31results about How to "Reduce inventory backlog" patented technology

The invention relates to the technical field of logistics supply chains, and discloses an intelligent logistics supply chain management method and system, and the method comprises the steps: obtaining the order data of a supply chain in real time; order analysis is carried out according to the receiving information, a delivery task is generated based on an order analysis result, and a first delivery path of the current delivery task is determined; planning an optimal path of supply chain order logistics distribution; inventory information of the supply chain is monitored and updated in real time, and the inventory capacity is dynamically adjusted. According to the method, order division can be realized, refined order management can be realized, a plurality of order sets which can be delivered once in a single pass can be determined, delivery order delivery tasks meeting conditions can be determined through the delivery target model, the optimal path can be dynamically adjusted, the delivery efficiency can be improved to the greatest extent, the delivery time can be shortened, and the logistics cost can be reduced. The inventory information is monitored in real time, the visibility of the inventory can be improved, and the inventory can be managed more scientifically.

The invention discloses a transparent logistics supply chain tracing management system fused with a block chain technology. The transparent logistics supply chain tracing management system comprises a data acquisition module, a block chain storage module, an intelligent contract module, a user interface module and a data analysis module, the data acquisition module is responsible for collecting real-time data of each link of a supply chain and transmitting the data to the block chain storage module; the block chain storage module uses SHA-256 and ECDSA algorithms to ensure the security and authenticity of the data; according to the method, the blockchain technology is utilized, data of all links of the supply chain is subjected to chaining storage, the data are open, transparent and non-tampering, and the credibility of the data is enhanced; through an SHA-256 Hash algorithm and an ECDSA signature algorithm, the security and integrity of the data in the transmission and storage process are ensured, and the data are prevented from being tampered or forged; and transactions and operations in the supply chain are automatically executed through the intelligent contract module according to a preset rule, so that human intervention is reduced, and the automation level and the operation efficiency of the system are improved.

The invention relates to the technical field of robots, and discloses a robot laser welding work center material distribution regulation and control method and related devices.The method comprises the steps that a welding production plan and a real-time material inventory condition of a robot laser welding work center are obtained; performing feature extraction on the welding production plan and the material inventory condition to obtain target features of the welding production plan and the material inventory condition; the target features of the two are input into a biologically heuristic material delivery intelligent regulation and control model based on fusion of a particle swarm optimization algorithm and an ant colony optimization algorithm, and a material delivery regulation and control strategy of the robot laser welding work center is obtained; and carrying out corresponding material distribution regulation and control on the robot laser welding work center according to the material distribution regulation and control strategy. According to the invention, timely supply of the materials can be guaranteed, and meanwhile, reduction of material distribution cost and reduction of stock overstock can be effectively realized.

The invention belongs to the technical field of engineering project material intelligent distribution management, and discloses an intelligent building material distribution management system, which comprises a monitoring area division module, a construction progress analysis module, a material demand identification module, a distribution route planning module, a distribution sequence planning module, a distribution process monitoring module and a distribution abnormity identification module. Whether material distribution needs to be carried out or not is judged based on the construction progress of each construction unit, smooth construction can be guaranteed through the analysis mode, material supply and construction requirements can be accurately matched, resource allocation is greatly optimized, and inventory overstock is reduced. According to the invention, during material distribution, the abnormal material weight condition and the abnormal equipment operation condition in the material distribution process corresponding to each target construction unit are analyzed, whether the material distribution process corresponding to each target construction unit is abnormal is judged, the weight abnormity can be accurately identified, the material allocation can be optimized, the supply and demand balance is ensured, and the material distribution efficiency is improved. Stock overstock and waste are reduced, and cost is reduced.

The invention discloses a television intelligent maintenance decision-making method, and the method comprises the following steps: obtaining historical work order information which at least comprises a television type, a fault description, a fault reason, a maintenance scheme, a maintenance category and used spare parts; constructing an intelligent fault library containing standard faults according to the historical work order information; establishing a knowledge graph according to the historical work order information and the intelligent fault database; acquiring new work order information, performing fault matching with the intelligent fault library according to fault information in the new work order information, and performing maintenance scheme recommendation according to the matched fault based on the knowledge graph; the invention also discloses a television intelligent maintenance decision-making system and a computer program. The intelligent, automatic and personalized television maintenance process can be realized, the quality and efficiency of the maintenance service can be greatly improved, the operation cost can be reduced, and the high-quality and reliable maintenance experience can be provided for the user.

