CN115542838B - Online monitoring and early warning method and system based on PLC intelligent control - Google Patents
Online monitoring and early warning method and system based on PLC intelligent control Download PDFInfo
- Publication number
- CN115542838B CN115542838B CN202211357223.4A CN202211357223A CN115542838B CN 115542838 B CN115542838 B CN 115542838B CN 202211357223 A CN202211357223 A CN 202211357223A CN 115542838 B CN115542838 B CN 115542838B
- Authority
- CN
- China
- Prior art keywords
- pipeline
- plc
- sequence
- wind power
- material conveying
- 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.)
- Active
Links
- 238000012544 monitoring process Methods 0.000 title claims abstract description 66
- 238000000034 method Methods 0.000 title claims abstract description 35
- 230000005856 abnormality Effects 0.000 claims abstract description 121
- 238000001514 detection method Methods 0.000 claims abstract description 65
- 238000005457 optimization Methods 0.000 claims description 30
- 230000006870 function Effects 0.000 claims description 22
- 238000013528 artificial neural network Methods 0.000 claims description 11
- 238000010276 construction Methods 0.000 claims description 8
- 230000000694 effects Effects 0.000 abstract description 23
- 230000002159 abnormal effect Effects 0.000 description 6
- 238000012549 training Methods 0.000 description 2
- 230000006978 adaptation Effects 0.000 description 1
- 210000004556 brain Anatomy 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 230000010365 information processing Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000012545 processing Methods 0.000 description 1
Classifications
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B19/00—Programme-control systems
- G05B19/02—Programme-control systems electric
- G05B19/04—Programme control other than numerical control, i.e. in sequence controllers or logic controllers
- G05B19/05—Programmable logic controllers, e.g. simulating logic interconnections of signals according to ladder diagrams or function charts
- G05B19/054—Input/output
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B2219/00—Program-control systems
- G05B2219/10—Plc systems
- G05B2219/13—Plc programming
- G05B2219/13142—Debugging, tracing
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- 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]
Landscapes
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Engineering & Computer Science (AREA)
- Automation & Control Theory (AREA)
- Pipeline Systems (AREA)
Abstract
The invention discloses an online monitoring and early warning method and system based on PLC intelligent control, and relates to the field of intelligent control, wherein the method comprises the following steps: determining a material conveying pipeline path according to the wind power material conveying device; traversing a material conveying pipeline path, determining a material conveying flow threshold sequence and a material conveying speed threshold sequence, and optimizing and determining pipeline valve control parameters and pipeline wind power control parameters based on the material conveying flow threshold sequence and the material conveying speed threshold sequence; inputting the pipeline valve control parameters and the pipeline wind power control parameters into a PLC circuit control parameter matching model to generate PLC circuit control parameters; inputting the real-time control parameter monitoring data into a PLC control abnormality detection model, generating an abnormality control parameter and carrying out PLC control abnormality early warning. The technical problem of the unusual monitoring effect of control to PLC not good among the prior art is solved. The technical effects of improving the control abnormality monitoring effect of the PLC, improving the accuracy of the early warning of the control abnormality of the PLC and the like are achieved.
Description
Technical Field
The invention relates to the field of intelligent control, in particular to an online monitoring and early warning method and system based on PLC intelligent control.
Background
With the wide application of online monitoring, the requirements of people on online monitoring are getting higher and higher. The PLC is combined with the on-line monitoring, and the on-line monitoring system is intelligently controlled through the PLC, so that the remote off-line automatic monitoring is realized, and the method has important practical significance. In the prior art, the control abnormality monitoring accuracy of the PLC is insufficient, the intelligent degree is low, the control abnormality monitoring effect of the PLC is poor, and the technical problem of relatively accurate PLC control abnormality early warning cannot be realized.
Disclosure of Invention
The application provides an online monitoring and early warning method and system based on PLC intelligent control. The technical problems that in the prior art, the control abnormality monitoring accuracy aiming at the PLC is insufficient, the intelligent degree is low, the control abnormality monitoring effect of the PLC is poor, and accurate PLC control abnormality early warning cannot be realized are solved.
In view of the above problems, the application provides an online monitoring and early warning method and system based on PLC intelligent control.
In a first aspect, the application provides an online monitoring and early warning method based on PLC intelligent control, wherein the method is applied to an online monitoring and early warning system based on PLC intelligent control, and the method comprises the following steps: acquiring basic information of a material to be conveyed, wherein the basic information of the material to be conveyed comprises the material to be conveyed, a material conveying starting point and a material conveying ending point; determining a material conveying pipeline path according to a wind-force material conveying device based on the material conveying starting point and the material conveying ending point; traversing the material conveying pipeline path, and determining a material conveying flow threshold sequence and a material conveying speed threshold sequence; optimizing and determining pipeline valve control parameters and pipeline wind power control parameters according to the material conveying flow threshold sequence and the material conveying speed threshold sequence; inputting the pipeline valve control parameters and the pipeline wind power control parameters into a PLC circuit control parameter matching model to generate PLC circuit control parameters; constructing a PLC control abnormality detection model according to the PLC circuit control parameters; inputting the real-time control parameter monitoring data into the PLC control abnormality detection model, generating abnormality control parameters and carrying out PLC control abnormality early warning.
