141 results about "Heuristic search algorithm" patented technology

The invention provides a private domain live broadcast room hotspot prediction and adaptive routing method based on swarm intelligence, and relates to the technical field of data processing, and the method comprises the steps: constructing a dynamic interest community through a dynamic graph neural network, extracting group interaction mode features through a multi-layer graph attention network, and carrying out the group interaction mode features; the method comprises the following steps: generating time sequence correlation features in combination with an improved bidirectional gating loop unit, extracting high-order semantic features, performing hotspot prediction through a multi-task learning framework, constructing a multi-level network topology model according to a prediction result, and performing path exploration in combination with an improved heuristic search algorithm to obtain an optimal transmission path. Flow distribution is carried out through a weighted polling algorithm, a deep learning distributed load balancing controller and a congestion control algorithm are combined, the data transmission rate is dynamically adjusted, and self-adaptive routing pushing of live broadcast content is achieved.

The invention discloses a ship dynamic path adjusting method combining visual navigation and radar data. The ship dynamic path adjusting method comprises the following steps: S1, collecting data; s2, carrying out pretreatment; s3, performing data fusion to generate an environment perception model; s4, combining the generative adversarial network and the space-time diagram neural network to construct an environment prediction model; s5, performing initial path planning by using a heuristic search algorithm to generate an initial path; s6, refining and optimizing the path by using a fast extension random tree algorithm to generate an optimal path; s7, generating future possible environment change according to the environment prediction model, and calculating a coexistence reserve path for each prediction scene in combination with the environment perception model; s8, when the current path is predicted to have risks in the future, switching to a preset standby path in advance; and S9, automatically generating a navigation control instruction, and transmitting the instruction to a ship automatic driving system. According to the invention, intelligent optimization of the ship path is realized through a multi-sensor fusion and environment prediction method.

The invention provides a mining intelligent watering dust fall method and system, electronic equipment and a storage medium, and relates to the field of watering dust fall. The method comprises the following steps: acquiring multi-source heterogeneous environment sensing data; preprocessing the multi-source heterogeneous environment sensing data to obtain preprocessed multi-source heterogeneous environment sensing data, and constructing a mining area holographic environment model based on the preprocessed multi-source heterogeneous environment sensing data; based on the holographic environment model of the mining area, extrapolating and estimating the dynamic change trend of the wind dust concentration by using a space-time sequence prediction algorithm to obtain a wind dust concentration distribution prediction result of each area of the mining area in a future time period; according to the technical scheme, dust suppression is taken as an optimization target, water mist parameters and a nozzle arrangement scheme are taken as decision variables, a mining area holographic environment model and a wind dust concentration distribution prediction result are taken as constraint conditions, an optimal spraying strategy meeting dust falling requirements is solved through a heuristic search algorithm, and the dust falling effect is improved and the water consumption is reduced through the technical scheme.

The invention discloses a V2X-based global path planning method for a mine unmanned driving cloud control platform, and relates to unmanned driving. The V2X-based global path planning method comprises the following steps: constructing a traffic semantic map data structure # imgabs0 #; according to task issuing or operation plan adjustment, generating path request data R, and performing time constraint, resource constraint and path optimality constraint verification on the path request data R; according to the R and # imgabs1 #, adopting a heuristic search algorithm based on a graph theory to carry out optimal path search on the road topological structure, and generating a global path P containing a node sequence and driving parameters; and acquiring obstacle data detected by the vehicle end through a local sensor, performing obstacle avoidance correction through a local path optimization algorithm according to the obstacle data and the global path P, and generating a local path meeting the safety distance constraint and the path deviation constraint. According to the method, on the premise of meeting multi-dimensional coupling constraints such as time-space, priority-resource, safety-efficiency and the like, the optimal driving path dynamically adapting to the complex environment of the well industry and mining is generated.

The embodiment of the invention discloses a power transmission line path planning method and device, electronic equipment and a storage medium, and the method comprises the steps: obtaining geographic information data of a power transmission line path planning region, carrying out the preprocessing of the geographic information data through a geographic information system, and constructing a grid cost surface considering influence factors; constructing an optimization model based on the grid cost surface according to the requirement of power transmission line path planning; solving the optimization model by using a heuristic search algorithm to obtain a preliminary path; further optimization processing is carried out based on the preliminary path, unnecessary corners and redundant paths are eliminated, and a target planning path is obtained; according to the method, a mathematical optimization model and a corresponding solution algorithm can be introduced, the optimal line path can be automatically generated on the basis of comprehensively considering various factors, the dependence on artificial experience is reduced, and the objectivity and scientificity of planning are improved, so that the efficiency and accuracy of high-voltage transmission line planning are remarkably improved.

