97 results about "Constraint satisfaction" patented technology

The invention is suitable for the field of transformer fault analysis, and provides a transformer potential fault mode identification method and device based on reverse derivation, and the method comprises the steps: generating a multi-field coupled feature puzzle model through real-time collection of four-dimensional physical quantity spatio-temporal data of temperature, vibration, oil chromatography, partial discharge and the like, and carrying out the topological matching of the feature puzzle model and a historical health file, and reversely deducing a fault coupling path for the abnormal vacancy region through a constraint satisfaction algorithm, reconstructing a missing feature block, decoupling and extracting interface abnormal features, dynamically optimizing model topology in combination with an equipment aging factor, and finally quantifying the full-life-cycle risk accumulation intensity and positioning a composite fault source. The method breaks through the limitation of a traditional single-parameter threshold value, achieves the early recognition and precise traceability of the multi-field coupling fault through a reverse derivation mechanism of the feature jigsaw blocks, is suitable for the health management of the whole life cycle of the transformer, and can remarkably improve the timeliness and accuracy of potential fault early warning.

The invention discloses an intelligent planning method and system for a weak current system in a smart park, and belongs to the technical field of weak current intelligent design. The method comprises the steps of performing feature extraction on the weak current multi-source data of the smart park to form a weak current feature set; a multi-dimensional semantic space is constructed, semantic association features are obtained, and node features, topological relations and constraint rules of the weak current system are determined; generating a weak current knowledge graph based on the information, and performing semantic alignment on the basic information of the park to obtain a final scene demand representation; performing graph reasoning and constraint calculation according to the representation to obtain a feasible region and constraint satisfaction condition, and generating a candidate construction scheme; and screening out an optimal construction scheme from the candidate schemes according to a preset comprehensive optimization strategy and sending the optimal construction scheme to a control center. According to the scheme, the weak current scheme is promoted from demand understanding to scheme optimization, and a coherent and verifiable automatic process is formed; therefore, the manual intervention is less, the design judgment is more accurate, and the finally output construction scheme has higher engineering reliability.

The invention discloses a reservoir multi-target optimization scheduling decision-making method based on digital twinning and AI simulation, and relates to the technical field of reservoir scheduling, the method effectively solves the problems of data fragmentation and information redundancy in traditional scheduling by constructing a multi-source data fused reservoir digital twinning body, realizes accurate virtual-real mapping of reservoir working conditions, and improves the scheduling efficiency. A high-reliability simulation basis is provided for subsequent optimization scheduling, and decision errors caused by data errors or model deviation are avoided; four core targets of flood control, power generation, ecology and water supply are considered, an engineering safety bottom line and rigidity requirements are defined through a layered constraint system, constraint satisfaction degree screening is enhanced by means of an improved multi-target optimization algorithm, and target weights can be dynamically adjusted according to a real-time scene. And after the non-dominated scheme set is optimized and output, candidate schemes are screened in combination with real-time demand quantitative scores.

The invention relates to the technical field of warehouse management, and discloses a virtual goods allocation dynamic mapping and elastic warehouse space management method and system. The method comprises the following steps: acquiring a storage space multi-source data set including goods allocation physical attributes, goods flow records and storage environment parameters; generating a topological relation graph based on the physical attributes of the goods allocation, generating a virtual goods allocation allocation strategy according to the goods flow record, and processing the storage environment parameters to generate capacity demand fluctuation characteristics; and inputting the result into a multi-dimensional decision model to generate an optimization vector, constructing an elastic adjustment strategy graph through a constraint satisfaction algorithm, and outputting a virtual goods allocation dynamic mapping scheme. The system comprises a multi-source data acquisition module, a topology modeling module, a strategy generation module, a capacity prediction module, a multi-dimensional decision module and an elastic adjustment module. The storage space utilization rate and the operation efficiency can be improved, environmental changes can be effectively coped with, and the enterprise operation cost is reduced.

The invention provides a rounded-corner container transportation path dynamic optimization scheduling method and system, and the method comprises the steps: quantifying three constraints of the size, the gravity center and the loading and unloading priority of a rounded-corner container, converting the three constraints into a matching formula, a path constraint threshold value and a weight rule, and constructing a weighted multi-objective optimization function in combination with the transportation cost, the time and the cargo damage risk; a genetic algorithm and ant colony algorithm mixed framework is built, a constraint adaptation layer is embedded to filter invalid solutions, and the iteration efficiency is improved; two types of algorithm operators are improved, and a constraint satisfaction degree, a loading and unloading priority and a dynamic parameter adjustment mechanism are fused; dividing multiple regions into sub-region optimization by adopting a divide-and-conquer strategy, and adapting to a large-scale dynamic scene through cross-region collaboration and local re-optimization; a full-dimension verification scheduling scheme in iteration is carried out, algorithm parameters are automatically adjusted based on constraint violation information, and iteration is terminated or constraint relaxation is started according to preset conditions; an improved algorithm is integrated to a dynamic scheduling system, real-time data are connected, parameters are optimized through a self-learning module, and a manual intervention interface is reserved.

The invention provides a large language model instruction optimization method based on constraint perception self-learning reasoning, which can be applied to the technical field of natural language processing and artificial intelligence. The method comprises the steps that in the cold start training stage, a thinking chain example generated by a pre-training language model is utilized to assist a target large language model in recognizing constraints in a training instruction set, and the constraints in the training instruction set comprise hard constraints and soft constraints; performing supervision and fine tuning on the target large language model by utilizing the thinking chain example and the training annotation data so as to enable a thinking chain generation process of the target large language model to meet a hard constraint and a soft constraint; in the self-learning training stage, a constraint satisfaction evaluation mechanism is constructed by using the hard constraint and the soft constraint, and a thinking chain generation strategy of the target large language model is optimized through a reinforcement learning algorithm based on the constraint satisfaction evaluation mechanism.

A neural-symbolic hybrid system for generating binary document formats directly from natural language input comprises a binary-aware hierarchical tokenizer operating across four levels (binary bytes, structural elements, semantic content, and concepts), a constraint satisfaction engine with 64 parallel processing cores for enforcing structural integrity and mathematical consistency, format-specific processors for Excel, PowerPoint, PDF, and CAD documents, and a formal verification system generating mathematical proofs of correctness. The system includes custom AI Document Generation Processor (AIDGP) silicon spanning 600 mm2 with specialized cores providing 500 TOPS processing power. Performance characteristics include 99.7% structural accuracy, 100% format compliance, 15.3 second average generation time for complex documents, and distributed capacity of 1,000,000 documents per hour. The system eliminates intermediate conversion steps while maintaining semantic preservation through hardware-accelerated constraint satisfaction and formal verification engines ensuring structural integrity, format compliance, and security through AES-256 encryption and automated regulatory compliance across 25+ international standards.

Quantum optimization with Rydberg atom arrays is provided. In particular, methods are provided for solving combinatorial graph optimization problems, constraint satisfaction problems, maximum independent set problems, algebraic problems, and factoring.

The invention discloses a competitive evolution multi-task optimization method, system and equipment, and aims to solve the problem that an existing method is difficult to carry out collaborative optimization on fuel cost and gas emission under the condition that power balance and unit capacity constraint conditions are met. The method comprises the following steps: constructing main and auxiliary double-population parallel search; state indexes representing population convergence, diversity and feasibility are calculated in real time; based on the current state, a cooperation mode and evolution operator combined action is adaptively selected through a deep reinforcement learning agent; rewards are calculated according to improvement of the population on cost, emission and constraint satisfaction after action execution; and training the intelligent agent by using the state, the action and the reward, and iteratively optimizing the decision strategy until the Pareto optimal scheduling scheme meeting the constraint is output. According to the method, adaptive intelligent guidance of the evolutionary process is realized, and the optimization quality, the convergence speed and the algorithm robustness of the economic emission scheduling scheme of the power system are remarkably improved.

