819 results about "Multi objective optimization algorithm" patented technology

The invention discloses a CAD automatic generation system and method based on intelligent model selection and application, and aims at achieving automatic modeling under the multi-modal design requirement. The system comprises a user interaction module for receiving multi-modal input such as natural language, sketch and voice; the intelligent demand analysis module is used for combining an industrial large language model and a product knowledge graph, combining semantic analysis and generating a structured demand; the intelligent model selection calculation module is used for matching the optimal parameter combination and the component list based on a multi-objective optimization algorithm; the CAD automatic generation module calls a parametric modeling engine to generate an editable three-dimensional model; the constraint solving module is used for processing hard constraints and soft constraints in real time and dynamically adjusting model parameters; and the model output and interaction module feeds back a design state and supports user iteration. The system realizes full-process automation from the design intention to the CAD model, improves the design efficiency and accuracy, and is suitable for the fields of mechanical design, intelligent manufacturing and the like.

The invention relates to the technical field of enterprise management, in particular to an intelligent decision support system and method based on cognitive logic and scenarized semanteme, and the method comprises the steps: obtaining enterprise decision data through multi-source data monitoring, carrying out the preprocessing, and generating cross-modal enterprise scene cognitive information; analyzing cross-modal association among the data, fusing a cognitive logical reasoning rule and a semantic understanding model, and constructing scenarized semantic decision fusion features; training an enterprise decision-making quality evaluation model based on historical cases, performing quality perception on a current decision-making scene, and outputting decision-making quality information; and an execution effect is judged in combination with an expected target, an optimization mechanism is started if the target is deviated, candidate schemes are generated by using a historical knowledge base and a multi-target optimization algorithm, an optimal solution is screened, and intelligent adjustment of a decision scheme is realized. According to the method, multi-source heterogeneous data and cognitive logic are fused, semantic understanding, dynamic evaluation and adaptive optimization capabilities of a decision system are enhanced, and scientificity and real-time performance of enterprise decision are improved.

The invention discloses an intelligent control and optimization system and method for the whole process of a photovoltaic power station. The method comprises the following steps: constructing a component-level high-fidelity digital twin through multi-source heterogeneous data fusion and an attention enhancement deep learning model; injecting annotated historical fault data, carrying out joint training by combining a feature embedding layer and an adversarial generative network, and generating a digital mapping body with fault evolution deduction capability; a three-layer architecture is introduced, reinforcement learning and a multi-objective optimization algorithm are fused, and autonomous decisions of power station level power output maximization, equipment loss minimization and maintenance cost optimization are realized in a virtual simulation environment constructed by a digital mapping body. According to the method, accurate dynamic mapping of the physical entity and the virtual space, fault pre-judgment and deduction and closed-loop control of global autonomous optimization are realized, the intelligent level and the full-life-cycle economy of the photovoltaic power station are improved, and the problems of insufficient fidelity, lack of fault deduction capability and low global optimization efficiency in the prior art are solved.

A full-automatic packaging scheduling method based on a multi-objective optimization algorithm, which method relates to the technical field of intelligent production scheduling in packaging workshops. The method comprises: on the basis of work orders to be packaged of a production line, extracting production data of the production line, and initializing parameters; constructing a multi-objective optimization model of the packaging production line; to address the problems of premature convergence and susceptibility to local optima in an INSGA-II algorithm, using the INSGA-II algorithm to improve an initialization method, crossover and mutation strategies and crossover and mutation factors, and performing solving on the basis of objective functions such as minimizing the maximum makespan, minimizing the maximum energy consumption and minimizing the total machine load, so as to generate an optimized scheduling scheme; and starting packaging operations on the basis of the generated optimized scheduling scheme. The method can increase the unit-time production capacity of a production line, and effectively solve the problems in packaging production scheduling, thereby reducing the production costs of enterprises and improving the packaging production efficiency.

The invention discloses a geological disaster emergency response path planning method based on reinforcement learning. The geological disaster emergency response path planning method comprises the following steps: S1, fusing and processing multi-source data; s2, constructing a four-dimensional space-time model based on the fused data; s3, multi-agent collaborative decision making is carried out, and a mixed agent system based on a Safe-PPO improved algorithm is deployed; a multi-objective optimization algorithm is adopted to balance the rescue efficiency and safety; deciding and outputting a leading path and a plurality of alternative paths; s4, performing conflict detection on the action of the path planning agent; s5, carrying out preferential selection on the paths subjected to conflict detection and global optimization; s6, issuing the path to a terminal for rescuing the action personnel; s7, dynamically monitoring the environment; s8, judging whether a path needs to be re-planned or not according to environment dynamic monitoring data; and S9, completing the task. According to the method, through reinforcement learning and multi-agent collaborative architecture, the risk penalty term is embedded in the reward function through the improved Safe-PPO algorithm, and the accuracy of path planning is improved.

The invention relates to the technical field of silver paste preparation, in particular to a silver paste conductivity data modeling and formula optimization system based on machine learning, which comprises a data acquisition and storage module, a preprocessing module, a characteristic influence analysis module, a formula optimization module, a simulation verification module and the like. The method comprises the following steps: acquiring original data of a silver paste formula, preprocessing, calculating influence coefficients of all components on target performance by utilizing a machine learning model, and identifying high and low influence components; the formula optimization module is combined with component content constraints and adopts a multi-objective optimization algorithm to generate candidate formulas; and the simulation verification module verifies the performance of the formula through sintering simulation and process adaptation, and feeds back optimization. According to the invention, the full-process intelligentization of the silver paste formula from data processing to optimization verification is realized, the conductivity and other performances of the silver paste are accurately improved, the research and development cost is reduced, the period is shortened, the suitability of the formula process is enhanced, and the research and development and industrial upgrading of the silver paste are promoted.

The invention provides a trailing suction dredger loading operation parameter optimization real-time recommendation system and method, and relates to the technical field of trailing suction dredger dredging engineering.The system comprises a data collection layer, a real-time calculation layer, an intelligent recommendation layer and a feedback control layer, the data collection layer is used for collecting trailing suction dredger loading operation parameters in real time, and the real-time calculation layer is used for calculating the real-time calculation layer; the real-time calculation layer can calculate energy consumption and yield in real time based on edge calculation equipment, the intelligent recommendation layer can generate a Pareto optimal solution set through a multi-objective optimization algorithm, and the dynamic weight adjustment module adjusts energy consumption and yield weight coefficients in real time according to the construction stage. The feedback control layer issues the collected loading operation parameters of the trailing suction dredger to an execution mechanism in real time through a PLC, and monitors the operation state of equipment to update a historical database, so that collaborative optimization of energy consumption and yield can be achieved, and the optimization precision is continuously improved through historical data.

The invention provides a laser remelting process parameter optimization method and system based on a multi-objective optimization algorithm. The method comprises the steps that the range of laser remelting process parameters on the surface of a cladding layer is limited; designing a process parameter combination by using an ELHS method, carrying out a laser remelting experiment by using different process parameter combinations on the premise of keeping the surface quality of the cladding layer consistent, and collecting performance index data after remelting; establishing a nonlinear mapping model between the process parameters and the performance indexes by using a PSO-XGBoost model; a Pareto optimal solution set is obtained through population and individual initialization, non-dominated sorting, congestion degree calculation, optimal solution calculation and global optimization of process parameters by adopting an MOEDO algorithm based on a nonlinear mapping model; and a CRITIC-TOPSIS decision system is utilized to carry out evaluation sorting on the Pareto optimal solution set, and an optimal process parameter combination is screened out. According to the method, the influence of the laser power, the scanning speed and the lap joint rate on the quality and performance of the laser remelting surface can be considered at the same time, and the limitation of traditional single process parameter optimization is broken through.

