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

Abstract

The present application relates to the technical field of digital production management, and particularly relates 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; using the production line monitoring network to perform real-time process production comprehensive monitoring on the power distribution equipment digital production line to obtain real-time production monitoring comprehensive data flow; using the production line digital process twin model to identify abnormal processes from the real-time production monitoring comprehensive data flow to obtain an abnormal production process list; obtaining order plan task data; based on the abnormal production process list, adjusting abnormal process parameters of the real-time production line digital twin model, and predicting the production completion time of the order plan task data to obtain order production predicted completion time data. The present application realizes accurate prediction and intelligent management of production progress by constructing a production line digital process twin model.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of digital production management, and particularly relates to a data control method and system for digital production of power distribution equipment. BACKGROUND

[0002] In the process of digital production of power distribution equipment, the data control method plays a crucial role. Effective data control can realize real-time collection, transmission, processing and analysis of production data, providing strong data support for production planning, production process monitoring, quality tracing, equipment maintenance and other links, thereby improving the fine and intelligent level of production management. However, in the digital production line of power distribution equipment, the actual running efficiency and capacity of different production links of the equipment are different due to factors such as machining precision requirements and differences in material properties. The traditional data control method often separates production tasks from physical production capacity when processing production progress data. Production tasks are usually based on theoretical planning models and consider ideal production processes and time arrangements, while the physical production capacity of the equipment is affected by various actual factors, such as performance degradation, fault maintenance, and running efficiency fluctuations under different process parameters. This separation results in an inability to accurately reflect the dynamic performance changes of the equipment in the actual production process when predicting progress, resulting in a large deviation between the predicted results and the actual production progress. SUMMARY

[0003] Therefore, the present application 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: identifying key production process equipment of the digital production line of power distribution equipment to obtain key production process equipment data; and constructing a digital process twin model of the production line according to the key production process equipment data;

[0006] Step S2: deploying production monitoring sensors on the digital production line of power distribution equipment to construct a production line monitoring network; and performing process node representative workpiece processing tests on the digital production line of power distribution equipment and performing production line capacity statistics to obtain production line benchmark capacity data;

[0007] Step S3: Real-time process production comprehensive monitoring is performed on the power distribution equipment digital production line by using the production line monitoring network to obtain real-time production monitoring comprehensive data flow; actual production process beat analysis is performed according to the real-time production monitoring comprehensive data flow to generate actual production process beat data; production parameter adjustment is performed on the production line digital process twin model according to the production line benchmark capacity data to obtain a real-time production line digital twin model; process deviation calculation is performed on the actual production process beat data by using the real-time production line digital twin model to obtain real-time production beat deviation rate data;

[0008] Step S4: Abnormal process identification is performed on the power distribution equipment digital production line by using the real-time production beat deviation rate data to obtain an abnormal production process list; order plan task data is obtained; abnormal process parameter adjustment is performed on the real-time production line digital twin model based on the abnormal production process list, and production completion time prediction is performed on the order plan task data to obtain order production predicted completion time data.

[0009] The application realizes real-time synchronization of virtual world and physical world by constructing a digital process twin model corresponding to the actual production line and integrating real-time production monitoring data into the model. Traditional planning models are often based on ideal conditions, ignoring factors such as equipment aging, fault maintenance, process parameter fluctuations, etc., resulting in large deviations in prediction results. This method updates the parameters of the digital twin model in real time, such as adjusting the production parameters in the model based on actual production process cycle time analysis results, so that the model can dynamically reflect the actual performance changes of the equipment, thereby improving the accuracy of production progress prediction and avoiding production plan delays or resource waste caused by prediction deviation. By real-time monitoring of the production line, actual production process cycle time data is obtained and compared with the digital twin model, and the production cycle deviation rate can be calculated in real time. This allows management personnel to timely detect deviations in the production process and take appropriate measures to adjust, avoiding the accumulation and amplification of deviations, thereby improving the controllability of production progress and the accuracy of prediction. For example, if the actual cycle of a certain process continuously deviates from the planned cycle, the system can timely issue a warning to remind management personnel to pay attention and find the cause, such as equipment failure, improper process parameter setting, etc., and timely take corrective measures to avoid affecting subsequent processes and final delivery date. Through real-time production cycle deviation rate data, the system can automatically identify abnormal processes and generate an abnormal process list. Based on this list, the system can adjust the parameters of the digital twin model for abnormal processes and re-predict order planning task data to obtain more accurate order production completion time data. This automated abnormality identification and processing mechanism reduces the need for human intervention, improves production management efficiency, and reduces the risk of human error. Therefore, the data control method for digital production of power distribution equipment constructs a digital process twin model of the production line, performs real-time process production comprehensive monitoring, obtains real-time production monitoring comprehensive data flow, identifies abnormal processes, adjusts the parameters of the real-time production line digital twin model based on the abnormal process list, and predicts the production completion time of order planning task data, solving the problem of separation of production tasks and physical production capacity in traditional data control methods, improving the accuracy of progress prediction, and realizing intelligent management of power distribution equipment production process.

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

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

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

[0013] Step S13: Extract key equipment process parameters and process material characteristic parameters according to key production process equipment data;

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

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

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

[0017] The present application establishes a physical model of the equipment processing process through finite element analysis, and analyzes the stress-strain and processing time relationship in combination with material characteristic parameters to 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 changes in equipment performance under different process parameters, material deformation, etc., thereby improving the prediction accuracy of the model. By integrating the equipment processing physical model and the equipment processing stress-strain time model, a more comprehensive production line digital process twin model is constructed. This model not only contains the geometric structure and motion characteristics of the equipment, but also contains the mechanical property change information 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 comprises the following steps:

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

[0020] Step S22: Determine equipment production performance monitoring sensors based on production line key process node data, and deploy production monitoring sensors on the digital production line of the distribution equipment to construct 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: Perform process node representative workpiece processing tests using pre-set production line operating environment parameters based on the production line key process node data, and collect equipment processing performance test data using the production line monitoring network to obtain equipment test raw data;

[0022] Step S24: error correction is performed on the equipment test raw data to obtain corrected test data;

[0023] Step S25: equipment unit time yield calculation is performed based on the corrected test data, and production line capacity statistics are performed to obtain production line benchmark capacity data.

[0024] The present application 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 for process node representative workpiece processing test, and the test data is corrected for errors to ensure the accuracy of the benchmark capacity data. By testing under standardized environmental parameters, the influence of environmental factors on the test results can be eliminated, thereby obtaining more reliable equipment performance data. In addition, error correction of the test raw data can further eliminate the influence of measurement error and system error, 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 can be more accurately identified, such as equipment efficiency decline, insufficient capacity, etc., and appropriate measures can be taken to adjust in a timely manner, thereby improving the overall efficiency and stability of the production line.

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

[0026] Step S31: real-time process production comprehensive monitoring of the digital production line of the power distribution equipment is performed using the production line monitoring network to obtain real-time production monitoring comprehensive data stream;

[0027] Step S32: the real-time production monitoring comprehensive data stream is analyzed and signal separated to obtain real-time process start and end monitoring signal, real-time machining equipment load monitoring signal, and real-time machining equipment energy consumption monitoring signal, respectively;

[0028] Step S33: actual production process beat analysis is performed according to the real-time process start and end monitoring signal to generate actual production process beat data;

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

[0030] Step S35: production parameter adjustment is performed on the production line digital process twin model based on the production line benchmark capacity data, and real-time running parameter synchronization is performed based on the production equipment actual operating parameters to obtain a real-time production line digital twin model;

[0031] Step S36: Based on the current production task data, the actual production process beat data is calculated for process deviation using the real-time production line digital twin model, and real-time production beat deviation rate data is obtained.

