27 results about "Iteration loop" patented technology

The invention discloses a large model and knowledge graph dual-drive-based guided reasoning system and method, belongs to the technical field of artificial intelligence reasoning, and solves the problem of how to improve the process reasoning ability of a large language model for engineering subject courses and the reliability of solving complex engineering problems. According to the method, metadata is extracted from teaching materials, structured problem representation is constructed, and a knowledge graph is constructed to form a complete knowledge system; the method comprises the following steps: decomposing a complex engineering problem into a structured solving plan with knowledge marks, and carrying out iterative loop based on a Monte Carlo tree search algorithm to generate a search tree comprising a plurality of high-quality candidate problem solving paths; then determining an optimal answer and a corresponding reasoning path in all simulated paths in a voting mode, performing confidence evaluation on each node of each path in the candidate path set, and further selecting a path of an optimal solution; and the process reasoning capability of the large language model on engineering subject courses and the reliability of solving complex engineering problems are effectively improved.

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 discloses a dynamic convergence control method and device for an iterative task in intelligent application, and belongs to the technical field of computers. Task data are acquired and initialized to obtain a plurality of data units, and a corresponding high-dimensional state vector is constructed for each data unit; the high-dimensional state vector is used for representing the change of content embedding, and performing iterative loop processing on the task data; after execution of one iteration loop processing period is completed, the high-dimensional state vectors of the related data units are updated, and updated high-dimensional state vectors are obtained; and based on a preset convergence condition, judging the updated high-dimensional state vector by using a semantic convergence controller to obtain a judgment result. According to the method, the high-dimensional state vector is judged and processed, so that resources are saved, and a high-quality result is generated; by introducing the semantic convergence controller, whether semantic oscillation occurs or not is judged, meanwhile, invalid optimization can be recognized, and the iterative task processing efficiency is improved.

The invention provides a dynamic scheduling method and system for a redundant executor based on a foreground theory and a Q learning evolutionary game method. The method comprises the following steps: constructing a strategy space, a profit structure and strategy evolution dynamics of the executor and a reward and punishment mechanism of an enhanced analyzer-regulator; according to the dynamic scheduling method, an executor simulates interaction under a tripartite game framework, iterative loop is carried out, and a strategy space of a mimicry defense system is output as a redundant executor after the loop is ended, the simulation interaction process comprises the following steps: the enhanced analyzer-regulator monitors and obtains objective income of each execution body in real time, calculates subjective perception utility of each execution body according to an income structure of the execution body, and then adjusts the subjective perception utility of the mimicry defense system according to a reward and punishment mechanism of the enhanced analyzer-regulator. And each execution body dynamically updates the strategy space of each execution body according to the adjusted subjective perception utility and strategy evolution.

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.

The invention provides a JAX-based high-performance discrete event simulation method and system, and the method comprises the steps: firstly completing environment configuration and initialization through an immutable data structure, and generating an EnvParas object with fixed static parameters; secondly, processing event logic by adopting a pure function steepv, and realizing side-effect-free functional event processing and state iteration by combining JAX condition control primitive and lax.scan iterative loop; then, a batch processing task is generated through job packaging, and job-level parallel acceleration is achieved on multiple GPU / TPU devices by means of jax. Jit compiling and jax. Vmap vectorization; and finally, packaging a standardized AI interactive interface, supporting external algorithm calling and finishing result post-processing. Functional programming and JAX hardware acceleration are combined, the simulation speed is increased, meanwhile, the event dynamic accuracy is guaranteed, reinforcement learning large-scale sampling can be seamlessly supported, and the method is suitable for AI application scenes in the field of operational research.

The invention belongs to the field of integrated circuit testing, and discloses a Shmoo testing method and device and a storage medium. The method comprises the steps of determining to-be-optimized Shmoo test parameters of an integrated circuit and defining a search range; an optimization objective function is established, an initial test parameter set is generated, and initial test or simulation is executed to obtain initial fitness data; executing an iterative loop of the grey wolf optimization algorithm, calculating a fitness value of a current test parameter set in each iteration, identifying an optimal test parameter, a suboptimal test parameter and a third optimal test parameter, and updating an algorithm control parameter; updating other test parameters according to the position information of the three test parameters, and carrying out boundary processing on the updated test parameters to keep the test parameters in a predefined search range; and when the termination condition is met, outputting the optimal test parameter as an optimal Shmoo test parameter combination, thereby determining the performance boundary of the integrated circuit or generating a Shmoo graph, and realizing more comprehensive and accurate Shmoo test.

