155 results about "Intelligent modeling" patented technology

The invention discloses an environment perception and command control intelligent deduction platform based on bionic unmanned equipment, and relates to the field of intelligent deduction, and the platform comprises the steps: collecting environment data through a multi-mode sensor, encrypting the environment data through an anti-interference communication relay module, and transmitting the encrypted environment data to a central control platform; the data fusion preprocessing module is used for processing multi-source data and outputting a structured data stream, and the environment intelligent modeling module is combined with a geographic information system to construct a three-dimensional dynamic environment model; an intelligent deduction module carries out multi-scene deduction based on the model, and a self-adaptive control strategy generation module converts a decision tree into an anti-disturbance control sequence; and the man-machine collaborative decision-making module fuses operator intervention to generate a final instruction, and the instruction distribution and equipment collaborative control module issues the instruction to the bionic unmanned equipment to realize intelligent collaborative control. The method has the advantages that bionic perception, anti-interference communication and intelligent deduction are integrated, efficient cooperation and adaptive control of unmanned equipment in a complex environment are achieved, and the intelligence and robustness of task execution are remarkably improved.

The invention discloses a high-time-resolution flow field testing method and system based on sparse moment measured values and an intelligent power system, and belongs to the field of flow field testing and data reconstruction in bridge wind engineering. According to the method, overall low-frequency and local high-frequency sparse flow field data are obtained through a four-pulse fixed-frequency variable-frequency laser system and a four-exposure high-speed imaging PIV sampling system; a 32-dimensional nonlinear modal coefficient is extracted through a multi-scale convolution flow field sparse feature extraction model, then an unsampled time step coefficient is predicted through an LSTM intelligent power system model, and finally the unsampled time step coefficient is input into a multi-scale convolution auto-encoder to reconstruct a high-time-resolution flow field. The method does not need to depend on a pressure sequence, reduces the hardware and data processing cost through sparse sampling, accurately captures the flow field dynamics characteristics through intelligent modeling, solves the problems of expensive high-frequency hardware, loss of low-frequency reconstruction data and difficulty in model training in a traditional PIV test, and is suitable for flow field dynamics research and engineering optimization.

The invention discloses an LLM (Logical Language Modelica) domain method for Modelica intelligent modeling optimization, and belongs to the field of computer-aided modeling. Structured information is converted and extracted by collecting field data of the new energy automobile; then, based on the RAG technology, intelligent blocking and vectorization processing is carried out on the structured information to form a structured knowledge base, and then a recall test is carried out by calculating a comprehensive weighted score to judge whether the structured knowledge base is available or not; then, the LLM is integrated into an AI-Agent framework, an AI-Agent is formed, and LLM parameters are configured; integrating the structured knowledge base which passes the test into the AI-Agent by utilizing an API (Application Program Interface) and an access protocol; and meanwhile, a specialized cue word template is designed and integrated into the AI-Agent, and a Modelica code generation template and related constraints are standardized. And finally, the user asks a question to the AI-Agent, the LLM works according to a specialized cue word instruction, an API interface is called to access the knowledge base, and Modelica codes which conform to language specifications and can be operated by the user are automatically output through the LLM. According to the invention, end-to-end conversion from unstructured input to high-fidelity model output is realized.

The invention provides a human body motor function decline risk prediction method based on multi-modal data evaluation. The method comprises the following steps: S1, collecting multi-modal data; s2, data preprocessing; s3, multi-dimensional feature extraction is carried out; s4, labeling and quantifying; s5, feature coding and fusion; s6, model training; s7, evaluating and verifying the model; and S8, outputting the model application. Through multi-modal data fusion and intelligent modeling, the defects in the prior art are effectively overcome.

