303 results about "Greedy algorithm" patented technology

The invention provides an intelligent boxing method and system. The intelligent boxing method comprises the steps of obtaining boxing task information and generating a boxing scheme. The boxing task information comprises the size, the weight, the stacking limitation, the number and the priority of each cargo, and the size, the bearing capacity and the occupation state of each loading unit. And under the condition that the size, bearing and stacking constraint conditions are met, a plurality of candidate boxing combinations are generated, the loading utilization rate, the residual space distribution and the shape matching degree are evaluated based on a preset optimization target, and an optimal scheme is selected. The system comprises an input terminal, a task information receiving module, a data storage module, a task and scheme storage module, an operation processing module and a final boxing scheme generation module. According to the method, multi-dimensional constraint matching and weight balance are considered, and space waste and transportation risks are avoided; the maximum greedy algorithm and the adaptive large neighborhood search optimization are combined to realize the combination of automation and experience. The method can be expanded to batch task scheduling and grouping sequencing, so that the calculation complexity is reduced, and the overall boxing and transportation efficiency is improved.

The invention discloses an information retrieval system and method based on semantic normalization, and relates to the technical field of artificial intelligence information, and the method comprises the steps: collecting a semantic query record input by a user, carrying out the preliminary semantic analysis, and generating structured data; on the basis of the structured data, entity disambiguation is carried out by utilizing a knowledge graph, abstract classes are generated through a neural network, calibration and dynamic weight adjustment are carried out, and high-confidence entity abstract classes and confidence scores are generated; entity abstract classes and confidence scores are combined with user contexts, an action-value function is calculated through a value network, and an optimal action is selected by utilizing a-greedy algorithm; executing semantic normalization mapping according to the optimal action, and obtaining an intermediate expression by using a meta-symbol dynamic generator; and performing index retrieval and multi-dimensional sorting based on the intermediate expression to generate a sorted retrieval result list. According to the method, the semantic fragmentation problem of multi-modal query is solved, and deep semantic alignment and dynamic weight calibration of heterogeneous data are realized.

The invention belongs to the technical field of electric power engineering digitization and intelligent drawing auditing, and particularly relates to a dynamic rule engine and multi-target auditing task decomposition method oriented to electric power drawings. Obtaining a power rule file, analyzing and verifying to generate qualified rules, and screening the qualified rules to obtain an effective rule pool; the method comprises the following steps: reserving candidate areas by preprocessing an electric power engineering drawing, generating structured cells through line segment classification and combination and table detection, extracting and cleaning a text, and affiliating the text to the corresponding cells to form a structured result; screening candidate rules from the effective rule pool, extracting auditing objects from the structured result, generating auditing points, calculating the priority of the auditing points, and packaging the auditing points into an assignment unit set capable of being executed in parallel; on the basis of the dispatching unit set, optimizing task distribution by adopting a dynamic model and a greedy algorithm in an execution environment with the maximum concurrency, and doubly optimizing task execution by quantifying benefits and verifying rules; and performing rule judgment and result measurement on a task execution result.

The invention provides a wireless resource scheduling method and device used in a scene in which a mobile phone is directly connected with a satellite, and relates to the technical field of satellite communication, and the method comprises the steps: constructing an electromagnetic map which represents the corresponding relation of a geographic position, a physical resource block and ground interference power in a satellite beam coverage area; link budgeting is carried out on the multiple user terminals in combination with the real-time orbit geometric parameters of the satellite and the electromagnetic map, and the signal to interference plus noise ratio of each user terminal on each physical resource block is predicted; based on the predicted signal to interference plus noise ratio, adopting a proportional fair marginal gain greedy algorithm to allocate an optimal continuous spectrum resource block for each user terminal; on the allocated spectrum resources, optimizing and reallocating the total transmitting power of the satellites by adopting a water injection algorithm; and a final spectrum-power joint scheduling scheme is generated, and subsequent link self-adaption and data transmission are guided. According to the invention, the spectrum efficiency, throughput and connection stability of the system are obviously improved.

