401 results about "Iteration process" patented technology

The invention relates to an intelligent agent system optimization method and device based on intelligent fault analysis and cross-generation knowledge inheritance, and belongs to the technical field of artificial intelligence. According to the method, interaction abnormal signals are captured in real time by deploying a lightweight log probe, and a tool benefit prediction model based on reinforcement learning is constructed to automatically generate an improvement proposal when the failure rate exceeds a threshold value; an agent genealogy map is established to realize automatic inheritance of a new agent on core memory and abandonment of failure knowledge, and a disastrous forgetting blocker is deployed to dynamically extract a functional module from a genealogy to deal with key capability degradation. Aiming at the problems of fault response lag, knowledge inheritance fracture, key capability degradation and the like in an intelligent agent system iteration process, the invention creatively provides a cooperation mechanism of an intelligent fault analysis layer and a cross-generation knowledge inheritance network, and the fault self-healing capability, version stability and service continuity guarantee level of the system are remarkably improved.

The invention belongs to the technical field of industrial intelligence, and relates to a deep reinforcement learning-based non-correlation parallel machine scheduling method, which comprises the following steps of: constructing a mathematical model suitable for non-correlation parallel machine scheduling; the machining time of each workpiece on each heterogeneous machine is collected, and normalization processing is carried out; initializing a genetic algorithm scheduling population and a depth Q network; constructing a deep reinforcement learning training framework; expressing a state vector by using the average fitness, the optimal fitness and the optimal individual code; operating parameters of the genetic algorithm are controlled by using the action space, and parameters of the deep Q network are updated by using a reward function; and dynamically controlling operator selection in a genetic algorithm iteration process by using the trained deep reinforcement learning model to obtain an optimal scheduling solution and realize scheduling of the non-correlation parallel machine. According to the method, a deep reinforcement learning algorithm is provided for solving similar problems in the manufacturing industry production scheduling field by analyzing a non-correlation parallel machine scheduling problem data model, and the production efficiency is improved.

The invention relates to an adaptive imperceptible three-dimensional point cloud-based target identification confrontation attack method. The method comprises the following steps of: obtaining a central point from an original point cloud based on the magnitude of a saliency and imperceptibility joint score; gaussian perturbation is carried out on the original point cloud based on the obtained center point to generate a confrontation point cloud, and function mapping is carried out on a dynamic interval of Gaussian perturbation in the process so as to avoid sudden change of perturbation and improve the attack success rate of the confrontation point cloud; constructing a loss function for iterative training, the loss function constraining disturbance through a joint regularization item, and adaptively and dynamically adjusting a regularization item weight parameter in an iteration process; continuously iteratively generating confrontation point clouds, and optimizing the confrontation point clouds by taking minimization of confrontation loss under constraint as a target so as to improve the imperceptibility of disturbance; and finally generating a three-dimensional confrontation point cloud. According to the method, good balance between the point cloud attack resistance imperceptibility and the resistance strength can be realized.

The invention discloses an expensive constrained multi-objective optimization method and system based on constrained hyper-volume expectation. The method comprises the following steps: firstly, initializing a population and training an agent model; then calculating a correlation coefficient between the solution with the best population convergence and the constraint violation degree of the solution, and performing classification processing; then, optimization is carried out on the agent model, and a new solution is selected by utilizing a filling criterion based on constraint hyper-volume expectation to carry out evaluation and is filled into a file; further selecting and screening individuals by using an improved environment to enter a next-generation population; and finally, judging whether a stop condition is reached or not, if so, outputting a final population, otherwise, turning to execute an iteration process. According to the method, the optimization efficiency is remarkably improved, local optimum is avoided, constraint conditions are precisely processed, and the population can be more effectively converged to a better feasible region.

