24 results about "Constraint programming" patented technology

The invention discloses a real-time operation optimization method and system for a multi-energy complementary electrolytic hydrogen production system, and the method comprises the steps: collecting real-time parameters of each module of the system, and inputting structured process knowledge to guide a neural network to predict a parameter change trend; based on a prediction result, constructing an optimization main model by using a distributed robust opportunity constraint programming algorithm, decomposing sub-models through a multi-main-body collaborative optimization algorithm based on ADMM, and solving the sub-models to obtain preliminary optimization parameters; and the photothermal effect is combined to induce the water phase regulation and control platform to regulate the medium state and the system operation parameters, closed-loop regulation is formed, and the final optimization parameters are output after the parameter deviation reaches the standard through iteration. The system comprises a multi-parameter collaborative acquisition unit, a structured knowledge driven prediction unit, a distributed robust optimization modeling unit, a multi-subject collaborative solving unit, a closed-loop parameter adjustment unit and an iterative optimization output unit, and all the units collaboratively realize real-time optimization of the system, so that the operation stability and economy are improved.

The invention discloses a single-satellite multi-APP satellite-borne software reconstruction method, which comprises the following steps: receiving a reconstruction instruction from a ground or satellite-borne control layer, and analyzing the reconstruction instruction to generate task description; the method comprises the following steps: according to an operation environment and a to-be-reconstructed APP list in a reconstruction instruction, after determining a dependency relationship and potential conflicts among all APPs in a satellite, constructing a weighted directed graph; the method comprises the following steps: acquiring current CPU, memory, power and bandwidth use conditions of all APPs in a satellite to form a resource matrix, and generating state snapshots of all the APPs at the same time; inputting the weighted directed graph, the resource matrix and the APP reconstruction constraint into a constraint planning solver to generate a plurality of feasible execution sequences; a cost function is adopted to calculate the overall cost of reconstruction of each feasible execution sequence, and the feasible execution sequence with the minimum overall cost is selected as a reconstruction execution sequence; and performing reconstruction according to the task description and the reconstruction execution sequence, performing security detection on each APP in the sequence, solidifying the APPs meeting the security detection, and otherwise, performing rollback operation on the APPs.

The application provides a kind of automatic driving taxi fleet operation management decision method considering demand space-time uncertainty, belongs to traffic control technical field, including the following steps: 1, information input;2, model building;3, model solution;4, scheme output;The application aims to maximize order profit and minimize vehicle mileage as the goal, while making overall decision optimization to vehicle order task, charging task, regional deployment task and parking task.On this basis, through random chance constraint programming, the randomness of user travel demand time and space distribution is considered to interfere with scheduling decisions, to improve the robustness of fleet operation management decision, to realize the dynamic and efficient matching of user travel demand and fleet resource space-time distribution, to improve the user order satisfaction rate, vehicle utilization rate and system operation efficiency of shared electric travel service.

The invention provides a method for estimating dispatching time of a semiconductor machine, which is characterized by comprising the following steps of: firstly, providing a semiconductor machine which comprises a plurality of cavities, then providing a plurality of batches of (Lot) wafer groups, each batch of wafer groups comprising a plurality of wafers, and then sending the plurality of batches of wafer groups to the semiconductor machine for a plurality of manufacture procedures, and according to the process conditions of the plurality of batches of wafer groups in the semiconductor machine, a simulation process flow chart is formulated by a system, and a dispatch time is calculated according to the simulation process flow chart, and the simulation process flow chart formulated by the system is obtained based on a constraint programming (CP) algorithm.

The application relates to the technical field of artificial intelligence, and discloses a cross-platform compatible vocational education software and hardware cooperative management method and system.The method comprises the following steps: generating an initial training plan by using a constraint programming algorithm, performing instruction conversion on a class scheduling instruction in the initial training plan to obtain a hardware control instruction set, generating knowledge mastery data by using data of a student terminal, combining the hardware control instruction set to construct a learning path atlas, generating a cross-platform optimization instruction stream based on a learning path atlas resource allocation strategy table, extracting a key resource optimization index to obtain the key resource optimization index, performing parameterization conversion to obtain optimization parameters of the constraint programming algorithm, feeding back the optimization parameters to the constraint programming algorithm to generate an updated training plan, and improving inter-process communication efficiency, reducing the occurrence rate of multi-task scheduling conflicts, and improving the timeliness of feedback data.

