optimization techniques. The improved network of k-LUTs is decomposed into a trial AIG representation, which may be further improved for example by applying Boolean-based optimization techniques. The current AIG representation may then be updated in accordance with the quality of the trial AIG representation produced by the current iteration. The final AIG representation from the iterations is synthesized to a netlist of standard cells.

The invention provides a sample generation method and device, a storage medium and a program product. The sample generation method comprises the following steps: inputting a first type of prompts into a sample generation model to obtain a sample set; taking the sample set as a current sample set in a first round of iteration process, and executing the following iteration process: performing clustering processing on the current sample set to obtain a plurality of class clusters; if the iteration ending condition is not met, determining a to-be-enhanced class cluster and a to-be-inhibited class cluster according to the number of samples contained in each class cluster; based on the to-be-enhanced class cluster and / or the to-be-inhibited class cluster, constructing a second class prompt comprising a negative constraint condition and / or a positive constraint condition; the negative constraint condition is used for indicating to reduce generation of samples belonging to the to-be-inhibited class cluster, and the positive constraint condition is used for indicating to increase generation of samples belonging to the to-be-enhanced class cluster; and inputting the second type of prompts into the sample generation model to obtain a new sample set, and taking the new sample set as a current sample set in the next round of iteration process.

The invention relates to a self-adaptive information negotiation method applicable to a continuous variable quantum key distribution system, which comprises the following steps of: estimating an optimal code rate that an LDPC (Low Density Parity Check) code should reach under a current quantum channel, converting an initial code rate into the optimal code rate by a receiver, and obtaining a low-reliability bit number l which needs to be disclosed in conversion calculation, the method comprises the following steps of: decoding an LLR (Local Library Ratio) by using Bob, sending the LLR to Alice through a classic channel in combination with an encryption technology, sorting absolute values of the LLR by Alice in a decoding iteration process, selecting l bit positions with low reliability, sending the bit positions to Bob, determining a bit value by Bob according to the bit positions with low reliability sent by Alice, and feeding back and sending the bit value to Alice, and correcting the LLR value by Alice according to bit information sent by Bob. According to the method disclosed by the invention, the code rate of the LDPC code can be flexibly adjusted along with the change of the state of the time-varying channel, and the negotiation efficiency is improved, so that the key rate of the system is improved. The method is excellent in performance in a time-varying channel environment, negotiation efficiency under a low signal-to-noise ratio condition can be remarkably improved, and an efficient and reliable information negotiation scheme is provided for practical application of a quantum secret communication system.

The invention provides a whole-process production scheduling method considering processing and assembling, which comprises the following steps: constructing a two-stage flexible flow shop scheduling model, scheduling processing tasks of all parts of a plurality of products on a parallel machine in the first stage, and scheduling assembling of the product parts and processing tasks of semi-finished products in the second stage; minimizing the maximum completion time and the total energy consumption of the machine is taken as a double-optimization target; a multi-target swarm intelligence algorithm is adopted to solve the model, population individuals represent a scheduling scheme through two-segment coding, the first segment of coding defines a process execution sequence, and the second segment of coding defines a machine selection result; in an iteration process, alternately executing a Thompson sampling strategy and a dedirectional sampling and generating strategy according to a preset probability, adaptively selecting a bottom layer optimization operator to realize global search, and constructing a guide solution set to realize local mining; and outputting a non-dominated solution set after iteration is ended, and obtaining a corresponding whole-process scheduling scheme after decoding.

The invention relates to the technical field of pipelines, in particular to a complex FLNG cabin three-dimensional pipeline layout method which comprises the following steps that S1, basic parameters are initialized; s2, calculating initial solutions by adopting an intelligent initial strategy based on obstacle density, and taking an optimal solution in the initial solutions as a current global optimal solution; s3, entering an iteration process, calculating an energy factor, if the energy factor is greater than 1, performing long-distance migration with the probability of 30%, and performing hole digging with the probability of 70%; otherwise, randomly foraging or avoiding the predator at equal probability; s4, periodically performing an elitism and neighborhood search strategy; s5, executing an adaptive orthogonal constraint relaxation strategy and boundary limitation operation; and S6, calculating and determining a final global optimal solution, and outputting a final layout result. According to the method, the calculation efficiency and the convergence speed are improved, the actual engineering situation is fit, and the engineering cost is reduced on the basis of preferentially meeting orthogonalization feasibility.

