Data control method and system for digital production of power distribution equipment

Abstract

The invention relates to the technical field of digital production management, in particular to a data control method and system for digital production of power distribution equipment. The method comprises the following steps: constructing a production line digital process twin model for a power distribution equipment digital production line; constructing a production line monitoring network; performing real-time process production comprehensive monitoring on the digital production line of the power distribution equipment by using a production line monitoring network to obtain a real-time production monitoring comprehensive data stream; performing abnormal process identification on the real-time production monitoring comprehensive data flow by using a production line digital process twinborn model to obtain an abnormal production process list; obtaining order plan task data; and performing abnormal process parameter adjustment on the real-time production line digital twin model based on the abnormal production process list, and performing production completion time prediction on the order plan task data to obtain order production prediction completion time data. According to the method, accurate prediction and intelligent management of the production progress are realized by constructing the digital process twinning model of the production line.

Description

Technical Field

[0001] The present invention relates to the technical field of digital production management, and in particular to a data control method and system for digital production of power distribution equipment. Background Art

[0002] Data control methods play a crucial role in the digital production of power distribution equipment. Effective data control enables the real-time collection, transmission, processing, and analysis of production data, providing strong data support for various aspects such as production planning, process monitoring, quality traceability, and equipment maintenance, thereby enhancing the refinement and intelligence of production management. However, in digital production lines for power distribution equipment, the actual operating efficiency and capacity utilization of equipment at different stages of the production process vary due to factors such as machining precision requirements and differences in raw material properties. Traditional data control methods often separate production tasks from the physical production capacity of equipment when processing production schedule data. Production tasks are typically formulated based on theoretical planning models that consider ideal production processes and schedules, while the physical production capacity of equipment is affected by a variety of practical factors, such as equipment aging, fault repairs, and fluctuations in operating efficiency under different process parameters. This separation results in schedule forecasts that fail to accurately reflect the dynamic performance changes of equipment during actual production, resulting in significant deviations between the predicted results and the actual production schedule. Summary of the Invention

[0003] Based on this, the present invention provides a data control method and system for digital production of power distribution equipment to solve at least one of the above technical problems.

[0004] To achieve the above-mentioned purpose, a data control method for digital production of power distribution equipment includes the following steps:

[0005] Step S1: Identify key production process equipment for the digital production line of power distribution equipment to obtain key production process equipment data; and construct a digital process twin model of the production line based on the key production process equipment data.

[0006] Step S2: Deploy production monitoring sensors on the digital production line of power distribution equipment to build a production line monitoring network; perform processing tests on representative workpieces at process nodes of the digital production line of power distribution equipment, and perform production line capacity statistics to obtain production line benchmark capacity data;

[0007] Step S3: Using the production line monitoring network to conduct real-time comprehensive process production monitoring of the digital production line of the power distribution equipment, obtaining a real-time production monitoring comprehensive data stream; performing actual production process beat analysis based on the real-time production monitoring comprehensive data stream to generate actual production process beat data; adjusting production parameters of the production line digital process twin model based on the production line benchmark capacity data to obtain a real-time production line digital twin model; using the real-time production line digital twin model to calculate process deviations on the actual production process beat data to obtain real-time production beat deviation rate data;

[0008] Step S4: Identify abnormal processes in the digital production line of power distribution equipment through real-time production beat deviation rate data to obtain a list of abnormal production processes; obtain order planning task data; adjust the abnormal process parameters of the real-time production line digital twin model based on the abnormal production process list, and predict the production completion time of the order planning task data to obtain order production predicted completion time data.

[0009] The present invention achieves real-time synchronization between the virtual and physical worlds by constructing a digital process twin model corresponding to the actual production line and integrating real-time collected production monitoring data into the model. Traditional planning models are often based on ideal conditions and ignore the influence of actual factors such as equipment aging, fault repair, and process parameter fluctuations, resulting in large deviations in prediction results. However, this method updates the parameters of the digital twin model in real time, for example, adjusting the production parameters in the model based on the results of the actual production process beat analysis, so that the model can dynamically reflect the actual performance changes of the equipment, thereby improving the accuracy of production schedule prediction and avoiding production plan delays or resource waste caused by prediction deviations. By monitoring the production line in real time, obtaining actual production process beat data, and comparing it with the digital twin model, the production beat deviation rate can be calculated in real time. This enables managers to promptly discover deviations in the production process and take appropriate measures to adjust them, avoiding the accumulation and amplification of deviations, thereby improving the controllability of the production schedule and the accuracy of the prediction. For example, if the actual beat of a process continues to deviate from the planned beat, the system can issue an early warning in a timely manner, reminding management personnel to pay attention and find the cause, such as equipment failure, improper process parameter settings, etc., and take corrective measures in a timely manner to avoid affecting subsequent processes and final delivery dates. Through real-time production beat deviation rate data, the system can automatically identify abnormal processes and generate a list of abnormal processes. Based on this list, the system can adjust the parameters of abnormal processes in the digital twin model and re-forecast the order planning task data to obtain more accurate order production prediction completion time data. This automated abnormality identification and processing mechanism reduces the need for manual intervention, improves the efficiency of production management, and reduces the risk of human error. Therefore, the data control method for digital production of distribution equipment of the present invention solves the problem of separation of production tasks and physical production capacity of equipment in traditional data control methods by constructing a digital process twin model of the production line, real-time process production comprehensive monitoring, obtaining real-time production monitoring comprehensive data stream, identifying abnormal processes, adjusting parameters of the real-time production line digital twin model based on the abnormal process list, and predicting production completion time for order planning task data. It improves the accuracy of progress prediction and realizes intelligent management of the distribution equipment production process.

[0010] Preferably, step S1 includes the following steps:

[0011] Step S11: extracting the production process flow of the digital production line of power distribution equipment to obtain production line process flow data;

[0012] Step S12: Identify key production process equipment based on the production line process data to obtain key production process equipment data;

[0013] Step S13: extracting key equipment process parameters and process material characteristic parameters based on key production process equipment data;

[0014] Step S14: setting process conditions based on key equipment process parameters, and establishing a physical model of the equipment processing process based on finite element analysis to obtain an equipment processing physical model;

[0015] Step S15: Analyzing the relationship between stress, strain and processing time of the characteristic parameters of the process material through the equipment processing physical model to generate an equipment processing stress, strain and time model;

[0016] Step S16: Construct a digital process twin model of the production line using the equipment processing physical model and the equipment processing stress-strain time model.

[0017] The present invention establishes a physical model of the equipment processing process through finite element analysis, and analyzes the relationship between stress, strain and processing time in combination with material characteristic parameters, thereby constructing an equipment processing stress-strain-time model that can better reflect the actual processing status of the equipment. This enables the digital twin model to more accurately simulate various complex situations in the actual production process, such as equipment performance changes and material deformation under different process parameters, thereby improving the prediction accuracy of the model. The equipment processing physical model and the equipment processing stress-strain-time model are integrated to construct a more comprehensive digital process twin model of the production line. This model not only includes the geometric structure and motion characteristics of the equipment, but also includes information on changes in the mechanical properties of the material during the processing process. This more comprehensive model can more accurately reflect various influencing factors in the production process, such as equipment aging, material batch differences, etc., thereby improving the accuracy of production progress prediction and the sensitivity of abnormal process identification.

[0018] Preferably, step S2 includes the following steps:

[0019] Step S21: Divide the digital process twin model of the production line into key process nodes to obtain key process node data of the production line;

[0020] Step S22: Determine equipment production performance monitoring sensors based on key process node data of the production line, and deploy production monitoring sensors on the digital production line of power distribution equipment to build a production line monitoring network; the equipment production performance monitoring sensors include equipment load rate monitoring sensors, equipment energy consumption monitoring sensors, and process start and end monitoring sensors;

[0021] Step S23: Based on the key process node data of the production line, a representative workpiece processing test is performed using the preset production line operating environment parameters, and the equipment processing performance test data is collected using the production line monitoring network to obtain the equipment test raw data;

[0022] Step S24: performing error correction on the original equipment test data to obtain corrected test data;

[0023] Step S25: Calculate the equipment output per unit time based on the corrected test data, and perform production line capacity statistics to obtain production line benchmark capacity data.

[0024] The present invention can accurately identify the process nodes that have the greatest impact on the production line capacity by dividing the digital twin model into key process nodes, and deploy corresponding sensors at these nodes, such as equipment load rate monitoring sensors, equipment energy consumption monitoring sensors, and process start and end monitoring sensors. The preset production line operating environment parameters are used to perform representative workpiece processing tests on process nodes, and the test data is error-corrected to ensure the accuracy of the benchmark capacity data. By testing under standardized environmental parameters, the interference of environmental factors on the test results can be eliminated, thereby obtaining more reliable equipment performance data. In addition, error correction is performed on the test raw data to further eliminate the influence of measurement errors and system errors, thereby improving the accuracy of the benchmark capacity data. By comparing the real-time monitoring data in the actual production process with the benchmark capacity data, abnormal conditions in the production process, such as decreased equipment efficiency, insufficient production capacity, etc., can be more accurately identified, and corresponding measures can be taken in a timely manner to make adjustments, thereby improving the overall efficiency and stability of the production line.

