122 results about "Global optimum" patented technology

The invention discloses a heterogeneous multi-agent entropy regularization resource allocation method for dynamic spectrum sharing, which belongs to the technical field of wireless network communication, and comprises the following steps: loading a basic trainer, and managing subsequent reinforcement learning; the method comprises the following steps: constructing a simulation environment, establishing a mathematical model, initializing parameter analysis, constructing a training environment and an evaluation environment, and creating an intelligent agent; generating initial empirical data by using a random strategy, filling an empirical playback buffer area, returning to a final state after a preset preheating step number is reached, and performing formal training to sample data from the empirical playback buffer area to update a network; starting a formal training process, interacting the intelligent agent with the environment, and storing empirical data to a buffer area; the strategy network is optimized through the strategy gradient, and the evaluation network is optimized through the TD error. By the adoption of the method, the problem of performance bottleneck caused by the fact that the strategy is converged to be suboptimal in spectrum allocation of a traditional DRL method is solved, and resource allocation efficiency close to global optimum is achieved through random strategy optimization.

The invention discloses an equipment abnormity monitoring method and system based on the Internet of Things, and relates to the technical field of intelligent operation and maintenance of the Internet of Things, and the method comprises the steps: collecting monitoring data to generate a high-dimensional original matrix, carrying out the optimization through employing a GCN model and combining with ACO, carrying out the updating through a comparison learning model and an FCM algorithm, and carrying out the searching of global optimum through employing a VAE model and combining with a PSO algorithm. The method comprises the steps of performing classification optimization based on K-means clustering and BSO, performing MLE calculation, updating dynamic causal KG through Granger causal test, generating a multi-modal result array through NSM, a scoring formula, a naive Bayesian model, a Mahalanobis distance formula and a logistic regression model, and performing optimization by using a fuzzy rule and GWO. According to the method, the GCN model is combined with the adaptive optimization algorithm, the precision and response speed of anomaly monitoring are improved, optimization is carried out by using the fuzzy rule and introducing the GWO based on multi-modal causal reasoning, and the reliability and efficiency of anomaly monitoring are improved.

The invention relates to a three-dimensional point cloud registration method and system based on a two-dimensional visual large model. A source point cloud and a target point cloud are decoupled into two-dimensional projection representation; constructing a structure consistency restoration domain, restoring geometric structure fracture and semantic deficiency in the projection image, and generating a dense and continuous restoration image; inputting the repaired image into a pre-trained visual geometry large model, carrying out cross-modal feature matching and pose resolving, and synchronously outputting a pixel-level matching confidence map; and inversely mapping a two-dimensional pose calculation result to a three-dimensional space to complete coarse registration, constructing a high-credibility anchor point set in the three-dimensional space by using a confidence map, and guiding a point cloud fine registration algorithm to converge to global optimum. According to the method, the registration large model for natural image training can be seamlessly migrated to the point cloud data, and retraining is not needed; in a weak texture and partially overlapped point cloud scene, a robust rough registration initial value can still be provided; the convergence speed and the anti-noise performance of fine registration are remarkably improved, and adaptive improvement from coarse to fine is achieved.

The invention relates to the technical field of railway line selection design, in particular to a railway line searching method, medium and equipment in a wide and dense obstacle environment. The method comprises the following steps: acquiring obstacles passed by a line and determining preferentially treated obstacles; and inserting an optimal line intersection point around the obstacle, adjusting the line scheme and repeating the point insertion operation until the spatial relationship requirement of the line and the obstacle is met, and finally generating a global optimal line conforming to the constraint. According to the method, through a priority obstacle handling strategy and a dynamic point insertion mechanism, key obstacles are intelligently recognized, intersection points are preferentially generated around the obstacles, the problem of circuit bypassing redundancy or repeated adjustment caused by an improper obstacle handling sequence in a traditional method is avoided, and the efficiency and quality of circuit scheme design in a complex environment are remarkably improved. Through point-by-point insertion type iterative search, it is ensured that each adjustment approaches to the global optimum direction, and compared with a traditional trial and error method, the calculation time consumption is reduced, and meanwhile local optimum is avoided.

