1058 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.

A checkout terminal is provided. The checkout terminal comprises a camera array, a display device, a weight scale, and a control circuit. The control circuit is configured to identify, based at least on images captured by the camera array, product identifiers associated with a plurality of different items placed in a placement area and on the weight scale, determine a combined weight of the plurality of different items based on product weight information stored in a product database, retrieve a weight measurement measured by the weight scale, detect for unaccounted items based on comparing the combined weight the plurality of different items and the weight measurement, and in the event that an unaccounted item is detected, display, via the display device, instructions to move one or more items to identify the unaccounted item.

The invention discloses a product traceability direction management and control system based on full-process monitoring, and relates to the technical field of product traceability management. The full-process management and control platform is in communication connection with a product data acquisition module, a product identification analysis module, a product data management module, a data traceability analysis module, a product abnormity management module, a process optimization module and an interaction management module. According to the product traceability direction management and control system based on full-process monitoring, through comprehensive monitoring and recording of raw material sources, production dates, processing details and quality inspection results, it is ensured that products meet preset quality standards in each production link, once any deviation is found, the system can immediately give out early warning, and the product traceability direction management and control system is convenient to use. In addition, a large amount of data is analyzed through a machine learning algorithm, key factors influencing the product quality can be recognized, and therefore a scientific basis is provided for quality improvement.

The invention discloses a high-precision industrial quality control system based on an AI large model and an application method, which are characterized by being applied to detection and measurement of industrial products. Comprising a product data acquisition module in an industrial production process, a data preprocessing module, an AI large model suitable for industrial quality control, a quality detection module, a result feedback and decision support module, a control module and a system management module, the application of the AI large model suitable for industrial quality control comprises the steps of constructing the AI large model suitable for industrial quality control, calling the AI large model suitable for industrial quality control, executing the control system, carrying out multi-modal data fusion, and carrying out multi-modal data fusion. The product data acquisition module in the industrial production process acquires data of multiple modes for quality detection and measurement, the AI large model suitable for industrial quality control adopts a data fusion algorithm, and the feedback module provides a visual detection result. And visually displaying the detected quality problem to a user in the form of an image and a chart.

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 discloses a squeeze casting forming quality detection device and system, and belongs to the field of quality detection.According to the shape and size of a squeeze casting, ultrasonic emission signal parameters are adjusted, and self-adaptive preprocessing is conducted on received ultrasonic echo signals; according to the method, data fusion and feature extraction are carried out on ultrasonic echo signals received by multiple channels, abnormal grid units are screened, the abnormal types of the abnormal grid units are identified through a deep learning model, then abnormal areas are divided, and the molding quality score of the extrusion casting part is obtained through comprehensive evaluation on the abnormal areas. Carrying out qualification judgment on the extrusion casting part; detection data is stored, managed and maintained, the detection data is classified, filed and indexed by constructing a product database, a squeeze casting quality tracing path is generated, and data backup and data recovery are regularly performed on the product database; the objective of the invention is to solve the problems of accurate and efficient quality detection and effective data management of extrusion castings.

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.

Apparatuses, systems, and methods to provider personalized online product fitting are disclosed. In a variety of embodiments, personalized shopping systems include an automated shopping assistant accessing product data, a matchmaking system accessing history data, preference data, and / or anatomical data measured using an automated shopping assistant apparatus, where the personalized shopping system can generate a personalized match based on the history data, preference data, and / or anatomical data. The automated shopping assistant apparatus may include depth sensors and / or image scanners which can capture a variety of 2D and / or 3D models. These models can be utilized to generate anatomical data. The anatomical data can be used to virtually try on a variety of items. Products may be personalized based on the history data, preference data, and / or anatomical data.

