16results about How to "Efficient search" patented technology

The application belongs to the field of workshop scheduling, and particularly discloses a semiconductor wafer probe test scheduling method and system based on an improved genetic algorithm, which adopts double-layer process-machine coding, and combines auxiliary resource taboo coding as an individual, effectively converts a complex scheduling problem into a coding form through an innovative coding and decoding strategy, and avoids traversal search on all selectable auxiliary resources; global-local hybrid search is carried out through the improved genetic algorithm, on the basis of machine level exchange with the traditional critical path-key block, search on the auxiliary resource immediately preceding work is increased, and neighborhood exploration is carried out based on a load balancing strategy, so that the search efficiency and solution quality are improved. The application can quickly and efficiently solve the semiconductor wafer probe test scheduling scheme, effectively improve the equipment utilization, speed up the scheduling efficiency, and break through the bottleneck of traditional scheduling.

The utility model discloses a kind of optical fiber sensing formula light-curing lamp of deformability, it is related to medical instrument technical field, including handle, the outer surface of handle is fixedly installed with lamp pole, the outer surface of handle is fixedly installed with display screen, the side of handle away from lamp pole is fixedly installed with charging end;The inside of lamp pole is provided with the adjusting mechanism of angle convenient to adjust, the adjusting mechanism includes the optical fiber light guide stick of deformability, the optical fiber light guide stick is movably installed in the outer surface of lamp pole.The optical fiber sensing formula light-curing lamp of deformability, angle adjustment is carried out by control switch groove silk drive optical fiber light guide stick, and optical fiber light guide stick is installed with sensor, the distance between real-time sensing and oral cavity interior photosensitive resin is facilitated, to provide position reference for medical staff, and angle adjustment is carried out by the optical fiber light guide stick of deformability, more accurate irradiation to oral cavity interior is realized, reduce the operation time of doctor, guarantee the solidification effect to photosensitive resin.

This invention provides a method for predicting the dominant frequency of blasting vibration by integrating parameter fluctuation processing and the Dream Optimization Algorithm (DOA), belonging to the field of underground engineering safety control technology. The system includes a data acquisition and uncertainty modeling module, a data preprocessing and feature construction module, a hyperparameter optimization module, and a prediction model training and result output module. The method obtains relevant parameters through on-site investigation and monitoring, and generates an extended sample set using a combination of probabilistic perturbation modeling and fuzzy triangular modeling. The data is preprocessed and features are selected. The DOA algorithm is used to globally search the hyperparameters of the Support Vector Regression (SVR) model, and the optimal hyperparameter combination is selected by combining a robustness fitness function. The SVR model is trained, and prediction results and uncertainty prediction intervals are generated. This invention can explicitly characterize the uncertainty of geological parameters, achieve efficient global optimization of hyperparameters, improve prediction accuracy, robustness, and generalization ability, and is applicable to different geological conditions and blasting scenarios. It can be extended to various blasting dynamic response prediction tasks.

This invention belongs to the field of marine engineering antifouling material technology and artificial intelligence optimization technology. It discloses a collaborative optimization method for the antifouling stability and safety of antifouling materials used in debris-blocking nets, comprising the following steps: Step 1, collecting multi-dimensional data and preparing samples of the antifouling materials for the debris-blocking nets; Step 2, constructing and training an AI prediction model: selecting key feature parameters from the multi-dimensional data, using these key feature parameters as core feature vectors to construct a dual-objective prediction model, which outputs predicted values ​​for the material's antifouling stability index and environmental safety index; Step 3, collaborative optimization under multiple constraints: setting multiple constraints for the material; using the hybrid meta-heuristic algorithm ALO-KHO to solve the multi-objective optimization problem; selecting the optimal solution from the Pareto optimal solution to obtain the parameter combination of the antifouling materials for the debris-blocking nets with the highest score. The prediction model of this invention has high accuracy, strong generalization ability, and significant multi-objective collaborative optimization effect.

The invention discloses a photovoltaic MPPT (Maximum Power Point Tracking) control algorithm, which relates to the technical field of photovoltaic control algorithms, utilizes the characteristics of simple structure, high convergence speed, high global optimization capability, wide applicability and the like of a gradient optimization algorithm, and improves the defects of premature convergence and sensitivity to control parameters of the gradient optimization algorithm. Meanwhile, the photovoltaic output energy efficiency and the stability of the photovoltaic output energy efficiency serve as the evaluation basis, an MPPT algorithm performance evaluation model is constructed through an analytic hierarchy process, photovoltaic MPPT performance is evaluated, and GTO performance is verified and improved.

