A prefabricated beam production system based on intelligent pipeline
The intelligent production line system solves the problems of low efficiency and high cost in traditional precast beam production, and realizes precise production planning, real-time inventory monitoring, on-demand delivery of raw materials, and real-time feedback of consumption data, thereby improving production collaboration efficiency and economy.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- CCCC SECOND HIGHWAY ENG CO LTD
- Filing Date
- 2025-09-09
- Publication Date
- 2026-07-21
AI Technical Summary
Traditional precast beam production methods suffer from low production efficiency, unreasonable resource allocation, and extensive management processes. These include reliance on manual experience for production planning, outdated raw material inventory management data, lack of scientific models to support raw material demand forecasting, and insufficient utilization of AGV equipment, resulting in high production costs and difficulty in controlling the production cycle.
The precast beam production system based on an intelligent production line includes a production planning module, a raw material inventory monitoring module, a raw material demand forecasting module, an intelligent delivery execution module, and a production execution feedback module. Through quantitative formulas, multi-sensor technology, ARIMA models, and Hungarian algorithms, it achieves precise production planning, real-time monitoring and optimization of inventory, on-demand delivery of raw materials, and real-time feedback of consumption data.
It has achieved a 30% or more improvement in the scientific nature and feasibility of production planning, a 20% to 40% increase in inventory turnover, a forecast error controlled within 8%, a 30% or more increase in AGV utilization, a delivery delay rate reduced to below 5%, a 8% to 15% reduction in production costs, a reduction in the time for tracing quality issues to within 1 hour, and a significant improvement in the accuracy and timeliness of management decisions.
Smart Images
Figure CN121390932B_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of precast beam production technology, specifically to a precast beam production system based on an intelligent assembly line. Background Technology
[0002] In the field of precast beam production, traditional production models generally suffer from low production efficiency, unreasonable resource allocation, and extensive management processes. Specifically: production planning relies on manual experience, making it difficult to accurately match equipment capacity with order demand, often resulting in poor process coordination or idle equipment; raw material inventory management uses manual counting and recording, leading to data lag and large errors, easily causing inventory backlogs or shortages. Fluctuations in the inventory of key raw materials such as steel bars and concrete often cause production interruptions or excessive capital tied up; raw material demand forecasting lacks scientific model support, relying mostly on simple estimations based on historical data, making it difficult to cope with external factors such as market price fluctuations and seasonal changes, resulting in unreasonable procurement plans; raw material delivery relies on manual scheduling, with low accuracy in path planning and timing control, insufficient utilization of AGV equipment, and risks of incorrect, missed, or delayed raw material delivery; and the collection of consumption data during the production process is not timely and cannot be fed back to the planning and inventory systems in real time, resulting in a lack of closed-loop management of "planning-production-feedback," ultimately leading to high production costs and difficulty in controlling the production cycle, hindering the intelligent and intensive development of precast beam production.
[0003] With the advancement of industrialized construction, the production scale of precast beams is constantly expanding, and the requirements for precision and efficiency in the production process are increasing. The traditional production model that relies on manual decision-making can no longer meet the needs of large-scale and multi-variety production. There is an urgent need to reconstruct the production management system through intelligent technology to achieve full-process digital control of production planning, inventory monitoring, demand forecasting, intelligent distribution, and execution feedback, so as to solve the efficiency bottlenecks and management pain points in the traditional model. Summary of the Invention
[0004] To solve the above-mentioned technical problems, a precast beam production system based on an intelligent production line is provided. This technical solution solves the above problems.
[0005] To achieve the above objectives, the technical solution adopted by the present invention is as follows:
[0006] A precast beam production system based on an intelligent production line includes: a production planning module, a raw material inventory monitoring module, a raw material demand forecasting module, an intelligent delivery execution module, and a production execution feedback module;
[0007] The production planning module is used to obtain the precast beam production plan and output the raw material demand list and delivery sequence.
[0008] The raw material inventory monitoring module is electrically connected to the production planning module and is used to collect raw material inventory data of steel bars and concrete in real time, establish an inventory database, and set a safety stock threshold.
[0009] The raw material demand forecasting module is electrically connected to the production planning module and the raw material inventory monitoring module. Based on the production plan and historical inventory data, a raw material consumption analysis model is established. Based on the output of the raw material consumption model, raw material consumption pattern parameters are obtained. Based on the current raw material consumption pattern parameters, the raw material demand parameters for future periods are predicted.
[0010] The intelligent delivery execution module is electrically connected to the raw material demand forecasting module and the raw material inventory monitoring module. Based on the raw material demand parameter forecasting results and inventory status, it automatically generates replenishment orders and delivers raw materials to designated processing stations according to the production sequence.
[0011] The production execution feedback module is electrically connected to the intelligent delivery execution module and is used to collect actual raw material consumption data and feed it back to the raw material demand forecasting module and the raw material inventory monitoring module for dynamic inventory adjustment.
