233 results about "Intelligent modeling" patented technology

The invention discloses a large model driven automobile model modeling method based on a Modelica language, and belongs to the technical field of intelligent modeling and automobile simulation. The method comprises the following steps: firstly, accurately analyzing a natural language demand into a structured triple by adopting a BERT-CRF (domain knowledge enhanced) multi-task model; matching an optimal component combination through a multi-objective optimization algorithm driven by a graph neural network, and cooperatively predicting an interdisciplinary parameter feasible region in combination with symbolic mathematical derivation and machine learning; a topological connection matrix is innovatively optimized by using a graph attention network, and intelligent generation and dynamic verification of simulation codes are realized by fusing a template engine and syntax tree analysis; and finally, constructing a multi-target reward function optimization control strategy through reinforcement learning, and establishing a closed-loop knowledge iteration mechanism. Compared with a traditional modeling method, through deep combination of the large model and Modelica, the technical difficulty of automobile system modeling is remarkably reduced while the preciseness of physical modeling is kept, and the method is particularly suitable for complex scenes such as new energy vehicle model development and intelligent driving system integration.

The invention belongs to the field of industrial thermal engineering and intelligent modeling, and discloses a heat exchanger heat exchange coefficient prediction method and system based on a physical information neural network. The method comprises the following steps: acquiring multi-dimensional operation data through a signal acquisition system, cleaning abnormal and blank values, standardizing, and segmenting into time sequence samples by adopting a sliding window method; a double-layer physical information long-short-term memory network is constructed, and a time sequence feature and a physical equation residual error are combined to generate a space-time fusion feature matrix. And a composite loss function including data loss, physical equation loss and physical consistency loss is designed, physical and data driving influences are balanced through hyper-parameter tuning, and accurate prediction of the heat exchange coefficient is achieved based on a gradient descent optimization model. The method combines field physical laws and data features, improves the reliability and physical interpretability of prediction, and is suitable for operation optimization of the heat exchanger of the desulfurization wastewater treatment system of the thermal power plant.

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 relates to the field of environment management, and discloses a watershed digital twinning environment real-time simulation and interaction platform fusing cloud edge collaboration and intelligent modeling, and the platform comprises the steps: carrying out the dynamic collection of the Yangtze River and Yellow River watershed, and generating environment state information organized according to the temporal-spatial resolution; associating the generated environment state information with multi-source environment data to form a multi-source knowledge basis of the drainage basin pollution field; establishing a rule knowledge base covering pollution identification, early warning and prevention and control business processes by utilizing a generative knowledge representation method, and generating a pollution event candidate identification result; carrying out fusion calculation on the pollution event candidate recognition result and multi-source heterogeneous data, constructing a digital twin simulation model, and eliminating data islands between modules and data sources; and executing linkage calculation on the pollution area and the risk node calibrated in the digital twinborn simulation model, generating environment state interaction information and synchronously updating the environment state interaction information to the digital twinborn environment. The method has the advantage that the regional water environment risk prevention and control capability is improved.

The invention relates to the technical field of point cloud processing, in particular to an intelligent modeling method based on point cloud data. The method comprises the following steps: scanning the interior of the transformer substation to obtain vision field overlapping graph data; performing point cloud density distribution field construction on the view field overlapping graph data to obtain point cloud density distribution field data; performing occlusion region extraction on the point cloud density distribution field data to obtain occlusion region data; performing structure connectivity calculation on the point cloud density distribution field data according to the occlusion region data to obtain occlusion region connectivity data; performing hole residual mapping on the occlusion area communication data to obtain hole residual data; performing graph neural network reasoning on the hole residual data to obtain category reasoning data; and performing physical rule fusion according to the category reasoning data to obtain point cloud complementation data, and performing point cloud model construction to obtain a point cloud model. According to the invention, the spatial coverage integrity of the point cloud data and the recognition precision of the occlusion area in the occlusion environment are improved, so that the accuracy of point cloud modeling is improved.

Disclosed herein are methods and systems for using artificial intelligence modeling techniques to train and execute an artificial intelligence model to analyze camera feed received from an ego to generate an occupancy data indicating whether different voxels within the ego's surroundings are occupied by an object having mass. A method comprises inputting, using a camera of an ego object, image data of a space around the ego object into an artificial intelligence model; predicting, by executing the artificial intelligence model, an occupancy attribute of a plurality of voxels; and generating a dataset based on the plurality of voxels and their corresponding occupancy attribute.

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 an intelligent path optimization control system for an industrial robot. The intelligent path optimization control system comprises a task input and analysis module, an environment sensing module, an intelligent modeling and path planning module, a feedback and execution module and a robot execution unit, the task input and analysis module sends a task instruction to the environment sensing module; the environment sensing module collects environment data, forms a dynamic semantic map and sends the dynamic semantic map to the intelligent modeling and path planning module. The intelligent modeling and path planning module sends a robot joint track instruction to the feedback and execution module; the robot motion unit drives the robot to execute trajectory tracking according to a motion control instruction issued by the feedback and execution module, and meanwhile, the feedback and execution module is connected with the weight coefficient input end of the intelligent modeling and path planning module to dynamically optimize the weight coefficient. Robot path planning is automatically adjusted to avoid collision, and it is ensured that the robot efficiently completes tasks.

