An industrial MES monitoring system with data interaction function

Abstract

The present invention provides an industrial MES monitoring system with data interaction function. This system realizes the improvement of the efficiency and intelligence of the coordinated management of factory production capacity. First, the MES system collects the real-time data of each factory and stores it; then, it predicts the production capacity requirements of each factory, and dynamically adjusts the prediction model in combination with historical production data, equipment status and market change trends. The scheduling optimization model automatically generates a scheduling plan based on the predicted production capacity requirements. After the generated scheduling plan is transmitted to the MES systems of each factory, finally, the factory automatically allocates resources according to the plan and according to the actual production situation, and the factory adjusts the priority and production order of tasks according to the actual execution situation. When problems such as equipment failures, production progress lags, and personnel absences occur, the scheduling plan is automatically adjusted. The present invention significantly improves the efficiency and intelligence of the coordinated management of factory production capacity through dynamic adjustment capabilities and adaptive optimization mechanisms.

Description

Technical Field

[0001] The present invention relates to the field of industrial production capacity management, and particularly to an industrial MES monitoring system with data interaction function. Background Art

[0002] With the rapid development of global manufacturing, especially in the production environment of multinational and multi-factory, how to effectively coordinate the production capacity of each factory, ensure the timely completion of production tasks, and maximize the utilization of resources has become a major challenge faced by manufacturing enterprises. Traditional production scheduling methods mostly rely on manual operations or simple calculation models, which are difficult to cope with complex and changeable production situations and cannot make full use of real-time data and intelligent decision-making mechanisms.

[0003] In the prior art, the MES system is used to monitor data such as production status, equipment utilization rate, and personnel scheduling in real time, and can generate production tasks and scheduling plans. This method enables the production process to respond to data changes in real time, thereby improving the production efficiency and accuracy. And by automatically generating production scheduling plans through production demand and equipment status prediction, it reduces manual intervention and improves the automation level of the scheduling process.

[0004] Although the prior art can automatically generate scheduling plans, it lacks the ability to adjust scheduling plans according to real-time data during the execution of production tasks. Traditional systems often can only make adjustments according to pre-determined plans, and cannot optimize the plan in real time according to actual changes in production (such as equipment failures, personnel absences, etc.). This may affect the production progress in case of emergencies, and the scheduling plan often fails to be effectively feedback and optimized after execution. Although task adjustments can be made, the feedback data is not fully utilized for continuous optimization, resulting in inconsistent performance of the scheduling plan in different production cycles and unable to continuously improve the system's adaptability and production efficiency. Summary of the Invention

[0005] To solve the above problems, the present invention introduces real-time data collection, prediction model adjustment, and feedback mechanism, enabling production scheduling to not only be automatically adjusted during task execution but also optimize the scheduling model according to actual production feedback. Based on this, the present invention proposes an industrial MES monitoring system with data interaction function.

[0006] For this reason, the technical solution adopted by the present invention is as follows:

[0007] Module 1: Factory status collection module, which monitors the production status, equipment production capacity, personnel scheduling, and order requirements of each factory in real time through the MES system, and generates real-time data including equipment utilization rate, production equipment load situation, personnel scheduling situation, and order progress according to the production status, equipment production capacity, personnel scheduling, and order requirements of each factory;

[0008] Module 2: Factory Capacity Prediction Module. According to the real-time data, use data analysis algorithms to predict the capacity requirements of each factory, generate the predicted values of the capacity requirements of each factory, and dynamically adjust the prediction model based on historical production data, equipment status, and market change trends;

[0009] Module 3: Production Scheduling Module. Establish a scheduling optimization model. The model automatically generates a cross-factory production scheduling plan according to the predicted values of the capacity requirements. The MES system transmits the production scheduling plan to each factory. The MES system generates specific production tasks according to the production scheduling plan, and executes the production tasks and updates the production status;

[0010] Module 4: Dynamic Scheduling Optimization Module. During the execution of the production tasks, the MES system adjusts the scheduling plan according to the real-time data in the production process, generates a new scheduling plan, analyzes the new scheduling plan through the system feedback mechanism, and optimizes the scheduling optimization model according to the analysis results.

[0011] Furthermore, the real-time data is the equipment utilization rate, the load situation of production equipment, the personnel scheduling situation, and the order progress.

