18 results about "Material requirements planning" patented technology

The invention discloses a production material accurate scheduling method and system under cooperation of WMS and MES, and relates to the technical field of material scheduling. Comprising a data acquisition and processing module which acquires data and preprocesses the data; the material demand planning module is used for constructing a material demand prediction model by using a time sequence analysis method, and decomposing a high-level production plan into specific material demand lists step by step according to a process route and process requirements; and the inventory management module is used for managing the materials, and dynamically distributing and adjusting the storage locations by adopting a clustering algorithm according to the characteristics of the materials and the layout of the warehouse. According to the invention, based on linkage of the WMS and the MES, full-chain closed-loop management from planning to execution is realized, and the cross-system cooperation efficiency is improved; a dynamic demand prediction system is constructed by adopting time sequence analysis, a regression model and a machine learning algorithm, a historical rule and market changes are considered, and the prediction precision is 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 invention discloses a flexible manufacturing-based DPA component matching method, which comprises the following steps of: establishing a DPA component requirement catalog, establishing a management database, and creating catalog entries for each component needing DPA detection; the method comprises the following steps: establishing DPA components, and creating a corresponding DPA component for each basic component needing to execute DPA detection in enterprise product bill of material structure management; the method comprises the following steps: establishing a corresponding relation between a DPA component requirement directory and a component, establishing a mapping relation between a DPA requirement directory and a DPA component, triggering to generate or call a corresponding DPA component demand according to the mapping relation, and realizing automatic conversion from a detection requirement to a specific execution material; and establishing a matched operation system, and integrating the system into a manufacturing execution system or a material demand planning module of an enterprise resource plan. According to the method, automatic identification of DPA detection requirements, intelligent matching of detection materials and accurate accounting of related cost can be realized.

1. The name of the design product: project management system graphical user interface for electronic device. 2. The use of the design product: the design product takes an electronic device as a carrier, and is used for operating a running project management system through the electronic device. 3. The design points of the design product: the interface content of the graphical user interface. 4. The picture or photo that best shows the design points: interface change state diagram 10. 5. The use of the graphical user interface: used for distinguishing project construction plan, material requirement plan, scheme plan, optimization plan, quality inspection plan, safety inspection plan, etc., establishing upper and lower correlation relationship, and facilitating scientific examination of each plan. Click the performance center button in the main view to enter interface change state diagram 1; click the total progress management button in interface change state diagram 1 to enter interface change state diagram 2; click the panoramic plan analysis button in interface change state diagram 2 to enter interface change state diagram 3; click the specific project "Sunshine City" button under the project department in interface change state diagram 3 to enter interface change state diagram 4; click the time window in interface change state diagram 4 to enter interface change state diagram 5; click the one-key full-open button in interface change state diagram 5 to enter interface change state diagram 6; click the query button in interface change state diagram 6 to enter interface change state diagram 7; click the full-screen button on the right side of the one-key expansion button in interface change state diagram 7 to enter interface change state diagram 8; click the logo icon in the upper left corner of the window in interface change state diagram 8 to enter interface change state diagram 9; check the task project in interface change state diagram 9 and click the one-key expansion button to enter interface change state diagram 10; click the right purple horizontal bar in interface change state diagram 10 to enter interface change state diagram 11.

The present invention discloses a method for manufacturing a packaging substrate for an LED electronic display screen, and relates to the technical field of circuit board processing for LEDs. The present invention integrates a material requirement planning (MRP) system with CNC cutting equipment and utilizes an intelligent typesetting algorithm based on a genetic algorithm to maximize the utilization of substrate materials and reduce waste. Secondly, in the intelligent drilling stage, an intelligent drilling adjustment system constructed by a deep Q learning algorithm is adopted to achieve real-time adjustment and optimization of drilling parameters, thereby ensuring hole position accuracy and hole shape consistency. In the intelligent plating process, a neural network model based on deep learning is utilized to monitor and adjust electroplating parameters in real time, thereby improving the uniformity and adhesion of the electroplating layer. Finally, in the pressing and browning and resin grinding stages, an automatic plate grinding machine is used for resin grinding to ensure the flatness of the substrate surface.

Systems and methods are directed toward managing demand, such as for manufacturing in complex supply and / or demand driven environments, including aerospace-industry products and systems, environments with a multitude of stakeholders, and other sophisticated organizations. A production request may be provided with a component identifier for a top-level module component to be produced. Data associated with the component identifier may be determined. The data may include one or more additional component identifiers for sub-components which are to be consumed to produce the top-level module component. Supply allocations for the top-level module component and the sub-components may be generated based at least in part on the data. One or more action signals may be provided to fulfill the production request for the top-level module component according to the supply allocations.