The present application relates to steel structure construction and assembly planning, and proposes a method for once-through of steel bar crossing through the through hole of cross steel column based on constraint programming; in order to solve the scratch, deadlock and rollback caused by the constraint of hole structure and fireproof coating, the method is: a scene model is established with the center of the through hole as the reference, the hole, coating, steel bar and reachable parameters are obtained; the contact normal is determined through geometric reasoning, the minimum bending radius is converted into the upper limit of the cumulative turning angle of the path, and the coating contact limit area is calibrated according to the friction and allowable indentation, and the incident angle range and allowed turning angle are given; the tool angle resolution is discretized and a safety margin is set, and the interlocking relationship table is formed in combination with mutual interference; the constraint satisfaction integer programming is carried out with the steel bar penetration sequence, the incident side, the incident angle and the guide as variables, and when it is not feasible, a detachable trumpet guide is installed at the high-risk hole edge for recalculation. The method ensures that the steel bar is once-through, avoids coating scratches and assembly deadlocks, and improves the construction feasibility.

The invention discloses a virtual power plant regulation and control method based on distributed energy and flexible load aggregation, and belongs to the technical field of power system operation and control. A cooperative control architecture comprising a resource layer, an aggregation layer, an optimization layer, a control layer and a market layer is constructed, and distributed optimization is realized by improving an ADMM algorithm; according to the method, opportunity-constrained programming and an ellipsoid uncertainty set are used for processing wind and light uncertainty, a GRU and a Dueling DQN are adopted to form a deep reinforcement learning load response mechanism, a resource layer completes equipment access and multi-frequency sampling, an aggregation layer clusters to generate an equivalent model and constructs a low-delay communication network, an optimization layer constructs a multi-objective function, and the multi-objective function is combined with an MIQP and a robust algorithm for solving. The control layer executes regulation and control through MPC rolling optimization and a priority strategy, and the market layer realizes transaction and income sharing. According to the method, the wind and light absorption rate is increased by 15%-20%, the calculation efficiency is 30% or above, and the method is suitable for a high-proportion renewable energy scene.

The invention relates to steel structure construction and assembly planning, and provides a one-time penetrating method for a reinforcing steel bar to penetrate through a cross-shaped steel column through hole based on constraint planning. In order to solve the problems of scratching, deadlock and rollback caused by orifice construction and fireproof coating constraint, the method comprises the steps that a scene model with the center of a through hole as the benchmark is established, and orifices, coatings, reinforcing steel bars and reachable parameters are obtained; determining a contact normal direction through geometric reasoning, converting the minimum bending radius into a path accumulated corner upper limit, calibrating a coating contact limiting area according to friction and allowable indentation, and giving an incident angle range and an allowable corner; discretizing according to the tool angle resolution, setting a safety margin, and forming an interlocking relation table by combining mutual interference; the rib penetrating sequence, the incident side, the incident angle and the guide piece are used as variables for constraint, integer programming is met, and when not feasible, a detachable horn mouth guide piece is additionally arranged on the high-risk hole edge for recalculation. According to the method, single-time penetration of the steel bars is ensured, coating abrasion and assembly deadlock are avoided, and the construction feasibility is improved.

The invention discloses a cold-chain logistics site selection and path planning method based on an improved discrete fireworks algorithm, and belongs to the technical field of intelligent decision and logistics management, and the method comprises the steps: S1, building a dual-target stochastic optimization model taking the minimization of the total cost of a system and the minimization of the carbon emission as targets, and taking the output uncertainty into consideration; s2, utilizing opportunity constraint programming to convert the randomly fluctuating yield into deterministic constraint, and setting the confidence level of decision; s3, mixed integer coding is used for generating an initial firework population representing cold storage site selection, vehicle type distribution and a path sequence; s4, in the iteration process of the algorithm, population individuals are searched through an explosion operator and a mutation operator; s5, utilizing a random opportunity constraint adjustment operator to carry out feasibility repair on a new solution generated by explosion and variation, so that the repaired solution meets the confidence level; and S6, performing non-dominated sorting and selection on the population through a Pareto dominating relationship, and outputting a cold storage site selection and path optimal scheme.

