Digital Workshop Energy Optimization Method and System

Abstract

The present invention discloses a digital workshop energy optimization method and system in the technical field of digital workshops. The method includes: creating multiple energy consumption category directories that match the workshop; classifying each instrument in the workshop according to a preset energy consumption classification rule, and matching the detection data of each instrument to each energy consumption category directory; dividing the detection data of multiple instruments in the same energy consumption category directory according to the pre-entered minimum electricity measurement unit information to obtain several electricity unit data sets; and analyzing the data of each electricity unit data set in the workshop to obtain energy consumption information and output an optimization prompt. This application has the effect of strengthening the management of the energy consumption quality by the energy monitoring platform and reducing energy consumption waste.

Description

Technical Field

[0001] This application relates to the technical field of digital workshops, and in particular to a digital workshop energy optimization method and system. Background Art

[0002] Energy equipment management refers to: based on the latest Internet of Things technology, by collecting the energy consumption data of various energy consumption monitoring points, enabling the factory to achieve comprehensive visualization of energy use, establishing an energy management system such as energy planning and assessment for the factory, and then helping the enterprise continuously optimize energy use and reduce the comprehensive energy consumption of the enterprise.

[0003] Currently, for energy monitoring platforms such as workshops, factory areas, and industrial parks, they generally focus on the real-time monitoring of energy consumption equipment and the visualization processing of energy consumption data, and can provide energy planning management basis for managers through waveform diagrams, pie charts, etc.; however, they lack the supervision of the quality of energy use. Therefore, this application proposes a new technical solution. Summary of the Invention

[0004] In order to strengthen the management of energy consumption quality by the energy monitoring platform and reduce energy consumption waste, this application provides a digital workshop energy optimization method and system.

[0005] In a first aspect, this application provides a digital workshop energy optimization method, adopting the following technical solution:

[0006] A digital workshop energy optimization method includes:

[0007] Create multiple energy consumption category directories that match the workshop;

[0008] Classify each instrument in the workshop according to the preset energy consumption classification rules, and match the detection data of each instrument to each energy consumption category directory;

[0009] Divide the detection data of multiple instruments in the same energy consumption category directory according to the pre-entered minimum electricity measurement unit information to obtain several electricity unit data sets; and,

[0010] Analyze the data of each electricity unit data set in the workshop to obtain energy consumption information and output an optimization prompt;

[0011] Among them, the data analysis of each electricity unit data set in the workshop includes:

[0012] Statistically calculate the electricity consumption, average electricity bill, peak-valley electricity consumption in the minimum electricity measurement unit period T2 to obtain a power distribution report;

[0013] Establish a one-to-one correspondence between the real-time effective output and electricity consumption of the equipment monitored by the instrument, and generate the energy consumption ratio information of the minimum electricity measurement unit according to the power distribution report, and synchronously mark the corresponding effective output of each instrument detection device;

[0014] Process the energy consumption ratio information according to the preset optimization hint logic, and output optimization hints.

[0015] Optionally, the processing of the energy consumption ratio information according to the preset optimization hint logic includes:

[0016] Taking T3 as the evaluation period, compare the energy consumption ratios of each device, and judge whether it increases. If so, execute the next judgment;

[0017] Judge whether the effective output increases. If so, record the power consumption unit dataset of this period as the incremental file; if not, evaluate the existence of abnormal risks.

[0018] Optionally, the evaluation of the existence of abnormal risks includes:

[0019] Compare the power outputs of other devices in the previous and next periods, and judge whether it decreases. If not, determine it as abnormal and output the energy consumption abnormality information as an optimization hint.

[0020] Optionally, when the effective output increases, perform historical data evaluation; the historical data evaluation includes:

[0021] Search for historical incremental files, call the same energy consumption ratio items, compare the current effective output with the effective output of the same energy consumption ratio items, and judge whether the difference is less than the preset abnormal threshold. If not, output the energy consumption increment abnormality information as an optimization hint.

