54 results about "Selection strategy" patented technology

The invention discloses a low-carbon economic operation optimization method for an electricity-hydrogen-heat comprehensive energy system, and belongs to the crossing field of intelligent energy systems and artificial intelligence, and the method comprises the following steps: 1, building a low-carbon economic dual-target operation optimization problem model of the electricity-hydrogen-heat comprehensive energy system; 2, constructing a dual-objective operation optimization problem solving framework with a third-generation non-dominated sorting genetic algorithm as a core; 3, modeling a population evolution decision problem in the genetic algorithm into a Markov decision process, and constructing a deep reinforcement learning agent and population evolution environment interaction mechanism; 4, training the intelligent agent by adopting a double-depth Q network algorithm, and outputting a genetic action according to an environment state; 5, obtaining a new filial generation in combination with a selection strategy associated with the reference points and genetic actions output by the intelligent agent; the step 4 and the step 5 are iteratively evolved until the maximum iteration generation number is reached, and the operation cost can be reduced under the same carbon emission condition.

The invention discloses an electric shovel fault diagnosis method based on a sparse evolutionary algorithm and a neural network. The method comprises the steps of 1, acquiring electric shovel data; 2, establishing a neural network; 3, generating a guide vector and selecting a parent by using a mating selection strategy in SPEA2; 4, clustering the decision variables into groups, and calculating the importance of each group according to the guide vector of the current population; 5, generating filial generations by using crossover mutation operators; and step 6, performing continuous iteration, selecting N solutions by using an environment selection strategy of SPEA2, and finally obtaining a group of solutions which meet the Pareto leading edge surface and have relatively high classification precision and relatively low neural network sparsity. Multiple solutions can be obtained through one-time operation to meet different electric shovel fault diagnosis requirements, and therefore the electric shovel fault diagnosis efficiency and precision can be improved.

The embodiment of the invention provides a point cloud denoising method and device, a medium and a product, and relates to the technical field of artificial intelligence. The method comprises the following steps: acquiring target point cloud data subjected to abnormal point elimination; according to the target point cloud data, constructing a density-based hierarchical clustering algorithm and a de-noising network of an operation selection strategy, and generating a de-noising model based on the de-noising network; and inputting the to-be-denoised point cloud data into the denoising model, and outputting the denoised point cloud data. According to the scheme, isolated noise possibly misleading path decision is filtered in advance, and interference is cleared for follow-up path selection; a denoising network based on a density hierarchical clustering algorithm and an operation selection strategy is constructed, a point cloud structure is accurately divided, representative elite points are screened in combination with the operation selection strategy, under extreme conditions, the elite points are preferentially used as core extension paths, noise point dominant decision making is avoided, and the accuracy of the system is improved. The problem that in the prior art, a single path is poor in adaptability in an extreme scene is solved, and the denoising accuracy is improved.

The invention relates to the technical field of intelligent manufacturing, in particular to an efficient and energy-saving flexible job shop scheduling method considering adjustment time. Comprising the steps of 1, initializing an initial population of an MTCP-SPEA algorithm; 2, executing a spatial awareness environment selection strategy; step 3, judging whether the current running time of the MTCP-SPEA algorithm reaches half of the total running time, if so, executing step 6, and otherwise, executing step 4; 4, a crossover operator and a mutation operator are adopted to evolve the current population, and current non-dominated individuals are selected to form a Pareto solution set; 5, executing local search operation and returning to the step 2; and step 6, executing a multi-thread-based constrained programming auxiliary optimization method to generate an improved elite set, screening out all non-dominated individuals from the improved elite set to form an optimal solution set, and outputting the optimal solution set. According to the method, the problem of efficient and energy-saving flexible job shop scheduling optimization considering time adjustment is effectively solved.

