574 results about "Product data" patented technology

The disclosed embodiments include computer-implemented systems and processes that generate and provision, in real time, directed digital content based on decomposed structured messaging data. For example, an apparatus may receive a plurality of messages that characterize first data exchanges initiated between a first counterparty and second counterparties during a first temporal interval. Each of the messages includes elements of message data associated with a real-time payment requested from the first counterparty by a corresponding one of the second counterparties. Based on the elements of message data, that apparatus may predict an occurrence of a second exchange of data that involves the first counterparty during a second temporal interval, and may transmit notification data that includes product data characterizing an available product associated with the predicted occurrence of the second data exchange to a device operable by the first counterparty for presentation within a digital interface.

The invention relates to the technical field of data processing, and discloses a block chain-based green product authentication traceability system. The system comprises a data acquisition module for acquiring product full-life-cycle green attribute data by using a multi-source sensing device; the block chain evidence storage module is used for hierarchically encrypting the data based on a hybrid hash algorithm to generate a non-tampering account book record; the intelligent contract auditing module automatically generates a green authentication result through a multi-rule verification model; the traceability query module is used for constructing a privacy protection query channel to output a verifiable traceability chain by applying a zero-knowledge proof technology; and the dynamic optimization module is used for adjusting the authentication standard according to the environmental policy change. According to the system, comprehensive acquisition, safe storage, accurate authentication, reliable tracing and standard dynamic optimization of green product data are realized, the authentication credibility and the supervision efficiency are improved, and the development of the green industry is promoted.

Systems and methods for providing item recommendations based on dual models with different levels of product data granularity are disclosed. In some embodiments, a disclosed method includes: receiving, from a computing device, a recommendation request for recommending items to a customer; determining, based on the recommendation request, at least one anchor item to be displayed to the customer; obtaining a first machine learning model trained based on a first product data granularity; obtaining a second machine learning model trained based on a second product data granularity; generating, using the first machine learning model and the second machine learning model, a ranked list of recommended items based on the at least one anchor item; and transmitting to the computing device the ranked list of recommended items to be displayed to the customer with the at least one anchor item.

The invention discloses a three-stage trigger type carbon emission factor dynamic updating system and method, particularly relates to the technical field of carbon emission management in an industrial flexible manufacturing process, and is used for solving the problem of confusion and distortion of carbon emission data of mixed products in an existing fixed period updating mode. Accurate product-level carbon accounting is realized through a three-level trigger mechanism. Firstly, energy consumption data are aggregated in real time based on a spatial geographic boundary to generate a spatial dimension carbon emission factor; when the space factor change rate exceeds a dynamic threshold value, triggering a product data redirection operation; detecting a product type change event to obtain a material code and a process route; constructing a virtual data acquisition domain and generating a dynamic membership matrix; shunting the redirected data to a virtual domain according to a matrix, and freezing abnormal data at the same time; and finally, outputting a dynamic carbon emission factor through event-triggered emergency calculation and periodic iteration double-track updating, and realizing accurate traceability of the product-grade carbon footprint in a flexible manufacturing environment.

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 relates to the technical field of intelligent optimization, and discloses a 3D printing powder proportion optimization method and system based on artificial intelligence. The method comprises the following steps: collecting historical data corresponding to a powder material based on a printing process of a 3D printed finished product, and performing multivariate analysis on the historical data to generate a plurality of powder compositions; the material simulation model is used for carrying out property simulation on any powder composition, outputting material property data and extracting a powder ratio meeting the printing requirement; the finished product prediction model performs finished product prediction on any powder ratio, outputs a finished product prediction result, performs matching optimization on the finished product prediction result and ideal finished product data, and screens out an optimal powder ratio and a corresponding optimal printing process; and performance testing is conducted on the actual printing finished product, if the actual printing finished product does not meet the requirement, the step is executed repeatedly, and a new optimal powder proportion and a new optimal printing technology are screened out again. According to the method, the precision of 3D printing powder proportion optimization is improved.

The invention relates to the technical field of deep learning, provides an optimization method of a defect detection model and defect detection equipment, and can be used for industrial quality inspection. According to the method, at least one to-be-detected product data set in production is used for carrying out multi-round iterative optimization on a defect detection model after pre-training, the defect that traditional model training is isolated from a production line environment is overcome, and closed-loop iterative detection and optimization of the model are achieved. In each optimization process, confidence coefficient learning and active learning are combined, the uncertainty of a model is quantified through confidence coefficient learning, a global confidence coefficient threshold value is determined according to the confidence coefficient corresponding to the defect type of each piece of to-be-detected product data, and the threshold value is dynamically adjusted by using the recall rate and the number of optimization times. And during active learning, sample data screening is carried out by using the adjusted global confidence threshold, so that blind labeling or over-fitting labeling is avoided, the quality of the screened samples is ensured, and the accuracy and generalization of a retraining defect detection model are improved.

