4445results about "Market data gathering" patented technology

A system and method for flexibly incorporating machine learning models into applications using a marketplace platform and distributed computational graph (DCG) architecture. The DCG enables dynamic selection, creation and incorporation of trained models with data sources and marketplaces for data, algorithms, simulation models, ontologies, knowledge corpora, and crowd or expert judgment. Multiple models can be used in series or parallel. An expert judgment marketplace allows human and artificial intelligence (AI) experts to score the accuracy of training data and model outputs. Consumers can select and rank AI agents or experts based on the helpfulness of their judgments. A symbolic knowledge corpora and retrieval augmented generation (RAG) marketplace enables selling access to proprietary datasets as RAGs and knowledge bases. The system includes knowledge corpora and RAG marketplaces with domain-specific components and user experience customization.

Techniques and systems for the dynamic generation of a map interface include generating temporal activity models by representing individual social media postings as having respective density distributions in time. Each posting's temporal density distribution spans multiple sequential time windows centered on the posting's timestamp, with density contributions decreasing in value for time windows further from the timestamp. The temporal models may be combined with spatial density distributions to generate comprehensive geo-temporal representations of social media activity. A graphical user interface displays an interactive map with overlay elements determined based on calculated activity attributes, including detected temporal patterns and anomalies identified by comparing current activity models against historical baselines. The modeling approach enables improved visualization of activity patterns while providing inherent privacy protection through probabilistic representation of individual posts.

The invention relates to the technical field of data analysis, in particular to an operation decision intelligent analysis method and system based on cross-domain data fusion. The method comprises the following steps: firstly, based on an enterprise multi-domain ontology knowledge base, performing entity identification and relation mapping on heterogeneous data from different business systems through a semantic mapping-based multi-source heterogeneous data dynamic fusion algorithm, and establishing a unified data model; then, a causal reasoning and deep learning fused hybrid intelligent decision engine is adopted to analyze and process the model; then, a multi-level causal relationship network among business variables is constructed through a causal relationship discovery algorithm by utilizing an analysis result of the hybrid intelligent decision engine, and an adaptive business scene analysis model based on reinforcement learning is used to dynamically adjust an analysis strategy according to business environment changes; generating a Pareto optimal decision scheme set through a multi-objective optimization algorithm, and outputting operation decision suggestions; according to the invention, the comprehensiveness and accuracy of intelligent analysis of enterprise operation decisions are improved.

An artificial intelligence driven system of systems may include a layered architecture for providing transaction support to various types of enterprises. A governance layer implements automated governance and policy enforcement through specialized governance modules utilizing generative AI technology. An enterprise layer supports enterprise functions by integrating management and control platforms with digital infrastructure. An offering layer creates and manages system offerings via content generation, personalization, and smart product modules. A transactions layer enables automated transaction orchestration through API integration, execution, and fulfillment modules. An operations layer manages AI systems through generation, training, verification and orchestration modules. A network layer provides adaptive networking capabilities through routing, protocol selection and communication modules. A data layer processes fused data from multiple sources using machine learning and AI systems. A resource layer manages computing, storage, and other resources through specialized resource modules.

An Al -based energy edge platform is provided herein with a wide range of features, components and capabilities for management and improvement of legacy infrastructure, coordination, and orchestration with distributed systems to support important use cases for a range of enterprises. An Al -based energy edge platform may include a graph neural network including a set of nodes respectively representing at least one distributed energy resource (DER) and a set of edges respectively interconnecting the set of nodes, wherein each edge represents at least one energy - related feature among at least two nodes of the set of nodes. The platform may incorporate emerging technologies to enable ecosystem and individual energy edge node efficiencies, agility, engagement, and profitability. Embodiments may forecast, plan for, and manage the demand and utilization of energy in greater distributed environments. Embodiments may employ intelligent provisioning, data aggregation, and analytics to leverage energy market connection, communication, and transaction enablement platforms.

