Cloud computing-based electrical manufacturing execution management system

Abstract

The application 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, a device health and state monitoring module and a production efficiency improvement module.In the application, the electrical production task progress, the device usage, the process parameters and the energy consumption data are captured to realize accurate scheduling of production resources, and then the operation mode, time and speed of the device are regulated, so that the resource allocation is more flexible, the energy consumption data is analyzed based on cloud computing, the energy use mode and the device load are optimized, effective energy distribution is realized, the energy efficiency is improved, the device fault can be predicted in advance by combining the device health evaluation model, the maintenance cycle is adjusted according to the device state, the production interruption caused by the device shutdown is avoided, the production scheduling is deeply analyzed, then the production sequence and the resource allocation are adjusted, the production efficiency is improved, and the production line reaches a higher level in stability and flexibility.

Description

TECHNICAL FIELD

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

[0002] The field of intelligent manufacturing technology improves the intelligent level of manufacturing industry through informatization, automation, digitization and networking technology. This field covers cutting-edge technologies such as artificial intelligence, cloud computing, big data analysis, Internet of Things (IoT), and robot technology, aiming to optimize production processes, improve production efficiency and product quality, and reduce production costs. The core concept of intelligent manufacturing is to realize the automation, flexibility and fine management of production processes through the integrated application of intelligent equipment and systems, to promote personalized customization production, and to support production decision-making through real-time data collection and analysis. The development of intelligent manufacturing not only improves the production capacity of traditional manufacturing industry, but also promotes the digital transformation of 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 for managing and optimizing the operation process of the production site. Through technologies such as cloud computing, big data, and Internet of Things, the system realizes real-time monitoring, data collection and analysis of the production process, thereby assisting enterprises to improve production efficiency, ensure product quality, reduce inventory costs, and effectively support flexible and customized production needs. MES can adjust production plans in real time, dynamically allocate resources according to actual production conditions, ensure the efficient operation of production lines, and provide data-driven decision support platforms for enterprises by integrating various production data, so that enterprises can quickly respond to market demand and improve overall competitiveness.

[0004] In the prior art, device scheduling and production plan adjustment rely on preset rules or historical data, lacking flexible response to real-time data, especially in terms of device usage status and energy consumption, it is difficult to achieve precise optimization of resource allocation, and its energy management and device maintenance are executed according to fixed cycles or preset conditions, which cannot fully respond to the actual state and load of the device, resulting in low energy efficiency of the device under light or heavy load, increasing non-critical energy consumption. In terms of device health monitoring, the existing system is based on simple fault statistics or single sensor monitoring, which cannot timely predict potential device failures, causing maintenance work to lag, which may affect production progress due to unexpected device downtime. The existing technology lacks comprehensive analysis capability for production bottlenecks, and the adjustment of production sequence and resource allocation is not flexible enough to effectively respond to complex and variable production demands, thereby affecting overall production efficiency, resulting in slow system response speed, low resource utilization rate, high production cost, and short device operation cycle, and showing a certain lag in responding to market demand. SUMMARY

[0005] The purpose of the present application is to solve the problems existing in the prior art, and the electrical manufacturing execution management system based on cloud computing is proposed.

[0006] In order to achieve the above-mentioned purpose, the present application adopts the following technical scheme: the electrical manufacturing execution management system based on cloud computing comprises:

[0007] The scheduling optimization module captures the electrical production task progress, equipment usage, process parameters and energy consumption data, optimizes the scheduling of the equipment through the cloud computing platform, adjusts the equipment operation mode, working time and speed, and generates the electrical production resource scheduling optimization result;

[0008] The energy management module optimizes the energy use mode of the electrical manufacturing equipment based on the electrical production resource scheduling optimization result, analyzes the energy consumption data by using cloud computing, adjusts the operation load of the equipment, generates the energy distribution strategy, optimizes the manufacturing equipment load and energy efficiency according to the energy distribution strategy combined with the energy efficiency evaluation data, and generates the energy saving optimization result;

[0009] The equipment health and state monitoring module monitors the running state of the manufacturing equipment through the cloud platform based on the energy saving optimization result, performs fault prediction and health management on the equipment combined with the equipment health evaluation model, generates the equipment health state result, adjusts the maintenance cycle according to the equipment health state result, and generates the equipment maintenance optimization scheme;

[0010] The production efficiency improvement module evaluates the influence of the current production scheduling and energy management on the production efficiency based on the equipment maintenance optimization scheme, analyzes the production bottleneck, generates the production bottleneck analysis result, adjusts the production sequence and resource scheduling according to the production bottleneck analysis result, and generates the electrical production line efficiency management scheme.

[0011] As a further scheme of the present application, the obtaining step of the electrical production resource scheduling optimization result is specifically:

[0012] By capturing the electrical production task progress, equipment usage, process parameters and energy consumption data, the current load, running power consumption and equipment utilization time of the equipment are analyzed, the equipment whose load value reaches the set initial threshold value is selected, and the equipment screening set meeting the load condition is generated;

[0013] According to the equipment screening set meeting the load condition, the running power consumption and equipment usage time data of the equipment are extracted, the equipment scheduling weight is calculated through the parameters of equipment usage time and load condition, the equipment is selected and scheduled through the weight, and the equipment scheduling priority weight result is obtained;

[0014] Based on the device scheduling priority weight result, in combination with the load and power consumption of the device, a standard threshold of power consumption utilization rate is set, devices meeting the conditions are screened to join a scheduling optimization list, and an electrical production resource scheduling optimization result is generated.

[0015] As a further scheme of the present application, the obtaining step of the energy distribution strategy is specifically:

[0016] Based on the electrical production resource scheduling optimization result, the energy consumption data and corresponding load information of each device are extracted, the real-time running load and consumed energy of each device are analyzed, and devices meeting the energy utilization conditions are screened to form a device energy consumption set meeting the conditions;

[0017] According to the device energy consumption set meeting the conditions, the power consumption of the device is analyzed, the running load and power of the device are combined, the energy use priority weight of the device is calculated, and the energy use priority of the device is obtained;

[0018] According to the energy use priority of the device, in combination with the real-time running power and load state of the device, the devices are sorted according to the priority, and the energy distribution of the devices is adjusted according to the sorting, and an energy distribution strategy is generated.

[0019] As a further scheme of the present application, the obtaining step of the energy saving optimization result is specifically:

[0020] According to the energy distribution strategy, the load data and energy efficiency data of the manufacturing device are analyzed, the load and energy efficiency data of the differential device are divided into time intervals, the statistical indicators of the average value and the standard deviation are calculated, and the interval device load and energy efficiency data set is obtained;

[0021] Based on the interval device load and energy efficiency data set, the correlation between the load interval and the energy efficiency performance is extracted, the influence of each load interval on the energy efficiency is calculated in combination with the optimization target, the mapping relationship between the key load point and the energy efficiency is analyzed, the energy efficiency score of the load interval is calculated, and an energy efficiency optimization model is generated;

[0022] According to the energy efficiency optimization model, energy efficiency adjustment is performed, the energy efficiency optimization result of each device load interval is analyzed, the load distribution is adjusted, and an energy saving optimization scheme is generated.

[0023] As a further scheme of the present application, the obtaining step of the device health state result is specifically:

[0024] According to the energy saving optimization result, the device running state information including temperature, vibration, and pressure parameters is extracted, the deviation of the current state is determined, the abnormal value of the running state of each device is calculated, and a device health state data set is generated;

[0025] The device health state data set is processed to remove missing and abnormal data, device differentiated state information is integrated into a unified health evaluation parameter vector through a multi-dimensional data fusion technology, and a device health evaluation data set is generated.

[0026] Based on the device health evaluation data set, health prediction is performed by comparing the state data of each device with the preset health threshold, and a device health state result is generated.

[0027] As a further scheme of the present application, the obtaining step of the device maintenance optimization scheme is specifically:

[0028] According to the device health state result, the health state is judged by analyzing the vibration frequency, temperature rise rate and pressure change rate of the device, a health state evaluation item is generated, and if the health state evaluation item exceeds the set threshold, it is determined that the maintenance period needs to be adjusted.

[0029] The health state evaluation item is called, and the maintenance period setting value is calculated by combining the device workload parameter and the original failure frequency parameter.

[0030] Based on the maintenance period setting value, the current maintenance period standard is compared, and if the maintenance period setting value is lower than the maintenance period standard, the maintenance period setting value is used to replace the current maintenance period, and a device maintenance optimization scheme is generated.

[0031] As a further scheme of the present application, the obtaining step of the production bottleneck analysis result is specifically:

[0032] According to the device maintenance optimization scheme, the device maintenance log and the energy use record are integrated, the running state of each device is analyzed, and the unit energy efficiency ratio of the device is calculated by combining the energy consumption parameter, to generate a preliminary analysis data set.

[0033] The preliminary analysis data set is subjected to data item decomposition, the load intensity of the workstation is calculated through workstation load ratio, and the ratio of energy consumption and unit efficiency load is calculated to identify the bottleneck node of load and energy consumption, and a functional data set is obtained.

[0034] The production bottleneck key indicators are extracted from the functional data set, the load intensity and energy efficiency level of the bottleneck node are classified and analyzed, and a production bottleneck analysis result is generated.

[0035] As a further scheme of the present application, the obtaining step of the electrical production line efficiency management scheme is specifically:

[0036] Based on the production bottleneck analysis result, the capacity utilization rate and the device standby time of each bottleneck are insufficient, combined with the material supply cycle of the bottleneck, each bottleneck data is executed item by item, and a bottleneck priority analysis result is established.

[0037] According to the bottleneck priority analysis result, the resource consumption rate, process flow efficiency and bottleneck frequency of each bottleneck are calculated item by item, and according to the starting time of the bottleneck and the total production time, the influence weight of resource allocation is calculated to obtain the bottleneck resource weight distribution result;

[0038] Based on the bottleneck resource weight distribution result, the production sequence and resource allocation are adjusted to obtain the electrical production line efficiency management scheme.

[0039] Compared with the prior art, the advantages and positive effects of the present application are that:

[0040] In the present application, by capturing electrical production task progress, equipment usage, process parameters and energy consumption data, accurate scheduling of production resources is realized to regulate the operation mode, working time and speed of the equipment, so that the resource allocation is more flexible, relying on cloud computing to analyze energy consumption data, the energy use mode and equipment load are optimized, and energy is effectively distributed, thereby improving energy efficiency, in combination with the equipment health evaluation model, equipment failure can also be predicted in advance, the maintenance cycle is adjusted according to the equipment state, the production interruption caused by unexpected equipment downtime is avoided, through dynamic health monitoring and maintenance plan optimization, the equipment life and operation stability are improved, the production operation cost is reduced, the efficiency of production scheduling and energy management is deeply analyzed, the production sequence and resource scheduling are adjusted according to the production bottleneck, and the production line operation efficiency is assisted to improve, which is complementary in realizing efficient use of resources, energy efficiency improvement, accurate equipment maintenance and production efficiency maximization, not only optimizes the resource scheduling and energy distribution in the production process, but also realizes real-time monitoring of the equipment state and improvement of the production efficiency, so that the production line can maintain stability while obtaining higher response speed and production flexibility. BRIEF DESCRIPTION OF DRAWINGS

[0041] Figure 1 The system flowchart of the present application;

[0042] Figure 2 The flowchart of the electrical production resource scheduling optimization result in the present application;

[0043] Figure 3 The flowchart of the energy distribution strategy in the present application;

[0044] Figure 4 The flowchart of the energy saving optimization result in the present application;

[0045] Figure 5 The flowchart of the equipment health state result in the present application;

[0046] Figure 6 The flowchart of the equipment maintenance optimization scheme in the present application;

[0047] Figure 7 Flow chart for bottleneck analysis result production in the present application;

[0048] Figure 8 Flow chart for electrical production line efficiency management scheme in the present application. DETAILED DESCRIPTION

[0049] In order to make the purpose, technical scheme and advantages of the present application more clear, the present application is further described in detail below in combination with the drawings and examples. It should be understood that the specific examples described herein are only used to explain the present application and do not limit the present application.

[0050] In the description of the present application, it should be understood that the terms "length", "width", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer" and the like indicate the orientation or positional relationship based on the orientation or positional relationship shown in the drawings, and are only for the convenience of describing the present application and simplifying the description, and do not indicate or imply that the device or element referred to must have a particular orientation, be constructed and operated in a particular orientation, and therefore cannot be understood as a limitation on the present application. In addition, in the description of the present application, the meaning of "a plurality of" is two or more, unless otherwise explicitly and specifically limited.

[0051] Please refer to Figure 1 The electrical manufacturing execution management system based on cloud computing comprises:

[0052] The scheduling optimization module captures electrical production task progress, equipment usage, process parameters and energy consumption data, and performs scheduling optimization on the equipment through the cloud computing platform, adjusts the equipment running mode, working time and speed, and generates electrical production resource scheduling optimization results;

[0053] The energy management module uses cloud computing to analyze energy consumption data based on the electrical production resource scheduling optimization results, optimizes the energy use mode of the electrical manufacturing equipment, adjusts the running load of the equipment, generates an energy distribution strategy, and optimizes the manufacturing equipment load and energy efficiency according to the energy distribution strategy combined with energy efficiency evaluation data, and generates an energy saving optimization result;

[0054] The equipment health and state monitoring module monitors the running state of the manufacturing equipment through the cloud platform based on the energy saving optimization result, combines the equipment health evaluation model to perform fault prediction and health management on the equipment, generates equipment health state results, adjusts the maintenance cycle according to the equipment health state results, and generates an equipment maintenance optimization scheme;

[0055] The production efficiency improvement module evaluates the influence of current production scheduling and energy management on production efficiency based on the equipment maintenance optimization scheme, analyzes production bottlenecks, generates production bottleneck analysis results, adjusts production order and resource scheduling according to the production bottleneck analysis results, and generates an electrical production line efficiency management scheme.

[0056] The electrical production resource scheduling optimization result includes production task allocation, equipment operation mode, process parameter setting, work time arrangement, energy allocation strategy includes energy consumption allocation ratio, load distribution, energy efficiency evaluation index, energy saving optimization result includes load configuration, energy efficiency improvement parameter, energy saving adjustment scheme, equipment health status result includes temperature monitoring index, pressure detection value, running time record, equipment maintenance optimization scheme includes maintenance time plan, fault prevention measure, health status management, production bottleneck analysis result includes production bottleneck point, resource allocation efficiency, scheduling response situation, electrical production line efficiency management scheme includes production order optimization, resource scheduling strategy, efficiency improvement plan.

[0057] Please refer to Figure 2 , the acquisition steps of the electrical production resource scheduling optimization result are:

[0058] By capturing electrical production task progress, equipment usage, process parameters and energy consumption data, analyzing the current load, running power consumption and equipment utilization time of the equipment, selecting the equipment whose load value reaches the set initial threshold, and generating the equipment screening set meeting the load condition;

[0059] According to the data set of equipment usage, first, collect the load value, running power consumption and equipment usage time of each equipment, set the load initial threshold through monitoring the equipment operation data, and classify the load condition of the equipment. For each equipment, compare its current load value with the load initial threshold. If the load value of the equipment is lower than the initial threshold, it means that the work load of the equipment is insufficient and does not meet the scheduling requirements of the current task, so 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 meeting the scheduling requirements. Through the screening step, all equipment data meeting the load condition will be retained and an equipment screening set will be generated. During the screening process, the data of the equipment not meeting the load requirements will be completely excluded to ensure that the subsequent equipment scheduling task can realize more efficient resource allocation, not only improving the resource utilization efficiency, but also ensuring that the equipment load does not exceed the load and avoiding equipment failure or performance decline caused by excessive load.

[0060] According to the equipment screening set meeting the load condition, extract the running power consumption and equipment usage time data of the equipment, calculate the equipment scheduling weight through the parameters of equipment usage time and load condition, and screen the equipment through the weight, using the formula:

[0061]

[0062] The device scheduling priority weight result is obtained, wherein, represents the device scheduling priority weight result, represents the number of devices meeting the load condition, is the average power consumption of the device, is the inverse of the use time of the device, is the current load condition of the device, represents the total number of devices;

[0063] The advantage of the formula is that by combining the power consumption, use time and load condition of the device, the scheduling priority of the device is evaluated in a weighted manner, ensuring that devices with heavy load, high power consumption and long use time are prioritized for scheduling, optimizing the overall scheduling efficiency of the devices. The formula can assist the scheduling system in prioritizing devices with high load, high energy consumption and long running time among multiple devices, thereby improving the overall production efficiency of the system;

[0064] The number of devices meeting the load condition is 15, which is obtained by collecting device usage data. This indicates that there are 15 devices in the current system that meet the scheduling conditions. Secondly, kW represents the average power consumption of the device, which is obtained from the power consumption data of the device during use. The device power consumption value is used to measure the energy consumption of the device. For devices with high power consumption, they should be prioritized for scheduling to optimize overall energy efficiency, is calculated by monitoring the total use time of the device. This data helps to assess the workload of the device and avoid excessive wear and tear caused by long-term operation of some devices, which affects the reliability of the overall device, is the current load ratio of the device, which is obtained from real-time monitoring system data, representing the ratio of the current load to the maximum load. After substituting the parameter values, the formula calculation result is:

[0065]

[0066] The result is used to reflect the priority weight of device scheduling, indicating the relative priority of device scheduling under certain load conditions. To improve the rationality of the scheduling strategy, a device type difference factor and a task matching degree weight are introduced when calculating the weight value to avoid the deviation caused by simply selecting devices according to load, power consumption and use time. Through this optimized scheduling weight model, devices with high power consumption or load are no longer blindly selected, but the functional attributes of the devices, current task requirements, historical performance and other multi-dimensional parameters are considered, so that the device scheduling is more targeted and accurate. With the improved scheduling algorithm, the system can allocate resources more finely according to the adjusted weight value, thereby improving device utilization efficiency, avoiding resource waste, and ensuring efficient and balanced production scheduling process.

[0067] Based on the device scheduling priority weight result, the standard threshold of power utilization is set according to the load and power consumption of the device, and the devices meeting the conditions are selected to join the scheduling optimization list to generate the electrical production resource scheduling optimization result;

[0068] After obtaining the device scheduling priority weight, in order to further improve the accuracy of scheduling, a device energy efficiency evaluation mechanism is introduced to screen the power utilization of the device. Unlike using the ratio of actual power consumption to maximum power consumption as a single screening index, this scheme introduces factors such as device operating condition matching degree and task energy efficiency adaptability to construct a multi-dimensional power utilization evaluation model. In the screening process, not only whether the power consumption reaches a certain threshold is considered, but also whether the energy consumption performance of the device under certain production tasks is reasonable. The standard threshold will be dynamically set according to the device type, running period, actual task load and historical efficiency data to avoid excluding normal devices due to low power consumption, and to reduce the misallocation of high-power devices due to task mismatch. In the final scheduling list, the devices not only meet the energy efficiency standard, but also highly match the scheduling tasks, ensuring that efficient and highly adaptive devices are allocated first. Through this optimization process, errors caused by single power determination can be effectively avoided, and the economic efficiency and overall energy efficiency of the system can be improved;

[0069] By introducing a standardized quantitative formula, the power utilization rate screening threshold is explicitly set. The specific steps include: first, based on historical equipment operation data, the power mean, load ratio and energy efficiency level of each type of equipment under normal load are counted, and the mean and standard deviation are calculated; second, the screening threshold formula is set, such as: power utilization rate threshold = (device standard power × load standard ratio) ± (k × standard deviation), where k is an adjustable tolerance coefficient to flexibly adjust the screening strictness; third, compare the real-time monitored equipment power consumption with the threshold, and only keep the equipment 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 realize adaptive screening. Through this way, the screening logic can be standardized, the scientificity and consistency of equipment selection can be improved, the deviation caused by experiential screening can be avoided, and the changes of different production task loads can also be dynamically responded, finally realizing the optimization of overall energy distribution and the improvement of energy efficiency level.

[0070] Please refer to Figure 3 The acquisition step of the energy distribution strategy is specifically:

[0071] Based on the electrical production resource scheduling optimization result, the energy consumption data and corresponding load information of each device are extracted, the real-time running load and consumed energy of each device are analyzed, and the devices meeting the energy utilization conditions are screened to form a device energy consumption set meeting the conditions;

[0072] According to the electrical production resource scheduling optimization result, first, the energy consumption data and corresponding load information of each device need to be extracted, which can be realized through the device running data recorded in the scheduling system, including the power consumption of the device under different load conditions. Then, the actual running load of each device is compared with the energy consumption, the relationship between device load and power is utilized, and the device specification parameters are combined to screen the devices with energy utilization rate lower than the preset threshold, which should be dynamically adjusted according to historical device data to ensure the rationality of the screening standard. For the devices meeting the conditions, their related parameters are continued to be extracted, the device performance evaluation model is further utilized to screen the devices with high energy efficiency, and a device energy consumption set meeting the conditions is formed, the energy efficiency data of the devices are summarized to form an energy consumption data set.

[0073] According to the device energy consumption set meeting the conditions, the power consumption of the device is analyzed, the running load and power of the device are combined, and the device energy use priority weight is calculated using the formula:

[0074]

[0075] The energy use priority of the device is obtained, wherein, represents the energy use priority of the device, represents the number of devices meeting the conditions, is the running 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;

[0076] The formula has the advantages that by introducing the device load ratio , the load weight factor and the scheduling adjustment parameter , the energy use priority of the device can be more accurately calculated, simple power and load weighting is avoided, the scheduling order of the device is optimized, and the reasonable allocation of energy is improved;

[0077] represents the number of eligible devices, is the running power of the device, is the current load ratio of the device, is the load weight factor, is the energy scheduling adjustment parameter;

[0078] In actual application, by dynamically monitoring the device power , the load ratio , the load weight factor , real-time parameters of each device are obtained, then the adjustment is made according to the overall load condition of the device;

[0079] The power of device 1 is 50kW, the load ratio is 0.8, and the load weight factor is 1.2.

[0080] The power of device 2 is 30kW, the load ratio is 0.5, and the load weight factor is 1.0.

[0081] The power of device 3 is 40kW, the load ratio is 0.6, and the load weight factor is 1.1.

[0082] According to the above formula, the calculation is as follows:

[0083]

[0084]

[0085] The result shows that the energy use priority of the device is 54.27, which represents the overall energy scheduling priority of the device. According to the value, the devices can be sorted, and the devices with higher energy consumption and heavier load are preferentially scheduled, so as to achieve a better energy allocation effect.

[0086] According to the energy use priority of the device, combined with the real-time running power and load state of the device, the device is sorted according to the priority, and the energy allocation of the device is adjusted according to the sorting to generate an energy allocation strategy;

[0087] According to the energy use priority of the device, combined with the real-time running power and load state of the device, the device is sorted according to the priority, and the energy allocation of the device is adjusted according to the sorting to generate an energy allocation strategy;

[0088] Please refer to Figure 4 , the energy saving optimization result obtaining step is specifically:

[0089] According to the energy allocation strategy, the load data and energy efficiency data of the manufacturing equipment are collected, and the load and energy efficiency data of the differentiated equipment are divided into time intervals, and the statistical indicators of the average value and standard deviation are calculated to obtain the interval equipment load and energy efficiency data set;

[0090] According to the energy allocation strategy, first, the load data and energy efficiency data of the manufacturing equipment are collected, and the load and energy efficiency data of the differentiated equipment are divided into time intervals, and the load and energy efficiency level of the equipment in each interval is calculated, and the data is statistically and arranged, and the average value, standard deviation and other statistical indicators are used to calculate the load and energy efficiency data set in the interval. The data set can help further analyze the energy efficiency performance of the equipment under different load conditions. From the load and energy efficiency data of the equipment, the power (P), voltage (V), load (L) and equipment loss (D) and other related data of each equipment in different time periods are obtained, which are used for subsequent analysis. The calculation of statistical indicators calculates the average value and standard deviation of the data by formula to describe the load and energy efficiency of the equipment in each time interval. The monitoring method of related data includes online data recording and periodic data acquisition. The energy efficiency of each load interval is calculated using the data, and the energy efficiency data set of the equipment under different load conditions is obtained for subsequent optimization analysis.

[0091] Based on the interval equipment load and energy efficiency data set, the correlation between the load interval and the energy efficiency performance is extracted, combined with the optimization target, the influence of each load interval on the energy efficiency is calculated, the mapping relationship between the key load point and the energy efficiency is analyzed, and the formula is:

[0092]

[0093] The energy efficiency score of the load interval is calculated to generate an energy efficiency optimization model, wherein, The energy efficiency score of the load interval is represented, The power is represented, The voltage is represented, The load is represented, The equipment loss is represented, The target threshold is represented;

[0094] The formula has the advantages that, by combining the effects of power, load, voltage, and equipment loss, the energy efficiency performance of the equipment under different working loads can be comprehensively reflected, and then accurate scoring basis for energy efficiency optimization is provided, including that after the loss parameter is added, the formula can effectively adjust and optimize the energy efficiency performance of the equipment, and avoid energy efficiency score deviation caused by not considering equipment loss;

[0095] : The power of the equipment under a specific load, measured value, the power is obtained by a monitoring device of the power system, and the unit is watt (W);

[0096] : The voltage of the equipment, which is obtained in real time by an ammeter and a voltmeter, and the unit is volt (V);

[0097] : The load of the equipment, which represents the load degree of the equipment within a given time, and the unit is kilowatt (kW), which is measured by a load sensor;

[0098] : The loss of the equipment during operation, which is represented as the part of the energy conversion process of the equipment that is not effectively utilized, and the unit is watt (W), which is calculated by a performance monitoring tool of the equipment;

[0099] : The target threshold of the energy efficiency of the equipment, which is set as the best energy efficiency value specified in the equipment manufacturer or industry standard, and the unit is watt / kilowatt-hour (kWh);

[0100] The set value is: , , , , ;

[0101] The formula is substituted and calculated:

[0102]

[0103]

[0104]

[0105] The result shows that the energy efficiency score under load is negative, indicating that the current loss of the device is too high, resulting in its energy efficiency performance not meeting expectations, and further optimization measures are needed.

[0106] According to the energy efficiency optimization model, the energy efficiency is adjusted, and by analyzing the energy efficiency optimization results of each device load interval, the load distribution is adjusted to generate an energy-saving optimization scheme.

[0107] According to the energy efficiency optimization model, first of all, the energy efficiency optimization results of each device load interval need to be analyzed, including extracting the energy efficiency score under each load interval from the model, and conducting a detailed analysis of the relationship between the energy efficiency score and the load interval. Through analysis, it can be identified that under the load interval, the device energy efficiency is ideal, and which interval has the space for energy efficiency optimization. First, calculate the energy efficiency score of each load interval through the model to obtain the energy efficiency score under each interval. The score reflects the level of change in device energy efficiency under specific load conditions. By comparing the scores of different intervals, the load interval with potential energy-saving optimization space can be identified, and the optimization priority can be determined. Auxiliary tools such as optimization algorithms and analysis tools will be used for analysis, and then the load distribution will be adjusted, and an energy-saving optimization scheme will be generated. Adjusting the load distribution is based on the analysis of optimization results and energy efficiency scores to ensure that the device can achieve the best energy efficiency state under different load conditions, thereby reducing energy consumption and improving the overall system energy efficiency performance.

[0108] Please refer to Figure 5 , the steps of obtaining the device health state result are as follows:

[0109] According to the energy-saving optimization results, extract the device operating state information, including temperature, vibration, pressure parameters, determine the deviation of the current state, calculate the abnormal value of each device operating state, and generate a device health state data set.

[0110] According to the device operating state information received by the cloud platform, first extract key parameters such as device temperature, pressure, vibration and load, etc. Use the sensor interface to obtain real-time data values, and compare them with the standard range in the historical record to identify abnormal values. By analyzing the trend of each device data, the frequency and duration of abnormal states are obtained, thereby preliminarily identifying the 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 is merged to form a unified health evaluation parameter set. This parameter set contains the performance of the device in different monitoring dimensions, generating a device health state data set to provide a basis for subsequent health assessment.

[0111] The device health state data set is processed to remove missing and abnormal data, and the device differentiated state information is integrated into a unified health evaluation parameter vector through multi-dimensional data fusion technology to generate a device health evaluation data set;

[0112] When processing the device health state data set, first, all sensor data is cleaned to remove offline and large error data points, and the data is normalized to make it suitable for subsequent fusion calculation. The health state data of multiple dimensions such as temperature, pressure, and vibration is combined through certain weight coefficients using weighted average to generate a comprehensive device health parameter, which will serve as a quantitative indicator of the overall health of the device. This process not only ensures the accuracy of the data, but also comprehensively reflects the running health of the device from multiple dimensions, ensuring the scientificity and accuracy of the device health evaluation, and laying a solid foundation for subsequent fault prediction and health management of the device.

[0113] Based on the device health evaluation data set, the health of each device is predicted by comparing the state data with the preset health threshold, using the formula:

[0114]

[0115] The device health state result is generated, wherein, represents the device health state result, represents the device temperature state parameter, represents the device vibration state parameter, represents the device pressure state parameter, is the weight coefficient;

[0116] The advantage of the formula is that it uses multi-dimensional data of the device (such as temperature, vibration, and pressure) and combines weight coefficients to comprehensively evaluate the health of the device, rather than relying on a single parameter. This method better adapts to the performance of the device under different working conditions, providing more accurate health evaluation results, especially for complex devices, significantly improving the accuracy and timeliness of fault prediction;

[0117] The temperature, vibration, and pressure data of the device are obtained through sensor monitoring, and the real-time values of , and are obtained, such as temperature , vibration mm / s, and pressure MPa. The weight coefficients of each dimension are set using empirical data or historical data, such as , , , and then the parameters are substituted into the formula to calculate:

[0118]

[0119]

[0120] The result shows that the current health status evaluation value of the equipment is 32.375, based on which it can be further judged whether the equipment is in a healthy state, whether it needs to be overhauled, or whether there is a potential failure risk.

[0121] Please refer to Figure 6 The obtaining step of the equipment maintenance optimization scheme is specifically:

[0122] According to the equipment health status result, the health status is judged by analyzing the vibration frequency, temperature rise rate and pressure change rate of the equipment, and a health status evaluation item is generated. If the health status evaluation item exceeds the set threshold, it is determined that the maintenance period needs to be adjusted.

[0123] The judgment of the equipment health status is the key first step of the maintenance period adjustment. Therefore, the key indicators of the equipment operation need to be monitored comprehensively, such as the vibration frequency, temperature rise rate and pressure change rate. The real-time data collection of the indicators is completed by the sensors installed on the equipment, which can capture the operation data of the equipment under different working conditions. In order to ensure the accuracy and practicability of the equipment monitoring data, the original operation data collected will be preliminarily preprocessed, but the traditional "outlier rejection" and "global smoothing" methods will not be simply used in the processing process. On the contrary, 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 the data that are mutated or exceed the statistical distribution range will be marked as "potential anomaly" instead of being directly deleted. Such data will enter the subsequent state diagnosis analysis module and be reserved as a reference clue for potential failure. The smoothing processing also uses the interval dynamic window weighting method, which is only executed in the scene where the trend judgment is needed, retains the key fluctuation characteristics, and avoids hiding the abnormal behavior of the equipment. 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 equipment, thereby enhancing the timeliness and accuracy of the fault prediction and providing more real and reliable data support for the equipment health assessment and maintenance strategy formulation. Ensure that the high-quality data enter the analysis stage. After the data are processed, statistical analysis is performed on each indicator, including calculating the average value and standard deviation, etc., to evaluate the deviation degree of the indicators from the equipment performance standard. These statistical results will directly affect the evaluation of the equipment health status. Once it is found that the equipment health indicators exceed the normal range, the adjustment demand of the maintenance period will be triggered, thereby generating a health status evaluation item to provide decision support for the next step of maintenance period adjustment.

[0124] The health status evaluation item is called, combined with the working load parameter and the original failure frequency parameter of the equipment, and the formula:

[0125]

[0126] The maintenance period setting value is calculated, wherein, represents the maintenance period setting value, represents the work load, represents the health state evaluation item, represents the original failure frequency, represents the original failure frequency parameter;

[0127] If specific numerical substitution and calculation are needed, we first need to assume the actual available data values, and the following example values are used for specific calculation process:

[0128] The set values are: the (work load parameter) is 150, the (health state evaluation item) is 0.85, the (original failure frequency) is 0.25;

[0129] To effectively identify the bottleneck nodes of load and energy imbalance in the production process, the system constructs a functional data set based on multi-dimensional data, and models the indicators through the load and energy consumption data at the workstation level. Among them, represents the actual load amount of the workstation in unit time, represents the energy consumption value in the same period; is an adjustment factor introduced for optimization model, which is used to reflect the weighted processing of the energy consumption sensitivity of the specific process, and and are respectively the preset proportional coefficients according to different production processes and equipment characteristics, the initial values of which are obtained by fitting historical production data and can be dynamically adjusted. In addition, represents the process performance score at the key process stage, which is used to quantify the performance of the workstation in the comprehensive scheduling efficiency, and is the evaluation factor of the comprehensive production task response speed. In order 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 manual intervention correction is supported. Through this model construction method, not only the actual meaning and source of each parameter are clear, but also the self-adaptive adjustment ability for different production environments is realized, so as to more scientifically identify the production bottleneck nodes and optimize the scheduling strategy and energy efficiency matching;

[0130] Calculate : ;

[0131] Calculate the product : ;

[0132] Calculate the square root : ;

[0133] Calculate the final : ;

[0134] The resulting maintenance cycle setting value is 20.76, which means that according to the current load and health status assessment of the equipment, the maintenance cycle setting value is 20.76 units of time, which can be used to adjust the maintenance strategy to ensure that the equipment is running in the best state.

[0135] Based on the maintenance cycle setting value, compare it with the current maintenance cycle standard. If the maintenance cycle setting value is lower than the maintenance cycle standard, replace the current maintenance cycle with the maintenance cycle setting value to generate an equipment maintenance optimization scheme.

[0136] The final adjustment of the maintenance cycle is based on the comparison and analysis of the existing cycle standard and the recommended cycle. By calculating the difference ratio between the two, it is determined whether the cycle needs to be updated. The recommended maintenance cycle is compared with the current cycle standard, and by calculating their difference, the adjustment requirement of the maintenance frequency is evaluated. If the new recommended cycle is shorter than the existing standard, it means that more frequent maintenance is needed to ensure the reliability and safety of the equipment operation. Through such comparison and analysis, the final cycle adjustment recommendation can be determined. If the setting 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.

[0137] Please refer to Figure 7 , the steps for obtaining the production bottleneck analysis result are as follows:

[0138] According to the equipment maintenance optimization scheme, integrate the equipment maintenance log and energy use record, analyze the running state of each equipment, and calculate the unit energy efficiency ratio of the equipment based on the energy consumption parameters to generate a preliminary analysis data set.

[0139] Integrate the collected equipment maintenance log and energy use record, analyze the running state of each equipment based on the equipment efficiency parameters, and analyze the running time and energy consumption of each equipment in detail. Through historical data comparison and analysis, calculate the unit time energy efficiency of each equipment, rank the energy efficiency of the equipment, and further analyze the correlation between equipment energy efficiency and production scheduling to ensure data consistency. 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 strategy. Classify and summarize the energy efficiency results according to equipment and generate a preliminary analysis data set.

[0140] Perform data item decomposition on the preliminary analysis data set, calculate the load intensity of the workstation through workstation load ratio, and calculate the ratio of energy consumption and unit efficiency load. Use the formula:

[0141]

[0142] Identify the bottleneck nodes of load and energy consumption, get the functional dataset, wherein, represent the workstation load, represent the energy consumption, and is the coefficient of adjusting the proportion relationship between load and energy consumption, represent the performance indicators of key production links, represent the production scheduling efficiency;

[0143] In order to identify the mismatch points between load and energy consumption and locate the production bottleneck, the system constructs a functional dataset and uses a formula for evaluation and analysis. In the formula, represents the average load value of each workstation in a specified period (unit: kW), which is directly extracted from the historical operation record of the workstation; represents the total energy consumption in the same period (unit: kWh), which is obtained by summarizing the energy consumption monitoring module; and are adjustment parameters, respectively representing the load weight factor and the energy consumption weight factor, which are used to regulate the contribution proportion of load and energy consumption to the weight of bottleneck identification, and their initial values are set to 1.0 by default, which can be adjusted adaptively according to different process sections through expert experience or historical training data; is the performance indicator (such as unit energy consumption ratio, production cycle efficiency, etc.) when the key process is executed, which measures the response speed and execution ability of the task in this process. All parameters and their value ranges can be set and adjusted in the system configuration interface, and are dynamically updated during the model running process. Through this clear parameter and transparent calculation method, the indexes in the bottleneck identification process have real physical meaning and operability, thereby improving the precise adaptation ability and control effect of the scheduling system in complex production environment;

[0144] The advantage of the formula is that by adjusting the coefficient of the proportion relationship between load and energy consumption and , the formula weight can be adjusted according to the actual production conditions to adapt to the specific needs 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;

[0145] Set the actual load of a workstation to 120 units, the energy consumption to 150 units, the coefficient of adjusting the proportion relationship between load and energy consumption to 0.8, to 1.2, and the formula calculation is:

[0146]

[0147] The results show that the production efficiency of the workstation under the current configuration is optimal, and and The selection reflects the actual adjustment of the device load and the energy efficiency weight.

[0148] According to the model calculation result, the current workstation shows high production efficiency under the configured parameters. This conclusion is not based on a single indicator, but a multi-dimensional efficiency score model formed by comprehensive evaluation of key performance parameters such as load intensity, unit energy consumption and production rhythm. The adjustment coefficient (load and energy consumption weight factor) and the energy and output ratio adjustment coefficient introduced in the model are generated by the system based on a large amount of historical operation data fitting, while allowing dynamic fine-tuning according to specific process requirements or production strategy. These two parameters are used to balance the proportion of device load in scheduling priority and the weight of unit energy consumption in output evaluation, respectively, to ensure the adaptability of the model in different scenarios. Through sensitivity analysis and simulation comparison, the current parameter configuration can achieve optimal scheduling and energy efficiency matching in most typical task scenarios, forming the current "efficiency optimal" conclusion. This conclusion is a data-driven result, supported by verification tests, providing a reliable basis for scheduling strategy adjustment and energy efficiency optimization.

[0149] Extract the production bottleneck key indicators from the functional data set, classify and analyze the load intensity and energy efficiency level of the bottleneck nodes, and generate the production bottleneck analysis result;

[0150] Extract the production bottleneck key indicators from the intermediate result set, mark and classify the high load and low efficiency nodes in the data set, use data clustering analysis method to distinguish different production bottleneck types, analyze the specific characteristics and causes of each type of bottleneck node in detail, compare the analysis result with the production flow chart, find out the key influencing points in the process, and formulate targeted improvement measures, finally generate the production bottleneck analysis result, which can accurately identify and solve the efficiency obstacles in the production process, so as to realize the optimization and improvement of the whole production process, not only increase the production efficiency, but also improve the utilization efficiency of resources, help to reduce the cost and improve the product quality;

[0151] To accurately identify and classify various types of production bottlenecks, the system introduces data clustering analysis method, based on the constructed functional data set to identify and classify the pattern of bottleneck nodes. The clustering algorithm used is an improved K-means algorithm, combined with four key dimensions of equipment operating load, unit energy efficiency ratio, task execution period, resource waiting time to form a multi-dimensional feature vector as the clustering input data set. The data sources include equipment maintenance logs, energy consumption records, and production rhythm tracking system to ensure the comprehensiveness and timeliness of the features. During the clustering process, the system automatically determines the optimal number of clusters through indicators such as silhouette coefficient and Davies-Bouldin index to ensure that the clustering results have good internal consistency and class separation. After each class of bottleneck node clustering, the system further analyzes its running mode, frequently appearing position in the process, and resource occupation status to identify the possible causes such as uneven equipment load, low energy efficiency, or poor material supply. Finally, through the induction of various bottleneck characteristics, data basis is provided for subsequent formulation of targeted scheduling strategies and equipment optimization schemes. This method enhances the structured understanding of complex production bottleneck problems, avoids the one-sidedness of human judgment, and improves the scientificity and universality of the solution.

[0152] Please refer to Figure 8 , the acquisition steps of the electrical production line efficiency management scheme are as follows:

[0153] Based on the production bottleneck analysis results, the capacity utilization rate and equipment standby time of each bottleneck are insufficient, combined with the material supply cycle of the bottleneck, the bottleneck priority analysis results are established by performing item-by-item sorting for each bottleneck data;

[0154] Based on the bottleneck data of the electrical production line, the capacity utilization rate and equipment standby time of each bottleneck are obtained, and the parameters are called one by one to quantitatively analyze the capacity utilization rate. The ratio of actual output to planned output of each device per unit time is calculated, and the capacity utilization rate is further compared based on the bottleneck data. The resource allocation demand of each bottleneck is quantified by the output rate difference, and the standby time parameter of the bottleneck is refined. The cumulative standby time is called as a supplementary index of bottleneck efficiency by filtering the interruption data during the bottleneck period and sampling the cumulative standby time of the equipment standby period multiple times. The material supply cycle is analyzed by adjusting the supply cycle parameter to evaluate the bottleneck priority data, so that the capacity utilization rate, standby time, and supply cycle data have consistency. Each bottleneck is executed item-by-item sorting to obtain the bottleneck priority sequence, and the bottleneck priority analysis results are established to provide data support for the subsequent steps.

[0155] According to the bottleneck priority analysis results, the resource consumption rate, process flow efficiency, and bottleneck frequency of each bottleneck are calculated one by one, and the influence weight of resource allocation is calculated according to the start time and total production time of the bottleneck. The formula is:

[0156]

[0157] The bottleneck resource weight distribution result is obtained, wherein, represents the bottleneck resource weight, represents the resource consumption rate, represents the manufacturing flow conversion efficiency, is the bottleneck frequency, is the total production time, is the bottleneck start time, is the bottleneck priority;

[0158] The formula has the beneficial effect that the resource consumption rate and the process flow conversion efficiency are combined, and the absolute time difference is introduced, thereby further improving the accuracy of the weight calculation;

[0159] represents the resource consumption rate, which can be obtained by monitoring the resource consumption amount of the equipment divided by the unit time;

[0160] represents the process flow conversion efficiency, which can be obtained by measuring the average completion time of the unit process;

[0161] represents the bottleneck frequency, which can be calculated by counting the number of times the bottleneck appears in each production process;

[0162] is the total production time, which is obtained by the difference between the process start time and the process end time;

[0163] is the bottleneck start time, that is, the time point at which the bottleneck first appears in the total production process;

[0164] represents the bottleneck priority sequence value;

[0165] Specific numerical calculation: let , , , , , ;

[0166] Substitute into the formula:

[0167]

[0168] The result shows that the bottleneck resource weight is 0.15, which is used to evaluate the occupation degree of the bottleneck to the resource, thereby providing a basis for subsequent resource scheduling.

[0169] Based on the bottleneck resource weight distribution result, the production sequence and resource allocation are adjusted to obtain the electrical production line efficiency management scheme;

[0170] The bottleneck resource weight distribution result is called, each bottleneck is compared according to the bottleneck priority and the resource weight, the resource weight value is taken as the reference factor of the bottleneck priority, the priority of each bottleneck in the production process is allocated one by one, the resource allocation priority of each bottleneck is compared and refined, the adaptability of the resource allocation priority under the condition of each bottleneck is ensured, the resource parameters are gradually allocated to each bottleneck link, the comparison result of the bottleneck priority and the resource allocation weight is called, the production sequence is further adjusted adaptively, the consistency of the bottleneck link in the resource allocation and the production sequence is ensured, the resource weight and the priority of each bottleneck link in the production line are gradually adjusted, and the electrical production line efficiency management scheme is obtained.

[0171] In order to improve the resource allocation efficiency of the whole production process, the system based on the identified bottleneck priority, the resource allocation demand of each bottleneck node in the process is fine management. The resource parameters here refer to three types of core production factors: ① equipment running time (unit: hour), ② energy allocation (unit: kWh), ③ labor and logistics support resources (unit: working hours and process flow rhythm), which are quantified through historical data and real-time monitoring model, and the weight is set based on task load, energy efficiency level and frequency in bottleneck process. In the allocation mechanism, first, the allocation priority of each bottleneck is obtained based on the bottleneck resource weight calculation model, which considers the bottleneck starting time, duration and resource consumption rate, and forms a quantifiable bottleneck score. Then the system allocates resources according to the priority, and after meeting the complete resource demand of high-priority bottlenecks, it allocates sub-optimal bottlenecks according to the marginal effect of the remaining resources. In 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 adaptation ability of scheduling strategy to various bottleneck situations, and ensures the dynamic balance of resource allocation in complex processes, effectively supporting the flexible scheduling and continuous optimization of electrical manufacturing execution system.

[0172] The above is only a preferred embodiment of the present application, not other forms of the present application, any skilled in the art can use the above disclosed technology to change or modify equivalent embodiments applied to other fields, but any simple modification, equivalent change and modification of the above embodiments without departing from the technical solution content of the present application, according to the technical essence of the present application, still belongs to the protection scope of the technical solution of the present application.

Claims

1. A cloud-based electrical manufacturing execution management system, characterized in that, The system includes: The scheduling optimization module captures data on electrical production task progress, equipment usage, process parameters, and energy consumption. It then uses a cloud computing platform to optimize equipment scheduling, adjusting equipment operating modes, working hours, and speeds to generate optimized electrical production resource scheduling results. Based on the electrical production resource scheduling optimization results, the energy management module uses cloud computing to analyze energy consumption data, optimizes the energy usage mode of electrical manufacturing equipment, adjusts the operating load of equipment, generates an energy allocation strategy, and optimizes the load and energy efficiency of manufacturing equipment according to the energy allocation strategy and energy efficiency assessment data to generate energy-saving optimization results. Based on the energy-saving optimization results, the equipment health and status monitoring module monitors the operating status of manufacturing equipment through a cloud platform, combines the equipment health assessment model to perform fault prediction and health management of the equipment, generates equipment health status results, adjusts the maintenance cycle according to the equipment health status results, and generates equipment maintenance optimization plans. The specific steps for obtaining the device health status result are as follows: Based on the energy-saving optimization results, extract equipment operating status information, including temperature, vibration, and pressure parameters, determine the deviation of the current status, calculate the abnormal values ​​of the operating status of each piece of equipment, and generate an equipment health status dataset. The equipment health status dataset 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 an equipment health assessment dataset. Based on the equipment health assessment dataset, health prediction is performed by comparing the status data of each device with a preset health threshold, and the equipment health status result is generated. The specific steps for obtaining the equipment maintenance optimization plan are as follows: Based on the equipment health status results, the health status is judged by analyzing the equipment's vibration frequency, temperature rise rate, and pressure change rate, and a health status assessment item is generated. If the health status assessment item exceeds the set threshold, it is determined that the maintenance cycle needs to be adjusted. The health status assessment item is invoked, and the maintenance cycle setting value is calculated by combining the equipment's workload parameters and original fault frequency parameters. Based on the maintenance cycle setting value, and compared with the current maintenance cycle standard, if the maintenance cycle setting value is lower than the maintenance cycle standard, the current maintenance cycle is replaced by the maintenance cycle setting value to generate an equipment maintenance optimization plan. Based on the equipment maintenance optimization scheme, the production efficiency improvement module assesses the impact of current production scheduling and energy management on production efficiency, analyzes production bottlenecks, generates production bottleneck analysis results, adjusts production sequence and resource scheduling according to the production bottleneck analysis results, and generates an electrical production line efficiency management scheme.

2. The cloud-based electrical manufacturing execution management system according to claim 1, characterized in that, The specific steps for obtaining the electrical production resource scheduling optimization results are as follows: By capturing data on electrical production task progress, equipment usage, process parameters, and energy consumption, the system analyzes the current load, operating power consumption, and equipment utilization time of the equipment, selects equipment whose load values ​​reach the set initial threshold, and generates a set of equipment that meets the load conditions. Based on the set of devices that meet the load conditions, extract the operating power consumption and usage time data of the devices. Calculate the device scheduling weight using the parameters of device usage time and load conditions. Filter and schedule devices according to the weights to obtain the device scheduling priority weight result. Based on the equipment scheduling priority weight results, combined with the equipment load and power consumption, a standard threshold for power consumption utilization is set, and qualified equipment is selected to be added to the scheduling optimization list, generating electrical production resource scheduling optimization results.

3. The cloud-based electrical manufacturing execution management system according to claim 2, characterized in that, The specific steps for obtaining the energy allocation strategy are as follows: Based on the electrical production resource scheduling optimization results, the energy consumption data and corresponding load information of each piece of equipment are extracted. By analyzing the real-time operating load and energy consumption of each piece of equipment, and screening out equipment that meets the energy utilization conditions, a set of equipment energy consumption that meets the conditions is formed. Based on the set of eligible equipment energy consumption, analyze the power consumption of the equipment, and calculate the priority weight of equipment energy use by combining the equipment's operating load and power; Based on 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 priority, and the energy allocation of the equipment is adjusted according to the sorting to generate an energy allocation strategy.

4. The cloud-based electrical manufacturing execution management system according to claim 3, characterized in that, The specific steps for obtaining the energy-saving optimization results are as follows: According to the energy allocation strategy, the load and energy efficiency data of the manufacturing equipment are used, and the load and energy efficiency data of the differentiated equipment are divided into time intervals. By calculating the statistical indicators of average value and standard deviation, the set of equipment load and energy efficiency data for each interval is obtained. Based on the set of equipment load and energy efficiency data in the aforementioned intervals, the correlation between load intervals and energy efficiency performance is extracted. Combined with the optimization objective, 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. Based on the energy efficiency optimization model, energy efficiency adjustments are made. By analyzing the energy efficiency optimization results for each equipment load range, load allocation is adjusted to generate an energy-saving optimization scheme.

5. The cloud-based electrical manufacturing execution management system according to claim 1, characterized in that, The specific steps for obtaining the production bottleneck analysis results are as follows: Based on the equipment maintenance optimization plan, integrate equipment maintenance logs and energy usage records, analyze the operating status of each device, and calculate the unit energy efficiency ratio of the device by combining energy consumption parameters, and generate a preliminary analysis dataset. The preliminary analysis dataset is decomposed into data items. The load intensity of the workstation is calculated by 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, thus obtaining a functional dataset. Key indicators of production bottlenecks are extracted from the functional dataset, and the load intensity and energy efficiency level of bottleneck nodes are classified and analyzed to generate production bottleneck analysis results.

6. The cloud-based electrical manufacturing execution management system according to claim 5, characterized in that, The specific steps for obtaining the electrical production line efficiency management solution are as follows: Based on the production bottleneck analysis results, the capacity utilization rate and equipment standby time of each bottleneck are obtained. Combined with the material supply cycle of the bottleneck, the data of each bottleneck is sorted item by item to establish the bottleneck priority analysis results. Based on the bottleneck priority analysis results, the resource consumption rate, process flow efficiency and bottleneck frequency of each bottleneck are calculated item by item. Based on the bottleneck start time and total production time, 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 an electrical production line efficiency management scheme.

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