CN114320862A - Energy-saving optimization method for air compressor - Google Patents
Energy-saving optimization method for air compressor Download PDFInfo
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Abstract
The invention discloses an energy-saving optimization method for an air compressor, which comprises the following steps: step S1) dividing the monitoring area; step S2) deploying the unmanned aerial vehicle and planning the flight route of the unmanned aerial vehicle; step S3) collecting the operation data of each group of air compressors and feeding back the operation data in real time; step S4), starting and allocating the air compressor, and collecting the running data of the air compressor in real time; step S5), optimizing and adjusting the PLC program of the air compressor, and performing simulation test. When the air compressor is in an abnormal state, the PLC program of the air compressor is optimized and adjusted, resource waste caused by frequent loading and unloading work of the air compressor is avoided, energy efficiency is improved, and energy-saving optimization of the air compressor is realized; the unmanned aerial vehicle is used for polling to avoid manual operation, so that the personal safety of workers is ensured; the abnormal data are fed back in time, energy loss caused by the fact that the abnormal state of the air compressor cannot be processed in time is avoided, and the air compressor is energy-saving and environment-friendly.
Description
Technical Field
The invention relates to the technical field of air compressors, in particular to an energy-saving optimization method of an air compressor.
Background
The air compressor is a device used for compressing gas, is similar to a water pump in structure and is a necessary public auxiliary device in a plurality of manufacturing factories, and the factories are usually unstable when compressing air, so that the air compressor is frequently loaded and unloaded, not only is the resource waste caused, but also the device loss is caused, therefore, the optimized operation of the air compressor is the key focus-focused work of a plurality of enterprises, and particularly, the energy-saving optimized operation of the air compressor is realized. With the arrival of an energy crisis, the research on the energy-saving optimization technology of the air compressor needs to be developed urgently, the air compressor optimization method in the prior art needs manual operation, potential threats are caused to the personal safety of workers, and due to the fact that the processing efficiency of the abnormal state of the air compressor is low, the abnormal state of the air compressor cannot be effectively processed in time, energy loss is caused, and energy conservation and environmental protection are not facilitated. For example, chinese patent No. CN111734614A discloses an air compressor system operation optimization method and apparatus, which can avoid frequent loading and unloading of the air compressor, and reduce energy consumption waste of the entire system, but require workers to manually check and adjust the air compressor, thereby reducing worker data collection efficiency, further reducing abnormal state processing efficiency of the air compressor, and at the same time, causing potential threats to the personal safety of the workers. In addition, the existing air compressor energy-saving method based on unmanned aerial vehicle routing inspection cannot feed back abnormal data in time, reduces the processing rate of abnormal operation state, causes energy waste, and is not beneficial to energy conservation and environmental protection.
Disclosure of Invention
The invention mainly aims to solve the problem that the safety and the timeliness of the air compressor energy-saving optimization method in the prior art are poor, and provides the air compressor energy-saving optimization method, wherein an unmanned aerial vehicle is adopted to collect the operation data of the air compressor, the operation state of the air compressor is detected according to the operation data, and when the air compressor is in an abnormal state, the PLC program of the air compressor is optimized and adjusted, so that the resource waste caused by frequent loading and unloading work of the air compressor is avoided, and the energy efficiency is improved; patrol and examine through unmanned aerial vehicle and avoid manual operation, guarantee staff's personal safety, improve the treatment effeciency of air compressor machine abnormal state simultaneously, avoid because can not in time make the processing to air compressor machine abnormal state and cause energy loss, energy-concerving and environment-protective.
