Energy consumption management method, computing device and computer readable storage medium

Abstract

The invention discloses an energy consumption management method, computing equipment and a computer readable storage medium. The energy consumption management method comprises the steps of determining instrument data of a target process; obtaining production management data of the target process; wherein the production management data is product information data related to the production activity of the target process; and determining energy consumption information of the target process according to the instrument data and the production management data. Thus, the energy consumption information of the target process is determined based on integration of the obtained instrument data and production management data of the target process, the labor cost for statistical management of the energy consumption of the process is reduced, and the efficiency and accuracy of energy consumption management are improved.

Description

Technical Field

[0001] The present application relates to the field of management technology, and in particular to an energy consumption management method, a computing device, and a computer-readable storage medium. Background Art

[0002] Factory energy consumption management is one of the key links in modern industrial production. With the continuous advancement of industrialization, the problem of energy consumption has become increasingly prominent.

[0003] At present, factories lack effective statistical methods for managing energy consumption in certain processes. When conducting energy consumption statistics, it is necessary to first count the power consumption at a fine-grained level, and then to count the process-related data, such as the number of effective stamping times in the stamping process, the spraying area in the painting process, etc.

[0004] If the existing technology is to relatively accurately meet the management needs of process energy consumption, it will need to invest a lot of manpower and time costs to complete the collection and statistics of basic data, and the accuracy of the statistical data cannot be guaranteed. Summary of the invention

[0005] The purpose of this application is to provide an energy consumption management method, a computing device and a computer-readable storage medium, which reduce the manpower cost of statistical management of process energy consumption and improve the efficiency and accuracy of energy consumption management.

[0006] To achieve the above objectives:

[0007] In a first aspect, an embodiment of the present application provides an energy consumption management method, characterized by comprising:

[0008] Determine the instrument data for the target process;

[0009] Acquire production management data of the target process; wherein the production management data is product information data related to the production activities of the target process; and determine energy consumption information of the target process based on the instrument data and the production management data.

[0010] In one embodiment, the determining of the instrument data of the target process includes:

[0011] Acquire initial instrument data of a target process, and perform data processing on the initial instrument data through edge computing;

[0012] The processed initial instrument data is determined as the instrument data of the target process.

[0013] In one embodiment, the obtaining of initial instrument data of the target process includes:

[0014] Acquire initial instrument data of each management node; wherein each management node is a management location set according to a production management dimension; the management node includes at least one of a factory node, a workshop node, a process node, and an equipment node;

[0015] According to the initial instrument data of the management node, the initial instrument data of the target process is correspondingly acquired.

[0016] In one embodiment, the production management data includes at least one of production work order information, equipment processing information and quality control information; the energy consumption information includes at least one of process production energy consumption information, process processing energy consumption information and qualified product energy consumption information;

[0017] The determining the energy consumption information of the target process according to the instrument data and the production management data includes at least one of the following:

[0018] Determining energy consumption information of process production of the target process according to the instrument data and the production work order information;

[0019] Determining energy consumption information of a process of the target process according to the instrument data and the equipment processing information;

[0020] The energy consumption information of qualified products in the target process is determined based on the instrument data and the quality control information.

[0021] In one embodiment, the method further comprises:

[0022] Determine the total energy consumption corresponding to the target process according to the instrument data and the preset energy consumption parameters; and / or,

[0023] The energy consumption cost corresponding to the target process is determined based on the instrument data and the preset energy consumption price.

[0024] In one embodiment, the method further comprises:

[0025] Inputting at least one of the production management data into a preset prediction model to output energy consumption prediction data based on the prediction model;

[0026] The preset prediction model is established based on an RNN variant and is determined by training and learning the prediction model according to historical data of the production management data of the target process.

[0027] In one embodiment, the method further comprises:

[0028] Perform energy consumption analysis according to the energy consumption information, energy consumption prediction data, and environmental information of the target process;

[0029] An analysis report is generated based on the energy consumption analysis result, and the analysis report is output.

[0030] In one embodiment, the energy consumption analysis includes at least one of a trend analysis of energy consumption indicators, a year-on-year analysis of a time period, and a month-on-month analysis.

[0031] In a second aspect, an embodiment of the present application provides a computing device, specifically comprising:

[0032] processor;

[0033] a memory for storing instructions executable by the processor;

[0034] The processor is configured to execute the instructions to perform the energy consumption management method as described in the first aspect.