The invention provides a truck-unmanned aerial vehicle cooperative dynamic scheduling method and system in emergency logistics, and relates to the technical field of unmanned aerial vehicle intelligent scheduling. Constructing a truck-unmanned aerial vehicle cooperative distribution network, wherein each truck is equipped with a cooperative unit comprising a large unmanned aerial vehicle and a small unmanned aerial vehicle; the network comprises warehouses, hub points and demand points; establishing a mixed integer programming model by taking minimization of the total operation cost as a target and taking electric quantity management, service distribution, a time window and collaborative feasibility as constraints; and solving the model by adopting a randomized greedy algorithm, and synchronously deciding a truck path and an unmanned aerial vehicle task in single iteration to obtain a truck path scheme, an unmanned aerial vehicle service distribution scheme and a dynamic electric quantity scheduling scheme. Therefore, the cost optimization and dynamic coordination of emergency logistics scheduling are realized, and the efficiency and feasibility of emergency distribution are effectively improved.

The invention discloses a dynamic planning method and system for an intelligent patrol point location of power transformation equipment, and belongs to the technical field of intelligent patrol of power systems, the dynamic planning method for the intelligent patrol point location of the power transformation equipment comprises the following steps: mapping dynamic features and static attributes to the same feature space and carrying out cross-modal association; constructing a defect severity model, and introducing defect severity in risk quantification calculation to generate a point location priority list; a transformer substation three-dimensional point cloud model is built, path nodes are initialized according to a point location priority list, and a greedy algorithm is utilized. A closed-loop decision-making system of multi-modal data fusion is constructed; according to the method, automatic planning driven by a risk quantification model based on defect history, generation of a three-dimensional space non-blind area coverage path, dynamic adjustment triggered by two factors of equipment change and inspection effect, deep collaborative analysis of machine account-defect-real-time data and continuous inspection of a complex scene are guaranteed by a semantic compensation mechanism.

The invention discloses an intelligent power grid load prediction and dynamic response coordinated scheduling method, and relates to the technical field of power system automation, and the method comprises the steps: accessing intelligent ammeters, distributed power controllers and other devices of Modbus, IEC61850 and DL / T645 protocols through a multi-protocol adaptive gateway, and achieving data standardization; time stamps are calibrated by means of Beidou time service and an IEEE1588PTP protocol, and it is ensured that multi-source data synchronization errors are controllable; deploying an edge computing node cluster, distributing high-priority tasks to low-load nodes through an edge coordinator in combination with a load fluctuation level and a greedy algorithm, and ensuring real-time processing efficiency; the edge nodes generate short-term load prediction, and the cloud platform outputs medium and long-term prediction based on a historical data training model; and finally, the coordinated scheduling decision module fuses the two types of prediction results and the real-time parameters of the power grid, and generates a dynamic instruction to control the output of the adjustable load and the distributed power supply.

The invention discloses a new energy automobile charging scheduling method and system based on deep learning, and the method comprises the steps: carrying out the preprocessing of data, and obtaining multi-source data; a self-attention mechanism of Transform is combined with a GNN graph neural network to establish a deep learning model, a time sequence is processed, a topological relation between geographic distribution of charging piles and a power grid load is modeled through GNN, and spatio-temporal joint features are output; meta-learning is introduced to dynamically adjust the weight of the spatio-temporal joint feature according to a real-time environment, and a charging demand prediction index is obtained; and solving an optimal charging pile distribution scheme through MIP mixed integer programming according to the charging demand prediction index, and generating a charging scheduling scheme by adopting a greedy algorithm. The total charging cost is reduced, the average waiting time of users is shortened, and the power grid load variance is reduced.