[0012] The equipment utilization rate: Monitor the operation of the equipment through the MES system, collect equipment status information and automatically collect it through sensors, PLCs, and SCADA systems. The formula is:

[0013] ;

[0014] Wherein, is the utilization rate of equipment i, is the actual working time of equipment i, is the available time of equipment i,

[0015] The load situation of production equipment: The MES system obtains the operating load of the equipment by integrating equipment sensors and calculates it through the following formula:

[0016] ;

[0017] Wherein, is the load of equipment i, is the production volume actually produced by equipment i, is the maximum output capacity of equipment i,

[0018] The personnel scheduling situation: The MES system records and manages the personnel scheduling situation of each factory in real time. The personnel scheduling information is calculated through the following formula:

[0019] ;

[0020] Among them, is the workload of worker j, is the working hours assigned to worker j, is the available working hours of worker j,

[0021] Regarding the order progress, the MES system tracks the progress of each order in real time through integration with the Enterprise Resource Planning (ERP) system and the order management system. The formula is:

[0022] ;

[0023] Among them, is the completion degree of the order, is the actual production volume, is the demand volume of the order,

[0024] After all real-time data is collected, calculated, and processed, it is stored through the MES system. The formula is:

[0025] ;

[0026] Among them, is the sum of all real-time production data, is the real-time production data of factory k, and k is the total number of factories.

[0027] Furthermore, the capacity demand prediction includes historical data processing and is predicted based on the weighted average of historical data and real-time data.

[0028] The historical data processing: Process the historical production data, including removing noise, filling missing values, and correcting outliers. The historical data set consists of the production records of the factory, including equipment utilization rate, production equipment load situation, personnel scheduling situation, and order progress.

[0029] The prediction based on the weighted average of historical data and real-time data: Generate a capacity demand prediction value through the weighted average method according to the historical data and the real-time data of the current factory. The prediction formula is as follows:

[0030] ;

[0031] Among them, is the capacity demand prediction value of factory k at time point t, is the smoothing factor used to balance the influence of historical data and real-time data, is the capacity prediction value at the previous moment, is the real-time production data of factory k, is the weight factor for historical data, is the historical production data of factory k.

[0032] Furthermore, the prediction model is adjusted such that during the production process, the MES system will collect data on equipment operation status, personnel scheduling, production progress, etc. in real time and feed it back to the prediction model. Whenever these data change, the model will be adjusted according to the feedback results. The formula is:

[0033] ;

[0034] where, is the prediction error of factory k, is the predicted value of the production capacity demand of factory k, is the actual production capacity of factory k,

[0035] According to the error value, the system corrects the prediction by adjusting the parameters of the model. Through real-time data feedback, the prediction model can automatically learn.

[0036] Furthermore, the market change trend, the fluctuations in market demand and raw material prices directly affect the prediction of production capacity demand. Therefore, it is necessary to integrate market data into the model and adjust the prediction results by analyzing the market change trend. The formula is:

[0037] ;

[0038] where, is the adjusted predicted value of the production capacity of factory k based on the influence of market demand, is the weight factor of market data.

[0039] Furthermore, for the scheduling optimization model, according to the predicted value of the production capacity demand, it automatically generates a cross-factory production scheduling plan. The key steps of the scheduling optimization model are:

[0040] 1) Determine the optimization objective; maximize resource utilization; reduce the production cycle; the objective function is:

[0041] ;

[0042] where Z is the total production cost, is the production cost related to factory k, is the scheduling volume of factory k;

[0043] 2) Determine the constraint conditions, including the maximum production capacity constraint of each factory; equipment utilization constraint; personnel scheduling constraint; order delivery date constraint; the formula is:

[0044] ;

[0045] Among them, is the maximum production capacity of factory k, is the maximum scheduling volume of factory k,

[0046] 3) Model solution. The specific steps are as follows: Input data, input the predicted production capacity demand value, production capacity of each factory, availability of equipment and personnel, order priority, and delivery time into the optimization model; Optimization calculation, calculate the optimal production scheduling plan by solving the objective function; Output the scheduling plan, generate a specific production scheduling plan.

[0047] 4) Generate a production scheduling plan. The key contents of the scheduling plan include: task assignment, production sequence, resource allocation. The generated scheduling plan is transmitted to the MES system of each factory in the following format:

[0048] Production scheduling plan = {task, production factory, equipment, personnel, production sequence, priority, delivery date}.

[0049] Furthermore, for the transmission and execution of the production scheduling plan, after the scheduling plan is generated, it is transmitted to each factory. After the MES system of the factory receives the scheduling plan, it will generate specific production tasks according to the content of the plan. The generation process is as follows:

[0050] 1) Definition of task content, define the specific content of the production task.

