Machine energy consumption management method and device, equipment and storage medium

Abstract

The invention relates to the technical field of energy consumption management, in particular to a machine energy consumption management method and device, equipment and a storage medium. The method comprises the following steps: according to a preset energy consumption prediction model and actual machine parameters of a target machine, obtaining predicted energy consumption data of the target machine, wherein the predicted energy consumption data comprise a plurality of energy consumption predicted values corresponding to a plurality of detection time periods; acquiring actual energy consumption data of the target machine, wherein the actual energy consumption data comprises a plurality of energy consumption detection values corresponding to the plurality of detection time periods; according to the energy consumption predicted value and the energy consumption detection value corresponding to the target detection time period, judging whether the energy consumption of the target machine table in the target time period is abnormal or not; and under the condition that the ratio of the number of the detection time periods of the abnormal energy consumption to the total number of the plurality of detection time periods is greater than a preset proportion threshold value, pushing alarm information of the target machine to the target equipment. According to the method, energy consumption fluctuation caused by real-time change of machine parameters can be considered, energy consumption in multiple time periods is monitored, whether alarm information is pushed or not is judged, the probability of false alarm and missing alarm is reduced, and an abnormal machine can be managed conveniently.

Description

Technical Field

[0001] The embodiments of the present application relate to the field of energy consumption management technology, and in particular, to a method and apparatus, device, and storage medium for managing energy consumption of a machine. Background Art

[0002] In manufacturing companies, the energy consumption of production equipment is an important factor affecting production costs, and implementing energy-saving strategies for equipment is a common means for companies to reduce costs and improve economic benefits. In related technologies, it is usually necessary to analyze the historical energy consumption data of equipment, such as monthly and quarterly average energy consumption data, to assist in energy-saving decisions.

[0003] However, for non-standard equipment, such as the production machines in solar cell factories, the processes and parameters used by various production units are different, and the parameters fluctuate in real time during the operation of the machines. If the energy consumption of the machines is predicted and evaluated by methods such as average value calculation, it may not only lead to insufficient prediction accuracy, but also cause waste of manpower due to frequent false alarms, making it difficult for technicians to find the cause of abnormal energy consumption. Summary of the invention

[0004] In view of this, the embodiments of the present application provide a machine energy consumption management method and device, equipment, and storage medium, which can take into account the energy consumption fluctuations caused by real-time changes in machine parameters, monitor the energy consumption in multiple time periods and determine whether to push alarm information, thereby reducing the probability of false alarms and missed alarms, so as to manage abnormal machines.

[0005] According to a first aspect of an embodiment of the present application, a method for managing machine energy consumption is provided, comprising:

[0006] According to a preset energy consumption prediction model and actual machine parameters of the target machine, predicted energy consumption data of the target machine is obtained, wherein the preset energy consumption prediction model is established based on historical energy consumption data and historical machine parameters of multiple machines, and the predicted energy consumption data includes multiple energy consumption prediction values ​​corresponding to multiple detection time periods;

[0007] Acquire actual energy consumption data of the target machine, wherein the actual energy consumption data includes a plurality of energy consumption detection values ​​corresponding to the plurality of detection time periods;

[0008] According to the energy consumption prediction value and the energy consumption detection value corresponding to the target detection period, determining whether the energy consumption of the target machine in the target period is abnormal, wherein the target period is any period among the multiple detection periods;

[0009] When the ratio of the number of detection periods of abnormal energy consumption to the total number of the plurality of detection periods is greater than a preset ratio threshold, the alarm information of the target machine is pushed to a target device, which is a device of a process responsible person.

[0010] As an optional implementation, in the first aspect of the embodiment of the present application, judging whether the energy consumption of the target machine in the target time period is abnormal according to the energy consumption prediction value and the energy consumption detection value corresponding to the target detection time period includes:

[0011] When the energy consumption detection value corresponding to the target detection period is greater than the energy consumption prediction upper limit, or the energy consumption detection value corresponding to the target detection period is less than the energy consumption prediction lower limit, it is determined that the energy consumption of the target machine in the target period is abnormal;

[0012] When the energy consumption detection value corresponding to the target detection period is less than or equal to the energy consumption prediction upper limit, and the energy consumption detection value corresponding to the target detection period is greater than or equal to the energy consumption prediction lower limit, it is determined that the energy consumption of the target machine in the target period is normal;

[0013] The energy consumption prediction upper limit and the energy consumption prediction lower limit are determined according to the energy consumption prediction value corresponding to the target detection period. As an optional implementation, in the first aspect of the embodiment of the present application, the method further includes:

[0014] The energy consumption prediction upper limit and the energy consumption prediction lower limit corresponding to each detection period are summed up respectively to obtain the total value of the energy consumption prediction upper limit and the total value of the energy consumption prediction lower limit corresponding to the target machine;

[0015] Sum the energy consumption detection values ​​corresponding to each detection period to obtain the total energy consumption detection value corresponding to the target machine;

[0016] When the energy consumption detection total value is greater than the energy consumption prediction upper limit total value, or the energy consumption detection total value is less than the energy consumption prediction lower limit total value, the alarm information of the target machine is pushed to the target device.

[0017] As an optional implementation manner, in the first aspect of the embodiment of the present application, after judging whether the energy consumption of the target machine in the target time period is abnormal according to the energy consumption prediction value and the energy consumption detection value corresponding to the target detection time period, the method further includes:

[0018] Calculate the energy consumption difference between the energy consumption detection value and the energy consumption prediction value corresponding to each energy consumption abnormality detection period;

[0019] Sum the energy consumption differences of all energy consumption abnormality detection periods to obtain the energy loss value corresponding to the target machine;

[0020] The target machine is hierarchically managed according to the energy consumption loss value corresponding to the target machine to determine the energy consumption level of the target machine. The hierarchical management includes sorting the energy consumption loss values ​​of multiple machines and setting the energy consumption level corresponding to each machine according to the sorting result.

[0021] As an alternative implementation, in the first aspect of the embodiments of the present application, after pushing the alarm information of the target machine tool to the target device, the method further includes:

[0022] When the energy consumption level of the target machine tool is at a preset level, obtain the alarm handling information corresponding to the target machine tool, and push the alarm handling information to the auditing device to audit the energy consumption situation of the target machine tool, where the auditing device is a device at a higher level than the person in charge of the process.

[0023] As an alternative implementation, in the first aspect of the embodiments of the present application, the method further includes:

[0024] According to the energy consumption data and machine tool parameters of the target machine tool in the first historical detection period, obtain the baseline energy consumption curve of the target machine tool, where the baseline energy consumption curve includes the corresponding relationship between the output and energy consumption of the target machine tool;

[0025] According to the energy consumption data and machine tool parameters of the target machine tool in the second historical detection period, obtain the new energy consumption curve of the target machine tool, where the new energy consumption curve includes the corresponding relationship between the output and energy consumption of the target machine tool, and the second historical detection period is a detection period after the first historical detection period;

[0026] According to the baseline energy consumption curve and the new energy consumption curve, determine the energy consumption improvement data of the target machine tool, where the energy consumption improvement data includes the ratio between the corrected unit energy consumption value of the target machine tool in the second historical detection period and the corrected unit energy consumption value of the first historical detection period, and the corrected unit energy consumption value is determined according to the energy consumption value of the target machine tool for producing a single workpiece at different outputs and the proportion of the energy consumption values corresponding to different outputs.

[0027] As an alternative implementation, in the first aspect of the embodiments of the present application, the method further includes:

[0028] When the corrected unit energy consumption value in the second historical detection period is less than the corrected unit energy consumption value in the first historical detection period, update the baseline energy consumption curve according to the new energy consumption curve.

[0029] In the second aspect of the embodiments of the present application, a machine tool energy consumption management device is provided, and the device includes:

[0030] An energy consumption prediction module, configured to obtain the predicted energy consumption data of the target machine tool according to a preset energy consumption prediction model and the actual machine tool parameters of the target machine tool, where the preset energy consumption prediction model is established according to the historical energy consumption data and historical machine tool parameters of multiple machine tools, and the predicted energy consumption data includes multiple energy consumption prediction values corresponding to multiple detection time periods;

[0031] An energy consumption acquisition module, used to acquire actual energy consumption data of the target machine, wherein the actual energy consumption data includes a plurality of energy consumption detection values ​​corresponding to the plurality of detection time periods;

[0032] an abnormality detection module, used to determine whether the energy consumption of the target machine in the target detection period is abnormal according to the energy consumption prediction value and the energy consumption detection value corresponding to the target detection period, wherein the target period is any period among the multiple detection periods;

[0033] The energy consumption alarm module is used to push the alarm information of the target machine to the target device when the ratio of the number of detection periods of abnormal energy consumption to the total number of the multiple detection periods is greater than a preset ratio threshold. The target device is the device of the process responsible person.

[0034] A third aspect of the present application provides a computer device, including a memory and a processor, wherein the memory stores a computer program that can be run on the processor, and when the processor executes the program, the method described in the first aspect of the embodiment of the present application is implemented.

[0035] A fourth aspect of the present application provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the method provided in the first aspect of the embodiment of the present application is implemented.

[0036] Compared with the related art, the embodiments of the present application have at least the following beneficial effects:

[0037] The method provided in the present application can, firstly, obtain the energy consumption prediction value of the target machine in multiple detection time periods according to the preset energy consumption prediction model and the actual machine parameters of the target machine, so as to provide a basis for energy consumption management; secondly, obtain the energy consumption detection values ​​of multiple detection time periods in the actual production process of the target machine, and determine whether each detection time period is abnormal by comparing the energy consumption prediction value and the energy consumption detection value of each detection time period; then, when the ratio of the number of detection time periods with abnormal energy consumption to the total number of multiple detection time periods is greater than a preset ratio threshold, the alarm information of the target machine is sent to the target equipment corresponding to the process manager, thereby reducing the probability of false alarm and missed alarm, and facilitating relevant personnel to take timely measures, thereby optimizing the energy consumption performance of the machine. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0039] Figure 1A A schematic diagram of an application scenario of the machine energy consumption management method provided in an embodiment of the present application;

[0040] Figure 1B A schematic diagram of another application scenario of the machine energy consumption management method provided in an embodiment of the present application;

[0041] Figure 2 A schematic diagram of a process flow of a machine energy consumption management method provided in an embodiment of the present application;

[0042] Figure 3 Another schematic diagram of a process flow of a machine energy consumption management method provided in an embodiment of the present application;

[0043] Figure 4 A schematic diagram of energy consumption data corresponding to different detection periods of a target machine provided in an embodiment of the present application;

[0044] Figure 5 A schematic diagram of another process flow of the machine energy consumption management method provided in the embodiment of the present application;

[0045] Figure 6 A schematic diagram of a process for determining energy consumption improvement data in a machine energy consumption management method provided in an embodiment of the present application;

[0046] Figure 7 A schematic diagram of a baseline energy consumption curve and a new energy consumption curve provided in an embodiment of the present application;

[0047] Figure 8 A schematic diagram of a structure of a machine energy consumption management device provided in an embodiment of the present application;

[0048] Fig. 9 A schematic diagram of the structure of a computer device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0049] In order to make the purpose, technical solution and advantages of the embodiments of the present application clearer, the specific technical solution of the present application will be further described in detail below in conjunction with the drawings in the embodiments of the present application. The following embodiments are used to illustrate the present application, but are not used to limit the scope of the present application.

[0050] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art to which this application belongs. The terms used herein are only for the purpose of describing the embodiments of this application and are not intended to limit this application.

[0051] In the following description, reference is made to “some embodiments”, which describe a subset of all possible embodiments, but it will be understood that “some embodiments” may be the same subset or different subsets of all possible embodiments and may be combined with each other without conflict.

[0052] It should be pointed out that the terms "first\second\third" involved in the embodiments of the present application are used to distinguish similar or different objects, and do not represent a specific ordering of the objects. It can be understood that "first\second\third" can be interchanged in a specific order or sequence where permitted, so that the embodiments of the present application described here can be implemented in an order other than that illustrated or described here.

[0053] In the manufacturing sector, reducing consumption is an important way for enterprises to reduce costs. In related technologies, enterprises can evaluate the energy consumption trend of equipment by analyzing the average energy consumption data of equipment over a period of time (such as monthly or quarterly data), or formulate corresponding energy consumption management measures based on the optimal energy consumption range given by the equipment manufacturer to achieve the goal of energy saving and consumption reduction.

[0054] However, for some non-standard equipment customized according to specific production processes, production processes or enterprise needs, take the production machines in solar cell factories as an example. First, the parameters of the machine equipment may fluctuate in real time during operation, which aggravates the uncertainty of energy consumption. For example, changes in parameters such as the operating load, ambient temperature, and production speed of the machine equipment in different time periods will have a significant impact on energy consumption, making it difficult to determine the optimal energy consumption range. Secondly, due to the different processes and parameters used by different production units, the energy consumption characteristics of the machine equipment are significantly different. If the average value calculation and other methods are still used to predict and evaluate the energy consumption of the machine, it will not only lead to serious lack of prediction accuracy, but also make it difficult to find whether the energy consumption of the machine equipment is abnormal or the cause of the abnormal energy consumption. It may also cause manpower waste due to frequent false alarms. When faced with a large number of false alarms, technicians find it difficult to accurately determine the cause of abnormal energy consumption and cannot take effective energy reduction measures in time. This not only affects the efficiency of the company's energy consumption management, but also increases unnecessary operating costs.

[0055] In view of this, the embodiments of the present application provide a machine energy consumption management method and device, equipment, and storage medium, which can take into account the energy consumption fluctuations caused by real-time changes in machine parameters, monitor the energy consumption in multiple time periods and determine whether to push alarm information, thereby reducing the probability of false alarms and missed alarms, so as to manage abnormal machines.

[0056] The following introduces the application scenarios of the machine energy consumption management method provided in the embodiments of the present application.

[0057] See also Figure 1A , Figure 1A A schematic diagram of an application scenario of the machine energy consumption management method provided in the embodiment of the present application, such as Figure 1A The application scenario schematic diagram shown may include a target machine 10 and a target device 20 .

[0058] The target machine 10 can be used to complete various production tasks in a manufacturing enterprise. For example, in a solar cell factory, the target machine may include but is not limited to components such as furnace tubes, air intake components, and temperature control components to complete the production process of solar cells.

[0059] In some possible embodiments, the machine energy consumption management method can be applied to a target machine 10, which may include a processor, so that the target machine 10 can deploy a preset energy consumption model locally or in the cloud to analyze and process the energy consumption data of the machine itself to determine whether the energy consumption of the target machine 10 is abnormal.

[0060] like Figure 1A As shown, the target machine 10 can establish a communication connection with the target device 20, wherein the target device 20 is the device of the process responsible person, so that when the target machine 10 generates an alarm message, the alarm message can be pushed to the target device 20 to remind the process responsible person to check and handle it, which helps to effectively manage and optimize the energy consumption of the target machine 10, reduce the unit consumption value required for the target machine 10 to produce a single product, and improve the economic benefits of the target machine 10.

[0061] See also Figure 1B , Figure 1B A schematic diagram of another application scenario of the machine energy consumption management method provided in the embodiment of the present application, such as Figure 1B The application scenario diagram shown may include a target machine 10 , a target device 20 and a server 30 .

[0062] It is understandable that in enterprises in the field of manufacturing, their production workshops are usually equipped with multiple machines to meet production needs. In order to achieve centralized monitoring and management of the energy consumption of each machine, in some possible embodiments, the machine energy consumption management method provided in the present application can be applied to the server 30. The server 30 can establish a communication connection with multiple machines including the target machine 10 to obtain the machine parameters of each machine, and obtain the predicted energy consumption data of each machine according to the preset energy consumption prediction model deployed locally or in the cloud on the server 30, and then compare it with the actual energy consumption data corresponding to each machine to determine whether the energy consumption of each machine is abnormal.

[0063] like Figure 1B As shown, the server 30 can communicate with the target device 20 and push the alarm information of the machines with abnormal energy consumption in each machine to the target device 20, so that the process responsible personnel can understand and handle the abnormal energy consumption in time. Through the centralized management and analysis of the server 30, the computing power requirements of the machine to process the energy consumption data by itself are reduced, and the energy consumption of the entire production workshop is optimized, reducing production costs.

[0064] It should be noted that if Figure 1A or Figure 1B The scene diagram shown is only an example and can be adjusted and expanded according to actual needs and is not limited here.

[0065] The following will introduce the implementation method of the machine energy consumption management method provided in the embodiment of the present application.

[0066] See also Figure 2 , Figure 2 A flow chart of a method for managing machine energy consumption provided in an embodiment of the present application is shown in FIG. Figure 2 As shown, the method may include the following steps:

[0067] S201, obtaining predicted energy consumption data of a target machine according to a preset energy consumption prediction model and actual machine parameters of the target machine. The preset energy consumption prediction model is established based on historical energy consumption data and historical machine parameters of multiple machines. The predicted energy consumption data includes multiple energy consumption prediction values ​​corresponding to multiple detection time periods.

[0068] In the embodiments of the present application, machine parameters refer to various parameters used by the machine in operation, which are used to describe and control the operating conditions and performance of the machine. For example, when the target machine is a production machine in a solar cell factory, the machine parameters may include parameters such as furnace temperature, introduced gas, and gas pressure. These parameters can be adjusted according to actual production needs and scenarios, and are not limited here.

[0069] Optionally, the actual machine parameters may include multiple sets of machine parameters corresponding to the target machine in multiple inspection periods. The length of the inspection period can be determined according to actual needs. For example, the inspection period can be 30 minutes, 60 minutes, etc., to achieve refined monitoring and management of the machine energy consumption and operating status.

[0070] Optionally, in the embodiment of the present application, the energy consumption value and the energy consumption prediction value refer to the unit consumption value required for the machine to produce a single workpiece. For example, when the workpiece is a solar cell, the multiple energy consumption prediction values ​​corresponding to the multiple detection time periods refer to the power consumption required for the target machine to produce a single solar cell in each detection time period.

[0071] In an embodiment of the present application, the preset energy consumption prediction model is established based on historical energy consumption data and historical machine parameters of multiple machines, wherein the multiple machines are machines of the same type as the target machine and can be used to produce the same workpieces.

[0072] Optionally, the preset energy consumption prediction model can be constructed based on machine learning algorithms such as random forest, XGBoost (eXtremeGradientBoosting) or LightGMB (Light GradientBoosting Machine). These algorithms are efficient and accurate, can effectively process large-scale data, and provide accurate energy consumption prediction results. The preset energy consumption prediction model constructed by these algorithms can better adapt to the energy consumption characteristics of the machine and improve the accuracy and reliability of energy consumption prediction.

[0073] Among them, random forest is an ensemble learning algorithm based on decision trees. It improves the accuracy and stability of prediction by building multiple decision trees and combining their results. It has the following characteristics: Strong anti-overfitting ability: Through random sampling and feature selection, random forest can effectively reduce the overfitting problem of a single decision tree. Processing large-scale data: Random forest can handle a large number of input features and has strong robustness to data distribution and outliers. XGBoost is an ensemble learning algorithm based on gradient boosting. It improves the accuracy and generalization ability of the model by optimizing the objective function and introducing regularization terms. It has the following characteristics: Efficient computing performance: XGBoost can quickly process large-scale data through multi-threading and distributed computing. Powerful feature importance evaluation: XGBoost can automatically evaluate the importance of features to help users understand which features have the greatest impact on energy consumption prediction. LightGMB is an efficient machine learning algorithm based on gradient boosting decision trees. It has the following characteristics: Efficient training speed: LightGBM significantly improves training speed and efficiency through histogram algorithm and leaf node-based growth strategy. Low memory usage: LightGBM can effectively reduce memory usage when processing large-scale data, and is suitable for use in resource-constrained environments. You can use algorithms such as random forest, XGBoost, or LightGBM to build a model for energy consumption data prediction based on actual needs, and there is no limitation here.

[0074] Taking the random forest as an example of the preset energy consumption prediction model, in the module construction process, the historical energy consumption data and historical machine parameters of multiple machines can be divided into training sets and test sets. For example, 80% of the historical energy consumption data and the corresponding historical machine parameters can be used as training sets, and the remaining 20% ​​of the historical energy consumption data and the corresponding historical machine parameters can be used as test sets to perform hyperparameter optimization and obtain the final preset energy consumption prediction model.

[0075] Optionally, before training the preset energy consumption prediction model, data requests may be made for historical energy consumption data and historical machine parameters of multiple machines, such as through missing value identification and processing, outlier identification and processing, etc., to ensure the quality and reliability of the data.

[0076] In the embodiment of the present application, actual machine parameters of the target machine may be used as input parameters of a preset energy consumption prediction model to obtain predicted energy consumption data output by the model including multiple energy consumption prediction values ​​corresponding to multiple detection time periods.

[0077] S202, obtaining actual energy consumption data of a target machine, where the actual energy consumption data includes a plurality of energy consumption detection values ​​corresponding to a plurality of detection time periods.

[0078] In the embodiment of the present application, the actual energy consumption data includes multiple energy consumption detection values ​​corresponding to multiple detection time periods, which can reflect the actual energy consumption of the target machine in different time periods. These energy consumption detection values ​​can be the usage of electricity consumption, gas consumption or other related energy, depending on the energy type of the target machine.

[0079] Exemplarily, when the target machine is a production machine for producing solar cells in a solar cell factory, the multiple energy consumption detection values ​​are multiple power consumption values ​​detected in multiple detection time periods.

[0080] Optionally, the energy consumption detection value corresponding to each detection period may refer to the detection unit consumption value of the target machine in each detection period.

[0081] It should be noted that in some scenarios, output is an important factor affecting the unit consumption value. By obtaining the unit consumption value of the target machine in each detection period, the energy efficiency of the target machine can be more intuitively reflected, providing a more accurate basis for energy consumption management and optimization, so as to evaluate whether the target machine has any abnormalities.

[0082] S203, judging whether the energy consumption of the target machine in the target period is abnormal according to the energy consumption prediction value and the energy consumption detection value corresponding to the target detection period, where the target period is any period among the multiple detection periods.

[0083] In an embodiment of the present application, after obtaining energy consumption prediction values ​​and energy consumption detection values ​​corresponding to multiple detection time periods, it is possible to determine whether the energy consumption of any target detection time period in the multiple detection time periods is abnormal by calculating the energy consumption deviation, calculating the difference size, etc.

[0084] In some possible embodiments, by calculating the energy consumption deviation between the energy consumption prediction value and the energy consumption detection value in the target detection period, it is determined whether the energy consumption is abnormal. The target energy consumption deviation corresponding to the target detection period can be calculated by the deviation calculation formula. The deviation calculation formula is:

[0085]

[0086] Where D is the target energy consumption deviation corresponding to the target detection period, W 0 is the predicted energy consumption value during the target detection period, W 1It is the energy consumption detection value during the target detection period.

[0087] Optionally, when the target energy consumption deviation is greater than or equal to a preset deviation threshold (eg, 10%), it is determined that the energy consumption of the target machine in the target time period is abnormal.

[0088] In some possible embodiments, the energy consumption difference between the energy consumption prediction value corresponding to the target detection period and the energy consumption prediction value can be calculated. When the energy consumption difference is a positive number and less than a preset first threshold, or when the energy consumption difference is a negative number and greater than a preset second threshold, it is determined that the energy consumption of the target detection period is normal; otherwise, it is determined that the energy consumption of the target detection period is abnormal.

[0089] In some possible embodiments, by comparing the energy consumption prediction value corresponding to the target detection period with the energy consumption prediction value, if the energy consumption prediction value is less than or equal to the energy consumption prediction value, it is determined that the energy consumption of the target detection period is normal; if the energy consumption prediction value is greater than the energy consumption prediction value, it is determined that the energy consumption of the target detection period is abnormal.

[0090] Optionally, any one or more combinations of the above methods may be used to determine whether the energy consumption of the target machine in each detection period is abnormal, which is not limited here.

[0091] In some possible embodiments, when it is determined that the energy consumption of the target machine in the target time period is abnormal, the method further includes: generating alarm information.

[0092] The alarm information may include but is not limited to the machine identification information of the target machine, the target time period, etc. The target machine may output the alarm information to the on-site operator through the display screen, speaker or indicator light, etc. provided thereon.

[0093] S204, when the ratio of the number of detection periods of abnormal energy consumption to the total number of multiple detection periods is greater than a preset ratio threshold, push the alarm information of the target machine to the target equipment, which is the equipment of the process responsible person.

[0094] In the embodiment of the present application, by analyzing the energy consumption prediction value and energy consumption detection value of multiple detection periods, it is possible to determine whether the energy consumption of each detection period is abnormal. If, in multiple detection periods, the ratio of the number of detection periods with abnormal energy consumption to the total number of detection periods is greater than a preset ratio threshold (e.g., 50%), it is considered that the energy consumption of the target machine is continuously abnormal, and the alarm information of the target machine needs to be pushed to the target device to remind the process responsible personnel to check and handle it.

[0095] It should be noted that the target device can be a mobile phone, tablet or other mobile device of the process manager, or a fixed workstation or monitoring terminal. By pushing the alarm information to the process manager in a timely manner, it can ensure that they can quickly understand the abnormal energy consumption of the target machine and take corresponding measures to deal with it, so as to reduce the unit consumption value of the target machine.

[0096] In some possible embodiments, when the ratio of the number of detection periods of abnormal energy consumption to the total number of multiple detection periods is greater than a preset ratio threshold, the machine energy consumption management method provided in the present application also includes: generating alarm information, which may include machine identification information of the target machine, the number of detection periods of abnormal energy consumption, and period information of each abnormal energy consumption detection period.

[0097] Optionally, the alarm information may also include actual machine parameters, energy consumption prediction values ​​or energy consumption detection values ​​corresponding to each abnormal energy consumption detection period, which is not limited here.

[0098] In some possible embodiments, for some more complex workpiece production processes, the target machine can perform multiple processes, and different processes correspond to different process managers. The machine energy consumption management method provided in this application also includes: determining the target device according to the target process corresponding to the target machine in multiple energy consumption abnormality detection periods. This ensures that the alarm information can be accurately pushed to the relevant process manager, improves the processing efficiency and pertinence of the alarm information, and thus more effectively manages the machine energy consumption.

[0099] Exemplarily, when the target process corresponding to the target machine in the detection time periods of multiple energy consumption anomalies is a heat preservation process, the target device is determined to be the device of the person in charge of the process of the heat preservation process.

[0100] In the embodiment of the present application, after the alarm information of the target machine is pushed to the target device, the process responsible personnel and relevant technical personnel can adjust the working parameters of the target machine or troubleshoot the fault to optimize the energy consumption performance of the target machine and realize machine energy consumption management.

[0101] In the machine energy consumption management method provided in the embodiment of the present application, the preset energy consumption prediction model and the actual parameters of the target machine are first used to obtain the energy consumption prediction value of the target machine in multiple detection time periods to provide a basis for energy consumption management. Next, the energy consumption detection value of the target machine in multiple detection time periods during the actual production process is obtained, and by comparing the energy consumption prediction value and the detection value of each time period, it is determined whether the energy consumption of each time period is abnormal. Finally, when the proportion of detection periods with abnormal energy consumption exceeds the preset threshold, an alarm message is sent to the target equipment of the process manager to reduce false alarms and missed alarms, so that relevant personnel can take timely measures to optimize the energy consumption performance of the machine.

[0102] The following is an introduction to an implementation method of determining whether the energy consumption during the target detection period is abnormal, which is implemented in an embodiment of the present application.

[0103] See also Figure 3 , Figure 3 Another flow chart of the machine energy consumption management method provided in the embodiment of the present application is as follows: Figure 3 As shown, the method may include the following steps:

[0104] S301, obtain predicted energy consumption data of the target machine according to a preset energy consumption prediction model and actual machine parameters of the target machine. The preset energy consumption prediction model is established based on historical energy consumption data and historical machine parameters of multiple machines. The predicted energy consumption data includes multiple energy consumption prediction values ​​corresponding to multiple detection time periods.

[0105] S302, obtaining actual energy consumption data of the target machine, where the actual energy consumption data includes a plurality of energy consumption detection values ​​corresponding to a plurality of detection time periods.

[0106] S303: When the energy consumption detection value corresponding to the target detection period is greater than the energy consumption prediction upper limit, or the energy consumption detection value corresponding to the target detection period is less than the energy consumption prediction lower limit, it is determined that the energy consumption of the target machine in the target period is abnormal.

[0107] In some possible embodiments, judging whether the energy consumption of the target machine in the target period is abnormal according to the energy consumption prediction value and the energy consumption detection value corresponding to the target detection period includes:

[0108] When the energy consumption detection value corresponding to the target detection period is greater than the energy consumption prediction upper limit, or the energy consumption detection value corresponding to the target detection period is less than the energy consumption prediction lower limit, the energy consumption of the target machine in the target period is judged to be abnormal, wherein the energy consumption prediction upper limit and the energy consumption prediction lower limit are determined based on the energy consumption prediction value corresponding to the target detection period.

[0109] Optionally, the energy consumption forecast upper limit of the target detection period is determined by increasing the energy consumption forecast value by an upper limit fluctuation threshold, and the energy consumption forecast lower limit of the target detection period is determined by decreasing the energy consumption forecast value by a lower limit fluctuation threshold.

[0110] In some possible embodiments, the upper fluctuation threshold and the lower fluctuation threshold corresponding to the target detection period are determined by historical machine parameters and historical energy consumption data of the target machine.

[0111] By using data statistics methods to analyze and process the historical machine parameters and historical energy consumption data of the target machine, the energy consumption data fluctuation range of the target machine under different machine parameters can be obtained. The method provided in this application can collect the historical energy consumption data of the target machine under different machine parameters, and then analyze the distribution of these data through statistical analysis methods such as fitting and calculating the mean, and then calculate the energy consumption fluctuation range under a certain confidence level, so as to determine the upper and lower fluctuation thresholds corresponding to the target machine under different machine parameters.

[0112] Figure 4 A schematic diagram of the energy consumption data corresponding to the target machine in different detection periods provided in the embodiment of the present application. Exemplarily, in some embodiments, the energy consumption data corresponding to the target machine in different detection periods can be as follows: Figure 4 As shown, each detection period is 1 hour, and there are 24 detection periods in total. Through the method provided in this application, the energy consumption detection value, energy consumption prediction value, energy consumption prediction upper limit and energy consumption prediction lower limit of the target machine in different detection periods can be obtained. Figure 4 The unit of the vertical axis is kWh.

[0113] It can be understood that if the actual value of the target machine in the target detection period is outside the predicted upper limit and predicted lower limit corresponding to the period, that is, the energy consumption detection value corresponding to the target detection period is greater than the energy consumption prediction upper limit, or the energy consumption detection value corresponding to the target detection period is less than the energy consumption prediction lower limit, the energy consumption of the target machine in the target period is abnormal, so as to count the number of detection periods with abnormal energy consumption, or control the target machine to generate alarm information in the detection period with abnormal energy consumption.

[0114] S304: When the energy consumption detection value corresponding to the target detection period is less than or equal to the energy consumption prediction upper limit and the energy consumption detection value corresponding to the target detection period is greater than or equal to the energy consumption prediction lower limit, it is determined that the energy consumption of the target machine in the target period is normal.

[0115] In some possible embodiments, judging whether the energy consumption of the target machine in the target period is abnormal according to the energy consumption prediction value and the energy consumption detection value corresponding to the target detection period includes:

[0116] When the energy consumption detection value corresponding to the target detection period is less than or equal to the energy consumption prediction upper limit, and the energy consumption detection value corresponding to the target detection period is greater than or equal to the energy consumption prediction lower limit, it is determined that the energy consumption of the target machine in the target period is normal.

[0117] In some possible embodiments, when the ratio of the number of detection periods of abnormal energy consumption to the total number of multiple detection periods is greater than a preset ratio threshold, the method provided in the present application also includes: generating an energy consumption diagram corresponding to the target machine, and sending the energy consumption diagram to the target device.

[0118] It should be noted that the energy consumption diagram can be Figure 4 As shown, the process responsible personnel can intuitively understand the energy consumption of the target machine and analyze the energy consumption trend, so as to manage and control energy consumption more effectively.

[0119] S305, when the ratio of the number of detection periods of abnormal energy consumption to the total number of multiple detection periods is greater than a preset ratio threshold, push the alarm information of the target machine to the target equipment, which is the equipment of the process responsible person.

[0120] In some possible embodiments, the machine energy consumption management method provided by the present application further includes:

[0121] The energy consumption prediction upper limit and energy consumption prediction lower limit corresponding to each detection period are summed up respectively to obtain the total value of the energy consumption prediction upper limit and the total value of the energy consumption prediction lower limit corresponding to the target machine;

[0122] Sum the energy consumption detection values ​​corresponding to each detection period to obtain the total energy consumption detection value corresponding to the target machine;

[0123] When the total value of energy consumption detection is greater than the total value of the upper limit of energy consumption prediction, or the total value of energy consumption detection is less than the total value of the lower limit of energy consumption prediction, the alarm information of the target machine is pushed to the target device.

[0124] It should be noted that in addition to determining whether to push the alarm information of the target machine to the target device based on the number of detection periods of abnormal energy consumption, in some possible embodiments, it is also possible to determine whether to push the alarm information by comparing the total energy consumption detection value with the total energy consumption prediction upper limit value and the total energy consumption prediction lower limit value, thereby improving the accuracy and reliability of energy consumption management.

[0125] Optionally, the above method of judging whether to push the alarm information of the target machine to the target device by the number of detection periods of abnormal energy consumption, and the method of judging whether to push the alarm information by comparing the total value of energy consumption detection with the total value of the upper limit of energy consumption prediction and the total value of the lower limit of energy consumption prediction, can be performed at the same time. When the energy consumption data of the target machine meets any condition, the alarm information will be pushed to the target device.

[0126] Please refer to Table 1, which is a schematic table of energy consumption data corresponding to the target device in different detection periods.

[0127] Table 1

[0128] Detection period 1 2 3 4 5 6 total Energy consumption detection value 22 59 59 66 29 45 280 Energy consumption forecast 32 40 36 40 33 39 220 Energy consumption forecast upper limit 37 49 39 49 43 60 277 Energy consumption forecast lower limit 18 17 23 17 18 24 117 Check whether the time period is abnormal no yes yes yes no no -

[0129] In some possible embodiments, the energy consumption data of the target machine is shown in Table 1, the total number of detection periods is 6, and the number of detection periods of abnormal energy consumption is 3. If the preset ratio threshold is 50%, it can be seen that the ratio of the number of detection periods of abnormal energy consumption to the total number of multiple detection periods is equal to 50%, which is not greater than the preset ratio threshold. Therefore, the alarm information of the target machine will not be pushed to the target device through the number of detection periods of abnormal energy consumption. However, when the energy consumption detection value, the energy consumption prediction upper limit and the energy consumption prediction lower limit corresponding to each detection period are summed up respectively, it can be seen that the total energy consumption detection value of the target machine in multiple detection periods is 280 (in degrees), the total energy consumption detection upper limit is 277, and the total energy consumption detection upper and lower limits is 177. Since the total energy consumption detection value is greater than the total energy consumption prediction upper limit, it is still necessary to push the alarm information of the target machine to the target device to improve the timeliness and effectiveness of the machine energy consumption management and ensure that the relevant personnel will not miss the abnormal energy consumption of the target machine.

[0130] By implementing the above technical solution, the machine energy consumption management method provided in this application can timely push the alarm information of the target machine to the target device by analyzing the energy consumption forecast upper limit and the energy consumption forecast lower limit corresponding to each detection time period, thereby effectively monitoring and managing the energy consumption of the target machine.

[0131] The following describes how to set the energy consumption level of a target machine in an embodiment of the present application so as to manage the machines according to different energy consumption levels.

[0132] See also Figure 5 , Figure 5 Another flow chart of the machine energy consumption management method provided in the embodiment of the present application is as follows: Figure 5 As shown, the method may include the following steps:

[0133] S501, obtain predicted energy consumption data of the target machine according to a preset energy consumption prediction model and actual machine parameters of the target machine. The preset energy consumption prediction model is established based on historical energy consumption data and historical machine parameters of multiple machines. The predicted energy consumption data includes multiple energy consumption prediction values ​​corresponding to multiple detection time periods.

[0134] S502, obtaining actual energy consumption data of a target machine, where the actual energy consumption data includes a plurality of energy consumption detection values ​​corresponding to a plurality of detection time periods.

[0135] S503, judging whether the energy consumption of the target machine in the target period is abnormal according to the energy consumption prediction value and the energy consumption detection value corresponding to the target detection period, where the target period is any period among the multiple detection periods.

[0136] S504, calculating the energy consumption difference between the energy consumption detection value and the energy consumption prediction value corresponding to each energy consumption abnormality detection period.

[0137] In some possible embodiments, after determining whether the energy consumption of the target machine in the target period is abnormal according to the energy consumption prediction value and the energy consumption detection value corresponding to the target detection period, the method further includes:

[0138] Calculate the energy consumption difference between the energy consumption detection value and the energy consumption prediction value corresponding to each energy consumption abnormality detection period;

[0139] The energy consumption differences of all energy consumption abnormality detection periods are summed up to obtain the energy loss value corresponding to the target machine;

[0140] The target machines are hierarchically managed according to the energy consumption loss values ​​corresponding to the target machines to determine the energy consumption levels of the target machines. The hierarchical management includes sorting the energy consumption loss values ​​of multiple machines and setting the energy consumption level corresponding to each machine according to the sorting results.

[0141] It should be noted that the energy consumption difference is the difference between the energy consumption detection value and the energy consumption prediction value in each energy consumption abnormality detection period. When the energy consumption difference is positive, it means that the actual energy consumption is higher than expected. When the energy consumption difference is negative, it means that the actual energy consumption is lower than expected.

[0142] S505, summing up the energy consumption differences in all energy consumption abnormality detection periods to obtain the energy loss value corresponding to the target machine.

[0143] By summing up the energy consumption differences during all energy consumption anomaly detection periods, the corresponding energy consumption loss value of the target machine can be obtained to quantify the energy consumption loss or difference of the target machine under abnormal conditions, which helps to identify and evaluate the severity of energy consumption anomalies and provide data support for subsequent energy consumption optimization.

[0144] It is understandable that the smaller the loss energy consumption value corresponding to the machine, the smaller the comprehensive difference between the energy consumption detection value and the energy consumption prediction value in several energy consumption anomaly detection periods, the closer the energy consumption performance of the machine is to expectations, and no special attention is needed. For machines with large loss energy consumption values, corresponding measures need to be taken to optimize and improve them in order to reduce energy consumption performance and improve production efficiency and economic benefits.

[0145] Refer to Table 2, which is another schematic table of energy consumption data corresponding to the target device in different detection time periods.

[0146] Table 2

[0147]

[0148]

[0149] Exemplarily, in some possible embodiments, the energy consumption data of the target machine can be as shown in Table 2. The total number of detection time periods is 6, and the detection time period of abnormal energy consumption is 3. The energy consumption differences corresponding to the detection time periods of abnormal energy consumption are -17, 35 and 9, and the corresponding loss energy consumption value of the target machine is 27.

[0150] S506, hierarchical management is performed on the target machine according to the energy consumption loss values ​​corresponding to the target machine to determine the energy consumption level of the target machine. The hierarchical management includes sorting the energy consumption loss values ​​of multiple machines and setting the energy consumption level corresponding to each machine according to the sorting result.

[0151] In some possible embodiments, after the energy consumption loss value corresponding to the target machine is obtained, the energy consumption level corresponding to each machine may be set by comparing the energy consumption loss values ​​corresponding to the target machine and other machines.

[0152] Optionally, the target machines are managed in a hierarchical manner according to the energy loss values ​​corresponding to the target machines to determine the energy consumption levels of the target machines, including:

[0153] Sort the energy consumption loss values ​​of multiple machines including the target machine from largest to smallest;

[0154] The energy consumption level of the target machine is determined according to the proportion interval in which the loss energy consumption value of the target machine ranks.

[0155] For example, it can be determined that the energy consumption level of the machines whose energy loss values ​​rank in the top 10% among multiple machines is level 1 (also called Class A), the energy consumption level of the machines ranked between 10% and 30% is level 2 (also called Class B), and the energy consumption level of the remaining machines is level 3 (also called Class C).

[0156] It should be noted that the division method of the proportion interval can be flexibly adjusted according to the actual situation and is not limited here. The higher the energy consumption ranking of a machine, the greater its energy loss value, and usually requires more priority attention and management to avoid consuming too much energy and causing unnecessary cost increases. In this way, through the hierarchical management of machines, enterprises can allocate resources and energy more targetedly, give priority to solving the problems of machines with high energy loss, thereby reducing the overall energy consumption level and saving production costs.

[0157] Optionally, for machines with energy consumption level 1 ranked in the top 10%, enterprises can arrange a dedicated technical team to conduct in-depth analysis to find the specific reasons for energy loss, such as equipment aging, unreasonable operating parameters or inadequate maintenance, and formulate targeted energy-saving measures, such as equipment upgrades, optimization of operating parameters or strengthening daily maintenance. For machines with energy consumption level 2 ranked between 10% and 30%, enterprises can regularly monitor and evaluate their operating status, formulate medium-term energy-saving plans based on the actual use of the equipment, and gradually reduce energy loss. For machines with energy consumption level 3 ranked after 30%, enterprises can appropriately reduce monitoring efforts and focus more resources and energy on machines with energy consumption loss more significantly at level 1 and 2, so as to achieve reasonable allocation and efficient use of resources.

[0158] S507, when the ratio of the number of detection periods of abnormal energy consumption to the total number of multiple detection periods is greater than a preset ratio threshold, push the alarm information of the target machine to the target equipment, which is the equipment of the process responsible person.

[0159] In some possible embodiments, after the alarm information of the target machine is pushed to the target device, the machine energy consumption management method provided by the present application further includes:

[0160] When the energy consumption level of the target machine is at the preset level, the alarm handling information corresponding to the target machine is obtained, and the alarm handling information is pushed to the auditing device to audit the energy consumption of the target machine. The auditing device is the device of the superior of the process responsible person.

[0161] By pushing the alarm handling information to the superiors of the process responsible personnel, such as department managers and other corresponding audit equipment, the company can promptly understand the abnormal energy consumption of the machine and take corresponding measures based on the audit results, such as approving emergency repairs, equipment optimization or adjusting production plans, etc., which helps to quickly respond to abnormal energy consumption problems and ensure the rationality and effectiveness of decision-making.

[0162] Optionally, different energy consumption levels can correspond to different audit devices to achieve hierarchical audit management, ensuring that machines with high energy consumption losses can receive timely and effective attention and processing, while avoiding excessive intervention in machines with low energy consumption losses, thereby improving management efficiency and targeted decision-making.

[0163] By implementing the above technical solution, the machine energy consumption management method provided by this application can evaluate the energy consumption level of the target machine and conduct hierarchical audit management of the machine, so that the enterprise can reasonably allocate resources and energy, give priority to solving the problems of machines with high energy consumption losses, thereby effectively reducing the overall energy consumption level and saving production costs.

[0164] The following describes how to evaluate the energy consumption performance of a target machine and determine the effectiveness of energy consumption management in an embodiment of the present application.

[0165] See also Figure 6 , Figure 6 A schematic diagram of a process for determining energy consumption improvement data in a machine energy consumption management method provided in an embodiment of the present application, such as Figure 6 As shown, the method may include the following steps:

[0166] S601, obtain predicted energy consumption data of the target machine according to a preset energy consumption prediction model and actual machine parameters of the target machine. The preset energy consumption prediction model is established based on historical energy consumption data and historical machine parameters of multiple machines. The predicted energy consumption data includes multiple energy consumption prediction values ​​corresponding to multiple detection time periods.

[0167] S602, obtaining actual energy consumption data of a target machine, where the actual energy consumption data includes a plurality of energy consumption detection values ​​corresponding to a plurality of detection time periods.

[0168] S603, judging whether the energy consumption of the target machine in the target period is abnormal according to the energy consumption prediction value and the energy consumption detection value corresponding to the target detection period, where the target period is any period among the multiple detection periods.

[0169] S604, when the ratio of the number of detection periods of abnormal energy consumption to the total number of multiple detection periods is greater than a preset ratio threshold, push the alarm information of the target machine to the target equipment, which is the equipment of the process responsible person.

[0170] S605 , obtaining a reference energy consumption curve of the target machine according to the energy consumption data and machine parameters of the target machine in the first historical detection period, wherein the reference energy consumption curve includes a corresponding relationship between the output and energy consumption of the target machine.

[0171] It should be noted that, in order to verify the energy consumption management effect of the target machine, the energy consumption curve of the target machine can be obtained according to a preset detection cycle, such as 1 month or 1 quarter, to evaluate whether its energy consumption performance reaches the expected target.

[0172] According to the preset statistical algorithm, the energy consumption data and machine parameters of the target machine in the first historical detection period can obtain the corresponding relationship (curve) between different parameters and energy consumption values.

[0173] For example, when the target machine is a production machine in a solar cell factory, based on preset statistical algorithms, such as regression analysis, it is possible to obtain curves corresponding to a variety of energy consumption values ​​and machine parameter energy consumption, such as energy consumption value and output energy consumption curve, energy consumption value and temperature energy consumption curve, etc., thereby helping to predict and manage machine energy consumption.

[0174] After obtaining the corresponding relationship curves between multiple energy consumption values ​​and machine parameters of the target machine in the first historical detection period, the benchmark energy consumption curve of the machine can be obtained by fitting the corresponding relationship curves between the energy consumption values ​​and the machine parameters.

[0175] In some possible embodiments, the upper and lower fluctuation thresholds corresponding to the target machine under different machine parameters can be determined through the benchmark energy consumption curve to determine the upper and lower energy consumption prediction limits corresponding to the target machine in the target detection period.

[0176] S606, obtaining a new energy consumption curve of the target machine according to the energy consumption data and machine parameters of the target machine in the second historical detection period, the new energy consumption curve including the corresponding relationship between the output and energy consumption of the target machine, the second historical detection period being the detection period after the first historical detection period.

[0177] It should be noted that the second historical detection cycle is the detection cycle after the first historical detection cycle, and the second historical detection cycle has the same time length as the first historical detection cycle, so as to effectively compare and evaluate the energy consumption of the target machine before and after energy consumption management.

[0178] Optionally, the second historical detection cycle may be a detection cycle after the first historical detection cycle, or the second historical detection cycle may be a historical cycle that is N cycles apart from the first historical detection cycle, where N is an integer greater than or equal to 1 and is not limited here.

[0179] By outputting the baseline energy consumption curve and the new energy consumption curve, the energy consumption changes of the target machine in different detection cycles can be intuitively displayed, so that relevant technical personnel can clearly and intuitively observe the effect of energy consumption management measures. If the new energy consumption curve is lower than the baseline energy consumption curve as a whole, it means that the energy consumption management measures are effective and the energy consumption of the target machine has been effectively controlled and reduced. On the contrary, if the new energy consumption curve is higher than the baseline energy consumption curve as a whole, it means that the energy consumption management measures may need to be further optimized and improved.

[0180] For example, see Figure 7 , Figure 7 A schematic diagram of the baseline energy consumption curve and the new energy consumption curve provided in the embodiment of the present application. Figure 7 As shown in the figure, the new energy consumption curve is lower than the baseline energy consumption curve, which means that, at the same output level, the energy consumption of the target machine in the second historical detection cycle is lower than the energy consumption baseline level of the first historical detection cycle. This shows that the energy consumption management measures of the target machine have achieved positive results and the energy consumption has been effectively controlled and reduced.

[0181] In some possible embodiments, the method provided by the present application further includes: outputting a reference energy consumption curve and a new energy consumption curve.

[0182] Optionally, outputting the baseline energy consumption curve and the new energy consumption curve includes: outputting the baseline energy consumption curve and the new energy consumption curve through a display screen of a target machine, or sending the baseline energy consumption curve and the new energy consumption curve to a target device to output the baseline energy consumption curve and the new energy consumption curve through a display screen of the target device.

[0183] S607: Determine energy consumption improvement data of the target machine according to the baseline energy consumption curve and the new energy consumption curve.

[0184] In some possible embodiments, the energy consumption improvement data includes the ratio of the corrected unit consumption value of the target machine in the second historical detection cycle to the corrected unit consumption value of the first historical detection cycle. The corrected unit consumption value is determined based on the energy consumption value of the target machine in producing a single workpiece at different outputs and the ratio of energy consumption values ​​corresponding to different outputs.

[0185] It should be noted that, taking the production machines in solar cell factories as an example, some non-standard equipment has significant differences in the unit consumption values ​​required to produce a single workpiece at different outputs. In order to more accurately evaluate the energy consumption performance of the machine, the corrected unit consumption value can be used for comparison and analysis.

[0186] In some possible embodiments, the method also includes: obtaining target energy consumption data of the target machine in the target detection cycle, the target energy consumption data including total energy consumption values ​​and energy consumption value ratios corresponding to multiple outputs; dividing the total energy consumption value corresponding to each output by the corresponding output to obtain the unit consumption value corresponding to each output; multiplying the unit consumption value corresponding to each output by the corresponding energy consumption value ratio to obtain the corrected unit consumption value corresponding to each output; summing the corrected unit consumption values ​​corresponding to each output, and determining the summation result as the corrected unit consumption value of the target detection cycle.

[0187] Please refer to Table 3, which shows the energy consumption and output data of the target machine in the first historical detection cycle.

[0188] Table 3

[0189] Yield Total energy consumption Energy consumption ratio Corrected unit consumption value (total energy consumption value / output*electricity proportion) 500 55000 5.64％ 6.21 1000 105000 10.77％ 11.31 1500 127500 13.08％ 11.12 2000 160000 16.41％ 13.13 2500 187500 19.23％ 14.42 3000 180000 18.46％ 11.08 4000 160000 16.41％ 6.56 total 975000 100.00％ 73.82

[0190] Please refer to Table 4, which shows the energy consumption and output data of the target machine in the second historical detection cycle.

[0191] Table 4

[0192]

[0193]

[0194] It should be noted that the unit of the energy consumption values ​​shown in Table 3 or Table 4 is kWh.

[0195] In one embodiment, as shown in Tables 3 and 4, the corrected unit consumption value of the target machine in the first historical detection period is 73.82, and the corrected unit consumption value of the target machine in the second historical detection period is 56.85. The ratio between the corrected unit consumption value of the second historical detection period and the corrected unit consumption value of the first historical detection period included in the energy consumption improvement data is 0.77, and the saving rate is 23%.

[0196] It can be understood that when the ratio between the corrected unit consumption value of the second historical detection cycle and the corrected unit consumption value of the first historical detection cycle is less than 1, it indicates that the energy consumption of the target machine has improved; and when the ratio is greater than 1, it indicates that the energy consumption of the target machine has deteriorated. The ratio can be output so that relevant technicians can intuitively evaluate the energy consumption management effect of the target machine through the energy consumption improvement data.

[0197] In some possible embodiments, the target improvement data further includes a ratio between a modified unit consumption value corresponding to each output in the second historical detection cycle and a modified unit consumption value corresponding to each output in the first historical detection cycle.

[0198] For example, as shown in Table 3 and Table 4, the target machine has a modified unit consumption value of 9.97 for a production of 2000 in the second historical detection cycle, and a modified unit consumption value of 13.13 for a production of 2000 in the first historical detection cycle. It can be seen that when the production is 2000, the ratio between the modified unit consumption value corresponding to the production in the second historical detection cycle and the modified unit consumption value corresponding to the production in the first historical detection cycle is 0.76, and the saving rate is 24%. In this way, the energy consumption changes under different production levels can be analyzed more carefully, thereby providing a more targeted reference for energy consumption management.

[0199] In some possible embodiments, the machine energy consumption management method further includes:

[0200] When the corrected unit consumption value of the second historical detection period is less than the corrected unit consumption value of the first historical detection period, the reference energy consumption curve is updated according to the new energy consumption curve.

[0201] It should be noted that the new energy consumption curve is used as the new baseline energy consumption curve to reflect the actual energy consumption level of the target machine after the energy consumption management measures are implemented. This update mechanism can ensure that the baseline energy consumption curve always reflects the latest energy consumption management effect and provides a more accurate reference for subsequent energy consumption forecasting and management.

[0202] By implementing the above technical solution, the machine energy consumption management method provided by this application can intuitively demonstrate the effect of energy consumption management measures by establishing a baseline energy consumption curve and a new energy consumption curve. By calculating the corrected unit consumption value and energy consumption improvement data, it provides technicians with quantitative evaluation indicators, improves the scientificity and accuracy of energy consumption management, and provides strong support for energy conservation and emission reduction of enterprises.

[0203] It should be understood that, although the steps in the above-mentioned flowcharts are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps is not strictly limited in order, and these steps can be executed in other orders. Moreover, at least a part of the steps in the above-mentioned flowcharts may include multiple sub-steps or multiple stages, and these sub-steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these sub-steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the sub-steps or stages of other steps.

[0204] Based on the foregoing embodiments, an embodiment of the present application provides a machine energy consumption management device, which includes the modules included and the units included in the modules, and can be implemented by a processor; of course, it can also be implemented by a specific logic circuit; in the implementation process, the processor can be a central processing unit (CPU), a microprocessor (MPU), a digital signal processor (DSP) or a field programmable gate array (FPGA), etc.

[0205] See also Figure 8 , Figure 8 A schematic diagram of a structure of a machine energy consumption management device provided in an embodiment of the present application is shown in FIG. Figure 8 As shown, the machine energy consumption management device should include an energy consumption prediction module 801, an energy consumption acquisition module 802, an abnormality detection module 803 and an energy consumption alarm module 804, wherein:

[0206] The energy consumption prediction module 801 is used to obtain the predicted energy consumption data of the target machine according to the preset energy consumption prediction model and the actual machine parameters of the target machine. The preset energy consumption prediction model is established according to the historical energy consumption data and historical machine parameters of multiple machines. The predicted energy consumption data includes multiple energy consumption prediction values ​​corresponding to multiple detection time periods.

[0207] The energy consumption acquisition module 802 is used to acquire the actual energy consumption data of the target machine, where the actual energy consumption data includes a plurality of energy consumption detection values ​​corresponding to a plurality of detection time periods;

[0208] The abnormality detection module 803 is used to determine whether the energy consumption of the target machine in the target period is abnormal according to the energy consumption prediction value and the energy consumption detection value corresponding to the target detection period, and the target period is any period in the multiple detection periods;

[0209] The energy consumption alarm module 804 is used to push the alarm information of the target machine to the target equipment when the ratio of the number of detection periods of abnormal energy consumption to the total number of multiple detection periods is greater than a preset ratio threshold. The target equipment is the equipment of the process responsible person.

[0210] In some possible embodiments, the abnormality detection module 803 is also used to judge that the energy consumption of the target machine in the target time period is abnormal when the energy consumption detection value corresponding to the target detection time period is greater than the upper limit of energy consumption prediction, or the energy consumption detection value corresponding to the target detection time period is less than the lower limit of energy consumption prediction; when the energy consumption detection value corresponding to the target detection time period is less than or equal to the upper limit of energy consumption prediction, and the energy consumption detection value corresponding to the target detection time period is greater than or equal to the lower limit of energy consumption prediction, judge that the energy consumption of the target machine in the target time period is normal; wherein the upper limit of energy consumption prediction and the lower limit of energy consumption prediction are determined based on the energy consumption prediction value corresponding to the target detection time period.

[0211] In some possible embodiments, the energy consumption alarm module 804 is also used to sum the energy consumption prediction upper limit and the energy consumption prediction lower limit corresponding to each detection time period, respectively, to obtain the total value of the energy consumption prediction upper limit and the total value of the energy consumption prediction lower limit corresponding to the target machine; to sum the energy consumption detection values ​​corresponding to each detection time period, to obtain the total energy consumption detection value corresponding to the target machine; and when the total energy consumption detection value is greater than the total value of the energy consumption prediction upper limit, or the total energy consumption detection value is less than the total value of the energy consumption prediction lower limit, push the alarm information of the target machine to the target device.

[0212] In some possible embodiments, the machine energy consumption management device also includes an energy consumption evaluation module. After determining whether the energy consumption of the target machine in the target time period is abnormal based on the energy consumption prediction value and the energy consumption detection value corresponding to the target detection time period, the energy consumption evaluation module is used to calculate the energy consumption difference between the energy consumption detection value and the energy consumption prediction value corresponding to each energy consumption abnormality detection time period; sum the energy consumption differences of all energy consumption abnormality detection time periods to obtain the loss energy consumption value corresponding to the target machine; perform hierarchical management on the target machine according to the loss energy consumption value corresponding to the target machine, and determine the energy consumption level of the target machine. The hierarchical management includes sorting the loss energy consumption values ​​of multiple machines, and setting the energy consumption level corresponding to each machine according to the sorting result.

[0213] In some possible embodiments, the energy consumption alarm module 804 is also used to obtain the alarm handling information corresponding to the target machine when the energy consumption level of the target machine is a preset level, and push the alarm handling information to the auditing device to audit the energy consumption of the target machine. The auditing device is the device of the superior of the process responsible person.

[0214] In some possible embodiments, the energy consumption evaluation module is further used to obtain a baseline energy consumption curve of the target machine based on the energy consumption data and machine parameters of the target machine in a first historical detection cycle, the baseline energy consumption curve including the corresponding relationship between the output and energy consumption of the target machine; obtain a new energy consumption curve of the target machine based on the energy consumption data and machine parameters of the target machine in a second historical detection cycle, the new energy consumption curve including the corresponding relationship between the output and energy consumption of the target machine, the second historical detection cycle is a detection cycle after the first historical detection cycle; determine energy consumption improvement data of the target machine based on the baseline energy consumption curve and the new energy consumption curve, the energy consumption improvement data including the ratio between the corrected unit consumption value of the target machine in the second historical detection cycle and the corrected unit consumption value in the first historical detection cycle, the corrected unit consumption value being determined based on the energy consumption value of the target machine in producing a single workpiece at different outputs and the ratio of the energy consumption values ​​corresponding to the different outputs.

[0215] In some possible embodiments, the energy consumption evaluation module is further configured to update the reference energy consumption curve according to the new energy consumption curve when the corrected unit consumption value of the second historical detection period is less than the corrected unit consumption value of the first historical detection period.

[0216] In some possible embodiments, the machine energy consumption management device further includes an output module, which is used to output a reference energy consumption curve and a new energy consumption curve.

[0217] The description of the above device embodiment is similar to the description of the above method embodiment, and has similar beneficial effects as the method embodiment. For technical details not disclosed in the device embodiment of the present application, please refer to the description of the method embodiment of the present application for understanding.

[0218] It should be noted that in the embodiments of this application Figure 8 The division of modules in the device shown is schematic and is only a logical function division. There may be other division methods in actual implementation. In addition, each functional unit in each embodiment of the present application may be integrated into a processing unit, or may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of a software functional unit. It may also be implemented in the form of a combination of software and hardware.

[0219] It should be noted that in the embodiment of the present application, if the above method is implemented in the form of a software function module and sold or used as an independent product, it can also be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of the embodiment of the present application can be essentially or partly embodied in the form of a software product that contributes to the relevant technology. The computer software product is stored in a storage medium, including several instructions to enable an electronic device to execute all or part of the methods described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as a U disk, a mobile hard disk, a read-only memory (ROM), a magnetic disk or an optical disk. In this way, the embodiment of the present application is not limited to any specific combination of hardware and software.

[0220] The present application embodiment provides a computer device, whose internal structure diagram can be as follows: Fig. 9 As shown. The computer device includes a processor, a memory and a network interface connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store data. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, the above method is implemented.

[0221] An embodiment of the present application provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the steps in the method provided in the above embodiment are implemented.

[0222] An embodiment of the present application provides a computer program product including instructions, which, when executed on a computer, enables the computer to execute the steps of the method provided in the above method embodiment.

[0223] Those skilled in the art will understand that Fig. 9 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0224] In one embodiment, the machine energy consumption management device provided in the present application can be implemented in the form of a computer program. The computer program can be Fig. 9The computer device shown in the figure is run. The memory of the computer device can store various program modules constituting the above-mentioned device. The computer program composed of various program modules enables the processor to execute the steps in the method of each embodiment of the present application described in this specification.

[0225] It should be noted here that the description of the above storage medium and device embodiments is similar to the description of the above method embodiments, and has similar beneficial effects as the method embodiments. For technical details not disclosed in the storage medium, storage medium and device embodiments of this application, please refer to the description of the method embodiments of this application for understanding.

[0226] It should be understood that "one embodiment" or "an embodiment" or "some embodiments" mentioned throughout the specification means that specific features, structures or characteristics related to the embodiment are included in at least one embodiment of the present application. Therefore, "in one embodiment" or "in one embodiment" or "in some embodiments" appearing throughout the specification may not necessarily refer to the same embodiment. In addition, these specific features, structures or characteristics can be combined in one or more embodiments in any suitable manner. It should be understood that in various embodiments of the present application, the size of the sequence number of the above-mentioned processes does not mean the order of execution, and the execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiment of the present application. The above-mentioned sequence numbers of the embodiments of the present application are only for description and do not represent the advantages and disadvantages of the embodiments. The above description of each embodiment tends to emphasize the differences between the various embodiments, and the same or similar aspects can be referenced to each other. For the sake of brevity, this article will not repeat them.

[0227] The term "and / or" in this article is only a description of the association relationship of associated objects, indicating that there may be three relationships. For example, object A and / or object B can represent three situations: object A exists alone, object A and object B exist at the same time, and object B exists alone.

[0228] It should be noted that, in this article, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, an element defined by the sentence "comprises a ..." does not exclude the existence of other identical elements in the process, method, article or device including the element.

[0229] In the several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. The embodiments described above are only schematic. For example, the division of the modules is only a logical function division. There may be other division methods in actual implementation, such as: multiple modules or components can be combined, or can be integrated into another system, or some features can be ignored, or not executed. In addition, the coupling, direct coupling, or communication connection between the components shown or discussed can be through some interfaces, and the indirect coupling or communication connection of devices or modules can be electrical, mechanical or other forms.

[0230] The modules described above as separate components may or may not be physically separated, and the components displayed as modules may or may not be physical modules; they may be located in one place or distributed on multiple network units; some or all of the modules may be selected according to actual needs to achieve the purpose of the present application.

[0231] In addition, all functional modules in the embodiments of the present application may be integrated into one processing unit, or each module may be a separate unit, or two or more modules may be integrated into one unit; the above-mentioned integrated modules may be implemented in the form of hardware or in the form of hardware plus software functional units.

[0232] A person skilled in the art can understand that all or part of the steps of implementing the above method embodiment can be completed by hardware related to program instructions, and the aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it executes the steps of the above method embodiment; and the aforementioned storage medium includes: mobile storage devices, read-only memories (ROM), magnetic disks or optical disks, etc., various media that can store program codes.

[0233] Alternatively, if the above-mentioned integrated unit of the present application is implemented in the form of a software function module and sold or used as an independent product, it can also be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the embodiment of the present application can essentially or in other words, the part that contributes to the relevant technology can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions for enabling an electronic device to execute all or part of the methods described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as mobile storage devices, ROMs, magnetic disks, or optical disks.

[0234] The methods disclosed in several method embodiments provided in this application can be arbitrarily combined without conflict to obtain new method embodiments.

[0235] The features disclosed in several product embodiments provided in this application can be arbitrarily combined without conflict to obtain new product embodiments.

[0236] The features disclosed in several method or device embodiments provided in this application can be arbitrarily combined without conflict to obtain new method embodiments or device embodiments.

[0237] The above is only an implementation method of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A machine energy consumption management method, characterized in that: include: According to a preset energy consumption prediction model and actual machine parameters of the target machine, predicted energy consumption data of the target machine is obtained, wherein the preset energy consumption prediction model is established based on historical energy consumption data and historical machine parameters of multiple machines, and the predicted energy consumption data includes multiple energy consumption prediction values ​​corresponding to multiple detection time periods; Acquire actual energy consumption data of the target machine, wherein the actual energy consumption data includes a plurality of energy consumption detection values ​​corresponding to the plurality of detection time periods; According to the energy consumption prediction value and the energy consumption detection value corresponding to the target detection period, determining whether the energy consumption of the target machine in the target period is abnormal, wherein the target period is any period among the multiple detection periods; When the ratio of the number of detection periods of abnormal energy consumption to the total number of the plurality of detection periods is greater than a preset ratio threshold, the alarm information of the target machine is pushed to a target device, which is a device of a process responsible person.

2. The method according to claim 1, characterized in that: The step of judging whether the energy consumption of the target machine in the target time period is abnormal according to the energy consumption prediction value and the energy consumption detection value corresponding to the target detection time period includes: When the energy consumption detection value corresponding to the target detection period is greater than the energy consumption prediction upper limit, or the energy consumption detection value corresponding to the target detection period is less than the energy consumption prediction lower limit, it is determined that the energy consumption of the target machine in the target period is abnormal; When the energy consumption detection value corresponding to the target detection period is less than or equal to the energy consumption prediction upper limit, and the energy consumption detection value corresponding to the target detection period is greater than or equal to the energy consumption prediction lower limit, it is determined that the energy consumption of the target machine in the target period is normal; The energy consumption prediction upper limit and the energy consumption prediction lower limit are determined according to the energy consumption prediction value corresponding to the target detection period.

3. The method according to claim 2, characterized in that The method further comprises: The energy consumption prediction upper limit and the energy consumption prediction lower limit corresponding to each detection period are summed up respectively to obtain the total value of the energy consumption prediction upper limit and the total value of the energy consumption prediction lower limit corresponding to the target machine; Sum the energy consumption detection values ​​corresponding to each detection period to obtain the total energy consumption detection value corresponding to the target machine; When the energy consumption detection total value is greater than the energy consumption prediction upper limit total value, or the energy consumption detection total value is less than the energy consumption prediction lower limit total value, the alarm information of the target machine is pushed to the target device.

4. The method according to any one of claims 1 to 3, characterized in that: After determining whether the energy consumption of the target machine in the target time period is abnormal according to the energy consumption prediction value and the energy consumption detection value corresponding to the target detection time period, the method further includes: Calculate the energy consumption difference between the energy consumption detection value and the energy consumption prediction value corresponding to each energy consumption abnormality detection period; Sum the energy consumption differences of all energy consumption abnormality detection periods to obtain the energy loss value corresponding to the target machine; The target machine is hierarchically managed according to the energy consumption loss value corresponding to the target machine to determine the energy consumption level of the target machine. The hierarchical management includes sorting the energy consumption loss values ​​of multiple machines and setting the energy consumption level corresponding to each machine according to the sorting result.

5. The method according to claim 4, characterized in that After pushing the alarm information of the target machine to the target device, the method further includes: When the energy consumption level of the target machine is at a preset level, the alarm handling information corresponding to the target machine is obtained, and the alarm handling information is pushed to an audit device to audit the energy consumption of the target machine. The audit device is the device of the superior of the process responsible person.

6. The method according to claim 1, characterized in that The method further comprises: Obtaining a reference energy consumption curve of the target machine according to the energy consumption data and machine parameters of the target machine in the first historical detection period, wherein the reference energy consumption curve includes a corresponding relationship between the output and energy consumption of the target machine; Obtaining a new energy consumption curve of the target machine according to the energy consumption data and machine parameters of the target machine in a second historical detection period, wherein the new energy consumption curve includes a corresponding relationship between the output and the energy consumption of the target machine, wherein the second historical detection period is a detection period after the first historical detection period; According to the baseline energy consumption curve and the new energy consumption curve, the energy consumption improvement data of the target machine is determined, and the energy consumption improvement data includes the ratio between the corrected unit consumption value of the target machine in the second historical detection cycle and the corrected unit consumption value in the first historical detection cycle. The corrected unit consumption value is determined based on the energy consumption value of the target machine in producing a single workpiece at different outputs and the ratio of energy consumption values ​​corresponding to different outputs.

7. The method according to claim 6, characterized in that The method further comprises: When the corrected unit consumption value of the second historical detection period is less than the corrected unit consumption value of the first historical detection period, the reference energy consumption curve is updated according to the new energy consumption curve.

8. A machine energy consumption management device, characterized in that: The device comprises: An energy consumption prediction module, used to obtain predicted energy consumption data of a target machine according to a preset energy consumption prediction model and actual machine parameters of the target machine, wherein the preset energy consumption prediction model is established according to historical energy consumption data and historical machine parameters of multiple machines, and the predicted energy consumption data includes multiple energy consumption prediction values ​​corresponding to multiple detection time periods; An energy consumption acquisition module, used to acquire actual energy consumption data of the target machine, wherein the actual energy consumption data includes a plurality of energy consumption detection values ​​corresponding to the plurality of detection time periods; an abnormality detection module, used to determine whether the energy consumption of the target machine in the target detection period is abnormal according to the energy consumption prediction value and the energy consumption detection value corresponding to the target detection period, wherein the target period is any period among the multiple detection periods; The energy consumption alarm module is used to push the alarm information of the target machine to the target device when the ratio of the number of detection periods of abnormal energy consumption to the total number of the multiple detection periods is greater than a preset ratio threshold. The target device is the device of the process responsible person.

9. An electronic device, characterized in that: The system comprises a processor and a memory, wherein the memory is used to store code instructions; the processor is used to run the code instructions to execute the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: Used to store a computer program, the computer program comprising instructions for implementing the method according to any one of claims 1 to 7.

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