In a second aspect, the application also provides an online monitoring and early warning system based on PLC intelligent control, wherein the system comprises: the information acquisition module is used for acquiring basic information of materials to be conveyed, wherein the basic information of the materials to be conveyed comprises the materials to be conveyed, a material conveying starting point and a material conveying end point; the path determining module is used for determining a material conveying pipeline path according to the wind power material conveying device based on the material conveying starting point and the material conveying ending point; the sequence determining module is used for traversing the material conveying pipeline path and determining a material conveying flow threshold sequence and a material conveying speed threshold sequence; the parameter determining module is used for optimally determining pipeline valve control parameters and pipeline wind power control parameters according to the material conveying flow threshold sequence and the material conveying speed threshold sequence; the circuit control parameter generation module is used for inputting the pipeline valve control parameters and the pipeline wind power control parameters into a PLC circuit control parameter matching model to generate PLC circuit control parameters; the construction module is used for constructing a PLC control abnormality detection model according to the PLC circuit control parameters; the abnormality early warning module is used for inputting real-time control parameter monitoring data into the PLC control abnormality detection model, generating abnormality control parameters and carrying out PLC control abnormality early warning.
One or more technical schemes provided by the application have at least the following technical effects or advantages:
Determining a material conveying starting point and a material conveying end point through basic information of a material to be conveyed, and determining a material conveying pipeline path by combining a wind-driven material conveying device; traversing a material conveying pipeline path, and determining a material conveying flow threshold sequence and a material conveying speed threshold sequence; optimizing and determining pipeline valve control parameters and pipeline wind power control parameters according to the material conveying flow threshold sequence and the material conveying speed threshold sequence; inputting the pipeline valve control parameters and the pipeline wind power control parameters into a PLC circuit control parameter matching model to generate PLC circuit control parameters; constructing a PLC control abnormality detection model according to the control parameters of the PLC circuit; inputting the real-time control parameter monitoring data into a PLC control abnormality detection model, generating an abnormality control parameter and carrying out PLC control abnormality early warning. The control abnormality monitoring accuracy and the intelligent degree of the PLC are improved, the control abnormality monitoring effect of the PLC is improved, the accuracy of PLC control abnormality early warning is improved, and the technical effect of accurate PLC control abnormality early warning is achieved.
Drawings
FIG. 1 is a schematic flow chart of an online monitoring and early warning method based on PLC intelligent control;
FIG. 2 is a schematic flow chart of determining a material conveying flow threshold sequence and a material conveying speed threshold sequence in an online monitoring and early warning method based on PLC intelligent control;
Fig. 3 is a schematic structural diagram of an online monitoring and early warning system based on PLC intelligent control.
Reference numerals illustrate: the system comprises an information acquisition module 11, a path determination module 12, a sequence determination module 13, a parameter determination module 14, a circuit control parameter generation module 15, a construction module 16 and an abnormality early warning module 17.
Detailed Description
The application provides an online monitoring and early warning method and system based on PLC intelligent control. The technical problems that in the prior art, the control abnormality monitoring accuracy aiming at the PLC is insufficient, the intelligent degree is low, the control abnormality monitoring effect of the PLC is poor, and accurate PLC control abnormality early warning cannot be realized are solved. The control abnormality monitoring accuracy and the intelligent degree of the PLC are improved, the control abnormality monitoring effect of the PLC is improved, the accuracy of PLC control abnormality early warning is improved, and the technical effect of accurate PLC control abnormality early warning is achieved.
Example 1
Referring to fig. 1, the application provides an online monitoring and early warning method based on PLC intelligent control, wherein the method is implemented by an online monitoring and early warning system based on PLC intelligent control, the system is applied to a wind power material conveying device, and the method specifically comprises the following steps:
Step S100: acquiring basic information of a material to be conveyed, wherein the basic information of the material to be conveyed comprises the material to be conveyed, a material conveying starting point and a material conveying ending point;
step S200: determining a material conveying pipeline path according to a wind-force material conveying device based on the material conveying starting point and the material conveying ending point;
specifically, information acquisition is carried out on the material to be conveyed, and basic information of the material to be conveyed is obtained. Further, a material conveying pipeline path is obtained based on the material conveying starting point and the material conveying ending point by combining the wind power material conveying device. The basic information of the materials to be conveyed comprises the materials to be conveyed, a materials conveying starting point and a materials conveying end point. The material to be conveyed is any material which is intelligently conveyed by using the wind power material conveying device. The material to be conveyed quantity comprises the mass and the quantity of the material to be conveyed. The material conveying starting point and the material conveying end point correspond to the starting point position and the end point position of the material to be conveyed. The wind power material conveying device is material conveying equipment for executing the online monitoring and early warning method based on PLC intelligent control. The material conveying pipeline path comprises specific path information of the wind power material conveying device for conveying materials according to a material conveying starting point and a material conveying end point. The technical effects of determining the material conveying pipeline path by analyzing the basic information of the material to be conveyed and the wind-force material conveying device are achieved, and accordingly the accuracy of the subsequent PLC control abnormality early warning is improved.