[0032] The application utilizes the production line monitoring network to collect process production comprehensive data in real time, and performs signal separation to obtain key production process information, such as process start and end time, equipment load rate, and energy consumption. This provides basic data for subsequent beat analysis and deviation calculation. According to the real-time process start and end monitoring signal, the actual production process beat is analyzed, and combined with the current production task data and the benchmark capacity data, the real-time updated digital twin model is used for process deviation calculation. This deviation analysis method based on real-time data and dynamic model can more accurately reflect the dynamic changes in the production process, avoiding the lag of static analysis methods. For example, even if the equipment load rate or energy consumption is within the normal range, but the actual process beat deviates, the system can timely identify and issue a warning. By obtaining the actual operating parameters of the production equipment and synchronizing them to 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 state of the equipment, such as equipment aging and performance fluctuations, thereby improving the accuracy and reliability of the deviation calculation. Through continuous real-time monitoring and deviation analysis, abnormal situations in the production process can be detected in a timely manner, such as process beat delay and equipment efficiency decline, and timely warning information can be provided to management personnel to take appropriate corrective measures to avoid production efficiency decline and product quality problems.

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

[0034] Step S331: Signal denoising processing is performed on the real-time process start and end monitoring signal, and key signal segment extraction is performed through a pre-set signal threshold to obtain a set of process start and end key signal segments;

[0035] Step S332: The set of process start and end key signal segments is matched with the process start and end signal segment through a pre-set process signal mode library to identify the start and end signals of each process of the production line, and the initial start and end signals of the production line process are generated;

[0036] Step S333: Time stamp calibration is performed on the initial start and end signals of the production line process to obtain calibrated start and end signals of the production line process;

[0037] Step S334: The duration of each process is calculated according to the calibrated start and end signals of the production line process to obtain single-process beat time data;

[0038] Step S335: Data cleaning and outlier processing are performed on the single-process beat time data to generate valid single-process beat data;

[0039] Step S336: The effective single process tact data is integrated according to the time sequence and the process production equipment number, and actual production process tact data is obtained.

[0040] The present application reduces the influence of noise interference on tact analysis by performing noise reduction processing and key signal segment extraction on real-time signals, and improves the accuracy of signal recognition. In the actual production environment, various interference factors will cause noise and fluctuations in the monitoring signal, affecting the judgment of process start and end time. The use of a preset process signal mode library for signal segment matching realizes the automatic recognition of process start and end signals. This avoids the subjectivity and inefficiency of manual signal recognition, and improves the automation level of tact analysis. The identified process start and end signals are time-stamped for calibration, ensuring the consistency and accuracy of different process tact time data. Due to factors such as response time and data transmission delay of different sensors, the time stamp may be biased. Through time stamp calibration, these biases can be eliminated, ensuring the consistency of different process tact time data in time, thereby improving the accuracy of the bias calculation. The single process tact time data is cleaned and processed for outliers, and is integrated in time sequence to obtain more reliable and orderly actual production process tact data. This provides high-quality data input for subsequent bias calculation and abnormal process identification, thereby improving the reliability and effectiveness of the entire system.

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

[0042] Step S361: Process task decomposition is performed according to the current production task data to obtain current production task decomposition data;

[0043] Step S362: Based on the current production task decomposition data, the real-time production line digital twin model is simulated and the simulation parameters are set, and the production line simulation calculation is performed to obtain production line process simulation process data;

[0044] Step S363: According to the production line process simulation process data, the stress-strain-time curve of each process is extracted to obtain the simulation stress-strain-time curve of each process;

[0045] Step S364: According to the simulation stress-strain-time curve of each process and the preset process operation material failure criterion, the process operation simulation processing time is extracted to obtain the theoretical production process tact data;

[0046] Step S365: The actual production process tact data is calculated for production tact deviation by the theoretical production process tact data, and real-time production tact deviation rate data is generated.

[0047] The present application simulates the parameter setting of the digital twin model based on current production task data, and performs simulation calculation, obtaining a simulation result that is more consistent with actual production tasks. The stress-strain-time curve of each process is extracted from the simulation result, and the theoretical production process cycle time data is calculated by combining the pre-set process operation material failure criteria. This cycle calculation method based on material failure criteria can more accurately reflect the processing characteristics and limit state of the material, thereby improving the accuracy of the theoretical cycle data. For example, the failure time of the material during processing can be more accurately predicted according to indicators such as the fatigue limit or fracture toughness of the material, thereby more accurately calculating the theoretical process cycle. 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 abnormal situations in the production process. For example, even if the actual cycle fluctuates within the normal range, if there is a persistent deviation from the theoretical cycle, it indicates a potential equipment failure or process problem. Through analysis of the real-time production cycle deviation rate data, the bottleneck links and potential risks in the production process can be better understood, and guidance can be provided for subsequent process optimization and equipment maintenance.

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

[0049] The theoretical production process cycle data and the actual production process cycle data are matched with the process equipment and time-aligned to generate production line process cycle comparison data;

[0050] The operation cycle time difference is calculated according to the production line process cycle comparison data to generate process cycle time difference values;

[0051] The process cycle time difference values are subjected to absolute value processing and production line process correlation to obtain single-process deviation values;

[0052] The single-process deviation values are normalized to generate single-process deviation percentage data;

[0053] Production line process statistical analysis is performed according to the single-process deviation percentage data to obtain real-time production cycle deviation rate data.

[0054] Theoretical and actual process beat data are matched and time-aligned, ensuring the accuracy and effectiveness of the deviation calculation. Due to the adjustment of the process execution sequence or the switching of the equipment in the actual production process, it is necessary to match and align the theoretical and actual data before performing the deviation calculation, to ensure that the comparison is the same process on the same equipment. By calculating the job beat time difference and performing absolute value processing, the single-process deviation value is obtained, and further normalization processing is performed to generate single-process deviation percentage data. This processing method of converting deviation values into percentage data more directly reflects the degree of deviation, facilitating comparison and analysis between different processes. For example, even if the beat time difference values of two processes are the same, if their reference beat times are different, their deviation percentages will also be different, thus more accurately reflecting the relative size of the deviation. Statistical analysis of single-process deviation percentage data obtains real-time production beat deviation rate data. This statistical analysis method can more comprehensively reflect the beat deviation of the entire production line and help managers identify processes or equipment with larger deviations, thus more targeted optimization and improvement.

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

[0056] Step S41: Abnormal process identification of the power distribution equipment digital production line is performed through real-time production beat deviation rate data, and an abnormal production process list is obtained;

[0057] Step S42: The abnormal production process list is fed back to the terminal device, and abnormal process running parameters are collected in real time to obtain abnormal process running parameters;

[0058] Step S43: Abnormal process parameter adjustment is performed on the real-time production line digital twin model using the abnormal process running parameters, and an abnormal production line digital twin model is obtained;

[0059] Step S44: Production line capacity attenuation coefficient calculation is performed according to real-time processing equipment load monitoring signals and real-time processing equipment energy consumption monitoring signals, and a production line capacity attenuation coefficient is generated;

[0060] Step S45: Order planning task data is obtained; production task decomposition is performed based on the order planning task data, and order planning production task data is obtained;

[0061] Step S46: Order task production simulation is performed on the order planning production task data using the abnormal production line digital twin model based on the production line capacity attenuation coefficient, and production completion time prediction is performed, and order production prediction completion time data is obtained.

[0062] The present application identifies abnormal processes through real-time production rhythm deviation rate data and collects the operating parameters of abnormal processes, thereby achieving rapid response and precise positioning of abnormal situations. This avoids the inefficient method of checking all processes one by one and improves the efficiency of abnormal processing. The abnormal process operating parameters are used to adjust the digital twin model, and an abnormal production line digital twin model that is closer to the actual production state is constructed. This provides a more accurate model basis for subsequent order task production simulation and completion time prediction. By feeding the actual operating parameters of abnormal processes back into the digital twin model, the production process under abnormal conditions can be simulated more accurately, thereby improving the reliability of the prediction results. The production line capacity decay coefficient is calculated according to the device load and energy consumption signals, and this coefficient is used for order task production simulation, which realizes effective quantification and prediction of capacity decay. This avoids the deviation of completion time prediction caused by capacity decay, improving the accuracy of prediction. For example, if the device load is too high or the energy consumption is too large, the capacity decay coefficient will increase, thereby more accurately reflecting the impact of capacity decline during simulation. Based on the abnormal production line digital twin model and order plan production task data, simulation is performed and the completion time of the order production is predicted. This simulation-based prediction method can more accurately predict the completion time of the order, providing a reliable basis for production plan adjustment and customer delivery date commitment. 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, optimizing production plans, etc., to ensure timely delivery of orders.