The invention discloses a railway station energy consumption intelligent management method and system based on artificial intelligence. The method comprises the steps that sensors are deployed in subsystems of a railway station, and environment parameters, equipment operation parameters and historical energy consumption data are collected; a unified communication protocol and a standard interface are adopted, and any one or more subsystems of air conditioner ventilation, cold and heat sources, illumination and elevators and escalators are interconnected; according to the real-time environment change and the load demand, carrying out self-adaptive control on the plurality of pieces of energy consumption equipment; based on a mechanism model and an artificial intelligence prediction model, energy consumption simulation and comparison are carried out on different control strategies, and the global maximum is automatically searched through an intelligent algorithm on the premise of meeting passenger comfort and safe operation. According to the technical scheme, through deep combination of mechanism modeling and artificial intelligence, high-precision prediction and adaptive regulation and control can be realized in railway station multi-system energy consumption management, and the energy utilization efficiency is comprehensively improved.

The invention relates to the technical field of search path planning, in particular to a search path planning method and device based on deep reinforcement learning and an evaluation method. The search path planning method comprises the following steps: establishing an environment matrix about detection capability based on search environment parameters, and setting a starting point; the trained neural network outputs a Q value corresponding to each action based on the current position of the intelligent agent, a reward function and an environment matrix, and the reward function is designed according to the current position, the distance between the reward function and a target point, the detection capability of the neighborhood of the current position and the number of unvisited areas; and the intelligent agent executes each action according to the action corresponding to the maximum Q value. The problems that in the prior art, efficiency and stability are often difficult to consider at the same time when a reinforcement learning algorithm faces a complex task, the path planning time is possibly too long or the result deviates from global optimum, and the actual application effect is affected can be solved.

The invention discloses an unmanned aerial vehicle urban inspection task allocation method based on a two-stage heuristic algorithm, and the method comprises the steps: regarding an unmanned aerial vehicle urban inspection task as a multi-dimensional constrained dynamic path planning problem, and simplifying the dynamic path planning problem into a two-stage static planning problem for solving; the first stage is called heuristic feasible solution set search, and after a round of decision is executed and state transition is completed, a feasible solution set of a next round of decision is heuristically searched in multi-dimensional constraints; the second stage is heuristic decision path planning, and an optimal decision path is searched in a feasible solution set of multiple rounds of decision. According to the method, through two-order decomposition, a complex dynamic programming problem is decomposed into two layers of static linear programming problems, and heuristic algorithms are used in the two layers; on one hand, the difficulty of solving the global optimum problem is reduced, and on the other hand, the poor quality of the overall solution caused by the relative optimum of each round of decision solution is avoided.

The invention discloses a knowledge graph-driven line storage space joint optimization scheduling model construction method. The method comprises the following steps: determining an optimal scheduling target and an overall target function of row storage space joint flood control scheduling, and setting constraint conditions; under a knowledge graph theoretical framework, constructing a response relationship between single-class line storage space scheduling and a river flood discharge state; selecting an excess peak water level exceeding a river embankment control water level as a coordination variable, decomposing the overall objective function into system association constraint equations of a river embankment, a reservoir, a flood storage and detention area and a flood drainage pump station, and reducing a multi-class line storage space joint scheduling problem into a problem of optimization scheduling of each single-class line storage space scheduling; and respectively solving by adopting an improved particle swarm algorithm to obtain an optimal scheduling process of each single-class line storage space. The method not only solves the problem that global optimum is difficult to ensure by directly applying a traditional optimization algorithm due to many row storage space optimization targets and a competitive relationship, but also reduces the calculation time of the model.

This invention discloses an IGBT lifetime prediction method based on a GA-Elman-LSTM combined model. Specifically, the method involves: acquiring IGBT aging data; preprocessing the data; using a GA-optimized Elman neural network to import the training dataset into the GA-Elman model for training; constructing error correction training and validation sets using the trained GA-Elman preliminary prediction model; training the LSTM error correction prediction model; and building a GA-Elman-LSTM error correction combined model based on the GA-Elman preliminary prediction model and the LSTM error correction prediction model for final combined prediction. This invention conveniently and efficiently enables the model to converge to the global optimum, effectively improving the model's prediction accuracy; it also improves the model's prediction stability and reduces prediction risk.