Step S300: traversing the material conveying pipeline path, and determining a material conveying flow threshold sequence and a material conveying speed threshold sequence;
further, as shown in fig. 2, step S300 of the present application further includes:
Step S310: according to the material conveying pipeline path, a feeding device is obtained;
Step S320: acquiring a plurality of first valves according to the feeding device, wherein the first valves are multi-channel valves and are in a normally closed state;
Step S330: dividing the feeding device according to the first valves to generate a first conveying pipeline and a second conveying pipeline until an Nth conveying pipeline;
Step S340: traversing the first conveying pipeline and the second conveying pipeline until the N conveying pipeline, and acquiring pipeline diameter information and the type of the material to be conveyed;
Step S350: and inputting the pipeline diameter information and the type of the material to be conveyed into a conveying parameter calibration table to obtain the material conveying flow threshold sequence and the material conveying speed threshold sequence.
Specifically, based on the material conveying pipeline path, a feeding device is determined, and a plurality of first valves are obtained. Further, the feeding device is divided according to the first valves, a first conveying pipeline and a second conveying pipeline … … N conveying pipeline are obtained, and pipeline diameter parameters and material conveying type parameters are acquired based on the first conveying pipeline and the second conveying pipeline … … N conveying pipeline, so that pipeline diameter information and material types to be conveyed are obtained. And inputting the pipeline diameter information and the type of the material to be conveyed into a conveying parameter calibration table by taking the pipeline diameter information and the type of the material to be conveyed as input information, so as to obtain a material conveying flow threshold sequence and a material conveying speed threshold sequence. The feeding device comprises specific feeding position information of the wind power material conveying device corresponding to the material conveying pipeline path. The first valves are multichannel valves of the feeding device, and are in a normally closed state. The first conveying pipeline and the second conveying pipeline … … N conveying pipeline are a plurality of conveying pipelines which are obtained by dividing the feeding device according to a plurality of first valves. The pipeline diameter information and the type of the materials to be conveyed comprise pipeline diameter parameters and material conveying type parameters corresponding to the N-th conveying pipeline of the first conveying pipeline and the second conveying pipeline … …. The conveying parameter calibration table comprises a plurality of preset pipeline diameter parameters, a plurality of preset material type parameters to be conveyed, a plurality of preset pipeline diameter parameters, a plurality of material conveying flow thresholds and a plurality of material conveying speed thresholds, wherein the material conveying flow thresholds and the material conveying speed thresholds correspond to the preset material type parameters to be conveyed. The conveying parameter calibration table can be constructed and obtained by inquiring and collecting historical conveying logs of a plurality of material conveying factories. The conveying parameter calibration table can be used for matching a material conveying flow threshold value and a material conveying speed threshold value according to the pipeline diameter parameter and the material type parameter to be conveyed. The material conveying flow threshold sequence comprises material conveying flow thresholds corresponding to each conveying pipeline in the N conveying pipeline of the first conveying pipeline and the second conveying pipeline … …. The material conveying speed threshold sequence comprises material conveying speed thresholds corresponding to each conveying pipeline in the first conveying pipeline and the N conveying pipeline of the second conveying pipeline … …. The technical effects of obtaining accurate and reliable pipeline diameter information and material type to be conveyed by matching the pipeline diameter information and the material type to be conveyed through the conveying parameter calibration table are achieved, and accordingly the accuracy of the pipeline valve control parameters and the pipeline wind power control parameters which are obtained later is improved.
Step S400: optimizing and determining pipeline valve control parameters and pipeline wind power control parameters according to the material conveying flow threshold sequence and the material conveying speed threshold sequence;
further, the step S400 of the present application further includes:
Step S410: determining a valve opening interval sequence according to the material conveying flow threshold sequence;
step S420: determining a pipeline wind power interval sequence according to the material conveying speed threshold sequence;
Specifically, based on the material conveying flow threshold sequence, valve opening intervals of the first valves are matched, and a valve opening interval sequence is obtained. The valve opening interval sequence comprises valve opening range information of a plurality of first valves corresponding to the first conveying pipeline and the second conveying pipeline … … N conveying pipeline under the condition that the material conveying flow threshold sequence is met, namely when the material conveying flow threshold corresponding to each conveying pipeline is met. Further, based on the material conveying speed threshold value sequence, wind power interval matching is carried out on the first conveying pipeline and the N conveying pipeline of the second conveying pipeline … …, and a pipeline wind power interval sequence is obtained. The pipeline wind power interval sequence comprises wind power size range information corresponding to the N-th conveying pipeline of the first conveying pipeline and the second conveying pipeline … … under the condition that the material conveying speed threshold value sequence is met, namely, when the material conveying speed threshold value corresponding to each conveying pipeline is met. The method achieves the technical effects that the accurate valve opening interval sequence and the pipeline wind power interval sequence are obtained by analyzing the material conveying flow threshold sequence and the material conveying speed threshold sequence, so that the accuracy of optimizing and determining the pipeline valve control parameters and the pipeline wind power control parameters is improved.