[0051] 2) Resource allocation.

[0052] 3) Production plan arrangement, allocate the task to a specific production line and shift, and determine the production sequence.

[0053] 4) Execute the production task. The MES system starts the production line and executes the task. During the production process, the MES system will monitor the progress of the task in real time and update the task status according to the actual execution situation;

[0054] The execution progress of the production task is updated through the following formula:

[0055] ;

[0056] Among them, is the new progress of task a, is the old progress of task a, is the progress adjustment amount of task a, is the smoothing factor, which is used to adjust the update speed of the production task.

[0057] Further, in the new scheduling scheme, when the system receives real-time feedback data from each factory during the execution of production tasks, the scheduling optimization module will automatically adjust the scheduling scheme according to these real-time data. The formula is as follows:

[0058] ;

[0059] Among them, is the new task priority, is the original task priority, is the actual operation data of equipment i, is the expected data, is the adjustment factor.

[0060] Further, in the analysis of the scheduling scheme, during the execution of production tasks, the system will collect the execution results in real time through the feedback mechanism. The feedback data includes production progress, resource utilization, and production bottlenecks. Through the feedback data, the system analyzes the actual effect of the execution of production tasks.

[0061] By analyzing the feedback data of the production status, the MES system evaluates the effect of the adjusted scheduling scheme. The specific analysis steps include:

[0062] 1) Difference from prediction, comparing the difference between the actual production progress and the predicted progress.

[0063] 2) Resource utilization efficiency, analyzing the usage of equipment and personnel.

[0064] 3) Bottleneck analysis, identifying the bottlenecks in the production process through feedback data.

[0065] Further, in the optimization of the scheduling optimization model, through the analysis of the scheduling scheme, the MES system will optimize the scheduling optimization model. The optimization process makes the scheduling scheme flexible and accurate by adjusting the model parameters and updating the algorithm.

[0066] Compared with the prior art, the advantages of the present invention are as follows:

[0067] 1. By monitoring various data in the production process in real time, the present invention can dynamically adjust the scheduling scheme during the execution of production tasks, effectively avoid production bottlenecks and resource waste, and improve production efficiency and resource utilization rate.

[0068] 2. By collecting the actual results of task execution through the feedback mechanism, analyzing the effect of the execution of production tasks in real time, and optimizing the scheduling scheme according to the feedback data, through this process, the system can self-learn and optimize, adapt to different production environments and task requirements, and thus continuously improve the scheduling accuracy and production efficiency in long-term operation.

[0069] 3. The present invention further integrates factors such as market demand data and raw material price changes, and can adjust the production capacity prediction model in real time, enabling the scheduling plan to not only respond to changes during the production process but also be adjusted according to market changes. This flexible prediction and adjustment mechanism makes production scheduling more in line with actual needs, reduces uncertainties during the production process, enables production scheduling to be optimized at a broader level, and enhances the market adaptability and flexibility of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0070] To more clearly illustrate the technical solutions in the embodiments of the present application, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the following drawings are only some embodiments of the present application. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0071] Figure 1 It is a schematic diagram of the overall framework of the present invention.

[0072] Figure 2 It is a schematic diagram of Module 1 of the present invention.

[0073] Figure 3 It is a schematic diagram of Module 2 of the present invention.

[0074] Figure 4 It is a schematic diagram of Module 3 of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0075] To achieve the above objectives, the present invention is implemented through the following technical solutions. In combination with the attached Figure 1 , the present invention provides an industrial MES monitoring system with data interaction function, and the system includes:

[0076] Module 1: Factory status collection module. In combination with the attached Figure 2 , it monitors the production status, equipment production capacity, personnel scheduling, and order requirements of each factory in real time through the MES system, and generates real-time data including equipment utilization rate, production equipment load situation, personnel scheduling situation, and order progress according to the production status, equipment production capacity, personnel scheduling, and order requirements of each factory.

[0077] Equipment utilization rate: The MES system monitors the operating conditions of the equipment in real time, including the start-stop status, operating speed, workload, etc. of the equipment, collects the status information of each equipment, and the operating status of the equipment is automatically collected through sensors, programmable logic controllers (PLCs), and supervisory control and data acquisition systems (SCADA), and the data is converted into digital signals and uploaded to the MES system. The equipment utilization rate is obtained through the following formula:

[0078] ;

[0079] Among them, is the utilization rate of equipment i, is the actual working time of equipment i, is the available time of equipment i. Through this formula, the MES system can obtain the status of each equipment in real time and monitor its utilization rate.

