1593 results about "Intelligent algorithms" patented technology

The invention provides a plant-network integrated intelligent linkage scheduling method and system based on real-time monitoring, and the method comprises the steps: collecting drainage data in a drainage pipe network, obtaining the working condition data of a sewage treatment plant, and obtaining meteorological early warning information; performing comprehensive analysis on the drainage data, the working condition data and the meteorological early warning information based on an intelligent algorithm model, and performing prediction to obtain an analysis result; the intelligent algorithm model is used for simulating the running state of the drainage pipe network and predicting the sewage amount, the water quality change and the mixed sewage overflow risk; and according to the analysis result, generating a dynamic linkage scheduling instruction for the storage tank, the intelligent catch basin and the sewage treatment plant. According to the invention, the running state of the drainage pipe network is accurately simulated through the intelligent algorithm model, the sewage amount, the water quality change and the mixed sewage overflow risk are predicted, then the dynamic linkage scheduling instruction for the regulation and storage tank, the intelligent catch basin and the sewage treatment plant is generated, and the situation that the environment is damaged by overflow is avoided.

The invention relates to the technical field of chip design, and discloses a chip dynamic power consumption scheduling method and system based on an intelligent algorithm. The method comprises the following steps of: firstly, acquiring instruction stream data operated by a chip in real time, extracting a feature vector comprising an instruction dynamic change vector and context associated data, and determining a power consumption prediction mapping parameter according to the feature vector; and when the parameter exceeds a preset threshold value, an accurate power consumption prediction result is generated by adjusting the weight of the convolutional neural network. Subsequently, a synchronous timing demand is calculated based on the instruction switching frequency and the data dependency, and an initial power supply configuration is determined. By monitoring task load classification signals, the power consumption distribution proportion is adjusted when the signals are lower than a threshold value, the optimized power supply configuration is obtained, and the improvement index of the resource distribution efficiency is calculated according to the optimized power supply configuration. And finally, according to the index, dynamically adjusting a limiting condition of a scheduling period, and forming a self-adaptive optimization framework, thereby realizing accurate prediction and dynamic optimization scheduling of the chip power consumption.

The invention discloses a data security protection method and system combined with big data analysis, and relates to the field of data security protection, and the method comprises the steps: building a multi-dimensional data collection and fusion data set according to API access records, external threat intelligence and context metadata association data; based on an isolated forest algorithm, calculating a sample path length to evaluate a behavior anomaly probability score; risk indexes of external threats, data sensitivity and permission exceptions are quantified respectively; constructing a data security risk comprehensive assessment model based on a weighted summation algorithm, and updating and optimizing the weight in the model by using a gradient boosting tree algorithm; and according to an output result of the data security risk comprehensive assessment model, setting a security risk level, and according to the security risk level, dynamically responding to a protection measure. The method has the advantages that continuous and dynamic risk assessment and automatic response to third-party data access behaviors are realized by fusing multi-source data and an intelligent algorithm.

The invention provides a transformer area line loss intelligent algorithm decision-making method based on a knowledge graph and a large model agent, semantic retrieval in the algorithm calling process is supported, dominant and implicit knowledge such as expert experience, technical standards and historical cases is coded in a unified mode, a mapping relation is established between algorithm nodes and process nodes in the knowledge graph, and the algorithm is optimized. An integrated reasoning path is formed, so that the intelligent agent can automatically combine and call an algorithm according to a task target, a large model is embedded into a transformer area line loss analysis process, and a hierarchical structure of'task analysis intelligent agent-algorithm decision intelligent agent-result analysis intelligent agent 'is formed; the task analysis agent is responsible for converting a problem input by a user into a structured task; the algorithm decision agent is responsible for algorithm selection and adaptive parameter adjustment; the result analysis agent is responsible for report generation and decision recommendation based on the knowledge base; according to the method, organized association can be carried out on scattered algorithm tools, expert experience and business rules, and a unified transformer area line loss agent tool integration framework is constructed.