Step S430: obtaining a fitness function:
Wherein, Representing the valve opening of an nth conveying pipeline, F n representing the wind power of the nth conveying pipeline, xi n representing the resistance coefficient of the nth conveying pipeline, T representing the fitness, N epsilon N;
Step S440: and optimizing based on the fitness function according to the valve opening interval sequence and the pipeline wind power interval sequence to generate the pipeline valve control parameters and the pipeline wind power control parameters.
Further, step S440 of the present application further includes:
Step S441: according to the valve opening interval sequence and the pipeline wind power interval sequence, an mth valve opening sequence and an mth pipeline wind power sequence are obtained;
Step S442: inputting the mth valve opening sequence and the mth pipeline wind power sequence into the fitness function to obtain mth fitness;
step S443: judging whether the m-th fitness is larger than or equal to the m-1 th fitness;
Step S444: if the opening sequence of the mth valve and the wind power sequence of the mth pipeline are larger than or equal to each other, setting the opening sequence of the mth valve and the wind power sequence of the mth pipeline as mth optimization results; if the value is smaller than the value, setting an mth-1 valve opening sequence and an mth-1 pipeline wind power sequence as the mth optimization result;
Step S445: judging whether m meets preset times, if so, determining the pipeline valve control parameters and the pipeline wind power control parameters according to the m-th optimization result.
Specifically, the first conveying pipeline and the second conveying pipeline … … and the N conveying pipeline are sequentially arranged to be N conveying pipelines, and the N conveying pipelines are in one-to-one correspondence with the first conveying pipeline and the second conveying pipeline … … and the N conveying pipeline. And then, the valve opening interval sequence and the pipeline wind power interval sequence are matched based on the nth conveying pipeline to obtain an nth valve opening interval sequence and an nth conveying pipeline wind power interval sequence corresponding to the nth conveying pipeline, and the nth valve opening interval sequence and the nth conveying pipeline wind power interval sequence are randomly selected to obtain an mth valve opening sequence and an mth pipeline wind power sequence. The mth valve opening sequence is any valve opening information in the nth valve opening interval sequence. The m-th pipeline wind power sequence comprises arbitrary wind power size information in the n-th pipeline wind power interval sequence. And then, taking the opening sequence of the mth valve and the wind power sequence of the mth pipeline as input information, and inputting the input information into a fitness function to obtain the mth fitness. In the function of the degree of adaptation,Characterizing the valve opening of the nth conveying pipeline, at this time,Is the input mth valve opening sequence. F n characterizes the wind force of the nth transport pipe, and F n is the input wind force sequence of the mth pipe. And xi n represents the resistance coefficient of the nth conveying pipeline, and the xi n can be obtained by collecting and inquiring the resistance parameters of the nth conveying pipeline. T represents fitness, N is N, and T is the mth fitness of the output.
Further, randomly selecting the nth valve opening interval sequence and the nth conveying pipeline wind power interval sequence again to obtain the mth-1 valve opening sequence and the mth-1 pipeline wind power sequence. The m-1 th valve opening sequence is any valve opening information different from the m-th valve opening sequence in the n-th valve opening interval sequence. The m-1 pipeline wind power sequence is any wind power magnitude information which is different from the m pipeline wind power sequence in the n-th conveying pipeline wind power interval sequence. And then, the m-1 valve opening sequence and the m-1 pipeline wind power sequence are used as input information, and the fitness function is input, so that the m-1 fitness is obtained. The m-1 th fitness is obtained in the same manner as the m-th fitness, and for brevity of description, details are not repeated here.
