132 results about "Algorithmic efficiency" patented technology

The invention relates to the technical field of natural language processing, and discloses a cross-domain vocabulary logical reasoning and extension method. According to the method, a multi-source cross-domain text data stream is received, cross-language alignment and semantic fusion are performed through a pre-trained semantic decoupling model, an adaptive semantic extension threshold model and a causal reasoning framework are constructed, logic interruption nodes are identified, a compensation strategy is configured, and collaborative parameters are iteratively optimized by using a distributed semantic collaborative learning framework. Multi-source data can be effectively integrated, semantic barriers can be broken, the vocabulary expansion range and mode can be accurately controlled, logic transmission coherence and accuracy are ensured, efficient collaboration of multi-language agents is achieved, algorithm efficiency and semantic accuracy are improved, the problem of semantic instability is monitored in real time, the problem of semantic instability is solved, and powerful support is provided for cross-domain natural language processing.

The invention relates to the technical field of electrolytic hydrogen production, and discloses a PEM electrolytic cell energy-economy collaborative optimization method based on digital twinning, which comprises the following steps: S1, calibrating parameters of a membrane electrode mathematical model by using a standard test for a single cell of a PEM electrolytic cell, and establishing a high-precision physical simulation model; s2, constructing a high-precision digital twin system based on the high-precision physical simulation model; s3, inputting multi-dimensional data of electricity price fluctuation, hydrogen demand prediction and equipment state, and performing collaborative optimization analysis through an optimization decision algorithm; s4, forming a PEM electrolytic cell control optimization instruction; and S5, the physical entity PEM electrolytic bath executes the instruction. According to the method, the'electricity price-demand-equipment 'dynamic relation is planned as a whole from the global perspective, the process steps cover data-driven modeling, multi-target decision, digital twinning cooperation and closed-loop feedback, balance needs to be achieved among algorithm efficiency, model precision and engineering feasibility, and energy-economy-life multi-target cooperation is achieved.

The invention provides a power grid energy efficiency optimization and load prediction management method and device, relates to the field of energy and power, and solves the technical problem of data storage and processing delay caused by poor database expansibility and low algorithm efficiency. The method comprises the following steps: integrating various sensor data and intelligent sensing terminal data to form multi-dimensional data; and sending the multi-dimensional data to an edge computing node for preprocessing, and storing the multi-dimensional data in a distributed storage cluster. Load data and multi-dimensional influence factors in the multi-dimensional data stored in the historical distributed storage cluster are acquired, and a prediction model is constructed. And analyzing the multi-dimensional data stored in the distributed storage cluster through an artificial intelligence analysis engine, capturing nonlinear and non-stationary change characteristics of the load, and obtaining real-time prediction data. And acquiring real-time operation data of each link of the power grid from the distributed storage cluster, and optimizing a scheduling strategy in combination with a dynamic loss characteristic model of the equipment. The method and the device are used in the power dispatching process.

The invention relates to the technical field of the Internet of Things, in particular to an edge computing privacy task unloading method, system and device based on reinforcement learning, and the method comprises the steps: obtaining computing task information and task unloading selection generated by each terminal device, and the task unloading selection comprises task selection local execution or task selection unloading to an edge server, local execution cost or unloading cost of each task is obtained based on the calculation; performing statistics to obtain the total cost of all tasks, and establishing an initial global optimization target; the privacy entropy of the task is calculated, and the privacy entropy of the task is used as unloading additional cost to be embedded into the initial global optimization target to update the global optimization target; and searching an optimal strategy of the updated global optimization target by adopting a single-agent reinforcement learning algorithm. The privacy guarantee strength and the dynamic scene adaptability are improved while the algorithm complexity is greatly reduced and the algorithm efficiency is improved, and the method can be suitable for large-scale node, high-dynamic and strong-privacy-constraint application scenes in edge calculation.