[0025] Preferably, step S3 includes the following steps:

[0026] Step S31: using the production line monitoring network to perform real-time comprehensive process production monitoring on the digital production line of the power distribution equipment, and obtaining a real-time production monitoring comprehensive data stream;

[0027] Step S32: performing monitoring data analysis on the real-time production monitoring integrated data stream and performing signal separation to obtain real-time process start and end monitoring signals, real-time processing equipment load monitoring signals, and real-time processing equipment energy consumption monitoring signals;

[0028] Step S33: Analyze the actual production process rhythm according to the real-time process start and end monitoring signals to generate actual production process rhythm data;

[0029] Step S34: Acquire actual operating parameters of production equipment and current production task data;

[0030] Step S35: Adjust the production parameters of the production line digital process twin model according to the production line benchmark capacity data, and synchronize the real-time operating parameters based on the actual operating parameters of the production equipment to obtain a real-time production line digital twin model;

[0031] Step S36: Based on the current production task data, the real-time production line digital twin model is used to calculate the process deviation of the actual production process rhythm data to obtain real-time production rhythm deviation rate data.

[0032] The present invention utilizes a production line monitoring network to collect comprehensive process data in real time and perform signal separation to obtain key production process information, such as process start and end times, equipment load rate, and energy consumption. This provides the foundational data for subsequent beat analysis and deviation calculation. Actual production process beat analysis is performed based on real-time process start and end monitoring signals. Combined with current production task data and baseline capacity data, process deviation calculation is performed using a real-time, updated digital twin model. This deviation analysis method, based on real-time data and a dynamic model, can more accurately reflect dynamic changes in the production process, avoiding the lag inherent in static analysis methods. For example, even if indicators such as equipment load rate or energy consumption are within normal ranges, if the actual process beat deviates, the system can promptly identify and issue an early warning. By acquiring the actual operating parameters of production equipment and synchronizing them into the digital twin model, real-time linkage between the virtual model and the physical equipment is achieved. This enables the digital twin model to more accurately reflect the actual operating status of the equipment, such as equipment aging and performance fluctuations, thereby improving the accuracy and reliability of deviation calculations. Through continuous real-time monitoring and deviation analysis, abnormal situations in the production process can be discovered in a timely manner, such as process cycle delays, reduced equipment efficiency, etc., and timely early warning information can be provided to managers so that they can take corresponding corrective measures in time to avoid reduced production efficiency and product quality problems.

[0033] Preferably, step S33 includes the following steps:

[0034] Step S331: performing signal noise reduction processing on the real-time process start and end monitoring signals, and extracting key signal segments using a preset signal threshold to obtain a set of process start and end key signal segments;

[0035] Step S332: using a preset process signal pattern library to perform process start and end signal segment matching on the process start and end key signal segment set, identifying the start and end signals of each process of the production line, and generating the initial start and end signals of the production line process;

[0036] Step S333: performing time stamp calibration on the initial start and end signals of the production line process to obtain the production line process calibration start and end signals;

[0037] Step S334: Calculate the duration of each process according to the start and end signals of the production line process calibration to obtain the single process takt time data;

[0038] Step S335: performing data cleaning and outlier processing on the single process tact time data to generate valid single process tact data;

[0039] Step S336: Integrate the effective single-process beat data by process production equipment number in chronological order to obtain the actual production process beat data.

[0040] This invention performs noise reduction processing and extracts key signal segments on real-time signals, effectively reducing the impact of noise interference on takt time analysis and improving signal recognition accuracy. In actual production environments, various interference factors can cause noise and fluctuations in monitoring signals, affecting the determination of process start and end times. By matching signal segments using a preset process signal pattern library, automatic identification of process start and end signals is achieved. This avoids the subjectivity and inefficiency of manual signal identification and improves the automation of takt time analysis. Timestamp calibration of identified process start and end signals ensures the consistency and accuracy of takt time data for different processes. Timestamp deviations can occur due to factors such as sensor response times and data transmission delays. Timestamp calibration eliminates these deviations, ensuring temporal consistency of takt time data for different processes, thereby improving the accuracy of deviation calculation. Data cleaning and outlier processing are performed on the single-process takt time data, followed by chronological integration, resulting in more reliable and organized actual production process takt data. This provides high-quality data input for subsequent deviation calculation and identification of abnormal processes, thereby improving the reliability and effectiveness of the entire system.

[0041] Preferably, step S36 includes the following steps:

[0042] Step S361: Decomposing the process tasks according to the current production task data to obtain the current production task decomposition data;

[0043] Step S362: Setting simulation parameters for the real-time production line digital twin model based on the current production task decomposition data, and performing production line simulation calculations to obtain production line process simulation data;

[0044] Step S363: extracting the stress-strain-time curve of each process according to the production line process simulation data to obtain the simulated stress-strain-time curve of each process;

[0045] Step S364: extracting the simulated processing time of each process according to the simulated stress-strain time curve of each process and the preset process material failure criterion to obtain theoretical production process takt data;

[0046] Step S365: Calculate the production rhythm deviation of the actual production process rhythm data using the theoretical production process rhythm data to generate real-time production rhythm deviation rate data.

[0047] The present invention sets simulation parameters for a digital twin model based on current production task data and performs simulation calculations, resulting in simulation results that better align with actual production tasks. The stress-strain-time curves for each process are extracted from the simulation results and, combined with preset process material failure criteria, theoretical production process cycle data is calculated. This cycle calculation method based on material failure criteria can more accurately reflect the processing characteristics and limit states of the material, thereby improving the accuracy of the theoretical cycle data. For example, based on indicators such as the material's fatigue limit or fracture toughness, the failure time of the material during processing can be more accurately predicted, thereby more accurately calculating the theoretical cycle data. By comparing the actual production process cycle data with the theoretical production process cycle data, real-time production cycle deviation rate data is calculated. This deviation analysis method based on theoretical and actual values ​​can more effectively identify abnormalities in the production process. For example, even if the actual cycle fluctuates within the normal range, a persistent deviation from the theoretical cycle indicates potential equipment failure or process problems. By analyzing real-time production beat deviation rate data, we can gain a deeper understanding of the bottlenecks and potential risks in the production process, and provide guidance for subsequent process optimization and equipment maintenance.

[0048] Preferably, step S365 includes the following steps:

[0049] Match the process equipment with the theoretical and actual production process beat data, align the timing, and generate production line process beat comparison data.

[0050] Calculate the operation takt time difference based on the production line process takt comparison data and generate the process takt time difference value;

[0051] Perform absolute value processing on process takt time differences and associate them with production line processes to obtain single process deviation values.

[0052] Normalize the single process deviation value to generate single process deviation percentage data;

[0053] Based on the single process deviation percentage data, the production line process statistical analysis is carried out to obtain the real-time production rhythm deviation rate data.

[0054] The present invention matches and aligns theoretical and actual process tact data, ensuring the accuracy and effectiveness of deviation calculations. Because actual production processes often involve adjustments to process execution order or equipment switching, the theoretical and actual data must be matched and aligned before deviation calculation to ensure that the tact data for the same process and the same equipment are being compared. By calculating the tact time difference and performing absolute value processing, a single process deviation value is obtained, which is then normalized to generate single process deviation percentage data. This conversion of deviation values ​​into percentage data more intuitively reflects the degree of deviation and facilitates comparison and analysis between different processes. For example, even if two processes have the same tact time difference, if their baseline tact times are different, their deviation percentages will also be different, more accurately reflecting the relative magnitude of the deviations. Production line process statistical analysis of the single process deviation percentage data yields real-time production tact deviation rate data. This statistical analysis method can more comprehensively reflect the tact deviation of an entire production line and help managers identify processes or equipment with significant deviations, enabling more targeted optimization and improvement.

[0055] Preferably, step S4 includes the following steps:

[0056] Step S41: identifying abnormal processes in the digital production line of power distribution equipment using real-time production beat deviation rate data to obtain a list of abnormal production processes;

[0057] Step S42: Feedback the abnormal production process list to the terminal device, and collect the abnormal process operation parameters in real time to obtain the abnormal process operation parameters;

[0058] Step S43: using the abnormal process operation parameters to adjust the abnormal process parameters of the real-time production line digital twin model to obtain the abnormal production line digital twin model;

[0059] Step S44: Calculating the production line capacity attenuation coefficient based on the real-time processing equipment load monitoring signal and the real-time processing equipment energy consumption monitoring signal to generate the production line capacity attenuation coefficient;

[0060] Step S45: Obtain order plan task data; decompose production tasks based on the order plan task data to obtain order plan production task data;

[0061] Step S46: Based on the production line capacity attenuation coefficient, the abnormal production line digital twin model is used to simulate the order task production data and predict the production completion time to obtain the order production prediction completion time data.