The invention discloses a content abstract generation method based on chapter structure analysis, and belongs to the technical field of natural language processing. The method comprises the steps that firstly, an original text is preprocessed, then text structure deep analysis is carried out, the text type of the text is recognized, an explicit / implicit text relation is extracted, and special symbols are introduced through a Prompt normal form for implicit text relation extraction to strengthen logic semantics; secondly, scoring sentences by adopting a double-path scoring mechanism in combination with a deep neural network model of chapter structure features and an optimized text sorting algorithm, and fusing scores through a logistic regression model; then, screening target sentences based on a chapter relation weighted secondary modulus function and a greedy algorithm, and finally, carrying out post-processing to generate an abstract. According to the abstract generation method, the chapter structure logic is deeply utilized, so that the problems of logic unsmoothness, information redundancy or key relation missing in the existing abstract generation are solved, and the semantic coherence and information integrity of the abstract are improved.

The invention relates to a program-controlled resistor card and a program-controlled resistor smooth control method and device thereof. The method comprises the following steps: acquiring a resistor card code combination corresponding to a current resistance value and a target resistance value; a difference mask is calculated through an XOR operation, and a relay set needing to be changed is identified; on the basis of a greedy algorithm strategy, a relay enabling the instantaneous resistance value to be closest to a target value is selected from the difference mask identification set in a successive single-step switching mode for operation; iteratively executing switching and state updating until the difference mask is zero; and finally outputting a switching completion signal. The device comprises a state acquisition module, a difference analysis module, a switching decision module and a driving execution module, realizes smooth transition in a resistance change process through ordered single-step relay switching control, effectively avoids extreme states such as an open circuit or a short circuit, remarkably improves switching precision and system reliability, and is suitable for the fields of precision test measurement and automatic control.

The invention discloses an edge personalized reasoning method and system based on heterogeneous resources. The method comprises the following steps: optimizing edge deployment and reasoning through three aspects of technologies: customizing a resource-aware knowledge base, constructing a knowledge graph and converting the knowledge graph into a Pareto optimal problem, dynamically adjusting according to end-side resources, cooperatively processing tasks by the edge and the end, and compressing data; heterogeneous end side efficient query, sparse indexing for low-computing-power equipment, kNN algorithm for high computing power, weighted fusion of prediction results, and greedy algorithm optimization storage; an edge-end collaborative reasoning framework is adopted, edge nodes process intensive tasks, end-sides execute personalized tasks, a server cuts and distributes a multi-level knowledge base, a differentiated retrieval strategy is designed, the framework constructed by the method can fully utilize resource characteristics of different devices, and the overall reasoning efficiency is improved. The edge-end cooperation mechanism can improve the reasoning complexity and depth through edge enhancement while meeting the end-side resource requirements, and balance the response speed and accuracy.

The invention discloses a reinforcement learning-based cable arrangement method and system in a power supply and distribution system, and a storage medium. The method comprises the following steps of S1, obtaining cable information, laying environment information and constraints required to be met by cable laying; s2, abstracting a bridge system into a weighted undirected topological graph; s3, calling a path planning algorithm based on reinforcement learning, and calculating an optimal cable laying path by taking a minimum comprehensive cost function including path length cost, plot ratio cost and precious penalty terms as an optimization target under the constraint that cable laying needs to be met; s4, calculating the center coordinate of each cable to be arranged in each bridge by adopting a greedy algorithm according to the optimal cable laying path, and arranging the cables according to the center coordinate; according to the method, the optimal laying and arrangement path can be planned on the premise of meeting various physical constraints of cable laying, and the method can automatically and dynamically adapt to different laying scenes.

The invention discloses an intelligent charging pile and a parking space dispatching management system and method. The system comprises a plurality of intelligent charging piles, a parking space monitoring device and a server. The server realizes cooperative intelligent management of the charging pile and the parking space through the charging pile management module, the parking space management module and the scheduling service module. The core of the system is that the scheduling service module dynamically distributes appropriate parking spaces and charging piles for charging requests of users on the basis of a greedy algorithm and comprehensive distance, charging efficiency, income, power grid load and other multi-dimensional factors. According to the invention, the problem of low resource utilization efficiency caused by separation of the charging pile and parking space management can be solved, and maximization of resource utilization, optimization of user experience and peak load shifting of a power grid load are realized.