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 discloses an intelligent human body modeling method and system based on a three-dimensional dot matrix, and relates to the field of intelligent modeling, and the method comprises the steps: carrying out the structural analysis of the semantic level of an original three-dimensional point cloud, and predicting a space displacement field pointing from the surface of observed clothes to the surface of an internal real human body under the semantic guidance. By applying the displacement field, double transformation of point cloud stripping and standardization is carried out on the point cloud, so that repeated fine tuning and optimization are carried out on the body type and posture parameters of the human body model in a standardized space, and finally a high-fidelity human body three-dimensional model is generated through driving. In this way, the complex clothes interference problem is deconstructed into a staged geometric mapping and parameter optimization process, and therefore the robustness and accuracy of human body three-dimensional reconstruction under the real wearing condition are effectively improved.

The invention discloses a Java enterprise-level rapid development method based on visual modeling. The method comprises the steps that modeling behaviors are collected to generate a cognitive map, a view is constructed, and operation behaviors are collected to generate cognitive feature data; generating a semantic constraint model, and fusing the cognitive features and the node features to construct the semantic constraint model; semantic consistency detection is executed, design and implementation differences are automatically compared, and bidirectional correction is triggered; generating a project engineering structure, and generating a structured code and an interface document according to the correction model; zero-shutdown migration evolution is executed, a migration relation graph is constructed, and seamless switching is carried out in a gray level mode; and performing semantic self-correction updating in the operation period, monitoring semantic offset and backfilling design content for closed-loop synchronization. According to the method, intelligent modeling and uninterrupted evolution of Java enterprise-level applications are realized through cognitive map driving and a multi-mode migration mechanism.

The invention provides an airway atomization flow intelligent drug delivery control method and system, and relates to the technical field of medical instruments.The method comprises the steps that basic information of the age, the weight and the illness state severity of a patient is obtained, and physiological parameters of the respiratory rate and the tidal volume of the patient are collected in real time through a sensor; inputting the basic information and the physiological parameters into a fuzzy adaptive control algorithm, and initializing an individual administration rule base according to the basic information; based on the real-time change trend of the respiratory rate and the tidal volume, three initial control index values including an atomization flow value, a drug concentration value and a time interval are dynamically generated through a membership function. According to the invention, through the full-process design of data acquisition, intelligent modeling, accurate execution and data closed loop, intelligentization of atomization treatment is realized.

The invention discloses a three-dimensional modeling method based on voice interaction and a large language model, and relates to the technical field of computer intelligent modeling. According to the method, on the basis of dialogue context management, a closed-loop'voice / text input-modeling script generation-execution monitoring-error feedback and repair 'process is constructed based on a Rhinoscriptsyntab library in combination with RAG (Retrieval Enhanced Generation) and an automatic error correction mechanism, so that a three-dimensional model can be incrementally modified in multiple rounds, and object attributes can be tracked; therefore, flexible response to complex modeling requirements is realized in the Rhino environment. According to the method, a designer can quickly generate and edit the three-dimensional model through a natural language instruction, so that the design efficiency is remarkably improved, the cognitive burden of software operation is reduced, and the continuity of creative thinking is kept.

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 relates to the field of software requirement modeling, and discloses a software requirement modeling method and system based on an artificial intelligence large model.The method comprises the following software requirement modeling steps that firstly, functional structure modeling is conducted, and then functional module modeling is conducted; for each last-stage function module obtained after checking and revising the function structure, function module modeling is carried out one by one according to the following steps: step 1, data structure modeling; step 2, system function modeling; 3, modeling a user interface; through the method, natural language processing and knowledge reasoning capabilities of the artificial intelligence large model can be integrated in a software demand modeling process, collaborative modeling of the artificial intelligence large model and people is realized, intelligent modeling is realized, and the efficiency and quality of software demand modeling 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 provides a flow and water content prediction method based on distributed optical fiber sound wave monitoring data, which comprises the following steps: step 1, extracting DAS monitoring data of different layer sections of a production well, and preprocessing; 2, DAS sound wave frequency band energy FBE of each layer section is calculated; step 3, performing LSTM high-precision intelligent modeling to realize liquid production capacity prediction; 4, LSTM model sensitivity analysis is carried out, and a main liquid production section is determined; 5, verifying a sensitivity analysis result by utilizing DTS temperature data; step 6, taking the liquid production capacity as a main control factor influencing the water content, and remodeling to realize short-term synchronous prediction of the flow and the water content; and 7, performing field application, and evaluating an intelligent modeling effect. According to the method, future flow prediction of the field production well can be achieved, the main liquid production section of the multi-layer (section) oil well is determined, short-term synchronous prediction of the future flow and the water content of the oil well is achieved, and intelligent management and control of oil field injection and production are facilitated.