The invention discloses a synchronous detection system for surface density and wall thickness uniformity of a composite bulletproof helmet, which belongs to the technical field of bulletproof helmet detection and comprises a detector cooperative control module, a two-parameter synchronous acquisition module, a data preprocessing module, a two-parameter cooperative constraint module and an intelligent modeling output module. Through the integrated design that the detection unit integrates the 3D scanning detector, the microwave thickness measuring probe and the beta-ray density detector, and in cooperation with the synchronous triggering and double-channel signal acquisition mode of the double-parameter synchronous acquisition module, synchronous acquisition of the surface density and wall thickness data of the helmet is realized; the problems that traditional step-by-step detection is low in efficiency and poor in data relevance are solved, the detection efficiency and the accuracy of quality judgment are greatly improved, meanwhile, a detection report containing process optimization suggestions is output, the problems that traditional detection results are not visual, and connection with the production process is not smooth are effectively solved, and the product quality is improved. And accurate guidance can be provided for mass production quality control and process adjustment.

The invention belongs to the technical field of space intelligent modeling, and relates to a space intelligent world modeling method based on a global NURBS parameter domain. Consistent mapping and management are carried out on the multi-Patch geometric objects in a global NURBS parameter domain; generating and optimizing a control point matrix and a weight matrix for geometric expression based on a global NURBS parameter domain; realizing automatic smooth transition of the multi-Patch geometry in the splicing area based on a continuity keeping mechanism of curvature and normal constraint; executing NURBS curved surface subdivision operation according to the curvature change rate and the geometric error threshold value; and performing global solution on the control point matrixes of all the Patches, and outputting a space intelligent world model which is continuous and differentiable in a global range and has consistent parameters. According to the method, the continuity and controllability of geometric modeling are remarkably improved. The invention further provides a spatial intelligent world modeling system based on the global NURBS parameter domain.

The invention discloses a multi-body system dynamics intelligent modeling calculation system and method based on a natural language, and belongs to the field of overall design of spacecrafts. The problems that in the prior art, when the simulation requirement related to extreme working conditions and strong nonlinear coupling is met, the period is long, manual experience is relied on, and the maintenance cost is high are solved. The system comprises a text segmentation embedding unit used for carrying out text segmentation embedding processing on a multi-body dynamics knowledge base and constructing a vectorized dynamics knowledge context; the large language model unit is used for receiving natural language task description input by a user, performing semantic understanding through LLM (Logistics Language Model) and generating a task analysis result according to dynamic knowledge context learning; and the task processing unit is used for classifying the tasks into inquiry tasks, command and control tasks or construction tasks according to the task analysis result, and calling corresponding modules according to LLM and MCP technologies. The method is used in the field of multi-body system dynamics modeling of spacecrafts.

The present application relates to the technical field of offshore engineering structure health monitoring, and specifically discloses a jack-up platform pile leg stress prediction machine learning method under a typhoon working condition, comprising: obtaining jack-up platform pile leg stress data and environmental disturbance information under a typhoon working condition; inverting the overall stress distribution of the pile leg to obtain inversion stress data; constructing a multi-dimensional fusion data set; building a neural network model based on a bidirectional gate recurrent unit and a timing attention mechanism; training and evaluating the neural network model using the multi-dimensional fusion data set to obtain a trained model; obtaining typhoon forecast data, inputting the trained model, and calculating to obtain pile leg stress prediction data, which is compared with a safety threshold to perform safety warning. The present application realizes high-precision stress prediction through data inversion and intelligent modeling, significantly improves warning efficiency, reduces maintenance frequency, conforms to international offshore engineering specifications, and is suitable for various offshore platforms and intelligent operation and maintenance.

The invention provides a coal-fired power plant SCR denitration system dynamic prediction method based on a parameter identification mechanism-data hybrid model, and relates to the technical field of intelligent modeling prediction. The method comprises the following steps: establishing an SCR dynamic mechanism model, completing parameter identification based on historical operation data, and obtaining mechanism predicted values of NOx concentration and ammonia escape amount; constructing a series model learning mechanism model prediction deviation of the liquid neural network and the long-short-term memory network; and carrying out weighted fusion on the mechanism prediction value and the prediction deviation to form a hybrid model with physical consistency and dynamic adaptive capacity, and realizing high-precision stable prediction of NOx emission and ammonia escape amount of the SCR outlet under the variable load working condition.