The invention provides a spatial heterogeneous vision positioning system and positioning method of a pineapple picking robot, relates to the technical field of picking robot positioning, and realizes high-precision spatial positioning and attitude estimation of the robot in a complex terrain environment by comprehensively utilizing multi-source information such as vision, inertial measurement, multispectrum and structured light. The positioning stability and the real-time performance are obviously improved; by embedding a multi-modal deep learning model, efficient non-destructive detection and intelligent classification of internal defects of pineapple fruits are realized, a mechanical arm is supported to dynamically adjust a picking strategy, fruits with better quality are preferentially selected for picking, and the picking quality and efficiency are improved; and meanwhile, the preset three-dimensional terrain model and the heuristic search algorithm are combined, the motion path of the robot is effectively planned, it is guaranteed that the robot safely and stably moves in the complex environment, and the mechanical damage risk is reduced.

The invention discloses a Multi-Point CTS-based chip clock design optimization method, and relates to the technical field of chip clock design optimization, and the method comprises the following steps: using a hardware description language VHDL to compile RTL codes describing circuit behaviors, using a DC EDA tool of Synopsys to convert the RTL codes into a logic netlist, using a standard algorithm H-Tree to construct a preliminary clock tree structure layout, and using the standard algorithm H-Tree to construct the logic netlist. Obtaining a primary clock tree topological graph; a Multi-Point CTS method is introduced, a plurality of clock sources are adopted to optimize the initial clock tree topological graph, and an optimized clock tree topological graph is obtained; a static time sequence analysis tool PrimeTime is adopted to analyze the optimized clock tree topological graph, and a time sequence analysis result is obtained; identifying the longest path from a clock source to a register in the optimized clock tree topological graph according to a time sequence analysis result, marking the longest path as a critical path, and finding the optimal position for placing a buffer in the critical path by adopting a heuristic search algorithm; and calculating a difference value between the actual delay and the maximum allowable delay of the optimized clock tree topological graph by using PrimeTime again.

The invention discloses an unmanned aerial vehicle cooperative inspection path planning and airport layout method and system for expressway multi-facility maintenance. According to the method, a multi-dimensional environment characterization system is constructed by collecting highway multi-source data, a comprehensive optimization model with the coverage range, the response time and the total cost as targets is established, an airport layout scheme is generated by adopting an adjustable service radius strategy and a heuristic search algorithm, and an optimal equilibrium solution set is extracted through multi-target sorting and screening. The task priority is determined based on the facility type and maintenance urgency, a multi-unmanned aerial vehicle cooperative route planning model is established, and a conflict-free circular inspection path is planned. And performing online rolling replanning by adopting a prediction control strategy, and integrating inspection data feedback to realize closed-loop self-adaptive optimization of the system. According to the invention, collaborative optimization of the airport layout and the inspection path is realized, the inspection efficiency is improved, the operation cost is reduced, and the adaptive ability of the system to deal with a dynamic environment is enhanced.

The invention provides a limit test method, system and device for an artificial intelligence model and a medium, and the method specifically comprises the steps: constructing a plurality of scene templates for different types of games, and generating a test case set by combining and transforming the scene templates according to a boundary condition input by a user; based on the test case set, according to a preset optimization objective function, adopting a heuristic search algorithm to determine an optimal test parameter combination meeting the artificial intelligence model; performing automatic testing on the artificial intelligence model according to the optimal test parameter combination, and generating a model test result; and analyzing the model test result, determining the performance of the artificial intelligence model under the boundary condition, generating a model test report, and carrying out optimization and re-test on the artificial intelligence model through the model test report. According to the method, full-process automation and intellectualization of game AI model testing are achieved, and the testing efficiency and the model quality are remarkably improved.

The invention relates to the technical field of intelligent network connection vehicles, in particular to an intersection signal lamp and intelligent network connection vehicle trajectory collaborative optimization method, which comprises the following steps: acquiring state information of all vehicles within a certain range of an intersection; obtaining mathematical expressions of signal lamp parameters of all lanes based on the entrance lanes of the intersection and a pre-established adaptive traffic signal model; generalized delays and vehicle tracks corresponding to different signal lamp parameters are obtained based on a pre-constructed vehicle delay estimation model; and solving an optimal signal lamp parameter and a corresponding vehicle track based on a heuristic search algorithm. Therefore, the problems that in the related technology, the real-time state of the traffic flow is not considered, the mixed traffic condition of mixed driving of the intelligent network connection vehicle and the human driving vehicle and the interaction relation between the signal lamp and the vehicle are not considered, so that the signal lamp parameter and the track of the intelligent network connection vehicle cannot be collaboratively optimized, the intersection traffic efficiency is low, and the traffic efficiency is poor are solved. And actual requirements cannot be met.