This application discloses a data hashing method, apparatus, electronic device, and storage medium, relating to the field of data processing technology. The method includes: acquiring at least one set of messages corresponding to the data to be hashed; generating new message words by expanding a portion of the message words in each set of messages; when there are multiple sets of messages, in the first iteration loop, generating a corresponding intermediate hash value based on a preset number of message words included in the first set of messages corresponding to the first iteration loop and a plurality of pre-configured initial values; generating the initial value for the first iteration round in the next iteration loop based on the intermediate hash value corresponding to the first iteration loop and the plurality of pre-configured initial values, and entering the next iteration loop, until the current iteration loop is the last, and determining the final hash value corresponding to the current iteration loop as the target hash value. This application can solve the problem that data hashing requires a large amount of logical resources, resulting in a large algorithm core area.

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.

A data processing service generates for iteratively applying a geospatial function to geospatial data. The generated code includes at least a first iterative loop and a second iterative loop. The data processing service compiles the generated code to generate compiled code that vectorized at least the second iterative loop. The data processing service receives a request from a client device to perform one or more data processing operations including applying the geospatial function to a data table of geospatial cell indices. The data processing service compiles the request into one or more tasks including at least a vectorized operation based on the compiled code and executes the one or more tasks by at least invoking the vectorized operation on the set of worker nodes.

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.

An embodiment of the disclosed invention is a computer-implemented method to build a schedule, which includes establishing a scheduling period and a number of tasks to be accomplished during the schedule, assigning an expected value to each of the tasks, and using an iterative RIDES loop to adjust the expected value for each task and rebuild the schedule until the expected value is optimized. Another embodiment is a computer-implemented method to optimize a schedule that includes using an iterative RIDES loop to determine whether to execute a first schedule allocation or continue to improve the schedule allocation. Another embodiment is a method for implementing a loop architecture to update an expected value for a task using data from the environment, wherein the expected task value is the maximum task value multiplied by a weighting factor.

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.

A data processing service generates for iteratively applying a geospatial function to geospatial data. The generated code includes at least a first iterative loop and a second iterative loop. The data processing service compiles the generated code to generate compiled code that vectorized at least the second iterative loop. The data processing service receives a request from a client device to perform one or more data processing operations including applying the geospatial function to a data table of geospatial cell indices. The data processing service compiles the request into one or more tasks including at least a vectorized operation based on the compiled code and executes the one or more tasks by at least invoking the vectorized operation on the set of worker nodes.

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 invention discloses a heterogeneous equipment maintenance task distribution system and method based on A2C reinforcement learning, and belongs to the technical field of reinforcement learning. The implementation method comprises the steps that 1, a server side establishes an A2C-GA hybrid model used for distribution of maintenance tasks of heterogeneous equipment in a workbench-task state, and hyper-parameters are configured for the model; the client side carries out parameter initialization on the working table-task state of the server side; 2, the server side updates the Actor network and the Critic network by utilizing action state interaction evaluation of the task allocation set, and performs parameter updating on the A2C model by utilizing the gradient loss of the Actor network and the Critic network; 3, executing the step 1 and the step 2 in an iterative loop mode until the fitness is improved to be less than 1%; 4, the server side visually outputs the task allocation set and the total time of task completion through the client side; compared with the prior art, the technical problem of improving the equipment maintenance task allocation accuracy in the existing heterogeneous equipment maintenance task allocation is solved.