The invention provides an intelligent modeling method and system for a self-adaptive regulation and control water-turbine generator set. Multi-source data in the operation process of a water-turbine generator set speed regulation system are collected, a bidirectional sparse attention residual network BiKAN is used for conducting preliminary prediction on the operation state of the system, and deviation signals are formed by the predicted data and actually-measured data. A local sparse variational mode decomposition LSVMD method is introduced to carry out multi-scale mode decomposition on prediction errors, an online sparse gated cycle unit network OSBiGRU is constructed, and dynamic capture and online correction of non-stationary errors are realized by taking decomposition errors and multi-source state features as input. A hierarchical progressive adaptive regulation and control mechanism is constructed, different regulation and control levels are dynamically divided according to a unified error evaluation criterion, and corresponding model updating strategies are driven. And dynamic optimal adjustment of model structure parameters is realized by using a triangular topology aggregation algorithm in which a Newton Raphson search rule strategy is introduced. Compared with the prior art, the modeling precision and the prediction stability of a complex nonlinear system are remarkably improved.

The invention discloses a heterogeneous fuzzy and game-based double-layer adaptive evolution CAD modeling command generation method, and belongs to the technical field of artificial intelligence and computer-aided design intelligent modeling. The method solves the technical problems that in an existing natural language driven CAD command generation technology, fuzzy semantics are difficult to quantify and map, heterogeneous commands are poor in adaptability, the executable performance of generated commands is low, and the reasoning process is black-box-shaped. According to the method, a semantic analysis-game generation-evolution refinement-verification backtracking-knowledge precipitation full-process closed-loop architecture is constructed, the method can be applied to the fields of industrial software, three-dimensional modeling, intelligent design systems and the like, the engineering performability, generalization ability and reasoning interpretability of command generation are improved, the generation diversity and feasibility are balanced, and the method is suitable for popularization and application. And the model iteration cost is reduced, and technical support is provided for industrial landing of the text-driven CAD modeling technology.

The invention relates to the technical field of business process modeling, in particular to a meta-model-driven interactive business process intelligent modeling method and system. The method comprises the following steps: constructing a meta-model framework containing core elements such as activities, events and gateways, and establishing a meta-model constraint rule base to define a structure legality rule of a BPMN2.0 specification; based on a domain ontology Schema and the domain exclusive large language model after instruction fine tuning, extracting a business entity and a process relationship thereof from the unstructured text; carrying out information integrity verification and logic consistency detection on the extracted BPMN elements by utilizing a constraint satisfaction algorithm and Petri net reachability analysis; displaying a difference label of the process sketch through an interactive verification interface, and realizing semantic correction and model iteration of a user on the process elements based on a natural language feedback mechanism; an XML code is generated by adopting a template-driven code generation strategy, and a flow chart is generated through a BPMN parser. According to the method, the business process modeling efficiency can be remarkably improved, and the manual intervention proportion in the modeling process is reduced.

The invention relates to the technical field of unmanned site modeling, and particularly discloses an intelligent modeling method, device and system for an unmanned site digital twin model, and the method comprises the steps: carrying out the construction of the digital twin model of an unmanned site through the data of an unmanned site and the basic environment around the unmanned site; and then optimizing the unmanned station digital twin model based on deployment data of an actual scene corresponding to each grid in the established unmanned station digital twin model, namely, following a mixed mode of combining mechanism modeling and data-driven modeling, and taking a physical model as a framework. And the parameters of the digital twin model of the unmanned station are calibrated by using real-time data, and the precision and generalization ability are balanced, so that the construction accuracy and reliability of the digital twin model of the station are improved, and the real-time monitoring accuracy and reliability of the coverage area of the station in a complex environment are improved. Therefore, the accuracy and timeliness of task dynamic planning, reconstruction and bookbinding are higher, so that when tasks need to be executed, the control accuracy and efficiency of single task and multi-task cooperation in the site can be improved, the possibility of task planning again is reduced, and the task scheduling efficiency and accuracy are improved.

