CN114879619A - Digital workshop energy optimization method and system - Google Patents
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- G05B19/41885—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM] characterised by modeling, simulation of the manufacturing system
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Abstract
The invention discloses a digital workshop energy optimization method and a system, belonging to the technical field of digital workshops, wherein the method comprises the following steps of; creating a plurality of energy consumption category catalogs of matched workshops; classifying each instrument of the workshop according to a preset energy consumption classification rule, and matching the detection data of each instrument to each energy consumption category; dividing the detection data of a plurality of meters in the same energy consumption category according to the pre-entered minimum electricity consumption metering unit information to obtain a plurality of electricity consumption unit data sets; and analyzing the data of each power unit data set of the workshop to obtain energy consumption information and outputting an optimization prompt. The energy consumption monitoring platform has the effects of enhancing the management of the energy consumption quality and reducing the energy consumption waste.
Description
Technical Field
The application relates to the technical field of digital workshops, in particular to a digital workshop energy optimization method and system.
Background
Energy device management refers to: based on the latest internet of things technology, the energy consumption data of various energy consumption monitoring points are collected, so that the plant can realize comprehensive visualization of energy use, and an energy management system such as energy planning and assessment is established for the plant, so that the enterprise can be helped to continuously optimize energy use and reduce comprehensive energy consumption of the enterprise.
At present, energy monitoring platforms of workshops, factories and parks generally focus on real-time monitoring of energy consumption equipment and visualization processing of energy consumption data, and energy planning management bases can be provided for managers through oscillograms, pie charts and the like; however, the method lacks of supervision on the energy use quality, so the application proposes a new technical scheme.
Disclosure of Invention
In order to enhance the management of an energy monitoring platform on energy consumption quality and reduce energy consumption waste, the application provides a digital workshop energy optimization method and system.
In a first aspect, the present application provides a digital vehicle-to-vehicle energy optimization method, which adopts the following technical scheme:
a digital vehicle-to-vehicle energy optimization method, comprising:
creating a plurality of energy consumption category catalogs of matched workshops;
classifying each instrument of the workshop according to a preset energy consumption classification rule, and matching the detection data of each instrument to each energy consumption category;
dividing the detection data of a plurality of meters in the same energy consumption category according to the pre-entered minimum electricity consumption metering unit information to obtain a plurality of electricity consumption unit data sets; and the number of the first and second groups,
analyzing data of each power consumption unit data set of the workshop to obtain energy consumption information and outputting an optimization prompt;
wherein the data analysis of each electricity usage unit data set of the plant comprises:
counting and calculating the electricity consumption, the average electricity charge and the peak-valley electricity consumption of the minimum electricity consumption metering unit period T2 to obtain a power distribution report;
establishing a one-to-one correspondence relationship between real-time effective output and power consumption of equipment monitored by the instruments, generating energy consumption ratio information of a minimum power consumption metering unit according to a power distribution report, and synchronously identifying corresponding effective output of each instrument detection equipment;
and processing the energy consumption ratio information according to preset optimization prompt logic, and outputting an optimization prompt.
Optionally, the processing the energy consumption ratio information according to the preset optimization prompt logic includes:
comparing the energy consumption occupation ratio of each device by taking T3 as an evaluation period, judging whether the energy consumption occupation ratio is increased, and if so, executing the next judgment;
judging whether the effective output is increased, if so, recording the electricity utilization unit data set of the period as an increment file; if not, then the presence of an abnormal risk is assessed.
Optionally, the assessing is at risk of an abnormality, comprising:
comparing the power output of other equipment in the two periods before and after, judging whether the power output is reduced, if not, judging that the power output is abnormal, and outputting energy consumption abnormal information as an optimization prompt.
Optionally, when the effective output increases, performing historical data evaluation; the historical data evaluation comprises the following steps:
searching the historical increment file, calling the same energy consumption proportion item, comparing the current effective output with the effective output of the same energy consumption proportion item, judging whether the difference is smaller than a preset abnormal threshold value, and if not, outputting energy consumption increment abnormal information as an optimization prompt.
Optionally, the invoking the same energy consumption duty term includes:
and calculating the similarity between the current increment file and other historical increment files by a similarity algorithm, and taking the historical increment file meeting a preset similarity threshold value as the same energy consumption proportion item.