[0022] Optionally, the calling of the same energy consumption ratio items includes:

[0023] Calculate the similarity between the current incremental file and other historical incremental files using the similarity algorithm, and take the historical incremental files that meet the preset similarity threshold as the same energy consumption ratio items.

[0024] Optionally, it further includes:

[0025] Obtain and receive the response method of the optimization hint and the feedback information of the result;

[0026] Identify the feedback information and determine the cause of the abnormality; and,

[0027] Classify the previous response methods according to the cause of the abnormality and generate an optimization plan.

[0028] Optionally, it further includes:

[0029] Taking the machine time as the horizontal axis and the energy consumption parameters of each device at the previous sampling time nodes as the vertical axis parameters, establish an energy consumption trend chart;

[0030] Obtain and receive the device selection instruction of the management personnel; and,

[0031] According to the device selection instruction, the energy consumption parameters of the specified device are displayed in the energy consumption trend chart.

[0032] In a second aspect, the present application provides a digital workshop energy optimization system, adopting the following technical solution:

[0033] A digital workshop energy optimization system includes a memory and a processor, and a computer program capable of being loaded and executed by the processor, such as any one of the above digital workshop energy optimization methods, is stored on the memory.

[0034] In summary, the present application includes at least one of the following beneficial technical effects: it can monitor the energy consumption of the workshop production equipment; at the same time, it also correlates the energy consumption ratio of the equipment with the real-time effective output, and based on this, analyzes the energy consumption quality of each device specifically, helping the management personnel to find possible energy consumption waste problems in production and reducing energy consumption waste. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 is the main process schematic diagram of the present application;

[0036] Figure 2 is the effect schematic diagram of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0037] The following will further elaborate on the present application in conjunction with the attached Figure 1-2 for a more detailed description.

[0038] The embodiment of the present application discloses a digital workshop energy optimization method.

[0039] Referring to Figure 1 , the digital workshop energy optimization method includes:

[0040] S101. Create multiple energy consumption category directories that match the workshop; wherein, the energy consumption category directory refers to: the directory of a certain type of energy consumption equipment; such as: the power consumption directory, the air conditioning power consumption directory, etc., which are specifically generated by receiving the creation instruction input by the management personnel.

[0041] It can be understood that the above energy consumption category directories can be created at the upper level, divided according to the functional areas of the factory area, to realize the energy consumption detection of each functional area; this embodiment mainly makes a specific explanation at the lower level and will not be elaborated further.

[0042] S102. Classify each instrument in the workshop according to the preset energy consumption classification rules, and match the detection data of each instrument to each energy consumption category directory;

[0043] Among them, the energy consumption classification rule, that is, for example: the air conditioning monitoring instrument is matched to the air conditioning power consumption directory.

[0044] It should be noted that in this application, the above-mentioned various instruments, including smart electricity meters at all levels and control and monitoring instruments of equipment, that is, the detected data is not limited to electricity consumption. Taking the workshop of a manufacturer that produces products such as aluminum honeycomb cores, aluminum honeycomb panels, and stone honeycomb composite panels as an example, the detected data may also include: the temperature, humidity, and pressure of cooling towers and boilers, the pressure of air compressors, the flow rate of water pumps, etc.

[0045] Regarding the monitoring of electricity consumption, specifically based on the above, such as:

[0046] 1. Install at least one main electricity meter for the main power distribution room, office, dormitory, Workshop 1, and Workshop 2 (configured and installed) to monitor the energy consumption of each area in the factory area;

[0047] 2. Install at least one electricity meter for the aluminum film composite line to monitor the energy consumption of aluminum film composite;

[0048] 3. Install at least one electricity meter for the extended baking furnace (cold press) to monitor the energy consumption during baking;

[0049] 4. Install at least two electricity meters for the single-sheet composite production line of aluminum honeycomb panels, and at least one is used to monitor the energy consumption of the composite device;

[0050] 5. Configure two electricity meters for the 1350 automatic line to monitor the energy consumption of overhead cranes, manipulators, etc.