The invention discloses a disturbance of consciousness stimulation regulation and control system and method fused with electroencephalogram connection recognition. The system comprises a simulated electroencephalogram signal data acquisition stage, a connection recognition analysis stage, a stimulation parameter optimization stage and an executable stimulation instruction conversion stage. The method has the following advantages and effects that a whole-electroencephalogram activity distribution diagram is generated by simulating an electroencephalogram signal data acquisition stage, a key connection area is identified, and a brain function connection map is generated by utilizing a function connection analysis network and a phase synchronization algorithm in a connection identification analysis stage; in the stimulation parameter optimization stage, space-time correlation between a whole electroencephalogram activity distribution map and a brain function connection map is established, a multi-objective optimization algorithm is adopted to generate an optimized stimulation parameter set through a fusion network, and finally, in the executable stimulation instruction conversion stage, the optimized stimulation parameter set is converted into an executable stimulation instruction based on a self-adaptive control model. Therefore, the accuracy and the self-adaptive capability of electroencephalogram signal stimulation regulation and control are remarkably improved.

The invention discloses a double-machine cooperative hoisting operation guiding system and method based on Beidou positioning, and the system comprises a coordinate data implantation unit which is used for pre-storing a standardized coordinate template with environment compensation parameters; the sensor network is used for acquiring crane position, load and environment data in real time; the crane positioning and guiding unit is used for generating a multi-dimensional environment correction factor, dynamically comparing coordinates, weighting and calculating deviation, and planning a synchronous walking path through a multi-objective optimization algorithm; and the hoisting control unit is used for constructing a synchronous control model containing moment balance and kinematics constraint and adjusting hoisting parameters by adopting a closed-loop algorithm. According to the method, millimeter-level positioning guiding and double-machine load self-adaptive balancing can be achieved, segment inclination and wind disturbance influences are effectively restrained, hoisting synchronization precision and operation safety are remarkably improved, high-steady-state anti-inclination control can be completed without newly adding hardware, and the method has high engineering practicability and popularization value.

According to the large-span steel structure refined construction method based on the simulation model, through whole-process refined simulation and optimization, the construction level of a large-span steel structure is remarkably improved, the construction scheme is determined based on a multi-objective optimization algorithm, accurate design of parameters such as hoisting, supporting and monitoring is achieved, a temperature field model is combined with actual measurement correction, and the construction efficiency is improved. The integration of optimization closure temperature, node semi-rigidity and initial defects enables simulation errors, support stability evaluation and optimization guarantee of safety coefficients, most construction risks can be predicted in advance on the whole, the field adjustment time is shortened, the formal construction period is shortened, the construction efficiency and safety are greatly improved, the obvious engineering value and economic benefits are achieved, and the method is suitable for popularization and application. The problem that an existing large-span steel structure construction method lacks a calculation model for predicting the risk in advance and shortening the construction period by combining multi-aspect data is solved.

The invention relates to a node semi-rigid modeling-based complex curved surface reticulated shell multi-objective form optimization method. According to the method, through integration of parametric modeling, numerical simulation and a multi-objective optimization algorithm, efficient optimization design of the irregular boundary free-form surface latticed shell structure is achieved. The control point coordinate matrix of the NURBS curved surface is used for generating a geometric model of the complex curved surface reticulated shell, and high-quality grid division is automatically generated; semi-rigid mechanical behaviors of nodes are simulated through combination of a rigid beam and a spring in a numerical model, semi-rigid characteristics of the nodes are integrated into a rod piece unit, and the mechanical performance of the structure is accurately evaluated; and a non-dominated sorting genetic algorithm is adopted, the minimization of the total strain energy of the structure, the minimization of the total weight of the structure and the maximization of the minimum node configuration degree serve as objective functions, and the multi-objective form optimization design of the complex curved surface reticulated shell is completed. The method is suitable for a single-layer latticed shell structure with any node form and any special-shaped contour boundary, and has wide applicability and good transportability.

The invention discloses an intelligent manufacturing real-time decision-making method and system based on digital twinning, and relates to the technical field of intelligent manufacturing. Comprising the following steps: S1, collecting multi-source operation data in a physical production system in real time; s2, dynamically constructing and updating a digital twinborn model of the physical production system based on the multi-source operation data acquired in real time; and S3, based on the digital twin model, monitoring the operation state of the physical production system in real time. According to the method, the multi-source operation data of the physical production system are collected in real time, and the digital twin model is dynamically updated, so that no lag deviation between the model and the physical system is ensured; when a dynamic disturbance event is identified, event-driven real-time simulation and predictive analysis are started immediately, delay caused by traditional batch processing is avoided, and disturbance influence can be accurately evaluated in combination with historical data and expert experience; and then an optimal decision scheme is generated and screened in real time through a multi-objective optimization algorithm.

