Energy consumption analysis method and device, medium and electronic equipment

Abstract

The embodiment of the invention discloses an energy consumption analysis method and device, a medium and electronic equipment, and relates to the technical field of lithium salt production energy consumption analys.The lithium salt production data of a batch needing energy consumption analysis is decomposed into all production links for deep analysis on the basis of the total energy consumption, and the energy consumption analysis efficiency is improved. The energy consumption data is converted according to a time sequence, curve expression of energy consumption is obtained, due to the fact that production devices in all production links are different, productivity requirements at different times are different, fluctuation of a first energy consumption curve cannot represent the relative normal value of energy consumption, and therefore standardization processing is carried out on the energy consumption, and the energy consumption is obtained. And converting to the same processing standard for comparison, and carrying out local peak search on the obtained second energy consumption curve, so that the part with abnormal fluctuation of the curve can be identified, the abnormal condition of energy consumption is finally obtained, and the improvement of the energy consumption analysis effect is realized.

Description

Technical Field

[0001] This application relates to the field of energy consumption analysis technology in lithium salt production, specifically to an energy consumption analysis method, apparatus, medium, and electronic equipment. Background Technology

[0002] In lithium salt production, electricity consumption is a significant factor in production costs. Existing energy consumption analysis methods focus on overall energy consumption, directly concluding whether energy consumption is too low or too high based on the relative magnitude of total energy consumption. However, lithium salt production involves various stages, and overall energy consumption cannot represent each individual stage. Analysis that does not delve into the specific production stages is ineffective and cannot provide effective guidance for optimizing production processes, identifying energy consumption anomalies, and determining energy conservation and reduction directions. Summary of the Invention

[0003] The main objective of this application is to provide an energy consumption analysis method, apparatus, medium, and electronic device, which aims to solve the problem of poor performance in energy consumption analysis of lithium salt production processes in the prior art.

[0004] To achieve the above objectives, the technical solutions adopted in the embodiments of this application are as follows: In a first aspect, embodiments of this application provide an energy consumption analysis method applied to lithium salt production, comprising the following steps: The total energy consumption data of the target batch of lithium salt production is decomposed to obtain several first energy consumption data; among them, the several first energy consumption data represent the energy consumption data of different production stages. The first energy consumption data is transformed based on the time series to obtain the first energy consumption curve; The first energy consumption curve is standardized based on production data to obtain the second energy consumption curve. Local peak searches are performed on the second energy consumption curve to identify abnormal energy consumption situations.

[0005] In one possible implementation of the first aspect, a local peak search is performed on the second energy consumption curve to obtain abnormal energy consumption situations, including: The local peak position is obtained by performing a local peak search on the second energy consumption curve; Based on the location of local peak values, the time periods of abnormal energy consumption can be obtained.

[0006] In one possible implementation of the first aspect, after performing a local peak search on the second energy consumption curve to obtain abnormal energy consumption conditions, the method further includes: Identify abnormal time series based on abnormal energy consumption periods; Based on the abnormal time series, obtain the operating status data of the production equipment in the corresponding production process; Based on the operating status data of the production equipment, identify equipment with abnormal energy consumption.

[0007] In one possible implementation of the first aspect, equipment with abnormal energy consumption is identified based on the operating status data of the production equipment, including: Input the operating status data of the production equipment into the operating status-energy consumption relationship model, and output simulated energy consumption data to identify equipment with abnormal energy consumption.

[0008] In one possible implementation of the first aspect, before inputting the operating status data of the production equipment into the operating status-energy consumption relationship model and outputting simulated energy consumption data to identify equipment with abnormal energy consumption, the method further includes: Preprocess historical production data; Based on the preprocessed historical production data, a model of the relationship between operating status and energy consumption is established.