The invention provides a distributed training method, device and system, equipment and a medium, and relates to the technical field of artificial intelligence, in particular to a distributed machine learning technology. The method comprises the following steps: calculating a current compression ratio and a current delay step length according to a bandwidth parameter and a transmission delay parameter of a current network; compressing the gradient of each round of calculation in the iteration process according to the current compression ratio to obtain a compression gradient, and updating an accumulative error term; when the number of iterations reaches the current delay step length, calculating a current update gradient according to a current compression gradient and an accumulative error term; and uploading the current update gradient to a server, so that the server performs aggregation according to the current update gradient uploaded by each computing node to obtain an aggregation gradient, and calculates global parameters of a model according to the aggregation gradient and issues the global parameters to each computing node. According to the method, the model precision is not lost in the process of reducing the communication overhead, so that the training efficiency and training precision of the model are improved.

The invention belongs to the technical field of communication, and particularly relates to a multiplication active code decoding method based on an SO-ORBGRAND decoder. The SO-ORBGRAND decoder outputs soft information by calculating a correct posterior probability of a conjecture code word sequence, and high-performance decoding is realized through soft information exchange and optimization processing between the row decoder and the column decoder. In the iteration process, a method for judging the validity of the code word through the check matrix is designed by utilizing the polarization characteristic of the multiplication active code, so that the iteration is supported to jump out in advance to reduce the complexity. The invention provides a multiplication active code decoding algorithm based on an SO-ORBGRAND decoder, and compared with various non-system multiplication active code decoding algorithms and the performance of traditional long-code polarization codes, the method has remarkable advantages in error correction performance; a method for judging the validity of the code word through the check matrix by utilizing the polarization characteristic design of the multiplication positive code is provided, the complexity is reduced while the judgment success rate is improved, and the implementation overhead is reduced.

The invention belongs to the technical field of electric digital data processing, and particularly relates to a method for optimizing errors of a logistics sorting encoder based on an improved grey wolf algorithm. According to the method, Logistic chaotic mapping and Gaussian perturbation are superposed to generate a diversity initial parameter population so as to break through the limitation of traditional random initialization, a fitness function is designed to quantify an angle compensation residual error, and parallel computing is utilized to accelerate evaluation. In the iteration process, global exploration and local development are dynamically balanced through adaptive convergence factors, a bimodal perturbation mechanism is constructed in combination with a differential evolution strategy and Levy flight variation, population effectiveness is maintained through reflection boundary processing, guiding of # imgabs0 # wolf is enhanced through dynamic weight distribution, the position of a leader wolf is updated by adopting an elitist retention strategy, and the population effectiveness is improved. And finally, outputting the optimal compensation parameter when the maximum number of iterations or the residual threshold is met, thereby improving the positioning precision and the operation efficiency of the logistics sorting system.

The invention relates to the technical field of intelligent manufacturing, and discloses a multi-objective optimization method and system for an aviation airborne product, so as to improve the overall performance of the aviation airborne product. The method comprises the steps of obtaining an initial population based on a series of initial model files of a target aviation airborne product, and determining a constraint condition and a target function of the target aviation airborne product; the constraint condition comprises the range of each iteration parameter; in each iteration process of obtaining the global optimal Pareto frontier according to the constraint condition, performing non-dominated sorting and congestion degree calculation on the tth generation of population through an algorithm, selecting individuals with excellent performance from a sorting result, and applying gradient-guided Gaussian disturbance to generate a (t + 1) th generation of new solution; the tth generation of population comprises cross progenies of the (t-1) th generation of population; and when the target iteration termination condition is met, iteration is terminated, and the global optimal Pareto front is obtained.