The invention discloses an electromagnetic metasurface sparse signal modeling method and device. The method comprises the steps that S1, sparse electromagnetic signals of an electromagnetic metasurface are acquired; s2, solving through signal sparse prior knowledge under the constraint of an electromagnetic constitutive equation to obtain a sparse signal distribution function; s3, according to the sparse signal distribution function, obtaining the coordinate system representation of the sparse signal in the Riemannian manifold space; and S4, according to the coordinate system representation of the sparse signal in the Riemannian manifold space, establishing a signal electromagnetic field manifold embedding model of the sparse signal mapped in the Riemannian space based on the electromagnetic metasurface. By the adoption of the technical scheme, the problems of an existing electromagnetic metasurface sparse signal modeling method in the aspects of algorithm efficiency, model adaptability and hardware collaboration are solved.

The invention discloses a CMIES low-carbon operation scheme generation method based on variable reduction multi-objective evolutionary optimization, and belongs to the technical field of integrated energy systems. Aiming at the multi-target complex constraint characteristic of the CMIES system, a dynamic variable reduction mechanism driven by constraint correlation is provided, key variables are accurately recognized through correlation analysis and constraint gain indexes, and the number of feasible solutions and constraint processing efficiency are effectively improved; a two-stage coevolution strategy is adopted to be fused with random reduction and dynamic analysis, global exploration and local development capacity is balanced in combination with a main and auxiliary population multi-strategy cooperation mechanism, and a Pareto solution set which is high in quality and uniform in distribution is generated; by constructing a low-dimensional auxiliary optimization problem and an inherited updating mechanism, the calculation complexity of a high-dimensional space is reduced, and the algorithm efficiency is remarkably improved while the quality of a solution set is ensured.

According to the safety coprocessor hybrid encryption method based on the SM2 / 3 / 4 domestic cryptographic algorithm, a novel hybrid encryption system based on the SM2 / 3 / 4 and considering encryption safety, efficiency and key management convenience is constructed on the basis of the characteristics of the SM2 algorithm, the SM3 algorithm and the SM4 algorithm, and the encryption / decryption and signature / verification process is achieved. And aiming at the efficiency problem of a domestic SM algorithm, an encryption / decryption and signature / verification scheme is designed and realized through pure software analysis and software and hardware (SW / HW) collaboration, so that optimal division of software and hardware is realized, and the algorithm efficiency is greatly improved.

The invention discloses a kitchen waste collection and transportation path optimization method based on an improved simulated annealing algorithm, belongs to the technical field of intelligent optimization and path planning, and mainly solves the technical bottlenecks of an existing kitchen waste collection and transportation path planning method in the aspects of dynamics, multi-objective optimization and algorithm efficiency. The method comprises an application layer, an algorithm layer and a data layer, and by fusing mixed neighborhood search, a dynamic punishment mechanism and a parallel computing architecture, the timeliness, the economical efficiency and the environmental protection property of path planning are remarkably improved. According to the method, real-time traffic and corruption risks can be dynamically responded, multi-target optimization weights are quantified, different city management strategies are adapted, the problems that a traditional algorithm is low in efficiency and prone to falling into local optimum are solved through multi-chain parallel acceleration and self-adaptive temperature control, and city-level large-scale node real-time scheduling requirements are supported.

The invention discloses a multi-target road transportation path planning method and system based on multiple parameters A * and NSGA-II, and belongs to the technical field of road transportation path optimization. The method comprises the following steps: constructing a large-scale road network directed graph structure; establishing a satellite orbit model and a risk distribution model, calculating a dynamic risk value of a roadside, and forming dynamic road network data; a three-stage initialization strategy is adopted to generate an initial population, transportation time and reconnaissance risks are taken as double targets, evolutionary optimization is carried out through an NSGA-II algorithm, and a Pareto optimal path set is output. The system comprises a corresponding road network module, a risk module and a planning module. According to the method, the technical problems of multi-target conflict, low initial population quality and satellite risk avoidance of a traditional method are solved, and the timeliness, the concealment and the algorithm efficiency of path planning are remarkably improved.