[0062] This invention identifies abnormal processes using real-time production cycle deviation data and collects their operating parameters, enabling rapid response and precise location of abnormal situations. This avoids the inefficient process-by-process screening and improves the efficiency of exception handling. The operating parameters of the abnormal processes are used to adjust the digital twin model, constructing a digital twin model of the abnormal production line that more closely reflects actual production conditions. This provides a more accurate model foundation for subsequent order task production simulation and completion time prediction. By feeding the actual operating parameters of the abnormal process into the digital twin model, the production process under abnormal conditions can be more accurately simulated, thereby improving the reliability of the prediction results. The production line capacity reduction coefficient is calculated based on equipment load and energy consumption signals and used in order task production simulation, effectively quantifying and predicting capacity reduction. This avoids completion time prediction errors caused by capacity reduction and improves prediction accuracy. For example, if equipment load is too high or energy consumption is excessive, the capacity reduction coefficient increases, more accurately reflecting the impact of capacity reduction in the simulation process. Simulation is performed based on the digital twin model of the abnormal production line and order-planned production task data, and order completion time prediction is predicted. This simulation-based forecasting method can more accurately predict order completion times, providing a reliable basis for adjusting production plans and meeting customer delivery commitments. For example, if the predicted completion time is later than the customer's required delivery date, appropriate measures can be taken in advance, such as increasing production resources and optimizing production plans, to ensure on-time delivery of the order.

[0063] Preferably, step S44 includes the following steps:

[0064] Step S441: Calculating the load rate performance attenuation coefficient of the real-time processing equipment load monitoring signal using a preset production equipment load rate-performance attenuation relationship curve to generate a load rate performance attenuation coefficient;

[0065] Step S442: Calculating the energy consumption performance attenuation coefficient of the real-time processing equipment energy consumption monitoring signal using a preset production equipment energy consumption-performance attenuation relationship curve to generate an energy consumption performance attenuation coefficient;

[0066] Step S443: performing weighted calculation of the production line capacity attenuation coefficient based on the load rate performance attenuation coefficient and the energy consumption performance attenuation coefficient to obtain the production line capacity attenuation coefficient.

[0067] The present invention calculates the load rate performance attenuation coefficient and the energy consumption performance attenuation coefficient based on preset load rate-performance attenuation curves and energy consumption-performance attenuation curves, respectively. This curve-based calculation method can more accurately reflect the impact of equipment load rate and energy consumption on equipment performance, avoiding the limitations of simple linear relationships. The preset curves can be fitted based on historical data or experimental data to more accurately reflect the actual performance attenuation patterns of the equipment. A weighted calculation of the load rate performance attenuation coefficient and the energy consumption performance attenuation coefficient yields a more comprehensive production line capacity attenuation coefficient. This weighted calculation method comprehensively considers the impact of both load rate and energy consumption on production capacity, avoiding the one-sidedness of single-factor analysis. For example, even if the equipment load rate is not high, excessive energy consumption can still lead to a decrease in production capacity. Through weighted calculation, the impact of these two factors can be combined to more accurately reflect the overall production line capacity attenuation. Applying the calculated production line capacity attenuation coefficient to order task production simulation can more accurately predict order completion times.

[0068] The present invention further provides a data control system for digital production of power distribution equipment, which executes the data control method for digital production of power distribution equipment as described above. The data control system for digital production of power distribution equipment includes:

[0069] The process twin modeling module is used to identify key production process equipment in the digital production line of power distribution equipment and obtain key production process equipment data; based on the key production process equipment data, a digital process twin model of the production line is constructed;

[0070] The capacity benchmarking module is used to deploy production monitoring sensors on the digital production line of power distribution equipment to build a production line monitoring network. It also conducts processing tests on representative workpieces at process nodes of the digital production line of power distribution equipment and performs production line capacity statistics to obtain production line benchmark capacity data.

[0071] The production variance analysis module is used to use the production line monitoring network to conduct real-time comprehensive process production monitoring of the digital production line of power distribution equipment, obtaining a real-time production monitoring comprehensive data stream; perform actual production process beat analysis based on the real-time production monitoring comprehensive data stream to generate actual production process beat data; adjust production parameters of the production line digital process twin model based on the production line benchmark capacity data to obtain a real-time production line digital twin model; use the real-time production line digital twin model to calculate process deviations on the actual production process beat data to obtain real-time production beat deviation rate data;

[0072] The production time prediction module is used to identify abnormal processes in the digital production line of distribution equipment through real-time production rhythm deviation rate data to obtain a list of abnormal production processes; obtain order planning task data; adjust the abnormal process parameters of the real-time production line digital twin model based on the abnormal production process list, and predict the production completion time of the order planning task data to obtain order production prediction completion time data. BRIEF DESCRIPTION OF THE DRAWINGS

[0073] Figure 1 A schematic flow chart of the steps of the data control method for digital production of power distribution equipment according to the present invention;

[0074] Figure 2 for Figure 1 Detailed implementation steps of step S2 in FIG.

[0075] Figure 3 for Figure 1 Detailed implementation steps of step S4 in FIG.

[0076] The purpose, features and advantages of the present invention will be further described with reference to the accompanying drawings and in conjunction with the embodiments. DETAILED DESCRIPTION

[0077] The following is a clear and complete description of the technical method of the present invention in conjunction with the accompanying drawings. It is obvious that the embodiments described are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without making any creative efforts are within the scope of protection of the present invention.

[0078] In addition, the accompanying drawings are merely schematic illustrations of the present invention and are not necessarily drawn to scale. Identical reference numerals in the figures denote identical or similar parts, and thus repetitive descriptions thereof will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities that do not necessarily correspond to physically or logically separate entities. These functional entities may be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor and / or microcontroller approaches.

[0079] It should be understood that although the terms "first," "second," and the like may be used herein to describe various elements, these elements should not be limited by these terms. These terms are used solely to distinguish one element from another. For example, a first element may be referred to as a second element, and similarly, a second element may be referred to as a first element, without departing from the scope of the exemplary embodiments. The term "and / or" as used herein includes any and all combinations of one or more of the listed associated items.

[0080] To achieve this, please refer to Figures 1 to 3 The present invention provides a data control method for digital production of power distribution equipment, comprising the following steps:

[0081] Step S1: Identify key production process equipment for the digital production line of power distribution equipment to obtain key production process equipment data; and construct a digital process twin model of the production line based on the key production process equipment data.

[0082] Step S2: Deploy production monitoring sensors on the digital production line of power distribution equipment to build a production line monitoring network; perform processing tests on representative workpieces at process nodes of the digital production line of power distribution equipment, and perform production line capacity statistics to obtain production line benchmark capacity data;

[0083] Step S3: Using the production line monitoring network to conduct real-time comprehensive process production monitoring of the digital production line of the power distribution equipment, obtaining a real-time production monitoring comprehensive data stream; performing actual production process beat analysis based on the real-time production monitoring comprehensive data stream to generate actual production process beat data; adjusting production parameters of the production line digital process twin model based on the production line benchmark capacity data to obtain a real-time production line digital twin model; using the real-time production line digital twin model to calculate process deviations on the actual production process beat data to obtain real-time production beat deviation rate data;

[0084] Step S4: Identify abnormal processes in the digital production line of power distribution equipment through real-time production beat deviation rate data to obtain a list of abnormal production processes; obtain order planning task data; adjust the abnormal process parameters of the real-time production line digital twin model based on the abnormal production process list, and predict the production completion time of the order planning task data to obtain order production predicted completion time data.

[0085] In an embodiment of the present invention, the data control method for digital production of power distribution equipment includes the following steps:

[0086] Step S1: Identify key production process equipment for the digital production line of power distribution equipment to obtain key production process equipment data; and construct a digital process twin model of the production line based on the key production process equipment data.

[0087] In an embodiment of the present invention, for a certain type of digital production line for power distribution cabinets, the Siemens Tecnomatix Plant Simulation software is first used to perform three-dimensional layout modeling of the production line. Key production process equipment is identified, including CNC punching machines, CNC bending machines, welding robots, assembly workstations, etc. Key production process equipment data is obtained, including equipment name, model, specifications, processing capabilities (for example, the punching speed of the punching machine is 100 times / minute, the bending angle accuracy of the bending machine is ±0.1°, and the welding speed of the welding robot is 50cm / s), spatial position coordinates (for example, the CNC punching machine is located at x=10m, y=5m, z=0m), input and output parameters (for example, the input parameters of the CNC punching machine are the thickness of the plate and the stamping shape, and the output parameters are the stamped plate), operating status parameters (for example, the equipment operating status: running / idle / fault), etc. The acquired equipment data is imported into the PlantSimulation software, and based on the actual production line layout and process flow, a three-dimensional virtual model containing the above-mentioned key equipment is constructed. This model is the digital process twin model of the production line, which includes the three-dimensional geometry, position information, motion trajectory and logic control program of each equipment.