Embodiments of the present application disclose a material demand planning method, equipment and medium. A period type table is created, wherein the period type table at least includes a period type name and a period span; based on the period type table, a period span related attribute is added in a preset independent demand to obtain a reference independent demand; and based on the period type table, a period span related attribute is added in a preset material dictionary table to obtain a reference material dictionary table; data in the reference independent demand is compared with data in the reference material dictionary table for processing, so as to determine a current material demand plan based on a comparison processing result; wherein the period span in the preset independent demand is related to material production or procurement days, and the period span in the preset material dictionary is related to material preparation time in advance. The above method reduces the workload of the developer.

The application belongs to the technical field of rural sewage treatment, and discloses a rural sewage treatment planning system and method, which comprises a planning collection device, which is used for obtaining construction range information and process requirement information, performing plan analysis on the construction range information and the process requirement information, and obtaining engineering plan information; the engineering plan information comprises progress plan information and material requirement plan information; a construction collection device is used for obtaining construction real-time information and the engineering plan information, performing completion condition analysis on the construction real-time information according to the engineering plan information, and obtaining construction completion result information. The application can effectively manage the sewage project, improve the timeliness of engineering management, and make the construction range information fit the actual sewage generation condition and treatment demand of the rural area.

The application provides a material demand planning method, system, device and medium, and relates to the technical field of material management. The method comprises the following steps: obtaining production consumption information, inventory information and in-transit logistics information of a target material; determining a basic demand quantity of the target material in a preset period and a material demand index according to the production consumption information and the inventory information of the target material; correcting the basic demand quantity according to the material demand index to obtain a target demand quantity of the target material; in the case that the target demand quantity is greater than 0, determining a delivery time corresponding to the target material according to the in-transit logistics information of the target material; and formulating a material demand plan of the target material by a pre-constructed material decision model according to the target demand quantity and the delivery time. The method can reduce the dependence on manual statistics, improve the accuracy and timeliness of the formulation of the material demand plan.

The application discloses a kind of main plan compilation system and method for solving customer order change.The main plan compilation method for solving customer order change is mainly realized in the main plan adjustment service based on local change strategy, the main plan local recompilation service based on reinforcement learning algorithm, capacity calculation service of system service layer.The system judges the numerical relationship between new demand capacity and residual capacity after receiving customer order change information, executes the main plan adjustment method based on local change strategy, generates new main plan compilation record according to the customer order increment allocation strategy based on multi-day residual capacity ratio and the non-urgent customer order reduction strategy, guarantees the premise of making full use of residual capacity, replaces, supplements original main plan, if still cannot meet the demand of customer order change by using local adjustment strategy, executes the main plan local recompilation method based on improved reinforcement learning algorithm, and recompiles part of main plan for the remaining days after change date.The application can quickly respond to customer order change, reduce large-scale adjustment of original main plan, and reduce the chain effect on material demand plan and other plans.

This invention discloses a production management method and system for single-phase prepaid smart meters, relating to the field of intelligent manufacturing. The method includes: collecting customer order data, inventory data, and supplier data; setting priority thresholds to determine the priority of pre-processed customer order numbers, defining nodes, edges, and attributes, and generating a production graph; constructing a dynamic production optimization model to obtain priority production batches through production batch optimization; allocating priority production batches using a linear programming algorithm to obtain production tasks for these batches, and analyzing these tasks to obtain an equipment requirement list; verifying the raw material requirements in the production tasks using material requirements planning (MRP) technology to obtain raw material warehousing information; and using a blockchain platform to compare the equipment requirement list and raw material warehousing information to generate a production completion report. This invention, by constructing a dynamic production optimization model, achieves intelligent sorting and optimization of production batches for single-phase prepaid smart meters.

The invention belongs to the technical field of nuclear power, and particularly relates to a group factory spare part purchasing and allocation decision-making method and device, a computer readable storage medium and electronic equipment. The method comprises the following steps: acquiring input data of spare part purchasing and allocation decisions of group factories; based on the input data, according to material demand plan types and batch type combinations of different spare parts, respectively determining the purchase quantity and the sharing quantity of each power plant; performing overall decision-making based on the purchase quantity and the sharing quantity of each power plant through the virtual power plant intelligent agent to obtain the total purchase quantity of the group plants; and performing local decision making through each entity power plant agent based on the total purchase quantity of the group plants to obtain a decision making scheme of each entity power plant. According to the method and the device, overall decision making is carried out through the virtual power plant intelligent agent, and then refined local decision optimization is carried out through each entity power plant intelligent agent on the basis, so that the accuracy of the obtained result is effectively improved.