[0009] In one possible implementation of the first aspect, the first energy consumption data is transformed according to the time series to obtain the first energy consumption curve, including: Divide the time series into units of time to obtain several unit time periods; Sum the energy consumption within each unit time period to obtain the energy consumption per unit time. A distribution map of energy consumption points per unit time is constructed by dividing the time series into units of time intervals on the horizontal axis and using the corresponding unit time consumption on the vertical axis. Curve fitting is performed on the points on the energy consumption point distribution map per unit time to obtain the first energy consumption curve.

[0010] In one possible implementation of the first aspect, the first energy consumption curve is standardized based on production data to obtain a second energy consumption curve, including: Based on the production data within a unit time period, a portion of the corresponding first energy consumption curve is standardized to obtain the second energy consumption curve.

[0011] Secondly, embodiments of this application provide an energy consumption analysis device applied to lithium salt production, comprising: The decomposition module is used to decompose the total energy consumption data of the target batch of lithium salt production and obtain several first energy consumption data; wherein, the several first energy consumption data represent the energy consumption data of different production stages. The conversion module is used to convert the first energy consumption data according to the time series to obtain the first energy consumption curve; The standardization module is used to standardize the first energy consumption curve based on production data to obtain the second energy consumption curve. The search module is used to search for local peaks in the second energy consumption curve to identify abnormal energy consumption situations.

[0012] Thirdly, embodiments of this application provide a computer-readable storage medium storing a computer program, which, when loaded and executed by a processor, implements the energy consumption analysis method provided in any of the first aspects above.

[0013] Fourthly, embodiments of this application provide an electronic device, including a processor and a memory, wherein, Memory is used to store computer programs; The processor is used to load and execute computer programs to cause electronic devices to perform energy consumption analysis methods as provided in any of the first aspects above.

[0014] Compared with the prior art, the beneficial effects of this application are: This application proposes an energy consumption analysis method, apparatus, medium, and electronic device. The method includes: decomposing the total energy consumption data of a target batch of lithium salt production to obtain several first energy consumption data; wherein the several first energy consumption data represent the energy consumption data of different production stages; transforming the first energy consumption data according to a time series to obtain a first energy consumption curve; standardizing the first energy consumption curve according to production data to obtain a second energy consumption curve; and performing local peak search on the second energy consumption curve to obtain abnormal energy consumption conditions. This application analyzes the energy consumption data of a batch of lithium salt production, breaking it down into various production stages based on the total energy consumption. The energy consumption data is then transformed into a time series representation, resulting in an energy consumption curve. Since the production equipment differs across stages, and capacity demands vary at different times, the fluctuations in the first energy consumption curve do not necessarily reflect the relative levels of energy consumption to normal values. Therefore, the curve is standardized and compared using the same standard. A local peak search is then performed on the resulting second energy consumption curve to identify abnormal fluctuations, ultimately revealing anomalies in energy consumption and improving the effectiveness of energy consumption analysis. Attached Figure Description

[0015] Figure 1 This is a schematic diagram of the electronic device structure of the hardware operating environment involved in the embodiments of this application; Figure 2 A flowchart illustrating the energy consumption analysis method provided in this application embodiment; Figure 3A schematic diagram of the modules of the energy consumption analysis device provided in the embodiments of this application; The diagram is labeled as follows: 101-Processor, 102-Communication bus, 103-Network interface, 104-User interface, 105-Memory. Detailed Implementation

[0016] It should be understood that the specific embodiments described herein are merely illustrative of this application and are not intended to limit this application.

[0017] See attached document Figure 1 , attached Figure 1 This is a schematic diagram of the electronic device structure of the hardware operating environment involved in the embodiments of this application. The electronic device may include: a processor 101, such as a central processing unit (CPU), a communication bus 102, a user interface 104, a network interface 103, and a memory 105. The communication bus 102 is used to realize the connection and communication between these components. The user interface 104 may include a display screen and an input unit such as a keyboard. Optionally, the user interface 104 may also include a standard wired interface and a wireless interface. The network interface 103 may optionally include a standard wired interface and a wireless interface (such as a Wi-Fi interface). The memory 105 may be a storage device independent of the aforementioned processor 101. The memory 105 may be a high-speed random access memory (RAM) or a stable non-volatile memory (NVM), such as at least one disk storage device. The processor 101 may be a general-purpose processor, including a central processing unit, a network processor, etc., or it may be a digital signal processor, an application-specific integrated circuit, a field-programmable gate array or other programmable logic device, discrete gate or transistor logic device, or discrete hardware component.

