20 results about "Constraint satisfaction problem" patented technology

The invention provides an intelligent learning plan generation method and device based on a large model, and belongs to the technical field of educational informatization. Identifying entities and key terms; constructing a course knowledge graph; carrying out matching calculation with courses through mixed retrieval on the basis of a course knowledge graph and key information features, and carrying out dual-channel collaborative multi-channel retrieval in combination with vector similarity and keyword matching; the multi-target course arrangement information is optimized based on a genetic algorithm, course arrangement is carried out in combination with a time planner of a constraint satisfaction problem model, and a course time arrangement plan is generated; and related lecturers and students are informed of confirmed results. According to the method, the curriculums and student requirements are matched, the comprehensiveness and accuracy of curriculum retrieval are improved through weighted sorting, the curriculums meeting the learning plan requirements can be found more accurately, and the curriculum matching efficiency is improved. A better course arrangement scheme is found through global search, the resource utilization rate is improved, and a scientific and reasonable course time arrangement plan is generated.

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.

A symbolic execution kernel for artificial general intelligence (AGI) and artificial superintelligence (ASI) systems is disclosed. The kernel comprises a cognitive logic module for constraint-based symbolic instruction execution, a cryptographic arbitration engine for ethical branch verification, and a runtime firewall for threat detection and symbolic graph mutation neutralization. Symbolic instructions are processed as constraint-satisfaction problems verified by satisfiability modulo theory solvers and cryptographically sealed for integrity. Behavioral sequences are preserved using Merkle hash trees, and multimodal inputs including electroencephalography signals undergo symbolic verification. Zero-knowledge proofs, dual-kernel consensus, and rollback logic provide resilience against faults and ethical drift. The architecture achieves arbitration within five microseconds and ensures lawful and deterministic execution under hardware or network compromise.

The present application relates to the technical field of intelligent tool parameter optimization, and particularly relates to an intelligent tool adaptive parameter optimization method and system, comprising: acquiring a multi-dimensional parameter set and state data in a running process, constructing a parameter influence relationship graph through causal inference and statistical correlation analysis; performing multi-level influence diffusion simulation based on the graph, identifying sensitive propagation links and oscillation loops, and constructing a constraint space topology; solving a multi-objective constraint satisfaction problem according to a current state and a target constraint, and generating a parameter configuration scheme with causal consistency; applying the scheme to an intelligent tool and collecting feedback, and updating influence weights and coupling coefficients in the graph using reinforcement learning. The present application realizes adaptive and accurate optimization of intelligent tool parameters, and improves running stability and performance.

This invention discloses an adaptive carbon footprint data processing method and system based on the coupling of risk perception feedback and large model inference constraints. The method includes: parsing the metadata of the original data object and calculating a data risk vector containing time deviation and physical conservation deviation; constructing an initial computational topology and transforming data requirements into a constraint satisfaction problem; if no solution is found, triggering topology adaptive reconstruction based on semantic reasoning; dynamically adjusting the inference hyperparameters of the generative model based on the data risk vector, and updating the mapping weights between risk and hyperparameters online using the logical deviation scores generated by subsequent source tracing audits. This invention solves the technical problem of inference illusion caused by high-uncertainty data by establishing a closed-loop coupled control mechanism between data risk and model behavior; and solves the technical problem of static workflows being unable to cope with process interruptions caused by missing data through runtime semantic reconstruction of the computational topology.

In one embodiment, a method comprises: parsing, using a large language model, a constraint satisfaction problem statement into a sequence of sentences corresponding to a plurality of individual problem sentences and a plurality of individual answer choices; invoking respective primitives from a set of logical-linguistic primitive functions to iteratively process i) each of the plurality of individual problem sentences to establish a respective problem constraint with accompanying arguments and ii) each of the plurality of individual answer choices to establish a respective answer choice constraint with accompanying arguments; storing, during invoking, the respective problem constraints into an aggregate list of problem constraints; and solving, after completion of the aggregate list of problem constraints, the constraint satisfaction problem statement as a validity output for each answer choice constraint given the aggregate list of problem constraints.

The invention provides a building design intelligent generation and compliance verification method and system, and the method comprises the steps: obtaining an operation intention or demand from a user from a first user side application or a second user side application, and guaranteeing the conflict resolution and merging synchronization in a data networking state between the first user side application and the second user side application; traversing the knowledge graph according to an operation intention or a demand, performing real-time semantic verification to obtain a reasoning result and a corresponding system action, extracting a constraint condition from the knowledge graph by combining the reasoning result and the corresponding system action when a user triggering scheme generation command is received, establishing a CSP constraint satisfaction problem model, and establishing a CSP constraint satisfaction problem model; a built-in solver is called to solve the CSP constraint satisfaction problem model, an optimization target is extracted according to a user triggering scheme generation command, feasible solutions are screened and sorted, Top-N optimal schemes are output, and the problems that in the prior art, hidden logic conflicts cannot be found based on a traditional rule engine, manual checking is needed, and post conflicts exist are solved.

A computer-implemented method comprising, automatically: analyzing a machine learning dataset which comprises multiple datapoints, to deduce constraints on features of the datapoints; generating a first set of CSP (Constraint Satisfaction Problem) rules expressing the constraints; based on a machine learning model which was trained on the dataset, generating a second set of CSP rules that define one or more perturbation candidates among the features of one of the datapoints; formulating a CSP based on the first and second sets of CSP rules; solving the formulated CSP using a solver; and using the solution of the CSP as a counterfactual explanation of a prediction made by the machine learning model with respect to the one datapoint.

The invention provides an airborne system architecture design method and system based on a constraint satisfaction problem, and belongs to the technical field of airborne system design, and the method specifically comprises the steps that an architecture decision model is constructed, the architecture decision model comprises a plurality of decision items and a plurality of alternative items corresponding to each decision item, and the alternative items are mutually independent; determining a measurement index of the decision item according to a development target of the airborne system; based on a constraint satisfaction problem model, in the architecture decision model, for each decision item, searching options conforming to a preset constraint rule in the alternative items, and obtaining an architecture scheme Pareto leading set; and performing multi-target tradeoff optimization on the architecture scheme Pareto leading-edge set to obtain an optimal architecture scheme. Through the processing scheme provided by the invention, the scientificity and efficiency of the architecture design of the airborne system are improved, and the influence of human factors on the decision result is also remarkably reduced.

This invention relates to the field of forklift control technology, and more particularly to a method and system for managing the lifting motion of a forklift. The method includes: establishing a set of action primitives defined by lifting motion constraints and a height deviation tolerance domain, and constructing a constraint action library; acquiring the constraint vectors of the forklift's external environment and the internal state vectors of the fork mechanism, and performing spatiotemporal registration to generate a comprehensive sensing dataset; generating a candidate action set for forklift height lifting by solving a constraint satisfaction problem model; calculating the risk coefficient of the candidate action set and selecting a target action strategy based on the risk coefficient; executing the target action strategy, synchronously collecting the execution effectiveness vector, adjusting the lifting motion constraints, and updating the constraint action library. This invention effectively solves the problems of lack of motion management and difficulty in rationally selecting and dynamically adjusting lifting motions during the height lifting process of existing forklifts, thus improving the safety, stability, and operational reliability of forklift height lifting operations.

The application discloses a multi-scene linkage enterprise office resource intelligent scheduling and management and control system, comprising a perception layer, a resource graph layer, an intention prediction layer, a scheduling engine layer and an execution control layer, the resource graph layer uniformly abstracts office resources into a space-time multi-modal heterogeneous graph, an online inference cross-domain linkage path is obtained through a graph neural network, global optimal resource linkage scheduling is realized, the intention prediction layer adopts an architecture combining edge cloud federation and differential privacy, edge nodes locally complete perception inference and inject Laplace noise, only an anonymous intention probability vector is uploaded, high-precision resource occupation prediction is realized on the premise of zero personal identifiable information uploading, the scheduling engine layer is composed of a double-track hybrid scheduling engine of a large language model semantic analysis track and a constraint satisfaction problem solving track, absolute exclusivity of resources in a multi-concurrent scene is ensured through optimistic lock atomic verification, and scheduling illusion risk is eliminated, and the application can improve resource utilization rate by more than 30%, and reduce invalid operation time of high-energy-consumption facilities by more than 40%.

The invention discloses an intelligent music creation auxiliary method based on a music theory, and belongs to the technical field of music creation. According to the method, intelligent assistance of a music creation process is realized by fusing multi-modal intention input and a structured music theory rule base, a pre-constructed structured music theory rule base is utilized to dynamically map a user intention into a computable constraint parameter, and on the basis, a problem modeling technology is satisfied through constraint, so that the problem modeling efficiency is improved. A music creation problem is converted into a feasible solution space search problem in mathematics, a music fragment conforming to the intention of a user is efficiently searched in a solution space by means of a constraint propagation and backtracking algorithm, a result is output in a multi-mode form, and meanwhile interpretable feedback of the creation process is provided. By means of the method, music theoretical knowledge and user personalized requirements can be effectively integrated, the accuracy and usability of the generated result are improved, meanwhile, dependence on professional literacy of the user is reduced, and efficient and intelligent music creation assistance is achieved.

The invention provides an intelligent design system and semiconductor automatic optical detection equipment, and relates to the field of artificial intelligence and industrial automation, the intelligent design system comprises a user interaction module, a central main agent and a plurality of professional sub agents; the central main agent analyzes the demand proposal to obtain a constraint satisfaction problem, decomposes the demand proposal into a plurality of professional sub-tasks with a dependency relationship, and distributes the professional sub-tasks to respective corresponding professional sub-agents; the plurality of professional sub-agents respond to respective professional sub-tasks, perform iterative calculation based on respective professional knowledge bases under the negotiation of the central main agent to generate an initial professional scheme conforming to the constraint satisfaction problem, and send the initial professional scheme to the central main agent; the central master agent generates a globally optimal solution based on the initial professional schemes received from the plurality of professional sub-agents. And an intelligent solution for simulating expert thinking and realizing cross-domain intelligent collaboration is provided.

The invention discloses a refrigerator model production scheduling method and device, computer equipment and a storage medium. The method comprises the steps that flexible constraint parameters input by a decision maker are received; constructing a constraint satisfaction problem model based on the received flexible constraint parameters and order data, and solving by adopting a constraint propagation technology to obtain a plurality of feasible solutions; performing multi-target quality evaluation on the plurality of feasible solutions, and screening out a flexible constraint parameter combination according with the preference of a decision maker based on a Pareto non-dominated sorting algorithm; constructing a constraint optimization model based on the screened flexible constraint parameter combination, and solving to generate an initial model scheduling scheme; and optimizing the initial model scheduling scheme by adopting a neighborhood search algorithm to improve an address intensive rate, and outputting a final model scheduling scheme. According to the embodiment of the invention, collaborative optimization of three types of key performance indexes is realized, and a prospective solution is provided for multi-service requirements in a production stage.

The invention relates to the technical field of training resource scheduling, in particular to a training resource management method. The method comprises the steps of firstly constructing a class missing influence analysis directed attribute graph, identifying influence clusters needing collaborative adjustment through a community discovery algorithm, then establishing a constraint satisfaction problem model for each influence cluster, generating a preliminary feasible scheme, and performing optimization by means of a teacher and site preference simulation multi-agent negotiation mechanism to obtain a class missing influence analysis result; and finally, carrying out multi-dimensional quantitative evaluation on the candidate schemes, namely constructing a coordinated dependency network to analyze execution complexity, calculating a failure risk probability by utilizing a Bayesian network, testing a space-time error-tolerant rate by adopting Monte Carlo simulation, comprehensively evaluating student commuting burden of site change, and selecting an optimal scheme through a multi-criterion decision algorithm. According to the method, the whole-process intelligent decision-making from conflict identification, scheme generation and optimization to risk assessment is realized, and the scheduling capability of a training institution for coping with a complex resource interruption event and the scheme reliability are effectively improved.

This invention discloses a vehicle VIN code recognition method and system based on machine vision, comprising: acquiring an original image of the vehicle; locating and geometrically correcting the original image to obtain a standardized image; obtaining initial recognition results and easily confused character positions based on the standardized image; generating a candidate character list for easily confused character positions based on the initial recognition results according to VIN code encoding rules; dividing the initial recognition results using optional check bits and constructing a VIN code encoding rule constraint satisfaction problem; performing hierarchical pruning based on the candidate character list through the constraint satisfaction problem; outputting candidate VIN recognition results based on the pruned list using a backtracking search algorithm; verifying the candidate VIN recognition results to obtain the VIN recognition result. This method utilizes standardized image geometric correction, temporal context feature modeling, and hierarchical constraint pruning to improve the accuracy and real-time performance of VIN code recognition, while also exhibiting good interpretability.

The invention discloses a pet intelligent product supply chain matching method based on a hierarchical AI architecture. The method comprises the following steps: firstly, obtaining a pet intelligent product modularization scheme customized by a user; then semantic enhancement and compatibility check are carried out through a product-manufacturer knowledge graph driven by symbology AI, and an initial manufacturer matching scheme is generated; mining a group reputation index of the similar user group by using a collaborative filtering algorithm in the linking AI; inputting the scheme, the reputation index and a constraint condition set including hardware compatibility, production cost and productivity into a constraint satisfaction problem solver for optimization solution, and outputting a final manufacturer matching scheme conforming to all constraints; and finally, generating a personalized explanation text through the generative AI. Through hierarchical fusion of three AI technologies, full-process automation from user personalized customization to manufacturer intelligent matching is realized, the problems of configuration conflict, insufficient resource integration and opaque decision are effectively solved, and the precision and efficiency of supply chain matching are remarkably improved.

The application provides a heterogeneous system data automatic clearing and cross-database reconciliation method for a travel business scene, comprising: receiving a travel business request, performing operational risk assessment through a reconciliation risk prediction model, and performing joint scheduling control through adaptive traffic peak clipping; based on a pre-constructed travel field ontology, the Schema alignment problem between heterogeneous systems is converted into a constraint satisfaction problem on a factor graph, and the field mapping relationship between the heterogeneous systems is automatically derived; based on the field mapping relationship, the operation causal dependence of the travel business request is tracked using a vector clock causal consistency protocol without a coordinator, and the target heterogeneous system operation is written through direct exchange of difference vector clocks between nodes; the written operation data is subjected to multi-dimensional reconciliation consistency proof, and the actual settlement or transfer of funds is driven, and the automatic clearing is completed. The application realizes cross-system real-time reconciliation and risk prediction, and ensures the strong consistency and auditability of global order data.

Disclosed is an approach for solving arbitrary constraint satisfaction problems. In some embodiments, the approach includes a process to generate a software representation of what is possible based on a system corresponding to the constraint satisfaction problem. The software representation comprises a state machine where different states can be reached using respective transitions or properties which are possible as determined based on a current state of the system and parameters thereof whether global or otherwise.

The invention provides a multi-constraint-driven intelligent arrangement method for rail transit station rooms. Comprising the following steps of S1) analyzing a CAD arrangement base map and extracting semantic information, S2) establishing room configuration information and constructing a classification system, S3) defining decision variables and mathematical modeling, S4) analyzing an evacuation path and calculating an evacuation distance, S5) performing hard constraint modeling, S6) setting a soft constraint optimization objective and constructing a multi-objective function, S7) solving a constraint satisfaction problem, and S8) comparing and selecting multiple schemes and performing visual display. The method has the advantages of being high in automation degree, high in constraint processing capacity, remarkable in optimization effect, high in specialization degree and high in scheme comparison and selection capacity.