79 results about "Computational problem" patented technology

The invention belongs to the technical field of electric power automation, and discloses a DTS intelligent interaction method and system fusing a large language model and a vector database, and the method comprises the steps: obtaining the text data of an electric power regulation and dispatcher training simulation system DTS field, and constructing a semantic vector knowledge base; based on the constructed semantic vector space mapping and knowledge base, performing problem reconstruction representation on the proposed problem, and then converting the problem reconstruction representation into a vector through an embedded network; retrieving in the constructed semantic vector knowledge base based on the converted vector, calculating the comprehensive similarity between the problem vector and the vector in the knowledge base, and retrieving the most relevant knowledge fragment; and driving a large language model to generate a final intelligent question and answer response by taking the most relevant knowledge fragment and a dialogue context provided by a memory mechanism as input. Efficient, accurate and intelligent questioning and answering of complex dispatching business problems are achieved, and a new artificial intelligence interaction tool is provided for training of a digital intelligent novel power system dispatcher.

The invention particularly relates to a cache, migration and unloading collaborative optimization method in a satellite-assisted ocean network, which comprises the following steps: constructing a satellite-assisted ocean network architecture, mapping task completion delay to system utility according to the satellite-assisted ocean network architecture, and constructing a target function with the purpose of maximizing the system utility under multi-dimensional constraints; converting an objective function optimization problem into a partially observable Markov decision process; and solving an objective function optimization problem based on attention-enhanced multi-agent reinforcement learning to obtain an optimal solution. According to the method, the problem of low-delay and efficient task calculation under the satellite-assisted ocean network architecture is solved.

The invention relates to the technical field of geographic information analysis, and discloses a rapid space-time road network K function graph generation method and system based on a range tree and a prefix counting array, a terminal and a storage medium, and the method comprises the following steps: aiming at the calculation problem of a single space-time road network K function and the calculation problem of generating the space-time road network K function graph; the problems are solved through data structures of a range tree and a prefix counting array, different methods are selected according to the structure of each edge, and the selected structures are used for calculating the space-time road network K function of multiple thresholds. According to the method, different data structures are selected for calculation according to different calculation problems, and the time complexity is remarkably reduced under the condition that the space complexity and the precision are not changed.

The invention provides a management, modeling and simulation method and system for heterogeneous computing task scheduling, and the method comprises the steps: generating a global logic task identifier set according to the input parameters of a computing problem, and dividing the set into a plurality of logic task blocks based on the internal attributes of logic task identifiers; mapping each logic task block into an atomic computing unit, and packaging a computing attribute for each atomic computing unit; based on a preset screening rule and the resource constraint information, the atomic computing units are recombined, and schedulable task instances are formed; and allocating the schedulable task instances to the computing resources for execution, and monitoring performance data of the execution to update the quantifiable computing attributes of the atomic computing units by using the performance data. According to the method, the problems that the utilization efficiency of heterogeneous computing resources is low and simulation prediction is distorted due to the fact that a traditional task scheduling scheme lacks a quantifiable task abstraction and dynamic recombination mechanism across hardware platforms are solved.

The invention belongs to the technical field of large model application, and relates to an enterprise technology demand prediction method and system based on large model driving, and the method comprises four parts: enterprise problem deep analysis, problem technology essence identification, technology knowledge base intelligent retrieval and multi-granularity technology demand prediction. In the deep analysis of enterprise problems, the problem description is subjected to structured processing, an entity relationship graph is constructed, and domain knowledge is aligned. The problem technology essence identification utilizes large model semantic understanding to analyze core challenges, and classifies technical problems through a multi-label classification model. And the technical knowledge base intelligently retrieves and calculates the similarity between the problem semantic vector and the knowledge graph, fuses multi-source data expansion, and screens candidate technologies according to the technology maturity. And performing multi-granularity technology demand prediction analysis on the core technology field and the subdivision direction to form a multi-level demand result. According to the invention, through systematic analysis and intelligent prediction, enterprise technology demands are accurately identified, and the method is suitable for scenes such as industry-university-research cooperation, technology transfer and innovative resource configuration.

