CN114035520A - Intelligent supervision system and method for air compression station - Google Patents
Intelligent supervision system and method for air compression station Download PDFInfo
- Publication number
- CN114035520A CN114035520A CN202111255371.0A CN202111255371A CN114035520A CN 114035520 A CN114035520 A CN 114035520A CN 202111255371 A CN202111255371 A CN 202111255371A CN 114035520 A CN114035520 A CN 114035520A
- Authority
- CN
- China
- Prior art keywords
- air compressor
- air
- cloud platform
- module
- compression station
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 230000006835 compression Effects 0.000 title claims abstract description 32
- 238000007906 compression Methods 0.000 title claims abstract description 32
- 238000000034 method Methods 0.000 title claims abstract description 24
- 238000004891 communication Methods 0.000 claims abstract description 29
- 238000012806 monitoring device Methods 0.000 claims abstract description 17
- 238000004458 analytical method Methods 0.000 claims abstract description 11
- 238000003062 neural network model Methods 0.000 claims description 18
- 238000005265 energy consumption Methods 0.000 claims description 15
- 238000001816 cooling Methods 0.000 claims description 12
- 238000013528 artificial neural network Methods 0.000 claims description 9
- 238000012937 correction Methods 0.000 claims description 9
- 238000012545 processing Methods 0.000 claims description 6
- 230000005540 biological transmission Effects 0.000 claims description 3
- 238000004364 calculation method Methods 0.000 claims description 3
- QVFWZNCVPCJQOP-UHFFFAOYSA-N chloralodol Chemical compound CC(O)(C)CC(C)OC(O)C(Cl)(Cl)Cl QVFWZNCVPCJQOP-UHFFFAOYSA-N 0.000 claims description 3
- 238000012549 training Methods 0.000 claims description 3
- 238000012423 maintenance Methods 0.000 description 6
- 238000012544 monitoring process Methods 0.000 description 5
- 230000003993 interaction Effects 0.000 description 4
- 230000002035 prolonged effect Effects 0.000 description 2
- 239000002699 waste material Substances 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 210000001503 joint Anatomy 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000008447 perception Effects 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B19/00—Programme-control systems
- G05B19/02—Programme-control systems electric
- G05B19/418—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]
- G05B19/41865—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 job scheduling, process planning, material flow
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B2219/00—Program-control systems
- G05B2219/30—Nc systems
- G05B2219/32—Operator till task planning
- G05B2219/32252—Scheduling production, machining, job shop
Landscapes
- Engineering & Computer Science (AREA)
- General Engineering & Computer Science (AREA)
- Manufacturing & Machinery (AREA)
- Quality & Reliability (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Automation & Control Theory (AREA)
- Control Of Positive-Displacement Pumps (AREA)
Abstract
The invention relates to an intelligent supervision system and method of an air compression station, belonging to the technical field of intelligent supervision of air compression stations; the technical problem to be solved is as follows: the improvement of the hardware structure of the intelligent supervision system of the air compression station is provided; the technical scheme for solving the technical problems is as follows: the system comprises a communication terminal, a cloud platform, a control end and an equipment operation monitoring device, wherein the equipment operation monitoring device is connected with the control end through a wire, the control end is communicated with the cloud platform, and the cloud platform is communicated with the communication terminal; the equipment operation monitoring device comprises at least one air compressor, each air compressor is provided with a flow meter, an electric energy meter and a camera, and the air compressor, the flow meter and the electric energy meter are respectively connected with a control end through leads; an analysis module for pre-judging the machine state of the air compressors is integrated on the cloud platform, and a prediction model for comprehensively adjusting and scheduling the running of the plurality of air compressors at no-load, unloading and stopping time is integrated on the control end; the invention is applied to the air compressor.
Description
Technical Field
The invention discloses an intelligent supervision system and method for an air compression station, and belongs to the technical field of intelligent supervision of air compression stations.