In order to achieve the purpose, the invention adopts the following technical scheme:
an energy-saving optimization method for an air compressor comprises the following steps: step S1) dividing the monitoring area; step S2) deploying the unmanned aerial vehicle and planning the flight route of the unmanned aerial vehicle; step S3) collecting the operation data of each group of air compressors and feeding back the operation data in real time; step S4), starting and allocating the air compressor, and collecting the running data of the air compressor in real time; step S5), optimizing and adjusting the PLC program of the air compressor, and performing simulation test. The invention provides an energy-saving optimization method of an air compressor, which comprises the following steps of collecting operation data of the air compressor by adopting an unmanned aerial vehicle, detecting the operation state of the air compressor according to the operation data, optimizing and adjusting a PLC (programmable logic controller) program of the air compressor when the air compressor is in an abnormal state, and realizing energy-saving optimization of the air compressor, wherein the specific process comprises the following steps:
(1) dividing a monitoring area: the working personnel enable the unmanned aerial vehicle to be in communication connection with the computer, meanwhile, the computer sends environment data to the unmanned aerial vehicle, and the unmanned aerial vehicle builds a panoramic model according to the environment data; the working personnel control the unmanned aerial vehicle to collect the regional information, and the unmanned aerial vehicle generates a comparison model according to the regional information; comparing the comparison model with the panoramic model, dividing the monitoring area, and marking each divided area as A1、A2、…、An-1、AnN is a natural number; generating a monitoring model corresponding to each area according to the comparison model and the panoramic model;
(2) deploying the unmanned aerial vehicle and planning the flight route of the unmanned aerial vehicle: staff deploy several groups of unmanned aerial vehicles to A1-AnPlanning a flight route according to the shortest time consumption principle according to the positions of all groups of air compressors marked in the monitoring models of all the regions;
(3) collecting operation data of each group of air compressors and feeding back the operation data in real time: the working personnel start the unmanned aerial vehicle, the unmanned aerial vehicle inspects all groups of air compressors according to the planned flight route, collects the operation data of all groups of air compressors in the corresponding area in real time, classifies the operation data and automatically generates an inspection record table;
(4) starting and allocating the air compressor, and collecting the operating data of the air compressor in work in real time: the unmanned aerial vehicle allocates the starting combination of the air compressors according to the operation data of each group of air compressors, collects the operation data of the air compressors in work in real time, and optimizes and modifies the allocation scheme according to the collected data;
(5) optimizing and adjusting the PLC program of the air compressor, and performing simulation test: the unmanned aerial vehicle is in communication connection with a PLC (programmable logic controller) of the air compressor, adjusts and labels a PLC program stored in the PLC, and feeds back an adjustment scheme to a worker through a computer; and the computer carries out simulation test according to the adjustment scheme, records the operation data of each group of air compressors in the simulation test, and compares the operation data with the data in the routing inspection recording table to generate an optimized log.
Compared with the prior optimized operation scheme of manually controlled air compressors, the method has the advantages that each set of air compressors in the monitored area is patrolled by the unmanned aerial vehicle, the terminal module of the unmanned aerial vehicle receives environmental data uploaded by workers, and a panoramic model of the monitored area is constructed according to the environmental data; each group of unmanned aerial vehicles construct a comparison model according to the acquired regional information; comparing the comparison model with the panoramic model, dividing the monitoring area and marking each divided area; generating a monitoring model of each area according to the comparison model and the panoramic model; the staff deploys a plurality of groups of unmanned aerial vehicles to each region, and plans the flight route according to the shortest time-consuming principle according to the positions of the air compressors marked in the monitoring model. The invention can autonomously plan the inspection area and improve the inspection efficiency of the unmanned aerial vehicle; and the shortest flight route that consumes time is planned by oneself, improves staff's data collection efficiency, and then improves air compressor machine abnormal state treatment effeciency, avoids causing the energy waste.
The unmanned aerial vehicle starts and allocates the air compressor according to the allocation scheme, collects the operating data of the air compressor in operation, automatically generates an operation recording table at the same time, and records all the collected operating data into the operation recording table; analyzing various operation data of the air compressor in the operation process, extracting abnormal change data in the operation data, and collecting the occurrence time of the abnormal data and the operation state of the air compressor by the unmanned aerial vehicle; and according to the running state of the air compressor, carrying out program positioning and marking on the corresponding PLC program, and automatically generating an adjusting scheme aiming at the corresponding PLC program. The method can feed back the abnormal data in time, improve the processing speed of the abnormal operation state, avoid energy loss caused by incapability of processing the abnormal state of the air compressor in time, save energy and protect environment. Meanwhile, according to the scheme, the PLC program of the air compressor is optimized and adjusted, resource waste caused by frequent loading and unloading of the air compressor is avoided, and the energy efficiency is improved.