The invention discloses a multi-unmanned-aerial-vehicle cooperative path planning method based on an improved MPGWO algorithm, and belongs to the technical field of path planning, and the method comprises the steps: initializing a multi-unmanned-aerial-vehicle path planning task which comprises the target function of path planning, the position of an unmanned aerial vehicle, parameters and other information; then, constructing a three-dimensional environment map which comprises complex terrains and static cylindrical obstacles; secondly, relevant algorithm parameters are initialized, a population is initialized based on an SPSO algorithm, and a high-quality initial solution is generated; layering according to the fitness values, dynamically adjusting the number and the distribution mode of the sub-populations, and evaluating the fitness values of the individuals; secondly, independently optimizing each sub-population by adopting an improved MPGWO algorithm, and updating all individual positions meeting constraint conditions; the sub-populations share the global optimum alpha wolf every certain iteration times, and the fitness value and the global optimum solution of each sub-population are updated; and when the algorithm reaches the maximum number of iterations, outputting an optimal path. According to the method, by improving the MPGWO algorithm, the optimization efficiency and the solution quality of multi-unmanned aerial vehicle cooperative path planning are improved, the feasibility of the path is ensured, and finally efficient path planning and obstacle avoidance are realized.

The invention provides a wheeled robot navigation planning method in a non-flat environment, which combines local optimum and global optimum, disassembles a long-distance trajectory planning problem in a complex environment, firstly solves a global distance optimum trajectory to provide constraints, then locally solves a navigation problem, approaches the global optimum by using the local optimum, and finally solves the navigation problem by using the global optimum. And the solving efficiency is greatly improved. In the optimization process, the algorithm uses the geometrical characteristics of the ground surface to carry out dimension reduction on the loss function of the track, the landing characteristics of the track are considered, the method can well adapt to various complex and extreme ground surface topological structures, and the real-time performance and the landing constraint of the wheeled robot are considered while it is guaranteed that the track is approximately optimal.

The invention belongs to the technical field of semi-active suspension control methods, and relates to a semi-active suspension control method based on an improved multi-objective particle swarm optimization (MOPSO) algorithm, which comprises the following steps of: performing adaptive grid division on an external file of the MOPSO algorithm to determine global optimum with the most diversity and global optimum with the most convergence; judging the evolution state of the population according to the distribution entropy variation of all optimal solutions in an external file, and selecting a global optimal gBest for guiding evolution; according to the variable quantity of the local crowding distance of the current population evolution, adjusting flight parameters of population evolution to balance the evolution trend of the population; and fusing the two core mechanisms, providing a multi-information fusion multi-target particle swarm optimization algorithm, and applying the multi-information fusion multi-target particle swarm optimization algorithm to determination of a semi-active suspension LQR control strategy weight coefficient. According to the novel multi-objective optimization algorithm, external archive information and current population dynamic information are cooperatively integrated, so that balance between convergence precision and population diversity is effectively realized.

The invention belongs to the technical field of unmanned system intelligent control and path planning, and discloses a multi-unmanned aerial vehicle path planning method based on a multi-population grey wolf optimization algorithm, and the specific technical scheme comprises the steps: 1, building an environment model and constraint conditions of unmanned aerial vehicle three-dimensional path planning; 2, constructing a multi-target cost function including fuel consumption cost, flight height cost, threat area cost, time cooperation cost and space cooperation cost; 3, optimizing the multi-target cost function by adopting a multi-population grey wolf optimization algorithm to obtain an optimal flight path; according to the method, the global search capability is remarkably enhanced, and the multi-group clustering strategy and the periodic convergence factor act together, so that the algorithm can effectively jump out of local optimum, a path scheme closer to global optimum is found in a complex solution space, the convergence speed and the calculation precision are greatly improved, the collaborative planning effect is excellent, and the method is suitable for large-scale popularization and application. The method shows strong environmental adaptability and robustness, and has a very high practical application value.