Background
At present, the management of the air compressor stations used in the market still depends on manual monitoring, and the air compressor stations are managed by manually judging the operation data of the air compressor.
Because the air compressor machine of manual operation opens and stops, when the gas consumption of lower end gas unit increases, the interior operation personnel of station just manual start-up air compressor machine is starting the air compressor machine, and from the operation to normal operating, often need two to three minutes's start-up time, very easily causes the pipe network pressure to drop suddenly, causes the end to use the air pressure low, influences original normal equipment operation. When the gas consumption of a gas unit is reduced, the air pressure of a pipe network is increased, the operation of air compressors is also unloaded, a plurality of air compressors are also unloaded at the same time, and the pulse type gas supply of the pipe network pressure not only causes electric energy waste, but also shortens the service life of equipment.
The operating personnel of the original air compression station only record the operating parameters of a single air compressor, and do not reasonably adjust the operating parameters of the air compressor, so that energy waste is caused, and the equipment is not operated in the working interval with the best efficiency.
The air compression station operating personnel only record the operation machine, maintenance and use condition in the air compression station, do not make a prejudgment on the use actual condition, the maintenance is only the so-called operation time given by a manufacturer to carry out maintenance, and the maintenance is not carried out according to the actual operation condition of the machine, so that some equipment is excessively maintained, some machines are not maintained timely, the energy consumption is increased, and even the machine equipment is damaged.
The operation data of the air compressor can only be checked on the air compressor, the production condition cannot be in butt joint with the management layer, and the platform management of the operation data of the air compression station cannot be carried out.
Disclosure of Invention
In order to overcome the defects in the prior art, the invention aims to solve the technical problems that: an improvement of the hardware structure of an intelligent supervision system of an air compression station is provided.
In order to solve the technical problems, the invention adopts the technical scheme that: an intelligent supervision system of an air compression station comprises a communication terminal, a cloud platform, a control end and an equipment operation monitoring device, wherein the equipment operation monitoring device is connected with the control end through a wire, the control end is communicated with the cloud platform, and the cloud platform is communicated with the communication terminal;
the equipment operation monitoring device comprises at least one air compressor, wherein each air compressor is provided with a flow meter, an electric energy meter and a camera, the air compressor, the flow meter and the electric energy meter are respectively connected with a control end through leads, and the camera is communicated with a communication terminal;
the cloud platform is integrated with an analysis module for pre-judging the machine state of the air compressors, and the control end is integrated with a prediction model for comprehensively adjusting and scheduling the running of the plurality of air compressors in no-load, unloading and stopping time.
The equipment operation monitoring device is also provided with an environment sensing module and cooling equipment, and the environment sensing module and the cooling equipment are respectively connected with the control end through leads.
The environment perception module comprises a temperature sensor and a humidity sensor, and the cooling device is specifically set to be a fan.
The control end comprises a control computer, a man-machine exchange unit and an Internet of things module, the Internet of things module and the man-machine exchange unit are respectively connected with the control computer through leads, and the control computer is communicated with the cloud platform through the Internet of things module;
the prediction model specifically adopts an LSTM neural network model with a correction algorithm to comprehensively adjust and schedule the running no-load, unloading and stopping time of the plurality of air compressors for prediction.
The control computer body adopts any one of a PLC, a singlechip or an industrial personal computer;
the man-machine exchange unit is specifically arranged as a touch display screen;
the internet of things module is specifically set as any one of a wifi module, a G module or an NB-IoT module, a Lora module and a ZigBee module.
The cloud platform is specifically set as a server or a server cluster, a storage module is arranged on the cloud platform and specifically comprises a storage hard disk and a cloud storage device, the operation data of the air compressor are stored in real time and stored in a backup mode, and the analysis processing module specifically adopts a weighted moving average algorithm to predict the machine state of the air compressor.
The communication terminal specifically comprises a PC terminal and a mobile terminal, and the communication terminal is communicated with the cloud platform in a wired or wireless communication mode.