[0088] Step S2: Deploy production monitoring sensors on the digital production line of power distribution equipment to build a production line monitoring network; perform processing tests on representative workpieces at process nodes of the digital production line of power distribution equipment, and perform production line capacity statistics to obtain production line benchmark capacity data;

[0089] In an embodiment of the present invention, sensors such as current sensors, vibration sensors, displacement sensors, temperature sensors, etc. are deployed on key equipment such as CNC punching machines, CNC bending machines, welding robots, and assembly workstations to collect real-time operating data of the equipment. The sensor data is transmitted to a data acquisition server via industrial Ethernet or a wireless network to build a production line monitoring network. Taking the production of 100 distribution cabinets of this model as an example, the deployed sensor network is used to collect production process data. During the production process, representative workpieces (such as distribution cabinet bodies, cabinet doors, and mounting plates) are processed and tested for process nodes such as CNC stamping, CNC bending, robot welding, and manual assembly. The processing time, waiting time, handling time, etc. of each process are recorded, and the total production time of 100 distribution cabinets is calculated. The total production time is divided by 100 to obtain the average production time of a single distribution cabinet, that is, the benchmark production capacity data of the production line. For example, the benchmark capacity is to produce 10 distribution cabinets per hour.

[0090] Step S3: Using the production line monitoring network to conduct real-time comprehensive process production monitoring on the digital production line of the power distribution equipment, and obtain a real-time production monitoring comprehensive data stream;

[0091] In an embodiment of the present invention, a production line monitoring network collects data from various sensors in real time, including equipment operating status, processing parameters, environmental parameters, and the like, to form a real-time production monitoring integrated data stream. For example, current and vibration data from a CNC punch press, angle and pressure data from a bending machine, and current, voltage, and speed data from a welding robot are collected. Based on the real-time production monitoring integrated data stream, the actual processing time of each process is analyzed. For example, the CNC punching process actually takes 2 minutes, the CNC bending process actually takes 3 minutes, and so on, to generate actual production process tact data. The benchmark production capacity data (for example, 10 units per hour) is converted into a standard tact time for each process, for example, the standard tact time for each process is 6 minutes. The actual production process tact data is compared with the standard tact time, and the tact deviation rate of each process is calculated. For example, the tact deviation rate of the CNC punching process is (2-6) / 6=-66.7%, the tact deviation rate of the CNC bending process is (3-6) / 6=-50%, and so on, to obtain real-time production tact deviation rate data. According to the actual production monitoring data, the parameters of the digital process twin model constructed in step S1 are adjusted, such as updating parameters such as equipment operating speed and processing time to match the actual status of the current production line, and a real-time production line digital twin model is obtained.

[0092] Step S4: Identify abnormal processes in the digital production line of power distribution equipment through real-time production beat deviation rate data to obtain a list of abnormal production processes; obtain order planning task data; adjust the abnormal process parameters of the real-time production line digital twin model based on the abnormal production process list, and predict the production completion time of the order planning task data to obtain order production predicted completion time data.

[0093] In an embodiment of the present invention, a beat deviation rate threshold is set, for example, ±10%. When the beat deviation rate of a certain process exceeds the threshold, the process is identified as an abnormal process. All abnormal processes are recorded in the abnormal production process list. For example, if the deviation rate of the CNC stamping process is -66.7%, which exceeds the threshold of -10%, the process is recorded as an abnormal process. Order planning task data is obtained, for example, an order requires 100 distribution cabinets. Based on the abnormal process list, the parameters of the corresponding abnormal processes in the real-time production line digital twin model are adjusted, such as reducing the processing speed parameters of the CNC stamping process. The adjusted real-time production line digital twin model is used to simulate and run the order planning task data, predict the order production completion time, and obtain the order production predicted completion time data.

[0094] Preferably, step S1 includes the following steps:

[0095] Step S11: extracting the production process flow of the digital production line of power distribution equipment to obtain production line process flow data;

[0096] Step S12: Identify key production process equipment based on the production line process data to obtain key production process equipment data;

[0097] Step S13: extracting key equipment process parameters and process material characteristic parameters based on key production process equipment data;

[0098] Step S14: setting process conditions based on key equipment process parameters, and establishing a physical model of the equipment processing process based on finite element analysis to obtain an equipment processing physical model;

[0099] Step S15: Analyzing the relationship between stress, strain and processing time of the characteristic parameters of the process material through the equipment processing physical model to generate an equipment processing stress, strain and time model;

[0100] Step S16: Construct a digital process twin model of the production line using the equipment processing physical model and the equipment processing stress-strain time model.

[0101] In one embodiment of the present invention, production process data for a certain type of power distribution cabinet digital production line is extracted through on-site inspections and process document analysis. This process data includes the name and sequence of each process, the input and output materials, the logical relationships between processes, and the detailed operating steps and parameters for each process. For example, a production process includes processes such as blanking, stamping, bending, welding, assembly, and inspection. The input of the blanking process is steel coils, and the output is cut steel plates; the input of the stamping process is cut steel plates, and the output is stamped components. The stamping and bending processes have a sequential relationship, and stamping must be completed before bending can proceed. The operating steps and parameters of each process also need to be recorded in detail. For example, the stamping process requires recording parameters such as the stamping die model, stamping pressure, and stamping speed to form the production line process data. For example, in the power distribution cabinet production process, CNC punching machines, CNC bending machines, welding robots, and automated assembly lines are identified as key production process equipment. Record static data such as the name, model, specifications, manufacturer, purchase date, rated power, processing accuracy, processing range, etc. of key equipment, as well as dynamic data such as the real-time operating status, fault records, and maintenance records of the equipment. Store these data in a structured manner to form key production process equipment data. Extract key equipment process parameters and process material characteristic parameters based on the key equipment identified in step S12. For example, for a CNC punch press, the extracted process parameters include punching speed, punching force, die gap, etc.; for a CNC bending machine, the extracted process parameters include bending angle, bending speed, rebound compensation value, etc. For cold-rolled steel plate materials commonly used in distribution cabinets, the extracted material characteristic parameters include elastic modulus, Poisson's ratio, yield strength, tensile strength, elongation, etc. Taking CNC bending as an example, according to the process parameters such as bending angle, bending speed, rebound compensation value extracted in step S13, set the corresponding process conditions in ABAQUS finite element analysis software. Establish a physical model of the equipment processing process based on the structural parameters and working principles of the key equipment. For example, a finite element model of a CNC bending machine bending a cold-rolled steel plate is established, and the model includes a bending mold, a steel plate, and constraints. The geometric shape and material properties of the mold need to be accurately defined, and the material properties of the steel plate use the material characteristic parameters of the cold-rolled steel plate extracted in step S13. The equipment processing physical model established in step S14, such as the finite element model of the CNC bending machine, is simulated and analyzed in ABAQUS. During the analysis, the material characteristic parameters of the cold-rolled steel plate extracted in step S13 are input into the model, and predefined process conditions are applied. Through simulation calculations, the stress and strain distribution of the steel plate during the bending process and the processing time are obtained. The stress and strain data of different time steps are extracted, the relationship between stress and strain and processing time is established, and a stress and strain time model of equipment processing is generated.Import the equipment processing physical model established in step S14 and the equipment processing stress-strain-time model generated in step S15 into digital factory simulation software such as Tecnomatix Plant Simulation. Connect the models of each key equipment according to the production line process data extracted in step S11 to construct a complete production line digital process twin model. This model not only includes the equipment's 3D geometry and motion trajectory, but also encompasses the physical and material properties of the equipment during processing. This model can simulate the real production process and enable process optimization and production forecasting.

[0102] As an example of the present invention, refer to Figure 2 As shown, Figure 1 Detailed implementation steps of step S2 are shown in the flowchart. In this example, step S2 includes:

[0103] Step S21: Divide the digital process twin model of the production line into key process nodes to obtain key process node data of the production line;

[0104] In an embodiment of the present invention, in the constructed digital process twin model of the digital production line of the power distribution cabinet, the entire production process is divided into several key process nodes according to the production process flow. For example, processes such as blanking, stamping, bending, welding, assembly, and inspection are defined as key process nodes. Each key process node contains the name of the process, the starting position coordinates (for example, the starting position coordinates of the welding process are x=10m, y=5m, z=2m), the ending position coordinates (for example, the ending position coordinates of the welding process are x=12m, y=5m, z=2m), the input and output materials, the required equipment (for example, the equipment required for the welding process is a welding robot), the standard working hours (for example, the standard working hours of the welding process are 5 minutes), and the relationship between the previous and next processes, etc., to form the key process node data of the production line.

[0105] Step S22: Determine equipment production performance monitoring sensors based on key process node data of the production line, and deploy production monitoring sensors on the digital production line of power distribution equipment to build a production line monitoring network; the equipment production performance monitoring sensors include equipment load rate monitoring sensors, equipment energy consumption monitoring sensors, and process start and end monitoring sensors;

[0106] In an embodiment of the present invention, the type and number of equipment production performance monitoring sensors that need to be deployed are determined based on the key process node data determined in step S21. For example, current sensors and voltage sensors are deployed at the key process nodes of a CNC punch press as equipment load rate monitoring sensors to monitor the real-time load rate of the punch press; power meters are deployed as equipment energy consumption monitoring sensors to monitor the energy consumption of the punch press; and photoelectric sensors are deployed as process start and end monitoring sensors to monitor the start and end of the stamping process. Corresponding sensors are also deployed at other key process nodes, such as welding robots and assembly lines. All sensors are connected to the data acquisition server via industrial Ethernet to build a production line monitoring network.

[0107] Step S23: Based on the key process node data of the production line, a representative workpiece processing test is performed using the preset production line operating environment parameters, and the equipment processing performance test data is collected using the production line monitoring network to obtain the equipment test raw data;

[0108] In an embodiment of the present invention, the production line operating environment parameters are set, such as temperature 25°C, humidity 60%, and voltage 220V. Based on the key process node data obtained in step S21, representative workpieces are selected for processing tests. For example, in the blanking process, standard-sized steel plates are selected for cutting tests; in the stamping process, representative distribution cabinet panels are selected for stamping tests. During the processing test, the production line monitoring network deployed in step S22 is used to collect data such as equipment load rate, energy consumption, and process start and end time in real time. The collected data is stored in a database to obtain the original data of the equipment test. For example, the current, voltage, power, and timestamp data of each stamping action in the stamping process are recorded.

[0109] Step S24: performing error correction on the original equipment test data to obtain corrected test data;

[0110] In this embodiment of the present invention, error correction is performed on the raw device test data collected in step S23. For example, a Kalman filter algorithm is used to filter sensor data such as current and voltage to remove noise interference; the least squares method is used to fit the measured data to eliminate systematic errors; and the data is calibrated according to the sensor's calibration curve to correct for inherent sensor errors. After error correction, more accurate corrected test data is obtained.

[0111] Step S25: Calculate the equipment output per unit time based on the corrected test data, and perform production line capacity statistics to obtain production line benchmark capacity data.

[0112] In this embodiment of the present invention, the equipment output per unit time for each key process node is calculated based on the corrected test data obtained in step S24. For example, based on the corrected test data for the stamping process, the number of stamping operations that can be completed per hour, i.e., the output per unit time of the stamping equipment, is calculated. Then, based on the production line process flow, the output per unit time of each key process node is comprehensively analyzed to calculate the production capacity of the entire production line. For example, taking into account the connection and waiting time between each process, the number of distribution cabinets that can be produced per hour by the entire production line is calculated to obtain the production line's baseline production capacity data.

[0113] Preferably, step S3 includes the following steps:

[0114] Step S31: using the production line monitoring network to perform real-time comprehensive process production monitoring on the digital production line of the power distribution equipment, and obtaining a real-time production monitoring comprehensive data stream;

[0115] Step S32: performing monitoring data analysis on the real-time production monitoring integrated data stream and performing signal separation to obtain real-time process start and end monitoring signals, real-time processing equipment load monitoring signals, and real-time processing equipment energy consumption monitoring signals;

[0116] Step S33: Analyze the actual production process rhythm according to the real-time process start and end monitoring signals to generate actual production process rhythm data;

[0117] Step S34: Acquire actual operating parameters of production equipment and current production task data;

[0118] Step S35: Adjust the production parameters of the production line digital process twin model according to the production line benchmark capacity data, and synchronize the real-time operating parameters based on the actual operating parameters of the production equipment to obtain a real-time production line digital twin model;

[0119] Step S36: Based on the current production task data, the real-time production line digital twin model is used to calculate the process deviation of the actual production process rhythm data to obtain real-time production rhythm deviation rate data.

[0120] In an embodiment of the present invention, a sensor network deployed on a digital production line for power distribution equipment, such as current sensors, voltage sensors, photoelectric sensors, and temperature sensors, collects data from various stages of the production line in real time. The collected data includes equipment operating status (e.g., running, stopped, faulted), processing parameters (e.g., current, voltage, power, speed), environmental parameters (e.g., temperature, humidity), and process start and end signals. This data is transmitted to a data acquisition server via industrial Ethernet or a wireless network, forming a comprehensive real-time production monitoring data stream, with data collected, for example, once per second. The comprehensive real-time production monitoring data stream acquired in step S31 is parsed according to a predefined data format. For example, information such as timestamps, sensor types, and sensor values ​​is parsed from the data stream. The data stream is then separated into different signals based on the functions and uses of different sensors. For example, data from photoelectric sensors is separated as real-time process start and end monitoring signals, data from current and voltage sensors is separated as real-time process load monitoring signals, and data from power meters is separated as real-time process energy consumption monitoring signals. The actual start and end times of each process are determined based on the real-time process start and end monitoring signals, such as the photoelectric sensor signals, separated in step S32. For example, when a photoelectric sensor detects a workpiece entering a workstation, the process start time is recorded; when a workpiece leaves the workstation, the process end time is recorded. The actual time taken for each process, i.e., the actual production process beat, is calculated. The actual beat data for each process is recorded in a time series format to generate actual production process beat data. The production line monitoring network acquires the actual operating parameters of the production equipment in real time, such as the actual punching speed of the CNC punch press, the actual bending angle of the CNC press brake, and the actual welding speed of the welding robot. Simultaneously, current production task data, such as the number of orders currently being produced, product model, and planned completion time, is obtained from the production management system. Based on pre-determined baseline production line capacity data, relevant parameters in the production line digital process twin model are adjusted, such as the equipment's processing speed and processing time, to ensure that the model's capacity is consistent with the baseline capacity. Simultaneously, the actual operating parameters of the production equipment acquired in step S34 are synchronized with the digital process twin model. For example, the actual punching speed of the CNC punch press is updated to the corresponding virtual device in the model, so that the model reflects the real-time operating status of the production line. Ultimately, a real-time production line digital twin model synchronized with the actual production line status is obtained. The real-time production line digital twin model obtained in step S35 is used in conjunction with the current production task data obtained in step S34, such as product model and order quantity, to simulate the execution of the current production task. The standard takt time for each process output by the model is compared with the actual production process takt time data obtained in step S33 to calculate the takt deviation rate for each process. For example, the standard takt time is subtracted from the actual takt time, and then divided by the standard takt time to obtain the real-time production takt deviation rate data.

[0121] Preferably, step S33 includes the following steps:

[0122] Step S331: performing signal noise reduction processing on the real-time process start and end monitoring signals, and extracting key signal segments using a preset signal threshold to obtain a set of process start and end key signal segments;

[0123] Step S332: using a preset process signal pattern library to perform process start and end signal segment matching on the process start and end key signal segment set, identifying the start and end signals of each process of the production line, and generating the initial start and end signals of the production line process;

[0124] Step S333: performing time stamp calibration on the initial start and end signals of the production line process to obtain the production line process calibration start and end signals;

[0125] Step S334: Calculate the duration of each process according to the start and end signals of the production line process calibration to obtain the single process takt time data;

[0126] Step S335: performing data cleaning and outlier processing on the single process tact time data to generate valid single process tact data;

[0127] Step S336: Integrate the effective single-process beat data by process production equipment number in chronological order to obtain the actual production process beat data.

[0128] In an embodiment of the present invention, the collected real-time process start and end monitoring signals, such as photoelectric sensor signals, are subjected to noise reduction processing using a wavelet transform method to remove high-frequency noise and interference from the signal. A signal threshold is set, such as a voltage value greater than 5V. When the signal amplitude exceeds the preset threshold, it is considered a valid signal segment. Signal segments exceeding the threshold are extracted, and the start and end times of each segment are recorded to form a set of key process start and end signal segments. A process signal pattern library is pre-established, containing standard start and end signal patterns for each process. For example, the start and end signal pattern for a stamping process is defined as: the signal first jumps from a low level to a high level, persists for a period of time, and then jumps from a high level to a low level. The set of key process start and end signal segments extracted in step S331 is matched with the process signal pattern library. For example, a dynamic time warping (DTW) algorithm is used to calculate the similarity between the signal segment and each pattern. The pattern with the highest similarity is selected as the process start and end signal corresponding to the signal segment. For example, if a signal segment matches the start and end signal pattern of a stamping process, the signal segment is identified as the start or end signal of the stamping process. All identified process start and end signals and their corresponding timestamps are recorded to generate the initial start and end signals for the production line process. Due to factors such as network delays and sensor errors, the timestamps of the initial start and end signals for the production line process may vary. All sensor times are synchronized using the Network Time Protocol (NTP). The timestamps of the initial start and end signals for the production line process are compared with the standard time to correct for time deviations. For example, if the timestamp of a process start signal lags behind the actual start time by 10 ms, 10 ms is added to the timestamp for calibration. The calibrated signals and their corresponding timestamps form the calibration start and end signals for the production line process. Based on the calibration start and end signals for the production line process obtained in step S333, the duration of each process is calculated. For example, if the calibration start time for a process is 10:00:00.001 and the calibration end time is 10:00:05.001, the duration of the process is 5 seconds. The duration of each process is recorded to generate the single-process takt time data. The single-process takt time data obtained in step S334 is cleaned and outlier processing is performed. For example, a boxplot method is used to identify and remove outliers. For example, if the takt time of a process is significantly greater or less than the takt time of other identical processes, the data is considered an outlier and is discarded. The cleaned data forms valid single-process takt data. Based on the production process flow and equipment number, the valid single-process takt data obtained in step S335 is consolidated in chronological order. For example, the takt data of multiple processes on the same equipment are arranged in chronological order to form the actual production process takt data for that equipment. Finally, the actual production process takt data of all equipment is consolidated to form the complete actual production process takt data.For example, the actual production process cycle data of equipment A is: [Process 1: 5s, Process 2: 3s, Process 3: 4s].

