1252 results about "Material data" patented technology

The invention provides a twin power station collision visual simulation method and system based on physical simulation, and relates to the technical field of data processing, and the method comprises the steps: obtaining a three-dimensional geometric model of a twin power station, carrying out space division, extracting geometric parameters, obtaining material data, constructing a material correlation model and a mechanical model, and carrying out fusion to obtain a physical simulation model; acquiring initial collision condition parameters as boundary conditions, performing local grid encryption, constructing a nonlinear kinetic equation of a collision process, performing time domain solution, determining equivalent stress distribution and equivalent plastic strain distribution, and generating a collision result; judging whether plastic deformation or fracture failure occurs, generating full collision process physical field data, generating a collision animation frame sequence, generating a stress distribution cloud picture and a plastic strain cloud picture, constructing a fracture special effect, and comprehensively generating a collision visual scene.

The invention provides a supply chain collaborative material management system and method, and relates to the technical field of material management, and the method comprises the steps: receiving a multi-enterprise heterogeneous material data flow, and constructing an industry knowledge graph based on the field-level semantic mapping of a dynamic ontology library; a standardized data table is generated; standardized data is injected into a distributed message queue for parallel processing, dynamic cache partitions are created according to material object labels, timestamp indexes are built in the dynamic cache partitions, a sliding window mechanism is adopted to scan data, and multi-strategy resolution is performed on conflict data in a window; performing conjoint analysis on the cleaned data to generate an enhanced analysis report, and activating a case transfer learning mechanism to update a model when the similarity between new data and historical cases exceeds a threshold value; constructing a material state transfer matrix for the abnormal data, simulating a correction operation in a digital twin environment, and triggering automatic correction; the problems of multi-enterprise heterogeneous data integration, dynamic data processing, intelligent analysis and the like are solved, and the collaboration efficiency and the management level of a supply chain are improved.

Systems, computer program products, and methods are described herein for building material data management. With reference to an example method, the method receives a first dataset formed of first data entries associated with a building material data source and generates a building material data entity based on the building material data source. The method includes outputting a representation of the building material data entity. The first dataset may be formed of data records (e.g., construction documents, user inputs, sensor data, or machines data), and the representation may be a visual representation may include notifications, visual presentations of the building material entity, etc.

The invention relates to a work order material intelligent tracking and dynamic updating system based on multi-source data fusion. The system comprises an acquisition module, a tracking module, an updating module, a man-machine interaction module and a storage module, the acquisition module is used for acquiring work order information from a bill of material, scheduling information and inventory and supplier data, and constructing a material data model; the tracking module monitors material inventory, consumption and material shortage risks based on the model, and realizes automatic state updating and graphical path recording in combination with bar code recognition and ambiguity analysis; the updating module predicts material requirements through a machine learning algorithm, optimizes a distribution plan and outputs a dynamic report; the interaction module supports multi-format data import, unstructured field classification and interrupted operation control; the storage module ensures credible synchronization and tamper-proofing performance of data among different systems through a block chain technology; according to the invention, high-efficiency management and dynamic allocation of work order materials can be realized in an environment without an MES system, and the production transparency and the resource utilization efficiency are improved.

The present invention relates to the field of ship design, and in particular to an automatic three-dimensional modeling method, system and device based on a two-dimensional ship CAD drawing. The method includes obtaining and parsing ship CAD drawing data, extracting metadata from the drawing, and identifying primitives; generating images through rendering technology for unidentified complex primitive areas, and performing preprocessing; inputting the preprocessed images into an object detection model, generating detection frames and category annotations using a deep learning algorithm, and performing coordinate conversion to accurately calibrate the positions of primitives; extracting geometric information, structural attributes and material data based on object detection results and primitive information, performing parametric expression conversion, and generating parametric model data of the ship structure; inputting the generated parametric data into a modeling instruction template, generating a ship CAD modeling instruction sequence, and executing the instructions through a CAD system to automatically construct a three-dimensional model of the ship. The present invention can realize the fully automated conversion from two-dimensional CAD drawings to three-dimensional modeling, thereby improving the efficiency and accuracy of ship design.