An intelligent supervision method for an air compression station comprises the following steps:
the method comprises the following steps: the method comprises the steps that a flow meter, an electric energy meter and a camera arranged on each air compressor are used for collecting the air consumption, the power consumption and the image data of the environment around the running machine of the air compressor in real time, meanwhile, the environment temperature and the humidity data of the running machine of the air compressor are collected in real time through an environment sensing module, the machine of each air compressor is collected, and the collected data are sent to a control computer of a control end;
step two: the control computer comprehensively analyzes and judges the plurality of air compressors in the air compression station according to the feedback data of each air compressor, controls the loading, unloading and stopping time of each air compressor, and determines the combined loading, unloading and stopping of the plurality of air compressor units by analyzing the energy consumption of each air compressor;
step three: the control computer sends the running data of all the air compressors, the loading, unloading, stopping and air consumption amount, the power consumption and the running environment data to the cloud platform for storage, and meanwhile, the cloud platform judges the machine state of each air compressor through analysis of historical data of the air compressors;
step four: and the control computer schedules the air compressor units in the air compressor station in real time according to the machine state prejudgment result of the cloud platform on each air compressor, stops the air compressor with overhigh energy consumption or fault, and recalculates the scheduling instruction according to the current air compressor capable of being put into operation.
The control steps of the loading, unloading and stopping time of the air compressor by the control computer in the step two are as follows:
establishing an LSTM neural network model, training the neural network through historical loading, no-load and shutdown data of each air compressor, setting a correction algorithm in the neural network, and correcting the current machine running state of each air compressor through the energy consumption of the air compressor within a certain period of time;
predicting the gas consumption at the next moment through an LSTM neural network model, and giving an optimal loading or unloading strategy of the air compressor or the air compressor unit in advance;
wherein the input variables of the LSTM neural network model comprise that of each air compressor: power transmission time, running time, flow during loading, running power consumption, ambient temperature, humidity and machine parameters;
the output variables of the LSTM neural network model include for each air compressor: loading time, unloading time, downtime;
the constraint conditions of the LSTM neural network model comprise that each air compressor: the method comprises the following steps of (1) increasing the air consumption pressure and time at the lower end of an air compressor, starting and stopping frequency of the air compressor, the output air pressure range of the air compressor, the pressure range of a pipe network of the air compressor and the output air pressure range of an air pressure station;
the correction algorithm specifically adopts an iterative calculation method, and corrects the input variable of the LSTM neural network according to the energy consumption condition of the air compressor in the recent period of time, so that the output predicted loading, unloading and shutdown time is corrected.
The cloud platform in the third step is used for prejudging the machine state of the air compressor, specifically, a weighted moving average method is adopted, the specific power of the air compressor is used as the weight, the current machine state of the air compressor is prejudged, and whether the air compressor is maintained or the energy consumption is excessively prejudged according to a prejudgment result.
Compared with the prior art, the invention has the beneficial effects that: the intelligent supervision system of the air compression station provided by the invention collects the air consumption and the power consumption of the air compressor through the flow meter and the electric energy meter, the camera is adopted to carry out video monitoring on the air compressor, the collected parameters are uploaded to the cloud platform through the control end, the cloud platform sends the operation parameter data to the communication terminal, the communication terminal can control the air compressor according to the operation state of the air compressor, the machine state of the air compressor and the idle load, unloading and shutdown time of the air compressor are predicted through the analysis processing module and the prediction model, a solution is provided for monitoring, managing and parameter recording and maintaining of a plurality of air compressors, and the service life of the air compressor is prolonged.
Drawings
The invention is further described below with reference to the accompanying drawings:
FIG. 1 is a schematic diagram of the system of the present invention;
in the figure: the system comprises a communication terminal 1, a PC terminal 2, a mobile terminal 3, a cloud platform 4, a control end 5, an Internet of things module 6, a human-computer interaction unit 7, a control computer 8, an equipment operation monitoring device 9, a cooling device 10, an environment sensing module 11, an air compressor 12, a flow meter 13, an electric energy meter 14 and a camera 15.