The invention relates to a statistical algorithm, in particular to a multi-material variety-based dynamic statistical algorithm. The system comprises a material identification unit, and the material identification unit is used for carrying out unique coding on different materials, including the variety, specification, size, weight and batch information of the materials; the data acquisition unit collects access records and position changes of materials through a camera, and the dynamic statistics updating unit is used for updating and monitoring stock information of the materials based on data acquired by the data acquisition unit; the purchase optimization unit updates transaction data of multiple materials based on dynamic statistics, generates a more reasonable and detailed material demand plan, and is used for optimizing a purchase plan of an enterprise; the user interaction and display unit is used for displaying real-time material statistical data and the optimized demand plan and giving an alarm when the material inventory exceeds a normal range; advanced image recognition, data processing and artificial intelligence technologies are integrated, so that the system is of great significance in improving the accuracy of material inventory management, reducing the inventory cost and optimizing the purchase decision.

Embodiments are directed to providing real-time forecasting for material requirements planning and inventory controls based on data from a variety of sources. More specifically, embodiments are directed to collecting data from sources including, but not limited to, manufacturing, inventory, and supplier data sources, and focusing on the most important data signals in near real-time, focusing on critical suppliers and inventory, and creating critical alerts while also enabling predictive analytics and machine learning to automate the processes and providing an ability to manage all the data.

This application relates to a method, system, device, and storage medium for calculating Material Requirements Planning (MRP). The method includes: loading basic data of an MRP task to be processed into the memory of a target computing node; dynamically calculating the balunable quantity of materials required for the MRP task to be processed using the memory and a balancing algorithm deployed on the target computing node; calculating the purchase quantity of materials required for the MRP task to be processed based on the balunable quantity; and calculating the material requirements plan corresponding to the MRP task to be processed based on the basic data, the purchase quantity, and the MRP algorithm deployed on the target computing node. The solution provided in this application can significantly improve the computational efficiency of material requirements planning.

The invention discloses a distribution control method and application of replacement materials in a material demand plan and electronic equipment. The method comprises the following steps: step (1), dividing the replacement materials into I-type replacement materials, II-type replacement materials and III-type replacement materials; step (2), formulating different distribution strategies for the three replacement materials; (3) determining the type of the replacement material; step (4), selecting a corresponding distribution strategy for distribution according to the type of the replacement material; a distribution strategy of class I replacement materials: distributing all the current net demand quantity to the replacement materials with the maximum difference between the historical distribution quantity and the required distribution quantity; a distribution strategy of class II replacement materials: distributing all the current net demand quantity to the replacement materials with the lowest historical quota rating; and the allocation strategy of the class III replacement materials is that the replacement materials are sequentially allocated according to the quantity to be allocated from large to small by applying a batch rule. According to the invention, reasonable and efficient distribution of the replacement materials in the existing material demand plan can be ensured, and the requirements of the replacement material plan are met.

A digital twin-based system for cell-based autonomous production consolidation in an enterprise resource planning (ERP) controlled discrete manufacturing environment, comprising: a module for generating a digital twin (101) configured to create and maintain a virtual representation of one or more physical production cells including the associated workstations, workflow structures, material dependencies and capacity constraints; a demand classification and cell grouping module (102) configured to receive production demand from an ERP system and classify the demand into cell-compatible demand clusters based on routing compatibility, product family attributes, and production cell capacities; a production consolidation engine (103) configured to generate consolidated planned orders at the cell level by aggregating the classified demand clusters and embedding cell-specific production intelligence into ERP planning objects; a real-time synchronization module (104) configured to collect operational data from manufacturing systems, including machine status, throughput rate, queue metrics and capacity utilization, and to update the digital twin models accordingly; a deviation detection module (105) configured to compare the real-time operating data with the parameters of the digital twin models to identify deviations that exceed predefined operating thresholds; and an autonomous recalibration engine (106) is configured such that, when a deviation is detected, the results of the material requirements planning at the level of the manufacturing cell are selectively regenerated and the production utilization is redistributed to one or more manufacturing plants (107).

The invention belongs to the technical field of pumped storage power station material scheduling and acceptance, and discloses an intelligent scheduling and unattended acceptance method for pumped storage power station construction materials. The initial scheduling instruction is generated based on the construction progress model, the material demand plan and the predefined scheduling rule, the latest demand time point and the scheduling trigger time point of the materials are scientifically calculated, it is ensured that the materials are supplied in time, stock overstock is avoided, the scheduling accuracy and efficiency are improved, and the scheduling cost is reduced. The problem that scheduling instruction generation is lagged and inaccurate in the prior art is solved. According to the method, the external dynamic influence factors are fused in real time in the acceptance process, the supplementary scheduling analysis is triggered in time, the supplementary scheduling demand is scientifically judged, the supplementary scheduling instruction is generated, the continuity of material supply is ensured, and the problem that the supplementary scheduling analysis is not timely and inaccurate in the prior art is effectively solved. And meanwhile, continuous optimization is performed by collecting historical data, so that the scheduling and acceptance performance is further improved.