Optionally, the method further includes:
acquiring and receiving a response mode of the optimization prompt and feedback information of a result;
identifying feedback information and determining an abnormal reason; and the number of the first and second groups,
and classifying the response modes of the previous times according to the abnormal reasons to generate an optimization scheme.
Optionally, the method further includes:
taking the machine time as a horizontal axis and taking the energy consumption parameters of each device of the past sampling time nodes as vertical axis parameters to establish an energy consumption trend graph;
acquiring and receiving an equipment selection instruction of a manager; and the number of the first and second groups,
and displaying the energy consumption parameters of the specified equipment on the energy consumption trend graph according to the equipment selection instruction.
In a second aspect, the present application provides a digital workshop energy optimization system, which adopts the following technical scheme:
a digital plant energy optimization system comprising a memory and a processor, the memory having stored thereon a computer program that can be loaded by the processor and executed to perform any of the digital plant energy optimization methods described above.
In summary, the present application includes at least one of the following beneficial technical effects: the energy consumption of workshop production equipment can be monitored; meanwhile, the energy consumption ratio and real-time effective output of the equipment are also correlated, the energy consumption quality of each equipment is analyzed in a targeted manner by taking the energy consumption ratio and the real-time effective output as the basis, managers are helped to find the problem of energy consumption waste possibly generated in production, and the energy consumption waste is reduced.
Drawings
FIG. 1 is a schematic flow chart of the main flow of the present application;
fig. 2 is a schematic view of the effect of the present application.
Detailed Description
The present application is described in further detail below with reference to figures 1-2.
The embodiment of the application discloses a digital workshop energy optimization method.
Referring to fig. 1, the digital plant energy optimization method includes:
s101, creating energy consumption type catalogs of a plurality of matched workshops; wherein, the energy consumption category list refers to: a directory of a certain class of energy consuming devices; such as: the power utilization catalog, the air conditioner utilization catalog and the like are generated by specifically receiving a creation instruction input by a manager.
It can be understood that the energy consumption category directory may be created at a previous stage, and divided by functional areas of a factory area to implement energy consumption detection of each functional area; the present embodiment is mainly explained in detail in the following stage, and is not described in detail.
S102, classifying each instrument of the workshop according to a preset energy consumption classification rule, and matching detection data of each instrument to each energy consumption category;
the energy consumption classification rules are as follows: the air conditioner monitoring instrument is matched with the air conditioner power utilization directory.
It should be noted that, in the present application, the above-mentioned meters, including the smart meters of each stage and the control and monitoring meters of the equipment, that is, the detected data is not limited to the power consumption. Take the factory workshop of products such as a certain production aluminum honeycomb core, aluminum honeycomb panel, stone material honeycomb composite sheet as an example, the detected data still can include: temperature and humidity, pressure of an air compressor, flow of a water pump and the like of the cooling tower and the boiler.
With regard to the monitoring of the electrical quantity, on the basis of the above, in particular, there are:
1. at least one total electric meter is assembled (configured and installed) for a total power distribution room, an office, a dormitory, a workshop 1 and a workshop 2 so as to monitor the energy consumption of each area of a factory area;
2. at least one ammeter is arranged for monitoring the aluminum film compounding energy consumption;
3. at least one electric meter is arranged for the lengthened baking furnace (cold press) to monitor the baking energy consumption;
4. at least two electric meters are assembled on the aluminum honeycomb plate single-sheet composite production line, and at least one electric meter is used for monitoring the energy consumption of the composite device;
5. two electric meters are configured for 1350 automatic lines, and energy consumption monitoring is carried out on a crane, a manipulator and the like.
It can be understood that energy consumption equipment in a cellular board production plant, such as a cladding machine, an edge bonding machine, a bending machine, and the like, may be determined by referring to the existing production process, and details are not repeated in this embodiment.
S103, dividing the detection data of the plurality of meters in the same energy consumption category according to the pre-recorded minimum electricity consumption metering unit information to obtain a plurality of electricity consumption unit data sets.
Referring to fig. 2, with respect to the minimum electricity usage measurement unit, i.e., as: dividing an air compressor in a power electricity utilization catalog as a minimum electricity utilization metering unit; under the unit, the air compressors 1-9 are distinguished by serial numbers, and detection data are collected in the same power utilization unit data set.
The foregoing S101-103 is a preprocessing performed by the method, that is, the following steps: and collecting the accessed meter data, wherein the collection frequency of each meter is 15-30 s. After the above-described flow is completed, the following analysis process may be developed.
And S2, analyzing the data of each power consumption unit data set of the workshop to obtain energy consumption information, and outputting an optimization prompt.
In one embodiment of the method, the method is not only directed to energy optimization, but also includes the following real-time monitoring of the instrument status items to meet various supervision requirements of management personnel:
monitoring the instrument, namely displaying the latest state of the instrument needing to be monitored;
the method comprises the steps that an instrument real-time curve is obtained, instrument data are displayed in a curve oscillogram mode, and the curve is refreshed every 3 seconds;
and analyzing the historical data of the instrument, graphically displaying the historical data of the instrument, inquiring the acquired data in a certain time period, and exporting the data information of the equipment in a certain time period by original data or by self-definition.
In an embodiment of the method, the step S2 includes:
1) and counting and calculating the total power consumption of each period T1 of the workshop, and comparing the period power consumption to obtain an energy consumption overview. For example: comparing the energy consumption of the current day with that of the previous day and the current month with that of the previous month.
The energy consumption overview is used for helping managers to know energy consumption changes of all stages from the level of the whole workshop so as to observe and know the influence of abnormal energy consumption on the whole body by combining with subsequent targeted energy consumption analysis results.
2) And counting and calculating the electricity consumption, the average electricity charge and the peak-valley electricity consumption of the minimum electricity consumption metering unit period T2 to obtain a power distribution report.
The period T2 is as follows: and obtaining a daily report, a weekly report and the like by hours, days, weeks and months. The report forms can provide a basis for managers to adjust energy plans and account related expenses on one hand, and on the other hand, the report forms are used for energy optimization.
3) Referring to FIG. 2, establishing a one-to-one correspondence relationship between real-time effective output of equipment monitored by the instruments and power consumption, generating energy consumption ratio information of a minimum power consumption metering unit according to a power distribution report, and synchronously identifying corresponding effective output of each instrument detection equipment; and the number of the first and second groups,
and processing the energy consumption ratio information according to preset optimization prompt logic, and outputting an optimization prompt.
In the method, the real-time effective output does not simply refer to the power calibrated by equipment, but the actually calculated effective work (output); regarding the calculation formula of the effective work of the existing equipment such as the air compressor, etc., the calculation formula is the prior art, and therefore, the description is omitted; in this embodiment, the effective output is constantly measured at another angle, taking a water pump as an example: and taking the flow in the detection data of the water pump as a real-time effective output parameter.
According to the content, the method can monitor the operation of the electric equipment in the workshop and can also monitor the energy consumption of workshop production equipment; meanwhile, the energy consumption ratio and real-time effective output of the equipment are also related, and the management personnel are helped to search the energy consumption waste problem possibly generated in the production and reduce the energy consumption waste by aiming at analyzing the energy consumption quality of each equipment based on the energy consumption ratio and the real-time effective output.
In an embodiment of the method, the processing the energy consumption ratio information according to the preset optimization prompt logic includes:
1) with T3 (e.g.: 2 hours) is an evaluation period, the energy consumption occupation ratio of each device is compared, whether the energy consumption occupation ratio is increased or not is judged, and if yes, the next judgment is executed;
judging whether the effective output is increased, if so, recording the electricity utilization unit data set of the period as an increment file; if not, then the presence of an abnormal risk is assessed.
For production equipment at each stage of a workshop, when no fault occurs and the output is invalid, the increase of energy consumption is the load increase in theory, and in order to improve the power, the most direct way is to check whether the effective output is increased or not when the energy consumption ratio is increased, so that whether the abnormality occurs or not can be evaluated, for example: the output waste due to the broken middle pipeline, the energy consumption increase due to the failure of equipment, the energy consumption waste due to the coordination of equipment at all levels of the production line, and the like.
It will be appreciated that if the anomaly is evaluated from the energy consumption ratio only, there is a greater chance of error, which will occur once the a device power is unchanged and the other B, C devices of the same unit are powered down. To avoid this misjudgment, the above evaluation has an abnormal risk, which includes:
comparing the power output (electricity consumption and gear position as basis) of other equipment (same unit) in two periods before and after, judging whether to reduce, if so, ending; if not, judging the system to be abnormal, and outputting energy consumption abnormal information as an optimization prompt.
2) For the condition that the effective output is increased, not only the incremental file is recorded, but also historical data evaluation is further executed; historical data profiling, comprising:
searching the historical increment file, calling the same energy consumption proportion item, comparing the current effective output with the effective output of the same energy consumption proportion item, judging whether the difference is smaller than a preset abnormal threshold value, and if not, outputting energy consumption increment abnormal information as an optimization prompt.
It is noted that the same energy consumption term in this context refers to the same device, or at least to the same unit, the same type of device.
According to the above, when the energy consumption is increased, the method can also transfer historical data, and the historical data is compared with the effective output increment to be used as an evidence, so as to evaluate whether the abnormality occurs.
It can be understood that, taking a car as an example, as the driving time increases, equipment aging and carbon deposition occur, and the power difference generated by the oil feeding force is the same. The above items aim at comparing the historical electricity consumption quality with the current electricity consumption quality, and help managers to find the problem of energy consumption waste possibly caused by equipment aging and the like in time.
In an embodiment of the present application, the same energy consumption duty is called, and not only the parameter of energy consumption duty is used as a criterion for searching and calling, specifically, it includes:
and calculating the similarity between the current increment file and other historical increment files by a similarity algorithm, and taking the historical increment file meeting a preset similarity threshold value as the same energy consumption proportion item.
Wherein, the similarity calculation method is a data similarity calculation method; any algorithm capable of calculating data similarity of multiple latitudes is selected, and details are not repeated in the prior art.
It should be noted that in the similarity calculation of this step, the valid output is first rejected, i.e. the parameter is not added to the similarity calculation, so as to avoid the cycle misjudgment.
In one embodiment of the method, the method further comprises:
acquiring and receiving a response mode of the optimization prompt and feedback information of a result;
identifying feedback information and determining an abnormal reason; and the number of the first and second groups,
and classifying the response modes of the past times according to the abnormal reasons (for example, the same reason is one type), and generating an optimization scheme.
In this way, after the administrator receives the optimization prompts all the time, the administrator needs to upload the response modes and results after responding to the prompts. The generation of the optimization scheme can be used as a reference for the manager when the same problem occurs next time, so as to guide the relevant personnel to complete the energy optimization more quickly.
It will be appreciated that the method may also, on the basis of the above: adding an abnormal reason into the increment file; when the method outputs the optimization prompt, the increment file is processed by a maximum expectation algorithm to obtain an estimation result of the estimated abnormal reason, namely the method can predict the reason of the energy consumption abnormality; even more, according to the abnormal reason, the optimization scheme of the highest similarity in the historical increment archives can be called and output to be used as a guide for the management personnel.
For energy management, the visual visualization of data plays an important role, and referring to fig. 2, therefore, the method further includes:
taking the machine time as a horizontal axis and taking the energy consumption parameters of each device of the past sampling time nodes as vertical axis parameters to establish an energy consumption trend graph;
acquiring and receiving an equipment selection instruction of a manager; and the number of the first and second groups,
and displaying the energy consumption parameters of the specified equipment on the energy consumption trend graph according to the equipment selection instruction.
Taking the graph as an example, an administrator can visually know the energy consumption development trend of each device at the moment, and can compare different devices of sampling points at all times.
The embodiment of the application also discloses a digital workshop energy optimization system.
The digital workshop energy optimization system comprises: comprising a memory and a processor, the memory having stored thereon a computer program that can be loaded by the processor and executed to perform any of the methods for digital plant energy optimization described above.
The above embodiments are preferred embodiments of the present application, and the protection scope of the present application is not limited by the above embodiments, so: all equivalent changes made according to the structure, shape and principle of the present application shall be covered by the protection scope of the present application.
Claims (8)
1. A method for optimizing energy in a digital vehicle, comprising:
creating a plurality of energy consumption category catalogs of matched workshops;
classifying each instrument of the workshop according to a preset energy consumption classification rule, and matching the detection data of each instrument to each energy consumption category;
dividing the detection data of a plurality of meters in the same energy consumption category according to the pre-entered minimum electricity consumption metering unit information to obtain a plurality of electricity consumption unit data sets; and the number of the first and second groups,
analyzing data of each power consumption unit data set of the workshop to obtain energy consumption information and outputting an optimization prompt;
wherein the data analysis of each electricity usage unit data set of the plant comprises:
counting and calculating the electricity consumption, the average electricity charge and the peak-valley electricity consumption of the minimum electricity consumption metering unit period T2 to obtain a power distribution report;
establishing a one-to-one correspondence relationship between real-time effective output and power consumption of equipment monitored by the instruments, generating energy consumption ratio information of a minimum power consumption metering unit according to a power distribution report, and synchronously identifying corresponding effective output of each instrument detection equipment;
and processing the energy consumption ratio information according to preset optimization prompt logic, and outputting an optimization prompt.
2. The digital workshop energy optimization method according to claim 1, wherein the processing of the energy consumption ratio information according to the preset optimization prompting logic comprises:
comparing the energy consumption occupation ratio of each device by taking T3 as an evaluation period, judging whether the energy consumption occupation ratio is increased, and if so, executing the next judgment;
judging whether the effective output is increased, if so, recording the electricity utilization unit data set of the period as an increment file; if not, then the presence of an abnormal risk is assessed.
3. The digital plant energy optimization method of claim 2, wherein the assessing is at risk of an anomaly comprising:
comparing the power output of other equipment in the two periods before and after, judging whether the power output is reduced, if not, judging that the power output is abnormal, and outputting energy consumption abnormal information as an optimization prompt.
4. The digital plant energy optimization method of claim 2, wherein: when the effective output is increased, performing historical data evaluation; the historical data evaluation comprises the following steps:
searching a historical increment file, calling the same energy consumption ratio item, comparing the current effective output with the effective output of the same energy consumption ratio item, judging whether the difference is smaller than a preset abnormal threshold value, and if not, outputting energy consumption increment abnormal information as an optimization prompt.
5. The digital plant energy optimization method of claim 4, wherein the invoking of the same energy consumption duty term comprises:
and calculating the similarity between the current increment file and other historical increment files by a similarity algorithm, and taking the historical increment file meeting a preset similarity threshold value as the same energy consumption proportion item.
6. The digital plant energy optimization method of claim 1, further comprising:
acquiring and receiving a response mode of the optimization prompt and feedback information of a result;
identifying feedback information and determining an abnormal reason; and the number of the first and second groups,
and classifying the response modes of the previous times according to the abnormal reasons to generate an optimization scheme.
7. The digital plant energy optimization method of claim 1, further comprising:
taking the machine time as a horizontal axis, taking the energy consumption parameters of each device of the past sampling time nodes as vertical axis parameters, and establishing an energy consumption trend graph;
acquiring and receiving an equipment selection instruction of a manager; and the number of the first and second groups,
and displaying the energy consumption parameters of the specified equipment on the energy consumption trend graph according to the equipment selection instruction.
8. A digital workshop energy optimization system is characterized in that: comprising a memory and a processor, said memory having stored thereon a computer program which can be loaded by the processor and which executes the method for energy optimization of a digital plant according to any one of claims 1 to 7.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116205467A (en) * | 2023-04-28 | 2023-06-02 | 扬州市职业大学(扬州开放大学) | High-efficiency consumption management system and method based on industrial Internet of things |
| CN116542538A (en) * | 2023-07-07 | 2023-08-04 | 中能国研（北京）电力科学研究院 | Industrial equipment energy consumption monitoring method and device, electronic equipment and storage medium |
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116205467A (en) * | 2023-04-28 | 2023-06-02 | 扬州市职业大学(扬州开放大学) | High-efficiency consumption management system and method based on industrial Internet of things |
| CN116542538A (en) * | 2023-07-07 | 2023-08-04 | 中能国研（北京）电力科学研究院 | Industrial equipment energy consumption monitoring method and device, electronic equipment and storage medium |
| CN116542538B (en) * | 2023-07-07 | 2023-09-08 | 中能国研（北京）电力科学研究院 | Industrial equipment energy consumption monitoring method and device, electronic equipment and storage medium |
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