[0035] In a third aspect, an embodiment of the present application provides a computer-readable storage medium, in which a computer program is stored. When the instructions in the computer-readable storage medium are executed by a processor of a computing device, the computing device is enabled to implement the energy consumption management method as described in the first aspect.

[0036] The energy consumption management method, computing device and computer-readable storage medium provided in the embodiment of the present application include: determining the instrument data of the target process; obtaining the production management data of the target process; wherein the production management data is product information data related to the production activities of the target process; and determining the energy consumption information of the target process based on the instrument data and the production management data. In this way, the energy consumption information of the target process is determined based on the integration of the acquired instrument data and the production management data, which reduces the manpower cost of statistical management of the energy consumption of the process and improves the efficiency and accuracy of energy consumption management. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 A schematic diagram of a flow chart of an energy consumption management method provided by an embodiment of the present invention;

[0038] Figure 2 A schematic diagram of the structure of a computing device provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0039] Exemplary embodiments will be described in detail herein, examples of which are shown in the accompanying drawings. When the following description refers to the drawings, the same numbers in different drawings represent the same or similar elements unless otherwise indicated. The implementations described in the following exemplary embodiments do not represent all implementations consistent with the present application. Instead, they are merely examples of devices and methods consistent with some aspects of the present application as detailed in the appended claims.

[0040] It should be noted that, in this article, the terms "include", "comprises" or any other variants 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 includes 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 "includes a ..." does not exclude the existence of other identical elements in the process, method, article or device including the element. In addition, components, features, and elements with the same name in different embodiments of the present application may have the same meaning or different meanings, and their specific meanings need to be determined by their explanation in the specific embodiment or further combined with the context of the specific embodiment.

[0041] It should be understood that, although the terms first, second, third, etc. may be used to describe various information in this article, these information should not be limited to these terms. These terms are only used to distinguish the same type of information from each other. For example, without departing from the scope of this article, the first information may also be referred to as the second information, and similarly, the second information may also be referred to as the first information. Depending on the context, the word "if" as used herein can be interpreted as "at the time of..." or "when..." or "in response to determination". Furthermore, as used in this article, the singular forms "one", "one" and "the" are intended to also include plural forms, unless there is an opposite indication in the context. It should be further understood that the terms "comprising", "including" indicate that there are described features, steps, operations, elements, components, projects, kinds, and / or groups, but do not exclude the existence, occurrence or addition of one or more other features, steps, operations, elements, components, projects, kinds, and / or groups. The terms "or" and "and / or" used herein are interpreted as inclusive, or mean any one or any combination. Thus, “A, B, or C” or “A, B and / or C” means “any of the following: A; B; C; A and B; A and C; B and C; A, B, and C.” An exception to this definition will occur only when a combination of elements, functions, steps, or operations are inherently mutually exclusive in some manner.

[0042] It should be understood that, although the various steps in the flowchart in the embodiment of the present application are displayed in sequence according to the indication of the arrows, these steps are not necessarily performed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and it can be performed in other orders. Moreover, at least a portion of the steps in the figure may include a plurality of sub-steps or a plurality of stages, and these sub-steps or stages are not necessarily performed at the same time, but can be performed at different times, and their execution order is not necessarily performed in sequence, but can be performed in turn or alternately with other steps or at least a portion of the sub-steps or stages of other steps.

[0043] It should be noted that, in this article, step codes such as S101, S102, etc. are used for the purpose of expressing the corresponding content more clearly and concisely, and do not constitute a substantial limitation on the sequence. When implementing the step, those skilled in the art may execute S102 first and then S101, etc., but these should all be within the scope of protection of this application.

[0044] It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0045] In the subsequent description, the suffixes such as "module", "component" or "unit" used to represent elements are only used to facilitate the description of the present application, and have no specific meanings. Therefore, "module", "component" or "unit" can be used in a mixed manner.

[0046] See also Figure 1 The embodiment of the present application provides an energy consumption management method, which can be implemented in software and / or hardware. In this embodiment, the energy consumption management method is applied to a computing device as an example. The energy consumption management method provided in this embodiment includes:

[0047] Step S101, determining instrument data of a target process.

[0048] The target process is a process for which energy consumption information needs to be obtained, which can be set by the user as required or by the system by default. Specifically, the target process may include spraying, stamping and other processes.