[0051] It can be understood that for the energy consumption equipment in the honeycomb panel production plant area, there are also such as coating machines, edge banding machines, bending machines, etc., which can be determined with reference to the existing production process and will not be elaborated in this embodiment.

[0052] S103. Divide the detection data of multiple instruments in the same energy consumption category directory according to the pre-entered minimum electricity measurement unit information to obtain several electricity consumption unit data sets.

[0053] Refer to Figure 2 , regarding the minimum electricity measurement unit, that is, for example: in the power consumption directory, one item of air compressor is divided as the minimum electricity measurement unit; under this unit, the air compressors are distinguished by serial numbers 1-9, and the detection data is summarized in the same electricity consumption unit data set.

[0054] The above S101-103 are the preprocessing for the implementation of this method, that is, for example: implementing the acquisition of the data of the connected instruments, and the acquisition frequency of each instrument is 15-30 s. After completing the above process, the following analysis process can be carried out.

[0055] S2. Analyze the data of each electricity consumption unit data set in the workshop to obtain energy consumption information and output an optimization prompt.

[0056] In one embodiment of the present method, the method is not only for energy optimization, but also includes the following items for real-time monitoring of instrument status to meet the diverse supervision needs of management personnel:

[0057] Instrument monitoring, which displays the latest status of the instruments to be monitored;

[0058] Real-time instrument curve, which obtains instrument data in real time and displays it in the form of a curve waveform diagram, refreshing every 3 seconds;

[0059] Analysis of instrument historical data, which graphically displays the historical data of the instruments, allows querying of the collected data within a certain time period, and enables export of the original data or customized export of the data information of a device within a certain time period.

[0060] In one embodiment of the present method, the above S2 includes:

[0061] 1) Statistically calculate the total electricity consumption of each cycle T1 in the workshop, and compare the electricity consumption of each cycle to obtain an energy consumption overview. For example: compare the energy consumption of the current day with the previous day, and that of the current month with the previous month.

[0062] The item of energy consumption overview is used to help management personnel understand the energy consumption changes at each stage from the perspective of the entire workshop, so as to observe and understand the impact of abnormal energy consumption on the whole in combination with the subsequent targeted energy consumption analysis results.

[0063] 2) Statistically calculate the electricity consumption, average electricity cost, peak-valley electricity consumption of the minimum electricity measurement unit cycle T2 to obtain a power distribution report.

[0064] For the above cycle T2, such as hours, days, weeks, months, daily reports, weekly reports, etc. can be obtained. The report can, on the one hand, provide a basis for management personnel to adjust the energy plan and calculate relevant costs, and on the other hand, is for the following energy optimization.

[0065] 3) Refer to Figure 2 , establish a one-to-one correspondence between the real-time effective output and electricity consumption of the equipment monitored by the instrument, and generate the energy consumption proportion information of the minimum electricity measurement unit according to the power distribution report, and synchronously mark the corresponding effective output of each instrument detection device; and,

[0066] Process the energy consumption proportion information according to the preset optimization prompt logic and output an optimization prompt.

[0067] In the present method, the above real-time effective output does not simply refer to the rated power of the equipment, but the actually calculated effective work done (output); the formula for calculating the effective work of existing equipment such as air compressors is prior art and will not be elaborated here; in this embodiment, from another perspective, the effective output is constantly measured. Taking a water pump as an example: the flow rate in the detection data of the water pump is used as the real-time effective output parameter.

[0068] According to the above content, in addition to monitoring the operation of the electrical equipment in the workshop, this method can also monitor the energy consumption of the production equipment in the workshop; at the same time, it also correlates the energy consumption ratio of the equipment with the real-time effective output, and based on this, analyzes the energy consumption quality of each equipment to help the management personnel find possible energy consumption waste problems in production and reduce energy consumption waste.

[0069] In an embodiment of this method, processing the energy consumption ratio information according to the preset optimization prompt logic includes:

[0070] 1). Taking T3 (such as: 2 hours) as the evaluation period, comparing the energy consumption ratios of each equipment, and judging whether it increases. If so, execute the next judgment;

[0071] Judge whether the effective output increases. If so, record the power consumption unit data set of this period as the incremental file; if not, evaluate that there is an abnormal risk.

[0072] For the production equipment at all levels in the workshop, when there is no failure and no invalid output, theoretically, the increase in energy consumption is due to the increase in load. In order to improve power, the most direct link is to check whether the effective output increases when the energy consumption ratio increases to evaluate whether there is an abnormality. For example: waste of output due to rupture of the intermediate pipeline, increase in energy consumption due to equipment failure, or waste of energy consumption due to poor coordination of equipment at all levels of the production line, etc.

[0073] It can be understood that if only evaluating the abnormality from the energy consumption ratio, there is a relatively high probability of error. After all, once the power of equipment A remains unchanged while the power of other equipment B and C of the same unit decreases, the above situation will occur. To avoid this misjudgment, the above evaluation of abnormal risk includes:

[0074] Compare the power outputs (based on power consumption and gear position) of other equipment (of the same unit) in the previous and current periods, and judge whether it decreases. If so, end; if not, determine it as abnormal and output the energy consumption abnormality information as an optimization prompt.

[0075] 2). For the situation where the effective output increases, not only record it as an incremental file, but also further perform historical data evaluation; historical data evaluation includes:

[0076] Search for the historical incremental file, call the item with the same energy consumption ratio, compare the current effective output with the effective output of the item with the same energy consumption ratio, and judge whether the difference is less than the preset abnormal threshold. If not, output the energy consumption increment abnormality information as an optimization prompt.

[0077] It should be noted that the item with the same energy consumption ratio here refers to the same equipment, or at least equipment of the same unit and the same type.

[0078] According to the above, when the energy consumption increases, this method will also mobilize historical data and use the increment of the effective output as evidence through comparison to evaluate whether an anomaly has occurred.

[0079] It can be understood that taking a car as an example, as the driving duration increases, equipment aging and carbon deposition occur, resulting in differences in the power generated with the same fuel supply force. The above items aim to compare the historical power consumption quality with the current situation to help managers promptly discover potential energy consumption waste problems caused by equipment aging and the like.

[0080] In an embodiment of the present application, when calling items with the same energy consumption ratio, it is not merely based on the parameter of energy consumption ratio as the standard for searching and calling. Specifically, it includes:

[0081] Calculate the similarity between the current increment file and other historical increment files using a similarity algorithm, and select the historical increment files that meet the preset similarity threshold as items with the same energy consumption ratio.

[0082] Among them, the similarity algorithm, that is, the data similarity calculation method; any algorithm that can calculate the data similarity in multiple dimensions can be selected, and this is prior art and will not be elaborated further.

[0083] It should be noted that in the similarity calculation of this step, the effective output is first excluded, that is, this parameter is not added to the similarity calculation to avoid circular misjudgment.

[0084] In an embodiment of this method, this method further includes:

[0085] Obtain and receive the response method and the feedback information of the result of the optimization prompt;

[0086] Identify the feedback information to determine the cause of the anomaly; and,

[0087] Classify the previous response methods according to the cause of the anomaly (for example: the same cause is in one category) to generate an optimization plan.

[0088] The above means that after managers receive optimization prompts each time, they need to upload both the response method and the result after responding to the prompt. The generation of the optimization plan can be used as a reference for managers when the same problem appears next time to guide relevant personnel to complete energy optimization faster.

[0089] It can be understood that on the above basis, this method can also: add the cause of the anomaly to the increment file; when the method outputs an optimization prompt, process the increment file using the maximum expectation algorithm to obtain an estimated result of the cause of the anomaly, so that this method can predict the cause of the energy consumption anomaly; even more, an optimization plan with the highest similarity in the historical increment file can be called according to the cause of the anomaly and output to guide managers.

