Energy consumption analysis method, device and equipment for tobacco logistics and medium
By acquiring and analyzing energy consumption and task data in tobacco logistics warehouses, calculating deviation ratios, and constructing energy consumption prediction models, the problem of low efficiency in traditional energy consumption management has been solved, and refined energy consumption management and cost optimization have been achieved.
Patent Information
- Application Number
- CN202411108344.4
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-08-13
- Publication Date
- 2026-02-13
AI Technical Summary
Traditional methods of energy consumption management in tobacco logistics rely on manual monitoring and analysis, which are inefficient, prone to errors, and make it difficult to accurately analyze and predict energy consumption data, and to detect and resolve abnormal energy consumption situations in a timely manner.
By acquiring daily actual energy consumption data and task data from tobacco logistics warehouses, the deviation ratio between actual and predicted energy consumption is calculated to identify abnormal energy consumption dates. These abnormal energy consumption dates are then analyzed in detail to construct an energy consumption prediction model, uncover the regular characteristics of abnormal energy consumption, and establish an energy consumption anomaly prediction model for early warning.
This has enabled refined energy consumption management, reduced the operating costs of tobacco logistics companies, and enhanced their competitiveness.
Smart Images
Figure CN121526019A_ABST
Abstract
Description
Technical Field
[0001] This disclosure relates to the field of tobacco logistics, and in particular to a method, apparatus, equipment, and medium for energy consumption analysis in tobacco logistics. Background Technology
[0002] In the tobacco logistics sector, energy management is a crucial aspect, directly impacting a company's operating costs and environmental impact. Traditional energy management methods primarily rely on manual monitoring and analysis, which is inefficient and prone to errors. In tobacco logistics, energy consumption in the storage area mainly includes lighting, equipment, and other energy consumption. This energy consumption data is closely related to the storage area's task data, such as inbound and outbound task volumes, and whether weekly maintenance work has been carried out. However, traditional energy management methods often struggle to accurately analyze and predict this energy consumption data, thus failing to promptly identify and resolve energy consumption anomalies. Summary of the Invention
[0003] In view of the shortcomings of the prior art described above, the purpose of this disclosure is to provide an energy consumption analysis method, apparatus, equipment and medium for tobacco logistics, so as to improve the precision of energy consumption management and achieve continuous optimization of energy consumption.
[0004] The first aspect of this disclosure provides an energy consumption analysis method for tobacco logistics, comprising: acquiring daily actual energy consumption data and daily task data for multiple tobacco logistics warehouses, wherein the actual energy consumption data includes actual values of lighting energy consumption, actual values of equipment energy consumption, and actual values of equipment maintenance; calculating the deviation ratio between the daily actual energy consumption of each warehouse and the predicted energy consumption constructed based on historical data, and determining the date on which the deviation ratio exceeds a preset threshold as an abnormal energy consumption date; and performing a detailed analysis of the task data on the abnormal energy consumption date to determine the cause of the abnormal energy consumption.
[0005] In an embodiment of the first aspect, the task data includes one or more combinations of inbound task volume, outbound task volume, and whether weekly maintenance work has been carried out.
[0006] In an embodiment of the first aspect, determining the date on which the deviation ratio exceeds a preset threshold as an abnormal energy consumption date includes: setting a threshold for the energy consumption deviation ratio to distinguish between normal and abnormal energy consumption dates; when the deviation ratio between the actual energy consumption and the predicted energy consumption of a certain storage area exceeds the set threshold, marking that day as an abnormal energy consumption date.
[0007] In an embodiment of the first aspect, the modeling process for predicting energy consumption includes: selecting parameters related to energy consumption; cleaning the selected parameters; performing regression analysis on the cleaned parameters and historical energy consumption data to determine the correlation coefficient of each parameter; setting a threshold for the correlation coefficient and selecting only parameters with correlation coefficients higher than the threshold; and establishing a mathematical model for energy consumption prediction based on the selected highly correlated parameters.
[0008] In an embodiment of the first aspect, it is determined that there is a positive correlation between the cumulative amount of the actual value of lighting energy consumption and the cumulative amount of the inventory task; using the cumulative amount of the inventory task as the independent variable and the cumulative amount of the actual value of lighting energy consumption as the dependent variable, a mathematical model is constructed between the cumulative amount of the inventory task and the cumulative amount of lighting energy consumption.
[0009] In an embodiment of the first aspect, the cumulative amount of actual device energy consumption is strongly correlated with the cumulative amount of tasks and the cumulative number of working days; further comprising: determining that the cumulative amount of actual device energy consumption is positively correlated with the cumulative amount of tasks and the cumulative number of working days; and constructing mathematical models between the cumulative amount of device energy consumption and the cumulative amount of tasks, and between the cumulative amount of device energy consumption and the cumulative number of working days, respectively, using the cumulative amount of tasks and the cumulative number of working days as independent variables and the cumulative amount of actual device energy consumption as the dependent variable.