The invention discloses a security storage chip energy consumption optimization method based on multi-objective optimization, and relates to the technical field of energy consumption optimization, and the method comprises the steps: inputting the threshold voltage distribution characteristics of a security storage chip of a candidate parameter set into an aging model, fitting the rule of the threshold voltage distribution characteristics along with the time under an accelerated aging condition, and obtaining an aging model; aging model parameters are obtained; calculating the data retention failure rate of the security storage chip in the target life cycle by using the aging model parameters, and obtaining an extended feasible region of the instant erasure security constraint and the long-term retention security constraint in combination with a data retention judgment standard; and establishing a multi-objective optimization function with reduction of operation energy consumption and maintenance energy consumption as optimization objectives in the extended feasible region, and obtaining a candidate solution set through a multi-objective optimization algorithm. According to the method, data retention prediction in the life cycle is realized, and energy consumption optimization and long-term reliability constraint are combined.

The invention belongs to the technical field of wind power generation, and particularly relates to a fault diagnosis and early warning method and system for a wind power plant, and the method comprises the steps: collecting the state data of a plurality of physical fields through a plurality of types of sensor arrays disposed at key parts of a fan; performing preprocessing and feature extraction on the data to obtain high-dimensional feature data; driving the field-level digital twinborn model to perform real-time simulation based on the high-dimensional feature data, and determining an initial fault recognition result and severity by comparing a simulation predicted value with actual sensing data and combining with an unsupervised anomaly detection algorithm; a diagnosis result is calibrated through multi-modal high-dimensional feature data fusion; predicting the remaining useful life of the key component based on the fault severity and the evolution trend thereof, and generating a collaborative operation and maintenance decision by using a multi-objective optimization algorithm; and an operation and maintenance decision is automatically executed through a closed-loop feedback execution mechanism. According to the invention, the accuracy of fault diagnosis and early warning of the wind power plant is improved.

The invention discloses a lead bonding method and device based on dynamic self-adaption, equipment and a storage medium. The method comprises the following steps: based on position information of a first bonding point, position information of a second bonding point and parameter information of a lead, performing path planning on lead bonding through a multi-objective optimization algorithm, and generating an initial bonding path in a preset line arc shape; bonding a lead to a first bonding point of the chip bonding pad and applying energy, pulling the lead to the substrate bonding pad according to the generated initial bonding path, and monitoring bonding path information of the lead in real time in the pulling process; and when the monitored bonding path information of the lead exceeds a preset threshold range, dynamically adjusting the initial bonding path, pulling the lead to a second bonding point of the substrate bonding pad based on the adjusted bonding path, and applying energy to the second bonding point to obtain a chip after lead bonding. According to the invention, the problem of short circuit or poor contact caused by lead overlapping can be avoided, and the lead bonding efficiency is effectively improved.

The invention discloses a production control method and system based on multi-source data fusion and an intelligent algorithm, and relates to the technical field of automatic control. Multi-dimensional production state information is collected through a sensor, multi-source heterogeneous data is integrated into a data set in a unified format through a data fusion technology, and the multi-dimensional production state information is obtained; a long short-term memory network is used for trend analysis and prediction, a multi-objective optimization algorithm is used for calculating an optimal control parameter, multi-loop cooperative adjustment is realized, an adjustment instruction is issued through a distributed control protocol, a priority ranking algorithm is introduced to ensure system stability, and a game theory negotiation algorithm is used for realizing dynamic resource allocation among production units. The production scheduling plan is optimized through the genetic algorithm, continuous monitoring and optimization can be achieved, control parameters can be adjusted in a self-adaptive mode, the production efficiency and the resource utilization rate are effectively improved, and intelligent automatic control under the complex production environment is achieved.

The invention discloses an automatic extraction method for semiconductor device model parameters, and the method comprises the steps: obtaining a multi-target response through the key parameters of a semiconductor device model, and constructing and training an agent model to predict a target response mean value and uncertainty parameters; constructing a multi-objective optimization collaborative circulation framework assisted by an agent model; generating a candidate parameter population in key parameters according to a position updating mechanism of a multi-objective optimization algorithm, predicting candidate parameters by using a trained proxy model, selecting the candidate parameters according to a prediction result to carry out real simulation evaluation, adding an obtained real sample into a non-dominated file, and updating the file based on a non-dominated relationship. Adding a training set dynamic updating agent model into the real sample, generating a potential search trajectory, selecting a representative representative Pareto approximate solution from the potential search trajectory, and updating the position of the population and the direction of the next-generation search trajectory according to the representative solution selected from the file; and terminating the circulation, outputting the solution set in the file, and checking.

The invention provides a part lightweight design method based on a random forest model, and the method comprises the steps: defining a design variable and a response target, and generating a sample point through a test design sampling method; geometric modeling and updating, finite element preprocessing, solving calculation and result extraction are automatically completed through parameterized scripts, and a sample data set is formed; independently training a random forest regression model for each performance response by using the sample data set, and constructing a quality rapid calculation model; verifying the random forest agent model until all agent models meet preset precision requirements; on the basis of the proxy model, constructing an optimization problem, carrying out optimization solution by adopting a multi-objective optimization algorithm, and outputting a Pareto optimal solution set; and selecting a candidate design scheme from the Pareto optimal solution set, executing high-precision finite element simulation, comparing a prediction result and a simulation result of the proxy model, and if an error is within an acceptable range and all performances meet design requirements, determining a final lightweight design scheme.

The invention relates to the technical field of data processing, and discloses a wind turbine generator optimization design method based on tidal current-wave-sediment coupling simulation. The method comprises the following steps: carrying out adaptive time step adjustment on marine environment data through a physical process intensity discrimination algorithm to obtain tidal current field, wave field and sediment transportation data; carrying out fluid-solid coupling calculation on the structural parameters of the wind turbine generator to obtain structural vibration response and ocean load distribution data, and predicting pile foundation scouring depth distribution; carrying out time-varying coefficient correction on the pile foundation bearing capacity parameters to obtain dynamic bearing capacity evaluation data; inputting a multi-objective optimization algorithm to obtain a Pareto optimal solution set; and obtaining a wind turbine generator optimization design scheme through engineering feasibility verification and screening. The technical problems that in existing offshore wind turbine generator design, tidal current-wave-sediment coupling simulation space-time scales are not matched, the pile foundation scouring prediction precision is insufficient, bearing capacity evaluation lacks time-varying characteristic consideration, and the multi-target optimization design capacity is insufficient are solved.

The invention discloses a short video content accurate recommendation system based on artificial intelligence image recognition, particularly relates to the technical field of short video personalized recommendation, and is used for solving the problem of matching of user interest and visual bearing capacity. The method comprises the following steps: extracting short video key frame spatial-temporal characteristics through a multi-scale convolutional neural network, constructing a cognitive load threshold curve in combination with a user micro gesture sequence, representing the tolerance range of a user to visual complexity in different time periods, and generating a visual complexity vector; calculating a visual load matching index by using an adaptive dynamic warping algorithm, and adjusting a candidate video sequence through a load penalty factor to generate an initial recommendation probability; modeling a user dynamic interest vector based on a gated loop unit network in combination with an attention mechanism; and finally, user interests and video semantics are fused through multi-dimensional features, a final recommendation list is generated through a multi-objective optimization algorithm under the constraint of visual load, and personalized recommendation of interest matching degree maximization and visual comfort optimization is realized.

A warehouse logistics scheduling system based on big data analysis specifically relates to the technical field of logistics scheduling, and comprises an order emergency degree calculation component which is configured to integrate order information, customer data and inventory records, perform standardization processing on multi-source heterogeneous data through a big data analysis technology, calculate order emergency degree indexes, and send the order emergency degree indexes to a server; the equipment scheduling optimization component is configured to output a scheduling execution coefficient of equipment based on the order emergency degree index and the equipment real-time state data, and the system dynamic balance component is configured to collect storage energy consumption data, path congestion data and equipment execution data, calculate a system dynamic adjustment coefficient through a multi-objective optimization algorithm and output the system dynamic adjustment coefficient. According to the warehouse logistics scheduling optimization system and method, through deep fusion of big data analysis, an intelligent closed loop of warehouse logistics scheduling is constructed, and the scheduling targets of high timeliness, low cost and stable operation of the system are finally achieved.

The invention discloses an intelligent knowledge management system for a clean energy project, and the system comprises the steps: collecting multi-source heterogeneous data from the clean energy project, and converting the multi-source heterogeneous data into structured knowledge input data; performing knowledge extraction on the structured knowledge input data, and aligning and fusing knowledge extracted from different data sources to construct a structured knowledge base; performing data processing on the structured knowledge base through a time-aware graph attention network T-GAT and a graph structure causal model SCM to construct a dynamic time sequence knowledge graph; according to user requirements and scenes, a decision result and a knowledge recommendation result are obtained through an integrated causal-driven multi-objective optimization algorithm and a dynamic time sequence knowledge graph, and user feedback data are output; and processing the user feedback data by fusing Temporal Graph Network and an online near-end strategy optimization Online PPO algorithm, and carrying out dynamic optimization and adaptive adjustment on the knowledge graph, the rule base and the decision model according to the user feedback data.

The invention provides a method and system for dispatching heterogeneous GPUs through cross-cluster management software, and relates to the technical field of GPU resource dispatching.The method comprises the steps that physical topology data, hardware specification data and historical task records of GPU nodes are obtained through the cross-cluster management software, an association degree matrix between GPU units is established, and a topology grouping set is generated; calculating a communication efficiency index based on topology detection analysis, constructing a topological graph model, and determining an optimal communication path of a multi-card task; generating a perception scheduling strategy in combination with a multi-objective optimization algorithm, and selecting an optimal GPU combination from the grouping set; in the task execution process, a scheduling strategy is dynamically adjusted according to the real-time cluster state and task efficiency data, continuous adaptation of a physical topological structure and task requirements is kept, and finally efficient collaborative scheduling of cross-cluster heterogeneous GPU resources is achieved. According to the method, the scheduling efficiency of heterogeneous GPU resources in a distributed computing scene and the overall performance of the system are improved.

The invention discloses an optical storage and charging dynamic power distribution control method and system based on AI prediction, and the method comprises the steps: collecting photovoltaic, energy storage, charging, power grid and environment multi-dimensional operation data through a sensor network, and obtaining a historical and real-time data set through preprocessing; training an AI joint prediction model by using the historical data set, and outputting a photovoltaic output and charging load demand prediction result; inputting system operation constraint condition parameters to delimit an optimization boundary; solving a power instruction reference value of each power module through a multi-objective optimization algorithm in combination with a prediction result and a constraint condition to realize global optimal distribution; and correcting the reference value based on the hardware operation parameters and the deviation between the predicted value and the real-time value, issuing an instruction, monitoring operation, and triggering fault protection. The method effectively solves the problems that a traditional system is rigid in power dispatching, low in photovoltaic consumption rate, large in energy storage life loss, remarkable in power grid impact, insufficient in prediction precision, high in energy transmission loss and weak in multi-target coordination.

The invention discloses an energy storage intelligent control system capable of meeting peak and frequency regulation scheduling requirements of a virtual power plant, and aims to realize efficient coordination of scheduling of an energy storage unit and the virtual power plant and guarantee stable operation of a power grid. The system comprises a data interaction module, a decision control module and an execution control module, the data interaction module receives peak regulation and frequency modulation instructions of a virtual power plant dispatching center in real time, and collects real-time data of an energy storage unit (charge state, charge and discharge power and health state) and a power grid (frequency and load); the decision control module generates a charging and discharging control strategy by adopting a multi-target optimization algorithm based on the data, and the strategy simultaneously meets the targets of peak regulation load stabilization and frequency modulation frequency stabilization and adapts to the charge and health state constraints of the energy storage unit; and the execution control module executes hierarchical cooperative charging and discharging control on the energy storage unit according to the strategy, when peak regulation and frequency modulation instructions conflict, execution is performed according to a priority rule preset by a scheduling center, and scheduling requirements and operation safety of the energy storage unit are effectively balanced.

