45 results about "Market needs" patented technology

The invention relates to the technical field of intelligent supply chain optimization, and according to the intelligent supply chain platform software optimization system and method based on the large model, the large model can dynamically predict market demand fluctuation by combining historical demand data with a real-time sales trend, so that prediction deviation caused by single data in a traditional method is avoided; according to the invention, shortage materials can be supplemented in time to cope with sudden demands, fund and storage cost occupied by excess stock can be reduced, supply shortage or excess risks are reduced, products are recommended through real-time matching based on portraits, demand matching precision is improved, the problem of poor user experience caused by lack of data support in a traditional recommendation mode is reduced, and user experience is improved. And through integrated regulation and control of links such as demand prediction, logistics, production, purchasing and recommendation by a large model, intelligent linkage of a whole process of a supply chain is realized, and an information barrier of independent operation of each link in a traditional mode is broken through.

The invention relates to a dynamic purchase plan generation system and method based on performance prediction. The system comprises a data collection module which is used for collecting and integrating historical sales data, inventory data, market demand data and supplier delivery data; the performance prediction module comprises a prediction model and is used for carrying out demand prediction based on historical data and selected features; the purchase plan generation module is used for making a purchase plan according to the prediction value generated by the performance prediction module, the current inventory condition and a preset purchase strategy, wherein the purchase strategy comprises the minimum order quantity, the maximum inventory quantity, the supplier delivery date and the like; the dynamic adjustment module is used for monitoring actual sales and inventory conditions in real time; the user interface module is used for displaying the prediction data and the purchase plan; and the report generation module is used for generating a prediction result. According to the system, complex data can be effectively integrated and managed, the accuracy of demand prediction is improved, and a purchase plan is optimized, so that efficient supply chain operation is realized.

The invention provides a power grid dynamic management and control method, device and equipment based on a multi-market subject game, and relates to the technical field of power system management. The method comprises the steps that market demand data and price fluctuation data are acquired, a multi-agent mixed game model constructed in advance is acquired, and the multi-agent mixed game model comprises a bidding decision-making layer and a market clearing layer; in a bidding decision-making layer, each power generation enterprise selects a corresponding game mode according to the market demand data and the price fluctuation data, and formulates a power supply strategy by taking maximization of profit as a target; each thermal power enterprise formulates a power supply strategy according to the power market supply-demand ratio and the thermal power enterprise market share based on a multi-agent depth deterministic strategy gradient MADDPG algorithm; and in the market clearing layer, the independent system operator determines the planned power generation amount and the market clearing price of each power generation enterprise according to the load constraint condition and the unit constraint condition by taking the minimum power purchase cost as a target. The decision-making accuracy of the power generation enterprise can be improved.

The invention belongs to the technical field of intelligent manufacturing, and discloses a demand-driven material demand planning system based on deep reinforcement learning and an optimization method. The system comprises a demand module which is used for collecting, processing and outputting related demands and supplier environment data; the demand-driven material demand planning module is used for generating an inventory replenishment plan, a production plan and a material purchasing plan; the simulation module is used for constructing and operating a simulation model so as to output a simulation experiment statistical result; the deep reinforcement learning module interacts with the demand-driven material demand planning module and the simulation module, carries out training by utilizing a built-in algorithm library, and outputs an optimization parameter set of a demand-driven material demand plan; and the execution module is used for converting the plan information into an operation instruction and feeding back actual operation data and an execution result so as to make up for the deficiency of static parameter configuration of the traditional demand-driven material demand plan, realize collaborative optimization of inventory level and service rate and enhance the response capability of an enterprise to market demand change.

A modular tumbler, vessel and plates made with a durable material that includes a NFC and a Bluetooth™ Low Energy (BLE) Beacon for commercial trade and item location purposes. The invention includes a modular tumbler, vessel and plate holder with accessories that will avoid usage of disposals items. The volume and size of the tumblers, vessel & plate can change according to market needs, lids and tumblers holders includes modular features as well to be able to fit the different accessories. The present invention include multiple modular features and accessories and allow tumblers, vessels and plates to be merge with Internet of things solutions by using the NFC & BLE chips to exchange data with Smart devices like Tablets, smart phones, computers, others.

This invention discloses a carbon quota management method and system for carbon asset trading, relating to the field of data management. The method includes: constructing a knowledge graph linking enterprises to the environment, and using a cross-modal attention mechanism to fuse multimodal data to classify enterprises by industry; using a data envelopment analysis algorithm to determine carbon emission risk thresholds for each industry, and constructing a two-layer game framework combined with a multi-head self-attention mechanism to generate a technological progress coefficient sequence to determine carbon quota ranges; constructing dynamic feature vectors and simulating carbon emission evolution paths under different scenarios using a Monte Carlo algorithm to output carbon emission accounting results; predicting the supply and demand trends of carbon quotas, calculating the supply and demand gap under different scenarios, and comparing the supply and demand gap with preset thresholds to trigger an adaptive adjustment mechanism. This invention can identify carbon emission risks, dynamically capture industry technological evolution and market demand changes, and improve the accuracy and flexibility of carbon quota management.

The invention discloses a day-ahead electricity price prediction method and device based on real-time market demand prediction. The method comprises the following steps: acquiring a market game expected index and traditional market data in a historical transaction period; constructing a fusion feature matrix based on the market game expected indexes in the historical transaction period and the traditional market data; matching the fusion feature matrix with preset historical day-ahead electricity price data to generate a training data set; training the integrated probability prediction model according to the training data set to obtain a target integrated probability prediction model; acquiring a market game expected index and traditional market data of a to-be-predicted period to construct a to-be-predicted feature matrix; inputting the to-be-predicted feature matrix into a target integrated probability prediction model to obtain a prediction processing result; and according to the prediction processing result, generating a day-ahead electricity price single-point prediction value and a day-ahead electricity price prediction interval. By implementing the scheme, more comprehensive and reliable price signals and risk quantification information can be provided for the operation optimization decision of the power system.

The application discloses a distributed power energy transaction settlement system, which comprises a distributed energy production end, a transaction center, a consumption end, a settlement center and a regional chain network, wherein the distributed energy production end, the transaction center, the consumption end and the settlement center are connected through the regional chain network; by introducing intelligent and automatic transaction and settlement processes, the overall efficiency of transaction is significantly improved; the intelligent and automatic processes can process transaction information in real time, quickly respond to market demand changes, the system can automatically match the demands of buyers and sellers, quickly complete transaction confirmation, and instantly generate settlement documents; the efficient processing mode not only shortens the transaction period, but also reduces errors and delays caused by human factors, so that the power energy transaction is more smooth and fast.

The utility model relates to the technical field of sugarcane peeling equipment, in particular to a sugarcane peeling device which comprises a plurality of pressing modules and a base which are arranged from top to bottom, and each pressing module is connected with the base through a fixing seat with a pressing spring. The sugarcane peeling device has the advantages that the connecting plate with the bending angle ranging from 150 degrees to 170 degrees is matched with the pressing spring so that the peeling tool bit can be attached to sugarcane, sugarcane peeling can be more stable, the peeling efficiency is greatly improved, the height-adjustable limiting supporting column is adopted for adjusting the gathering degree of the pressing module towards the center of the base, operation is easy, and practicability is high. Convenience and rapidness are achieved, and market requirements are better met.

An object of the system according to the embodiment is to evaluate market suitability of a business idea of a user and provide a specific action plan.SOLUTION: A system includes an idea input part, a market evaluation part, a customer specification part, and a competition analysis part. The idea input unit inputs an idea of a user. A market evaluation part evaluates the market needs on the basis of the idea input by the idea input part. A customer specification part specifies a target customer layer on the basis of the market needs evaluated by the market evaluation part. The competition analysis unit analyzes a situation of a competitor based on the target customer layer specified by the customer specification unit.SELECTED DRAWING: Figure 1

The invention relates to the technical field of supply chain scheduling, and discloses a supply chain production-transportation-sales cooperative scheduling method and system, and a storage medium. The system comprises a production data acquisition module, a transportation data acquisition module and a sales data acquisition module which are respectively used for acquiring plans, schedules, inventory, routes, states, costs, orders, prediction and market demand data of corresponding links; the production, transportation and sales association modeling module is used for establishing an association model based on the collected data and generating a collaborative scheduling prediction result; the dynamic feature extraction module combines historical and real-time data to extract dynamic features and identify a scheduling demand spatial-temporal change rule; the resource optimization scoring module calculates the resource adjustment priority and the allocation proportion according to the dynamic characteristics and the prediction result, and generates an optimization scheduling scheme; and the self-adaptive scheduling execution module adjusts production, transportation and sales scheduling in real time based on the optimization scheme to realize collaborative adaptive change. The system can break data islands and improve supply chain cooperation efficiency and strain capability.

This invention provides an e-commerce promotion method based on enterprise conditions and market demand, belonging to the field of marketing and promotion technology. By introducing enterprise production status data such as energy consumption and environmental protection cost ratios, and inventory backlog pressure coefficients, and considering market demand fluctuation factors such as search path complexity, social media viral activity indicators, and conversion competition rates, this invention makes product promotion strategies more precise and flexible. By analyzing the lag effect coefficient in the supply chain to correct demand evaluation, it obtains more accurate demand fluctuation predictions, providing a reliable basis for formulating final product ranking and promotion strategies. This not only significantly improves the accuracy of e-commerce promotion strategies but also enhances the enterprise's competitiveness in a dynamic environment.

The invention discloses an electricity market demand response management triggering privacy protection method based on an aggregation game, and belongs to the technical field of electricity market demand management. The method comprises the following steps: acquiring a real-time state of a generator by using a state-space equation; constructing an aggregation game model of the multi-turbine generator to optimize the profit of the generator; constructing a continuous time random noise generation model; transmitting a noise-added aggregation function estimation result and a noise estimation signal to other specified generators; determining an optimized output strategy of the generator; and constructing a state space expression of the turbine generator, and driving the generator to output power according to a predetermined strategy. According to the method, privacy protection is introduced to trigger dynamic average consistent control, time-varying Nash equilibrium point search and model prediction control, the total output electric power of the generator set in a dynamic environment meets the market power requirement, the strategy is adjusted to maximize the income of the generator set, communication resources are saved, and the power efficiency of the generator set is improved. And the privacy information of the generator is protected through the power value transmitted in the random noise interference network.

The application discloses a kind of decision method and system of power capacity market demand response based on scene method, determine the basic assumption of the participation capacity market of demand side response supplier, based on basic assumption, determine the uncertainty factor of the demand side response supplier decision model to be established;Determine the probability distribution of uncertainty factor, extract the different scene of the demand side response supplier decision model to be established;Determine the number of scenes that need to be removed, remove scene based on preset rule;Based on the scene after removal, establish demand side response supplier decision model;Establishing deterministic decision model, random decision model as contrast model;To demand side response supplier decision model, deterministic decision model and random decision model are solved, obtain the participation capacity of demand side response supplier, income and loss;Calculate the probability of violation, based on the probability of violation and the income and loss of demand side response supplier, determine the optimal number of removed scenes.

The invention relates to the technical field of human resource management, in particular to a flexible employment matching method and system based on big data, and the method comprises the steps: obtaining a post description text to establish behavior features, constructing a task association path to generate post intention elements, and building a flexible task distribution chart in combination with market demands; the job seeker information is integrated, the skill adaptation degree is evaluated, a skill matching list is generated, according to the skill matching list, resource allocation and job seeker behavior records are combined, a task combination with the optimal resource and time is screened out, and a flexible employment matching result is generated. According to the method, a behavior feature network is constructed by extracting post semantics, task intentions are aggregated based on logic paths, a task distribution system is constructed in combination with market demands, the skill matching degree is quantized through multi-dimensional evaluation, task paths are optimized through a resource and time balance model, and a closed loop of post demands, task extension and man-post matching is formed. And the matching timeliness, stability and collaboration are improved.

An object of the system according to the embodiment is to match advanced technologies with market needs and promote commercialization and monetization.SOLUTION: A system includes an acquisition unit, an element analysis unit, a collection unit, an organization unit, a matching unit, and a mediation unit. The acquisition unit acquires market needs. The element analysis part analyzes the technical element on the basis of the market needs acquired by the acquisition part. The collection part collects public technical information. The organization unit organizes and classifies the technical information collected by the collection unit. The matching unit matches technologies based on the technology information organized and classified by the organizing unit. The mediation unit proposes a combination of technologies matched by the matching unit to the company and mediates a contract.SELECTED DRAWING: Figure 1

The application provides a marketing strategy dynamic simulation optimization system data processing method and device, and relates to the field of data processing. The method comprises the following steps: establishing an industry market demand prediction model with a specified period based on industry characteristic data; establishing a market competition situation and market influence model based on historical market data, and determining the competition type of each market participant in the market competition structure in the target industry through the market competition situation and market influence model; establishing a production capacity and energy storage model based on the production process characteristic data, the upper limit of production capacity data and the inventory turnover level data of the target industry; constructing a market dynamic twin engine based on the production capacity and energy storage model, the market competition situation and market influence model and the industry market demand prediction model; and dynamically simulating the marketing strategies corresponding to different competition types through the market dynamic twin engine to obtain optimal marketing strategy data with a specified period.

The invention relates to a market demand prediction method and system for electricity-hydrogen coupling technology equipment, and the method comprises the steps: carrying out the energy consumption preference analysis of different user types in the potential application field of an electricity-hydrogen coupling technology, and obtaining the user portraits of all types of users; constructing a technical utility value function and a market demand function of the electricity-hydrogen coupling technical equipment based on the user portrait; wherein the market demand function is constructed based on a technical utility value function; comparing and analyzing competition elements of the electricity-hydrogen coupling technical equipment and the traditional equipment, and assigning scores to competition element parameters in the market demand function to obtain technical utility value of the electricity-hydrogen coupling technical equipment; based on the technical utility value, the market demand of the electro-hydrogen coupling technical equipment is obtained. Compared with the prior art, the method has the advantages of high market demand prediction accuracy and the like.

This application discloses a talent credit risk assessment method and system based on dynamic weight allocation. The method includes: acquiring talent assessment-related information from multiple data sources; constructing a talent capability profile model encompassing education, skills, innovation, and practical dimensions based on this information; real-time analysis of industrial policy texts in a regional economic planning database; dynamically adjusting the weight coefficients in the talent assessment according to industrial policy guidance and market demand changes; calculating a comprehensive capability score for the talent based on these weight coefficients across three dimensions: education, skills, and industrial contribution; generating a talent credit risk assessment model; establishing a credit product matching rule base based on score intervals; recommending suitable credit products based on the talent's comprehensive capability score; and deploying a federated learning architecture across multiple financial institutions to collaboratively optimize the talent credit risk assessment model. This application achieves precision, efficiency, and security in quantifying talent capabilities and matching financial products.

The invention discloses an edge computing power transaction method based on reinforcement learning, and relates to the technical field of edge computing and artificial intelligence, and the method comprises the steps: obtaining edge computing power resource data, market data and historical transaction data; constructing a state space based on the edge computing power resource data, calculating a state vector, setting a value range of a preliminary transaction based on the market data and the historical transaction data, and obtaining an action space based on the value range; designing a reward function based on the market demand model to obtain an experience tuple; inputting the trained double-depth Q network based on the experience tuple, outputting a Q value corresponding to the action, and selecting the action with the highest Q value as a transaction decision; executing a transaction decision, and performing adjustment feedback on the dual-depth Q network according to the market data and the historical transaction data; the learning efficiency and accuracy of the pricing strategy are improved by adopting a reinforcement learning algorithm; an objective pricing mechanism based on data driving is established, and the objectivity and scientificity are ensured by using historical transaction data, computing power resource data and market data.

The embodiment of the invention discloses a supply chain quality and pricing design method and system considering a reference quality effect. The method comprises the following steps: S1, constructing a supply chain dynamic quality and pricing decision model considering the reference quality effect in a competitive environment; s2, determining an optimal solution of each supply chain member decision variable and an optimal value of a profit function based on the supply chain dynamic quality considering the reference quality effect and the pricing decision model; and S3, performing sensitivity analysis on the supply chain member optimal decision and the optimal profit formed based on the optimal value in S2 to determine a final quality and pricing decision analysis scheme. According to the method, the supply chain dynamic quality and pricing decision model considering the reference quality effect in a competitive environment is constructed, and the dynamic reference quality effect is brought into consideration factors of market demands in a supply chain correlation analysis technology, so that theoretical guidance is provided for enterprises to reduce enterprise production cost and improve performance.

An object of the system according to the embodiment is to perform dynamic pricing and advertisement adjustment based on market demand and competition information.SOLUTION: A system includes a collection unit, an analysis unit, a proposal unit, and an adjustment unit. The collection unit collects market demand and competition information. The analysis unit analyzes the information collected by the collection unit. The proposal unit proposes appropriate pricing on the basis of the analysis result obtained by the analysis unit. The adjustor automatically adjusts the advertisement based on the pricing suggested by the suggester.SELECTED DRAWING: Figure 1

The invention belongs to an evaluation method, and provides a new energy marketization consumption evaluation method and a related device for solving the problem that the market demand is not adapted to the condition of multi-type and complex transaction rules and inter-provincial and inter-provincial transaction mutual coupling at present. The method comprises the following steps: performing evaluation based on power grid basic boundary data, multi-type market transaction data and government data including non-market electric quantity, and performing new energy consumption capability calculation by adopting a new energy power system time sequence production simulation method according to the power grid basic boundary data to obtain a basic new energy consumption evaluation result; and then, according to the basic new energy consumption evaluation result, evaluating a new energy marketization consumption condition, and after a marketization measure for promoting new energy consumption is implemented, respectively evaluating a promotion effect of development of new energy inter-provincial spot transaction on new energy consumption and a promotion effect of regional peak regulation auxiliary service on new energy consumption. And a more comprehensive and accurate new energy consumption capability assessment method is provided.

The invention discloses a clean energy and fire coal collaborative electric power transaction method, device, equipment and medium, relates to the technical field of electric power market transaction, and is adapted to an electric power market multi-power structure by splitting contract electric quantity into clean energy electric quantity and fire coal electric quantity so as to meet clean energy and fire coal collaborative transaction requirements. The method fits the current situation of diversification of a power energy structure, and a power transaction price calculation scheme covers independent pricing and collaborative pricing, so that independent accounting of sub-power supplies is supported, multi-power-supply collaboration can be realized, different transaction scenes and market requirements can be adapted, and the universality of the method is enhanced. The application focuses on the power market transaction pain point, and the constructed transaction method can balance clean energy and fire coal transaction, optimize the transaction logic of the market subject, assist the market in efficient and stable operation, and accord with the power marketization reform direction.

The invention provides a market demand influence-oriented monthly production plan adjustment intelligent optimization method and system, and relates to the technical field of intelligent manufacturing and production plan optimization. The method specifically comprises the steps of obtaining commodity future data of a target bulk commodity, and constructing a two-dimensional observation vector sequence; constructing an HMM demand prediction model and performing training; inputting the two-dimensional observation vector sequence into a trained HMM demand prediction model, and performing state decoding on the output of the model to obtain a demand state sequence and a state transition matrix; a multi-scene plan generation model is used for monthly production planning, and an effective alternative plan scheme set of the target bulk commodity is obtained; and screening the effective alternative plan scheme set by using a production utility evaluation model to obtain an optimal plan scheme. According to the invention, three links of market demand prediction, multi-scene plan generation and intelligent evaluation optimization are organically connected in series to form a complete and data-driven decision closed loop.

The invention provides a circuit-assisted market operation strategy optimization method and device based on model analysis, relates to the field of power markets, and solves the problem that in the prior art, when a market is optimized, due to unstable power supply of renewable resources, excessive production capacity of an adjusted market operation strategy is possibly caused, and the market operation strategy optimization efficiency is improved. And the development of a power system is not facilitated. The method comprises the following steps: acquiring operation parameter data of a power system and market transaction data of a power market; analyzing the operation parameter data of the power system to obtain market demand data; adjusting a transaction strategy based on the market demand data and the market transaction; predicting the market demand data according to the operation parameter data of the power system to obtain predicted demand data; evaluating the market risk according to the predicted demand data to obtain a risk level; and implementing risk management and control measures according to the risk level. The method and the device are used in a circuit-assisted market operation strategy optimization process.

The present application relates to data analysis technology, specifically to a cross-border market intelligent recommendation method and system based on multi-dimensional customs data fusion analysis, including a customs data analysis module and a competitive data analysis module; the present application classifies product information in a tree form, integrates multi-dimensional information such as import volume, trade flow, competitive data, and deeply mines market segmentation demand; and uses a linear regression model to predict import volume trend and time to reach the estimated maximum value, eliminates the interference of accidental factors, and ensures the stability of the recommended market demand; through analysis of trade correlation and product complementarity, enterprises can choose markets with strong product complementarity and little influence from competitors, reducing trade fluctuation risk; through quantifying the competitive difference between the enterprise and competitors; through a weighted scoring model, the target market is dynamically evaluated and sorted, and countries with high stability and strong competitiveness are preferentially recommended.

By constructing a multi-source data fusion collection system and a dynamic feature engineering method, firstly, five-dimensional data such as user behaviors, competitive product prices and supply chains are collected in real time through a distributed crawler cluster; secondly, performing weighted processing on historical data by using a time decay factor, and constructing a demand price elastic coefficient and a competition sensitivity index; performing dynamic prediction through a three-layer hybrid model, and simulating a market game in combination with an adversarial training mechanism; and finally, generating a Pareto optimal solution set based on a four-dimensional objective function, and outputting a differential pricing strategy through a business rule engine, thereby solving the problem that a traditional pricing model is insufficient in response to dynamic factors such as market demand elasticity and user behaviors, and improving the pricing efficiency in combination with a hybrid machine learning model and a multi-objective optimization strategy. And the span of price adjustment from minute-level response to hour-level decision is realized.

The invention discloses an optimal electricity price decision-making method of an electricity selling company based on a decision-making model, and belongs to the technical field of electricity markets. According to the method, the problem that the uncertainty of the market demand cannot be fully considered in the existing method is solved, the historical optimal electricity price is combined with the actual market condition and the profit target by constructing the decision tree model, and the optimal electricity price decision can be quickly provided for the electricity selling company based on historical experience; by constructing a multi-dimensional decision model integrating market demands, competition situations, cost structures and profit targets, the relation between the market demands and electricity prices is accurately analyzed for electricity selling companies, responses of competitors are predicted, and the dependency relation between cost and demand quantity is quantified; and the optimal electricity price with the maximum profit is solved through an optimization algorithm, so that the decision-making efficiency and accuracy of the electricity selling company in a complex market environment are improved, the market competitiveness and economic benefits of the electricity selling company are improved, and it is ensured that advantages are kept in intense market competition.

The invention relates to a data analysis technology, in particular to a cross-border market intelligent recommendation method and system based on multi-dimensional customs data fusion analysis, and the system comprises a customs data analysis module and a competition data analysis module. According to the method, the product information is subjected to tree-shaped hierarchical division, and multi-dimensional information such as the import amount, the trade flow direction and the competition data is integrated, so that market subdivision requirements are deeply mined; a linear regression model is used for predicting the trend of the import volume and the time for reaching the estimated maximum value, interference of accidental factors is eliminated, and it is ensured that the recommended market demand is stable; through analysis of trade correlation and product complementarity, an enterprise can select a market which has strong complementarity with own products and is slightly influenced by competitors, and the risk of trade fluctuation is reduced; the competitive power difference between the competitor and the competitor is quantified; through a weighted scoring model, the target market is dynamically evaluated and sorted, and countries with high stability and strong competitiveness are preferentially recommended.