2969 results about "Dynamic simulation model" patented technology

The invention discloses a hydraulic engineering equipment data intelligent management system based on digital twinning, and belongs to the technical field of hydraulic engineering. Comprising an intelligent perception and data fusion module for realizing real-time acquisition and standardized processing of cross-modal data; the knowledge graph construction and causal reasoning module is used for constructing an intelligent knowledge system capable of autonomously learning and semantic reasoning; the digital twin modeling and simulation module is used for realizing dynamic simulation and scene deduction of a full life cycle and providing limit working condition simulation and risk assessment support; the intelligent prediction and health management module is responsible for performing real-time monitoring, fault early warning and residual service life prediction on the equipment state, and generating personalized intelligent maintenance strategies for different working conditions; the visualization and decision support module is used for visually presenting the equipment operation data and the analysis result and providing intelligent decision recommendation; and the cloud edge collaboration and system integration module realizes cross-platform interoperation and continuous integration through distributed computing and micro-service architecture.

The invention discloses an industrial robot real-time adaptive control method and system based on digital twinning, and relates to the technical field of industrial robots. The digital twin engine module runs a high-fidelity dynamics simulation model and an environment interaction model, performs real-time state estimation, abnormal working condition recognition and twin parameter dynamic updating, is seamlessly integrated with the control execution module, and provides decision support with high robustness and high adaptability for an industrial scene; the adaptive control module performs online rolling optimization on a control strategy based on a deep reinforcement learning algorithm, generates joint space trajectory correction, tail end precision compensation and dynamic load adaptability optimal instructions, and realizes parameter adaptive setting through fuzzy logic or a neural network; and the fault diagnosis module performs multi-scale time sequence analysis by using an LSTM and convolutional neural network fusion model, detects position offset, moment sudden change or temperature overrun and other abnormalities, and triggers emergency shutdown, sound-light alarm and an adaptive recovery strategy.

The invention relates to an automatic driving large model training optimization method based on multi-scene data balance. Comprising the following steps: (1) constructing a real vehicle high-speed driving scene library; (2) constructing a visual language automatic driving large model, training by adopting an iterative training framework based on a real vehicle high-speed driving scene library, designing a multi-task joint loss function and a weight adaptive adjustment strategy, realizing multi-task target balance, and obtaining a trained visual language automatic driving large model; (3) dynamic simulation is carried out for the automatic driving working condition, and a high-fidelity simulation data test set is constructed based on simulation data; and (4) performing hyper-parameter optimization and lightweight processing on the trained visual language automatic driving large model according to the high-fidelity simulation data test set to obtain a scene data balanced automatic driving large model. According to the method, the large model reasoning speed is increased, and resource occupation is reduced, so that the judgment capability of the large model on dynamic working conditions and high-risk scenes is remarkably improved.

The invention relates to the technical field of bridge engineering, in particular to a cable structure bridge design method adopting a BIM (Building Information Modeling) model, which comprises the steps of three-dimensional bridge foundation model construction, cable structure parameterization configuration, nonlinear mechanical simulation, construction error modeling, feedback optimization and the like. Through introduction of structured geological data and NURBS curved surface reconstruction, a geological-structure integrated model is realized. Adopting a genetic algorithm and a particle swarm optimization method to intelligently configure pile foundation and cable parameters; a construction error closed-loop adjustment mechanism is constructed through dynamic simulation and real-time tension feedback control; and finally, parameter correction write-back and BIM delivery model integration is realized. According to the invention, the design precision, the construction stage adaptability and the digital delivery integrity of the cable structure can be improved, and the method is suitable for cable structure bridge engineering with complex geology and high-precision control requirements.

