Digital factory production management method and system

Abstract

The invention relates to a digital factory production management method and system, and the method comprises the steps: predicting a future initial order demand based on a data analysis tool and historical sales data, and adjusting the initial order demand based on a current market trend and a current market strategy corresponding to a target peer, and obtaining a target order demand; obtaining the required production capacity and the equipment utilization rate corresponding to each piece of production equipment based on the target order demand; acquiring real-time operation data corresponding to each piece of production equipment, and predicting maintenance requirements corresponding to the real-time operation data, the required production capacity and the equipment utilization rate based on the trained deep learning model; and making a maintenance plan of each production device in advance based on the maintenance demand and the maintenance priority corresponding to each production device. The production equipment is subjected to preventive maintenance, the reliability and stability of the production equipment are improved, the use of maintenance resources is optimized, and the maintenance efficiency and quality are improved.

Description

Technical Field

[0001] The present application relates to the technical field of digital management, and in particular to a digital factory production management method and system. Background Art

[0002] In the current manufacturing environment, digital factory production management has become a key means to improve production efficiency, reduce costs and enhance market competitiveness. However, traditional production management methods often rely on manual experience and intuition to make decisions, resulting in low production efficiency, waste of resources and extended order delivery cycles.

[0003] Traditional production management methods also have shortcomings in equipment maintenance. They usually rely on regular maintenance and post-failure repairs, lacking in-depth analysis and prediction of real-time operating data of production equipment. This often leads to problems such as untimely equipment maintenance, high maintenance costs, and production interruptions, which seriously affect production efficiency. Therefore, there is an urgent need for a digital factory production management solution that can solve the problem of insufficient optimization of maintenance plans. Summary of the invention

[0004] In order to solve the problem of insufficient optimization of maintenance plans, the present application provides a digital factory production management method and system.

[0005] In a first aspect, the present application provides a digital factory production management method, comprising: Based on data analysis tools and historical sales data, forecast future initial order demand, and adjust the initial order demand based on current market trends and the current market strategies of target peers to obtain target order demand, wherein the historical sales data includes sales products, sales quantity, sales amount, sales location and sales time; Based on the target order demand, obtain the required production capacity and the equipment utilization rate corresponding to each production equipment; Acquire the real-time operation data corresponding to each of the production equipment, and predict the maintenance requirements corresponding to the real-time operation data, the required production capacity and the equipment utilization rate based on the trained deep learning model; Based on the maintenance requirements and the maintenance priorities corresponding to each of the production equipment, a maintenance plan for each of the production equipment is formulated in advance to perform preventive maintenance.

[0006] The beneficial effects of this application are: more accurate target order demand can be obtained, the accuracy and timeliness of order demand forecasting can be improved, a reliable basis can be provided for production planning, and market changes and competitive situations can be better adapted. Preventive maintenance of production equipment can improve the reliability and stability of production equipment, extend the service life of production equipment, reduce production interruptions and losses caused by production equipment failures, optimize the use of maintenance resources, and improve maintenance efficiency and quality.

[0007] Further, after obtaining the real-time operation data corresponding to each of the production equipment, the method further includes: Based on a preset data analysis tool, the real-time operation data is analyzed to obtain multiple target factors affecting production efficiency and product quality; Based on the plurality of target factors, the target order demand, the production capacity and the equipment utilization rate, the operating parameters of each of the production equipment are adjusted.

[0008] The beneficial effects of adopting the above further solution are: by accurately adjusting the working parameters of the production equipment, the production process can be optimized, the production efficiency and product quality can be improved. The integration of automatic control reduces the frequency and cost of manual intervention and improves the intelligent level of production management.

[0009] Further, after obtaining the target order demand, the method further includes: Based on the target order demand, the arrival time period of the raw materials corresponding to each of the sales products and the average sales volume per unit period are obtained, and the safety stock volume is calculated based on the arrival time period and the average sales volume; Based on the safety stock, the replenishment point and replenishment strategy of each of the raw materials are calculated, so that when the replenishment point is reached, the raw materials are replenished based on the replenishment strategy, and the replenishment strategy includes one or more of quantitative replenishment, periodic replenishment and demand-driven replenishment.

[0010] The beneficial effects of adopting the above further scheme are: automated replenishment operations can be realized. By formulating and implementing effective replenishment strategies, timely supply of raw materials is ensured, and the reliability and stability of the supply chain are improved. Inventory costs are reduced, inventory turnover is improved, and the competitiveness of enterprises is enhanced. Intelligent management of the supply chain is achieved, and the transparency and traceability of the supply chain are improved.

[0011] Further, after obtaining the target order demand, the method further includes: Acquire real-time production data corresponding to each initial sub-area in the processing and production operation area, wherein the production data includes equipment utilization, production efficiency, processing quality and energy consumption; Based on the target order demand and the real-time production data, the division of each of the initial sub-areas is dynamically adjusted, and the adjusted sub-areas are used as current sub-areas.

[0012] The beneficial effects of adopting the above further scheme are: by dynamically adjusting the sub-area division, the production layout can be optimized, and production efficiency and product quality can be improved. It helps to better meet the target order requirements, improve customer satisfaction and market competitiveness. By optimizing the production area layout, energy consumption and production costs can be reduced, and the economic benefits of the enterprise can be improved.

