CN114542444A - Intelligent monitoring method and system for air compressor - Google Patents
Intelligent monitoring method and system for air compressor Download PDFInfo
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- CN114542444A CN114542444A CN202210051742.1A CN202210051742A CN114542444A CN 114542444 A CN114542444 A CN 114542444A CN 202210051742 A CN202210051742 A CN 202210051742A CN 114542444 A CN114542444 A CN 114542444A
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F04—POSITIVE - DISPLACEMENT MACHINES FOR LIQUIDS; PUMPS FOR LIQUIDS OR ELASTIC FLUIDS
- F04B—POSITIVE-DISPLACEMENT MACHINES FOR LIQUIDS; PUMPS
- F04B51/00—Testing machines, pumps, or pumping installations
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- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
- Y02P90/02—Total factory control, e.g. smart factories, flexible manufacturing systems [FMS] or integrated manufacturing systems [IMS]
Abstract
The invention discloses an intelligent monitoring method and system for air compressors, which are used for obtaining basic information of a first air compressor; performing pressure acquisition on a first air compressor through a pressure acquisition device to obtain a first pressure set; carrying out temperature acquisition on a first air compressor to obtain a first temperature set; acquiring a first image of the air compressor to obtain a first image acquisition result, and acquiring a first correction parameter according to the image acquisition result and the basic information; establishing an abnormal parameter comparison model, inputting a first temperature set and a first pressure set into the abnormal parameter comparison model, and acquiring first abnormal information; correcting the first abnormal information through the first correction parameter to obtain a second abnormal parameter; and carrying out abnormity early warning of the first air compressor based on the second abnormity information. The technical problem that in the process of monitoring the air compressor in the prior art, data acquisition and analysis cannot be accurately carried out in real time, so that the problem positioning cannot be accurately carried out due to untimely monitoring of the air compressor is solved.
Description
Technical Field
The invention relates to the field related to intelligent monitoring, in particular to an intelligent monitoring method and system for an air compressor.
Background
The air compressor is a device capable of compressing gas, the electric energy consumption of the air compressor in China at present reaches about 10% of the energy consumption of the whole industry, the price of one air compressor is in the range of hundreds of thousands to tens of thousands yuan, and thousands of yuan is needed for maintenance once. The user not only needs the safe and stable operation of air compressor machine, expects simultaneously can effectual reduction air compressor machine's running cost, therefore in time carries out the unusual early warning of operational facilities and is just particularly important. In the prior art, parameters need to be observed on a panel of an equipment controller in real time, manual monitoring cannot be carried out for 24 hours, and when equipment breaks down, a fault point cannot be accurately and rapidly located.
However, in the process of implementing the technical scheme of the invention in the application, the technology at least has the following technical problems:
in the process of monitoring the air compressor in the prior art, data acquisition and analysis can not be accurately carried out in real time, so that the technical problems that the problem location cannot be accurately carried out due to untimely monitoring of the air compressor occur.
Disclosure of Invention
The application solves the technical problems that in the process of monitoring the air compressor in the prior art, data collection and analysis cannot be accurately carried out in real time, so that the problem positioning cannot be accurately carried out due to untimely monitoring of the air compressor, the data collection of the air compressor can be timely carried out, data analysis is carried out by combining with fault air compressor data, and the technical effect of timely and accurately carrying out fault analysis and early warning of the air compressor is achieved.
In view of the above problems, the present application provides an intelligent monitoring method and system for an air compressor.
In a first aspect, the present application provides an intelligent monitoring method for an air compressor, the method is applied to an intelligent monitoring system, the intelligent monitoring system is in communication connection with a pressure acquisition device, a temperature acquisition device and an image acquisition device, and the method includes: acquiring basic information of a first air compressor; performing pressure acquisition on the first air compressor through the pressure acquisition device to obtain a first pressure set; acquiring the temperature of the first air compressor through the temperature acquisition device to obtain a first temperature set; acquiring an image of the first air compressor through the image acquisition device to obtain a first image acquisition result, and acquiring a first correction parameter according to the image acquisition result and the basic information; establishing an abnormal parameter comparison model, inputting the first temperature set and the first pressure set into the abnormal parameter comparison model, and obtaining first abnormal information; correcting the first abnormal information through the first correction parameter to obtain a second abnormal parameter; and carrying out abnormity early warning of the first air compressor based on the second abnormity information.
On the other hand, this application still provides an air compressor machine intelligence monitored control system, the system includes: the first obtaining unit is used for obtaining basic information of the first air compressor; the second obtaining unit is used for carrying out pressure collection on the first air compressor through a pressure collecting device to obtain a first pressure set; the third obtaining unit is used for carrying out temperature collection on the first air compressor through a temperature collecting device to obtain a first temperature set; the fourth obtaining unit is used for carrying out image acquisition on the first air compressor through an image acquisition device, obtaining a first image acquisition result and obtaining a first correction parameter according to the image acquisition result and the basic information; a fifth obtaining unit, configured to construct an abnormal parameter comparison model, input the first temperature set and the first pressure set into the abnormal parameter comparison model, and obtain first abnormal information; a sixth obtaining unit, configured to correct the first abnormal information according to the first correction parameter, and obtain a second abnormal parameter; and the first early warning unit is used for carrying out abnormity early warning on the first air compressor based on the second abnormity information.
In a third aspect, the present invention provides an electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the steps of the method of the first aspect when executing the program.
In a fourth aspect, the present application provides a computer program product comprising a computer program and/or instructions which, when executed by a processor, performs the steps of the method of any one of the first aspect.
One or more technical solutions provided in the present application have at least the following technical effects or advantages:
acquiring basic information of air compressor equipment, and acquiring pressure data of the air compressor equipment through a pressure acquisition device to obtain a first pressure set; acquiring the temperature of the air compressor equipment through temperature acquisition equipment to obtain a first temperature set; the image acquisition device acquires images of the air compressor equipment to obtain a first image acquisition result, wherein the first image acquisition result is an analysis result of the environment of the air compressor equipment, an abnormal parameter comparison model is input based on the first pressure set and the first temperature set to obtain a first abnormal comparison result of the air compressor, a first correction parameter is obtained through the environment analysis result and the basic information, the first abnormal comparison result is corrected based on the first correction parameter to obtain a second abnormal parameter, and the first air compressor is subjected to abnormal early warning based on the second abnormal parameter to timely acquire data of the air compressor, analyze the data by combining with data of a fault air compressor and then timely and accurately perform fault analysis and early warning of the air compressor.
The foregoing description is only an overview of the technical solutions of the present application, and the present application can be implemented according to the content of the description in order to make the technical means of the present application more clearly understood, and the following detailed description of the present application is given in order to make the above and other objects, features, and advantages of the present application more clearly understandable.
Drawings
Fig. 1 is a schematic flow chart of an intelligent monitoring method for an air compressor according to the present application;
fig. 2 is a schematic flow diagram illustrating a loading and running time early warning process of the intelligent air compressor monitoring method according to the present application;
fig. 3 is a schematic flow chart of equipment rotation in the intelligent air compressor monitoring method according to the present application;
fig. 4 is a schematic flow chart of the method for intelligently monitoring an air compressor according to the present application for obtaining the first abnormal information;
fig. 5 is a schematic structural diagram of an intelligent air compressor monitoring system according to the present application;
fig. 6 is a schematic structural diagram of an electronic device according to the present application.
Description of reference numerals: a first obtaining unit 11, a second obtaining unit 12, a third obtaining unit 13, a fourth obtaining unit 14, a fifth obtaining unit 15, a sixth obtaining unit 16, a first warning unit 17, an electronic device 50, a processor 51, a memory 52, an input device 53, and an output device 54.
Detailed Description
The application solves the technical problems that in the process of monitoring the air compressor, data collection and analysis cannot be accurately carried out in real time, and the problem positioning cannot be accurately carried out due to untimely monitoring of the air compressor in the prior art, achieves the technical effects of timely carrying out data collection of the air compressor, carrying out data analysis by combining with data of a fault air compressor and further carrying out fault analysis and early warning of the air compressor timely and accurately. Embodiments of the present application are described below with reference to the accompanying drawings. As can be appreciated by those skilled in the art, with the development of technology and the emergence of new scenarios, the technical solutions provided in the present application are also applicable to similar technical problems.
The terms "first," "second," and the like in the description and in the claims of the present application and in the above-described drawings are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the terms so used are interchangeable under appropriate circumstances and are merely descriptive of the manner in which objects of the same nature are distinguished in the embodiments of the application. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of elements is not necessarily limited to those elements, but may include other elements not expressly listed or inherent to such process, method, article, or apparatus.
Summary of the application
The air compressor is a device capable of compressing gas, the electric energy consumption of the air compressor in China at present reaches about 10% of the energy consumption of the whole industry, the price of one air compressor is in the range of hundreds of thousands to tens of thousands yuan, and thousands of yuan is needed for maintenance once. The user not only needs the safe and stable operation of air compressor machine, expects simultaneously can effectual reduction air compressor machine's running cost, therefore it is very important to carry out the unusual early warning of operation equipment in time. In the prior art, continuous data cannot be acquired by manual inspection and meter reading, post-analysis is difficult to perform, and problems of problem positioning are solved in time.
In view of the above technical problems, the technical solution provided by the present application has the following general idea:
the application provides an intelligent monitoring method for an air compressor, which is applied to an intelligent monitoring system, wherein the intelligent monitoring system is in communication connection with a pressure acquisition device, a temperature acquisition device and an image acquisition device, and the method comprises the following steps: acquiring basic information of a first air compressor; performing pressure acquisition on the first air compressor through the pressure acquisition device to obtain a first pressure set; acquiring the temperature of the first air compressor through the temperature acquisition device to obtain a first temperature set; acquiring an image of the first air compressor through the image acquisition device to obtain a first image acquisition result, and acquiring a first correction parameter according to the image acquisition result and the basic information; establishing an abnormal parameter comparison model, inputting the first temperature set and the first pressure set into the abnormal parameter comparison model, and obtaining first abnormal information; correcting the first abnormal information through the first correction parameter to obtain a second abnormal parameter; and carrying out abnormity early warning of the first air compressor based on the second abnormity information.
Having thus described the general principles of the present application, various non-limiting embodiments thereof will now be described in detail with reference to the accompanying drawings.
Example one
As shown in fig. 1, the present application provides an intelligent monitoring method for an air compressor, the method is applied to an intelligent monitoring system, the intelligent monitoring system is in communication connection with a pressure acquisition device, a temperature acquisition device and an image acquisition device, and the method includes:
step S100: acquiring basic information of a first air compressor;
step S200: performing pressure acquisition on the first air compressor through the pressure acquisition device to obtain a first pressure set;
particularly, the intelligent monitoring system is a system for performing air compressor parameter real-time acquisition and analysis and air compressor early warning, and the intelligent monitoring system is in communication connection with the pressure acquisition device, the temperature acquisition device and the image acquisition device and can perform mutual data interaction. The pressure acquisition device is equipment for carrying out pressure acquisition and measurement composed of pressure sensors, generally speaking, the pressure sensors comprise capacitance type, diffused silicon type, ceramic type, strain gauge type and the like, and high-temperature pressure sensors of corresponding models are selected according to the acquisition precision requirement and the distribution range of pressure. The temperature acquisition device is a device which is composed of temperature sensors and can acquire temperature, and the temperature sensors comprise resistance sensors and thermocouple sensors. And according to the measurement precision requirement, combining the actual cylinder exhaust temperature to select the temperature sensor. The image acquisition device is equipment capable of acquiring images, and generally uses an industrial camera or a monitoring camera. And reading information of the first air compressor through the intelligent monitoring system to obtain basic information of the first air compressor, wherein the basic information comprises production information, use information, volume, air displacement, preset exhaust pressure, air temperature and air pressure information of each stage of cylinder, working power, working mode and the like of the first air compressor. Through the acquisition of the basic information, data support is provided for the subsequent accurate analysis of the first air compressor.
Further, the pressure acquisition devices are distributed in the cylinders of all stages and at the output end of the first air compressor, and are used for acquiring the internal pressure and the output pressure of the cylinders of all stages in real time. And the models of the pressure acquisition devices of the cylinders at all levels are different according to different pressure characteristics of the cylinders at all levels. The process of acquiring the pressure signals by the pressure acquisition device comprises the process of acquiring continuous pressure signals and acquiring pressure signals at preset time intervals, each pressure signal is provided with an identification of an acquisition position and an acquisition time, and the first pressure set is obtained according to the acquired set of the pressure signals.
Step S300: acquiring the temperature of the first air compressor through the temperature acquisition device to obtain a first temperature set;
step S400: acquiring an image of the first air compressor through the image acquisition device to obtain a first image acquisition result, and acquiring a first correction parameter according to the image acquisition result and the basic information;
specifically, the temperature acquisition devices are arranged at the positions of the cylinders at all levels, and are used for acquiring the real-time temperature of the operation process of the first air compressor. And under the condition of no special acquisition instruction, carrying out temperature acquisition with the same rule as the pressure acquisition device, wherein each temperature acquisition data has a temperature position identifier, a time identifier and the like. And obtaining the first temperature set according to the continuous plasticizer set and the distribution data set acquired by the temperature acquisition device. The image acquisition device is acquisition equipment for estimating the actual environment of the first air compressor, the image acquisition device is arranged according to the position of the first air compressor, the image acquisition of the first air compressor is carried out according to the image acquisition device, the estimation of environmental influence factors of the first air compressor is carried out according to the acquisition result of the image, and the environmental change of the air compressor caused by other factors, such as the sealing of the environment of the air compressor caused by human factors, and the like, such as whether the first air compressor is irradiated by sunlight, whether the environment is humid, seeper water and air are not easy to circulate, and the like are also included. According to a first image set acquired by an image acquisition device, the environment where the first air compressor is located is evaluated to obtain a first real-time environment evaluation result, and a first correction parameter is obtained based on the first real-time environment evaluation result and the environment of the first air compressor requiring operation. Through the acquisition of the first correction parameter, data support is provided for subsequent fault location of the running state of the air compressor, and further the technical effect of more accurate fault location can be achieved.
Step S500: establishing an abnormal parameter comparison model, inputting the first temperature set and the first pressure set into the abnormal parameter comparison model, and obtaining first abnormal information;
step S600: correcting the first abnormal information through the first correction parameter to obtain a second abnormal parameter;
step S700: and carrying out abnormity early warning of the first air compressor based on the second abnormity information.
Specifically, the abnormal parameter comparison model is a model for parameter abnormality detection obtained by machine learning of an expert system, the abnormal parameter comparison model is a model obtained after training processing by using operation data of equipment of the same type of the first air compressor as basic data and using abnormal data and a problem analysis result for processing the abnormal data as identification data, fault matching positioning is performed according to the abnormality of temperature and pressure information at different positions and the magnitude of abnormal numerical values, when the output result of the abnormal parameter comparison model meets a predetermined requirement, the training of the model is ended, the collected first temperature set and the collected first pressure set are input into the abnormal parameter comparison model, and the characteristics of continuous change of temperature and the parameters collected at intervals, continuous change of pressure and interval change are based on, comparing whether the parameter characteristics of the current first air compressor are similar to or consistent with those of the historical data, and temporarily considering that the current first air compressor is in a normal state when the comparison result does not have similar or high-matching-degree analog data; when the comparison result has similar or higher matching degree analog data, temporarily considering that the current state of the first air compressor is abnormal; and performing abnormal analog positioning according to the comparison result to obtain the first abnormal information. And correcting the first abnormal information according to a first correction parameter analyzed by the environmental information. For example, when the first abnormal information is that the internal temperature of the air compressor is abnormal and the matched abnormal reason is load operation, the obtained first correction parameter is direct sunlight and air temperature rise in the environment, the internal temperature abnormality cannot be determined as matched load operation and also includes environmental variation factors, at this time, the first abnormality information should be corrected according to the degree of the environmental influence, the matching result is divided into two parts of abnormality of environmental influence and abnormality of load operation, second abnormal information is obtained according to the correction result, the operation of the first air compressor is monitored and early warned based on the second abnormal information, so that the data acquisition of the air compressor is carried out in time, and data analysis is carried out by combining with the data of the fault air compressor, so that the technical effect of carrying out fault analysis and early warning on the air compressor timely and accurately is achieved.
Further, as shown in fig. 2, step S800 of the present application further includes:
step S810: obtaining equipment operation parameters of the first air compressor;
step S820: carrying out equipment loading and running time parameter extraction according to the equipment running parameters to obtain first loading and running time;
step S830: obtaining historical equipment operating parameters of the first air compressor, and obtaining first abnormal identification parameters based on abnormal evaluation of the historical equipment operating parameters on the first loading and operating time;
step S840: and after the second abnormal information is identified based on the first abnormal identification parameter, performing abnormal early warning on the first air compressor.
Specifically, the intelligent monitoring device is in communication connection with the first air compressor and can perform mutual data transmission, the intelligent monitoring device is used for acquiring real-time operation parameters of the first air compressor, namely the operation parameters of the device comprise operation power information, set power information and the like, starting time, starting state and the like, first loading and running time is obtained according to the operation parameters of the device, the first loading and running and the first running time reflect the current starting state of the first air compressor, and whether the air compressor is correctly used or not or whether appropriate receiver capacity exists to meet the requirement can be determined by evaluating the time. And acquiring data of first loading and running time when the first air compressor performs historical running normal, and acquiring historical running parameters of the equipment. Eliminating the maximum value and the minimum value of loading and running time in the historical running parameters of the equipment, carrying out average value solving on the residual data, carrying out deviation comparison on the first loading and running time and the historical data based on the average value solving result, and obtaining the first abnormal identification parameter according to the degree of deviation; the second abnormal information identification is carried out based on the first abnormal identification parameters, the heritage early warning is carried out based on the second abnormal information after the abnormal identification is carried out, the unfolding analysis is carried out by starting the abnormal dimensionality, data support of multiple dimensionalities is provided for more accurately carrying out the abnormal analysis, and then a foundation is tamped for carrying out the timely abnormal analysis and detection.
Further, as shown in fig. 3, step S900 of the present application further includes:
step S910: obtaining first running time and first air supply quantity information of the first air compressor;
step S920: obtaining a first evaluation coefficient of the first air compressor according to the basic information and the first air supply amount information;
step S930: obtaining a first comprehensive evaluation parameter of the first air compressor based on the first evaluation coefficient and the first running time;
step S940: and carrying out equipment rotation of the first air compressor based on the first comprehensive evaluation parameter.
Specifically, in the operation process of an air compressor in an enterprise, a plurality of air compressors are generally required to be matched to complete the supply of the actual air consumption. On the premise of ensuring the supply of the preset air consumption, in order to ensure the normal work of the air compressors, a plurality of reserved air compressors are required to be reserved for the rotation of the equipment, so that the faults and the abnormity of the equipment caused by the operation fatigue of single equipment are avoided. In order to enable each device to be capable of performing device rotation more uniformly and scientifically, the first operation time and the first air supply quantity information of the first air compressor are collected, the first air compressor is subjected to load assessment according to the basic information of the first air compressor, and the first assessment coefficient is obtained according to an assessment result, wherein the first assessment coefficient reflects the load state of the first air compressor in current air supply quantity. And obtaining comprehensive evaluation parameters of the first air compressor according to the first evaluation coefficient and the first running time.
Further, the air compressors which finish the supply of the actual air consumption all pass through the rules, the comprehensive evaluation parameters of the air compressors are obtained, and the standby machines are sequentially alternated according to the numerical values of the comprehensive evaluation parameters of the air compressors. Through the comprehensive evaluation parameters, the equipment is scientific and reasonable in turn, the equipment state of the air compressor is guaranteed, and the technical effect of improving the running stability of the equipment is achieved.
Further, as shown in fig. 4, step S600 of the present application further includes:
step S610: obtaining a first sample data set, wherein samples of the first sample data set are all device data with the same parameters as those of the first air compressor, and each group of data in the first sample data set has identification information of an abnormal result;
step S620: inputting the parameter data in the first sample data set into an abnormal parameter comparison model, performing supervised learning of the abnormal parameter comparison model through the identification information, and ending the training process of the abnormal parameter comparison model when the output result of the abnormal parameter comparison model meets a first preset requirement;
step S630: inputting the first temperature set and the first pressure set into the abnormal parameter comparison model to obtain a first matching result and first matching degree information;
step 640: and acquiring the first abnormal information according to the first matching result and the first matching degree information.
Specifically, the first sample data set is a set of device operation data of the same type as the first air compressor, the first sample data set includes both normal operation data of each device and parameter data in an abnormal state, and each set of data in the first sample data set has identification information of an abnormal result/a normal operation result. Dividing the first sample data set into a sample set and a test set, performing supervision training on an abnormal parameter comparison model based on the sample set, performing testing on the abnormal parameter comparison model based on the test set, finishing training on the abnormal parameter comparison model when a test result meets a preset stability requirement, inputting the first temperature set and the first pressure set into the abnormal parameter comparison model at the moment, and obtaining a first matching result and first matching degree information, wherein the first matching result is a matched abnormal type, and the first matching degree is the matching degree of a parameter state of the first air compressor and the matched abnormal type. And acquiring the first abnormal information based on the first matching result and the first matching degree information, and constraining the first matching degree and the first matching result to make the finally acquired first abnormal information more rigorous, so that the first abnormal information is more accurate, and a basis is provided for accurate early warning and tamping.
Further, step S400 of the present application further includes:
step S410: acquiring environmental parameter information according to the image acquisition result;
step S420: performing environmental abnormality degree evaluation on the first air compressor based on the environmental parameter information and the basic information to obtain a first evaluation result;
step S430: and obtaining a first correlation influence coefficient according to the first evaluation result and the first temperature set, and obtaining the first correction parameter based on the first correlation influence coefficient.
Specifically, according to the image acquisition result, the environment where the first air compressor is located is analyzed, including spatial position, spatial tightness, spatial size, whether the first air compressor is directly irradiated by sunlight at certain time nodes, rarefaction condition of air, humidity and the like, the environment parameter information is obtained based on the acquired information, the preset requirement information of installation is obtained according to the basic information of the first air compressor, the environment influence degree of the current environment information is evaluated based on the preset requirement information, and a first evaluation result is obtained. The first evaluation result can cause direct temperature abnormality of the first air compressor or cause load increase of the first air compressor, so that temperature abnormality of the first air compressor is indirectly caused, a first correlation influence coefficient of the temperature influence of the current environment on the first air compressor is obtained according to the first evaluation result, and the first correction parameter is obtained based on the first correlation influence coefficient. Through the acquisition of the first correlation influence coefficient, more accurate data support of correlation influence is provided for accurate analysis of temperature abnormity, and further a foundation is provided for accurate abnormity positioning and tamping of the first air compressor.
Further, step S1000 of the present application further includes:
step S1010: obtaining a maintenance history of the first air compressor;
step S1020: extracting early warning historical information of the first air compressor to obtain first early warning frequency information and first early warning grade information;
step S1030: constructing a first weight distribution evaluation parameter, and performing weight distribution of the first early warning frequency information and the first early warning grade information based on the first weight distribution evaluation parameter to obtain a first weight distribution result;
step S1040: obtaining a first maintenance evaluation index based on the first early warning frequency information, the first early warning grade information and the first weight distribution result, and obtaining a first maintenance reminding time based on the first maintenance evaluation index and the maintenance history record;
step S1050: and sending the first maintenance reminding time to first equipment.
Specifically, the early warning level is an early warning rule classified according to different severity degrees of early warning. Through intelligent monitoring system calls the historical data of first air compressor machine maintenance, obtains maintenance historical record. Through the intelligent monitoring system, early warning historical information of the first air compressor is extracted, the first early warning frequency information and the first early warning grade information are obtained according to the extraction result, and the first early warning frequency information and the first early warning grade information comprise two parts, namely the early warning condition before maintenance and the early warning condition after maintenance. The first weight distribution evaluation parameter is preset parameters of frequency information for early warning and level information weight distribution for early warning, the first early warning frequency information and the first early warning level information are subjected to weight distribution based on the first weight distribution evaluation parameter to obtain a first weight distribution result, a first maintenance evaluation index for maintenance is obtained based on the first weight distribution result and the frequency parameters and the level parameters for early warning, the current state of the first air compressor is evaluated based on the first maintenance evaluation index and the maintenance history record, the first maintenance reminding time is selected according to the state evaluation result, and the first maintenance reminding time is sent to first equipment, wherein the first equipment is a computer or a mobile phone/other communication equipment of personnel in charge of the first air compressor.
Further, step S1100 of the present application further includes:
step S1110: acquiring first gas demand information;
step S1120: acquiring basic equipment information of first combined equipment, wherein the first combined equipment comprises the first air compressor;
step S1130: performing energy consumption evaluation of the gas supply combination of the equipment according to the equipment basic information and the first gas demand information to obtain a first energy consumption evaluation result;
step S1140: and coordinating the operation parameters of the first air compressor based on the first energy consumption evaluation result.
Specifically, the first gas demand information is total gas demand information of an enterprise, air compressor equipment is called according to the total gas demand information to obtain basic information of each called air compressor equipment, the first air compressor is one of the first combined equipment, the gas production rate responsible for the equipment of each air compressor is combined and distributed on the premise that the normal operation of each air compressor is met to obtain a first combined distribution set, the total energy consumption is calculated by combining the basic information of each air compressor equipment on the basis of the combined distribution set, the total energy consumption is calculated on all combined results of the energy production rate meeting the normal operation of each air compressor, the energy consumption is compared according to the total energy consumption calculation results to obtain a distribution combination of the gas production rate with the minimum energy consumption, and the operation parameters of the first air compressor are coordinated on the basis of the distribution combination, the technical effect of reducing the total energy consumption is achieved on the premise of meeting the requirement of total gas supply and ensuring normal operation of equipment.
In summary, the intelligent monitoring method and system for the air compressor provided by the application have the following technical effects:
1. acquiring basic information of air compressor equipment, and acquiring pressure data of the air compressor equipment through a pressure acquisition device to obtain a first pressure set; acquiring the temperature of the air compressor equipment through temperature acquisition equipment to obtain a first temperature set; the image acquisition device is used for acquiring images of the air compressor equipment to obtain a first image acquisition result, wherein the first image acquisition result is an analysis result of the environment of the air compressor equipment, an abnormal parameter comparison model is input based on the first pressure set and the first temperature set to obtain a first abnormal comparison result of the air compressor, a first correction parameter is obtained through the environment analysis result and the basic information, the first abnormal comparison result is corrected based on the first correction parameter to obtain a second abnormal parameter, the abnormal early warning of the first air compressor is performed based on the second abnormal parameter, the data acquisition of the air compressor is performed in time, the data analysis is performed by combining with the data of the fault air compressor, and the technical effect of performing the fault analysis early warning of the air compressor in time and accurately is achieved.
2. Because the dimension of starting abnormity is adopted for unfolding analysis, data support of multiple dimensions is provided for more accurate analysis of abnormity, and further a foundation is laid for timely abnormity analysis and detection tamping.
3. Due to the adoption of the mode of acquiring the comprehensive evaluation parameters, the rotation of the equipment is more scientific and reasonable, and the technical effects of ensuring the equipment state of the air compressor and improving the operation stability of the equipment are further achieved.
4. Due to the adoption of the constraint mode of two parameters of the first matching degree and the first matching result, the finally obtained first abnormal information is more rigorous, so that the first abnormal information is more accurate, and a basis is provided for accurate early warning and tamping.
Example two
Based on the same inventive concept as the intelligent monitoring method for the air compressor in the foregoing embodiment, the present invention further provides an intelligent monitoring system for the air compressor, as shown in fig. 5, the system includes:
a first obtaining unit 11, where the first obtaining unit 11 is configured to obtain basic information of a first air compressor;
a second obtaining unit 12, where the second obtaining unit 12 is configured to perform pressure collection on the first air compressor through a pressure collection device to obtain a first pressure set;
a third obtaining unit 13, where the third obtaining unit 13 is configured to perform temperature collection on the first air compressor through a temperature collection device to obtain a first temperature set;
a fourth obtaining unit 14, where the fourth obtaining unit 14 is configured to perform image acquisition on the first air compressor through an image acquisition device, obtain a first image acquisition result, and obtain a first correction parameter according to the image acquisition result and the basic information;
a fifth obtaining unit 15, where the fifth obtaining unit 15 is configured to construct an abnormal parameter comparison model, and input the first temperature set and the first pressure set into the abnormal parameter comparison model to obtain first abnormal information;
a sixth obtaining unit 16, where the sixth obtaining unit 16 is configured to perform the first abnormal information correction through the first correction parameter, and obtain a second abnormal parameter;
and the first early warning unit 17 is used for performing abnormity early warning on the first air compressor based on the second abnormity information.
Further, the system further comprises:
a seventh obtaining unit, configured to obtain an equipment operating parameter of the first air compressor;
an eighth obtaining unit, configured to perform device loading and runtime parameter extraction according to the device runtime parameter, so as to obtain a first loading and runtime;
a ninth obtaining unit, configured to obtain an equipment historical operation parameter of the first air compressor, and obtain a first abnormal identification parameter based on abnormal evaluation of the first loading and operation time by the equipment historical operation parameter;
and the second early warning unit is used for carrying out abnormity early warning on the first air compressor after the second abnormal information is identified based on the first abnormal identification parameter.
Further, the system further comprises:
a tenth obtaining unit, configured to obtain first operation time and first air supply amount information of the first air compressor;
an eleventh obtaining unit configured to obtain a first evaluation coefficient of the first air compressor based on the basic information and the first air supply amount information;
a twelfth obtaining unit, configured to obtain a first comprehensive evaluation parameter of the first air compressor based on the first evaluation coefficient and the first operating time;
and the first rotation unit is used for performing equipment rotation of the first air compressor based on the first comprehensive evaluation parameter.
Further, the system further comprises:
a thirteenth obtaining unit, configured to obtain a first sample data set, where samples of the first sample data set are all device data with the same parameters as the first air compressor, and each group of data in the first sample data set has identification information of an abnormal result;
the first construction unit is used for inputting the parameter data in the first sample data set into an abnormal parameter comparison model, performing supervised learning on the abnormal parameter comparison model through the identification information, and ending the training process of the abnormal parameter comparison model when the output result of the abnormal parameter comparison model meets a first preset requirement;
a fourteenth obtaining unit, configured to input the first temperature set and the first pressure set into the abnormal parameter comparison model, and obtain a first matching result and first matching degree information;
a fifteenth obtaining unit, configured to obtain the first abnormal information according to the first matching result and the first matching degree information.
Further, the system further comprises:
a sixteenth obtaining unit, configured to obtain environment parameter information according to the image acquisition result;
a seventeenth obtaining unit configured to perform environmental abnormality degree evaluation of the first air compressor based on the environmental parameter information and the basic information, and obtain a first evaluation result;
an eighteenth obtaining unit, configured to obtain a first correlation influence coefficient according to the first evaluation result and the first temperature set, and obtain the first correction parameter based on the first correlation influence coefficient.
Further, the system further comprises:
a nineteenth obtaining unit configured to obtain a maintenance history of the first air compressor;
a twentieth obtaining unit, configured to extract early warning history information of the first air compressor, and obtain first early warning frequency information and first early warning level information;
the second construction unit is used for constructing a first weight distribution evaluation parameter, and performing weight distribution of the first early warning frequency information and the first early warning grade information based on the first weight distribution evaluation parameter to obtain a first weight distribution result;
a twenty-first obtaining unit, configured to obtain a first maintenance assessment index based on the first early warning frequency information, the first early warning level information, and the first weight distribution result, and obtain a first maintenance reminding time based on the first maintenance assessment index and the maintenance history record;
the first sending unit is used for sending the first maintenance reminding time to first equipment.
Further, the system further comprises:
a twenty-second obtaining unit for obtaining first gas demand information;
a twenty-third obtaining unit, configured to obtain device basis information of a first combined device, where the first combined device includes the first air compressor;
a twenty-fourth obtaining unit, configured to perform energy consumption evaluation of an air supply combination of the device according to the device basic information and the first air demand information, and obtain a first energy consumption evaluation result;
and the first coordination unit is used for coordinating the operation parameters of the first air compressor based on the first energy consumption evaluation result.
Various changes and specific examples of the intelligent air compressor monitoring method in the first embodiment of fig. 1 are also applicable to the intelligent air compressor monitoring system in this embodiment, and through the foregoing detailed description of the intelligent air compressor monitoring method, those skilled in the art can clearly know the implementation method of the intelligent air compressor monitoring system in this embodiment, so for the sake of brevity of the description, detailed description is not repeated here.
Exemplary electronic device
The electronic device of the present application is described below with reference to fig. 6.
Fig. 6 illustrates a schematic structural diagram of an electronic device according to the present application.
Based on the inventive concept of the intelligent monitoring method for the air compressor in the foregoing embodiment, the present invention further provides an electronic device, and the electronic device according to the present application is described below with reference to fig. 6. The electronic device may be a removable device itself or a stand-alone device independent thereof, on which a computer program is stored which, when being executed by a processor, carries out the steps of any of the methods as described hereinbefore.
As shown in fig. 6, the electronic device 50 includes one or more processors 51 and a memory 52.
The processor 51 may be a Central Processing Unit (CPU) or other form of processing unit having data processing capabilities and/or instruction execution capabilities, and may control other components in the electronic device 50 to perform desired functions.
The memory 52 may include one or more computer program products that may include various forms of computer-readable storage media, such as volatile memory and/or non-volatile memory. The volatile memory may include, for example, Random Access Memory (RAM), cache memory (cache), and/or the like. The non-volatile memory may include, for example, Read Only Memory (ROM), hard disk, flash memory, etc. One or more computer program instructions may be stored on the computer-readable storage medium and executed by the processor 51 to implement the methods of the various embodiments of the application described above and/or other desired functions.
In one example, the electronic device 50 may further include: an input device 53 and an output device 54, which are interconnected by a bus system and/or other form of connection mechanism (not shown).
The embodiment of the invention provides an intelligent monitoring method for an air compressor, which is applied to an intelligent monitoring system, wherein the intelligent monitoring system is in communication connection with a pressure acquisition device, a temperature acquisition device and an image acquisition device, and the method comprises the following steps: acquiring basic information of a first air compressor; performing pressure acquisition on the first air compressor through the pressure acquisition device to obtain a first pressure set; acquiring the temperature of the first air compressor through the temperature acquisition device to obtain a first temperature set; acquiring an image of the first air compressor through the image acquisition device to obtain a first image acquisition result, and acquiring a first correction parameter according to the image acquisition result and the basic information; establishing an abnormal parameter comparison model, inputting the first temperature set and the first pressure set into the abnormal parameter comparison model, and obtaining first abnormal information; correcting the first abnormal information through the first correction parameter to obtain a second abnormal parameter; and carrying out abnormity early warning of the first air compressor based on the second abnormity information. The technical problems that in the process of monitoring the air compressor, data acquisition and analysis cannot be accurately carried out in real time, and therefore the air compressor cannot be timely monitored and problem location cannot be accurately carried out in the prior art are solved, the technical effects that data acquisition of the air compressor is timely carried out, data analysis is carried out by combining with fault air compressor data, and then fault analysis and early warning of the air compressor are timely and accurately carried out are achieved.
Through the above description of the embodiments, those skilled in the art will clearly understand that the present application can be implemented by software plus necessary general-purpose hardware, and certainly can also be implemented by special-purpose hardware including special-purpose integrated circuits, special-purpose CPUs, special-purpose memories, special-purpose components and the like. Generally, functions performed by computer programs can be easily implemented by corresponding hardware, and specific hardware structures for implementing the same functions may be various, such as analog circuits, digital circuits, or dedicated circuits. However, for the present application, the implementation of a software program is more preferable. Based on such understanding, the technical solutions of the present application may be substantially embodied in the form of a software product, which is stored in a readable storage medium, such as a floppy disk, a usb disk, a removable hard disk, a ROM, a RAM, a magnetic disk, or an optical disk of a computer, and includes several instructions for causing a computer device to execute the method according to the embodiments of the present application.
In the above embodiments, the implementation may be wholly or partially realized by software, hardware, firmware, or any combination thereof. When implemented in software, may be implemented in whole or in part in the form of a computer program product.
The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the procedures or functions described in accordance with the present application are generated, in whole or in part. The computer may be a general purpose computer, a special purpose computer, a network of computers, or other programmable device. The computer instructions may be stored on or transmitted from a computer-readable storage medium to another computer-readable storage medium, which may be magnetic (e.g., floppy disks, hard disks, tapes), optical (e.g., DVDs), or semiconductor (e.g., Solid State Disks (SSDs)), among others.
It should be appreciated that reference throughout this specification to "one embodiment" or "an embodiment" means that a particular feature, structure or characteristic described in connection with the embodiment is included in at least one embodiment of the present application. Thus, the appearances of the phrases "in one embodiment" or "in an embodiment" in various places throughout this specification are not necessarily all referring to the same embodiment. Furthermore, the particular features, structures, or characteristics may be combined in any suitable manner in one or more embodiments. It should be understood that, in the various embodiments of the present application, the sequence numbers of the above-mentioned processes do not mean the execution sequence, and the execution sequence of the processes should be determined by the functions and the inherent logic, and should not constitute any limitation to the implementation process of the present application.
Additionally, the terms "system" and "network" are often used interchangeably herein. The term "and/or" herein is merely an association describing an associated object, meaning that three relationships may exist, e.g., a and/or B, may mean: a exists alone, A and B exist simultaneously, and B exists alone. In addition, the character "/" herein generally indicates that the former and latter related objects are in an "or" relationship.
It should be understood that in this application, "B corresponding to A" means that B is associated with A, from which B can be determined. It should also be understood that determining B from a does not mean determining B from a alone, but may be determined from a and/or other information.
Those of ordinary skill in the art will appreciate that the elements and algorithm steps of the examples described in connection with the embodiments disclosed herein may be embodied in electronic hardware, computer software, or combinations of both, and that the components and steps of the examples have been described in a functional general in the foregoing description for the purpose of illustrating clearly the interchangeability of hardware and software. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
In short, the above description is only a preferred embodiment of the present disclosure, and is not intended to limit the scope of the present disclosure. Any modification, equivalent replacement, improvement and the like made within the spirit and principle of the present application shall be included in the protection scope of the present application.
Claims (10)
1. The intelligent monitoring method for the air compressor is characterized by being applied to an intelligent monitoring system, wherein the intelligent monitoring system is in communication connection with a pressure acquisition device, a temperature acquisition device and an image acquisition device, and the method comprises the following steps:
acquiring basic information of a first air compressor;
performing pressure acquisition on the first air compressor through the pressure acquisition device to obtain a first pressure set;
acquiring the temperature of the first air compressor through the temperature acquisition device to obtain a first temperature set;
acquiring an image of the first air compressor through the image acquisition device to obtain a first image acquisition result, and acquiring a first correction parameter according to the image acquisition result and the basic information;
establishing an abnormal parameter comparison model, inputting the first temperature set and the first pressure set into the abnormal parameter comparison model, and obtaining first abnormal information;
correcting the first abnormal information through the first correction parameter to obtain a second abnormal parameter;
and carrying out abnormity early warning of the first air compressor based on the second abnormity information.
2. The method of claim 1, wherein the method further comprises:
obtaining equipment operation parameters of the first air compressor;
carrying out equipment loading and running time parameter extraction according to the equipment running parameters to obtain first loading and running time;
obtaining historical equipment operating parameters of the first air compressor, and obtaining first abnormal identification parameters based on abnormal evaluation of the historical equipment operating parameters on the first loading and operating time;
and after the second abnormal information is identified based on the first abnormal identification parameter, performing abnormal early warning on the first air compressor.
3. The method of claim 1, wherein the method further comprises:
obtaining first running time and first air supply quantity information of the first air compressor;
obtaining a first evaluation coefficient of the first air compressor according to the basic information and the first air supply amount information;
obtaining a first comprehensive evaluation parameter of the first air compressor based on the first evaluation coefficient and the first running time;
and performing equipment rotation of the first air compressor based on the first comprehensive evaluation parameter.
4. The method of claim 1, wherein the method further comprises:
obtaining a first sample data set, wherein samples of the first sample data set are all device data with the same parameters as those of the first air compressor, and each group of data in the first sample data set has identification information of an abnormal result;
inputting the parameter data in the first sample data set into an abnormal parameter comparison model, performing supervised learning of the abnormal parameter comparison model through the identification information, and ending the training process of the abnormal parameter comparison model when the output result of the abnormal parameter comparison model meets a first preset requirement;
inputting the first temperature set and the first pressure set into the abnormal parameter comparison model to obtain a first matching result and first matching degree information;
and acquiring the first abnormal information according to the first matching result and the first matching degree information.
5. The method of claim 1, wherein the method further comprises:
acquiring environmental parameter information according to the image acquisition result;
performing environmental abnormality degree evaluation on the first air compressor based on the environmental parameter information and the basic information to obtain a first evaluation result;
and obtaining a first correlation influence coefficient according to the first evaluation result and the first temperature set, and obtaining the first correction parameter based on the first correlation influence coefficient.
6. The method of claim 1, wherein the method further comprises:
obtaining a maintenance history of the first air compressor;
extracting early warning historical information of the first air compressor to obtain first early warning frequency information and first early warning grade information;
constructing a first weight distribution evaluation parameter, and performing weight distribution of the first early warning frequency information and the first early warning grade information based on the first weight distribution evaluation parameter to obtain a first weight distribution result;
obtaining a first maintenance evaluation index based on the first early warning frequency information, the first early warning grade information and the first weight distribution result, and obtaining a first maintenance reminding time based on the first maintenance evaluation index and the maintenance history record;
and sending the first maintenance reminding time to first equipment.
7. The method of claim 1, wherein the method further comprises:
acquiring first gas demand information;
acquiring basic equipment information of first combined equipment, wherein the first combined equipment comprises the first air compressor;
performing energy consumption evaluation of the gas supply combination of the equipment according to the equipment basic information and the first gas demand information to obtain a first energy consumption evaluation result;
and coordinating the operation parameters of the first air compressor based on the first energy consumption evaluation result.
8. The utility model provides an air compressor machine intelligent monitoring system which characterized in that, the system includes:
the first obtaining unit is used for obtaining basic information of the first air compressor;
the second obtaining unit is used for carrying out pressure collection on the first air compressor through a pressure collecting device to obtain a first pressure set;
the third obtaining unit is used for carrying out temperature collection on the first air compressor through a temperature collecting device to obtain a first temperature set;
the fourth obtaining unit is used for carrying out image acquisition on the first air compressor through an image acquisition device, obtaining a first image acquisition result and obtaining a first correction parameter according to the image acquisition result and the basic information;
a fifth obtaining unit, configured to construct an abnormal parameter comparison model, input the first temperature set and the first pressure set into the abnormal parameter comparison model, and obtain first abnormal information;
a sixth obtaining unit, configured to correct the first abnormal information according to the first correction parameter, and obtain a second abnormal parameter;
and the first early warning unit is used for carrying out abnormity early warning on the first air compressor based on the second abnormity information.
9. An electronic device comprising a processor and a memory; the memory is used for storing; the processor is used for executing the method of any one of claims 1 to 7 through calling.
10. Computer program product comprising a computer program and/or instructions, characterized in that the computer program and/or instructions, when executed by a processor, implement the steps of the method of any one of claims 1 to 7.
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