122 results about "Sequence optimization" patented technology

The invention relates to the technical field of construction control, and particularly discloses a method and a system for dynamically regulating and controlling the installation precision of an inclined-span steel box arch bridge based on machine learning. Wherein the real-time data comprises real-time stress of the arch rib, real-time deformation of the arch rib, real-time environment wind speed and real-time hoisting parameters; constructing a prediction model capable of analyzing the coupling effect of the wind load and the hoisting unbalance load based on a large amount of historical construction data; inputting real-time data into the prediction model to obtain a real-time coupling effect analysis result; outputting a hoisting sequence optimization instruction based on the decision engine, the coupling effect analysis result and a preset mechanical constraint condition of the obliquely-spanning steel box arch bridge; based on the deviation between the real-time deformation amount of the arch rib and a preset installation precision threshold value, a cable force grading adjustment scheme is generated; construction efficiency of the inclined-span steel box arch bridge is effectively improved, construction safety and installation precision are guaranteed, and stability and reliability of the bridge structure are guaranteed.

The invention discloses a service starting sequence optimization method and device, electronic equipment and a storage medium, and the method comprises the steps: obtaining a multi-mode data set of at least one service in a substrate management controller, creating a target function, taking each service in the at least one service as a node to construct a target tree structure, and obtaining a target tree structure; the optimization objective of the objective function is to minimize the total start time of at least one service; and based on the target function and the target tree structure, determining a target node corresponding to the optimal evaluation index in each layer of structure, and finally starting the service corresponding to each target node in sequence according to the hierarchical relationship between the determined target nodes. By means of the method, the technical problem that in the related technology, flexibility is poor when all services in the baseboard management controller are started in a predefined static sequence is solved, and the technical effect of optimizing the service starting sequence through the self-adaptive dynamically-changed operation environment is achieved.

The invention relates to the technical field of production management, in particular to a production scheduling optimization system based on data analysis, which comprises a process data trend analysis module, a station resource real-time acquisition module, a scheduling sequence optimization judgment module, a rhythm coordination check module and a scheduling dynamic optimization module. According to the method, the process duration trend is analyzed in real time, continuous growth is accurately recognized, rhythm parameters are dynamically corrected, the stable process rhythm is ensured, efficient resource connection is achieved based on the station task completion time and the matching task and station idle starting time, the task sequence is dynamically adjusted according to the station idle state, and task connection is optimized; the beat proportion of adjacent tasks is verified, the task interval is adjusted, the beat difference is controlled within a reasonable range, beat sudden change is prevented from interfering with production, the rapid response ability to production changes is integrally enhanced, task and resource collaborative optimization is achieved, the overall scheduling efficiency and beat consistency are improved, and the production continuity and the resource utilization rate are improved.

The invention relates to a resource allocation and process planning optimization method under complex constraint conditions, which comprises the following steps of: representing various constraint conditions of a process planning process by utilizing a double-layer directed acyclic graph, and generating and constructing an initial feasible solution set meeting all the constraint conditions; initializing a state space, wherein the state space comprises state variables of a process number, an average process length, reward value fluctuation and fitness; noise is collected and coupled to a Q network, action selection is carried out, and low-level heuristic algorithm options are output; generating a new taboo sequence by using the selected low-level heuristic algorithm, and optimizing the current feasible solution; switching a low-level heuristic algorithm according to the improvement amplitude and the execution duration; and outputting an optimal processing feature sequence and process division combination after multiple times of training. Compared with the prior art, the method has the advantages that multiple process decisions can be optimized at the same time, sub-optimal results caused by separate optimization processes are avoided, processing time and resource consumption are remarkably reduced, and production efficiency and scheme adaptability are improved.

The invention discloses a simulation IC layout wiring sequence optimization and wiring method based on reinforcement learning, and the method comprises the steps: obtaining wiring data which comprises a wiring region, an obstacle region, a wire net set, and the starting point and terminal point of each wire net; gridding the wiring space, and constructing a multi-channel image according to the wiring data; a multi-channel image is used as a state, the problem of simulating IC line net sorting is solved based on a Markov decision process, and the method comprises the steps that a line sequence selection model is constructed, the current multi-channel image is used as input, a probability vector and a predicted value are output, and line net sorting is completed; according to the sorting result of all the wire nets, in combination with the wiring area, the obstacle area and the starting point and the ending point of each wire net, wiring is carried out on each wire net in sequence, and a wiring result is obtained; and calculating a performance index of a wiring result as an award, constructing a total loss function in combination with a predicted value, and optimizing a line sequence selection model. According to the invention, the efficiency of simulating IC wiring is effectively improved, and the performance result of the layout is improved.