[0013] Further, adjusting the working parameters of each of the production equipment based on the multiple target factors, the target order demand, the production capacity and the equipment utilization rate includes: For each of the current sub-regions, based on the multiple target factors, the target order demand, the production capacity, the equipment utilization rate and the first mapping relationship, the working parameter adjustment requirements of each of the production equipment corresponding to the current sub-region are predicted, and the first mapping relationships are preset between different current sub-regions and different production equipment; Based on the adjustment requirements of each of the working parameters, the working parameters of each of the production equipment are automatically adjusted to adapt to different processing goals.

[0014] The beneficial effect of adopting the above further solution is that by accurately predicting and adjusting the working parameters of the production equipment, the production process can be optimized, waste and bottlenecks in the production process can be reduced, and production efficiency can be improved.

[0015] Further, after acquiring the real-time operation data corresponding to each of the production equipment, the method further includes: For each of the current sub-areas, if a device failure is detected in the production equipment, a target time required for fault repair is predicted based on the fault type, fault severity and historical fault recovery time corresponding to the device failure; For each of the current sub-areas, based on the target time, the current production demand corresponding to the current sub-area and the remaining production capacity, the production volume delayed due to the failure of the production equipment is predicted; a second mapping relationship is preset between different current sub-areas and different current production demands, and the current production demand is the current workload after allocation based on the target order demand; For each of the current sub-regions, based on the production volume, calculating the capacity gap of the current sub-region; Based on the production capacity gap, a production adjustment strategy is generated, which includes adjusting the production plan, reallocating production tasks, and scheduling spare equipment or production lines.

[0016] The beneficial effects of adopting the above further scheme are: by quickly predicting the time for fault repair and the delayed production volume, a production adjustment strategy can be quickly formulated and implemented to shorten the production recovery time. By internal deployment and reallocation of production tasks, the remaining production capacity can be fully utilized to reduce resource waste. By predicting and responding to production equipment failures in advance, the risk of production interruption and loss caused by failures can be reduced.

[0017] Further, after taking the adjusted sub-region as the current sub-region, the method further includes: For each employee, a talent profile corresponding to the employee is constructed based on the basic information corresponding to the employee, and the talent profile includes the employee's skills, work efficiency, potential ability and communication preference; Based on each of the current sub-regions and each of the talent portraits, an employee comprehensive deployment strategy is obtained, and the employee comprehensive deployment strategy is used to allocate the most suitable employee to the most suitable current sub-region.

[0018] The beneficial effects of adopting the above further scheme are: by assigning employees to the most suitable current sub-areas, employees' strengths can be brought into play, and job satisfaction and sense of achievement can be improved. The high matching degree between employees and current sub-areas facilitates employees to adapt to the work environment more quickly, improve work efficiency and productivity. With the implementation of the comprehensive employee deployment strategy, enterprises can respond to changes in production needs more flexibly and improve the overall flexibility of the production system.

[0019] In a second aspect, the present application provides a digital factory production management system, comprising: The order demand forecasting module is used to forecast the future initial order demand based on the data analysis tool and historical sales data, and adjust the initial order demand based on the current market trend and the current market strategy corresponding to the target peers to obtain the target order demand. The historical sales data includes sales products, sales quantity, sales amount, sales location and sales time; A first acquisition module is used to acquire the required production capacity and the equipment utilization rate corresponding to each production equipment based on the target order demand; A maintenance demand prediction module is used to obtain the real-time operation data corresponding to each of the production equipment, and predict the maintenance demand corresponding to the real-time operation data, the required production capacity and equipment utilization rate based on the trained deep learning model; The maintenance module is used to formulate a maintenance plan for each of the production equipment in advance based on the maintenance requirements and the maintenance priorities corresponding to each of the production equipment, so as to perform preventive maintenance.

[0020] In a third aspect, the present application provides an electronic device, including a processor and a memory, wherein the processor is coupled to the memory; The processor is used to execute the computer program stored in the memory so that the electronic device performs the method as described in any one of the first aspects.

[0021] In a fourth aspect, the present application provides a computer-readable storage medium, comprising a computer program or instructions, which, when executed on a computer, causes the computer to execute the method as described in any one of the first aspects. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 A flowchart of a digital factory production management method according to an embodiment of the present application; Figure 2 This is a structural block diagram of a digital factory production management system according to an embodiment of the present application; Figure 3 This is a structural block diagram of an electronic device according to an embodiment of the present application. DETAILED DESCRIPTION

[0023] The present application is further described in detail below in conjunction with the accompanying drawings.

[0024] The embodiment of the present application provides a digital factory production management method, which can be executed by a device, which can be a server or a terminal device, wherein the server can be an independent physical server, a server cluster or a distributed system composed of multiple physical servers, or a cloud server providing cloud computing services. The terminal device can be a smart phone, a tablet computer, a desktop computer, etc., but is not limited thereto.

[0025] like Figure 1 As shown, a digital factory production management method is shown, with electronic equipment as the execution subject, and the main process of the method is described as follows (steps S101 to S104): Step S101: Based on data analysis tools and historical sales data, predict future initial order demand, and adjust the initial order demand based on current market trends and current market strategies corresponding to target peers to obtain target order demand. The historical sales data includes sales products, sales quantity, sales amount, sales location and sales time.

[0026] In this embodiment, historical sales data can be extracted from the sales system of the enterprise, including key information such as sales products, sales quantity, sales amount, sales location and sales time, etc. These data can also be cleaned and formatted to ensure the accuracy and consistency of the data.