One or more systems, devices, computer program products and / or computer-implemented methods of use provided herein relate to an intelligent and automated system to solve quantum computing related problems. The computer-implemented system can comprise a memory that can store computer-executable components. The computer-implemented system can further comprise a processor that can execute the computer-executable components stored in the memory, wherein the computer-executable components can comprise a recommendation component that can employ a machine learning model to generate, based on an input, a recommendation comprising a combination of entities comprising, one or more quantum circuits, one or more algorithms, one or more quantum hardware units, one or more error mitigation or error correction techniques, and one or more quantum procedures, to solve a defined problem comprised in the input.

The invention relates to method (200) and computing device (101) for decomposition of one or more computational problems The method (200) comprises obtaining (201) performance information of the one or more network nodes, the performance information comprising idle time of the plurality of tasks hosted on the one or more network nodes, and resource capability of the one or more network nodes. Upon determining (204) that a threshold condition on the idle time of one or more tasks is fulfilled, determining (203) one or more re-deploying configurations for a subset of the plurality of the tasks, based on the idle time information of the plurality of tasks, and on the resource capability of the one or more network nodes; and re- deploying (205), based on one of the one or more re-deploying configurations, the tasks of the subset associated with the one of the one or more re-deploying configurations.

The embodiment of the invention relates to the technical field of large language models, and discloses a knowledge graph abstract method, system and device and a medium. Calculating the semantic similarity between the question and the triple in the knowledge graph, and screening top-K related facts according to the semantic similarity; generating a question-oriented evidence abstract according to the top-K related facts, and performing knowledge distillation and preference alignment on the evidence abstract in the generation process; and carrying out specific task processing on the generated evidence abstract by adopting direct preference optimization. Compared with a traditional triple linearization and free text generation method, more key evidences can be contained in a short text length, and the question answering efficiency of the model is improved. The technical problem of low evidence abstract generation efficiency can be at least solved.

A quantum simulation method for solving a computational problem using a solid-state quantum system, the method comprising the steps of: passing a laser through a material; in the material, evolving at least some of a plurality of atoms in a first state into at least some of a plurality of atoms in a second state upon receiving energy from the laser to form at least one exciton; selecting at least one exciton site on the material wherein the at least one exciton site is separated from a neighbouring at least one exciton site by a distance less than a Rydberg blockade radius; mapping the computational problem into a problem Hamiltonian of the solid-state quantum system; measuring at least a portion of plurality of the at least one excitons to obtain a read-out of the solid-state quantum system; and determining a solution to the computational problem from the read-out.

An embodiment sends, at a first time, from a boundary server to a client system in response to a challenge request, a challenge specifying a computational problem to be solved by the client system, the boundary server specified in a challenge list sent to the client system. An embodiment receives, at a second time, at the boundary server, a challenge response from the client system, the challenge response comprising a solution to the computational problem. An embodiment generates, at the boundary server, a certificate encoding an elapsed time between the first time and the second time, the certificate usable by the client system to prove a location of the client system. An embodiment sends, from the boundary server to the client system, the certificate.

The invention discloses an air-water cross-medium shell whole-course trajectory firing table compiling method, and belongs to the technical field of outer ballistic cross-medium trajectory correction theories, cross-medium fluid dynamics and artillery firing table compiling. The method comprises numerical simulation of an air trajectory, a water entry trajectory and a water trajectory and a whole-course trajectory shot table fast interpolation algorithm. Wherein the air trajectory is solved based on a six-degree-of-freedom trajectory model; the water entry trajectory and the underwater trajectory are obtained by solving a fluid control equation by adopting a fluid volume (VOF) multiphase flow model, a turbulence model, a cavitation model and a dynamic grid technology; the full-ballistic shot table fast interpolation algorithm is realized based on a least square method and a linear interpolation algorithm. The method is suitable for the cannonball trajectory air-water cross-medium condition, the rapid and accurate calculation problem of firing elements of an artillery fire control system in the air-water cross-medium environment of the firing table is solved, the continuity of the cross-medium full-trajectory firing table is achieved, the blank of the cross-medium firing table is filled up, and the cross-domain combat ability of an artillery weapon system is effectively improved.