The invention discloses a dynamic optimization method based on a particle swarm optimization algorithm, and relates to the technical field of intelligent optimization algorithms, and the method comprises the steps: initializing the particle swarm optimization algorithm; calculating a target vector value of each particle in the particle swarm, executing Pareto comparison, screening the first 15% of particles as elite particles, and updating the particle swarm to join in optimization; adopting Alpha-Stable distribution to generate random disturbance, and executing dynamic mutation operation on particle positions; maintaining and updating the external archive storing the non-dominated solution; s4, performing quantitative evaluation on the current iteration state of the algorithm based on a preset convergence judgment criterion, and if the convergence condition is not met, returning to S3 and S4 to perform loop execution until the convergence condition is met, and terminating the iteration process; according to the method, aiming at the performance defects of the particle swarm, the optimization performance of the algorithm is pertinently improved in different stages through a dynamic variation mode, and a feasible solution is provided for the dynamic variation of the particle swarm algorithm.

The invention discloses a flexible job shop energy-saving scheduling optimization method considering a light storage condition and a load characteristic, and relates to the technical field of industrial scheduling, and the method comprises the steps: building a flexible job shop energy-saving scheduling problem model based on mixed integer programming, and setting an optimization target and a constraint condition; an ant colony algorithm is improved, an ant colony is divided into dynamic multi-level search, and a pheromone matrix is optimized; the updating effect of pheromones in the ant colony algorithm in the iteration process is optimized; optimizing the optimal solution of each generation of the ant colony algorithm by adopting a graph neural network off-line learning neighborhood search method; and stopping iteration when a preset termination condition is met, and outputting an optimal scheduling scheme. According to the method, a more efficient and flexible energy-saving scheduling strategy is developed to effectively coordinate the productivity and the energy efficiency, energy optimization and cost reduction in the production process are achieved, actual technical support is provided for energy-saving scheduling of the flexible job shop, and a method system for the workshop scheduling problem is enriched.

The invention belongs to the field of resource allocation, particularly relates to a multi-chain resource allocation technology based on a neural network, and provides the following scheme that the multi-chain resource allocation technology comprises a block chain multi-chain system and further comprises the following steps: S1, preparing training data by using a neural network training module: preparing the training data by using the neural network training module; s2, training a node optimization distribution module: dynamically adjusting network parameters through an adaptive optimization algorithm based on the training data set processed by the neural network training module, and controlling an iteration process by adopting an early stop mechanism and a learning rate attenuation strategy until a loss function is converged to a preset threshold value, according to the method, node attributes and global load information are dynamically fused through the graph neural network, intelligent optimal distribution of nodes is achieved, meanwhile, the over-smoothing problem caused by multi-layer stacking can be avoided while light weight of the model is guaranteed, and through intelligent decision making and dynamic adaptation capacity, the optimal distribution of the nodes is achieved. And the problems of resource mismatching, system bottleneck and expansibility are solved.

The invention discloses an MBSE model version pedigree iteration method and system based on a graph theory and a knowledge graph. The method comprises the following steps: constructing an MBSE model of an aerospace craft and mapping the MBSE model into an attribute graph; a directed acyclic graph is adopted to record a version iteration process, management is carried out through a version number system comprising a main version number, a combined version number and a revised version number, and a differential storage strategy is adopted; and performing change detection and merging based on a baseline, accurately identifying changes through a model comparison algorithm including internal attributes, internal relationships, overall relationships and difference analysis, and processing conflicts in collaborative design. According to the method, atomic-scale version control is realized, the problems of missing change traceability, low verification efficiency and multi-baseline conflict are solved, and the efficiency and quality of aerospace craft full-life-cycle model management are improved.