The invention discloses an electric power and carbon management collaborative optimization method and system for a multi-industry symbiotic zero-carbon park, and the method comprises the steps: constructing a mixed integer programming model which comprises an electric power dispatching sub-model, a carbon collection sub-model and a source-sink matching sub-model, and defining a coupling relation between an electric power dispatching variable and a carbon management variable; designing a multi-objective optimization function, and bringing coal consumption cost, start-stop cost, carbon transaction cost, wind curtailment penalty cost, deep peak regulation cost, peak regulation compensation benefit and transportation cost into a unified optimization framework; and setting time-space coordination constraint conditions, including power balance constraint, output upper and lower limit constraint, climbing rate constraint, capture-transport volume balance constraint and carbon sink capacity limitation constraint, to realize coordination optimization of power production and carbon logistics in time and space dimensions. According to the method, the total cost of the system is effectively reduced, the utilization efficiency of carbon sink resources is improved, real-time controllability of carbon emission intensity is ensured, and the problem of global optimum deficiency caused by a traditional two-stage optimization method is solved.

The invention discloses an artificial intelligence model hybrid quantification method and device based on multi-objective optimization, and relates to the technical field of artificial intelligence. The method comprises the following steps: randomly generating a super-average mixed quantized sample for a target model; parallel mixed quantization is carried out on the target model, and each mixed quantization sample and a mixed quantization result form a mixed quantization data set; using an evolutionary algorithm and a Bayesian neural network model to iteratively screen out a set of optimal mixed quantized samples from the mixed quantized data set; and screening out an optimal mixed quantized sample which enables the precision and the size of the target model to reach optimal balance, quantizing the target model to obtain an optimal quantized model, and finely adjusting the optimal quantized model by using the target domain data set to obtain a final quantized model. The method combines transfer learning, an evolutionary algorithm and a Bayesian neural network-assisted multi-objective optimization algorithm to improve the model precision and consider the model size at the same time, thereby realizing multi-objective global optimization.

The invention relates to an engineering optimization method and system based on a whale optimization algorithm, and a medium, and belongs to the technical field of intelligent optimization algorithms. Comprising the following steps: S1, initializing parameters and populations: setting a population scale, a maximum number of iterations, dimensions and upper and lower bounds of variables, generating an initial population, and initializing a global optimal solution and a current optimal solution; s2, executing an individual variation strategy: recording a global optimal individual position and a current iteration optimal individual position; s3, executing a unified search strategy; s4, executing a group communication strategy; and S5, judging whether the current number of iterations reaches the maximum number of iterations, if so, outputting a globally optimal solution, otherwise, adding 1 to the current number of iterations and returning to the step S2 to continue iteration. The algorithm provided by the invention aims to improve the global search capability and convergence precision of the algorithm and maintain the time complexity equivalent to that of the original WOA at the same time.

The invention relates to the technical field of ultrasound, in particular to an ultrasonic phased array sparse arraying method. Comprising the following steps: firstly, constructing a planar rectangular ultrasonic array model and determining basic parameters; setting a fitness function by taking a minimum maximum sidelobe level (MSLL) as an optimization criterion; secondly, introducing a tent chaotic mapping-good point set mixing method to obtain a classical solution population; a quantum bit coding strategy is introduced in the coding stage, individuals in the population are expressed as quantum bits, a standard NOA strategy is executed on the classical solution population to update the population, then the update quantity is synchronized to the quantum bits, and finally a new generation of classical solution population is generated through quantum rotation and collapse operation and the fitness is evaluated; integrating a covariance matrix adaptive evolution strategy (CMA-ES) in the optimization iteration process, and dynamically adjusting the shape, direction and step length of search distribution; and finally updating the global optimum. According to the method, the array element distribution of the sparse array is optimized through the multi-strategy fusion ENOA algorithm, the peak side lobe level of the array can be remarkably reduced, the anti-interference capability and precision of target detection are improved, and meanwhile, the hardware cost of the array is reduced.

The invention discloses a non-linear constraint coupling driven implicit modeling optimization method and system and a memory. The method comprises the steps of firstly obtaining nonlinear constraint data of a target geological interface, then generating initial implicit function representation through a Hermite radial basis function based on an initial control point of the target geological interface, and then obtaining a nonlinear coupling driving optimization objective function about implicit function representation of the target geological interface according to the data type of nonlinear constraint, and optimizing through an implicit gradient flow algorithm to obtain a geological interface reconstruction model and a global optimization target geological interface model. The invention further provides a computer readable memory, the memory contains a computer program for realizing the implicit modeling optimization method for nonlinear constraint coupling driving, the computer program can be read and executed, and the computer program, a programmable logic controller and a processor form an optimization system. According to the system, nonlinear constraint data of a large number of geological interfaces are fully coupled, an implicit modeling framework is constrained, and therefore high-precision modeling of the three-dimensional geological interfaces is achieved.

The invention discloses a multi-spectral radiation temperature measurement method and system for improving a quantum particle swarm, relates to the field of radiation temperature measurement and photoelectric signal processing, and solves the problems that when an existing standard PSO algorithm is applied to high-dimensional and nonlinear multi-spectral inversion, local extreme values are likely to be trapped, and the self-adaptive capacity is poor. Setting parameters, generating an initial particle position by using Tent chaotic mapping, calculating the particle fitness of an initialized population, updating initial individual optimum and global optimum, and calculating an average optimal particle position; updating the optimal particle position through a Levy flight mechanism; and repeating the steps, calculating the multispectral radiation temperature measurement target function value of the updated position, updating the initial individual optimum and the global optimum, and if an error threshold value or the maximum number of iterations is met, outputting the global optimum solution. The method is also suitable for the application field of inverting the real temperature and the spectral emissivity of the target from the multi-channel spectral radiation signals and the like.

The invention provides a coal machine equipment production plan optimization method based on a variable neighborhood search algorithm, and relates to the technical field of engineering scheduling. The method comprises the following steps: 1, initializing algorithm parameters; 2, constructing a priority ranking set and a total cost function; 3, setting and optimizing an initial solution; 4, generating a neighborhood solution set and updating the neighborhood solution set to obtain a candidate solution set; 5, selecting a neighborhood structure; 6, searching a neighborhood to obtain a local optimal solution; 7, updating the optimal solution; 8, optimizing the optimal solution; and 9, outputting a globally optimal solution after the execution of the number of iterations is completed. According to the method, the initial solution and the neighborhood structure of the variable neighborhood search algorithm are improved, the quality of the initial solution is optimized, premature falling into local optimum is avoided, the approximate optimal solution can be obtained for the coal machine equipment production plan optimization problem, more effective decision support is provided for decision makers, and therefore the efficiency and benefits of equipment batch production are improved.

The invention discloses a Czochralski crystal growth aperture contour extraction method, which comprises the steps of obtaining and preprocessing a crystal growth image, weighting and calculating a grey-scale map according to red, green and blue channels, carrying out normalization processing on a grey-scale value, calculating a gradient amplitude image by adopting an edge detection operator, generating an initial contour set, and extracting the aperture contour of the Czochralski crystal growth according to the initial contour set. Iterative optimization is carried out through a group optimization algorithm, a function containing constraint conditions such as a curve opening, an overlapping definition domain and average width and a double-target weighted sum of a gradient mean value and a regional gray mean value is used as an optimization function, and after 20-50 times of iteration, a global optimal aperture contour is output. According to the method, the robust and accurate extraction of the aperture contour is realized by introducing the optimization function of the multi-constraint and double-target weighted sum and combining the group optimization algorithm. According to the method, manual intervention is not needed, key data can be obtained in real time, support is provided for accurate regulation and control of Czochralski method crystal equal-diameter growth, and the yield and the automation level of crystal production are effectively improved.

The invention provides a wireless communication method, system and device based on fusion of a bidirectional intelligent metasurface and non-orthogonal multiple access of a movable element. The method comprises the following steps: receiving a superposed signal sent by a base station; based on a preset constraint condition, calculating the historical channel state information through a preset operation optimization algorithm to generate a driving instruction; according to the driving instruction, driving each movable element to move to a target position; electromagnetic regulation and control are carried out on the received superposed signal to generate an electromagnetic regulation and control signal, and the electromagnetic regulation and control signal is transmitted to the user terminal; the user terminal converts and decodes the electromagnetic regulation and control signal to generate a user data stream; and enabling the user terminal to estimate the current channel state based on the current user data flow, and generating and outputting updated channel state information. According to the method, the layout form of the metasurface can be dynamically optimized, and higher anti-interference capability and better multi-user fairness are realized while the performance is ensured to approach global optimum.

The invention discloses a laying hen lossless data processing method based on improved XGBoost, and relates to the technical field of data processing, discrete feature numeralization is realized through adaptive tag coding, abnormal value detection and non-linear mapping correction are combined, a robustness purification feature set is constructed, and a layer lossless data processing method based on the improved XGBoost is obtained. The problems that a traditional method is sensitive to sensor noisy points and lacks an effective data cleaning mechanism are solved, and prediction precision reduction caused by extreme sample interference is avoided. And performing nonlinear expansion on the purification features by using a polynomial kernel, and accurately capturing a coupling relationship between the features. The global optimal hyper-parameter optimization of the XGBoost integrated regression model is driven by high-dimensional features, so that the parameter optimization efficiency is improved, local optimum is avoided, the method adapts to a multi-source heterogeneous and high-noise nonlinear data scene of laying hen breeding, the processing precision and generalization ability of the model are remarkably improved, reliable data support is provided for accurate management and quality monitoring of laying hen breeding, and the method is suitable for large-scale popularization and application. And efficient landing of the intelligent breeding technology is promoted.

The invention discloses a monocular vector high-precision map data association method based on geometric context, and relates to the technical field of map data association. Extracting a map landmark set in a current frame view range from the map through the initial vehicle pose, and projecting a point set to a pixel plane; performing structured modeling on each point set in the detection road sign set and the projection road sign set; constructing a cost matrix for the current estimated vehicle pose; introducing a gating mechanism based on map distance and a self-adaptive gating range based on map depth, and eliminating matching pairs with inconsistent geometric structures; complete data association is obtained through cost matrix calculation, optimal bipartite graph matching is performed on the cost matrix by adopting a Hungary algorithm, and a data association relationship meeting global optimum is obtained. According to the method, feature information of road signs in a vector high-precision map is fully utilized, outlier matching relation pairs are eliminated through self-adaptive gating, finally, data association is conducted through a Hungary algorithm, and matching robustness and accuracy are improved.

This invention discloses a network path optimization method and system. The method includes: adjusting the adaptive weights of the heuristic function in the underlying model LPA* algorithm to intelligently change the magnitude of the heuristic function's influence with iteration; simultaneously, introducing a bias p in the K-value calculation to improve the algorithm's search efficiency; and performing unbalanced allocation of initial pheromones for the ant colony algorithm based on the initial path calculated by the underlying model. The upper-layer model applies the ACO algorithm to plan fiber optic network paths on a grid map to obtain the globally optimal network path. This invention, through the design of a hierarchical algorithm, provides the ant colony algorithm with prior information including pre-selected paths, enhancing the traditional ant colony algorithm's ability to guide pre-selected areas in path planning. This enables faster search for feasible paths, effectively solving the problems of slow search speed and susceptibility to local optima in the traditional ACO algorithm, and significantly improving the overall performance of the fiber optic network planning system.

The application discloses a kind of urban road network vehicle macroscopic path planning method for common congestion, belong to traffic path planning field;The method uses double-layer planning model, including macroscopic path planning and local path planning;In macroscopic path planning, the effective management of large-scale urban road network is realized by using existing traffic subarea division method and macroscopic traffic flow distribution strategy;Local path planning is then based on the traffic subarea passing order given by macroscopic planning, and within each traffic subarea, A* algorithm is used to plan a time-consuming shortest path for vehicles from the boundary of the previous subarea to the boundary of the next subarea. In order to deal with the common traffic congestion of urban road network, the application uses the flow balance theory to distribute traffic flow from the macroscopic level under the global perspective. According to the macroscopic path planning algorithm, the flow between the current traffic subareas is dispatched, so that the urban traffic flow reaches the global optimum.