Further, whether the mth fitness is larger than or equal to the mth-1 fitness is judged, if the mth fitness is larger than or equal to the mth-1 fitness, the mth valve opening sequence and the mth pipeline wind power sequence are set as mth optimization results, and at the moment, the mth optimization results comprise the mth valve opening sequence and the mth pipeline wind power sequence. And if the m-th fitness is smaller than the m-1-th fitness, setting the m-1-th valve opening sequence and the m-1-th pipeline wind power sequence as m-th optimization results, wherein the m-th optimization results comprise the m-1-th valve opening sequence and the m-1-th pipeline wind power sequence. And further, performing iterative optimization based on the mth optimization result, judging whether m meets the preset times or not, namely judging whether the iterative optimization times of the mth optimization result meet the preset times or not, and if the iterative optimization times of the mth optimization result meet the preset times, determining the pipeline valve control parameters and the pipeline wind power control parameters based on the mth optimization result of which the iterative optimization times meet the preset times. The preset times comprise preset iteration optimizing times thresholds. The pipeline valve control parameters and the pipeline wind power control parameters comprise valve opening information and wind power information of a first conveying pipeline and a second conveying pipeline … … which correspond to an mth optimizing result of which the iterative optimizing times meet the preset times. The method achieves the technical effects that the valve opening interval sequence and the pipeline wind power interval sequence are subjected to iterative optimization for the preset times through the fitness function, the pipeline valve control parameters and the pipeline wind power control parameters with high accuracy and high fitness are obtained, and therefore the intelligence and the accuracy of PLC control abnormality early warning are improved.
Step S500: inputting the pipeline valve control parameters and the pipeline wind power control parameters into a PLC circuit control parameter matching model to generate PLC circuit control parameters;
Further, the step S500 of the present application further includes:
step S510: acquiring pipeline conveying PLC control record data and constructing a PLC circuit control parameter matching model;
further, step S510 of the present application further includes:
Step S511: acquiring pipeline valve control parameter record data and pipeline valve PLC control parameter record data according to the pipeline conveying PLC control record data;
step S512: constructing a pipeline valve PLC control parameter matching half model based on an artificial neural network according to the pipeline valve control parameter recording data and the pipeline valve PLC control parameter recording data;
Step S513: acquiring pipeline wind power control parameter record data and pipeline wind power PLC control parameter record data according to the pipeline conveying PLC control record data;
Step S514: constructing a pipeline wind power PLC control parameter matching half model based on an artificial neural network according to the pipeline wind power control parameter recording data and the pipeline wind power PLC control parameter recording data;
Step S515: and combining the pipeline valve PLC control parameter matching half model and the pipeline wind power PLC control parameter matching half model to generate the PLC circuit control parameter matching model.
Step S520: inputting the pipeline valve control parameters and the pipeline wind power control parameters into the PLC circuit control parameter matching model to generate the PLC circuit control parameters.
Specifically, based on the first conveying pipeline and the N conveying pipeline of the second conveying pipeline … …, pipeline conveying PLC control record information is acquired through big data, and pipeline conveying PLC control record data are obtained. The pipeline conveying PLC control record data comprises pipeline valve control parameter record data, pipeline valve PLC control parameter record data, pipeline wind power control parameter record data and pipeline wind power PLC control parameter record data. Further, based on the artificial neural network, the pipeline valve control parameter record data and the pipeline valve PLC control parameter record data are subjected to continuous self-training learning until a convergence state is obtained, and the pipeline valve PLC control parameter matching half model is obtained. And further, based on the artificial neural network, the pipeline wind power control parameter record data and the pipeline wind power PLC control parameter record data are subjected to continuous self-training learning until a convergence state is obtained, and the pipeline wind power PLC control parameter matching half model is obtained. And then combining the obtained pipeline valve PLC control parameter matching half model and the pipeline wind power PLC control parameter matching half model to obtain a PLC circuit control parameter matching model. And further, taking the pipeline valve control parameters and the pipeline wind power control parameters as input information, inputting the input information into a PLC circuit control parameter matching model, and obtaining the PLC circuit control parameters.
The pipeline valve control parameter record data comprises a plurality of historical valve opening information of a plurality of first valves corresponding to the N-th conveying pipeline of the first conveying pipeline and the second conveying pipeline … …. The pipeline valve PLC control parameter record data comprise electric control conveying parameters such as voltage, current, alternating frequency and the like corresponding to the pipeline valve control parameter record data. The artificial neural network is a nonlinear information processing system which consists of a large number of processing units and has self-adaption, self-organization and self-learning capabilities. The artificial neural network processes information by simulating the brain neural network to process and memorize information. The pipeline valve PLC control parameter matching half model has the functions of performing intelligent analysis and PLC control parameter matching on the input pipeline valve control parameters. The pipeline wind power control parameter record data comprises a plurality of historical wind power size information corresponding to the N-th conveying pipeline of the first conveying pipeline and the second conveying pipeline … …. The pipeline wind power PLC control parameter record data comprise electric control conveying parameters such as voltage, current, alternating frequency and the like corresponding to the pipeline wind power PLC control parameter record data. The pipeline wind power PLC control parameter matching half model has the functions of intelligently analyzing the input pipeline wind power control parameters and matching the PLC control parameters. The PLC circuit control parameter matching model comprises a pipeline valve PLC control parameter matching half model and a pipeline wind power PLC control parameter matching half model. The PLC circuit control parameters comprise pipeline valve control parameters, voltage, current, alternating frequency and other electric control conveying parameters corresponding to the pipeline wind power control parameters. The method has the advantages that PLC control record data are conveyed through a pipeline, a PLC circuit control parameter matching model with strong adaptability and high generalization performance is constructed, and pipeline valve control parameters and pipeline wind power control parameters are subjected to matching analysis through the PLC circuit control parameter matching model, so that accurate PLC circuit control parameters are obtained, and the accuracy and the comprehensive technical effect of monitoring control abnormality of the PLC are improved.