In one embodiment, in response to a challenge request, a challenge is sent from a border server to a client system at a first time, the challenge specifying a computing problem to be solved by the client system, the border server being specified in a list of challenges sent to the client system. One embodiment receives, at a second time, a challenge response from the client system at the border server, the challenge response including a solution to the compute question. One embodiment generates, at a border server, a certificate encoding a time elapsed between a first time and a second time, the certificate being usable by a client system to attestation a location of the client system. In one embodiment, credentials are sent from a border server to a client system.

A trust chain security trusted computing and data transmission method and device based on an intelligent computing platform and a storage medium, the method comprising intercepting and analyzing transmission data in a virtual machine and a platform system data transmission link, transferring safe data to a target link, and transferring unknown safe data to a designated platform system confusion area for confusion safe processing; using a log layered mining method to analyze the transmission data in the transmission link to analyze the association view formed by the association relationship of the virtual machine; according to the association view, the influence of the sensitive operation of any virtual machine on other virtual machines is used to realize dynamic maintenance of the trust chain between different virtual machines on the cloud; the system uses a benefit and cost ratio index to sort the trust relationship of different virtual machines, and the virtual machine with a larger benefit and cost ratio is given priority to establish a trust relationship. The method can realize dynamic maintenance of the trust chain of the virtual machine on the cloud, solve the problem of dynamic calculation of the priority of the trustworthiness of the virtual machine, and improve the operation efficiency of the cloud service network.

The invention relates to a thick and thin grid fast interpolation technology for wall turbulence calculation, and aims to solve the problems of large calculation amount and complex implementation process of an existing interpolation method. Half of the minimum grid spacing of the fine grids is used as a judgment standard, and interpolation modes are intelligently switched. For each fine grid point, when the distance between the fine grid point and the nearest coarse grid point is smaller than the standard, the physical value of the nearest coarse grid point is directly given to the fine grid point; otherwise, if the distance is greater than or equal to the standard, interpolation is carried out by adopting an inverse distance weighted average method. According to the method, half of the minimum fine grid scale is defined to serve as a standard for judging whether the fine grid point is directly assigned by the physical quantity on the nearest coarse grid amplitude point or not, and on the premise that it is ensured that physical information is not distorted, the complex calculation problem is simplified. The tedious calculation process in a traditional method is avoided, the algorithm complexity is effectively reduced, and the interpolation calculation speed and efficiency are remarkably improved.

The invention discloses a car insurance secret state modeling method based on multi-key fully homomorphic encryption, and belongs to the technical field of big data and data security. The method comprises the following steps: each participant independently generates an encryption key pair, encrypts local data, and uploads the encrypted data to a secure computing environment; performing generalized linear regression (GLM) model training and prediction in the ciphertext domain; after training is completed, all parties perform collaborative decryption through private key shares to obtain a model output result; according to the method, fully homomorphic encryption (FHE), privacy protection modeling and a generalized linear regression (GLM) algorithm are comprehensively applied, so that joint modeling and prediction of multi-party car insurance data in a ciphertext state are realized, and the privacy protection and efficient calculation problems in car insurance risk modeling and pricing participated by multiple parties are solved; and the requirements of effective fusion and privacy protection of multi-party data are met.

A method for managing a group of processing units configured to solve a cryptographic problem. The method includes a control unit of the group for: connecting to at least one computing devices network which collaborate in the search for a solution to a cryptographic problem by calculating possible solutions to the problem, determining a computing power available for each processing unit of the group; calculating a computing power of the group, based on the available computing power of each processing unit of the group, determining possible remaining solutions of the cryptographic problem which are to be calculated by the computing devices network, determining whether the computing power available of the group allows calculation of the possible remaining solutions in a predetermined time, and in the affirmative case, allocating the computing power available in the group of processing units to the calculation of the solution of the cryptographic problem.

Systems and methods in accordance with the present disclosure can implement a parallel processing system, such as a graphics processing unit (GPU)-based system, to generate solutions to complex computational problems. Aspects of this technical solution can retrieve a plurality of solutions each representing a plurality of values in a multi-dimensional space, allocate, to one or more processing units associated with the one or more circuits and having a parallelized configuration, one or more of the plurality of solutions, modify, by the one or more processing units according to the parallelized configuration, at least one value of the one or more solutions allocated to the one or more processing units, to determine a plurality of modified solutions, and output, from the plurality of modified solutions, according to one or more criteria indicating a diversity among the plurality of modified solutions, a selected solution.

The invention discloses an intelligent inspection problem identification and data processing method and system based on a multi-modal large model, and relates to the technical field of intelligent inspection data processing, and the method comprises the steps: carrying out the preprocessing of multi-modal original data collected in the inspection process, and extracting the feature representation corresponding to each modal; inputting the feature representation into a cross-modal fusion network, and obtaining a fusion feature representation by constructing an inter-modal weight matrix; inputting the fusion feature representation into a pre-trained multi-modal large model for identification processing, adopting a multi-label classification algorithm to complete inspection problem type identification and calculate a problem risk score, and outputting a problem identification result; and constructing a correlation graph by taking a problem identification result as a node, analyzing the correlation strength between the problems by using a graph neural network, identifying a problem propagation path and an influence range, and generating a structured patrol report. According to the method, the accuracy of patrol problem identification, the scientificity of risk assessment and the structuralization and interpretability of result expression can be improved.

The application discloses an intelligent retrieval method and device for a knowledge base and a storage medium, and belongs to the technical field of information processing and artificial intelligence. The method comprises the following steps: acquiring a user question, and constructing a feature fusion model and a fine arrangement language model; extracting a knowledge text feature set of a preset knowledge base based on the feature fusion model, and associating the knowledge text feature set with the knowledge base to obtain a knowledge database; extracting a question feature of the user question based on the feature fusion model; matching the question feature with the knowledge database piece by piece, and calculating the similarity between the question feature and the knowledge text features of each knowledge point in the knowledge base based on a similarity algorithm to obtain a coarse arrangement set; and performing fine arrangement on the knowledge points of the knowledge base corresponding to the coarse arrangement set based on the fine arrangement language model by a text matching algorithm to obtain a retrieval result. The arrangement accuracy and the generalization of the intelligent retrieval for the knowledge base are improved by the method.

The invention provides a target event punishment scheme generation method and device, equipment and a medium, is used for the technical field of data prediction, and can solve the problem of low efficiency and accuracy of punishment conclusion generation in the prior art. Comprising the following steps: determining corresponding text data, and performing semantic recognition on the text data to obtain a plurality of information nodes; based on the target event, determining a plurality of event problems and corresponding problem vectors, and calculating the similarity between the problem vectors and the information node vectors; determining a corresponding target information node according to the similarity and a preset similarity threshold, and judging whether to iterate the target information node or not according to the question category; if yes, performing iteration on each association node according to a preset number of iterations to obtain a plurality of target information nodes; determining node information corresponding to the target information node, and determining a punishment scheme corresponding to the target event according to the node information corresponding to each event problem; the accuracy and efficiency of generating the case punishment scheme are improved.

The present invention relates to a method for enhancing the privacy of delegated quantum computing, the method comprising a client (A) whose objective is to solve a computational problem based on sensitive data and / or using a sensitive algorithm, and a cloud computing service provider (B) which has superior quantum computing capabilities to the client (A) and is therefore capable of solving the problem and / or executing the client's desired algorithm, the method comprising a sequence including a) an optical emitter (10) controlled by the client (A) emits at least one pulse (11) having a specific quantum state (S1); b) a quantum emitter (20) controlled by the provider (B) receives the pulse (11); and c) the quantum emitter (20) emits a single photon (21) supporting a photon qubit (Q2), thereby defining a relationship between the quantum state (S1) of the pulse (11) and the photon qubit (Q2). The present invention also relates to a system for carrying out this method.

The invention discloses a fusion calculation method of a quantum classical hybrid calculation system and a related device, and the method comprises the steps: carrying out the splitting processing of a task request applied by the quantum classical hybrid calculation system, so as to obtain calculation subtasks independently completed by quantum calculation, classical calculation and intelligent calculation; in response to the split calculation sub-task type, configuring the heterogeneous calculation resources to the corresponding calculation sub-tasks; the heterogeneous computing resources comprise quantum computing resources, classical computing resources and intelligent computing resources; the computing resources are evaluated and analyzed according to the resource evaluation and intelligent analysis module to obtain a corresponding scheduling decision basis; inserting the calculation subtasks into the corresponding task queues, and scheduling the task queues based on the scheduling decision basis, the user-defined scheduling strategy and the first linkage relationship of the calculation subtasks; according to the method, a classical and efficient fusion calculation technical scheme of quantum is provided, and an efficient solution is provided for a complex calculation problem through computing power complementation.

The application relates to the technical field of electrical engineering, and particularly discloses a multi-section power transmission limit calculation method based on multi-target migratory reinforcement learning, which comprises the following steps: considering transient power angle and voltage stability constraints, establishing a reverse search model for power transmission limit calculation; adopting a decomposition strategy to decompose the multi-section coupled limit calculation problem of the reverse search model into sub-problem models; converting different sub-problem models into Markov decision processes, and combining a neighborhood-based parameter migration strategy and an improved DDPG algorithm to solve the problems; and realizing end-to-end output of the Pareto front of the multi-section coupled limit through training of the model. The method has better convergence and diversity compared with traditional multi-target meta-heuristic algorithms.

Fast simultaneous feasibility testing (SFT) for management of an electrical power grid is achieved through various innovations. The computation problem relates to evaluation of candidate solutions for external power flows into a power grid, with respect to predetermined constraints and contingencies. A perturbation approach with precomputation is extended to encompass grid states (e.g. time periods) in addition to contingencies. Advantages derive from: fewer factorizations or inversions of large matrices; decoupling of state-dependent and contingency-dependent perturbations, leaving relatively few perturbations jointly dependent on both state and contingency; or making approximations by discarding small jointly dependent terms. Significant computation reductions allow a single workstation to perform SFT for 36 hours of a day-ahead cycle.

The invention relates to the technical field of cloud edge computing, and particularly provides a cloud task migration decision-making method based on deep reinforcement learning, which comprises the following steps of: constructing a target function by taking maximization of a cloud task success rate as a target; a deep reinforcement learning framework is constructed, and an LSTM network model is adopted to observe and predict the current state of the system based on the system history; describing a decision process under the condition that the system environment is uncertain based on a partially observable Markov decision process; and taking the current state of the system as the input of the improved deep reinforcement learning model, and obtaining a migration strategy of the cloud task based on the improved deep learning model. According to the method, a proper service migration mechanism is designed aiming at the cloud edge calculation problem in a mobile state, so that the low delay of the service is ensured, and modeling is carried out on the delay and reliability of the service migration process; and a DCPPOSM algorithm based on deep reinforcement learning is designed for effective migration decision making with an optimization objective of maximizing a task execution success rate.