[0080] Production equipment load situation: The MES system obtains the operating load of the equipment by integrating equipment sensors, calculates the ratio of the equipment load to its maximum bearing capacity, and the load data of the equipment is updated based on real-time information during the production process to ensure that the working status of the equipment is reflected in real time. The load information is calculated through the following formula:

[0081] ;

[0082] Among them, is the load of equipment i, is the production volume actually produced by equipment i, is the maximum output capacity of equipment i.

[0083] Personnel scheduling situation: The MES system records and manages the personnel scheduling situation of each factory in real time, including the work tasks, working hours arrangements, vacation situations, etc. of each worker. The personnel scheduling information is collected in real time through means such as radio frequency identification (RFID) systems or face recognition technologies. The working status and location information of each worker are uploaded to the MES system in real time, and the system automatically calculates the work load and task allocation of each worker to ensure the efficiency of personnel scheduling. The personnel scheduling information is calculated through the following formula:

[0084] ;

[0085] Among them, is the work load of worker j, is the working hours assigned to worker j, is the available working hours of worker j.

[0086] Order progress: The MES system, through integration with the enterprise resource planning system (ERP) and the order management system, tracks the progress of each order in real time, including the order receipt date, production progress, estimated delivery date, and actual delivery date. This information is obtained through an automated order tracking module to ensure the accuracy and timeliness of order progress data. The order demand data is represented by the following formula:

[0087] ;

[0088] Among them, is the completion degree of the order, is the actual production volume, is the demand quantity of the order. By calculating the order fulfillment rate, the MES system can adjust the production tasks in real time to ensure on-time delivery.

[0089] Data Upload and Storage: After all real-time data is collected, calculated, and processed, it is uploaded to the central database or cloud platform for storage through the unified data transmission module of the MES system. The data storage format is unified to support subsequent data analysis and processing. The data collection module verifies all collected data in real time to ensure the integrity, accuracy, and consistency of the data. The data storage method is achieved through the following means:

[0090] ;

[0091] Among them, is the sum of all real-time production data, is the real-time production data of factory k, and k is the total number of factories. Through this method, the MES system can achieve centralized storage and fast query of data for subsequent scheduling optimization and production capacity prediction.

[0092] Module 2: Factory Production Capacity Prediction Module, combined with Appendix Figure 3 , uses data analysis algorithms to predict the production capacity requirements of each factory, generates the predicted values of the production capacity requirements of each factory, and dynamically adjusts the prediction model according to historical production data, equipment status, and market change trends.

[0093] Production capacity demand prediction includes historical data processing, and prediction is based on the weighted average of historical data and real-time data.

[0094] Historical Data Processing: In the prediction process, it is first necessary to process the historical production data, including removing noise, filling in missing values, and correcting outliers. The historical data set consists of the production records of the factory, including equipment utilization rate, production equipment load situation, personnel scheduling situation, and order progress.

[0095] Prediction Based on the Weighted Average of Historical Data and Real-Time Data: According to the historical data and the real-time data of the current factory, the predicted value of the production capacity demand is generated through the weighted average method. The prediction formula is as follows:

[0096] ;

[0097] Among them, is the predicted value of the production capacity demand of factory k at time point t, is the smoothing factor used to balance the influence of historical data and real-time data, is the predicted value of the production capacity at the previous moment, is the real-time production data of factory k, is the weight factor for historical data, is the historical production data of factory k. This formula combines the weighted average of historical production data and real-time production data. By adjusting and values, the model can adaptively change in actual production and accurately predict future production capacity requirements.

[0098] Prediction model adjustment: During the production process, the MES system will collect data on equipment operation status, personnel scheduling, production progress, etc. in real time and feedback it to the prediction model in real time. Whenever these data change, the model will adjust according to the feedback results. For example, when the equipment load is higher than expected or the production progress lags behind, the model needs to be dynamically adjusted to reflect these changes.

[0099] Feedback adjustment formula: Assume that the error between the predicted value of the model and the actual production capacity is , then the feedback mechanism is adjusted through the following formula:

[0100] ;

[0101] where is the prediction error of factory k, is the predicted value of the production capacity demand of factory k, is the actual production capacity of factory k,

[0102] According to the error value, the system corrects the prediction by adjusting the parameters of the model. For example, if the prediction error is large, it is necessary to increase the weight of historical data or real-time data to reduce the error of future predictions.