The invention belongs to the technical field of intelligent control and energy conservation of a thermal power plant, and particularly relates to a full-process optimization method for a pneumatic ash conveying system based on multi-mode sensing fusion, dynamic digital twin modeling and self-adaptive reinforcement learning control and a closed-loop verification and self-healing system. A sensing-analysis-decision-execution-self-optimization closed-loop architecture is constructed, and real-time working condition matching, energy consumption optimization and equipment service life prolonging in the ash conveying process are achieved through data communication collection, data analysis and calculation, control result output, control instruction execution and system iterative circulation. The technical problems of control lag, high energy consumption and fast equipment loss of a traditional system are solved.

The invention discloses a feasible pump acceleration column and constraint generation method, and relates to the technical field of energy system planning, and the method comprises the steps: obtaining basic parameters of a three-layer mixed integer optimization problem, and constructing an original problem model based on the basic parameters; a column and constraint generation framework is adopted, an original problem model is decomposed into a main problem model and sub-problem models, dual processing is conducted on the sub-problem models, and a single-layer max model is obtained; calculating through a feasible pump accelerated column and constraint generation algorithm to obtain a main problem target solution and a sub-problem target solution, and updating the lower bound and the upper bound of an original problem model; judging whether a convergence condition is met or not, and if yes, directly outputting a target solution; and if not, iterative loop calculation is carried out until the convergence condition is met, and a target solution is obtained. According to the method, the global optimality of the solution can be guaranteed, the solving efficiency can be remarkably improved, and the method has good expandability and is suitable for solving the large-scale three-layer mixed integer optimization problem.

The invention discloses a security constraint unit commitment efficient solving method and system based on neighborhood search, and relates to the technical field of power system operation optimization, and the method comprises the steps: setting an initial iteration frequency, constructing a UC model without network security constraint, and obtaining an initial UC solving result; according to a UC model solving result, safety checking is carried out on the power transmission line, and a specific unit set is screened; building a neighborhood search cutting plane based on a specific unit set, adding the neighborhood search cutting plane into the UC model, and solving the UC model again; and after the UC model is solved again, iterative circulation is carried out, and the unit operation state and the planned output are output. According to the method, the search space of the mixed integer programming problem is effectively reduced, the invalid search range is reduced, the solving speed of the security constraint unit commitment problem is increased, the strict requirement of real-time scheduling of a power system on the solving time can be met, and the method is suitable for large-scale popularization and application. The unit combination scheme obtained through efficient solution can realize optimization of the system operation cost, and the economic benefit of the power system is improved.

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 invention discloses a graph database fault query simplification method and device based on a large language model, and belongs to the technical field of computer software, and the method comprises the steps: obtaining an original query statement for triggering the collapse of a graph database and associated collapse information of the original query statement, and constructing an iterative control architecture for managing query simplification, execution and verification processes; inputting the original query statement and the crash information into a large language model, so that the large language model generates a simplified query statement according to a preset simplified rule set; executing the simplified query statement, and detecting whether the graph database crashes again; based on execution and detection results, state updating and iterative loop control are carried out through an iterative control architecture; and when a preset termination condition is met, outputting a minimum query statement for triggering the crash of the graph database. According to the method, the manual intervention requirement is remarkably reduced, the automation degree is high, the simplification efficiency is improved, meanwhile, multiple LLM models and graph database systems are compatible, and good expandability and adaptability are achieved.

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 provides a robotized manufacturing workshop scheduling method and device based on a coalition game. The method provided by the invention comprises the following steps: establishing an optimization model taking minimization of maximum completion time as a single target; performing process sorting optimization based on the optimization model and particle swarm optimization, encoding the process sorting into discrete particles, updating the particle speed through an exchange operation, evaluating a process sequence corresponding to the particles with the maximum completion time, iteratively updating individual and global optimal sequences, and obtaining an initial process execution sequence; based on the optimization model and the initial process execution sequence, adopting an alliance formation game to carry out robot task allocation optimization, and through iterative optimization of a task utility function and a robot utility function, controlling a robot to adjust alliance members according to utility to obtain a robot alliance allocation scheme; and based on the initial process execution sequence and the robot alliance allocation scheme, dynamically adjusting the process execution sequence and the robot alliance allocation scheme through an iterative loop to obtain an optimal scheduling scheme.

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.