8 results about "Production optimization" patented technology

This application provides a method and system for optimizing forging production driven by digital twins and real-time data feedback, belonging to the field of process optimization technology. The method includes: parsing the contextual data chain of related parameters based on forging production target optimization parameters; constructing a digital twin space and establishing a real-time data interaction path; updating the digital twin space using the real-time data interaction path; acquiring pre-processed product variables; simulating the production process and processing parameters through the digital twin space; and outputting a forging production process optimization strategy that achieves the optimization target, used for deploying the production process of pre-processed products. This application solves the technical problem in the prior art where the lack of simulation and optimization of process parameters before processing makes it difficult to detect and adjust process problems in a timely manner during processing, further affecting production efficiency. By using digital twins for full-process production pre-simulation, optimization before production is achieved, reducing product defect rates and improving production efficiency.

This application relates to a production optimization method, system, electronic device, and storage medium based on surplus fabrics. The method includes: acquiring inventory data of surplus fabrics; extracting fabric attribute parameters for each batch of surplus fabrics from the inventory data; wherein the fabric attribute parameters include fabric type, fabric specifications, color parameters, available quantity, batch constraint rules, and / or historical loss indicators; inputting the fabric attribute parameters into a surplus fabric production capacity model to determine the production quantity of each batch of surplus fabrics; generating new product production plans and best-selling product production plans when the production quantity exceeds a target threshold; and determining a target production execution plan based on the new product production plans and best-selling product production plans. This enables accurate quantitative assessment and efficient utilization of surplus fabrics, effectively reducing warehousing and capital occupation costs, while simultaneously considering production efficiency and feasibility, and improving the refinement and intelligence level of garment production management.

The application provides a whole milk powder production optimization method and device, electronic equipment and storage medium, relates to the dairy production technical field, and includes the following steps: acquiring the nutritional component data of raw milk in a plurality of milk cans; grouping the plurality of milk cans based on the complementarity between the nutritional component data of the raw milk in each milk can, obtaining the production batch corresponding to each milk can; determining the process adjustment parameter for adjusting the nutritional components of the raw milk of each production batch based on the difference between the nutritional component data of the raw milk of each production batch and the target nutritional component data; and producing whole milk powder based on the process adjustment parameter of each production batch. The method and device provided by the application convert the original production process relying on manual experience into an automatic production process through scientific complementarity grouping and accurate process parameter calculation, and consider both product quality and economic benefits in the whole milk powder production process.

The application relates to the technical field of prefabrication plants, and discloses a production optimization method based on a digital twin production line model, which comprises the following steps: establishing initial models of all processes, simulating the production line by adopting Monte Carlo sampling, discovering capacity and quality risks, putting forward production suggestions and management points, tracking worker and equipment production data in real time during the operation of the production process, updating model parameters, updating the model into a twin model, simulating by Monte Carlo sampling, predicting capacity and quality risks, putting forward a production optimization scheme, continuously updating model parameters and the twin model in the subsequent production process, and finally solidifying into a twin model highly matched with the existing production line. The existing production line twin technology focuses on data acquisition and display, a linear model is adopted for the prediction of capacity and quality, and the variation of samples is not considered enough. The application provides a nonlinear model and key parameters, and the matching of the production line is realized through dynamic updating, which is of great significance to actual production.

The application belongs to the technical field of oil and gas field development engineering, and discloses an intelligent low-carbon oil reservoir parameter inversion and injection-production optimization system, which comprises a data acquisition and access module, an intelligent oil reservoir parameter inversion module, a low-carbon injection-production collaborative optimization module and a scheme execution and instruction issuing module. The intelligent closed-loop system constructed by fusing data driving and physical models effectively solves the core pain points of the single optimization target, the lack of regional collaboration and the dynamic response capability of the prior art. The creativity of the application lies in that: for the first time, the "carbon footprint" is internalized as an optimization target, realizing the systematic balance between economic benefits and low-carbon emission reduction; through integrated scheduling of multiple sources and multiple sinks and collaborative optimization of multiple fluids, the regional resource utilization efficiency and the overall recovery rate are improved; with the help of real-time inversion and rolling optimization mechanism, adaptive dynamic decision-making capability is formed, and the robustness of the system in dealing with uncertainty is significantly enhanced, providing an innovative solution for intelligent and green development of oil fields.

The present application provides a kind of converter energy consumption and carbon emission diagnosis and optimization method based on production rhythm, belong to steel metallurgical process intelligent diagnosis technical field.Input with converter process production rhythm parameter, operating parameter and material structure parameter, oxygen consumption, converter gas recovery and steam recovery are predicted by long short-term memory network, and energy consumption and carbon emission of current converter working condition are calculated accordingly;Based on multidimensional production data, the reference working condition is obtained by clustering algorithm;The deviation index between the current working condition and the reference working condition is constructed and the working condition is diagnosed, and the influencing factors are sorted according to the contribution degree;On this basis, the converter production is optimized combined with working condition deviation and influence weight.This method uses entropy weight method, integrates energy efficiency and carbon emission index, constructs working condition evaluation system, selects the reference working condition with optimal energy efficiency and carbon emission by clustering algorithm, and realizes converter production optimization.

The present application relates to the production optimization control method and system of thermoplastic elastomer coated ABS in the field of new materials, comprising: obtaining the physicochemical property data of multi-component system from the thermoplastic elastomer formula database, extracting the epoxy group reactivity and chain segment flexibility control parameters by using the principal component analysis algorithm, and obtaining the compatibility characteristic vector between components; if the interface binding force distribution data is lower than the preset threshold value, then by adjusting the polyester chain segment proportion and the maleic anhydride SEBS addition amount, new formula parameters are generated, and the optimized multi-component system formula is obtained; from the stable die pressure distribution data, the pre-dispersion crosslinking point and the nano-enhanced synergy processing dynamic balance characteristics are extracted, whether the preset processing stability threshold is satisfied is judged, and the processing process stability evaluation result is obtained.