The invention provides a service scheme generation method and system, and relates to the technical field of computer application, and the method comprises the steps: obtaining user demand information which comprises a service type, a service scene, a resource constraint condition and an expected effect description; semantic analysis is performed on the user demand information based on the comprehensive domain knowledge graph, core service elements are extracted, and a user demand model is constructed; according to the user demand model, candidate service resources are matched in a service resource library through a heuristic search algorithm, and candidate service schemes are generated; a multi-objective optimization algorithm is adopted, real-time resource state data and user feedback information are combined, the candidate service schemes are dynamically adjusted, and the final service scheme is output, so that user requirements can be accurately analyzed, service resources are efficiently matched, the generated service scheme is optimized, knowledge and resource iteration is realized, and the scheme generation efficiency and quality are improved.

The invention relates to the technical field of nuclear power robots, discloses a nuclear power robot accurate positioning and path planning method, and aims to construct a three-dimensional environment model for a complex and changeable environment in a nuclear power station, and express the environment as a grid map containing environment characteristic information. According to the method, a comprehensive state space is defined, the three-dimensional position, obstacle distribution, radiation intensity and energy state of the nuclear power robot are covered, and real-time updating of dynamic obstacles is considered. The action space defines all possible moving actions of the robot so as to ensure the planning flexibility. By defining a comprehensive cost function, the length of a path, radiation exposure, energy consumption and collision risk are evaluated, and a strategy is formulated with the purpose of minimizing accumulated cost. According to the method, the heuristic search algorithm is combined with the reinforcement learning strategy to carry out path planning, so that the planning precision and adaptability are improved.

The present invention belongs to the field of agricultural machinery, and relates to a multi-sensor fusion adaptive detection method for a stone picker and an intelligent stone picker. The detection data of several sensors on the stone picker are collected, and a pre-trained transfer learning model in different environments is constructed. The transfer learning model is trained based on multi-task learning and adaptive gradient pruning, and the hyperparameters of the transfer learning model are adjusted through Bayesian optimization to obtain the optimal object recognition model under the current soil environment; based on a heuristic search algorithm, the parameter configuration of the sensor and the optimal object recognition model is automatically adjusted in combination with detection accuracy and energy consumption indicators; previous detection data are collected, and a clustering algorithm is used to determine whether there is a new pattern in the object detection result. If so, the optimal strategy of the object recognition model is generated through deep reinforcement learning and an environmental reward mechanism. Accurate detection, classification and efficient cleaning of stones and construction waste in reclaimed land and agricultural land are achieved, and manual labor is reduced.

The invention belongs to the field of power distribution network operation control, and discloses a power distribution network global optimization regulation and control method, system and device and a medium, and the method comprises the steps: obtaining multi-dimensional indexes including regional modularity, regional reactive power balance degree and regional active power balance degree in a power distribution network; based on a comprehensive evaluation mechanism of a multi-dimensional index, an optimal power distribution network region division result is obtained by utilizing iterative calculation of a heuristic search algorithm; a power distribution network global optimization model with cost optimization as a target is constructed so as to realize control of the comprehensive cost of the power distribution network; on the basis of a distributed optimization algorithm, a global optimization problem of the power distribution network is decomposed into a cloud main problem and a side terminal problem, and global optimality of resource allocation among different power distribution network regions is realized by solving the cloud main problem; and the optimal control strategy in the power distribution network area is realized by using the control result of the comprehensive cost of the power distribution network and the solution of the edge terminal problem. And the coordination control of the global massive controllable resources of the power distribution network is realized.

The invention discloses an intelligent advertisement resource allocation method based on dynamic planning and heuristic search, and relates to the technical field of advertisements, and the method comprises the steps: S1, obtaining historical flow data and predefined time node data, S2, generating an initial advertisement resource scheduling plan by using a dynamic planning algorithm, and S3, generating an advertisement resource scheduling plan based on a real-time flow trend and a time node. S4, using a heuristic search algorithm to optimize the corrected resource scheduling plan, and relieving the peak load pressure; the intelligent advertisement resource allocation method based on dynamic planning and heuristic search has good expandability and actual landing performance, and the resource scheduling plan is corrected according to the time node and the flow trend to solve the problem of overlarge peak load pressure.

