12 results about "Iteration loop" patented technology

The invention provides an SQL statement generation method and system based on execution of feedback iterative correction, and relates to the technical field of large language models.The method comprises the steps that a query request of a user is received, and metadata information of a target database is obtained; generating a current SQL statement based on the query request and the metadata information; pre-executing the current SQL statement in a database sandbox environment; obtaining a pre-execution result, directly outputting the pre-execution result if the pre-execution result succeeds, and performing iterative loop if the pre-execution result fails; and analyzing error information in the pre-execution result, and determining an error type. According to the method, the illusion problem of a large language model is fundamentally solved, and the semantic correctness of SQL generation is ensured. And meanwhile, the iterative correction process replaces low-efficiency manual cue word adjustment, so that the use experience and the problem solving efficiency can be remarkably improved. In addition, the whole pre-execution process is completed in a sandbox environment, so that the influence of potential misoperation on production data is effectively isolated, and the safety of the system is enhanced.

A method of detecting superimposed SCMA signals from multiple user equipment in a receiver of an OTFS communication system includes initializing an executing an iteration loop, in which a mean of all a posteriori estimates of the transmitted signal determined so far is calculated, which a posteriori estimates are based on the OTFS-demodulated received signal, the corresponding channel matrix, and the mean vectors and variances determined for each UE. The mean of all a posteriori estimates determined so far is used for determining vectors and variances for each UE, further using the probabilities of the non-zero elements of the respective UEs codebooks. The iteration is repeated until a termination criterion is met.

The invention relates to the technical field of generative artificial intelligence, and particularly discloses a diffusion model machine forgetting training method and system for preventing sensitive content generation, and the method comprises the steps: generating a pseudo sample set; calculating a significance gradient and a significance mask of generator parameters based on the pseudo sample set, and screening key parameters related to forgetting semantics; performing selective gradient updating on the pseudo-fractional network parameters based on the saliency mask; taking the updated parameters of the pseudo-fractional network as a supervision reference of a behavioral distillation stage, sampling forgetting class pseudo samples and reserved class pseudo samples, and guiding training of a generator network; executing gradient updating on the key parameters marked by the saliency mask for forgotten pseudo samples; executing all-parameter gradient updating for the reserved pseudo samples; and after iterative loop, obtaining a generator network of which the specific category is forgotten. According to the method, a selective updating mechanism of structure perception is introduced, so that the accuracy, stability and retention performance of forgetting sensitive contents are improved.

A method of detecting superimposed SCMA signals from multiple user equipment in a receiver of an OTFS communication system includes initializing an executing an iteration loop, in which a mean of all a posteriori estimates of the transmitted signal determined so far is calculated, which a posteriori estimates are based on the OTFS-demodulated received signal, the corresponding channel matrix, and the mean vectors and variances determined for each UE. The mean of all a posteriori estimates determined so far is used for determining vectors and variances for each UE, further using the probabilities of the non-zero elements of the respective UEs codebooks. The iteration is repeated until a termination criterion is met.

The application discloses an edge computing task offloading method and system based on soft clustering and dynamic constraints, and relates to the technical field of edge computing. The method comprises the following steps: clustering robots, constructing a membership matrix, and mapping the cluster center back to the individual decision space of the robot; an evolutionary iteration cycle is performed, in which, during the cycle, a dynamic constraint relaxation factor is determined according to the real-time proportion of feasible solutions, the dynamic constraint relaxation factor is set to 0 for safety level constraints and deadline constraints, a constraint violation degree vector is calculated for the candidate solution, and the candidate solution is screened according to the feasibility boundary box; the candidate solution is Pareto sorted; a safety level index is calculated, and when a preset safety threshold is met, a Pareto optimal solution set is extracted; and a task offloading scheme represented by the Pareto optimal solution set is executed. The application solves the problem that the traditional method is completely ineffective when the feasible solution density is less than 10 ‑15 , ensures 100% safety compliance rate, improves search efficiency, and solves the dimension disaster problem.

The invention belongs to the technical field of gear transmission systems, and particularly discloses a gear meshing optimization method and system based on random progressive iteration and regularization, and the method comprises the following steps: carrying out the data sampling and preliminary fitting based on a gear tooth surface curve model; aiming at the preliminary fitting, setting an adaptive double-layer optimization framework which comprises a first layer of control point updating strategy optimization and a second layer of parameter distribution optimization, constructing an objective function, and realizing adaptive fusion optimization in the same iterative loop to obtain an optimized involute curve and a curved surface; and for the meshing region, introducing second-order difference smoothing, carrying out multi-objective optimization, and outputting a finally fitted NURBS control point P and an optimized parameter mapping weight w. By adopting the technical scheme, on the basis of least square approximation (LSPIA), the adaptive double-layer optimization technology is integrated, and the fitting precision, the fairness and the convergence speed are remarkably improved by jointly optimizing a control point updating strategy and parameter distribution.