The invention discloses a relation enhancement type named entity recognition method and device, a medium and a program product, and relates to the field of data processing. The method comprises the following steps: performing feature extraction on text data through a BERT model to obtain a plurality of tokens and semantic feature vector sequences; performing different-scale convolution operations by using a convolutional neural network to obtain local character combination features, and fusing the local character combination features with the semantic feature vector sequence to form a feature enhanced semantic feature vector sequence; the Transform model determines a long-distance dependency relationship of semantic feature vectors in the sequence by using an attention mechanism; generating a long entity structure model based on the long-distance dependency relationship, and then determining an enhanced semantic feature vector sequence; and performing tag sequence optimization processing on the named entity through a conditional random field layer, determining sequence labeling loss, boundary detection loss and relation prediction loss, determining total loss, and adjusting a tag sequence so as to determine the named entity. According to the method, the accuracy and integrity of named entity recognition can be effectively improved.

The invention discloses a model node execution sequence optimization method and electronic equipment. The method comprises the steps of obtaining a model structure file of a to-be-executed model; according to the model structure file, obtaining operation processes of the to-be-executed model, an intermediate data value generated by each operation process and a connection relation between the operation processes; constructing a directed acyclic graph by taking the operation process as a model node, taking the connection relationship as a connection sequence of each model node and taking the intermediate data value as an edge; the current actual memory is the minimum, the execution sequence of each model node is obtained based on the directed acyclic graph, and the current actual memory comprises the current occupied memory value subtracting the released intermediate data value. According to the method, the optimal model node execution sequence is determined in a software automatic analysis mode by taking the minimum current actual memory as a target, so that the execution sequence of each operation node in the to-be-executed model is optimized, and long-time occupation of the memory is avoided.

The invention provides a lean production-oriented self-adaptive digital intelligent scheduling method and system, and relates to the technical field of data processing, and the method comprises the steps: taking an initial resource allocation scheme as the basis of task sequence optimization, constructing a time detection line on a time axis, detecting the overlapping condition of different tasks in an execution period, and recognizing resource conflict points; generating a preliminary scheduling plan under the condition of meeting the equipment capacity and man-hour constraint; based on multi-source data of a production site, evaluating the preliminary scheduling plan, updating scheduling parameters, and generating an optimized scheduling scheme; and converting the optimized scheduling scheme into an equipment control instruction, and transmitting the equipment control instruction to a control terminal of a production unit to complete closed-loop optimization of production resources. Reasonable adaptation and efficient scheduling of production resources can be achieved, the production efficiency is improved, and resource waste and conflicts are reduced.

The invention discloses an assembly sequence planning method based on a multi-strategy improved flower pollination algorithm, and relates to the research field of assembly sequence planning. According to the method, on the basis of assembly body information, the assembly direction transformation cost, the assembly tool transformation cost and the assembly operation difficulty cost are set as assembly cost evaluation indexes of the assembly sequence, and a sequence evaluation system is constructed through quantitative evaluation of the constructed assembly sequence cost. The improved flower pollination algorithm based on multiple strategies is provided, the convergence efficiency of assembly sequence planning optimization can be improved, the method has more advantages than a single algorithm, the problem that sequence optimization falls into a local optimal solution can be solved, and a new thought and an efficient method are provided for assembly planning research. The method aims at solving the problems that in industrial product assembly sequence planning, a flower pollination algorithm is weak in solution capacity in a discrete spatial domain, prone to falling into local optimum, low in solution efficiency and the like, the optimal assembly sequence is rapidly solved, then the production time and cost of industrial products are reduced, and the assembly quality is improved.

The invention discloses a sequence optimization method for balancing functions and yield. The sequence optimization method comprises the following steps: acquiring an initial amino acid sequence, a cell environment and a nucleic acid expression quantity; obtaining an initial nucleic acid sequence according to the initial amino acid sequence; obtaining a yield fraction through a preset yield module according to the initial nucleic acid sequence, the cell environment and the nucleic acid expression quantity; obtaining at least one function score through at least one preset function module according to the initial amino acid sequence; and simultaneously optimizing the initial amino acid sequence and the initial nucleic acid sequence through an optimization module according to the yield fraction and the at least one functional fraction to obtain an optimized amino acid sequence and an optimized nucleic acid sequence. According to the invention, the functional module and the yield module are integrated, and multi-objective optimization of protein sequences and corresponding nucleic acid sequences is realized through the optimization module.