The invention discloses a safe evacuation path planning method under a strong wind condition of island engineering. According to the technical scheme, the method is characterized by comprising the steps that firstly, the physical space of an engineering site is discretized into a plurality of grid units, and a computable graph structure is constructed; then, acquiring multi-source dangerous area information generated in real time, and quantifying the dangerous area information into a dynamic risk value of each grid unit; then, acquiring the real-time position of the constructor as a starting point, and presetting an emergency exit as an ending point; and finally, on the constructed risk grid map, a heuristic search algorithm such as A * is adopted, and the geometric distance and the dynamic risk value between grid units are fused by a cost function of the heuristic search algorithm, so that an optimal evacuation path considering safety and efficiency is quickly searched. According to the method, the safety risk is quantified and fused into the path optimization process, so that the quick response to the dynamic environment and the real-time re-planning of the path are realized, and the efficiency and safety of personnel evacuation under the emergency condition are remarkably improved.

The invention belongs to the field of distributed system and multi-robot formation control, and provides a multi-robot elastic formation control method and system based on a state machine, and the method comprises the steps: initializing and updating an occupation grid map, and generating a Euclidean symbol distance field; on the basis of taking the updated occupied grid map as a search space, searching local path points of each robot by utilizing a heuristic search algorithm, and generating a local path; constructing an optimized objective function by taking dynamics feasibility, obstacle avoidance, avoidance between robots and formation keeping as constraint cost, and optimizing a local path to obtain an optimized path; the method comprises the following steps: periodically detecting potential collision of robots in an optimized path walking process, and carrying out mixed path planning on multiple robots on a Euclidean coincidence distance field by utilizing a cooperative mechanism of leader path offset mapping and local autonomous adjustment according to a detection result; according to the invention, system states can be dynamically switched according to environment changes, and cooperative control of formation maintenance and obstacle crossing is realized.

The invention is suitable for the technical field of road construction, and provides a wind power plant road intelligent line selection method based on multi-dimensional constraint dynamic modeling, and the method comprises the steps: obtaining a multi-dimensional constraint condition of wind power plant road line selection, and constructing a constraint model; generating a slope grid map based on a digital elevation model, and fusing the slope grid map with a constraint map layer reflecting geological stability and a land cover type to generate a dynamic cost surface; adopting an improved heuristic search algorithm to perform parallel path search from the road starting point to each target fan position, generating a plurality of initial feasible paths, and calculating a comprehensive cost value; screening a candidate line set according to a comparison result of the comprehensive cost values; receiving real-time updating data in a construction process and environment monitoring, inputting the real-time updating data into the constraint model to trigger model updating, performing iterative optimization on the candidate line set based on the updated constraint model, and outputting a final line selection scheme; and the line selection efficiency, economy and reliability are effectively improved.

The invention discloses an efficient distributed machine learning training system based on a server-free architecture, and the system comprises a modeling module which is used for obtaining an input workload for modeling; the resource configuration module is configured with an optimized mathematical model and used for obtaining optimal parameter configuration based on the optimized mathematical model on the basis that the modeling module completes modeling; optimizing the relation between double-layer parameters and system-level parameters of mathematical model modeling server-free distributed training and training delay, cost and convergence efficiency; and the K-REDUCE training framework is used for triggering the server-free function, and the server-free function carries out distributed training according to the K-REDUCE framework. According to the method, a mathematical model is constructed to describe the relationship between parameters and performance, and optimal parameter configuration is searched by adopting a heuristic search algorithm based on pruning, so that the training performance is effectively improved, the economic cost is reduced, system resources are dynamically adjusted, and the optimal training performance is realized.

The invention relates to the technical field of intelligent security and protection, and discloses an intelligent security and protection alarm method and system based on an intelligent park. The method comprises the following steps: acquiring original security and protection data by using sensing equipment arranged in a smart park; performing standardization and redundancy elimination processing on the original security and protection data, dividing safety areas of the park through a pattern recognition algorithm, calculating a load intensity index of each area, and generating a security and protection situation summary; according to the security situation summary, a heuristic search algorithm is adopted to determine alarm trigger parameter initial values including detection sensitivity and alarm intervals, and a basic alarm parameter group is generated; performing nonlinear correction on the basic alarm parameter group, and outputting an environment perception alarm parameter group; training the historical security data set by adopting a neural network model, and analyzing the interaction between the load and the threat to obtain a threat diffusion model; and comparing the threat diffusion model with the real-time security data points, performing difference measurement, reconstructing alarm trigger logic according to a measurement result, and completing autonomous regulation and control of security and protection of the smart park.