[0018] Those skilled in the art will understand that the appendix Figure 1 The structure shown does not constitute a limitation on the electronic device and may include more or fewer components than shown, or combine certain components, or have different component arrangements.

[0019] As attached Figure 1 As shown, the memory 105, which serves as a storage medium, may include an operating system, a network communication module, a user interface module, and an energy consumption analysis device.

[0020] In the appendix Figure 1In the electronic device shown, the network interface 103 is mainly used for data communication with the network server; the user interface 104 is mainly used for data interaction with the user; the processor 101 and the memory 105 in this application can be set in the electronic device, and the electronic device calls the energy consumption analysis device stored in the memory 105 through the processor 101 and executes the energy consumption analysis method provided in the embodiment of this application.

[0021] See attached document Figure 2 Based on the hardware device of the foregoing embodiments, embodiments of this application provide an energy consumption analysis method applied to lithium salt production, comprising the following steps: S10: Decompose the total energy consumption data of the target batch of lithium salt production to obtain several first energy consumption data; wherein, the several first energy consumption data represent the energy consumption data of different production stages.

[0022] In the specific implementation process, the target batch of lithium salt production, that is, the batch of lithium salt production that requires energy consumption analysis, is defined as the total energy consumption data, which is the total energy consumed in producing this batch of lithium salt. This is further broken down based on the production process, that is, the total energy consumed at each stage of production.

[0023] S20: Transform the first energy consumption data according to the time series to obtain the first energy consumption curve.

[0024] In the specific implementation process, the energy consumption data of each production stage is transformed, with time series data as the horizontal axis and energy consumption magnitude as the vertical axis, converting the primary energy consumption data into a curve representation. Specifically, the primary energy consumption data is transformed based on the time series to obtain the primary energy consumption curve, including: Divide the time series into units of time to obtain several unit time periods; Sum the energy consumption within each unit time period to obtain the energy consumption per unit time. A distribution map of energy consumption points per unit time is constructed by dividing the time series into units of time intervals on the horizontal axis and using the corresponding unit time consumption on the vertical axis. Curve fitting is performed on the points on the energy consumption point distribution map per unit time to obtain the first energy consumption curve.

[0025] In the specific implementation process, in order to facilitate the analysis of energy consumption anomalies, the energy consumption is divided into units of time, and the total energy consumption within a unit time period is counted as the energy consumption per unit time period. The unit time consumption is obtained by dividing the unit time period in the time series sequentially as the horizontal axis, such as 1, 2, 3, etc. The unit time consumption obtained by summing the corresponding units is used as the vertical axis. In this way, a series of discrete points are constructed to obtain a distribution map of unit time energy consumption points. The first energy consumption curve obtained by curve fitting these points can be smoother, the fluctuations will not be too drastic, the data complexity will be reduced, and it will be more conducive to analysis and identification.

[0026] S30: Standardize the first energy consumption curve based on production data to obtain the second energy consumption curve.

[0027] In practice, due to differences in production equipment at each stage and varying capacity demands at different times, fluctuations in the first energy consumption curve cannot be considered an abnormality in energy consumption relative to normal values. Standardization is necessary to unify analysis standards and further reduce curve complexity, making analysis and identification more effective. Specifically, the first energy consumption curve is standardized based on production data to obtain the second energy consumption curve, including: Based on the production data within a unit time period, a portion of the corresponding first energy consumption curve is standardized to obtain the second energy consumption curve.

[0028] In the specific implementation process, according to the division of unit time periods in the aforementioned embodiment, production data for the corresponding time period is obtained. Production data may include output. The first energy consumption curve is standardized. In fact, based on the output within a unit time period and the required energy consumption per unit time, the energy consumption required for one unit output within a unit time period is obtained. After this processing, the entire curve is expressed in terms of energy consumption per unit output. Under ideal working conditions, the second energy consumption curve should tend to be a horizontal line. Abnormal energy consumption that is too low or too high will show obvious local peaks on the curve.

[0029] S40: Perform a local peak search on the second energy consumption curve to obtain abnormal energy consumption conditions.

[0030] In the specific implementation process, the standardized second energy consumption curve appears as a horizontal line. Abnormal energy consumption will cause the curve to fluctuate similarly to a sine function. Local peak searches are used to obtain local amplitudes, thereby identifying abnormal energy consumption changes. Specifically, a local peak search is performed on the second energy consumption curve to obtain abnormal energy consumption conditions, including: The local peak position is obtained by performing a local peak search on the second energy consumption curve; Based on the location of local peak values, the time periods of abnormal energy consumption can be obtained.

[0031] By searching for local peaks, the location of the local peak is determined. Then, queries are performed in both directions from that location until multiple consecutive data points on both sides show fluctuations within the allowable range. This indicates stable energy consumption, and the intermediate interval can be considered the period of abnormal energy consumption. After identifying the abnormal period, the corresponding abnormal time series is determined on the time series chart. Then, based on the abnormal time series, the operating status data of production equipment in the production process is obtained. By identifying the abnormal operating status, the equipment with abnormal energy consumption can be accurately located, providing clear direction for energy saving, efficiency improvement, and process optimization. In other words: After performing a local peak search on the second energy consumption curve to identify abnormal energy consumption conditions, the method also includes: Identify abnormal time series based on abnormal energy consumption periods; Based on the abnormal time series, obtain the operating status data of the production equipment in the corresponding production process; Based on the operating status data of the production equipment, identify equipment with abnormal energy consumption.

[0032] In one embodiment, identifying equipment with abnormal energy consumption based on the operating status data of the production equipment includes: Input the operating status data of the production equipment into the operating status-energy consumption relationship model, and output simulated energy consumption data to identify equipment with abnormal energy consumption.

[0033] In practical implementation, the location of equipment with abnormal energy consumption can utilize the operating status-energy consumption relationship model. Specifically, before inputting the operating status data of the production equipment into the operating status-energy consumption relationship model and outputting simulated energy consumption data to determine the equipment with abnormal energy consumption, the method also includes: Preprocess historical production data; Based on the preprocessed historical production data, a model of the relationship between operating status and energy consumption is established.

[0034] Historical production data is preprocessed, such as by completion and data cleaning, to improve data quality. Then, a relationship model between operating status and energy consumption is established through machine learning or regression analysis. The operating status represents the production process, such as motor speed and calcination temperature. The relationship model represents the quantitative relationship between energy consumption and the process. By predicting the theoretical energy consumption under different process parameters, i.e., under different operating statuses, i.e., simulated energy consumption data, equipment with abnormal actual energy consumption can be identified. This can then guide relevant personnel to repair, replace, or develop new equipment for abnormal equipment.

[0035] In this embodiment, the lithium salt production data of a batch requiring energy consumption analysis is broken down into various production stages based on the total energy consumption. Then, the energy consumption data is transformed into a time series to obtain an energy consumption curve. Since the production equipment in each production stage is different and the capacity demand is different at different times, the fluctuations of the first energy consumption curve cannot represent the level of energy consumption relative to the normal value. Therefore, it is standardized and transformed to the same processing standard for comparison. By performing local peak search on the obtained second energy consumption curve, the abnormal parts of the curve fluctuation can be identified, and the abnormal situation of energy consumption can be obtained, thereby improving the effect of energy consumption analysis.