[0010] In the first aspect of the embodiment, the method further includes: recording and analyzing the causes of the abnormal energy consumption to form an energy consumption anomaly case library; analyzing the data in the case library to uncover the regularity characteristics of the abnormal energy consumption; constructing an energy consumption anomaly prediction model based on the regularity characteristics of the abnormal energy consumption to provide early warning of potential abnormal energy consumption; and feeding back the warning results to the staff to guide them in adjusting future task data.
[0011] A second aspect of this disclosure discloses an energy consumption analysis device for tobacco logistics, comprising: an acquisition module for acquiring daily actual energy consumption data and daily task data for multiple tobacco logistics warehouses, wherein the actual energy consumption data includes actual values of lighting energy consumption, actual values of equipment energy consumption, and other actual energy consumption; a calculation module for calculating the deviation ratio between the daily actual energy consumption of each warehouse and the predicted energy consumption constructed based on historical data, and determining dates on which the deviation ratio exceeds a preset threshold as abnormal energy consumption dates; and an analysis module for performing detailed analysis of the task data on abnormal energy consumption dates to determine the cause of the abnormal energy consumption.
[0012] This disclosure discloses a third aspect of an electronic device, the electronic device comprising: a processor and a memory; wherein the memory is used to store a computer program; and the processor is used to execute the computer program stored in the memory to cause the electronic device to perform the energy consumption analysis method for tobacco logistics as described in any of the first aspects.
[0013] The fourth aspect of this disclosure discloses a computer-readable storage medium having a computer program stored thereon, which is executed by an electronic device as described in any of the first aspects, the method for energy consumption analysis of tobacco logistics.
[0014] As described above, the energy consumption analysis method for tobacco logistics disclosed herein acquires daily actual energy consumption data and task data from multiple storage areas, calculates the deviation ratio between actual and predicted energy consumption, and conducts detailed analysis of abnormal energy consumption dates to determine the causes of abnormal energy consumption, thereby achieving refined energy consumption management. Furthermore, by analyzing the causes of abnormal energy consumption, adjustments can be made to future task data, reducing the operating costs of tobacco logistics companies and enhancing their competitiveness. Attached Figure Description
[0015] Figure 1 A flowchart illustrating an embodiment of the energy consumption analysis method for tobacco logistics is shown.
[0016] Figure 2 A task count statistics chart is shown in one embodiment of this disclosure.
[0017] Figure 3 A statistical chart of weekday data is shown in one embodiment of this disclosure.
[0018] Figure 4 A statistical chart of electricity consumption data for each category is shown in one embodiment of this disclosure.
[0019] Figure 5 This diagram illustrates the fitting relationship between the number of tasks entered into the database and lighting energy consumption in one embodiment of this disclosure.
[0020] Figure 6 This diagram illustrates the fitting relationship between the number of inbound tasks after excluding rest days and lighting energy consumption in one embodiment of the present disclosure.
[0021] Figure 7 This diagram illustrates the fitting relationship between the cumulative amount of data entry tasks and the cumulative amount of lighting energy consumption in one embodiment of this disclosure.
[0022] Figure 8 A schematic diagram of a module for an energy consumption analysis device for tobacco logistics is shown in one embodiment of this disclosure.
[0023] Figure 9 A schematic diagram of the circuit structure of an electronic device according to an embodiment of the present disclosure is shown. Detailed Implementation
[0024] The following specific examples illustrate the implementation of this disclosure. Those skilled in the art can easily understand other advantages and effects of this disclosure from the information disclosed herein. This disclosure can also be implemented or applied through other different specific embodiments, and various details in this disclosure can be modified or changed according to different viewpoints and application modules without departing from the spirit of this disclosure. It should be noted that, unless otherwise specified, the embodiments and features in the embodiments of this disclosure can be combined with each other.
[0025] Although not explicitly defined, all terms, including technical and scientific terms used herein, shall have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure pertains. Terms defined in commonly used dictionaries shall be further interpreted as having a meaning consistent with the relevant technical literature and the message of the present disclosure, and shall not be over-interpreted as having an ideal or overly formulaic meaning unless otherwise defined.
[0026] The technical solutions of the present invention will now be described in detail with reference to the accompanying drawings.
[0027] like Figure 1 The diagram shows a flowchart of an energy consumption analysis method for tobacco logistics according to an embodiment of the present disclosure, including steps S11-S13.