[0129] Preferably, step S36 includes the following steps:

[0130] Step S361: Decomposing the process tasks according to the current production task data to obtain the current production task decomposition data;

[0131] Step S362: Setting simulation parameters for the real-time production line digital twin model based on the current production task decomposition data, and performing production line simulation calculations to obtain production line process simulation data;

[0132] Step S363: extracting the stress-strain-time curve of each process according to the production line process simulation data to obtain the simulated stress-strain-time curve of each process;

[0133] Step S364: extracting the simulated processing time of each process according to the simulated stress-strain time curve of each process and the preset process material failure criterion to obtain theoretical production process takt data;

[0134] Step S365: Calculate the production rhythm deviation of the actual production process rhythm data using the theoretical production process rhythm data to generate real-time production rhythm deviation rate data.

[0135] In an embodiment of the present invention, current production task data is obtained. For example, a production order includes 100 sets of distribution cabinets, each of which includes a cabinet body, cabinet doors, mounting plates, and other components. Based on the distribution cabinet's BOM (bill of materials) and process flow, the production task is broken down into individual processes. For example, producing 100 distribution cabinets requires cutting 1,000 steel plates, punching 2,000 holes, bending 3,000 edges, welding 4,000 welds, and assembling 5,000 parts. The decomposed process task data, including information such as the process name, materials required for the process, equipment required for the process, and the number of processes, is stored in a database to form the current production task decomposition data. The current production task decomposition data obtained in step S361 is used as input parameters to set simulation parameters for the real-time production line digital process twin model. For example, the number of parts to be processed, material properties, and equipment parameters are set. The production line is simulated using discrete event simulation methods. During the simulation process, the model simulates the execution of each process, including material flow, equipment operation, and process processing time. The stress, strain, time and other data generated for each process during the simulation process are recorded to form the production line process simulation process data. From the production line process simulation process data obtained in step S362, the stress, strain and time data of each process are extracted. For example, the stress and strain values ​​of each time step in the stamping process are extracted. The extracted data are plotted as a stress-strain-time curve, for example, with time as the horizontal axis and stress and strain as the vertical axis to draw a three-dimensional curve graph. A corresponding stress-strain-time curve is generated for each process to form a simulated stress-strain-time curve for each process. The process operation material failure criteria are pre-set. For example, when the stress of the material exceeds the yield strength, the material is considered to have failed. Based on the simulated stress-strain-time curve of each process obtained in step S363, it is determined whether the material of each process has failed. For example, in the stress-strain-time curve of the stamping process, the time point when the stress reaches the yield strength is found. The time from the start of the process to the failure of the material is the simulated processing time of the process. The simulated processing time of each process is recorded to obtain the theoretical production process beat data. Compare the theoretical production process beat data obtained in step S364 with the actual production process beat data to calculate the beat deviation rate of each process. For example, the beat deviation rate is calculated using the following formula: (actual beat - theoretical beat) / theoretical beat × 100%. Record the beat deviation rate of each process to generate real-time production beat deviation rate data. For example, the theoretical beat of the stamping process is 2 seconds, and the actual beat is 2.2 seconds. The beat deviation rate of the stamping process is (2.2-2) / 2 × 100% = 10%.

[0136] Preferably, step S365 includes the following steps:

[0137] Match the process equipment with the theoretical and actual production process beat data, align the timing, and generate production line process beat comparison data.

[0138] Calculate the operation takt time difference based on the production line process takt comparison data and generate the process takt time difference value;

[0139] Perform absolute value processing on process takt time differences and associate them with production line processes to obtain single process deviation values.

[0140] Normalize the single process deviation value to generate single process deviation percentage data;

[0141] Based on the single process deviation percentage data, the production line process statistical analysis is carried out to obtain the real-time production rhythm deviation rate data.

[0142] In this embodiment of the present invention, theoretical production process takt data includes the theoretical takt time for each process and the corresponding process equipment ID. Actual production process takt data includes the actual takt time for each process, the corresponding process equipment ID, and a timestamp. First, the theoretical data and actual data are matched based on the process equipment ID. For example, the theoretical takt data for a stamping process with the equipment ID "CNC001" is matched with the actual takt data for a stamping process with the same equipment ID. Then, time alignment is performed based on the timestamps of the actual data, arranging the theoretical and actual takt times of different processes on the same equipment in chronological order. For example, equipment "CNC001" completed the stamping process at 10:00:00 with an actual takt time of 2.2 seconds and a theoretical takt time of 2 seconds; it completed the bending process at 10:00:05 with an actual takt time of 3.1 seconds and a theoretical takt time of 3 seconds. This data is arranged in chronological order to generate production line process takt comparison data, which includes the process equipment ID, process name, theoretical takt time, actual takt time, and timestamp. Traverse each record in the production line process takt comparison data and calculate the difference between the actual takt time and the theoretical takt time. For example, if the stamping process on equipment "CNC001" has an actual takt time of 2.2 seconds and a theoretical takt time of 2 seconds, the takt time difference for this process is 2.2 - 2 = 0.2 seconds. Record the takt time difference for each process to generate process takt time difference data, which includes the process equipment ID, process name, takt time difference, and timestamp. For example, if the stamping process on equipment "CNC001" has a takt time difference of 0.2 seconds, its absolute value is 0.2 seconds. Then, based on the production line process flow, associate each process with its preceding and following processes. For example, if the stamping process is followed by the bending process, the absolute time difference of the stamping process is associated with the bending process. Finally, obtain the deviation value for each process, namely the single process deviation value, which includes the process equipment ID, process name, single process deviation value, and timestamp. Select a baseline value, such as the maximum value of all process deviations. Divide the deviation value of each process by the baseline value to obtain the normalized deviation value, that is, the single process deviation percentage data. For example, assuming that the maximum deviation value of all processes is 0.5 seconds, and the deviation value of the stamping process of the equipment "CNC001" is 0.2 seconds, then the deviation percentage of this process is 0.2 / 0.5 = 0.4, or 40%. For example, calculate the average deviation percentage, maximum deviation percentage, minimum deviation percentage, etc. of all processes. Based on different statistical indicators, such as the average deviation percentage, the overall beat deviation of the production line can be evaluated to obtain real-time production beat deviation rate data. For example, if the average deviation percentage of all processes is calculated to be 20%, it can be considered that the real-time beat deviation rate of the current production line is 20%.

[0143] As an example of the present invention, refer to Figure 3As shown, Figure 1 Detailed implementation steps of step S4 are shown in the flowchart. In this example, step S4 includes:

[0144] Step S41: identifying abnormal processes in the digital production line of power distribution equipment using real-time production beat deviation rate data to obtain a list of abnormal production processes;

[0145] In an embodiment of the present invention, a beat deviation rate threshold is set, for example, ±15%. The real-time production beat deviation rate data is compared with the preset threshold. For example, if the real-time beat deviation rate of a process exceeds +15% or is lower than -15%, the process is determined to be an abnormal process. All process information determined to be abnormal, including process name, equipment ID, beat deviation rate, etc., is recorded in the abnormal production process list. For example, if the beat deviation rate of the stamping process of the "CNC001" equipment is 20%, which exceeds the threshold, the process is added to the abnormal production process list.

[0146] Step S42: Feedback the abnormal production process list to the terminal device, and collect the abnormal process operation parameters in real time to obtain the abnormal process operation parameters;

[0147] In this embodiment of the present invention, the list of abnormal production processes generated in step S41 is sent to the corresponding terminal device. For example, the abnormal stamping process information for the "CNC001" device is sent to the control terminal of that device. After receiving the abnormal process information, the terminal device immediately begins collecting the operating parameters of the process, such as stamping speed, punching force, current, voltage, temperature, etc. The collected parameter data is uploaded to the data collection server in real time, forming the abnormal process operating parameters.

[0148] Step S43: using the abnormal process operation parameters to adjust the abnormal process parameters of the real-time production line digital twin model to obtain the abnormal production line digital twin model;

[0149] In this embodiment of the present invention, the operating parameters of the abnormal process collected in step S42 are used to update the parameters of the corresponding process in the real-time production line digital twin model. For example, the actual parameters of the "CNC001" machine, such as the stamping speed, punching force, current, voltage, and temperature, are updated to the corresponding virtual device in the model. Through parameter adjustment, the digital twin model can more accurately reflect the actual operating status of the abnormal process, resulting in a digital twin model of the abnormal production line.

[0150] Step S44: Calculating the production line capacity attenuation coefficient based on the real-time processing equipment load monitoring signal and the real-time processing equipment energy consumption monitoring signal to generate the production line capacity attenuation coefficient;

[0151] In an embodiment of the present invention, real-time processing equipment load monitoring signals and real-time processing equipment energy consumption monitoring signals are analyzed. For example, the average load rate and average energy consumption of the equipment are calculated. The average load rate and average energy consumption of the current equipment are compared with the rated load rate and rated energy consumption of the equipment to calculate the production line capacity reduction coefficient. For example, the capacity reduction coefficient can be calculated using the following formula: 1-(current average load rate / rated load rate)×(current average energy consumption / rated energy consumption). The calculated capacity reduction coefficient is recorded to generate the production line capacity reduction coefficient.

[0152] Step S45: Obtain order plan task data; decompose production tasks based on the order plan task data to obtain order plan production task data;

[0153] In an embodiment of the present invention, order planning task data, such as order number, product model, order quantity, planned start time, planned completion time, etc., is obtained from the production management system. Based on the product BOM (bill of materials) and process flow, the order planning task is decomposed into production tasks for each process. For example, the task of producing 100 sets of distribution cabinets is decomposed into the task of cutting 1,000 steel plates, the task of punching 2,000 holes, the task of bending 3,000 edges, etc. The decomposed production task data, including the process name, process quantity, required equipment, required materials, etc., is stored in the database to form the order planning production task data.

[0154] Step S46: Based on the production line capacity attenuation coefficient, the abnormal production line digital twin model is used to simulate the order task production data and predict the production completion time to obtain the order production prediction completion time data.

[0155] In an embodiment of the present invention, based on the production line capacity attenuation coefficient calculated in step S44, the digital twin model of the abnormal production line obtained in step S43 is used to simulate the order task production data obtained in step S45. During the simulation process, the influence of the capacity attenuation coefficient is taken into account, for example, the simulated processing time of each process is multiplied by the capacity attenuation coefficient. Through simulation calculation, the actual completion time of each process is predicted, and finally the production completion time of the entire order is predicted. The predicted completion time is compared with the planned delivery date of the order to determine whether the order can be completed on time.

[0156] Preferably, step S44 includes the following steps:

[0157] Step S441: Calculating the load rate performance attenuation coefficient of the real-time processing equipment load monitoring signal using a preset production equipment load rate-performance attenuation relationship curve to generate a load rate performance attenuation coefficient;

[0158] Step S442: Calculating the energy consumption performance attenuation coefficient of the real-time processing equipment energy consumption monitoring signal using a preset production equipment energy consumption-performance attenuation relationship curve to generate an energy consumption performance attenuation coefficient;

[0159] Step S443: performing weighted calculation of the production line capacity attenuation coefficient based on the load rate performance attenuation coefficient and the energy consumption performance attenuation coefficient to obtain the production line capacity attenuation coefficient.

[0160] In an embodiment of the present invention, a curve is pre-established to correlate the load rate and performance degradation of production equipment. For example, this curve is fitted by collecting and analyzing long-term equipment operating data. The curve describes the relationship between the equipment load rate and the performance degradation coefficient. This curve can be obtained using methods such as polynomial fitting and exponential fitting and stored as a function or lookup table. Load monitoring signals from processing equipment, such as current and voltage, are collected in real time to calculate the equipment's real-time load rate. For example, the real-time current value is divided by the equipment's rated current value to obtain the load rate. The calculated real-time load rate is used as input and substituted into a preset load rate-performance degradation curve to obtain the corresponding load rate-performance degradation coefficient. For example, assuming a load rate of 80%, a load rate-performance degradation coefficient of 0.95 is calculated by looking up a table or substituting it into a function. A curve is pre-established to correlate the energy consumption and performance degradation of production equipment. For example, a curve is fitted by collecting and analyzing long-term equipment operating data. The curve describes the relationship between the equipment's energy consumption and the performance degradation coefficient. The curve can be obtained using methods such as polynomial fitting and exponential fitting and stored as a function or lookup table. Energy consumption monitoring signals from processing equipment, such as power values, are collected in real time. Take the real-time energy consumption data as input and substitute it into the preset energy consumption-performance attenuation relationship curve to obtain the corresponding energy consumption performance attenuation coefficient. For example, assuming that the real-time power is 10kW, the energy consumption performance attenuation coefficient calculated by looking up the table or substituting it into the function is 0.92. Determine the weights of the load rate performance attenuation coefficient and the energy consumption performance attenuation coefficient based on expert experience or historical data. For example, set the load rate weight to 0.7 and the energy consumption weight to 0.3. Multiply the load rate performance attenuation coefficient calculated in step S441 and the energy consumption performance attenuation coefficient calculated in step S442 by the corresponding weights, and then add them together to obtain the production line capacity attenuation coefficient. For example, assuming that the load rate performance attenuation coefficient is 0.95 and the energy consumption performance attenuation coefficient is 0.92, then the production line capacity attenuation coefficient is 0.95×0.7+0.92×0.3=0.941, and the production line capacity attenuation coefficient is obtained.

[0161] The present invention further provides a data control system for digital production of power distribution equipment, which executes the data control method for digital production of power distribution equipment as described above. The data control system for digital production of power distribution equipment includes:

[0162] The process twin modeling module is used to identify key production process equipment in the digital production line of power distribution equipment and obtain key production process equipment data; based on the key production process equipment data, a digital process twin model of the production line is constructed;

[0163] The capacity benchmarking module is used to deploy production monitoring sensors on the digital production line of power distribution equipment to build a production line monitoring network. It also conducts processing tests on representative workpieces at process nodes of the digital production line of power distribution equipment and performs production line capacity statistics to obtain production line benchmark capacity data.

[0164] The production variance analysis module is used to use the production line monitoring network to conduct real-time comprehensive process production monitoring of the digital production line of power distribution equipment, obtaining a real-time production monitoring comprehensive data stream; perform actual production process beat analysis based on the real-time production monitoring comprehensive data stream to generate actual production process beat data; adjust production parameters of the production line digital process twin model based on the production line benchmark capacity data to obtain a real-time production line digital twin model; use the real-time production line digital twin model to calculate process deviations on the actual production process beat data to obtain real-time production beat deviation rate data;

[0165] The production time prediction module is used to identify abnormal processes in the digital production line of distribution equipment through real-time production rhythm deviation rate data to obtain a list of abnormal production processes; obtain order planning task data; adjust the abnormal process parameters of the real-time production line digital twin model based on the abnormal production process list, and predict the production completion time of the order planning task data to obtain order production prediction completion time data.

[0166] The present application is to establish a physical model of the equipment processing process through finite element analysis, and analyze the relationship between stress, strain and processing time in combination with material characteristic parameters, and construct an equipment processing stress-strain-time model that can better reflect the actual processing state of the equipment. This enables the digital twin model to more accurately simulate various complex situations in the actual production process, such as equipment performance changes and material deformation under different process parameters, thereby improving the prediction accuracy of the model. The equipment processing physical model and the equipment processing stress-strain-time model are integrated to construct a more comprehensive digital process twin model of the production line. The model not only includes the geometric structure and motion characteristics of the equipment, but also includes information on changes in the mechanical properties of the material during the processing process. This more comprehensive model can more accurately reflect the various influencing factors in the production process, such as equipment aging, material batch differences, etc., thereby improving the accuracy of production progress prediction and the sensitivity of abnormal process identification.

[0167] The present invention is therefore intended to be illustrative and non-restrictive in all respects, with the scope of the invention being defined by the appended claims rather than the foregoing description, and all changes that come within the meaning and range of equivalents of the application documents are intended to be embraced therein.

[0168] The foregoing description is intended only to provide specific embodiments of the present invention, which will enable those skilled in the art to understand and implement the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention is not intended to be limited to the embodiments shown herein, but is to be construed in the widest possible manner consistent with the principles and novel features disclosed herein.

Claims

1. A data control method for digital production of power distribution equipment, characterized in that: The following steps are involved: Step S1: Identify key production process equipment on the digital production line of power distribution equipment to obtain key production process equipment data; Build a digital process twin model of the production line based on key production process equipment data; Step S2: Deploy production monitoring sensors on the digital production line of power distribution equipment to build a production line monitoring network; Conduct representative workpiece processing tests at process nodes on the digital production line for power distribution equipment, and perform production line capacity statistics to obtain baseline production line capacity data. Step S3: Using the production line monitoring network to conduct real-time comprehensive process production monitoring on the digital production line of the power distribution equipment, obtaining a real-time production monitoring comprehensive data stream; performing actual production process beat analysis based on the real-time production monitoring comprehensive data stream, and generating actual production process beat data; Adjust the production parameters of the production line digital process twin model based on the production line benchmark capacity data to obtain a real-time production line digital twin model. Use the real-time production line digital twin model to calculate the process deviation of the actual production process beat data to obtain real-time production beat deviation rate data. Step S4: Identify abnormal processes in the digital production line of power distribution equipment through real-time production beat deviation rate data to obtain a list of abnormal production processes; obtain order planning task data; adjust the abnormal process parameters of the real-time production line digital twin model based on the abnormal production process list, and predict the production completion time of the order planning task data to obtain order production predicted completion time data.

2. The data control method for digital production of power distribution equipment according to claim 1, characterized in that: Step S1 includes the following steps: Step S11: extracting the production process flow of the digital production line of power distribution equipment to obtain production line process flow data; Step S12: Identify key production process equipment based on the production line process data to obtain key production process equipment data; Step S13: extracting key equipment process parameters and process material characteristic parameters based on key production process equipment data; Step S14: setting process conditions based on key equipment process parameters, and establishing a physical model of the equipment processing process based on finite element analysis to obtain an equipment processing physical model; Step S15: Analyzing the relationship between stress, strain and processing time of the characteristic parameters of the process material through the equipment processing physical model to generate an equipment processing stress, strain and time model; Step S16: Construct a digital process twin model of the production line using the equipment processing physical model and the equipment processing stress-strain time model.

3. The data control method for digital production of power distribution equipment according to claim 1, characterized in that: Step S2 includes the following steps: Step S21: Divide the digital process twin model of the production line into key process nodes to obtain key process node data of the production line; Step S22: Determine equipment production performance monitoring sensors based on key process node data of the production line, and deploy production monitoring sensors on the digital production line of power distribution equipment to build a production line monitoring network; the equipment production performance monitoring sensors include equipment load rate monitoring sensors, equipment energy consumption monitoring sensors, and process start and end monitoring sensors; Step S23: Based on the key process node data of the production line, a representative workpiece processing test is performed using the preset production line operating environment parameters, and the equipment processing performance test data is collected using the production line monitoring network to obtain the equipment test raw data; Step S24: performing error correction on the original equipment test data to obtain corrected test data; Step S25: Calculate the equipment output per unit time based on the corrected test data, and perform production line capacity statistics to obtain production line benchmark capacity data.

4. The data control method for digital production of power distribution equipment according to claim 1, characterized in that: Step S3 includes the following steps: Step S31: using the production line monitoring network to perform real-time comprehensive process production monitoring on the digital production line of the power distribution equipment, and obtaining a real-time production monitoring comprehensive data stream; Step S32: performing monitoring data analysis on the real-time production monitoring integrated data stream and performing signal separation to obtain real-time process start and end monitoring signals, real-time processing equipment load monitoring signals, and real-time processing equipment energy consumption monitoring signals; Step S33: Analyze the actual production process rhythm according to the real-time process start and end monitoring signals to generate actual production process rhythm data; Step S34: Acquire actual operating parameters of production equipment and current production task data; Step S35: Adjust the production parameters of the production line digital process twin model according to the production line benchmark capacity data, and synchronize the real-time operating parameters based on the actual operating parameters of the production equipment to obtain a real-time production line digital twin model; Step S36: Based on the current production task data, the real-time production line digital twin model is used to calculate the process deviation of the actual production process rhythm data to obtain real-time production rhythm deviation rate data.

5. The data control method for digital production of power distribution equipment according to claim 4, characterized in that: Step S33 includes the following steps: Step S331: performing signal noise reduction processing on the real-time process start and end monitoring signals, and extracting key signal segments using a preset signal threshold to obtain a set of process start and end key signal segments; Step S332: using a preset process signal pattern library to perform process start and end signal segment matching on the process start and end key signal segment set, identifying the start and end signals of each process of the production line, and generating the initial start and end signals of the production line process; Step S333: performing time stamp calibration on the initial start and end signals of the production line process to obtain the production line process calibration start and end signals; Step S334: Calculate the duration of each process according to the start and end signals of the production line process calibration to obtain the single process takt time data; Step S335: performing data cleaning and outlier processing on the single process takt time data to generate valid single process takt time data; Step S336: Integrate the effective single-process beat data by process production equipment number in chronological order to obtain the actual production process beat data.

6. The data control method for digital production of power distribution equipment according to claim 4, characterized in that: Step S36 includes the following steps: Step S361: Decomposing the process tasks according to the current production task data to obtain the current production task decomposition data; Step S362: Setting simulation parameters for the real-time production line digital twin model based on the current production task decomposition data, and performing production line simulation calculations to obtain production line process simulation data; Step S363: extracting the stress-strain-time curve of each process according to the production line process simulation data to obtain the simulated stress-strain-time curve of each process; Step S364: extracting the simulated processing time of each process according to the simulated stress-strain time curve of each process and the preset process material failure criterion to obtain theoretical production process takt data; Step S365: Calculate the production rhythm deviation of the actual production process rhythm data using the theoretical production process rhythm data to generate real-time production rhythm deviation rate data.

7. The data control method for digital production of power distribution equipment according to claim 6, characterized in that: Step S365 includes the following steps: Match the process equipment with the theoretical and actual production process beat data, align the timing, and generate production line process beat comparison data. Calculate the operation takt time difference based on the production line process takt comparison data and generate the process takt time difference value; Perform absolute value processing on process takt time differences and associate them with production line processes to obtain single process deviation values. Normalize the single process deviation value to generate single process deviation percentage data; Based on the single process deviation percentage data, the production line process statistical analysis is carried out to obtain the real-time production rhythm deviation rate data.

8. The data control method for digital production of power distribution equipment according to claim 4, characterized in that: Step S4 includes the following steps: Step S41: identifying abnormal processes in the digital production line of power distribution equipment using real-time production beat deviation rate data to obtain a list of abnormal production processes; Step S42: Feedback the abnormal production process list to the terminal device, and collect the abnormal process operation parameters in real time to obtain the abnormal process operation parameters; Step S43: using the abnormal process operation parameters to adjust the abnormal process parameters of the real-time production line digital twin model to obtain the abnormal production line digital twin model; Step S44: Calculating the production line capacity attenuation coefficient based on the real-time processing equipment load monitoring signal and the real-time processing equipment energy consumption monitoring signal to generate the production line capacity attenuation coefficient; Step S45: Obtain order plan task data; decompose production tasks based on the order plan task data to obtain order plan production task data; Step S46: Based on the production line capacity attenuation coefficient, the abnormal production line digital twin model is used to simulate the order task production data and predict the production completion time to obtain the order production prediction completion time data.

9. The data control method for digital production of power distribution equipment according to claim 8, characterized in that: Step S44 includes the following steps: Step S441: Calculating the load rate performance attenuation coefficient of the real-time processing equipment load monitoring signal using a preset production equipment load rate-performance attenuation relationship curve to generate a load rate performance attenuation coefficient; Step S442: Calculating the energy consumption performance attenuation coefficient of the real-time processing equipment energy consumption monitoring signal using a preset production equipment energy consumption-performance attenuation relationship curve to generate an energy consumption performance attenuation coefficient; Step S443: performing weighted calculation of the production line capacity attenuation coefficient based on the load rate performance attenuation coefficient and the energy consumption performance attenuation coefficient to obtain the production line capacity attenuation coefficient.

10. A data control system for digital production of power distribution equipment, characterized in that: The data control method for digital production of power distribution equipment according to claim 1 is used to execute the data control system for digital production of power distribution equipment, comprising: The process twin modeling module is used to identify key production process equipment in the digital production line of power distribution equipment and obtain key production process equipment data; based on the key production process equipment data, a digital process twin model of the production line is constructed; The capacity benchmarking module is used to deploy production monitoring sensors on the digital production line of power distribution equipment to build a production line monitoring network. It also conducts processing tests on representative workpieces at process nodes of the digital production line of power distribution equipment and performs production line capacity statistics to obtain production line benchmark capacity data. The production variance analysis module is used to use the production line monitoring network to conduct real-time comprehensive process production monitoring of the digital production line of power distribution equipment, obtaining a real-time production monitoring comprehensive data stream; perform actual production process beat analysis based on the real-time production monitoring comprehensive data stream to generate actual production process beat data; adjust production parameters of the production line digital process twin model based on the production line benchmark capacity data to obtain a real-time production line digital twin model; use the real-time production line digital twin model to calculate process deviations on the actual production process beat data to obtain real-time production beat deviation rate data; The production time prediction module is used to identify abnormal processes in the digital production line of distribution equipment through real-time production rhythm deviation rate data to obtain a list of abnormal production processes; obtain order planning task data; adjust the abnormal process parameters of the real-time production line digital twin model based on the abnormal production process list, and predict the production completion time of the order planning task data to obtain order production prediction completion time data.

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