Intelligent dynamic management system for production progress and efficiency of production line

Abstract

The invention provides an intelligent dynamic management system for the production progress and efficiency of a production line, belongs to the technical field of production line management, and aims at achieving the intelligent dynamic management of the production progress and efficiency of the production line through real-time data collection and analysis, flexible time dimension configuration and accurate calculation of key indexes such as the production progress and the production efficiency. Accurate calculation and dynamic management of the production progress and the production efficiency of the production line can be realized, and the production progress can be effectively estimated and the technological process can be optimized, so that the overall management level and the production efficiency of the production line are improved, and the production cost is reduced.

Description

Technical Field

[0001] This invention relates to the field of production line management technology, and in particular to an intelligent dynamic management system for production line progress and efficiency, applicable to various industrial production scenarios such as discrete manufacturing and process manufacturing. It enables precise calculation and dynamic management of production line progress and efficiency, and allows for effective prediction of production progress and optimization of processes. Background Technology

[0002] In modern manufacturing, precise control of production schedules and optimization of production efficiency are key to enhancing enterprise competitiveness. However, traditional production management systems typically rely on static planning, lacking the ability to dynamically adjust to actual production conditions. For example, static planning management, which depends on production plans with a fixed time dimension, cannot adapt to changes in the dynamic production environment; data acquisition is lagging, and production data is not updated in a timely manner, resulting in blind spots in progress monitoring; forecast accuracy is insufficient, and production completion time estimates rely on experience-based judgments without data support; efficiency analysis is crude, making it difficult to quantify production efficiency deviations and accurately optimize processes; and the time dimension is singular, only supporting fixed time dimensions (such as shifts / hours), failing to match the needs of different production cycles. This leads to problems such as inaccurate tracking of production schedules, low production efficiency, and resource waste. Summary of the Invention

[0003] To address the above technical issues, this invention provides an intelligent dynamic management system for production line progress and efficiency. Through real-time data collection and analysis, it flexibly configures the time dimension and accurately calculates key indicators such as production progress and efficiency, thereby achieving accurate prediction of production progress and optimization of the process flow, thus improving the overall management level and production efficiency of the production line and reducing production costs.

[0004] The technical solution of this invention is:

[0005] An intelligent dynamic management system for production line progress and efficiency includes:

[0006] The data acquisition module collects production data in real time through industrial IoT devices and MES system interfaces, and supports multiple data input methods such as sensors, RFID, and manual entry.

[0007] The planning management module calculates the planned production quantity for each time interval based on the shift schedule and production plan, according to the configurable time dimension.

[0008] The real-time computing engine calculates output per unit time based on production data acquired by the data acquisition module and uses a sliding time window algorithm.

[0009] The intelligent prediction module uses the exponential moving average algorithm EMA to predict the remaining production time, and the calculation formula is "remaining time = (total planned quantity - completed quantity) / EMA (recent production efficiency)";

[0010] Efficiency analysis module: calculate the ratio of actual production efficiency to planned production efficiency, clearly show the difference between actual production efficiency and planned target of the production line; at the same time, calculate the unit product production time (IST);

[0011] Optimization decision module: when the efficiency ratio calculated by the efficiency analysis module is less than the set threshold, trigger optimization suggestions.

[0012] Further,

[0013] The data acquisition module obtains various data on the production line, including equipment running state, product processing quantity, production time, and provides basis for subsequent data processing and analysis.

[0014] Further,

[0015] Users use the planning management module to flexibly set time dimensions according to different production tasks and scheduling arrangements, meet diversified production needs, and realize dynamic adjustment of production plan calculation.

[0016] Further,

[0017] The sliding time window algorithm dynamically calculates the output in different time periods according to the set time window size, ensuring the timeliness and accuracy of output calculation.

[0018] The production progress is calculated by the formula "production progress (%) = (∑actual output / ∑planned output) × 100%".

[0019] Further,

[0020] The intelligent prediction module estimates the remaining production estimated consumption time and estimated production completion time by analyzing historical production data and current production trends.

[0021] According to historical production data and current production trends, the remaining production time is predicted by using the exponential moving average algorithm EMA; when the prediction result shows that the production may not be completed on time, the production plan is adjusted according to the prediction result.

[0022] Further,

[0023] The optimization decision module identifies process bottlenecks based on IST analysis, and provides targeted process optimization solutions combined with actual production conditions. DETAILED DESCRIPTION

[0024] In order to make the purposes, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below. Obviously, the described embodiments are some of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative work fall within the protection scope of the present application.

[0025] The present application provides a production line production progress and efficiency intelligent dynamic management system, comprising the following modules:

[0026] Data acquisition module: real-time acquisition of production data through industrial internet of things (IIoT) devices and MES system interface, supporting multiple data input methods such as sensors, RFID, manual input, etc. This module can quickly and accurately obtain various data on the production line, such as equipment running status, product processing quantity, production time, etc., providing a basis for subsequent data processing and analysis.

[0027] Plan management module: according to the scheduling plan and production plan, the planned production quantity of each time interval is calculated according to the configurable time dimension (such as minutes, quarter hours, hours, shifts, etc.). Users can flexibly set the time dimension according to different production tasks and scheduling arrangements to meet diverse production needs and realize dynamic adjustment of production plan calculation.

[0028] Real-time calculation engine: based on the production data obtained by the data acquisition module, the sliding time window algorithm is used to calculate the unit time yield. The sliding time window algorithm can dynamically calculate the yield in different time periods according to the set time window size, ensuring the timeliness and accuracy of yield calculation. The production progress is calculated by the formula "production progress (%) = (∑actual yield / ∑planned yield) x 100%". Through real-time production data analysis on the production line, the accuracy and timeliness of actual production quantity calculation are ensured, and the current production status of the production line is intuitively reflected.

[0029] Intelligent prediction module: using the exponential moving average algorithm EMA, the remaining production time is predicted, and the calculation formula is "remaining time = (total planned quantity - completed quantity) / EMA (recent production efficiency)". Through analysis of historical production data and current production trend, this module can accurately estimate the remaining production time and the estimated production completion time, providing a scientific basis for production plan adjustment.

[0030] Efficiency analysis module: calculates the ratio of actual production efficiency to planned production efficiency, clearly showing the difference between actual production efficiency of the production line and the planned target; at the same time, calculates the unit product production time (IST). Through analysis of production efficiency ratio and IST, the running efficiency of the production line can be deeply understood, and the efficiency bottleneck in the production process can be found.

[0031] Optimization decision module: Trigger optimization suggestions when the efficiency ratio calculated by the efficiency analysis module is less than the set threshold. This module identifies process bottlenecks based on IST analysis and provides targeted process optimization solutions for managers, such as adjusting equipment parameters, optimizing operation processes, and reallocating production tasks, to achieve fine management of production lines.

[0032] The working content of the system is as follows:

[0033] 1. Data acquisition module deployment: Install various sensors on the production line to collect real-time equipment operation state data; collect product processing quantity information through RFID technology; at the same time, set up manual input ports to record some special production data. Connect the industrial Internet of Things (IIoT) device and the MES system interface to ensure that production data can be transmitted to the system in real time and accurately.

[0034] 2. Plan management module configuration: According to the enterprise's scheduling plan and production order, managers can flexibly set the time dimension in the plan management module for different production tasks.

[0035] 3. Real-time calculation and monitoring: The real-time calculation engine uses the sliding time window algorithm to calculate the unit time output and production progress based on the production data obtained by the data acquisition module. Managers can intuitively see the real-time production progress of the production line through the system interface. If the production progress is found to be lagging behind, timely measures can be taken to adjust.

[0036] 4. Production prediction and plan adjustment: The intelligent prediction module uses the Exponential Moving Average algorithm (EMA) to predict the remaining production time based on historical production data and current production trends. When the prediction result shows that production may not be completed on time, managers can adjust the production plan according to the prediction result, such as increasing manpower or adjusting equipment operation parameters.

[0037] 5. Efficiency analysis and optimization: The efficiency analysis module periodically calculates the ratio of actual production efficiency to planned production efficiency, as well as the unit product production time (IST). When the efficiency ratio is found to be less than the set threshold, the optimization decision module triggers optimization suggestions, identifies the process bottleneck as a certain process with too long processing time based on IST analysis, and provides optimization solutions for managers, such as adjusting equipment parameters for that process, optimizing operation processes, and reallocating production tasks. After managers adjust according to the optimization scheme, the production efficiency of the production line is significantly improved.

[0038] The above merely describes the preferred embodiments of the present application, which are used for explaining the technical solutions of the present application, and are not used for limiting the protection scope of the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. An intelligent dynamic management system for production line production progress and efficiency, characterized in that, It comprises: a data acquisition module that acquires production data in real time through industrial Internet of Things devices and MES system interfaces, supports multiple data input methods such as sensors, RFID, and manual input, etc. a plan management module that calculates the planned production quantity for each time interval according to the scheduling plan and production plan, and according to the configurable time dimension; a real-time calculation engine that calculates the unit time output using a sliding time window algorithm based on the production data obtained by the data acquisition module; an intelligent prediction module that uses the Exponential Moving Average algorithm EMA to predict the remaining production time, with the formula "remaining time = (total planned quantity - completed quantity) / EMA (recent production efficiency)"; an efficiency analysis module that calculates the ratio of actual production efficiency to planned production efficiency, clearly showing the difference between actual production efficiency and planned target of the production line, and also calculates the unit product production time IST; an optimization decision module that triggers optimization suggestions when the efficiency ratio calculated by the efficiency analysis module is less than the set threshold.

2. The system according to claim 1, characterized in that, The data acquisition module acquires various data on the production line, including equipment operating status, product processing quantity, and production time, providing a basis for subsequent data processing and analysis.

3. The system according to claim 1, characterized in that, Users can use the plan management module to flexibly set the time dimension according to different production tasks and scheduling arrangements, meet diverse production needs, and realize dynamic adjustment of production plan calculation.

4. The system according to claim 1, characterized in that, The sliding time window algorithm dynamically calculates the output in different time periods according to the set time window size, ensuring the timeliness and accuracy of output calculation.

5. The system according to claim 4, characterized in that, The production progress is calculated using the formula "production progress (%) = (∑actual output / ∑planned output) × 100%".

6. The system according to claim 1, characterized in that, The intelligent prediction module estimates the estimated consumption time and estimated completion time of the remaining production by analyzing historical production data and current production trends.

7. The system according to claim 6, characterized in that, According to historical production data and current production trends, the Exponential Moving Average algorithm EMA is used to predict the remaining production time; when the prediction result shows that the production may not be completed on time, the production plan is adjusted according to the prediction result.

8. The system according to claim 1, characterized in that, The optimization decision module identifies process bottlenecks based on IST analysis, and provides targeted process flow optimization solutions based on actual production conditions.