The invention discloses a BOM reconstruction method for large equipment manufacturing, and relates to the field of digitization. The BOM reconstruction method for large equipment manufacturing comprises the following steps: step 1, enterprise evaluation: according to the data size of enterprise development equipment, the enterprise scale, the enterprise supply management mode and the project division characteristics, evaluating an enterprise; and 2, according to the characteristics of different enterprises, dividing the construction paths into an EBOM-PBOM-MBOM construction path, an EBOM-MBOM construction path and an EBOM-PBOM construction path. According to the large-scale equipment manufacturing BOM reconstruction method, each enterprise plans and manufactures BOM according to the characteristics of developed product data size, enterprise scale, enterprise supply management mode and project division, and effective management of manufacturing end process data is realized by an optimal, most convenient and most economical process configuration management scheme; by taking effective management of product development process data as a starting point, a reconstruction mode and architecture of PBOM and MBON are planned.

Emission footprints can be calculated and provided for consumption by software applications. To do this, product data including a quantity of a product, components of the product, and activities for the product are obtained. Emission factor data for the components indicating a carbon dioxide impact of the components per unit are obtained. One or more emissions footprints are calculated per product, per material, and per activity based on a time period, the quantity, the components, the activities, and the emission factor data. Each of the emissions footprints indicates an amount of carbon dioxide per unit for a corresponding product, material, or activity. The calculated emissions footprints for the time period are provided via an application programming interface or as a published event.

The present invention relates to digital farming. In order to support seasonal recommendations and planning, an apparatus (10) for generating an application scheme for controlling a harmful organism on an agricultural field. The apparatus comprises an input unit (12), a processing unit (14), and an output unit (16). The input unit (12) is configured to receive information on an expected presence of the harmful organism for an upcoming period. The processing unit (14) is configured to determine a rough control schedule comprising a plurality of control time periods for controlling the harmful organism, based on the information about an expected presence of the harmful organism for the upcoming period; determine, based on product data that comprises information on a plurality of agricultural products, at least one program of control measures to be sequentially applied to the agricultural field to control the harmful organism at the plurality of control time periods, wherein each control time period is associated with at least one respective control measure selected from a chemical measure or a mechanical measure; and generate the application scheme that comprises the determined rough control schedule with the plurality of control time periods and / or the at least one program of control measures. The output unit (16) is configured to provide the generated application scheme.

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 data mining, in particular to a method and system for intelligently generating a sheet metal part process model. The method comprises the following steps: acquiring structural data of a sheet metal part processing workshop, and carrying out topological structure analysis to generate a processing technology model; acquiring design data of sheet metal parts, establishing a three-dimensional model, and performing machining simulation by using a process model to obtain simulation data; integrating historical processing finished product feature data, extracting surface features, and obtaining actual finished product data; finished product parameterization description is carried out on the machining simulation data, surface defects of a finished product are evaluated, and defect data are obtained; the simulation machining stress distribution uniformity is evaluated, and surface quality influence factors are calculated; and optimizing the processing technology model according to the quality influence factor, and uploading to a management platform to execute a simulation generation task. The accuracy of the sheet metal part process model can be 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.

System and method for context prediction via application-specific process-to-product causal networks for generating manufacturing insights. A process graph is obtained that comprises a set of data nodes, including operation specific process data nodes and product data nodes. A probabilistic graph model (PGM) is learned for the industrial process based on the process graph and historic process and product data collected for the industrial process in respect of the data nodes The PGM comprises a computed structure and a set of relationship parameters.

The invention discloses a full-life-cycle-oriented product management system and management method, and relates to the technical field of product management, and the method comprises the steps: dividing a product life cycle, and determining the product data management content of each stage; performing preliminary analysis on the product data in the cache by using an AI technology; according to a root node number and a version number of a product, combining with the preliminary analysis report, recursively obtaining product data of each sub-node, constructing a virtual simulation model by utilizing a digital twin technology and combining with a classification result, carrying out full life cycle tracking through a block chain technology, and carrying out detection and warning by utilizing an AI technology; when the life cycle of the product is finished, product data updating is stopped; according to the method, the product life cycle is divided, the data management content of each stage is clarified, and the AI technology and the digital twinning technology are introduced, so that automatic acquisition, anomaly detection and simulation modeling of the product data are realized, and the overall efficiency and accuracy of product data management are remarkably 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.