[0036] See attached document Figure 3 Based on the same inventive concept as in the foregoing embodiments, this application also provides an energy consumption analysis device for lithium salt production, comprising: The decomposition module is used to decompose the total energy consumption data of the target batch of lithium salt production and obtain several first energy consumption data; wherein, the several first energy consumption data represent the energy consumption data of different production stages. The conversion module is used to convert the first energy consumption data according to the time series to obtain the first energy consumption curve; The standardization module is used to standardize the first energy consumption curve based on production data to obtain the second energy consumption curve. The search module is used to search for local peaks in the second energy consumption curve to identify abnormal energy consumption situations.

[0037] Those skilled in the art should understand that the division of the various modules in the embodiments is merely a logical functional division. In actual applications, they can be fully or partially integrated into one or more actual carriers. These modules can be implemented entirely in software through processing unit calls, entirely in hardware, or a combination of software and hardware. It should be noted that each module in the energy consumption analysis device in this embodiment corresponds one-to-one with each step in the energy consumption analysis method in the aforementioned embodiments. Therefore, the specific implementation of this embodiment can refer to the implementation of the aforementioned energy consumption analysis method, which will not be repeated here.

[0038] Based on the same inventive concept as in the foregoing embodiments, embodiments of this application also provide a computer-readable storage medium storing a computer program, which, when loaded and executed by a processor, implements the energy consumption analysis method provided in the embodiments of this application.

[0039] Based on the same inventive concept as in the foregoing embodiments, embodiments of this application also provide an electronic device, including a processor and a memory, wherein, Memory is used to store computer programs; The processor is used to load and execute computer programs to cause electronic devices to perform energy consumption analysis methods as provided in the embodiments of this application.

[0040] In some embodiments, the computer-readable storage medium may be a memory such as FRAM, ROM, PROM, EPROM, EEPROM, flash memory, magnetic surface memory, optical disk, or CD-ROM; or it may be a device including one or any combination of the above-mentioned memories. The computer may be a variety of computing devices, including smart terminals and servers.

[0041] In some embodiments, executable instructions may take the form of a program, software, software module, script, or code, written in any form of programming language (including compiled or interpreted languages, or declarative or procedural languages), and may be deployed in any form, including as a standalone program or as a module, component, subroutine, or other unit suitable for use in a computing environment.

[0042] As an example, executable instructions may, but do not necessarily, correspond to files in the file system. They may be stored as part of a file that holds other programs or data, for example, in one or more scripts in a Hyper Text Markup Language (HTML) document, in a single file dedicated to the program in question, or in multiple collaborative files (e.g., a file that stores one or more modules, subroutines, or code sections).

[0043] As an example, executable instructions can be deployed to execute on a single computing device, or on multiple computing devices located in one location, or on multiple computing devices distributed across multiple locations and interconnected via a communication network.

[0044] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or system that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or system. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or system that includes that element.

[0045] The sequence numbers of the embodiments in this application are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0046] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as read-only memory / random access memory, magnetic disk, optical disk) and includes several instructions to cause a multimedia terminal device (which may be a mobile phone, computer, television receiver, or network device, etc.) to execute the methods described in the various embodiments of this application.

[0047] In summary, the present application provides an energy consumption analysis method, apparatus, medium, and electronic device. The method includes: decomposing the total energy consumption data of a target batch of lithium salt production to obtain several first energy consumption data; wherein the several first energy consumption data represent the energy consumption data of different production stages; transforming the first energy consumption data according to a time series to obtain a first energy consumption curve; standardizing the first energy consumption curve according to production data to obtain a second energy consumption curve; and performing local peak search on the second energy consumption curve to obtain abnormal energy consumption conditions. This application analyzes the energy consumption data of a batch of lithium salt production, breaking it down into various production stages based on the total energy consumption. The energy consumption data is then transformed into a time series representation, resulting in an energy consumption curve. Since the production equipment differs across stages, and capacity demands vary at different times, the fluctuations in the first energy consumption curve do not necessarily reflect the relative levels of energy consumption to normal values. Therefore, the curve is standardized and compared using the same standard. A local peak search is then performed on the resulting second energy consumption curve to identify abnormal fluctuations, ultimately revealing anomalies in energy consumption and improving the effectiveness of energy consumption analysis.