[0012] Preferably, the production planning module specifically includes:
[0013] The capacity assessment unit is used to calculate capacity using parameters such as the rated production efficiency of each piece of equipment on the production line, the number of units, the daily effective operating time of the equipment, and the equipment maintenance time, through a capacity calculation formula. The capacity calculation formula is as follows:
[0014]
[0015] In the formula, C represents the total production capacity. The overall efficiency coefficient of the equipment is 0.8-0.9. For the first The rated production efficiency of the equipment For the first The effective operating time of each piece of equipment, and output of a capacity assessment report;
[0016] The demand calculation unit is used to calculate the raw material demand based on precast beam type parameters, production quantity of each type, and raw material consumption parameters per unit of that type of precast beam, generating a detailed raw material demand list. The formula for calculating the raw material demand is as follows:
[0017]
[0018] In the formula, For the first Total demand for various raw materials For the first Production quantity of precast beams Parameters consumed per unit;
[0019] The timing planning unit takes the standard operating time of each production process and the preparation time required for the connection between processes as input, and combines the demand list output by the demand calculation unit to obtain the delivery completion time of each raw material through reverse calculation, and outputs a raw material delivery timing table arranged by time nodes.
[0020] Preferably, the raw material inventory monitoring module specifically includes:
[0021] The data acquisition unit is used to collect the weight data of raw materials based on the weighing sensors in the raw material warehouse, identify the batch label information of raw materials through RFID, record the storage area of raw materials through the position sensor, and output real-time inventory data including inventory quantity, storage location, entry time and batch number.
[0022] The threshold setting unit is used to obtain the safety stock threshold by taking the historical average daily consumption of raw materials, the average delivery cycle of suppliers, and the demand fluctuation coefficient as inputs, and using the safety stock calculation formula. The safety stock calculation formula is as follows:
[0023]
[0024] In the formula, This represents the average daily consumption. For delivery cycle, This is the demand fluctuation coefficient. This is the safety stock threshold;
[0025] The early warning triggering unit is used to compare the real-time inventory quantity output by the data acquisition unit with the safety stock threshold set by the threshold setting unit in real time. When the inventory quantity is detected to be lower than the safety stock threshold, an inventory early warning information containing the raw material name, current inventory, and suggested purchase quantity is immediately sent to the system management terminal.
[0026] The database maintenance unit is used to store real-time inventory data, raw material inbound records, and outbound records into the inventory database in a time series, and to generate daily, weekly, and monthly inventory reports on a regular basis.
[0027] Preferably, the raw material inventory monitoring module further includes:
[0028] The periodic inventory unit is used to conduct a comprehensive inventory check of raw material inventory at a fixed time every week using an automated inventory robot. It uses weighing and counting methods to obtain the actual inventory quantity of each raw material and records the inventory time, participants, and inventory process data.
[0029] The discrepancy analysis unit is used to obtain the inventory discrepancy value by taking the inventory record quantity and the actual inventory quantity as input. When the ratio of the discrepancy value to the average daily consumption exceeds 5%, it outputs an inventory data correction instruction.
[0030] The traceability and verification unit is used to automatically retrieve data from the inbound ledger, warehouse monitoring video, and raw material requisition registration form when the discrepancy analysis unit detects that the inventory discrepancy exceeds the limit. It then traces the cause of the discrepancy and generates a discrepancy cause analysis report and improvement suggestions.
[0031] Preferably, the raw material demand forecasting module specifically includes:
[0032] The model building unit is used to construct a raw material consumption analysis model using the daily raw material consumption data of the past 6 months, the corresponding production plan data, and inventory change data as inputs, employing the ARIMA time series model. The model formula is as follows:
[0033]
[0034] In the formula, It is an autoregressive polynomial. These are the autoregressive coefficients. Let the order be the autoregressive order. For lag operators, for Order difference operator, It is the difference order. for Real-time raw material consumption data, The moving average coefficient is... The moving average order is... Given a white noise sequence, the optimal parameters of the model are determined using the AIC criterion. , , , It is a moving average polynomial;
[0035] The parameter extraction unit is used to extract parameters such as the consumption frequency, consumption intensity, and consumption fluctuation range of raw materials from historical consumption data through model training, and output a parameter analysis report.
[0036] The forecasting calculation unit is used to calculate the raw material demand forecasts for the next 7, 15, and 30 days, taking the constructed ARIMA model and the recent production plan as inputs, and output the demand forecast results for each time period.
[0037] The model update unit is used to compare the predicted value and the actual consumption value to calculate the prediction error. When the error exceeds 8%, it automatically calls historical data to retrain the model and adjust the parameters.
[0038] Preferably, the raw material demand forecasting module further includes:
[0039] The influencing factors unit is used to collect external factors such as market raw material price fluctuations, seasonal changes, holiday shutdown plans, and supplier delivery cycle fluctuations, and assigns influence weights to each factor through the analytic hierarchy process.
[0040] The regression correction unit is used to establish a multiple linear regression model to correct the demand forecast using ARIMA prediction results and factor weights as input, and outputs the corrected demand forecast. The formula for the multiple linear regression model is as follows:
[0041]
[0042] In the formula, This is the revised demand. For ARIMA prediction results, For constant terms, For the first The regression coefficients of each factor For the first The weights of each factor For the first The quantitative values of each factor are used to output the corrected demand forecast results.
[0043] The error assessment unit is used to obtain the deviation rate between the corrected predicted value and the actual consumption value. When the deviation rate exceeds 5%, the model retraining process is triggered and the cause of the error is recorded.
[0044] Preferably, the intelligent delivery execution module specifically includes:
[0045] The order generation unit is used to take the demand forecast for the next 30 days and the real-time inventory quantity as input. When the sum of the inventory quantity and the expected delivery quantity during the procurement cycle cannot meet the forecast demand, it automatically generates a replenishment order containing the raw material name, specifications, purchase quantity, suggested delivery date, and a list of alternative suppliers.