Detailed Description
As shown in fig. 1, an intelligent supervision system of an air compression station includes a communication terminal 1, a cloud platform 4, a control end 5 and an equipment operation monitoring device 9, where the equipment operation monitoring device 9 is connected to the control end 5 through a wire, the control end 5 communicates with the cloud platform 4, and the cloud platform 4 communicates with the communication terminal 1;
the equipment operation monitoring device 9 comprises at least one air compressor 12, each air compressor 12 is provided with a flow meter 13, an electric energy meter 14 and a camera 15, wherein the air compressors 12, the flow meters 13 and the electric energy meters 14 are respectively connected with the control end 5 through leads, and the cameras 15 are communicated with the communication terminal 1;
an analysis processing module for pre-judging the machine state of the air compressors is integrated on the cloud platform 4, and a prediction model for comprehensively adjusting and scheduling the running of the plurality of air compressors in no-load, unloading and stopping time is integrated on the control end 5.
The equipment operation monitoring device 9 is also provided with an environment sensing module 11 and a cooling device 10, wherein the environment sensing module 11 and the cooling device 10 are respectively connected with the control end 5 through leads.
The environment sensing module 11 includes a temperature sensor and a humidity sensor, and the cooling device 10 is specifically configured as a fan.
The control end 5 comprises a control computer 8, a man-machine exchange unit 7 and an internet of things module 6, the internet of things module 6 and the man-machine exchange unit 7 are respectively connected with the control computer 8 through leads, and the control computer 8 is communicated with the cloud platform 4 through the internet of things module 6;
the prediction model specifically adopts an LSTM neural network model with a correction algorithm to comprehensively adjust and schedule the running no-load, unloading and stopping time of the plurality of air compressors for prediction.
The control computer 8 specifically adopts any one of a PLC, a singlechip or an industrial personal computer;
the man-machine exchange unit 7 is specifically set as a touch display screen;
the internet of things module 6 is specifically set to be any one of a wifi module, a 4G module or an NB-IoT module, a Lora module and a ZigBee module.
The cloud platform 4 is specifically set as a server or a server cluster, a storage module is arranged on the cloud platform 4 and specifically comprises a storage hard disk and a cloud storage device, the operation data of the air compressor is stored in real time and stored in a backup mode, and the analysis processing module specifically adopts a weighted moving average algorithm to predict the machine state of the air compressor.
The communication terminal 1 specifically comprises a PC terminal 2 and a mobile terminal 3, and the communication terminal 1 communicates with the cloud platform 4 in a wired or wireless communication mode.
The intelligent supervision system of the air compression station has the following specific working process:
the control computer 8 in the control end 5 collects the working state of the equipment operation monitoring device 9, the collected data comprises the air consumption of the air compressor, the power consumption of the air compressor and the operation environment of the air compressor, the air consumption of the air compressor is collected by the flow meter 13, the collected data is converted into electric signals to be transmitted to the control computer 8, the power consumption is collected by the electric energy meter 14, the collected signals are transmitted to the control computer 8, the operation environment comprises the operation temperature and the humidity of the air compressor, the environment sensing module 11 is internally provided with the temperature sensor and the humidity sensor, the temperature sensor converts the collected temperature information into the electric signals to be transmitted to the control computer 8, the humidity sensor converts the collected humidity information into the electric signals to be transmitted to the control computer 8, and the control computer 8 collects the air consumption, the power consumption and the operation environment of the air compressor, The power consumption of the air compressor, the running temperature and the humidity of the air compressor are transmitted to the cloud platform 4 through the Internet of things module 6, the cloud platform 4 transmits the air consumption, the power consumption of the air compressor, the running temperature and the humidity of the air compressor to the communication terminal 1, and management personnel can conveniently monitor the running terminal of the air compressor at the communication terminal.
The control end 5 is further provided with a human-computer interaction unit 7, in this embodiment, the human-computer interaction unit 7 is preferably a touch display screen, and the air consumption of the air compressor, the power consumption of the air compressor, the operating temperature of the air compressor and the humidity can also be displayed on the touch display screen. In this embodiment, can predetermine air consumption, power consumption, temperature and humidity of air compressor machine through human-computer interaction unit 7, in the air compressor machine operation process, if carry out the suggestion of reporting to the police after air consumption, power consumption, temperature and humidity exceed the default. The alarm information can be sent to the communication terminal 1 through the cloud platform 4. When the temperature exceeds a preset value, the control computer 8 drives a fan in the cooling device 10 to operate for cooling.
The communication terminal 1 is also connected with the camera 15 through a network, so that the running state of the air compressor can be checked.
In this embodiment, control computer 8 can save the tolerance, power consumption, temperature and humidity information of air compressor machine to backup to cloud platform 4 is last, makes things convenient for the maintenance and the inquiry in later stage.
The invention discloses an intelligent supervision system of an air compression station, which is characterized in that air consumption and power consumption of an air compressor are collected through a flow meter and an electric energy meter, a camera is adopted to carry out video monitoring on the air compressor, collected parameters are uploaded to a cloud platform through a control end, the cloud platform sends operation parameter data to a communication terminal, the communication terminal can control the air compressor according to the operation state of the air compressor, the intelligent supervision system has a simple structure, hardware support is provided for intelligent monitoring, management and parameter recording and maintenance of a plurality of air compressors, and the service life of the air compressor is prolonged.
The invention also provides an intelligent supervision method of the air compression station, which comprises the following steps:
the method comprises the following steps: the method comprises the steps that a flow meter, an electric energy meter and a camera arranged on each air compressor are used for collecting the air consumption, the power consumption and the image data of the environment around the running machine of the air compressor in real time, meanwhile, the environment temperature and the humidity data of the running machine of the air compressor are collected in real time through an environment sensing module, the machine of each air compressor is collected, and the collected data are sent to a control computer of a control end;
step two: the control computer comprehensively analyzes and judges the plurality of air compressors in the air compression station according to the feedback data of each air compressor, controls the loading, unloading and stopping time of each air compressor, and determines the combined loading, unloading and stopping of the plurality of air compressor units by analyzing the energy consumption of each air compressor;
step three: the control computer sends the running data of all the air compressors, the loading, unloading, stopping and air consumption amount, the power consumption and the running environment data to the cloud platform for storage, and meanwhile, the cloud platform judges the machine state of each air compressor through analysis of historical data of the air compressors;
step four: and the control computer schedules the air compressor units in the air compressor station in real time according to the machine state prejudgment result of the cloud platform on each air compressor, stops the air compressor with overhigh energy consumption or fault, and recalculates the scheduling instruction according to the current air compressor capable of being put into operation.
The control steps of the loading, unloading and stopping time of the air compressor by the control computer in the step two are as follows:
establishing an LSTM neural network model, training the neural network through historical loading, no-load and shutdown data of each air compressor, setting a correction algorithm in the neural network, and correcting the current machine running state of each air compressor through the energy consumption of the air compressor within a certain period of time;
predicting the gas consumption at the next moment through an LSTM neural network model, and giving an optimal loading or unloading strategy of the air compressor or the air compressor unit in advance;
wherein the input variables of the LSTM neural network model comprise that of each air compressor: power transmission time, running time, flow during loading, running power consumption, ambient temperature, humidity and machine parameters;
the output variables of the LSTM neural network model include for each air compressor: loading time, unloading time, downtime;
the constraint conditions of the LSTM neural network model comprise that each air compressor: the method comprises the following steps of (1) increasing the air consumption pressure and time at the lower end of an air compressor, starting and stopping frequency of the air compressor, the output air pressure range of the air compressor, the pressure range of a pipe network of the air compressor and the output air pressure range of an air pressure station;
the correction algorithm specifically adopts an iterative calculation method, and corrects the input variable of the LSTM neural network according to the energy consumption condition of the air compressor in the recent period of time, so that the output predicted loading, unloading and shutdown time is corrected.
The cloud platform in the third step is used for prejudging the machine state of the air compressor, specifically, a weighted moving average method is adopted, the specific power of the air compressor is used as the weight, the current machine state of the air compressor is prejudged, and whether the air compressor is maintained or the energy consumption is excessively prejudged according to a prejudgment result.
It should be noted that, regarding the specific structure of the present invention, the connection relationship between the modules adopted in the present invention is determined and can be realized, except for the specific description in the embodiment, the specific connection relationship can bring the corresponding technical effect, and the technical problem proposed by the present invention is solved on the premise of not depending on the execution of the corresponding software program.
Finally, it should be noted that: the above embodiments are only used to illustrate the technical solution of the present invention, and not to limit the same; while the invention has been described in detail and with reference to the foregoing embodiments, it will be understood by those skilled in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; and the modifications or the substitutions do not make the essence of the corresponding technical solutions depart from the scope of the technical solutions of the embodiments of the present invention.
Claims (10)
1. An intelligent supervision system of an air compression station comprises a communication terminal (1), a cloud platform (4), a control end (5) and an equipment operation monitoring device (9), wherein the equipment operation monitoring device (9) is connected with the control end (5) through a wire, the control end (5) is communicated with the cloud platform (4), and the cloud platform (4) is communicated with the communication terminal (1);
the equipment operation monitoring device (9) comprises at least one air compressor (12), each air compressor (12) is provided with a flow meter (13), an electric energy meter (14) and a camera (15), wherein the air compressors (12), the flow meters (13) and the electric energy meters (14) are respectively connected with the control end (5) through leads, and the cameras (15) are communicated with the communication terminal (1);
an analysis processing module for pre-judging the machine state of the air compressors is integrated on the cloud platform (4), and a prediction model for comprehensively adjusting and scheduling the running of the plurality of air compressors in no-load, unloading and stopping time is integrated on the control end (5).
2. An intelligent supervision system for an air compression station according to claim 1, characterised in that: the equipment operation monitoring device (9) is also internally provided with an environment sensing module (11) and a cooling device (10), wherein the environment sensing module (11) and the cooling device (10) are respectively connected with the control end (5) through leads.
3. An intelligent supervision system for an air compression station according to claim 2, characterised in that: the environment sensing module (11) comprises a temperature sensor and a humidity sensor, and the cooling device (10) is specifically set to be a fan.
4. An intelligent supervision system for an air compression station according to claim 1, characterised in that: the control end (5) comprises a control computer (8), a man-machine exchange unit (7) and an Internet of things module (6), the Internet of things module (6) and the man-machine exchange unit (7) are respectively connected with the control computer (8) through leads, and the control computer (8) is communicated with the cloud platform (4) through the Internet of things module (6);
the prediction model specifically adopts an LSTM neural network model with a correction algorithm to comprehensively adjust and schedule the running no-load, unloading and stopping time of the plurality of air compressors for prediction.
5. An intelligent supervision system for an air compression station according to claim 4, characterised in that: the control computer (8) specifically adopts any one of a PLC, a singlechip or an industrial personal computer;
the man-machine exchange unit (7) is specifically set as a touch display screen;
the Internet of things module (6) is specifically set to be any one of a wifi module, a 4G module or an NB-IoT module, a Lora module and a ZigBee module.
6. An intelligent supervision system for an air compression station according to claim 1, characterised in that: the cloud platform (4) is specifically set as a server or a server cluster, a storage module is arranged on the cloud platform (4), specifically comprises a storage hard disk and a cloud storage device, the storage hard disk and the cloud storage device are used for storing the running data of the air compressor in real time and storing the running data in a backup mode, and the analysis processing module specifically adopts a weighted moving average algorithm to predict the machine state of the air compressor.
7. An intelligent supervision system for an air compression station according to claim 1, characterised in that: the communication terminal (1) specifically comprises a PC terminal (2) and a mobile terminal (3), and the communication terminal (1) is communicated with the cloud platform (4) in a wired or wireless communication mode.
8. An intelligent supervision method of an air compression station is characterized in that: the method comprises the following steps:
the method comprises the following steps: the method comprises the steps that a flow meter (13), an electric energy meter (14) and a camera (15) which are arranged on each air compressor (12) are used for collecting the air consumption, the power consumption and the image data of the environment around a machine in which the air compressor operates in real time, meanwhile, an environment sensing module is used for collecting the environment temperature and humidity data in which the air compressor operates in real time, the machine of each air compressor is collected, and the collected data are sent to a control computer (8) of a control end (5);
step two: the control computer (8) comprehensively analyzes and judges the plurality of air compressors in the air compression station according to the feedback data of each air compressor, controls the loading, unloading and stopping time of each air compressor, and determines the combined loading, unloading and stopping of the plurality of air compressor units by analyzing the energy consumption of each air compressor;
step three: the control computer (8) sends the running data of all the air compressors, the loading, unloading, stopping and air consumption amount, the power consumption and the running environment data to the cloud platform (4) for storage, and meanwhile, the cloud platform (4) judges the machine state of each air compressor in advance through analysis of historical data of the air compressors;
step four: and the control computer (8) schedules the air compressor units in the air pressure station in real time according to the machine state prejudgment result of the cloud platform (4) on each air compressor, stops the air compressor with overhigh energy consumption or fault, and recalculates the scheduling instruction according to the air compressor which can be put into operation at present.
9. An intelligent supervision method for an air compression station according to claim 8, characterized in that: the control steps of the loading, unloading and stopping time of the air compressor by the control computer (8) in the step two are as follows:
establishing an LSTM neural network model, training the neural network through historical loading, no-load and shutdown data of each air compressor, setting a correction algorithm in the neural network, and correcting the current machine running state of each air compressor through the energy consumption of the air compressor within a certain period of time;
predicting the gas consumption at the next moment through an LSTM neural network model, and giving an optimal loading or unloading strategy of the air compressor or the air compressor unit in advance;
wherein the input variables of the LSTM neural network model comprise that of each air compressor: power transmission time, running time, flow during loading, running power consumption, ambient temperature, humidity and machine parameters;
the output variables of the LSTM neural network model include for each air compressor: loading time, unloading time, downtime;
the constraint conditions of the LSTM neural network model comprise that each air compressor: the method comprises the following steps of (1) increasing the air consumption pressure and time at the lower end of an air compressor, starting and stopping frequency of the air compressor, the output air pressure range of the air compressor, the pressure range of a pipe network of the air compressor and the output air pressure range of an air pressure station;
the correction algorithm specifically adopts an iterative calculation method, and corrects the input variable of the LSTM neural network according to the energy consumption condition of the air compressor in the recent period of time, so that the output predicted loading, unloading and shutdown time is corrected.
10. An intelligent supervision method for an air compression station according to claim 8, characterized in that: the cloud platform (4) in the third step is used for prejudging the machine state of the air compressor, specifically, a weighted moving average method is adopted, the specific power of the air compressor is used as the weight, the current machine state of the air compressor is prejudged, and whether the air compressor is maintained or excessively consumes energy is prejudged according to the prejudging result.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111255371.0A CN114035520A (en) | 2021-10-27 | 2021-10-27 | Intelligent supervision system and method for air compression station |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111255371.0A CN114035520A (en) | 2021-10-27 | 2021-10-27 | Intelligent supervision system and method for air compression station |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN114035520A true CN114035520A (en) | 2022-02-11 |
Family
ID=80135526
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202111255371.0A Pending CN114035520A (en) | 2021-10-27 | 2021-10-27 | Intelligent supervision system and method for air compression station |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114035520A (en) |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115079634A (en) * | 2022-07-15 | 2022-09-20 | 重庆中环建设有限公司 | Air compression station variable frequency speed regulation system and method based on 5G Internet of things |
| CN115167319A (en) * | 2022-08-06 | 2022-10-11 | 广东鑫钻节能科技股份有限公司 | Air compressor start and stop control system and control method based on Internet of things cloud platform |
| CN115163473A (en) * | 2022-06-22 | 2022-10-11 | 上海施耐德日盛机械集团科技有限公司 | Real-time online cloud detection system for air compressor |
| CN115263735A (en) * | 2022-08-06 | 2022-11-01 | 广东鑫钻节能科技股份有限公司 | Data centralized management and control platform based on operation of air compression station |
| TWI806611B (en) * | 2022-04-25 | 2023-06-21 | 緯創資通股份有限公司 | Optimization systems and methods for operating air compressor groups |
-
2021
- 2021-10-27 CN CN202111255371.0A patent/CN114035520A/en active Pending
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| TWI806611B (en) * | 2022-04-25 | 2023-06-21 | 緯創資通股份有限公司 | Optimization systems and methods for operating air compressor groups |
| CN115163473A (en) * | 2022-06-22 | 2022-10-11 | 上海施耐德日盛机械集团科技有限公司 | Real-time online cloud detection system for air compressor |
| CN115079634A (en) * | 2022-07-15 | 2022-09-20 | 重庆中环建设有限公司 | Air compression station variable frequency speed regulation system and method based on 5G Internet of things |
| CN115167319A (en) * | 2022-08-06 | 2022-10-11 | 广东鑫钻节能科技股份有限公司 | Air compressor start and stop control system and control method based on Internet of things cloud platform |
| CN115263735A (en) * | 2022-08-06 | 2022-11-01 | 广东鑫钻节能科技股份有限公司 | Data centralized management and control platform based on operation of air compression station |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN114035520A (en) | Intelligent supervision system and method for air compression station | |
| US8046180B2 (en) | Model-based determination of power source replacement in wireless and other devices | |
| CN104820413B (en) | A kind of energy-conservation cluster control system of pneumatics station | |
| CN201129829Y (en) | Central air conditioner changeable temperature difference energy-saving control system | |
| CN110299874B (en) | WIFI-based intelligent control system for switched reluctance motor | |
| CN111950201B (en) | Full life cycle monitoring system and method for sealing device for pump | |
| CN117171590A (en) | Intelligent driving optimization method and system for motor | |
| CN116914939A (en) | Big data-based energy storage management system, method and energy storage power station system | |
| CN115081723A (en) | Intelligent factory energy management system and management method | |
| CN117350507A (en) | Virtual power plant scheduling system | |
| CN117650628B (en) | Energy efficiency management system based on self-adaptive preconditioning scene | |
| CN108332355A (en) | A kind of energy conserving system and power-economizing method for communication base station | |
| CN113606111A (en) | Energy-saving protection system based on air compression station and implementation method thereof | |
| CN112711680A (en) | Air compression intelligent operation and maintenance method based on Internet of things | |
| CN112032939A (en) | Control method of heat exchange system | |
| CN216052788U (en) | Supervision platform of air compression station | |
| CN115933398A (en) | Air compression system linkage control energy-saving method and system based on Internet of things | |
| CN112610459A (en) | Intelligent group control energy-saving system and method for air compressors | |
| CN114718855A (en) | Compressed air medium intelligent system prediction system | |
| CN211823008U (en) | Self-optimizing group control system of air-cooled modular machine | |
| CN114183888A (en) | Intelligent control system of 5G base station air conditioner based on narrowband Internet of things | |
| CN117249537B (en) | Virtual power plant scheduling and control system and method based on central air conditioner | |
| US20230376093A1 (en) | System and method for proactively controlling an environmental condition in a server rack of a data center based at least in part on server load | |
| CN111427268B (en) | Coal mine energy optimization control system and optimization control method | |
| CN212060891U (en) | Coal mine energy optimization control system |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication |