14 results about "Selective strategy" patented technology

The invention discloses an intelligent matching recommendation system and method for a hardware supply chain, and particularly relates to the technical field of intelligent matching recommendation. The method comprises the steps that original demand information submitted by a purchaser is analyzed, key fields are extracted, and feature vectors are generated; by comparing current and historical purchase records, a difference field is identified, a two-dimensional vector is constructed according to a change amplitude and a service weight, and difference risks are divided into four levels by using a clustering algorithm. And the system further calculates function dependence, historical co-occurrence and purchase quantity correlation among the materials, and quantifies a strong association relationship. In combination with the difference level and the strong association index, the system calculates a comprehensive urgency degree, and a supplier selection strategy is determined according to the comprehensive urgency degree: when the urgency degree is low, suppliers are globally screened through similarity matching; and when the urgency degree is high, the historical cooperative suppliers are preferentially recommended, so that fine grading of purchase difference risks and intelligent supplier decision making are realized, and the supply chain management efficiency is improved.

The application relates to a power distribution network self-healing regulation parameter solving optimization method and device, computer equipment, a computer readable storage medium and a computer program product. The method comprises the following steps: randomly generating an initial population in a multi-dimensional solution space, dividing the initial population, and continuously performing iterative updating of the population; monitoring the stagnation state and diversity index of the population; in the case that the stagnation state and the diversity index meet preset conditions, triggering at least one improved strategy among a stagnation disturbance strategy based on golden sine segmentation, a dynamic variation and selection strategy based on adaptive distribution, and a diversity maintenance strategy based on congestion degree calculation and niche technology, and updating the population; when the population reaches a preset iteration number or a convergence condition, taking a global optimal solution as a power distribution network self-healing regulation parameter. The method can improve the improved optimization algorithm, and finally enables the power distribution network self-healing regulation, and solves a more optimal and reliable operation control scheme.

The invention relates to the technical field of unmanned aerial vehicle operation of low-altitude economic intelligent logistics, in particular to a multi-distribution-center unmanned aerial vehicle distribution route planning method, which comprises the following steps of: establishing a model taking minimization of a weighted total flight distance, minimization of robust flight time and maximization of a demand satisfaction rate as decision targets; determining a distribution center by adopting a greedy selection strategy of geometric coverage and dispersity priority; and solving to obtain a Pareto frontier solution set. According to the method, the multi-target optimization model containing the flight time, the flight distance and the demand satisfaction rate is constructed, the delivery centers are selected by adopting the greedy selection strategy based on geometric coverage and dispersity, and the Pareto frontier solution set is solved by applying the NSGA-II algorithm, so that the balance of multiple decision targets is realized, the adaptability to a multi-delivery-center scene is improved, and the method has the advantages of being simple in structure, convenient to operate and high in efficiency. The distribution center selection is optimized to improve the coverage range and the service efficiency, and an optimization tradeoff scheme among the service quality, the distribution efficiency and the operation cost is provided for decision makers.

The present invention relates to the technical field of workshop production scheduling, and particularly to a distributed hybrid flow shop scheduling method for rocket tank production. Taking the minimization of the maximum completion time of products as the objective function, a distributed hybrid flow shop scheduling model including the assembly process is constructed. This method designs a genetic algorithm framework with multi-layer coding, and introduces an elite selection strategy and a neighborhood search operator into it. The elite selection strategy ensures that high-quality individuals are retained during the iteration process, accelerating the convergence speed of the algorithm; the network diagram method is used to identify the critical path, and the neighborhood search is carried out on the critical workpieces to optimize the production scheduling bottleneck, improve the quality of the solution, and avoid falling into local optima. The present invention can not only greatly improve the production scheduling efficiency of rocket tanks, but also has good applicability and universality, and can be widely applied to other types of distributed hybrid flow shop scheduling environments.

The present invention belongs to the technical field of cross-border payment settlement analysis and management, and discloses a cross-border payment channel intelligent allocation system based on transaction type; comprising: a type identification module: used for collecting transaction order data, extracting features from the transaction order data to obtain transaction order features, using the transaction order features as input of a type identification model, and obtaining a transaction type label; a risk assessment module: used for collecting risk data, extracting features from the collected risk data to obtain risk assessment features, using the risk assessment features as input of a risk analysis model, and obtaining a risk assessment score; based on real-time data collection technology and stream computing technology, the payment process is monitored in real time, and when the risk level exceeds a preset risk threshold, an early warning mechanism is immediately triggered; the present invention dynamically adjusts the selection strategy of the payment channel according to factors such as real-time market conditions and performance changes of the payment channel, and provides a precise payment channel allocation plan.

The invention provides a distribution box with a fault early warning function, and belongs to the technical field of distribution boxes, and the distribution box comprises a distribution box body, a current sensor, a voltage sensor, a temperature sensor, a frequency detector, a communication interface and a control chip. Three different receptive field convolution kernels of a multi-scale feature extraction layer are used for extracting local feature modes of different time scales in parallel, a comprehensive feature vector is generated through a feature fusion integration layer, and a self-adaptive gating expert network model is operated to carry out specialized fault recognition and probability distribution calculation. A self-adaptive processing frequency adjustment mechanism is executed to dynamically adjust the sampling frequency, an abnormal state detection confirmation mechanism is implemented to avoid misjudgment through continuous verification, a gating weight dynamic adjustment mechanism is implemented to optimize an expert selection strategy, and finally a graded early warning output layer is generated to realize an accurate fault type. The technical problem of insufficient fault early warning accuracy of the power distribution system is solved.

