CN117128162A - Intelligent energy air compression station energy-saving control system and control method - Google Patents
Intelligent energy air compression station energy-saving control system and control method Download PDFInfo
- Publication number
- CN117128162A CN117128162A CN202311094864.XA CN202311094864A CN117128162A CN 117128162 A CN117128162 A CN 117128162A CN 202311094864 A CN202311094864 A CN 202311094864A CN 117128162 A CN117128162 A CN 117128162A
- Authority
- CN
- China
- Prior art keywords
- data
- energy consumption
- energy
- air compressor
- model
- 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 27
- 238000007906 compression Methods 0.000 title claims abstract description 27
- 238000000034 method Methods 0.000 title claims description 42
- 238000005265 energy consumption Methods 0.000 claims abstract description 216
- 238000012544 monitoring process Methods 0.000 claims abstract description 62
- 238000004458 analytical method Methods 0.000 claims abstract description 37
- 238000005457 optimization Methods 0.000 claims abstract description 24
- 238000012545 processing Methods 0.000 claims abstract description 24
- 238000004519 manufacturing process Methods 0.000 claims abstract description 7
- 238000013486 operation strategy Methods 0.000 claims abstract description 7
- 238000012549 training Methods 0.000 claims description 19
- 238000004140 cleaning Methods 0.000 claims description 12
- 238000007781 pre-processing Methods 0.000 claims description 10
- 238000012360 testing method Methods 0.000 claims description 10
- 230000008859 change Effects 0.000 claims description 7
- 238000003062 neural network model Methods 0.000 claims description 7
- 238000010219 correlation analysis Methods 0.000 claims description 6
- 238000012417 linear regression Methods 0.000 claims description 6
- 230000002596 correlated effect Effects 0.000 claims description 5
- 239000011159 matrix material Substances 0.000 claims description 4
- 238000012937 correction Methods 0.000 claims description 3
- 230000001419 dependent effect Effects 0.000 claims description 3
- 238000012806 monitoring device Methods 0.000 claims description 3
- 238000013450 outlier detection Methods 0.000 claims description 3
- 238000012216 screening Methods 0.000 claims description 3
- 230000000007 visual effect Effects 0.000 claims description 3
- 238000004134 energy conservation Methods 0.000 abstract description 3
- 230000000694 effects Effects 0.000 description 7
- 238000011156 evaluation Methods 0.000 description 7
- 230000005540 biological transmission Effects 0.000 description 5
- 230000000875 corresponding effect Effects 0.000 description 5
- 239000002184 metal Substances 0.000 description 5
- 230000002159 abnormal effect Effects 0.000 description 4
- 238000004422 calculation algorithm Methods 0.000 description 4
- 230000009286 beneficial effect Effects 0.000 description 3
- 230000008901 benefit Effects 0.000 description 3
- 230000006872 improvement Effects 0.000 description 3
- 238000007726 management method Methods 0.000 description 3
- 230000001276 controlling effect Effects 0.000 description 2
- 238000013500 data storage Methods 0.000 description 2
- 238000009826 distribution Methods 0.000 description 2
- 230000006870 function Effects 0.000 description 2
- 238000005070 sampling Methods 0.000 description 2
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 2
- 230000004075 alteration Effects 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 238000013135 deep learning Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 230000000593 degrading effect Effects 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 230000007613 environmental effect Effects 0.000 description 1
- 238000010801 machine learning Methods 0.000 description 1
- 238000012423 maintenance Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000005381 potential energy Methods 0.000 description 1
- 230000008569 process Effects 0.000 description 1
- 230000002035 prolonged effect Effects 0.000 description 1
- 238000007619 statistical method Methods 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 238000012800 visualization Methods 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/20—Design optimisation, verification or simulation
-
- 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
- F04B49/00—Control, e.g. of pump delivery, or pump pressure of, or safety measures for, machines, pumps, or pumping installations, not otherwise provided for, or of interest apart from, groups F04B1/00 - F04B47/00
- F04B49/02—Stopping, starting, unloading or idling control
-
- 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
- F04B49/00—Control, e.g. of pump delivery, or pump pressure of, or safety measures for, machines, pumps, or pumping installations, not otherwise provided for, or of interest apart from, groups F04B1/00 - F04B47/00
- F04B49/02—Stopping, starting, unloading or idling control
- F04B49/022—Stopping, starting, unloading or idling control by means of pressure
-
- 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
- F04B49/00—Control, e.g. of pump delivery, or pump pressure of, or safety measures for, machines, pumps, or pumping installations, not otherwise provided for, or of interest apart from, groups F04B1/00 - F04B47/00
- F04B49/06—Control using electricity
- F04B49/065—Control using electricity and making use of computers
-
- 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
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01D—MEASURING NOT SPECIALLY ADAPTED FOR A SPECIFIC VARIABLE; ARRANGEMENTS FOR MEASURING TWO OR MORE VARIABLES NOT COVERED IN A SINGLE OTHER SUBCLASS; TARIFF METERING APPARATUS; MEASURING OR TESTING NOT OTHERWISE PROVIDED FOR
- G01D21/00—Measuring or testing not otherwise provided for
- G01D21/02—Measuring two or more variables by means not covered by a single other subclass
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/21—Design, administration or maintenance of databases
- G06F16/215—Improving data quality; Data cleansing, e.g. de-duplication, removing invalid entries or correcting typographical errors
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
- G06F18/214—Generating training patterns; Bootstrap methods, e.g. bagging or boosting
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/24—Classification techniques
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N20/00—Machine learning
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N5/00—Computing arrangements using knowledge-based models
- G06N5/01—Dynamic search techniques; Heuristics; Dynamic trees; Branch-and-bound
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
- G06Q10/06312—Adjustment or analysis of established resource schedule, e.g. resource or task levelling, or dynamic rescheduling
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2119/00—Details relating to the type or aim of the analysis or the optimisation
- G06F2119/08—Thermal analysis or thermal optimisation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2119/00—Details relating to the type or aim of the analysis or the optimisation
- G06F2119/14—Force analysis or force optimisation, e.g. static or dynamic forces
Abstract
The invention discloses an energy-saving control system of an intelligent energy air compression station, which comprises the following components: the energy consumption monitoring equipment is used for monitoring and recording the energy consumption data of the air compressor in real time; the data processing module is used for transmitting the energy consumption data recorded by the energy consumption monitoring equipment to the energy-saving control system for processing and analysis; the energy consumption model building module is used for building an energy consumption model of the air compressor based on historical and real-time energy consumption data; the energy consumption analysis module is used for analyzing the real-time data by using the energy consumption model and evaluating the current energy consumption condition; the energy consumption optimizing module optimizes the operation strategy of the air compressor according to the planned production task and the energy consumption target; the intelligent scheduling decision module is used for performing intelligent scheduling decision through a decision model; the intelligent energy air compression station energy-saving control system can monitor the pressure, the temperature and the flow in all aspects, has high data utilization efficiency and accuracy, can help to understand the energy consumption rule and the influencing factors of the air compressor in depth, and provides accurate basis for energy conservation optimization.
