21 results about "Knapsack problem" patented technology

The invention relates to the technical field of furniture board blanking, solves the problems of low material utilization rate and high algorithm complexity caused by board texture direction constraint and'one-step cutting 'process limitation in the prior art, and provides a three-stage mixed heuristic blanking method for panel furniture. Comprising the following steps: S1, generating a texture single blank standard length set according to blank data; s2, according to all the single blank block strips, all composite strips with the maximum value under the current condition are obtained through calculation; s3, generating all single blank levels with the maximum value according to the generated single blank composite strip combination; S4, abstracting the single blank levels into a solution combination of a knapsack problem to generate a plate L * W; s5, according to coding of a heuristic sequencing algorithm combined with genetic annealing and decoding calculation of an imprecise positioning algorithm in the three stages, an optimal layout mode under the current condition is obtained; and S6, generating a plate layout drawing according to the optimal layout mode, wherein the plate layout drawing is the optimal plate two-dimensional shearing blanking mode. According to the three-stage mixed heuristic blanking method for panel furniture provided by the invention, through collaborative optimization of the three-stage imprecise layout and the genetic simulated annealing algorithm, an optimal panel layout mode with a high utilization rate can be obtained; and meanwhile, the texture direction constraint in furniture artificial board blanking is ingeniously solved through a blank and strip rotation limiting method, and the operation time of the algorithm can be shortened through a grading solving mode combining a standard size set and three-stage imprecise layout.

The invention relates to the field of artificial intelligence and optimization algorithms, in particular to an intelligent evolution optimization method based on large language model driving. According to the method, semantic comprehension and reasoning capabilities of large language models such as DeepSeek and the like are deeply embedded into a core process of a traditional evolutionary optimization algorithm (PSO, GA), a'semantic cognition-dynamic decision 'double-loop mechanism is constructed, and full-process optimization from problem modeling, intelligent search to explainable output is realized. According to the method, the effectiveness of the method is verified on two typical optimization scenes of a 0-1 knapsack problem (discrete type) and Rosenbrock and Sphere function (continuous type), and through semantic-driven population initialization, dynamic parameter regulation and control and a closed loop feedback mechanism, the core problems that a traditional algorithm is low in initial solution quality, prone to local optimum, insufficient in calculation efficiency and the like are effectively solved.

The invention provides an intelligent scheduling method and device for a scheduling platform. The method comprises the following steps: evaluating the task value of each task by using a value evaluation model; evaluating resource consumption during task execution through a resource evaluation model on the basis of resource requirements during task execution, evaluating the total amount of all resources, and collecting uncompleted tasks; building a backpack problem model based on the task value, the resource consumption, the total amount of all resources and the uncompleted tasks of each task; and solving the knapsack problem model in combination with the scheduling score card to obtain an optimal task combination, and allocating the tasks to corresponding resources or execution units for execution according to the optimal task combination. The dependency, the weight and the resource consumption sequence among the tasks are optimized by automatically analyzing the task values of the tasks; and calculating an optimal task combination in real time by adopting a knapsack algorithm model according to the scheduling progress and the server resource remaining condition. And dynamic changes of task characteristics and resource consumption are fully considered, so that the efficiency and the stability of the system are improved.

A wind power grid impact damping suppression method based on branch and bound and knapsack optimization control can realize efficient and rapid suppression of wind power fluctuation. The device is characterized by combining the advantages of thyristor switched filter (TSF) and active power filter (APF), and developing active damping generator technology based on knapsack optimization control for wind power grid impact suppression. The developed ADG technology has the following advantages: ① It has the characteristics of TSF harmonic impact suppression, voltage mutation and high cost performance; ② It has the characteristics of APF output damping continuous adjustable and high precision; ③ Based on branch and bound, the wind power impact is classified as the reference of the maximum damping of ADG output, which effectively improves the rapidity and stability of ADG response; ④ The knapsack problem (UKP) is used to realize the optimal switching control of ADG, which meets the minimum cost and efficient wind power disturbance impact suppression.

The invention discloses a data processing method and device for scene visualization, and the method comprises the steps: determining a scheduling scene type when a scene visualization demand is received, determining a corresponding scheduling mechanism according to the scheduling scene type, scheduling a corresponding node set according to the scheduling mechanism, and carrying out the loading display, so as to complete the visualization process of a scene, the scheduling mechanism is an internal and external memory scheduling mechanism based on a leap-over type detail level model, and the scheduling mechanism at least comprises a direct scheduling mechanism, a scheduling mechanism of a father node substitution method or a scheduling mechanism of a knapsack problem solving method.