The invention is suitable for the technical field of automobile energy management and data driving, and provides an engine transient fuel consumption correction method and system based on data driving, and the method comprises the following steps: collecting multi-source data in a vehicle operation process in real time; preprocessing the multi-source data, and outputting effective working condition data; based on various artificial neural networks, according to the effective working condition data, respectively constructing three transient fuel consumption correction sub-models; performing linear weighted fusion on the three transient fuel consumption correction sub-models to obtain a total transient fuel consumption correction model; and correcting the transient fuel consumption of the engine according to the total transient fuel consumption correction model. According to the method, the multi-source data in the vehicle running process are collected in real time, the total transient fuel consumption correction model is established through data driving and intelligent modeling means, accurate prediction and real-time correction of transient fuel consumption are achieved, and therefore the fuel economy and the energy management optimization effect of the hybrid electric vehicle under the actual running working condition are improved.

The invention discloses a project task dependency relationship intelligent modeling system and method based on a knowledge graph, and belongs to the technical field of project task modeling, and the method specifically comprises the steps: obtaining project task image data, carrying out the preprocessing of the obtained project task image data, and according to the preprocessed project task image data, carrying out the intelligent modeling of the project task dependency relationship; the method comprises the steps of constructing a preliminary project task knowledge graph, analyzing direct and indirect dependency relationships between project tasks, updating the preliminary project task knowledge graph to obtain the project task knowledge graph, and optimizing project task scheduling based on the project task knowledge graph and in combination with attribute information of the project tasks. The priority and resource allocation of project task execution are determined, and project task nodes and dependency relationships in the knowledge graph are automatically updated based on real-time data and project progress; the priority and resource allocation between project tasks can be processed in real time, so that a project manager can obtain the latest dependency relationship and progress condition of the tasks in real time.

The invention provides an Internet of Things computing scheduling system for multi-device fusion, which relates to the field of electric digital data processing and comprises a heterogeneous device sensing and intelligent modeling module, a semantic task analysis and intelligent decomposition module, a multi-target collaborative scheduling and self-adaptive distribution module and a full-life-cycle feedback and continuous optimization module. The heterogeneous equipment perception and intelligent modeling module is used for being responsible for constructing panoramic cognition of Internet of Things equipment, and the semantic task analysis and intelligent decomposition module is used for achieving intelligent conversion from an application task to schedulable atomic operation. The multi-objective collaborative scheduling and self-adaptive allocation module realizes intelligent balanced computing resource scheduling based on a multi-objective optimization theory; the full life cycle feedback and continuous optimization module is used for constructing a closed loop optimization mechanism from execution to learning to realize self-evolution of the system; according to the system, the intelligent level and the execution efficiency of computing scheduling of the Internet of Things are remarkably improved, and a reliable scheduling guarantee is provided for large-scale heterogeneous Internet of Things applications.

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 discloses a geological mineral data intelligent modeling method and system based on multilevel space-time intrinsic coding, and the method comprises the steps: generating a 9-level adaptive grid through employing an improved HEALPix subdivision algorithm for the geological complexity and target data density of a to-be-detected region; based on a 9-level adaptive grid, encoding space time-varying grid units of existing geological mineral resource data to obtain a geological feature space matrix; constructing a multi-modal data register based on Transform, inputting the 9-level adaptive grid and the geologic feature space matrix into the multi-modal data register, and outputting an associated feature matrix; using a hybrid interpolation model to perform hybrid interpolation on the correlation feature matrix to obtain a full-grid correlation correlation interpolation matrix; and a CNN error correction module is used to optimize the full-grid correlation degree correlation interpolation matrix to obtain a geological mineral resource space-time intrinsic code, and geological mineral resource data intelligent modeling is realized based on the geological mineral resource space-time intrinsic code.

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 a urea warehouse three-dimensional modeling and intelligent scheduling system, and belongs to the technical field of urea warehouse intelligent modeling and scheduling, and the system specifically comprises the steps: collecting the internal image of a urea warehouse in real time, carrying out the preprocessing of the collected internal image of the urea warehouse, generating the three-dimensional point cloud data of the urea warehouse through a computer vision technology, and carrying out the calculation of the three-dimensional point cloud data. Constructing a three-dimensional model of the urea warehouse, constructing a space-time thermodynamic diagram, and intelligently generating a scheduling scheme based on a real-time inventory state, a warehouse space use condition, a task condition and a transportation demand; spatial layout, article positions and types of the urea warehouse are scanned and analyzed in real time, a three-dimensional space model of the urea warehouse and a constructed space-time thermodynamic diagram can be dynamically generated according to data collected in real time, obstacles can be found in advance, the operation path of the mechanical arm can be intelligently planned, the path of the mechanical arm does not need to be adjusted or re-planned according to the obstacles, and the working efficiency is improved. And the carrying time is saved, and the operation efficiency of the mechanical arm is greatly improved.