A system for material detection and identification includes an interface configured to access a material database associating each of a plurality of materials with a resonance frequency and one or more transmission parameters; an RF transmitter configured to, for each material of at least a subset of the plurality of materials in the material database transmit into an environment an RF signal the resonance frequency for the material using the one or more transmission parameters; an RF receiver configured to receive a response signal from the environment; and a processor configured to analyze the response signal for resonance characteristics that indicate a presence of the material and identify the material to a user if the presence of the material is indicated by the resonance characteristics.

The invention discloses an intelligent welding forming method and system for a green building steel heating radiator, and the method comprises the following steps: carrying out the surface defect recognition of a steel heating radiator base material based on an AI visual inspection system, recognizing a qualified base material, and automatically matching the type of a welding material from a material database according to the material and thickness parameters of the qualified base material. Welding parameters are intelligently matched through AI visual inspection and a neural network model, a laser and friction stir hybrid welding process is combined, traditional manual operation is replaced, the welding efficiency and precision are improved, and the problems of uneven welding seams and the like are solved; welding data are analyzed in real time through a multi-mode AI model, parameters are dynamically adjusted, intelligent defect recognition and repair welding are achieved in cooperation with 3D visual inspection, and the quality stability is improved through whole-process monitoring; smoke dust is treated through an environment-friendly process, acid pickling is replaced with mechanical rust removal, efficient recycling of materials is achieved through waste recycling, a green manufacturing system is constructed, and the sustainable development requirement of green buildings is met.

A system for material detection and identification includes an interface configured to access a material database associating each of a plurality of materials with one or more corresponding resonance frequencies; an RF transmitter configured to, for each material of at least a subset of the plurality of materials in the material database, transmit into an environment an RF signal at a first resonance frequency for the material; an RF receiver configured to receive a response signal from the environment for each RF signal; and a processor configured to analyze each response signal for resonance characteristics that indicate a presence of the material and identifying the material to a user if the presence of the material is indicated by the resonance characteristics.

A method for material detection is described. The method may include extracting, from a material database, a resonance frequency for the target material. The method may further include comparing an application type to entries in a scan database. The scan database may store pre-defined scanning patterns and corresponding application types. The method may include extracting, from the scan database, a scan sequence for the application type. Also, the method may include instructing a gimbal to follow positions in the scan sequence. The method may also include transmitting into an environment an RF signal when the gimbal is at the positions in the scan sequence. The method may further include receiving a response signal from the environment. The method may include analyzing the response signal for resonance characteristics that indicate a presence of the target material. Additionally, the method may include generating a haptic feedback when the target material is detected.

A method includes accessing a material database associating each of a plurality of materials with one or more corresponding resonance frequencies; for each material of at least a subset of the plurality of materials in the material database: transmitting, via an RF transmitter, an RF signal into a target at a first resonance frequency for each material; receiving, via an RF receiver, a response signal from the target; and analyzing the response signal for resonance characteristics that indicate a presence of each material in the target; generating a report of each material in the target indicated by the resonance characteristics; and integrating the report with at least one of: a timestamp; image or video data from a camera; or geolocation data from a geolocation sensor.

A system for material detection and identification includes an interface configured to access a. material database associating each of a plurality of materials with one or more corresponding resonance frequencies: an RF transmitter configured to, for each material of at least a subset of the plurality of materials in the material database, transmit into an environment an RF signal at a first resonance frequency for the material; an RF receiver configured, to receive a response signal from the environment for each RF signal; and a processor configured to analyze each response signal for resonance characteristics that indicate a presence of the material and identifying the material to a user if the presence of the material is indicated by the resonance characteristics.

The invention discloses an AI-based intelligent factory dynamic production scheduling optimization method and system, and belongs to the technical field of factory dynamic production scheduling, and the method comprises the following steps: obtaining and integrating the working states of various types of equipment in a factory, the stock position states of different materials, and the manual data; the production order state, the process route requirement, the plan and the completion time of each process and the delivery date requirement of the customer are determined; generating an initial production scheduling plan through an AI optimization algorithm in combination with supply chain material data, factory storage space, equipment switching cost, production rules, each order process dependency relationship and a production target; according to the method, the initial scheduling plan is generated by acquiring and integrating multiple types of production data, the dynamic events are monitored, multiple schemes are generated through evaluation, the optimal scheme is selected through economic model evaluation, and production refinement, dynamic response and benefit optimization are achieved.

A system for material detection and identification including: an RF transmitter configured for transmitting into an environment an RF signal at a first resonance frequency for a target material, wherein the first resonance frequency is obtained from a material database associating each of a plurality of materials with one or more corresponding resonance frequencies; an RF receiver configured for receiving a resultant response signal from the environment; and a processor configured for: analyzing the resultant response signal for resonance characteristics that indicate a presence of the target material, wherein analyzing includes, if the resonance characteristics are detected and no other material in the material database shares similar resonance characteristics for the first resonance frequency, reporting to a user that the target material has been identified.

The invention relates to a work order material intelligent tracking and dynamic updating system based on multi-source data fusion. The system comprises an acquisition module, a tracking module, an updating module, a man-machine interaction module and a storage module, the acquisition module is used for acquiring work order information from a bill of material, scheduling information and inventory and supplier data, and constructing a material data model; the tracking module monitors material inventory, consumption and material shortage risks based on the model, and realizes automatic state updating and graphical path recording in combination with bar code recognition and ambiguity analysis; the updating module predicts material requirements through a machine learning algorithm, optimizes a distribution plan and outputs a dynamic report; the interaction module supports multi-format data import, unstructured field classification and interrupted operation control; the storage module ensures credible synchronization and tamper-proofing performance of data among different systems through a block chain technology; according to the invention, high-efficiency management and dynamic allocation of work order materials can be realized in an environment without an MES system, and the production transparency and the resource utilization efficiency are improved.

The invention relates to the field of electric power material warehouse management and control, in particular to an electric power material digital intelligent professional warehouse management and control method, which comprises the following steps: acquiring electromagnetic spectrum data, identifying a central frequency point energy distribution diagram and an electromagnetic interference intensity level, constructing a three-dimensional electromagnetic field intensity distribution model of a professional warehouse, and dynamically adjusting the working mode of an RFID reader-writer; the method comprises the following steps: deploying a multi-modal check node in a storage area of a professional warehouse, and generating a composite check result through an infrared contour matching method and a UWB coordinate verification method when the confidence coefficient of electric power material data read by an RFID reader-writer is lower than a preset confidence coefficient threshold value; updating an electric power material inventory database according to a composite verification result, and performing electric power material inventory state analysis in real time through an edge computing node; the problems that inventory data distortion and material positioning deviation are caused by reduction of the accuracy rate of RFID in a strong electromagnetic environment, and inventory accuracy management of an electric power material warehouse is seriously restricted are solved.

The invention relates to the technical field of marking control, and discloses a control method and system for product marking. The method comprises the steps of scanning the surface of a part to obtain material data; performing three-dimensional positioning according to the material data to obtain coordinate data; marking parameters are screened according to the coordinate data; using the parameters to control the marking device to generate process data; analyzing the process data to evaluate the marking quality; and optimizing parameters by using the quality data and storing the parameters in a database to form a control scheme. According to the invention, accurate identification of the material and shape characteristics of the part can be realized, the optimal marking position and process parameters are automatically determined according to the identification result, real-time monitoring and adjustment are carried out in the marking process, the marking quality is comprehensively evaluated and fed back and optimized after the real-time monitoring and adjustment are completed, and the consistency and reliability of the marking quality are improved.