The invention discloses an integrated intelligent modeling and capacity expansion method and equipment for new energy equipment of a distribution network. The method comprises the following steps: querying a graph of modeled power equipment and unmodeled power equipment in a main network of a power grid; constructing a graph neural network according to the graph and the unmodeled power equipment; dividing the graph neural network into a plurality of independent area networks; inserting influence factors in the graph neural network when the network of each district has a fault historically; if the insertion is completed, predicting the fault of the power equipment in the graph neural network; calculating a deviation degree between the predicted fault and a historical fault; and when the deviation degree is greater than or equal to a preset first threshold value, generating a modeling extension measure for drawing a new graph for the unmodeled power equipment. The method continuously follows the change of the power grid, maintains the typicality of the main grid graph of the power grid, reduces the graph redundancy, can maintain the conciseness of the main grid graph of the power grid, and maintains the fault monitoring efficiency based on the main grid graph of the power grid.

The invention discloses a hospital performance evaluation system based on multi-source scientific research data collaboration, and the system comprises a multi-source data collection module which is used for collecting scientific research performance original data from an internal information system of a hospital and an external scientific research database; the data collaborative processing module is connected to the multi-source data acquisition module and is used for cleaning, integrating and standardizing the scientific research performance original data to generate high-quality collaborative data; the performance indicator modeling module is connected to the data collaborative processing module and is used for calculating the high-quality collaborative data based on a data envelope analysis model and a radial basis function neural network to generate a performance evaluation result; and the performance management platform is connected to the performance indicator modeling module and is used for displaying the performance evaluation result. According to the invention, automatic acquisition, cooperative processing, intelligent modeling and visual management of scientific research data can be realized, and accurate, efficient and dynamic evaluation can be carried out on scientific research performance of hospitals, departments and individuals.

1. Name of the product in this design: Graphical User Interface for Generating Product Lifecycle Models for Electronic Devices. 2. Purpose of this design: An electronic device. 3. The key design feature of this product is its graphical user interface. 4. The picture or photo that best illustrates the key design points: Design 1 front view. 5. Design 1 is designated as the basic design. 6. Purpose of the graphical user interface: It is used for intelligent modeling of products to achieve automatic generation and visualization of the product's entire lifecycle model. 7. Human-computer interaction method of graphical user interface: Design 1 main view displays the design interface of the product life cycle model of the selected product. The top left corner of the main view of Design 1 displays the product name of the selected product. The main view of Design 1 displays the "raw material acquisition" stage, "manufacturing" stage, "product transportation / delivery" stage, "product use" stage and "end of life" stage in the form of horizontal swimlanes from top to bottom. You can click / touch the "AI Intelligent Modeling" button in the upper left corner of the Design 1 main view to intelligently generate the modeling information of the selected product's full life cycle model and trigger the interface to change from the Design 1 main view to the Design 1 interface change state diagram 1. The left-hand list in Design 1 Interface Change Status Diagram 1 displays the generated modeling information for the selected product's entire lifecycle model. In Design 1 Interface Change State Diagram 1, you can select X target generation items by checking the boxes, and trigger the interface to change from Design 1 Interface Change State Diagram 1 to Design 1 Interface Change State Diagram 2, where X is a positive integer. You can click / touch the "Add X to Model" button in the lower left corner of Design 1 interface change state diagram 2 to generate a project to build the full life cycle model of the selected product using the selected X targets, and trigger the interface to change from Design 1 interface change state diagram 2 to Design 1 interface change state diagram 3. Design 1 Interface Change State Diagram 3 shows the full lifecycle model of the selected product. Design 2 main view displays the design interface for the product lifecycle model of the selected product. The top left corner of the main view of Design 2 displays the product name of the selected product. The main view of Design 2 displays the "Raw Material Acquisition", "Production and Manufacturing", "Product Transportation / Delivery", "Product Use" and "End of Life" stages of the product life cycle in the form of horizontal swimlanes from top to bottom. You can click / touch the "AI Intelligent Modeling" button in the upper left corner of the Design 2 main view to intelligently generate the modeling information of the selected product's full life cycle model, and trigger the interface to change from the Design 2 main view to the Design 2 interface change state diagram 1. The left-hand list in Design 2 Interface Change State Diagram 1 shows the generated modeling information of the selected product's full lifecycle model. In Design 2 Interface Change State Diagram 1, you can select n target generation items by checking the boxes, and trigger the interface to change from Design 2 Interface Change State Diagram 1 to Design 2 Interface Change State Diagram 2, where n is an integer. In Design 2 Interface Change State Diagram 2, you can click / touch the "Refresh" button in the left-hand list to regenerate the generated items other than the selected n target generated items, and trigger the interface to change from Design 2 Interface Change State Diagram 2 to Design 2 Interface Change State Diagram 3. The left-hand list in Design 2 Interface Change State Diagram 3 shows the regenerated results of the modeling information for the selected product's entire lifecycle model. In the interface change state diagram 3 of Design 2, the X target generation items can be selected by checking the boxes, and the interface will change from the interface change state diagram 3 of Design 2 to the interface change state diagram 4 of Design 2, where X is a positive integer. You can click / touch the "Add X to Model" button in the lower left corner of Design 2 interface change state diagram 4 to generate a project to build the full life cycle model of the selected product using the selected X targets, and trigger the interface to change from Design 2 interface change state diagram 4 to Design 2 interface change state diagram 5. Design 2 Interface Change State Diagram 5 shows the full lifecycle model of the selected product. Design 3's main view displays the design interface for the product's entire lifecycle model. The top left corner of the Design 3 main view displays the product name of the selected product. The Design 3 main view shows the "Raw Material Acquisition", "Production and Manufacturing", "Product Transportation / Delivery", "Product Use" and "End of Life" stages of the product life cycle from top to bottom in the form of horizontal swimlanes. You can click / touch the "AI Intelligent Modeling" button in the upper left corner of the Design 3 main view to intelligently generate the modeling information of the selected product's full life cycle model, and trigger the interface to change from the Design 3 main view to the Design 3 interface change state (Figure 1). The left-hand list in Design 3 Interface Change State Diagram 1 shows the generated modeling information for the selected product's entire lifecycle model. In Design 3 Interface Change State Diagram 1, you can select X target generation items by checking X target generation items, and trigger the interface to change from Design 3 Interface Change State Diagram 1 to Design 3 Interface Change State Diagram 2, where X is a positive integer. You can click / touch the "Add X to Model" button in the lower left corner of Design 3 interface change state diagram 2 to generate a project to build the full life cycle model of the selected product using the selected X targets, and trigger the interface to change from Design 3 interface change state diagram 2 to Design 3 interface change state diagram 3. Design 3 Interface Change State Diagram 3 shows the full lifecycle model of the selected product. You can click / touch the selected model node in Design 3 Interface Change State Diagram 3 to view the details of the selected model node and trigger the interface to change from Design 3 Interface Change State Diagram 3 to Design 3 Interface Change State Diagram 4. Design 4 main view displays the design interface for the product lifecycle model of the selected product. The top left corner of Design 4's main view displays the product name of the selected product. Design 4's main view shows the "Raw Material Acquisition," "Production and Manufacturing," "Product Transportation / Delivery," "Product Use," and "End of Life" stages of the product's entire life cycle in horizontal swimlanes from top to bottom. You can click / touch the "AI Intelligent Modeling" button in the upper left corner of the Design 4 main view to bring up the AI ​​tool floating window and trigger the interface to change from the Design 4 main view to the Design 4 interface change state (Figure 1). You can click / touch the "Start Generation" button in the AI ​​tool floating window in Design 4 interface change state diagram 1 to intelligently generate the modeling information of the selected product's full life cycle model, and trigger the interface to change from Design 4 interface change state diagram 1 to Design 4 interface change state diagram 2. The left-hand list in Design 4 Interface Change State Diagram 2 shows the generated modeling information for the selected product's entire lifecycle model. In Design 4 Interface Change State Diagram 2, you can view the details of the selected generated item by clicking / touching the list on the left, and trigger the interface to change from Design 4 Interface Change State Diagram 2 to Design 4 Interface Change State Diagram 3. 8. Other situations requiring explanation: Other explanation: "X" in the interface represents a text, number or symbol content area.

