70 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 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 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 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 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 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 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 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.

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.

The invention relates to the field of intelligent design, provides a cultural and creative product design method and system based on perceptual engineering, provides effective basic information for personalized emotion demands and market demands by obtaining basic form information and comprehensive emotion information of a target cultural and creative product, and designs an emotion demand analysis method. According to the method, weight combination calculation is carried out in combination with a CRITIC analysis method and an entropy weight method, emotion demand weights are accurately obtained, the design pertinence is further improved, then a design sketch generation method is designed, the design sketch of a target cultural and creative product is rapidly generated through contour extraction processing and diffusion generation processing, the design process efficiency is greatly improved, and the design cost is reduced. An emotion mapping model is further designed, the design sketch is optimized through an empirical mode decomposition module, a kernel principal component analysis module and a self-attention enhancement module, the comprehensiveness and adaptability of the design are further improved, and the pertinence and efficiency of the creative product design are improved.

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 a supply chain green emission reduction and coordination method based on Bayesian update inference, which is applied to a two-stage supply chain system composed of a product supplier and a product retailer, and is characterized in that the product retailer predicts the market demand of the next sales period according to historical data; the market demand prediction precision is improved through Bayesian update inference, then a retailer places an order to a supplier according to the predicted market demand after the precision is improved to report the order quantity, and the predicted market demand information is synchronously transmitted to the supplier to realize information sharing; through supply chain upstream and downstream collaborative integration and deployment, suppliers can adjust production tasks according to predicted market demands, it is guaranteed that participants of the whole supply chain can reasonably coordinate resources and adjust operation strategies under accurate market demand prediction, and green emission reduction and coordination of the supply chain are achieved. According to the method, the vicious circle of prediction distortion-excessive production-resource waste-carbon emission increase in a traditional supply chain is effectively broken.

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 value evaluation, in particular to a short video traffic futures transaction and value evaluation system, which realizes accurate comparison and matching of cross-platform traffic through deep mining of periodic feature offset, accurate recognition of click intensity difference between platforms and standardized adjustment, and improves the accuracy of value evaluation of the short video traffic futures transaction and value evaluation. The value evaluation has higher applicability and operability, the periodic structure of the data is analyzed through the causal forest algorithm, potential influence factors in flow fluctuation can be deeply identified, the predictability of short video flow and the stability of future value evaluation are improved, and through accurate judgment of the long and short-term memory network in conversion deviation, the value evaluation accuracy is improved. According to the method, the operation response characteristics of the content can be in butt joint with market demands, quantitative prediction of future traffic value is realized, a feasible decision basis is provided for a platform and a content creator, the decision quality of market participants is improved, and the efficiency and reliability of traffic futures trading are optimized.

The present invention discloses an integrated optimization method for production planning and scheduling facing market demand uncertainty, including: S1: initializing the parameters and population of the algorithm; S2: passing each particle into the production planning layer to calculate the optimal cost and production indicators; S3: passing the production indicators of each particle into the production scheduling layer to calculate the sum of costs for all scheduling periods; S4: calculating the objective function of each particle and evaluating it; S5: updating the positions of all particles; S6: executing steps S2 - S4; S7: determining whether the convergence condition is met. If it is met, the optimal production plan and scheduling results are output; if not, return to S5. The present invention effectively solves the problem that the optimal solution effect of production planning and scheduling is not ideal or even infeasible due to market demand uncertainty through a three-layer structural model of a market demand uncertainty layer, a production planning layer, and a production scheduling layer.

The present invention discloses a method, device and electronic device for rounding the mantissa of market demand sheets, which are used to solve the problems of high cost, large number of input personnel, long time consumption, difficulty in large-scale implementation, and easy expiration of products due to failure to process the mantissa in time in the existing mantissa rounding method. The present invention includes: obtaining market demand sheets of a preset product and determining the mantissa type of the preset product; calculating the mantissas of the market demand sheets corresponding to the mantissa type; determining the comprehensive priority of each market demand sheet according to the mantissa; and performing mantissa rounding on each market demand sheet according to the comprehensive priority.