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.

The embodiment of the invention provides a marketing short video generation method and device based on a large language model, and the method comprises the steps: carrying out the matching degree analysis of a hot title and product data based on the large language model, and outputting a hot topic which is most matched with a product according to the sequence of the matching degree from high to low; searching a hot video related to the most matched hot topic in a video platform, and extracting an oral copy in the hot video based on an automatic voice recognition technology; generating a creation copywriting by using a large language model based on the oral copywriting, the hot topics and the product data; and performing similarity matching on the embedded vector corresponding to the created copywriting and the semantic vector of the video slice in the database, and sorting and generating a mixed video meeting the duration requirement according to the similarity.

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 relates to the technical field of product data management, in particular to a food package printing material batch traceable management method and system, and the method comprises the following steps: collecting the production time of each production batch, constructing time data which comprises year, month, day, hour, minute and second, and creating a time code based on the time data; the time code is combined with the data of the production batch. According to the method, the time data of each production batch, which is accurate to second, is collected, the unique time code is constructed, and the time code and the batch production data are closely combined, so that a traceable time stamp data packet is formed, accurate correspondence between production information and time is ensured, and more efficient information integration and storage management are realized. In the storage process, classification and indexing are carried out in combination with batch time marking data packets, a data retrieval mechanism capable of rapidly responding is established, the information positioning speed and accuracy are improved, and the efficiency and reliability of batch query and tracking are improved.

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.

An apparatus for the identification of dynamic sub-targets is disclosed. The apparatus includes a processor and a memory communicatively connected to the processor. The memory instructs the processor to receive a plurality of entity data comprising a plurality of product data. The processor identifies one or more static targets as a function of the plurality of entity data. The processor identifies a first set of dynamic sub-targets as a function of the one or more static targets and the plurality of product data. The memory instructs the processor to iteratively determine a static target status as a function of the first set of dynamic sub-targets and the one or more static targets. The processor identifies a second set of dynamic sub-targets as a function of the static target status. The processor generates a target report as a function of the static target status and the second set of dynamic sub-targets.

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.

The invention discloses an intelligent recommendation method, apparatus and device, and a storage medium. The method comprises the steps of obtaining historical user data and product data corresponding to a diagnostic products; according to the b preset use scenes and the historical user data, constructing a historical user portrait; b is a positive integer; determining a recommendation rule according to the c preset user demands, the product data and the historical user portraits; according to product data, historical user portraits and recommendation rules, the a diagnosis products are classified, and a classification label sets are obtained; obtaining interaction content between the user and a preset large language model; determining user preference information according to the interaction content; determining a target classification tag set from the a classification tag sets according to the user preference information; and outputting related recommendation content of the target diagnosis product corresponding to the target classification label set to the user through a preset large language model. The product recommendation method and device are beneficial to improving the product recommendation accuracy.

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.

Systems such as computer-aided design, visualization, and manufacturing (“CAD”) systems, product lifecycle management (“PLM”) systems, product data management (“PDM”) systems, and similar systems, all manage data for products and other items (collectively, “Product Data Management” systems or PDM systems). More specifically, in such systems the enablement of an industrial manufacturing environment for detecting possible collisions of movable objects in an industrial manufacturing environment is necessary.

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 relates to the technical field of rubber corrugated pipe processing quality detection. The invention relates to an effect evaluation method for processing quality detection of a rubber corrugated pipe. The method comprises the following steps: S1, acquiring process parameters of rubber corrugated pipe production equipment; s2, acquiring historical production data, performing finished product data screening on the historical production data in combination with the process parameters, and establishing a finished product database; s3, collecting a finished product image of the rubber corrugated pipe, and performing similarity matching analysis on the finished product image and the finished product database; according to the method, analysis and decision making are carried out based on a large amount of production data, errors caused by subjective judgment are avoided, in the abnormal scoring link, weights are distributed according to the importance degree of difference characteristics, the quality problem is quantified, data support and a reasonable detection strategy are provided for quality detection and improvement, high-risk products are detected preferentially, and the detection efficiency is improved. Unnecessary detection cost is reduced, and reworking and scrapping cost caused by unqualified products is reduced at the same time.

A method includes: receiving product data associated with a product; identifying at least one brand characteristic of a brand associated with the product, based on the product data; identifying at least one attribute of the product based on the product data; generating an attribute insight output based on at least the at least one attribute of the product; receiving demographic data associated with the product; generating a demographic insight output based on at least the demographic data associated with the product; receiving a suggested strategy output, wherein the suggested strategy output is generated based on, at least, the attribute insight output and the demographic insight output; generating a content brief output based on the suggested strategy output; and providing, at a display, an actionable output that includes at least the content brief output.

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.