752 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 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.

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 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.

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.

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.

A system and method are provided for modifying a graphic narrative to feature select products / services. Panels of the graphic narrative are segmented into elements (e.g., using semantic segmentation models like Fully Convolutional Networks), which are then identified as backgrounds, foregrounds, text bubbles, objects, characters etc. (e.g., using artificial intelligence (AI) classifiers like K-means classifiers). The identified elements are compared to a product database to select products suitable to be promoted by modifying selected elements, resulting in a version of the graphic narrative having modified panels in which the selected element(s) are changed to feature the selected products. For example, generative AI methods can redraw image elements or redraft text to place the selected products within the context of the graphic narrative. Links or watermarks can be placed in the modified panels to provide users with access to interact with or purchase the promoted products.

The invention discloses a severe convection refined collaborative observation method based on adjacent weather radar networking, and the method comprises the steps: selecting a weather radar station for networking as a networking member, and transmitting PPI data scanned by each group of networking members in real time to a data buffer for accumulation in a stream transmission communication mode; transmitting the data accumulated in the data buffer area to an algorithm server, processing and packaging the real-time PPI data on the algorithm server, and constructing a networking cooperation minute observation data set; observing the data set by networking collaboration minutes, developing networking products released by the service, and storing the generated products into a product data pool; and the downstream customer extracts the required product from the product data pool. According to the method, the application potential of multi-weather radar joint observation data in short-term and temporary services is fully excavated, the scanning updating frequency of a severe convection system is improved to 1 minute, and the observation grid horizontal density is encrypted to a sub-kilometer level.

The invention discloses an intelligent optimal scheduling system and method based on multi-target production, relates to the technical field of intelligent scheduling optimization, and solves the problems that a scheduling scheme is difficult to adapt to the change of a production environment due to the fact that the scheduling scheme is generated by adopting a fixed weight or a static priority in the prior art. And the accuracy of the multi-target production scheduling scheme is low. A product comprehensive function is constructed according to product data, equipment data, order data and inventory data; generating a scheduling scheme according to the product comprehensive function and performing production scheduling according to the scheduling scheme; an alarm signal is generated according to equipment data, a product comprehensive function of multiple product production is constructed, multiple objective functions are combined through adaptive weights, the optimal state can be dynamically adjusted according to real-time production data, the accuracy of a production scheduling scheme is improved, meanwhile, the equipment and order states are monitored in real time, and the production scheduling efficiency is improved. An early warning signal can be sent in time, and the situation that equipment cannot work normally or orders are not processed in time is avoided.

The invention discloses a key product data acquisition method and device, equipment and a storage medium, and relates to the technical field of computers. The method comprises the following steps: reading newly-added key product data from a backboard of cluster equipment according to a first path through a platform module of a cluster, and storing the newly-added key product data to a cache; the first path comprises a platform module, a monitoring module, a storage case service module and a backboard in sequence; reading initial key product data from a backboard of cluster equipment according to a second path through a case management module of the cluster, and reading the newly added key product data from the cache through the case management module; the second path sequentially comprises a case management module, a storage case service module and a back plate; and obtaining complete key product data based on the newly added key product data and the initial key product data. The reading time of key product data can be shortened, and the case management module is prevented from reading the product data overtime.

The invention discloses a data mining analysis method applied to a sandwich production control system, and relates to the technical field of data mining. The method comprises the following steps: collecting multi-source data of a sandwich production control system; predicting the production state of the production control system by adopting a stream data processing technology; performing quality prediction on the sandwich by adopting a deep learning algorithm; performing defect detection on the sandwich by using a computer vision technology; performing fault prediction by adopting a deep learning algorithm and a correlation analysis algorithm; optimizing the production scheduling of each production link by adopting an optimization algorithm; and product data of the sandwich are collected and product traceability is carried out. On the basis of data mining, various technologies are adopted to process and analyze the mined data, so that the production efficiency and quality are improved, and the production cost is reduced; by predicting the production quality and carrying out defect detection and fault detection, the production management level is improved, and the optimization of the production process, the accurate control of the quality and the effective management of the cost are realized.

The embodiment of the invention discloses an intelligent production method and equipment of a composite insulation board, a medium, application and an intelligent production technology, which are used for solving the problem of unstable quality of an existing production mode. The method comprises the following steps: determining target product data of a to-be-detected composite insulation board according to workshop order information and historical detection data; detecting the defect connected graph and the boundary contour according to a preset camera and ultrasonic equipment, performing regional grouping on the defect graph in combination with the layer structure data, inputting a classification model to obtain a first detection result, and comparing the boundary contour to determine a second detection result. And positioning an optimizable production link based on the first detection result and the second detection result, performing simulation operation on an optimizable production environment by using a digital twin model, and identifying variable bottleneck parameters. And inputting the variable bottleneck parameters, the production link parameter range and historical production data into a least square model, calculating tuning data, and adjusting the composite insulation board according to the tuning data to realize closed-loop production.

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 relates to the technical field of after-sales service, and discloses an after-sales service system and method based on an electric tool, the system comprises the electric tool, an information acquisition unit is used for acquiring working parameters and fault information of the electric tool; the control unit is used for sending a control instruction to the electric tool and receiving detection information returned by the electric tool according to the control instruction; the communication unit is used for uploading data acquired by the electric tool to the cloud server; the cloud server is used for carrying out fault classification and health detection on equipment data formed by the working parameters, the fault information and the detection information; performing multi-dimensional division on the equipment data, and generating a product after-sales portrait according to a dimensional division result; and the client is used for obtaining the target data from the cloud server according to the query instruction and visually displaying the target data. According to the invention, the product data can be accurately obtained after the electric tool is sold, the after-sales service optimization of the product is realized based on the product data, and the user experience is improved.

The embodiment of the invention relates to a data label processing method and device, equipment, a medium and a program product. The data label processing method comprises the steps of obtaining a mapping relation of a data label under a target dimension, wherein the mapping relation indicates a corresponding relation between a plurality of universal labels of data and a plurality of user-defined labels of the target dimension; receiving a data processing strategy, wherein the data processing strategy at least indicates a target universal label in the plurality of universal labels and a target operation corresponding to the target universal label; based on the mapping relationship, determining a target custom tag corresponding to the target universal tag from the plurality of custom tags; and executing the target operation on the target data with the target custom tag. Therefore, the data labels can be conveniently and uniformly managed, and the existing label system of each dimension does not need to be modified.

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.