The invention provides a reentrant complex manufacturing system simulation evaluation method, and relates to the field of manufacturing system simulation. According to the method, the complexity characteristic of a manufacturing system is fully considered, including reentrancy, order random arrival, order splitting and machine type mixing, and the applicability of the simulation method is improved. According to the method, multiple operation priority value calculation rules are provided for two key links of operation release and operation dispatch, a combined strategy is designed by adopting a predicted influence function and constraint planning, the operation property and workshop state influence are comprehensively considered, the number of working products of a manufacturing system is effectively controlled, and meanwhile, the working efficiency of the manufacturing system is improved. And the order on-time delivery rate of an enterprise is improved. A plurality of performance test targets adopted by the invention can effectively describe the state of the production system and measure the effectiveness of the strategy provided by the invention. In a word, the simulation method provided by the invention can assist an enterprise administrator in evaluating the rationality of the production plan on the premise of not changing the actual production state, so that the decision-making cost is reduced.

The invention belongs to the technical field of automatic control, and discloses a large production workshop operation intelligent scheduling and automatic closed-loop control method, which comprises the following steps: 1) modeling a problem as a Markov process, and defining a state space, an action space and a transfer function; 2) extracting features from the heterogeneous graph by using a graph neural network, and enhancing the node relation modeling capability; 3) training the strategy model through a behavior cloning method, and using an optimal solution generated by constraint programming as training data; 4) designing a constraint programming real-time solving capability predictor, and dynamically evaluating the solving difficulty of the instance; and 5) jointly constructing a solution by combining behavior cloning and constraint programming, and balancing the solving speed and quality. According to the method, the advantages of deep learning and a traditional optimization method are combined, and the scheduling efficiency and the quality are remarkably improved; the method has good expandability and is suitable for examples with different scales and complexity; the method can be widely applied to flexible job shop scheduling and other combination optimization problems.

This invention relates to the interdisciplinary field of intelligent control and constraint programming, and provides a stage intelligent control method and system based on multi-device collaboration. To address the inherent spatiotemporal lag between equipment execution and actor behavior caused by the "post-event response" mode of traditional stage control, the method establishes a unified spatiotemporal reference, captures the continuous motion trajectory flow of actors in real time, and directly encodes it into dynamic spatiotemporal constraints. These constraints are then fused with the physical response characteristics of the stage execution equipment to construct a multi-dimensional fusion constraint space. Feasible state sequences are solved in real time within this multi-dimensional fusion constraint space, generating instruction sets with execution timestamps, which are then distributed to each stage execution device for synchronous execution. Through a constraint satisfaction mechanism of "direct behavior encoding - constraint fusion - real-time solution," real-time and precise collaboration between multiple stage devices and actor behavior is achieved, significantly enhancing the artistic expression of stage performances and the creative freedom of actors.

The application discloses a transmission shaft assembly part scheduling method, system and electronic equipment based on multi-target constraint programming, which comprises the following steps: obtaining annual scheduling plan data of a whole vehicle, constructing a first target function and a first constraint condition of an assembly annual plan; obtaining assembly annual plan data; constructing a second target function and a second constraint condition of a part annual plan; obtaining part annual plan data; constructing a third target function and a third constraint condition of an assembly monthly plan based on the assembly annual plan data; obtaining assembly monthly plan data according to the third target function and the third constraint condition; constructing a fourth target function and a fourth constraint condition of a part monthly plan; obtaining part monthly plan data; and outputting a scheduling optimization scheme according to the part monthly plan data. Compared with the prior art, the scheduling method provided by the application can quickly adjust the production plan according to market demand changes, reduce product quality fluctuations, and significantly improve production efficiency.

The invention provides a vehicle gauge chip final test intelligent scheduling platform and method, which not only can realize dynamic optimization scheduling, but also can ensure absolute consistency of scheduling results in different hardware environments, and solves concurrency conflicts when manual scheduling and automatic scheduling are parallel. The platform comprises a data acquisition module; a multi-constraint production scheduling algorithm engine based on a constraint programming solver, which generates a plurality of optimization schemes through a dynamic weight strategy; the intelligent resource matching module supports seamless cooperation of physical and virtual resources; and the decision support module integrates a localized large language model and provides closed-loop intelligence from natural language query to weight parameter optimization suggestions and conflict repair script generation. According to the technical scheme, the equipment utilization rate, the production scheduling decision efficiency and the system operation reliability are remarkably improved.