[0027] Use data analysis tools such as Excel and Python's Pandas library to conduct in-depth analysis of historical sales data to identify sales trends, seasonal fluctuations, and cyclical patterns. Then, select appropriate forecasting models (such as time series analysis, machine learning algorithms, etc.) to forecast initial order demand in the future.

[0028] Through market research, industry reports and competitor analysis, we can obtain the current market trends and market strategies of target peers. We use this information as input to dynamically adjust the initial order demand to reflect market changes and competition, thereby obtaining more accurate target order demand, improving the accuracy and timeliness of order demand forecasts, providing a reliable basis for production planning, and being able to better adapt to market changes and competition, thus improving the company's market responsiveness and competitiveness.

[0029] Step S102: Based on the target order demand, the required production capacity and the equipment utilization rate corresponding to each production equipment are obtained.

[0030] According to the target order demand, combined with the company's production process, process requirements and equipment capabilities, the required production capacity is evaluated. Production capacity can include determining the production capacity of the production line, etc.

[0031] Through the production management system or equipment monitoring system, the operating status and output data of each production equipment can be obtained in real time. These data are used to calculate the equipment utilization rate, that is, the ratio of the actual equipment operation time to the total available time. This ensures that the production capacity matches the order demand and avoids overproduction or underproduction. It is easy to find the problem of low equipment utilization in time and provide a basis for equipment optimization and upgrading.

[0032] Step S103: Acquire the real-time operation data corresponding to each of the production equipment, and predict the maintenance requirements corresponding to the real-time operation data, the required production capacity and the equipment utilization rate based on the trained deep learning model.

[0033] Through sensors, IoT technology and other means, the operating data of each production equipment is collected in real time. The operating data can be temperature, pressure or vibration, etc. Based on historical operating data and known maintenance records, deep learning models (such as convolutional neural networks, recurrent neural networks, etc.) are trained. Deep learning models can learn the operating mode and fault characteristics of equipment, thereby predicting future maintenance needs.

[0034] The real-time operation data, required production capacity and equipment utilization are input into the trained deep learning model. The deep learning model will output the prediction results, including maintenance time, maintenance type and required spare parts, etc. It is convenient to find hidden equipment failures in advance, reduce equipment failure rate, optimize maintenance plans, and reduce unnecessary downtime and maintenance costs.

[0035] Step S104: Based on the maintenance requirements and the maintenance priorities corresponding to the various production equipment, a maintenance plan for each of the production equipment is formulated in advance to perform preventive maintenance.

[0036] According to the prediction results and the importance and urgency of the production equipment, the maintenance needs of each production equipment are prioritized to obtain the maintenance priority of each production equipment. According to the priority sorting results, a detailed maintenance plan can be formulated, which includes maintenance time, maintenance personnel, required spare parts and tools, etc. According to the maintenance plan, preventive maintenance is carried out on the production equipment, including equipment inspection, cleaning, lubrication, replacement of spare parts, etc. At the same time, the data and results of the maintenance process are recorded to provide a basis for subsequent model optimization and fault analysis.

[0037] By performing preventive maintenance on production equipment, the reliability and stability of production equipment are improved, the service life of production equipment is extended, production interruptions and losses caused by production equipment failures are reduced, the use of maintenance resources is optimized, and maintenance efficiency and quality are improved.

[0038] In this embodiment, after step S103, it also includes: based on a preset data analysis tool, analyzing the real-time operation data to obtain multiple target factors affecting production efficiency and product quality; based on the multiple target factors, the target order demand, the production capacity and the equipment utilization rate, adjusting the working parameters of each of the production equipment.

[0039] After obtaining the real-time operating data of each production equipment, data preprocessing is first performed, including data cleaning (noise removal, missing value filling, etc.), format conversion (ensuring the uniformity of data format for subsequent analysis) and outlier detection (identifying and processing abnormal data to avoid interference with analysis results).

[0040] Based on preset data analysis tools (such as statistical software SPSS, R language, Python's Pandas and NumPy libraries, or machine learning platforms such as TensorFlow, PyTorch, etc.), in-depth analysis of pre-processed real-time operation data is performed. These tools can combine multiple analysis methods, such as regression analysis, correlation analysis, cluster analysis, principal component analysis, etc., to identify multiple key target factors that affect production efficiency and product quality.

[0041] Regression analysis is used to study the quantitative relationship between independent variables (various parameters in real-time operation data) and dependent variables (production efficiency, product quality) to determine which factors have a significant impact on the target variable; correlation analysis is used to evaluate the correlation between different real-time operation data and identify potential synergies or conflict points; cluster analysis is used to classify similar real-time operation data into the same category, which helps to discover patterns or abnormal behaviors in the production process; principal component analysis is used to reduce data dimensions and extract the principal components that best represent the original data set to facilitate subsequent analysis and decision-making.

[0042] Based on the analysis results, target factors that have a significant impact on production efficiency and product quality are screened out, such as equipment temperature, vibration frequency, pressure level, current fluctuation, etc.

[0043] Through in-depth analysis of real-time operation data, we can accurately identify the target factors that affect production efficiency and product quality, and provide a scientific basis for optimizing the production process. It helps to timely discover potential production problems, prevent quality accidents, and improve production stability and reliability.

[0044] Based on the identified target factors, combined with the target order demand, production capacity and equipment utilization, the adjustment strategy of the corresponding production equipment working parameters is obtained. The adjustment strategy may include determining the direction (such as increase, decrease, optimization, etc.), magnitude and timing of the adjustment.

[0045] In this embodiment, the adjustment strategy can be integrated into the automated control system to achieve real-time adjustment of the working parameters of the production equipment. By accurately adjusting the working parameters of the production equipment, the production process can be optimized, and the production efficiency and product quality can be improved. The integration of automated control reduces the frequency and cost of manual intervention and improves the intelligent level of production management. The continuous monitoring and feedback mechanism ensures the accuracy and effectiveness of parameter adjustment, which helps to achieve continuous optimization of the production process.

[0046] In this embodiment, after step S101, it also includes: based on the target order demand, obtaining the arrival time period and the estimated average sales volume per unit period of the raw materials corresponding to each of the sales products, and calculating the safety stock based on the arrival time period and the average sales volume; based on the safety stock, calculating the replenishment point and replenishment strategy of each of the raw materials, so that when the replenishment point is reached, the raw materials are replenished based on the replenishment strategy, and the replenishment strategy includes one or more of quantitative replenishment, periodic replenishment and demand-driven replenishment.

[0047] In this embodiment, based on the communication information with the supplier, the arrival time period of the raw materials corresponding to each sales product can be obtained, including the transportation time and the warehousing time, etc. Considering the factors that may affect the arrival time, such as holidays and weather changes, the arrival time period can be appropriately adjusted.

[0048] Based on historical sales data, seasonal and cyclical trends of each sales product are analyzed. Combined with target order demand, using statistical methods such as moving average method and exponential smoothing method, average sales volume in the future can be predicted.

[0049] Based on the arrival time cycle of raw materials and the expected average sales volume per unit cycle, the safety stock calculation formula (such as the economic order quantity model EOQ, safety stock formula, etc.) is used to calculate the safety stock.

[0050] By accurately calculating the safety stock level, the inventory cost and customer service level are balanced, the risk of inventory backlog and out-of-stock is avoided, the flexibility and responsiveness of the supply chain are improved, and the company is helped to better respond to market changes.

[0051] Based on the safety stock and the average sales volume per unit cycle, the replenishment point of each raw material is calculated. The replenishment point refers to the inventory level that triggers replenishment when the inventory level drops to a certain level.

[0052] There is a correspondence between the characteristics and demand patterns of raw materials and the replenishment strategy. According to the correspondence, the characteristics of the current raw materials and the current demand pattern, a suitable replenishment strategy is selected, including one or more of quantitative replenishment, periodic replenishment and demand-driven replenishment.

[0053] Among them, quantitative replenishment means: when the inventory level drops to the replenishment point, replenishment is carried out in a fixed quantity; periodic replenishment means: replenishment is carried out according to a fixed cycle (such as weekly, monthly), and the replenishment quantity is adjusted according to actual demand; demand-driven replenishment means: replenishment is carried out according to real-time demand information (such as customer orders, sales forecasts, etc.) to achieve real-time response of the supply chain.

[0054] In this embodiment, the replenishment strategy is integrated into the supply chain management system to realize automated replenishment operations. By formulating and implementing effective replenishment strategies, the timely supply of raw materials is ensured, and the reliability and stability of the supply chain are improved. Inventory costs are reduced, inventory turnover is improved, and the competitiveness of enterprises is enhanced. Intelligent management of the supply chain is realized, and the transparency and traceability of the supply chain are improved.

[0055] In this embodiment, after step S101, it also includes: acquiring real-time production data corresponding to each initial sub-area in the processing and production operation area, wherein the production data includes equipment utilization, production efficiency, processing quality and energy consumption; based on the target order demand and the real-time production data, dynamically adjusting the division of each initial sub-area, and using the adjusted sub-area as the current sub-area.

[0056] In each initial sub-area, sensors or data acquisition devices can be installed to monitor the operating status of the equipment in real time, including operating time, downtime, etc. Through the data acquisition system, the information of these sensors or data acquisition devices is collected regularly, and the equipment utilization rate is calculated.

[0057] Use the production management system or MES (Manufacturing Execution System) to record the completion of production tasks in each initial sub-area, including production quantity, production cycle, etc. By analyzing the completion of these tasks, the production efficiency is calculated.

[0058] Set up quality inspection points in each initial sub-area, conduct quality inspections on the products produced, record the number of qualified products and the number of unqualified products, and calculate the unqualified product rate as an indicator of processing quality.

[0059] Energy consumption monitoring equipment is installed to monitor the energy consumption of each initial sub-area in real time, including electricity, water, gas, etc. Energy consumption data is collected regularly through the data acquisition system and statistical analysis is performed.

[0060] Through real-time monitoring and data analysis, the real-time production data of each initial sub-area can be accurately obtained, providing a basis for subsequent dynamic adjustments. It helps to timely discover problems and bottlenecks in the production process and improve production efficiency and product quality.

[0061] According to market demand and sales forecast, determine the target order demand, including product type, quantity, delivery date, etc. Compare the real-time production data of each initial sub-area and analyze the differences in equipment utilization, production efficiency, processing quality and energy consumption.

[0062] Dynamically adjust the division of each initial sub-area based on the evaluation results of target order demand and real-time production data. Divide sub-areas with high production efficiency, high equipment utilization, good processing quality and low energy consumption into key production areas, giving priority to meeting target order demand. Rectify or optimize sub-areas with low production efficiency, low equipment utilization, poor processing quality or high energy consumption, or divide them into auxiliary production areas for the production of non-urgent or low-priority products. Develop a detailed adjustment plan, including adjustment time, adjustment content, and adjusted sub-area division.

[0063] By dynamically adjusting the sub-area division, the production layout can be optimized, production efficiency and product quality can be improved. This helps to better meet the target order requirements, improve customer satisfaction and market competitiveness. By optimizing the production area layout, energy consumption and production costs can be reduced, and the economic benefits of the enterprise can be improved.

[0064] In this embodiment, adjusting the working parameters of each of the production equipment based on the multiple target factors, the target order requirements, the production capacity and the equipment utilization rate includes: For each of the current sub-regions, based on the multiple target factors, the target order demand, the production capacity, the equipment utilization rate and the first mapping relationship, the working parameter adjustment requirements of each of the production equipment corresponding to the current sub-region are predicted, and the first mapping relationships are preset between different current sub-regions and different production equipment; Based on the adjustment requirements of each of the working parameters, the working parameters of each of the production equipment are automatically adjusted to adapt to different processing goals.

[0065] Collect real-time production data for each current sub-area, including target factors such as equipment utilization, production efficiency, processing quality, energy consumption, and key information such as target order demand, production capacity, and equipment utilization. Integrate this information to form a complete data set to provide a basis for subsequent analysis.

[0066] According to the production process, equipment layout and production goals, the first mapping relationship between different current sub-areas and different production equipment is preset. It may involve matching of information such as equipment type, production capacity, and processing range. It ensures that each current sub-area can accurately correspond to the relevant production equipment, providing a basis for subsequent adjustment of working parameters.

[0067] Based on machine learning or statistical methods, a prediction model is constructed that can predict the adjustment requirements of the working parameters of each production equipment based on the collected data. The model should consider the combined impact of multiple target factors, target order requirements, production capacity, and equipment utilization to ensure the accuracy and reliability of the prediction results. The prediction model is applied to the actual data to output the adjustment requirements of the working parameters of the production equipment corresponding to each current sub-area. These requirements can include adjustment range, adjustment direction, adjustment time, etc.

[0068] According to the prediction results, formulate specific production equipment working parameter adjustment strategies. This includes determining the specific parameters to be adjusted (such as speed, temperature, pressure, etc.), adjustment range and steps, etc. Integrate the adjustment strategy into the automation control system to ensure that the system can automatically perform adjustment operations.

[0069] During the adjustment process, the operating status and working parameters of the production equipment are monitored in real time to ensure that the adjustment process proceeds smoothly. The detailed information of each adjustment operation is recorded, including the adjustment time, parameter values ​​before and after the adjustment, adjustment effect, etc. An adjustment report is generated to provide data support for subsequent production management and optimization.

[0070] By accurately predicting and adjusting the working parameters of production equipment, the production process can be optimized, waste and bottlenecks in the production process can be reduced, thereby improving production efficiency.

[0071] Optimization of working parameters helps improve processing quality, reduce defective rate, and enhance overall product quality and customer satisfaction. Through reasonable parameter adjustment, energy consumption and raw material consumption in the production process can be reduced, thereby reducing production costs and improving the economic benefits of the enterprise.

[0072] In this embodiment, after step S103, the following steps are further included: For each of the current sub-areas, if a device failure is detected in the production equipment, a target time required for fault repair is predicted based on the fault type, fault severity and historical fault recovery time corresponding to the device failure; For each of the current sub-areas, based on the target time, the current production demand corresponding to the current sub-area and the remaining production capacity, the production volume delayed due to the failure of the production equipment is predicted; a second mapping relationship is preset between different current sub-areas and different current production demands, and the current production demand is the current workload after allocation based on the target order demand; For each of the current sub-regions, based on the production volume, calculating the capacity gap of the current sub-region; Based on the production capacity gap, a production adjustment strategy is generated, which includes adjusting the production plan, reallocating production tasks, and scheduling spare equipment or production lines.

[0073] Based on sensors, monitoring systems and fault diagnosis software, the operating status of production equipment can be monitored in real time, and once an abnormality or fault is detected, it can be identified immediately. Fault identification can be based on the fault type (such as mechanical fault, electrical fault, software fault, etc.) and fault severity (such as minor, moderate, severe).

[0074] In this embodiment, a fault database is also established in the electronic device to record various types of faults that occurred in the past and their repair time. The relationship between the fault type, fault degree and repair time is analyzed to establish a prediction model. By inputting the currently detected fault type and fault degree into the prediction model, the target time required for fault repair can be predicted.

[0075] According to the target order demand, combined with the production plan and capacity allocation, the current production demand of each current sub-region is determined. Through the preset second mapping relationship, it is ensured that each current sub-region can accurately correspond to its current production demand.

[0076] Consider the overall impact of the faulty equipment on the production line and evaluate the production capacity of the remaining production equipment after the failure. A comprehensive analysis can be conducted based on factors such as equipment type, production speed, and workload.

[0077] Combine the target time, current production demand and remaining production capacity to predict the production volume that will be delayed due to production equipment failure. The prediction should take into account factors such as the location of the faulty equipment in the production process and its impact on upstream and downstream processes.

[0078] In this embodiment, the capacity gap refers to the part that cannot meet the current production demand due to production equipment failure. For each current sub-region, its capacity gap is calculated based on the predicted delayed production volume. At the same time, considering the remaining production capacity of other current sub-regions, it is evaluated whether the capacity gap can be made up through internal deployment.

[0079] According to the capacity gap, adjust the production plan of the affected current sub-region and give priority to the production of key products or urgent orders. If internal deployment cannot completely make up for the capacity gap, consider reallocating production tasks to other sub-regions or production lines. If spare equipment or production lines are available, dispatch them to the affected current sub-region to resume production as soon as possible. Scheduling should take into account factors such as equipment compatibility, transportation time, installation and commissioning.

[0080] By quickly predicting the time to repair a fault and the amount of production that will be delayed, you can quickly formulate and implement a production adjustment strategy to shorten the time to resume production. By internally allocating and reallocating production tasks, you can make full use of the remaining production capacity and reduce resource waste. By predicting and responding to production equipment failures in advance, you can reduce the risk of production interruptions and losses caused by failures.

[0081] In this embodiment, after the adjusted sub-region is used as the current sub-region, the method further includes: for each employee, based on the basic information corresponding to the employee, constructing a talent portrait corresponding to the employee, wherein the talent portrait includes the employee's skills, work efficiency, potential ability and communication preference; based on each of the current sub-regions and each of the talent portraits, obtaining an employee comprehensive calling strategy, wherein the employee comprehensive calling strategy is used to assign the most suitable employee to the most suitable current sub-region.

[0082] In this embodiment, basic information of employees, including but not limited to educational background, work experience, position, past project participation, etc., can be collected from sources such as human resource management systems, employee files, and performance evaluation records.

[0083] Evaluate employees' skills and work efficiency through skills testing, work sample evaluation, and feedback from colleagues / superiors. Skills can include professional skills, soft skills, etc.; work efficiency can be measured by indicators such as the speed, quality, and accuracy of completing tasks. Combine information such as employees' career development paths, training records, and personal career plans to identify employees' potential capabilities, that is, the skills or abilities that employees may develop in the future. Understand employees' communication preferences, including communication style (such as direct, indirect), communication frequency, and communication media (such as face-to-face, email, instant messaging), etc. through daily communication observations, questionnaires, or team feedback.

[0084] By integrating the above basic information, a detailed talent profile can be constructed for each employee. The talent profile can be presented in an easy-to-understand and easy-to-use way, such as through a chart, tag cloud or scoring system.

[0085] Analyze the specific needs of each sub-region for employees based on the production needs, skill requirements, work intensity and other factors of each current sub-region. Use the information in the talent portrait to match employees with the needs of the current sub-region. The matching process should comprehensively consider the employee's skills, work efficiency, potential and communication preferences, as well as the specific needs of the sub-region.

[0086] On the basis of preliminary matching, we make optimization adjustments by considering factors such as employees’ existing work arrangements, workload balance, and team structure to ensure that employee allocation meets both sub-regional needs and employees’ personal development.

[0087] Based on the optimized employee allocation, formulate a comprehensive employee transfer strategy. The strategy should include specific employee transfer plans, training plans (if necessary), communication plans, etc. to ensure that employees can smoothly transition to new jobs.

[0088] By assigning employees to the most suitable current sub-areas, employees' strengths can be brought into play, and job satisfaction and sense of achievement can be improved. The high degree of matching between employees and current sub-areas makes it easier for employees to adapt to the work environment more quickly, improve work efficiency and productivity. With the implementation of the comprehensive employee deployment strategy, enterprises can respond to changes in production needs more flexibly and improve the overall flexibility of the production system.

[0089] Based on the same technical concept, the present application also provides a digital factory production management system, such as Figure 2 As shown, the digital factory production management system 200 mainly includes: The order demand forecasting module 201 is used to forecast the future initial order demand based on the data analysis tool and the historical sales data, and adjust the initial order demand based on the current market trend and the current market strategy corresponding to the target peers to obtain the target order demand, wherein the historical sales data includes sales products, sales quantity, sales amount, sales location and sales time; A first acquisition module 202 is used to acquire the required production capacity and the equipment utilization rate corresponding to each production equipment based on the target order demand; The maintenance demand prediction module 203 is used to obtain the real-time operation data corresponding to each of the production equipment, and predict the maintenance demand corresponding to the real-time operation data, the required production capacity and the equipment utilization rate based on the trained deep learning model; The maintenance module 204 is used to formulate a maintenance plan for each of the production equipment in advance based on the maintenance requirements and the maintenance priorities corresponding to each of the production equipment, and perform preventive maintenance.

[0090] Optionally, after the maintenance demand prediction module 203, the method further includes: A second acquisition module is used to analyze the real-time operation data based on a preset data analysis tool to obtain multiple target factors that affect production efficiency and product quality; The parameter adjustment module is used to adjust the working parameters of each of the production equipment based on the multiple target factors, the target order demand, the production capacity and the equipment utilization rate.

[0091] Optionally, after the order demand prediction module 201, the method further includes: A third acquisition module is used to acquire the arrival time period of the raw materials corresponding to each of the sales products and the average sales volume per unit period based on the target order demand, and calculate the safety stock based on the arrival time period and the average sales volume; A replenishment calculation module is used to calculate the replenishment point and replenishment strategy of each of the raw materials based on the safety stock, so that when the replenishment point is reached, the raw materials are replenished based on the replenishment strategy, and the replenishment strategy includes one or more of quantitative replenishment, periodic replenishment and demand-driven replenishment.

[0092] Optionally, after the order demand prediction module 201, the method further includes: A fourth acquisition module is used to acquire real-time production data corresponding to each initial sub-area in the processing and production operation area, wherein the production data includes equipment utilization, production efficiency, processing quality and energy consumption; The partition adjustment module is used to dynamically adjust the division of each of the initial sub-areas based on the target order demand and the real-time production data, and use the adjusted sub-areas as the current sub-areas.

[0093] Optionally, the parameter adjustment module includes: a parameter adjustment demand prediction submodule, configured to predict, for each current subregion, the working parameter adjustment demand of each of the production equipment corresponding to the current subregion based on the plurality of target factors, the target order demand, the production capacity, the equipment utilization rate and the first mapping relationship, wherein the first mapping relationship is preset between different current subregions and different production equipment; The automatic adjustment submodule is used to automatically adjust the working parameters of each of the production equipment based on the adjustment requirements of each of the working parameters to adapt to different processing goals.

[0094] Optionally, after the maintenance demand prediction module 203, the method further includes: A failure time prediction module is used to predict the target time required for fault repair based on the fault type, fault degree and historical fault recovery time corresponding to the equipment fault for each of the current sub-areas if a device failure is detected in the production equipment; A production volume prediction module is used to predict the production volume delayed due to the failure of production equipment for each current sub-area based on the target time, the current production demand corresponding to the current sub-area and the remaining production capacity; a second mapping relationship is preset between different current sub-areas and different current production demands, and the current production demand is the current workload after allocation based on the target order demand; A capacity gap calculation module, configured to calculate the capacity gap of each current sub-region based on the production volume; The production adjustment module is used to generate a production adjustment strategy based on the production capacity gap, and the production adjustment strategy includes adjusting the production plan, reallocating production tasks, and scheduling spare equipment or production lines.

[0095] Optionally, after adjusting the partition module, the following is further included: A portrait building module is used to build a talent portrait corresponding to each employee based on the basic information corresponding to the employee, and the talent portrait includes the employee's skills, work efficiency, potential ability and communication preference; The fifth acquisition module is used to acquire a comprehensive employee deployment strategy based on each of the current sub-regions and each of the talent portraits, wherein the comprehensive employee deployment strategy is used to allocate the most suitable employee to the most suitable current sub-region.

[0096] In one example, the module in any of the above systems can be one or more integrated circuits configured to implement the above methods, such as: one or more application specific integrated circuits (ASICs), or, one or more digital signal processors (DSPs), or, one or more field programmable gate arrays (FPGAs), or a combination of at least two of these integrated circuit forms.

[0097] For another example, when the modules in the system can be implemented in the form of a processing element scheduling program, the processing element can be a general-purpose processor, such as a central processing unit (CPU) or other processor that can call a program. For another example, these modules can be integrated together and implemented in the form of a system-on-a-chip (SOC).

[0098] Various objects such as various messages / information / equipment / network elements / systems / devices / actions / operations / processes / concepts that may appear in this application are named. It can be understood that these specific names do not constitute a limitation on the relevant objects. The names assigned may change with factors such as scenarios, contexts or usage habits. The understanding of the technical meaning of the technical terms in this application should be mainly determined from the functions and technical effects embodied / executed in the technical scheme.

[0099] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and modules described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0100] Those of ordinary skill in the art will appreciate that the modules and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0101] Based on the same technical concept, the present application also provides an electronic device, such as Figure 3 As shown, the electronic device 300 includes a processor 301 and a memory 302 , and may further include an information input / information output (I / O) interface 303 , one or more of a communication component 304 , and a communication bus 305 .

[0102] The processor 301 is used to control the overall operation of the electronic device 300 to complete all or part of the steps in the above-mentioned digital factory production management method; the memory 302 is used to store various types of data to support the operation of the electronic device 300, and these data may include, for example, instructions for any application or method used to operate on the electronic device 300, and data related to the application. The memory 302 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk.

[0103] The I / O interface 303 provides an interface between the processor 301 and other interface modules, and the above-mentioned other interface modules can be keyboards, mice, buttons, etc. These buttons can be virtual buttons or physical buttons. The communication component 304 is used to test the wired or wireless communication between the electronic device 300 and other devices. Wireless communication, such as Wi-Fi, Bluetooth, Near Field Communication (NFC), 2G, 3G or 4G, or a combination of one or more of them, so the corresponding communication component 304 can include: Wi-Fi components, Bluetooth components, NFC components.

[0104] The communication bus 305 may include a path to transmit information between the above components. The communication bus 305 may be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus, etc. The communication bus 305 may be divided into an address bus, a data bus, a control bus, etc.

[0105] The electronic device 300 can be implemented by one or more application specific integrated circuits (ASIC), digital signal processors (DSP), digital signal processing devices (DSPD), programmable logic devices (PLD), field programmable gate arrays (FPGA), controllers, microcontrollers, microprocessors or other electronic components to execute the digital factory production management method given in the above embodiment.

[0106] The electronic device 300 may include, but is not limited to, a mobile terminal such as a digital broadcast receiver, a PDA (Personal Digital Assistant), a PMP (Portable Multimedia Player), etc., and a fixed terminal such as a digital TV, a desktop computer, etc., and may also be a server, etc.

[0107] Based on the same technical concept, the present application also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the above-mentioned digital factory production management method are implemented.

[0108] The computer-readable storage medium may include: a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and other media that can store program codes.

[0109] The terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not expressly listed, or also includes elements inherent to such process, method, article, or apparatus.