This invention discloses a method, system, and device for detecting weak seismic signals based on instantaneous phase analysis, relating to the field of reservoir prediction. The method includes: 1. Inputting a three-dimensional seismic data volume; 2. Extracting the amplitude data of the current seismic trace and recording it as a one-dimensional array; 3. Performing a -90° phase shift on the one-dimensional array to obtain a phase shift array; 4. Performing a time-frequency continuous wavelet transform on the phase shift array to obtain multiple common-frequency amplitude arrays; 5. Performing a Hilbert transform on each common-frequency amplitude array to obtain the corresponding instantaneous phase array; 6. Calculating the second derivative of each instantaneous phase array with respect to the time variable n to obtain a three-dimensional data volume indicating weak signals at the current seismic trace's common frequency; 7. Changing the seismic trace and repeating steps 2-6 to obtain multiple three-dimensional data volumes indicating weak signals at the common frequency; 8. Selecting the optimal weak signal indicator data volume. This invention can effectively find the three-dimensional data volume indicating weak signals, improving the accuracy of detection.

The application discloses an artificial intelligence psychological evaluation method and system based on multi-modal input, and belongs to the technical field of artificial intelligence, and solves the problem that the existing system does not consider individual differences of the tested person to generate a customized evaluation strategy, resulting in insufficient evaluation targeting of the tested person, the method comprises the following steps: generating a personalized evaluation psychological evaluation strategy of the tested person based on basic information of the tested person, collecting multi-modal evaluation information of the tested person in real time, and identifying and analyzing a comprehensive state characteristic vector based on a pre-constructed psychological evaluation model; the application utilizes basic information to construct an initial feature portrait, uses test appeal as a constraint, adopts a collaborative attention network to generate strategy demand information, and individually adjusts item priorities based on an adjusted feature portrait, so that an evaluation strategy that adapts to individual cognitive preferences and emotional characteristics is generated, different individual differences can be adapted to, and implicit and defensive psychological problems of the tested person can be accurately captured, and the accuracy and efficiency of psychological evaluation are improved.

The application relates to the technical field of network security, and discloses an encrypted traffic identification system and method based on multi-modal fusion, which realizes intelligent type identification of network attacks on encrypted traffic in network communication by combining different network attack type standard encrypted traffic abnormal connection interaction feature information established by combining artificial intelligence algorithms and standards with network attack type standard encrypted traffic abnormal connection interaction feature information; scientific detection of network attack types on encrypted traffic in network communication is realized by combining different network attack type standard encrypted traffic abnormal connection protocol feature information established by combining data analysis and science, multi-level intelligent analysis of network attack types based on communication time sequence features, communication packet size features, communication traffic statistical features, communication connection interaction features and communication connection protocol features of encrypted traffic is realized, and intelligent and efficient supervision of communication network anomalies based on multi-modal behavior features of encrypted traffic is realized.

This paper proposes a baseline-length-constrained high-precision relative positioning method for BeiDou, comprising the following steps: First, establishing a double-difference relative positioning model using the pseudorange and carrier phase raw observations of the reference station and the rover station; Second, obtaining a strictly baseline-length-constrained objective function based on the double-difference relative positioning model according to the least squares criterion; Third, employing an integer ambiguity search space amplification strategy to achieve effective ambiguity search, and accurately estimating the rover station's position relative to the reference station. This invention utilizes the often underutilized baseline length information of dual antennas in real-world scenarios to improve the relative positioning solution performance, exhibiting higher reliability compared to the traditional unconstrained case. The method employs an integer ambiguity search space amplification strategy, thereby extending the standard LAMBDA algorithm to baseline-length-constrained relative positioning, achieving effective integer ambiguity search.

The application relates to a kind of unmanned aerial vehicle auxiliary-based vehicle task unloading method, its steps are: first, the task queue model, calculation model, communication model of vehicle, roadside unit and unmanned aerial vehicle and the energy consumption and collection model of unmanned aerial vehicle are constructed;Second, under the constraint conditions such as meeting task queue stability, long-term energy sustainability of unmanned aerial vehicle, the optimization problem with the minimum long-term average total task processing delay of system as target is constructed, and the long-term optimization problem is converted into deterministic single time slot optimization problem using Lyapunov optimization;Finally, in each time slot, according to the current system state, the deterministic optimization problem is solved using genetic algorithm, so that the task unloading and unmanned aerial vehicle path planning scheme of current time slot are obtained.The method can effectively improve the task processing efficiency and guarantee the system queue stability, reduce the task processing delay, and thus improve the overall performance of the vehicle edge computing network in the dynamic uncertain environment combined with the Lyapunov optimization framework.

