448 results about "Material data" patented technology

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 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 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.

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.

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 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 relates to the technical field of knowledge graph completion, in particular to a metal material data-oriented multi-modal knowledge graph completion method. The method comprises the following steps: acquiring multi-modal data of a target data source, and preprocessing the multi-modal data to obtain a text entity, a text entity vector and an image content vector; extracting a text entity relationship triple and a table entity relationship triple from the text entity and the preprocessed structured table data to obtain an entity relationship triple; updating the text entities based on the text entity vector and the image content vector to obtain an entity set, and disambiguating the entities to obtain a standardized entity set; and constructing a target knowledge graph according to the standardized entity set and the entity relationship triad, and performing relationship completion on the target knowledge graph. In this way, the problems that an existing method lacks semantic alignment and information complementation technologies among modals, and complex dependency relationships among entities in the multi-modal knowledge graph cannot be fully considered can be solved.

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 method includes detecting an application-specific trigger for an application-specific apparatus; 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 a target associated with the application-specific apparatus, an RF signal at a first resonance frequency for a material of the set of materials; receiving a response signal from the target; analyzing the response signal for resonance characteristics that indicate a presence of the material in the target; and, in response to the presence of the material being indicated, determining whether an application-specific action is required; and if the application-specific action is required, actuating at least one integration communication apparatus associated with the application-specific apparatus to perform at least one application-specific task.

The invention discloses an electronic film cutting control method and system based on digital twinning. The method comprises the steps that tool and material interaction data are collected and denoised, time domain and frequency domain characteristics of the tool and material interaction data are extracted, spectral distribution is calculated through Fourier transform, and material characteristic fluctuation mode data are determined; if the threshold value is exceeded, judging that the toughness of the material is changed, obtaining time sequence data, obtaining a material difference feature vector, inputting the material difference feature vector into a support vector machine model for classification, matching a material database, and determining an optimized control parameter set; a deep neural network is adopted to predict the optimized control parameter set, and environment variables are fused to obtain a final dynamic control instruction set; and if the final dynamic control instruction is not matched with the current cutter state, a stable machining path is obtained through closed-loop feedback cycle updating. According to the method, the digital twin of the cutter and the material is constructed, the physical machining process is mapped in real time, the machining precision and adaptability can be remarkably improved, and intelligent response to the material difference is achieved.

The application relates to the field of information technology, in particular to an intelligent processing method and system for new media data. The method comprises the following steps: reading historical new media material data; analyzing the historical new media material data, calculating an availability estimation value of the historical new media material, and sorting and screening the historical new media resource according to the availability estimation value; storing the analyzed historical new media material data in a classified manner, and establishing a multidimensional index of the historical new media material data; receiving a new media content generation demand, converting the new media content generation demand into a new media material matching condition; screening and sorting new media materials based on the new media material matching condition, and selecting the first N new media material data as basic new media material data; and fusing a theme content based on the basic new media material, generating multi-form new media content, and realizing intelligent processing of high-efficiency, high-quality and self-optimizable new media content.

The invention relates to the technical field of constructional engineering data processing, in particular to an engineering quality monitoring method and device and electronic equipment, and the method comprises the steps: periodically collecting constructional engineering data; determining a fracture form coefficient according to the fracture data; performing dynamic index evaluation on the material data and the environment data to obtain a material degradation rate; constructing an initial stress vector and an actually measured stress vector according to the stress data, and analyzing a stress deviation angle based on the initial stress vector and the actually measured stress vector; constructing an energy-time decay model based on the acoustic emission data to determine an acoustic emission cumulant; performing data fusion analysis on the fracture form coefficient, the material degradation rate, the stress deviation angle, the acoustic emission cumulant and the environmental data to obtain a structural health index; and judging the building engineering quality according to the structural health index. According to the invention, accurate analysis of the building engineering quality is realized.

The invention discloses a parameter optimization method for photoresist, which comprises the following steps: collecting process multi-source data, preprocessing to construct a data set, and generating environment and equipment disturbance vectors; photoresist material data are extracted, multiple features are fused in a dimensionality reduction mode, and photoresist dynamic vectors are generated; acquiring process time sequence data, inputting the process time sequence data into the improved CycleNet network, and extracting process dynamic characteristics; constructing a tensor input solving unit, calculating the acid concentration and the developing thickness, and predicting the imaging quality; the photoresist dynamic vector is evolved, and a complete disturbance set is generated in combination with the multiple disturbance vectors; and constructing a robust optimization target, evaluating a parameter imaging effect, and obtaining optimal process parameter output. According to the method, robust optimization and stable control of key parameters of the photoetching process are realized by constructing a parameter optimization process fusing photoresist dynamic evolution, process time sequence disturbance and multi-source disturbance perception.

The invention discloses a material state management and monitoring method and system based on a manufacturing life cycle, and relates to the technical field of intelligent manufacturing. The method comprises the steps that a material state model is established based on a manufacturing life cycle and comprises state codes, and the state codes are used for representing state stages and state attributes of materials; key business events of materials of multiple business systems are obtained, business data of all the materials are obtained through cross-system synchronous analysis, state transition is executed in combination with a preset state transition rule, and a transition path is generated; state codes, service data and migration paths of all the materials are presented through a visual carrier, and the visual carrier comprises a multi-dimensional retrieval entry; and identifying the abnormal material based on the state transition rule, and generating an early warning notification. Through adoption of the method and the device, unification of material data and states of a plurality of business systems in a manufacturing life cycle is realized, consistency and real-time performance of material state information are ensured, and meanwhile, full-process visual monitoring and abnormal accurate identification of the material states are achieved.

The invention discloses a material database system with enhanced artificial intelligence. The material database system comprises an interactive user interface module, a data management and service module, a visual processing module and an AI analysis and intelligent dialogue module. Structured and unstructured material data are managed in a centralized manner through a unified database and cloud storage, and a standardized data center is constructed; by integrating a chemical structure rendering library and a 3D graphic engine, real-time rendering from a CIF file to an interactive 3D model is realized, and a dynamically generated XRD spectrogram is displayed through an interactive chart library, so that the interpretability of data is greatly improved; by introducing an AI application development framework and an LLM, the system is endowed with intelligent analysis ability exceeding simple data retrieval, and professional material interpretation can be provided; by integrating the RAG system, a traditional search mode is upgraded into an intelligent dialogue mode, so that a user can explore material data like a dialogue with an expert.

The invention relates to the technical field of electric power storage, and particularly discloses an electric power storage resource global collaborative management method and system, and the method comprises the steps: constructing an electric power professional warehouse data graph with an Internet of Things physical ID as a core node, the Internet of Things physical ID being a unique physical identifier of an electric power storage resource; associating material data of a safety supervision warehouse, a regulation and control warehouse and a logistics warehouse, and integrating key data entities of a safety tool verification system, a scheduling equipment management system and a work order management system; a unified data format and a data mapping rule are defined, and standardized interaction of cross-system heterogeneous data is achieved. According to the method, standardized real-time synchronization of cross-system data is realized by constructing a power professional warehouse data graph and relying on an internet of things entity ID active synchronization engine; by using a multi-dimensional rule engine, based on a professional warehouse type-material attribute-business scene three-dimensional rule base, accurate management and control are provided for warehouse-in, warehouse-in, warehouse-out and exception handling of materials, and the asset utilization efficiency is effectively improved.

The invention relates to a CNC machining material unified management system based on intelligent early warning and multi-dimensional fusion, and belongs to the technical field of intelligent manufacturing and industrial informatization. The invention aims to solve the technical challenges in the existing CNC processing material management. The system adopts a three-layer architecture, comprises a data layer, a business logic layer and an application layer, and realizes the functions of unified modeling, classified management, batch tracking, inventory early warning and the like of material data. Through the intelligent tool breakage prediction module, the two-stage position management module, the intelligent inventory prediction module and the like, the system can automatically identify tool breakage abnormity, optimize the storage position, predict the inventory depletion date and generate intelligent replenishment suggestions. The system improves the management efficiency, reduces the inventory cost and the stockout risk, is suitable for the fields of intelligent manufacturing and industrial informatization, and meets the requirements of digital transformation of enterprises.

The invention discloses a power distribution network construction material demand analysis and prediction method, which comprises the following analysis and prediction steps: S1, collecting historical material data of a power distribution network construction project, the historical material data comprising project attribute data and material usage data; s2, preprocessing the material consumption data, including data cleaning, standardization and normalization processing; s3, carrying out dimension reduction processing on the material data based on a principal component analysis method; s4, performing clustering analysis on the material project attributes based on a clustering analysis algorithm; s5, parameters of the extreme learning machine are optimized based on a particle swarm algorithm, the optimized extreme learning machine is adopted to construct a power distribution network construction material demand prediction model, and the number and amount of power distribution network construction material demands are predicted; the system can effectively improve the material management level of a power enterprise, and improves the efficiency and quality of a material demand plan.

The invention relates to the technical field of production management, and discloses a material data change management method, device and equipment and a medium which are applied to a manufacturing execution system.The method comprises the steps that planned order material data of a current vehicle is acquired; based on the planned order material data, determining a material use quantity required by production of each station, and eliminating missing material data to generate processed material data; comparing the plan order material data with the processed material data to generate a loading difference list according to a comparison result; and updating the planned order material data according to the loading difference list to obtain target order material data. According to the technical scheme provided by the invention, accurate material breakpoint control can be realized, so that material change, trial assembly and production plans are kept consistent.

The invention belongs to the technical field of automobile manufacturing logistics, and particularly relates to an automobile part tray stowage optimization method based on a genetic algorithm, and the method comprises the steps: 0, obtaining material demand data; the method comprises the steps that 1, all materials belonging to the same supplier and sent to the same factory crossing are merged into independent material groups; step 2, for each material group, calculating the minimum vertical stacking column number of each material; step 3, taking the first-level required materials in each material group as loading objects, and optimizing a loading scheme by using a genetic algorithm to obtain a first-level material loading scheme; step 4, for one material group, acquiring all secondary demand material data, and performing ascending order arrangement according to demand time; then, the sorted secondary materials are traversed in sequence, and whether the secondary materials can be filled into the remaining position of the tray or not is judged; and a demand optimization loading scheme of the material group is obtained. According to the method, collaborative optimization of tray space utilization rate maximization and tray use number minimization can be realized.

The invention provides a multi-algorithm fusion-based water-based scratch-resistant plastic gloss oil formula optimization method, which comprises the following steps of: obtaining coating component data and environment variable data from a preset material database, and constructing an initial data set; performing crossover mutation operation on the initial formula parameters by adopting a genetic algorithm to generate a formula candidate set; according to the component proportion in the formula candidate set, obtaining a hardness index and a flexibility index through simulation, and performing grouping processing on the candidate set to obtain a grouped candidate set; for the grouped candidate set, adopting a support vector machine to carry out classified training on flexibility indexes of the high-hardness group, extracting performance deviation characteristics, and determining an adjustment vector; the formula parameters of the low-hardness group are corrected according to the adjustment vector, and the scratch resistance simulation data and the bending resistance simulation data are fused to obtain a corrected formula set; the coating performance under the environment variable change is sampled by adopting Monte Carlo simulation, performance fluctuation distribution is extracted, and an optimization formula and a process combination are determined.

The invention discloses an intelligent material information management method and device based on LangGraph and multiple agents. The method comprises the steps that an unstructured material description text input by a user is acquired; calling a preset LangGraph state diagram to drive a plurality of agents to perform feature analysis, attribute extraction, multi-level category classification and attribute standardization on the unstructured material description text according to a preset workflow; and obtaining multi-level category analysis information and standardized attribute data of the material to form structured material description data, and storing the structured material description data. According to the method, the LangGraph state diagram technology is innovatively adopted to construct a multi-agent processing assembly line, all agents achieve information transmission and cooperative work by sharing the SkuState state, and automatic structured processing, intelligent classification and standardized management of material data are achieved.

The invention relates to the technical field of Internet of Things, and particularly discloses an Internet of Things-based engineering construction progress dynamic regulation and control method and system, which automatically collects personnel, machinery and material data of a construction site through Internet of Things sensing equipment, replaces traditional manual recording, solves the problem of data islands, and improves the working efficiency. The method comprises the following steps: calculating a human influence factor based on a personnel state, quantifying configuration rationality and working efficiency, calculating a mechanical influence factor according to mechanical operation efficiency and a safety state, evaluating comprehensive performance of equipment, obtaining a material influence factor through material supply and inventory data, and monitoring supply timeliness and inventory stability. The three are fused to obtain a construction progress comprehensive deviation index, if the construction progress comprehensive deviation index exceeds a preset range, an adjustment instruction is automatically generated, resource allocation is dynamically optimized, real-time diagnosis and intelligent regulation and control of progress management and control are achieved, and the problem of traditional management lag is effectively solved.

Method for detecting and evaluating an event during the operation of a sorting plant, wherein the method steps are executed on a monitoring system for machine parameters and / or material data streams of the sorting plant such that the monitoring system continuously records sensor data from aggregates and / or sensor data from machines and / or sensor data from machine components and / or sensor data from material streams 2; such that, after the sensor data of at least one sensor fulfills a detection criterion 5 defined for the respective sensor, in particular a defined threshold value, a timestamp is set in the memory, an event criterion is created, the existing sensor data remain stored assigned to the event criterion, and a pattern recognition routine is started,wherein the sensor data acquired from the timestamp onwards in a time interval of a predetermined duration are evaluated by means of the pattern recognition routine in such a way that a confidence value of 5 is repeatedly determined, and / or in such a way that, in the time interval, a decision is repeatedly made on the basis of the sensor data and / or on the basis of the currently determined confidence value of 5, such that a fault message is assigned to the event criterion and this is sent to at least one fault reporting point 13, 15, 16 or such that an event message is assigned to the event criterion and this is sent to a reporting point and the pattern recognition routine is executed again or such that the pattern recognition routine is aborted 14.,

The invention provides a cloud chain collaborative material intelligent management system for small and medium-sized enterprises, and relates to the technical field of intelligent management, material data of different enterprises are collected and stored in an isolated manner, corresponding labels are given to different materials which are scanned to be warehoused, and the collected material data and the given label data are encrypted and transmitted to a cloud chain collaborative layer; the cloud chain collaboration layer carries out structured storage on the received material data and label data, and integrates private cloud, alliance chain and differential privacy gateway technologies to realize data non-sharing collaboration; the intelligent application layer constructs a demand prediction model to predict material demands, generates a purchase list according to a demand prediction result in combination with an actual inventory condition, carries out purchase based on the purchase list, and carries out the lightweight collaborative application of edge calculation, block chains and federated learning technologies in the material management of small and medium-sized enterprises. The problems of data islands, low collaboration efficiency, high deployment cost and the like in material management are solved.