The invention discloses a control method applied to self-piercing riveting, and relates to the technical field of self-piercing riveting control. According to the method, riveting material data are collected in real time through a multi-source data fusion interface, the high-dynamic deformation process of a riveting head is captured, deformation rate and rebound value characteristic values are extracted, and a multi-dimensional process parameter set is constructed. And comprehensively analyzing the hardness, thickness and plastic dissipation characteristics of the material, and establishing an anti-deformation capability evaluation function. By introducing the fluctuation characteristics of the punching force, the nonlinear relation between the punching force and the elastic deformation of the riveting rod is analyzed, a hybrid compensation model is constructed, and a riveting dynamic coupling compensation vector is output. And generating a compensation weight matrix through an adaptive optimization algorithm, and dynamically adjusting the stamping force and the displacement of the riveting rod in combination with an anti-saturation control strategy. And dynamically adjusting a gain parameter through multi-scale filtering and a Lyapunov stability criterion, and triggering iterative updating of a compensation weight parameter during out-of-tolerance so as to optimize a control effect. Riveting precision and stability are improved, and the service life of equipment is prolonged.

The invention relates to a Bayesian optimization-based ruthenium, tin and indium doped lithium-rich manganese-based electrocatalyst screening method, electronic equipment and a storage medium, and the method comprises the following steps: generating a uniform ruthenium, tin and indium doped lithium-rich manganese-based material data set based on Dirac distribution, and generating a doped structure through element replacement; calculating hydroxyl adsorption energy and extracting descriptors to construct a data set to train a machine learning model; performing cell expansion operation on the initial electrocatalyst structure, predicting hydroxyl adsorption energy of each adsorption site, and mapping adsorption energy deviation into current density characterization component activity in combination with Boltzmann distribution; selecting an initial component based on the obtained component activity information, and constructing an agent model by utilizing Bayesian optimization; and determining a to-be-sampled component through an expectation improvement strategy, sampling and updating the agent model, and outputting an optimal doping proportion. Compared with the prior art, the method has the advantages that the ruthenium, tin and indium doped lithium-rich manganese-based electrocatalyst with excellent catalytic performance can be efficiently screened out, and the experiment cost is reduced.

The invention belongs to the technical field of crucible coating optimization, and relates to an intelligent coating optimization method for quartz crucible surface treatment. According to the method, coating material data, surface treatment environment data, coating process data and performance test data are collected, the performance test data serve as a real result label of a training set, denoising, missing value processing and normalization are carried out on the collected data, and through a Pearson's correlation coefficient and a principal component analysis method, the coating material data, the surface treatment environment data, the coating process data and the performance test data are obtained. According to the method, a group of key features are screened out, data dimensions are reduced, main information influencing the coating performance is reserved, a deep neural network model is trained, the coating performance is predicted through the model, and coating process parameters are optimized by adopting a particle swarm optimization algorithm based on the predicted coating performance, so that the coating performance is optimized. According to the method, the optimal parameter combination can be effectively searched, blindness and high cost of a traditional optimization method are avoided, a closed-loop intelligent coating optimization process is formed, and the optimization efficiency and performance of the coating process are remarkably improved.

The invention discloses a statistical analysis-based engineering cost dynamic adjustment method and system, and aims to solve the problems of many errors and poor accuracy and reliability when the engineering cost is changed due to factors such as material price fluctuation, the method comprises the steps of obtaining a price change message and extracting related data, matching an engineering material data table by using a preset template, and obtaining an engineering material data table; the system queries and processes price data according to a change material identifier, carries out source diversification processing on the change price data, and also relates to initial construction and dynamic updating of an engineering cost template, the system is composed of a price change module, an engineering material data table module, a to-be-changed module, a to-be-replaced module and other modules, and all the modules work cooperatively to realize corresponding functions. The method has the following advantages that on the aspect of data processing, multi-channel data are automatically integrated, prices are fused, and the operation risk is reduced; in the aspect of template construction and updating, historical data and algorithms are utilized, and the material matching accuracy is improved; during price adjustment, the confidence interval is calculated based on statistical analysis, and the accuracy and reliability of the project cost are effectively guaranteed.

The invention provides a material data curve identification and fitting method based on deep learning, and relates to the technical field of material data extraction, and the method comprises the steps: obtaining an image file containing a plurality of material data curves; separating the material data curve from the image background by adopting an image segmentation technology, and extracting feature points of the material data curve through a convolutional neural network; and taking the feature points as control points, performing parametric fitting on the curve through polynomial or spline interpolation, and generating a smooth continuous curve. The method further comprises the following steps: detecting the position and boundary of a coordinate axis in the image file through an image processing technology; the OCR technology is used to identify the scale label on the coordinate axis, and the deep learning model is combined to analyze the numerical value and unit of the scale. According to the method, through innovative technologies such as multi-modal feature fusion, self-adaptive preprocessing and intelligent data labeling, precise recognition of complex curves, multi-curve separation and association, intelligent recognition of non-standard coordinate axes and efficient real-time processing are achieved.

The invention belongs to the field of intelligent manufacturing, and particularly relates to an intelligent scheduling method for collaborative manufacturing of tunnel boring machines, in the method, multi-source heterogeneous data including design data, process data, production data, material data and the like are acquired, and a rich data basis is provided for intelligent scheduling. The data fusion method based on the ontology is adopted, and the problems of semantic heterogeneity and structural heterogeneity of multi-source heterogeneous data are effectively solved. By constructing the tunnel boring machine manufacturing field ontology model, semantic association and sharing of data from different sources are realized, and the availability and understandability of the data are improved. An algorithm combining mixed integer programming and deep reinforcement learning is provided for intelligent scheduling of tunnel boring machine collaborative manufacturing. A robust optimization method is introduced to enhance the robustness of the scheduling scheme, and the influence of uncertain factors such as raw material supply and equipment maintenance in the manufacturing process of the tunnel boring machine is considered.

The invention belongs to the field of material data, and discloses a simulation data generation method for metal material multi-mode fusion. Comprising the steps of collecting data and performing feature extraction to obtain feature vectors; through multi-modal method fusion, a metal material multi-modal feature training data set is obtained; and constructing a metal material simulation data diffusion model and a diffusion model loss function conforming to physical condition constraints, training the diffusion model, and outputting high-quality simulation metal material data conforming to the basic physical law of the material. According to the simulation data generation method for multi-modal fusion of the metal material provided by the invention, physical constraints are introduced, so that the generated simulation data meet experimental data from a statistical angle and meet a basic physical rule; the simulation data of the metal material can be effectively generated under the condition of limited data, and the experiment and calculation cost can be remarkably reduced; and meanwhile, numerical modal data and image modal data are fused, so that the structure-performance evolution relationship of the material is better met.

The invention discloses a battery material combination method and device and a computer storage medium, and relates to the technical field of new energy materials, and the method comprises the steps: generating one or more groups of candidate material combinations based on battery material data; extracting attribute features of the candidate material combination, and converting the attribute features into feature vectors; and inputting the feature vector into a pre-trained prediction model to obtain a performance prediction result of the candidate material combination. According to the method, the candidate material combination is generated, and the performance of the candidate material combination is predicted by using the prediction model, so that efficient screening and safety performance prediction of the battery material combination are realized, the research and development efficiency of the battery material is remarkably improved, and meanwhile, the selection range of the battery material combination is also expanded.

The invention discloses a material data quality evaluation method based on a large model Agent, and relates to the technical field of material science and intelligent data management. According to the method, firstly, based on a rule set and through a real-time feedback and cyclic detection mechanism, original material data is corrected, and it is ensured that data entering follow-up evaluation has high quality; and secondly, based on the knowledge base, deep analysis is performed on the speciality, logic consistency and rationality of the to-be-evaluated material data by utilizing an RAG technology and an LLMs model, and a data quality evaluation result based on the knowledge base is generated. And meanwhile, various machine learning algorithms are used for carrying out multi-aspect detailed detection on the to-be-evaluated material data to obtain a plurality of data quality evaluation results based on the machine learning algorithms, and visual and quantitative evaluation results are provided through a visual method. And then, multi-dimensional evaluation information is integrated through a large model Agent, and a high-interpretation and structured comprehensive quality evaluation result is generated.