[0103] Through real-time data feedback, the prediction model can automatically learn. Specifically, the prediction model can be updated through the backpropagation algorithm. Each time new production data is received, the model calculates the error and updates the weights, enabling the model to continuously adapt to the new production environment.

[0104] For example, the model can identify the complex relationships between variables such as equipment status, production progress, and personnel scheduling. By optimizing its internal parameters, the prediction accuracy of the model for long time series is continuously improved.

[0105] Market change trends: The fluctuations of market demand and raw material prices directly affect the prediction of production capacity requirements. Therefore, it is necessary to integrate market data into the model and adjust the prediction results by analyzing the market change trends. For example, when the market demand increases, the predicted production capacity value can be correspondingly increased, and vice versa.

[0106] The changing trend of market data can be modeled through regression analysis or time series analysis, and the parameters of the production capacity prediction model can be automatically adjusted according to market changes. The specific market trend adjustment formula is as follows:

[0107] ;

[0108] where, is the predicted production capacity value of factory k after adjustment considering the impact of market demand, is the weight factor of market data.

[0109] Module 3: Production scheduling module. Combining with Appendix Figure 4 , the model automatically generates a cross-factory production scheduling plan based on the predicted production capacity demand value, transmits the production scheduling plan to the MES system of each factory. The MES system generates specific production tasks according to the production scheduling plan and performs the execution of production tasks and updates the production status.

[0110] The establishment of the scheduling optimization model is based on the predicted production capacity demand value. The goal of the model is to automatically generate the optimal production scheduling plan according to factors such as production demand, equipment load, personnel scheduling, and order priority of each factory. This optimization model mainly includes the following key steps:

[0111] 1) Determine the optimization objective. The optimization objective is usually to minimize the production cost, including equipment usage cost, labor cost, material transportation cost, etc.; maximize the resource utilization rate to ensure the efficient utilization of production resources such as equipment and personnel; reduce the production cycle. Through reasonable task arrangement, shorten the production cycle and improve production efficiency. The objective function can be expressed as:

[0112] ;

[0113] where, Z is the total production cost, is the production cost related to factory k, is the scheduling volume of factory k,

[0114] 2) Determine the constraint conditions. When establishing the scheduling optimization model, various constraint conditions in the production process must be considered, mainly including: the maximum production capacity constraint of each factory to ensure that the production capacity of each factory will not be overloaded; the equipment utilization rate constraint, and the production tasks of the equipment need to be scheduled according to the actual available time and load of the equipment; the personnel scheduling constraint, the personnel of each factory need to be reasonably arranged according to the task requirements to avoid overtime work or overstaffing; the order delivery date constraint, the scheduling plan must ensure that the customer's order requirements are completed on time. The formula is:

[0115] ;

[0116] where, is the maximum production capacity of factory k, is the maximum scheduling volume of factory k. These constraints ensure the feasibility of the production scheduling plan, while optimizing the priority of production tasks, the utilization rate of resources, and the maximization of production capacity.

[0117] 3) Model solving. The scheduling optimization model is solved using optimization algorithms such as linear programming (LP) or integer programming (IP). These algorithms can efficiently solve the production task arrangements of each factory to ensure the optimal allocation of resources. In practice, heuristic algorithms or genetic algorithms (GA) may need to be combined to address complex multi-objective optimization problems.

[0118] The specific steps of model solving are as follows: Input data, input data such as the predicted value of production capacity demand, the production capacity of each factory, the availability of equipment and personnel, the priority of orders, and the delivery time into the optimization model; Optimization calculation, calculate the optimal production scheduling plan by solving the objective function; Output the scheduling plan, generate a specific production scheduling plan, including the production tasks, production order, resource allocation, etc. of each factory.

[0119] 4) Generate a production scheduling plan. The production scheduling plan includes the production task arrangements, equipment scheduling, personnel scheduling, material distribution, etc. of each factory. The key contents of the scheduling plan include: Task assignment, each production task is assigned to a specific factory and equipment to ensure the production is completed on time; Production order, arrange the production order of each factory to avoid resource conflicts and ensure production continuity; Resource allocation, reasonably allocate production resources according to the equipment utilization situation and personnel load of each factory to maximize the resource utilization rate.

[0120] The generated scheduling plan can be transmitted to the MES system of each factory in the following format:

[0121] Production scheduling plan = {task, production factory, equipment, personnel, production order, priority, delivery date}.

[0122] Transmission and execution of the production scheduling plan: Once the scheduling plan is generated, the MES system transmits the plan to each factory. After receiving the scheduling plan, the MES system will generate specific production tasks according to the plan content and execute the production according to the actual production conditions and resource configuration of the factory.