The invention discloses a land degradation supervision method based on remote sensing monitoring, and relates to the technical field of land degradation monitoring. The method is used for solving the problems of hidden degradation risk identification and dynamic propagation simulation. According to a multi-temporal thermal infrared remote sensing image, daily variation characteristics of surface temperature are extracted through spectral analysis, a recessive salinization risk distribution map is constructed by combining measured data of soil salinity, and interference of vegetation coverage is overcome. And multi-source remote sensing and geographic data are fused, dominant driving factors significantly associated with the salting risk are screened, and the degeneration cause is clarified. And in combination with ecological restoration potential and treatment resource constraints, calculating an antagonism index of a driving factor and the restoration potential, and dividing degradation threat levels. And a natural hydrology and human activity dual-path propagation model is constructed, a risk space-time evolution process is simulated, and a future hotspot migration path and an optimal blocking window are predicted. According to the method, accurate early warning and active prevention and control of land degradation are realized through a technical chain of risk identification, driving analysis, threat grading and dynamic simulation.

The invention relates to the technical field of landslide motion simulation, in particular to a landslide motion simulation method based on a motion model. The method comprises the following steps: acquiring data of a target landslide area; generating complete topographic feature data through landform identification and difference complementation; performing topographic analysis on the data; identifying a landslide-prone area, constructing a three-dimensional model, judging a critical state, performing risk weight calculation, and generating a probability distribution set of the landslide-prone area; performing path analysis to obtain an initial motion track and a probability prediction model thereof, constructing a three-dimensional dynamic simulation scene, performing iterative analysis, finally generating a landslide motion simulation result, and establishing a dynamic risk early warning and emergency response model to improve the prevention and control capability of landslide disasters. According to the invention, a more accurate and more reliable landslide motion simulation method is realized.

The invention relates to the field of image analysis, in particular to a temporal bone disease classification method and system based on a multi-modal medical image fusion technology. The method comprises the following steps: acquiring a multi-modal image of a patient, performing adaptive distortion correction, and generating a standardized image set; performing layer-by-layer anatomical structure semantic segmentation and multi-modal image fusion on the standardized image set to construct an image fusion framework; according to the image fusion framework, performing intelligent recognition on the fine structure of the temporal bone, and constructing a personalized temporal bone anatomical structure chart; performing tissue function state analysis and digital pathology dynamic simulation based on the personalized temporal bone anatomical structure chart, and constructing a digital pathology model; and performing intelligent pathological feature classification based on the digital pathological model to obtain an intelligent classification report. According to the method, rapid, efficient and accurate temporal bone disease classification is realized.

The invention belongs to the technical field of urban planning, and provides a digital twinborn-based urban update space intelligent planning and evaluation system, which comprises a data acquisition module for fusing multi-source heterogeneous data in real time, and constructing and updating an urban digital twinborn body; the intelligent planning module generates candidate schemes by combining targets and constraints based on the digital twins and utilizing artificial intelligence; the simulation deduction module performs multi-dimensional dynamic simulation on the candidate scheme, and feeds back and adjusts parameter optimization to generate a planning scheme; the intelligent evaluation module evaluates a planning scheme based on a simulation result through a quantitative index; the monitoring feedback module continuously monitors the actual operation state of the to-be-updated area; according to the invention, through the Internet of Things and the digital twinborn model, the actual condition of a city is dynamically reflected, the integrity and timeliness of data are ensured, and a city updating scheme continuously adapts to city changes.

The invention relates to the field of chip verification, and provides a chip verification method, device and equipment, a medium and a chip. The method comprises the following steps: establishing a dynamic simulation verification environment of a to-be-verified design, and generating a plurality of test scenes and corresponding test excitation according to a design specification definition; sending the test excitation to a driver through a sequencer, converting the test excitation into a signal conforming to a to-be-verified design interface protocol by the driver, and transmitting the signal to the to-be-verified design; the monitor collects an input signal and an output signal of a to-be-verified design in real time, converts the input signal and the output signal into transactions, and transmits the transactions to the scoreboard; comparing the reference model with an output signal of the to-be-verified design to generate a dynamic simulation verification result; according to the dynamic simulation result, the state space is reduced, state space traversal is carried out on a design part which is not covered by dynamic simulation in the design to be verified, and a formal verification analysis result is generated; and evaluating the to-be-verified design according to the formal verification analysis result.