Description
Technical Field
The invention relates to an intelligent energy air compression station energy-saving control system and a control method.
Background
The compressed air in modern industry is used as one of three common power sources in factories, and has the characteristics of wide sources, high compression ratio, convenient transportation and the like, so that the compressed air is widely applied in the field of industrial manufacturing.
The air compression station energy-saving control system is an automatic system for optimizing and saving energy consumption in the operation process of an air compressor, and the core aim is to minimize energy consumption and improve the energy efficiency of the system by reasonably adjusting and controlling the operation parameters of the air compressor.
The existing air compression station energy-saving control system generally adopts pressure adjustment control, and the outlet pressure of an air compression unit is monitored and adjusted to meet production requirements and reduce energy consumption as much as possible. The system can adjust the running state of the air compressor unit according to actual needs, and avoid excessively high or excessively low outlet pressure, so that the energy-saving system mainly depends on pressure monitoring to adjust, and lacks monitoring on temperature and flow, has low data utilization efficiency and accuracy, is difficult to deeply understand the energy consumption rule and influence factors of the air compressor, and cannot provide accurate basis for energy saving optimization.
Disclosure of Invention
Aiming at the defects in the prior art, the invention aims to provide the intelligent energy air compressor station energy-saving control system and the intelligent energy air compressor station energy-saving control method which can monitor the pressure, the temperature and the flow in all aspects, have high data utilization efficiency and accuracy, can help to understand the energy consumption rule and influencing factors of the air compressor in depth and provide accurate basis for energy saving optimization.
The technical scheme adopted for solving the technical problems is as follows:
wisdom energy air compression station energy-saving control system includes:
the energy consumption monitoring equipment is used for monitoring and recording the energy consumption data of the air compressor in real time;
the data processing module is used for transmitting the energy consumption data recorded by the energy consumption monitoring equipment to the energy-saving control system for processing and analysis;
the energy consumption model building module is used for building an energy consumption model of the air compressor based on historical and real-time energy consumption data;
the energy consumption analysis module is used for analyzing the real-time data by using the energy consumption model and evaluating the current energy consumption condition;
the energy consumption optimizing module optimizes the operation strategy of the air compressor according to the planned production task and the energy consumption target;
and the intelligent scheduling decision module is used for performing intelligent scheduling decision through a decision model based on the energy consumption analysis and the optimization result.
Preferably, the energy consumption monitoring device comprises an electric power instrument arranged on a power line of the air compressor, a pressure sensor arranged on a main gas pipeline of the air compressor, a flowmeter arranged on the gas pipeline of the air compressor, and a temperature sensor arranged on an air inlet pipeline, an air outlet pipeline, a cooler outlet pipeline and a cooler return pipeline of the air compressor.
Another technical problem to be solved by the invention is to provide an energy-saving control method of an intelligent energy air compression station, which comprises the following steps:
installing energy consumption monitoring equipment in the air compression station, and monitoring and recording energy consumption data of the air compressor in real time;
transmitting the data acquired by the energy consumption monitoring equipment to an energy-saving control system for processing and analysis;
based on historical data and real-time data, an energy consumption model of the air compressor is built;
analyzing the real-time data by using an energy consumption model, and evaluating the current energy consumption condition;
based on the energy consumption analysis and the optimization result, performing intelligent scheduling decision through a decision model;
and (5) automatically starting and stopping, switching and adjusting the running state and parameters of the air compressor based on the intelligent scheduling decision result.
Preferably, the method for installing the energy consumption monitoring device in the air compression station comprises the following steps:
an electric power instrument is arranged on a power supply circuit of the air compressor and used for monitoring and recording the use condition of electric energy in real time;
a pressure sensor is arranged on a main gas pipeline of the air compressor and used for monitoring the air pressure in the pipeline in real time;
a flowmeter is arranged on a gas pipeline of the air compressor and is used for measuring the flow of air in real time;
and a temperature sensor is arranged on an air inlet pipeline, an air outlet pipeline, a cooler outlet and a cooler return pipeline of the air compressor.
Preferably, the method for transmitting the data acquired by the energy consumption monitoring equipment to the energy-saving control system comprises the following steps:
and using the data collector or gateway equipment as a bridge to collect and transmit the data of the energy consumption monitoring equipment to the energy-saving control system.