The application discloses a kind of based on visual perception's intelligent modeling generation system of moving head lamp, belong to stage lighting control technical field.The application includes: visual perception unit, obtains moving head light column three-dimensional information and establishes lamp space position database;Modeling case library is used to store the preset commonly used moving head light column modeling template;Space constraint analysis module is used to analyze the modeling space constraint that can be realized based on the actual space layout of on-site moving head lamp;AI modeling generation module is automatically generated based on lamp space distribution moving head light column basic modeling that adapts current lamp position layout;Modeling output interface is exported to console use.The application realizes the automatic generation of moving head lamp basic modeling based on on-site lamp position space perception for the first time, provides a series of position adaptation modeling scheme for lighting designer, and greatly improves programming efficiency.

This invention relates to the fields of tunnel engineering and 3D geological modeling technology, specifically to an intelligent modeling method and system for 3D geological uncertainty in tunnels based on transition probability geostatistics and sparse boreholes. The method includes: S1, integrating multi-source data to construct a 3D geological conceptual model; S2, statistically characterizing a one-dimensional transition probability matrix, calculating and fitting a spatial continuity and 3D anisotropic variability function model; S3, calculating the prior spatial probabilities and transition adjustment factors for various lithologies, obtaining the posterior lithology distribution of nodes through Bayesian intelligent updating, and initially assigning lithology categories to nodes through random sampling; S4, assigning the most probable lithology category to each grid node, calculating the variance of lithology values ​​across all implementations, and measuring model uncertainty; S5, outputting the optimal 3D uncertainty model for the tunnel; and S6, verification and evaluation. This invention can effectively achieve 3D heterogeneous modeling and explicit quantification of uncertainty under strong geological constraints.

The invention relates to the technical field of micro-terrain intelligent identification, in particular to a power transmission line icing micro-terrain intelligent identification method and system based on icing simulation. Comprising the following steps: S1, determining position data of a power transmission line, and obtaining remote sensing data covering the power transmission line according to the position data; s2, establishing an icing micro-topography database, and performing micro-topography classification on the icing micro-topography database; the defects in the prior art are effectively overcome through multi-source data fusion and intelligent modeling, a multi-source fusion scheme of SAR data, multispectral data, DEM data and meteorological data is adopted, comprehensive extraction of three-dimensional features of terrains, airflow and water vapor is achieved in combination with accurate position data of a power transmission line management end, and the accuracy of the three-dimensional features of the terrains, the airflow and the water vapor is improved. The problems of single feature dimension and incomplete data coverage in the prior art are solved, richer basic data support is provided for microtopography recognition, and the relevance between the features and the icing mechanism is remarkably improved.

The application discloses a power transformer temperature monitoring method, device and equipment, and relates to the technical field of power equipment state monitoring and intelligent modeling. First, according to the load factor, the average winding temperature and the position of the tap switch of the power transformer at the current moment, the physical model of the power transformer is used to calculate the heat generation power at the current moment; the heat generation power at the current moment is brought into a heat balance equation for solving to obtain the top-layer oil temperature simulation value at the current moment; then, the physical characteristics at the current moment and a preset number of moments before the current moment are combined to form an enhanced feature sequence; the enhanced feature sequence is input into a deviation prediction model to obtain the dynamic deviation of the top-layer oil temperature at the current moment, the top-layer oil temperature simulation value at the current moment is calibrated, and the top-layer oil temperature monitoring value is obtained. The application provides prior knowledge through the physical model, combines data-driven learning of the deviation dynamic characteristics, and applies stability constraints, so that high-precision and stable oil temperature prediction is realized.

The present application relates to a real-time artificial intelligence system for production line edge, which comprises a data collection system, a statistical analysis system, an artificial intelligence modeling system and an automatic tuning system. The data collection system is used to preprocess and clean the data. The statistical analysis system analyzes the preprocessed and cleaned data to generate a dataset. The artificial intelligence modeling system is used to run and evaluate the dataset with multiple models to complete the creation and deployment of an AI model. Finally, when the AI model is deployed on a production line, the automatic tuning system is used to automatically tune the AI model according to the latest data of the production line.