Disclosed in the present invention is an optimized dispatching method for a virtual power plant, which method is applied to a virtual power plant dispatching control center in a virtual power plant dispatching system. The method comprises: predicting next-day wind power and photovoltaic output values and power demand conditions of electric vehicles in a load aggregator on the day ahead; acquiring a next-day output scheme reported by each output unit in a virtual power plant dispatching system, wherein the next-day output scheme is determined by each output unit on the basis of a day-ahead electricity selling price, electric-vehicle charging and discharging strategies and the output characteristics and costs of other output units; and performing coordinated optimization on the basis of the next-day output schemes reported by the output units and a virtual power plant optimized dispatching model which takes demand responses and environmental costs into account, so as to obtain a final next-day output plan, and issuing the final next-day output plan to each output unit, such that each output unit performs execution on the basis of the final next-day output plan. The present invention makes load dispatching in a virtual power plant more flexible.

A system may include an Al convergence system of systems for a value chain network environment.

The invention provides a supply chain sales anomaly detection and root cause analysis system and method fused with a knowledge graph, and the system comprises a demand collection and preprocessing module which is used for connecting an order system, a supply chain system, a customer relationship management system and an external data source, and completing the data cleaning, entity analysis and feature extraction; the supply chain knowledge graph construction module is used for defining an entity type and a relationship type; the real-time anomaly detection module is used for accessing a sales index data stream, performing anomaly detection in combination with lightweight filtering and a graph neural network model, and calculating node and global anomaly scores; and the visual report generation module is used for automatically generating a visual report. According to the method, the dynamic supply chain knowledge graph is constructed, the graph neural network is applied, multi-source heterogeneous data is deeply fused, the complex dependency relationship between entities is effectively captured, the accuracy and timeliness of sales anomaly detection are remarkably improved, automatic positioning of abnormal root causes and evidence chain tracing are achieved, and the analysis efficiency is greatly improved.

The invention relates to the technical field of user behavior analysis, and discloses an activity matching method and system based on user behaviors, and the method comprises the steps: carrying out the full-dimension collection of behavior data of a user in a multi-channel marketing environment, and obtaining a structured user behavior data set; constructing dynamic user portrait data based on the structured user behavior data set; performing three-layer tagging processing of basic attributes, content features and experience values on the original activity data to obtain structured activity tag data; performing asynchronous iterative calculation and intelligent weighted matching according to the dynamic user portrait data and the structured activity tag data to obtain a personalized activity recommendation list; and executing a multi-brand cooperation strategy and global constraint processing on the personalized activity recommendation list to obtain a target matching scheme meeting cross-scene requirements. According to the method, coordinated activity recommendation in a multi-brand environment is realized, and the technical problem of mutual conflict of different brand activity recommendation is solved.

The invention discloses a marketing decision analysis system and method based on artificial intelligence, and aims to improve the intelligent level of marketing decision, optimize resource allocation and improve the rate of return on investment. The system comprises a data analysis module, a prediction evaluation module, an AI decision engine and a knowledge management module. The data analysis module obtains marketing-related data from a plurality of data sources and generates a market insight result. And the prediction evaluation module predicts the potential effect of the marketing scheme by adopting a statistical analysis method based on the market insight result and the historical marketing data, and obtains a marketing prediction result. And the AI decision engine receives the market insight result and the marketing prediction result, and determines an optimal marketing decision scheme based on multi-round dialogue management, knowledge graph construction and decision reasoning technologies. According to the marketing decision analysis system and method, accurate, efficient and reusable marketing decisions are realized in a data driving mode, and the scientificity and the performability of enterprise marketing strategies are improved.

To improve efficiency of idea proposal support.SOLUTION: A proposal support system executes: acquiring a specific first word from a co-occurrence network in which each first word of a first word group in a first sentence group including a first field name in at least one information source out of a first information source related to a first field and a second information source related to a second field different from the first field is a node, and a co-occurrence relation between two first words is a link connecting the nodes; extracting company names in the second field relating to the specific first word, from a second sentence group including a second field name and the specific first word from the at least one information source; associating the specific first word, a specific second word, and the company names in the second field, based on appearance information related to the specific second word that co-occurs with the specific first word in a second word group in the second sentence group; and performing output processing of outputting an analysis result in a displayable manner.SELECTED DRAWING: Figure 5

The provided are a price elasticity analysis and prediction method and model based on deep learning. The model consists of a CNN layer and an RNN layer. The method comprises the following steps: S1, collecting historical data and merging the historical data into a multi-dimensional time series dataset; S2, extracting sentiment data and trend data from market news and social media; S3, inputting the data obtained into CNN for data preprocessing and feature extraction; S4, inputting the feature extracted by CNN into RNN for time series analysis; S5, training and optimizing model: using Adam algorithm to adjust the learning rate adaptively, and combining the momentum method and RMSProp algorithm to improve the generalization ability and prediction accuracy of the model. The provided combines the advantages of CNN and RNN, which can understand and predict the complex relationship between price and market behavior more comprehensively and accurately.