Preferably, the method for establishing the energy consumption model of the air compressor comprises the following steps:
collecting historical data and real-time data of an air compressor, wherein the historical data and the real-time data comprise electric energy data, pipeline pressure data, pipeline flow data, pipeline temperature data and cooler temperature data;
selecting proper characteristic variables from the collected data as input of an energy consumption model;
preprocessing the collected data, including data cleaning, outlier processing and missing value filling;
training a linear regression model by using historical data, and evaluating the performance of the model by using the decision coefficients;
and verifying and optimizing the trained model by using real-time data to obtain an optimized energy consumption model.
Preferably, the method for evaluating the current energy consumption condition comprises the following steps:
collecting historical data and real-time data of an air compressor, wherein the historical data and the real-time data comprise electric energy data, pipeline pressure data, pipeline flow data, pipeline temperature data and cooler temperature data;
preprocessing real-time data, including data cleaning, outlier detection and correction;
taking the preprocessed real-time data as input, and inputting the input into the established energy consumption model;
predicting the real-time data by using an energy consumption model to obtain a predicted energy consumption value;
comparing the actual energy consumption value with the predicted energy consumption value, and evaluating the current energy consumption condition;
and analyzing the current energy consumption condition by observing the change trend of the energy consumption.
Preferably, the method for performing intelligent scheduling decision through the decision model based on the energy consumption analysis and the optimization result comprises the following steps:
preprocessing collected electric energy data, pipeline pressure data, pipeline flow data, pipeline temperature data and cooler temperature data, including data cleaning, outlier processing and missing value filling;
selecting a proper feature set from the preprocessed electric energy data, pipeline pressure data, pipeline flow data, pipeline temperature data and cooler temperature data through correlation analysis;
dividing the feature set into a training set and a testing set, wherein the training set is used for training and parameter adjustment of the model, and the testing set is used for evaluating the generalization capability of the model;
training the neural network model by using the training set;
and predicting the real-time data by using the trained neural network model, and generating an energy-saving control decision based on the prediction result.
Preferably, the method for selecting the proper feature set from the preprocessed electric energy data, the pipeline pressure data, the pipeline flow data, the pipeline temperature data and the cooler temperature data through the correlation analysis comprises the following steps:
calculating a correlation coefficient between each feature and the energy-saving index;
based on the calculated correlation coefficient, visual search can be performed, and a correlation matrix is drawn;
setting a threshold value of a correlation coefficient to screen out features which are obviously correlated with the target variable;
screening out the characteristics with the correlation with the target variable exceeding a threshold value from the characteristics;
after the feature set is selected, a multiple collinearity check is performed.
Preferably, the method for performing the multiple collinearity check is:
collecting a dataset for analysis, including all features and target variables;
the data is preprocessed, including data cleaning, missing value filling and standardization.
Selecting one target feature as a dependent variable, and fitting a linear regression model by using other features as independent variables;
for each feature, its variance expansion factor VIF is calculated as follows:
VIF＝1/(1-R^2)
wherein R2 is a coefficient of determination of the linear relationship of the feature to other arguments;
judging the collinearity between the features according to the size of the VIF, and if the VIF of one feature is greater than the threshold value 10, indicating that higher collinearity exists;
if the high collinearity exists between the features, selecting one of the high correlation features, and eliminating other correlation features;
re-fitting the linear model according to the processed feature set, and calculating an updated VIF value, wherein if the co-linearity problem is solved, the VIF value should be lower than a threshold value of 10;
if the co-linearity problem still exists, iteratively executing the steps until the co-linearity requirement of the model is met.
The beneficial effects of the invention are as follows:
the energy consumption monitoring equipment is arranged to monitor and record the energy consumption data of the air compressor in real time, so that accurate knowledge of the energy consumption condition is provided, basic data is provided for energy saving optimization, and the data acquired by the energy consumption monitoring equipment are transmitted to an energy saving control system for processing and analysis, so that centralized management and analysis of a large amount of data can be realized, and the utilization efficiency and accuracy of the data are improved; the method has the advantages that the energy consumption rule and influence factors of the air compressor can be deeply understood through analyzing the result of the energy consumption model, a basis is provided for energy conservation optimization, real-time data are analyzed through the energy consumption model, the current energy consumption condition is estimated, abnormal energy consumption or high energy consumption states can be timely found through real-time monitoring and analysis, a reference is provided for subsequent decisions, and intelligent scheduling decisions are performed through the decision model based on the energy consumption analysis and the optimization result. According to the real-time energy consumption condition and the optimization target, the optimization of energy consumption is realized by automatically starting and stopping, switching and adjusting the running state and parameters of the air compressor, and unnecessary energy consumption and running cost can be reduced by implementing intelligent scheduling decision, so that the energy efficiency of the air compressor is improved. The energy-saving effect is improved, and the energy cost and the environmental influence of enterprises are reduced; the scheme utilizes advanced technical means to realize automatic control and intelligent management of the air compression station, reduces the requirements of manual intervention and operation, and improves the working efficiency and the reliability of the system.
Drawings
FIG. 1 is a schematic block diagram of an intelligent energy air compression station energy saving control system of the present invention;
FIG. 2 is a flow chart of the intelligent energy air compression station energy-saving control method of the invention.
Detailed Description
The principles and features of the present invention are described below with reference to the drawings, the examples are illustrated for the purpose of illustrating the invention and are not to be construed as limiting the scope of the invention. The invention is more specifically described by way of example in the following paragraphs. Advantages and features of the invention will become more apparent from the following description and from the claims.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. The terminology used herein in the description of the invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. The term "and/or" as used herein includes any and all combinations of one or more of the associated listed items.
Examples
Referring to fig. 1, the intelligent energy air compression station energy-saving control system includes:
the energy consumption monitoring equipment is used for monitoring and recording the energy consumption data of the air compressor in real time;
the data processing module is used for transmitting the energy consumption data recorded by the energy consumption monitoring equipment to the energy-saving control system for processing and analysis;
the energy consumption model building module is used for building an energy consumption model of the air compressor based on historical and real-time energy consumption data;
the energy consumption analysis module is used for analyzing the real-time data by using the energy consumption model and evaluating the current energy consumption condition;
the energy consumption optimizing module optimizes the operation strategy of the air compressor according to the planned production task and the energy consumption target;
and the intelligent scheduling decision module is used for performing intelligent scheduling decision through a decision model based on the energy consumption analysis and the optimization result.