The invention relates to a toxic text collection method and system based on retrieval enhancement generation, and the method comprises the steps: firstly obtaining target text data through a crawling platform, manually constructing a small-scale initial data set through a cold start mode, and injecting the initial data set into a knowledge base as basic data; and performing semantic retrieval on each batch of target texts, obtaining the first k texts with semantic similarity from the knowledge base, reasoning by adopting a plurality of large language models through thinking chain reasoning in combination with the texts, generating a toxic label, and labeling the target texts to be labeled in the current batch. And injecting the labeled target text into a knowledge base, continuously collecting and labeling data in an iteration mode, and continuously optimizing the performance of a retriever through an incremental learning mechanism in the iteration process so as to realize toxic text collection. According to the method, large-scale, fine-grained and high-consistency toxic text tagging corpora can be efficiently accumulated, and the problems of high tagging cost, inconsistent quality and poor expansibility in traditional toxic text data collection are remarkably relieved.

A quantum computer system comprises first and second registers which are used to separate a quantum state into multiple subspaces of a 2n dimensional Hilbert space. A quantum state comprising 2n elements is defined on the first register, the first register comprising n qubits; a quantum state is defined on the second register, the second register comprising one or more qubits; and receiving a value k, where k is a binary integer such that 0 =< k =< n. The method further performing a bit-wise iteration process comprising: (i) performing a quantum entanglement between the registers to separate the quantum state on the first register into distinct subspaces of the Hilbert space which are indexed by the entangled values on the second register, and (ii) measuring an outcome on the second register to find a match with a portion of k, wherein said portion of k increases incrementally with the iteration process until the match is with all of k. The bit-wise iteration is used to separate the quantum state on the first register into subspaces of the Hilbert space, wherein elements of the subspaces have different Hamming weights, and one subspace contains only elements of Hamming weight k.

The invention discloses an active power distribution network energy storage optimization configuration method and system based on an improved parrot algorithm, and the method comprises the steps: building a multi-target energy storage optimization configuration model with node voltage deviation, energy storage cost and power distribution network loss as target functions according to a preset constraint condition; a chaos theory is introduced to initialize a parrot algorithm population, in each iteration process, weights of foraging behaviors, staying behaviors, communication behaviors and stranger afraid behaviors in the parrot algorithm are dynamically adjusted according to fitness values of individuals and current iteration times, Gaussian variation or Cauchy variation is selected according to variation probabilities, and a parrot algorithm is obtained. Mutation operation is carried out on the parrot individuals in the iteration process, and finally an improved parrot algorithm is obtained; and solving the multi-target energy storage optimal configuration model according to the improved parrot algorithm, and outputting an energy storage optimal configuration result. Efficient collaborative optimization of multiple objective functions is realized, and more accurate and comprehensive decision support is provided for energy storage optimization configuration of the active power distribution network.

The invention relates to a power distribution network reactive power scheduling scheme generation method and device, computer equipment, a computer readable storage medium and a computer program product. The method comprises the steps of randomly generating a plurality of power distribution network reactive power scheduling schemes based on parameters of a power distribution network system, and establishing a fitness function; based on a fitness function, iteratively optimizing the plurality of schemes, each iteration process comprising: based on fitness values of the plurality of schemes of the last iteration, determining a target scheme of the current iteration; based on the target scheme and a piecewise nonlinear decreasing weight strategy, performing first optimization, second optimization and horizontal crossover operation on the plurality of schemes of the last iteration to obtain a plurality of schemes of the current iteration, and based on the fitness function, determining fitness values of the plurality of schemes of the current iteration; and when the number of iterations reaches a preset number of iterations, taking the optimal scheme of the last iteration as an optimized power distribution network reactive power scheduling scheme. The method can prevent from falling into a local optimal solution during optimization.

The invention belongs to the field of coherent combination, and discloses a multi-strategy segmented fusion adaptive SPGD algorithm for a laser coherent combination system. The method is used for solving the problems that in a traditional SPGD algorithm, due to the fact that a fixed gain coefficient and disturbance amplitude exist, the convergence speed and stability of the algorithm are contradictory, and the optimal performance is difficult to achieve in different environments and system states. According to the method, the algorithm is divided into three stages in the iteration process, and self-adaptive adjustment of the gain coefficient and the disturbance amplitude is realized according to the characteristics of a hardware system in combination with three different iteration strategies. According to the method, the convergence speed of the algorithm can be remarkably improved, meanwhile, the stability of the algorithm can be effectively improved, the algorithm has higher robustness in consideration of hardware system characteristics, and the good application prospect and feasibility of the algorithm are shown.

The invention provides a microgrid scheduling method, and the method comprises the steps: constructing a first optimization model which comprises a plurality of objective functions and a plurality of constraint conditions; converting the first optimization model into a second optimization model in a QUBO form; the second optimization model is solved through a mixed quantum approximate optimization algorithm to obtain a micro-grid scheduling strategy, the mixed quantum approximate optimization algorithm comprises a quantum approximate optimization algorithm and a classical algorithm, the quantum approximate optimization algorithm is used for solving the second optimization model through quantum evolution in the multi-round iteration process, and the classical algorithm is used for solving the second optimization model through quantum evolution in the multi-round iteration process. The classical algorithm is used for adjusting optimization parameters of the quantum approximate optimization algorithm based on candidate solutions generated by quantum evolution in each iteration process, parallel exploration of multiple potential solutions is achieved, and therefore the probability of finding an optimal solution is improved within feasible time.

The invention discloses a heterogeneous cluster task scheduling fusion method based on Q learning and a genetic algorithm, and belongs to the technical field of intelligent scheduling. Collaborative optimization is carried out through a dynamic feedback mechanism of Q-Learning and the global search capability of the genetic algorithm: in an initialization stage, a cluster state space and an action space are defined, and a multi-Q-value table and an initial population are generated; during task scheduling, nodes are selected based on a # imgabs0 #-greedy strategy, reward values are calculated, and a Q value table is updated; meanwhile, the scheduling scheme is coded into chromosomes, a new population is generated through roulette selection, crossover and variation, and a Q value table is optimized; in the iteration process, the convergence is judged according to the Q value change or the population fitness change, and the strategy is dynamically adjusted. According to the method, the advantages of double algorithms are fused, local optimum is avoided, the task processing efficiency and the resource utilization rate are remarkably improved, adaptive strategy updating during cluster state change is supported, and the method is suitable for an efficient task scheduling scene of a large-scale heterogeneous cluster.

The invention provides a field modeling large model fine tuning method and system, and the method comprises the steps: extracting core elements and main tasks in a field business from the demand description of a business field, starting with the elements and the tasks, defining instruction modes of sub-tasks, and quantifying and evaluating the completeness of an automatic modeling result. And the fine-tuned large model is optimized according to the evaluation result, the completeness of the model is effectively improved by utilizing a fine-tuning-quantification-evaluation-optimization iteration process, and the method can be suitable for field scenes with relatively high reliability requirements.

The invention relates to the technical field of sparse matrix multiplication, and discloses a high-performance SpMM kernel implementation method based on a dense tensor core. The method comprises the following steps: storing a sparse matrix in a BlockNM sparse format, and pre-fetching metadata in the sparse matrix into a shared memory of a GPU (Graphics Processing Unit); asynchronously copying the sparse matrix and the dense matrix to a shared memory, setting a four-stage pipeline scheduling strategy, and controlling SpMM operations of different iteration processes; loading the sparse matrix and the dense matrix in the shared memory into a register, setting an offset for each thread accessing the shared memory, and enabling each thread to access different banks in the shared memory based on the offset; the matrix multiplication and addition operation is executed in the register to obtain a calculation result, the calculation result is temporarily stored in the shared memory from the register, then the calculation result in the shared memory is written back to the global memory, and the efficiency of sparse matrix multiplication is improved.

The invention discloses a layered LDPC (Low Density Parity Check Code) decoding method based on syndrome feedback self-adaption, and aims to solve the problem of poor decoding performance caused by fixed correction factors and poor adaptability to different check matrixes and channel conditions in the existing LDPC decoding algorithm. The method comprises the following steps of: firstly, dividing rows of a check matrix into a plurality of independent layers by adopting a hierarchical scheduling architecture so as to accelerate propagation and convergence of decoding information; secondly, in each iteration process of hierarchical decoding, a self-adaptive mechanism based on syndrome feedback is used, and a core offset factor beta in an offset minimum sum algorithm is adjusted in real time; the mechanism intelligently increases or decreases the offset factor by diagnosing the current decoding state (i.e., the number of unsatisfied check equations) to help the decoding process jump out of local optimum and stably converge. A simulation result shows that compared with a traditional offset minimum sum algorithm, the decoding performance is effectively improved and the bit error rate is reduced on the premise that the complexity is not obviously increased, and particularly, the performance gain is obvious in a medium-high signal-to-noise ratio region, and the robustness is higher.

The embodiment of the invention provides a column hierarchical message passing decoding method, a column hierarchical message passing decoding device and a related component, and the decoding method comprises the steps: carrying out the column-by-column decoding of a basis matrix, and calculating the initial prior channel information and a second hard decision sequence of a variable node; calculating an initial V2C symbol value transmitted by the variable node to the connected check node according to the initial prior channel information, completing message updating of the check node by using the V2C symbol value after the previous iteration (the initial V2C symbol value in the first iteration) and row information in the subsequent iteration, and completing updating of the variable node by using the updated check node. Therefore, row information updating of the basis matrix is realized, and the storage overhead of messages transmitted by decoding nodes in the iteration process is effectively reduced.

The invention relates to the technical field of computers, and discloses an information source coding matrix data processing method based on a modified conjugate gradient algorithm. According to the method, sparsity constraint and a fixed-point quantization mechanism are fused in iterative optimization, and a sparse fixed-point coding matrix meeting hardware deployment requirements is directly generated through gradient shielding, correction conjugate parameter calculation and Armijo criterion line search and synchronous execution of hard threshold sparseness and fixed-point quantization after each update. According to the method, the sparsity constraint and the fixed-point quantization mechanism are embedded in the iteration process of the modified conjugate gradient algorithm, so that end-to-end alignment of coding matrix optimization and hardware deployment requirements is realized.

The invention discloses a multi-strategy improved fox-monkey optimization algorithm for global optimization and application thereof, and belongs to the technical field of optimization algorithms, the method comprises the following steps: firstly, using Chebyshev chaotic mapping to initialize a population so as to improve the diversity and coverage range of the initial population; secondly, a difference population evolution strategy is introduced in the iteration process, and the algorithm is prevented from falling into local optimum in the later period; and finally, introducing a crisscross strategy to enhance global search capability and maintain population diversity. 20 benchmark test functions and a part of complex functions in a cec2017 function set are adopted for a simulation experiment, and the experiment result shows that the optimization precision, the convergence speed and the stability of the improved algorithm are obviously improved. The improved algorithm is applied to the engineering optimization problem, compared with an original algorithm, the convergence precision of the algorithm in three actual engineering problems is improved by 19.56%, 19.18% and 6.7% respectively, the standard deviation is reduced by 91.44%, 27.56% and 92.19% respectively, and the feasibility of the improved algorithm is further verified.

Some aspects utilize an iterative process to synthesis a logic network within an ASIC using lookup table (LUT) optimization techniques. The logic network may be represented by an And-Inverter graph (AIG), as described in more detail below. On each iteration, the current AIG representation of the logic network is mapped to a network of k-LUTs. A k-LUT is a lookup table that can represent any function of k variables. This network may then be improved using LUT optimization techniques. The improved network of k-LUTs is decomposed into a trial AIG representation, which may be further improved for example by applying Boolean-based optimization techniques. The current AIG representation may then be updated in accordance with the quality of the trial AIG representation produced by the current iteration. The final AIG representation from the iterations is synthesized to a netlist of standard cells.

The invention discloses an efficient routing algorithm for offshore buoy networking. The problem that existing offshore buoy networking information transmission is poor in real-time performance and stability is mainly solved. According to the scheme, the method comprises the following steps: inputting the position of an offshore buoy network, constructing a communication link connectivity model, and abstracting a network link topology by means of a graph theory representation mode; comprehensively considering network efficiencies such as time delay, packet loss rate and network load balancing, and determining link cost between two buoy nodes; aiming at the defect that the ant colony algorithm is easy to fall into local optimum, two-point improvement is carried out on the basic ant colony algorithm. The method comprises the following steps: firstly, during construction of initial pheromones, in a first iteration process of an algorithm, 20% of ants in a population are selected, a path with the minimum cost is selected based on a neighbor algorithm, and the remaining ants adopt random selection paths, so that the convergence of the algorithm is improved, and meanwhile, the diversity of the population is ensured; and a second point, aiming at the problem that a large number of ants are easy to walk to a repeated path when the ant colony is converged to the local optimum in the later stage of iteration, adding repeated check operation in an algorithm updating process. According to the specific operation, when 20% of ants select repeated paths, the ants of the repeated paths select new paths in a random mode, the path search space is expanded, and the algorithm performance is improved. According to the method, the efficient route of buoy networking communication can be dynamically planned, and the real-time performance and the stability of the network are met.

The invention provides a sample generation method and device, a storage medium and a program product. The sample generation method comprises the following steps: inputting a first type of prompts into a sample generation model to obtain a sample set; taking the sample set as a current sample set in a first round of iteration process, and executing the following iteration process: performing clustering processing on the current sample set to obtain a plurality of class clusters; if the iteration ending condition is not met, determining a to-be-enhanced class cluster and a to-be-inhibited class cluster according to the number of samples contained in each class cluster; based on the to-be-enhanced class cluster and / or the to-be-inhibited class cluster, constructing a second class prompt comprising a negative constraint condition and / or a positive constraint condition; the negative constraint condition is used for indicating to reduce generation of samples belonging to the to-be-inhibited class cluster, and the positive constraint condition is used for indicating to increase generation of samples belonging to the to-be-enhanced class cluster; and inputting the second type of prompts into the sample generation model to obtain a new sample set, and taking the new sample set as a current sample set in the next round of iteration process.

The invention relates to the technical field of high-dimensional feature selection, and provides a multi-party high-dimensional unbalanced federal evolution feature selection method, which comprises the following steps that: firstly, a plurality of participants jointly train a gaan network through federal learning, each participant uploads parameters to a federal server for aggregation after convergence, and then the server returns new parameters to each participant, so that a multi-party high-dimensional unbalanced federal evolution feature selection is realized; and updating a local ga parameter. Therefore, the global gaan network can be better trained by combining the data of the participants. According to the data imbalance problem existing in each participant, virtual samples are generated through the trained global ga network for completion, so that the data of each participant is balanced, then an agent model is used for evaluating the particle fitness of each participant, and an evolutionary algorithm is used for iterating a population, so that the particle fitness of each participant is evaluated. In the iteration process, the most characteristic subsets of the participants can be uploaded to a federated server, and the server carries out operation to obtain the subset with the optimal characteristic after aggregation of the participants and returns the subset to the participants.

The invention relates to a self-adaptive information negotiation method applicable to a continuous variable quantum key distribution system, which comprises the following steps of: estimating an optimal code rate that an LDPC (Low Density Parity Check) code should reach under a current quantum channel, converting an initial code rate into the optimal code rate by a receiver, and obtaining a low-reliability bit number l which needs to be disclosed in conversion calculation, the method comprises the following steps of: decoding an LLR (Local Library Ratio) by using Bob, sending the LLR to Alice through a classic channel in combination with an encryption technology, sorting absolute values of the LLR by Alice in a decoding iteration process, selecting l bit positions with low reliability, sending the bit positions to Bob, determining a bit value by Bob according to the bit positions with low reliability sent by Alice, and feeding back and sending the bit value to Alice, and correcting the LLR value by Alice according to bit information sent by Bob. According to the method disclosed by the invention, the code rate of the LDPC code can be flexibly adjusted along with the change of the state of the time-varying channel, and the negotiation efficiency is improved, so that the key rate of the system is improved. The method is excellent in performance in a time-varying channel environment, negotiation efficiency under a low signal-to-noise ratio condition can be remarkably improved, and an efficient and reliable information negotiation scheme is provided for practical application of a quantum secret communication system.