[0046] The timing control unit is used to determine the departure time of the AGV based on the delivery schedule and the optimal route distance, and controls the AGV to depart at the calculated time and adjust the travel speed to ensure that the raw materials are delivered to the designated workstation on time.
[0047] The task allocation unit is used to optimize the allocation of delivery tasks using the current number of tasks to be delivered, the real-time status of each AGV, its load capacity, and its current position, and outputs AGV task scheduling instructions.
[0048] Preferably, the intelligent delivery execution module further includes:
[0049] The status monitoring unit is used to collect the AGV's running position, travel speed, remaining power, and load weight parameters in real time through positioning devices, speed sensors, power sensors, and load sensors installed on the AGV, and output the equipment operation status report once per second.
[0050] The fault warning unit is used to analyze the collected parameters in real time. When it is detected that the AGV travel speed is less than 50% of the rated speed, the remaining power is less than 20%, the load weight exceeds the rated load by 10%, and the vibration amplitude exceeds the threshold, it immediately sends a fault warning message and retrieves the backup equipment.
[0051] The maintenance planning unit takes historical operating data, fault records, and maintenance records of the AGV as input, predicts the remaining life of key components through a life prediction model, and outputs an AGV maintenance cycle table and preventive maintenance recommendations.
[0052] Preferably, the production execution feedback module specifically includes:
[0053] The production execution feedback module specifically includes:
[0054] The consumption acquisition unit is used to collect data on the actual consumption quantity, consumption time, and corresponding production process of each batch of raw materials in real time by using weighing sensors installed at the steel bar processing station and flow meters installed at the concrete pouring station, and to generate a raw material actual consumption data table according to the production batch.
[0055] The inventory update unit is used to obtain the inventory update quantity by taking the actual consumption quantity and the current inventory quantity as input, generate inventory data update instructions and synchronize them to the inventory database;
[0056] The model feedback unit is used to classify and label the actual consumption data according to time, process and precast beam type, and use it as training samples to supplement the historical dataset of the demand prediction module, thereby increasing the influence weight of recent data to optimize the prediction model parameters.
[0057] The quality traceability unit is used to receive raw material quality problem information from the production quality inspection module, mark the batches of raw materials with quality problems, automatically trace the supplier information, warehousing inspection report, quality certificate and requisition record data of the batch of raw materials, and generate a raw material quality traceability report.
[0058] Preferably, the production execution feedback module further includes:
[0059] The consumption analysis unit is used to take historical consumption data as input, obtain the differences in raw material consumption for different types of precast beams and different production teams through comparative analysis, identify abnormal consumption processes and teams, and output a consumption difference analysis report.
[0060] The process optimization unit is used to propose suggestions for improving raw material utilization based on consumption anomalies and production process parameters.
[0061] The cost accounting unit is used to calculate raw material costs based on actual consumption data and raw material purchase prices, according to precast beam type and production batch. It analyzes the deviation between actual and planned costs, and outputs a cost accounting report and cost control recommendations. The cost calculation formula is as follows:
[0062]
[0063] In the formula, where For actual cost, For the first Actual consumption of various raw materials For the first The unit price of the raw materials purchased, This represents the number of samples.
[0064] Compared with the prior art, the beneficial effects of the present invention are as follows:
[0065] 1. By electrically connecting and linking the five modules of production planning, inventory monitoring, demand forecasting, intelligent distribution, and execution feedback, an integrated intelligent framework of "planning-inventory-forecasting-distribution-feedback" is constructed. This breaks down the information silos of traditional production. The real-time collected consumption data not only dynamically updates the inventory database but also serves as a training sample to optimize the prediction model. This enables full-process digital control from planning to consumption feedback, solves the problem of data lag, and significantly improves the response speed of production adjustments.
[0066] 2. In the production planning stage, precise matching of production capacity and demand is achieved through quantitative formulas and dynamic correction mechanisms; inventory management integrates multi-sensor technology and dynamic threshold algorithms, combined with real-time early warning to avoid backlog or stockouts and improve inventory turnover; demand forecasting adopts the ARIMA model and multiple regression correction, introduces external factors to quantify weights, and controls forecasting errors; intelligent delivery improves equipment utilization and reduces delivery delay rates by optimizing AGV task allocation and timing control through algorithms, thereby comprehensively improving resource allocation efficiency.
[0067] 3. Relying on RFID tags and full-chain data recording, the time for tracing quality issues has been shortened from the traditional 2-3 days to within 1 hour, strengthening quality control capabilities; by analyzing consumption differences to identify weak links, and combining process optimization suggestions to improve raw material utilization, cost deviations are accurately calculated based on actual data, resulting in a reduction in overall production costs; multi-dimensional data reports provide quantitative basis for management decisions, reducing reliance on experience and achieving a refined upgrade in quality, cost, and management. Attached Figure Description
[0068] Figure 1This is a system framework diagram of the present invention;
[0069] Figure 2 This is a system flowchart of the present invention. Detailed Implementation
[0070] The following description is intended to disclose the invention and enable those skilled in the art to implement it. The preferred embodiments described below are merely examples, and other obvious variations will occur to those skilled in the art.