The invention discloses an operation system, method and equipment of a live working robot with an intelligent distribution network, and a storage medium, and relates to the technical field of intelligent distribution networks of robots, and the system comprises a first man-machine interaction module which supports bidirectional interaction with an operator through voice, characters, images and the like; the second multi-mode sensing module senses the equipment state, the obstacle and the environment change in the working environment in real time through a visual sensor, an auditory sensor, a force sensor and the like; the big language model processes and analyzes the instruction through a natural language and generates a task execution step, and adjusts a task strategy according to the perception data; the decision planning module formulates a task path and resource scheduling in combination with a multi-objective optimization algorithm; the safety monitoring module monitors the safety state in the operation process in real time and triggers early warning; the anomaly detection and processing module receives the early warning information and starts an autonomous navigation and path planning technology; according to the system, autonomous and safe operation of the robot is achieved, and task execution efficiency, adaptability and flexibility are improved.

The invention provides an intelligent production scheduling process connection optimization method based on a product structure tree, relates to the field of intelligent manufacturing, and solves the technical problem that process conflicts and resource contention are easy to exist in the prior art. The method comprises the following steps: constructing a dynamic evolution product structure tree; performing process decomposition based on the product structure tree, generating a process sequence and identifying a key path; performing multi-dimensional conflict detection on the process sequence, and performing conflict resolution according to a detection result; the multi-dimensional conflicts comprise space conflicts, resource conflicts and time sequence conflicts; and based on the process sequence after conflict resolution, generating an optimal production scheduling plan through a multi-objective optimization algorithm, collecting data in real time in the production process, and adjusting the optimal production scheduling plan based on the multi-index deviation between the plan and the reality.

The invention discloses an unmanned aerial vehicle rescue scheduling optimization method and device based on semantic translation modeling, and relates to the technical field of unmanned aerial vehicle emergency rescue intelligent scheduling, and the method comprises the steps: obtaining emergency rescue task description information; a natural language rule in the task description information is converted into a standardized constraint expression, and a response time target, a coverage range target, resource parameters and network structure parameters are recognized; constructing an initial scheduling model, and solving the initial scheduling model through an improved multi-objective optimization algorithm to obtain an initial scheduling scheme of the unmanned aerial vehicle; and when an interference event in the execution process of the initial scheduling scheme is detected, constructing a dynamic re-optimization model by taking the initial scheduling scheme as a disturbance reference, solving the dynamic re-optimization model by taking the scheme disturbance cost and the arrival late time as optimization targets, and outputting a repair scheduling scheme and an interpretable decision result. And the accuracy, the real-time performance and the decision credibility of emergency rescue scheduling are obviously improved.

The invention discloses a high-flux electron beam quality cooperative control system and method, belongs to the technical field of electron beam control, and aims to solve the problems that the beam intensity and quality are difficult to consider, the subsystem collaboration is poor and the stability is insufficient. The cooperative control module is in communication connection with the sensing module and is used for receiving the state parameters, analyzing the state parameters based on a preset multi-target optimization algorithm and generating a cooperative control instruction, and the execution module is in communication connection with the cooperative control module and is used for receiving the cooperative control instruction and executing the cooperative control instruction according to the cooperative control instruction. Cooperative control of beam intensity and quality is realized. Through a multi-objective optimization algorithm, the limitation of traditional single parameter control is broken through, the beam intensity is larger than or equal to 100 mA, the emittance is smaller than or equal to 5 pi mm mrad and the energy dispersion is smaller than or equal to 0.5% can be achieved at the same time, and the technical problem that the beam intensity and the emittance are difficult to consider is solved.