The invention relates to the technical field of panel processing, in particular to a panel furniture workpiece typesetting method based on an improved genetic algorithm, which comprises the following steps: acquiring parameter information of a to-be-typeset workpiece; and performing classification based on the parameter information of all the workpieces to be typeset to obtain a plurality of workpiece categories, endowing each workpiece category with a unique type code, constructing an initial population according to the category and the number of the workpieces to be typeset, and inputting the initial population into the improved genetic algorithm to obtain an optimal chromosome. According to the method, the workpieces with the same size and shape are classified into workpieces with the same specification, and a repeated gene coding mode is adopted, so that the defect that actual repeated chromosomes are generated when a plurality of workpieces with the same specification are processed in a traditional unique coding mode is effectively avoided, and the algorithm efficiency is improved. And secondly, in the gene crossover operation, an improved ordered crossover and order crossover strategy is introduced, and a random deletion mechanism is combined, so that the algorithm population diversity and the solution space coverage are effectively improved, and the search divergence and convergence efficiency are improved.

The invention discloses an unmanned aerial vehicle cluster task allocation method based on a large language model optimization genetic algorithm, and belongs to the field of computers. The method comprises the following steps: setting a specific chromosome coding mode; generating a multi-constraint initial population; calculating fitness to quantify the advantages and disadvantages of individual genes of the population; when the optimal individual meets the requirement or the maximum iteration round is reached, ending; retaining the optimal individual as a filial generation; generating a batch of new filial generation individuals by the large language model, and fusing the new filial generation individuals with the current filial generation population; calling an optimized large language model to analyze individual chromosome semantics, and outputting an evolutionary potential score; obtaining an individual comprehensive selection probability by integrating the fitness and the evolution potential score, and executing a selection operation; and selecting individuals based on the individual comprehensive selection probability to carry out crossover and mutation operation to generate offspring. The large language model is embedded into the core link of the genetic algorithm, and the algorithm efficiency is improved by improving the population diversity of the genetic algorithm in the unmanned aerial vehicle cluster task allocation scene.

The invention discloses a tractor trailer robot track generation method based on a polynomial and a symbolic distance field, and the method comprises the steps: dividing grids through a point cloud map, estimating the curvature and normal direction of each grid, extracting obstacle information, and calculating a symbolic distance value corresponding to each grid; taking the initial pose of the tractor trailer robot as a root node, and based on the obstacle information, utilizing a multi-end-point heuristic graph search algorithm for spatial search in SE (2) to generate an initial path for arriving at the target area; and in combination with the symbol distance value corresponding to each grid, modeling the trajectory generation of the tractor trailer robot as a constraint optimization problem, and solving the constraint optimization problem by taking the initial path as an initial value to obtain a final trajectory. The problem that track generation is difficult due to complex kinematics, high state space dimension and deformable structure of the traction trailer robot is solved, the problems that an existing algorithm is low in efficiency and loses solution space are solved, and efficient track generation of the traction trailer robot is achieved.

The invention discloses a low-orbit constellation measurement and control resource scheduling scheme based on an improved genetic algorithm, and the scheme comprises the steps: firstly building a model through satellite simulation software STK, obtaining resource data, building a scheduling distribution model, converting measurement and control resources and measurement and control tasks into a mathematical form, and carrying out the measurement and control of the low-orbit constellation, thereby achieving the high-dimensional and multi-constraint problems of the measurement and control of the low-orbit constellation. A profit value is specified for each measurement and control task, an arc section is used as a measurement and control main body according to constraint conditions, a function expression for evaluating the advantages and disadvantages of a scheme is specified, finally, an improved genetic algorithm is applied, improved binary coding is adopted, the measurement and control arc section is used as a gene unit, a tournament selection mechanism is utilized, and data preprocessing and multiple constraint judgment correction are carried out. And solving an optimal scheme. The scheme can efficiently process complex scheduling, reduce invalid search and improve algorithm efficiency and scheme feasibility, meets actual operation requirements, and is suitable for scenes such as commercial satellite measurement and control.