The present invention relates to a sampling apparatus and method robust against side-channel attacks, the method comprising the steps of: (a) receiving a random bit; (b) setting an iteration loop index i to 0 and determining whether the random bit is greater than or equal to NRCDT[i]; (c) if the random bit is greater than or equal to NRCDT[i], an adjustment value in an i-th iteration loop is set to 1, and if the random bit is smaller than NRCDT[i], the adjustment value in the iteration loop is set to 0; (d) updating a random integer by subtracting the adjustment value from 18; and (e) repeating steps (b) to (d) while increasing i by 1 until i reaches 17, thereby obtaining a finally updated random integer, wherein NRCDT[i] has a value obtained by bit-inverting RCDT[i] of a base sampler.

The application discloses a flexible job shop scheduling method and device based on sub-batch division, and comprises the following steps: in the iteration cycle step, individual sorting is performed on the initial population according to fitness, a guide wolf individual is selected from the sorted population, and an individual is selected to establish an external file; the guide wolf individual is used to guide and optimize the subordinate wolf individual in the population through the grey wolf optimization algorithm; the coding of the individual in the initial population is randomly adjusted based on sub-batch division to expand the search range; the coding of the individual in the initial population is optimized and adjusted in a local structure; and the external file established by the individual selected from the population is output, and a scheduling scheme is selected from the external file. By using the above technical scheme, the calculation amount of production scheduling can be reduced, and unnecessary time consumption can be reduced through reasonable sub-batch division; the population evolution is accelerated, and the convergence speed is accelerated by designing multiple guide mechanisms; and for the decision wolf individual, a local search based on a key task is designed to find a better solution.

The application discloses a micro-grid optimal scheduling method based on an improved MOJAYA algorithm, first sets a micro-grid target function and constraint conditions, initializes a population: generates an initial population and an empty archive set. Then, the initial population and the empty archive set are combined, and the total fitness of all individuals in the combined set is calculated, the total fitness F(i) is composed of the original fitness R(i) and the congestion density D(i), and the archive set is updated; finally, the optimal individual and the worst individual are identified through the total fitness F(i), the population individual is updated through an MH sampling method; an iterative loop is performed, and finally, the archive set is output, and the individuals in the archive set represent the power generation output scheme of each micro power supply after the micro-grid optimal scheduling. The application solves the problem of weak global search ability of the existing technology and the problem of being easily trapped into local optimization.

The invention relates to the technical field of network security, and discloses an SHA-2 algorithm acceleration method and system based on an RISC-V platform, and the method comprises the steps: compiling a compression function of an SHA-2 algorithm through an RISC-V assembly instruction set; performing data stream recombination and sliding window register mapping on a message expansion process of the SHA-2 algorithm, and mapping sequential access to a plurality of word arrays on the algorithm into cyclic coverage access to a plurality of registers or memory units; performing complete expansion on multiple rounds of iteration loops in the compression function, and statically and sequentially arranging calculation of each round of iteration; operation optimization of RISC-V instruction characteristics is carried out on the SHA-2 algorithm; and adjusting the calculation sequence in the sub-function and the lifetime of the intermediate result to reduce the number of temporary registers. According to the method, the compression function in the SHA-2 algorithm is optimized by using the RISC-V assembly instruction set, so that the operation efficiency is remarkably improved, the processing speed and performance are improved, and the optimization effect range is expanded; the memory access frequency is reduced, and the calculation efficiency is improved.

The application discloses an edge computing task offloading method and system based on soft clustering and dynamic constraints, and relates to the technical field of edge computing. The method comprises the following steps: clustering robots, constructing a membership matrix, and mapping the cluster center back to the individual decision space of the robot; an evolutionary iteration cycle is performed, in which, during the cycle, a dynamic constraint relaxation factor is determined according to the real-time proportion of feasible solutions, the dynamic constraint relaxation factor is set to 0 for safety level constraints and deadline constraints, a constraint violation degree vector is calculated for the candidate solution, and the candidate solution is screened according to the feasibility boundary box; the candidate solution is Pareto sorted; a safety level index is calculated, and when a preset safety threshold is met, a Pareto optimal solution set is extracted; and a task offloading scheme represented by the Pareto optimal solution set is executed. The application solves the problem that the traditional method is completely ineffective when the feasible solution density is less than 10 ‑15 , ensures 100% safety compliance rate, improves search efficiency, and solves the dimension disaster problem.

This invention belongs to the field of artificial intelligence and software engineering technology, specifically disclosing a method for generating causal consistency test cases based on graph reasoning and execution feedback. The method includes: constructing a code knowledge graph; parsing natural language problem reports to obtain structured information; performing a hybrid search on the code knowledge graph to obtain a list of candidate code nodes related to the natural language problem report; starting with high-scoring nodes in the candidate code node list, performing graph exploration on the code knowledge graph to obtain a complete context code set; based on the structured information and the complete context code set, executing an iterative loop of "planning-generation-execution-critique" to obtain "causally correct" failed test cases; generating candidate code patches to repair the target code; and applying the failed test cases to the repaired code for testing. This invention solves the problems of semantic gaps and understanding difficulties, context focusing and localization difficulties, and causal reproduction difficulties in existing technologies.