The invention discloses a large-scale cluster target electromagnetic scattering analysis method and system based on a parallel equivalence principle, and the method specifically comprises the steps: carrying out the geometric modeling and equivalent surface construction of each target unit in a cluster target, and generating discrete surface element information; starting a parallel computing process, and distributing a target-equivalent face by adopting a greedy algorithm; a calculation process group corresponding to each sub-domain is partitioned according to a two-dimensional Cartesian coordinate grid, a transfer operator matrix between the sub-domains is partitioned according to the same two-dimensional calculation process grid, global parallel iterative solution circulation is carried out, and the equivalent surface flow of the sub-domain is updated; and the converged equivalent surface flow of each sub-domain is used as an equivalent source, and the radar cross section RCS of the whole cluster target in the specified observation direction is synthesized through far-field radiation integration. The method has good parallel expandability and calculation efficiency, is suitable for complex and diverse cluster target scenes, reduces memory requirements and calculation time overhead, improves calculation efficiency, and is high in engineering practicability.

The invention discloses a tool semantic completion and tool map construction method based on a large language model. The method comprises the following steps: collecting an API document of a target tool, performing structured processing to form a standard tool set, and constructing a map ontology on the basis of the standard tool set; complementing tool function description and parameter semantics by utilizing a tool call log and combining an entity recognition technology; a semantic embedding and greedy algorithm is adopted to determine a dependency direction between tools, a tool chain simulation scene is generated through a large model, and an entity relationship edge is constructed in combination with a multi-model voting mechanism. According to the invention, a set of closed-loop verifiable tool map generation mechanism is innovatively constructed; by introducing a large model semantic enhancement technology, map noise caused by loss of tool document information is effectively reduced, and the construction precision of the tool map is remarkably improved.

The invention relates to the field of circuit breaker grid-connected and off-grid conversion, in particular to a system for realizing grid-connected and off-grid conversion based on a residual current protection circuit breaker, which is characterized in that a data sensing module acquires data through a Modbus-RTU protocol, processes the data through a linear active disturbance rejection control algorithm and outputs a pure data sequence; the power-off state analysis module predicts the power-off probability in combination with an echo state network algorithm, judges the island state through an improved island detection algorithm, fuses multi-source information by utilizing a D-S evidence theory, and judges whether the mains supply is powered off or not to generate a control instruction; the cooperative verification module carries out priority ranking on PCS based on an analytic hierarchy process and a fuzzy evaluation method and carries out cooperative control on off-grid mode switching by confirming the state of the commercial power, and the commercial power recovery module calculates the maximum recoverable power according to the rated power of the PCS and the SOC of a battery through load priority scheduling and a greedy algorithm, and gradually recovers load power supply; the system significantly improves the anti-interference capability of grid-connected and off-grid conversion, and is suitable for regions with unstable power grids. Customers feed back that the system is charged depending on an original photovoltaic system under the condition that the mains supply is in long-term power failure, so that the power utilization condition is greatly improved, and the power utilization difficulty is relieved.

The invention belongs to the technical field of unmanned aerial vehicle control, and discloses an unmanned aerial vehicle group control method and device, electronic equipment and a storage medium, and the method comprises the steps: obtaining the adjacent unmanned aerial vehicle information of each unmanned aerial vehicle in an unmanned aerial vehicle group in a GNSS limited or GNSS failure environment, and obtaining the adjacent unmanned aerial vehicle information of each unmanned aerial vehicle in the unmanned aerial vehicle group based on the adjacent unmanned aerial vehicle information through a division criterion and a greedy algorithm; dividing the unmanned aerial vehicle group into a plurality of unmanned aerial vehicle dynamic groups, calculating to obtain positioning information of each unmanned aerial vehicle in the corresponding unmanned aerial vehicle dynamic group by applying an extended Kalman filtering algorithm according to a target positioning optimization function and in combination with the multi-source sensor real-time data of the unmanned aerial vehicles and the adjacent unmanned aerial vehicle information, and determining the unmanned aerial vehicle dynamic group based on the target task information and in combination with the positioning information. A virtual spring-damping model is utilized to cooperatively control dynamic grouping of the unmanned aerial vehicles; through the extended Kalman filtering algorithm, the target positioning optimization function and the preset virtual spring-damping model, dynamic grouping of the unmanned aerial vehicles is cooperatively controlled, and the control efficiency of the unmanned aerial vehicle group is improved.

The invention discloses a method for rapidly screening a molecular marker combination for paternity test, which comprises the following steps: constructing a same allele Boolean matrix of possible parent pairs on candidate molecular markers according to typing information of all potential parents on the candidate markers, on the basis, a distinguishing ability matrix of the candidate molecular markers to all family pairs is constructed, and finally, a marker set capable of distinguishing molecular generation population parent sources is screened out by using algorithms such as a greedy algorithm and the like. According to the method, based on parent genotype information, a non-redundant effective marker set for offspring group parent traceability can be screened out from a large number of candidate molecular markers, and the marker set can be accurately and rapidly obtained in the face of mixed groups with more than multiple coefficients and under the condition of parent part marker typing deletion.

Techniques and architecture are described for a method that includes an in network compute (INC) manager receiving from switches of a fat tree configured network, arithmetic logic unit (ALU) capacity of the switches. Based at least in part on the ALU capacity of the switches and bandwidth, the INC manager determines one or more switches within each tier that are capable of supporting the processing units and based at least in part on the determining, the INC manager selects a first switch as a root, wherein the first switch is included within a tier of switches having intermediate tiers of switches located between the tier and the plurality of processing units within the fat tree configured network. The INC manager creates one or more paths of switches within each of the intermediate tiers from the root to the plurality of processing units to provide a constrained disjoint spanning tree of switches.

The invention discloses an emergency communication guarantee method and system based on dynamic resource scheduling, and particularly relates to the technical field of wireless communication. According to the method, communication requirements, network states and environment interference multi-dimensional data are collected through a multi-source sensor, noise reduction and normalization preprocessing are carried out on the data, and a standardized data set is generated; fusing subjective and objective weights by adopting an analytic hierarchy process-entropy weight method, and calculating a dynamic priority value of each communication demand in combination with a service rule; then, a resource scheduling strategy library is called, channels, bandwidths and base station access resources are distributed through an improved greedy algorithm according to the priority and the resource margin, and an initial scheduling scheme is generated; and after the scheme is executed, starting full-link quality real-time monitoring, and if any one of key parameters such as bit error rate, time delay and the like is lower than a preset threshold value, triggering closed-loop dynamic adjustment: updating data, recalculating priority and calling an emergency optimization strategy to adjust resource allocation in a targeted manner until the link quality reaches the standard.

The invention discloses a method and device for comprehensively optimizing test resources, electronic equipment and a storage medium, and belongs to the technical field of software test.The method comprises the steps that a test case set is classified through a high-dimensional data clustering algorithm, and a plurality of test case classes are obtained; selecting representative test cases from each test case class based on an improved greedy algorithm to form a target test case subset; performing priority ranking on the target test case subset by adopting a firefly group optimization algorithm to obtain a ranking result; and optimizing distribution and scheduling of test resources according to the sorting result so as to preferentially execute high-priority test cases and realize resource load balancing. By combining high-dimensional data clustering, an improved greedy algorithm and a firefly group optimization algorithm, the problem of large-scale test case management in the interconnection and interworking test of heterogeneous AC and AP equipment is solved, comprehensive optimization management of test resources is realized, and the test efficiency and the system quality are remarkably improved.

The invention discloses a UAV positioning and joint deployment and resource allocation method based on a radar, and relates to the technical field of UAV communication, the angle of a UAV relative to the radar is measured by deploying an EMVS-MIMO radar, the distance is determined by combining pulse ranging, and the three-dimensional position of the UAV can be estimated; according to the method, UAV deployment and GU association are optimized based on a BCD method, firstly, rapid convergence of a particle swarm algorithm and global optimization capacity of a grid search algorithm are combined, and UAV deployment is optimized; then, recalculating the GU association according to the new UAV deployment; a constrained particle swarm optimization algorithm is adopted to optimize the UAV transmitting power, and a variable learning rate is introduced to dynamically adjust the inertia weight, so that particles are helped to find a globally optimal solution; based on different requirements of communication and positioning GU on the bandwidth, a greedy algorithm is adopted to allocate the bandwidth of the UAV, and the fairness between the GUs is ensured.

The invention provides a maintenance resource configuration and scheduling method, and the method comprises the steps: determining a resource configuration point and the minimum number of resources in a given range, completing the planning of a resource scheduling path for avoiding obstacles and threats, enabling the recovery degree of equipment to be not lower than a target threshold value, and enabling the total time to be shortest. Comprising the following steps: (1) on the basis of a weighted K-means clustering algorithm, taking a clustering center of a plurality of to-be-repaired equipment clusters as a resource configuration point according to position information of to-be-repaired equipment and a repair task weight; (2) allocating resources to each resource allocation point based on a Hungary algorithm, and allocating the resources at the corresponding resource allocation points to the to-be-repaired equipment to ensure that the allocated resources can adapt to the fault of the to-be-repaired equipment; (3) selecting key equipment from the to-be-repaired equipment based on a priority greedy algorithm and preferentially allocating resources for key faults of the key equipment; and (4) planning of avoiding obstacles and threats is carried out on the path from each resource to the allocated to-be-repaired equipment based on an A * algorithm. According to the invention, efficient resource configuration and scheduling optimization are realized.

The invention discloses a hyperspectral image waveband selection method and system based on a three-dimensional convolution auto-encoder. The method comprises the following steps: carrying out block processing on an original hyperspectral image; designing a three-dimensional space-spectrum reconstruction network based on a global spectrum sensing module; constructing a combined loss function of a reconstruction error term and a sparse regularization constraint term, and performing iterative updating on the network by using an Adam optimizer; constructing a wave band comprehensive evaluation criterion based on triple constraints and a dynamic fusion mechanism; and successively selecting an optimal wave band by adopting a greedy algorithm according to a comprehensive evaluation criterion, and outputting a wave band selection result of the hyperspectral image. Through fusion of a three-dimensional spatial spectrum reconstruction module and a multi-head self-attention mechanism, local and global association of hyperspectral data is comprehensively mined, and deep joint representation of spectrum and spatial information is realized. Meanwhile, sparse regularization constraint is introduced into a loss function, and the network is guided to focus the most valuable key wave band.

According to the public service facility supply and demand matching site selection method and system based on the geographic grid, the geographic grid is adopted to integrate multi-source heterogeneous data, data such as population and land utilization are mapped to the unified grid, statistics and space anomaly inspection are combined to guarantee data quality, manual operation is reduced, the technical threshold is lowered, and the method and system are suitable for large-scale popularization and application. Non-professionals can carry out analysis through a standardized process, a precise data foundation is laid for subsequent work, land space planning and current situation data are fused, a population prediction model is constructed to deduce future distribution, planning data are incorporated into analysis, a result is made to adapt to 5-10-year city development requirements, perspectiveness and practical operation are considered, and the method is suitable for popularization and application. And finally, quantizing an accessibility gap through a Gaussian two-step moving search method, iteratively screening an optimal scheme in combination with a greedy algorithm, determining a facility construction priority order, avoiding resource idleness and repeated construction, and ensuring that newly-added facilities efficiently fill the service gap.

The invention relates to an underwater marshalling path planning method and system, and the method comprises the steps: carrying out the rasterization of a target region in a water area search region based on the environment data of the water area search region, and generating a detection capability matrix; based on the detection capability matrix, generating a minimum open coverage center point set of the detection range of the marshalling serving as a whole for the target area; taking the minimum open coverage center point set as a traversal object, generating an initial candidate path sequence population through a greedy algorithm, and executing iterative optimization on paths in the initial candidate path sequence population by utilizing a multi-objective optimization algorithm to generate a global optimal path; decomposing the global optimal path into an independent path of each platform in the marshalling; and smoothing all the independent paths based on a preset constraint condition to obtain an actual application path of each platform in the marshalling, and completing underwater marshalling path planning. According to the method, real-time calculation is carried out by utilizing environment information, so that the training time, the hardware cost and the trouble of data annotation are saved.

The invention provides a multi-mode unmanned aerial vehicle countering method and system based on ground-air cooperation, relates to the technical field of unmanned aerial vehicle countering, and solves the problems that an existing unmanned aerial vehicle countering technology is low in cooperation efficiency, weak in environmental adaptability and insufficient in real-time response capability. In the method, a ground station uses a radar to scan a target unmanned aerial vehicle in a monitoring airspace, calculates basic parameters of the target unmanned aerial vehicle, constructs a threat degree evaluation model, obtains the threat degree of the target unmanned aerial vehicle, and uses a greedy algorithm to carry out target distribution between a countering unmanned aerial vehicle and the target unmanned aerial vehicle to form a distribution scheme; the distribution scheme is uploaded to the countering unmanned aerial vehicle, and a target unmanned aerial vehicle distributed to the countering unmanned aerial vehicle in a monitoring airspace is recognized through a built-in lightweight visual recognition algorithm; a target unmanned aerial vehicle is continuously tracked through a built-in visual tracking algorithm, and a corresponding countering means is dynamically selected based on recognition and tracking states. According to the invention, the countering response speed of the unmanned aerial vehicle and the stability of target tracking in a complex environment are smoothly improved.

The invention discloses a dynamic batch hybrid flow shop scheduling method based on a state-driven iterative greedy algorithm, which relates to the technical field of production scheduling, and comprises the following steps: analyzing main scheduling problems influencing a production cycle; constructing a machine fault response mechanism; representing the solution in a symbolization manner; designing a state-driven iterative greedy algorithm based on problem characteristics and a coding and decoding strategy; constructing a dynamic batch hybrid flow shop scheduling method based on a state-driven iterative greedy algorithm; the method is compared with a common method. According to the method, machine faults are avoided as far as possible by optimizing the batch sequence, batch splitting and machine allocation, and a mixed fault response mechanism in which the preset rule and the DDQN decision are combined is provided for inevitable machine faults, so that the negative influence of the faults on production scheduling is reduced, and the production scheduling efficiency is improved. The disturbance intensity can be adjusted in real time according to the characteristics of the current solution and the historical optimal solution, dynamic allocation of computing resources is achieved, and the flexibility of the algorithm is improved.

The invention discloses a mobile ad hoc network route planning method and device based on LSTM node trajectory prediction, and the method comprises the steps: S1, adding mobile data containing the speed and position of a node into a Hello message, and periodically broadcasting the Hello message by each node in a mobile ad hoc network; s2, obtaining discrete mobile data of each node in the mobile ad hoc network through a Hello message, preprocessing the discrete mobile data, predicting the position of each node after a certain time period by using a trained LSTM model, and further obtaining position area data of a hop neighbor node of a local node within a certain time period; s3, based on the position prediction data of each node, a local node performs multi-point relay MPR selection in a one-hop neighbor node set by adopting a greedy algorithm; according to the method, while the balanced routing overhead is ensured, the data packet delivery rate is effectively improved, the average end-to-end delay is reduced, and the reliability requirement of the ad hoc network in the dynamic environment is met.

The application discloses a dynamic communication path selection and switching method for a full interconnection network, specifically comprising the following steps: renumbering all nodes in the full interconnection network; planning a communication path between any two nodes in the full interconnection network; after all the communication paths are sorted out, each communication path is tried in turn, and the message sending time corresponding to each path is recorded; and the time of acquiring the message transmission of each path is taken as the basis to select the shortest message transmission path from the historical record of message transmission as the message transmission path in the future. The application is based on the improved e greedy algorithm, dynamically adjusts the communication path of concurrent tasks, frequently selects and switches the communication path between communication nodes, avoids the subnet area that may have a conflict in the interconnection network, and further reduces the communication delay and the processing time of concurrent tasks in the network.