The invention provides an underground cavern intelligent construction sequencing optimization method and system, and the method comprises the steps: firstly integrating geological parameters, construction machinery parameters and historical construction cases to construct a construction method database, and then determining a layering height parameter set through a dynamic matching algorithm according to the spatial topological relation between geomechanical indexes and a three-dimensional geological model in the database, according to construction machinery and layering height parameters, a transverse partition and axial partition parameter set is generated by using an octree space subdivision algorithm, then composite coding is performed based on multiple attributes of the three-dimensional geologic model and the parameters, and a unique construction block coding set is generated; and finally, performing construction sequence optimization on the code set by using a multi-objective optimization algorithm to generate a Pareto optimal solution set containing construction period, cost and stability optimization paths, and dynamically adjusting construction sequence parameters in the solution set according to surrounding rock deformation data to realize intelligent construction sequencing optimization of the underground cavern.

The invention provides an intelligent iron tower construction process simulation and optimization method based on digital twinning, which is applied to the technical field of intelligent iron tower construction, and comprises the following steps: obtaining a human intervention event sequence and a construction execution state transition sequence corresponding to each construction stage in a construction process, the human intervention event sequence at least comprises a manual confirmation trigger moment, an unplanned pause trigger type and a manual intervention duration, and the construction execution state transition sequence at least comprises a construction step start-stop state, a state duration and a state switching result; according to the method, the causal influence of human intervention behaviors before and after construction execution state transition is quantified, the causal influence weight is constructed, and the construction sequence is optimized in combination with digital twin construction simulation, so that the problems that construction sequence optimization in the prior art lacks quantifiable constraint parameters and results are unstable are solved; the construction steps are sensitive and stable, the construction sequence can be predicted and executed, and the reliability of construction management decisions and the risk controllability of the construction process are improved.

The invention discloses a damaged road network recovery sequence optimization method with maximum toughness as a target based on user balance flow distribution, and aims to solve the problems of low recovery efficiency of a post-disaster traffic road network and lack of scientific basis for decision making. The method comprises the following implementation steps: firstly, constructing a damaged road network toughness evaluation framework, and defining a toughness index as a reciprocal of a product of recovery time and total transit time in combination with a traffic network topological structure and a flow demand; secondly, establishing damaged road network toughness optimization scene parameters, abstracting a road network into a connected graph, and defining a recovery scheme set; then, a mixed integer linear programming model with the maximum toughness as the target is constructed, a nonlinear problem is converted into a linear problem through secant approximation, and constraint conditions such as flow balance and travel requirements are set; and finally, performing global optimization solution on the model by using a high-performance solver to generate an optimal recovery scheme. According to the method, the road network toughness can be scientifically quantified, the recovery sequence is optimized, blindness of experience decision making is avoided, the passing efficiency of a post-disaster traffic system is rapidly improved, efficient and scientific decision support is provided for post-disaster traffic recovery, and the method has important practical application value.

The invention discloses a test sequence optimization method and device, equipment and a storage medium. Comprising the steps of obtaining function point related data, and generating a function point test sequence based on an upper confidence bound algorithm; defining a global resource occupancy matrix, and according to the function point test sequence, sequentially importing the function points, traversing and querying the global resource occupancy matrix, and generating a parallel scheduling task; and executing the parallel scheduling task, and monitoring resource conflict conditions and test failure information in real time. By comprehensively considering related data of the function points, intelligent sorting of the function points is achieved, and the problems that in a traditional sorting method, exploration and utilization are difficult to balance and global dynamic optimization is lacked are solved. By reasonably distributing parallel tasks, resource conflicts are avoided, the utilization rate of limited test resources is improved, and efficiency reduction caused by unreasonable resource planning is reduced. Resource conflicts are found and processed in time through real-time monitoring, the conflicts are prevented from influencing the test process, test failures can be quickly responded, and the test efficiency is improved.