This invention discloses a cost- and reliability-aware profit optimization method for a heterogeneous multi-server system. The method includes: establishing a heterogeneous multi-server system model to obtain expected service revenue and multi-server system utilization; establishing a soft error reliability model to formulate the application domain investment selection and profit maximization problem as a constrained nonlinear optimization problem; utilizing an iterative method based on a penalty function to obtain the optimal multi-server system configuration, minimum cost, and maximum profit for any single application domain, and converting the profit and configuration optimization problem of the heterogeneous multi-server system into a 0-1 knapsack problem; and utilizing a dynamic programming-based algorithm and a greedy-based algorithm to solve the optimal application domain investment strategy and the corresponding maximum profit. This method can consider budget constraints and the soft error reliability of service requests while configuring the heterogeneous multi-server system configuration, thereby improving cloud service quality and provider profits.

This invention discloses a mobile edge caching method based on user location prediction. For multi-base station caching scenarios, this method caches a portion of files in nodes of a mobile edge network with storage capabilities. When users request these cached files, they can obtain them directly through the edge nodes without sending requests to the server via the backhaul link, avoiding duplicate file transmission and alleviating pressure on the operator's backhaul link. Simultaneously, a user location prediction mechanism is introduced, using an LSTM model to predict user locations, obtain the user group within the base station's service area for a certain time period, and calculate the file request distribution. Under cache capacity constraints, by optimizing the global caching strategy, a problem of maximizing operator caching revenue is constructed, simplified to a knapsack problem, and a dynamic programming algorithm is used to find the optimal solution for the caching strategy of each base station at each time step. This effectively reduces backhaul link overhead and improves the operator's caching revenue.

The present invention discloses a non-uniform memory access resource allocation method based on a parallel adaptive auction algorithm. The method includes the following steps: when the operating system starts, obtain the node information and task information in the non-uniform memory access architecture; regard the resource problem of the non-uniform memory access architecture as a knapsack problem with location constraints, and use the parallel adaptive auction algorithm for optimization and solution to obtain the allocation result. This application combines economic theory and computer science, enables tasks to independently bid for resources under the auction mechanism, and effectively decomposes the problem into independent parallel computing sub-problems, making the bidding strategies of each task independent of each other, and realizing more effective and efficient allocation of memory resources; when independently bidding for resources, the present invention combines the DQN model with the solution of the advertiser strategy under the auction mechanism, improves the stability of the DQN model, realizes the autonomous decision-making of tasks based on local information, and thus realizes decentralized and efficient resource allocation.

Based on the theoretical approach of the ordered multiple knapsack problem, this application employs a greedy algorithm (i.e., loop statements) to solve the intelligent scheduling problem and the combination optimization problem of different steel grades and thicknesses / widths in the production scheduling process of hot-rolled strip endless continuous casting and rolling. Under the premise of satisfying processing constraints, through reasonable rolling planning, it fulfills various requirements of the production task and can quickly find the global optimal solution, minimizing the penalty value (i.e., finding the production order that minimizes the penalty value). Applied to the rolling production process of hot-rolled strip endless continuous casting and rolling, this application can maximize the utilization of production resources (maximizing the ratio of total revenue to total cost), ensure that orders are completed within the delivery period, reduce the randomness and volatility problems caused by manual scheduling, improve production efficiency, and reduce production costs.

The invention belongs to the technical field of federated learning, and relates to a federated learning client selection method based on multi-factor dynamic evaluation, and the method comprises the steps: calculating positive factors of a client in a current state through a model owner, and then selecting the client to participate in federated learning through a participant selection algorithm based on a knapsack problem; the client downloads the global model, carries out training by using the local data set, and sends the trained local model to the block chain; the client side calculates cosine similarity between the client side and local models of other client sides and calculates mutual reliability between the client sides according to the cosine similarity; a model owner reads mutual credibility among the clients and calculates group reputation of each client; and the model owner evaluates the effort value of the client in this round of training according to the local model of the client. According to the method, collaborative optimization of malicious node filtering, inert client excitation and class imbalance relieving can be achieved from the source, and the overall efficiency and safety of the federated learning system are remarkably improved.

The application provides a multi-objective artificial bionical optimization method for solving knapsack problems, comprising the following steps: S1, setting artificial bionical population parameters and control parameter values, initializing positions and fitness values of each artificial bionical in the artificial bionical population; S2, simulating foraging, dormancy and reproduction behaviors of the artificial bionical; S3, updating the positions and fitness values of the artificial bionical according to step S2; S4, converting a continuous solution space into a discrete solution space through discretization processing; S5, selectively selecting part of the goods according to characteristics of all the goods; S6, repeating steps S2-S5, iteratively updating the positions and fitness values of the artificial bionical until the iteration termination condition is met, the method simulates foraging, dormancy and reproduction stages, and can guide the search direction to turn to other insufficiently explored areas for a multi-objective optimization problem, so that a better global solution can be found.

The present invention belongs to the technical field of federated learning, and relates to a method for selecting federated learning clients based on multi-factor dynamic evaluation. The model owner calculates the positive factors of clients in the current state, and then uses a participant selection algorithm based on the knapsack problem to select clients to participate in federated learning; the clients download the global model, use the local dataset for training, and send the trained local model to the blockchain; the clients calculate the cosine similarity between their local models and those of other clients, and calculate the mutual trust degree between clients based on the cosine similarity; the model owner reads the mutual trust degree between each client and calculates the group reputation of each client; the model owner evaluates the effort value of each client in this round of training according to the local model of the client. The present invention can realize the collaborative optimization of malicious node filtering, lazy client incentive and class imbalance mitigation from the source, and significantly improve the overall efficiency and security of the federated learning system.

The present application provides a slab design method, apparatus, device, medium, and product, which obtain order information corresponding to multiple orders; obtain constraint rules, which include a first conversion rule between the slab and the mother roll, a second conversion rule between the mother roll and the daughter plate, and a slab processing rule; input the constraint rules and the order information corresponding to the multiple first orders into a first model to obtain an initial order combination scheme corresponding to multiple available slabs, and the initial order combination scheme is used to determine the initial design size corresponding to each available slab; for any first slab among the multiple available slabs, if the initial design size of the first slab does not meet the slab processing rule, based on a genetic algorithm and a knapsack problem, the initial order combination scheme is adjusted to obtain a target order combination scheme corresponding to the multiple available slabs. The embodiments of the present application can improve the effect of order combination and reduce production costs.

The invention discloses a 6G outdoor hotspot scene network planning method based on limited backtracking, which relates to the technical field of wireless communication and comprises the following steps: 1) constructing a system model in an outdoor hotspot scene and analyzing system interference; 2) introducing deployment cost and base station downlink capacity limitation, and constructing a 6G outdoor hotspot scene base station deployment optimization problem into a similar multi-dimensional knapsack problem; 3) solving an optimal base station deployment scheme by using a limited backtracking dynamic programming algorithm fused with a user association mechanism; and 4) evaluating and analyzing the performance of the proposed scheme according to two indexes of the coverage rate and the average data transmission rate of the user. According to the method, the optimal deployment number and position of the base stations in the 6G outdoor hotspot scene can be effectively determined, the deployment cost and load balance are considered, and the network coverage rate and the data transmission rate are remarkably improved.

This disclosure relates to the field of computer technology and provides a picking location planning method, a picking location planning device, a computer-readable storage medium, and an electronic device. The picking location planning method includes: constructing a total picking cost function for the order based on the picking decision variables and picking costs corresponding to each location; constructing an initial problem model based on the total picking cost function of the order combined with a first demand constraint associated with each target item category; transforming the initial problem model to obtain K knapsack problem models corresponding to the K target item categories; solving each knapsack problem model using a dynamic programming algorithm to obtain each initial non-picking location set corresponding to each target item category; and determining the picking location set corresponding to each target item category based on the K initial non-picking location sets. This disclosure enables faster acquisition of optimal picking locations.

The application discloses a kind of federal learning client selection methods and systems for heterogeneous intelligent transportation system, comprising the following steps: S1, establish global prediction model based on federal learning protocol, model client selection problem as two-dimensional complete knapsack problem;S2, obtain the CPU execution frequency of all clients and the physical distance between client and edge server;S3, according to the CPU execution frequency of client and the physical distance with edge server, the client is clustered by heterogeneous perception clustering method;S4, the clustering result obtained is maximized by client selection method The number of clients participating in training within the deadline.Compared with the prior art, by increasing the number of clients participating in training within the deadline, the training efficiency and prediction accuracy can be improved within a limited time.

The invention discloses a method for solving an MCKP problem and a program product, and the method comprises the steps: carrying out the relaxation processing of a debit and credit risk control model based on a linear relaxation technology and a penalty term technology, and converting the debit and credit risk control model into a form of a multi-choice knapsack problem MCKP; the converted MCKP problem is preprocessed on the basis of the dominance theory, non-dominated solutions are eliminated in the O (n2) complexity, and the optimal slope parameter of the problem is determined on the basis of the non-dominated solutions; on the basis of the split theory, the optimal slope parameter is utilized, the minimum solution space and the maximum solution space are searched in the solution space of the loan combination, and the final solution of the loan combination is solved in the linear complexity of O (n) through a greedy algorithm; calculating a precision control result of a final solution based on a linear relaxation theory and an integer programming theory; vectorization operation is adopted in the whole process to accelerate data processing, it is ensured that each step of operation reaches the theoretically fastest speed, and therefore the efficiency and accuracy of loan risk control processing are improved. The precision is high and controllable, and the precision performance is particularly excellent on the ultra-large scale MCKP problem.

On the basis of a theoretical method for the ordered multi-knapsack problem, by using a greedy algorithm, i.e., loop statements, the present application solves the problem of combinatorial optimization among different steel grades and different thicknesses and widths in intelligent scheduling and production scheduling processes of endless continuous casting and rolling of hot-rolled strip steel; and while meeting machining constraint conditions, various aspects of requirements in production tasks are fulfilled by means of rational rolling planning, and a global optimal solution can be found more quickly so as to minimize a penalty value (i.e., to obtain a production sequence that minimizes the penalty value). The application is applied to a rolling production process of endless continuous casting and rolling of hot-rolled strip steel, and can realize the maximum utilization of production resources in endless continuous casting and rolling of hot-rolled strip steel (the ratio of total revenue to total cost is maximized), thereby ensuring that orders are completed within a delivery period, mitigating the problems of randomness and volatility caused by manual scheduling, improving production efficiency, and lowering production costs.

The invention discloses a local disturbance agent-based hybrid expert model heterogeneous pruning method, which comprises the following steps of: S1, acquiring hierarchical structure information of a MoE model to be pruned, determining the number of MoE layers contained in the model, and setting a total expert activation budget after pruning; s2, evaluating output representation of each MoE layer under different activation expert numbers by using a pre-selected calibration data set, calculating representation similarity of the output representation relative to high-quality baseline configuration, and obtaining a value function of the layer when an expert is adopted; s3, modeling a hierarchical Top-K activation pruning problem into a multi-choice knapsack problem under the constraint of total budget based on a value function, and solving the optimal expert activation number configuration of each MoE layer; s4, according to the optimal expert activation number configuration, the reasoning routing logic of the MoE model is updated; the method is used for solving the problem that in the prior art, a uniform pruning strategy with the low Top-K value is adopted for all layers in a unified mode, the calculation cost is reduced, and meanwhile the model performance is obviously reduced.

The application relates to the technical field of computer data processing, and provides a storage-computing separation database query optimization method and system for massive data analysis, which solves the technical problems of low system resource utilization and high query response delay. The method comprises the following steps: collecting business query logs and metadata information of all basic tables; constructing a syntax tree based on a query text, and calculating fluctuation indexes of each basic table; constructing a query topology graph based on a fingerprint sequence to obtain a candidate substructure set; taking the remaining available space as a space constraint, determining a target substructure combination through a multi-dimensional knapsack problem, and generating a query rewriting rule; matching the target fingerprint sequence with the query rewriting rule to determine a target substructure; replacing the target substructure with a read operation of pre-computed data corresponding to the target substructure combination; and generating an execution plan for optimizing the query, so that transparent rewriting of the query based on the pre-computed result is realized, and the query performance is maximally improved under the condition of limited maintenance cost.

This invention proposes a parking space planning method based on dynamic programming and bin packing algorithm, comprising: Step 1, constructing an abstract layout of the apron, wherein the abstract layout includes setting the area of ​​the apron and defining obstacles and service lanes in the apron area; Step 2, combining the aircraft types to be parked according to their available taxiing methods to form multiple combinations of aircraft types and taxiing methods, calculating the footprint of each combination of aircraft types and taxiing methods, and calculating the width of the right taxiway based on the footprint; Step 3, treating the parking space planning problem as a complete knapsack problem; each combination of aircraft types is regarded as an item to be packed, the value of each aircraft type is regarded as the item value, the available area of ​​the apron is regarded as the knapsack capacity, determining whether each combination of aircraft types can be parked on the apron according to the parking conditions, and obtaining all possible parking space planning results in the order of each row.