The invention discloses a test case automatic generation method and device, electronic equipment and a storage medium. The method comprises the steps of obtaining a to-be-tested program; analyzing the to-be-tested program to generate an initial population; determining a fitness function; performing iterative optimization on the initial population based on the fitness function to obtain an optimal individual; and taking the optimal individual as a target test case for the to-be-tested program. According to the technical scheme, the boundary value use case generated based on the program control flow diagram is fused with the candidate use case to generate the initial population, the coverage rate of critical paths in the population can be effectively increased, the proportion of redundant use cases can be effectively reduced, and under the same test target, the scale of the use case set is reduced, and the test execution time is shortened. The hierarchical fitness function design reduces the proportion of high-time-consumption operation cases and improves the test efficiency. All stages of operation of the genetic algorithm are combined with all stages of operation of the artificial bee colony algorithm, and algorithm efficiency is improved.

The invention discloses a heavy-load freight direct-current electric locomotive on-board platform and a train operation curve calculation method. Step-by-step iterative optimization is carried out through a dynamic programming algorithm. Presetting operation steps and step lengths, and defining a selectable action set in each step length; and with the current state of the train as a starting point, evaluating each action in the action set in each iteration step, and calculating the next train state which may be transferred to. And evaluating all possible train state changes by adopting a target cost function, sorting according to the cost values from small to large, and preferentially selecting the action with the minimum cost value for subsequent calculation. And if all the action cost values of the current step exceed the limit, backtracking to the previous iteration step for reselection. And after iteration to the maximum step number, storing all train state changes, and finally obtaining an optimal train operation curve under the constraint of the target function. The method optimizes a control strategy through a dynamic programming algorithm, improves prediction precision, optimizes action selection, accelerates convergence, reduces redundancy, and improves operation stability, safety and algorithm efficiency.

The invention relates to the technical field of three-dimensional reconstruction, in particular to application of a non-local hierarchical adaptive propagation three-dimensional reconstruction method in cultural relic heritage protection. Through bilateral Gaussian weighted luminosity consistency cost calculation, a robust solution is provided for a low texture region, a shielding problem and an illumination change problem in multi-view matching, and technical support is provided for high-quality three-dimensional reconstruction of a complex scene. The hierarchical sampling strategy combines the advantages of coarse sampling and fine sampling, the optimal direction is screened in the coarse sampling stage, invalid calculation is reduced, precise propagation is performed in the high-confidence direction in the fine sampling stage, checkerboard division ensures the calculation independence of adjacent pixels, and the algorithm efficiency is improved; by introducing non-local operation and flexible sampling radius setting, hypothesis propagation efficiency and depth estimation accuracy are effectively improved in a low-texture area, a shielding scene and a complex scene, and a more reliable technical guarantee is provided for high-quality 3D reconstruction.

The invention discloses a distributed heterogeneous flexible flow shop batch processing scheduling method and system, relates to the technical field of distributed production scheduling in the manufacturing industry, and aims to solve the problems that an existing scheduling method is not comprehensive in constraint consideration, poor in energy consumption optimization and low in algorithm efficiency. According to the method, a mixed integer linear programming model containing multiple constraints such as release time and sequence-related preparation time is constructed, a learning-assisted dual-objective co-evolution framework is established, and the maximum completion time and the total energy consumption are synchronously optimized by combining mixed initialization, global-local search collaboration, decision reinforcement learning operator selection and a collaborative energy-saving strategy. The release time, the sequence-related preparation time, the inter-stage transportation time and the batch processing scheduling are simultaneously considered in the distributed heterogeneous flexible flow shop scheduling for the first time, the established mixed integer linear programming model better fits the actual production scene, and the method fits the actual production scene, is good in energy consumption optimization effect and can be adapted to the non-ferrous metal metallurgy aluminum production process.

