2550results about "Market data gathering" patented technology

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.

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.

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 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.

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.

The invention relates to the technical field of digital marketing, and discloses an omni-channel marketing funnel intelligent attribution model and an automatic budget distribution system, and the model comprises a data collection module which is used for collecting data through a contact recording device and a user identification device which are disposed at a marketing terminal, and a behavior tracking device and a channel classification device which are disposed at a user side. Acquiring information such as multi-channel marketing contact interaction data, user behavior data and channel types; the path analysis module realizes data path analysis through time sequence synchronization of a time alignment unit, feature matching of a path association unit and dynamic adjustment of a computing power scheduling unit; the attribution calculation module generates the conversion attribution weight of each channel based on the matching data; and the budget mapping module establishes a dynamic mapping relationship between the weight and the budget and updates the dynamic mapping relationship. The system outputs the attribution result through the model application module, and the budget execution module distributes the budget according to the dynamic mapping, so that the channel contribution can be accurately evaluated, the scientific distribution of the marketing budget is realized, and the marketing efficiency and the rate of return on investment are improved.

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 personalized scheme generation method, device, equipment and medium, and the method comprises the steps: collecting multi-modal data, and carrying out the preprocessing of the multi-modal data to generate a preprocessed data set and a data gateway lead; the fusion processing module performs cross-modal interaction and attention interaction processing on the preprocessed data set based on the data gateway association to generate a comprehensive feature vector; inputting the comprehensive feature vector into a recognition model to generate an object dynamic portrait; generating a personalized optimization path set based on the dynamic image of the object; and extracting a demand intensity item, a preference weight item and an emotional state item, and generating a personalized scheme in combination with the personalized optimization path set. According to the method, the object dynamic portrait containing the demand, preference and emotional state is generated through multi-modal data fusion and feature extraction, and a personalized scheme is output in combination with an optimized path mechanism, so that demand description comprehensiveness and scheme generation precision are realized.

A dynamic real estate valuation system based on multiparametric market indicators, which includes the following: a valuation engine configured to generate real-time results for property valuation; a multitude of distributed data ingestion and processing units configured to capture heterogeneous data sources, including historical property transaction data, real-time property listings, zoning and land use records, macroeconomic indicators, geospatial data, environmental sensor outputs, and sentiment-derived metrics; a model orchestration control unit comprising a stack of machine learning models, wherein the models include at least a gradient boosting decision tree model, a long-short-term memory (LSTM) time series forecaster, and an enhancement learning module that iteratively optimizes the model parameters based on the observed evaluation accuracy; a data contextualization controller configured to apply dynamic weighting to each input parameter based on the geographic, temporal, and market context by executing decay functions and location-specific rule matrices; a physical property valuation terminal (PVT) that includes an edge processing unit (EPU), geolocation circuitry, secure communication interfaces and a touch-based user interface; a valuation book subsystem configured to hash the valuation output, timestamp, and signatures of the input record into a blockchain-based distributed ledger; wherein the system is designed to continuously recalibrate its valuation results by comparing the predicted valuations with the actual sales or rental prices, and wherein the physical terminal is designed to produce a legally certifiable valuation document with embedded provenance data.

The invention relates to the technical field of automobile sales volume prediction, in particular to an automobile sales volume multi-dimensional prediction model construction system and method. Comprising a data acquisition unit; a data preprocessing unit; a dynamic feature engineering unit; a multi-model collaborative prediction unit; a model training optimization unit; and a prediction result output unit. According to the method, dynamic screening and iterative updating of multi-dimensional prediction features are achieved through the dynamic feature engineering unit, feature importance quantification, real-time contribution degree feedback and feature set stability verification are combined, the prediction feature set can be adaptively adjusted along with the prediction period and data change, interference of low-efficiency features on the prediction process is effectively avoided, and the prediction efficiency is improved. The adaptability of the features and the sales volume prediction demand is improved; according to the invention, a time sequence-non-time sequence dual-channel prediction framework is constructed based on the multi-model collaborative prediction unit, so that the influence of single prediction dimension limitation on the result is reduced, and the stability of sales prediction precision is improved.