The invention provides a multi-AGV flexible job shop active scheduling method based on an improved VNS-INSGA-II algorithm and a related device, and belongs to the technical field of shop active scheduling. According to the method, a multi-objective optimization model for active scheduling of the multi-AGV flexible job shop is constructed, and constraint conditions of the multi-objective optimization model are set; the VNS-INSGA-II algorithm is improved by adopting a multi-layer coding mode, a hybrid population initialization mode, a time-varying coefficient-based dual-strategy binary tournament selection strategy and a variable neighborhood search algorithm, and the improved VNS-INSGA-II algorithm is obtained; based on a set constraint condition of the multi-objective optimization model, an improved VNS-INSGA-II algorithm is adopted to solve decision variables in an objective function of the established multi-objective optimization model for active scheduling of the multi-AGV flexible job shop, and a Pareto frontier is obtained; and selecting a group of solutions from the Pareto frontier to obtain an active scheduling result, thereby carrying out active scheduling on the multi-AGV flexible job shop. According to the invention, the problems of low stability and low scheduling precision of the scheduling system are solved.

The invention belongs to the field of project group decision making, and particularly relates to a dynamic strategic target-oriented project group reconstruction method and system, and the method comprises the steps: obtaining a project adjustment decision scheme according to a constraint condition; inputting the strategic targets, the project attributes and the decision-making schemes into a large language model to obtain preference scores of the decision-making schemes, and then sorting the decision-making schemes to obtain a sorting result; setting a state; the strategy network generates a corresponding weight vector according to the sorting result and selects an execution action to obtain a selection strategy; according to the initial state and the execution action, state conversion is carried out to obtain a next state, and the extraction weight of each training sample is calculated; updating the strategy network according to the extraction weight and the training sample to obtain an optimized strategy network; and obtaining a project group adjustment result according to the sorting result and the optimization strategy network. The method has the effects that the project is objectively decided, and the project combination is accurately changed according to the changing dynamic strategy target.

The invention belongs to the crossing field of artificial intelligence and an electric power system, and discloses an electric power customer service oriented large model prompt thinking chain construction method and related device.Firstly, a target topic type is determined according to an electric power customer service problem, and then comprehensive representation of all fused thinking chains of the target topic type is obtained; and selecting a fusion thinking chain corresponding to the comprehensive representation with the highest similarity based on the similarity, and taking the fusion thinking chain as a large model prompt thinking chain of the electric power customer service problem for the electric power customer service. According to the method, the selection strategy based on the topic type is adopted, the complexity of the selection strategy is effectively reduced, and the selection efficiency and the cue word quality are improved. Meanwhile, the comprehensive representation of each fusion thinking chain of the target topic type adopts a multi-thinking chain example fusion analysis strategy based on a graph attention network, and the reasoning process information of multiple thinking chain examples is integrated, so that the reasoning stability and accuracy of the finally selected fusion thinking chain are improved; the problem of instability caused by reasoning based on a single thinking chain example is effectively solved.

The present application provides a method for predicting point cloud attributes, an encoder, a decoder, and a storage medium. During the prediction process of point cloud attributes, different target adjacent point selection strategies are designed according to the distribution of duplicate points to determine at least one target adjacent point of the current point, and the attributes of the current point are predicted based on the reconstructed attribute information of the at least one target adjacent point, thereby improving the efficiency of point cloud attribute prediction.

The invention relates to the technical field of artificial intelligence, can be applied to business system platforms of financial science and technology, medical treatment and health and the like, and discloses a strategy optimization method, device, equipment and medium of a multi-modal action model. Obtaining relation dependency, constructing an initial multi-modal action model in combination with the initial training parameter group, performing fine tuning on the initial multi-modal action model by using the obtained task specific data to obtain a fine-tuned multi-modal action model, and obtaining an environment interaction data set of a target environment; and performing interactive sampling on the environment interaction data set one by one by using the fine-tuning multi-modal action model to generate a plurality of target interaction tracks, and performing strategy optimization on a selection strategy in the fine-tuning multi-modal action model to obtain a target selection strategy. According to the method, the model selection strategy accuracy of the multi-modal action model is improved when the multi-modal action model faces a new situation or data is insufficient.