The invention discloses a drainage pipeline multi-parameter sensing trenchless repair dynamic regulation and control method and system. Through a fusion technology path of multi-source comprehensive data acquisition, edge calculation real-time cross validation of abnormity, pipeline state simulation analysis of defect levels, intelligent algorithm dynamic adjustment of repair parameters and geographic information visualization platform full-process monitoring, closed-loop management from abnormity identification to repair regulation and control is realized. A comprehensive data set is formed through multi-source data acquisition, an edge computing technology is utilized to quickly identify abnormities, a simulation technology is combined to evaluate the severity of defects, then process parameters are optimized and repaired based on defect levels, and a regulation and control instruction is generated through linkage of a visual platform and a pump station dispatching system. And finally, a pipeline repair and operation regulation and control scheme is formed through integration, and the repair effect and the system stability are ensured. According to the method, the pipeline abnormity processing accuracy and the repairing efficiency are remarkably improved, and a technical guarantee is provided for safe and stable operation of an urban drainage system.

The invention provides an autonomous adjustment method and system for a thermal power plant desulfurization wastewater dosing device, and the method comprises the steps: collecting multiple parameters such as flow, pH value, heavy metal ion and suspended matter concentration in real time through on-line monitoring instruments arranged at a water inlet end, a reaction unit and a water outlet end; constructing and continuously updating a dynamic mathematical model based on historical data and a reaction mechanism; a control system integrating intelligent algorithms such as a neural network and fuzzy control is utilized, and optimal dosing parameters are calculated in combination with the model and real-time data; a dosing pump or a valve is driven to execute precise dosing; and finally, closed-loop optimization is formed through effluent quality feedback. According to the invention, automatic, accurate and self-adaptive adjustment of the dosing device is realized, the defects that a traditional mode depends on manpower and response lags behind are overcome, stable and standard discharge of desulfurization wastewater is effectively guaranteed, meanwhile, medicament consumption and operation cost are remarkably reduced, and the automation level and the capability of coping with working condition fluctuation of the system are improved.

The invention relates to the field of numerical control of numerical control machine tools, in particular to an intelligent cutter matching selection control method and system for a numerical control machine tool. The method comprises the specific implementation steps that a machining task digital instruction, numerical control machine tool parameters and a tool historical database are obtained, a joint parameter space is constructed, and candidate tools and initial numerical control parameters are screened; operation feedback parameters are obtained, a stability lobe graph is generated, and a machinable interval is calibrated; a multi-objective optimization model is utilized to evaluate candidate tools, an optimization scheme is output, and parameters and early warning signals are adjusted; a machine tool is controlled in a closed-loop mode through PID, tool parameters are reconfigured when a threshold value is exceeded, a lobe graph is updated, a numerical control feedback strategy is generated, and a dynamic feedback path optimization database is constructed. Through an intelligent algorithm and closed-loop control integrated by a numerical control system, adaptive optimization of tool matching and cutting parameters is realized, and the machining stability and efficiency are improved.

The invention discloses a cable-stayed bridge cross brace optimization system based on system reliability and an intelligent algorithm, which relates to the technical field of bridge engineering, and comprises a multi-source parameter input module used for collecting and processing data such as a main beam section, stay cable parameters, a strain sequence, a vibration sequence, a satellite cloud picture, a traffic monitoring flow, laser point cloud and the like; and outputting the structured design data, the monitoring data flow, the dynamic load spectrum, the probability distribution model and the construction error correction parameters. According to the invention, all-dimensional data such as design, monitoring, load, materials and construction errors are integrated through the multi-source parameter input module, and multi-level and dynamic reliability evaluation is carried out on the component, the subsystem and the system level by using the system reliability analysis module, so that the component failure probability, the system reliability index time sequence and the like can be output; therefore, the real reliability level of the cross brace of the cable-stayed bridge in the whole life cycle is reflected more accurately, and the one-sidedness of a traditional method is avoided.