Through energy consumption monitoring equipment, the system can monitor and record the energy consumption data of the air compressor in real time, and accurate energy consumption information is provided. The enterprise can know the energy consumption condition in time and take corresponding measures in a targeted way. The system is provided with a data processing module which can transmit the energy consumption data to the energy-saving control system for processing and analysis. Thus, useful information can be extracted from the mass data to assist enterprises in energy consumption analysis, assessment and optimization.
Through the energy consumption model building module, the system can build an energy consumption model of the air compressor based on historical and real-time energy consumption data. The model can better understand the change rule of the energy consumption and provide basis for subsequent energy consumption analysis and optimization; the energy consumption analysis module analyzes the real-time data by using the energy consumption model, and can evaluate the current energy consumption condition. By comprehensively analyzing the energy consumption data, the system can find out the problems of abnormal energy consumption, energy consumption peak and the like and help enterprises to adjust the operation strategy in time.
The energy consumption optimizing module can optimize the operation strategy of the air compressor according to the planned production task and the energy consumption target. Through intelligent scheduling decision, the system can reasonably arrange operations such as switching on and off, load distribution and the like of equipment according to energy consumption analysis and optimization results so as to achieve an energy-saving effect.
The energy consumption monitoring equipment comprises an electric power instrument arranged on a power line of an air compressor, a pressure sensor arranged on a main gas pipeline of the air compressor, a flowmeter arranged on the gas pipeline of the air compressor, and a temperature sensor arranged on an air inlet pipeline, an air outlet pipeline, a cooler outlet and a cooler return pipeline of the air compressor.
For the electric power instrument, the electric power instrument is arranged on a power line of the air compressor; for the pressure sensor, it is mounted on the main gas pipe; for the flowmeter, it is installed on the gas pipeline; for the temperature sensor, the temperature sensor is arranged on an air inlet pipeline, an air outlet pipeline, a cooler outlet and a cooler return water pipeline; and connecting the installed equipment with a monitoring system. The output signals of the electric power instrument, the pressure sensor, the flowmeter and the temperature sensor can be connected into the monitoring system by utilizing a proper interface and a transmission cable, so that the transmission and the acquisition of data are ensured.
According to the actual situation, parameters and settings of the monitoring system are configured. Setting information such as sampling frequency, data storage mode, alarm threshold value and the like so as to meet the requirement of energy consumption monitoring; and testing and debugging the installed equipment and the configured monitoring system. Ensuring that each device can normally collect data and correctly communicate with the monitoring system. And performing some test cases to verify the accuracy and stability of the data.
And displaying and analyzing the collected energy consumption data according to the function of the monitoring system. The energy consumption data can be counted, displayed in a graph and analyzed in a trend through a monitoring interface or special data analysis software, so that a user is helped to know the energy consumption condition
Referring to fig. 2, an energy-saving control method for an intelligent energy air compression station comprises the following steps:
installing energy consumption monitoring equipment in the air compression station, and monitoring and recording energy consumption data of the air compressor in real time;
transmitting the data acquired by the energy consumption monitoring equipment to an energy-saving control system for processing and analysis;
based on historical data and real-time data, an energy consumption model of the air compressor is built;
analyzing the real-time data by using an energy consumption model, and evaluating the current energy consumption condition;
based on the energy consumption analysis and the optimization result, performing intelligent scheduling decision through a decision model;
and (5) automatically starting and stopping, switching and adjusting the running state and parameters of the air compressor based on the intelligent scheduling decision result.
The air compression station is provided with energy consumption monitoring equipment, including an electric power meter, a pressure sensor, a flowmeter, a temperature sensor and the like. The equipment monitors and records the energy consumption data of the air compressor in real time; and transmitting the data acquired by the energy consumption monitoring equipment to an energy-saving control system for processing and analysis. The data may be transmitted to the energy-saving control system using sensors and data transmission techniques, such as wired or wireless networks.
And establishing an energy consumption model of the air compressor based on the historical data and the real-time data. The data can be analyzed and modeled by using a machine learning algorithm or a statistical method so as to predict and estimate the energy consumption condition of the air compressor; and analyzing the real-time data by using the energy consumption model, and evaluating the current energy consumption condition. Comparing the actual energy consumption with the theoretical energy consumption, potential energy consumption problems and room for improvement are found.
Based on the energy consumption analysis and the optimization result, the intelligent scheduling decision is made by utilizing a decision model. The optimal starting and stopping, switching and adjusting the running state and parameters of the air compressor are determined through an algorithm and a rule so as to achieve the energy-saving aim; and automatically controlling and adjusting the running state and parameters of the air compressor according to the intelligent scheduling decision result. The automatic start-stop, switching and adjustment can be realized through the automatic control system, so that manual intervention is avoided, and the energy-saving effect is improved.
The method for installing the energy consumption monitoring equipment in the air compression station comprises the following steps:
an electric power instrument is arranged on a power supply circuit of the air compressor and used for monitoring and recording the use condition of electric energy in real time;
a pressure sensor is arranged on a main gas pipeline of the air compressor and used for monitoring the air pressure in the pipeline in real time;
a flowmeter is arranged on a gas pipeline of the air compressor and is used for measuring the flow of air in real time;
and a temperature sensor is arranged on an air inlet pipeline, an air outlet pipeline, a cooler outlet and a cooler return pipeline of the air compressor.