[0123] The MES system automatically generates specific production tasks according to the production scheduling plan and automatically allocates resources according to the task content. The process of task generation includes the following contents:

[0124] Definition of task content: The specific content of the production task, such as the production quantity, production process requirements, required equipment, etc.

[0125] Resource Allocation: Allocate resources based on equipment availability, personnel arrangements, material inventory, etc.;

[0126] Production Plan Arrangement: Assign tasks to specific production lines and shifts, and determine the production sequence.

[0127] Execute Production Tasks: Once the production tasks are generated, the MES system will start the production line and execute the tasks. During the production process, the MES system will monitor the progress of the tasks in real time and update the task status according to the actual execution situation.

[0128] The execution progress of the production tasks is updated through the following formula:

[0129] ;

[0130] where, is the new progress of task a, is the old progress of task a, is the progress adjustment amount of task a, is the smoothing factor, which is used to adjust the update speed of production tasks.

[0131] Module Four: Dynamic Scheduling Optimization Module. During the execution of the production tasks, the MES system adjusts the scheduling plan according to the real-time data in the production process, generates a new scheduling plan, analyzes the new scheduling plan through the system feedback mechanism, and optimizes the scheduling optimization model according to the analysis results.

[0132] New Scheduling Plan: When the system receives real-time feedback data from each factory during the execution of production tasks, the scheduling optimization module will automatically adjust the scheduling plan according to these real-time data. For example:

[0133] Equipment Failure. When a device fails, the system will adjust the production tasks of that device and assign the tasks to other available devices to avoid delays in the production schedule;

[0134] Production Schedule Lags. If the production task progress lags behind the expectation, the system will accelerate the production progress by rearranging the production sequence, adjusting working hours or increasing manpower, etc.;

[0135] Personnel Absence. If a worker is absent or there is a shortage of personnel on the production line, the system will rearrange the production tasks according to the scheduling situation of the remaining personnel and adjust the personnel allocation;

[0136] Order Demand Changes. If the market demand or order volume changes, the system will adjust the priority of production tasks to meet the new demand.

[0137] Based on these changes, the MES system adjusts the scheduling plan based on real-time data. For example, when the equipment load When it is higher than expected, the system reduces the load by adjusting the task allocation of the equipment. The adjustment formula is:

[0138] ;

[0139] Among them, is the new task priority, is the original task priority, is the actual load of equipment i, is the expected load, is the adjustment factor.

[0140] According to the feedback and adjustment of real-time data, the system automatically generates a new scheduling plan, and reallocates production tasks, adjusts the equipment load, adjusts the working hours and tasks of workers, etc. The new scheduling plan will reflect all changes in the production process, ensure that production tasks can be completed on time, and maximize the use efficiency of resources.

[0141] Scheduling plan analysis: During the execution of production tasks, the system will collect the execution results in real time through a feedback mechanism. These feedback data will be used to analyze whether the current scheduling plan is effective and help the system identify problems in the production process (such as equipment failures, production delays, etc.). The feedback data includes:

[0142] Production progress: The completion and delay status of tasks;

[0143] Resource usage: The usage of equipment, personnel, and materials;

[0144] Production bottlenecks: Which tasks or links have bottlenecks, resulting in production delays.

[0145] Through these feedback data, the system can analyze the actual effect of the execution of production tasks and judge the effectiveness of the scheduling plan. If the system finds that the current plan cannot meet the production requirements or there are problems, it will readjust the scheduling plan.

[0146] By analyzing the feedback data of the production status, the MES system can evaluate the effect of the adjusted scheduling plan. The specific analysis steps include:

[0147] Difference from the prediction: Compare the difference between the actual production progress and the predicted progress, calculate the error and analyze the reasons;

[0148] Resource utilization efficiency: Analyze the usage of equipment and personnel, and evaluate whether the resources are fully utilized;

[0149] Bottleneck analysis: Identify the bottlenecks in the production process through feedback data and analyze the reasons for the bottlenecks.

[0150] Based on the feedback analysis results, the MES system optimizes the scheduling optimization model. The optimization process adjusts the model parameters or updates the algorithm to make the scheduling plan more flexible and accurate. For example, by introducing dynamic learning algorithms (such as deep learning, reinforcement learning, etc.), the MES system can make adaptive adjustments based on historical feedback data and real-time data, thereby improving the accuracy of the scheduling model and production efficiency.