The invention relates to the technical field of economic resource management, and discloses an economic resource management optimization method based on intelligent decision making. According to the method, multi-source heterogeneous data, including resource stock, demand fluctuation and the like, of an economic system are collected firstly; a dynamic resource pool is divided based on multi-dimensional feature analysis, and a nonlinear optimization model is constructed to predict resource supply and demand changes so as to generate an allocation scheme; and then optimizing a distribution path by using a multi-stage decision tree, updating model parameters through an adaptive learning mechanism according to market feedback and constraint condition changes, and correcting a deployment scheme in real time. In addition, key technical details such as an elastic quota adjustment formula and a fuzzy clustering algorithm membership function are given. According to the method, complex data can be effectively integrated, resources are scientifically scheduled, supply and demand are accurately predicted, a distribution path is optimized, environmental changes are adapted, the efficiency and benefits of economic resource management are remarkably improved, and scientific and reasonable resource management decision support is provided for economic subjects.

A value chain network control tower system comprises a processor and memory configured to execute a know your model system that manages the complete lifecycle of Al models in enterprise environments. The know your model system performs model intake and registration actions including model documentation collection, registration procedures, metadata collection, input / output interface standardization, legal and licensing validation checks, and security validation. The system conducts comprehensive model evaluation and risk assessment actions by analyzing foundational properties, task performance, safety and risk management, alignment and compliance characteristics, operational metrics, and tooling transparency capabilities. The know your model system executes model deployment actions through automated environment validation, predeployment approval processes, and controlled production deployment with continuous monitoring.

Systems and methods for controlling rights related to digital knowledge are disclosed. A sample system may include an input system to receive digital knowledge from a user, a tokenization system to tokenize the digital knowledge and a ledger management system to create, manage, and store things on a distributed ledger and provide provable access to the digital knowledge. A smart contract system may create a smart contract including triggering action is and respond with a defined smart contract action on an occurrence of the triggering event. The smart contract system may also process commitments to the smart contract.

A real-time inventory management system that includes: a centralized control platform module configured to receive, consolidate, and process inventory data from multiple sources, including warehouses, retail stores, and distribution centers; an IoT and sensor module with RFID tags, barcode scanners, and smart sensors configured to track inventory items, monitor environmental conditions, and update inventory records in real time; a machine learning and demand forecasting module configured to analyze historical inventory data, market trends, and external factors to predict future demand and optimize inventory levels; an automatic replenishment and replenishment module configured to dynamically generate and execute replenishment orders based on real-time inventory levels and forecasted demand; a data integration and synchronization module configured to enable seamless communication and data synchronization with external systems, including Enterprise Resource Planning (ERP) platforms and supplier networks; a user interface and dashboard module configured to display real-time inventory status, actionable insights, and key performance indicators (KPIs) for decision-making.

The invention discloses an electric power marketing management information platform daily electric power fitting method and related equipment, and relates to the technical field of electric power data management, and the method comprises the steps: obtaining historical load data, external environment data and equipment operation state data of a target region; constructing an initial load fitting model based on the historical load data; extracting a date characteristic factor according to a preset time classification rule, and dynamically correcting the initial load fitting model based on the date characteristic factor to generate a corrected load model; generating a multi-source fusion feature based on the external environment data and the equipment operation state data; and inputting the multi-source fusion features into the corrected load model, and outputting a target load prediction result.

In some embodiments, a configured artificial intelligence system includes a plurality of intelligence models; a scoring system configured to generate know-your-model scores that quantify suitability for specific tasks of each model; a model execution system configured to provide standardized execution environment for the plurality of intelligence models; a training and reinforcement system configured to monitor outcomes relating to decisions or predictions made by the plurality of intelligence models and use outcome data as feedback to reinforce model performance; and a governance and analysis system configured to ensure model operations comply with governance standards. The intelligence controller may be configured to receive task requests, analyze task complexity, decompose tasks into manageable subtasks, and dynamically select appropriate models from the plurality of intelligence models to execute each subtask based on model suitability and performance characteristics.

The present disclosure is directed to enabling asynchronous analytics via an intercept device. The intercept device may intercept a request transmitted by a user device to a host server, the request indicating data of interest to be provided by the host server in a response; transmit the request to the host server; asynchronously extract analytic data from the request; transmit the analytic data to an analytics server for performance of analytics on the analytic data; receive, from the analytics server, a result of performance of the analytics on the analytic data; determine, based at least in part on analyzing the result, whether the response from the host server is to be modified; selectively effect modification of the response based at least in part on determining whether the response from the host server is to be modified; and transmit the response to the user device. Various other aspects are contemplated.

The invention discloses a purchase demand prediction method and system based on big data analysis, and relates to the technical field of supply chain management and intelligent prediction, and the method comprises the steps: collecting multi-source heterogeneous data, and carrying out the data fusion and preprocessing; based on a causal inference technology, key influence factors of purchase demand changes are analyzed, and a purchase demand prediction model is constructed; dynamically adjusting the prediction model by using real-time data, and generating a real-time or periodic purchase demand prediction result; carrying out data simulation and model optimization aiming at an extreme scene of a purchase demand; and optimizing a supply and demand matching strategy based on a prediction result, and carrying out iterative improvement on the prediction model through a feedback mechanism. Through multi-source heterogeneous data fusion, causal inference, dynamic adjustment and feedback optimization mechanisms, high-precision purchase demand prediction is realized, a supply and demand matching strategy is optimized, the adaptability of the model to market fluctuation and extreme scenes is enhanced, the purchase cost is effectively reduced, and the supply chain management efficiency and stability are improved.

The invention belongs to the technical field of intelligent supply chain management and optimization, discloses a product supply chain comprehensive optimization system and method based on big data, and aims to solve the problem that a traditional supply chain management system based on historical data and a static model is difficult to quickly adapt to changes. Forming a real-time evaluation vector; integrating the environment data, constructing an environment feature vector, and training to obtain a risk prediction model in combination with a real-time evaluation vector, thereby obtaining a risk probability of each node in the future, and obtaining a risk evaluation vector; based on the risk assessment vector, in combination with the inventory level and the transportation path, performing joint optimization on the inventory configuration and the distribution path to generate an optimization strategy; updating each node of the supply chain according to the optimization strategy, monitoring the state of the updated supply chain in real time, and generating a cost prediction vector when abnormity is monitored; and taking corrective measures on the supply chain according to the cost prediction vector to ensure stable operation of the supply chain.

The invention provides an identification early warning and law prompting method based on big data, and the method comprises the steps: collecting the multi-dimensional behavior data of a user in real time, including identity features, historical transaction prices and real-time environment variables; performing space-time association processing on the data to generate a space-time aligned feature tensor; on the basis of user price sensitivity grading, differential weighted fusion is carried out on the feature tensors, and a classification feature matrix is generated; a dynamic deviation index is calculated, a price rationality comprehensive score is generated, and early warning is triggered by a low score; inputting a legal clause matching engine to generate an early warning report; and executing a multi-level intervention strategy according to the early warning level. The method can help a user identify potential price differences, provides technical support for a related platform, and enables the user to find and process price abnormity behaviors more accurately.

The invention provides a cross-platform user behavior data intelligent aggregation and analysis processing method and system, and relates to the technical field of data processing, and the method comprises the steps: obtaining multi-platform user historical behavior data, carrying out the time sequence sorting, carrying out the session segmentation based on the time sequence data, obtaining a behavior session sequence, and constructing a behavior migration probability graph; an intention recognition agent and a behavior prediction agent are deployed through a decentralized federal reinforcement learning framework, cross-platform cooperative training is realized by using a differential privacy mechanism, and anti-factual reasoning is performed based on a multi-layer causal relationship graph to generate a user intention portrait, so that the cross-platform data analysis accuracy is improved, and the privacy protection capability is enhanced.

A tokenization system / platform / method for minting asset-class backed tokens on a distributed ledger is described herein. A tokenization system / platform receives a token configuration indicating multiple revenue-generating assets of different types and a minting party. The platform configures smart contracts to manage revenue associated with revenue-generating assets, including a first function to mint asset-class backed tokens backed by the plurality of assets and a second function to computationally apportion and distribute revenue among blockchain addresses of token owners. After deploying the configured smart contracts on the distributed ledger, the platform initiates blockchain transactions to invoke the smart contract functions, including a first transaction that mints tokens and assigns them to purchasing users' blockchain addresses, and one or more second transactions that distribute cryptocurrency corresponding to revenue among token owners. The system / platform / method may provide for fractional ownership and management of different types of revenue-generating assets and / or portfolios of revenue-generating assets.

The invention relates to the technical field of data processing, and provides a trade big data processing and user behavior prediction method and system.The trade big data processing and user behavior prediction method comprises the steps that transaction data, browsing behavior data and feedback evaluation data of a user on a cross-border e-commerce platform are obtained to generate a user behavior pattern sequence, and then a user interaction graph is constructed; and carrying out edge weight updating and node label classification to obtain a high-dimensional relation graph, carrying out similarity partition processing to obtain a predicted popularity index graph, and predicting the future cross-border commodity interaction behavior of the target user group by using the predicted popularity index graph. By acquiring transaction data, browsing behavior data and feedback evaluation data of users and generating a prediction popularity index graph, multi-dimensional data are effectively and comprehensively processed, accurate prediction of cross-border commodity interaction behaviors of a target user group is realized, and the user experience is improved when a user intention deep relationship is processed and cross-time-period behavior evolution analysis is performed. The problems of low model dimension and insufficient prediction precision exist.

The invention relates to the technical field of artificial intelligence, can be applied to business scenes of financial science and technology, medical health and the like, and discloses a product decision optimization method, device, equipment and medium based on a knowledge graph. And generating a user feature portrait in combination with the user related information, taking the user feature portrait and the information in the knowledge graph as an input state, taking the product information set as an action space, training and generating a decision model based on a preset incentive mechanism, outputting a target item by using the decision model, and updating the knowledge graph and the decision model based on user feedback information. The comprehensiveness of an input state is improved through a fusion modeling mode containing user information, product information and environment information, a dynamic updating mechanism is constructed in combination with a knowledge graph and user feedback, a decision model is driven to be continuously optimized, and then the pertinence of a recommendation result and the self-adaptive capacity of a system are improved.

A method includes obtaining, by one or more processors, a plurality of images, executing, by the one or more processors, a domain-specific target audience machine learning model to generate domain performance scores for the plurality of images, ranking, by the one or more processors, the plurality of images according to the domain performance scores for the plurality of images, and generating, by the one or more processors, a record comprising one or more images of the plurality of images based on the rankings of the plurality of images.

The invention relates to the technical field of artificial intelligence, can be applied to business system platforms of financial science and technology, medical health and the like, and discloses a multi-source data-based customer portrait generation method, device and equipment and a medium, and the method comprises the steps of obtaining structured data and unstructured data of a target customer in a data source; separately performing privacy desensitization processing on the structured data and the unstructured data to obtain structured desensitization data and unstructured desensitization data; extracting multi-modal features and time sequence features of the structured desensitization data and the unstructured desensitization data, and performing feature fusion on the multi-modal features and the time sequence features to obtain fusion features; constructing a target label set according to the fusion features, and analyzing real-time label weight distribution of the label set by using a federal learning model; and updating the target label set according to the real-time label weight distribution to obtain a real-time label set, and generating a real-time portrait according to the real-time label set. The accuracy of a customer portrait generation result can be improved.

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 project cost risk monitoring method and a project cost management platform, and the method comprises the steps: S1, carrying out the real-time collection and standardization processing of multi-source data: collecting dynamic data in real time through an Internet of Things device at a construction site, carrying out the butt joint of a design drawing, a financial system and the like, obtaining static data, and employing a data cleaning, conversion and integration technology; s2, carrying out risk factor identification and correlation analysis based on a knowledge graph; s3, implementing risk quantitative prediction and early warning driven by artificial intelligence; s4, real-time monitoring and automatic verification of contract performance of blockchain enabling are realized; s5, providing dynamic cost adjustment and optimization decision support; through real-time collection and standardization processing of multi-source data, data islands of all parties participating in a project are broken, and accuracy, integrity and timeliness of key data such as construction progress and cost expenditure are ensured. Based on big data analysis and artificial intelligence prediction, more accurate risk assessment and cost prediction are provided for project managers.