[0049] In one embodiment, step S101, determining instrument data of a target process, includes:

[0050] The initial instrument data of the target process is obtained, and the initial instrument data is processed through edge computing; the processed initial instrument data is determined as the instrument data of the target process.

[0051] Specifically, in one embodiment, obtaining initial instrument data of a target process includes:

[0052] Obtain initial instrument data of each management node; wherein each management node is a management location set according to the production management dimension; the management node includes at least one of a factory node, a workshop node, a process node, and an equipment node; according to the initial instrument data of the management node, obtain the initial instrument data of the target process accordingly.

[0053] Optionally, based on the production management dimension, the first-level nodes can be divided from the workshop dimension (first-level workshop node), and then under the workshop, the nodes can be divided according to the process dimension (second-level process node). Under the same process, if it contains multiple workstations and multiple equipment, the nodes can be further divided according to the actual situation (third-level equipment node). Here, the tree-shaped organizational structure of the energy consumption of the process is completed through the previous steps. The root node is the factory, the next level of the root node is defined as the first-level workshop node, the next level of the workshop node is defined as the second-level process node, the next level of the process node is defined as the equipment node, and the next level of the equipment node is each instrument node.

[0054] In one embodiment, the instrument node is a leaf node, which is a terminal node and has no child nodes. In the instrument node, at least one instrument is set to detect the energy consumption data of the target process based on the type and energy consumption of the target process.

[0055] For example, if workshop A has three main processes: P1, P2, and P3. Create a first-level node workshop A, and create three second-level nodes P1, P2, and P3 under it. Then, associate the relevant instruments with the second-level nodes. For example, if process P1 consists of five electricity meters and three gas meters, then associate all eight instruments with the P1 node.

[0056] In other embodiments, if different processes share the same high-energy-consuming equipment, the total energy consumption of the target process and at least one other process can be obtained based on the instrument corresponding to the high-energy-consuming equipment. Here, the energy consumption data of the target process and the energy consumption data of at least one other process can be determined based on a preset energy consumption ratio. Alternatively, the total energy consumption obtained can be directly used as the energy consumption data of the target process for statistics.

[0057] Optionally, multiple meters are connected through an edge computing gateway to simultaneously collect and process the initial meter data generated by these meters. For example, the edge computing gateway performs preliminary cleaning and aggregation on the collected initial meter data, removes redundant and invalid data, and extracts key information. In this way, the amount of preprocessed data is reduced, the amount of data transmitted to the server is reduced, and the burden on the application server is effectively reduced. At the same time, even in the case of network instability or server downtime, the edge computing gateway can still run independently and cache data, ensuring the continuity and reliability of data transmission.

[0058] Exemplarily, when the management node setting includes: workshop node, process node, equipment node, the initial instrument data recorded by at least one instrument is obtained. In one embodiment, each instrument is assigned a unique number as an identifier, so that the matching relationship between the instrument and each node set can be determined by the identifier, and the association between the instrument reading and the corresponding equipment, process, and workshop is established. It can be understood that when the initial instrument data recorded by the instrument is obtained, the identification information corresponding to the instrument is also obtained, and the equipment, process, and workshop information corresponding to the initial instrument data can be determined by the identification information, that is, the initial instrument data under each equipment node, the initial instrument data under each process node, and the initial instrument data under each workshop node are determined by the initial instrument data and the identifier recorded by at least one instrument obtained. In this way, after obtaining the initial instrument data recorded by the instrument, since the initial instrument data obtained may include not only energy consumption data, but also other information, it is necessary to clean the initial instrument data through the edge computing gateway, and aggregate the energy consumption data obtained by cleaning to generate instrument data for determining the target process.

[0059] Optionally, the meter is installed on the management node and connected to the edge computing gateway through networking to directly upload the data collected by the digital meter to the IoT platform or other servers in real time to achieve persistent data storage. At the same time, the initial meter data can be obtained based on the set time interval, such as every 5 seconds or 30 seconds. The specific time interval for obtaining the initial meter can be customized according to the energy consumption characteristics of different management nodes. For example, for high-energy consumption equipment, a shorter acquisition time interval can be set. In this way, the timeliness and accuracy of energy consumption data are ensured, which helps to timely reflect the changes in various energy consumption in the production process and provide reliable data support for management personnel.

[0060] Specifically, corresponding to different types of energy consumption, different meters can be set to collect data, including electricity meters to count electricity energy consumption, water meters to count water energy consumption, gas meters to count gas energy consumption, etc.