[0028] Step S11: Obtain daily actual energy consumption data and daily task data for multiple tobacco logistics warehouses. The actual energy consumption data includes actual values of lighting energy consumption, actual values of equipment energy consumption, and other actual energy consumption.
[0029] Specifically, tobacco logistics warehouses typically include a raw material formulation warehouse (LT system), an auxiliary material balancing warehouse (DM system), and a finished cigarette case warehouse (FG system). These warehouses are crucial links in the tobacco logistics process, involving a large amount of material storage and transfer. Actual energy consumption data for these warehouses can be obtained through the current cigarette factory's energy management system. The energy management system provides a web interface through which users can query the daily energy consumption of each department's electricity consumption nodes. To facilitate data analysis and use, the energy management system can export this data in Excel format for easy integration of energy consumption data with task data. The actual energy consumption data includes actual values for lighting energy consumption, equipment energy consumption, and equipment maintenance energy consumption. The actual lighting energy consumption reflects the energy consumption of the warehouse's lighting system, the actual equipment energy consumption reflects the energy consumption of the warehouse's equipment operation, and the actual equipment maintenance energy consumption includes the energy consumption generated by a series of equipment configured to ensure the normal operation and logistics efficiency of the warehouse, such as conveyors, stacker cranes, AGVs (Automated Guided Vehicles), palletizing robots, air conditioning, etc.
[0030] This energy consumption data is closely related to the task data of the warehouse area, which can be obtained through an existing WMS warehouse management system. The WMS system can count the number of inbound and outbound tasks in each warehouse area by time, by warehouse area, and by shift, and create data forms. The primary keys of these data forms are usually named in the form of "time + warehouse area + task code" to facilitate data retrieval.
[0031] In some implementations, the task data includes inbound task volume, outbound task volume, and one or more combinations of whether weekly maintenance work has been carried out. This task data directly reflects the workload and operational status of the warehouse area.
[0032] Specifically, the inbound workload refers to the quantity of materials received by the warehouse within a certain period of time, which directly affects the warehouse's lighting and equipment energy consumption. For example, when the inbound workload increases, the warehouse may need more lighting to ensure visibility in the working environment, and may also need more equipment to handle the handling and storage of materials.
[0033] Outbound task volume refers to the quantity of materials issued from the warehouse within a certain period of time. An increase in outbound task volume may lead to a reduction in warehouse lighting energy consumption, as some areas may no longer require additional lighting, but at the same time, more equipment may be needed to handle the handling and loading of materials.
[0034] Whether weekly maintenance work has been carried out is also an important data point, as it can impact the energy consumption for lighting in the storage area. During weekly maintenance, staff regularly inspect and maintain the equipment and facilities within the storage area. To ensure that staff can clearly see the details of the equipment and facilities during maintenance, it may be necessary to increase lighting intensity or extend lighting time.
[0035] Step S12: Calculate the deviation ratio between the actual daily energy consumption of each storage area and the predicted energy consumption constructed based on historical data, and determine the date when the deviation ratio exceeds the preset threshold as the abnormal energy consumption date.
[0036] In some implementations, determining the date when the deviation ratio exceeds a preset threshold as an abnormal energy consumption date includes: setting a threshold for the energy consumption deviation ratio to distinguish between normal and abnormal energy consumption dates; when the deviation ratio between the actual energy consumption and the predicted energy consumption of a certain storage area exceeds the set threshold, that day is marked as an abnormal energy consumption date.
[0037] More specifically, firstly, by collecting and organizing historical energy consumption data, an energy consumption prediction model can be established. This model considers multiple energy-related factors, such as workload, weather conditions, and equipment operating status, and uses appropriate statistical methods (such as linear regression and time series analysis) to predict future energy consumption. Then, for each storage area, daily actual energy consumption data is collected and compared with the predicted energy consumption. By calculating the deviation ratio between actual and predicted energy consumption, a quantitative indicator can be obtained to assess whether daily energy consumption is abnormal. The formula for calculating the deviation ratio is:
[0038] Deviation ratio = (Actual energy consumption - Predicted energy consumption) / Predicted energy consumption × 100%
[0039] In this formula, actual energy consumption is the energy consumption value actually measured in the storage area on that day, while predicted energy consumption is the expected energy consumption value calculated based on historical data and a prediction model. Then, a threshold for the proportion of energy consumption deviation is set to distinguish between normal and abnormal energy consumption days. This threshold can be determined based on the specific circumstances of the storage area, historical data, and industry standards. Typically, this threshold is set within a relatively reasonable range to accurately distinguish between normal and abnormal situations in practical applications. When the proportion of deviation between the actual and predicted energy consumption of a storage area exceeds the set threshold, that day is marked as an abnormal energy consumption day. This marking helps storage area staff to promptly identify and address abnormal energy consumption situations, thereby taking appropriate measures to optimize energy management.
