110 results about "Problem solution" patented technology

The invention relates to a knowledge graph question answering method combining a large model and a graph neural network, which comprises a question decomposition module, a sub-question solving module and an answer reasoning module, the method is used for solving the problem that the size of a problem sub-graph is difficult to determine in a multi-hop problem in a graph neural network method, a sub-problem solving module is composed of a problem sub-graph retrieval stage and a reasoning path construction stage, in the problem sub-graph retrieval stage, a similarity definition mode is given, calculation is carried out through a Personalized PageRank algorithm, and the problem sub-graph retrieval stage is used for solving the problem sub-graph size. And meanwhile, in order to improve the accuracy of GNN answering, the question sub-graphs of the subsequent questions are combined with the question sub-graphs of the previous questions, so that the dynamic expansion of the question sub-graphs is realized. In the inference path construction stage, candidate answers are obtained through a pre-trained graph neural network, and then an inference path is constructed by using a BFS algorithm. And finally, the reasonability of the reasoning path is judged through an answer reasoning module so as to give sub-question answers. The invention provides a knowledge graph question and answer framework combining a large model and a graph neural network, and the performance of the model on a multi-hop problem is effectively improved.

The present invention discloses a knowledge credibility measurement method based on knowledge graph, which comprises the following steps: first, using knowledge representation learning algorithm to vectorize entities and relationships in knowledge graph, and then using energy function and sigmoid function to calculate the credibility of relationship; then, according to attribute standard set, the attribute value of entity in knowledge graph is evaluated, and attribute value credibility is calculated by combining word forest similarity and edit distance; then, a question-answering system is constructed, and the question-answering system and question-answering standard set are used to calculate the credibility of knowledge in knowledge graph for problem solving; finally, combining the credibility of relationship, the credibility of attribute value and the credibility of problem solving, and performing weighted summation according to different weights to obtain the credibility of knowledge graph. The invention is helpful to correct the erroneous knowledge in knowledge graph and improve the accuracy of knowledge in knowledge graph.

The invention discloses an uncertain system Lyapunov stability reinforcement learning control algorithm based on the worst situation. The uncertain system Lyapunov stability reinforcement learning control algorithm comprises the following steps: S1, modeling a system dynamics model and an uncertain boundary through a relu activation network; s2, determining a robustness condition, and pre-determining the area of an attraction domain by using the robustness condition; s3, determining the uncertainty of the dynamic model and the robustness guarantee RL under state estimation; s4, establishing network parameterization; and S5, carrying out numerical simulation on the inverted pendulum and the quadrotor unmanned aerial vehicle. According to the uncertainty system Lyapunov stability reinforcement learning control algorithm based on the worst situation, the most violating state can still be accurately found, so that the stability under uncertainty is forced, a geometric view of the existence of a robust RL problem solution is given to explain the robustness and the ability thereof, and the robustness and the ability of the robustness RL problem solution are improved. The numerical simulation of the inverted pendulum and the four-rotor aircraft under various uncertainties proves the effectiveness of the proposed method.

The invention discloses a user solution recommendation method based on enterprise user portrait analysis. The recommendation method comprises the following steps: S1, collecting first enterprise information of a to-be-recommended target user and second enterprise information of a historical user forming a solution; s2, forming a to-be-recommended user portrait and a historical user portrait based on the first enterprise information and the second enterprise information; s3, forming a hypergraph network by taking a plurality of historical user portrait vectorization as object nodes and taking a plurality of enterprise problem solution vectorization as result nodes; s4, performing community division on the hypergraph network; s5, searching a community most related to the user problem vector from the hypergraph network; and S6, performing similarity comparison on the object node in the community most related to the user problem vector and the to-be-recommended user portrait, selecting the object node with high similarity as the closest enterprise, and taking the enterprise problem solution corresponding to the object node as a solution recommendation result.

The invention discloses a path planning method and device for battery replacement, equipment and a storage medium. The method comprises the following steps: S1, establishing a model: defining variables; an objective function; constraint conditions; s2, selecting a solution algorithm: adopting an improved ant colony algorithm and combining with a local search strategy to improve the efficiency of problem solution; in the ant colony algorithm, an optimal charging station insertion heuristic mode is introduced, and the most suitable charging station insertion position is found for the path exceeding the maximum mileage of the vehicle or the unmanned aerial vehicle; and S3, sensitivity analysis: performing sensitivity analysis on main factors influencing path planning to optimize model parameters. According to the method, multiple factors of the electric vehicle and the unmanned aerial vehicle in the logistics distribution process, especially the influence of the battery replacement strategy, are comprehensively considered, more accurate and efficient path planning is realized, and by introducing the improved ant colony algorithm and the local search strategy, the problem solving efficiency is improved, and the calculation cost is reduced.

The present invention relates to a method which can be performed by an online system, and provides a method for mathematics teaching through implicit condition finding, the method comprising the steps of: constructing a DBMS which manages an implicit condition corresponding to a condition which is indirectly suggested or needs to be inferred by a learner himself / herself, in addition to a suggested condition which is directly suggested in a mathematical question; teaching the learner the implicit condition by using a concept description material written on the basis of the DBMS; and teaching the learner to find the implicit condition by using a problem solution material written on the basis of the DBMS. According to the present invention, a learner can solve a problem by inferring an implicit condition by himself / herself.

The invention provides a multi-level game decision-making system fusing situation knowledge, belongs to the field of game decision-making in artificial intelligence, and aims to solve the problem that multiple red-blue adversarial solution schemes are difficult to adapt to cross-domain multi-scene and complex multi-task migration multiplexing, generalization modeling of a game solution process is carried out based on a layering thought, and a multi-level game decision-making algorithm is obtained. Each layer of game problem solving comprises a game environment structured processor, a game information processor, a game state estimator, a game state deduction / simulator, a multi-level value transmission and strategy generator and a strategy action conversion executor, and different scenes can be quickly modeled; and a proper algorithm solving strategy is selected in each stage according to the general game solving step without repeatedly developing a new system, so that the aim of multi-scene multiplexing of one set of framework is fulfilled.

The invention relates to the technical field of artificial intelligence, discloses a customized question-answering system and method based on a large language model, and aims to solve the problems that an existing question-answering system is poor in accuracy and lacks permission adaptation ability. The question and answer platform is linked with a local large language model, an online large language model, a knowledge graph, a business knowledge base, an authority management module, a multi-modal data processing module and a feedback module; the enterprise communication tool receives the multi-modal question data sent by the user, and receives and displays a question and answer result; the multi-modal data processing module converts multi-modal problem data into corresponding text information, the authority management module obtains user authority information and adjusts access content and editing authority of a knowledge graph and a business knowledge base of a user, and the knowledge graph and the business knowledge base generate corresponding retrieval results. And the large language model generates a question-answer result according to the retrieval result. According to the method, the accuracy of professional knowledge questions and answers is improved, and permission adaptation is realized.

The invention discloses a distribution network planning problem automatic allocation solution measure algorithm library based on artificial intelligence, which comprises the steps of constructing a distribution network planning typical problem definition standard library model, constructing an artificial intelligence algorithm library, automatically analyzing distribution network planning typical problem solution measures, and automatically distributing the distribution network planning typical problem solution measures. The method comprises the following steps: automatically analyzing power distribution network planning typical problem solving measure process visualization, machine learning and deep learning analysis engine, intelligent fault problem positioning, artificial intelligence-based dynamic load problem prediction, and transformer area low voltage problem cause intelligent analysis. According to the distribution network planning problem automatic allocation solution measure algorithm library based on artificial intelligence, a unified problem definition standard library is established, the definition standard of the distribution network planning problem is standardized, and the early-stage planning analysis efficiency and accuracy of a power grid can be improved; and power grid planning and construction can be guaranteed, and disaster prevention and reduction power supply planning and construction can be promoted actively.

Example systems perform complex optimization tasks with improved efficiency via neural meta-optimization of experts. In particular, provided is a machine learning framework in which a meta-optimization neural network can learn to fuse a collection of experts to provide a predicted solution. Specifically, the meta-optimization neural network can learn to predict the output of a complex optimization process which optimizes over outputs from the collection of experts to produce an optimized output. In such fashion, the meta-optimization neural network can, after training, be used in place of the complex optimization process to produce a synthesized solution from the experts, leading to orders of magnitude faster and computationally more efficient prediction or problem solution.

This invention provides a method for constructing boundary conditions for a superhydrophobic cylindrical surface based on OpenFOAM, comprising the following steps: (1) reading the parameter setting file; (2) obtaining the position of the center of the cylindrical surface; (3) finding the position of the center of the first layer of grid near the cylindrical surface and its tangential velocity; (4) finding the position of the center of the second layer of grid near the cylindrical surface and its tangential velocity; (5) calculating the sum of the distances between the center of the cell and the center of the cylindrical surface; (6) calculating and updating the velocity of the cylindrical surface; (7) performing flow numerical simulation, repeating S2~S7 until the calculation is completed. This invention is based on OpenFOAM and has been further developed to construct a superhydrophobic surface boundary condition suitable for flow around a cylinder. This boundary condition supports parallel computation of the entire calculation process, improving computational efficiency and the scale of the problem solution.

The invention provides a system internal user satisfaction closed-loop management optimization method and device, and the method comprises the steps: constructing a plurality of role groups which are used for carrying out satisfaction process management, and at least comprise a first role group, a second role group and a third role group; an initial question list is obtained, and the initial question list comprises a plurality of reference questions; obtaining a screening result of the first role group for the plurality of reference questions based on a preset screening rule, and updating a question list ledger based on the screening result, the screening result including one or more target questions; obtaining a first verification result of the second role group for the target question; obtaining to-be-processed problems determined by the third role group based on the first verification result, and a problem solution corresponding to each to-be-processed problem; and updating the problem list ledger by the first role group based on the to-be-processed problem and the problem solution. Therefore, the user satisfaction cooperation management efficiency can be effectively improved.

The embodiment of the invention provides a code compilation optimization method and device based on algorithm representation, electronic equipment and a storage medium, and belongs to the technical field of code compilation testing. The method comprises the steps of obtaining an initial binary file and a problem optimization instance corresponding to a source code, screening and finely tuning candidate compilation optimization models based on a first reference problem solution, and reconstructing a second reference problem solution through a finely-tuned target compilation optimization model, and performing code compilation optimization on the initial binary file based on a target compilation optimization item determined by the target reconstruction solution. Wherein the target compilation optimization model is used for representing an intelligent algorithm for solving the optimization problem instance. According to the embodiment of the invention, the intelligent algorithm most related to the problem optimization instance can be determined by screening and fine tuning the candidate compilation optimization model, compilation testing does not need to be carried out on each different compilation optimization item combination through the intelligent algorithm, automation of code compilation optimization is realized, and the efficiency and accuracy of code compilation optimization are improved.

The invention relates to the technical field of biomolecule electrostatic characteristic prediction, and discloses a partial differential equation solving method based on a finite volume element method. According to the method, an unstructured grid matched with a biomolecular interface is constructed, a solution of a Poisson-Boltzmann equation (PBE) is separated into an analytical solution G, a linear interface problem solution psi and a nonlinear interface problem solution by adopting a decomposition technology, and a non-homogeneous flux condition at the interface is processed by utilizing a controlled volume decomposition technology; linear and nonlinear problems are solved through a finite volume element method, and final electrostatic potential distribution and solvation free energy are obtained through assembling. The method solves the problems of insufficient precision and low efficiency when a traditional numerical method is used for processing a complex interface non-homogeneous flux condition, has second-order L2 convergence and first-order H1 convergence, and can be efficiently applied to the biophysical research fields such as protein solvation free energy prediction and the like.

The invention discloses a large-scale disassembly line balance optimization system and method based on transfer learning. The system comprises a single-target optimization module used for correspondingly storing each single-target problem optimization solution to an independent knowledge base; the multi-objective optimization module is used for performing multi-objective problem optimization by using an evolutionary algorithm; the mapping relation building module is used for building an integer type mapping relation from each single-target problem solution set of the current generation to a multi-target problem solution set when transfer learning is triggered; and the transfer learning execution module is used for obtaining a transfer solution on the multi-target problem according to the multiple single-target problem optimization solutions in the independent knowledge base and the integer type mapping relation, and obtaining an initial population of next-generation multi-target problem optimization according to the transfer solution. Based on a transfer learning mechanism and an evolutionary algorithm, the problem of large-scale disassembly line balance optimization under the conditions of discrete decision space, high variable correlation and complex constraint is effectively solved, algorithm convergence is remarkably accelerated, and the solution set quality is improved.

The invention discloses a robust self-interference suppression beamforming method based on a multi-missile collaborative scene, which comprises the following steps of: considering a non-ideal condition, constructing a coupling channel model containing a circular symmetry Gaussian distribution error, and constructing an expression of power of a transmitting signal and a self-interference signal. Based on a self-interference signal power expression, an optimization problem for self-interference suppression transmitted beam forming with robust estimation errors is constructed; wherein the optimization target is set to maximize the transmitting beam gain; the set constraint condition is that it is ensured that the power of self-interference signals coupled to all receiving array elements in the receiving sub-arrays is smaller than a set upper limit threshold value; and transmitting link full-feed power upper limit constraint. According to an optimization problem, an original non-convex opportunity constraint problem is converted into a convex problem to be solved through an approximation method of positive semi-definite relaxation and a Bernshi inequality, so that a transmitted beam weight vector solution is obtained, the robust self-interference suppression capability under a non-ideal coupling channel is realized, and normal receiving of a desired signal is ensured.

The invention provides a problem solution obtaining method and device based on a large model and electronic equipment, and relates to the technical field of artificial intelligence, in particular to the application fields of generative search, intelligent document editing, intelligent assistants, virtual humans and the like. According to the specific implementation scheme, current error reporting information of a target system is obtained; acquiring prompt information; wherein the prompt information comprises N groups of optimization reference information; n is an integer greater than or equal to 1; the optimization reference information comprises historical error reporting information related to the target system and a historical problem solution for the historical error reporting information; and obtaining a current problem solution for the current error report information by using the large model based on the prompt information.

The invention discloses a prompt optimization method and system for a large language model of an electric power system. The method comprises the following steps: acquiring data information of a target electric power system and data information of a corresponding large language model; constructing an initial prompt population of a fault diagnosis task of the target power system, and generating a diversified strategy in combination with domain problem description and a general thinking mode; performing mutation operation on the task prompt through a large language model to generate a variant adapted to a specific scene; screening to obtain a power grid problem solution by adopting a tournament selection mechanism; adjusting the strategy of the next round of evolution based on population KL divergence analysis; and prompt optimization of the large language model of the target power system is realized. According to the method, the prompt optimization of the large language model of the electric power system is realized, the reliability is higher, the accuracy is better, and the applicability of the scheme is better.

The invention provides an intelligent general drawing auxiliary layout method and system based on module calling and automatic verification, and is applied to the field of data processing application. According to the application, module calling and preprocessing are performed on basic data, a space module library G1 module is called in a one-key manner, data circulation from G1 to G3 is realized, and static spacing data, sunlight bar shadow graph data, a building number interval value, unit assembly basic data, a building-level house type index and a general graph-level business state index are generated based on a multi-city rule billboard; processing the static spacing data, the sunlight bar shadow graph data, the building number interval value, the unit assembly basic data, the building-level house type index and the general graph-level business state index to generate a compliance verification result; processing the compliance verification result to generate potential problem scheme information; and processing the potential problem scheme information based on the feature dimension to generate intelligent general graph auxiliary layout abnormal information.

The invention discloses a method and a device for determining two fluid parameters of a nuclear reactor, a medium and equipment. The method comprises the following steps: determining a preset constraint condition and a two-fluid control equation of a nuclear reactor according to a working condition variable of the nuclear reactor; constructing a target loss function according to a preset constraint condition and the two-fluid control equation; iteratively training by minimizing the target loss function to obtain a parameter prediction model; and inputting the target working condition parameters into the parameter prediction model to obtain characteristic parameters of the two-phase flow behavior of the nuclear reactor under the target working condition parameters. According to the method provided by the invention, the control equation is embedded into the network model, so that the complexity of a problem solution space is remarkably reduced, the requirement on training sample data is reduced, the quick solution of the two-fluid equation of the nuclear reactor in a complex and changeable environment is realized, and meanwhile, the prediction reliability and generalization ability of two-phase characteristic parameters are considered.

The invention discloses a road network quality optimization method and device and electronic equipment. The method comprises the following steps: acquiring poor-quality data in a road grid network; processing the poor-quality data through a drive test agent according to a ReAct mechanism, and generating a problem solution corresponding to the poor-quality data; whether the problem solutions can meet preset requirements or not is judged, if the problem solutions cannot meet the preset requirements, defects existing in the reflection problem solutions are simulated, and according to the defects, the problem solutions corresponding to the poor-quality data are iteratively generated; if the maximum number of iterations is reached or the problem solution can meet a preset requirement, confirming the problem solution as a target solution; and optimizing the poor-quality data according to the target solution, thereby realizing road network quality optimization based on the road test agent ReAct mechanism. The method can improve the efficiency and accuracy of road network quality optimization.

Systems and methods for automatically identifying and resolving problem instances in data service workloads are disclosed. In some embodiments, a disclosed method includes: identifying a problem instance for a workload associated with a plurality of data service platforms; determining, using at least one machine learning model, a problem solution based on the problem instance and a catalog of problem solutions; executing the problem solution including operations across the plurality of data service platforms; and recovering the workload in accordance with a determination that the problem instance is resolved by the problem solution.

The application discloses a signal analysis method, system, device and medium based on quadratic constraint quadratic programming problem solution prediction, and belongs to the technical field of artificial intelligence. The method comprises the following steps: acquiring a target programming problem of a power transmission signal, wherein the target programming problem comprises a quadratic objective function and a quadratic constraint condition; encoding the quadratic objective function and the quadratic constraint condition to generate a target tripartite graph of the target programming problem; inputting the target tripartite graph into a message passing model; analyzing a plurality of nodes in the target tripartite graph and feature edges between the plurality of nodes by using the message passing model to obtain problem attributes of the target programming problem output by the message passing model, wherein the problem attributes comprise feasibility, an objective value and an optimal solution; and performing signal analysis on the power transmission signal based on the problem attributes. The method can process quadratic constraint quadratic programming problems in real time, and improves the analysis efficiency of the power transmission signal.

An analysis device according to one embodiment of the present invention comprises: a cyclic symmetry dual problem solution unit for inputting, to a dual problem based on an optimal transport problem with strongly convex regularization term, a first and second vector that are a portion of the symmetric components of first and second input data that have cyclic symmetry as well as a cost matrix that is a portion of the symmetric components of third input data that has cyclic symmetry, and outputting an optimal solution to the dual problem; and an optimal transport problem solution unit for using the optimal solution outputted by the cyclic symmetry dual problem solution unit to find and output an optimal solution and optimal objective function value of the optimal transport problem with strongly convex regularization term.

The invention discloses a dynamic examination system based on XR augmented reality and an implementation method, and relates to the field of XR technology and education examination fusion. Aiming at the problems of weak anti-cheating ability, insufficient foreign language scenario, limited occupational qualification examination practical operation evaluation and the like of a traditional examination, the system integrates intelligent anti-cheating, immersive scenario examination, multi-dimensional evaluation and AI digital human invigilation functions, including a complete version and a simplified version. The complete version system realizes full-process safety monitoring and scene assessment through an XR intelligent head-mounted display (supporting perspective / immersion dual-mode switching and integrating a multi-mode biological recognition module, a full-band detection module and the like), scene adaptive interaction equipment and an AI digital human invigilation engine; the simplified version is a low-cost transition scheme, a lightweight XR head-mounted display is adopted, synchronous display with a traditional computer host is supported, basic anti-cheating and scenarized functions are reserved, and the system is suitable for small and medium-sized examinations. According to the method, the examination safety and the evaluation accuracy can be remarkably improved, the deployment cost is reduced, the method is suitable for scenes such as large-scale selection examinations, foreign language evaluation and occupational qualification examinations, layered landing of the XR technology in the educational examination field is achieved, and examination evaluation is promoted to be transformed from'knowledge memory 'to'problem solution'.

The invention discloses a visual shared whiteboard system and method for solving a group collaboration mathematical problem, and belongs to the technical field of computer science and human-computer interaction crossing. The system comprises a multi-modal fusion input module used for converting input such as handwriting and voice into a structured mathematical object; the real-time semantic understanding and knowledge graph engine is used for constructing a collaborative cognitive model; the collaborative process management module based on the directed hypergraph is used for carrying out formalized modeling and navigation on a nonlinear problem solving process; the context-aware intelligent auxiliary module is used for providing verification, recommendation and guiding consensus achievement; and a multi-user cooperation engine ensures stable cooperation experience. According to the method, the problems of professional expression, real-time cooperation splitting, group thinking process management difficulty and the like are solved, and a unified, intelligent and efficient integrated cooperation environment is provided for group solving of complex mathematical problems.

According to the circulating mill disclosed by the invention, the internal circulating pipe interface is arranged in the area between the axial projection line of the impeller air inlet on the front cover or the rear cover and the axial projection line of the centrifugal fan inlet along the impeller, so that new problems brought by a grading problem solution constructed for solving the high pressure problem in the circulating mill in the background art are eliminated; and the pressure in the circulating mill is reduced, so that the rotating resistance of the impeller is reduced, and the energy consumption of the mill is reduced.

The present application discloses a liner alliance resource scheduling method, device, electronic device and storage medium, which relates to the field of resource scheduling technology. The method includes: constructing a random programming model for liner alliance resource scheduling; calculating the expected revenue of the container to be scheduled as gradient information based on the random programming model and the liner schedule; constructing a mixed integer linear programming subproblem based on the gradient information and solving it to obtain the subproblem solution result; determining the container demand scenario, and solving the deterministic problem of the demand scenario based on the subproblem solution result to obtain a deterministic result; updating the gradient information based on the dual value of the deterministic result; returning to constructing the mixed integer linear programming subproblem based on the gradient information and solving it to obtain the subproblem solution result, until the iteration end condition is reached, and the final subproblem solution result is obtained; and scheduling each container based on the final subproblem solution result. The present application can improve the utilization efficiency of containers and cabin space.

The invention discloses a distribution network planning problem automatic allocation solution measure algorithm library based on artificial intelligence, which comprises the steps of constructing a distribution network planning typical problem definition standard library model, constructing an artificial intelligence algorithm library, automatically analyzing distribution network planning typical problem solution measures, and automatically distributing the distribution network planning typical problem solution measures. The method comprises the following steps: automatically analyzing power distribution network planning typical problem solving measure process visualization, machine learning and deep learning analysis engine, intelligent fault problem positioning, artificial intelligence-based dynamic load problem prediction, and transformer area low voltage problem cause intelligent analysis. According to the distribution network planning problem automatic allocation solution measure algorithm library based on artificial intelligence, a unified problem definition standard library is established, the definition standard of the distribution network planning problem is standardized, and the early-stage planning analysis efficiency and accuracy of a power grid can be improved; and power grid planning and construction can be guaranteed, and disaster prevention and reduction power supply planning and construction can be promoted actively.

The application discloses a new energy automobile product whole life cycle problem management method based on a knowledge graph, which comprises the following steps: step one: inputting a problem, searching the problem in a historical problem database to determine whether the problem is a repeated problem; if yes, step seven is executed; if no, step two is executed; step two: classifying the problem based on professional knowledge and experience to determine the type and nature of the problem; step three: finding measures for solving the problem in the historical problem database and proposing a problem solution; step four: evaluating the feasibility and effectiveness of the proposed problem solution; if the evaluation is passed, step five is executed; if the evaluation is not passed, step three is executed; step five: practically verifying the problem solution; if the verification is passed, step six is executed; if the verification is not passed, step three is executed; step six: uploading problem data to a data center to update the historical problem database; and step seven: completing the problem solution.