923 results about "Market place" patented technology

A method and a system for providing at least one of a process automation and artificial intelligence (PAAI), a market aggregation, or an embedded marketplace relating to transactions are disclosed. In particular, a method and a system for automation of transactions in a transaction environment (e.g., marketplace or a set of marketplaces) are disclosed. A method and a system for managing transactions in a transaction environment (e.g., marketplace or a set of marketplaces) are disclosed. A method and a system for automating processing of transactions in a transaction environment (e.g., marketplace or a set of marketplaces) are disclosed. A method and a system for automated orchestration of a transaction environment (e.g., marketplace or a set of marketplaces) are disclosed. A method and a system for augmenting of services in a transaction environment (e.g., marketplace or a set of marketplaces) are disclosed.

The invention relates to the technical field of artificial intelligence, and discloses a multi-country compliance intelligent auditing method, device and equipment for cross-border e-commerce and a medium, and the method comprises the steps: obtaining the law and regulation revision frequency of each specified trade country; setting a regulation capturing period for a corresponding specified trade country according to the regulation revision frequency; based on the law and regulation capturing period of each specified trade country, obtaining a law and regulation text of each specified trade country; analyzing the law and regulation text by utilizing the semantic analysis capability of a preset large language model to obtain analysis content; constructing a structured knowledge graph based on the analysis content; and obtaining a trade text, and checking the compliance of the trade text based on the structured knowledge graph. The method has the beneficial effects that the method is helpful to quickly adapt to the regulation change of the global market, the violation risk is reduced, and the competitiveness and market adaptability of enterprises are improved.

Various aspects of a prediction market ecosystem are described for punters and market makers to speculate on occurrence and non-occurrence of future events by placing bets on operators that take bets within their jurisdictions and can buy other punters bets that are placed with other operators through an exchange server that matches and executes trade orders that also allows operators to participate on the exchange to manage their risks and records inter-operator transactions generated as the result of executing the trade orders.

Systems and methods for optimizing the combination of products and services a business offers to customers, identifying a combination of top markets a business offers to customers for growth opportunities, and optimizing advertisements. In one implementation, the disclosed system includes at least one processing device and at least one non-transitory memory containing software code configured to cause the processing device to: gather customer data and financial institution data from a plurality of data sources; extract a plurality of customer behavior features and a plurality of financial institution behavior features; process the customer behavior features and financial institution behavior features using one or more trained foundation models; input the foundation model outputs and a plurality of goal inputs into a trained product model; and output a natural-language product response.

The invention discloses a marketing strategy optimization management system based on six elements of order transaction, and belongs to the field of communication management systems, in terms of data acquisition, multi-source comprehensive information collection enables enterprises to perceive consumer demands in all directions and no longer blindly grope, a dynamic feature modeling unit fuses various kinds of data into six-dimensional feature vectors, and the six-dimensional feature vectors are integrated into a database; multiple factors of commodities, users and festivals are balanced and considered, a solid foundation is laid for a marketing strategy, a commodity-festival-user ternary association graph constructed by a festival graph generation engine enables commodities to be pushed in a targeted manner, a personalized strategy is generated by an intelligent decision module, the matching degree of the commodities and consumers is improved, and the marketing efficiency is improved. The overall continuous feedback optimization mechanism of the system enables the marketing strategy to be like a continuously evolved life entity, can better adapt to the market change, and improves the sales probability of commodities.

Computerized systems and methods are disclosed for automating Configure to Order (CTO) and Quote to Order (QTO) processes. Methods include receiving user inputs for desired product configurations, retrieving corresponding data from a bill of materials database, and calculating optimized pricing through intelligent rules based on real-time market data. Automated quotes are generated and transferred to orders in a vendor system, selected based on pre-set criteria like vendor reputation and delivery time. Validation steps reduce errors, and real-time reports are generated. The system integrates a Real-Time Data Mesh for data aggregation, a Single Pane of Glass User Interface for user interactions, and Advanced Analytics and Machine Learning Modules for implementing rule-based and learning algorithms. The system is accessible across various devices and standardizes data for uniform consumption, while also employing machine learning models to continually optimize processes. Notifications are sent to users upon successful execution of orders or completion of quotes.