Systems, methods, and apparatuses for customer engagement that receive a product catalog including information associated with a plurality of products; encode product data by at least one of generating a reverse text index associated with a plurality of products in the product catalog or vectorizing embeddings of the information associated with the plurality of products in the product catalog; store the encoded product data in a product catalog database; receive input from an end user; at least one of convert the end user input to a text query or create input vectors by vectorizing embeddings associated with the input; retrieve a list of products from the product catalog database based on at least one of the text query or the input vectors associated with the input; and output a response to the end user, wherein the response includes a link to information of products in the list of products.

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 e-commerce platform profit analysis method and system and a commodity management method. The method comprises the steps of obtaining commodity data and order data of an e-commerce platform by using a timed task, updating or newly adding the commodity data to a first database according to the number of a commodity, and updating or newly adding the order data to a second database table according to the number of an order. And performing integration processing on the data of the first database table and the second database table, and performing calculation through a preset big data model to obtain a new and old customer ratio, an actual cost price and an actual profit of the e-commerce platform. And carrying out weighted analysis on the new and old customer growth rates, the actual cost prices and the actual profits of different e-commerce platforms, and outputting a customer change-profit expectation trend analysis result of the e-commerce platform. Compared with the prior art, the method has the advantages that the relation between data is analyzed, and the cost fluctuation of the commodity can be mastered in time.

A user analysis method and system based on big data, a product and a medium relate to the field of commodity data processing, and the method comprises the following steps: obtaining a specific operation sequence of a current user in a current full-decreasing sales promotion scene, extracting an order-on decision behavior feature in the specific operation sequence according to a preset user behavior analysis rule, and obtaining a decision-on decision behavior feature of the order-on decision behavior feature; the order-on decision behavior feature comprises an abnormal shopping behavior; on the basis of the make-on decision behavior characteristics, calculating a make-on possibility score for each commodity purchased by the user, determining a recommendation weight of each commodity according to the make-on possibility score, and when the make-on possibility score of a target commodity exceeds a preset score threshold value, determining that the target commodity is a recommended commodity; and reducing the recommendation weight of the target commodity in the current full-reduction sales promotion scene to a preset weight. By implementing the method, the accuracy of the e-commerce platform to push commodities to the user can be improved.

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 provides a sintering raw material test data and raw material ratio real-time correction method, and belongs to the technical field of electronic data processing, and the method comprises the steps: obtaining historical raw material test data, historical sintering process data and historical finished product data in a plurality of historical specified time periods, and determining a plurality of sinter component targets; determining an important parameter vector and a contribution algorithm of each sinter component target; constructing a prediction model of each sinter component target; determining an influence parameter vector of each sinter component target; determining current test correction data; and acquiring a plurality of historical test correction data, determining a feedback value based on the current test correction data and the plurality of historical test correction data, and generating a correction report. The data quality and analysis optimization scientificity can be improved, the scientificity and reliability of a prediction result are ensured, dynamic adjustment and automatic correction and optimization of theoretical and actual closed-loop feedback are achieved, and preposition and intelligentization of quality control management are achieved.

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.

System and method for prevention of counterfeiting of products using combination of packaging codes and blockchain technology is provided. Hash of a first product code is generated by encrypting scanned product data associated with a product received. Second product code is generated by encrypting first product code for combining with distinct first product code Quick Response (QR) part values. Hash of a third product code is generated by combining hash of first product code and hash of second product code. Hash of third product code is divided into a hash of a first sub-code and a hash of a second sub-code. Lastly, a comparison of a scanned third product code placed on the product is performed with requested user ID, generated hash of first product code, second product code and hash of third product code to ascertain a match therebetween for preventing counterfeiting of the product.