The invention provides a fracture-vug reservoir inter-well three-dimensional communication path multi-target search method and system, and the method comprises the steps: building a three-dimensional space model of a fracture-vug reservoir based on seismic multi-attribute data, mapping a carbonate reservoir to a three-dimensional space, and carrying out the grid division; according to the coherent amplitude data and the well distribution condition of each well pair in the search space, adaptively optimizing a coherent amplitude threshold and a seismic attribute fusion weight by adopting a multi-objective optimization algorithm by taking the communication length and the volume as optimization objectives, and searching to obtain grouped inter-well communication paths meeting set requirements corresponding to the current well pair; according to the method, an evaluation function improved heuristic search algorithm is designed to search a single path in a search space, and a self-adaptive crossover and mutation probability improved multi-objective algorithm is proposed to optimize threshold parameters to obtain a plurality of inter-well communication paths conforming to a real seismic structure, so that the problems of limited path analysis and poor reliability in the prior art can be solved; and efficient and comprehensive search of inter-well communication paths is realized.

The invention discloses a multi-constraint path planning method and device based on intelligent heuristic search, and is suitable for a mobile robot in a complex and narrow environment. According to the method, a multi-source sensor is used for environment perception and fusion mapping, and a grid map is generated; a neural heuristic search algorithm based on a Mama structure and a selective state space model (Selective SSM) is adopted on the cost map, and a guide graph is generated; a continuous and collision-free initial path is generated through self-adaptive step size control in combination with robot kinematics constraints and channel characteristics; further establishing a multi-target constraint model based on a robot center Euclidean symbol distance field (RC-ESDF), and executing path smoothing and safety optimization by using a priori-guided particle swarm optimization algorithm (Prior-guided PSO) to obtain an optimal path meeting dynamic consistency; the system corrects the path in real time through a closed-loop feedback mechanism and triggers dynamic re-planning, so that the path feasibility, safety and real-time adaptive capacity of the robot in a limited environment are remarkably improved.

The invention provides a road inspection method and device, a storage medium and electronic equipment, and the method comprises the steps: obtaining a plurality of inspection points on a target road, determining a vehicle inspection position point according to each inspection point, the vehicle inspection position point being a position where an image collection device of an automatic driving vehicle can shoot the inspection point, the inspection points are in one-to-one correspondence with the vehicle inspection position points; adopting a heuristic search algorithm to calculate and obtain an inspection route at least according to all the vehicle inspection position points; and controlling the automatic driving vehicle to inspect the inspection points in sequence according to the inspection route to obtain corresponding inspection images, uploading all the inspection images to a back-end server to determine an inspection result, and inspecting the target road. The method has higher stability, can adapt to more extreme working environments and collect more stable detection data, and meanwhile, the automatic driving vehicle is provided with various abundant high-precision sensors, so that target defects and quality can be monitored more comprehensively.

The invention discloses a carbon flow optimization method and device for edge calculation under vehicle-road cooperation and a medium, and relates to the technical field of traffic carbon emission. The method comprises the steps that a real-time state parameter sequence and environment information of a vehicle are acquired, and at least two candidate driving strategies are generated through a built-in carbon emission digital twinborn model; according to the candidate driving strategies, the corresponding expected carbon emission amount is calculated by inquiring the emission factor mapping table of the specific power of the vehicle and integrating; packaging the candidate driving strategy and the corresponding expected carbon emission into a bidding packet, and sending the bidding packet to an edge computing node on the roadside; based on the received bidding package, carbon right reverse auction is initiated, a combined optimization problem is solved by adopting a heuristic search algorithm, and a bid winning strategy set is determined; and generating a cooperative driving strategy according to the bid winning strategy set, and issuing the cooperative driving strategy to the corresponding vehicle end node. By means of the method, information distortion is restrained, and direct and real-time optimization control over regional total carbon emission is achieved.