[0061] Optionally, step S102, obtaining production management data of the target process; wherein the production management data is product information data related to the production activities of the target process;

[0062] In one embodiment, the production management data includes at least one of production work order information, equipment processing information, and quality control information.

[0063] Among them, the production work order information can be obtained through the Manufacturing Execution System (MES). Among them, the production work order is issued by the planning department to the production workshop, and requires the production workshop to execute the production business process instructions, recording the production information content of the product and the by-products; the workshop management personnel collect materials and organize production according to the production work order issued by the planning department, and schedule and allocate workshop resources. In one embodiment, the production work order information includes product type, quantity, unit spraying area, unit welding length, etc. Among them, MES is an information system located between the Enterprise Resource Planning (ERP) system and the factory / workshop level control system, which is used to track, record and monitor the production process of the product, including the management and scheduling of production work orders.

[0064] The equipment processing information can be obtained through the central control system. Here, the equipment processing information includes the number of stamping times of the product, the number of products stamped per time, etc. Among them, the central control system is the core of factory automation, which is used to monitor and control production equipment in real time to ensure that the equipment runs efficiently and stably according to the predetermined program.

[0065] Quality control information can be obtained through the Quality Management System (QMS), which includes relevant information on qualified products, including quality control information on qualified products. Among them, QMS is a management system that ensures that the quality of products and services meets customer needs and relevant regulations, covering activities such as quality assurance, quality control, and quality improvement.

[0066] Step S103: Determine the energy consumption information of the target process according to the instrument data and the production management data.

[0067] The energy consumption information may be at least one type of energy consumption information about the target process, such as water, electricity, gas, oil, etc. In one embodiment, at least one type of energy consumption information of the target process is determined at the same time and integrated based on the target process.

[0068] Here, when integrating energy consumption information, different statistical periods can be set up according to the management needs of the factory, such as natural month, natural quarter, financial month, etc.

[0069] In one embodiment, the production management data includes at least one of production work order information, equipment processing information, and quality control information; the energy consumption information includes at least one of process production energy consumption information, process processing energy consumption information, and qualified product energy consumption information;

[0070] Determine the energy consumption information of the target process based on instrument data and production management data, including at least one of the following:

[0071] Determine the energy consumption information of the target process production based on instrument data and production work order information;

[0072] Determine the energy consumption information of the target process based on instrument data and equipment processing information;

[0073] Determine the energy consumption information of qualified products in the target process based on instrument data and quality control information.

[0074] Optionally, when the target process is spraying, the instrument data of spraying includes electrophoretic pre-treatment energy consumption, ventilation energy consumption, and energy consumption of other related equipment, the production work order information includes the quantity and area of ​​products, and the energy consumption information of process production includes the energy consumption per unit spraying area, the energy consumption per unit spraying area can be determined based on the electrophoretic pre-treatment energy consumption, ventilation energy consumption, energy consumption of other related equipment and the quantity and area of ​​products. The formula is: Energy consumption per unit spraying area = (electrophoretic pre-treatment energy consumption + ventilation shared energy consumption + other related equipment energy consumption) / (product a area * product a quantity + product b area * product b quantity).

[0075] When the instrument data includes the energy consumption of the hydraulic press and the inert gas consumption, the production order information includes the weld length of a single product, and the energy consumption information of the process production includes the energy consumption per unit welding length, the energy consumption per unit welding length of the target process can be determined based on the energy consumption of the hydraulic press, the inert gas consumption and the weld length of a single product by the formula: energy consumption per unit welding length = (energy consumption of the welding machine + inert gas consumption) / weld length of a single product.

[0076] Optionally, when the instrument data includes the energy consumption of the hydraulic press, the equipment processing information includes the number of stamping times, and the energy consumption information of the process processing includes the energy consumption of a single stamping, the energy consumption of a single stamping of the target process can be calculated based on the energy consumption of the hydraulic press and the number of stamping times by the formula: single stamping energy consumption = hydraulic press energy consumption / number of stamping times.

[0077] When the instrument data includes the energy consumption of the hydraulic press, the equipment processing information includes the number of products stamped at a time, and the energy consumption information of the process processing includes the energy consumption of stamping a single product, the energy consumption of stamping a single product of the target process can be calculated based on the energy consumption of the hydraulic press and the number of products stamped at a time by the formula: energy consumption of stamping a single product = energy consumption of the hydraulic press / number of products stamped at a time.