The invention discloses a multi-granularity multi-dimensional power transmission and transformation equipment digital twinning mechanism method and system, and relates to the technical field of power transmission and transformation equipment digital modeling, and the method comprises the steps: collecting multi-dimensional physical field data, and carrying out the preprocessing; constructing a layered multi-physics field coupling model; carrying out integration and parameter adjustment on the output of the layered multi-physics field coupling model to form a unified multi-physics field coupling model; and performing simultaneous analysis on multiple physical fields to complete dynamic simulation and fault prediction. By improving the finite element analysis method and the hierarchical multi-physics field modeling technology, the technical problem that the operation state of the power transmission and transformation equipment in a complex multi-physics field coupling environment is difficult to accurately monitor and predict the fault in real time is technically solved, and the effects of improving the monitoring precision and enhancing the fault diagnosis real-time performance and the prediction accuracy are achieved.

The invention relates to the technical field of high-pressure dredge pump simulation calculation, and discloses a construction and application method of a high-pressure efficient dredge pump simulation calculation model. A model is established based on dynamic fluid-solid coupling parameters (mud pulsating pressure, impeller dynamic stress and the like), soil multiphase rheological parameters (particle phase volume fraction, viscous resistance coefficient and the like) and pipeline transient resistance parameters (transient pressure drop gradient and the like), and factors such as rotating speed-power matching and the like are incorporated to calculate an operation envelope and a critical failure threshold. A multi-physics field coupling analysis system comprising parameter configuration, dynamic simulation (integrating a flow field and a structural vibration solver) and a safety evaluation module is developed, after actually measured data and self-defined parameters are input, dynamic flow velocity distribution, pressure pulsation characteristics and the like are obtained through fluid-solid bidirectional coupling calculation, and an optimal configuration scheme is generated. The system supports database management, multi-rheological model matching and three-dimensional visualization, the simulation precision and the engineering efficiency are improved, and support is provided for performance optimization and safety evaluation of the high-pressure working condition.

The invention discloses an underground water infiltration replenishment evaluation method and equipment based on digital twinning, and relates to the technical field of underground water digital twinning evaluation, and the method comprises the steps: traversing a target area to carry out multi-source sensing collection; performing underground water infiltration simulation to generate an infiltration full-process data set, and obtaining an infiltration coupling model; geologic structure parameters of the target area are extracted, and a three-dimensional geologic structure model is constructed; performing association mapping on the infiltration coupling model and the three-dimensional geological structure model to obtain a dynamic simulation result; deviation analysis is carried out, automatic correction optimization is carried out, and an underground water infiltration supply evaluation report is generated. According to the method, the technical problems of insufficient multi-source data fusion, low space-time simulation precision and weak model dynamic correction capability in underground water infiltration supply evaluation in the prior art are solved, and underground water infiltration supply dynamic simulation and digital twinborn automatic correction of multi-source data coupling are realized; and the technical effect of improving the space-time precision of groundwater infiltration supply evaluation is achieved.

The invention provides an air conditioner working condition monitoring method and system based on the Internet of Things. The method comprises the following steps: monitoring the air conditioning system and the Internet of Things sensing data of the environment where the air conditioning system is located; performing dynamic simulation on the air conditioning system based on the sensing data of the Internet of Things to obtain a real-time simulation working condition of the air conditioning system; the real-time simulation working condition is input into the fault feature decoupling model, and potential fault risks existing in the air conditioning system are predicted from multiple dimensions so as to obtain multiple prediction results of the potential fault risks; a dynamic coordination algorithm is adopted, dynamic traceability and dynamic traceability and collaborative decision making are carried out on the multiple paths of prediction results, control chain conflicts among the multiple paths of prediction results are eliminated, and potential fault traceability information is obtained; and displaying the real-time simulation working condition and the corresponding potential fault traceability information through a real-time monitoring interface of the air conditioning system. Real-time working condition simulation and intelligent fault tracing of the air conditioning system can be achieved, the fault early warning accuracy and system coordination of the air conditioning equipment are improved, and the operation efficiency of the air conditioning equipment is improved.