The invention relates to the technical field of credit and loan business approval management, and discloses a credit and loan business approval task automatic distribution system, method and device, and the key points of the technical scheme are as follows: obtaining and configuring a permission setting strategy, a reexamination post selection strategy, an approval post distribution strategy and a permission automatic adjustment strategy; the permission matching strategy is used for setting approval permissions for the tasks and approvers; the reexamination post selection strategy is used for configuring an approver of a reexamination post according to the approval authority of the main reexamination person; the examination and approval post distribution strategy is used for distributing a primary examination person and a reexamination person with matched permissions for a task according to the task and the examination and approval permissions of an examination and approval person; the authority automatic adjustment strategy is used for automatically adjusting the approval authority of the task and the approver according to the dynamic authority parameters; in response to the task allocation instruction, receiving a target task; and according to the target task, calling a permission setting strategy, a reexamination post selection strategy, an approval post distribution strategy and an automatic permission adjustment strategy, and distributing a primary reexamination person and a reexamination person to the target task.

The invention discloses a statistical result release-oriented multi-mode optimization differential privacy mechanism recommendation method, which comprises the following steps of: randomly generating statistical result release data, filtering a part of statistical distribution result release data as a training set, and performing feature space division on the training set according to a specific data mode; setting a utility maintenance target according to the data mode, and evaluating a test result by adopting a utility loss evaluation index to obtain an average utility maintenance target evaluation result; executing a differential selection strategy on the average utility maintenance target evaluation result, and selecting an optimal differential privacy mechanism to obtain a feature space output result; and performing feature space mining based on the training set and the optimal differential privacy mechanism, and selecting the differential privacy mechanism with the highest global near-optimal probability as a recommended differential privacy mechanism. According to the method, the recommendation precision and stability of a differential privacy mechanism in a complex query scene are remarkably improved.

This invention discloses a deep learning-based method for detecting concrete structures, comprising the following steps: acquiring images or video frames from the same area to form an observation dataset; establishing a set of defect hypotheses and constructing evidence items; constructing an observation action primitive library; calculating scores and performing quality gating using an observation quality scoring network based on MobileNetV3-Small; obtaining candidate defect instances through coarse detection; generating candidate regions and type distributions and forming multi-view packages; generating observation sequences and determining trigger states under time budget and reshoot count budget constraints; inputting an improved DUSt3R and outputting a structured evidence package upon triggering; fusing the type distribution and evidence package to determine if the criteria are met and outputting the results; updating the observation sequence if the criteria are not met; generating evidence data packages and updating the primitive selection strategy. This invention improves detection reliability, measurement consistency, and result traceability, and is suitable for intelligent detection scenarios of concrete structure defects.

The invention discloses a multi-resource scheduling method, device and system and a storage medium, and relates to the technical field of computer software. Independently maintaining an allocation unit priority queue and an interval index according to a resource dimension, and adopting'priority to expected allocation unit / allocation band 'for mapping and band reservation quota; when a scheduling task is accessed online, interval overlapping query is performed, and candidate allocation units are screened in combination with conditions such as time, resources, priorities, weights, capacity and retention constraints; a self-adaptive allocation unit selection strategy with expected allocation band priority is adopted, self-adaptive preferential allocation selection is carried out in an expected allocation band, when no candidate exists in the band, the candidate overflows to an adjacent allocation band or a new allocation unit, and limited preemptible / delayed is supported in a hard constraint scene; the strategy is updated, online learning and optimization are carried out based on historical conflicts and the band utilization rate, and the method is suitable for time sequence arrangement and visualization of generalized scheduling items while data are efficiently processed.

The application provides a distribution box with a fault early warning function, and belongs to the technical field of distribution boxes.The distribution box comprises a distribution box body, a current sensor, a voltage sensor, a temperature sensor, a frequency detector, a communication interface and a control chip.Eight-dimensional time sequence data vectors are collected, preprocessed, and the local feature patterns of different time scales are extracted in parallel by using three different receptive field convolution kernels of a multi-scale feature extraction layer.A comprehensive feature vector is generated by a feature fusion integration layer, a specialized fault recognition and probability distribution calculation are performed by running an adaptive gating expert network model, a sampling frequency is dynamically adjusted by executing an adaptive processing frequency adjustment mechanism, a false judgment is avoided by implementing an abnormal state detection confirmation mechanism through continuity verification, an expert selection strategy is optimized by implementing a gating weight dynamic adjustment mechanism, and finally a hierarchical early warning output layer is generated to realize accurate fault types, thereby solving the technical problem of insufficient accuracy of fault early warning of a power distribution system.

The application discloses a cigarette retail order automatic packing method based on a hybrid adaptive genetic spotted hyena optimization algorithm, which comprises the following steps: step one, planning a general design flow of the cigarette automatic packing, and clearly defining an optimization target, variables, constraint conditions and the interrelation among various size data; step two, establishing a multi-objective optimization model with priority for the actual cigarette packing problem, and quantitatively expressing a plurality of constraint conditions in the actual operation flow; step three, improving the spotted hyena optimization algorithm by using an improved adaptive genetic algorithm, a chaos initialization and a roulette wheel selection strategy, and obtaining the best scheme of the cigarette retail order automatic packing by solving the cigarette packing model through the obtained hybrid adaptive genetic spotted hyena optimization algorithm. The application example provides an optimization algorithm for the cigarette retail order automatic packing, can solve the problems of large space waste, large number of cigarette packs and large consumption of packing materials in the traditional packing method, and provides effective technical support for the cigarette retail order automatic packing and other box-type cargo packing.