Preferably, the specific process of step S1 includes the following steps: step A1), uploading environmental data to a terminal module of the unmanned aerial vehicle by a worker; step A2) the unmanned aerial vehicle builds a panoramic model of the monitored area according to the environmental data; step A3) unmanned aerial vehicle Collection area letterInformation; step A4) the unmanned aerial vehicle builds a monitoring area comparison model according to the area information; step A5) comparing the comparison model with the panoramic model, dividing the monitoring area, and marking each divided area as A1、A2、…、An-1、AnN is a natural number; step A6) generating a monitoring model corresponding to each area according to the comparison model and the panoramic model. A terminal module of the unmanned aerial vehicle receives environmental data uploaded by workers and constructs a panoramic model of a monitored area according to the environmental data; each group of unmanned aerial vehicles builds a comparison model according to the collected regional information, compares the comparison model with the panoramic model, divides the monitoring region and generates the monitoring model corresponding to each region.
Preferably, the specific process of planning the flight path of the unmanned aerial vehicle in step S2 includes the following steps: step B1) marking the positions of the air compressors of each group in the corresponding area monitoring model; step B2), each group of unmanned aerial vehicles plans a flight route according to the position of the air compressor in the corresponding monitoring model; step B3) calculating the time consumption of each group of flight routes; step B4) selecting the flight route with the shortest time consumption and feeding back the selected flight route to the staff; step B5) the staff manually adjusts the flight path of the unmanned aerial vehicle through the computer. Marking the positions of all groups of air compressors in the monitoring models of the corresponding areas, and planning a plurality of groups of flight routes by all groups of unmanned aerial vehicles according to the positions of the air compressors in the corresponding monitoring models; calculating the time consumption of each group of flight routes, and selecting the flight route with the shortest time consumption, wherein the specific calculation formula is as follows: h is L/s, wherein h represents flight time, L represents total route length, and s represents unmanned plane flight speed; and feeding back the selected flight route to the staff, and manually adjusting the flight route of the unmanned aerial vehicle by the staff through a computer.
Preferably, the specific process of step S3 includes the following steps: step C1), each group of unmanned aerial vehicles collects various operation data of the air compressors operating in the corresponding areas, and classifies the data according to temperature, operation time, outlet flow of the air compressors and real-time power of the air compressors; step C2), calculating the energy efficiency of the corresponding air compressor by the data calculation module of each group of unmanned aerial vehicles; step C3) recording the data collected and calculated by each group of unmanned aerial vehicles into the patrol record table, and sending the patrol record table to the computer in a wireless transmission mode. Each group of unmanned aerial vehicles collects various operation data of the air compressors operating in the corresponding areas and classifies the operation data according to temperature, operation time, outlet flow of the air compressors and real-time power of the air compressors; each group of unmanned aerial vehicle data calculation modules calculates the energy efficiency of the corresponding air compressor, and the specific calculation formula is as follows: n is M ÷ f, wherein N represents the energy efficiency of the air compressor, M represents the outlet flow of the air compressor, and f represents the real-time power of the air compressor; and recording each group of data into the patrol recording table, and sending the patrol recording table to the computer in a wireless transmission mode.
Preferably, the specific process of step S4 includes the following steps: step D1), the staff uploads the allocation scheme to the terminal module of the unmanned aerial vehicle; step D2) according to the allocation scheme, allocating the air compressor starting combination; step D3) collecting the operation data of the air compressor in real time and automatically generating an operation record table; step D4) carrying out characteristic analysis on the data influencing the energy efficiency of the air compressor in the operation record table and extracting the data; step D5), constructing a deep neural network model, and fitting the energy efficiency curve of the air compressor through the deep neural network model according to the data extracted in the step D4; step D6) verifying the logic of the energy efficiency curve to be synthesized, modifying the allocation scheme according to the verification result, and simultaneously feeding back the modified allocation scheme to the staff; and D7) operating the air compressor according to the modified allocation scheme, and simultaneously feeding back various operation data of the air compressor to the staff. Each group of air compressors is correspondingly started, combined and allocated according to an allocation scheme uploaded by a worker; the unmanned aerial vehicle collects data of the running air compressor according to the allocation scheme, automatically generates an operation record table and records each collected data into the operation record table; carrying out characteristic analysis on data influencing the energy efficiency of the air compressor, and extracting the data; constructing a deep neural network model, and fitting an energy efficiency curve of the air compressor through the deep neural network model; verifying the fitted energy efficiency curve logic, modifying the allocation scheme according to the verification result, and feeding back the allocation scheme to the staff; and operating the air compressor according to the modified allocation scheme, and feeding back various operating data of the air compressor to the workers.
Preferably, in the step S5, optimizing a specific process of adjusting the PLC program of the air compressor, including the following steps) step E1) of analyzing various operation data of the air compressor, and extracting variation abnormal data therein; step E2) the unmanned aerial vehicle collects the abnormal data occurrence time; step E3) carrying out program positioning and labeling on the corresponding PLC program according to the air compressor operation data; and E4) automatically generating an adjusting scheme according to the corresponding PLC program and feeding back the adjusting scheme to the staff. Analyzing various data in the operation process of the air compressor, and extracting data with abnormal changes; collecting abnormal data occurrence time and an air compressor running state by the unmanned aerial vehicle; according to the running state of the air compressor, carrying out program positioning and marking on the corresponding PLC program; and (4) generating an adjusting scheme for the corresponding PLC program and feeding the adjusting scheme back to the working personnel.
Preferably, in step S5, the simulation test includes the following specific steps: and the computer carries out simulation test according to the adjustment scheme, simultaneously records the operation data of each group of air compressors in the simulation test, and compares the operation data with the data in the routing inspection recording table to generate an optimized log.
Preferably, the formula for calculating the time taken for each set of routes is:
h＝L÷s
wherein h represents the flight time consumption of the unmanned aerial vehicle, L represents the total length of the route, and s represents the flight speed of the unmanned aerial vehicle.
Preferably, the formula for calculating the energy efficiency of the air compressor is as follows:
N＝M÷f
wherein, N represents the air compressor machine efficiency, and M represents air compressor machine export flow, and f represents the air compressor machine real-time power.
Therefore, the invention has the advantages that:
(1) when the air compressor is in an abnormal state, the PLC program of the air compressor is optimized and adjusted, resource waste caused by frequent loading and unloading work of the air compressor is avoided, energy efficiency is improved, and energy-saving optimization of the air compressor is realized;
(2) the unmanned aerial vehicle is used for polling to avoid manual operation, so that the personal safety of workers is ensured; meanwhile, the processing efficiency of the abnormal state of the air compressor is improved, energy loss caused by the fact that the abnormal state of the air compressor cannot be processed in time is avoided, and the air compressor is energy-saving and environment-friendly.
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FIG. 1 is a flow chart of the present invention.
Detailed Description
The invention is further described with reference to the following detailed description and accompanying drawings.
As shown in fig. 1, an energy-saving optimization method for an air compressor includes the following steps: step S1) dividing the monitoring area; step S2) deploying the unmanned aerial vehicle and planning the flight route of the unmanned aerial vehicle; step S3) collecting the operation data of each group of air compressors and feeding back the operation data in real time; step S4), starting and allocating the air compressor, and collecting the running data of the air compressor in real time; step S5), optimizing and adjusting the PLC program of the air compressor, and performing simulation test. The invention provides an energy-saving optimization method of an air compressor, which comprises the following steps of collecting operation data of the air compressor by adopting an unmanned aerial vehicle, detecting the operation state of the air compressor according to the operation data, optimizing and adjusting a PLC (programmable logic controller) program of the air compressor when the air compressor is in an abnormal state, and realizing energy-saving optimization of the air compressor, wherein the specific process comprises the following steps:
(1) dividing a monitoring area: the working personnel enable the unmanned aerial vehicle to be in communication connection with the computer, meanwhile, the computer sends environment data to the unmanned aerial vehicle, and the unmanned aerial vehicle builds a panoramic model according to the environment data; the working personnel control the unmanned aerial vehicle to collect the regional information, and the unmanned aerial vehicle generates a comparison model according to the regional information; comparing the comparison model with the panoramic model, dividing the monitoring area, and marking each divided area as A1、A2、…、An-1、AnN is a natural number; generating a monitoring model corresponding to each area according to the comparison model and the panoramic model;
(2) deploying the unmanned aerial vehicle and planning the flight route of the unmanned aerial vehicle: staff deploy several groups of unmanned aerial vehicles to A1-AnPlanning a flight route according to the shortest time consumption principle according to the positions of all groups of air compressors marked in the monitoring models of all the regions;
(3) collecting operation data of each group of air compressors and feeding back the operation data in real time: the working personnel start the unmanned aerial vehicle, the unmanned aerial vehicle inspects all groups of air compressors according to the planned flight route, collects the operation data of all groups of air compressors in the corresponding area in real time, classifies the operation data and automatically generates an inspection record table;
(4) starting and allocating the air compressor, and collecting the operating data of the air compressor in work in real time: the unmanned aerial vehicle allocates the starting combination of the air compressors according to the operation data of each group of air compressors, collects the operation data of the air compressors in work in real time, and optimizes and modifies the allocation scheme according to the collected data;
(5) optimizing and adjusting the PLC program of the air compressor, and performing simulation test: the unmanned aerial vehicle is in communication connection with a PLC (programmable logic controller) of the air compressor, adjusts and labels a PLC program stored in the PLC, and feeds back an adjustment scheme to a worker through a computer; and the computer carries out simulation test according to the adjustment scheme, records the operation data of each group of air compressors in the simulation test, and compares the operation data with the data in the routing inspection recording table to generate an optimized log.
The specific process of step S1 includes the following steps: step A1), uploading environmental data to a terminal module of the unmanned aerial vehicle by a worker; step A2) the unmanned aerial vehicle builds a panoramic model of the monitored area according to the environmental data; step A3) unmanned aerial vehicle collecting area information; step A4) the unmanned aerial vehicle builds a monitoring area comparison model according to the area information; step A5) comparing the comparison model with the panoramic model, dividing the monitoring area, and marking each divided area as A1、A2、…、An-1、AnN is a natural number; step A6) generating a monitoring model corresponding to each area according to the comparison model and the panoramic model. A terminal module of the unmanned aerial vehicle receives environmental data uploaded by workers and constructs a panoramic model of a monitored area according to the environmental data; each group of unmanned aerial vehicles builds a comparison model according to the collected regional information, compares the comparison model with the panoramic model, divides the monitoring region and generates the monitoring model corresponding to each region.
The specific process of planning the flight route of the unmanned aerial vehicle in the step S2 includes the following steps: step B1) marking the positions of the air compressors of each group in the corresponding area monitoring model; step B2), each group of unmanned aerial vehicles plans a flight route according to the position of the air compressor in the corresponding monitoring model; step B3) calculating the time consumption of each group of flight routes; step B4) selecting the flight route with the shortest time consumption and feeding back the selected flight route to the staff; step B5) the staff manually adjusts the flight path of the unmanned aerial vehicle through the computer. Marking the positions of all groups of air compressors in the monitoring models of the corresponding areas, and planning a plurality of groups of flight routes by all groups of unmanned aerial vehicles according to the positions of the air compressors in the corresponding monitoring models; calculating the time consumption of each group of flight routes, and selecting the flight route with the shortest time consumption, wherein the specific calculation formula is as follows: h is L/s, wherein h represents flight time, L represents total route length, and s represents unmanned plane flight speed; and feeding back the selected flight route to the staff, and manually adjusting the flight route of the unmanned aerial vehicle by the staff through a computer.
The specific process of step S3 includes the following steps: step C1), each group of unmanned aerial vehicles collects various operation data of the air compressors operating in the corresponding areas, and classifies the data according to temperature, operation time, outlet flow of the air compressors and real-time power of the air compressors; step C2), calculating the energy efficiency of the corresponding air compressor by the data calculation module of each group of unmanned aerial vehicles; step C3) recording the data collected and calculated by each group of unmanned aerial vehicles into the patrol record table, and sending the patrol record table to the computer in a wireless transmission mode. Each group of unmanned aerial vehicles collects various operation data of the air compressors operating in the corresponding areas and classifies the operation data according to temperature, operation time, outlet flow of the air compressors and real-time power of the air compressors; each group of unmanned aerial vehicle data calculation modules calculates the energy efficiency of the corresponding air compressor, and the specific calculation formula is as follows: n is M ÷ f, wherein N represents the energy efficiency of the air compressor, M represents the outlet flow of the air compressor, and f represents the real-time power of the air compressor; and recording each group of data into the patrol recording table, and sending the patrol recording table to the computer in a wireless transmission mode.
The specific process of step S4 includes the following steps: step D1), the staff uploads the allocation scheme to the terminal module of the unmanned aerial vehicle; step D2) according to the allocation scheme, allocating the air compressor starting combination; step D3) collecting the operation data of the air compressor in real time and automatically generating an operation record table; step D4) carrying out characteristic analysis on the data influencing the energy efficiency of the air compressor in the operation record table and extracting the data; step D5), constructing a deep neural network model, and fitting the energy efficiency curve of the air compressor through the deep neural network model according to the data extracted in the step D4; step D6) verifying the logic of the energy efficiency curve to be synthesized, modifying the allocation scheme according to the verification result, and simultaneously feeding back the modified allocation scheme to the staff; and D7) operating the air compressor according to the modified allocation scheme, and simultaneously feeding back various operation data of the air compressor to the staff. Each group of air compressors is correspondingly started, combined and allocated according to an allocation scheme uploaded by a worker; the unmanned aerial vehicle collects data of the running air compressor according to the allocation scheme, automatically generates an operation record table and records each collected data into the operation record table; carrying out characteristic analysis on data influencing the energy efficiency of the air compressor, and extracting the data; constructing a deep neural network model, and fitting an energy efficiency curve of the air compressor through the deep neural network model; verifying the fitted energy efficiency curve logic, modifying the allocation scheme according to the verification result, and feeding back the allocation scheme to the staff; and operating the air compressor according to the modified allocation scheme, and feeding back various operating data of the air compressor to the workers.
In the step S5, optimizing and adjusting the specific process of the PLC program of the air compressor, comprising the following steps) E1) analyzing various operation data of the air compressor, and extracting abnormal change data; step E2) the unmanned aerial vehicle collects the abnormal data occurrence time; step E3) carrying out program positioning and labeling on the corresponding PLC program according to the air compressor operation data; and E4) automatically generating an adjusting scheme according to the corresponding PLC program and feeding back the adjusting scheme to the staff. Analyzing various data in the operation process of the air compressor, and extracting data with abnormal changes; collecting abnormal data occurrence time and an air compressor running state by the unmanned aerial vehicle; according to the running state of the air compressor, carrying out program positioning and marking on the corresponding PLC program; and (4) generating an adjusting scheme for the corresponding PLC program and feeding the adjusting scheme back to the working personnel.
In step S5, the simulation test includes the following specific steps: and the computer carries out simulation test according to the adjustment scheme, simultaneously records the operation data of each group of air compressors in the simulation test, and compares the operation data with the data in the routing inspection recording table to generate an optimized log.
The above-described embodiments are only preferred embodiments of the present invention, and are not intended to limit the present invention in any way, and other variations and modifications may be made without departing from the spirit of the invention as set forth in the claims.
Claims (9)
1. An energy-saving optimization method for an air compressor is characterized by comprising the following steps:
step S1: dividing a monitoring area;
step S2: deploying an unmanned aerial vehicle and planning a flight route of the unmanned aerial vehicle;
step S3: collecting operation data of each group of air compressors and feeding back the operation data in real time;
step S4: starting and allocating an air compressor, and collecting operating data of the air compressor in work in real time;
step S5: and optimizing and adjusting the PLC program of the air compressor, and performing simulation test.
2. The energy-saving optimization method of the air compressor as claimed in claim 1, wherein the specific process of step S1 includes the following steps:
step A1: the method comprises the steps that a worker uploads environmental data to a terminal module of the unmanned aerial vehicle;
step A2: the unmanned aerial vehicle builds a panoramic model of the monitored area according to the environmental data;
step A3: collecting area information by the unmanned aerial vehicle;
step A4: the unmanned aerial vehicle builds a monitoring area comparison model according to the area information;
step A5: comparing the comparison model with the panoramic model, dividing the monitoring area, and marking each divided area asA1、A2、…、An-1、AnN is a natural number;
step A6: and generating a monitoring model corresponding to each area according to the comparison model and the panoramic model.
3. The air compressor energy-saving optimization method according to claim 2, wherein the specific process of planning the flight path of the unmanned aerial vehicle in the step S2 includes the following steps:
step B1: marking the positions of all groups of air compressors in the corresponding area monitoring models;
step B2: each group of unmanned aerial vehicles plans a flight route according to the position of the air compressor in the corresponding monitoring model;
step B3: calculating the time consumption of each group of flight routes;
step B4: selecting a flight route with the shortest time consumption and feeding back the selected flight route to the staff;
step B5: the staff manually adjusts unmanned aerial vehicle's flight route through the computer.
4. The energy-saving optimization method of the air compressor as claimed in claim 1, wherein the specific process of step S3 includes the following steps:
step C1: each group of unmanned aerial vehicles collects various operation data of the air compressors operating in the corresponding areas, and classifies the data according to temperature, operation time, outlet flow of the air compressors and real-time power of the air compressors;
step C2: the data calculation module of each group of unmanned aerial vehicles calculates the energy efficiency of the corresponding air compressor;
step C3: and recording data acquired by each group of unmanned aerial vehicles and calculated into the patrol record table, and sending the patrol record table to the computer in a wireless transmission mode.
5. The energy-saving optimization method of the air compressor as claimed in claim 1, wherein the specific process of step S4 includes the following steps:
step D1: the staff uploads the allocation scheme to a terminal module of the unmanned aerial vehicle;
step D2: allocating the starting-up combination of the air compressor according to an allocation scheme;
step D3: collecting the operation data of the air compressor in the work in real time, and automatically generating an operation record table;
step D4: performing characteristic analysis on data influencing the energy efficiency of the air compressor in the operation record table and extracting the data;
step D5: constructing a deep neural network model, and fitting an energy efficiency curve of the air compressor through the deep neural network model according to the data extracted in the step D4;
step D6: verifying the logic of the energy efficiency curve to be synthesized, modifying the allocation scheme according to the verification result, and simultaneously feeding back the modified allocation scheme to the staff;
step D7: and operating the air compressor according to the modified allocation scheme, and simultaneously feeding back various operating data of the air compressor to the workers.
6. The energy-saving optimization method for the air compressor according to claim 1, wherein in the step S5, the specific process of optimizing and adjusting the PLC program of the air compressor comprises the following steps:
step E1: analyzing various operation data of the air compressor, and extracting variation abnormal data in the operation data;
step E2: the unmanned aerial vehicle collects the occurrence time of abnormal data;
step E3: according to the operation data of the air compressor, carrying out program positioning and marking on the corresponding PLC program;
step E4: and automatically generating an adjusting scheme according to the corresponding PLC program and feeding back the adjusting scheme to the working personnel.
7. The energy-saving optimization method of the air compressor as claimed in claim 6, wherein in the step S5, the simulation test comprises the following specific steps: and the computer carries out simulation test according to the adjustment scheme, simultaneously records the operation data of each group of air compressors in the simulation test, and compares the operation data with the data in the routing inspection recording table to generate an optimized log.
8. The energy-saving optimization method of the air compressor as claimed in claim 3, wherein the formula for calculating the time consumed by each set of flight paths in step B3 is as follows:
h＝L÷s
wherein h represents the flight time consumption of the unmanned aerial vehicle, L represents the total length of the route, and s represents the flight speed of the unmanned aerial vehicle.
9. The energy-saving optimization method for the air compressor according to claim 4, wherein the formula for calculating the energy efficiency of the air compressor in the step C2 is as follows:
N＝M÷f
wherein, N represents the air compressor machine efficiency, and M represents air compressor machine export flow, and f represents the air compressor machine real-time power.
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