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

[0064] Step S441: Calculate the load rate performance decay coefficient by presetting the production equipment load rate-performance decay relationship curve to the real-time processing equipment load monitoring signal, and generate the load rate performance decay coefficient;

[0065] Step S442: Calculate the energy consumption performance decay coefficient by presetting the production equipment energy consumption-performance decay relationship curve to the real-time processing equipment energy consumption monitoring signal, and generate the energy consumption performance decay coefficient;

[0066] Step S443: Perform production line capacity decay coefficient weighted calculation based on the load rate performance decay coefficient and the energy consumption performance decay coefficient, and obtain the production line capacity decay coefficient.

[0067] The present application calculates the load rate performance attenuation coefficient and the energy consumption performance attenuation coefficient based on the preset load rate-performance attenuation relationship curve and the energy consumption-performance attenuation relationship curve respectively. This calculation method based on the relationship curve can more accurately reflect the influence of the device load rate and energy consumption on the device performance, and avoids the limitations of simple linear relationship. The preset relationship curve can be fitted according to historical data or experimental data, so as to more accurately reflect the actual performance attenuation law of the device. The load rate performance attenuation coefficient and the energy consumption performance attenuation coefficient are weighted and calculated to obtain a more comprehensive production line capacity attenuation coefficient. This weighting calculation method can comprehensively consider the influence of the load rate and the energy consumption on the capacity, and avoids the one-sidedness of single factor analysis. For example, even if the device load rate is not high, if the energy consumption is too large, the capacity will still decrease. Through the weighted calculation, the influence of the two factors can be comprehensively considered, so as to more accurately reflect the overall capacity attenuation of the production line. The calculated production line capacity attenuation coefficient is applied to order task production simulation, which can more accurately predict the completion time of the order.

[0068] The present application also 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, and comprises:

[0069] A process twin modeling module is configured to identify key production process equipment of the digital production line of the power distribution equipment to obtain key production process equipment data, and to construct a digital process twin model of the production line according to the key production process equipment data.

[0070] A capacity benchmarking module is configured to deploy production monitoring sensors on the digital production line of the power distribution equipment to construct a production line monitoring network, to perform process node representative workpiece processing tests on the digital production line of the power distribution equipment, and to perform production line capacity statistics to obtain production line benchmark capacity data.

[0071] A production difference analysis module is configured to perform real-time process production comprehensive monitoring on the digital production line of the power distribution equipment by using the production line monitoring network to obtain real-time production monitoring comprehensive data flow, to perform actual production process cycle time analysis according to the real-time production monitoring comprehensive data flow to generate actual production process cycle time data, to perform production parameter adjustment on the digital process twin model of the production line according to the production line benchmark capacity data to obtain a real-time production line digital twin model, and to perform process deviation calculation on the actual production process cycle time data by using the real-time production line digital twin model to obtain real-time production cycle time deviation rate data.

[0072] The production time prediction module is used for identifying abnormal processes of the power distribution equipment digital production line through real-time production tact bias rate data, obtaining an abnormal production process list; obtaining order plan task data; adjusting abnormal process parameters of the real-time production line digital twin model based on the abnormal production process list, and predicting the production completion time of the order plan task data to obtain order production predicted completion time data. BRIEF DESCRIPTION OF DRAWINGS

[0073] Figure 1 The step flow diagram of the data control method for the power distribution equipment digital production of the application;

[0074] Figure 2 The detailed implementation step flow diagram of step S2 in the application; Figure 1 The detailed implementation step flow diagram of step S2 in the application;

[0075] Figure 3 The detailed implementation step flow diagram of step S4 in the application; Figure 1 The detailed implementation step flow diagram of step S4 in the application;

[0076] The implementation, functional features and advantages of the application will be further described with reference to the embodiments and the accompanying drawings. DETAILED DESCRIPTION

[0077] The technical method of the application will be described clearly and completely below with reference to the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the application, rather than all the embodiments. Based on the embodiments in the application, all other embodiments obtained by those skilled in the art without creative labor fall within the protection scope of the application.

[0078] In addition, the accompanying drawings are only schematic illustrations of the application, and are not necessarily drawn to scale. The same reference numerals in the drawings represent the same or similar parts, and thus repeated descriptions thereof will be omitted. Some block diagrams shown in the drawings are functional entities, which do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in the form of software, or in one or more hardware modules or integrated circuits, or in different network and / or processor methods and / or microcontroller methods.

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

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

[0081] Step S1: key production process equipment of the power distribution equipment digital production line is identified to obtain key production process equipment data; and a production line digital process twin model is constructed according to the key production process equipment data;

[0082] Step S2: production monitoring sensors are deployed on the power distribution equipment digital production line to construct a production line monitoring network; process node representative workpiece processing tests are performed on the power distribution equipment digital production line, and production line capacity statistics are performed to obtain production line benchmark capacity data;

[0083] Step S3: real-time process production comprehensive monitoring is performed on the power distribution equipment digital production line by using the production line monitoring network to obtain real-time production monitoring comprehensive data flow; actual production process beat analysis is performed according to the real-time production monitoring comprehensive data flow to generate actual production process beat data; production parameter adjustment is performed on the production line digital process twin model according to the production line benchmark capacity data to obtain a real-time production line digital twin model; process deviation calculation is performed on the actual production process beat data by using the real-time production line digital twin model to obtain real-time production beat deviation rate data;

[0084] Step S4: abnormal process identification is performed on the power distribution equipment digital production line by using the real-time production beat deviation rate data to obtain an abnormal production process list; order plan task data is obtained; abnormal process parameter adjustment is performed on the real-time production line digital twin model based on the abnormal production process list, and production completion time prediction is performed on the order plan task data to obtain order production predicted completion time data.

[0085] In the embodiment of the application, the data control method for digital production of power distribution equipment comprises the following steps:

[0086] Step S1: key production process equipment of the power distribution equipment digital production line is identified to obtain key production process equipment data; and a production line digital process twin model is constructed according to the key production process equipment data;

[0087] In the embodiment of the application, for a certain type of power distribution cabinet digital production line, first, the Siemens Tecnomatix Plant Simulation software is used to model the three-dimensional layout of the production line. Key production process equipment is identified, including numerical control punch, numerical control bending machine, welding robot, assembly workstation, etc. Key production process equipment data is obtained, including equipment name, model, specification, processing capacity (for example, punch press speed is 100 times / min, bending angle accuracy of bending machine is ±0.1°, welding speed of welding robot is 50 cm / s), spatial position coordinates (for example, numerical control punch is located at x=10 m, y=5 m, z=0 m), input and output parameters (for example, numerical control punch input parameters are plate thickness and punch shape, and output parameters are punched plate), running state parameters (for example, equipment running state: running / idle / failure), etc. The obtained equipment data is imported into the Plant Simulation software, and a three-dimensional virtual model containing the above-mentioned key equipment is constructed according to the actual production line layout and process flow. The model is a digital process twin model of the production line, which contains the three-dimensional geometric shape, position information, motion trajectory and logic control program of each equipment.