The invention relates to the field of power management, in particular to a load power consumption management control method and system based on time-of-use electricity price, and the method comprises the steps: obtaining the historical power consumption data of each user, and constructing a fitness function of a load scheduling scheme; setting an initial population; calculating a variable coefficient of each user in each time period; for each individual, weighting a preset initial variation rate according to the variation coefficient of each user in each time period, and obtaining a weighted variation rate of each user in each time period; and iteratively updating the initial population by using the weighted variation rate, outputting the individual with the highest fitness in the current population as the optimal load scheduling scheme, and performing optimal scheduling of the user load according to the optimal load scheduling scheme. According to the method, the variation rate is dynamically adjusted, and excellent individuals can be quickly identified in the iteration process, so that redundancy calculation is reduced, the efficiency of the algorithm is improved, and the algorithm can give an optimal scheduling scheme in time.

The invention provides a whole-process production scheduling method considering processing and assembling, which comprises the following steps: constructing a two-stage flexible flow shop scheduling model, scheduling processing tasks of all parts of a plurality of products on a parallel machine in the first stage, and scheduling assembling of the product parts and processing tasks of semi-finished products in the second stage; minimizing the maximum completion time and the total energy consumption of the machine is taken as a double-optimization target; a multi-target swarm intelligence algorithm is adopted to solve the model, population individuals represent a scheduling scheme through two-segment coding, the first segment of coding defines a process execution sequence, and the second segment of coding defines a machine selection result; in an iteration process, alternately executing a Thompson sampling strategy and a dedirectional sampling and generating strategy according to a preset probability, adaptively selecting a bottom layer optimization operator to realize global search, and constructing a guide solution set to realize local mining; and outputting a non-dominated solution set after iteration is ended, and obtaining a corresponding whole-process scheduling scheme after decoding.

The invention relates to the technical field of pipelines, in particular to a complex FLNG cabin three-dimensional pipeline layout method which comprises the following steps that S1, basic parameters are initialized; s2, calculating initial solutions by adopting an intelligent initial strategy based on obstacle density, and taking an optimal solution in the initial solutions as a current global optimal solution; s3, entering an iteration process, calculating an energy factor, if the energy factor is greater than 1, performing long-distance migration with the probability of 30%, and performing hole digging with the probability of 70%; otherwise, randomly foraging or avoiding the predator at equal probability; s4, periodically performing an elitism and neighborhood search strategy; s5, executing an adaptive orthogonal constraint relaxation strategy and boundary limitation operation; and S6, calculating and determining a final global optimal solution, and outputting a final layout result. According to the method, the calculation efficiency and the convergence speed are improved, the actual engineering situation is fit, and the engineering cost is reduced on the basis of preferentially meeting orthogonalization feasibility.

A method and device for querying a minimum group Steiner tree on graph data, and a medium, for graph data and group Steiner tree query given by an application scene, obtaining a query core vertex set and a minimum core vertex, performing a multi-round division-combination iteration process, constructing a result tree, i.e., the minimum group Steiner tree, and obtaining the minimum group Steiner tree. Group Steiner tree search on graph data is realized, an approximate solution with relatively high quality is quickly solved in a large-group-number scene, and a graph data query request of an application scene is responded. The invention provides a rapid approximate solution query method for a group Steiner tree problem with a large group number, the solution time is reduced to a second level from a day level, a high-quality solution is returned within the time acceptable by a user, and the problem that the query response time is too long or even overtime in a graph data query application scene is avoided.