The invention discloses a Nash negotiation-based energy management scheduling method for an optical storage and charging integrated energy station, and the method comprises the steps: constructing a multi-target optimization model which comprehensively considers the interest demands of an electric vehicle user, a charging station operator and a power grid operator; determining participants of Nash negotiation as a charging station operator, a power grid operator and an electric vehicle user, and determining a strategy space of each participant; on the basis of the Nash negotiation theory, all the benefit subjects are regarded as negotiation participants, utility functions of all the benefit subjects are constructed, and negotiation reference points of all the benefit subjects are determined; solving a Nash equilibrium solution of the multi-objective optimization model, and determining an optimal energy management scheduling strategy through iterative negotiation; and according to the optimal energy management scheduling strategy, real-time control and scheduling are carried out on the optical storage and charging integrated energy station. According to the invention, the flexibility, adaptability and economy of an energy scheduling scheme can be improved, and multi-party win-win and stable and efficient operation of the system are realized.

The invention relates to the technical field of port operation state sensing and monitoring, and particularly discloses a port operation state sensing and monitoring system based on a data center, which monitors a wind speed value and a visibility value at each current time point in real time through a micro weather station array, and synchronously obtains tidal phase data of an observatory. Comprehensively generating multi-dimensional meteorological data in a set time period; dynamically calculating the berthing safety coefficient of the target ship through a multi-dimensional constraint condition in combination with the load tonnage of the target ship; according to the target ship berthing safety coefficient, a dynamic ship berthing strategy of the target ship is generated; through comprehensive application of multi-dimensional meteorological data acquisition, berthing safety coefficient calculation, dynamic ship berthing strategy generation and a feedback terminal, a port manager can monitor and optimize the ship berthing process in real time. Therefore, the operation safety and efficiency of the port are improved, a more scientific and flexible berthing scheme is provided for the ship, and the sustainability and economic benefits of port operation are ensured.

The invention discloses an AI-based enterprise special asset diversified value-added service method and system, and the method comprises the steps: receiving multi-source heterogeneous data of enterprise special assets, and obtaining an enterprise special asset data graph containing asset attributes, market environments and historical disposal records; inputting the enterprise special asset data atlas into a pre-trained AI-based deep value evaluation model, and generating an asset value evaluation report including basic values, potential value-added paths and risk factors; according to the asset value evaluation report, utilizing a multi-target optimization algorithm to generate a plurality of preliminary value-added scheme sets for different value-added targets in parallel; and inputting the initial value-added scheme set into an intelligent matching engine, carrying out scheme feasibility verification and suitability screening, and outputting an optimal customized value-added service scheme package. By utilizing the embodiment of the invention, the accuracy of asset value analysis and the potential mining efficiency can be improved, and dynamic optimization and risk controllability of an asset value-added path are realized.

The invention discloses an intelligent customer acquisition and user behavior analysis system based on AI full ecology, and relates to the technical field of artificial intelligence and big data marketing, and the system comprises a multi-source data collection module which collects internal and external multi-channel data of an enterprise; the data cleaning module processes abnormal and missing values and unifies data formats; the user portrait construction module extracts multi-dimensional labels to describe user features; the intelligent customer obtaining module uses transfer learning to screen high-potential customers, and the user behavior analysis module mines behavior logic by means of Transform; the intelligent recommendation engine is fused with multiple algorithms to realize personalized recommendation, and the real-time decision module is combined with rules and reinforcement learning to optimize marketing. According to the method, multi-source data integration and deep analysis are realized, clients are accurately identified, behavior requirements are mined, the client obtaining efficiency and the conversion rate are improved, the marketing effect is optimized through personalized recommendation and intelligent decision, and a whole-process intelligent marketing solution from data acquisition to decision execution is provided for enterprises.