[0040] The energy consumption of tobacco logistics disclosed herein is analyzed using linear regression analysis. The principle of linear regression analysis is to assume that there is a linear relationship between actual energy consumption and influencing factors, and to express this relationship through a mathematical model.
[0041] In some embodiments, it is first necessary to select parameters related to energy consumption. These parameters may include the workload of the storage area (such as the amount of inbound and outbound tasks), the number of working days (such as working days with weekly maintenance and working days without weekly maintenance), etc. The selection of these parameters needs to be determined based on the actual situation of the storage area and the factors affecting energy consumption.
[0042] Furthermore, the selected parameters need to be cleaned. Data cleaning is a crucial step in data preprocessing, aiming to ensure data quality and usability, laying a solid foundation for subsequent data analysis and processing. In the energy consumption analysis of tobacco logistics warehouses, data cleaning includes both automated cleaning and manual verification, aiming to solve problems such as data inconsistency, incompleteness, non-compliance, and data redundancy.
[0043] Data cleaning includes two processes: automated cleaning and manual processing.
[0044] The automatic data cleaning process includes outlier removal, missing value handling, and data normalization. For example, in the energy consumption system data, if the energy consumption data for a certain day is zero, it may be because there was no meter reading during the holiday period. Such data is considered an outlier and needs to be automatically removed. If some data is missing, interpolation, mean imputation, or other statistical methods can be used to estimate these missing values. The data format is standardized to ensure that all data is stored and analyzed in a uniform format.
[0045] Manual review includes data verification and anomaly identification. Specifically, due to temporary adjustments to production tasks, data such as task volume, workdays, and weekly maintenance may change temporarily, requiring manual review and adjustment to ensure accuracy. Outliers not identified during the automatic cleaning process require further manual identification and processing.
[0046] In addition, if there are differences in dimensions and units after data cleaning, normalization is required so that different types of data can be compared and analyzed on the same scale.
[0047] These steps can improve data quality, reduce the probability of errors during data processing, and ensure the accuracy and validity of the data.
[0048] Then, regression analysis is performed on the selected parameters and historical energy consumption data. Regression analysis is a statistical method used to study the relationship between two or more variables. In this embodiment, regression analysis is used to determine the linear relationship between actual energy consumption and each parameter, and to calculate the correlation coefficient of each parameter. The correlation coefficient reflects the degree of correlation between actual energy consumption and each parameter, and its value ranges from -1 to 1, with a closer value to 1 indicating a stronger correlation.
[0049] Next, a threshold for the correlation coefficient is set. This threshold is determined based on the actual conditions of the reservoir area and the factors influencing energy consumption; a reasonable threshold range is typically chosen. Only parameters with correlation coefficients higher than the threshold are retained for use in building the mathematical model for energy consumption prediction.
[0050] Finally, based on the selected highly relevant parameters, a mathematical model for energy consumption prediction is established. This model can be used to predict energy consumption at a future point in time, providing a reference for energy management in the reservoir area. The mathematical model typically takes the form of linear equations.
[0051] In some implementations, the actual value of lighting energy consumption is strongly correlated with the amount of data to be entered into the database; the modeling process includes: determining that there is a positive correlation between the actual value of lighting energy consumption and the amount of data to be entered into the database; using the amount of data to be entered into the database as the independent variable and the actual value of lighting energy consumption as the dependent variable, constructing a mathematical model between the amount of data to be entered into the database and lighting energy consumption.
[0052] In another embodiment, the modeling process for the strong correlation between the cumulative actual energy consumption of the equipment and the cumulative amount of tasks and the cumulative number of working days includes: determining that the cumulative actual energy consumption of the equipment is positively correlated with the cumulative amount of tasks and the cumulative number of working days; and using the cumulative amount of tasks and the cumulative number of working days as independent variables and the cumulative actual energy consumption of the equipment as the dependent variable to construct mathematical models between the cumulative energy consumption of the equipment and the cumulative amount of tasks, and between the cumulative energy consumption of the equipment and the cumulative number of working days.
[0053] To better illustrate the above embodiments, the modeling process of lighting energy consumption in the auxiliary material warehouse is used as a specific example below.