The embodiments of the present application belong to the field of artificial intelligence and relate to an information retrieval method, apparatus, computer equipment, and storage medium. The method comprises: parsing a question text to obtain text entity information, the text entity information including each entity in the question text, the entity type of each entity, and the entity relationship between each entity; selecting an initial template that matches the text entity information, and filling the initial template according to the text entity information to obtain a first question template; transforming the first question template to obtain multiple second question templates; calculating the similarity between the question template and each inventory question template in the question and answer library, the question template including the first question template and multiple second question templates; determining the inventory question template that matches the question template based on the obtained similarity, and determining the answer information corresponding to the inventory question template as the answer information of the question text. The present application also relates to blockchain technology, and the answer information can be stored in the blockchain. The present application improves the accuracy of information retrieval.

The invention relates to the technical field of pumped storage, and discloses a pumped storage power station hydraulic energy parameter calculation method based on a particle swarm algorithm, which comprises the following steps: acquiring the position and speed of the current round of iteration of each particle and the individual optimal position of the previous round of iteration, and acquiring the global optimal position of the previous round of iteration of the particle swarm; correcting the position when the lift water head ratio constraint is not met; calculating the fitness; when the pumped storage power station does not meet the symmetric constraint of the standby storage capacity, local optimization is carried out; the fitness is calculated again after optimization, and the individual optimal position and the global optimal position are updated; updating the speed and position of each particle in the current iteration; and repeating the iteration process until an iteration stop condition is reached, and determining the target hydraulic energy parameters based on the global optimal position of the last round of iteration. Through the particle swarm optimization and multi-condition constraint, the method is suitable for the hydroenergy parameter calculation problem with strong nonlinearity, the globally optimal solution is efficiently and accurately searched, the calculation accuracy is improved, and a reliable basis is provided for the design and operation of a power station.

The invention discloses a virtual power plant distributed optimization and feasible region analysis method and system. The method comprises the following steps: establishing an interactive physical model of a virtual power plant and a power distribution network; based on an interactive physical model, relaxation decoupling is carried out on wide-area and neighborhood time coupling constraints caused by the state of charge of the energy storage system through a Lyapunov optimization theory, and a multi-period optimization problem of the virtual power plant is converted into a single-period optimization sub-problem; aiming at the single-period optimization sub-problem, adopting a particle swarm optimization algorithm HSIPSO improved based on a hybrid strategy and fusing the particle swarm optimization algorithm HSIPSO into an ADMM distributed optimization framework to form an ADMM-HSIPSO algorithm, and carrying out distributed solution on the single-period optimization sub-problem; converting a multi-period virtual power plant feasible region representation problem into a calculation problem of each scheduling period feasible region; a mathematical model of the feasible region of the virtual power plant is constructed, and an ADMM-HSIPSO algorithm is adopted for solving; the coordination and unification of the optimal distribution of the dispatching power of the power distribution network among the virtual power plants and the privacy protection in the virtual power plants are realized.

A distance calculation system and method for clustering algorithms are proposed. Based on the information distance theory with Coriolis complexity, a unified method for information distance calculation, lossless compressed distance calculation, and lossy compressed distance calculation is designed, and a compressed distance calculation system under lossy conditions is formed. This solves the problem of lossy compressed distance calculation for continuous data such as images and videos, and forms a complete and unified compressed distance calculation method and system applicable to any data type.

This invention relates to the field of knowledge graph collaborative filtering algorithms, specifically a knowledge graph-based method for predicting quality problems in aviation products. The specific steps of the method are as follows: S1, collaborative filtering algorithm; S2, similarity calculation; S3, problem cause prediction based on ICF algorithm; S4, quality problem prediction based on UCF algorithm. By fusing the semantic similarity of causes and the similarity of causes based on historical information, the similarity of the causes leading to different quality problems is calculated, thereby predicting the cause of a specific quality problem. Simultaneously, by calculating the similarity of quality problems caused by the causes of the problems, correlation analysis of quality problems is achieved, meaning that the occurrence of one quality problem may lead to the occurrence of another quality problem. This enables rapid control and early prevention of quality problems.