The invention discloses a sparse auto-encoder-based cell type deconvolution modeling method and system, and relates to the technical field of artificial intelligence technology and bioinformatics. Single cell RNA sequencing data is input into a trained sparse auto-encoder model to obtain a predicted cell type; the sparse auto-encoder model training process comprises the following steps: generating simulated bulk transcriptome data by using single-cell RNA sequencing data to construct a training set; constructing a sparse auto-encoder model, simulating bulk transcriptome data, generating a predicted cell type proportion through an encoder, and generating reconstructed bulk transcriptome data through a decoder according to the predicted cell type proportion; constructing a total loss function based on the KL divergence function, the reconstruction error and the prediction error so as to optimize trainable parameters in the sparse auto-encoder model; according to the deconvolution modeling method and system, the problems of low cell type prediction accuracy and low algorithm efficiency in the prior art are solved, and rapid and efficient bioinformatics analysis is realized.

The invention belongs to the technical field of power grid dispatching, and discloses an energy system low-carbon dispatching method and device based on deep reinforcement learning, and the method comprises the steps: changing the input structure of a decision network through the extraction of Riemannian flow pattern features and the analysis of group distribution; the input of a global decision network in the prior art is changed from environment state information to global state information and streaming feature group distribution and prediction group distribution at the next moment. The input of a single local decision network in the prior art is changed from local state information to flow pattern feature group distribution, predicted group distribution at the next moment and a corresponding unit state; according to the method, the input dimension of the decision network and the complexity of decision search are remarkably reduced, so that the algorithm efficiency is effectively improved, and rapid scheduling of the energy system is realized.

The invention relates to the technical field of robot positioning, in particular to an automatic robot positioning method based on vision assistance, which comprises a vision acquisition module (1), a multi-modal data fusion module (2), a deep learning feature extraction module (3), a self-adaptive positioning optimization module (4) and an execution control module (5). The visual collection module (1) collects multi-source data through the camera (11), the infrared sensor (12) and the laser radar (13), after the multi-mode data fusion module (2) integrates the multi-source data, key features are extracted through the deep learning feature extraction module (3), then the self-adaptive positioning optimization module (4) dynamically adjusts weights and corrects errors, and finally accurate positioning and navigation are achieved through the execution control module (5). According to the invention, through the adaptive weight adjustment system (24), the adaptive feature enhancement system (34) and the dynamic error compensation system (44), the positioning precision and robustness in a complex environment are significantly improved, and meanwhile, the intelligent obstacle avoidance system (53) ensures safe operation. According to the invention, the problems of insufficient complex environment adaptability, limited positioning precision and low algorithm efficiency in the prior art can be effectively solved.

The invention provides an alignment method and device for a three-dimensional model pose and a human pelvis pose, and relates to the technical field of medical image registration, and the method comprises the steps: obtaining pelvis 2D data of a patient in a pelvis minimally invasive surgery and a pre-constructed model pose of a three-dimensional model of the patient; the method comprises the following steps: determining a plurality of initial pose offset matrixes according to the positions of a plurality of interest points in pre-configured pelvis 3D data and the target position of each interest point in the body of a patient, determining a target pose offset matrix in the plurality of initial pose offset matrixes, and adjusting the model pose according to the target pose offset matrix to obtain a target pose. The model pose of the three-dimensional model is aligned with the pelvic pose of the patient. According to the method, the globally optimal solution is searched by adopting the step-by-step subdivision search strategy, the branching and trimming steps are realized, the scale of the search space is reduced, the algorithm efficiency is improved, and the method has higher adaptability and universality for the shape alignment problem.

This invention provides an iterative measurement device and method for the surface shape of a cylindrical axially stressed component, belonging to the field of component measurement technology. It solves the problems of low measurement accuracy, single measurement method, low algorithm efficiency, and insufficient precision of existing measurement devices. This measurement device can detect the perpendicularity of the cylindrical axially stressed component, ensuring its vertical placement. It can stably adjust the height of the probe detection mechanism and the laser measurement mechanism, maintaining stable positions without wobbling, thus satisfying the requirement of measuring the outer contour of the cylindrical axially stressed component's surface twice. By controlling the main unit in conjunction with the probe detection mechanism and the laser measurement mechanism, it achieves rapid and accurate measurement of the cylindrical axially stressed component's appearance shape. The measurement method of this invention does not cause errors in the measured specimen's appearance due to the irregular shape of the track itself. Utilizing an iterative algorithm, it can achieve precise measurement of the appearance of the cylindrical axially stressed component and the track.