The invention provides a power marketing archive intelligent management method, which comprises the steps of receiving archive data through a power data acquisition interface, matching an electricity price feature code in an archive based on a real-time electricity price policy identifier, and injecting a version calibration mark into unmatched data; constructing an electricity consumption abnormity prediction model based on the user historical load mode and the environmental factors; when a query request is responded, dynamic penalty parameters are generated based on the user behavior portrait and the rule base; a quantum key distribution path is generated based on a power grid topological structure, and cascade encryption is implemented along physical connection nodes; dynamically associating user operation authority with a power grid operation risk signal, and automatically disabling core operation in a high-risk state; and analyzing archive data in a security isolation environment to drive line loss optimization, electric charge tracing and fault pre-judgment service flow in real time, and outputting feedback archive update based on the service. According to the invention, the safety, reliability and operation efficiency of power marketing archive management are improved.

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 an object portrait label set generation method, device and equipment and a medium, and the method comprises the steps: collecting and integrating multi-source data, generating a multi-modal data set, and cleaning and enhancing the data set; extracting a portrait label set based on the historical structured information of the object, obtaining a multi-modal cue word set, and constructing a unified representation sequence; inputting the unified representation sequence into a multi-modal model, obtaining fusion representation and extracting a derivative factor candidate set; and performing consistency verification on the cross-modal data to obtain a consistency factor set, and finally fusing the consistency factor set with the structured portrait label set to generate a unified portrait system and output an object portrait label set. According to the method, the accurate and comprehensive customer portrait is generated by combining multi-modal data and cross-modal consistency verification, the portrait accuracy is improved, and support is provided for accurate marketing and personalized service.

Certain aspects of the disclosure provide artificial intelligence (AI) methods and systems for generating personalized social media content with trend integration. A method generally includes retrieving data from data sources that includes customer interactions with a business, and inventory data of the business, determining trending-product pairs that increase engagement of the customers with products recorded in the inventory data of the business based on the retrieved data. A generative artificial intelligence (AI) model is used to generate one or more of a caption, a hashtag, and a promotional image that are personalized to each of the customers in response to receiving prompts that contain information about the customers, information about trending-product pairs, and social media platforms of the customers. The method sends one or more of the captions, the hashtags, and the promotional images that are personalized to the customers to social media platforms of the customers.

A raw material system includes a product manufacturing demand estimation system programmed to calculate an expected demand for a product at a future point in time. An environment detection system identifies at least one of an environmental condition or an environmental event. A raw material production system estimates a raw material availability at the future point in time based on the expected demand and the environmental condition / event. A raw material requirement system calculates a required raw material amount to manufacture the product at the future point in time based on the expected demand and the environmental condition / event. A raw material procurement system autonomously configures a futures contract for procurement of at least a portion of the required raw material amount in response to the required raw material amount calculation exceeding the raw material availability estimation.

The invention discloses a bidding and incentive combined strategy optimization method, system and device for a source-load double-side peak regulation auxiliary service market, and a storage medium, and belongs to the technical field of electricity market transaction. The method comprises the following steps: constructing a load aggregator and thermal power generating unit-oriented double-market joint optimization model according to parameter data of a peak regulation auxiliary service market and an incentive demand response market; the double-market joint optimization model is formalized into a Markov decision process; constructing a multi-task multi-agent reinforcement learning training framework; and solving the multi-task multi-agent reinforcement learning training framework through an asynchronous training multi-task multi-agent flexible action-evaluation algorithm to obtain a load aggregator bidding and incentive optimal joint strategy and an optimal bidding amount and quotation strategy of the thermal power generating unit. According to the method, game behaviors among multiple market participants and joint decision behaviors of demand-side market subjects in double markets are considered at the same time, and the method is widely applicable to multi-task agent collaborative optimization in the electricity market.

The invention relates to the technical field of customer relationship management, and discloses an intelligent customer life cycle management SCRM system, which comprises a multi-source data acquisition and fusion engine; a dynamic customer portrait modeling engine; a customer life cycle state evaluation engine; a predictive intervention engine; an intelligent strategy decision engine; a task distribution and execution engine; and a feedback closed loop and learning engine. According to the intelligent customer life cycle management SCRM system and method, by setting a multi-modal decision engine, taking a customer state as input, learning an optimal strategy of maximizing a long term value (LTV) through a DQN / PPO algorithm, when a customer is in a high loss risk state, triggering an exclusive retention scheme for the customer, and generating a strategy according to a comprehensive conversion rate, cost and satisfaction target, so that the customer life cycle management is realized. Moreover, the personalized content template can be automatically generated, the optimal information channel is accurately sent to different clients, personalized matching is carried out for the clients, and the loss rate of the clients is reduced.

The invention relates to the field of data processing, and provides a cross-border e-commerce intelligent product selection system and method based on multi-platform data fusion, and the method comprises the steps: carrying out the data collection and preprocessing of a plurality of cross-border e-commerce platforms, constructing a neural network model through optimal hyper-parameters, training, and generating a selected product recommendation list. Meanwhile, the structural parameters and the search direction of the selected product model are continuously updated, and the selected product recommendation list is dynamically updated. According to the system, multi-platform commodity data are integrated, versioning feature snapshots are constructed, hyper-parameter selection of a multi-snapshot version evaluation strategy optimization model with a consistent time sequence is adopted, the generalization ability of a commodity selection model to a time-varying data environment is enhanced, the possibility that the accuracy of the model is reduced due to the change of data distribution is reduced, and the accuracy of the commodity selection model is improved. Meanwhile, dynamic updating of the selected product recommendation list is achieved, and it is ensured that the recommendation strategy can be fully matched with preference migration of the selected product trend.

System and methods are provided for achieving data standardization and normalization through an Agnostic Data Format (ADF) architecture. ADFs systems and processes provide a transformative bridge, enabling disparate data sources to converge into a unified and standardized format within the Real-Time Data Mesh (RTDM) framework. This dynamic process utilizes Artificial Intelligence (AI) and Machine Learning (ML) algorithms to interpret and align diverse data attributes. The ADF management system, integrated into a dynamic event-driven architecture, allows vendors to interact with RTDM by translating and standardizing their data. The synchronized data integrates canonically, incorporating real-time updates and collaborative decision-making across the distribution platform. This innovative approach enhances operational efficiency, enables data-driven decision-making, and provides users improved ability to use data within the distribution ecosystem.

The invention provides an automatic consumption tag analysis system and method based on multi-agent collaboration, and the system comprises a data processing agent which is used for collecting data from a social media platform and carrying out the data preprocessing; the label identification intelligent agent is used for extracting consumption labels of different dimensions from the preprocessed data; the sentiment analysis agent is used for carrying out context modeling and sentiment tendency recognition and binding the recognized sentiment tendency to the corresponding consumption label; the label normalization agent is used for performing clustering and normalization processing on all consumption labels bound with emotional tendencies to generate a structured multi-layer label atlas; the central scheduling agent is used for scheduling other agents, generating a label analysis result by using the multi-layer label atlas and sending the label analysis result to the user; according to the invention, based on a multi-agent architecture, structured analysis is carried out on user tags, behavior attributes and consumption intentions in social media contents, so that high-precision and high-efficiency intelligent consumption insight is realized.

Some embodiments provide a system to control retail product allocation, comprising: an anomaly detection system applying a series of anomaly detection models to business metric data to identify an anomaly of a category of products; a contextualization detection system applying contextual models to data relative to the anomaly and identifying contextual factors; a causal detection system applying causal inference and determination models to sets of relevance data as a function of the contextual factors to determine influence attribution factors that are predicted to have been factors in causing the threshold variation, and apply attribution prioritization models to define relevancy scores to the influence attribution factors and prioritize the influence attribution factors; a personalization recommendation system applying personalization models to the prioritized influence attribution factors and the contextual factors as a

A social media platform provides a map-based graphical user interface (GUI) for accessing social media content submitted for public accessibility via the social media platform supported by the map-based GUI. The GUI includes a map providing interactive location-based searching functionality in that selection of a target location by the user in the GUI, such as by tapping or clicking at the target location, triggers a search for social media content having geo-tag data indicating geographic locations within a geographical search area centered on the target location. A search period for which content is returned is dynamically variable based on the duration for which the tap or click is held.

An autonomous futures contract orchestration platform includes a set of processors programmed with a set of non-transitory computer-readable instructions. The instructions include receiving, from a data source, an indication associated with a product that relates to an entity that purchases or sells the product. The instructions include predicting a baseline cost of purchasing or selling the product at a future point in time based on the indication. The instructions include retrieving a futures cost, at a current point in time, of a futures contract for an obligation to the purchasing or selling the product for delivery or performance of the product at the future point in time. The instructions include executing a smart contract for the futures contract based on the baseline cost and the futures cost. The instructions include orchestrating the delivery or performance of the product at the future point in time.

A method for prioritizing predictive model data streams includes receiving, by a first device, a plurality of predictive model data streams. Each predictive model data stream includes a set of model parameters for a corresponding predictive model. Each predictive model is trained to predict future data values of a data source. The method includes prioritizing, by the first device, priorities to each of the plurality of predictive model data streams. The method includes selecting at least one of the predictive model data streams based on a corresponding priority. The method includes parameterizing, by the first device, a predictive model using the set of model parameters included in the selected predictive model data stream. The method includes predicting, by the first device, future data values of the data source using the parameterized predictive model.

The invention discloses a sales full-process digital management system and method for enabling a large model, and relates to the technical field of artificial intelligence and large models, and the system comprises a dynamic data twinning construction module which integrates multi-source data to generate customer digital twinning; the intention pre-judgment neural engine is used for pre-judging the intention of the client in advance by using an improved Transform; the dynamic game quotation engine is used for generating quotation in combination with customer sensitivity and competitive product strategies; the self-adaptive process arrangement module is used for dynamically adjusting the process based on a causal graph; a multi-dimensional risk early warning network is used for monitoring risks in real time; the situational knowledge distillation module is used for generating lightweight knowledge capsules; the closed-loop optimization feedback module is used for continuously iterating the model; and intelligent management of the whole sales process is realized. According to the invention, multi-source data are integrated to construct customer digital twinning, intention is accurately pre-judged, an optimal quotation is dynamically generated, a process is intelligently arranged, risks are early warned in real time, and intelligent closed-loop management from data to decision is realized.

The invention discloses a campus intelligent consumption analysis system and method based on multi-dimensional data fusion, and relates to the technical field of electric digital data processing. The system comprises a terminal feature fusion module, an anomaly identification analysis processing module and a classification verification and synchronization module. The terminal feature fusion module fuses terminal feature data and time feature data in the original multi-dimensional data to generate behavior association features, and data dispersion is reduced; the anomaly identification analysis processing module performs anomaly identification and processing on the behavior association features to form a feature data set and verify the validity, so that the detection accuracy is improved; and the classification verification and synchronization module uploads the verified classification result as a core data set to a specified service platform and synchronizes the core data set to a database of the specified service platform, so that the platform processing efficiency is improved. Through cooperation of multiple modules, efficient fusion, accurate anomaly detection and data synchronization of campus consumption data are realized, and comprehensiveness of campus consumption analysis and processing efficiency of a service platform are improved.

A computer-implemented method for computational textual search to find and display search identified information in documents. Search queries are processed over one or more documents either at a server or user device using match schemes that produce both binary (match or no match) and non-binary (i.e. multiple matching values) results and have relative fuzziness relationships. A fuzzier match scheme implies more results and fewer false negatives and a less fuzzy match scheme implies fewer results and fewer false positives. This fuzziness relationship allows, without changing a search string, for users to quickly change from a match scheme to a fuzzier or less fuzzy match scheme depending on user evaluation of results as containing too many false negatives or too many false positives—without becoming a programmer of complex search string metadata or an expert user of advanced search capabilities.