27 results about "Market needs" patented technology

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 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.

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.

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

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 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.

A system is provided.SOLUTION: A system, comprising: means for forecasting market demand; means for optimizing logistics; means for reducing food waste; and means for collecting and preprocessing data.SELECTED DRAWING: Figure 1

The application discloses a kind of method and system for evaluating the security and economy of load demand response process, belong to electric power market demand response technical field.The method of the present application comprises: based on industrial and commercial user load, the analysis model of load demand response process is established;For the production process of target industrial and commercial user load, the correlation between the production process is established, the correlation is divided into multiple minimum subcut sets;Determine the economic influence of the multiple minimum subcut sets on target industrial and commercial user load demand response process;Determine the security and economy of target industrial and commercial user load demand response process.The present application can be flexibly organized according to spot market price, the adjustment amplitude and duration, so as to maximize the benefit.

The invention relates to the technical field of e-commerce management, in particular to a cross-border e-commerce supply chain data analysis method and system based on artificial intelligence, and the method comprises the steps: based on commodity transaction data of a cross-border e-commerce transaction platform in a specific sales cycle, responding to a condition that a first transaction growth rate of a commodity in the commodity transaction data exceeds a preset growth amplitude threshold value; determining first potential purchase popularity distribution of the commodity in the target market region based on the first market region distribution data; based on the first potential purchase popularity distribution, obtaining historical supply chain operation data of the commodities in the corresponding target market region, and performing association rule analysis on the historical supply chain operation data to obtain an association operation feature mode of the commodities in each link of the supply chain. According to the invention, by dynamically adjusting the inventory management and control strategy, the inventory distribution can be optimized in real time according to the market demand change and the commodity transaction data, so that the phenomena of inventory overstock and stockout are reduced, and the overall inventory turnover rate is improved.

According to the method, multi-dimensional data of an electric power market and an electric power system are obtained in real time and subjected to standardized preprocessing, then a plurality of risk factors including market price fluctuation, load fluctuation and equipment faults are calculated, and an isolation score is obtained by using an isolation forest algorithm; and a comprehensive abnormal score is obtained through weighted fusion of the first classification result and the second classification result, so that the risk level of the power market at any moment is accurately and dynamically determined, and when the risk level reaches a preset condition, the system automatically triggers a response mechanism, obtains current power system state data and market demands, calculates the system risk level, and finally performs power supply. According to the method and the system, an optimal control strategy is selected for intervention based on a heuristic optimization algorithm, so that full-process automation and intelligentization from data acquisition, risk identification to response decision making are realized, the problems of insufficient data fusion capability and response lagging in the prior art are solved, and the real-time performance and accuracy of risk monitoring and the operation reliability of a power system are remarkably improved.

The invention discloses an industrial chain information service system based on big data, and relates to the technical field of industrial chain information services, the system comprises a market information acquisition unit and a sales information acquisition unit, the market information acquisition unit is used for acquiring market information of products, and the sales information acquisition unit is used for acquiring sales information of the products; the market information acquisition unit is used for acquiring market information, the market information comprises competitor information, market demands, market trends and price changes, and transmitting the acquired market information to the data preprocessing unit, the sales information acquisition unit is used for acquiring sales information of products, and the sales information comprises order information, sales amount, sales channels, sales areas and customer feedback. And the acquired sales information is transmitted to the data preprocessing unit. According to the invention, opinions and feedback of consumers on products and markets can be obtained through the market information acquisition unit, and a large amount of quantitative and qualitative data can be obtained for market research and consumer insight.

The invention relates to the field of purchase data automatic real-time response, in particular to a purchase data automatic real-time response method for market demand fluctuation, which comprises the following steps: constructing a multi-source data acquisition module to acquire market demand, industry dynamics, inventory and historical purchase data in real time; carrying out cleaning, abnormal value elimination and normalization processing on the data; calculating a demand fluctuation coefficient based on a weighted linear regression fitting formula, and dividing fluctuation grades according to a preset threshold value; according to the fluctuation level and the comprehensive supply and demand difference, automatically matching a purchase quantity and a purchase period adjustment strategy; the fluctuation model precision and the purchasing strategy effect are evaluated through a dual verification mechanism, and model parameters and a rule base are dynamically optimized based on feedback data to form a closed-loop adaptive optimization process. Through adoption of the technical scheme, high-sensitivity perception, accurate quantification and intelligent response to market fluctuation can be realized, and timeliness and accuracy of purchase decision and system self-evolution capability are remarkably improved.