[0151] An industrial MES monitoring system with data interaction function proposed by the present invention ensures the smooth execution of production tasks through real-time data collection, dynamic adjustment of the scheduling plan, and feedback mechanism, and can automatically optimize the scheduling plan according to the real-time changes during the production process. Through this adaptive adjustment mechanism, the production resources are optimally configured, the production efficiency is improved, and the cost is effectively controlled. This method provides an efficient and intelligent solution for cross-factory production capacity coordination management.

[0152] The present invention provides an industrial MES monitoring system with data interaction function, aiming to optimize the execution process of production tasks, improve production efficiency and reduce costs through dynamic scheduling and real-time data feedback. Compared with the existing technologies, the present invention has significant advantages, can adjust production tasks, equipment and personnel scheduling in real time, and optimize through the feedback mechanism during the production process, thereby effectively improving the production capacity utilization rate, reducing the production cycle and production costs.

[0153] First, the MES system collects the real-time data of each factory and stores it. Then, it predicts the production capacity requirements of each factory through the weighted average method, and dynamically adjusts the prediction model in combination with historical production data, equipment status and market change trends. This process improves the adaptability of the prediction to complex and changeable production environments through the self-learning ability of the model. The scheduling optimization model automatically generates a scheduling plan based on the predicted production capacity requirements. After the generated scheduling plan is transmitted to the MES systems of each factory, the factory automatically allocates resources according to the plan and the actual production situation. The factory adjusts the priority and production order of tasks according to the actual execution situation. When problems such as equipment failures, production progress lags, and personnel absences occur, the scheduling plan is automatically adjusted. This kind of adaptive adjustment enables the production process to quickly respond to emergencies and ensures the timely completion of production tasks.

[0154] In summary, the advantages of the present invention lie in its dynamic adjustment ability and adaptive optimization mechanism, which can automatically adjust production tasks according to real-time data, and can also be continuously optimized through the feedback mechanism to ensure that the production process is more efficient, flexible and accurate. Especially when facing changes in the production environment, it can quickly respond, minimize production delays and optimize resource utilization. The present invention provides an efficient and intelligent solution for cross-factory production capacity coordination management.

[0155] The above are only specific embodiments of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present application can easily think of changes or substitutions, which should all be covered within the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims described above.

Claims

1. An industrial MES monitoring system with data interaction function, characterized in that: The system includes: Module 1: Factory status collection module, which monitors the production status, equipment capacity, personnel scheduling and order requirements of each factory in real time through the MES system, and generates real-time data including equipment utilization, production equipment load, personnel scheduling and order progress according to the production status, equipment capacity, personnel scheduling and order requirements of each factory; Module 2: Factory capacity forecasting module, which uses data analysis algorithms to forecast the capacity demand of each factory based on the real-time data, generates forecast values ​​of the capacity demand of each factory, and dynamically adjusts the forecasting model based on historical production data, equipment status and market change trends; The capacity forecast is made by using the weighted average of the processed factory historical data and real-time data, and the formula is: ； in, is the predicted capacity demand of factory k at time point t, is a smoothing factor used to balance the impact of historical data and real-time data. is the predicted capacity value at the previous moment, is the real-time production data of factory k, is the weight factor of historical data, is the historical production data of factory k; Module 3: Production scheduling module, which establishes a scheduling optimization model. The model automatically generates a cross-factory production scheduling plan based on the predicted capacity demand value. The MES system transmits the production scheduling plan to each factory. The MES system generates specific production tasks based on the production scheduling plan, and executes the production tasks and updates the production status. The optimization goal of the scheduling optimization model is to minimize the total production cost of all factories, and the constraints are the capacity constraints, equipment utilization constraints, personnel scheduling constraints and order delivery constraints of each factory; Module 4: Dynamic scheduling optimization module. During the execution of the production task, the MES system adjusts the scheduling plan according to the real-time data in the production process, generates a new scheduling plan, analyzes the new scheduling plan through the system feedback mechanism, and optimizes the scheduling optimization model based on the analysis results.

2. An industrial MES monitoring system with data interaction function according to claim 1, characterized in that: The formula for equipment utilization is: ； in, is the utilization rate of equipment i, is the actual working time of device i, is the available time of device i, The production equipment load is calculated by the following formula: ； in, is the load of device i, is the actual output of equipment i, is the maximum output capacity of device i, The calculation formula for the personnel dispatch situation is: ； in, is the workload of worker j, is the working time assigned to worker j, is the available working time of worker j, The calculation formula for the order progress is: ； in, For order completion, is the actual production volume, is the order quantity, All real-time data is collected, calculated and processed and then stored in the MES system. The formula is: ； in, is the sum of all real-time production data, is the real-time production data of factory k, and k is the total number of factories.

