1001 results about "Material data" patented technology

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.

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 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 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.

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.

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.

The invention discloses an AI-based BOM material information automatic coding method, which comprises the following steps of: unifying data structures of BOMs of products at different stages, storing the data structures as a single BOM data set, screening single data with a single bit number, comparing part of data with a standard database, cleaning and then comparing when the data are not consistent with the standard database; a knowledge graph, vector conversion, cosine distance and the like can be used for further processing so as to convert single bill of material data into standard data; for data which cannot be matched, multiple strategies can be adopted to select matching entries; the invention further relates to format recognition of the bill of materials and selection of processing tools so as to ensure data integrity. When data of a single bill of material is denoised, key parameters are extracted in combination with multiple data sources, association rules are mined through a data mining algorithm, and denoising operation is executed. The method can improve the accuracy and standardization degree of material data, greatly improves the coding efficiency, and achieves the intelligentization and refinement of material management.

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.