[0078] Optionally, when the instrument data includes electrophoresis pre-treatment energy consumption, ventilation energy consumption, and other related equipment energy consumption, the quality control information includes the number of qualified products, and the energy consumption information of qualified products includes the unit spraying area energy consumption of qualified products, the unit spraying area energy consumption of qualified products of the target process can be determined according to the electrophoresis pre-treatment energy consumption, ventilation energy consumption, other related equipment energy consumption and the number of qualified products. It can be expressed as: unit spraying area energy consumption of qualified products = (electrophoresis pre-treatment energy consumption + ventilation energy consumption + other related equipment energy consumption) / number of qualified products. Here, considering the impact of unqualified products, energy consumption is considered based on the number of qualified products. For example, during the statistical period, if a total of 80 are produced and there are 2 unqualified products, the number of qualified products should be 78. In this way, based on the consideration of the number of qualified products, the energy consumption results can be made more accurate.

[0079] In one embodiment, the method further comprises:

[0080] Determine the total energy consumption corresponding to the target process based on the instrument data and the preset energy consumption parameters; and / or,

[0081] Determine the energy cost corresponding to the target process based on instrument data and preset energy consumption prices.

[0082] Optionally, when the preset energy consumption parameters include the number of energy types and the standard coal conversion coefficient of energy (for example, the parameters of GB / T 2589-2020 can be referred to), and the instrument data includes the energy consumption of a single energy consumption type, the total energy consumption corresponding to the target process can be determined according to the number of energy types, the standard coal conversion coefficient of energy and the energy consumption of a single energy consumption type. The formula can be expressed as follows:

[0083]

[0084] Where E represents the standard total energy consumption of the target process, n represents the number of energy types, and E i Expressed as the actual consumption of the i-th energy, K i Expressed as the standard coal equivalent coefficient of the i-th energy source (for example, the coefficient in GB / T 2589-2020 can be referred to).

[0085] Optionally, the energy consumption cost corresponding to the target process is determined based on the product of the calculated total standard energy consumption of the target process and the energy consumption price corresponding to the energy. Here, different energy types and price plans for each energy type can be determined, including fixed unit price, step unit price and mixed unit price of the power grid.

[0086] In summary, in the energy consumption management method provided in the above embodiment, the energy consumption information of the target process is determined based on the integration of the acquired instrument data and production management data, which reduces the manpower cost of statistical management of the energy consumption of the process and improves the efficiency and accuracy of energy consumption management.

[0087] In one embodiment, the method further comprises:

[0088] Inputting at least one production management data into a preset prediction model to output energy consumption prediction data based on the prediction model;

[0089] Among them, the preset prediction model is established based on the RNN variant, and is determined by training and learning the prediction model according to the historical data of the production management data of the target process.

[0090] First, select appropriate Recurrent Neural Network (RNN) variants (such as Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU)) for modeling. The input layer receives multi-dimensional time series data, the hidden layer consists of multiple layers of LSTM or GRU units, and the output layer is used to predict future energy consumption values ​​or device status. At the same time, a convolutional neural network (CNN) can be added for feature extraction, and combined with the CNN-RNN architecture to improve the model's ability to process spatiotemporal data.

[0091] Secondly, the historical data of the production management data of the target process is used for model training, and the model performance is evaluated through the validation set to adjust the model parameters. Here, the mean square error can be used as the loss function, and the parameters can be updated through the Adam optimizer. At the same time, cross-validation and grid search methods can also be used to optimize hyperparameters to ensure the generalization ability and stability of the model.

[0092] Finally, the trained RNN model is determined as a prediction model to predict real-time data and generate future energy consumption values. In this way, the equipment operating parameters can be adjusted in real time according to the prediction results to achieve energy consumption optimization. An automated prediction and adjustment mechanism is built to reduce human intervention and improve system response speed.

[0093] Here, in the actual production environment, the RNN model and optimization strategy can be continuously improved based on feedback to improve the energy consumption management level. By developing user interfaces and visualization tools, the energy consumption prediction and adjustment effects can be displayed to facilitate operator monitoring and decision-making.

[0094] In one embodiment, the method further comprises:

[0095] Conduct energy consumption analysis according to the target process’s energy consumption information, energy consumption forecast data, and environmental information;

[0096] Generate an analysis report based on the energy consumption analysis results and output the analysis report.