Various embodiments described herein relate to providing and / or employing a system and a method for managing a plurality of market-based instruments in a facility. In this regard, emission factors corresponding to a plurality of assets in the facility are stored in an emission factor repository. As a result, total actual emissions corresponding to the plurality of assets is calculated based on the stored emission factors. Then the total actual emissions are compared with a carbon emission target, wherein the carbon emission target corresponds to a specific period. Furthermore, total emissions are predicted in the facility for the specific period based on the stored emission factors and a result of the comparison. Accordingly, the predicted total emissions in the facility is displayed via a user interface of a display device.

The invention relates to the technical field of electric power systems, in particular to an electric power spot market risk prevention and control method and system considering market subject behaviors. Acquiring operation data of generating capacity, electricity demand, market price and power grid state of the electric power spot market; on the basis of the acquired operation data, a coupling degree evaluation index and a stability evaluation index are introduced to carry out prediction and anomaly detection on a market trend, and influence factors of connection of a large-scale generator set and a small-scale power grid are particularly concerned; analyzing a market subject behavior pattern through transaction frequency, price volatility and market participation degree characteristic quantitative indexes, and identifying market manipulation and violation operation risks; market dynamics are monitored in real time, a risk threshold value is set, and an alarm is triggered when a risk index exceeds a set standard; early warning information of different levels is automatically sent out according to the risk degree, and corresponding countermeasures and suggestions are provided; and a supervision mechanism is helped to make a quick response through a visual interface.

The invention relates to the technical field of big data intelligent decision making, and provides a household appliance commodity dynamic pricing system and method based on multi-source data fusion, and the system comprises a data collection server, a feature extraction engine, a model training cluster, and a strategy execution terminal. The feature extraction engine comprises a time sequence feature constructor used for generating sliding window statistics; the semantic analysis unit is used for analyzing a commodity detail page text description to generate a brand premium coefficient; and the image recognition module is used for predicting the willingness to pay by the user through the commodity main image quality score. Through collaborative design of the feature extraction engine and the model training cluster, the dynamic response capability and the data application depth which are deficient in a traditional pricing mechanism are realized, traditional household appliance merchants are assisted to adapt to market changes, the customer satisfaction and the market share are improved, and the survival and development of the merchants are guaranteed.

The invention discloses a management method and system based on a customer life cycle, and the method comprises the steps: recognizing a high-potential customer group through market behavior data, combining a low-threshold conversion path, remarkably improving the first-time purchase conversion rate of potential customers, precisely positioning a high ROI channel through data burying point and attribution analysis, optimizing an advertisement putting strategy, and reducing the customer obtaining cost. By automatically triggering a guide course and real-time behavior information, the period from registration to first use of a new customer is shortened, core function advantages are highlighted, and cognition and trust of the customer to a product are quickly established.

The invention relates to the technical field of information, and particularly discloses an online mall platform intelligent management method and system, and the method comprises the following steps: S1, constructing a data collection system, and obtaining platform operation data, user behavior data and external market data in real time; s2, acquiring historical sales data, analyzing the historical sales data by adopting a Prophet time sequence model, predicting a future sales trend and inventory requirements, and generating a dynamic replenishment suggestion; s3, optimizing inventory management through a dynamic programming algorithm, calculating an optimal inventory threshold value in combination with external market data, and performing automatic replenishment triggering; s4, establishing a user portrait according to the user behavior data, and generating a personalized coupon strategy based on a collaborative filtering algorithm; s5, dynamically matching the work order and the customer service according to a multi-attribute decision algorithm; and S6, storing quality assurance information, and ensuring non-tampering and traceability of construction time, material batches and technician ID data. Therefore, the operation efficiency and the user experience of the shopping mall platform are improved.

The invention relates to a trading platform that shifts focus from market prediction to disciplined execution, capital management, and trading psychology. By disclosing the general market trend in advance, users are empowered to train and operate within a known directional framework. The system generates synthetic charts using directional random walks anchored at algorithmically or randomly selected convergence points from historical data. This recreates real trends while introducing stochastic variability between anchors. A simulated order book models supply-demand dynamics using Poisson-distributed flows, capturing sentiment, liquidity shifts, and large trades. Features include Accelerated Time Compression for fast-forwarded trade simulation and a Rewind Trade mechanism allowing entry at the start of validated candlesticks. The rewind logic applies to both futures and spot markets, including staking actions. Safeguards such as rewind limits, volume caps, and LP prioritization preserve fairness. The platform supports multi-timeframe execution and bridges strategic training with interactive trading, enhancing decision quality and behavioral discipline.

The invention discloses a cross-border e-commerce commodity dynamic pricing method and system based on merchant multi-dimensional data, and relates to the technical field of cross-border e-commerce pricing, and the method comprises the steps: S1, multi-dimensional data collection: collecting structured data and unstructured data in a merchant operation process; by collecting and analyzing the unstructured data, the defect that an existing pricing model only depends on the structured data is overcome, the user price attitude and commodity value evaluation are accurately captured, the price elasticity coefficient is corrected by fusing the unstructured data and the structured data, the accuracy of price sensitivity calculation is improved, and the price sensitivity calculation efficiency is improved. Through data feedback and strategy adjustment after price adjustment, a dynamic closed loop is formed, it is ensured that a pricing strategy can adapt to user feedback and market changes in real time, the problems that an existing pricing method is inaccurate in pricing and not timely in strategy optimization are effectively solved, and finally the dual effects of merchant profit space guarantee and commodity market competitiveness improvement are achieved.

The invention discloses a supplier material market price evaluation method and system based on AI networking search, belongs to the technical field of electronic commerce and data processing, and provides a double-weight confidence model based on time sequence fluctuation and statistical deviation, data are grouped according to platform types and suppliers, a price fluctuation oscillogram is generated for each group of data, and the price fluctuation oscillogram is calculated. Calculating average price and standard deviation in the group; the global average price is introduced, the confidence coefficient of a single group of data is dynamically quantified through a normalization formula, low weight is given to high-fluctuation and high-deviation data, noise interference is effectively suppressed, and the reference of the finally output optimized market price is improved; in addition, structured data with timestamps are generated and the grouping average price and confidence factor of each platform are synchronously output, so that purchasing personnel are assisted to transversely compare the quotation rationality of suppliers; and finally, the output optimized market price is fused with multi-source high-confidence data, so that enterprises are helped to accurately grasp the market price interval, and the bargaining capability and the purchasing efficiency are improved.

The invention relates to the technical field of dynamic price adjustment of electronic price cards, and discloses a dynamic price adjustment strategy optimization method for electronic price cards based on machine learning, and the method comprises the steps: constructing a multi-source data sensing network, and collecting historical transaction data, competitive product price information and user interaction behavior data; generating a multi-dimensional composite feature set covering time, commodity attributes and market environment through feature engineering processing; training a dynamic price adjustment prediction model by using the feature set, and outputting an initial price adjustment strategy parameter table; calculating a strategy deviation value and triggering a correction mechanism based on sales data fed back by the electronic price tag in real time; and synchronizing the optimized price adjustment strategy to the electronic price card terminal, and updating the training sample library to continuously improve the model. According to the invention, data-driven intelligent price adjustment is realized, the accuracy, the real-time performance and the adaptability of a price strategy are remarkably improved, and the problems that a traditional price adjustment method is lagged in response and depends on artificial experience are effectively solved.

A system and method for optimizing a media campaign comprises using a solver on various input data to generate a media campaign solution that meets one or more optimization goals. An optimization program comprising the solver generates a media campaign solution based on store location data, trade areas, audience definitions, audience weights, medial channel data and one or more constraints. Each media campaign solution can include spend data for each of a plurality of the media channels.

The invention relates to an electricity market price difference prediction method and system based on dynamic similar day matching. The method comprises the steps of obtaining electricity transaction data; processing the power transaction data to obtain a bidding space index and a price difference direction index; calculating the similarity between the day-ahead electric power spot price data and the real-time electric power spot price data; calculating a comprehensive similarity index according to the similarity; screening out data of the comprehensive similarity index in a set range to form a similar day set, and training a price difference classification prediction model by using the similar day set; and predicting the spot price difference direction of a prediction day by using the price difference classification prediction model. According to the method, training data samples are screened through a similarity algorithm, the over-fitting risk caused by invalid data volume is reduced, then the influence of extreme prediction of an individual algorithm is reduced through model diversity, and finally a robust probabilistic price difference direction prediction result is output.

The invention belongs to the field of intelligent scheduling of an energy system, and provides a coupling market operation optimization method and system based on a multi-agent game, a coupling market operation model is established through a power distribution market operation model and an operator operation model, and operation data among a power distribution market, an operator and a user market can be correlated, so that the operation efficiency of the power distribution market is improved. Furthermore, by obtaining a double-layer game model and a target function set, the electricity purchase cost of the user and operation target data of the active power distribution network and operator operation are correlated, and the target function set can be constrained and solved based on a target constraint function set; and the parameters of the double-layer game model and the coupling market operation model can be optimized according to the solving result of the target function, so that the stable operation of the whole coupling market can be ensured, and the operation requirements among a power distribution market, an operator and a user market can be considered.

A system and method for providing reliable, legally compliant access to geographically restricted ATSC television signals via user-owned devices hosted in a datacenter. The invention verifies the physical address of each applicant to ensure eligibility within the designated market area before installation. It eliminates the need for rooftop or indoor antennas, resolving common ATSC reception challenges, including interference, terrain obstruction, and lightning risks. Designed for ease of use by non-technical users, the system provides consistent high-quality reception through a managed datacenter infrastructure. The invention complies with legal boundaries set by the U.S. Supreme Court's ruling in ABC v. Aereo by ensuring individualized device ownership and avoiding centralized retransmission. An optional encrypted group addressing mode further reduces energy and hardware redundancy while enforcing regional access through a conditional access system with access limited to persons with validated addresses and members of the datacenter.

The invention relates to the technical field of cloud services, in particular to a purchase quotation method and system based on clustering and association rules, and the method comprises the following steps: collecting historical purchase order data of internal products of an enterprise and external related market data, the historical purchase order data comprising product description; carrying out cleaning and standardized preprocessing on the collected data; the method has the advantages that through clustering based on product description, products such as servers and switches with different descriptions but similar essence are sorted and classified, data chaos caused by description differences is eliminated, and association rules mined on the basis are more targeted. Compared with a procurement quotation mode purely depending on experience, the procurement quotation method has the advantages that the procurement quotation accuracy is greatly improved, an enterprise can purchase required products at a more reasonable price, and cost waste caused by price judgment errors is avoided.

A method, system, and program product includes an integrated artificial intelligent system for generating a work order by including processes configured to execute demand forecasting, tailored pricing, service provider matching, and dynamic job order management. An example leverages data analytics to forecast demand, determine tailored prices, match users with nearby service professionals, teams, and offers via a marketplace network, and manage job orders using a dynamic work order management tool. The system provides service delivery and enhances customer satisfaction. Generating the work order may include performing a task selected from a list that includes at least one of: demand forecasting, tailored pricing, service provider matching, and dynamic job order management. Based on the performed task, the system may match a user with nearby service professionals via a marketplace network to generate a job order and manage the job order.

The invention relates to a transaction mechanism and a clearing method for an electric power alternative spot market, and aims to solve the adjustment problem caused by access of high-proportion renewable energy in a novel electric power system. The method comprises the following steps: firstly, defining volume adjustment commodities and a transaction mechanism thereof, generating and correcting a system adjustment demand curve based on an adjustment demand curve reported by a market subject, collecting the volume adjustment quotation of each market subject to construct a clearing model, carrying out clearing by adopting an equal volume adjustment increment criterion, and determining a bid-winning volume. And dynamically adjusting the adjustment demand of the system through multiple rounds of adjustment transactions until the adjustment demand converges to zero. The method further comprises an external quantity adjustment price determination method and a transaction surplus allocation mechanism, and can eliminate the negative extrinsic property of the adjustment capability, realize the marketization configuration of the adjustment capability, improve the market operation transparency and predictability, reduce the total electricity purchase cost of the system, simplify the market clearing process, and improve the user experience. The method is suitable for a novel power system with high-proportion renewable energy access.

The invention discloses a method for participating in spot goods electric energy-flexibility market clearing through energy storage. The method comprises the steps of obtaining a multi-time-scale power sequence; processing the power sequence to obtain a corrected polyhedron point set containing space node correlation characteristics and a real physical power scale; based on the corrected polyhedral point set, a double-layer decision model is constructed, the double-layer decision model comprises an upper-layer energy storage power station decision model and a lower-layer dispatching center decision model, and the upper-layer energy storage power station decision model is used for maximizing participation in spot goods electric energy-flexibility market income. The lower-layer dispatching center decision model is used for minimizing the electricity purchase-flexibility service cost; obtaining constraint conditions of the double-layer decision model according to the corrected polyhedral point set; and on the basis of the double-layer decision model and the constraint condition, a comprehensive double-layer optimization model is constructed, and the comprehensive double-layer optimization model is used for realizing dynamic balance of market income maximization and service cost minimization to obtain an optimization scheme.

Systems and methods for optimizing the combination of products and services a business offers to customers, identifying a combination of top markets a business offers to customers for growth opportunities, and optimizing advertisements. In one implementation, the disclosed system includes at least one processing device and at least one non-transitory memory containing software code configured to cause the processing device to: gather customer data and financial institution data from a plurality of data sources; extract a plurality of customer behavior features and a plurality of financial institution behavior features; process the customer behavior features and financial institution behavior features using one or more trained foundation models; input the foundation model outputs and a plurality of goal inputs into a trained product model; and output a natural-language market response.

The invention discloses a purchase demand prediction method combined with PMC material management and control, and belongs to the technical field of material management and control data processing, and the method comprises the steps: obtaining a material consumption sequence and corresponding timestamp data, and employing a time sequence decomposition method to separate trend components and seasonal components, and obtaining a consumption rule feature sequence; if the fluctuation amplitude of the consumption rule feature sequence exceeds a preset threshold value, determining the consumption rule feature sequence as a basic demand predicted value sequence; obtaining a market price index sequence, and calculating a correlation coefficient between the basic demand predicted value sequence and the market price index sequence; if the absolute value exceeds a set threshold value, updating a basic demand predicted value sequence through a time sequence model; generating a purchase plan sequence according to the basic demand predicted value sequence and the inventory average value; and generating a final output report. The invention discloses a problem that a current material management and control mode cannot capture key changes in time and is difficult to balance contradictions between inventory and production.

The invention discloses a transaction decision-making method and system in a market joint clearing environment, and relates to the technical field of electric power spot market transaction, and the method comprises the steps: S1, obtaining a predicted value of a node marginal electricity price, and constructing a quotation optimization model of each power generation main body; s2, designing an agent, and training the agent by taking the quotation optimization model as a reward function; s3, generating quotation data based on the trained agents, and constructing a joint clearing model of all power generation subjects; s4, calculating the actual value of the node marginal electricity price based on the joint clearing model, and calculating the deviation between the actual value and the predicted value; s5, judging whether the deviation exceeds a preset threshold value or not, if so, taking the actual value as a predicted value, and returning to the step S1 for closed-loop learning; and if not, taking the quotation data generated by each agent as a final quotation strategy. According to the invention, global optimization and intelligent collaboration of power generation subject transaction decisions can be realized in a complex market environment.

The invention discloses a method for optimizing a market bidding participation strategy of a sea wind energy storage system, and the method comprises the steps: constructing a double-layer game model which comprises a day-ahead electric energy market and a standby market, and an intra-day real-time scheduling and deviation adjustment model, and carrying out the short-term prediction of wind power output, a standby call rate, a day-ahead opponent market quotation and an intra-day market electricity price. And generating a typical scene set in combination with Monte Carlo simulation and confidence interval analysis. In the day-ahead stage, on the basis of the typical scene set and the double-layer game model, bidding power, bidding price, standby capacity configuration and an energy storage charging and discharging plan of the sea wind energy storage system in the electric energy market and the standby market are determined; and in the intra-day stage, combining real-time prediction data and a day-ahead bidding result, and utilizing a real-time scheduling and deviation adjustment model to dynamically correct the intra-day bidding power and adjust an energy storage operation strategy. The method can effectively improve the income and risk management and control capability of the sea wind energy storage system in a complex market environment, and achieves the optimal bidding and operation decision under the cooperation of multiple markets.

The embodiment of the invention relates to the field of data processing, and provides a pledged cargo management method based on AI vision and a related device, and the method comprises the steps: carrying out the multi-dimensional vision monitoring of a current pledged cargo based on an AI vision technology, and obtaining the multi-dimensional vision monitoring data; according to the market value multi-source data and the multi-dimensional visual monitoring data, constructing a target market-goods dual-channel valuation model; performing market value estimation on the current pledged goods based on a target market-goods dual-channel value estimation model to obtain current market value data; when the current market value data meets a preset condition, generating pledged cargo risk early warning information; and carrying out risk early warning according to the pledged cargo risk early warning information. By adopting the method provided by the invention, more accurate and more effective risk management of the current pledged goods can be realized based on the AI vision technology.

The invention discloses a cross-border e-commerce marketing strategy intelligent management system based on big data, and relates to the technical field of cross-border e-commerce marketing strategy management.The cross-border e-commerce marketing strategy intelligent management system utilizes a heterogeneous graph neural network to construct a user-commodity-culture relation graph, and dynamic weights are given to nodes through an attention mechanism; differences of consumers in each region in historical transactions, click rates and culture similarities are effectively captured, so that the problem of insufficient adaptability of a traditional fixed rule model in a multi-culture market is solved; carrying out emotion and semantic feature extraction on a multi-language comment text by adopting a Transform structure, and realizing rapid self-adaption of the model under the condition of data scarcity in combination with a meta-learning strategy; according to the combination, deep semantic information reflecting emotions and preferences of different regions can be accurately extracted, and cross-culture adaptability recommendation results are generated by fusing image embedding and text processing results, so that marketing contents better meet personalized requirements of consumers in various regions.

The invention provides an automobile after-service pricing model optimization method under dynamic supply and demand balance, and relates to the technical field of pricing model optimization. The pricing model optimization method comprises the following steps of accessing a government policy issuing platform and an industry association database, capturing related policies and macroeconomic indicators of automobile post-service in real time, performing semantic analysis on financial news and policy interpretation articles, extracting potential factors, and performing classification and analysis on the potential factors. A competitor official website, an e-commerce platform and a social media account are monitored for 7 * 24 hours, a real-time feedback module is embedded in a service platform, customers are encouraged to evaluate services, prices and new product demands in time, a market signal threshold model is established, and the market quality is improved. And early warning thresholds are set for the policy change frequency, the competitor price fluctuation amplitude and the customer demand growth rate index. Through real-time market dynamic monitoring, scientific quantitative analysis, intelligent and accurate decision making and rapid iteration strategies, the enterprise pricing ability and market competitiveness are effectively improved.