[0048] The above description is only a preferred embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

Claims

1. An energy consumption analysis method, characterized in that, Applied to lithium salt production, it includes the following steps: The total energy consumption data of the target batch of lithium salt production is decomposed to obtain several first energy consumption data; wherein, the several first energy consumption data represent the energy consumption data of different production stages; The first energy consumption data is transformed according to the time series to obtain the first energy consumption curve; The first energy consumption curve is standardized based on production data to obtain the second energy consumption curve. A local peak search is performed on the second energy consumption curve to identify abnormal energy consumption situations.

2. The energy consumption analysis method according to claim 1, characterized in that, The step of performing a local peak search on the second energy consumption curve to obtain abnormal energy consumption conditions includes: Perform a local peak search on the second energy consumption curve to obtain the local peak position; Based on the location of the local peak, the time period of abnormal energy consumption is obtained.

3. The energy consumption analysis method according to claim 2, characterized in that, After performing a local peak search on the second energy consumption curve to obtain abnormal energy consumption conditions, the method further includes: Based on the abnormal energy consumption periods, determine the abnormal time series; Based on the abnormal time series, obtain the corresponding operating status data of the production equipment in the production process; Based on the operating status data of the production equipment, identify equipment with abnormal energy consumption.

4. The energy consumption analysis method according to claim 3, characterized in that, The step of identifying equipment with abnormal energy consumption based on the operating status data of the production equipment includes: The operating status data of the production equipment is input into the operating status-energy consumption relationship model, and the simulated energy consumption data is output to identify equipment with abnormal energy consumption.

5. The energy consumption analysis method according to claim 4, characterized in that, Before inputting the operating status data of the production equipment into the operating status-energy consumption relationship model and outputting simulated energy consumption data to identify equipment with abnormal energy consumption, the method further includes: Preprocess historical production data; Based on the preprocessed historical production data, the operating status-energy consumption relationship model is established.

6. The energy consumption analysis method according to claim 1, characterized in that, The step of transforming the first energy consumption data according to the time series to obtain the first energy consumption curve includes: Divide the time series into units of time to obtain several unit time periods; The energy consumption within each unit time period is summed to obtain the energy consumption per unit time. A distribution map of energy consumption points per unit time is constructed by dividing the time series into units of time intervals on the horizontal axis and using the corresponding unit time consumption on the vertical axis. Curve fitting is performed on the points on the energy consumption point distribution map per unit time to obtain the first energy consumption curve.

7. The energy consumption analysis method according to claim 6, characterized in that, The step of standardizing the first energy consumption curve based on production data to obtain the second energy consumption curve includes: Based on the production data within the unit time period, a portion of the corresponding first energy consumption curve is standardized to obtain the second energy consumption curve.

8. An energy consumption analysis device, characterized in that, Applications in lithium salt production, including: The decomposition module is used to decompose the total energy consumption data of the target batch of lithium salt production and obtain several first energy consumption data; wherein, the several first energy consumption data represent the energy consumption data of different production stages. The conversion module is used to convert the first energy consumption data according to the time series to obtain the first energy consumption curve; The standardization module is used to standardize the first energy consumption curve based on production data to obtain the second energy consumption curve. The search module is used to perform local peak searches on the second energy consumption curve to obtain abnormal energy consumption conditions.

9. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is loaded and executed by the processor, it implements the energy consumption analysis method as described in any one of claims 1-7.

10. An electronic device, characterized in that, Including processor and memory, among which, The memory is used to store computer programs; The processor is used to load and execute the computer program to cause the electronic device to perform the energy consumption analysis method as described in any one of claims 1-7.

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