735 results about "Material data" patented technology

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.

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.

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 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.

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.

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.

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.

A method includes detecting interaction by a user with an entry detector; collecting credentials from the user; and, if the credentials indicate that the user is authorized to enter, accessing a material database associating each of a set of materials with one or more corresponding resonance frequencies; and for each material of the set of materials: transmitting, into an environment associated with the user, an RF signal at a first resonance frequency for a material of the set of materials; receiving a response signal from the environment; analyzing the response signal from the environment for resonance characteristics that indicate a presence of the material; and in response to the presence of the material being indicated: determining whether an action is required for the material; and if the action is required for the material, actuating an access apparatus to perform the action.

The invention relates to the technical field of cost control, in particular to a project cost progress management control method and system, and the method comprises the following steps: collecting attendance equipment material data, combining and calculating resource expenditure density, deducing cost pressure in combination with processes and positions to generate a pressure value sequence, and superposing payment records to calculate daily payment intensity; inputting an abnormal factor algorithm to detect deviation to generate a response fault, analyzing process change to screen a risk section to generate a structural fault, extracting a progress and payment intensity ratio to screen an asynchronous period, constructing a control time axis to analyze a relationship between expenditure density and payment intensity slope, and generating a progress management control result set. Attendance checking, equipment operation and material consumption data are collected to derive cost pressure, abnormal deviation is recognized, risks are early warned, the process and resource change trend is analyzed, the relationship between progress and payment intensity is coordinated, the precision, real-time performance and response efficiency of cost control are improved, and it is guaranteed that a project investment target is smoothly achieved.

The embodiment of the invention provides a multi-mode unmanned material checking method based on combination of RFID and machine vision, the method is applied to a mobile module and a camera module, an RFID read-write module is arranged on a mobile trolley, and the method comprises the following steps: in response to movement of the mobile module, the RFID read-write module scans a target material to obtain RFID identification data, and filters and de-duplicates the RFID identification data to obtain a target material; an RFID identification result is determined; dividing a shooting range of the camera module based on a scanning area of the RFID read-write module, and detecting a material box and a material surface sheet label in video image data when the video image data is shot; and identifying the material surface sheet label to obtain material data information, and matching the material data information with the RFID identification result to obtain a multi-modal material matching result.

The invention discloses a multi-scene wireless network rapid deployment method based on an electromagnetic field digital twinning technology, and relates to the technical field of computer application, and the method comprises the following steps: S1, constructing a three-dimensional digital twinning model of a corresponding scene, and integrating a building structure, equipment layout and material attribute data, and establishing a scene special material database containing metal equipment, a concrete wall body and an electromagnetic shielding layer. According to the multi-scene wireless network rapid deployment method provided by the invention, the three-dimensional digital twin model is constructed, and the building structure, the equipment layout and the material attribute data are integrated, so that the precise simulation of the environment is realized, the wireless network deployment accuracy is improved, the deployment scheme can better adapt to the dynamic change of the environment, and the deployment efficiency is improved. The improved O-FDTD algorithm is adopted to simulate electromagnetic field propagation in a building in real time, and the problem that the calculation efficiency is low when a traditional FDTD algorithm processes the metal edge effect and a moving object is effectively solved.

The invention belongs to the crossing field of artificial intelligence and material informatics, discloses a multi-layer material knowledge graph construction method and system based on a large model, and solves the problems of low utilization rate of multi-modal information, flat knowledge representation, uncontrollable quality and insufficient entity alignment precision in the prior art. The method comprises the following steps: analyzing material science and technology literatures, extracting multi-modal information of texts, tables and images, and preprocessing the multi-modal information into structured data; constructing a three-layer semantic framework of a concept term layer, a material entity layer and a material data layer; extracting knowledge by a large model layer by layer to generate a triple, and performing multi-dimensional quality evaluation and dynamic threshold filtering; entity fusion is realized by adopting hybrid alignment of semantic embedding and literal similarity and large model discrimination, and an atlas is maintained by combining incremental updating and version management. According to the method, knowledge integrity and accuracy are improved, entity heterogeneous scenes in the material field are adapted, whole-process automation is achieved, and high-quality knowledge support is provided for material research and development and data sharing.

A method for material detection 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 generating a haptic feedback to indicate a directionality or a proximity to the target material. The method may include analyzing the response signal for resonance characteristics that indicate a presence of the target material.

A system for currency verification includes a currency apparatus configured for receiving alleged currency to be verified; at least one processor; a non-transitory computer-readable medium configured for storing instructions that, when executed by the at least one processor, perform a method comprising: accessing a material database associating each of a set of materials found in authentic currency with one or more corresponding resonance frequencies; for each material of the set of materials: transmitting into the alleged currency an RF signal at a first resonance frequency for each material; receiving a response signal from the alleged currency; and analyzing the response signal for resonance characteristics that indicate a presence of each material; and a display device configured for outputting that the alleged currency is authentic if the presence of at least a subset of the set of materials found in the authentic currency is indicated.

The invention discloses a fastener heat treatment-oriented multi-source data fusion processing method, which relates to the field of data fusion and comprises five steps of multi-source data acquisition, data preprocessing, feature extraction, fusion modeling and result optimization. The multi-source data acquisition terminal acquires process data, material data, quality detection data and environment data; data cleaning and standardization are carried out in data preprocessing; the feature extraction terminal extracts a time sequence feature parameter, a component feature parameter, a quality feature parameter and an interference feature parameter; fusion modeling is combined with mechanism prior and data driving to realize fusion of a feature layer and a decision layer; and result optimization: smoothly correcting the fusion result, and outputting quality evaluation and process adjustment suggestions. According to the method, through deep fusion of multi-source data, the accuracy and stability of fastener heat treatment quality evaluation are improved, adaptive optimization of process parameters is realized, and the method is suitable for heat treatment whole-process management and control in a structured industrial scene.

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 the technical field of movie and television animation rendering, and particularly provides a movie and television animation material optimization method and system applied to real-time rendering, and the method comprises the steps: firstly obtaining a material data set of a target scene, which comprises a plurality of material units composed of basic material attributes and corresponding rendering effect attributes; performing multi-level feature extraction on the material data set to obtain a material feature set and a rendering association feature set of each material unit, performing matching processing on the two feature sets to generate an optimization strategy set of the material units, and dynamically adjusting a rendering parameter set of the material units according to the optimization strategy set to obtain a rendering parameter set of the material units; and finally, performing real-time rendering based on the adjusted rendering parameter set, and outputting a rendering picture of the target scene, thereby effectively optimizing real-time rendering of movie and television animation materials, and improving rendering quality and efficiency.

The invention discloses a numerical control machine tool dynamic characteristic matching and machining path planning method and system for efficient cutting, and relates to the technical field of intelligent manufacturing and numerical control machining path optimizing.The method comprises the following steps that on the basis of a CAD three-dimensional drawing of a machined part, the CAD three-dimensional drawing is imported into CAM software, the machined surface and the non-machined surface of the part are determined, and the machining surface and the non-machined surface of the part are matched; various machining schemes and technological parameters of different machining faces of the part are generated through CAM software in combination with drawing parameter requirements; and for the generated machining schemes under the process parameters of the different machining surfaces, the machining rigidity and vibration parameters are calculated in combination with part material data, and meanwhile, the standard rigidity and vibration parameters of the current numerical control machine tool are collected. According to the method, the dynamic performance of the numerical control machine tool is combined, the tool path planning in the CAM programming process and the technological parameters are matched, and tailoring and force measuring are carried out according to numerical control programming of different machine tools.

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.

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 relates to the technical field of DC fan blades, in particular to a fan blade model obtaining method and system and a medium. The method comprises the following steps: collecting point cloud data of a DC fan blade and extracting a main curvature direction, reconstructing a three-dimensional fan blade based on the main curvature direction, collecting heat dissipation demand data, carrying out demand load matching on the three-dimensional fan blade, generating fan heat dissipation demand load data, then carrying out heat source distribution inversion, and carrying out heat source distribution inversion. The method comprises the following steps: carrying out axial segmented projection through heat source distribution data, carrying out local heat influence simulation, carrying out heat dissipation structure continuation simulation by combining the influences, carrying out limit boundary constraint projection on a three-dimensional fan blade, determining a blade compensation direction, carrying out compensation path query according to the direction, and carrying out adaptive screening through material data. And performing compensation simulation on the three-dimensional fan blade through the compensation path, and constructing a compensated three-dimensional fan blade model. According to the method, accurate and reliable fan blade model optimization is realized.

The invention provides a redundant material data cleaning system based on a large model, belongs to the technical field of industrial intelligence and data governance, and realizes end-to-end semantic cleaning by utilizing the deep semantic understanding capability of a pre-trained large language model. Material description is converted into a high-dimensional semantic vector, semantic similarity is accurately calculated in a unified vector space, materials with the same essence are recognized beyond literal difference, and redundant items are automatically merged through intelligent clustering. The method not only can deeply capture semantic association between core attributes and effectively neglect noise, but also has the potential of fusing structured attributes to perform more robust judgment, significantly improves cleaning precision and efficiency, and overcomes inherent limitations of a traditional assembly line method.

The invention relates to the technical field of protection systems, in particular to a multi-source heterogeneous data fusion heat-proof porous material permeability multi-scale prediction method, which comprises the following steps: establishing a heat-proof porous material data set according to a collected three-dimensional structure of a heat-proof porous material; a permeability prediction model is established, the permeability prediction model is used for processing the structure image through a convolution module and a dimension reduction encoder in sequence, processing the structure characteristic parameters through a dimension raising encoder, processing through a cross-modal attention mechanism, and processing through regression prediction to obtain a permeability prediction value; based on the heatproof porous material data set, performing hyper-parameter optimization and training on the permeability prediction model by adopting a TPE sampler and a median pruning device; inputting to-be-evaluated sample data into the trained permeability prediction model to obtain a permeability prediction value; according to the method, the accuracy and efficiency of predicting the macroscopic permeability of the thermal protection material can be improved.

The invention relates to the technical field of data sampling, in particular to a neural network training data set generation method and device, equipment and a storage medium, and the method comprises the steps: obtaining the shape data, material data and target load position space of a part virtual model; determining boundary constraint conditions of the part virtual model according to the shape data and the material data; carrying out load point stratified sampling in the target load position space to obtain a plurality of sampling points and sampling parameters; according to the boundary constraint condition and the sampling parameters of the plurality of sampling points, constructing a simulation working condition, performing topological structure simulation under the simulation working condition, and determining a part topological structure corresponding to the simulation working condition according to a simulation result; and generating a training data set based on the simulated working condition and the corresponding model topological structure, training a neural network model by using the training data set, and generating a part topological structure under the target real working condition by using the trained neural network model. Therefore, the problem that the quality of the training data set is poor in the related technology is solved.

The invention relates to the technical field of geotechnical material local modeling, in particular to a geotechnical material data driving local modeling method and device based on a knowledge migration strategy, and the method comprises the steps: building a first mapping data pair based on the stress-strain physical quantity of a test curve sampling point, and building a high-fidelity data set of a geotechnical material; constructing a second mapping data pair based on the stress-strain state of the simulation curve sampling point and the intermediate physical quantity label, and establishing a low-fidelity data set of the geotechnical material; pre-training the target data-driven constitutive model by using the low-fidelity data set to generate an initial geotechnical material data-driven constitutive model; and driving the model to be fine-tuned by using the high-fidelity data set for the initial geotechnical material data, and generating final geotechnical material data to drive the model. Therefore, the problem of insufficient generalization ability and prediction precision of a data-driven model due to limited training data and unpredictability of a stress path in the prior art is solved.

The invention belongs to the technical field of data management. According to the material data cleaning method and system fusing the large language model and the knowledge graph, the knowledge graph stores core knowledge such as an enterprise material classification system, term definition and quality rules in a structured triple form, and a clear standard basis is provided for cleaning operation; accurate structured knowledge anchor points and standardized constraints are provided for the large language model, the illusion of the large language model is effectively inhibited, and it is ensured that the output content conforms to industry specifications; the big language model injects strong natural language understanding, fuzzy semantic analysis and unstructured information extraction capabilities into the knowledge graph, makes up for the flexibility deficiency of the knowledge graph, and deeply fuses the two to jointly solve the key problem of what the text semantics is understood and whether the semantics meets the standard or not. And the material data cleaning precision is improved.

Provided in the embodiments of the present application are a 3D video generation method and an electronic device. The method is applied to the electronic device. The method comprises: on the basis of captured data, acquiring 3D video material data, wherein the captured data comprises a 2D video and / or a 2D image; on the basis of the 3D video material data, analyzing a 3D video scene type; on the basis of the 3D video scene type, acquiring a new-view generation model corresponding to the 3D video scene type; and using the new-view generation model corresponding to the 3D video scene type to generate 3D video data on the basis of the 3D video material data. On the basis of the method of the embodiments of the present application, at a 3D video data generation stage, a corresponding new-view generation model is called on the basis of a 3D video scene type, and thus a more complete scene synthesis view can be acquired on the basis of a new-view synthesis algorithm that uses deep learning and artificial intelligence, such that when 3D video data is played, a user can immersively view 3D video content from different angles, thereby providing a stronger sense of 3D and richer 3D video content.