[0071] Reference Figure 1 As shown, a precast beam production system based on an intelligent production line includes: a production planning module, a raw material inventory monitoring module, a raw material demand forecasting module, an intelligent delivery execution module, and a production execution feedback module.
[0072] Reference Figure 2 As shown, the production planning module is used to obtain the precast beam production plan and output the raw material demand list and delivery sequence.
[0073] The raw material inventory monitoring module is electrically connected to the production planning module to collect raw material inventory data of steel bars and concrete in real time, establish an inventory database, and set safety stock thresholds.
[0074] The raw material demand forecasting module is electrically connected to the production planning module and the raw material inventory monitoring module. Based on the production plan and historical inventory data, a raw material consumption analysis model is established. Based on the output of the raw material consumption model, raw material consumption pattern parameters are obtained. Based on the current raw material consumption pattern parameters, the raw material demand parameters for future periods are predicted.
[0075] The intelligent delivery execution module is electrically connected to the raw material demand forecasting module and the raw material inventory monitoring module. Based on the raw material demand parameters and inventory status, it automatically generates replenishment orders and delivers raw materials to designated processing stations according to the production sequence.
[0076] The production execution feedback module is electrically connected to the intelligent delivery execution module. It is used to collect actual raw material consumption data and feed it back to the raw material demand forecasting module and the raw material inventory monitoring module for dynamic inventory adjustment.
[0077] The production planning module specifically includes:
[0078] The capacity assessment unit is used to calculate capacity using parameters such as the rated production efficiency of each piece of equipment on the production line, the number of units, the daily effective operating time of the equipment, and the equipment maintenance time, through a capacity calculation formula. The capacity calculation formula is as follows:
[0079]
[0080] In the formula, C represents the total production capacity. The overall efficiency coefficient of the equipment is 0.8-0.9. For the first The rated production efficiency of the equipment For the first The system calculates the effective operating time of each piece of equipment and outputs a capacity assessment report. The demand calculation unit uses precast beam type parameters, the production quantity of each type, and the raw material consumption parameters per unit of that type of precast beam as input to calculate the raw material demand and generate a detailed raw material demand list. The formula for calculating the raw material demand is as follows:
[0081]
[0082] In the formula, For the first Total demand for various raw materials For the first Production quantity of precast beams The unit is a consumption parameter; the timing planning unit is used to take the standard operation time of each production process and the preparation time required for the connection between processes as input, and combine the demand list output by the demand calculation unit to obtain the delivery completion time of each raw material through reverse calculation, and output the raw material delivery timing table arranged by time node.
[0083] By using quantitative formulas to accurately calculate capacity and demand, and introducing the comprehensive efficiency coefficient of equipment to dynamically correct capacity assessment results, combined with the time-series planning method of reverse calculation, the problem of mismatch between "capacity-demand-delivery" in traditional production planning is solved, thereby improving the scientific nature and feasibility of planning.
[0084] The raw material inventory monitoring module specifically includes:
[0085] The data acquisition unit is used to collect the weight data of raw materials based on the weighing sensors in the raw material warehouse, identify the batch label information of raw materials through RFID, record the storage area of raw materials through the position sensor, and output real-time inventory data including inventory quantity, storage location, entry time and batch number.
[0086] The threshold setting unit is used to obtain the safety stock threshold by taking the historical average daily consumption of raw materials, the average delivery cycle of suppliers, and the demand fluctuation coefficient as inputs, and using the safety stock calculation formula. The safety stock calculation formula is as follows:
[0087]
[0088] In the formula, This represents the average daily consumption. For delivery cycle, This is the demand fluctuation coefficient. This is the safety stock threshold;
[0089] The early warning triggering unit is used to compare the real-time inventory quantity output by the data acquisition unit with the safety stock threshold set by the threshold setting unit in real time. When the inventory quantity is detected to be lower than the safety stock threshold, an inventory early warning information containing the raw material name, current inventory, and suggested purchase quantity is immediately sent to the system management terminal. The database maintenance unit is used to store real-time inventory data, raw material inbound records, and outbound records into the inventory database in time series, and regularly generate daily, weekly, and monthly inventory reports.
[0090] By integrating multi-sensor data acquisition technology with dynamic threshold algorithms, and introducing a demand fluctuation coefficient, the system achieves dynamic adaptation of safety stock, solving the problem that traditional static inventory thresholds cannot cope with demand fluctuations. Combined with a real-time early warning mechanism, it enables precise and intelligent management and control of inventory.
[0091] The raw material inventory monitoring module also includes:
[0092] The regular inventory unit is used to conduct a comprehensive inventory check of raw material stock at fixed times each week using automated inventory robots. It obtains the actual inventory quantity of each raw material by weighing and counting, and records the inventory time, participating personnel, and inventory process data. The discrepancy analysis unit is used to obtain the inventory discrepancy value by taking the recorded inventory quantity and the actual inventory quantity as input. When the ratio of the discrepancy value to the average daily consumption exceeds 5%, it outputs an inventory data correction instruction. The traceability and verification unit is used to automatically retrieve data from the warehouse entry ledger, warehouse monitoring video, and raw material requisition registration form when the discrepancy analysis unit finds that the inventory discrepancy exceeds the limit, trace the cause of the discrepancy, and generate a discrepancy cause analysis report and improvement suggestions.
[0093] It pioneered a closed-loop inventory verification mechanism of "automated inventory counting - discrepancy analysis - traceability verification". By quantifying the discrepancy threshold to trigger the correction process, and combining multi-dimensional data traceability, it solves the problem of difficulty in tracing errors in traditional manual inventory counting, thereby improving the reliability of inventory data and the level of precision in inventory management.
[0094] The raw material demand forecasting module specifically includes:
[0095] The model building unit is used to construct a raw material consumption analysis model using the daily raw material consumption data of the past 6 months, the corresponding production plan data, and inventory change data as inputs, employing the ARIMA time series model. The model formula is as follows:
[0096]
[0097] In the formula, It is an autoregressive polynomial. These are the autoregressive coefficients. Let the order be the autoregressive order. For lag operators, for Order difference operator, It is the difference order. for Real-time raw material consumption data, The moving average coefficient is... The moving average order is... Given a white noise sequence, the optimal parameters of the model are determined using the AIC criterion. , , , It is a moving average polynomial;
[0098] The parameter extraction unit is used to extract parameters such as the consumption frequency, consumption intensity, and consumption fluctuation range of raw materials from historical consumption data through model training, and outputs a parameter analysis report; the prediction calculation unit is used to calculate the predicted raw material demand for the next 7 days, 15 days, and 30 days using the constructed ARIMA model and recent production plans as input, and outputs the demand forecast results for each time period; the model update unit is used to compare the predicted values and actual consumption values to calculate the prediction error. When the error exceeds 8%, it automatically calls historical data to retrain the model and adjust the parameters.
[0099] By applying the ARIMA time series model to the forecasting of raw material demand for precast beams, the problem of poor adaptability of traditional forecasting models is solved through the automatic model update mechanism. This enables the dynamic extraction of consumption pattern parameters and continuous optimization of forecasting accuracy, thereby improving the scientificity and timeliness of demand forecasting.
[0100] The raw material demand forecasting module also includes:
[0101] The Influencing Factors unit collects external factors such as market raw material price fluctuations, seasonal changes, holiday shutdown plans, and supplier delivery cycle fluctuations, and assigns influence weights to each factor using the analytic hierarchy process (AHP). The Regression Correction unit uses ARIMA forecast results and factor weights as input to build a multiple linear regression model to correct the demand forecast, outputting the corrected demand forecast result. The formula for the multiple linear regression model is:
[0102]
[0103] In the formula, This is the revised demand. For ARIMA prediction results, For constant terms, For the first The regression coefficients of each factor For the first The weights of each factor For the first The system quantifies the values of each factor and outputs the corrected demand forecast. The error assessment unit is used to obtain the deviation rate between the corrected forecast and the actual consumption. When the deviation rate exceeds 5%, the model retraining process is triggered and the cause of the error is recorded.
[0104] By introducing a quantitative weighting correction mechanism for external influencing factors and using a combination algorithm of ARIMA model and multiple linear regression, the problem of traditional forecasting ignoring external factors such as market and seasons is solved, and demand forecasting is upgraded from "historical fitting" to "dynamic adaptation", further reducing forecasting bias.
[0105] The intelligent delivery execution module specifically includes:
[0106] The order generation unit takes the 30-day demand forecast and real-time inventory as input. When the sum of the inventory and the expected delivery quantity within the procurement cycle cannot meet the forecast demand, it automatically generates a replenishment order containing the raw material name, specifications, purchase quantity, suggested delivery date, and a list of alternative suppliers. The timing control unit determines the departure time of the AGV based on the delivery schedule and optimal route distance through time calculations, controls the AGV to depart at the calculated time and adjusts its travel speed to ensure that raw materials are delivered to the designated workstation on time. The task allocation unit takes the current number of tasks to be delivered, the real-time status of each AGV, its load capacity, and its current position as input, optimizes the allocation of delivery tasks through the Hungarian algorithm, and outputs AGV task scheduling instructions.
[0107] By integrating inventory forecasting and intelligent algorithms, the entire delivery process is optimized. The Hungarian algorithm is used to achieve optimal allocation of AGV tasks, and time-series control is combined to ensure timely delivery of raw materials. This solves the problems of low efficiency and insufficient utilization of AGVs in traditional manual scheduling, and improves the intelligence and precision of the delivery system.
[0108] The intelligent delivery execution module also includes:
[0109] The status monitoring unit collects real-time data on the AGV's operating position, speed, remaining battery power, and load weight using positioning devices, speed sensors, power sensors, and load sensors installed on the AGV, and outputs an equipment operating status report once per second. The fault early warning unit analyzes the collected parameters in real time and immediately sends a fault early warning message and retrieves backup equipment when the AGV's speed is less than 50% of the rated speed, the remaining battery power is less than 20%, the load weight exceeds the rated load by 10%, or the vibration amplitude exceeds the threshold. The maintenance planning unit uses the AGV's historical operating data, fault records, and maintenance records as input to predict the remaining lifespan of key components through a life prediction model and outputs an AGV maintenance cycle table and preventive maintenance recommendations.
[0110] Establish a full lifecycle management mechanism for AGV equipment, encompassing "status monitoring, fault early warning, and preventive maintenance." By implementing real-time monitoring of multiple parameters and threshold early warning, combined with a lifespan prediction model, the mechanism facilitates a shift from "fault repair" to "preventive maintenance," reducing equipment downtime risks and enhancing the stability of the delivery system.
[0111] The production execution feedback module specifically includes:
[0112] The consumption acquisition unit is used to collect data on the actual consumption quantity, consumption time, and corresponding production process of each batch of raw materials in real time by using weighing sensors installed at the steel bar processing station and flow meters installed at the concrete pouring station, and to generate a raw material actual consumption data table according to the production batch.
[0113] The inventory update unit takes the actual consumption quantity and the current inventory quantity as input, obtains the inventory update quantity, generates inventory data update instructions, and synchronizes them to the inventory database. The model feedback unit classifies and labels the actual consumption data according to time, process, and precast beam type, and uses it as training samples to supplement the historical dataset of the demand forecasting module, increasing the influence weight of recent data to optimize the forecasting model parameters. The quality traceability unit receives raw material quality problem information from the production quality inspection module, marks the batches of raw materials with quality problems, automatically traces the supplier information, warehousing inspection report, quality certificate, and requisition record data of the batch of raw materials, and generates a raw material quality traceability report.
[0114] Establish a two-way feedback mechanism based on real-time consumption data to achieve dynamic updates of inventory data and provide fresh training samples for demand forecasting models. Combined with the full-chain traceability function of quality, it solves the pain points of asynchronous "consumption-inventory-forecast" data and difficulty in tracing quality problems in traditional production.
[0115] The production execution feedback module also includes:
[0116] The consumption analysis unit uses historical consumption data as input to obtain raw material consumption differences for different precast beam types and production teams through comparative analysis, identifies abnormal consumption processes and teams, and outputs a consumption difference analysis report. The process optimization unit, based on consumption anomalies and combined with production process parameters, proposes suggestions to improve raw material utilization. The cost accounting unit uses actual consumption data and raw material purchase prices as input to calculate raw material costs by precast beam type and production batch, analyzes the deviation between actual and planned costs, and outputs a cost accounting report and cost control suggestions. The cost calculation formula is as follows:
[0117]
[0118] In the formula, where For actual cost, For the first Actual consumption of various raw materials For the first The unit price of the raw materials purchased, The number of samples;
[0119] By deeply integrating consumption data analysis with process optimization and cost control, and identifying weak links in production through quantitative differences, the value transformation from "data collection" to "cost optimization" is realized, providing precise data support for production process improvement and cost control, and improving the economy and precision of production.
[0120] In summary, the advantages of this invention are as follows:
[0121] By connecting and designing a closed-loop data system for five modules—production planning, raw material inventory monitoring, demand forecasting, intelligent delivery, and execution feedback—the system breaks down information silos in traditional production processes, enabling full-process digital management from production planning to raw material consumption feedback. This constructs an integrated intelligent framework of "planning-inventory-forecasting-delivery-feedback," significantly improving production collaboration efficiency.
[0122] The quantitative formula is used to accurately calculate the production capacity and raw material demand. The comprehensive efficiency coefficient of the equipment is introduced to dynamically correct the production capacity assessment results. The reverse time series calculation method is combined to determine the raw material delivery time, which solves the mismatch between "production capacity-demand-delivery" in traditional planning and improves the scientificity and feasibility of production planning by more than 30%.
[0123] By integrating multi-sensor real-time data acquisition technology with dynamic safety stock algorithms, inventory thresholds are precisely set based on historical average daily consumption, delivery cycles, and demand fluctuation coefficients. Combined with a real-time early warning mechanism, inventory is dynamically adapted, avoiding capital tied up in inventory and preventing production interruptions caused by stockouts, thereby increasing inventory turnover by 20% to 40%.
[0124] The innovative application of the ARIMA time series model combined with a multiple linear regression correction mechanism not only captures consumption patterns through historical data, but also introduces quantitative weights for external influencing factors such as market prices and seasonal changes, thereby upgrading demand forecasting from "historical fitting" to "dynamic adaptation". The forecasting error is controlled within 8%, providing reliable data support for procurement planning.
[0125] By optimizing AGV task allocation through the Hungarian algorithm and accurately calculating departure time by combining it with a timing control model, raw materials can be delivered on time and as needed. At the same time, an AGV full life cycle management mechanism is built to reduce the risk of equipment downtime through status monitoring, fault early warning and preventive maintenance. AGV utilization rate is increased by more than 30% and delivery delay rate is reduced to below 5%.
[0126] Establish a two-way feedback mechanism based on real-time consumption data. Actual consumption data not only updates the inventory database synchronously, but also serves as a training sample to optimize the parameters of the demand forecasting model, ensuring real-time linkage between "consumption-inventory-forecasting" data. This solves the problem of data lag in traditional production and improves the response speed of production adjustments by 50%.
[0127] By using RFID tags for identification and full-chain data recording, batch traceability of raw materials can be achieved from warehousing to use. When quality problems occur, key information such as suppliers and inspection records can be quickly located. The traceability time for quality problems is shortened from the traditional 2-3 days to within 1 hour, thereby improving quality control capabilities.
[0128] By analyzing consumption differences to identify weak links in production, and combining process optimization suggestions to improve raw material utilization, and by accurately calculating cost deviations based on actual consumption data and purchase prices, we can provide data support for cost control, reduce overall production costs by 8% to 15%, and significantly improve the economic efficiency of precast beam production.
[0129] The system provides quantitative decision-making support for production management by outputting multi-dimensional data such as daily inventory reports, capacity assessment reports, and cost accounting reports. This reduces reliance on experience in traditional management and significantly improves the accuracy and timeliness of management decisions.
[0130] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the claimed invention. The scope of protection claimed by the appended claims and their equivalents is defined.
Claims
1. A precast beam production system based on an intelligent assembly line, characterized in that, include: Production planning module, raw material inventory monitoring module, raw material demand forecasting module, intelligent delivery execution module, and production execution feedback module; The production planning module is used to obtain the precast beam production plan and output the raw material demand list and delivery sequence. The raw material inventory monitoring module is electrically connected to the production planning module and is used to collect raw material inventory data of steel bars and concrete in real time, establish an inventory database, and set a safety stock threshold. The raw material demand forecasting module is electrically connected to the production planning module and the raw material inventory monitoring module. Based on the production plan and historical inventory data, a raw material consumption analysis model is established. Based on the output of the raw material consumption model, raw material consumption pattern parameters are obtained. Based on the current raw material consumption pattern parameters, the raw material demand parameters for future periods are predicted. The intelligent delivery execution module is electrically connected to the raw material demand forecasting module and the raw material inventory monitoring module. Based on the raw material demand parameter forecasting results and inventory status, it automatically generates replenishment orders and delivers raw materials to designated processing stations according to the production sequence. The production execution feedback module is electrically connected to the intelligent delivery execution module and is used to collect actual raw material consumption data and feed it back to the raw material demand forecasting module and the raw material inventory monitoring module for dynamic inventory adjustment. The production planning module specifically includes: The capacity assessment unit is used to calculate capacity using parameters such as the rated production efficiency of each piece of equipment on the production line, the number of units, the daily effective operating time of the equipment, and the equipment maintenance time, through a capacity calculation formula. The capacity calculation formula is as follows: ； In the formula, C represents the total production capacity. The overall efficiency coefficient of the equipment is 0.8-0.
9. For the first The rated production efficiency of the equipment For the first The effective operating time of each piece of equipment, and output of a capacity assessment report; The demand calculation unit is used to calculate the raw material demand based on precast beam type parameters, production quantity of each type, and raw material consumption parameters per unit of that type of precast beam, generating a detailed raw material demand list. The formula for calculating the raw material demand is as follows: ； In the formula, For the first Total demand for various raw materials For the first Production quantity of precast beams Parameters consumed per unit; The timing planning unit takes the standard operating time of each production process and the preparation time required for the connection between processes as input, and combines the demand list output by the demand calculation unit to obtain the delivery completion time of each raw material through reverse calculation, and outputs a raw material delivery timing table arranged by time nodes.
2. The precast beam production system based on an intelligent assembly line according to claim 1, characterized in that, The raw material inventory monitoring module specifically includes: The data acquisition unit is used to collect the weight data of raw materials based on the weighing sensors in the raw material warehouse, identify the batch label information of raw materials through RFID, record the storage area of raw materials through the position sensor, and output real-time inventory data including inventory quantity, storage location, entry time and batch number. The threshold setting unit is used to obtain the safety stock threshold by taking the historical average daily consumption of raw materials, the average delivery cycle of suppliers, and the demand fluctuation coefficient as inputs, and using the safety stock calculation formula. The safety stock calculation formula is as follows: ； In the formula, This represents the average daily consumption. For delivery cycle, This is the demand fluctuation coefficient. This is the safety stock threshold; The early warning triggering unit is used to compare the real-time inventory quantity output by the data acquisition unit with the safety stock threshold set by the threshold setting unit in real time. When the inventory quantity is detected to be lower than the safety stock threshold, an inventory early warning information containing the raw material name, current inventory, and suggested purchase quantity is immediately sent to the system management terminal. The database maintenance unit is used to store real-time inventory data, raw material inbound records, and outbound records into the inventory database in a time series, and to generate daily, weekly, and monthly inventory reports on a regular basis.
3. The precast beam production system based on an intelligent assembly line according to claim 2, characterized in that, The raw material inventory monitoring module also includes: The periodic inventory unit is used to conduct a comprehensive inventory check of raw material inventory at a fixed time every week using an automated inventory robot. It uses weighing and counting methods to obtain the actual inventory quantity of each raw material and records the inventory time, participants, and inventory process data. The discrepancy analysis unit is used to obtain the inventory discrepancy value by taking the inventory record quantity and the actual inventory quantity as inputs. When the ratio of the discrepancy value to the average daily consumption exceeds 5%, it outputs an inventory data correction instruction. The traceability and verification unit is used to automatically retrieve data from the inbound ledger, warehouse monitoring video, and raw material requisition registration form when the discrepancy analysis unit detects that the inventory discrepancy exceeds the limit. It then traces the cause of the discrepancy and generates a discrepancy cause analysis report and improvement suggestions.
4. The precast beam production system based on an intelligent assembly line according to claim 1, characterized in that, The raw material demand forecasting module specifically includes: The model building unit is used to construct a raw material consumption analysis model using the daily raw material consumption data of the past 6 months, the corresponding production plan data, and inventory change data as inputs, employing the ARIMA time series model. The model formula is as follows: ； In the formula, It is an autoregressive polynomial. These are the autoregressive coefficients. Let the order be the autoregressive order. For lag operators, for Order difference operator, Let be the difference order. for Real-time raw material consumption data, The moving average coefficient is... The moving average order is... Given a white noise sequence, the optimal parameters of the model are determined using the AIC criterion. , , , It is a moving average polynomial; The parameter extraction unit is used to extract parameters such as the consumption frequency, consumption intensity, and consumption fluctuation range of raw materials from historical consumption data through model training, and output a parameter analysis report. The forecasting calculation unit is used to calculate the raw material demand forecasts for the next 7, 15, and 30 days, taking the constructed ARIMA model and the recent production plan as inputs, and output the demand forecast results for each time period. The model update unit is used to compare the predicted value and the actual consumption value to calculate the prediction error. When the error exceeds 8%, it automatically calls historical data to retrain the model and adjust the parameters.
5. A precast beam production system based on an intelligent assembly line according to claim 4, characterized in that, The raw material demand forecasting module also includes: The influencing factors unit is used to collect external factors such as market raw material price fluctuations, seasonal changes, holiday shutdown plans, and supplier delivery cycle fluctuations, and assigns influence weights to each factor through the analytic hierarchy process. The regression correction unit is used to establish a multiple linear regression model to correct the demand forecast using ARIMA prediction results and factor weights as input, and outputs the corrected demand forecast. The formula for the multiple linear regression model is as follows: In the formula, This is the revised demand. For ARIMA prediction results, For constant terms, For the first The regression coefficients of each factor For the first The weights of each factor For the first The quantitative values of each factor are used to output the corrected demand forecast results. The error assessment unit is used to obtain the deviation rate between the corrected predicted value and the actual consumption value. When the deviation rate exceeds 5%, the model retraining process is triggered and the cause of the error is recorded.
6. A precast beam production system based on an intelligent assembly line according to claim 1, characterized in that, The intelligent delivery execution module specifically includes: The order generation unit is used to take the demand forecast for the next 30 days and the real-time inventory quantity as input. When the sum of the inventory quantity and the expected delivery quantity during the procurement cycle cannot meet the forecast demand, it automatically generates a replenishment order containing the raw material name, specifications, purchase quantity, suggested delivery date, and a list of alternative suppliers. The timing control unit is used to determine the departure time of the AGV based on the delivery schedule and the optimal route distance, and controls the AGV to depart at the calculated time and adjust the travel speed to ensure that the raw materials are delivered to the designated workstation on time. The task allocation unit is used to optimize the allocation of delivery tasks using the current number of tasks to be delivered, the real-time status of each AGV, its load capacity, and its current position, and outputs AGV task scheduling instructions.
7. A precast beam production system based on an intelligent assembly line according to claim 6, characterized in that, The intelligent delivery execution module also includes: The status monitoring unit is used to collect the AGV's running position, travel speed, remaining power, and load weight parameters in real time through positioning devices, speed sensors, power sensors, and load sensors installed on the AGV, and output the equipment operation status report once per second. The fault warning unit is used to analyze the collected parameters in real time. When it is detected that the AGV travel speed is less than 50% of the rated speed, the remaining power is less than 20%, the load weight exceeds the rated load by 10%, and the vibration amplitude exceeds the threshold, it immediately sends a fault warning message and retrieves the backup equipment. The maintenance planning unit takes historical operating data, fault records, and maintenance records of the AGV as input, predicts the remaining life of key components through a life prediction model, and outputs an AGV maintenance cycle table and preventive maintenance recommendations.
8. A precast beam production system based on an intelligent assembly line according to claim 1, characterized in that, The production execution feedback module specifically includes: The production execution feedback module specifically includes: The consumption acquisition unit is used to collect data on the actual consumption quantity, consumption time, and corresponding production process of each batch of raw materials in real time by using weighing sensors installed at the steel bar processing station and flow meters installed at the concrete pouring station, and to generate a raw material actual consumption data table according to the production batch. The inventory update unit is used to obtain the inventory update quantity by taking the actual consumption quantity and the current inventory quantity as input, generate inventory data update instructions and synchronize them to the inventory database; The model feedback unit is used to classify and label the actual consumption data according to time, process and precast beam type, and use it as training samples to supplement the historical dataset of the demand prediction module, thereby increasing the influence weight of recent data to optimize the prediction model parameters. The quality traceability unit is used to receive raw material quality problem information from the production quality inspection module, mark the batches of raw materials with quality problems, automatically trace the supplier information, warehousing inspection report, quality certificate and requisition record data of the batch of raw materials, and generate a raw material quality traceability report.
9. A precast beam production system based on an intelligent assembly line according to claim 8, characterized in that, The production execution feedback module also includes: The consumption analysis unit is used to take historical consumption data as input, obtain the differences in raw material consumption for different types of precast beams and different production teams through comparative analysis, identify abnormal consumption processes and teams, and output a consumption difference analysis report. The process optimization unit is used to propose suggestions for improving raw material utilization based on consumption anomalies and production process parameters. The cost accounting unit is used to calculate raw material costs based on actual consumption data and raw material purchase prices, according to precast beam type and production batch. It analyzes the deviation between actual and planned costs, and outputs a cost accounting report and cost control recommendations. The cost calculation formula is as follows: ； In the formula, where For actual cost, For the first Actual consumption of various raw materials For the first The unit price of the raw materials purchased, This represents the number of samples.