This invention relates to the field of intelligent reliability engineering technology for electromechanical products, specifically an adaptive failure prediction method for motors based on digital twin-based intelligent optimization. Through physically-driven intelligent modeling, a physical information neural network is used to automatically learn time-varying coupling coefficients, achieving an organic fusion of data-driven approaches and physical constraints. Failure paths are dynamically reconstructed by using a particle swarm optimization algorithm to identify dominant failure modes in real time and dynamically adjust the weights of the failure propagation chain. A prediction-verification-correction closed loop is implemented, leveraging digital twins and Bayesian inference to achieve continuous self-evolution of the model and constantly improve prediction accuracy. Intelligent design of test schemes is employed, using a multi-objective genetic algorithm to optimize and accelerate test conditions, improving test efficiency and failure mode coverage. Probabilistic lifetime prediction outputs a remaining lifetime distribution with confidence intervals, quantifying prediction uncertainty.

The invention discloses an energy management balance scheduling optimization method and system based on deep learning, and the method comprises the steps: collecting multi-source feature data, carrying out the standardization and preprocessing, and obtaining structured multi-dimensional time series data; constructing a node relation graph by using an improved dynamic graph attention model, and generating a global attention matrix; node features are updated according to the global attention matrix, optimized features are formed, and a scheduling strategy is generated; optimizing the scheduling strategy by adopting an improved Harlisia eagle algorithm to obtain an optimized scheduling scheme; a node operation strategy is adjusted according to the optimization scheme, and power and load distribution is completed; and a final operation scheme is generated and dynamically corrected. According to the method, the improved dynamic graph attention model is constructed, and the Harris eagle optimization algorithm combining a boundary escape mechanism and a dynamic energy regulation and control mechanism is combined, so that intelligent modeling of multi-source data of an energy network, multi-target optimization of a scheduling strategy and dynamic balance scheduling of energy operation are realized.

The invention relates to the technical field of ocean engineering structure health monitoring, and particularly discloses a self-elevating platform pile leg stress prediction machine learning method under an offshore typhoon working condition, and the method comprises the steps: obtaining self-elevating platform pile leg stress data and environment disturbance information under a typhoon working condition; inverting the overall stress distribution of the pile leg to obtain inversion stress data; constructing a multi-dimensional fusion data set; building a neural network model based on a bidirectional gating circulation unit and a time sequence attention mechanism; training and evaluating a neural network model by using the multi-dimensional fusion data set to obtain a trained model; typhoon forecast data are obtained and input into the trained model, pile leg stress forecast data are obtained through calculation and compared with a safety threshold value, and safety early warning is carried out. According to the method, high-precision stress prediction is realized through data inversion and intelligent modeling, the early warning efficiency is remarkably improved, the maintenance frequency is reduced, the international ocean engineering specifications are met, and the method is suitable for various ocean platforms and intelligent operation and maintenance.

The invention discloses an online distributed parameter optimization method and system for intelligent modeling of a complex system, and relates to the technical field of electric digital data processing. The method comprises the following steps that: an end side adopts an adaptive recursive ridge regression algorithm to recursively update the weight of an output layer of a feedforward neural network in real time so as to quickly track the dynamic state of a data stream; when the edge side monitors that the performance continuously declines, node pruning based on the optimal data set is triggered, and node growth is selected and randomly configured; when concept drift is detected, the edge side uploads data and a model state, the cloud side carries out anti-forgetting deep optimization on the feature extraction module, and optimization parameters are downloaded to cooperate with reconstruction and deployment. The method solves the problems of insufficient dynamic working condition adaptability, low computing resource efficiency and easy knowledge forgetting in the prior art, realizes optimal balance of model precision, adaptive speed and resource consumption, and is especially suitable for online modeling of complex dynamic systems such as intelligent control of high-speed trains.

