102 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 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 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 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 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 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 application discloses a box welding sequence optimization method based on a Kriging model and an NSGA-II algorithm and belongs to the field of welding process optimization. The method firstly establishes a box inherent strain finite element model, establishes welding sequence constraint conditions according to a weld distribution and actual process requirements; effective welding sequence samples are generated by adopting a random key coding strategy and Latin hypercube sampling, welding deformation response data are acquired through finite element simulation, and a Kriging welding deformation prediction proxy model is constructed; subsequently, a multi-objective optimization function is constructed, the trained Kriging model is taken as an evaluation model, the welding sequence is iteratively optimized by using an NSGA-II genetic algorithm, and a Pareto optimal solution set is obtained; and finally, an optimal welding sequence scheme is screened based on an ideal point distance criterion. The application replaces a large number of repeated finite element simulations with a proxy model, combines a multi-objective non-dominated sorting genetic algorithm, significantly reduces optimization calculation amount and time cost, and improves optimization efficiency of large box welding deformation control.

The invention relates to the technical field of computers, in particular to a compiler optimization method, a server, a computer program product and a storage medium. According to the method, a state embedding vector corresponding to a program needing to be optimized serves as input of a reinforcement learning agent; the state embedding vector represents deep semantics of a program needing to be optimized; analyzing the state embedding vector through a reinforcement learning agent, and determining a sequence optimization action corresponding to a program needing to be optimized; a reward value corresponding to the sequence optimization action meets an application condition; and optimizing the program needing to be optimized according to the sequence optimization action to obtain an optimized program. In this way, certain limitation of compiler optimization is reduced.

The present application relates to the technical field of cloud computing platform automation deployment, and particularly relates to a self-adaptive deployment method and system for a heterogeneous environment of a signal creation, and to three major technical bottlenecks of low multi-CPU architecture adaptation efficiency, frequent software dependency conflicts and complex security baseline configuration in the process of localization, and an intelligent heterogeneous computing resource scheduling engine and a dynamic security policy generation mechanism are innovatively proposed. The method comprises: constructing a heterogeneous resource portrait through hardware feature automatic identification technology, and realizing component installation sequence optimization based on a DAG dependency relationship analysis algorithm and Kahn topological sorting; creating an adaptive network security policy, and realizing real-time perception of the target system firewall state through a probe. Compared with the traditional deployment mode, the present application supports cross-architecture compatibility, solves the problem of dependency conflicts, improves the deployment efficiency, and guarantees the consistency and security of the system.

The invention relates to a robot action sequence optimization method and device for chemical experiment operation tasks, and the method comprises the steps: collecting a corresponding expert data set for each chemical experiment operation task, and carrying out the quantification of a corresponding precision requirement, a smoothness feature and task duration according to the expert data set; determining the size of an action block with the adaptive length of each chemical experiment operation task according to the quantized task characteristics; replacing the size setting of a fixed block in the ACT model with the size of an action block with a self-adaptive length, and training the ACS-ACT model by adopting a corresponding expert data set; and according to the observation information, a corresponding trained ACS-ACT model is adopted for reasoning, and predicted future actions are obtained. According to the method, after the block size and the output action are optimized, the task execution effect is obviously improved, and especially for tasks with relatively high precision and smoothness requirements, the task time consumption, the success rate and the action smoothness of the method are superior to those of an original ACT model.

The invention discloses an accompanying moving line optimization method and system based on feedback driving. The method comprises the following steps: establishing a diffusion model of a plane gridding mode based on a real accompanying environment; taking the multi-modal satisfaction score as a reward signal input, and establishing a personalized reward model based on diffusion model mapping; guiding the diffusion model to denoise by using a corresponding reward prediction function in the personalized reward model, and generating a plurality of candidate moving line sequences; and continuously updating the multi-modal satisfaction score and historical moving line data, performing gradient guidance on the diffusion model, and optimizing and outputting a plurality of optimal moving lines based on the candidate moving line sequence. Therefore, gradient guidance is carried out on the diffusion model by adopting the multi-modal satisfaction score, so that the diffusion model outputs the optimal moving line meeting the individual demand of the accompanying object, the accompanying person can obtain the operation guidance with extremely high reference value, the individual demand of the accompanying object is met, and the accompanying experience is remarkably improved.