For the electric power instrument, the electric power instrument is arranged on a power line of the air compressor; for the pressure sensor, it is mounted on the main gas pipe; for the flowmeter, it is installed on the gas pipeline; for the temperature sensor, the temperature sensor is arranged on an air inlet pipeline, an air outlet pipeline, a cooler outlet and a cooler return water pipeline; and connecting the installed equipment with a monitoring system. The output signals of the electric power instrument, the pressure sensor, the flowmeter and the temperature sensor can be connected into the monitoring system by utilizing a proper interface and a transmission cable, so that the transmission and the acquisition of data are ensured.
According to the actual situation, parameters and settings of the monitoring system are configured. Setting information such as sampling frequency, data storage mode, alarm threshold value and the like so as to meet the requirement of energy consumption monitoring; and testing and debugging the installed equipment and the configured monitoring system. Ensuring that each device can normally collect data and correctly communicate with the monitoring system. And performing some test cases to verify the accuracy and stability of the data.
And displaying and analyzing the collected energy consumption data according to the function of the monitoring system. The energy consumption data can be counted, displayed in a graph and analyzed in a trend through a monitoring interface or special data analysis software, so that a user can be helped to know the energy consumption condition.
The method for transmitting the data acquired by the energy consumption monitoring equipment to the energy-saving control system comprises the following steps:
and using the data collector or gateway equipment as a bridge to collect and transmit the data of the energy consumption monitoring equipment to the energy-saving control system.
The method for establishing the energy consumption model of the air compressor comprises the following steps:
collecting historical data and real-time data of an air compressor, wherein the historical data and the real-time data comprise electric energy data, pipeline pressure data, pipeline flow data, pipeline temperature data and cooler temperature data;
selecting proper characteristic variables from the collected data as input of an energy consumption model;
preprocessing the collected data, including data cleaning, outlier processing and missing value filling;
training a linear regression model by using historical data, and evaluating the performance of the model by using the decision coefficients;
and verifying and optimizing the trained model by using real-time data to obtain an optimized energy consumption model.
By establishing an energy consumption model of the air compressor, the energy consumption condition of the air compressor can be estimated and predicted more accurately. The model can utilize a plurality of characteristic variables and consider the interrelationship between the characteristic variables, so that the prediction accuracy is improved; by analyzing the energy consumption model, energy consumption problems and room for improvement can be identified. Based on the analysis result of the model, corresponding energy-saving measures can be formulated, and the running state and parameters of the air compressor are optimized so as to realize the maximization of the energy-saving effect.
The energy consumption model not only can be used for prediction and evaluation of energy consumption, but also can be used as a basis for intelligent scheduling decision. Based on the analysis result of the model, corresponding decision rules and algorithms can be formulated, so that automatic start-stop, switching and adjustment of the air compressor are realized, manual intervention is reduced, and the operation efficiency and energy-saving effect are improved; the energy consumption model can be verified and optimized according to the real-time data, and the accuracy and the adaptability of the model are maintained. The model is updated and optimized continuously, so that the method can be better adapted to different working conditions and requirements, and real-time energy consumption control and optimization are realized; the energy consumption model is constructed based on the collected historical data and real-time data, and the information of the data is fully utilized. The energy consumption control and optimization are carried out in a data driving mode, so that the scientificity and accuracy of decision making can be improved, and the actual requirements can be better met.
The method for evaluating the current energy consumption condition comprises the following steps:
collecting historical data and real-time data of an air compressor, wherein the historical data and the real-time data comprise electric energy data, pipeline pressure data, pipeline flow data, pipeline temperature data and cooler temperature data;
preprocessing real-time data, including data cleaning, outlier detection and correction;
taking the preprocessed real-time data as input, and inputting the input into the established energy consumption model;
predicting the real-time data by using an energy consumption model to obtain a predicted energy consumption value;
comparing the actual energy consumption value with the predicted energy consumption value, and evaluating the current energy consumption condition;
and analyzing the current energy consumption condition by observing the change trend of the energy consumption.
By collecting and evaluating the real-time data, the energy consumption condition of the current air compressor can be timely obtained. The method is beneficial to monitoring and managing the energy consumption in real time, and avoids the occurrence of overhigh energy consumption or abnormal conditions; according to the scheme, the evaluation is carried out based on the actual data, and the accuracy and the reliability of an evaluation result are improved. The energy consumption model predicts by utilizing a plurality of characteristic variables, comprehensively considers the influence of various factors on energy consumption, and makes the evaluation more scientific.
By comparing the actual energy consumption value with the predicted energy consumption value, energy consumption deviation and abnormal situation can be found. By analyzing the change trend of the energy consumption, the energy consumption problem and the improvement space can be identified, and measures can be taken in a targeted manner to optimize the energy consumption; the energy consumption evaluation can reflect not only the energy consumption condition, but also the running state of the air compressor indirectly. By analyzing the change trend of the energy consumption, whether the air compressor operates normally or not can be judged, and whether the problems of faults, unbalanced load and the like exist or not can be judged; the energy consumption evaluation result can provide important reference for decision making. According to the energy consumption condition, corresponding energy-saving measures and operation strategies can be formulated, and the operation efficiency and the energy utilization efficiency of the air compressor are optimized.
Based on the energy consumption analysis and the optimization result, the method for carrying out intelligent scheduling decision through the decision model comprises the following steps:
preprocessing collected electric energy data, pipeline pressure data, pipeline flow data, pipeline temperature data and cooler temperature data, including data cleaning, outlier processing and missing value filling;
selecting a proper feature set from the preprocessed electric energy data, pipeline pressure data, pipeline flow data, pipeline temperature data and cooler temperature data through correlation analysis;
dividing the feature set into a training set and a testing set, wherein the training set is used for training and parameter adjustment of the model, and the testing set is used for evaluating the generalization capability of the model;
training the neural network model by using the training set;
and predicting the real-time data by using the trained neural network model, and generating an energy-saving control decision based on the prediction result.