Step S600: constructing a PLC control abnormality detection model according to the PLC circuit control parameters;
further, the step S600 of the present application further includes:
step S610: traversing the PLC circuit control parameters, and collecting a plurality of groups of PLC circuit control parameter sample data sets, wherein the plurality of groups of PLC circuit control parameter sample data sets are control parameters for normal operation, and the characteristic value data quantity of any control parameter is more than or equal to 2;
step S620: acquiring a first group of control parameter sample data sets according to the plurality of groups of PLC circuit control parameter sample data sets;
step S630: based on the first group of control parameter sample data sets, carrying out multi-layer division according to data characteristic values, and constructing a first PLC control abnormality detection tree, wherein any one layer of the first PLC control abnormality detection tree defines a sample data set of the characteristic values;
Step S640: according to the ith group of control parameter sample data set, carrying out multi-layer division according to the data characteristic values, and constructing an ith PLC control abnormality detection tree, wherein any one layer of the ith PLC control abnormality detection tree limits a sample data set storing one characteristic value;
Step S650: merging the first PLC control abnormality detection tree to the ith PLC control abnormality detection tree to generate the PLC control abnormality detection model.
Step S700: inputting the real-time control parameter monitoring data into the PLC control abnormality detection model, generating abnormality control parameters and carrying out PLC control abnormality early warning.
Specifically, a plurality of groups of PLC circuit control parameter sample data sets are collected, wherein each group of PLC circuit control parameter sample data sets comprises a plurality of groups of PLC circuit control parameters which work normally and data characteristic values corresponding to the plurality of groups of PLC circuit control parameters, and the data quantity of any one control parameter characteristic value is more than or equal to 2. And then, randomly selecting a plurality of groups of PLC circuit control parameter sample data sets to obtain a first group of control parameter sample data sets. And carrying out multi-layer division on the first group of control parameter sample data sets according to the data characteristic values to obtain a first PLC control abnormality detection tree. The first PLC controls the sample data set of any one level of the anomaly detection tree to define a characteristic value. The first PLC control abnormality detection tree comprises a plurality of layers of data corresponding to a first group of control parameter sample data sets, the plurality of layers of data are all normal data, the data quantity of any layer of normal data is more than or equal to 2, if the input real-time control parameter monitoring data are normal data, and certain layer of data falling into the first PLC control abnormality detection tree can be fused with the layer of data, if the real-time control parameter monitoring data cannot fall into the layer of data, other layers of data of the subsequent first PLC control abnormality detection tree and the subsequent plurality of PLC control abnormality detection trees are traversed. If they cannot fall, they are regarded as abnormal. Eventually, individual branch nodes will be generated, and the number is 1. And then, the method is the same as the construction method of the first PLC control abnormality detection tree, a plurality of groups of PLC circuit control parameter sample data sets are randomly selected and divided in a multi-layer mode, and a second PLC control abnormality detection tree … … ith PLC control abnormality detection tree is constructed. The second PLC controlled anomaly detection tree … …, the ith PLC controlled anomaly detection tree, defines a sample dataset storing a characteristic value at any level. Further, the first PLC control abnormality detection tree and the second PLC control abnormality detection tree … … ith PLC control abnormality detection tree are combined to obtain a PLC control abnormality detection model. And further, the control parameters of the PLC circuit are collected in real time to obtain real-time control parameter monitoring data, the real-time control parameter monitoring data is used as input information and is input into a PLC control abnormality detection model to obtain abnormal control parameters, and PLC control abnormality early warning is carried out according to the abnormal control parameters. The PLC control abnormality detection model comprises a first PLC control abnormality detection tree and a second PLC control abnormality detection tree … … ith PLC control abnormality detection tree. The real-time control parameter monitoring data comprises real-time PLC circuit control parameters. The abnormal control parameters comprise abnormal PLC circuit control parameters in real-time control parameter monitoring data. The technical effects of carrying out abnormality detection on real-time control parameter monitoring data through the PLC control abnormality detection model, generating abnormal control parameters to carry out PLC control abnormality early warning and improving the accuracy of the PLC control abnormality early warning are achieved.
In summary, the online monitoring and early warning method based on PLC intelligent control provided by the application has the following technical effects:
1. Determining a material conveying starting point and a material conveying end point through basic information of a material to be conveyed, and determining a material conveying pipeline path by combining a wind-driven material conveying device; traversing a material conveying pipeline path, and determining a material conveying flow threshold sequence and a material conveying speed threshold sequence; optimizing and determining pipeline valve control parameters and pipeline wind power control parameters according to the material conveying flow threshold sequence and the material conveying speed threshold sequence; inputting the pipeline valve control parameters and the pipeline wind power control parameters into a PLC circuit control parameter matching model to generate PLC circuit control parameters; constructing a PLC control abnormality detection model according to the control parameters of the PLC circuit; inputting the real-time control parameter monitoring data into a PLC control abnormality detection model, generating an abnormality control parameter and carrying out PLC control abnormality early warning. The control abnormality monitoring accuracy and the intelligent degree of the PLC are improved, the control abnormality monitoring effect of the PLC is improved, the accuracy of PLC control abnormality early warning is improved, and the technical effect of accurate PLC control abnormality early warning is achieved.
2. By analyzing the material conveying flow threshold sequence and the material conveying speed threshold sequence, an accurate valve opening interval sequence and a pipeline wind power interval sequence are obtained, so that the accuracy of optimizing and determining the pipeline valve control parameters and the pipeline wind power control parameters is improved.
3. The valve opening interval sequence and the pipeline wind interval sequence are subjected to iterative optimization for preset times through the fitness function, so that the pipeline valve control parameters and the pipeline wind control parameters with high accuracy and high fitness are obtained, and the intelligence and the accuracy of PLC control abnormality early warning are improved.
4. The PLC control record data is conveyed through the pipeline, a PLC circuit control parameter matching model with strong adaptability and high generalization performance is constructed, and pipeline valve control parameters and pipeline wind power control parameters are subjected to matching analysis through the PLC circuit control parameter matching model, so that accurate PLC circuit control parameters are obtained, and the accuracy and the comprehensiveness of monitoring of control abnormality of the PLC are improved.
Example two
Based on the same inventive concept as the online monitoring and early warning method based on the intelligent control of the PLC in the foregoing embodiment, the invention also provides an online monitoring and early warning system based on the intelligent control of the PLC, please refer to fig. 3, the system includes:
the information acquisition module 11 is used for acquiring basic information of materials to be conveyed, wherein the basic information of the materials to be conveyed comprises a material to be conveyed amount, a material conveying starting point and a material conveying end point;
A path determining module 12, wherein the path determining module 12 is used for determining a material conveying pipeline path according to a wind power material conveying device based on the material conveying starting point and the material conveying ending point;
a sequence determining module 13, wherein the sequence determining module 13 is used for traversing the material conveying pipeline path and determining a material conveying flow threshold sequence and a material conveying speed threshold sequence;
A parameter determining module 14, wherein the parameter determining module 14 is configured to optimally determine a pipeline valve control parameter and a pipeline wind power control parameter according to the material conveying flow threshold sequence and the material conveying speed threshold sequence;
The circuit control parameter generation module 15 is used for inputting the pipeline valve control parameter and the pipeline wind power control parameter into a PLC circuit control parameter matching model to generate a PLC circuit control parameter;
The construction module 16 is used for constructing a PLC control abnormality detection model according to the PLC circuit control parameters;
The abnormality pre-warning module 17 is used for inputting real-time control parameter monitoring data into the PLC control abnormality detection model, generating abnormality control parameters and carrying out PLC control abnormality pre-warning.
Further, the system further comprises:
the feeding device acquisition module is used for acquiring a feeding device according to the material conveying pipeline path;
The valve acquisition module is used for acquiring a plurality of first valves according to the feeding device, wherein the first valves are multichannel valves and are in a normally closed state;
The conveying pipeline generation module is used for dividing the feeding device according to the first valves to generate a first conveying pipeline, a second conveying pipeline and an N conveying pipeline;
The first execution module is used for traversing the first conveying pipeline and the second conveying pipeline until an N conveying pipeline, and acquiring pipeline diameter information and the type of a material to be conveyed;
The second execution module is used for inputting the pipeline diameter information and the type of the materials to be conveyed into a conveying parameter calibration table to obtain the material conveying flow threshold sequence and the material conveying speed threshold sequence.
Further, the system further comprises:
the valve opening interval sequence determining module is used for determining a valve opening interval sequence according to the material conveying flow threshold sequence;
the pipeline wind power interval sequence determining module is used for determining a pipeline wind power interval sequence according to the material conveying speed threshold value sequence;
The fitness function acquisition module is used for acquiring fitness functions:
Wherein, Representing the valve opening of an nth conveying pipeline, F n representing the wind power of the nth conveying pipeline, xi n representing the resistance coefficient of the nth conveying pipeline, T representing the fitness, N epsilon N;
And the third execution module is used for optimizing based on the fitness function according to the valve opening interval sequence and the pipeline wind power interval sequence to generate the pipeline valve control parameters and the pipeline wind power control parameters.