A method accessing a pre-trained specific material database associating each of a plurality of materials with a corresponding material profile, each material profile including one or more parameters including at least one of a transmit frequency and a response frequency; receiving a selection of a target material from a user; identifying first material profile associated with the target material using the pre-trained specific material database; transmitting, via an RF detection device, an RF signal into the target material using the one or more parameters for the target material associated with the first material profile; receiving, via the RF detection device, a response signal from the target material; analyzing the response signal using an AI algorithm to determine whether resonance characteristics of the response signal indicate a presence of the target material; and notifying the user if the presence of the target material is indicated by the resonance characteristics.

Disclosed herein is a method for authorizing a user of a device to perform at least one operation on the device that requires authentication, including receiving, in response to receiving the unlock request, at least one image of the user using a camera located on the device while the user is being illuminated with the flood infrared illumination and / or the patterned infrared illumination; generating infrared pattern image data for the region of interest from the at least one image; extracting material data from the infrared pattern image data and determining at least one occlusion from material data; and allowing or declining the user to perform at least one operation on the device that requires authentication based on the material data and / or the determined occlusion.

The invention relates to the technical field of material storage management, in particular to a BIM-based construction site material monitoring management method and system. The method comprises the following steps: obtaining a construction project drawing; performing drawing information extraction on the construction project drawing to obtain construction project material information data and construction project structure data; performing construction project three-dimensional modeling on the construction project material information data and the construction project structure data to generate construction project three-dimensional modeling data; obtaining total construction material data; and carrying out material information modeling on the total construction material data by using a BIM technology to generate total construction material modeling data. According to the invention, accurate three-dimensional modeling, intelligent marking, regionalized storage and real-time allocation of the construction site materials are realized through the BIM technology, and the accuracy of construction site material monitoring management and the emergency response real-time performance are improved.

The invention discloses a method, a system and equipment for generating an oral video of a digital human, and a medium, and the method comprises the steps: obtaining oral copywriting and video material data, and analyzing and determining a timestamp of the copywriting in a video material through a multi-mode large model; converting the copywriting into audio data, preprocessing the audio data, and combining the audio data with the timestamp to generate first video data; and generating a digital human according to a user demand, and combining the digital human with the first video after image matting processing to obtain an oral playing video. According to the method, a traditional template generation mode is broken through, and customized production of the digital human broadcast video is realized through a multi-modal semantic matching and personalized digital human generation technology; and meanwhile, audio and video accurate synchronization, high-quality image matting and synthesis technologies are adopted, so that the content adaptability and the video quality are guaranteed, and the flexibility, the efficiency and the effect of digital population broadcast video production are remarkably improved.

The invention discloses a verification system and method for a material formula through a confidence interval, and relates to the technical field of material informatics and intelligent research and development decision. Comprising a data acquisition module used for acquiring candidate formula basic data, historical experiment basic data, environment associated data, material recessive data and equipment state data and preprocessing the acquired data; according to the method, the final error value is obtained through fusion, the adjusted final credible interval is constructed, the problems that an existing material performance prediction tool can only output a point prediction result and lacks a stable credible interval, and engineers are difficult to assess that performance reaches the standard and actually and successfully grasp are solved, the coverage rate is verified through the verification set, the error scale is scaled, and the reliability of the material performance prediction tool is improved. It is ensured that the credible interval meets the preset coverage requirement, successful mastering of performance standard reaching can be quantified, an engineer does not need to depend on experience judgment any more, and the accuracy of performance evaluation is improved.