The invention relates to the technical field of data fusion, in particular to a power distribution equipment whole life cycle management system and method based on data fusion. The method comprises the following steps: acquiring multi-modal real-time sensing data and an equipment management log, and performing pre-semantic extraction and coding compression to obtain an edge semantic fragment; analyzing the topological structure of the power distribution equipment by using the equipment management log, and performing federal feature distributed modeling on the edge semantic fragment by using the topological structure of the power distribution equipment to obtain federal feature data of the power distribution equipment; state evolution causal inference is carried out based on the federation characteristic data of the power distribution equipment to obtain an equipment state evolution path; performing operation state dynamic simulation and prediction evaluation according to the equipment state evolution path to obtain a dynamic twin evolution body; and carrying out equipment operation parameter iterative optimization on the equipment management log according to the dynamic twin evolution body to obtain an optimized equipment operation parameter group. According to the invention, the use efficiency and the operation reliability of equipment are improved, and the maintenance cost is reduced.

The invention discloses a belt conveyor fault identification and prediction method and system, and relates to the technical field of digital intelligent monitoring of belt conveyors, and the method comprises the steps: collecting the operation data of a belt conveyor in real time through various sensing devices arranged on the belt conveyor, analyzing the collected video data and multi-modal data through an AI algorithm, and carrying out the real-time monitoring of the operation data of the belt conveyor; analyzing the operation state and fault prior information of parts and materials of the conveyor, inputting the information into a dynamic simulation model, simulating the common influence of various faults on the belt conveyor through Monte Carlo simulation, and calculating the probability prediction values of the various faults; finally, an analysis result is displayed in real time in combination with modes of graphic display, probability curves and the like, and working personnel are timely notified of occurred and about-to-occur faults; meanwhile, fault probability prediction and operation data serve as training data again to conduct incremental learning on the AI algorithm, and the accuracy of the AI algorithm is continuously improved.

The invention relates to the technical field of energy management and optimization, and discloses a resource allocation dynamic simulation optimization method based on energy condition constraints, which comprises the following steps: step 1, deploying a sensor network and a data acquisition system, monitoring an energy supply condition and a consumption mode in real time, and performing multi-dimensional collection on data; 2, the collected data are cleaned and analyzed, a dynamic energy condition report is generated, and the report comprises energy supply and demand prediction, energy price fluctuation and carbon emission intensity information; and step 3, constructing a resource allocation dynamic simulation model according to the collected data and reports. The resource allocation dynamic simulation optimization method based on the combination of real-time energy state recognition and self-adaptive weight adjustment is adopted, the technical effect of flexible scheduling according to dynamic changes of energy conditions is achieved, and it is ensured that the resource allocation process can be responded in time, and the system operation efficiency is optimized.

The invention relates to the technical field of river channel desilting treatment, in particular to a dynamic simulation and optimization decision-making-based river channel silt treatment method, which comprises the following steps of: acquiring and treating historical and real-time multi-dimensional environmental data of a to-be-treated river channel and an influence area of the to-be-treated river channel; a river basin process simulation system capable of simulating river water power, sediment transportation, pollutant migration and transformation and ecological response processes is constructed, decision variables forming a river regulation scheme are defined in a parameterized mode, a multi-target optimization problem is defined and set in a formalized mode, a multi-target optimization engine is adopted, and the river regulation scheme is optimized. The method comprises the following steps: embedding a drainage basin process simulation system as a core evaluation module into an optimization iterative loop, implementing an optimization scheme, and continuously monitoring the environmental state change in the implementation process and after the implementation; the method can accurately predict the long-term and short-term influence of different treatment schemes in multiple aspects of hydrodynamic force, silt, pollutant migration and transformation, ecological response and the like, and solves the problem of comprehensive treatment of sludge.