3. The industrial MES monitoring system with data interaction function according to claim 1 is characterized in that: The processing of the historical data includes removing noise, filling missing values ​​and correcting outliers. It consists of the factory's production records, including equipment utilization, production equipment load, personnel scheduling, and order progress.

4. The industrial MES monitoring system with data interaction function according to claim 3 is characterized in that: The dynamic adjustment prediction model is that during the production process, the MES system will collect real-time data and feed it back to the prediction model. When the real-time data changes, the model will be adjusted according to the feedback results. The formula is: ； in, is the prediction error of plant k, is the predicted capacity demand of factory k, is the actual production capacity of factory k, According to the error value ,The system corrects the prediction by adjusting the parameters of the model, and the ,prediction model can learn automatically through real-time data feedback.

5. The industrial MES monitoring system with data interaction function according to claim 4 is characterized in that: Integrate market data into the model and adjust the forecast results by analyzing market trends. The formula is: ； in, is the adjusted forecast value of factory k’s production capacity based on market demand, is the weight factor of market data.

6. The industrial MES monitoring system with data interaction function according to claim 5 is characterized in that: The scheduling optimization model automatically generates a cross-factory production scheduling plan based on the predicted capacity demand value. The steps of the scheduling optimization model are: 1) Determine the optimization goal, maximize resource utilization, and reduce production cycle; the function of the optimization goal is: ； Where Z is the total production cost, is the production cost associated with factory k, is the dispatch quantity of factory k, 2) Determine the constraints, including the maximum capacity constraint of each factory, equipment utilization constraint, personnel scheduling constraint, and order delivery date constraint; the formula is: ； in, is the maximum production capacity of factory k, is the maximum dispatch quantity of factory k, 3) Model solution: The specific steps are: input the predicted value of capacity demand, the production capacity of each factory, the availability of equipment and personnel, the priority of orders and the delivery time into the optimization model; calculate the optimal production scheduling plan by solving the objective function; 4) Generate a production scheduling plan. The content of the scheduling plan includes: task allocation, production sequence, resource allocation, and the generated scheduling plan is transmitted to the MES system of each factory in the following format: Production scheduling plan = {task, production plant, equipment, personnel, production sequence, priority, delivery date}.

7. The industrial MES monitoring system with data interaction function according to claim 1 is characterized in that: The transmission and execution of the production scheduling plan. After the scheduling plan is generated, it is transmitted to each factory. After the factory's MES system receives the scheduling plan, it will generate specific production tasks according to the content of the plan. The generation process is specifically as follows: 1) Task content definition, define the specific content of the production task, 2) Resource allocation, 3) Production planning and scheduling, assigning tasks to specific production lines and shifts, and determining the production sequence, 4) Execute production tasks. The MES system starts the production line and executes the tasks. During the production process, the MES system monitors the progress of the tasks in real time and updates the task status based on the actual execution status. The execution progress of the production task is updated by the following formula: ； in, is the new progress of task a, is the old progress of task a, is the progress adjustment of task a, It is a smoothing factor used to adjust the update speed of production tasks.

8. An industrial MES monitoring system with data interaction function according to claim 7, characterized in that: The scheduling optimization module automatically adjusts the scheduling plan according to real-time data. The formula is: ； in, For the new task priority, is the original task priority, is the actual operation data of device i, For expected data, is the adjustment factor.

9. The industrial MES monitoring system with data interaction function according to claim 1 is characterized in that: The scheduling plan analysis, during the execution of production tasks, the system will collect the execution results in real time through the feedback mechanism. The feedback data includes production progress, resource usage, and production bottlenecks. Through the feedback data, the system analyzes the actual effect of the execution of production tasks. By analyzing the feedback data of production status, the MES system evaluates the effect of the adjusted scheduling plan. The specific analysis steps include: 1) Differences from forecasts, comparing the differences between actual production progress and forecast progress, 2) Resource utilization efficiency, analyzing the use of equipment and personnel, 3) Bottleneck analysis: identifying bottlenecks in the production process through feedback data.

10. An industrial MES monitoring system with data interaction function according to claim 1 or 9, characterized in that: The scheduling optimization model is optimized. Through the scheduling solution analysis, the MES system will optimize the scheduling optimization model. The optimization process is achieved by adjusting model parameters and updating algorithms.

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