[0054] First, a statistical summary was compiled of the auxiliary material warehouse's daily workdays, daily task quantities, and daily electricity consumption from December 2022 to March 2023. Daily task data was compiled from 6:00 AM to 6:00 AM the following day, including both manual and automated tasks performed by the automated material handling trolley; the data was sourced from the WMS system. Daily electricity consumption was also compiled from 6:00 AM to 6:00 AM the following day, focusing on lighting-related electricity usage. Workday statistics were based on the production situation in Workshop 2. For example... Figure 2 As shown, this displays the data on the number of auxiliary material inventory tasks from December 2022 to March 2023; Figure 3 The image shows workday data from December 2022 to March 2023, where 1 represents a workday and 0 represents a rest day. Figure 4 As shown, this table displays electricity consumption data for various categories from December 2022 to March 2023, with orange indicating lighting energy consumption data.
[0055] Then, linear regression analysis was performed on the above data, with a correlation threshold of 0.99. The table below shows the correlation coefficients of the relevant parameters and the fitted equations.
[0056]
[0057] Table 1 above shows that there is a strong correlation between the cumulative amount of incoming tasks and the cumulative amount of actual lighting energy consumption. Therefore, a mathematical model for predicting lighting energy consumption of auxiliary material warehouse can be constructed as y = 1.0981x - 262.22.
[0058] In some implementations, besides using linear analysis methods, energy consumption analysis in tobacco logistics warehouses can also employ ARIMA (Autoregressive Integral Moving Average) time series models for more in-depth analysis of energy consumption data with low correlation coefficients. The ARIMA model is a method for predicting time series data that considers trends, seasonality, and random fluctuations in the data.
[0059] For example, firstly, the preprocessed air conditioning energy consumption data was analyzed, revealing a clear seasonal trend. For instance, energy consumption is higher in summer, and higher in months, with peak production periods before February and after October each year.
[0060] To better describe this seasonal trend, we chose the ARIMA(2,1,1) model for fitting. In this model, p=2 indicates two autoregressive terms, d=1 indicates first-order differencing, and q=1 indicates a moving average term. Furthermore, parameter estimation is a crucial step in ARIMA model analysis, revealing the specific values of each parameter that determine how the model predicts time series data. In the ARIMA(2,1,1) model, parameter estimation results include autoregressive coefficients (AR coefficients) and moving average coefficients (MA coefficients).
[0061] The AR coefficient represents the degree of influence of electricity consumption at a past point in time on electricity consumption at the current point in time. In this embodiment, two autoregressive coefficients of 0.7 and 0.4 from the ARIMA(2,1,1) model are used as examples. 0.7 indicates that the electricity consumption at the previous two points in time has a 70% influence on the current electricity consumption. 0.4 indicates that the electricity consumption at the previous two points in time has a 40% influence on the current electricity consumption. These two coefficients sum to 1, meaning that the electricity consumption at the previous two points in time jointly determine the current electricity consumption.
[0062] The moving average (MA) coefficient represents the degree of influence of the random error term on the current power consumption at the current time point. In this embodiment, we take a moving average coefficient of -0.5 for the ARIMA(2,1,1) model as an example. -0.5 indicates that the influence of the random error term on the current power consumption at the current time point is negative 50%. This means that the random error term has a negative impact on the current power consumption at the current time point, that is, the presence of the random error term will lead to a lower predicted value of the current power consumption at the current time point.
[0063] Analysis using the ARIMA model can more accurately predict electricity consumption over a future period, providing a reference for energy management in reservoir areas. For example, the prediction results from the ARIMA model show that electricity consumption over the next 30 days will continue to follow a seasonal trend, with higher energy consumption in summer and during peak production periods.
[0064] Step S13: Perform a detailed analysis of the task data for the abnormal energy consumption dates to determine the cause of the abnormal energy consumption.
[0065] Specifically, firstly, based on the abnormal energy consumption dates derived from the predicted energy consumption model, a preliminary analysis can be conducted using the task data for that day. Task data includes information such as the amount of incoming tasks, the amount of outgoing tasks, and whether weekly maintenance work was performed. By comparing the task data from abnormal energy consumption dates with those from normal energy consumption dates, the possible causes of the abnormal energy consumption can be preliminarily determined.
[0066] Then, historical video recordings and alarm records for that day are retrieved for in-depth analysis. Historical video recordings can provide information on the warehouse's operation that day, such as personnel operations and equipment operating status, which helps identify operational errors or equipment malfunctions that may lead to abnormal energy consumption. Historical alarm records can provide information on abnormal situations that occurred in the warehouse that day, such as equipment malfunctions and abnormal temperature and humidity, which helps determine the specific causes of abnormal energy consumption.
[0067] By comprehensively analyzing task data, historical video recordings, and historical alarm records, the specific causes of abnormal energy consumption can be determined. For example, if the predicted energy consumption model shows that the lighting for auxiliary material receiving is on for a longer period on a certain day, combining the task data and historical video recordings for that day, it can be found that the longer lighting is on due to insufficient manpower for receiving materials during holidays. In this case, energy consumption can be reduced by adjusting personnel arrangements or optimizing work processes.
[0068] For example, if the predicted energy consumption model shows an abnormal increase in equipment energy consumption on a certain day, combining the task data and historical alarm records for that day can reveal that the equipment was undergoing maintenance on the weekly maintenance day, resulting in shorter equipment uptime and thus increased energy consumption. In this case, energy consumption can be reduced by adjusting the weekly maintenance plan or optimizing the equipment maintenance process.
[0069] By comprehensively analyzing task data, historical video recordings, and historical alarm records, the causes of abnormal energy consumption can be more accurately identified, and corresponding measures can be taken to reduce energy consumption. This method helps to improve the precision of energy consumption management in the reservoir area and achieve continuous optimization of energy consumption.
[0070] Furthermore, the causes of the abnormal energy consumption are recorded and analyzed to form an energy consumption anomaly case library; the data in the case library is analyzed to uncover the regularity of abnormal energy consumption; based on the regularity of the abnormal energy consumption, an energy consumption anomaly prediction model is constructed to provide early warning of potential abnormal energy consumption; the warning results are fed back to the staff to guide them in adjusting future task data.
[0071] Specifically, when abnormal energy consumption is detected, relevant data is immediately recorded, including the date of the anomaly, storage area, task data, historical video recordings, and historical alarm records. This data is then analyzed to identify the specific cause of the abnormal energy consumption. All data from abnormal energy consumption cases is compiled into a database and stored in a specific format for easy querying and analysis. Further analysis of the data in this case database reveals patterns in abnormal energy consumption, such as seasonal fluctuations and energy consumption anomalies under specific task loads.
[0072] Based on the identified patterns, statistical methods (such as linear regression and support vector machines) are used to construct an energy consumption anomaly prediction model. This model can predict future energy consumption based on current task data and historical patterns. The prediction model is applied to real-time task data; if the prediction results exceed the normal range, it is considered potentially abnormal energy consumption, and an immediate warning is issued to staff. The warning results are then fed back to staff, guiding them to adjust future task data to reduce potential abnormal energy consumption.
[0073] Furthermore, if an energy consumption anomaly prediction model indicates that electricity consumption will continue to increase in the near future, staff will promptly consider increasing the power supply to ensure that future production needs are met. This can be achieved by negotiating with the power supplier to increase the power supply, or by installing additional power generation equipment within the plant area, such as backup generators or solar power systems.
[0074] The above methods enable comprehensive management and control of energy consumption in tobacco logistics warehouses. The establishment of an energy consumption anomaly case database and the application of predictive models help to promptly identify and address potential abnormal energy consumption, thereby reducing warehouse operating costs.
[0075] It should be specifically noted that the flowchart representations of the embodiments described above in this disclosure can be understood as representing modules, segments, or portions of code comprising one or more sets of executable instructions configured to implement specific logical functions or processes. Furthermore, the scope of the preferred embodiments of this disclosure includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved.
[0076] like Figure 8 As shown, this disclosure presents an energy consumption analysis device 80 for tobacco logistics. It should be noted that the principle and technical implementation of the energy consumption analysis device for tobacco logistics can be referenced from the previous embodiments of the energy consumption analysis device method for tobacco logistics (e.g., Figure 1 Therefore, this embodiment will not repeat the details.
[0077] Specifically, the energy consumption analysis device 80 for tobacco logistics includes: an acquisition module 81, a calculation module 82, and an analysis module 83, wherein,
[0078] The acquisition module 81 is used to acquire daily actual energy consumption data and daily task data for multiple tobacco logistics warehouses. The actual energy consumption data includes actual values of lighting energy consumption, actual values of equipment energy consumption, and actual values of other energy consumption.
[0079] The calculation module 82 is used to calculate the deviation ratio between the actual daily energy consumption of each storage area and the predicted energy consumption constructed based on historical data, and to determine the date when the deviation ratio exceeds a preset threshold as an abnormal energy consumption date.
[0080] The analysis module 83 is used to perform detailed analysis of the task data on dates with abnormal energy consumption in order to determine the cause of the abnormal energy consumption.
[0081] It should be noted that, in Figure 8 The various functional modules in the embodiments can be implemented, in whole or in part, through software, hardware, firmware, or any combination thereof. When implemented in software, they can be implemented, in whole or in part, in the form of a program instruction product. A program instruction product includes one or a set of program instructions. When the program instructions are loaded and executed on a computer, all or part of the flow or function according to this disclosure is generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The program instructions can be stored in a computer-readable storage medium or transferred from one computer-readable storage medium to another.
[0082] and, Figure 8 The apparatus disclosed in the embodiments can be implemented through other modular division methods. The apparatus embodiments shown above are merely illustrative. For example, the module division is only a logical functional division, and in actual implementation, there may be other division methods. For example, a group of modules or modules may be combined or dynamically integrated into another system, or some features may be ignored or not executed. Furthermore, the shown or discussed mutual coupling, direct coupling, or communication connection may be through some interfaces, and the indirect coupling or communication connection between devices or modules may be electrical or other forms.
[0083] in addition, Figure 8The functional modules and sub-modules in the embodiments can be dynamically integrated within a single processing unit, or each module can exist physically independently, or two or more modules can be dynamically integrated within a single unit. These dynamic units can be implemented in hardware or as software functional modules. If these dynamic units are implemented as software functional modules and sold or used as independent products, they can also be stored in a computer-readable storage medium. This storage medium can be a read-only memory, a hard disk, or an optical disk, etc.
[0084] like Figure 9 The diagram shown illustrates the structure of an electronic device according to an embodiment of this disclosure.
[0085] The electronic device can execute computer program instructions to perform tasks such as... Figure 1 The method in any of these embodiments. For example, the electronic device may be a server group / server, desktop computer, laptop computer, etc., for running such... Figure 1 The electronic tag radio frequency identification method in the text. Alternatively, the electronic device can be a cloud server / server group, distributed computing node system, etc., that communicates remotely with a local terminal, and executes... Figure 5 Energy consumption analysis methods for tobacco logistics.
[0086] The electronic device 90 includes a bus 91, a processor 92, and a memory 93. The processor 92 and the memory 93 can communicate via the bus 91. The memory 93 can store program instructions. The processor 92 implements the method steps in the previous embodiments by executing the program instructions in the memory 93, such as... Figure 1 Any one of the methods.
[0087] Bus 91 can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be categorized as address buses, data buses, control buses, etc. For ease of representation, although only one thick line is used in the diagram, this does not indicate that there is only one bus or one type of bus.
[0088] In some embodiments, processor 92 may be implemented as a central processing unit (CPU), microprocessor unit (MCU), system-on-chip (System-on-Chip), or field-programmable array (FPGA). Memory 93 may include volatile memory for temporary data storage during program execution, such as random access memory (RAM).
[0089] The memory 93 may also include non-volatile memory for data storage, such as read-only memory (ROM), flash memory, hard disk drive (HDD), or solid-state disk (SSD).
[0090] In some embodiments, the electronic device 90 may further include a communicator 94. The communicator 94 is used for communication with external devices. In specific examples, the communicator 94 may include one or more wired and / or wireless communication circuit modules. For example, the communicator 94 may include one or more of, such as a wired network card, a USB module, a serial interface module, etc. The wireless communication protocols followed by the wireless communication module include one or more of the following: Nearfield Communication (NFC), Infrared (IR), Global System for Mobile Communications (GSM), General Packet Radio Service (GPRS), Code Division Multiple Access (CDMA), Wideband Code Division Multiple Access (WCDMA), Time Division Code Division Multiple Access (TD-SCDMA), Long Term Evolution (LTE), Bluetooth (BT), and Global Navigation Satellite System (GNSS).
[0091] This disclosure also provides a computer-readable storage medium, characterized in that it stores program instructions, which are executed, for example... Figure 1 Energy consumption analysis method for tobacco logistics in the embodiments.
[0092] That is, the method steps in the above embodiments are implemented as software or computer code that can be stored in a recording medium (such as CD ROM, RAM, floppy disk, hard disk or magneto-optical disk), or implemented as computer code that is originally stored in a remote recording medium or a non-transitory machine-readable medium and will be stored in a local recording medium after being downloaded via a network, so that the method represented herein can be stored in such software processing on a recording medium using a general-purpose computer, a special processor or programmable or special hardware (such as ASIC or FPGA).
[0093] In summary, the embodiments of this disclosure provide an energy consumption analysis method, apparatus, electronic device, and medium for tobacco logistics. By acquiring daily actual energy consumption data and task data from multiple storage areas, the deviation ratio between actual and predicted energy consumption is calculated, and detailed analysis is performed on dates with abnormal energy consumption to determine the causes, thus achieving refined energy consumption management. Furthermore, by analyzing the causes of abnormal energy consumption, adjustments can be made to future task data, reducing the operating costs of tobacco logistics companies and enhancing their competitiveness.
[0094] The above embodiments are merely illustrative of the principles and effects of this disclosure and are not intended to limit this disclosure. Any person skilled in the art can modify or alter the above embodiments without departing from the spirit and scope of this disclosure. Therefore, all equivalent modifications or alterations made by those skilled in the art without departing from the spirit and technical concept disclosed in this disclosure should still be covered by the claims of this disclosure.
Claims
1. A method for energy consumption analysis of a tobacco stream, characterized by, include: Obtain daily actual energy consumption data and daily task data for multiple tobacco logistics warehouses. The actual energy consumption data includes at least the actual values of lighting energy consumption, equipment energy consumption, and equipment maintenance energy consumption. Calculate the deviation ratio between the actual daily energy consumption of each storage area and the predicted energy consumption built based on historical data, and determine the date when the deviation ratio exceeds the preset threshold as the abnormal energy consumption date; Analyze task data on dates with abnormal energy consumption to determine the cause of the abnormal energy consumption.
2. The energy consumption analysis method for tobacco logistics according to claim 1, characterized in that, The task data includes the number of inbound tasks, the number of outbound tasks, and whether one or more of the weekly maintenance tasks have been carried out.
3. The energy consumption analysis method for tobacco logistics according to claim 1, characterized in that, The dates on which the deviation ratio exceeds a preset threshold are defined as abnormal energy consumption dates, including: Set a threshold for the energy consumption deviation ratio to distinguish between normal and abnormal energy consumption days; When the deviation ratio between the actual energy consumption and the predicted energy consumption in a certain reservoir area exceeds a set threshold, that day is marked as an abnormal energy consumption day.
4. The energy consumption analysis method for tobacco logistics according to claim 1, characterized in that, The modeling process for predicting energy consumption includes: Select parameters related to energy consumption; Perform data cleaning on the selected parameters; The selected parameters after data cleaning are subjected to regression analysis with historical energy consumption data to determine the correlation coefficient of each parameter. Set a threshold for the correlation coefficient, and only select parameters with correlation coefficients higher than the threshold; A mathematical model for energy consumption prediction is established based on the selected highly relevant parameters.
5. The energy consumption analysis method for tobacco logistics according to claim 4, characterized in that, The actual value of lighting energy consumption is strongly correlated with the amount of inventory to be processed; further including: The cumulative amount of the actual value of the lighting energy consumption is positively correlated with the cumulative amount of the inventory task. Using the cumulative amount of the inbound tasks as the independent variable and the cumulative amount of the actual lighting energy consumption as the dependent variable, a mathematical model is constructed between the cumulative amount of inbound tasks and the cumulative amount of lighting energy consumption.
6. The energy consumption analysis method for tobacco logistics according to claim 4, characterized in that, The cumulative actual energy consumption of the equipment is strongly correlated with the cumulative amount of tasks and the cumulative number of working days; further including: The cumulative amount of actual equipment energy consumption is positively correlated with the cumulative amount of tasks and the cumulative number of working days, respectively. Using the cumulative amount of tasks and the cumulative number of working days as independent variables, and the cumulative amount of actual equipment energy consumption as the dependent variable, mathematical models are constructed between the cumulative amount of equipment energy consumption and the cumulative amount of tasks, and between the cumulative amount of equipment energy consumption and the cumulative number of working days.
7. The energy consumption analysis method for tobacco logistics according to claim 1, characterized in that, Also includes: The causes of the abnormal energy consumption are recorded and analyzed to form an energy consumption anomaly case library; Analyze the data in the case library to uncover the regularity of abnormal energy consumption; Based on the regularity characteristics of the abnormal energy consumption, an energy consumption anomaly prediction model is constructed to provide early warning of potential abnormal energy consumption. The early warning results will be fed back to the staff to guide them in adjusting future task data.
8. An energy consumption analysis device for tobacco logistics, characterized in that, include: The acquisition module is used to acquire daily actual energy consumption data and daily task data for multiple tobacco logistics warehouses. The actual energy consumption data includes actual values of lighting energy consumption, actual values of equipment energy consumption, and actual values of other energy consumption. The calculation module is used to calculate the deviation ratio between the actual daily energy consumption of each storage area and the predicted energy consumption built based on historical data, and to determine the date when the deviation ratio exceeds the preset threshold as the abnormal energy consumption date. The analysis module is used to perform detailed analysis of task data on dates with abnormal energy consumption in order to determine the cause of the abnormal energy consumption.
9. An electronic device, characterized in that, The electronic device includes: Processor and memory; The memory is used to store computer programs; The processor is used to execute the computer program stored in the memory to cause the electronic device to perform the energy consumption analysis method for tobacco logistics as described in any one of claims 1 to 7.
10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by an electronic device, the program implements the energy consumption analysis method for tobacco logistics as described in any one of claims 1 to 7.