The invention provides a model training method, system and device and a storage medium, and belongs to the technical field of artificial intelligence. According to the method, the back propagation process of a first model layer group of a model is executed in a mode of accessing a video memory of a GPU, the back propagation process of a second model layer group is executed in a mode of direct memory access, and due to the fact that the direct memory access mode does not need weight loading, weight loading is not needed, and therefore weight loading is not needed. The time for waiting for weight loading in the model iteration process can be shortened, in addition, the process of generating the gradient of the second model layer group and the process of writing the generated gradient of the first model layer group into the memory of the CPU are executed in parallel, and the processing efficiency is improved. The process of writing the gradient of the generated second model layer group into the memory of the CPU and the process of updating the weight of the first model layer group are executed in parallel, so that the time required for writing the generated gradient into the memory of the CPU can be masked, the time of each iteration is shortened, and the efficiency of model training is improved.

The invention provides a source network load storage cooperative control optimization method based on an improved selective transmission algorithm, and belongs to the technical field of power system dispatching. An energy storage device equivalent circuit model considering the temperature effect is established, and the state of charge is estimated in real time through Kalman filtering; a power system is modeled into a directed graph network flow model to set power balance constraints, an improved genetic algorithm based on tent chaotic mapping is adopted to solve an optimization model, a network flow algorithm is embedded in an iteration process to check feasibility, and premature convergence is prevented by adaptively adjusting crossover probability and mutation probability. And finally, outputting a Pareto optimal solution set, and selecting a scheduling scheme with optimal comprehensive performance according to an ideal point method. The technical problems of high system operation cost and insufficient new energy consumption rate caused by the fact that multi-objective optimization solution is easy to fall into local optimum in the source network load storage cooperative control optimization process are solved.

The invention discloses a decoupling design method and system for reliability optimization of an angular contact ball bearing. The method comprises the following steps: determining initial design parameters of the angular contact ball bearing; constructing a reliability optimization design model which takes maximization of the rated dynamic load as a target and comprises probability reliability constraint processed by a penalty function; constructing a design variable and failure probability mapping agent model, specifically, identifying and quantifying uncertainty parameters, constructing a quasi-statics model and a limit state function, calculating the failure probability of a sample point through a probability reliability method, and training the agent model taking the design variable as input and the failure probability as output; and based on the proxy model, solving the reliability optimization design model by using an optimization algorithm, and outputting optimal bearing design parameters. According to the method, the reliability analysis process and the optimization iteration process are decoupled, so that the optimization efficiency is greatly improved while the design robustness is ensured.

An algorithm design method based on a large-scale language model comprises the steps that algorithm design requirements are received, and the algorithm design requirements comprise problem description; generating at least one candidate algorithm based on the algorithm design requirements; the to-be-evolved algorithm is iteratively evolved through the large-scale language model to obtain a target algorithm conforming to algorithm design requirements, the to-be-evolved algorithm comprises an evolved algorithm and / or a candidate algorithm, in each iteration process, a prompt is generated based on the to-be-evolved algorithm, and the prompt is used for guiding the large-scale language model to evolve. Therefore, through an explicit guidance mode, in the process of generating the algorithm by using the large-scale language model, clear guidance can be provided for the large-scale language model, so that the large-scale language model automatically generates the algorithm with relatively high accuracy, and the finally designed algorithm can be comparable with or even surpass the algorithm of manual customization design; and the difficulty of algorithm design is reduced.

The invention relates to the technical field of robot motion, in particular to a variable load working condition heavy-load mechanical arm kinematics parameter calibration method and system. The method comprises the steps that joint angle parameters and theoretical Standard D-H model parameters are determined; the theoretical position of the end effector in the ideal state is determined; the actual position is determined, and the position error of the end effector is determined; the deflection under the preset load condition is determined, and corrected D-H model parameters are obtained; determining a load effect based on the parameter structure characteristics; introducing the load effect as a correction item into a parameter updating formula of a preset LM algorithm, and designing an adaptive compensation coefficient to adjust the contribution degree of the load effect; and dynamically adjusting the adaptive compensation coefficient through a convergence index in an iteration process of the LM algorithm. According to the method, the calibration precision of the kinematics parameters of the mechanical arm can be improved under the variable load working condition.

The invention discloses a material warehouse management system based on path collaborative planning, and relates to the field of material warehouse management, the system comprises the following components: a data acquisition module, a data processing module, a path planning module and an equipment control module; according to the method, a multi-objective function containing equipment energy consumption parameters and operation time is constructed, an improved dynamic weight Pareto evolutionary algorithm is adopted for solving, the two objectives of energy consumption and operation time can be dynamically balanced, an energy consumption-efficiency coupling coefficient is introduced in the algorithm iteration process, and the energy consumption and efficiency of the equipment can be effectively improved. The optimization degree is adjusted in real time according to the optimal solution of the current solution set, the situation that the performance of another target is greatly reduced due to excessive pursuit of a single target is avoided, the comprehensive benefit of path planning is remarkably improved through the intelligent balance mechanism, and the system can keep an efficient and energy-saving operation state in different operation scenes.

This invention discloses a noise reduction method, a bearing fault diagnosis method, and a system based on dual sparse dictionary adaptive learning. The method performs wavelet decomposition on the signal to be denoised to obtain high-frequency and low-frequency signals, constructing high-frequency and low-frequency matrices. Then, threshold-adaptive DDTF dictionary learning is applied to both the high-frequency and low-frequency matrices. Specifically, an initial dictionary is set and learned to obtain an initial sparse coefficient matrix. An adaptive threshold update is then applied to the initial sparse coefficient matrix, selecting sparse coefficients from the initial sparse coefficient matrix in descending order as dynamic thresholds during the iteration process to update the initial sparse coefficients. After determining the final threshold, the final sparse coefficient matrix is ​​obtained, and the dictionary is updated again to obtain sub-signals. Finally, inverse transformation and matrix rearrangement inverse operations are performed on the obtained sub-signals to obtain the denoised signal. This invention combines wavelet decomposition and DDTF to construct a dual sparse pattern, effectively improving the sparse representation capability of a fixed basis.

The application discloses a parameter correction method and device, electronic equipment and a storage medium. The method comprises the following steps: obtaining the fitness value of any generation in the iteration process of a preset optimization algorithm, and a pre-calibrated fitness correction coefficient, wherein the fitness correction coefficient comprises a positive correction coefficient and a negative correction coefficient; further, determining a target correction coefficient based on the fitness value in the positive correction coefficient and the negative correction coefficient, correcting the fitness value based on the target correction coefficient to determine a fitness correction value, updating the preset optimization algorithm based on each fitness correction value, improving the adaptability of the preset optimization algorithm, thereby improving the optimization accuracy of the preset optimization algorithm; further, performing parameter optimization on the carbon load model based on the optimization algorithm with high optimization accuracy, improving the accuracy of the model parameters, thereby making the error of the target carbon load value estimated by the carbon load model smaller, and improving the estimation accuracy of the carbon load value.

The invention discloses a safe and efficient distribution and incremental updating method and system for an AI large model, and belongs to the technical field of artificial intelligence, cloud computing and data security. The method comprises the following steps: firstly, performing quantification or pruning compression on an original large-scale machine learning model to reduce the volume of data to be distributed; and then encrypting the compressed model and storing the compressed model to distributed nodes in a fragmented manner. Through an intelligent scheduling server, on the basis of the hardware capability and network topology of edge nodes, a proper fragment downloading source is distributed to the edge nodes, and parallel high-speed downloading and local recombination loading are achieved. For model version updating, the central server calculates binary system difference between a new version and an old version and generates an increment updating package, only the increment package is distributed to an edge node, the node locally applies updating to reconstruct a new version model, and updating flow and delay are greatly reduced. According to the method, high bandwidth utilization rate, low transmission overhead, full-link security protection and intelligent resource scheduling of the hundred-GB-to-TB-level large model in the cross-region distribution and iteration process are realized, and the efficiency and security of model deployment and updating are remarkably improved.

An RNN operator computing method, device, equipment, computer program product and medium are provided. The method is for an RNN operator. First, sequence data containing a plurality of time step input matrices is acquired, and the input matrices are subjected to single matrix-matrix multiplication operation with a layer input weight matrix, thereby generating, at one time, intermediate result matrices corresponding to all time steps for gated calculation. Subsequently, based on a binary semaphore mechanism, the production completion state of the batch of intermediate result matrices is synchronized among a plurality of parallel computing units of a chip. Once the production is confirmed to be completed, each computing unit performs iterative update calculation of the hidden space state in time step order using the ready intermediate result matrices. In the iteration process, the production state of the latest hidden space state is also synchronized between adjacent time step computing units through the binary semaphore mechanism, to ensure the correctness of the time sequence dependency, and finally the hidden space state output corresponding to the entire input sequence is obtained.

The invention relates to the technical field of textile manufacturing, in particular to a scheduling method based on fusion of a large language model and an improved simulated annealing algorithm, and the method comprises the following steps: obtaining production data; performing problem description, setting constraint conditions, taking minimization of the maximum completion time as a target function, constructing a scheduling problem model, setting an iteration process, and repeatedly executing the iteration process; generating an initial scheme according to the production data and the problem description; in the iteration process, alternately calling the large language model and the improved simulated annealing algorithm for solving, and generating a to-be-verified scheduling scheme; the verification intelligent agent verifies the scheduling scheme to be verified, and after multiple iterations, an optimal solution is finally determined, and an optimal scheduling scheme is output. After the technical scheme is adopted, the maximum completion time of the generated scheduling scheme is relatively short, the generation speed of the scheduling scheme is relatively high, and the scheduling scheme can be ensured to be executable.

The application discloses a power distribution network prediction auxiliary state estimation method and system based on an improved iteration process, and the method comprises the following steps: establishing a nonlinear discrete time state space model; generating a group of Sigma points through an unscented transformation, and substituting the Sigma points into a state transition equation to calculate a state prediction value and a state prediction error covariance matrix at a current time; substituting the Sigma points into a measurement equation to obtain a measurement prediction value, and calculating a Kalman gain; calculating a process noise covariance matrix at a next time; and correcting the state prediction value and the state prediction error covariance matrix at the current time to obtain an optimal state estimation value and an error covariance matrix thereof. The application avoids the risk of losing semi-positive definiteness of the covariance matrix in the iteration process from the algorithm mechanism, significantly enhances the numerical stability and robustness of the UKF algorithm, and ensures reliable application in high-dimensional complex power distribution network state estimation.

The application discloses a quantum linear solving method and device based on quantum adiabatic linear algorithm, and a medium, and relates to the technical field of quantum computing. The method comprises the following steps: obtaining a linear system to be processed, and constructing a Krylov subspace matched with the linear system; determining a subspace equation set of the linear system based on the Krylov subspace according to the linear system and the Krylov subspace; constructing a quantum circuit corresponding to the quantum adiabatic linear algorithm according to the subspace equation set, and solving an approximate solution of the linear system in the Krylov subspace according to the quantum circuit. The application can combine the subspace method with the quantum adiabatic linear algorithm, perform dimension reduction processing on the original high-dimensional linear equation set in the quantum adiabatic linear algorithm through the subspace method, so that the dimension of the linear equation set actually solved in the iteration process is smaller than the dimension of the original linear equation set. Therefore, the demand of the quantum discrete adiabatic linear algorithm for computing resources is reduced, and the quantum discrete adiabatic linear algorithm can be implemented in an NISQ chip and a classical computer.

The embodiment of the invention discloses a test case generation method, an automatic test method and a computer program product. The method is used for generating a corresponding test case for an iteration function to be applied to an online environment in a function iteration process of a target application program, and comprises the following steps: obtaining an atomic instruction set, and providing a target page comprising an information submission area and an instruction display area; obtaining a test step submitted by a test user in a natural language description mode through the information submission area, calling an AI model, and generating a test instruction corresponding to the test step by the AI model according to the content described by the natural language and the atomic instruction set; obtaining a test instruction generated by the AI model, and displaying the test instruction through an instruction display area so as to verify the integrity and the execution sequence of the test instruction corresponding to a plurality of test steps split by the target iteration function; and performing instruction combination on the plurality of test instructions to obtain a corresponding test case so as to execute the test case to perform automatic test on the target iteration function.