Electrical manufacturing execution management system based on cloud computing

Abstract

The invention relates to the technical field of intelligent manufacturing, in particular to an electrical manufacturing execution management system based on cloud computing, which comprises a scheduling optimization module, an energy management module, an equipment health and state monitoring module and a production efficiency improvement module. According to the method, accurate scheduling of production resources is realized by capturing electrical production task progress, equipment use conditions, process parameters and energy consumption data, then the operation mode, time and speed of equipment are regulated and controlled, resource allocation is more flexible, the energy consumption data are analyzed by relying on cloud computing, the energy use mode and equipment load are optimized, and the energy utilization efficiency is improved. Effective energy distribution is realized, energy efficiency is improved, equipment faults can be predicted in advance in combination with an equipment health assessment model, a maintenance period is adjusted according to an equipment state, production interruption caused by equipment shutdown is avoided, deep analysis is performed on production scheduling, then a production sequence and resource distribution are adjusted, production efficiency is improved, and production efficiency is improved. And the stability and the flexibility of the production line reach a higher level.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent manufacturing, and particularly to an electrical manufacturing execution management system based on cloud computing. Background Art

[0002] The intelligent manufacturing technology field improves the intelligent level of the manufacturing industry through informationization, automation, digitization, and networking technologies. This field covers cutting-edge technologies such as artificial intelligence, cloud computing, big data analysis, Internet of Things (IoT), and robotics, aiming to optimize the production process, improve production efficiency and product quality, and reduce production costs. The core concept of intelligent manufacturing realizes the automation, flexibility, and refined management of the production process through the integrated application of intelligent devices and systems, promotes personalized customized production, and supports production decision-making through the real-time collection and analysis of data. The development of intelligent manufacturing not only improves the production capacity of traditional manufacturing industries but also promotes the digital transformation of the manufacturing industry, bringing higher production flexibility and lower resource waste.

[0003] Among them, the electrical manufacturing execution management system is a key component in the field of intelligent manufacturing, mainly used to manage and optimize the operation process on the production site. Through technologies such as cloud computing, big data, and the Internet of Things, this system realizes the real-time monitoring, data collection, and analysis of the production process, thereby assisting enterprises in improving production efficiency, ensuring product quality, reducing inventory costs, and effectively supporting flexible and customized production requirements. MES can adjust the production plan in real time, dynamically allocate resources according to the actual production situation, and ensure the efficient operation of the production line. By integrating various production data, the electrical manufacturing execution management system provides an enterprise with a data-driven decision-making support platform, enabling the enterprise to quickly respond to market demands and enhance its overall competitiveness.

[0004] In the prior art, equipment scheduling and production plan adjustment mostly rely on preset rules or historical data, lacking flexible response to real-time data. Especially in terms of equipment usage conditions and energy consumption, it is difficult to achieve precise and optimized allocation of resources. Its energy management and equipment maintenance are carried out according to fixed cycles or preset conditions, unable to fully respond to the actual state and load of the equipment, resulting in low energy efficiency of the equipment under light or heavy load conditions and increasing non-critical energy consumption. In terms of equipment health monitoring, the existing systems are based on simple fault statistics or single-sensor monitoring, unable to predict potential equipment failures in a timely manner, making the maintenance work lag behind, and easily causing the production schedule to be affected due to unexpected equipment shutdowns. The prior art lacks the ability to comprehensively analyze production bottlenecks, and the adjustment of production sequence and resource allocation is not flexible enough, making it difficult to effectively respond to complex and changing production requirements, thus affecting the overall production efficiency, resulting in a slow system response speed, low resource utilization rate, high production costs, shortened equipment operation cycle, and showing a certain lag in quickly responding to market demands. Summary of the Invention

[0005] The object of the present invention is to solve the disadvantages existing in the prior art, and a cloud computing-based electrical manufacturing execution management system is proposed.

[0006] To achieve the above object, the present invention adopts the following technical solutions: The cloud computing-based electrical manufacturing execution management system includes: The scheduling optimization module captures the progress of electrical production tasks, equipment usage, process parameters, and energy consumption data, and through the cloud computing platform, optimizes the scheduling of equipment, adjusts the operating mode, working hours, and speed of the equipment, and generates an optimized result of electrical production resource scheduling; The energy management module, based on the optimized result of the electrical production resource scheduling, uses cloud computing to analyze the energy consumption data, optimizes the energy usage mode of electrical manufacturing equipment, adjusts the operating load of the equipment, generates an energy distribution strategy, and based on the energy distribution strategy, combines the energy efficiency evaluation data to optimize the load and energy efficiency of the manufacturing equipment, and generates an energy-saving optimization result; The equipment health and status monitoring module, based on the energy-saving optimization result, monitors the operating status of manufacturing equipment through the cloud platform, combines the equipment health assessment model to predict faults and manage the health of the equipment, generates an equipment health status result, and adjusts the maintenance cycle according to the equipment health status result, and generates an optimized equipment maintenance plan; The production efficiency improvement module, based on the optimized equipment maintenance plan, evaluates the impact of the current production scheduling and energy management on production efficiency, analyzes production bottlenecks, generates a production bottleneck analysis result, and adjusts the production sequence and resource scheduling according to the production bottleneck analysis result, and generates an electrical production line efficiency management plan.

[0007] As a further solution of the present invention, the specific steps for obtaining the optimized result of the electrical production resource scheduling are as follows: By capturing the progress of electrical production tasks, equipment usage, process parameters, and energy consumption data, analyzing the current load, operating power consumption, and equipment utilization time of the equipment, selecting the equipment with the load value reaching the set initial threshold, and generating a set of equipment screening that meets the load conditions; According to the set of equipment screening that meets the load conditions, extract the operating power consumption and equipment utilization time data of the equipment, calculate the equipment scheduling weight through the parameters of equipment utilization time and load conditions, screen and schedule the equipment through the weight, and obtain the equipment scheduling priority weight result; Based on the equipment scheduling priority weight result, combined with the load and power consumption of the equipment, set the standard threshold of power consumption utilization rate, screen the equipment that meets the conditions and add it to the scheduling optimization list, and generate an optimized result of electrical production resource scheduling.

[0008] As a further solution of the present invention, the specific steps for obtaining the energy distribution strategy are as follows: Based on the optimized results of the electrical production resource scheduling, extract the energy consumption data and corresponding load information of each device. By analyzing the real-time operating load and the amount of energy consumed by each device, and screening the devices that meet the energy utilization conditions, a set of device energy consumption that meets the conditions is formed; According to the set of device energy consumption that meets the conditions, analyze the power consumption situation of the devices. Combining the operating load and power of the devices, calculate the priority weight of device energy use to obtain the energy use priority of the devices; According to the energy use priority of the devices, combine the real-time operating power and load status of the devices, sort the devices according to the priority, and adjust the energy distribution of the devices based on the sorting to generate an energy distribution strategy.

[0009] As a further solution of the present invention, the specific steps for obtaining the energy-saving optimization results are as follows: According to the energy distribution strategy, obtain the load data and energy efficiency data of the manufacturing devices, and divide the load and energy efficiency data of different devices into time intervals. By calculating statistical indicators such as the average value and standard deviation, obtain the set of load and energy efficiency data of the devices in the interval; Based on the set of load and energy efficiency data of the devices in the interval, extract the correlation between the load interval and the energy efficiency performance. Combining the optimization objective, calculate the impact of each load interval on the energy efficiency, analyze the mapping relationship between the key load points and the energy efficiency, calculate the energy efficiency score of the load interval, and generate an energy efficiency optimization model; According to the energy efficiency optimization model, perform energy efficiency adjustment. By analyzing the energy efficiency optimization results of each device load interval, adjust the load distribution to generate an energy-saving optimization plan.

[0010] As a further solution of the present invention, the specific steps for obtaining the device health status results are as follows: According to the energy-saving optimization results, extract the device operation status information, including temperature, vibration, and pressure parameters, determine the deviation of the current status, calculate the abnormal value of the operation status of each device, and generate a device health status data set; Process the device health status data set, remove the missing and abnormal data, and through multi-dimensional data fusion technology, integrate the device differential status information into a unified health assessment parameter vector to generate a device health assessment data set; Based on the device health assessment data set, perform health prediction by comparing the status data of each device with the preset health threshold to generate the device health status results.

[0011] As a further solution of the present invention, the specific steps for obtaining the device maintenance optimization plan are as follows: Based on the device health status result, judge the health status by analyzing the vibration frequency, temperature rise rate, and pressure change rate of the device, generate health status evaluation items, and if the health status evaluation items exceed the set threshold, it is determined that the maintenance cycle needs to be adjusted; Call the health status evaluation items, and calculate the set value of the maintenance cycle by combining the workload parameters and the original failure frequency parameters of the device; Based on the set value of the maintenance cycle, compare it with the current maintenance cycle standard. If the set value of the maintenance cycle is lower than the maintenance cycle standard, use the set value of the maintenance cycle to replace the current maintenance cycle to generate a device maintenance optimization plan.

[0012] As a further solution of the present invention, the steps for obtaining the production bottleneck analysis result are specifically as follows: According to the device maintenance optimization plan, integrate the device maintenance log and the energy usage record, analyze the operating status of each device, and calculate the unit energy efficiency ratio of the device in combination with the energy consumption parameters to generate a preliminary analysis data set; Decompose the data items of the preliminary analysis data set, calculate the load intensity of the workstation through the workstation load ratio, and calculate the ratio of the energy consumption to the unit efficiency load to identify the bottleneck nodes of the load and energy consumption to obtain a functional data set; Extract the key indicators of the production bottleneck from the functional data set, classify and summarize the load intensity and energy efficiency level of the bottleneck nodes to generate the production bottleneck analysis result.

[0013] As a further solution of the present invention, the steps for obtaining the electrical production line efficiency management plan are specifically as follows: Based on the production bottleneck analysis result, calculate the production capacity utilization rate and the equipment standby duration of each bottleneck, and combine the material supply cycle of the bottleneck to perform item-by-item sorting for each bottleneck data to establish a bottleneck priority analysis result; According to the bottleneck priority analysis result, calculate the resource consumption rate, process flow efficiency, and bottleneck frequency of each bottleneck item by item, and calculate the influence weight of the resource allocation according to the start time of the bottleneck and the total production duration to obtain the bottleneck resource weight distribution result; Based on the bottleneck resource weight distribution result, adjust the production order and resource allocation to obtain the electrical production line efficiency management plan.

[0014] Compared with the prior art, the advantages and positive effects of the present invention are as follows: In the present invention, by capturing the progress of electrical production tasks, equipment usage, process parameters, and energy consumption data, precise scheduling of production resources is achieved. Based on this, the operating mode, working hours, and speed of equipment are regulated, making resource allocation more flexible. Relying on cloud computing to analyze energy consumption data, the energy usage pattern and equipment load are optimized, and energy is effectively allocated, thereby improving energy efficiency. Combining with the equipment health assessment model, it is also possible to predict equipment failures in advance, adjust the maintenance cycle according to the equipment status, and avoid production interruptions caused by unexpected equipment downtime. Through dynamic health monitoring and maintenance plan optimization, the equipment life and operating stability are improved, and the production operation cost is reduced. A deep analysis is carried out on the efficiency impact of production scheduling and energy management. The production sequence and resource scheduling are adjusted for production bottlenecks to assist in improving the operation efficiency of the production line. They complement each other in achieving efficient resource utilization, energy efficiency improvement, precise equipment maintenance, and maximization of production efficiency. Not only the resource scheduling and energy allocation in the production process are optimized, but also the real-time monitoring of equipment status and the improvement of production efficiency are achieved, enabling the production line to maintain stability while obtaining higher response speed and production flexibility. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 is the system flow chart of the present invention; Figure 2 is the flow chart of the optimization result of electrical production resource scheduling in the present invention; Figure 3 is the flow chart of the energy allocation strategy in the present invention; Figure 4 is the flow chart of the energy-saving optimization result in the present invention; Figure 5 is the flow chart of the equipment health status result in the present invention; Figure 6 is the flow chart of the equipment maintenance optimization plan in the present invention; Figure 7 is the flow chart of the production bottleneck analysis result in the present invention; Figure 8 is the flow chart of the electrical production line efficiency management plan in the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0016] In order to make the objectives, technical solutions, and advantages of the present invention clearer and more understandable, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0017] In the description of the present invention, it should be understood that the orientation or positional relationship indicated by terms such as "length", "width", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc. is based on the orientation or positional relationship shown in the drawings. It is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation. Therefore, it should not be construed as a limitation to the present invention. In addition, in the description of the present invention, the meaning of "a plurality of" is two or more, unless otherwise specifically defined.

[0018] Please refer to Figure 1 , the cloud computing-based electrical manufacturing execution management system includes: The scheduling optimization module captures the progress of electrical production tasks, equipment usage, process parameters, and energy consumption data, and optimizes the scheduling of equipment through the cloud computing platform, adjusts the operation mode, working hours, and speed of the equipment, and generates the optimized result of electrical production resource scheduling; Based on the optimized result of electrical production resource scheduling, the energy management module uses cloud computing to analyze energy consumption data, optimizes the energy usage mode of electrical manufacturing equipment, adjusts the operation load of the equipment, generates an energy distribution strategy, and based on the energy distribution strategy, combines energy efficiency evaluation data to optimize the load and energy efficiency of manufacturing equipment, and generates an energy-saving optimization result; Based on the energy-saving optimization result, the equipment health and status monitoring module monitors the operation status of manufacturing equipment through the cloud platform, combines the equipment health assessment model to predict faults and manage the health of the equipment, generates the equipment health status result, adjusts the maintenance cycle according to the equipment health status result, and generates an equipment maintenance optimization plan; Based on the equipment maintenance optimization plan, the production efficiency improvement module evaluates the impact of the current production scheduling and energy management on production efficiency, analyzes production bottlenecks, generates a production bottleneck analysis result, adjusts the production sequence and resource scheduling according to the production bottleneck analysis result, and generates an electrical production line efficiency management plan.

[0019] The optimized result of electrical production resource scheduling includes production task allocation, equipment operation mode, process parameter setting, working time arrangement. The energy distribution strategy includes energy consumption distribution ratio, load distribution, and energy efficiency evaluation index. The energy-saving optimization result includes load configuration, energy efficiency improvement parameter, and energy-saving adjustment plan. The equipment health status result includes temperature monitoring index, pressure detection value, and operation duration record. The equipment maintenance optimization plan includes maintenance time plan, fault prevention measures, and health status management. The production bottleneck analysis result includes production bottleneck points, resource allocation efficiency, and scheduling response situation. The electrical production line efficiency management plan includes production sequence optimization, resource scheduling strategy, and efficiency improvement plan.

[0020] Please refer toFigure 2 , the steps for obtaining the optimization result of electrical production resource scheduling are specifically as follows: By capturing the progress of electrical production tasks, equipment usage, process parameters, and energy consumption data, analyzing the current load, operating power consumption, and equipment utilization time of the equipment, selecting the equipment with the load value reaching the set initial threshold, and generating a set of equipment screening that meets the load conditions; According to the dataset of equipment usage, first collect parameters such as the load value, operating power consumption, and equipment usage time of each equipment. By monitoring the equipment operation data, set the initial load threshold to classify the load situation of the equipment. For each equipment, compare its current load value with the initial load threshold. If the load value of the equipment is lower than the initial threshold, it means that the workload of the equipment is insufficient and does not meet the scheduling requirements of the current task. Therefore, it needs to be excluded. If the load value of the equipment is higher than or equal to the threshold, the equipment can be regarded as a qualified equipment and meets the scheduling requirements. Through the screening step, all the equipment data that meets the load conditions will be retained, and a set of equipment screening will be generated. During the screening process, for the equipment that does not meet the load requirements, its data will be completely excluded to ensure that the subsequent equipment scheduling tasks can achieve more efficient resource allocation, not only improving the resource utilization efficiency, but also ensuring that the equipment load does not operate overloaded, avoiding equipment failures or performance degradation caused by overloading.

[0021] According to the set of equipment screening that meets the load conditions, extract the operating power consumption and equipment usage time data of the equipment. Through the parameters of equipment usage time and load situation, calculate the equipment scheduling weight, and screen and schedule the equipment by weight, using the formula:

[0022] Get the equipment scheduling priority weight result, where, represents the equipment scheduling priority weight result, represents the number of equipment that meets the load conditions, is the average power consumption of the equipment, is the reciprocal of the usage time of the equipment, is the current load situation of the equipment, represents the total number of equipment; The benefit of the formula is that by combining the power consumption, usage time, and load situation of the equipment, it evaluates the scheduling priority of the equipment in a weighted manner, ensuring that the equipment with heavier load, higher power consumption, and longer usage time is scheduled first, optimizing the overall scheduling efficiency of the equipment. This formula can assist the scheduling system to preferentially select the equipment with higher load, high energy consumption, and longer running time among multiple equipment, thereby improving the production efficiency of the overall system; The number of devices representing the load conditions is obtained by collecting device usage. The number of devices is 15, indicating that 15 devices in the current system meet the scheduling conditions. Secondly, kW represents the average power consumption of the device, which is obtained based on the power consumption data during the device's use. The device power consumption value is used to measure the energy consumption of the device. For devices with higher power consumption, they should be scheduled first to optimize the overall energy efficiency. , which is calculated by monitoring the total usage time of the device. This data helps to evaluate the workload of the device and avoid excessive wear of some devices due to long-term operation, which affects the reliability of the overall device. is the current load ratio of the device, obtained from the data acquired by the real-time monitoring system, representing the ratio of the current load of the device to the maximum load. After substituting the above parameter values, the formula calculation result is:

[0023] This result is used to reflect the priority weight of device scheduling, indicating the relative priority of device scheduling under specific load conditions. To improve the rationality of the scheduling strategy, a device type difference factor and a task matching degree weight need to be introduced when calculating this weight value to avoid biases caused by simply selecting devices based on load, power consumption, and usage time. Through this optimized scheduling weight model, instead of blindly giving priority to devices with high power consumption or load, multi-dimensional parameters such as the functional attributes of the device, the current task requirements, and the historical performance are comprehensively considered, making device scheduling more targeted and accurate. With the improved scheduling algorithm, the system can allocate resources more precisely according to the adjusted weight value, thereby improving the device utilization efficiency, avoiding resource waste, and ensuring the efficiency and balance of the production scheduling process.

[0024] Based on the device scheduling priority weight result, combined with the load and power consumption of the device, a standard threshold for power consumption utilization rate is set, and devices meeting the conditions are selected to be added to the scheduling optimization list to generate the electrical production resource scheduling optimization result; After obtaining the priority weight of device scheduling, in order to further improve the accuracy of scheduling, a device energy efficiency evaluation mechanism is introduced to screen the power consumption utilization rate of devices. Different from using only "the ratio of actual power consumption to maximum power consumption" as a single screening indicator, this solution introduces factors such as the matching degree of device operating conditions and the energy efficiency adaptability of tasks to build a multi-dimensional power consumption utilization rate evaluation model. During the screening process, not only consider whether the power consumption reaches a certain set threshold, but also pay more attention to whether the energy consumption performance of the device under specific production tasks is reasonable. The standard threshold will be dynamically set according to device type, operating cycle, actual task load, and historical efficiency data, avoiding mis-eliminating normal devices due to low power consumption, and also reducing the mis-priority of high-power devices due to task mismatch. In the final formed scheduling list, the devices not only meet the energy efficiency standards, but also highly match the scheduling tasks, ensuring the priority allocation of high-efficiency and high-adaptability device resources. Through this optimization process, the error caused by single power judgment can be effectively avoided, and the economy of scheduling and the overall energy efficiency of the system can be improved; By introducing a standardized quantization formula, the screening threshold of power consumption utilization rate is clearly set. The specific steps include: First, based on historical device operation data, calculate the power mean, load ratio, and energy efficiency level of each type of device under normal load, and calculate its mean and standard deviation; Second, set the screening threshold formula, such as: power consumption utilization rate threshold = (device standard power × load standard ratio) ± (k × standard deviation), where k is an adjustable tolerance coefficient used to flexibly adjust the screening strictness; Third, compare the real-time monitored device power consumption with this threshold, and only retain the devices within the threshold range to enter the energy efficiency optimization process; Finally, dynamically adjust the k value according to the real-time production environment to achieve adaptive screening. In this way, the screening logic can be standardized, the scientificity and consistency of device selection can be improved, the deviation caused by empirical screening can be avoided, and at the same time, the changes in different production task loads can be dynamically responded to, ultimately achieving the optimization of overall energy distribution and the improvement of energy efficiency level.

[0025] Please refer to Figure 3 , the steps for obtaining the energy distribution strategy are specifically as follows: Based on the optimization results of electrical production resource scheduling, extract the energy consumption data and corresponding load information of each device. By analyzing the real-time operating load and energy consumption of each device, and screening the devices that meet the energy utilization conditions, a set of device energy consumption that meets the conditions is formed; According to the optimization results of electrical production resource scheduling, it is first necessary to extract the energy consumption data of each device and its corresponding load information, which can be achieved through the device operation data recorded in the scheduling system, including the electrical energy consumption of the device under different load conditions. Then, compare the actual operating load of each device with the amount of energy it consumes. Utilize the relationship between device load and power, combined with the specification parameters of the device, to screen out devices with energy utilization rates lower than the preset threshold, which should be dynamically adjusted according to historical device data to ensure the rationality of the screening criteria. For the qualified devices, continue to extract their relevant parameters, use the device performance evaluation model to further screen out devices with higher energy efficiency, and form a set of device energy consumption that meets the conditions. Summarize the energy efficiency data of the devices to form an energy consumption dataset.

[0026] According to the set of device energy consumption that meets the conditions, analyze the power consumption situation of the devices, combine the operating load and power of the devices, calculate the energy usage priority weight of the devices, using the formula:

[0027] Obtain the energy usage priority of the devices, where, represents the energy usage priority of the device, represents the number of devices that meet the conditions, is the operating power of the device, is the current load ratio of the device, is the load weight factor, is the energy scheduling adjustment parameter, represents the total number of devices; The advantage of the formula is that by introducing the device load ratio , the load weight factor and the scheduling adjustment parameter , it can calculate the energy usage priority of the devices more accurately, avoid simple power and load weighting, optimize the scheduling order of the devices, and improve the rational distribution of energy; represents the number of devices that meet the conditions, is the operating power of the device, is the current load ratio of the device, is the load weight factor, is the energy scheduling adjustment parameter; In practical applications, through the dynamic monitoring of the device power , the load ratio , the load weight factor , obtain the real-time parameters of each device, then adjust according to the overall load situation of the devices; The power of Device 1 is 50 kW, the load ratio is 0.8, and the load weight factor is 1.2; The power of Device 2 is 30 kW, the load ratio is 0.5, and the load weight factor is 1.0; The power of Device 3 is 40 kW, the load ratio is 0.6, and the load weight factor is 1.1; Calculate according to the above formula:

[0028]

[0029] The result shows that the energy usage priority of the device is 54.27, representing the overall energy scheduling priority of the device. According to this value, the devices can be sorted to preferentially schedule the devices with higher energy consumption and heavier loads in order to achieve a better energy distribution effect.

[0030] According to the energy usage priority of the device, combined with the real-time operating power and load status of the device, sort the devices according to the priority, and adjust the energy distribution of the device according to the sorting to generate an energy distribution strategy; According to the energy usage priority of the device, combined with the actual operating power and load status of the device, sort all eligible devices from high to low according to the priority. Determine the scheduling order of the devices through the sorting, and adjust the energy distribution of the devices according to the sorting results. It is necessary to normalize the energy consumption of all devices to ensure that devices with different powers and loads can be compared on the same scale. According to the normalized data, combined with the priority sorting of the devices, perform energy distribution scheduling to ensure that, under the condition that the total energy demand remains unchanged, give priority to high-priority devices. Generate an energy distribution plan according to the adjusted energy distribution strategy and implement it in the scheduling of the devices.

[0031] Please refer to Figure 4 , and the specific steps for obtaining the energy-saving optimization results are as follows: According to the energy distribution strategy, by creating the load data and energy efficiency data of the manufacturing equipment, and dividing the load and energy efficiency data of the differentiated equipment into time intervals, obtain the set of load and energy efficiency data of the interval equipment by calculating statistical indicators such as the average value and standard deviation; According to the energy distribution strategy, it is first necessary to collect the load data and energy efficiency data of manufacturing equipment, divide the load and energy efficiency data of different equipment into time intervals, calculate the load and energy efficiency levels of the equipment within each interval, statistically analyze and organize the data, and calculate the load and energy efficiency data sets within the interval using statistical indicators such as the average value and standard deviation. The data set can help further analyze the energy efficiency performance of the equipment under different load conditions. Relevant data such as power (P), voltage (V), load (L), and equipment loss (D) of each equipment at different time periods are obtained from the load and energy efficiency data of the equipment, divided into time intervals for subsequent analysis. The calculation of statistical indicators uses formulas to calculate the average value and standard deviation of the data to describe the load and energy efficiency of the equipment in each time interval. The monitoring methods of relevant data include online data recording and periodic data collection. The energy efficiency of each load interval is calculated using the data to obtain the energy efficiency data set of the equipment under different load conditions for subsequent optimization analysis.

[0032] Based on the set of equipment load and energy efficiency data for intervals, extract the correlation between the load intervals and energy efficiency performance, combine the optimization objectives, calculate the impact of each load interval on energy efficiency, and analyze the mapping relationship between key load points and energy efficiency through the formula:

[0033] Calculate the energy efficiency score for the load interval to generate an energy efficiency optimization model, where, represents the energy efficiency score for the load interval, represents power, represents voltage, represents load, represents equipment loss, represents the target threshold; The advantage of the formula is that by combining the effects of power, load, voltage, and equipment loss, it can comprehensively reflect the energy efficiency performance of the equipment under different working loads, and thus provide an accurate scoring basis for energy efficiency optimization. After adding the loss parameter, the formula can effectively adjust and optimize the energy efficiency performance of the equipment, avoiding energy efficiency scoring deviations caused by the omission of equipment loss. : The power of the equipment at a specific load, measured value, the power is obtained by the monitoring device of the power system, unit is watt (W); : The voltage of the equipment, obtained in real time through an ammeter and a voltmeter, unit is volt (V); : The load of the equipment, indicating the load level of the equipment within a given time, unit is kilowatt (kW), measured by a load sensor; Loss generated during the operation of the device, which is expressed as the part of the device energy conversion process that is not effectively utilized, with the unit of watt (W) and is calculated through the device performance monitoring tool; Target threshold of device energy efficiency, which is set as the best energy efficiency value specified in the device manufacturer's or industry standard, with the unit of watt per kilowatt-hour (kWh); Set value: , , , , ; Substitute into the formula for calculation:

[0034]

[0035]

[0036] This result indicates that the energy efficiency score under the load is negative, which means that the current loss of this device is too high, resulting in its energy efficiency performance not meeting expectations and further optimization measures are required.

[0037] According to the energy efficiency optimization model, perform energy efficiency adjustment. By analyzing the energy efficiency optimization results of each device load interval, adjust the load distribution to generate an energy-saving optimization plan; According to the energy efficiency optimization model, first, it is necessary to analyze the energy efficiency optimization results of each device load interval, including extracting the energy efficiency score of each load interval from the model and conducting a detailed analysis in combination with the relationship between the energy efficiency score and the load interval. Through the analysis, it is possible to identify in which load intervals the device energy efficiency is relatively ideal and which intervals have room for energy efficiency optimization. First, calculate the energy efficiency score of each load interval through the model to obtain the energy efficiency score of each interval. The score reflects the change level of the device energy efficiency under specific load conditions. By comparing the scores of different intervals, it is possible to identify the load intervals with potential energy-saving optimization space and determine the priority order of optimization. Auxiliary tools such as optimization algorithms and analysis tools will be used for analysis, and then the load distribution will be adjusted to generate an energy-saving optimization plan. The adjustment of the load distribution is based on the analysis of the optimization results and the energy efficiency score to ensure that the device can achieve the best energy efficiency state under different load conditions, thereby reducing energy consumption and improving the overall energy efficiency performance of the system.

[0038] Please refer to Figure 5 , the specific steps for obtaining the device health status result are as follows: Extract the device operation status information according to the energy-saving optimization results, including temperature, vibration, and pressure parameters, determine the deviation of the current status, calculate the outlier of the operation status of each device, and generate the device health status dataset; According to the device operation status information received by the cloud platform, first extract key parameters such as device temperature, pressure, vibration, and load, use the sensor interface to obtain the data values in real time, compare them with the standard range in the historical record, identify outliers, and obtain the frequency and duration of the abnormal status by analyzing the change trend of each device's data, so as to initially identify potential problems of the device. By introducing a filtering mechanism, abnormal sensor data and noise data are removed to ensure the accuracy and reliability of the data. Finally, through data fusion technology, data from different sources are merged to form a unified health assessment parameter set, which contains the performance of the device in different monitoring dimensions, generates the device health status dataset, and provides a basis for subsequent health assessment.

[0039] Process the device health status dataset, remove missing and abnormal data, and integrate the device differential status information into a unified health assessment parameter vector through multi-dimensional data fusion technology to generate the device health assessment dataset; When processing the device health status dataset, first clean all sensor data, remove data points with disconnection and large errors, and normalize the data to make it suitable for subsequent fusion calculations. Adopt the weighted average method to merge the health status data of multiple dimensions such as temperature, pressure, and vibration through certain weight coefficients to generate a comprehensive device health parameter, which will be used as a quantitative indicator of the overall health status of the device. This process can not only ensure the accuracy of the data, but also comprehensively reflect the operation health of the device from multiple dimensions, ensure the scientificity and accuracy of the device health assessment, and lay a solid foundation for subsequent fault prediction and health management of the device.

[0040] Based on the device health assessment dataset, perform health prediction by comparing the status data of each device with the preset health threshold, using the formula:

[0041] Generate the device health status result, where represents the device health status result, represents the device temperature status parameter, represents the device vibration status parameter, represents the device pressure status parameter, is the weight coefficient; The advantage of the formula is that it can comprehensively evaluate the health status of the device by using the multi-dimensional data of the device (such as temperature, vibration, pressure) and combining the weight coefficients, rather than relying solely on a single parameter. This method can better adapt to the performance of the device under different working conditions and provide more accurate health assessment results. It has significant advantages especially for complex devices, and can significantly improve the accuracy and timeliness of fault prediction; Obtain the temperature, vibration and pressure data of the device through sensor monitoring, and get 、 and real-time values, such as temperature , vibration mm / s, pressure MPa. Use empirical data or historical data to set the weight coefficients for each dimension, such as , , , and then substitute each parameter into the formula for calculation:

[0042]

[0043] The result shows that the current health status evaluation value of the device is 32.375. Based on this value, it is possible to further determine whether the device is in a healthy state, whether maintenance is required, or whether there are potential fault risks.

[0044] Please refer to Figure 6 , and the specific steps for obtaining the device maintenance optimization plan are as follows: According to the device health status result, judge the health status by analyzing the vibration frequency, temperature rise rate, and pressure change rate of the device, generate health status evaluation items, and if the health status evaluation items exceed the set threshold, it is determined that the maintenance cycle needs to be adjusted; The judgment of the device health status is the crucial first step in the adjustment of the maintenance cycle. Therefore, it is necessary to comprehensively monitor the key operation indicators of the device, such as vibration frequency, temperature rise rate, and pressure change rate. The real-time data collection of the indicators is completed by sensors installed on the device, and the sensors can capture the operation data of the device under different working conditions. To ensure the accuracy and usability of the device monitoring data, the collected original operation data will be preliminarily preprocessed, but in the processing process, the traditional methods of "outlier removal" and "global smoothing" will no longer be simply adopted. Instead, a hierarchical processing strategy is introduced: First, the obvious sensor errors or data missing are identified and repaired through the signal-to-noise ratio algorithm, and for the data with mutations or beyond the statistical distribution range, they will be marked as "potential anomalies" rather than directly deleted. Such data will enter the subsequent state diagnosis and analysis module and be retained as reference clues for potential faults. The smoothing process also adopts the interval dynamic window weighting method and is only executed in the scenarios where trend judgment is required, retaining the key fluctuation characteristics and avoiding covering up the abnormal behavior of the device. Through this adjustment strategy, the system can not only improve the data processing quality but also retain the sensitivity to the potential anomalies of the device, thus enhancing the timeliness and accuracy of fault prediction and providing more real and reliable data support for the device health assessment and maintenance strategy formulation. Ensure that the data entering the analysis stage is of high quality. After the data is processed, statistical analysis is carried out on each indicator, including calculating its average value, standard deviation, etc., and evaluating its deviation degree from the device performance standard. These statistical results will directly affect the assessment of the device health status. Once it is found that the device health indicators exceed the normal range, the adjustment requirement of the maintenance cycle will be triggered, thus generating the health status assessment items and providing decision-making support for the next adjustment of the maintenance cycle.

[0045] Call the health status assessment items, combine the workload parameters and the original failure frequency parameters of the device, and use the formula:

[0046] Calculate the set value of the maintenance cycle, where, represents the set value of the maintenance cycle, represents the workload, represents the health status assessment item, represents the original failure frequency, represents the original failure frequency parameter; If specific numerical substitution and calculation are required, first we need to assume the actually available data values. The following is the specific calculation process with example numerical values: The set numerical values are: (Workload parameter) is 150, (Health status assessment item) is 0.85, (Original failure frequency) is 0.25; In order to effectively identify the bottleneck nodes where load and energy consumption are unbalanced during the production process, the system constructs a functional data set based on multidimensional data, and performs indicator modeling through workstation-level load and energy consumption data. Among them, represents the actual load of the workstation per unit time, represents the energy consumption value in the same period; the adjustment factor introduced to optimize the model is used to reflect the weighted processing of the sensitivity of a specific process to energy consumption, and are respectively proportional coefficients preset according to different production processes and equipment characteristics, and their initial values are obtained by fitting historical production data and can be adjusted dynamically. In addition, represents the process performance score under the key process stage, which is used to quantify the performance of the workstation in comprehensive scheduling efficiency, and is the evaluation factor of the comprehensive production task response speed. To ensure the applicability and interpretability of the model, all parameter settings are generated by the system self-learning module based on long-term data training, and support manual intervention correction. Through this model construction method, not only the actual meaning and source of each parameter are clarified, but also the adaptive adjustment capability for different production environments is realized, so as to more scientifically identify production bottleneck nodes and optimize the matching of scheduling strategies and energy efficiency; calculate : ; Calculate the product : ; Calculate square root : ; Calculate the final : ; The maintenance cycle setting value obtained It is 20.76, which means that according to the current load and health status assessment of the equipment, the maintenance cycle is set to 20.76 unit time. This result can be used to adjust the maintenance strategy to ensure that the equipment operates in the best condition.

[0047] Based on the maintenance cycle setting value, the current maintenance cycle standard is compared. If the maintenance cycle setting value is lower than the maintenance cycle standard, the maintenance cycle setting value is used to replace the current maintenance cycle to generate an equipment maintenance optimization plan; The final adjustment of the maintenance cycle is based on a comparative analysis of the existing cycle standard and the recommended cycle. The difference ratio between the two is calculated to determine whether the cycle needs to be updated. The recommended maintenance cycle is compared with the current cycle standard, and the difference between them is calculated to evaluate the need for adjustment of the maintenance frequency. If the new recommended cycle is shorter than the existing standard, it means that more frequent maintenance is required to ensure the reliability and safety of equipment operation. Through such comparative analysis, the final cycle adjustment recommendation can be determined. If the set value is adjusted to be lower than the existing standard, the decision will directly affect the formulation and implementation of the maintenance plan, thereby achieving optimization and efficiency improvement in equipment maintenance management.

[0048] Please refer to Figure 7 , and the steps for obtaining the production bottleneck analysis results are specifically as follows: According to the equipment maintenance optimization plan, integrate the equipment maintenance logs and energy usage records, analyze the operating status of each device, and combine with the energy consumption parameters to calculate the unit energy efficiency ratio of the device, generating a preliminary analysis dataset; Integrate the collected equipment maintenance logs and energy usage records, analyze the operating status of each device based on the equipment efficiency parameters, and detailedly analyze the operating time and energy consumption of each device. Through historical data comparison and analysis, calculate the unit time energy efficiency of each device, rank the energy efficiency of the devices to identify inefficient devices, further utilize the energy usage records to analyze the correlation between device energy efficiency and production scheduling, standardize the data to ensure data consistency, and based on the processed data, apply multi-dimensional data analysis technology to generate a detailed energy efficiency report. This report will directly affect the formulation of production efficiency improvement strategies, summarize the energy efficiency results by device category and generate a preliminary analysis dataset.

[0049] Decompose the data items in the preliminary analysis dataset, calculate the load intensity of the workstation through the workstation load ratio, and calculate the ratio of energy consumption to unit efficiency load, using the formula:

[0050] Identify the bottleneck nodes of load and energy consumption to obtain a functional dataset, where represents the workstation load, represents the energy consumption, and are coefficients for adjusting the proportional relationship between load and energy consumption, represents the performance index of key production links, represents the production scheduling efficiency; To identify the mismatch points between load and energy consumption and locate production bottlenecks, the system constructs a functional data set and uses a formula for evaluation and analysis. In the formula, represents the average load value (unit: kW) of each workstation within a specified period, which is directly extracted from the historical operation records of the workstation; represents the total energy consumption during the same period (unit: kWh), which is obtained by summarizing through the energy consumption monitoring module; and are adjustment parameters, representing the load weight factor and the energy consumption weight factor respectively, used to regulate the contribution ratio of load and energy consumption to the bottleneck identification weight. Their initial values are default set to 1.0 by the system and can be adaptively adjusted according to different process sections through expert experience or historical training data; is the performance index during the execution of key processes (such as unit energy consumption ratio, production cycle efficiency, etc.), and measures the response speed and execution ability of the task under this process. All parameters and their value ranges can be set and calibrated in the system configuration interface and are dynamically updated during the model operation. Through this way with clear parameters and transparent calculation, it is ensured that each index in the bottleneck identification process has real physical meaning and operability, thus improving the precise adaptation ability and regulation effect of the scheduling system in complex production environments; The benefit of the formula is that by adjusting the coefficients of the ratio relationship between load and energy consumption and it is possible to adjust the formula weights according to the actual production conditions to meet the specific requirements of different production environments, optimize the energy efficiency and scheduling efficiency in the production process, and enhance the flexibility of energy and efficiency management in the production process; Suppose a workstation has an actual load of 120 units, and the energy consumption is 150 units. The coefficient for adjusting the ratio relationship between load and energy consumption is set to 0.8, is set to 1.2, and the formula calculation is as follows:

[0051] This result indicates that the production efficiency of the workstation is relatively optimal under the current configuration, and and The selection of reflects the actual adjustment of the weights of equipment load and energy efficiency.

[0052] According to the model calculation results, the current workstation shows high production efficiency under the configured parameters. This conclusion is not based on a single indicator, but is obtained through a multi-dimensional efficiency scoring model formed by comprehensively evaluating key performance parameters such as load intensity, unit energy consumption, and production rhythm. The adjustment coefficients introduced in the model (load and energy consumption weight factor) and (energy and output ratio adjustment coefficient), their initial values are generated by the system based on a large amount of historical operation data fitting, and at the same time, dynamic fine-tuning is allowed according to specific process requirements or production strategies. These two parameters are used to weigh the proportion of equipment load in scheduling priority and the weight of unit energy consumption in output evaluation respectively, ensuring the adaptability of the model in different scenarios. Through sensitivity analysis and simulation comparison, the current parameter configuration can achieve better scheduling and energy efficiency matching in most typical task scenarios, forming the current inference of "relatively optimal efficiency". This inference is data-driven and supported by verification tests, providing a credible basis for scheduling strategy adjustment and energy efficiency optimization.

[0053] Extract the key indicators of production bottlenecks from the functional dataset, classify and summarize the load intensity and energy efficiency levels of bottleneck nodes, and generate the production bottleneck analysis results; Extract the key indicators of production bottlenecks from the intermediate result set, mark and classify the high-load and low-efficiency nodes in the dataset, use data clustering analysis methods to distinguish different types of production bottlenecks, analyze the specific characteristics and causes of each type of bottleneck node in detail, compare the analysis results with the production flow chart, find the key influencing points in the process, formulate targeted improvement measures, and finally generate the production bottleneck analysis results, which can accurately identify and solve the efficiency obstacles in the production process, thereby realizing the optimization and improvement of the overall production process, not only increasing production efficiency, but also improving the utilization efficiency of resources, helping to reduce costs and improve product quality; In order to accurately identify and classify various types of production bottlenecks, the system introduces a data clustering analysis method to perform pattern recognition and classification of bottleneck nodes based on the constructed functional data set. The clustering algorithm used is an improved K-means algorithm, which combines four key dimensions such as equipment operating load, unit energy efficiency ratio, task execution cycle, and resource waiting time to form a multidimensional feature vector as the clustering input data set. The data sources include equipment maintenance logs, energy consumption records, and production rhythm tracking systems to ensure the comprehensiveness and timeliness of the features. During the clustering execution process, the system automatically determines the optimal number of clusters through indicators such as the silhouette coefficient and the Davies-Bouldin index to ensure that the clustering results have good internal consistency and category separation. After clustering each type of bottleneck node, the system further analyzes its operating mode, the location where it frequently appears in the process flow, and the resource occupancy status, and identifies that its cause may be uneven equipment load, low energy efficiency, or poor material supply. Finally, by summarizing the characteristics of various bottlenecks, a data basis can be provided for the subsequent formulation of targeted scheduling strategies and equipment optimization plans. This method enhances the ability to understand complex production bottleneck problems in a structured manner, avoids the one-sidedness of human judgment, and improves the scientific nature and universality of the solution.

[0054] See also Figure 8 , the specific steps for obtaining the efficiency management plan for the electrical production line are: Based on the production bottleneck analysis results, the capacity utilization rate and equipment standby time of each bottleneck are taken into consideration, and combined with the bottleneck material supply cycle, each bottleneck data is sorted item by item to establish the bottleneck priority analysis results; Based on the bottleneck data of the electrical production line, the capacity utilization rate and equipment standby time and other parameters of each bottleneck are obtained, and the parameters are called one by one to conduct a quantitative analysis of the capacity utilization rate. Specifically, the ratio of the actual output of each equipment per unit time to the planned output is calculated. The capacity utilization rate is further compared based on the bottleneck data, and the resource allocation demand of each bottleneck is quantified by the output rate difference. The standby time parameters of the bottleneck are refined, and the cumulative time of the equipment standby period is recorded through multiple sampling, and the interruption data during the bottleneck period is screened. The cumulative standby time is called as a supplementary indicator of the bottleneck efficiency. At the same time, the material supply cycle is analyzed, and the bottleneck priority data is evaluated by adjusting the supply cycle parameters to make the capacity utilization rate, standby time and supply cycle data consistent. Each bottleneck is sorted item by item to obtain the bottleneck priority sequence. At the same time, the bottleneck priority analysis results are established to provide data support for subsequent steps.

[0055] According to the results of the bottleneck priority analysis, the resource consumption rate, process flow efficiency and bottleneck frequency of each bottleneck are calculated item by item, and the impact weight of resource allocation is calculated according to the start time of the bottleneck and the total production time, using the formula:

[0056] Obtain the bottleneck resource weight distribution result, where represents the bottleneck resource weight, represents the resource consumption rate, represents the manufacturing flow efficiency, is the bottleneck frequency, is the total production duration, is the bottleneck start time, is the bottleneck priority; The advantage of the formula is that it combines the resource consumption rate and the process flow efficiency, and introduces the absolute time difference, further improving the accuracy of weight calculation; represents the resource consumption rate, which can be obtained by dividing the resource consumption of the monitoring device by the unit time; represents the process flow efficiency, which can be obtained by measuring the average completion time of the unit process; represents the bottleneck frequency, which can be calculated by counting the number of times the bottleneck appears in each production process; is the total production duration, which is obtained from the difference between the start and end times of the process; is the bottleneck start time, that is, the time point when the bottleneck first appears in the total production process; represents the bottleneck priority sequence value; Specific numerical calculation: Let , , , , , ; Substitute into the formula:

[0057] The result shows that the bottleneck resource weight is 0.15, which is used to evaluate the degree of resource occupation by the bottleneck, so as to provide a basis for subsequent resource scheduling.

[0058] Based on the bottleneck resource weight distribution result, adjust the production order and resource allocation to obtain the electrical production line efficiency management plan; Call the result of the bottleneck resource weight distribution, compare each bottleneck according to the bottleneck priority and resource weight, take the value of the bottleneck resource weight as the basis, use the resource weight value as a reference factor for the bottleneck priority, and allocate the priority of each bottleneck in the production process one by one. Compare and refine the resource allocation priority of each bottleneck to ensure the adaptability of the resource allocation priority under each bottleneck condition. Gradually allocate resource parameters to each bottleneck link. At the same time, call the comparison result of the bottleneck priority and resource allocation weight to further adjust the production order adaptively to ensure the consistency of the bottleneck link in resource allocation and production order. Gradually adjust the resource weight and priority of each bottleneck link in the production line to obtain an electrical production line efficiency management plan; To improve the resource allocation efficiency of the overall production process, the system conducts refined management on the resource allocation requirements of each bottleneck node in the process based on the identified bottleneck priority. Resource parameters here refer to three types of core production factors: ① Equipment operation duration (unit: hour), ② Energy distribution volume (unit: kWh), ③ Manpower and logistics support resources (unit: man-hours and section transfer beats), which are all quantified through historical data and real-time monitoring models, and weights are set based on task load, energy efficiency level, and frequency of occurrence in bottleneck processes. In the allocation mechanism, first, calculate the allocation priority of each bottleneck based on the bottleneck resource weight calculation model. This priority comprehensively considers the bottleneck start time, continuous impact duration, and resource consumption rate to form a quantifiable bottleneck score. Subsequently, the system allocates resources level by level according to the priority. After meeting the complete resource requirements of high-priority bottlenecks, allocate sub-optimal bottlenecks according to the marginal effect of the remaining resources. During the allocation process, the system allows simulation prediction and load balancing strategy intervention to avoid single-point overload or resource waste. Finally, this method realizes the structured, transparent, and adjustable management of resource parameters, improves the adaptability of the scheduling strategy to various bottleneck scenarios, and ensures the dynamic balance of resource allocation in complex processes, effectively supporting the flexible scheduling and continuous optimization goals of the electrical manufacturing execution system.

[0059] The above is only a preferred embodiment of the present invention, and it is not intended to limit the present invention in other forms. Any person skilled in the art may use the disclosed technical content to make changes or modifications into equivalent embodiments with equivalent changes and apply them to other fields. However, as long as it does not depart from the technical solution content of the present invention, any simple modification, equivalent change, and modification made to the above embodiments based on the technical essence of the present invention still fall within the protection scope of the technical solution of the present invention.

Claims

1. An electrical manufacturing execution management system based on cloud computing, characterized in that, The system includes: The scheduling optimization module captures the progress of electrical production tasks, equipment usage, process parameters, and energy consumption data, and optimizes the scheduling of equipment through a cloud computing platform. It adjusts the operating mode, working hours, and speed of the equipment to generate an optimized result for the scheduling of electrical production resources. Based on the optimized result of the electrical production resource scheduling, the energy management module uses cloud computing to analyze the energy consumption data, optimizes the energy usage mode of electrical manufacturing equipment, adjusts the operating load of the equipment, generates an energy distribution strategy, and combines the energy efficiency evaluation data to optimize the load and energy efficiency of the manufacturing equipment to generate an energy-saving optimization result. Based on the energy-saving optimization result, the equipment health and status monitoring module monitors the operating status of manufacturing equipment through the cloud platform, combines the equipment health assessment model to perform fault prediction and health management on the equipment, generates an equipment health status result, and adjusts the maintenance cycle according to the equipment health status result to generate an optimized equipment maintenance plan. Based on the optimized equipment maintenance plan, the production efficiency improvement module evaluates the impact of the current production scheduling and energy management on production efficiency, analyzes production bottlenecks, generates a production bottleneck analysis result, and adjusts the production sequence and resource scheduling according to the production bottleneck analysis result to generate an efficiency management plan for the electrical production line.

2. The cloud computing-based electrical manufacturing execution management system according to claim 1, wherein The specific steps for obtaining the optimized result of the electrical production resource scheduling are as follows: By capturing the progress of electrical production tasks, equipment usage, process parameters, and energy consumption data, analyzing the current load, operating power consumption, and equipment utilization time of the equipment, and selecting the equipment with the load value reaching the set initial threshold, a set of equipment screening that meets the load conditions is generated. According to the set of equipment screening that meets the load conditions, the operating power consumption and equipment usage time data of the equipment are extracted. Through the parameters of equipment usage time and load conditions, the equipment scheduling weight is calculated, and the scheduling equipment is screened by weight to obtain the equipment scheduling priority weight result. Based on the equipment scheduling priority weight result, combined with the load and power consumption of the equipment, a standard threshold for power consumption utilization rate is set, and the equipment that meets the conditions is screened and added to the scheduling optimization list to generate an optimized result for the scheduling of electrical production resources.

3. The cloud computing-based electrical manufacturing execution management system according to claim 2, wherein The specific steps for obtaining the energy distribution strategy are as follows: Based on the optimized result of the electrical production resource scheduling, the energy consumption data and corresponding load information of each equipment are extracted. By analyzing the real-time operating load and energy consumption of each equipment, and screening the equipment that meets the energy utilization conditions, a set of equipment energy consumption that meets the conditions is formed. According to the set of equipment energy consumption that meets the conditions, the power consumption situation of the equipment is analyzed. Combined with the operating load and power of the equipment, the equipment energy usage priority weight is calculated to obtain the energy usage priority of the equipment. According to the energy usage priority of the equipment, combined with the real-time operating power and load status of the equipment, the equipment is sorted according to the priority, and the energy distribution of the equipment is adjusted according to the sorting to generate an energy distribution strategy.

4. The cloud computing-based electrical manufacturing execution management system according to claim 3, wherein, The specific steps for obtaining the energy-saving optimization result are as follows: According to the described energy distribution strategy, by using the load data and energy efficiency data of manufacturing equipment, and dividing the load and energy efficiency data of different equipment into time intervals, statistical indicators such as average value and standard deviation are calculated to obtain the set of load and energy efficiency data of interval equipment; Based on the set of load and energy efficiency data of interval equipment, the correlation between the load interval and energy efficiency performance is extracted. Combining with the optimization goal, the impact of each load interval on energy efficiency is calculated, the mapping relationship between key load points and energy efficiency is analyzed, the energy efficiency score of the load interval is calculated, and an energy efficiency optimization model is generated; According to the energy efficiency optimization model, energy efficiency adjustment is carried out. By analyzing the energy efficiency optimization results of each equipment load interval, the load distribution is adjusted to generate an energy-saving optimization plan.

5. The cloud computing-based electrical manufacturing execution management system according to claim 4, characterized in that, The specific steps for obtaining the equipment health status result are as follows: According to the energy-saving optimization result, the equipment operation status information, including temperature, vibration, and pressure parameters, is extracted, the deviation of the current status is determined, the abnormal value of the operation status of each equipment is calculated, and a set of equipment health status data is generated; The set of equipment health status data is processed to remove missing and abnormal data. Through multi-dimensional data fusion technology, the differentiated status information of the equipment is integrated into a unified health assessment parameter vector to generate a set of equipment health assessment data; Based on the set of equipment health assessment data, health prediction is carried out by comparing the status data of each equipment with the preset health threshold to generate the equipment health status result.

6. The cloud computing-based electrical manufacturing execution management system according to claim 5, characterized in that The specific steps for obtaining the equipment maintenance optimization plan are as follows: According to the equipment health status result, by analyzing the vibration frequency, temperature rise rate, and pressure change rate of the equipment, the health status is judged to generate health status assessment items. If the health status assessment items exceed the set threshold, it is determined that the maintenance cycle needs to be adjusted; The health status assessment items are called, and combined with the workload parameters and original failure frequency parameters of the equipment, the set value of the maintenance cycle is calculated; Based on the set value of the maintenance cycle, a comparison is made with the current maintenance cycle standard. If the preliminary set value of the maintenance cycle is lower than the maintenance cycle standard, the set value of the maintenance cycle is used to replace the current maintenance cycle to generate an equipment maintenance optimization plan.

7. The cloud computing-based electrical manufacturing execution management system according to claim 6, wherein The specific steps for obtaining the production bottleneck analysis result are as follows: According to the equipment maintenance optimization plan, the equipment maintenance logs and energy usage records are integrated, the operation status of each equipment is analyzed, and combined with the energy consumption parameters, the unit energy efficiency ratio of the equipment is calculated to generate a preliminary analysis data set; The data items of the preliminary analysis data set are decomposed. The load intensity of the workstation is calculated through the workstation load ratio, and the ratio of energy consumption to unit efficiency load is calculated to identify the bottleneck nodes of load and energy consumption, and a functional data set is obtained; The key indicators of production bottlenecks are extracted from the functional data set, and the load intensity and energy efficiency level of the bottleneck nodes are classified and inductively analyzed to generate the production bottleneck analysis result.

8. The cloud computing-based electrical manufacturing execution management system according to claim 7, characterized in that, The specific steps for obtaining the electrical production line efficiency management plan are as follows: Based on the production bottleneck analysis results, the capacity utilization rate and equipment standby duration of each bottleneck are insufficient. Combining with the material supply cycle of the bottleneck, item-by-item sorting is performed for each bottleneck data to establish the bottleneck priority analysis results; According to the bottleneck priority analysis results, the resource consumption rate, process flow efficiency, and bottleneck frequency of each bottleneck are calculated item by item, and based on the start time of the bottleneck and the total production duration, the influence weight of resource allocation is calculated to obtain the bottleneck resource weight distribution results; Based on the bottleneck resource weight distribution results, the production sequence and resource allocation are adjusted to obtain the electrical production line efficiency management plan.

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