An annealing unit performs a first search for an annealing solution to lower an objective function value in the vicinity of a constraint satisfaction solution, by using a first solver. A constraint logic unit performs a second search for a constraint satisfaction solution in the vicinity of an annealing solution obtained by the annealing unit, by using a second solver, and performs iterative processing of the first search and the second search to obtain an optimum solution.

The invention discloses an underground pipe network data intelligent detection method and system based on multi-source data fusion, particularly relates to the technical field of underground pipe network data processing, and is used for solving the problems of low accuracy and poor reliability caused by the fact that pipeline connection relation judgment depends on artificial experience in the prior art. The method comprises the following steps: constructing an original data set containing pipeline point space position information and pipeline attribute information; connection information integrity analysis, connection relation analysis based on an engineering rule and a space rule, graph signal processing evaluation combined with dynamic response correlation and network flow distribution compatibility, network structure robustness and cascade failure risk analysis, multi-agent game simulation and constraint satisfaction analysis are carried out in sequence; and finally, an accurate and reliable pipeline network topological relation graph is automatically generated.

The invention discloses a BIM-based power grid digital twin modeling method and device and a storage medium, and relates to the technical field of electronic data processing, and the method comprises the steps: based on power grid project parameters, automatically generating a BIM initialization model according with a specification from a parameterized BIM element library through a constraint satisfaction algorithm; collecting multi-source data with a unified space-time reference through a multi-mode sensing network; constructing a joint optimization objective function fusing geometric, semantic and physical laws, solving by using multi-source data to obtain the optimal adjustment amount of the model, and generating a high-precision power grid BIM model; and injecting real-time and historical data into a hybrid simulation engine integrating data driving and a physical mechanism model, and driving a BIM model to generate a dynamic digital twinborn body with real-time mapping and prediction capabilities. According to the method, parameterized BIM of typical design and multi-source real-time data of a construction site are deeply fused, and the power grid infrastructure digital twinborn body which can dynamically evolve and accurately map a physical entity is constructed.

The invention relates to the technical field of business process modeling, in particular to a meta-model-driven interactive business process intelligent modeling method and system. The method comprises the following steps: constructing a meta-model framework containing core elements such as activities, events and gateways, and establishing a meta-model constraint rule base to define a structure legality rule of a BPMN2.0 specification; based on a domain ontology Schema and the domain exclusive large language model after instruction fine tuning, extracting a business entity and a process relationship thereof from the unstructured text; carrying out information integrity verification and logic consistency detection on the extracted BPMN elements by utilizing a constraint satisfaction algorithm and Petri net reachability analysis; displaying a difference label of the process sketch through an interactive verification interface, and realizing semantic correction and model iteration of a user on the process elements based on a natural language feedback mechanism; an XML code is generated by adopting a template-driven code generation strategy, and a flow chart is generated through a BPMN parser. According to the method, the business process modeling efficiency can be remarkably improved, and the manual intervention proportion in the modeling process is reduced.

The application discloses a kind of multi-agent driven network sentiment regulation method and system based on reinforcement learning, belong to artificial intelligence and network information processing technical field.The method obtains text, speech, image, video, behavior, relationship and propagation path data, constructs space-time heterogeneous interaction graph and extracts graph structure feature;Combined with multi-modal basic feature, establish causal attention sentiment state representation;Multi-agent collaborative decision-making environment, sentiment regulation action set, constraint reward function and counterfactual propagation environment are constructed;Collaborative communication training is carried out using multi-agent constrained reinforcement learning, and the output joint regulation strategy;According to sentiment feedback, behavior feedback and propagation feedback, closed-loop update is carried out, and according to negative sentiment proportion, sentiment entropy, propagation risk index and constraint satisfaction rate, iterative control is carried out.The application can improve the accuracy, collaboration, robustness and constraint controllability of network sentiment regulation.

The invention relates to a resource negotiation scheduling method for an NAS system and an intelligent system, and relates to the field of the NAS system, the method is used for a control unit of the NAS system, the control unit is connected with a demand sensing module, and the resource negotiation scheduling method comprises the following steps: S1, obtaining demand sensing parameters of the NAS system through the demand sensing module; s2, analyzing the demand perception parameters, and generating a multi-dimensional resource availability dynamic graph; s3, in combination with the content of the multi-dimensional resource availability dynamic graph, running a negotiation algorithm based on constraint satisfaction to realize negotiation scheduling of resources; and S4, adjusting a resource negotiation scheduling result under the condition that an abnormal state occurs. End-side AI equipment in the prior art, such as an NAS system, is generally faced with the situation that user operation is stuck when the AI is used; on the basis of the scheme provided by the invention, the use experience of a user on the NAS system can be smoother.

The application provides a kind of industrial vertical field corpus construction data cleaning method, belong to industrial big data technical field, the application is through collecting multi-modal industrial data and recording original time stamp, unified time reference is generated using dynamic time warping algorithm for time alignment, cross-modal fusion feature vector is generated by multi-modal attention mechanism learning feature interaction weight, key data section is identified based on event-driven trigger rule and is associated as event data package, quality evaluation and abnormal calibration are carried out through step response consistency detection, multi-modal knowledge graph is constructed and cross-modal alignment model based on dual optimization constraint satisfaction framework is used for feature completion, finally, data quality is ensured through adaptive verification process, solve the technical problems that multi-modal industrial data is difficult to ensure alignment accuracy and original information integrity in time dimension alignment and semantic level fusion process.

The present invention relates to the technical field of warehouse management, and discloses a method and system for virtual location dynamic mapping and elastic warehouse space management. The method includes obtaining a multi-source data set of the warehouse space, including the physical attributes of the locations, the goods flow records, and the warehouse environment parameters; generating a topological relationship graph based on the physical attributes of the locations, generating a virtual location allocation strategy according to the goods flow records, and processing the warehouse environment parameters to generate the capacity demand fluctuation characteristics; inputting the above results into a multi-dimensional decision-making model to generate an optimization vector, and then constructing an elastic adjustment strategy graph through a constraint satisfaction algorithm, and outputting a virtual location dynamic mapping scheme. The system includes a multi-source data acquisition module, a topological modeling module, a strategy generation module, a capacity prediction module, a multi-dimensional decision-making module, and an elastic adjustment module. The present invention can improve the utilization rate of the warehouse space and the operation efficiency, effectively cope with environmental changes, and reduce the enterprise operation cost.

This invention discloses a hierarchical multi-agent reinforcement learning-based trip recommendation method and system, belonging to the field of trip recommendation technology. The hierarchical multi-agent reinforcement learning-based trip recommendation method includes: acquiring basic POI data, user historical behavior data, and contextual information; constructing a dynamic heterogeneous POI graph and generating POI embedding representations; constructing multi-type user preference representations and calculating the Bayesian user preference posterior distribution, and calculating candidate POI preference scores; constructing a hierarchical multi-agent reinforcement learning model, including, from high to low, a region selection agent, an agent action set generation unit, a POI selection agent, a time scheduling agent, and a path optimization agent; and outputting the final personalized trip route based on the hierarchical multi-agent reinforcement learning model. This invention can efficiently generate executable trips that balance personalization, path quality, and hard constraint satisfaction in large-scale POI scenarios, significantly improving recommendation accuracy and user experience.

The invention discloses a cascade reservoir scheduling method based on multi-population self-adaption, which comprises the following steps: establishing a cascade reservoir scheduling (CRS) model, taking maximized power generation, maximized desilting and maximized ecological rate as optimization targets of the CRS model, and determining constraint conditions according to the optimization targets of the CRS model and operation requirements of a cascade reservoir system; and then performing operation on the optimization target by using a cascade reservoir scheduling method based on multi-population self-adaption. According to the invention, by designing the single-target population and the double-target population, the conflict difficulty between target optimization and constraint satisfaction is reduced; waste of computing resources caused by low-efficiency populations is reduced through a population self-adaptive activation mechanism; by designing an environment selection mechanism based on bidirectional information sharing, the effectiveness of knowledge migration is improved, and the calculation complexity is reduced; the method is high in robustness and can be suitable for reservoir data in different years.

The invention provides an operation arrangement method and system based on learning condition information, and the method comprises the steps: collecting multi-source learning condition information; grouping class students based on an improved clustering algorithm, constructing a knowledge point topological graph by using an effective learning condition vector set, calculating a conduction weight, calculating a time sequence weighted weak frequency in combination with a dynamic mastery degree sequence, screening a high-frequency and high-conductivity weak combination, and generating a central vector cluster; constructing a multi-objective optimization function, and solving and outputting an optimal clustering number by taking maximization of a contour coefficient, minimization of an inter-group knowledge point overlapping rate and maximization of a teaching constraint satisfaction degree as objectives; calculating the similarity between the effective learning condition vector and the center vector cluster based on the Power distance, and iteratively updating the clustering center to convergence; and matching candidate homework questions according to each group of feature tags by adopting a mixed recommendation algorithm of content recommendation and collaborative filtering. According to the embodiment of the invention, the problems that the learning condition information cannot be fully utilized and the pertinence of the operation arrangement is insufficient in the existing operation arrangement method are solved.

The invention relates to the technical field of artificial intelligence and industrial process modeling, in particular to a time series data generation method and device based on a Gamma variational auto-encoder, and the method comprises the steps: carrying out the modeling of potential variables in the variational auto-encoder through Gamma distribution, and carrying out the modeling of the potential variables when the variational auto-encoder is trained, firstly, each variable value of original time sequence data in a training set is normalized according to a constraint condition to serve as training input, and then a micro-weight parameterization sampling mechanism is introduced to sample Gamma distribution constructed by encoder output parameters, so that the problem that training is unstable under the condition of small shape parameters in a traditional sampling method is solved. Therefore, the trained variational auto-encoder can significantly improve the constraint satisfaction rate and physical consistency of the generated time sequence data. According to the method, the high-quality time sequence data with good engineering availability can be stably generated.

The invention discloses an improved reservoir dispatching safety reinforcement learning method based on a Lagrange method, and relates to the technical field of reservoir dispatching. The Lagrange duality method has obvious advantages in the aspect of security constraint processing, and compared with a corrective method and a penalty method, the Lagrange method has the advantages that the constraint weight is adaptively adjusted, so that the target optimization performance is remarkably improved while the constraint is satisfied; experimental results show that the method is superior to other methods in the aspects of constraint satisfaction and generating capacity, and the constraint violation degree is reduced by more than 80%; according to the method, an action mask mechanism is adopted, through the synergistic effect of preposed defense and posterior optimization, the training stability is effectively improved, a complete rule mask can precisely define a safety boundary, the violation rate is reduced to zero from 21%-79%, and meanwhile strategy convergence is accelerated.

The invention discloses a deep reinforcement learning and mathematical optimization-based consumption capability evaluation method and system, and the method comprises the steps: training a pre-built new energy consumption evaluation model based on a historical wind and light output sequence scene, in the training process, neural network parameter updating is carried out through deep reinforcement learning layer solution, and a t moment integer variable is given; if t is equal to the last moment of the whole year, transmitting a whole-year integer variable to the mathematical optimization layer, taking the whole-year integer variable as a fixed parameter, solving to obtain the optimal power generation power, counting the new energy consumption amount, calculating an award, and returning an obtained result to the deep reinforcement learning layer to update neural network parameters, obtaining a trained new energy consumption evaluation model; and inputting a to-be-evaluated wind and light output sequence scene into the trained new energy consumption evaluation model to complete consumption capability evaluation. According to the method, the balance of calculation efficiency, constraint satisfaction and solving precision can be realized.

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 generalized Nash equilibrium search method and system based on a neural network, and relates to the technical field of distributed control, and the method comprises the specific steps: aggregation game problem definition and dynamic model construction, disturbance approximation and constraint transformation design, aggregation estimation and optimization method design, and parameter range determination and monitoring and verification. According to the method, the Lyapunov function is constructed through the Lyapunov stability theory, and the value range of the adjustment parameters is scientifically determined in combination with key parameters such as the Lipschitz constant, so that the innovation not only ensures the convergence of the distributed time-varying generalized Nash equilibrium optimization method, but also improves the optimization efficiency of the distributed time-varying generalized Nash equilibrium optimization method. In addition, mathematical requirements such as a global Lipxiss condition, non-empty closed convexity and continuous differentiable convexity are met, a design-verification closed loop is formed by monitoring an intelligent agent strategy evolution curve and a constraint satisfaction condition, and it is ensured that the optimal state can be continuously kept in the actual operation process.

The application discloses a dynamic optimization method for unmanned aerial vehicle online trajectory planning, and belongs to the technical field of unmanned aerial vehicle autonomous control, and comprises the following steps: constructing a finite time domain optimal control problem containing a target function and multiple constraints at each sampling time; converting continuous control input into a segmented function by control parameterization technology, converting an infinite dimension problem into a finite dimension nonlinear programming problem; converting time-varying speed and obstacle avoidance constraints into integral penalty items for processing by using a constraint transcription method; introducing a terminal controller and a terminal area constraint to ensure recursive feasibility and asymptotic stability of the system; solving the nonlinear programming problem on-line, and applying the first control instruction to the unmanned aerial vehicle. The application realizes real-time, safe and efficient trajectory generation of the unmanned aerial vehicle in a complex environment by optimizing problem reconstruction and calculation simplification, and provides reliable technical support for unmanned aerial vehicle autonomous tasks under the premise of strictly ensuring constraint satisfaction and closed loop stability.

Disclosed in the present invention is a formal verification method for realizing an optimized multiplier by cooperation of SCA and SAT. The formal verification method comprises: a reverse engineering algorithm systematically recovers an adder tree from an optimized multiplier; 2) a constraint satisfaction algorithm uses only an adder to complete generation of a reference multiplier by means of a constraint condition; and 3) a verification method based on SCA and SAT combines the complementary advantages of SCA and SAT. In the verification framework, the present invention introduces a reference multiplier generator for generating a correct reference multiplier which has both a structure similar to the optimized multiplier and a clear adder boundary. The clear adder boundary makes it possible to use SCA-based verification to prove the correctness of the multiplier. By using the structural similarity of the reference multiplier and the optimized multiplier, the reference multiplier is then used as a known correct model and used for SAT-based verification of the optimized multiplier.

An annealing unit uses a first solver to perform a first search for an annealing solution through lowering of an objective function value in the neighborhood of a constraint satisfying solution, a mixed integer programming problem optimization unit uses a second solver to perform a second search for a constraint satisfying solution through lowering of a value of an objective function of a linear expression in the neighborhood of the annealing solution obtained by the annealing unit, and iterative processing of the first search and the second search is performed to obtain an optimal solution.

To solve a binary quadratic optimization problem including complicated logic constraints without breaking the constraints in an optimization system.SOLUTION: An annealing unit uses a first solver to perform a first search for an annealing solution so as to lower an objective function value in the vicinity of a constraint satisfaction solution, and a constraint logic unit uses a second solver to perform a second search for a constraint satisfaction solution in the vicinity of the annealing solution obtained by the annealing unit, and an optimal solution is obtained by executing repetitive processing of the first search and the second search.SELECTED DRAWING: Figure 2

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.