The invention belongs to the technical field of labor tool management, and particularly relates to a multifunctional labor tool operation management system, which comprises an acquisition module, a PCBA (Printed Circuit Board Assembly) module, a wear detection module, a service life prediction module and a work quality analysis module, according to the invention, authority management is carried out through the RFID card reading unit, in-place and integrity detection is carried out on the labor tool by using the micro contact switch and the electric conduction contact reed, the state of the labor tool is monitored through the Internet of Things detection unit, and data transmission is realized through wireless communication, so that safe use and management of the labor tool are ensured; by configuring the wireless data transmission unit, the working state and the state of the labor tool are displayed in real time, the intuition and the effectiveness of management are further improved, the use permission, the in-place state and the integrity of the labor tool are effectively managed, and safe use of the labor tool is guaranteed.

The invention relates to the field of clothing, in particular to a customizable image generation method for clothing orders, which comprises the following steps: acquiring initial style data of clothing and generating a clothing database; obtaining body type parameters of a user, and performing matching in a clothing database based on the body type parameters to obtain a to-be-selected clothing group; obtaining a target garment selected by the user in the to-be-selected garment group, and obtaining an adjustment parameter of the user for the target garment; adjusting and decomposing the target garment based on the adjustment parameter to generate a garment pattern; and generating a clothing order based on the clothing pattern. Customers can obtain customized clothes completely meeting the requirements of themselves according to personal preferences and body characteristics. According to the method, the customer satisfaction is improved, and meanwhile, the problem of stock overstock in a traditional batch production mode is also reduced.

The invention discloses a fresh food intelligent distribution method and distribution system, and the method comprises the steps: obtaining a distribution region range and real-time weather data, and deploying a sensor to monitor the transportation and storage environment of fresh food; acquiring historical purchase data of a user, and predicting purchase categories in a special period by using a machine learning algorithm; according to the area range, a distribution route is optimized by adopting an intelligent algorithm; according to purchase data in a special time period, notifying a user of pre-purchase through a short message in advance, and integrating orders to distribute and deliver in advance; the fresh food is automatically recognized, classified and marked by adopting image recognition and voice recognition technologies; an automatic goods shelf, intelligent goods picking and an unmanned aerial vehicle are adopted, and fast and flexible fresh food distribution is achieved; establishing a plurality of supply sources, setting an early warning system, and monitoring each link of a fresh food supply chain; according to the fresh food intelligent distribution method and distribution system, more data sources and advanced prediction models are integrated, intelligent machine learning is adopted, and the fresh food demand prediction accuracy is improved.

The present invention discloses a storage goods management method, system, equipment and medium based on inventory-to-sales ratio, which relates to the technical field of warehouse management, including: constructing a data extraction neural network model, extracting characteristic information of the aggregated neighborhood nodes of the historical order data of commercial enterprises, obtaining the central time node aggregated characteristic vector of the historical order data of the N+1th commercial enterprise after the aggregation of the Nth commercial enterprise, and using the central time node aggregated characteristic vector as the input value of the neuron of the data extraction neural network model, setting the replenishment cycle, and outputting the combined characteristic vector of the historical order data arranged in chronological order; setting the same replenishment cycle, and obtaining the combined characteristic vector of the industrial enterprise production data within the same time; constructing an inventory-to-sales ratio demand forecasting model, inputting the combined characteristic vector of the historical order data and the combined characteristic vector of the industrial enterprise production data into the inventory-to-sales ratio demand forecasting model, and outputting the storage goods management result. The present invention can improve the storage efficiency of goods.

The present application provides a cross-border e-commerce comprehensive intelligent service method for multi-dimensional data analysis, including: obtaining sales data and return data of different regions in different time periods, including associated sales channels and associated product categories, and calculating the return volume and sales volume of each region in different time periods; classifying the sales data and return data according to the storage conditions corresponding to the associated product categories, each category corresponding to a warehouse category, and calculating the total return volume and the total sales volume corresponding to different warehouse categories; judging whether to establish a target warehouse in a target area according to the return volume and sales volume corresponding to the target warehouse category; for a recommended area identified as a recommended area for establishing a target warehouse in the target area, obtaining cross-border express delivery fees and warehousing cost data of the recommended area and adjacent areas, and evaluating the establishment cost of the target warehouse in the recommended area and adjacent areas.

The present invention discloses an agent distribution decision and inventory management optimization system based on GA-BP algorithm. By establishing a collaborative, forecasting and replenishment (CPFR) supply chain management model, it fundamentally realizes that suppliers, agents and the like fairly share relevant information on the supply chain. Each node enterprise on the supply chain shares the information on the supply chain on time, quickly and fairly, and negotiates according to its own capabilities and the information obtained to make a reasonable production or sales plan. In the present invention, by optimizing the weights through a genetic algorithm (GA), the obtained weights are applied to the BP network for training, effectively avoiding falling into a local optimal solution, and through a specific analysis of the enterprise's raw material safety inventory, the various factors affecting the safety inventory are understood in detail. Combined with the characteristics of the enterprise's raw material inventory, a good prediction effect is achieved, the unfavorable status of the enterprise's inventory management is improved, and the enterprise's profits are expanded.

The invention provides a traditional Chinese medicine supply chain collaborative management system and method based on an industrial internet, and solves the problems of traditional Chinese medicine supply chain collaborative management and the like, and the method comprises the following steps: S1, supply chain data collection and preprocessing; s2, selecting a data analysis algorithm; s3, carrying out model training; s4, detecting the prediction performance of the model, and evaluating the risk of the supply chain; and S5, supply chain collaborative management is carried out. The method has the advantages of good management effect, high safety and the like.

The invention relates to the technical field of intelligent storage management, in particular to an intelligent three-dimensional storage system for a transformer assembly line. Comprising a material incoming area, an automatic conveying subsystem and an intelligent dispatching subsystem. The material incoming area comprises a multifunctional three-dimensional goods shelf and a material recognition device. The material recognition device is used for recognizing the type, number and position information of materials, and the automatic conveying subsystem is used for conveying the materials from a material incoming area to a production line and comprises a conveying belt, an automatic guiding trolley, a suspension type crane and an annular rail car; the intelligent scheduling subsystem comprises a data processing module and a path planning module; the data processing module is used for receiving, processing and analyzing real-time data of the materials, and the real-time data of the materials comprises data uploaded by the material recognition device and production requirements of a production line; the path planning module is used for planning the conveying path of the materials from the material incoming area to the production line according to the real-time data of the materials. According to the technical scheme, the material management efficiency can be improved.

The invention discloses a double-channel supply chain system for cyclicity prediction and replenishment, and relates to the field of supply chain method accessory devices, and the double-channel supply chain system specifically comprises a closed-loop information tracing system. A circular economy index sharing system; a digital collaborative platform system; a third-party mechanism authentication and evaluation system; according to the double-channel supply chain system for cyclical prediction and replenishment, when the double-channel supply chain is systematically used by using cyclical prediction, inventory record errors can be timely found and corrected by periodically and cyclically checking the inventory condition, the phenomenon of excessive inventory or stockout caused by inaccurate data is reduced, and the work efficiency is improved. The accuracy of inventory management is improved; the circulation detection is beneficial to the real-time understanding of the sales dynamic state and the stock state of each channel, and the replenishment decision is made according to the data, so that the stock level can be adjusted more finely, the unnecessary stock overstock is reduced, the storage cost is reduced, the customer demand is ensured to be met, and the stockout loss is avoided.

This invention discloses a digital collaborative management system for the flexible textile production chain, relating to the field of textile production technology. The invention includes a data acquisition module, a data analysis module, a batch management module, a production module, and a database. First, based on sales information, return information, and remaining inventory of each flexible product, it determines whether each flexible product needs to be produced. The flexible products requiring production are referred to as "marked flexible products." Second, the raw materials required for each marked flexible product are acquired. The requirements of the raw materials for each marked flexible product on the production process and the mutual influence between the raw materials required for each marked flexible product are analyzed. Then, based on the analysis results, production batches for each marked flexible product are set, and finally, production is carried out. This reduces the frequency of inventory backlog and ensures the quality and production rate of flexible products.

The invention discloses a cigarette material demand prediction and inventory optimization method based on a time sequence model. The method comprises the following steps: acquiring cigarette production multi-source data, and constructing a standardized data set in combination with derived feature data calculated based on the cigarette production multi-source data; inputting the standardized data set into a pre-trained time sequence model, and outputting probabilistic demand prediction results respectively corresponding to the plurality of quantiles; calculating a relative confidence interval width according to a probabilistic demand prediction result; generating a matched target inventory value based on a comparison result of the relative confidence interval width and a preset confidence threshold; according to the target inventory numerical value and the current inventory data in the cigarette production multi-source data, the replenishment amount of the cigarette materials is calculated, an inventory optimization instruction is generated to optimize the inventory, the balance between the inventory and the risk is achieved, the cigarette production continuity is guaranteed, the inventory overstock and the storage cost are reduced, and the cigarette production efficiency is improved. And the lean and intelligent level of tobacco material inventory management is improved.

The invention relates to a WEEE recycling supply chain inventory optimization method based on evolutionary reinforcement learning, and belongs to the technical field of supply chain management and artificial intelligence. According to the method, a multi-level inventory cooperative control model is constructed, an inventory optimization problem is modeled as a Markov decision process, and an evolution reinforcement learning algorithm is utilized to train an intelligent agent to learn an optimal strategy. Core innovations include design state space capture inventory dynamics, action space processing continuation to discrete decision mapping, and cost-based minimization of reward functions. According to the algorithm, a population evolution mechanism and experience playback optimization are fused, and the learning efficiency and stability are improved through parallel exploration and sample screening. According to the method, the uncertainty of the recovery amount and demand can be effectively dealt with, the inventory cost is reduced, the supply chain robustness and sustainability are enhanced, and a self-adaptive intelligent solution is provided for WEEE management.

The invention provides a method for estimating the length of a secondary cable of a transformer substation, and the method comprises the following steps: space division and working principle division: dividing the transformer substation into a plurality of fields according to the space layout, and dividing the fields into a plurality of intervals according to the working principle; inputting basic data of the transformer substation, wherein the basic data comprises basic information such as total capacity, voltage class and main wiring system of the transformer substation; counting the number of cables or optical cables: counting the number of cables or optical cables of different specifications in each interval according to design and specific project requirements; estimating the length of the cable or the optical cable: calculating the length of the cable or the optical cable in each interval by using a related formula; and outputting a calculation result: summarizing and outputting the calculated lengths of the cable or the optical cable. According to the method, the actual length of the cable in a complex laying environment can be truly reflected, the estimation precision is remarkably improved, the operation safety and reliability of the transformer substation are enhanced, the operation and maintenance cost in the whole life cycle can be reduced, and the overall economic benefit of the transformer substation is improved.

The utility model discloses a water tank empty real exchange goods shelf device relates to the technical field of logistics operation, wherein, water tank empty real exchange goods shelf device includes frame, conveying mechanism, first push mechanism, second push mechanism and elevating system, and conveying mechanism includes first conveying assembly and second conveying assembly that first conveying assembly and second conveying assembly are spaced apart on the frame along the vertical direction, and elevating system is used for moving material from the discharge end of second conveying part to the feed end of third conveying part. The utility model discloses the technical scheme through adopting four oblique settings conveying part, forms the continuous goods box circulation path, and effectively simplifies the conveying flow, and improves work efficiency, through adopting first push mechanism and second push mechanism, can realize the accurate transfer of goods box between different levels, through adopting elevating system, realizes the automation exchange of empty box and real box, ensures that empty box can be in time back to logistics system and continue using, guarantees the stability and continuity of production rhythm.

The utility model relates to an RJ45 socket connector, which is provided with an insulating main body, a first terminal module and a second terminal module, the first terminal module is assembled on the insulating main body and is provided with a contact part and a first splicing part, and the contact part is in an inclined posture in a plugging cavity of the insulating main body; and the second terminal module is assembled on the insulating main body and provides a welding part and a second splicing part, the welding part extends out of the insulating main body, and the second splicing part is in contact conduction with the first splicing part, so that the first terminal module and the second terminal module are electrically connected together. The terminals are sectionally modularized, assembled on the insulating main body and then connected together in a conducting manner, so that the automatic production is facilitated, the assembling structure and quality are ensured, the disassembly, assembly and maintenance are facilitated, the forming size and the structure of the terminals are stable, the second terminal module can be selectively assembled, the production requirements of an SMT (Surface Mount Technology) or a DIP (Double In-line Package) process are met, and the production efficiency is improved. The connector has interchangeability and standardization, manufacturing and implementation of the connector are greatly improved, and stock overstock of accessory production is reduced.

The invention discloses a range hood spare part multistage inventory intelligent distribution system and method, and belongs to the technical field of range hood spare part inventory management and control, and the system comprises range hood equipment, a plurality of self-service spare part shelves, an integrated spare part warehouse, a spare part management system access terminal, a spare part management system server, and a supplier spare part warehouse. With the spare part management system server as a connection center, the range hood equipment, the self-service spare part goods shelf, the spare part management system access terminal and the supplier spare part warehouse are connected together through a computer communication network. According to the invention, interconnection and intercommunication of internal and external digital systems of an enterprise are realized, data of multi-stage spare part inventory positions are integrated, the spare part demand of the next period of the current time is predicted based on the optimized random forest predictor, and high efficiency, intelligence and refinement of spare part management are realized.

The present invention discloses an intelligent matching method for WiFi module software, and relates to the technical field of smart home appliance development. The present invention comprises the following steps: determining the corresponding relationship between the product WiFi software solution and the serial number before product design; after the WiFi module is powered on, detecting its own state; after the WiFi module receives the return command from the product electronic control, it stops sending the query command, and according to the serial number of the received product electronic control return command; after the WiFi module finds the corresponding software solution, it executes the WiFi module firmware solution and exits the factory detection mode and enters the user mode. The present invention integrates multiple sets of WiFi module software solutions into the same WiFi module firmware, uses the serial number reported by the electronic control to find the WiFi module software solution corresponding to the product, and then executes and switches to the user mode, performs intelligent matching on the WiFi module software, reduces the types of materials of the factory production module, and avoids the product return due to human installation errors.

The invention relates to the field of digital operation monitoring management, in particular to a digital operation monitoring management platform, which comprises a personnel authorization module, a data acquisition module, a network data analysis module, an equipment management module and an early warning processing module, the network data analysis module is used for receiving network data information and analyzing the network data information respectively, the personnel authorization module carries out authority distribution according to personal information corresponding to each worker, and the digital operation monitoring management platform can provide comprehensive operation data and deep analysis results, provides a scientific and accurate basis for enterprise decision making, and improves the enterprise safety. Therefore, the decision quality and success rate are improved.

The embodiment of the invention provides a material full-process outage control method and system, electronic equipment and a storage medium, and belongs to the field of material control in the manufacturing industry. The method comprises the following steps: determining whether the order number of a saleable whole vehicle product in a specified time is zero or not and / or determining whether the saleable whole vehicle product does not receive an order after the specified time or not, and if so, changing the sale state of the saleable whole vehicle product into a stop state; obtaining related information of a finished automobile product; according to the whole vehicle BOM data, calculating a special part of the disabled product and obtaining special part information; and determining the relationship between the deactivation time of the whole vehicle product sales state and the specified time, and carrying out purchase early warning or purchase limitation on the next-site special parts according to the relationship. According to the technical scheme, the purchasing early warning is issued by recognizing the material to be eliminated in advance, and the eliminated material is subjected to purchasing limitation and production stop, so that the excessive purchasing is prevented, the existing inventory is quickly modified and digested, the product research and development efficiency is promoted, the inventory overstock is reduced, and the production cost is reduced.

This application discloses a warehouse logistics sniffing method, device, and medium based on big data analysis, relating to the field of logistics technology. The warehouse logistics sniffing method includes: determining a global adjacency matrix of goods based on the local adjacency matrix of goods in all warehouses; determining global multidimensional attribute features of each good based on the local multidimensional attribute features of each good in all warehouses; constructing a probabilistic graphical model using the global adjacency matrix and global multidimensional attribute features as observation data; inferring the probability distribution of sales type of each good through the probabilistic graphical model; constructing a product association graph based on the global adjacency matrix and the probability distribution of sales type of each good; each node in the product association graph carries the local multidimensional attribute features of the good in each warehouse and the historical sales sequence; inputting the product association graph into a time-series graph convolutional network to output the estimated demand of each good in each warehouse; and completing the inventory allocation of each warehouse based on the estimated demand.

The application discloses a coal type sales planning and sales time collaborative control method and system based on a coal enterprise, and relates to the technical field of coal sales management.The application integrates multi-source data through a data fusion algorithm and other technical means, solves the problems of data dispersion and different formats, provides high-quality data support for subsequent analysis, accurately predicts sales trends and price fluctuations by using a long short-term memory neural network and a regression analysis model, helps enterprises optimize sales strategies, reduces inventory backlog risks, dynamically adjusts sales tasks by means of linear programming and reinforcement learning algorithms in combination with real-time data, improves market response speed and sales efficiency, and effectively improves the scientific nature and flexibility of coal enterprise sales management and enhances market competitiveness.

The invention relates to the technical field of frequency digging transformation, in particular to an intensive frequency digging transformation method for upgrading and updating of a frequency converter, which comprises the following steps of: removing kernel components of an old frequency converter, including a rectification unit and an inversion unit; a basic framework structure and a communication programmed control system of original equipment are reserved; installing a new high-performance frequency converter kernel component, and accessing an original PLC system through a DP bus protocol; pLC program control software and a communication protocol are updated and upgraded, and the compatibility of a new system and an original system is ensured. The reserved original equipment comprises a control cabinet box body, a bus copper plate, a PLC system and a driving motor peripheral, an electric driving system is integrated and replaced, so that leap-type improvement in performance and great reduction in research and development cost are achieved, and the control cabinet is suitable for being applied to integration of various systems and various projects. Vector control and servo control can be achieved in an optimized mode, and single-machine and multi-machine transmission work of complex tasks can be met.

The invention discloses a wholesale demand prediction method based on a machine learning algorithm, and the method comprises the steps: recognizing seasonal commodities according to the historical sales data of the commodities; according to the historical sales data of the commodities, identifying complementary commodities of the seasonal commodities; judging time synchronism and time hysteresis of demand changes of the complementary commodities and the seasonal commodities; calculating seasonal indexes of seasonal commodities, and identifying market stages of complementary commodities; according to the sales data of the commodities, identifying complementarity modes of seasonal commodities and complementary commodities thereof in different market stages; identifying market stages of the complementary commodities in a future preset time period, and generating wholesale purchase schemes of the seasonal commodities and the complementary commodities thereof; and judging the execution effect of the current wholesale purchase scheme, and adjusting the parameters of the wholesale purchase scheme. According to the method, the supply chain response speed and the market adaptability in wholesale trade can be effectively improved, and enterprises are assisted to keep competitive advantages in a complex and changeable market environment.

The present application relates to the clothing e-commerce technical field, specifically to the intelligent product selection and inventory prediction system based on clothing e-commerce platform.The present application extracts the popular color data, version data and fabric data in the clothing text and image of social media, and matches and analyzes the version parameters and fabric parameters of the clothing on sale on the platform, obtains the matching degree of each clothing on sale and the current popular trend, and provides a quantifiable screening basis for product selection.The present application selects target clothing according to the matching degree, inputs the version parameters, fabric parameters and historical sales of the target clothing into a multi-period sales prediction model, and outputs the predicted sales of each target clothing in different sales periods, realizes the association of product selection and periodical sales prediction based on the popular trend matching degree, is beneficial to realize sales-oriented inventory, inventory-oriented replenishment, reduces inventory backlog and shortage risk.