The invention discloses a load power balance regulation and control method and device based on knowledge graph and random sequence optimization, a medium and a product, and relates to the field of power system dispatching, and the method comprises the steps: constructing a power grid topology knowledge graph according to power grid physical equipment and corresponding topology connection and operation constraints; performing probability distribution fitting on the power output of the renewable energy according to the output data to obtain the output of the renewable energy; obtaining a plurality of typical output scenes; taking the state of the power grid physical equipment after fault isolation as an initial state, and constructing a random Markov decision process; searching in a state space by using an improved heuristic search algorithm by taking an initial state as a starting point, minimizing an accumulated cost function as a target, taking a hard constraint as a constraint condition and taking a topological distance as a heuristic function to obtain an optimal operation sequence; verifying the optimal operation sequence to obtain a final operation sequence; the power of the power grid is regulated and controlled; according to the invention, the fault response speed can be improved, and rapid adaptive decision in a complex scene can be realized.

The invention relates to a diffusion model reasoning acceleration method based on feature caching and distributed parallelism, and the method comprises the steps: exploring a caching method through a heuristic search algorithm, and confirming features corresponding to time steps needing to be cached; and performing distributed reasoning according to the caching scheme. The efficiency problem of an existing diffusion model in an image generation task in a high-resolution image generation scene is solved; the reasoning process of the diffusion model is efficiently processed based on the feature cache and the distributed reasoning architecture, and the resource utilization rate and the reasoning efficiency are improved.

The invention relates to the field of aviation safety, in particular to a low-altitude flight safety co-guidance system, which comprises a flight airspace delimiting module, an image intelligent identification module, a position determination module, a collision risk judgment module, a route planning module and a management database, and is characterized in that an identification model is constructed through a convolutional neural network, and a flight airspace image is acquired; the method comprises the following steps: identifying a moving object and a fixed obstacle in a flight airspace, performing position marking on the moving object and the fixed obstacle in a flight airspace image, predicting a movement track of the moving object in the flight airspace image, further judging whether the aircraft has a collision risk, and determining the collision risk position of the aircraft. A heuristic search algorithm is utilized to output a safe flight path fragment, the whole process from environment perception to risk response is covered, and the possibility of collision accidents of the aircraft is greatly reduced.

The invention discloses an unmanned aerial vehicle route planning method, device and equipment and a medium, and the method comprises the steps: obtaining the real-time transient wind field data of an unmanned aerial vehicle, and generating the flow field information of time-space correlation according to the real-time transient wind field data; a flight energy consumption model is established based on the flow field information, the flight energy consumption model is adopted to calculate energy consumption in different stages, node energy consumption attribute values are obtained, and the different stages comprise a level flight stage, a climbing stage and a descending stage; and the node energy consumption attribute value is input into the heuristic search algorithm model to obtain the current path planning with the lowest energy consumption, so that the accuracy of the route planning is improved, and the route energy consumption is saved.

The invention provides a building standard floor electromechanical pipeline modular arrangement sequence optimization method, and belongs to the technical field of building construction, and the method comprises the steps: firstly building a fine building BIM model, and dividing an electromechanical system into small modules; then analyzing the relationship between the modules, and determining a necessary sequence and a variable sequence; a weighted directed graph network is established for the variable sequence, and a heuristic function is defined. And searching an optimal arrangement path in the network by adopting an improved A * algorithm, and generating a detailed arrangement scheme. And finally, collision detection and simulation installation are carried out in the BIM model, and the feasibility of the scheme is verified. If problems exist, weight adjustment and optimization are returned until a final scheme is obtained. According to the optimization method, the BIM technology, the graph theory and the heuristic search algorithm are combined, electromechanical pipeline arrangement in a complex building can be effectively optimized, and the technical problem that in the prior art, modular arrangement sequence optimization of electromechanical pipelines of a building standard floor is difficult to achieve quickly and efficiently is solved.

The invention relates to the technical field of laboratory air ventilation, in particular to a ventilation cabinet airflow dynamic response optimization method based on digital twinning. The specific implementation process comprises the following steps: collecting a state vector sequence to predict a window motion track, identifying an operation intention and generating a virtual boundary condition; constructing a fluid mechanics digital twinborn model to simulate airflow dynamic characteristics, calculating and outputting airflow prediction data including a fluid resistance coefficient and turbulence intensity distribution, and generating an airflow optimization instruction for minimizing surface wind speed fluctuation by using a heuristic search algorithm; and monitoring a virtual-real deviation value, triggering a dynamic correction mechanism when a drift interval is exceeded, and carrying out online transfer learning on model parameters by utilizing the updated state vector sequence. According to the method, the risks of response lag and airflow oscillation are reduced through window trajectory prediction and digital twinborn simulation, meanwhile, errors caused by equipment aging are corrected through an online transfer learning mechanism, and the use precision and safety of long-term operation of the laboratory fume hood are ensured.