The invention discloses a distribution box room environment parameter integrated measurement method, and particularly relates to the field of multi-parameter measurement, and the method comprises the steps: firstly constructing a machine room three-dimensional model and a sensor correlation degree matrix, then building a heat balance, humidity diffusion and airflow motion model, and calibrating parameters; deploying a measurement system and calibrating through dual synchronization and cross check; establishing a sensor confidence evaluation system and an adaptive threshold based on 72-hour reference data; fusing calibration data by adopting a three-level correlation calibration mechanism; four types of measurement modes and conversion logics are designed, and measurement resources are dynamically allocated through environmental risk assessment; multi-dimensional state parameters are extracted, a comprehensive evaluation value is calculated through normalization, dynamic weighting and combinatorial algorithms, and five-level early warning response is achieved; according to the method, multiple models and an intelligent algorithm are integrated, the parameter measurement precision and the environment risk identification efficiency are improved, the response delay is reduced, the fault diagnosis accuracy is improved, and reliable technical support is provided for safe operation and maintenance of the distribution box room.

The invention relates to the technical field of forestry intelligent management and control, and discloses a forestry optimization management and control method and system based on artificial intelligence. The method comprises the following steps: collecting multi-source environment monitoring data such as soil humidity, illumination intensity and vegetation growth indexes in a forestry area, aligning a time sequence through a dynamic time warping algorithm, and separating steady-state and transient components; detecting anomalies through an isolated forest algorithm and generating a thermodynamic diagram, and obtaining a potential disease and pest outbreak area through causal reasoning in combination with historical records; constructing a dynamic growth reference curve, and identifying a disturbance sensitive area by using a generative adversarial network and a convolutional neural network; and integrating and generating a management and control priority partition map, optimizing resource allocation through deep reinforcement learning, outputting a task sequence, and driving equipment to execute irrigation, fertilization or pest control operation. According to the method, multiple artificial intelligence algorithms are fused, accurate control over the whole forestry process is achieved, and the method adapts to the dynamically-changing forestry environment.

According to the large power grid reactive power optimization method and device, the storage medium and the computer equipment provided by the invention, the advantages of the two algorithms are fully exerted through the hybrid chaos quantum particle swarm optimization algorithm and the dimension-by-dimension convex space search algorithm. According to the chaotic quantum particle swarm algorithm, the global search capability and the capability of jumping out of local optimum of a particle swarm are enhanced by utilizing the characteristics of quantum behaviors and chaotic mapping, and the problem of premature convergence of a traditional heuristic intelligent algorithm is avoided. And according to the dimension-by-dimension convex space search algorithm, fine search is carried out on each excellent particle in different dimensions, a local optimal solution is determined, and the search precision and efficiency are further improved. According to the design of the hybrid algorithm, special optimization is carried out aiming at the characteristics of a reactive power optimization problem model, such as variable property difference, constraint complexity and the like, and the technical defects of poor optimization effect and optimization efficiency of an optimization solution algorithm in the prior art are effectively overcome.

The invention discloses a gelatin solution temperature control optimization method for donkey-hide gelatin processing, and belongs to the technical field of swarm intelligence optimization. Comprising the steps that a gelatin solution temperature closed-loop PID control system in the donkey-hide gelatin decocting process is constructed, an improved side hand spider turning optimization algorithm is configured to serve as a parameter setting core, the self-adaptive setting process of PID control parameters is executed, and a global optimal control parameter vector obtained through optimization is mapped to a donkey-hide gelatin solution temperature PID controller module. The method focuses on the PID parameter setting problem of donkey-hide gelatin liquid temperature control, introduces the improved side hand turning spider optimization algorithm as a swarm intelligence algorithm to set the donkey-hide gelatin liquid temperature PID control parameters, and can effectively overcome the defect that the side hand turning spider optimization algorithm is easy to fall into local optimum. And the temperature of the donkey-hide gelatin liquid can be controlled more excellently.

The invention provides an unmanned aerial vehicle distribution task scheduling optimization method considering an endurance state, and belongs to the field of intelligent manufacturing. The method comprises the following steps: aiming at the problems of takeoff sequence, task allocation and endurance state change of an unmanned aerial vehicle in a distribution task, constructing a power consumption model based on flight stage division; under constraint conditions of unmanned aerial vehicle electric quantity, loading capacity, safe flight height and the like, converting the scheduling scheme into a three-layer integer sequence including a take-off sequence, a task allocation sequence and an execution sequence for expression and optimization; a task interruption and battery replacement decision-making mechanism and a battery replacement station selection cost function under the condition of insufficient electric quantity are introduced, and a swarm intelligence algorithm is adopted for solving, so that the optimization target of minimizing the total task completion time, the total battery replacement frequency and the flight distance standard deviation is achieved.

The invention provides an ocean current interference resistant fault-tolerant control method and system for a robot multi-motor propulsion system, and the method comprises the steps: inputting the actual and expected position error and speed error of an underwater robot into a self-adaptive backstepping sliding mode path tracking controller, and obtaining the thrust needed by tracking motion; introducing a propeller weight matrix representing the fault degree of the propeller into a weighted pseudo-inverse method, optimizing the propeller weight matrix by adopting an intelligent algorithm to obtain an optimal propeller weight matrix, and performing thrust distribution on thrust required by tracking motion through the weighted pseudo-inverse method to obtain a thrust distribution result; according to the operation and thrust distribution results of the multiple motors, the parameters of all the motors are adjusted in a self-adaptive mode through a multi-motor fault-tolerant control strategy; and each motor remotely controls the underwater robot based on the parameters after self-adaptive adjustment, and obtains the actual position and speed of the underwater robot at the current moment for loop closing.

The invention relates to the crossing field of agricultural intelligent algorithms and intelligent equipment technologies, in particular to an environment regulation and control model algorithm and algorithm model platform fusing crop physiology. According to the model algorithm, through four-step calculation of spatio-temporal heterogeneity adaptation, multi-source data dynamic coupling, stress self-adaptive adjustment and self-learning iterative optimization, a partition precise regulation and control decision value is generated; the algorithm model platform comprises a data acquisition layer (a partition sensor and a multi-source interface), a feature processing layer (a three-dimensional feature library and data preprocessing), a model algorithm layer (deploying the model algorithm) and a platform application layer (partition regulation and control and closed loop feedback), and full-link cooperation of algorithm calculation, platform execution, effect feedback and algorithm iteration is achieved. The crop yield is increased by 15-20%, water resources are saved by 25-30%, and the method is suitable for multi-crop facility agriculture scenes.

The invention discloses a method for correcting a predicted value of inherent frequency of a bridge under an operation condition, and belongs to the technical field of bridge monitoring, and the method comprises the following steps: S1, collecting bridge data; s2, data preprocessing; s3, environment and load interference decoupling is carried out; s4, constructing a model; s5, correcting the AI driving model; s6, verifying and iterating the mechanism; according to the method, breakthrough is made in three aspects of environmental interference stripping, parameter dynamic optimization and intelligent algorithm iteration, so that the prediction precision is improved, the engineering efficiency is optimized, the safety is ensured, the environmental interference stripping technology can effectively eliminate external environmental noise, and the accuracy of prediction data is ensured; a parameter dynamic optimization technology can dynamically adjust model parameters according to real-time data, so that a prediction result is closer to reality; the intelligent algorithm iteration continuously optimizes the model through machine learning and improves the intelligent level of prediction, and the method of updating posteriori distribution through Bayesian is adopted, so that the prediction model can be continuously corrected according to newly acquired data, and the prediction accuracy is ensured.

The invention relates to the technical field of intelligent power grids, in particular to an urban power distribution network whole-process scene chain closed-loop monitoring method and system fusing digital twinning. The method comprises the following steps of: fusing a weighted evidence theory and a dynamic time bending algorithm to acquire multi-source data, and constructing a physical mechanism-data driven hybrid enhanced digital twinborn model; based on the model, abnormity is researched and judged through an improved isolated forest algorithm, grading alarms are generated, simulation deduction is conducted through Monte Carlo-Latin hypercube sampling, an optimal strategy is screened through a non-dominated sorting genetic algorithm III with uncertainty, and instructions are executed after graph neural network topology verification. Feedback is formed by the collection result, and the strategy is optimized through meta-learning and Bayesian optimization calibration model. The system comprises six cooperation units, an intelligent algorithm and a digital twinning technology are integrated, the power distribution network abnormity identification precision, decision scientificity and adaptive capacity are improved, and safe and efficient operation is guaranteed.

The invention discloses an expressway reconstruction and extension traffic diversion method, system and device and a medium, and the method comprises the steps: obtaining multi-source dynamic traffic data, and constructing a road network origin-destination matrix through data fusion and demand deduction; a traffic flow distribution result is obtained by constructing a system optimal traffic distribution model; identifying the first congestion time period, and calculating the traffic flow exceeding the residual traffic capacity in the congestion time period to obtain the traffic flow to be shunted; solving an optimal shunting path under the target of minimizing the total travel time of the system through a hybrid intelligent algorithm to obtain an optimal shunting path scheme; and verifying the optimal shunting path scheme by using microscopic traffic simulation software, updating traffic data based on a simulation result, and carrying out iterative optimization to obtain a differentiated traffic organization scheme. According to the invention, the traffic flow passing efficiency during the highway reconstruction and extension period is effectively improved.

The invention relates to a robot track synchronization control method. According to the technical scheme, efficient collection and over-distance transmission of neural signals are achieved through the quantum entanglement technology, the brain movement intention is analyzed in combination with a high-sensitivity sensor and an intelligent algorithm, an instruction is transmitted to a robot power driving system through a low-delay communication architecture, and real-time synchronization of man-machine tracks is achieved through a two-way feedback system. Meanwhile, the movement flexibility of the robot is improved by adopting a lightweight design, the problems of high delay, low precision, poor interaction experience and the like in traditional man-machine synchronous control are solved, and the method can be widely applied to the fields of exoskeleton robots, industrial collaborative robots, rehabilitation medical robots and the like. The method has the advantages of being low in delay, high in precision, capable of improving interaction experience and wide in application range.

The Portable Digital Twin Robotic System based on Computer Vision for the Identification, Tracking, and Destruction of Suicide Drones is an innovative system that autonomously identifies, tracks, and destroys suicide drones by leveraging Digital Twin and computer vision technology. This system analyzes visual data using artificial intelligence algorithms and performs precise positioning in real-time. Aided by the Digital Twin, the precise simulation of the system's dynamics and movement enables the optimization of paths and precise control of the robot, while the real-time communication between the digital model and the physical robot provides the capability for immediate updates of control algorithms and predictive maintenance. The portable design and the capability to be connected to various types of weapons make this system suitable for installation on military and non-military platforms and improves the security of sensitive areas.

The invention relates to the technical field of automobile dynamics control, in particular to an ambulance suspension intelligent agent based on deep reinforcement learning and an optimization method thereof, and the optimization method comprises the steps: S1, initializing an evaluation network and a control strategy network; s2, adopting a swarm intelligence algorithm to initialize a population; s3, the number M of evolution generations of the controller and the time step T of each generation are set, in each time step, the initial fitness of the controller is set to be 0, and the fitness of the controller is updated according to the reward value and the auxiliary reward of the current time step; s4, after each intergeneration is finished, updating the population according to the fitness by adopting a swarm intelligence algorithm, and training the evaluation network and the control strategy network to update network parameters; and S5, after M generations of evolution are completed, screening out the controller with the highest fitness from the controllers, and setting the network parameters corresponding to the controller in the control strategy network. The invention is at least beneficial to improving the adaptability of the ambulance to different working conditions.

The invention provides a coal ash composite material goaf closed filling parameter intelligent matching method, and belongs to the technical field of deep learning and mining engineering crossing. According to the method, dynamic optimization and accurate matching of the filling parameters are realized through combination of data driving and an intelligent algorithm. The method comprises four core links: multi-source data perception and fusion, a material performance prediction model, key parameter identification and boundary constraint, and intelligent matching and optimization decision. According to the method, the modeling capability of the model for the complex coupling relationship is improved through the attention mechanism and the feature cross network; the performance evolution trend of the material under different proportions and process conditions is accurately predicted, key regulation and control parameters are automatically identified, and efficient search and optimal matching of a parameter space are achieved through a strategy gradient method. According to the method, multi-source data can be fused, key parameters can be dynamically identified, and the intelligent matching and optimizing capability is achieved.

The invention discloses a micro-grid optimization scheduling method based on an improved Loueer fox algorithm, relates to the technical field of micro-grid operation, and realizes collaborative optimization scheduling of a distributed power supply, an energy storage device, a load and the like in a micro-grid system by simulating various perception and behavior strategies of the Loueer fox. Aiming at the demand of distributed power supply system micro-grid optimization scheduling, the micro-grid system operation economy is taken as a main target, a function corresponding to a micro-power supply scheduling scheme is constructed, improvement is carried out based on an RFO algorithm, and a quantum fractal annealing disturbance strategy, a hyperbolic tangent self-adaptive olfactory disturbance strategy and a Riemannian metric accumulation disturbance strategy are introduced, so that the optimal scheduling of the micro-power supply system is realized. Through the fusion of the triple innovation strategies, the defects of poor population diversity, easy premature convergence, insufficient balance capability, low precision and the like when the original RFO algorithm and the traditional intelligent algorithm are used for solving the complex optimization problem are solved, and the efficient solution of the micro-grid optimization scheduling scheme is realized.

The invention discloses a production control management system for ultrathin stones, which mainly realizes intelligent management and control of the whole production process of the ultrathin stones and solves the problems of unstable quality and low efficiency in traditional production. The system comprises eight core units, namely a process parameter configuration unit, a grinding control unit, a fusion layer formation control unit, a thickness monitoring unit, a standing curing monitoring unit, a quality detection unit, a data recording and tracing unit and a central control unit, and the units are electrically connected and coordinated to construct a whole-process closed-loop management and control system. The system adopts technologies such as an intelligent algorithm, deep learning, machine learning, image recognition and big data analysis. A process parameter configuration unit generates an adaptive scheme in combination with raw material fluctuation; the grinding and fusion layer control unit corrects equipment parameters in real time and detects abnormity; the thickness monitoring and standing curing unit accurately regulates and controls the thickness and the curing environment; the quality detection unit is used for realizing defect classification and performance pre-judgment; and the data recording and tracing unit is associated with the whole-process data through the unique identifier.

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

The invention relates to the technical field of data processing, and discloses a pharmaceutical enterprise financial risk identification method, which comprises the following steps: constructing a dynamic panoramic financial behavior database through real-time fusion of multi-source heterogeneous data; using an intelligent algorithm to automatically detect an abnormal cash flow sequence significantly deviating from the health model; performing deep association mining on the same-period financial events and constructing a causal association map; dynamically quantifying a risk open value and a conduction path based on Monte Carlo simulation; generating a structured early warning report real-time push decision node; according to the invention, full-process dynamic management and control from data fusion, risk identification, cause tracing to quantitative early warning are realized, and the problems of low efficiency, narrow coverage, easy omission and difficulty in capturing complex and modeled anomalies due to the fact that financial cash flow anomalies are identified mainly depending on manual report checking or simple threshold alarm in traditional financial risk management are solved. And the active prevention and control capability of an enterprise on financial service risks such as cash flow breakage, debt paying crisis and operation interruption is obviously improved.

The invention relates to the technical field of wind power plant design, in particular to a wind power plant booster station site selection optimization method and system based on a multi-objective genetic algorithm and three-dimensional terrain constraint. The method comprises the following steps: firstly, constructing unified program grid data fused with geographic constraints; establishing a static land use cost model containing an orientation weight by using a rotation convolution algorithm; then, dynamic cost calculation logic of fan-power grid collaborative topology based on candidate coordinates, multi-constraint road dynamic path finding and site earth-rock balance iteration is defined; and finally, constructing a multi-target optimization model of comprehensive multi-dimensional cost, and performing iterative optimization through a genetic algorithm to obtain a Pareto optimal site selection coordinate. Through coupling of geographical constraints, the cost model and the intelligent algorithm, the problem of complex mountain site selection is solved, global collaborative optimization of land, earthwork, road and electrical cost is realized, and scientificity, economy and construction feasibility of wind power plant design are remarkably improved.

The invention provides a port logistics intelligent simulation and decision optimization platform based on a digital twin technology, and the platform comprises a basic layer which provides calculation, storage, network and basic software support; the resource layer integrates data resources, model resources and algorithm resources and is used for managing port multi-source data, business process models and intelligent algorithms; the digital twinborn layer is used for constructing and maintaining a high-fidelity digital twinborn body synchronous with a physical port and realizing virtual-real mapping, dynamic data fusion and model self-evolution; the simulation layer is used for deducing based on the digital twin and comprises a simulation engine and an event driving and data interaction module; and the application layer is used for providing a user interface, analyzing decision and optimizing functions and supporting scheme simulation, evaluation and closed-loop optimization. According to the method, the high-fidelity digital twinborn body of the port can be constructed, real-time mapping, accurate simulation, prospective prediction and intelligent decision making of the whole process of port logistics are achieved, and a technical closed loop of data acquisition, digital twinborn, intelligent decision making and virtual-real linkage is formed.

The invention discloses a particle accelerator multi-particle dynamics efficient simulation method, system and device and a medium. The method comprises the steps that a small number of macro particles are sampled from an initial macro particle beam to form a new macro particle beam; performing multi-particle beam flow dynamics simulation on the new macro particle beams by adopting a PIC algorithm, and projecting the new macro particle beams to four two-dimensional phase spaces in a distributed manner to generate low-resolution phase diagrams of the four two-dimensional phase spaces; obtaining beam physical parameters based on the new macro particle beam; and inputting the low-resolution phase diagram and the beam physical parameters into a beam halo super-resolution network, and outputting a high-resolution phase diagram of each phase space to realize multi-particle beam flow dynamics simulation. Therefore, by combining a traditional numerical calculation method and an artificial intelligence technology, the problem that an existing artificial intelligence algorithm is weak in generalization ability in the field of beam dynamics simulation is solved, rapid and high-precision simulation of the large-scale multi-particle beam flow dynamics process is achieved, and the method is applied to the field of particle accelerator beam dynamics simulation.

The invention relates to the field of low-altitude economy and geographic information, in particular to a low-altitude scene-based twinborn data rapid updating method, which comprises the following steps of: acquiring multi-source sensing data of a target low-altitude area in real time; comparing with a historical twinborn model, and identifying a change area and evaluating a priority by using an artificial intelligence algorithm; dynamically generating an optimal unmanned aerial vehicle data acquisition route by combining the airspace information and priority of the change area; then the unmanned aerial vehicle is controlled to collect updated data along the route; performing lightweight real-time three-dimensional reconstruction on the updated data to form a local updated model; and finally, fusing the model with a historical twinborn model to generate a final target twinborn model. Through intelligent change identification, dynamic route planning, lightweight reconstruction and incremental updating, the updating efficiency is significantly improved, the resource consumption is reduced, the real-time accuracy of the model is ensured, and the method is suitable for multiple fields such as low-altitude economy and urban governance.