The invention discloses a food risk prevention and control intelligent remote early warning method and system, and the method comprises the steps: collecting environment and operation data in real time through an edge calculation terminal, screening low-entropy data based on a data conflict entropy value, writing the low-entropy data into a block feature chain, and carrying out the encryption transmission; fusing the block feature chain data and the historical behavior game data of the participants by using a space-time diagram convolutional network to generate a hybrid risk index; constructing a digital twinborn body, calculating a cross-modal propagation coefficient through transfer learning, dynamically simulating a risk diffusion path, and generating a prevention and control strategy candidate set; quantizing an early warning level confidence interval by adopting a quantum random number algorithm, and triggering a differentiated encryption early warning instruction through an intelligent contract; and finally, aggregating early warning response data by using federal learning to reversely deduce a risk source, and synchronously correcting the block chain weight and the digital twinborn parameters. According to the invention, full-cycle risk tracing, dynamic simulation and collaborative early warning of the food supply chain are realized, and the risk identification precision and the prevention and control response efficiency are effectively improved.

The invention discloses a digital twinborn-based irrigation area water distribution scheduling method and system, and relates to the technical field of water distribution scheduling, and the method comprises the steps: collecting data, and constructing a digital twinborn model; dynamically simulating water resources in an irrigation area by using a hydrological model simulation technology based on a digital twinborn model; performing irrigation demand prediction by using a machine learning algorithm according to a simulation result; optimizing a water distribution scheduling model according to a prediction result; performing real-time scheduling control according to the scheduling strategy; performing scheduling effect evaluation according to the scheduling control data; adjusting a scheduling strategy according to an evaluation result; and performing irrigation area water resource management and long-term optimization by using an irrigation area water distribution scheduling system based on digital twinning according to an adjustment result. According to the method, the limitation of static weight distribution of a traditional method is overcome, the accuracy and flexibility of water distribution scheduling are improved, the actual requirements of a complex irrigation area environment are met, the maintenance cost and the operation risk are reduced, and efficient and stable operation of an irrigation area water distribution system is guaranteed.

The invention discloses a fracture structure multi-scale modeling method based on multi-source data fusion, and belongs to the field of geological modeling, and the method comprises the following steps: S1, collecting and preprocessing multi-source data; s13, aligning the data, and integrating the data from different sources together; s2, three-dimensional geologic modeling: S21, generating an initial three-dimensional grid by using geologic modeling software to obtain an initial three-dimensional geologic body model; s22, dynamically adjusting the density of the initial three-dimensional grid by using a self-adaptive grid division technology; s23, using a machine learning algorithm to identify and classify faults; s3, expanding the three-dimensional geologic body model macroscopically and microcosmically; and S4, performing dynamic simulation and prediction by using the three-dimensional geologic body model. By adopting the fracture structure multi-scale modeling method based on multi-source data fusion, the accuracy and reliability of the fracture structure model are improved by integrating multiple data sources and multi-scale analysis.

The invention relates to the technical field of safety supervision, in particular to a building engineering quality safety supervision system and method based on multi-source data. The method comprises the following steps: acquiring positioning data of engineers and instruments, identifying a movement track, generating a dynamic simulation building engineering model to analyze abnormal construction behaviors, performing incentive backtracking analysis to identify the root of the abnormal behaviors, predicting the development trend of the abnormal behaviors, delimiting a focusing supervision area according to prediction data, and performing focusing supervision on the abnormal behaviors. The method comprises the following steps: carrying out rejection path analysis, forming candidate paths, carrying out space mapping and speed optimal matching on the candidate paths, determining an optimal rejection path, and carrying out engineering repair management according to the optimal path when the safety quality is lower than a threshold value after the engineering safety quality is evaluated. According to the invention, a more efficient and safer construction engineering quality safety supervision method is realized.

The invention relates to the technical field of BIM building optimization, in particular to a BIM-based prefabricated building design construction optimization management system and method. The method comprises the following steps: acquiring a panoramic monitoring image of a building area and a construction design initial BIM model; carrying out regional space structure evolution and construction scene three-dimensional simulation on the panoramic monitoring image of the building region, and constructing a three-dimensional construction scene graph; identifying assembly type building component information according to the construction design initial BIM model so as to generate building component assembly demand characteristics; performing dynamic construction sequence simulation optimization on the three-dimensional construction scene graph and the construction design initial BIM model based on building component assembly demand characteristics, and constructing a dynamic optimization construction sequence; and carrying out available material transportation path analysis on the three-dimensional construction scene graph so as to generate an optimal operation path of each material. According to the invention, real-time collaborative management based on the BIM and the cloud is realized, the building construction quality is improved, and the construction design decision is optimized.

The invention discloses a network attack active trapping method based on intelligent scheduling, and the method comprises the steps: constructing a dynamic honeypot environment according to a real business system mirror image, simulating the interaction logic and data characteristics of a real business system, and generating a honeypot system; analyzing a network equipment log of an access source according to the security situation awareness platform, and capturing multi-dimensional features to identify attack traffic; dynamically generating a drainage strategy according to an identification result of the security situation awareness platform, and seamlessly switching attack traffic to a honeypot system through load balancing equipment; and recording an attack behavior chain in the honeypot system, extracting an attack tool fingerprint and tracking an attacker identity. Through dynamic simulation environment construction, intelligent traffic scheduling, full-chain traceability and intelligent resource management, high-simulation trapping, accurate attack shunting, credible electronic evidence chain generation and efficient resource utilization are realized, and the active defense efficiency is remarkably improved.

The invention provides a scene reproduction and augmented reality simulation training method for floating installation. The method comprises the following steps: acquiring an external environment and an equipment state in real time; acquiring operation behavior data of the trainee in the training process, performing timestamp matching on the operation behavior data and the dynamic simulation scene, constructing a synchronous model, calculating a key performance index based on the synchronized data to quantify the operation effect of the trainee, and obtaining an operation performance record; when it is detected that the student fails to operate, a failure scene of the student is reconstructed based on currently collected operation behavior data and a dynamic simulation scene, and a failure path is analyzed by using a reinforcement learning model; generating an optimized floating track and an optimized posture adjustment path according to the failure scene of the reconstructed student and the optimization suggestion; and the trainee completes the floating support installation operation according to the optimized path displayed by the augmented reality equipment. According to the invention, the efficiency and effect of floating support installation training can be greatly improved, and a more efficient and intelligent solution is provided for the ocean engineering field.

The invention relates to the technical field of state detection and fault diagnosis, in particular to an intelligent early warning and fault diagnosis system for a thermal power plant, which comprises a multi-source data acquisition module for acquiring data in real time; the edge computing node is used for performing noise filtering and abnormal value correction on the acquired data; the digital twin modeling unit is used for constructing a dynamic simulation model of the equipment based on a physical model and historical data; the hybrid analysis engine is used for positioning early abnormal detection and fault sources; the visual early warning interface is used for dynamically displaying the health state and the fault probability of the equipment and generating a graded alarm signal; according to the invention, the multi-source data acquisition module acquires equipment multi-dimensional signals in real time, after edge computing node filtering and denoising, a digital twin modeling unit constructs a precise simulation model, a hybrid analysis engine fuses LSTM and a Bayesian algorithm, fault features are deeply mined, data weights are optimized, and the fault detection accuracy is improved. According to the system, the accuracy and timeliness of diagnosis are remarkably improved.

The invention relates to the technical field of flood storage and detention area regulation and control, in particular to a flood storage and detention area risk-benefit dynamic game regulation and control method based on digital twinborn. Performing dynamic simulation on the real-time flood event based on a digital twinborn model to obtain probability distribution data; performing multi-target iterative optimization solution on the historical flood diversion regulation and control strategy based on the probability distribution data to generate a multi-target optimal balance set; generating a real-time controllable gate regulation and control instruction set on the basis of the digital twinborn model and the optimal tradeoff set; and in the process of real-time gate regulation and control through the gate regulation and control instruction set, carrying out quantitative evaluation on the regulation and control risk and benefit of the flood storage and detention area. According to the method, high-precision, dynamic and intelligent regulation and control of the flood storage and detention area are realized, accurate quantitative evaluation of risks and benefits of the flood storage and detention area is realized, a scientific basis is provided for flood control decision, and flood disaster loss and ecological influence are effectively reduced.

The invention discloses an urban ecological unit scale water conservation function evolution simulation system, relates to the technical field of ecological hydrological simulation, and is technically characterized in that the system combines an InVEST model, a water balance method, a geographic detector and a Moran index to construct a multi-source data fused water conservation evaluation method. According to the system, key factors are obtained through remote sensing data and geostatistical data, a mapping model between water yield and actually measured runoff depth is established based on a regression fitting relation, and a spatial heterogeneity analysis and factor interaction detection mechanism is introduced, so that time sequence dynamic simulation and spatial pattern recognition of a water conservation function are realized. The system can be widely applied to the fields of urban water resource regulation and control, ecological space planning, hydrological safety evaluation and the like.

The invention relates to the technical field of sewage treatment, in particular to a dynamic simulation method for a lake and reservoir sewage sludge deposition process. The method comprises the following steps: collecting lake and reservoir hydrological station data, carrying out abnormal value filtering and missing value completion, generating a complete hydrological sequence, carrying out water flow state subdivision to obtain flow state hierarchical data, reconstructing a multi-layer hydrodynamic field, forming a hydrological dynamic field set, carrying out suspended matter analysis, and obtaining a hydrological dynamic field model; the method comprises the following steps of: extracting sewage sludge data, deconstructing transportation characteristics of the sewage sludge data, generating transportation dynamic data, performing water body stress tensor decomposition to obtain stress distribution data, performing substance transportation simulation on a complete hydrological sequence by using the stress distribution data, establishing a multi-scale sedimentation dynamic model, acquiring water body sedimentation change data in real time, comparing the water body sedimentation change data with the model, and if inconsistency exists, judging whether the water body sedimentation change data is consistent or not. If yes, the model is finely adjusted in real time, and an optimized multi-scale deposition dynamic model is generated. The more accurate dynamic simulation method for the lake and reservoir sewage and sludge deposition process is achieved.

The invention relates to the technical field of land resource management, in particular to a land resource space optimization configuration method and system, and the method comprises the following steps: dividing grid cells based on a land research region, setting the initial land utilization state of each cell, comparing the land suitability of adjacent cells, updating the land utilization type, and determining the land utilization state of each cell; and recursively calculating the change in each time step, and iteratively executing the transfer specification to obtain a land utilization change state sequence. According to the invention, through recursive calculation and land utilization state updating, land utilization dynamic simulation, data continuity and regional adaptability enhancement, vacancy value filling and neural network adjustment, data and environmental factor feedback adjustment, prediction precision and trend judgment capability are improved, and economic, social and ecological benefits are quantified. The land use proportion and spatial layout are optimized, the land resource allocation efficiency is improved, policy adjustment and scene simulation are combined, differentiated decision support is provided, and social fairness and efficient resource utilization are promoted.

The invention provides a customized energy-saving air conditioner control method and device for industrial process requirements, and is applied to the technical field of data processing. According to the method, electromagnetic interference and vibration noise are eliminated through Kalman filtering, and a standardized process-environment-energy consumption correlation sequence is generated; and converting the three-dimensional process load map into a three-dimensional process load map, and establishing an individualized air conditioner dynamic load prediction model in combination with heat and humidity characteristics of a scene through fluid dynamic simulation and self-adaptive grid division. Extracting process priority factors to construct an adjustment matrix, calculating an air conditioner parameter combination under target energy consumption, extracting energy consumption characteristics, dividing standard exceeding risk levels, and constructing a multi-dimensional characteristic matrix; and comparing real-time data with historical data to identify abnormity, and generating an energy consumption optimization correction factor. Users are grouped according to enterprise conditions, key factors are screened by using a gradient boosting tree, a personalized energy-saving control model is constructed by fusing multiple information, target parameters are output, and a real-time adjustment instruction and a time-phased energy-saving strategy are generated in combination with a time sequence.