412 results about "Product data" patented technology

The invention relates to the technical field of data processing, in particular to a product package online design display method and system. The method comprises the following steps of obtaining packaging target product data and determining the outer contour of a product, reconstructing a simulation to-be-packaged product frame, obtaining a packaging leakproofness requirement and estimating a packaging use scene, generating packaging process constraint data, analyzing entity packaging constraints, and fusing the packaging constraint data to support packaging process simulation. The method comprises the following steps: generating simulated product packaging data, constructing a packaging demand reverse environment field according to a leakproofness demand, carrying out leakproofness re-measurement on the simulated product packaging data, generating packaging design leakproofness data, carrying out packaging backtracking optimization through the data, finally obtaining qualified product packaging design data, and carrying out online visual display. According to the invention, efficient and intelligent product package online design and display are realized.

The invention discloses a product data question and answer method based on a large model and mixed retrieval and related equipment. The method comprises the following steps: constructing a double-track knowledge base based on multi-source heterogeneous product data; performing multi-dimensional analysis on the current query statement in combination with the large model to obtain a multi-dimensional analysis result; the multi-dimensional analysis result comprises a query vector, a structured query instruction and a keyword; inputting the query vector into a vector database for retrieval to obtain a vector retrieval result; inputting the structured query instruction into a graph database for retrieval to obtain an entity relationship retrieval result; inputting the keyword into the text knowledge fragment for keyword matching to obtain a keyword retrieval result; and combining the vector retrieval result, the entity relationship retrieval result and the keyword retrieval result, and sorting and screening the combined mixed retrieval result through a large language model to obtain target answer data. The method can significantly improve the accuracy and reliability of the retrieval result, and can be widely applied to the technical field of artificial intelligence.

One variation of a method includes: accessing an image of an inventory structure captured by a robotic system while navigating through a store; detecting a slot in the inventory structure in the image; based on features extracted from the image, identifying a set of product units of a first product type occupying the slot; accessing a target product type assigned to the slot by a store representation; based on features extracted from the image, identifying an electronic shelf label depicted in the image, corresponding to the slot, and advertising the target product type; and, in response to the first product type differing from the target product type assigned to the slot, accessing a set of product data corresponding to the first product type from a product database, and, transmitting the set of product data to the electronic shelf label for rendering within an electronic display of the electronic shelf label.

The invention discloses an intelligent stationery supply chain optimization system based on big data analysis, belongs to the field of big data analysis, and aims to solve the problem of how to construct a high-precision demand prediction model through real-time and accurate data acquisition and processing to quickly identify and deal with abnormal conditions. The system comprises a data acquisition module, a storage management module, an intelligent prediction module, a mining analysis module and a supply chain cooperation module. The Internet of Things automatically collects supply chain product data, uploads the data to the cloud platform, stores the data in a database after cleaning, and classifies and adaptively compresses the data according to access evaluation indexes. A demand prediction model is constructed based on historical data, periodical and triggered updating is carried out, and the order demand of a product is predicted. According to the method, historical data with frequent access is screened for abnormal mode identification and reason classification, dynamic adjustment and collaboration of supply chains are realized through an information sharing platform, inventory purchase is optimized, and decision support is provided for abnormal modes.

The invention relates to an e-commerce cold start commodity sales prediction method and device based on big data, equipment and a medium, and the method comprises the steps: constructing a multi-source heterogeneous data set through integrating internal data of an e-commerce platform and competing product data of a third-party platform, and generating standardized commodity attributes and a cross-platform association relationship through cleaning and category mapping; constructing a knowledge graph based on commodity attributes and supply chain features, and extracting vectorization features representing commodity semantic association; fusing the knowledge graph and the cross-platform data to generate a virtual historical sales volume sequence of the target commodity; and the confidence coefficient of the virtual sequence is dynamically corrected, and the multi-modal fusion prediction of the time sequence model and the structured feature model is combined, so that high-precision sales volume estimation can be realized. According to the method, dependence of a traditional method on historical data is broken through, multi-platform heterogeneous information is effectively utilized, prediction robustness in a cold start scene is improved through a dynamic optimization mechanism, and accurate inventory management and marketing decision support is provided for e-commerce enterprises.

The invention relates to a dynamic control method and system for elastic sheet punch forming in the field of high-end equipment manufacturing, and the method comprises the steps: obtaining optimized pressure distribution data, and calculating the punching speed adjustment amount of each region in combination with a preset model of the impact of the punching speed on the deformation of an elastic sheet, thereby obtaining a speed distribution adjustment matrix; the stamping speed data and the pressure distribution data which are adjusted in real time are adopted to drive stamping equipment to execute dynamic stamping operation, and the size and performance measurement data of the formed elastic piece are obtained; obtaining the size measurement data of the formed elastic sheet, and scanning the surface of the elastic sheet through a high-precision three-dimensional scanner to obtain an elastic sheet size deviation distribution matrix; and the updated stamping process parameters are adopted, stamping operation is circularly executed, dimensional deviation and performance consistency data are continuously collected, and finally elastic piece product data meeting the precision requirement are obtained.

The invention discloses a product image-text and document association identifier generation method fusing a PLM coding rule, relates to the technical field of product data management, and is used for solving the problem that an image-text and document association identifier is not clear. According to the method, document information vectors are constructed by fusing PLM coding rules and collecting document metadata of design drawings, process documents, quality management and bill-of-material systems, an image-text document structure mapping chain is generated through a structure mapping judgment model, a unique association identifier of the image-text document is generated in combination with a preset template, and the unique association identifier of the image-text document is obtained. And real-time updating is carried out during structure replacement, hierarchical reconstruction or configuration change, cross-system serial number semantic alignment is realized through a heterogeneous coding analysis rule base, heterogeneous naming conflicts are solved, identifiers are written into a product data management platform, an index relationship is established, hierarchical retrieval, version switching and historical backtracking are supported, and the product data management efficiency is improved. And the consistency and cooperation efficiency of product research and development and quality control are improved.

The invention discloses a storage management method, system and device of a goods frame and a medium. The method comprises the steps of obtaining first product data of a first waste electronic product in a to-be-stored goods frame, second product data of a second waste electronic product in a target goods frame on a target goods shelf, environment data of the target goods shelf and to-be-stored position data of each to-be-stored position in the target goods shelf; generating a first risk category label based on the first product data, and generating a second risk category label based on the second product data; determining a storage conflict index of the to-be-stored goods frame and the target goods frame; calculating a storage adaptation score of the to-be-stored goods frame at each to-be-stored position and determining a to-be-stored position set; determining a storage and transportation path set of each to-be-stored position; determining an electrostatic induction interference intensity distribution sequence and a magnetic interference intensity distribution sequence of each storage transportation path; and determining a target storage and transportation path of the to-be-stored goods frame. The reliability of goods frame storage can be improved.

The invention relates to an intelligent mold calibration optimization method based on big data analysis. The method comprises the following steps: firstly, collecting current state data of a mold, including shape, size, wear degree and production environment parameters, and simultaneously obtaining ideal state data of a product; and constructing a mold-product correlation model by utilizing a physical simulation algorithm, and simulating a product forming process under different production parameters. Key control parameters are determined based on simulation results, the parameters are optimized through a machine self-learning algorithm, and an intelligent calibration optimization strategy is generated. Actual product data is collected in real time and compared with ideal state data, a product data difference value is generated, and an optimization strategy and key control parameters are dynamically adjusted according to the product data difference value. And finally, the production parameters of the mold are automatically adjusted according to the optimization strategy, intelligent calibration optimization is achieved, and the product quality and the production efficiency are improved.

A system for receiving a product dataset associated with a product comprising one or more subproducts, a first corresponding product dataset associated with a first corresponding product, and a second corresponding product dataset associated with a second corresponding product. A system may determine a product model output corresponding to the product, wherein the product model output is a single, value-weighted indicator of the product. A system may determine a first variability index corresponding to the first corresponding product based at least on the product model output and the first corresponding product dataset, and a second variability index corresponding to the second corresponding product based at least on the product model output and the second corresponding product dataset. A system may display a visual indicator on a graphical user interface the first variability index and the second variability index.

A system for generating a bill of materials comprising one or more product recommendations aligned with customer requirements, comprises a bill of materials processing device. The bill of materials processing device is configured to execute an AI-based recommendation engine. The recommendation engine is configured to receive customer data from one or more user inputs, retrieve product data from a product data source, and retrieve transaction data from a transaction data source. The recommendation engine includes a pre-processing module, training module, and inference module. The pre-processing module executes one or more pre-processing techniques to translate the data into a program-compatible format. The training module executes training and inference processes to generate the bill of materials. The training processes put the data in better condition for the inference module. The inference module uses two or more product recommendation processes to evaluate the data and to generate the BOM in a machine-readable format.

The disclosed embodiments include computer-implemented systems and processes that provision, in real-time, targeted digital content associated with an initiated data exchange based on structured messaging data. For example, an apparatus may receive a message associated with a real-time payment requested from a first counterparty by a second counterparty. The message may include elements of message data disposed within corresponding message fields, and based on at least the elements of message data, the apparatus may generate product data characterizing a product available to the first counterparty and transmit first notification data including digital content associated with the available product and with the data exchange to a first device operable by the first counterparty. Based on response data generated by the first device, the apparatus may perform operations that provision the available product to the first counterparty in accordance with the product data.

An apparatus for performing product matching may include: a processor configured to receive a search as product item for searching for a catalog product that matches a target product; classify the target product in a product taxonomy tree including a plurality of taxonomy nodes, by identifying a taxonomy node to which the target product belongs, among the taxonomy nodes; obtain product data associated with the target product from an internal source and an external source and measure data quality; extract attributes from the product data based on a machine learning model; validate the attribute in response to the attribute corresponding to defined mandatory attribute of the taxonomy node to which the target product belongs, and provide a search result based on the validated attributes; re-rank searched results governed by relevancy score and display matched set of products to the customer above defined re-rank confidence score.

A system is provided.SOLUTION: A system, comprising: means for inputting height data; means for uploading a whole body image; means for selecting product data; means for transmitting the height data and the whole body image to a server; means for generating a list of available products and transmitting the list to a terminal; means for transmitting data of a selected product to the server; means for synthesizing an image of the product with the whole body image at an appropriate size and position to generate a fitting image; and means for transmitting the generated fitting image to the terminal.SELECTED DRAWING: Figure 1

The invention discloses a project and intellectual property achievement association evaluation method and system, and relates to the technical field of evaluation methods, and the method comprises the following steps: S1, data acquisition: obtaining project data, intellectual property achievement data and product data; s2, association model construction: constructing a project-intellectual property achievement-product association model based on the project data, the intellectual property achievement data and the product data; s3, matching degree calculation: calculating a multi-dimensional matching degree between the project and the intellectual property achievement based on the association model; s4, performing dynamic correction; and S5, value evaluation. According to the method, through quantitative analysis of a claim modification track in the review opinion reply process, especially the core index of the calculation technology abandon-retention ratio, patents which are subjected to large limitation shrinkage and of which the core protection range is seriously shrunk can be effectively discriminated, and association degree overestimation caused by neglecting the review game process is avoided.

The disclosed technology includes a computer-implemented technique for generating images of synthetic products that are manufacturable as physical products is disclosed. The system receives user input describing a conceptual product via a user interface, including text and / or image data. Based on the input, a manufacturer-specific or general generative model is selected and provided with a text-based prompt derived from the conceptual product description. The model generates one or more images of synthetic products, each conforming to manufacturing constraints. The images are presented to the user, each optionally associated with pricing and delivery information, and represent products that are manufacturable and acquirable via the interface. The system may incorporate manufacturability analysis, quality assurance, and model training using user feedback and product data. The invention further encompasses non-transitory storage media and systems implementing these methods for on-demand product visualization and acquisition.

The invention discloses a product data real-time monitoring and analysis method based on a commodity bar code, and a management end supervises the possible risk of a target product stored at a sales end based on a meteorological condition. And if factors possibly causing risks to the storage environment appear before the target product is delivered out of the warehouse, an alarm is given to the sales end to prompt the sales end to process the target product, so that further loss caused by blind flow into the market is avoided. Moreover, according to the method provided by the invention, a sales code instead of a preset code is adopted when the target product is supervised, so that even if an attacker wants to tamper the sales record of the target product so as to miss the target product with risks, the attacker can be perceived by the management end, and risk management can be realized. Therefore, according to the method provided by the invention, the product data real-time monitoring analysis based on the commodity bar code can be realized by adopting a data processing technology with a management, supervision or prediction purpose.

Provided herein are systems, methods, and articles for generating and handling units or records derived from non-commodifiable work products, and the like. The disclosure provides for generating a certification record, including receiving work product data and generating a digitized record derived from the work product data.

A computer system extracts product data from a website and correlates product records from multiple sources to one another as corresponding to the same product. A website is crawled efficiently by rendering webpages using a virtual browser that ignores blacklisted elements, extracts data from objects without rendering, and suppressing retrieval of remote resources. Data is extracted according to engine control statements including a selector and extractor. A website may be crawled repeatedly and changes in extracted data may be detected and flagged. Engine control statements may be automatically changed in response to detecting a change in the configuration of the website. Images of product records may be correlated with one another by first comparing text of the product records and selecting images for comparison based on composition. Images are compared using a machine learning model. Images determined to be similar may be presented to a human for a correlation decision.

The invention discloses an e-commerce platform cross-channel data fusion and analysis optimization method. User behavior data, transaction data, commodity data and market information are collected in real time from multiple channels (such as official websites, mobile applications, social media and offline stores); performing standardization processing, abnormal value detection and missing value completion on the acquired data, and expanding small sample data by using a data enhancement technology to improve data quality; and encrypting the sensitive data. Through a self-attention mechanism and a deep learning method, correlation and features among different channel data can be accurately captured, so that a more comprehensive and efficient user portrait is generated. The analysis result after data fusion is more accurate, a reliable basis can be provided for platform decision making, the differential privacy technology and the encryption protocol are adopted, and the safety of user personal information and sensitive data is effectively guaranteed. The user trust of the platform can be improved, and the legal compliance of the platform in multiple regions can be ensured.

The invention discloses a discrete manufacturing multi-view BOM dynamic derivation method and system based on a tree-shaped network structure, and belongs to the technical field of intelligent manufacturing and product data management. The method comprises the following steps: constructing a tree body as a single product data source, and storing a hierarchical relationship, attribute definition and engineering semantics of a product in a tree structure; based on the tree body, dynamically matching derivation rules through a mesh mapping relation library; executing rule matching, semantic verification, structure reconstruction and dictionary verification by utilizing a dynamic view derivation engine to generate a target BOM view; semantic constraint and verification are carried out on the derivation process through a BOM data dictionary module; and realizing access control and audit based on role, scene and data sensitivity through the security and authority control module. According to the method, a new generation of xBOM dynamic derivation architecture which takes a tree form as an ontology, takes a net form as mapping, is configurable in rule and is strong in data specification is constructed; the fundamental problem of discrete manufacturing multi-view BOM management is solved, and engineering rationality and service flexibility are both achieved.

The invention discloses a device and method for overturning and quality inspection of multi-specification rectangular tubes, and the method comprises the steps: constructing a complete square tube precise positioning and overturning detection system through the cooperative work of a square tube automatic grabbing workstation, an overturning device, an end part limiting device, a jacking centering device, a laser detection system and a visual detection system; according to the system, full-automatic grabbing, precise positioning and posture adjustment of the square pipes of multiple specifications can be achieved, meanwhile, comprehensive collection and analysis of product data are completed, and therefore the quality inspection efficiency and the detection precision are remarkably improved, and a scientific basis is provided for production process optimization. The technical bottleneck of existing equipment in square tube quality inspection and production is overcome, the universality, the automation level and the detection capacity of the equipment are improved, and the high-standard requirement of the building manufacturing industry for the quality of components can be met.

The application provides a financial product data pushing method, device and equipment and a storage medium, and belongs to the technical field of big data. The method comprises the following steps: obtaining user basic data, obtaining product attribute data, obtaining graph feature data, determining graph modeling data, determining ontology domain objects, and determining ontology domain objects. Ontology extraction processing is performed on the graph modeling data according to the ontology domain objects, and ontology concept data is obtained; instance data is obtained from pre-stored product data; resource description framework data is obtained; a first knowledge graph is generated; a target learning model is constructed according to a pre-built multi-relation co-occurrence matrix and a pre-stored graph convolutional neural network model; a second knowledge graph is generated; a target recommendation model is generated; target user data is input into the target recommendation model, and target financial product data is generated; and the target financial product data is pushed to a target user terminal. The application improves the accuracy and interpretability of the pushed financial product data.

The application discloses a warehouse partition storage management method based on warehousing product data and relates to the technical field of warehouse management.The application first divides the warehouse into three virtual functional areas according to a virtual partition ratio, binds virtual attribute parameters to storage locations in each virtual functional area, then extracts product turnover attribute data to be warehoused, matches a corresponding target functional area by means of an intelligent model, and finally sorts product sequences in the target functional area according to warehousing time, and sequentially completes target location allocation.The application discards the traditional fixed physical partition mode, realizes dynamic division of logical partitions without changing the hardware layout of the warehouse, combines location turnover scoring to complete partition classification, matches high turnover goods with the storage location with the highest operation efficiency, relies on an artificial intelligence model to analyze product turnover data to replace artificial experience to determine partition attribution, greatly improves matching accuracy, and allocates locations based on warehousing time to fully utilize idle locations released after delivery and revitalize warehouse resources.

The invention belongs to the technical field of automation control, and particularly relates to a temperature and humidity control method and system for a board card processing environment, and the method comprises the steps: obtaining process scheduling information, material sensitivity information of a product database, and a real-time environment measurement value of a sensor; calculating a process transition index representing a process smooth transition state based on the process scheduling information, calculating a product sensitivity correction factor based on the material sensitivity information, and carrying out conditional fusion on the process transition index and the product sensitivity correction factor to generate a dynamic smooth coefficient; predicting an environment value of the next moment based on the dynamic smoothing coefficient; and performing feedback control based on the predicted value. According to the invention, smooth transition and differential fine control of the control strategy are realized, and the stability of the production process and the product quality are improved.

A processor-implemented method includes receiving, at a processor, from a transceiver and without human intervention, location data associated with a target. The processor receives, from the transceiver, product data for a resupply request referencing the target, the product data including at least one of a product type or a product quantity. The processor generates, without human intervention, unmanned autonomous vehicle (UAV) mission data based on the location data and the product data, the UAV mission data including a representation of at least one UAV and flight path data for the at least one UAV. The UAV mission data is caused to transmit to at least one UAV controller to cause the at least one UAV controller to initiate navigation of the at least one UAV according to the UAV mission data.

This disclosure relates to methods, apparatuses, systems, and the like for determining a product data set for producing a product based on the product data set using history data sets. The disclosure may contribute to the united nation's sustainable development goals of "industry, innovation, and infrastructure", "responsible consumption and production".

A method for determining at least one operation to be performed by at least one robot for machining at least one component is provided. An electronic computing device determines product data which characterizes the at least one component. The electronic computing device determines system data which characterizes a system including the robot for performing the at least one operation. The electronic computing device generates a simulation model that simulates the system and the at least one component based on the product data and the system data. The electronic computing device performs a simulation by way of the simulation model, as a result of which the electronic computing device determines process data describing multiple step groups, each of the step groups comprising multiple sub-steps of the at least one operation which differ from one another.

The invention provides a heterogeneous data source processing method and apparatus, and an electronic device. The method comprises the steps of accessing a relational database corresponding to a heterogeneous data source; mapping the physical table in each data source into a business-oriented logic data model; based on the logic data model borne by each product, defining an association relationship, a service capability parameter and a business rule for a main table and a plurality of levels of sub-tables of a group of hierarchical association data structures corresponding to each product; generating a user interaction interface matched with each product in combination with the logic data model and the association relationship of each product, the service capability parameter and the business rule; associating the logic data model of each product with a user interaction interface based on a business topic, and providing a visual arrangement canvas to define an association configuration relationship between product data from different data sources; and weaving the cross-product data configured based on the association configuration relationship into a unified virtual data view, and packaging the virtual data view into callable data service.

We provide the system. [Solution] A means of collecting product data from multiple commercial facilities, A method for predicting demand using machine learning techniques, A means for optimally allocating goods among multiple commercial facilities based on predicted demand, A means of monitoring and updating information on changes in product inventory in real time, A means of analyzing user purchasing behavior and formulating sales strategies, A means of providing information on product inventory status within commercial facilities via a smartphone app, A system that includes means for instructing commercial facilities to replenish their stock based on demand forecasts that take weather information and event information into account.