The invention discloses a traffic control subarea optimization and self-adaptive adjustment method and system, relates to the technical field of subarea optimization, and is used for solving the problem that the coordination effect in the peak period of traffic flow is poor due to the lack of dynamic response to real-time flow data when an existing traffic signal coordination system carries out regulation and control based on experience or a fixed algorithm. The coordination effect is quantified by calculating the overlapping length and the directional delay, so that the optimization process is more objective, the coordination effect of different initial phase differences can be quantified, the optimal initial phase difference is selected, a group of coordination control schemes is generated by considering the coordination of non-adjacent intersections, the problem of global incoordination caused by local optimization is reduced, and the optimization efficiency is improved. According to the method, factors such as path length and traffic flow weight are introduced, coordination deviation between local optimum and global optimum is calculated, coordination loss is quantified more accurately, a smooth transition strategy is adopted, and sudden traffic fluctuation caused by overlarge phase difference change is avoided.

The application provides a serial batch processing scheduling method based on a hybrid particle swarm algorithm in a fuzzy environment, and relates to the field of production scheduling.The method comprises the following steps: encoding and analyzing workpieces and machines based on machine indexes; in the case of parallel machine scheduling, initializing algorithm parameters based on the hybrid particle swarm algorithm, initializing a population by a heuristic algorithm, analyzing fitness, and updating the speed and position of particles; determining the minimization of the maximum completion time as an objective, executing a variable neighborhood descent local search strategy, and updating the local optimum and the global optimum; and in the case that an iteration index exceeds an iteration threshold, determining the end of the algorithm and outputting a target result to represent the allocation and scheduling information of the workpieces and machines.The application considers fuzzy processing time, learning effect and deterioration effect, and considers the scheduling problems of single machines and non-single machines in a fuzzy environment based on actual production conditions, which is helpful for iron and steel plants to formulate production strategies and reasonably arrange the allocation and scheduling of workpieces and machines.

The invention relates to the field of data processing, and provides a distribution comb contour determination method based on three-dimensional data acquisition, a medium and equipment. The method comprises the following steps: clustering an obtained contour curve of a specific area of the head of a user to generate a centroid-like curve, dividing the centroid-like curve into segmented centroid curves according to a preset interval, and screening segment curves with the similarity reaching the standard in a corresponding cluster as fitting candidate curves; and establishing a multi-objective optimization function containing curvature continuity, second-order derivability and anatomy adaptation degree, performing global optimal combination on the fitting candidate curves by using a dynamic programming algorithm to generate an initial contour point location sequence, and performing smooth optimization to form a contour curve of the distribution comb attached to the scalp side. According to the method, user head shape feature categories are divided through clustering analysis, an initial hash point set is generated by using a clustering center, the curve fitting degree is optimized in combination with a standardized smoothing algorithm, and finally a standard curvature template adaptive to a corresponding group is output.

This application provides a method, device, medium, and program product for determining the envelope region of reference points for parts. The method includes: obtaining the initial spatial coordinates of all reference points for a target type of parts; determining the first reference coordinates corresponding to all parts and the second reference coordinates corresponding to each part; then determining the offset of the initial spatial coordinates based on the first and second reference coordinates, and adjusting the initial spatial coordinates using the offset to obtain the corrected spatial coordinates of each part reference point; finally, combining the reference point types of all parts and the corrected spatial coordinates to determine the envelope region of all reference points for the target type of parts. This application's embodiment adjusts the initial spatial coordinates based on the offset of each part and calculates the envelope region of the part reference points based on the corrected spatial coordinates, achieving a part envelope region that approaches global optimum, significantly reducing the structural redundancy and development cost of part measurement supports.