[0088] Step S2: Production monitoring sensor deployment is performed on the power distribution equipment digital production line to construct a production line monitoring network; process node representative workpiece processing tests are performed on the power distribution equipment digital production line, and production line capacity statistics are performed to obtain production line baseline capacity data;

[0089] In the embodiment of the application, sensors such as current sensors, vibration sensors, displacement sensors, temperature sensors, etc. are deployed on key equipment such as numerical control punch, numerical control bending machine, welding robot, assembly workstation, etc. to collect real-time running data of the equipment. The sensor data is transmitted to the data acquisition server through industrial Ethernet or wireless network to construct a production line monitoring network. Taking the production of 100 units of the type of power distribution cabinet as an example, the production process data is collected by using the deployed sensor network. In the production process, representative workpieces (such as power distribution cabinet body, cabinet door, mounting plate) are processed at process nodes such as numerical control punching, numerical control bending, robot welding, and manual assembly. The processing time, waiting time, and handling time of each process are recorded, and the total production time of 100 units of power distribution cabinet is counted. The total production time is divided by 100 to obtain the average production time of a single power distribution cabinet, i.e. the production line baseline capacity data, for example, the baseline capacity is 10 units of power distribution cabinet per hour.

[0090] Step S3: Real-time process production comprehensive monitoring is performed on the power distribution equipment digital production line by using the production line monitoring network to obtain real-time production monitoring comprehensive data flow;

[0091] In this embodiment of the invention, data from various sensors, including equipment operating status, processing parameters, and environmental parameters, are collected in real time through a production line monitoring network to form a comprehensive real-time production monitoring data stream. For example, current and vibration data from CNC punching machines, angle and pressure data from bending machines, and current, voltage, and speed data from welding robots are collected. Based on the comprehensive real-time production monitoring data stream, the actual processing time of each process is analyzed. For example, the actual processing time for a CNC punching process is 2 minutes, the actual processing time for a CNC bending process is 3 minutes, and so on, generating actual production process cycle time data. The baseline production capacity data (e.g., 10 units per hour) is converted into a standard cycle time for each process, for example, a standard cycle time of 6 minutes for each process. The actual production process cycle time data is compared with the standard cycle time to calculate the cycle time deviation rate for each process. For example, the cycle time deviation rate for a CNC punching process is (2-6) / 6 = -66.7%, the cycle time deviation rate for a CNC bending process is (3-6) / 6 = -50%, and so on, obtaining real-time production cycle time deviation rate data. Based on actual production monitoring data, the parameters of the digital process twin model constructed in step S1 are adjusted, such as updating parameters like equipment operating speed and processing time, to match the actual state of the current production line and obtain a real-time production line digital twin model.

[0092] Step S4: Identify abnormal processes in the digital production line of the power distribution equipment using real-time production cycle 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 list of abnormal production processes, and predict the production completion time of the order planning task data to obtain the order production prediction completion time data.

[0093] In this embodiment of the invention, a cycle time deviation rate threshold is set, for example, ±10%. When the cycle time 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%, exceeding the threshold of -10%, then the process is recorded as an abnormal process. Order planning task data is obtained, for example, an order requiring 100 power 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, for example, reducing the processing speed parameter of the CNC stamping process. The adjusted real-time production line digital twin model is used to simulate the order planning task data to predict the order production completion time, thus obtaining the order production predicted completion time data.

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

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

[0096] Step S12: Key production process equipment identification is performed according to the production line process flow data, and key production process equipment data is obtained;

[0097] Step S13: Key equipment process parameters and process material characteristic parameters are extracted according to the key production process equipment data;

[0098] Step S14: Process conditions are set based on the key equipment process parameters, and a physical model of the equipment processing process is established based on finite element analysis, and an equipment processing physical model is obtained;

[0099] Step S15: The stress-strain and processing time relationship of the process material characteristic parameters is analyzed through the equipment processing physical model, and an equipment processing stress-strain time model is generated;

[0100] Step S16: The equipment processing physical model and the equipment processing stress-strain time model are constructed into a production line digital process twin model.

[0101] In the embodiment of the application, for a certain type of power distribution cabinet digital production line, the production process data is extracted through on-site investigation of the production site and analysis of process documents. The process data includes the names, sequences, input and output materials of each process, the logical relationship between processes, and the detailed operation steps and parameters of each process. For example, the production process includes processes such as blanking, stamping, bending, welding, assembly, and testing. The input of the blanking process is a steel coil, and the output is a cut steel plate; the input of the stamping process is a cut steel plate, and the output is a stamped part; there is a sequence relationship between the stamping and bending processes, and the bending process must be completed after the stamping process. The operation steps and parameters of each process also need to be recorded in detail, for example, the stamping process needs to record parameters such as stamping die model, stamping pressure, and stamping speed, forming the production line process data. For example, in the power distribution cabinet production process, numerical control punch, numerical control bending machine, welding robot, and automatic assembly line are identified as key production process equipment. The names, models, specifications, manufacturers, purchase dates, rated powers, machining accuracies, and machining ranges of the key equipment are recorded, as well as the real-time running state, fault record, and maintenance record of the equipment. These data are stored in a structured manner to form the key production process equipment data. According to the key equipment identified in step S12, the key equipment process parameters and process material characteristic parameters are extracted. For example, for the numerical control punch, the extracted process parameters include stamping speed, stamping force, die gap, etc.; for the numerical control bending machine, the extracted process parameters include bending angle, bending speed, springback compensation value, etc. For the cold-rolled steel plate material commonly used in power distribution cabinets, the extracted material characteristic parameters include elastic modulus, Poisson's ratio, yield strength, tensile strength, elongation, etc. Taking numerical control bending as an example, according to the bending angle, bending speed, springback compensation value, etc. extracted in step S13, the corresponding process conditions are set in the ABAQUS finite element analysis software. According to the structure parameters and working principle of the key equipment, the physical model of the equipment processing process is established. For example, a finite element model of the numerical control bending machine bending cold-rolled steel plate is established, which includes the bending die, the steel plate, and the constraint conditions. The geometry and material properties of the die need to be defined accurately, and the material properties of the steel plate use the cold-rolled steel plate material characteristic parameters extracted in step S13. Using the device processing physical model established in step S14, such as the finite element model of the numerical control bending machine, simulation analysis is performed in ABAQUS. During the analysis, the cold-rolled steel plate material characteristic parameters extracted in step S13 are input into the model, and the pre-defined process conditions are applied. Through simulation calculation, the stress and strain distribution of the steel plate in the bending process and the processing time are obtained. The stress and strain data at different time steps are extracted to establish the relationship between stress and strain and processing time, and the device processing stress and strain time model is generated.The device processing physical model established in step S14 and the device processing stress-strain time model generated in step S15 are imported into a digital factory simulation software such as Tecnomatix Plant Simulation. The models of the respective key devices are connected according to the production line process data extracted in step S11 to construct a complete production line digital process twin model. The model not only contains three-dimensional geometric information and motion trajectories of the devices, but also contains physical properties and material properties in the device processing process, can simulate the real production process, and can perform process optimization and production prediction.

[0102] As an example of the present application, reference is made to Figure 2 As shown in Fig. 1, Figure 1 The detailed implementation step flowchart of step S2 in the example includes:

[0103] Step S21: The production line digital process twin model is divided into key process nodes to obtain production line key process node data;

[0104] In the embodiment of the present application, in the constructed digital power distribution cabinet digital production line digital process twin model, the entire production process is divided into a plurality of key process nodes according to the production process. For example, the processes of 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 welding process starting position coordinates x = 10 m, y = 5 m, z = 2 m), the termination position coordinates (for example, the welding process termination position coordinates x = 12 m, y = 5 m, z = 2 m), the input and output materials, the required equipment (for example, the welding process required equipment is a welding robot), the standard working hours (for example, the welding process standard working hours is 5 minutes), and the correlation between the previous and subsequent processes, etc. information, forming production line key process node data.

[0105] Step S22: Based on the production line key process node data, the device production performance monitoring sensor is determined, and the production monitoring sensor is deployed on the digital power distribution equipment production line to construct a production line monitoring network; the device production performance monitoring sensor includes a device load rate monitoring sensor, a device energy consumption monitoring sensor, and a process start and end monitoring sensor;

[0106] In the embodiment of the present application, according to the key process node data determined in step S21, the type and quantity of equipment production performance monitoring sensors to be deployed are determined. For example, current sensors and voltage sensors are deployed as equipment load rate monitoring sensors at the key process nodes of the numerical control punch press, for monitoring the real-time load rate of the punch press; a power meter is deployed as an equipment energy consumption monitoring sensor, for monitoring the energy consumption of the punch press; a photoelectric sensor is deployed as a process start-stop monitoring sensor, for monitoring the start and end of the stamping process. On other key process nodes, such as welding robots and assembly lines, corresponding sensors are also deployed. All sensors are connected to the data acquisition server through an industrial Ethernet to build a production line monitoring network.

[0107] Step S23: Process node representative workpiece machining tests are performed based on the production line key process node data and using preset production line operating environment parameters, and equipment machining performance test data is collected using the production line monitoring network to obtain equipment test raw data;

[0108] In the embodiment of the present application, the production line operating environment parameters are set, for example, temperature 25℃, humidity 60%, voltage 220V. Based on the key process node data obtained in step S21, representative workpieces are selected for machining tests. For example, in the blanking process, a standard size steel plate is selected for cutting tests; in the stamping process, a representative power distribution cabinet panel is selected for stamping tests. During the machining tests, the production line monitoring network deployed in step S22 is used to collect data such as equipment load rate, energy consumption, process start-stop time, etc. in real time. The collected data is stored in a database to obtain equipment test raw data. For example, the current, voltage, power, and time stamp data of each stamping action in the stamping process are recorded.

[0109] Step S24: Error correction is performed on the equipment test raw data to obtain corrected test data;

[0110] In the embodiment of the present application, error correction is performed on the equipment test raw data collected in step S23. For example, Kalman filtering algorithm is used to filter the current, voltage, and other sensor data to remove noise interference; least squares method is used to fit the measurement data to eliminate system errors; the data is calibrated according to the calibration curve of the sensor to correct the error of the sensor itself. After error correction, more accurate corrected test data is obtained.

[0111] Step S25: Equipment unit time output is calculated based on the corrected test data, and production line capacity is counted to obtain production line benchmark capacity data.

[0112] In the embodiment of the present application, based on the corrected test data obtained in step S24, the device unit time yield of each key process node is calculated. For example, according to the corrected test data of the stamping process, the number of stamping times that can be completed per hour, i.e. the unit time yield of the stamping device, is calculated. Then, according to the production line process flow, the unit time yields of various key process nodes are comprehensively analyzed, and the production capacity of the entire production line is counted. For example, considering the connection and waiting time between processes, the number of power distribution cabinets that can be produced per hour on the entire production line is calculated to obtain the baseline production capacity data of the production line.

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

[0114] Step S31: Real-time process production comprehensive monitoring of the power distribution equipment digital production line is performed using the production line monitoring network to obtain a real-time production monitoring comprehensive data stream;

[0115] Step S32: The real-time production monitoring comprehensive data stream is monitored and analyzed, and signal separation is performed to obtain real-time process start-stop monitoring signals, real-time processing device load monitoring signals, and real-time processing device energy consumption monitoring signals, respectively;

[0116] Step S33: Actual production process beat analysis is performed according to the real-time process start-stop monitoring signals to generate actual production process beat data;

[0117] Step S34: Actual operation parameters of production equipment and current production task data are obtained;

[0118] Step S35: Production parameter adjustment is performed on the production line digital process twin model according to the production line baseline production capacity data, and real-time running parameter synchronization is performed based on the actual operation parameters of the production equipment to obtain a real-time production line digital twin model;

[0119] Step S36: Process deviation calculation is performed on the actual production process beat data using the real-time production line digital twin model based on the current production task data to obtain real-time production beat deviation rate data.

[0120] In the embodiment of the present application, through the deployment of sensor networks such as current sensors, voltage sensors, photoelectric sensors, temperature sensors, etc. on the digital production line of power distribution equipment, real-time data of each link of the production line is collected. The collected data includes the running state of the equipment (such as running, stopping, fault), processing parameters (such as current, voltage, power, speed), environmental parameters (such as temperature, humidity), and process start and end signals, etc. These data are transmitted to the data acquisition server through industrial Ethernet or wireless network to form real-time production monitoring comprehensive data stream, such as collecting data once a second. The real-time production monitoring comprehensive data stream obtained in step S31 is parsed according to the pre-defined data format. For example, the timestamp, sensor type, sensor value, etc. in the data stream are parsed. Then, according to the function and purpose of different sensors, the data stream is separated into different signals. For example, the data of the photoelectric sensor is separated out as a real-time process start and end monitoring signal, the data of the current and voltage sensor is separated out as a real-time processing equipment load monitoring signal, and the data of the power meter is separated out as a real-time processing equipment energy consumption monitoring signal. According to the real-time process start and end monitoring signal separated out in step S32, such as the photoelectric sensor signal, the actual start time and end time of each process are determined. For example, when the photoelectric sensor detects that the workpiece enters the station, the process start time is recorded; when the workpiece leaves the station, the process end time is recorded. The actual time consumption of each process, i.e. the actual production process cycle, is calculated. The actual cycle data of each process is recorded in the form of time series to generate actual production process cycle data. The actual running parameters of the production equipment are obtained in real time through the production line monitoring network, such as the actual punching speed of the numerical control punch, the actual bending angle of the numerical control bending machine, the actual welding speed of the welding robot, etc. At the same time, the current production task data is obtained from the production management system, such as the number of orders currently being produced, product model, planned completion time, etc. According to the pre-determined baseline capacity data of the production line, the related parameters in the digital process twin model of the production line are adjusted, such as adjusting the processing speed, processing time, etc. of the equipment, so that the capacity of the model remains consistent with the baseline capacity. At the same time, the actual running parameters of the production equipment obtained in step S34 are synchronized to the digital process twin model, such as updating the actual punching speed of the numerical control punch to the corresponding virtual equipment in the model, so that the model can reflect the real-time running state of the production line. Finally, the real-time production line digital twin model synchronized with the actual production line state is obtained. Using the real-time production line digital twin model obtained in step S35, combined with the current production task data obtained in step S34, such as product model and order quantity, the execution process of the current production task is simulated. According to the standard cycle time of each process output by the model, the actual production process cycle data obtained in step S33 is compared to calculate the cycle deviation rate of each process. For example, subtract the actual cycle from the standard cycle, and then divide by the standard cycle to obtain the real-time production cycle deviation rate data.

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

[0122] Step S331: Signal denoising processing is performed on the real-time process start-stop monitoring signal, and key signal segments are extracted through a preset signal threshold to obtain a set of process start-stop key signal segments;

[0123] Step S332: The set of process start-stop key signal segments is matched with process start-stop signal segments by using a preset process signal mode library, the start and end signals of each process of the production line are identified, and initial start-stop signals of the processes of the production line are generated;

[0124] Step S333: Time stamp calibration is performed on the initial start-stop signals of the processes of the production line to obtain calibrated start-stop signals of the processes of the production line;

[0125] Step S334: The duration of each process is calculated according to the calibrated start-stop signals of the processes of the production line to obtain single-process tact time data;

[0126] Step S335: Data cleaning and abnormal value processing are performed on the single-process tact time data to generate valid single-process tact data;

[0127] Step S336: The valid single-process tact data is integrated according to time sequence and process production equipment number to obtain actual production process tact data.

[0128] In the embodiment of the present application, the collected real-time process start-stop monitoring signals, such as photoelectric sensor signals, are denoised using wavelet transform method to remove high-frequency noise and interference in the signals. A signal threshold is set, for example, a voltage value greater than 5V. When the signal amplitude exceeds the preset threshold, it is considered as a valid signal segment. The signal segment exceeding the threshold is extracted, and the start time and end time of each segment are recorded to form a process start-stop key signal segment set. A process signal mode library is established in advance, which contains the standard start-stop signal mode of each process. For example, the start-stop signal mode of the stamping process is defined as: the signal first jumps from low level to high level, and after a period of time, it jumps from high level to low level. The process start-stop key signal segment set extracted in step S331 is matched with the process signal mode library. For example, the dynamic time warping (DTW) algorithm is used to calculate the similarity between the signal segment and each mode. The mode with the highest similarity is selected as the process start-stop signal corresponding to the signal segment. For example, a signal segment matches the start-stop signal mode of the stamping process, and the signal segment is identified as the start or end signal of the stamping process. All identified process start-stop signals and their corresponding time stamps are recorded to generate production line process initial start-stop signals. Due to network delay or sensor error, the time stamps of the production line process initial start-stop signals are deviated. The network time protocol (NTP) is used to synchronize the time of all sensors, and the time stamps of the process initial start-stop signals are compared with the standard time to correct the time deviation. For example, if the start signal timestamp of a process lags behind the actual start time by 10ms, the timestamp is corrected by adding 10ms. The calibrated signal and its corresponding timestamp form the production line process calibrated start-stop signal. According to the production line process calibrated start-stop signal obtained in step S333, the duration of each process is calculated. For example, the calibrated start time of a process is 10:00:00.001, and the calibrated end time is 10:00:05.001, so the duration of the process is 5 seconds. The duration of each process is recorded to obtain single-process cycle time data. The single-process cycle time data obtained in step S334 is cleaned and processed for abnormal values. For example, the box plot method is used to identify and eliminate abnormal values. For example, if the cycle time of a process is much larger or much smaller than the cycle time of other identical processes, the data is considered as an abnormal value and is eliminated. The cleaned data forms valid single-process cycle data. According to the production process flow and equipment number, the valid single-process cycle data obtained in step S335 is integrated in time sequence. For example, the cycle data of multiple processes on the same equipment is arranged in time sequence to form the actual production process cycle data of the equipment. Finally, all the actual production process cycle data of the equipment is integrated together to form complete actual production process cycle data.For example, the actual production process cycle data of device A is: [process 1: 5s, process 2: 3s, process 3: 4s].

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

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

[0131] Step S362: based on the current production task decomposition data, the simulation parameter setting of the real-time production line digital twin model is carried out, and the production line simulation calculation is carried out to obtain the production line process simulation process data;

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

[0133] Step S364: according to the simulation stress-strain-time curve of each process and the preset process operation material failure criterion, the process operation simulation processing time is extracted to obtain the theoretical production process cycle data;

[0134] Step S365: calculate the production cycle deviation of the actual production process cycle data through the theoretical production process cycle data to generate real-time production cycle deviation rate data.

[0135] In the embodiment of the present application, the current production task data is obtained, for example, a production order contains 100 sets of power distribution cabinets, each set of power distribution cabinet contains cabinet body, cabinet door, mounting plate and other components. According to the BOM (bill of materials) and process flow of the power distribution cabinet, the production task is decomposed into each process. For example, 1000 steel plates are needed for cutting, 2000 holes are needed for stamping, 3000 edges are needed for bending, 4000 welding points are needed for welding, and 5000 parts are needed for assembly to produce 100 sets of power distribution cabinets. The decomposed process task data, including process name, process required materials, process required equipment, process quantity and other information, is stored in the database to form the current production task decomposition data. The current production task decomposition data obtained in step S361 is used as an input parameter to set the simulation parameters of the real-time production line digital process twin model. For example, the number of parts to be processed, material properties, equipment parameters and the like are set. The production line is simulated and calculated using a discrete event simulation method. During the simulation process, the model simulates the execution process of each process, including the flow of materials, the operation of equipment, the processing time of the process and the like. The stress, strain, time and other data of each process generated during the simulation process are recorded to form the production line process simulation process data. The stress, strain and time data of each process are extracted from the production line process simulation process data obtained in step S362. For example, the stress and strain values of each time step in the stamping process are extracted. The extracted data is plotted into a stress-strain-time curve, for example, a three-dimensional curve graph is plotted with time as the horizontal coordinate and stress and strain as the vertical coordinate. Each process generates a corresponding stress-strain-time curve to form the simulation stress-strain-time curve of each process. The process operation material failure criterion is set in advance, for example, when the stress of the material exceeds the yield strength, the material is considered to fail. According to the simulation stress-strain-time curve of each process obtained in step S363, it is judged whether the material of each process fails. For example, in the stress-strain-time curve of the stamping process, the time point at which the stress reaches the yield strength is found. The time from the start of the process to the failure of the material is the simulation processing time of the process. The simulation processing time of each process is recorded to obtain the theoretical production process cycle time data. The theoretical production process cycle time data obtained in step S364 is compared with the actual production process cycle time data to calculate the cycle deviation rate of each process. For example, the cycle deviation rate is calculated using the following formula: (actual cycle time-theoretical cycle time) / theoretical cycle time x 100%. The cycle deviation rate of each process is recorded to generate real-time production cycle deviation rate data. For example, the theoretical cycle time of the stamping process is 2 seconds, and the actual cycle time is 2.2 seconds, so the cycle deviation rate of the stamping process is (2.2-2) / 2 x 100%=10%.

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

[0137] The theoretical production process beat data and the actual production process beat data are matched with process equipment, and are time-aligned to generate production line process beat comparison data;

[0138] According to the production line process beat comparison data, the operation beat time difference is calculated to generate process beat time difference values;

[0139] The process beat time difference values are subjected to absolute value processing, and the production line processes are associated to obtain single-process deviation values;

[0140] The single-process deviation values are subjected to normalization processing to generate single-process deviation percentage data;

[0141] According to the single-process deviation percentage data, the production line process statistical analysis is performed to obtain real-time production beat deviation rate data.

[0142] In the embodiments of the present application, the theoretical production process cycle time data includes the theoretical cycle time of each process and the corresponding process equipment ID. The actual production process cycle time data includes the actual cycle time of each process, the corresponding process equipment ID and the timestamp. First, the theoretical data and the actual data are matched according to the process equipment ID. For example, the theoretical stamping process cycle time data of the equipment ID "CNC001" is matched with the actual stamping process cycle time data of the same equipment ID. Then, according to the timestamp of the actual data, the theoretical cycle time and the actual cycle time of different processes on the same equipment are arranged in time sequence. For example, the equipment "CNC001" completed the stamping process at 10:00:00, the actual cycle time is 2.2 seconds, and the theoretical cycle time is 2 seconds; completed the bending process at 10:00:05, the actual cycle time is 3.1 seconds, and the theoretical cycle time is 3 seconds. These data are arranged in time sequence to generate production line process cycle comparison data, including process equipment ID, process name, theoretical cycle time, actual cycle time and timestamp. Traverse each record in the production line process cycle comparison data, calculate the difference between the actual cycle time and the theoretical cycle time. For example, the actual cycle time of the stamping process of the equipment "CNC001" is 2.2 seconds, and the theoretical cycle time is 2 seconds, so the cycle time difference of the process is 2.2-2=0.2 seconds. Record the cycle time difference of each process to generate process cycle time difference data, including process equipment ID, process name, cycle time difference and timestamp. For example, the cycle time difference of the stamping process of the equipment "CNC001" is 0.2 seconds, and its absolute value is 0.2 seconds. Then, according to the production line process flow, each process is associated with its associated process before and after. For example, the stamping process is followed by the bending process, so the absolute time difference of the stamping process is associated with the bending process. Finally, the deviation value of each process is obtained, that is, the single-process deviation value, including process equipment ID, process name, single-process deviation value and timestamp. Select a reference value, for example, select the maximum value of all process deviation values as the reference value. Divide the deviation value of each process by the reference value to obtain the normalized deviation value, that is, the single-process deviation percentage data. For example, assuming that the maximum value of all process deviation values 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 the process is 0.2 / 0.5=0.4 or 40%. For example, calculate the average deviation percentage, the maximum deviation percentage and the minimum deviation percentage of all processes. According to different statistical indicators, such as the average deviation percentage, the overall cycle deviation of the production line can be evaluated to obtain real-time production cycle deviation rate data. For example, the average deviation percentage of all processes is 20%, so the real-time cycle deviation rate of the current production line is considered to be 20%.

[0143] As an example of the present application, reference is made to Figure 3As shown, Figure 1 The detailed implementation step flowchart of step S4 in the embodiment is shown in the figure. In this embodiment, step S4 includes the following steps.

[0144] Step S41: Abnormal process identification is performed on the power distribution equipment digital production line by using real-time production tact time deviation rate data, and an abnormal production process list is obtained.

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

[0146] Step S42: The abnormal production process list is fed back to the terminal device, and real-time collection of abnormal process running parameters is performed, and abnormal process running parameters are obtained.

[0147] In the embodiment, the abnormal production process list generated in step S41 is sent to the corresponding terminal device, for example, the punching process abnormal information of the "CNC001" device is sent to the control terminal of the device. After the terminal device receives the abnormal process information, it immediately starts collecting the running parameters of the process, such as punching speed, punching force, current, voltage, temperature, etc. The collected parameter data is uploaded to the data collection server in real time to form abnormal process running parameters.

[0148] Step S43: Abnormal process parameter adjustment is performed on the real-time production line digital twin model by using the abnormal process running parameters, and an abnormal production line digital twin model is obtained.

[0149] In the embodiment, the abnormal process running parameters 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 punching speed, punching force, current, voltage, temperature, etc. of the "CNC001" device are updated to the corresponding virtual device in the model. Through parameter adjustment, the digital twin model can more accurately reflect the actual running state of the abnormal process, and an abnormal production line digital twin model is obtained.

[0150] Step S44: Production line capacity attenuation coefficient calculation is performed according to real-time machining device load monitoring signals and real-time machining device energy consumption monitoring signals, and a production line capacity attenuation coefficient is generated.

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

[0152] Step S45: obtaining order planning task data; based on the order planning task data, production task decomposition is performed to obtain order planning production task data;

[0153] In the embodiment of the present application, the 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. According to the product BOM (bill of materials) and process flow, the order planning task is decomposed into production tasks of each process. For example, the task of producing 100 power distribution cabinets is decomposed into the tasks of cutting 1000 steel plates, stamping 2000 holes, bending 3000 edges, etc. The decomposed production task data, including 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 order task production simulation is performed on the order planning production task data by using the abnormal production line digital twin model, and the production completion time prediction is performed to obtain the order production predicted completion time data.

[0155] In the embodiment of the present application, based on the production line capacity attenuation coefficient calculated in step S44, the order task production simulation is performed on the order planning production task data obtained in step S45 by using the abnormal production line digital twin model obtained in step S43. In the simulation process, the influence of the capacity attenuation coefficient is considered, for example, the simulation 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 the production completion time of the whole order is finally predicted. The predicted completion time is compared with the order planning delivery date to determine whether the order can be completed on time.

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

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

[0158] Step S442: Calculate the energy consumption performance attenuation coefficient of the real-time processing equipment energy consumption monitoring signal through the preset production equipment energy consumption-performance attenuation relationship curve, and generate the energy consumption performance attenuation coefficient;

[0159] Step S443: Perform production line capacity attenuation coefficient weighted calculation 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 the embodiment of the application, a production equipment load rate-performance attenuation relationship curve is established in advance, for example, by collecting and analyzing long-term running data of the equipment, a curve is fitted, which describes the relationship between the load rate of the equipment and the performance attenuation coefficient. The curve can be obtained by polynomial fitting, exponential fitting, etc., and stored in the form of a function or a lookup table. The load monitoring signal of the processing equipment is collected in real time, such as current, voltage, etc. The real-time load rate of the equipment is calculated. For example, the real-time current value is divided by the rated current value of the equipment to obtain the load rate. The calculated real-time load rate is taken as input and substituted into the preset load rate-performance attenuation relationship curve to obtain the corresponding load rate performance attenuation coefficient. For example, assuming that the load rate is 80%, the load rate performance attenuation coefficient is calculated to be 0.95 by looking up the table or substituting the function. A production equipment energy consumption-performance attenuation relationship curve is established in advance. For example, by collecting and analyzing long-term running data of the equipment, a curve is fitted, which describes the relationship between the energy consumption of the equipment and the performance attenuation coefficient. The curve can be obtained by polynomial fitting, exponential fitting, etc., and stored in the form of a function or a lookup table. The energy consumption monitoring signal of the processing equipment is collected in real time, such as power value. The real-time energy consumption data is taken as input and substituted 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 is calculated to be 0.92 by looking up the table or substituting the function. According to expert experience or historical data, the weights of the load rate performance attenuation coefficient and the energy consumption performance attenuation coefficient are determined. For example, the load rate weight is set to 0.7 and the energy consumption weight is set to 0.3. The load rate performance attenuation coefficient calculated in step S441 and the energy consumption performance attenuation coefficient calculated in step S442 are multiplied by the corresponding weights respectively, and then added 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 application also 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, and comprises:

[0162] A process twin modeling module is configured to identify key production process equipment of the digital production line of the power distribution equipment, to obtain key production process equipment data; and to construct a digital process twin model of the production line according to the key production process equipment data.

[0163] A capacity benchmarking module is configured to deploy production monitoring sensors on the digital production line of the power distribution equipment, to construct a production line monitoring network; to perform process node representative workpiece processing tests on the digital production line of the power distribution equipment, and to perform production line capacity statistics, to obtain production line benchmark capacity data.

[0164] A production difference analysis module is configured to perform real-time process production comprehensive monitoring on the digital production line of the power distribution equipment by using the production line monitoring network, to obtain real-time production monitoring comprehensive data flow; to perform actual production process beat analysis according to the real-time production monitoring comprehensive data flow, to generate actual production process beat data; to perform production parameter adjustment on the digital process twin model of the production line according to the production line benchmark capacity data, to obtain a real-time production line digital twin model; and to perform process deviation calculation on the actual production process beat data by using the real-time production line digital twin model, to obtain real-time production beat deviation rate data.

[0165] A production time prediction module is configured to perform abnormal process identification on the digital production line of the power distribution equipment by using the real-time production beat deviation rate data, to obtain an abnormal production process list; to obtain order plan task data; to perform abnormal process parameter adjustment on the real-time production line digital twin model based on the abnormal production process list, and to perform production completion time prediction on the order plan task data, to obtain order production predicted completion time data.

[0166] The application establishes a physical model of the equipment processing process through finite element analysis, and analyzes the stress-strain and processing time relationship combined with material characteristic parameters, to 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 changes in equipment performance under different process parameters, material deformation, etc., thereby improving the prediction accuracy of the model. The equipment processing physical model and the equipment processing stress-strain time model are fused to construct a more comprehensive production line digital process twin model. This model not only contains the geometric structure and motion characteristics of the equipment, but also contains the mechanical property change information 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.

[0167] Therefore, the embodiments should be regarded, at any point, as being exemplary and not limiting, the scope of the application being defined by the appended claims and not by the above description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein.