The invention relates to the technical field of welded beams, in particular to a multi-target welded beam design method based on a grey prediction evolutionary algorithm, which comprises the following steps: randomly generating a first-generation solution set, a second-generation solution set and a third-generation solution set of a multi-target welded beam mathematical model; mining evolutionary trends of three solutions randomly selected from the first-generation solution set, the second-generation solution set and the third-generation solution set by using a grey prediction propagation operator, and predicting a solution in the intermediate-generation solution set; after the intermediate generation solution set is generated, constructing a candidate solution set, and performing non-dominated sorting on the candidate solution set to obtain a non-dominated leading edge set; selecting a fourth-generation solution set from the non-dominated leading edge set by using an adaptive environment selection mechanism, namely, autonomously switching a selection strategy according to the evolution condition of the solution; and finally, through algorithm iteration, a group of Pareto optimal solutions which are fully converged and are diverse and uniform in distribution are obtained. According to the method, the flexibility and adaptability of designing the multi-target welding beam are improved, and the application effect and economic benefits of the welding beam in actual engineering are promoted.

The present application provides a memory management method and a storage device. The method comprises: based on an operation event, obtaining a selection strategy corresponding to the operation event through a performance mapping table; determining a target virtual block combination from a plurality of candidate virtual block combinations according to the selection strategy; and responding to the operation event using the target virtual block combination. Thus, the performance and / or operation stability of the storage device can be effectively improved.

The invention belongs to the technical field of distributed machine learning training and configurable software system performance optimization, and particularly relates to a distributed machine learning training configuration optimization method based on an improved genetic algorithm, and the method comprises the following steps: S1, importing a performance prediction model; s2, feature gene coding; s3, performing population initialization; s4, evaluating the fitness; s5, selecting a strategy; s6, performing a cross strategy; s7, performing a self-adaptive variation strategy; s8, population replacement and integration; and S9, executing S4 and checking a termination condition, and if not, executing S5 to S8. The overall structure, the round training time and the video memory occupancy performance prediction model provided by the embodiment of the invention are optimized and can be popularized to distributed machine learning training systems with different architectures and network topologies, and meanwhile, priori knowledge in the distributed training process is fully utilized; the parallel configuration parameters are guided to search in the directions of shortening of round training time, increasing of video memory occupation and relatively balanced load division, and better optimization effect and convergence speed are achieved.

The invention provides a point cloud attribute prediction method, an encoder, a decoder and a storage medium, in a point cloud attribute prediction process, different target adjacent point selection strategies are designed according to the distribution condition of repeated points, at least one target adjacent point of a current point is determined, and according to the reconstruction attribute information of the at least one target adjacent point, the current point is predicted according to the reconstruction attribute information of the at least one target adjacent point. And attribute prediction is carried out on the current point, so that the point cloud attribute prediction efficiency is improved.

The invention discloses a full-coverage path planning method. The method comprises the following steps: determining a grid map of an area to be subjected to path planning, and determining a transition probability matrix corresponding to the grid map; an initial population corresponding to the grid map is determined according to the transition probability matrix and preset selection strategies, and the preset selection strategies comprise a greedy selection strategy and a fuzzy selection strategy; determining a local distance contribution value of a target path in the initial population, executing a multi-modal disturbance operation on the target path according to the local distance contribution value, and generating a filial generation population corresponding to the initial population; and performing iterative optimization according to the initial population and the offspring population until an optimal path corresponding to the grid map is determined. According to the invention, technical problems of path redundancy, slow convergence speed and poor adaptability to a complex environment existing in a path planning algorithm in a complex obstacle scene in the prior art are solved.

The invention discloses a multi-objective optimization design method and system for an automobile top compression resistance structure considering parameter uncertainty, and aims to solve the problem that reliability, safety and light weight are difficult to consider into consideration due to performance fluctuation of a design scheme under the influence of the parameter uncertainty. The method comprises the steps that parameter uncertainty is represented in an interval form, and an interval multi-objective optimization model of the top compression resistance structure is established; after the design group and the archiving set are initialized, a multi-output Gaussian process model is established for each optimization target so as to provide accurate response prediction; executing a dominating-decomposing dual-mechanism search strategy to guide the design group to evolve, and improving the quality of an optimal solution; and outputting the optimal design scheme in the archiving set through an interval inflection point selection strategy. Compared with a traditional optimization design method, the solution set reliability comprehensive index is improved by 11% +, the design efficiency is improved by 50.0%, the requirements for reliability, safety and light weight are effectively considered, and the design period is greatly shortened.