According to the scheme, an energy consumption model is constructed by preprocessing real-time data and selecting features. This model can use hidden rules and associations in the historical data to predict future energy consumption. Based on the prediction results, intelligent scheduling decisions can be generated, and energy consumption optimization and saving are realized; the neural network model is utilized to predict real-time data, so that energy-saving control decisions can be quickly and accurately generated. Through training and parameter adjustment of a deep learning algorithm, the accuracy and generalization capability of the model can be improved, so that the decision is more efficient and accurate.
Through intelligent scheduling decision, the running state and parameters of the air compressor can be optimized and adjusted according to real-time conditions. For example, parameters such as electric energy input, pipeline pressure, flow and the like are adjusted so as to achieve the optimal energy consumption effect. The operation efficiency of the air compressor can be improved, the energy consumption is reduced, and the service life of equipment is prolonged; and the decision model is utilized to carry out intelligent scheduling decision, so that the real-time monitoring and management of energy consumption can be realized. By continuously collecting and analyzing the real-time data, the energy consumption condition can be dynamically adjusted and optimized, potential problems can be found in time, corresponding measures are taken for adjustment, and the energy utilization efficiency is improved; the energy-saving control decision generated by the scheme can effectively reduce energy consumption. By optimizing the operation parameters and states of the air compressor, the energy consumption which is too high or invalid is avoided, and the purpose of energy conservation is achieved. This helps enterprises to reduce energy costs and increase economic benefits
The method for selecting the proper feature set from the preprocessed electric energy data, the pipeline pressure data, the pipeline flow data, the pipeline temperature data and the cooler temperature data through the correlation analysis comprises the following steps:
calculating a correlation coefficient between each feature and the energy-saving index;
based on the calculated correlation coefficient, visual search can be performed, and a correlation matrix is drawn;
setting a threshold value of a correlation coefficient to screen out features which are obviously correlated with the target variable;
screening out the characteristics with the correlation with the target variable exceeding a threshold value from the characteristics;
after the feature set is selected, a multiple collinearity check is performed.
By calculating the correlation coefficient between each feature and the energy saving index, the correlation between different features and the target variable can be quantified. Doing so may select features based on the data itself, rather than subjective judgment. Features with high correlation between features and target variables are more likely to have an effect on energy saving indicators, so the features are selected as a final feature set; the correlation matrix is drawn to intuitively show the correlation between the features and the target variable. Through visualization, a stronger correlation relationship exists between the characteristics and the target variable, so that the characteristic distribution and the correlation mode of the data can be understood.
Setting a correlation coefficient threshold can help to screen out features that are significantly correlated to the target variable. Thus, the interference of irrelevant features on the feature set can be reduced, so that the feature set is more compact and effective; after the feature set is selected, multiple collinearity checks can be performed to exclude the case of high correlation between features. Multiple collinearity may lead to model instability, reducing the interpretability and generalization ability of the model. The quality of the feature set and the performance of the model can be improved by checking and processing the multiple collinearity; by selecting the feature set with higher correlation with the target variable, the interference of irrelevant features on the model can be reduced, and the accuracy and generalization capability of the model are improved. Therefore, the energy-saving index can be predicted and optimized better, and the purpose of energy saving is achieved.
The method for performing the multiple collinearity check is as follows:
collecting a dataset for analysis, including all features and target variables;
the data is preprocessed, including data cleaning, missing value filling and standardization.
Selecting one target feature as a dependent variable, and fitting a linear regression model by using other features as independent variables;
for each feature, its variance expansion factor VIF is calculated as follows:
VIF＝1/(1-R^2)
wherein R2 is a coefficient of determination of the linear relationship of the feature to other arguments;
judging the collinearity between the features according to the size of the VIF, and if the VIF of one feature is greater than the threshold value 10, indicating that higher collinearity exists;
if the high collinearity exists between the features, selecting one of the high correlation features, and eliminating other correlation features;
re-fitting the linear model according to the processed feature set, and calculating an updated VIF value, wherein if the co-linearity problem is solved, the VIF value should be lower than a threshold value of 10;
if the co-linearity problem still exists, iteratively executing the steps until the co-linearity requirement of the model is met.
Multiple collinearity checking is a common method that is straightforward and easy to implement. By calculating the VIF value, the degree of collinearity between the features can be rapidly judged; collinearity can lead to highly correlated features in the model, which carry similar information between them, creating redundancy. Redundant information can be reduced by eliminating co-linearity features, and the simplicity and the interpretation of the model are improved.
Collinearity can lead to model instability, making the prediction of the model very sensitive to small changes in the input data. By solving the problem of collinearity, the stability of the model can be improved, so that the model is more robust to the change of input data; collinearity may lead to uncertainty in model coefficients, degrading the interpretation of the model. The model coefficient can be more credible and easier to explain by processing the collinearity, so that the influence of the characteristics on the target variable can be understood; multiple collinearity may cause inaccurate coefficient estimation of the model, increasing prediction error; collinearity may also lead to instability of feature selection, and different feature subsets may result in different models, making the model results unreliable. By multiple collinearity checking and processing, these potential problems can be avoided, improving the quality and reliability of the model
The beneficial effects of the invention are as follows:
in the scheme, the ultrasonic signals are processed and analyzed by the computer, the related information of cracks in the metal structural member can be rapidly and accurately obtained, the analysis and calculation are carried out by combining the basic theory of application mechanics, the durability and the safety of the metal structural member are evaluated, important references are provided for maintenance of the structural member, meanwhile, the finite element model is used for analyzing the metal structural member by the computer so as to determine the parameter information of the metal structural member, and finally, the durability of the metal structural member is comprehensively evaluated through evaluation of parameters such as crack expansion rate, fatigue cycle number and the like.