[0097] Optionally, the energy consumption information, energy consumption forecast data, and environmental information of the target process are integrated, and further energy consumption analysis is performed, including: by comparing the actual energy consumption with the predicted energy consumption, analyzing the trend of energy consumption over time, and identifying high energy consumption periods and equipment. You can also explore the correlation between production activities, environmental factors, and energy consumption, such as the impact of temperature changes on energy consumption. Or identify outliers or mutations in energy consumption data, which may indicate equipment failure, improper operation, or data recording errors. Here, after completing the energy consumption analysis, the output can be displayed in the form of graphics or tables.

[0098] In one embodiment, the energy consumption analysis includes at least one of a trend analysis of energy consumption indicators, a year-on-year analysis of a time period, and a month-on-month analysis.

[0099] Optionally, the energy consumption index values ​​of each process can be displayed based on trend analysis. For example, the energy consumption index per unit spraying area will be displayed as a bar chart, and the average temperature per day will be displayed as a line chart in the same chart. In this way, users can clearly see the impact of temperature on energy consumption while viewing the trend changes of energy consumption indicators, making it easier to understand the relationship between the two. Here, trend analysis focuses on discovering long-term change patterns and is suitable for observing trends and influencing factors that continue to change.

[0100] Optionally, the current year-on-year comparison can be determined based on the year-on-year analysis to reduce the impact of other environmental factors, such as temperature. The year-on-year analysis adds output or output value as auxiliary data, so that users can clearly see the impact relationship of the indicator value under different output / output values. Here, the year-on-year analysis focuses on annual comparisons that exclude seasonal and cyclical effects, which is suitable for evaluating annual performance or results.

[0101] Optionally, the data of a certain period can be compared with the data of the immediately previous period based on the month-on-month analysis to observe short-term changes in the data. In energy consumption analysis, month-on-month analysis helps to identify recent fluctuations in energy consumption, such as abnormal energy consumption caused by equipment failure or improper operation. For example, by comparing the energy consumption per unit spraying area on a daily or weekly basis, sudden increases or decreases in energy consumption can be discovered in a timely manner, so that a quick response can be made to adjust production strategies or troubleshoot equipment problems. Here, the month-on-month analysis focuses on short-term data changes and is suitable for quickly discovering problems and adjusting strategies.

[0102] In one embodiment, an environmental detection instrument is also provided, and the current environmental information is determined based on the environmental detection instrument; wherein the environmental information is data information corresponding to environmental factors that affect energy consumption changes. For example, a reliable local temperature data source can be established, or a digital thermometer can be installed in the workshop. If a digital thermometer is installed, it needs to be installed at multiple locations in the workshop, and the average value is used as the real-time collected temperature value.

[0103] Based on the same inventive concept as the above embodiments, an embodiment of the present invention provides a computing device, such as Figure 2 As shown, the computing device includes: a processor 210 and a memory 211 storing a computer program; wherein, Figure 2 The processor 210 shown in the figure is not used to indicate that the number of the processor 210 is one, but is only used to indicate the positional relationship of the processor 210 relative to other devices. In actual applications, the number of the processor 210 may be one or more; similarly, Figure 2 The memory 211 shown in the figure has the same meaning, that is, it is only used to refer to the position relationship of the memory 211 relative to other devices. In practical applications, the number of memories 211 can be one or more. When the processor 210 runs the computer program, the above energy consumption management method is implemented.

[0104] The computing device may also include: at least one network interface 212. The various components in the computing device are coupled together via a bus system 213. It is understood that the bus system 213 is used to achieve connection and communication between these components. In addition to the data bus, the bus system 213 also includes a power bus, a control bus, and a status signal bus. However, for the sake of clarity, the bus system 213 is not used in the example above. Figure 2 Various buses are labeled as bus system 213.

[0105] The memory 211 may be a volatile memory or a non-volatile memory, or may include both volatile and non-volatile memories. The non-volatile memory may be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), a magnetic random access memory (FRAM), a flash memory, a magnetic surface memory, an optical disk, or a compact disc read-only memory (CD-ROM); the magnetic surface memory may be a disk memory or a tape memory. The volatile memory may be a random access memory (RAM), which is used as an external cache. By way of example and not limitation, many forms of RAM are available, such as static random access memory (SRAM), synchronous static random access memory (SSRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDRSDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous link dynamic random access memory (SLDRAM, SyncLink Dynamic Random Access Memory), and direct RAM bus random access memory (DRRAM, Direct Rambus Random Access Memory).The memory 211 described in the embodiments of the present invention is intended to include but is not limited to these and any other suitable types of memories.

[0106] The memory 211 in the embodiment of the present invention is used to store various types of data to support the operation of the computing device. Examples of these data include: any computer program used to operate on the computing device, such as an operating system and an application; contact data; phone book data; messages; pictures; videos, etc. Among them, the operating system includes various system programs, such as a framework layer, a core library layer, a driver layer, etc., which are used to implement various basic services and process hardware-based tasks. The application program may include various applications, such as a media player, a browser, etc., which are used to implement various application services. Here, the program that implements the method of the embodiment of the present invention may be included in the application program.

[0107] Based on the same inventive concept as the above-mentioned embodiment, this embodiment further provides a computer-readable storage medium, in which a computer program is stored. The computer-readable storage medium may be a ferromagnetic random access memory (FRAM), a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), a flash memory, a magnetic surface memory, an optical disc, or a compact disc read-only memory (CD-ROM); it may also be various devices including one or any combination of the above memories, such as a mobile phone, a computer, a tablet device, a personal digital assistant, etc. When the computer program stored in the computer-readable storage medium is executed by the processor, the energy consumption management method applied to the above-mentioned computing device is implemented. For the specific steps implemented when the computer program is executed by the processor, please refer to Figure 1 The description of the illustrated embodiment will not be repeated here.

[0108] The technical features of the above-described embodiments may be arbitrarily combined. To make the description concise, not all possible combinations of the technical features in the above-described embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0109] In this document, the terms "comprises," "comprising," or any other variations thereof, are intended to cover a non-exclusive inclusion of elements other than those listed and may also include additional elements not expressly listed.

[0110] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. An energy consumption management method, characterized in that: include: Determine the instrument data for the target process; Acquire production management data of the target process; wherein the production management data is product information data related to the production activities of the target process; The energy consumption information of the target process is determined according to the instrument data and the production management data.

2. The method according to claim 1, characterized in that The instrument data for determining the target process includes: Acquire initial instrument data of a target process, and perform data processing on the initial instrument data through edge computing; The processed initial instrument data is determined as the instrument data of the target process.

3. The method according to claim 2, characterized in that The obtaining of initial instrument data of the target process includes: Acquire initial instrument data of each management node; wherein each management node is a management location set according to a production management dimension; the management node includes at least one of a factory node, a workshop node, a process node, and an equipment node; According to the initial instrument data of the management node, the initial instrument data of the target process is correspondingly acquired.

4. The method according to claim 1, characterized in that: The production management data includes at least one of production order information, equipment processing information and quality control information; the energy consumption information includes at least one of process production energy consumption information, process processing energy consumption information and qualified product energy consumption information; The determining the energy consumption information of the target process according to the instrument data and the production management data includes at least one of the following: Determining energy consumption information of process production of the target process according to the instrument data and the production work order information; Determining energy consumption information of a process of the target process according to the instrument data and the equipment processing information; The energy consumption information of qualified products in the target process is determined based on the instrument data and the quality control information.

5. The method according to claim 1, characterized in that The method further comprises: Determine the total energy consumption corresponding to the target process according to the instrument data and the preset energy consumption parameters; and / or, The energy consumption cost corresponding to the target process is determined based on the instrument data and the preset energy consumption price.

6. The method according to claim 1, characterized in that The method further comprises: Inputting at least one of the production management data into a preset prediction model to output energy consumption prediction data based on the prediction model; The preset prediction model is established based on an RNN variant and is determined by training and learning the prediction model according to historical data of the production management data of the target process.

7. The method according to claim 1, characterized in that The method further comprises: Perform energy consumption analysis according to the energy consumption information, energy consumption prediction data, and environmental information of the target process; An analysis report is generated based on the energy consumption analysis result, and the analysis report is output.

8. The method according to claim 7, characterized in that The energy consumption analysis includes at least one of a trend analysis of energy consumption indicators, a year-on-year analysis of a time period, and a month-on-month analysis.

9. A computing device, characterized in that include: A processor and a memory for storing executable instructions; wherein the processor is configured to execute the instructions to implement the energy consumption management method according to any one of claims 1-8.

10. A computer-readable storage medium, characterized in that: When the instructions in the computer-readable storage medium are executed by a processor, the energy consumption management method according to any one of claims 1 to 8 is implemented.

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