The application discloses a rapeseed moisture tolerance evaluation method and system based on multi-source data fusion, relates to the field of agricultural information technology, and synchronously acquires airspace remote sensing images, ground environment monitoring data and crop phenotype physiological indexes in the system operation; time and space alignment and feature extraction are carried out; a dynamic evaluation model based on deep learning is constructed, multi-dimensional features are fused to generate a whole growth period moisture tolerance evaluation index; and moisture tolerance grades are divided according to the index, and spatial visualization results and disaster loss suggestions are output. The system comprises data acquisition, preprocessing, core evaluation and result display modules, and integrates an expert decision support unit to realize closed-loop management. Through deep fusion of multi-source heterogeneous data and intelligent modeling, the application realizes real-time, quantitative and non-destructive accurate evaluation of rapeseed moisture tolerance, and significantly improves evaluation efficiency, accuracy and cross-region applicability.

The invention discloses a plasticity constitutive parameter inversion method for a zinc sulfide infrared optical brittle material, and belongs to the technical field of material mechanical property characterization. According to the method, a zinc sulfide material nanoindentation simulation model is constructed, a nanoindentation experiment is carried out, meanwhile, an Oliver-Pharr method and a Johnson-Cook constitutive equation are adopted to obtain actual material hardness, elastic modulus and plasticity parameters of zinc sulfide to serve as input material parameters, and an automatic simulation experiment is carried out; the method comprises the following steps: acquiring a simulation displacement-load curve under different constitutive parameter combinations, performing prediction optimization on the displacement-load curve by using a prediction network to obtain a Pareto frontier solution, and constructing an inversion model by using an entropy weight method to obtain an optimal solution in a Pareto frontier solution set. According to the research, accurate characterization of the plasticity constitutive behavior of the zinc sulfide infrared optical brittle material is realized, and reliable method support can be provided for constitutive parameter identification and intelligent modeling of other brittle polycrystalline materials.

The invention relates to the technical field of sensor detection, in particular to a digital model verification method for reliability of a weighing sensor. According to the technical scheme, the method comprises the following steps: constructing a digital intelligent modeling module, wherein the module comprises a processor, a D / A converter, an A / D converter, an excitation switch circuit and a signal sampling circuit; and in a zero load state of the sensor, applying a first standard excitation signal to the sensor through the digital intelligent modeling module. According to the method, on-line and quantitative evaluation of the recessive performance degradation of the sensor is realized on the premise of not disassembling a machine and not stopping production through a digital model and a reverse verification technology; soft faults which are difficult to recognize by a traditional method can be found early, and predictive maintenance decisions are supported; the effective service life of the sensor is prolonged through dynamic compensation, and the fault positioning and operation and maintenance efficiency of a multi-sensor system is improved, so that the comprehensive utilization rate of equipment is remarkably improved, and the full-life-cycle maintenance cost is reduced.

The application relates to the technical field of application server middleware performance test, and particularly discloses an application server middleware performance test system. The system comprises a business scene intelligent modeling module, an adaptive load generation and scheduling module, a full-stack performance data acquisition and correlation analysis module and a test environment automatic management module. Through the cooperative work of the above modules, the test script is automatically generated from natural language or logs, the load pressure is dynamically adjusted based on real-time feedback, the full-stack performance data correlation analysis and root cause positioning are realized, and the test environment is automatically constructed and elastically expanded, so that the efficiency, authenticity and automation level of the performance test are improved.

The invention discloses an open channel system predictive control method based on a polynomial parameterized physical information neural network, which comprises the following steps of: 1, carrying out dynamic discretization on an open channel system, 2, constructing a polynomial parameterized physical information neural network model, a differentiable polynomial fitting function is adopted to approach a system state trajectory; 3, state evolution prediction based on self-circulation; and 4, model prediction controller design. The invention relates to the technical field of water conservancy project automatic control and intelligent modeling, and aims to provide an open channel system prediction control method based on a polynomial parameterization physical information neural network. In order to solve the problems of low modeling precision, poor training efficiency, weak generalization ability and insufficient control performance of an open channel system under the condition of limited data in the prior art, the method embeds physical constraints into a neural network, adopts a polynomial parameterized structure to accelerate training, and finally realizes efficient and accurate water level prediction and control of the open channel system.