The above-mentioned embodiments of the present invention are not intended to limit the scope of the present invention, and the embodiments of the present invention are not limited thereto, and all kinds of modifications, substitutions or alterations made to the above-mentioned structures of the present invention according to the above-mentioned general knowledge and conventional means of the art without departing from the basic technical ideas of the present invention shall fall within the scope of the present invention.
Claims (10)
1. Wisdom energy air compression station energy-saving control system, its characterized in that includes:
the energy consumption monitoring equipment is used for monitoring and recording the energy consumption data of the air compressor in real time;
the data processing module is used for transmitting the energy consumption data recorded by the energy consumption monitoring equipment to the energy-saving control system for processing and analysis;
the energy consumption model building module is used for building an energy consumption model of the air compressor based on historical and real-time energy consumption data;
the energy consumption analysis module is used for analyzing the real-time data by using the energy consumption model and evaluating the current energy consumption condition;
the energy consumption optimizing module optimizes the operation strategy of the air compressor according to the planned production task and the energy consumption target;
and the intelligent scheduling decision module is used for performing intelligent scheduling decision through a decision model based on the energy consumption analysis and the optimization result.
2. The intelligent energy air compression station energy saving control system according to claim 1, wherein: the energy consumption monitoring equipment comprises an electric power instrument arranged on a power line of an air compressor, a pressure sensor arranged on a main gas pipeline of the air compressor, a flowmeter arranged on the gas pipeline of the air compressor, and a temperature sensor arranged on an air inlet pipeline, an air outlet pipeline, a cooler outlet and a cooler return pipeline of the air compressor.
3. The intelligent energy air compression station energy-saving control method is characterized by comprising the following steps of:
installing energy consumption monitoring equipment in the air compression station, and monitoring and recording energy consumption data of the air compressor in real time;
transmitting the data acquired by the energy consumption monitoring equipment to an energy-saving control system for processing and analysis;
based on historical data and real-time data, an energy consumption model of the air compressor is built;
analyzing the real-time data by using an energy consumption model, and evaluating the current energy consumption condition;
based on the energy consumption analysis and the optimization result, performing intelligent scheduling decision through a decision model;
and (5) automatically starting and stopping, switching and adjusting the running state and parameters of the air compressor based on the intelligent scheduling decision result.
4. The intelligent energy air compression station energy saving control method according to claim 3, wherein the method for installing the energy consumption monitoring equipment in the air compression station is as follows:
an electric power instrument is arranged on a power supply circuit of the air compressor and used for monitoring and recording the use condition of electric energy in real time;
a pressure sensor is arranged on a main gas pipeline of the air compressor and used for monitoring the air pressure in the pipeline in real time;
a flowmeter is arranged on a gas pipeline of the air compressor and is used for measuring the flow of air in real time;
and a temperature sensor is arranged on an air inlet pipeline, an air outlet pipeline, a cooler outlet and a cooler return pipeline of the air compressor.
5. The intelligent energy air compressor energy saving control method according to claim 4, wherein the method for transmitting the data collected by the energy consumption monitoring device to the energy saving control system is as follows:
and using the data collector or gateway equipment as a bridge to collect and transmit the data of the energy consumption monitoring equipment to the energy-saving control system.
6. The intelligent energy air compressor station energy-saving control method according to claim 4, wherein the method for building the energy consumption model of the air compressor is as follows:
collecting historical data and real-time data of an air compressor, wherein the historical data and the real-time data comprise electric energy data, pipeline pressure data, pipeline flow data, pipeline temperature data and cooler temperature data;
selecting proper characteristic variables from the collected data as input of an energy consumption model;
preprocessing the collected data, including data cleaning, outlier processing and missing value filling;
training a linear regression model by using historical data, and evaluating the performance of the model by using the decision coefficients;
and verifying and optimizing the trained model by using real-time data to obtain an optimized energy consumption model.
7. The intelligent energy air compressor station energy saving control method according to any one of claims 4 to 7, wherein the method for evaluating the current energy consumption condition is:
collecting historical data and real-time data of an air compressor, wherein the historical data and the real-time data comprise electric energy data, pipeline pressure data, pipeline flow data, pipeline temperature data and cooler temperature data;
preprocessing real-time data, including data cleaning, outlier detection and correction;
taking the preprocessed real-time data as input, and inputting the input into the established energy consumption model;
predicting the real-time data by using an energy consumption model to obtain a predicted energy consumption value;
comparing the actual energy consumption value with the predicted energy consumption value, and evaluating the current energy consumption condition;
and analyzing the current energy consumption condition by observing the change trend of the energy consumption.
8. The intelligent energy air compressor station energy-saving control method according to any one of claims 4 to 7, characterized in that:
based on the energy consumption analysis and the optimization result, the method for carrying out intelligent scheduling decision through the decision model comprises the following steps:
preprocessing collected electric energy data, pipeline pressure data, pipeline flow data, pipeline temperature data and cooler temperature data, including data cleaning, outlier processing and missing value filling;
selecting a proper feature set from the preprocessed electric energy data, pipeline pressure data, pipeline flow data, pipeline temperature data and cooler temperature data through correlation analysis;
dividing the feature set into a training set and a testing set, wherein the training set is used for training and parameter adjustment of the model, and the testing set is used for evaluating the generalization capability of the model;
training the neural network model by using the training set;
and predicting the real-time data by using the trained neural network model, and generating an energy-saving control decision based on the prediction result.
9. The intelligent energy air compressor station energy saving control method according to claim 8, wherein the method for selecting a suitable feature set from the preprocessed electric energy data, the pipeline pressure data, the pipeline flow data, the pipeline temperature data and the cooler temperature data by correlation analysis is as follows:
calculating a correlation coefficient between each feature and the energy-saving index;
based on the calculated correlation coefficient, visual search can be performed, and a correlation matrix is drawn;
setting a threshold value of a correlation coefficient to screen out features which are obviously correlated with the target variable;
screening out the characteristics with the correlation with the target variable exceeding a threshold value from the characteristics;
after the feature set is selected, a multiple collinearity check is performed.
