31 results about "Constraint programming" patented technology

The present invention provides systems and methods for dynamic detection and prevention of electronic fraud and network intrusion using an integrated set of intelligent technologies. The intelligent technologies include neural networks, multi-agents, data mining, case-based reasoning, rule-based reasoning, fuzzy logic, constraint programming, and genetic algorithms. The systems and methods of the present invention involve a fraud detection and prevention model that successfully detects and prevents electronic fraud and network intrusion in real-time. The model is not sensitive to known or unknown different types of fraud or network intrusion attacks, and can be used to detect and prevent fraud and network intrusion across multiple networks and industries.

A novel approach to test-suite reduction based on network maximum flows. Given a test suite T and a set of test requirements R, the method identifies a minimal set of test cases which maintains the coverage of test requirements. The approach encodes the problem with a bipartite directed graph and computes a minimum cardinality subset of T that covers R as a search among maximum flows, using the classical Ford-Fulkerson algorithm in combination with efficient constraint programming techniques. Test results have shown that the method outperforms the Integer Linear Programming (ILP) approach by 15-3000 times, in terms of the time needed to find the solution. At the same time, the method obtains the same reduction rate as ILP, because both approaches compute optimal solutions. When compared to the simple greedy approach, the method takes on average 30% more time and produces from 5% to 15% smaller test suites.

A multi-day and multi-person trip planning system comprises a planning graph of nodes interconnected by transit arcs which both have associated time-variable costs and time. A user specifies user objectives within trip destinations and customizes a plurality of user preferences for a multi-day, multi-person, multi-modal trip. An optimizer compiles a plurality of permutations of visiting plans within the planning graph which satisfy the specified user objectives including routing to a plurality of trip destinations. The optimizer compares the permutations using mixed integer programming or constraint programming to identify at least one optimized visiting plan having a lowest aggregate associated cost or shortest travel time that also satisfies a plurality of constraints determined by the customizable user preferences or environment condition. Then the user interface presents at least one optimized visiting plan to a user or route related coupons.

The invention provides a power transformer risk assessment method based on a fault tree. The power transformer risk assessment method comprises the following steps that 1) according to the given fault tree, fuzzy judgment matrixes R are built by using the fuzzy analytic hierarchy process; 2) according to the fuzzy judgment matrixes R, a constraint programming problem equation set is written out, wherein the severity weights omega i of various fault causes are obtained according to the constraint programming problem equation set, and the severity weights vector W satisfying the equation that W=[omega 1, omega 2,...omega n](T) is obtained; 3) according to the severity weights vector W, the optimal solution of the severity weight omega i enabling an adaptive value Z to be minimum is obtained; 4) the optimal solution of the severity weight omega i is substituted into the formula (4) to obtain the risk coefficients of the fault causes of a transformer. According to the power transformer risk assessment method, matrixes achieving fuzzy judgment consistency do not need to be built, and risk of the transformer can be assessed under the circumstance that the fuzzy judgment matrixes are not consistent; compared with the method in which the matrixes achieving fuzzy judgment consistency have to be built in the prior art, the power transformer risk assessment method is simple in algorithm, rapid and relatively high in precision.

The invention discloses a demand response modeling and reliability evaluation method for a load aggregator, and the method comprises the steps: firstly building a response uncertainty model of a userinterruptible load based on a fuzzy mathematic theory and consumer psychology; secondly, determining interruptible load parameters of resident users and demand response task parameters of a load aggregator; further, establishing a demand response decision model of the load aggregator based on a fuzzy opportunity constraint programming theory; and finally, defining a demand response reliability index of the load aggregator, and respectively solving corresponding evaluation results. According to the method, the load aggregator can be helped to make a reliable demand response decision under the condition of considering the demand side resource response uncertainty, and the demand response reliability level of the load aggregator is effectively quantified and evaluated, so that reference can be provided for research and development of domestic load aggregators.

The invention discloses a circuit breaker reliability evaluation method based on a glowworm optimization algorithm. The circuit breaker reliability evaluation method comprises the following steps: determining a main fault mode of the circuit breaker; establishing a circuit breaker fault tree; establishing a fuzzy judgment matrix between the fault factors of the circuit breaker; converting a fuzzy consistency qualification used for solving each fault factor weight into a constrained optimization problem; according to an established constraint programming problem equation, importing the glowworm optimization algorithm to solve the severity weight of each fault factor; and establishing a circuit breaker reliability evaluation model to carry out reliability evaluation and obtain a circuit breaker reliability evaluation result. When the fuzzy judgment matrixes are inconsistent, circuit breaker reliability evaluation can be carried out, and the practicability of the method is improved. In addition, when the circuit breaker reliability evaluation method calculates the severity weight of each fault factor, the glowworm optimization algorithm is introduced. Compared with the traditional calculation method, the circuit breaker reliability evaluation method has the advantages of being high in convergence precision and high in convergence speed and can more simply and quickly realize circuit breaker reliability evaluation.

Systems and methods to define a scenario of conditions comprising the steps of defining at least one condition for at least one educational objective, the at least one condition being represented by a constraint and scheduling the conditions into a scenario of conditions. In some embodiments, the scheduling is performed by analyzing the constraints using constraint programming. In some embodiments, the constraints comprise mathematical or computational constraints representing a range of variables. Also disclosed are systems and methods to monitor a scenario of conditions.

A design engine systematically explores a design space associated with a design problem related to mechanical assemblies. The design engine implements a constraint programming approach to produce mechanical assembly configurations that adhere to a set of design constraints. For each feasible configuration, the design engine then optimizes various parameters to generate design options that meet a set of design objectives. With these techniques, the design space can be explored very quickly to generate significantly more feasible design options for the mechanical assembly than possible with conventional manual approaches. Accordingly, numerous design options can be generated that may otherwise never be produced using those conventional approaches.

It is difficult to select parameter values for constraint programming problem solvers which will yield good performance. Automated tuning of such problem solvers on a per problem instance basis may be used and this involves learning a function for predicting the runtime of a problem solver depending on parameter values of the problem solver and features of the problem instance being solved. However, it takes time for such prediction functions to be learnt, either during operation of a problem solver or offline, using specified examples. To address this, information about such a prediction function is shared between two or more problem solvers to improve performance. A sharing system may be used to receive prediction function information and send this to problem solvers.

The invention relates to an energy storage power station dynamic configuration method, system and device considering uncertainty, and the method comprises the steps: S100, building a wind power output model based on a fuzzy opportunity constraint programming method, and building a photovoltaic output model based on an uncertainty set; s200, determining a locating and sizing strategy of the energy storage power station by adopting a PV curve; s300, considering the randomness of the user demand response, and establishing a price-based user demand response uncertainty model; and S400, establishing an energy storage dynamic planning model based on the constructed wind power output model, the photovoltaic output model, the user demand response uncertainty model, the addressing and sizing strategy and the corresponding relationship between the power grid energy storage mode and the user demand response, and solving the energy storage power station configuration mode based on the energy storage dynamic planning model. According to different seasons, time periods and power grid operation scene replacement conditions, the energy storage configuration mode is dynamically updated, energy storage devices are increased and decreased in time, flexible distribution and dynamic layout of energy storage are achieved, and the stability of the system is improved.

Embodiments presented herein provide techniques for executing a block-based (BB) workflow to solve a constraint programming (CP) model related to a semiconductor manufacturing environment. Embodiments include receiving at least one BB workflow comprising a plurality of blocks. The plurality of blocks may specify a set of operations. Embodiments include accessing a plurality of block definitions corresponding to the plurality of blocks. Embodiments include executing the at least one BB workflow by performing the set of operations based on the plurality of block definitions, including extracting data from the semiconductor manufacturing environment, the data comprising both static data and dynamic data related to equipment in the manufacturing environment, creating the CP model based on the data and at least one constraint defined in the BB workflow, using a solver to determine a solution to the CP model; and publishing the solution to at least one component in the semiconductor manufacturing environment.

The invention relates to a relaxation method for optimization of an intelligent manufacturing controller unit based on an equilibrium constraint programming problem. The relaxation method can solve a C-stable point of MPVCC mathematical programming including vertical complementarity constraints and no longer assumes a KKT point, and an algorithm conforms to a termination criterion. The method is based on t-MPVCC mathematical programming, utilizes an MPVCC-MFCQ constraint specification to solve an approximate KKT point, replaces the KKT point with the approximate KKT point to solve an MPVCC programming problem and proves that a theoretical result is true under the MPVCC-MFCQ constraint specification, the algorithm is effective, and the method solves a problem of controlling computation of application software.

The present invention provides a calculation task assignment method and system of a community cloud system. The method includes the steps of: obtaining the number of available on-board computers, the computing capability of each on-board computer, and a plurality of calculation tasks that need to be allocated; according to each on-board computer The calculation tasks undertaken and the transmission power of the transmission calculation tasks between the data center and each on-board computer are established using a single-objective multi-constraint programming method to establish a mathematical model of the objective function and constraints; a single-objective multi-constraint genetic algorithm is used to solve the mathematical model To obtain the optimal calculation task allocation scheme; according to the optimal calculation task allocation scheme, the calculation tasks are allocated to the corresponding on-board computer for processing. Using the idle on-board computer in the parking lot in the community, part of the computing tasks in the data center are handed over to the on-board computer for processing, so that the design of the data center does not need to be based on the maximum demand for access in the community, thereby avoiding the need for community cloud system hardware resources. Waste of vehicle computer hardware resources.