This invention belongs to the field of hybrid flow shop scheduling technology in intelligent manufacturing, and particularly relates to a hybrid flow shop scheduling optimization method considering periodic preventive maintenance. The method constructs a DCABC-CP hybrid algorithm that integrates dual-population cooperative artificial bee colony and constraint programming. Through parameter initialization, dual-population hybrid encoding initialization, iterative optimization using hired bees, observer bees, and scout bees, adaptive population cooperation based on Thompson sampling multi-armed slot machine, forward and reverse decoding re-evaluation, and problem-specific local search, when the algorithm reaches 40% of its total execution time, the current optimal solution is imported into the CP model for precise optimization. Finally, it outputs a scheduling scheme that minimizes the maximum completion time while satisfying the periodic preventive maintenance constraint. This invention effectively balances global search and local optimization, enhances the ability to handle maintenance constraints, and significantly outperforms traditional algorithms in terms of solution efficiency and quality, making it suitable for large-scale hybrid flow shop scheduling scenarios.

This invention belongs to the field of hybrid flow shop scheduling technology in intelligent manufacturing, and particularly relates to a hybrid flow shop scheduling optimization method considering periodic preventive maintenance. The method constructs a DCABC-CP hybrid algorithm that integrates dual-population cooperative artificial bee colony and constraint programming. Through parameter initialization, dual-population hybrid encoding initialization, iterative optimization using hired bees, observer bees, and scout bees, adaptive population cooperation based on Thompson sampling multi-armed slot machine, forward and reverse decoding re-evaluation, and problem-specific local search, when the algorithm reaches 40% of its total execution time, the current optimal solution is imported into the CP model for precise optimization. Finally, it outputs a scheduling scheme that minimizes the maximum completion time while satisfying the periodic preventive maintenance constraint. This invention effectively balances global search and local optimization, enhances the ability to handle maintenance constraints, and significantly outperforms traditional algorithms in terms of solution efficiency and quality, making it suitable for large-scale hybrid flow shop scheduling scenarios.

The invention discloses an online tuning method and system for sintering waste heat power generation, and solves the problems of unstable operation and limited power generation efficiency of a waste heat power generation system in the prior art. The method comprises the following steps: obtaining real-time operation data of a whole process of a sintering waste heat power generation process; on the basis of real-time data, the sensible heat potential of sinter entering the circular cooler is predicted online through a firing quantity prediction model; on the basis of the sensible heat potential and to-be-optimized process control parameters, the sintering waste heat power generation power is predicted on line through a full-process energy efficiency model; solving and determining an optimal process control parameter corresponding to the current sintering ore sensible heat potential by adopting a constrained programming algorithm by taking generated power maximization as a target; and finally, closed-loop tuning control is carried out on the sintering waste heat power generation system based on the optimal parameters. According to the method, the upstream waste heat fluctuation can be accurately sensed, the downstream operation parameters are collaboratively optimized in a feedforward manner, and the power generation efficiency and the operation stability of the non-afterburning type sintering waste heat power generation system are remarkably improved.

The invention belongs to the technical field of electric power traffic fusion network scheduling, and particularly discloses a dynamic carbon emission factor-based electric power-traffic coupling network collaborative optimization method, which comprises the following steps of: constructing a double-layer optimization model for describing electric power-traffic coupling network collaborative operation; performing equivalent reconstruction and linearization processing on an upper-layer model of the double-layer optimization model, and converting the upper-layer model into a mixed integer quadratic constraint programming problem to obtain a reconstructed double-layer optimization model; and an upper layer model and a lower layer model of the reconstructed double-layer optimization model are solved alternately, coupling variables are updated until an equilibrium state of the electric power-traffic coupling network is reached, an equilibrium solution of the reconstructed double-layer optimization model is output, and collaborative optimization of the electric power-traffic coupling network is completed. According to the method, the problem of how to determine effective user incentive measures to realize optimal collaborative scheduling of economy and emission reduction is solved.

The invention belongs to the field of integrated circuit safety design, and relates to a time sequence sensing automatic replacement method for a circuit multi-threshold disguise unit, which comprises the following steps of: obtaining an original circuit design netlist after layout and wiring and a time sequence path set obtained through time sequence analysis, and setting a number constraint of disguise unit replacement; constructing a multi-objective optimization model through a Lagrangian multiplier method by taking maximization of circuit safety as an objective and taking a time sequence path and a disguise unit replacement number as constraints; solving the multi-target optimization model by adopting a hybrid algorithm combining constrained programming and simulated annealing so as to determine a logic node set to be replaced by a multi-threshold camouflage unit in the circuit; replacement of a multi-threshold camouflage unit is performed in a circuit in accordance with the set of logic nodes. The method is mainly applied to a later engineering change stage of a safety-critical chip, the physical safety of the chip can be improved on the premise that the time sequence performance is not sacrificed, and the method is completely compatible with a mainstream electronic design automation tool chain.

The invention discloses an automobile production scheduling method based on constraint programming. The method comprises the following steps: S1, obtaining to-be-scheduled order data, a hard constraint rule and a soft constraint rule; s2, processing the obtained to-be-scheduled order data to obtain the sum of single-dimension data in all orders; the constraint condition of each dimension in the hard constraint rule is compared with the sum of corresponding dimension data in all orders, and if any dimension does not meet the hard constraint rule, scheduling is ended, and corresponding dimension data which do not meet the hard constraint rule are returned; s3, solving the to-be-scheduled order by adopting hard constraint, and if a solution exists, carrying out optimal solution solving on the to-be-scheduled order based on a soft constraint rule to obtain a scheduling result; and if the hard constraint solution does not have the solution, performing iterative discarding on the hard constraint rule to obtain the hard constraint rule when the solution exists, and entering the soft constraint rule to perform optimal solution solution on the to-be-scheduled order to obtain an optimal scheduling result and the corresponding hard constraint rule under the condition of hard constraint rule conflict.

The invention provides a method for estimating the dispatching time of semiconductor machine, which is characterized by comprising the following steps: firstly, providing a semiconductor machine with a plurality of chambers, then providing a plurality of lot wafer groups, wherein each lot wafer group comprises a plurality of wafers, then sending the lot wafer groups to the semiconductor machine for a plurality of processes, and according to the process conditions of the lot wafer groups in the semiconductor machine, a simulated process flow chart is developed by a system, and a dispatching time is calculated according to the simulated process flow chart, wherein the simulated process flow chart developed by the system is obtained based on a constraint programming (CP) algorithm.

The invention relates to the technical field of travel itinerary automatic scheduling and optimization based on constrained programming, in particular to a travel itinerary intelligent scheduling method and system for multiple scenic spots of a travel destination. The method comprises the following steps: firstly, constructing a refined data model comprising scenic spot multi-entrance nodes, traffic cost, user preferences and time constraints; a scenic spot candidate set is obtained through ticket filtering and priority ranking preprocessing; then, a constraint planning theory is adopted to carry out deterministic solution on the multi-day journey, the open time window, multi-entrance mutual exclusion and secondary entrance complex reality constraint of the scenic spot are strictly processed, and the performability and stability of the solution are ensured; and finally, outputting an optimal travel plan according to a multi-target strategy. According to the invention, efficient and reliable intelligent journey planning meeting actual requirements is realized.

The application discloses a job shop production scheduling method based on constraint programming, relates to the technical field of job shop production scheduling, and comprises the following steps: S100, estimating the range of the optimal solution target value; S200, interval initialization; S300, updating the interval; S400, updating the interval midpoint; and S500, outputting the solution.The constraint propagation algorithm is improved for the job shop production scheduling problem, the improved constraint propagation algorithm is applied to constraint programming, the solving speed of the constraint programming is improved under the premise of guaranteeing the solving quality, the solving quality and the solving efficiency are considered, the management cost of the job shop production scheduling is reduced, the production time of orders is reduced, and the method has high practicability.

The invention discloses a traditional Chinese medicine nursing resource optimization and allocation method based on machine learning. The method comprises the following steps: generating a standardized traditional Chinese medicine nursing service multi-source data set; generating a stable nursing demand baseline feature map by using an average fusion path; generating a sensitive nursing demand compensation feature map; outputting a nursing demand heat map and a urgency degree mask; forming a hard constraint set and a soft constraint set; solving the multi-objective optimization function system by adopting constraint programming to obtain an initial feasible resource scheduling scheme meeting all hard constraint sets; and generating a final resource scheduling scheme. According to the method, while the time-space demand heat map segmentation accuracy can be greatly improved, the individual physique and period rule in traditional Chinese medicine nursing can be effectively reflected, and peak shifting, false peak and resource waste caused by data drift of a shift arrangement scheme can be remarkably reduced.