The application discloses an active domain adaptation semantic segmentation method, system, device and storage medium, selects and labels taking superpixels as units, which is different from image-level and pixel-level labeling methods, and the superpixel-level labeling greatly improves labeling efficiency by only assigning a semantic category to each superpixel; in addition, different from the existing scheme based on the uncertainty selection labeling strategy, the application focuses on difficult example samples in the domain adaptation scene, and proposes a selection strategy based on domain information quantity to label superpixels most valuable for domain adaptation learning; by adopting the superpixel-level labeling method and the selection strategy based on the domain information quantity, the application greatly reduces the labeling cost while improving the labeling quality, and guarantees the performance of the domain adaptation semantic segmentation.

The invention belongs to the technical field of transport capacity scheduling, and provides a ship transport capacity configuration optimization decision-making method and system, a medium and a server, and the method comprises the steps: predicting the transport price of each route in each plan period in the future through employing Neuro-ARIMA based on the historical transport price data of energy and bulk material waterway transportation; constructing a transport capacity configuration optimization model by taking net income maximization in a planning period as a target; and solving the transport capacity configuration optimization model by adopting an improved genetic-greedy strategy transport capacity optimization algorithm of domain knowledge guided coevolution to obtain an optimal transport capacity configuration strategy, including an optimal allocation scheme of each transport capacity source, a selection strategy of ship types, a contract signing number and a route allocation plan. According to the method, the coupling model of the task requirements and the transport capacity resources is constructed, collaborative allocation of multi-source transport capacity is realized under the condition that the transport price is uncertain, the adaptability and the utilization efficiency of a transport capacity structure are improved, decision support is provided for enterprises, and reasonable allocation of the transport capacity resources in a dynamic environment is realized.

The invention discloses a lightweight attitude recognition model construction method and device and an attitude recognition method. The construction method comprises the following steps: defining an attitude recognition model; defining a deep search space; the deep search space defines a cascading number space of deep stacking modules in the posture recognition model; defining a fusion selection space according to the deep search space; the fusion selection space defines a selection strategy space for selecting a fusion object by a fusion layer in the posture recognition model; and in the deep search space and the fusion selection space, searching a lightweight attitude recognition model for attitude recognition. According to the method, the lightweight attitude recognition model with both precision and operation speed can be quickly searched, so that the lightweight attitude recognition model can be deployed on a small platform such as an unmanned aerial vehicle.

According to the federated learning optimization method based on the self-adaptive loss threshold, the threshold is dynamically calculated and adjusted according to the error change trend in the training process, so that the client selection strategy is optimized. Secondly, the invention provides a two-factor selection strategy which can select different selection strategies according to different training stages. And finally, a historical information interaction mechanism is provided, so that the client selection is not only based on the current performance index, but also can be flexibly adjusted according to the change trend of the historical performance. According to the mechanism, the client selection process can adapt to the requirements of different training stages, and the training stability and convergence efficiency can be remarkably improved. Experimental results show that compared with a Pow-d method, the global model accuracy rates of the AHIBCS on the three public data sets of COVID-19, FMNIST and CIFAR-10 are improved by 2.1%, 2.4% and 2.7% respectively.

The invention discloses a three-generation genome SV detection method based on deep learning, and belongs to the technical field of structural variation detection. The problem that an existing structure variation detection method is poor in accuracy is solved. The method comprises the following steps: firstly, extracting variation features from a comparison result with a reference genome, obtaining a submatrix of a fragment according to the extracted variation features, then carrying out deep coding on the submatrix of the fragment through a convolutional neural network to obtain coding features, splicing the coding features of a plurality of fragments into a 2000bp feature matrix, and finally, extracting the feature matrix; a global dependency relationship among the fragments is captured through a Transform network, so that a variation region can be accurately identified in a longer sequence; after variation is detected, variation sites are subjected to clustering analysis, breakpoint positions are accurately positioned, an automatic support reading selection strategy is designed, support readings can be automatically screened according to comparison quality, false positive areas are further filtered, and the reliability of a detection result is ensured. The method disclosed by the invention can be applied to SV detection in the third-generation genome.

The application discloses a distributed process planning and workshop scheduling integrated optimization method. The application firstly classifies four decision sub-problems of the distributed process planning and workshop scheduling integrated optimization problem into two categories: selection problem and sequencing problem. Then, the selection problem is regarded as a main problem, and the sequencing problem is regarded as a sub-problem, and the two problems are solved respectively, and two groups of Benders optimality cuts and a Benders optimality cut selection strategy are designed to feed back the solving condition of the iteration to the main problem, so that the iteration between the main problem and the sub-problem is realized until the convergence rule is satisfied. The application is not only suitable for the production scene with high requirement on the calculation time, but also suitable for the production scene with high requirement on the quality of the solution. A large number of simulation experiments verify that the application can realize the fast solution of the distributed process planning and workshop scheduling integrated optimization problem, and ensure the quality of the solution.

The present invention discloses an automated penetration method and system based on the Rainbow algorithm. The method comprises the following steps: obtaining information about the environment to be tested, determining the target network corresponding to the penetration test task and the host information of the target network; determining the attack target and executing the corresponding penetration decision action based on the information about the environment to be tested; constructing a first reward function for the attack target using a first neural network model, and constructing a second reward function for the attack tactics using a second neural network model; calculating a first reward value for the attack target based on the first reward function, and calculating a second reward value for the attack tactics based on the second reward function; dynamically updating the selection strategy for the attack target, and dynamically updating the selection strategy for the attack tactics. The training cost of the present invention is low and the efficiency is high, and the decision accuracy of the model can be improved. The system can be widely applied in the field of computer technology.

This application belongs to the field of ultra-high-speed electromagnetic launch design analysis, specifically disclosing a design method for a wound-type electromagnetic launch system. The method includes: analyzing the electromagnetic performance of the wound-type electromagnetic launch system based on its thrust model to determine a first selection strategy for the capacitor in the system's excitation source; analyzing the system thrust, capacitor discharge in the system's excitation source, and changes in the pulse power device current to determine a second selection strategy for the number of turns in the drive coil and the number of turns in the launcher's armature coil; performing parameter selection analysis on the repetitive peak voltage, pulse peak current, critical rate of rise of the on-state current, and current pulse width of the system's excitation source to determine a third selection strategy for the pulse power device in the system's excitation source; and designing the wound-type electromagnetic launch system based on the first, second, and third selection strategies. This application enables the efficient and high-precision rapid design of wound-type electromagnetic launch systems.

The application relates to a prompt word optimization method and device of a text evaluator and a storage medium, and belongs to the technical field of artificial intelligence. The application first initializes a selection strategy cluster, which comprises a plurality of design factor selection strategies of an evaluation prompt word. In each iteration, each selection strategy in the selection strategy cluster is disturbed to generate a new selection strategy. The evaluation prompt word is determined based on the new selection strategy, and an evaluation result is generated on a verification set with artificial evaluation by using a large language model. The correlation coefficient of the evaluation result and the artificial evaluation is calculated, and based on the correlation coefficient, the selection strategy cluster is updated from the current selection strategy cluster and the new selection strategy. The application adopts an iterative search method guided by a heuristic function to optimize the selection strategy, and the selection strategies of a plurality of design factors in the prompt word are optimized, the search range of the prompt word is expanded, and the evaluation performance of the text evaluator is improved.

The present application provides a method and apparatus for generating intelligent vulnerability scanning strategies based on reinforcement learning. In this embodiment, based on the device feature vector of the target device perceived in real time and the historical scanning results of the target device through reinforcement learning, a current scanning plug-in selection strategy is dynamically generated to ensure that the dynamically generated scanning plug-in selection strategy has strong dynamic adaptability, thereby reducing false positives and false negatives. Further, with the help of the reward mechanism of reinforcement learning, according to the historical performance of different scanning plug-ins and the current scanning results of the target device (and even plus user feedback information), the Actor model in the reinforcement learning model is optimized and adjusted in real time to continuously optimize the subsequent scanning plug-in selection strategy and improve the detection efficiency and accuracy.

This invention provides a multi-objective design method for medium-thick plate billets, relating to the field of automation technology. The invention extracts the design process characteristics of medium-thick plate billets, transforms production objectives and process constraints into mathematical expressions, and establishes a multi-objective system optimization model for the design process of medium-thick plate billets. Orders are grouped according to thickness and steel quality codes. Heuristic algorithms are designed to generate initial medium-thick plate billet design schemes. Different heuristic algorithms are constructed according to objective priorities to cover the generation process of different initial feasible billet design schemes. A billet sub-plate crossover strategy and a billet sub-plate mutation strategy are designed to improve the initial feasible design schemes through crossover and mutation processes. A design scheme selection strategy selects the Pareto optimal scheme in the calculation process to form a scheme pool. A scheme reduction strategy is used to reduce the number of schemes in the scheme pool and adjust the sub-plate distribution on different medium-thick plates within the same design scheme. Finally, a design scheme within a limited number of schemes is given.

The application discloses a flexible job shop scheduling method based on an improved particle swarm genetic hybrid algorithm, which comprises the following steps: initializing parameters; initializing a population; solving the fitness value of the initialized population, and recording the optimal position and optimal chromosome of individuals and the population; updating the particle speed and position according to the optimal value of the last generation population; performing selection operation by adopting a composite selection strategy based on an elite solution reservation and roulette; performing selection operation, crossover operation and mutation operation on the genetic population by adopting ESI and FEC strategies, and replacing the corresponding individual if a better individual is evolved; searching the genetic population by adopting a variable neighborhood search algorithm, and replacing the corresponding individual if a better individual is evolved, to generate a next generation; judging whether to terminate the iterative search; and judging whether the iterative termination condition is met. The flexible job shop scheduling method based on the improved particle swarm genetic hybrid algorithm can compensate for each other's shortcomings and increase the local search capability.

The invention discloses a steel cutting and assembling method and system, electronic equipment and a storage medium, and relates to the field of material assembling. According to the method, all components needing to be cut and assembled are decomposed according to an overall design drawing of a device, the target length and number of each component are determined, and a total candidate combination list of all the components is determined; adjusting a filtering condition in combination with the real-time excess material data and a historical pruning mode, and filtering the total candidate combination list according to a preset constraint condition and the filtering condition to reserve an effective combination; determining a Pareto optimal combination list of each component from the effective combination through a state transition equation and multi-target dynamic programming, and determining an optimal combination of each component from the Pareto optimal combination list according to a combination selection strategy recommended by a preset reinforcement learning model; and the cutting mode and the assembling mode of the steel are generated according to the optimal combination. According to the technical scheme, the steel cutting and assembling scheme is optimized, the steel utilization rate is increased, and the cost is reduced.

This invention relates to the field of autonomous driving technology and provides an intelligent quadtree decomposition path planning method for complex dynamic scenarios. The method includes: modeling the quadtree decomposition depth selection problem as a Markov decision process and extracting environmental features as state input; employing an improved Q-learning algorithm to learn the optimal depth selection strategy and constructing a composite reward function that balances planning success rate, path quality, computational efficiency, and depth adaptability; based on the learned strategy, adaptively selecting the quadtree decomposition depth according to environmental features, constructing the quadtree, and performing path planning, while simultaneously combining global planning and local replanning to avoid dynamic obstacles. This invention achieves adaptive matching between quadtree decomposition depth and environmental complexity, solving the problem that a fixed depth cannot adapt to dynamic environmental changes, and significantly improving computational efficiency and robustness in dynamic scenarios while ensuring planning accuracy.

The invention provides an eVTOL safety obstacle avoidance method based on maximum trajectory entropy SAC reinforcement learning, which is used for solving the safety obstacle avoidance problem of flying eVTOL in urban dynamic and static environments, and comprises the following steps: designing a constraint set by using an eVTOL kinetic equation, and constraining a selection strategy of an MDP model based on the constraint set to obtain an initial CMDP model; performing iterative training on the initial CMDP model, and optimizing the initial CMDP model by using a maximum trajectory entropy SAC algorithm in each iterative process to obtain a trained CMDP model; and selecting an optimal current action by using the trained CMDP model. Compared with a traditional maximum entropy reinforcement learning algorithm based on a strategy entropy item, the method is higher in convergence speed, more stable in performance and high in safety obstacle avoidance task completion rate, and a new thought is provided for eVTOL automatic driving function design.