[0090] For the energy management system, the intuitive visualization of data plays an important role. Refer to Figure 2 , so this method further includes:

[0091] Taking the machine time as the horizontal axis and the energy consumption parameters of each device at each sampling time node as the vertical axis parameters, an energy consumption trend chart is established;

[0092] Obtaining and receiving the device selection instruction of the management personnel; and,

[0093] Displaying the energy consumption parameters of the specified device in the energy consumption trend chart according to the device selection instruction.

[0094] Taking the figure as an example, the administrator can intuitively understand the energy consumption development trend of each device at this time, and can compare different devices at each sampling point.

[0095] The embodiment of the present application also discloses a digital workshop energy optimization system.

[0096] The digital workshop energy optimization system includes a memory and a processor. A computer program capable of being loaded and executed by the processor as any one of the above digital workshop energy optimization methods is stored on the memory.

[0097] The above are all the preferred embodiments of the present application. The protection scope of the present application is not limited accordingly. Therefore, all equivalent changes made according to the structure, shape, and principle of the present application should be covered within the protection scope of the present application.

Claims

1. A digital workshop energy optimization method, characterized in that, Including: Create multiple energy consumption catalogs for matching workshops; Classify each instrument in the workshop according to the preset energy consumption classification rules, and match the detection data of each instrument to each energy consumption catalog; Divide the detection data of multiple instruments in the same energy consumption catalog according to the pre-entered minimum electricity measurement unit information to obtain several electricity unit data sets; And, Analyze the data of each electricity unit data set in the workshop to obtain energy consumption information and output an optimization prompt; Among them, the data analysis of each electricity unit data set in the workshop includes: Statistically calculate the electricity consumption, average electricity bill, peak-valley electricity consumption in the minimum electricity measurement unit period T2 to obtain a power distribution report; Establish a one-to-one correspondence between the real-time effective output of the equipment monitored by the instrument and the electricity consumption, and generate the energy consumption proportion information of the minimum electricity measurement unit according to the power distribution report, and synchronously identify the corresponding effective output of each instrument detection equipment; Process the energy consumption proportion information according to the preset optimization prompt logic and output an optimization prompt; The processing of the energy consumption proportion information according to the preset optimization prompt logic includes: Taking T3 as the evaluation period, compare the energy consumption proportions of each equipment, judge whether it increases, if so, execute the next judgment; Judge whether the effective output increases. If so, record the electricity unit data set of this period as an incremental file; if not, evaluate that there is an abnormal risk; The evaluation of the existence of abnormal risks includes: Compare the power outputs of other equipment in the previous and next periods, judge whether it decreases, if not, determine it as abnormal, and output the energy consumption abnormal information as an optimization prompt; When the effective output increases, perform historical data evaluation; the historical data evaluation includes: Search for historical incremental files, call the same energy consumption proportion items, compare the current effective output with the effective output of the same energy consumption proportion items, and judge whether the difference is less than the preset abnormal threshold. If not, output the energy consumption increment abnormal information as an optimization prompt.

2. The digital workshop energy optimization method according to claim 1, wherein The calling of the same energy consumption proportion items includes: Calculate the similarity between the current incremental file and other historical incremental files by the similarity algorithm, and take the historical incremental file that meets the preset similarity threshold as the same energy consumption proportion item.

3. The digital workshop energy optimization method according to claim 1, characterized in that Also including: Obtain and receive the response method of the optimization prompt and the feedback information of the result; Identify the feedback information and determine the cause of the abnormality; And, Classify the previous response methods according to the cause of the abnormality and generate an optimization plan.

4. The digital workshop energy optimization method according to claim 1, characterized in that Also including: Taking the machine time as the horizontal axis and the energy consumption parameters of each equipment at each sampling time node as the vertical axis parameters, establish an energy consumption trend chart; Obtain and receive the equipment selection instructions of the management personnel; And, According to the equipment selection instructions, display the energy consumption parameters of the specified equipment in the energy consumption trend chart.

5. A digital workshop energy optimization system, characterized in that: Including a memory and a processor, and a computer program capable of being loaded and executed by the processor is stored on the memory, which is a digital workshop energy optimization method according to any one of claims 1 to 4.

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