[0110] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In the description of this application, the meaning of "plurality" is at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

[0111] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, without contradiction.

[0112] Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limitations on the present application. Ordinary technicians in this field can change, modify, replace and modify the above embodiments within the scope of the present application.

Claims

1. A digital factory production management method, characterized in that: include: Based on data analysis tools and historical sales data, forecast future initial order demand, and adjust the initial order demand based on current market trends and the current market strategies of target peers to obtain target order demand, wherein the historical sales data includes sales products, sales quantity, sales amount, sales location and sales time; Based on the target order demand, obtain the required production capacity and the equipment utilization rate corresponding to each production equipment; Acquire the real-time operation data corresponding to each of the production equipment, and predict the maintenance requirements corresponding to the real-time operation data, the required production capacity and the equipment utilization rate based on the trained deep learning model; Based on the maintenance requirements and the maintenance priorities corresponding to each of the production equipment, a maintenance plan for each of the production equipment is formulated in advance to perform preventive maintenance.

2. A digital factory production management method according to claim 1, characterized in that: After acquiring the real-time operation data corresponding to each of the production equipment, the method further includes: Based on a preset data analysis tool, the real-time operation data is analyzed to obtain multiple target factors affecting production efficiency and product quality; Based on the multiple target factors, the target order demand, the production capacity and the equipment utilization rate, the working parameters of each of the production equipment are adjusted.

3. A digital factory production management method according to claim 1 or 2, characterized in that: After obtaining the target order demand, the method further includes: Based on the target order demand, the arrival time period of the raw materials corresponding to each of the sales products and the average sales volume per unit period are obtained, and the safety stock volume is calculated based on the arrival time period and the average sales volume; Based on the safety stock, the replenishment point and replenishment strategy of each of the raw materials are calculated, so that when the replenishment point is reached, the raw materials are replenished based on the replenishment strategy, and the replenishment strategy includes one or more of quantitative replenishment, periodic replenishment and demand-driven replenishment.

4. A digital factory production management method according to claim 2, characterized in that: After obtaining the target order demand, the method further includes: Acquire real-time production data corresponding to each initial sub-area in the processing and production operation area, wherein the production data includes equipment utilization, production efficiency, processing quality and energy consumption; Based on the target order demand and the real-time production data, the division of each of the initial sub-areas is dynamically adjusted, and the adjusted sub-areas are used as current sub-areas.

5. A digital factory production management method according to claim 4, characterized in that: The adjusting the working parameters of each of the production equipment based on the multiple target factors, the target order demand, the production capacity and the equipment utilization rate includes: For each of the current sub-regions, based on the multiple target factors, the target order demand, the production capacity, the equipment utilization rate and the first mapping relationship, the working parameter adjustment requirements of each of the production equipment corresponding to the current sub-region are predicted, and the first mapping relationships are preset between different current sub-regions and different production equipment; Based on the adjustment requirements of each of the working parameters, the working parameters of each of the production equipment are automatically adjusted to adapt to different processing goals.

6. A digital factory production management method according to claim 5, characterized in that: After acquiring the real-time operation data corresponding to each of the production equipment, the method further includes: For each of the current sub-areas, if a device failure is detected in the production equipment, a target time required for fault repair is predicted based on the fault type, fault severity and historical fault recovery time corresponding to the device failure; For each of the current sub-areas, based on the target time, the current production demand corresponding to the current sub-area and the remaining production capacity, the production volume delayed due to the failure of the production equipment is predicted; a second mapping relationship is preset between different current sub-areas and different current production demands, and the current production demand is the current workload after allocation based on the target order demand; For each of the current sub-regions, based on the production volume, calculating the capacity gap of the current sub-region; Based on the production capacity gap, a production adjustment strategy is generated, which includes adjusting the production plan, reallocating production tasks, and scheduling spare equipment or production lines.

7. A digital factory production management method according to claim 4, characterized in that: After taking the adjusted sub-region as the current sub-region, the method further includes: For each employee, a talent profile corresponding to the employee is constructed based on the basic information corresponding to the employee, and the talent profile includes the employee's skills, work efficiency, potential ability and communication preference; Based on each of the current sub-regions and each of the talent portraits, an employee comprehensive deployment strategy is obtained, and the employee comprehensive deployment strategy is used to allocate the most suitable employee to the most suitable current sub-region.

8. A digital factory production management system, characterized in that: include: The order demand forecasting module is used to forecast the future initial order demand based on the data analysis tool and historical sales data, and adjust the initial order demand based on the current market trend and the current market strategy corresponding to the target peers to obtain the target order demand. The historical sales data includes sales products, sales quantity, sales amount, sales location and sales time; A first acquisition module is used to acquire the required production capacity and the equipment utilization rate corresponding to each production equipment based on the target order demand; A maintenance demand prediction module is used to obtain the real-time operation data corresponding to each of the production equipment, and predict the maintenance demand corresponding to the real-time operation data, the required production capacity and equipment utilization rate based on the trained deep learning model; The maintenance module is used to formulate a maintenance plan for each of the production equipment in advance based on the maintenance requirements and the maintenance priorities corresponding to each of the production equipment, so as to perform preventive maintenance.

9. An electronic device, characterized in that: comprising a processor and a memory, wherein the processor is coupled to the memory; The processor is configured to execute a computer program stored in the memory, so that the electronic device executes the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The method comprises a computer program or an instruction, which, when executed on a computer, causes the computer to execute the method according to any one of claims 1 to 7.

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