The application provides an m sequence optimization method for a satellite-ground integrated sensing system test, comprising the following steps: step 1, reference sequence performance evaluation: the reference performance comprises a reference peak-to-average power ratio, a reference peak-to-side lobe ratio, a reference integrated side lobe ratio and a reference Doppler tolerance; step 2, comprehensive multi-dimensional cost function construction and mixed optimization strategy: step 201, constructing a comprehensive multi-dimensional cost function capable of weighted adjustment: step 202, defining a normalization function based on the reference performance: step 203, configuring the performance on demand based on the weight coefficient: step 204, minimizing the cost function by using a mixed optimization algorithm; and step 3, generating a final integrated sensing test sequence. The application realizes the collaborative optimization of multiple key performances of communication and sensing for the first time, and can generate a special sequence with excellent comprehensive performance, which can meet the stringent integrated satellite-ground sensing system test requirements.

The invention relates to the technical field of deep learning, and discloses a nucleic acid sequence optimization method, system and device, a storage medium and a program product, and the method comprises the following steps: obtaining an initial nucleic acid sequence; determining a candidate nucleic acid sequence population based on the initial nucleic acid sequence; the candidate nucleic acid sequence population comprises a plurality of candidate nucleic acid sequences; inputting the candidate nucleic acid sequences into a pre-constructed nucleic acid sequence performance prediction model to obtain a plurality of performance indexes in one-to-one correspondence with the candidate nucleic acid sequences; calculating the comprehensive performance of the candidate nucleic acid sequence based on the plurality of performance indexes of the candidate nucleic acid sequence; and determining a target nucleic acid sequence based on the comprehensive performance of the candidate nucleic acid sequence. On the basis of the initial nucleic acid sequence, the candidate nucleic acid sequences are determined, and a plurality of performance indexes of each candidate nucleic acid sequence are predicted. The optimal nucleic acid sequence is jointly determined through a plurality of performance indexes, and the interpretability, rationality and accuracy of nucleic acid sequence design can be effectively improved.

The invention discloses an existing operation station shield receiving construction step sequence optimization method and system based on digital twinning, and the method comprises the steps: collecting the associated data of an existing operation station, constructing a digital twinning basic model, achieving the full-factor digital mapping of entity engineering, and carrying out the parameterization and lightweight optimization. And establishing dynamic association between the model component and the original data. Dynamic monitoring and construction process data are collected in real time and are uniformly accessed through an integrated platform, and a real-time synchronization mechanism of the data and a model is established after preprocessing and screening. Based on the model and integrated data, setting a multi-dimensional optimization constraint, embedding a dynamic optimization algorithm, simulating, deducing and optimizing a construction step sequence by taking safety, high efficiency and low risk as targets, and generating an initial scheme; and when a preset trigger event occurs, re-optimization is carried out, and an adaptive scheme is output. And the scheme is pushed to a construction execution end, an execution state is tracked, actual and preset data deviations are compared, and an early warning mechanism is established. And smooth construction of the existing operation station is ensured.

The application relates to the technical field of intelligent scheduling, in particular to an intelligent scheduling and resource optimization allocation method based on dynamic task priority, which comprises the following steps: collecting task information and load information, constructing a multi-dimensional task priority calculation model, dynamically adjusting the priority of the task, determining the task scheduling sequence according to a dynamic priority task scheduling sequence optimization algorithm based on the priority of the task, intelligently allocating resources based on the priority of the task and the task scheduling sequence, simultaneously acquiring monitoring data and quantitatively processing the monitoring data, optimizing the task scheduling sequence based on quantitative feedback, and adjusting the priority of the task and the resource allocation in real time. The application effectively improves the efficiency of task scheduling, reduces energy consumption, and optimizes resource utilization by dynamically adjusting the priority of the task, optimizing the task scheduling sequence, introducing branch optimization and local search strategies, and combining real-time monitoring and feedback mechanisms, and is suitable for resource management and scheduling optimization in a multi-task environment.