[0168] The foregoing is considered as illustrative only of the principles of the application. Numerous modifications and changes will readily occur to those skilled in the art, and the generic principles defined herein can be applied to other embodiments without departing from the spirit or scope of the application. Therefore, the application is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A data control method for digital production of power distribution equipment, characterized by, The method comprises the following steps: Step S1: identifying key production process equipment of the power distribution equipment digital production line to obtain key production process equipment data; According to the key production process equipment data, a production line digital process twin model is constructed, and step S1 comprises: Step S11: extracting the production process flow of the power distribution equipment digital production line to obtain production line process flow data; Step S12: identifying key production process equipment according to the production line process flow data to obtain key production process equipment data; Step S13: extracting key equipment process parameters and process material characteristic parameters according to the key production process equipment data; Step S14: setting process conditions based on the 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 stress-strain and processing time relationship of the process material characteristic parameters through the equipment processing physical model to generate an equipment processing stress-strain time model; Step S16: constructing a production line digital process twin model from the equipment processing physical model and the equipment processing stress-strain time model; Step S2: deploying production monitoring sensors on the power distribution equipment digital production line to construct a production line monitoring network; performing representative workpiece processing tests on process nodes of the power distribution equipment digital production line, and performing production line capacity statistics to obtain production line baseline capacity data; Step S3: comprehensively monitoring the real-time process production of the power distribution equipment digital production line using the production line monitoring network to obtain real-time production monitoring comprehensive data flow; analyzing actual production process beat based on the real-time production monitoring comprehensive data flow to generate actual production process beat data; adjusting production parameters of the production line digital process twin model based on the production line baseline capacity data to obtain a real-time production line digital twin model; calculating process deviation of the actual production process beat data using the real-time production line digital twin model to obtain real-time production beat deviation rate data; Step S4: identifying abnormal processes of the power distribution equipment digital production line through real-time production beat deviation rate data to obtain an abnormal production process list; obtaining order plan task data; adjusting abnormal process parameters of the real-time production line digital twin model based on the abnormal production process list, and predicting production completion time of the order plan 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 S2 comprises the following steps: Step S21: dividing key process nodes of the production line digital process twin model to obtain production line key process node data; Step S22: determining equipment production performance monitoring sensors based on the production line key process node data, and deploying production monitoring sensors on the power distribution equipment digital production line to construct a production line monitoring network; the equipment production performance monitoring sensors comprise equipment load rate monitoring sensors, equipment energy consumption monitoring sensors, and process start and end monitoring sensors; Step S23: Based on the production line key process node data, process node representative workpiece machining tests are performed using preset production line operating environment parameters, and equipment machining performance test data is collected using the production line monitoring network to obtain equipment test raw data; Step S24: The equipment test raw data is error-corrected to obtain corrected test data; Step S25: Based on the corrected test data, equipment unit time yield is calculated, and production line capacity is counted to obtain production line benchmark capacity data.

3. 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: Real-time process production comprehensive monitoring of the power distribution equipment digital production line is performed using the production line monitoring network to obtain real-time production monitoring comprehensive data flow; Step S32: The real-time production monitoring comprehensive data flow is monitored data parsed, and signal separation is performed to obtain real-time process start and end monitoring signals, real-time machining equipment load monitoring signals, and real-time machining equipment energy consumption monitoring signals, respectively; Step S33: Actual production process cycle time analysis is performed according to the real-time process start and end monitoring signals to generate actual production process cycle time data; Step S34: Production equipment actual operating parameters and current production task data are obtained; Step S35: Production parameter adjustment is performed on the production line digital process twin model based on the production line benchmark capacity data, and real-time operating parameter synchronization is performed based on the production equipment actual operating parameters to obtain a real-time production line digital twin model; Step S36: Based on the current production task data, process deviation calculation is performed on the actual production process cycle time data using the real-time production line digital twin model to obtain real-time production cycle deviation rate data.

4. The data control method for digital production of power distribution equipment according to claim 3, characterized in that, Step S33 includes the following steps: Step S331: Signal denoising processing is performed on the real-time process start and end monitoring signals, and key signal segments are extracted through a preset signal threshold to obtain a process start and end key signal segment set; Step S332: Process start and end signal segment matching is performed on the process start and end key signal segment set using a preset process signal mode library to identify production line process start and end signals to generate production line process initial start and end signals; Step S333: Time stamp calibration is performed on the production line process initial start and end signals to obtain production line process calibrated start and end signals; Step S334: Each process duration calculation is performed according to the production line process calibrated start and end signals to obtain single-process cycle time data; Step S335: Data cleaning and outlier processing are performed on the single-process cycle time data to generate valid single-process cycle time data; Step S336: The valid single-process cycle time data is integrated according to time sequence to obtain actual production process cycle time data.

5. The data control method for digital production of power distribution equipment according to claim 3, characterized in that, Step S36 includes the following steps: Step S361: Process task decomposition is performed according to the current production task data to obtain current production task decomposition data; Step S362: Simulation parameter setting is performed on the real-time production line digital twin model based on the current production task decomposition data, and production line simulation calculation is performed to obtain production line process simulation process data; Step S363: Extract the stress-strain-time curve of each process according to the production line process simulation process data to obtain the process simulation stress-strain-time curve; Step S364: According to the process simulation stress-strain-time curve and the preset process operation material failure criterion, the process operation simulation processing time is extracted to obtain the theoretical production process cycle data; Step S365: Calculate the production cycle deviation of the actual production process cycle data by the theoretical production process cycle data, and generate real-time production cycle deviation rate data.

6. The data control method for digital production of power distribution equipment according to claim 5, wherein, Step S365 includes the following steps: Match the theoretical production process cycle data and the actual production process cycle data with the process equipment, and perform time alignment to generate production line process cycle comparison data; Calculate the operation cycle time difference according to the production line process cycle comparison data to generate the process cycle time difference value; Absolute value processing is performed on the process cycle time difference value, and the production line process is associated to obtain a single process deviation value; The single process deviation value is normalized to generate a single process deviation percentage data; According to the single process deviation percentage data, the production line process statistical analysis is carried out to obtain the real-time production cycle deviation rate data.

7. The data control method for digital production of power distribution equipment according to claim 3, characterized by, Step S4 includes the following steps: Step S41: Identify abnormal processes in the power distribution equipment digital production line through real-time production cycle deviation rate data to obtain an abnormal production process list; Step S42: The abnormal production process list is fed back to the terminal device, and the abnormal process running parameter is collected in real time to obtain the abnormal process running parameter; Step S43: Adjust the abnormal process parameter of the real-time production line digital twin model using the abnormal process running parameter to obtain the abnormal production line digital twin model; Step S44: Calculate 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; based on the order plan task data, production task decomposition is carried out to obtain order plan production task data; Step S46: Based on the production line capacity attenuation coefficient, the order task production simulation is carried out on the order plan production task data using the abnormal production line digital twin model, and the production completion time is predicted to obtain the order production predicted completion time data.

8. The data control method for digital production of power distribution equipment according to claim 7, characterized in that, Step S44 includes the following steps: Step S441: Calculate the load rate performance attenuation coefficient of the real-time processing equipment load monitoring signal based on the preset production equipment load rate-performance attenuation relationship curve to generate the load rate performance attenuation coefficient; Step S442: Calculate the energy consumption performance attenuation coefficient of the real-time processing equipment energy consumption monitoring signal based on the preset production equipment energy consumption-performance attenuation relationship curve to generate the energy consumption performance attenuation coefficient; Step S443: Based on the load rate performance attenuation coefficient and the energy consumption performance attenuation coefficient, the production line capacity attenuation coefficient is weighted to obtain the production line capacity attenuation coefficient.

9. A data control system for digital production of power distribution equipment, characterized by A data control method for performing the power distribution equipment digital production of claim 1, the data control system of the power distribution equipment digital production comprises: A process twin modeling module is configured to identify key production process equipment of the power distribution equipment digital production line to obtain key production process equipment data, and construct a digital process twin model of the production line according to the key production process equipment data. A capacity benchmarking module is configured to deploy production monitoring sensors on the power distribution equipment digital production line to construct a production line monitoring network, and perform process node representative workpiece processing tests on the power distribution equipment digital production line to perform production line capacity statistics to obtain baseline production capacity data of the production line. A production difference analysis module is configured to perform real-time process production comprehensive monitoring on the power distribution equipment digital production line by using the production line monitoring network to obtain real-time production monitoring comprehensive data flow, perform actual production process beat analysis according to the real-time production monitoring comprehensive data flow to generate actual production process beat data, adjust production parameters of the production line digital process twin model according to the baseline production capacity data of the production line to obtain a real-time production line digital twin model, and perform process deviation calculation on the actual production process beat data by using the real-time production line digital twin model to obtain real-time production beat deviation rate data. A production time prediction module is configured to identify abnormal production processes of the power distribution equipment digital production line by using the real-time production beat deviation rate data to obtain an abnormal production process list, obtain order plan task data, adjust abnormal process parameters of the real-time production line digital twin model based on the abnormal production process list, and predict production completion time of the order plan task data to obtain order production predicted completion time data.

Images