10. The intelligent energy air compressor station energy saving control method according to claim 8, wherein the method for performing the multiple collinearity check is as follows:
collecting a dataset for analysis, including all features and target variables;
the data is preprocessed, including data cleaning, missing value filling and standardization.
Selecting one target feature as a dependent variable, and fitting a linear regression model by using other features as independent variables;
for each feature, its variance expansion factor VIF is calculated as follows:
VIF＝1/(1-R^2)
wherein R2 is a coefficient of determination of the linear relationship of the feature to other arguments;
judging the collinearity between the features according to the size of the VIF, and if the VIF of one feature is greater than the threshold value 10, indicating that higher collinearity exists;
if the high collinearity exists between the features, selecting one of the high correlation features, and eliminating other correlation features;
re-fitting the linear model according to the processed feature set, and calculating an updated VIF value, wherein if the co-linearity problem is solved, the VIF value should be lower than a threshold value of 10;
if the co-linearity problem still exists, iteratively executing the steps until the co-linearity requirement of the model is met.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311094864.XA CN117128162A (en) | 2023-08-28 | 2023-08-28 | Intelligent energy air compression station energy-saving control system and control method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311094864.XA CN117128162A (en) | 2023-08-28 | 2023-08-28 | Intelligent energy air compression station energy-saving control system and control method |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN117128162A true CN117128162A (en) | 2023-11-28 |
Family
ID=88854129
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202311094864.XA Pending CN117128162A (en) | 2023-08-28 | 2023-08-28 | Intelligent energy air compression station energy-saving control system and control method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN117128162A (en) |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20180306188A1 (en) * | 2017-04-19 | 2018-10-25 | Abac Aria Compressa S.P.A. | Compressor provided with an electronic pressure switch and method of regulating the pressure within such a compressor |
| CN113537644A (en) * | 2021-08-23 | 2021-10-22 | 中冶赛迪技术研究中心有限公司 | Multi-air compression station dynamic collaborative optimization regulation and control system and method |
| CN113883692A (en) * | 2021-10-28 | 2022-01-04 | 湖北合合能源科技发展有限公司 | Intelligent energy management system for energy conservation of air conditioner |
| CN113962100A (en) * | 2021-10-27 | 2022-01-21 | 重庆电子工程职业学院 | Automatic optimization intelligent energy control system and method based on big data analysis |
-
2023
- 2023-08-28 CN CN202311094864.XA patent/CN117128162A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20180306188A1 (en) * | 2017-04-19 | 2018-10-25 | Abac Aria Compressa S.P.A. | Compressor provided with an electronic pressure switch and method of regulating the pressure within such a compressor |
| CN113537644A (en) * | 2021-08-23 | 2021-10-22 | 中冶赛迪技术研究中心有限公司 | Multi-air compression station dynamic collaborative optimization regulation and control system and method |
| CN113962100A (en) * | 2021-10-27 | 2022-01-21 | 重庆电子工程职业学院 | Automatic optimization intelligent energy control system and method based on big data analysis |
| CN113883692A (en) * | 2021-10-28 | 2022-01-04 | 湖北合合能源科技发展有限公司 | Intelligent energy management system for energy conservation of air conditioner |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109240244B (en) | Data-driven equipment running state health degree analysis method and system | |
| CN117176560B (en) | Monitoring equipment supervision system and method based on Internet of things | |
| CN113036913B (en) | Method and device for monitoring state of comprehensive energy equipment | |
| CN111486552A (en) | Method for identifying water supply temperature strategy of chilled water of air conditioner based on subentry metering data | |
| CN112728723A (en) | Method for monitoring and analyzing operation energy efficiency of refrigeration machine room by intelligent group control system | |
| CN106567964B (en) | A kind of flow control valve monitoring method based on DCS data | |
| CN114879619A (en) | Digital workshop energy optimization method and system | |
| CN116989432B (en) | Energy-saving operation optimization scheduling method and system for air conditioning system | |
| CN116720854B (en) | Equipment coordination control method and system based on intelligent patrol | |
| CN117032120A (en) | Integrated intelligent cloud control system and control method for air compression station | |
| CN117128162A (en) | Intelligent energy air compression station energy-saving control system and control method | |
| CN108709426B (en) | Sintering machine air leakage fault online diagnosis method based on frequency spectrum characteristic bilateral detection method | |
| CN111931969A (en) | Merging unit equipment state prediction method based on time sequence analysis | |
| CN115936676A (en) | Equipment health management method and system based on digital twins | |
| CN108229850B (en) | Urban energy consumption and environmental protection data management method and system based on industrial production network | |
| CN117082105B (en) | Environment-friendly intelligent hospital facility monitoring system and method | |
| CN117273402B (en) | Energy-saving management system and method for glass deep processing production line based on Internet of Things technology | |
| CN117308275B (en) | Temperature difference-based pipeline connection abnormality detection method and system | |
| CN117406685B (en) | Intelligent control optimizing management system of building equipment suitable for green low-carbon building | |
| KR102556151B1 (en) | Method for predicting production information in the energy-consuming food industry using IIoT transmitters based on machine learning and method for optimizing energy | |
| TWI783593B (en) | Intelligent production line equipment precise preventive maintenance and energy consumption visual monitoring system and method | |
| CN114414938B (en) | Dynamic response method and system for power distribution network faults | |
| KR102573254B1 (en) | System for predicting and analyzing trouble of mechanical equipment using federated learning | |
| CN117490193A (en) | Group control method for central air-conditioning water system | |
| CN117869278A (en) | Energy-saving control method and system for air compressor unit |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |