99 results about "Market needs" patented technology

The invention provides a multi-factory collaborative production management method, and the method comprises the steps: obtaining market demand information, and real-time equipment operation state data, real-time material inventory information, historical sales parameters, production demand data and production process evaluation information of each factory; an industrial knowledge graph is constructed for the production demand data and the production process evaluation information based on a large language model, the market demand information and the historical sales data are analyzed to obtain production configuration data, and a dynamic production demand model is generated; according to the real-time equipment operation state data and the real-time material inventory information, a basic capacity allocation scheme is determined and corrected, and a collaborative production scheduling instruction set is obtained for collaborative production management; according to the method, the unified industrial knowledge graph is constructed through the large language model, the data utilization rate is improved, the production scheduling scheme is generated based on the real-time dynamic production demand model, the order delivery punctuality rate is improved, the traceability relation chain fully covered in the production process is constructed, the comprehensive utilization of equipment is improved, and the logistics scheduling cost is reduced.

The invention discloses a purchase management decision support system driven by an AI large model, and particularly relates to the technical field of intelligent purchase management. A data acquisition module obtains historical and current purchase, market price, inventory, sales and customer feedback data of commodities; the clustering analysis module divides purchasing groups based on inventory states and market prices; the self-portrait construction module generates a comprehensive self-portrait through client feedback data; the market analysis module analyzes sales data and predicts future demand changes; the optimization adjustment module optimizes the purchase quantity in combination with demand prediction, customer preferences and purchase group data, so that the accuracy and efficiency of purchase decision making are improved; through intelligent data processing and model prediction, the limitation of dependence on experience and static rules in traditional purchase management is avoided, fluctuation of market demands can be responded in real time, the purchase quantity is ensured to be matched with the market demands, inventory overstock and shortage phenomena are avoided, and customer satisfaction and market competitiveness are improved.

The invention relates to the technical field of market demand analysis, in particular to a big data-based cross-border e-commerce market demand prediction system, which comprises a culture trend identification module, a sudden demand evaluation module and a demand prediction output module, wherein the culture trend identification module is used for capturing text data of sudden popular culture in a short period through a multi-dimensional data source; the sudden demand evaluation module is combined with sudden popular culture to analyze and quantify demand fluctuation for hot commodities in a short period; and the demand prediction output module provides promotion and marketing activity suggestions of hot commodities and inventory adjustment strategies according to the demand change prediction result. According to the method, the dependence of a traditional demand prediction method on a long-term trend is broken through, the accuracy of short-term demand prediction is remarkably improved, and a cross-border e-commerce platform is helped to respond to market changes in time.

The invention provides an intelligent industry chain optimization method and system based on artificial intelligence, and relates to the technical field of industry chain optimization. The method comprises the following steps: collecting historical industry chain data of an enterprise, constructing a training data set, and training to obtain a market demand prediction model; performing data segmentation on the historical industry chain data, inputting the data into the market demand prediction model, generating a plurality of market demand prediction values, and determining a first prediction fluctuation range; obtaining target industry chain data and inputting the target industry chain data into the market demand prediction model to generate a first demand prediction result; performing data fluctuation analysis and residual fluctuation analysis on the target industry chain data, and correcting the first predicted fluctuation range to generate a second abnormal fluctuation range; and generating a second demand prediction result based on the first demand prediction result and the second abnormal fluctuation range, and optimizing the industry chain of the enterprise according to the second demand prediction result. According to the invention, the intelligent level of enterprise industry chain optimization is enhanced.

The invention relates to the technical field of logistics digital management, in particular to a logistics digital management system based on an AI large model, and the system comprises a demand prediction module which collects market trend data, seasonal change information and promotion activity records, combines the market trend data, seasonal change information and promotion activity records into a market data set, calculates the current commodity demand probability, and generates a market demand matrix; and adjusting Bayesian network parameters according to the market demand matrix to obtain a demand prediction value of each commodity. According to the method, the demand prediction result is dynamically generated after the market trend data, the seasonal change information and the promotion activity records are integrated, and the fluctuation characteristics of the market environment can be captured, so that the commodity demand change is predicted, and the problem of information loss in traditional single-point prediction is avoided. In the aspect of inventory management, real-time updating of the inventory state can be achieved by means of ideal inventory level calculation and execution of a dynamic inventory adjustment plan, and the risks of resource waste and inventory structure imbalance are reduced.

The invention discloses a multi-mode electronic commerce data integrated management system, and relates to the technical field of electronic commerce data management. The multi-modal electronic commerce data integrated management system comprises a data acquisition module used for acquiring the current sales volume and commodity sales data of each commodity after the current period is ended, a data analysis module used for analyzing the customer flow index and the customer emotion fluctuation index of each commodity based on the commodity sales data, and a data storage module used for storing the customer flow index and the customer emotion fluctuation index of each commodity. The prediction analysis module is used for predicting the sales prediction quantity of each commodity in the next period based on the customer flow index and the customer emotion fluctuation index of each commodity, and the inventory calling module is used for carrying out scheduling supplementary processing on each commodity based on the sales prediction quantity of the next period. According to the method, the customer flow index and the customer emotion fluctuation index are integrated, the purchase intention and market demand change of the customer can be reflected more comprehensively, and the sales volume of each commodity can be accurately predicted through the multi-dimensional data fusion mode.

The embodiment of the invention provides a material processing method and device, equipment, a storage medium and a program. The method comprises the following steps: acquiring demand driving data of a target material and market environment data corresponding to the target material; based on a deep learning algorithm, determining a sales prediction result of the target material in a set time period according to the demand driving data, and determining market demand distribution of the target material in the set time period according to the market environment data; determining a production strategy according to the sales prediction result and the market demand distribution, and performing production operation on the target material based on the production strategy; and based on the actual market information in the production operation process, the production strategy is adjusted according to a reinforcement learning algorithm and a multi-objective optimization algorithm, and production operation is performed on the target material based on the adjusted production strategy. According to the scheme, market demand changes and emergencies can be dynamically handled, and efficient processing of the material data is realized.

The invention relates to the field of intelligent design, and provides an automobile product front face design method and system based on perceptual engineering, which accurately captures user demands and optimization targets by extracting and classifying emotion keywords, provides effective basic information for personalized emotion demands and market demands, and improves the user experience. An automobile front face form generation method is designed, an automobile front face form database is constructed, the design process efficiency is greatly improved so as to achieve the purpose of quickly responding to market demands, an emotion demand analysis method is designed, weights of emotion keywords are comprehensively sorted in a subjective and objective combination mode, and the market demands are rapidly responded. According to the method, the front face of the automobile product is designed, so that the deviation between the design scheme and the market demand is avoided, the design efficiency is greatly improved, a mapping model is further designed for scheme optimization to accurately respond to the emotional demand of the user, the design scheme which is more creative, personalized and differentiated is produced, and the pertinence and efficiency of the front face design method for the automobile product are improved.

The invention discloses an inventory optimization system and method based on multi-source data, and belongs to the technical field of inventory optimization. The production data and the sales data are acquired and fused to obtain the multi-source data, the LSTM neural network is trained by using the multi-source data, and the future market demand is predicted; on the basis of future market demands, in the aspect of space resource consumption and the aspect of time resource consumption, target functions and constraint conditions are set respectively, the corresponding target functions are solved, and the minimum warehouse resource consumption is obtained through calculation; processing the historical sales data by using a moving average method, and predicting the future sales volume of the warehouse products; combining the future sales volume with the existing stock volume and the in-transit volume, and calculating the replenishment volume; obtaining logistics data, determining the replenishment time according to the logistics transportation time, and adjusting the replenishment amount according to the transportation capacity; and based on the replenishment time and the replenishment amount, using a dynamic planning algorithm to obtain an optimal distribution frequency, and matching the optimal distribution frequency with the inventory to obtain an inventory use plan.

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 the technical field of brand marketing, in particular to a brand marketing system and method based on a block chain. Comprising a market monitoring module, a dynamic pricing module, a consumption prediction module and an evaluation optimization module. The market data is monitored in real time through the market monitoring module, a multivariate linear regression model between market demands and prices is established through the dynamic pricing module, the price of the brand is dynamically adjusted by taking profit maximization as a target, the consumption prediction module is utilized to construct a random forest model, and the price of the brand is dynamically adjusted. Each consumer is classified into a corresponding level according to a comprehensive score, personalized marketing suggestions are generated, the price of a brand is dynamically adjusted according to the change of market demands, the price of the brand can be ensured to conform to the change of the market in real time, the purpose of brand promotion is achieved, targeted marketing of consumer groups is achieved, and the market promotion efficiency is improved. And the marketing quality and efficiency of the brand are improved.

The invention discloses a main material and equipment price selection method and system for a maintenance project, and relates to the technical field of power maintenance project price selection, and the method comprises the steps: extracting field information from a material price library, and recognizing a core word through a semantic analysis model; constructing a core word index file, and performing word segmentation and matching processing on retrieval keywords; and sorting the matched data based on a weight rule, and generating a recommendation result for a user to refer to. According to the method, a dynamic and real-time data basis is provided for subsequent analysis, so that the system can better adapt to continuously changing market requirements, the reading speed of the index file is increased, the search sensitivity and accuracy are improved, the requirement of a user for instant information is met, and the user experience is improved. According to the method, the recommendation transparency and credibility are improved, and the user experience and participation sense are improved, so that the pertinence and rationality of decision making are improved, and efficient market response and material selection strategy optimization are realized.

The invention discloses a multi-dimensional technical achievement evaluation method and system, and the method comprises the following steps: extracting a technical keyword of a technical scheme, obtaining a related technical scheme, evaluating a technical innovation value, a technical width value, a technical independence value and a technical science and technology value of the technical scheme through a related technical file, and obtaining a scientific value grade; acquiring a market demand value, a marketization capability value and a market value grade according to the similar technical scheme; evaluating a cost feasible value, a technical benefit value and an economic value grade according to a difference value between the technical scheme and the similar technical scheme; extracting a first inventor and an invention unit of the technical scheme, and obtaining an authoritative value grade of the technical scheme; determining a weight by using an analytic hierarchy process and an entropy weight method and obtaining a technical scheme grade; the evaluation comprehensiveness is guaranteed through multi-aspect evaluation, the evaluation result is more accurate and effective, the weights are obtained through the analytic hierarchy process and the entropy weight method, the subjectivity of manually setting the weights is reduced, and the accuracy and comprehensiveness of weight distribution are guaranteed.

The invention relates to the technical field of enterprise resource management, in particular to an enterprise management optimization method and system based on big data, and the method comprises the following steps: capturing data of employee skills, a project execution process and customer interaction according to enterprise resource planning in an enterprise, analyzing the captured data, and carrying out the multi-dimensional information classification processing; and constructing an enterprise resource comprehensive data set. According to the method, the flow and utilization efficiency of the resources is improved by establishing the graphic database to integrate the enterprise resources and converting the data elements into the network nodes and the connecting edges, and the resource flow and utilization efficiency is improved by identifying and analyzing the association strength of the nodes and the weights of the edges in the resource network. Through detailed flow path analysis, an enterprise can formulate a scientific resource allocation strategy according to market requirements and current resource situations, resource allocation and use are optimized after dynamic resource allocation is implemented, the strategy is adjusted in time by comparing and analyzing resource efficiency and cost effectiveness before and after optimization, and the market response speed and competitiveness of the enterprise are remarkably improved.

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 discloses a fabric inventory optimization method and system, and the method comprises the steps: collecting market, sales and production data; predicting a fabric demand according to the collected data; planning fabric purchase and production plans according to the predicted demands, and making inventory space planning in advance; and monitoring the fabric inventory level in real time, and adjusting the purchase and production plan according to the inventory level. According to the invention, market demands and trends can be reflected in real time, fabric demands can be effectively predicted and planned, the inventory level can be dynamically adjusted, and optimal management of fabric inventory is realized.

The invention discloses an intelligent embroidery pattern generation method and system based on a machine learning model, and the method comprises the steps: collecting embroidery pattern design demands in real time according to an e-commerce platform, social media and market trend data, and obtaining a real-time updated data set; according to the data set, multi-modal features of the embroidery patterns are extracted, pattern feature identifiers with cultural features are generated, and fused pattern features are obtained; according to the fused pattern features, generating an embroidery pattern meeting the design requirements of the minority, and obtaining a preliminary embroidery pattern; and according to the preliminary embroidery pattern, compressing the small sample generation model into a lightweight model suitable for small and medium-sized enterprises or mobile terminal equipment so as to obtain an embroidery pattern generated in real time. According to the embodiment of the invention, market demands can be responded in real time, small embroidery patterns with cultural characteristics and high quality are generated, and the practical applicability of the generated model on small and medium-sized enterprises and mobile terminal equipment is improved.

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 discloses a market demand-based product research and development process and management method. The method comprises the following steps of 1, market demand acquisition and standardized modeling; step 2, demand decomposition and association degree quantization; step 3, project information integration and plan generation; 4, performing dynamic plan management and conflict detection; step 5, task execution and state tracking; and step 6, demand closed-loop verification and feedback. According to the method, the requirements are integrated through standardized modeling, quantifiable research and development input is realized, and the problem of analysis fragmentation is solved; by means of automatic tracing and dynamic management and control, the demand change influence is accurately evaluated, and resource waste is avoided; through whole-process standardized collaborative operation, human intervention is reduced, a PDCA cycle optimization mechanism is formed, human error risks are reduced, market demand full-life-cycle closed-loop management is achieved, and research and development efficiency and quality are 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.

The present invention discloses a supply chain production scheduling optimization method and system based on multi-source data. The method extracts supplier procurement data through a data crawler, analyzes the commonality of raw materials in combination with a knowledge graph, and generates a raw material commonality list; based on delivery records and product attributes, a linear regression is used to predict the quality stability value, and the process similarity is calculated through a text similarity algorithm to construct a process similarity matrix; in combination with historical sales data, a multivariate linear regression and time series analysis are used to construct a market demand prediction model. The complex relationships among raw material commonality, process similarity, and market demand are characterized through a graph neural network to generate a product association network diagram, and the Pareto optimization is used to find the optimal balance point between delivery time and resource utilization rate to form a production scheduling plan; finally, an integer programming algorithm is used to comprehensively evaluate the performance score, quality stability value, raw material commonality list, and production scheduling plan to optimize the supplier portfolio. The present invention improves the production scheduling efficiency and accuracy and meets complex business requirements.

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 invention discloses a product pricing method, system and device based on data analysis, and the method comprises the steps: obtaining a product related data set, and obtaining a product feature data set through analysis and feature extraction; a comprehensive feature data set is obtained through the comprehensive feature data model and the product feature data set, and comprehensive feature data comprises sales comprehensive feature data, price comprehensive feature data, customer comprehensive feature data and inventory comprehensive feature data; establishing a plurality of product pricing pre-training models, and analyzing the product feature data set and the comprehensive feature data set to obtain product pricing prediction data; obtaining product pricing data through model training and model reasoning; and comprehensively analyzing the product pricing data to obtain a product pricing result. According to the invention, through comprehensive analysis of product related data, dynamic pricing of products of different time, markets, clients and categories is realized, the products are accurately priced in real time, and market demands are met.

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.

To provide a system.SOLUTION: A system includes: means for generating a human resource development cartel; means for analyzing past feedback by AI; means for multiplying real-time market needs and trends; and means for visualizing optimum carrier environment and steps, necessary skill, and subjects.SELECTED DRAWING: Figure 1

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 discloses an automobile part information construction method and device based on export requirements and a storage medium, and relates to the field of automobile part information analysis. The method comprises the steps that after market characteristics of each market needing to be exported are determined, automobile parts corresponding to the market characteristics are obtained; associating the automobile part corresponding to each market feature with the market feature to form a market feature module; defining corresponding label information for each market feature module; and forming the market feature module corresponding to the label information required by the market needing to be exported into the automobile part information of the market needing to be exported. According to the method, the operation process is remarkably simplified, the working efficiency is improved, only one-time analysis is carried out when the automobile part corresponding to the market feature is initially determined, secondary analysis is not needed subsequently, the error probability is further reduced, and the working precision is improved.