The invention discloses an automatic file analysis method based on a semantic evolution graph. The method comprises the following steps: generating a policy document standardized corpus; obtaining a policy document structured corpus unit set; constructing an initial semantic map snapshot containing a version stamp; establishing a time slice semantic evolution graph sequence; obtaining a multi-time sequence fusion vector; generating a multi-time sequence optimization word embedding vector matrix; performing time continuous regularization on the multi-time-sequence optimization word embedding vector matrix, outputting time continuous word embedding vectors, and mapping the time continuous word embedding vectors to nodes and edges of the time slice semantic evolution graph sequence to form a global semantic evolution graph fusion representation sequence; and taking the global semantic evolution graph fusion representation sequence as feature input to obtain a policy document analysis result set. According to the method, the timeliness, the accuracy and the interpretability of automatic analysis of the policy document are greatly improved.

The invention discloses an order-driven mixed box stacking method, system and equipment and a medium. According to the method, a stacking process is decoupled into two associated subtasks of active warehouse-out decision and real-time stacking execution; the problems of frequent stack transfer and invalid reciprocating carrying caused by scattered goods combination in a physical layout limited scene in an existing offline stacking algorithm are effectively solved, and meanwhile, the space utilization rate bottleneck caused by lack of order global view in a traditional online stacking algorithm is also overcome; and a brand-new framework support and a global optimization decision view angle are provided for efficient disassembly operation in a limited physical space. An energy-based warehouse-out sequence optimization algorithm is introduced into a warehouse-out decision-making layer, and the algorithm deeply fuses the local fine sampling capability of Langevin dynamics and the global evolution capability of a genetic algorithm, so that the technical defects that a traditional heuristic rule is difficult to capture complex geometric features of goods and is easy to fall into local optimum are overcome; and the three-dimensional warehouse is guided to realize active and efficient replenishment scheduling.

The invention provides an automobile foot mat multi-style mixed flow production scheduling method based on customer order data, and the method comprises the steps: carrying out the standardization and feature vector generation of order process parameters, combining with the real-time state collection of production equipment, building a static process similarity and dynamic equipment response cost evaluation model, and fusing multi-source data to analyze the dynamic switching cost. The method comprises the following steps: establishing a scheduling sequence, realizing scheduling sequence optimization by using an improved NSGA-II multi-target genetic algorithm, comprehensively considering switching cost, equipment utilization rate and production line rhythm, finally dynamically selecting an optimal scheduling scheme through a TOPSIS decision method and a workshop load rate, and continuously correcting and evaluating model parameters by using an online incremental learning algorithm, thereby realizing scheduling sequence optimization. According to the method, the production scheduling efficiency and the resource utilization rate are improved, the equipment switching loss is remarkably reduced, and flexible production self-adaptive optimization is realized.

The invention relates to the technical field of industrial scheduling, and discloses a flexible job shop self-organizing scheduling-oriented multi-target dual hyper-heuristic method, which comprises the following steps of: generating a process selection rule and an interval selection rule by utilizing a genetic programming rule, and generating a rule set; based on a deep reinforcement learning method, a dynamic decision strategy is constructed in combination with a rule set, and multi-step action sequence optimization is carried out when self-organizing scheduling is triggered; and generating an instance through random combination of parameter indexes of a predetermined dynamic event, and calculating a performance index according to the generated instance to realize feasibility verification. Through a dynamic collaborative optimization mechanism of self-organizing scheduling, an autonomous decision closed loop can be realized under multiple disturbances such as equipment failure, processing fluctuation, new order insertion and the like, and the self-healing capability, response speed and anti-interference toughness of a production system in a dynamic environment are remarkably improved; and an intelligent decision-making scheme with an autonomous evolution capability is provided for a complex scheduling problem in an intelligent manufacturing scene.

The invention discloses an automatic control system of an electric agricultural machine, and relates to the technical field of control systems. The system comprises an atomic task decomposition and energy consumption modeling module, which is used for decomposing a macroscopic job into three types of atomic tasks of movement, job and action and establishing a refined energy consumption model; the global sequence optimization module is used for optimizing an atomic task sequence and distributing an energy budget by taking total energy as a constraint and taking comprehensive benefit maximization as a target; the real-time energy consumption monitoring and budget cancel-after-verification module is used for tracking execution and accounting energy consumption deviation; and the dynamic re-planning decision module triggers the re-optimization of the residual atomic task parameters or sequences when the deviation threatens the total atomic task. Through atomic task level energy budget management, global energy optimal planning before operation and dynamic adaptive adjustment in operation are realized, the problems of low operation reliability and low energy utilization efficiency caused by limited electric quantity of the electric agricultural machinery are solved, and the effective operation quantity of single charging is improved.

The invention provides a containerized AI micro-service mirror image layer scheduling method and device, and relates to the technical field of containerized AI micro-service deploy.The method includes the steps that an optimized objective function for mirror image layer scheduling is constructed through a target node in an edge cloud network system, a mirror image layer is loaded and modeled into a sequence optimization problem with the high forward sub-modularity, and the sequence optimization problem is solved; and solving the optimization objective function by using a greedy sequence insertion algorithm to obtain a loading sequence of all mirror image layers. On this basis, a task queue of each thread in the thread set is determined in combination with a thread load minimum priority strategy; according to the method, global optimization of the loading sequence of the mirror image layer is realized, and the scheduling strategy can be dynamically adjusted according to the mirror image structure and the node resource state, so that the cold start time delay of the AI micro-service is effectively reduced, the multi-thread resource utilization rate is improved, and the deployment flexibility of the micro-service system is enhanced.

The invention provides a power distribution network power supply recovery method considering node importance and recovery dependence. The method comprises the following steps: 1) obtaining fault information of a power distribution network in real time; 2) comprehensively evaluating the importance weight of each node by adopting a comprehensive weight method combining an analytic hierarchy process and an entropy weight method; 3) using a reverse breadth-first search algorithm from the fault points to identify a repair dependency relationship among the fault points, and determining an upstream fault set which needs to be repaired firstly when any fault point is repaired; 4) quantifying the reduction amount of the ice melting device deployed before the disaster on the fault recovery time, and correcting the fault recovery time of each fault point in the fault set; 5) constructing a repair sequence optimization model with the goal of minimizing the total weighted load loss amount; 6) solving the repair sequence optimization model by the scheduling control center by adopting an improved genetic algorithm, and generating an optimal sequence for fault repair; and 7) dynamically adjusting the optimal sequence of fault repair.

The invention discloses an unmanned aerial vehicle low-delay navigation method based on multi-modal large model reasoning and reinforcement learning sequence optimization, and relates to the field of multi-modal large models and reinforcement learning, and the method comprises the following steps: constructing a target function fusing a supervision fine tuning loss item and a reinforcement learning loss item; performing gradient descent optimization on parameters of the multi-modal large model based on a loss function to generate a trained navigation model; inputting an environment image and a task instruction acquired by the unmanned aerial vehicle into a navigation model for reasoning, and outputting an action sequence consisting of k actions and corresponding time sequence identifiers; and storing the action sequence into an action buffer pool, sequentially taking out the head-of-queue action for execution, synchronously receiving a new action sequence, replacing the action at the corresponding position in the buffer pool according to the time sequence identifier, and maintaining the number of the actions in the buffer pool to be k. And a future action sequence is generated in real time through a multi-mode large model, so that the unmanned aerial vehicle realizes continuous, low-delay, stable and reliable sequence flight.

The invention provides a fluorite feeding sequence optimization method which comprises the following steps: collecting detected detection data of a detected fluorite delivery source unit, and recording mixed fluorite data, production conditions and production standards; constructing a feeding sequence optimization model, and improving a discrete state transition algorithm for solving; and an optimal subscript solution is obtained by adopting a discrete state transition algorithm, and an optimal fluorite feeding sequence is obtained according to updating and termination conditions of the solution. Aiming at the problem that an existing modeling mode lacks a dynamic association mechanism for the feeding sequence, the dynamic association relation between the feeding sequence and the calcium fluoride accumulated grade is accurately described, and the stability of the whole feeding process of the pre-reactor is ensured by taking the minimum calcium fluoride grade fluctuation variance as a target function.

The invention relates to the technical field of power system load flow calculation and quantum calculation, in particular to a power system load flow calculation method and system based on quantum singular value transformation. According to the method, a target function needing to be approached by the QSVT and an approximation polynomial meeting parity and bounded performance are designed, and the polynomial is converted into a QSP phase sequence; and then a QSVT circuit is built in combination with the unitary operator U and the phase sequence to solve a quantum state, correction values of the voltage and the phase angle in the quantum state are extracted through quantum amplitude estimation, and the correction values are fed back to a classic controller for iteration, so that the voltage and the phase angle of the power system are updated until load flow calculation is converged. According to the method, polynomial approximation and phase sequence optimization are realized by using the QSVT framework, the calculation complexity is reduced, and the problem of convergence is avoided.