The invention discloses a cyclone feeding concentration prediction method, relates to the field of iron ore grinding and separation feeding concentration prediction, and aims to solve the problem that an ore grinding process cannot be effectively optimized in real time due to the fact that ore grinding parameters are difficult to measure in real time in the current ore separation production process. According to the method, a lion group optimization algorithm is improved, the global search capability is improved by changing a lion king updating mode and introducing an attenuation factor, the problem that the algorithm is trapped in a local optimal solution is avoided, a dynamic learning strategy is introduced for a parent lion and learning lion updating mode, and the local search capability is optimized; the performance of the lion group algorithm in a complex prediction task is effectively improved, and the limitation of a traditional algorithm in solving a high-dimensional complex problem is avoided.

The present disclosure provides a layout structure determination method and device, equipment and a storage medium, relates to the technical field of computers, in particular to the technical field of quantum computers, quantum chips and quantum simulation. The specific implementation scheme is: obtaining a target search range of a geometric parameter of a chip layout structure to be designed based on a target parameter required by the chip layout structure to be designed; the target search range represents a value range of the geometric parameter of the chip layout structure to be designed; the value of the geometric parameter can affect a capacitance parameter of the chip layout structure; obtaining a target value of the geometric parameter of the chip layout structure to be designed in the target search range based on the target parameter; obtaining a target chip layout structure based on the target value of the geometric parameter; the value of the geometric parameter of the target chip layout structure is the target value, and difference information between a simulation parameter corresponding to the target chip layout structure and the target parameter satisfies a preset difference requirement.

The application discloses a single-page application search method, device and equipment and a computer storage medium. The single-page application search method comprises the following steps: receiving a hypertext markup language (HTML) file and data resources of a single-page application of a target user; parsing the HTML file to obtain a document object model (DOM) tree, and loading a JavaScript file relied on by the HTML file; executing the JavaScript file, replacing corresponding nodes in the DOM tree with the HTML file and the data resources of the target user, and generating a first static HTML resource; calling an anchor point parser, replacing anchor point links in the first static HTML resource with non-anchor point links, and obtaining a second static HTML resource; and sending the second static HTML resource to a gateway. In this way, the network crawler can comprehensively capture the content of the single-page application, and the search engine can effectively search the related content of the single-page application.

A rapid radioactive source location device and method are disclosed, relating to the field of nuclear radiation detection technology. The rapid radioactive source location device includes a housing assembly, a collimator assembly, a camera, and Geiger tube nuclear radiation sensors. The housing assembly includes a top cover and a base. The collimator assembly includes collimator A and collimator B. The camera includes a lens and an active pixel sensor. The lens slides into the inner hole of collimator A. The active pixel sensor is located inside the lens and faces it. Multiple Geiger tube nuclear radiation sensors are slidably installed in the inner holes of each collimator B. The advantages of this invention are that it utilizes the high sensitivity of the Geiger tube nuclear radiation sensor at extremely low radiation intensities and the radiation response of the active pixel sensor at low to high radiation intensities, meeting the wide-range detection requirements for searching and locating radioactive sources, and enabling radioactivity level detection in various complex environments.

The application discloses a kind of multi-agent multi-objective optimization workshop scheduling method based on man-machine cooperation.First, three objective functions including process sequencing, machine selection, speed selection and worker selection are constructed to optimize maximum completion time, total energy consumption and machine load balancing.Second, a distributed constraint processing mechanism based on local resource agent (LRA) is designed, and each LRA autonomously decides speed and worker allocation based on local resource state to ensure the feasibility of the scheduling scheme.Finally, a hierarchical multi-agent evolutionary algorithm is used to evolve collaboratively between global planning layer and field execution layer, and efficiently search for the Pareto optimal solution set through distributed evaluation, environment selection and adaptive policy adjustment.The application overcomes the problems of single resource constraint, rigid processing model and limited optimization objectives in traditional scheduling, achieving multi-objective collaborative optimization under double resource and multi-speed constraints, and significantly improving the greenness, balance and efficiency of production scheduling.

The invention provides an automatic virtual coronary intervention planning method and device based on deep reinforcement learning, and the method comprises the following steps: obtaining a geometric model and physiological parameters of a target coronary artery, and generating a state vector representing a blood vessel environment based on a state encoder; according to the state vector, outputting an action vector based on an Actor network in an Actor-Critic model of Bayesian optimization; calculating a corresponding hemodynamic index based on the coronary artery virtual implantation simulation environment according to the action vector and the geometric model, and calculating a reward value according to the index; according to the reward value, the action vector is evaluated based on a Critic network, and parameters of an Actor-Critic network model are updated; and iteratively executing the steps of state vector generation, action vector output, reward value calculation and model parameter updating until a preset convergence condition is reached, and outputting a final stent implantation planning scheme. Through interaction of a state-action-reward-update circulation mechanism of reinforcement learning, output of an optimal implantation scheme of the coronary stent is realized.