The application provides a super block management method and device and electronic equipment, comprising: obtaining a target storage block in a storage unit in a plurality of NandFlash; the block number of the target storage block is the same; initializing a minimum heap container; constructing a super block according to the target storage block; writing a first data structure of a first sub-super block in the super block in a full data state into a first minimum heap in the minimum heap container; and writing a second data structure of a second sub-super block in the super block in an unwritten data state into a second minimum heap in the minimum heap container; and managing the write operation and the garbage collection operation of the super block through the first minimum heap and the second minimum heap. The method improves the garbage collection and wear leveling algorithm efficiency of the SSD firmware.

The invention discloses a single pixel panoramic imaging (SPPI) method based on a Walsh-Hadamard (W-H) base. The SPPI technology is widely applied to the fields of automatic driving, pipeline detection, low-altitude economy and the like, and according to a traditional scheme, although efficient panoramic perception is achieved through combination of a convex mirror and an imaging algorithm, the algorithm efficiency and precision still need to be optimized. According to the invention, a W-H orthogonal basis optimization panoramic annular modulation mode is introduced, and the SPPI technology is improved on one-dimensional and two-dimensional imaging algorithms. The method mainly comprises the following steps: 1) constructing a two-dimensional orthogonal ring modulation framework by adopting a W-H orthogonal basis to replace a random mode; 2) building a panoramic imaging system based on a passive modulation single-pixel camera; and 3) improving a single-pixel panoramic imaging model based on two-dimensional W-H base orthogonality and a V2DALM algorithm, and optimizing the panoramic imaging adaptation capability of the algorithm. According to the method, the imaging speed and quality are considered, and the optimal SPPI scheme can be selected for different application scene requirements such as high fidelity, high speed and speed-fidelity balance.

The invention relates to the technical field of holography, in particular to a color hologram generation method based on an approximate secondary phase mask. The method comprises the following steps: constructing an approximate secondary phase; extracting an optimized approximate secondary phase mask from a spatial domain or a frequency domain according to the approximate secondary phase; and decomposing a target image into RGB three-channel amplitudes, and combining the optimized approximate secondary phase mask with the three-channel amplitudes to obtain a color hologram. According to the method, the calculation efficiency can be improved, a hybrid optimization strategy is provided, a high-quality initial phase is quickly generated through an approximate secondary phase mask, the phase distribution is refined by combining limited GS iteration (10-30 times), the single frame generation time is compressed to be within 5 seconds, the efficiency is improved by more than 50% compared with that of a traditional GS algorithm, high-frequency noise is inhibited through regular phase distribution, and the calculation efficiency is improved. The phase uniformity is improved in combination with double-domain optimization, so that the PSNR value of the reconstructed image exceeds 28dB, the SSIM value exceeds 0.9, and the noise power is reduced by more than 30% compared with a random phase method.

The invention relates to the field of mechanical arm path planning, in particular to a path optimization obstacle avoidance method based on an improved Inform-RRT * mechanical arm motion planner. Firstly, parameter information required by path planning is acquired, then an initial step length is determined according to an environment, a random sampling point is generated, a nearest node is found, and a new node is generated according to the step length; continuously updating path points and father nodes, and optimizing path cost; by using the rewiring process in the RRT * algorithm, the radius search of the fixed step length is changed into the dynamic step length, the path length is shortened, and the algorithm efficiency is improved. In combination with an APF function, an artificial potential field function in the APF function is fused into an Inform-RRT * algorithm, and a good convergence effect of the algorithm is used for making up the defects that the number of nodes is too large and convergence is slow before the traditional Inform-RRT * forms an elliptical convergence domain. And finally, the improved path planning obstacle avoidance algorithm is added into an ompl planning library, and a path planning obstacle avoidance experiment is completed in ROS MoveIt in combination with a UR3 mechanical arm model. The number of sampling points is effectively reduced, and the working efficiency of the mechanical arm is improved.

The application discloses a kind of based on the energy-saving method of urban rail comprehensive system optimization of multi-vehicle cooperation, comprising the following steps: according to basic input data, establish train-line-net-graph comprehensive system coupling model;Establish the time-space-speed three-dimensional state network of train operation;According to train motion characteristics, determine the train control operating mode transfer rule between adjacent time stages in time-space-speed three-dimensional state network;The train state under each time stage of time-space-speed three-dimensional state network is reduced dimension, and effective state space domain is obtained;According to multiple body dynamic programming algorithm, train-line-net-graph comprehensive system coupling model, train control operating mode transfer rule and effective state space domain, output optimal collaborative multi-train three-dimensional trajectory.The application can significantly reduce the iteration number of tidal flow calculation, greatly improve the algorithm efficiency, can simultaneously optimize the running process of all trains in multi-train system, realize system state optimization.

The present invention discloses a laser SLAM loop detection method based on semantic information, comprising: performing spherical projection on the laser point cloud in the current environment and obtaining the depth information of each point in the two-dimensional image generated after the projection; performing semantic segmentation through a fully convolutional neural network and generating a weight matrix using the obtained semantic labels; aggregating and rotating the weight matrix through a sliding window to generate a global descriptor with rotation invariance; constructing a Kd-Tree to search historical frames and obtain candidate frames based on time thresholds and distance thresholds; obtaining the angular difference between two frames of point clouds using the global descriptor, and performing geometric verification through an ICP algorithm with an initial angle to determine the final loop frame and obtain a closed-loop position. The present invention solves the problem that the existing technology does not fully utilize the semantic information of the environment when performing loop detection using pure laser, resulting in low algorithm efficiency.

The invention provides a scheduling method for dual-objective optimization of a multi-agent flow shop under time-of-use electricity price, and relates to the technical field of intelligent manufacturing and production scheduling. According to the method, unified modeling is carried out on the total power cost TEC and the total customer completion time TCTA, and a dual-target mixed integer programming model is constructed; a double-node decision tree structure is constructed, and synchronous processing of task sorting and processing time interval distribution is achieved; analyzing the structural characteristics of the multi-agent flow shop under the constraint of time-of-use electricity price, designing a series of inequality constraints and multiple pruning rules, and compressing a search space on the premise of ensuring the integrity of a feasible solution; the method comprises the following steps of: performing dual-objective cooperative solution, embedding a branch and bound algorithm into an improved epsilon-constraint framework to perform iterative solution, accurately obtaining a Pareto frontier, and providing multiple optimal scheduling schemes balanced between cost and service quality for decision makers. According to the method, dual decisions of task sorting and cycle allocation can be realized, and the problems of decision model splitting, low solving algorithm efficiency and the like are solved.

The invention provides a multi-path routing method suitable for a non-order-preserving routing standard, and relates to the technical field of network routing, and the method comprises the steps: carrying out the network modeling of a common routing problem into a directed graph, and abstracting the directed graph into a routing algebra; aiming at the routing algebra of which the routing standard meets the monotonicity but does not meet the order-preserving property, obtaining the order-preserving property through a maximum order-preserving reduction technology; and calculating first K optimal paths from a source node to a destination node in the directed graph through a multi-path algorithm based on the routing algebra after order-preserving reduction, and setting an algorithm efficiency and path balance mechanism of the multi-path algorithm. According to the scheme, the method can be applied in a non-order-preserving environment, the application range of a routing algorithm is remarkably expanded, and the method can be widely applied to a routing decision scene in a complex network environment and has the advantages of being high in universality, stable in performance and high in path quality.