The invention relates to the technical field of carbon fiber layering sequence optimization, and particularly discloses a carbon fiber layering sequence optimization method based on a genetic algorithm, and the method comprises the steps: firstly generating mechanical partition data through finite element analysis, and initializing a layering sequence population with engineering reasonability; then, combining performance simulation and a manufacturability rule base to calculate a fitness value, and synchronously considering rigidity, strength and process feasibility; an excellent design module is protected through structured crossover operation, the mutation probability is dynamically adjusted based on the matching degree of the local principal stress direction and the laying layer angle, and the algorithm is guided to jump out of local optimum; and finally, iteratively generating a global optimal layering sequence considering both mechanical properties and manufacturability. According to the method, mechanical analysis and genetic algorithm evolutionary mechanisms are fused, so that the manufacturing, research and development period of the carbon fiber bicycle is remarkably shortened, the trial and error cost is reduced, and an innovative solution is provided for efficient design of a carbon fiber composite material structure.

The invention discloses a cascade reservoir pre-flood joint hydro-fluctuation sequence and hydro-fluctuation scheme construction method, which comprises the following steps: based on a pre-flood state parameter set of a cascade reservoir, carrying out conditional disturbance on forecast deviation, generating a multi-scene hydrological operation input set, and quantifying non-hydro-fluctuation risk distribution parameter data representing a water level over-limit possibility; constructing a pre-flood joint hydro-fluctuation optimization model in which a hydro-fluctuation sequence decision variable is introduced, and converting non-hydro-fluctuation risk distribution parameter data into a risk constraint condition therein; solving a pre-flood joint hydro-fluctuation optimization model according to the multi-scene hydrological operation input set to obtain hydro-fluctuation sequence optimization result data meeting a safety threshold; and performing pattern recognition on the hydro-fluctuation sequence optimization result data, extracting a hydro-fluctuation sequence pattern and generating corresponding pre-flood hydro-fluctuation scheme rule base data. According to the method, the hydro-fluctuation sequence is brought into the optimization decision, and the non-hydro-fluctuation risk is explicitly constrained, so that the cooperative improvement of flood control safety and power generation benefits of the cascade reservoir in a strong uncertain environment is realized.

The present invention relates to the field of neural network optimization technology, and in particular to a method for optimizing cross-layer memory access bandwidth to support deep neural networks. The method comprises the following steps: 1. designing a bandwidth model for single-layer block optimization and loop sequence optimization; 2. performing single-layer independent block optimization and loop sequence optimization; 3. performing cross-layer joint block optimization and loop sequence optimization; and 4. performing composite block optimization and loop sequence optimization. The method can implement a data block strategy for minimum DRAM access, a loop unrolling control strategy, and a data storage mapping update strategy, achieving a fine-grained data access and data flow solution that maximizes data reuse, and effectively supporting automatic code generation and computational mapping optimization for optimizing computational and memory access efficiency.

The invention relates to a PCB mounting workshop scheduling problem optimization method and system based on a joint mounting sequence, and the method comprises the following steps: building a PCB mounting workshop scheduling solving model based on the joint mounting sequence, which comprises two sub-models: a PCB mounting mixed flow workshop scheduling multi-objective optimization sub-model and a chip mounter mounting sequence optimization sub-model; and establishing a joint optimization framework to realize joint optimization of the two sub-models. And solving a mounting sequence optimization problem of the chip mounter by adopting an optimal foraging algorithm of double-layer decision. The upper layer starts from workshop production scheduling, and efficient configuration and comprehensive benefit maximization of workshop resources are achieved; the lower layer focuses on the optimization of the mounting process of the chip mounter in the workshop, the minimization of the mounting time is realized, the two are combined, the workshop scheduling layer inputs the PCB information to the chip mounter layer, the optimization result of the chip mounter is output to the workshop scheduling layer according to the information, and the production sequence of the PCB and the mounting optimization on the chip mounter are decided, so that the overall production efficiency is optimal.

The invention discloses a single-target amino acid sequence optimization system based on comprehensive scoring and bit-by-bit iterative optimization. The system comprises the following modules: a mutation probability evaluation module, a target affinity scoring module and a comprehensive scoring module. The comprehensive scoring module and the bit-by-bit iterative optimization strategy of the system significantly improve the optimization efficiency, can quickly screen out high-quality sequences with high affinity and functional rationality in a complex sequence space, reduces the calculation complexity, and ensures that the screening result has scientific rationality and application value. The technology can accelerate optimization design of antibody drugs, target screening of polypeptide vaccines and functional improvement of protein engineering, and provides a reliable tool for drug research and development, precision medicine, personalized treatment and industrial biotechnology.