Further, the system further comprises:
the fourth execution module is used for acquiring an mth valve opening sequence and an mth pipeline wind power sequence according to the valve opening interval sequence and the pipeline wind power interval sequence;
The fitness obtaining module is used for inputting the mth valve opening sequence and the mth pipeline wind power sequence into the fitness function to obtain mth fitness;
the fitness judging module is used for judging whether the mth fitness is greater than or equal to the mth-1 fitness;
The optimization result determining module is used for setting the mth valve opening sequence and the mth pipeline wind power sequence as mth optimization results if the optimization result determining module is larger than or equal to the mth valve opening sequence and the mth pipeline wind power sequence; if the value is smaller than the value, setting an mth-1 valve opening sequence and an mth-1 pipeline wind power sequence as the mth optimization result;
And the control parameter determining module is used for judging whether m meets the preset times or not, and if so, determining the pipeline valve control parameters and the pipeline wind power control parameters according to the m-th optimization result.
Further, the system further comprises:
the fifth execution module is used for acquiring the PLC control record data of pipeline transportation and constructing the PLC circuit control parameter matching model;
The PLC circuit control parameter generation module is used for inputting the pipeline valve control parameters and the pipeline wind power control parameters into the PLC circuit control parameter matching model to generate the PLC circuit control parameters.
Further, the system further comprises:
The control parameter record data acquisition module is used for acquiring pipeline valve control parameter record data and pipeline valve PLC control parameter record data according to the pipeline conveying PLC control record data;
The sixth execution module is used for constructing a pipeline valve PLC control parameter matching half model based on an artificial neural network according to the pipeline valve control parameter recording data and the pipeline valve PLC control parameter recording data;
the pipeline wind power record data acquisition module is used for acquiring pipeline wind power control parameter record data and pipeline wind power PLC control parameter record data according to the pipeline conveying PLC control record data;
the seventh execution module is used for constructing a pipeline wind power PLC control parameter matching half model based on an artificial neural network according to the pipeline wind power control parameter recording data and the pipeline wind power PLC control parameter recording data;
And the merging module is used for merging the pipeline valve PLC control parameter matching half model and the pipeline wind power PLC control parameter matching half model to generate the PLC circuit control parameter matching model.
Further, the system further comprises:
The sample acquisition module is used for traversing the PLC circuit control parameters and acquiring a plurality of groups of PLC circuit control parameter sample data sets, wherein the plurality of groups of PLC circuit control parameter sample data sets are control parameters for normal operation, and the data quantity of any one control parameter characteristic value is more than or equal to 2;
the first group of control parameter sample data set acquisition module is used for acquiring a first group of control parameter sample data sets according to the plurality of groups of PLC circuit control parameter sample data sets;
The first PLC control abnormality detection tree construction module is used for carrying out multi-layer division according to the data characteristic values based on the first group of control parameter sample data sets to construct a first PLC control abnormality detection tree, wherein any one layer of the first PLC control abnormality detection tree defines a sample data set of the characteristic values;
The system comprises an ith PLC control anomaly detection tree construction module, a first control parameter sample data set, a second control parameter sample data set, a third control parameter sample data set, a fourth control parameter sample data set and a fourth control parameter sample data set, wherein the ith PLC control anomaly detection tree construction module is used for carrying out multi-layer division according to the ith control parameter sample data set and the third control parameter sample data set;
the abnormality detection tree merging module is used for merging the first PLC control abnormality detection tree to the ith PLC control abnormality detection tree to generate the PLC control abnormality detection model.
The application provides an online monitoring and early warning method based on PLC intelligent control, wherein the method is applied to an online monitoring and early warning system based on PLC intelligent control, and the method comprises the following steps: determining a material conveying starting point and a material conveying end point through basic information of a material to be conveyed, and determining a material conveying pipeline path by combining a wind-driven material conveying device; traversing a material conveying pipeline path, and determining a material conveying flow threshold sequence and a material conveying speed threshold sequence; optimizing and determining pipeline valve control parameters and pipeline wind power control parameters according to the material conveying flow threshold sequence and the material conveying speed threshold sequence; inputting the pipeline valve control parameters and the pipeline wind power control parameters into a PLC circuit control parameter matching model to generate PLC circuit control parameters; constructing a PLC control abnormality detection model according to the control parameters of the PLC circuit; inputting the real-time control parameter monitoring data into a PLC control abnormality detection model, generating an abnormality control parameter and carrying out PLC control abnormality early warning. The technical problems that in the prior art, the control abnormality monitoring accuracy aiming at the PLC is insufficient, the intelligent degree is low, the control abnormality monitoring effect of the PLC is poor, and accurate PLC control abnormality early warning cannot be realized are solved. The control abnormality monitoring accuracy and the intelligent degree of the PLC are improved, the control abnormality monitoring effect of the PLC is improved, the accuracy of PLC control abnormality early warning is improved, and the technical effect of accurate PLC control abnormality early warning is achieved.
The technical features of the above embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the description.
The specification and drawings are merely exemplary of the present application, and the present application is intended to cover modifications and variations of the present application provided they come within the scope of the application and its equivalents.
Claims (6)
1. The online monitoring and early warning method based on the PLC intelligent control is characterized in that the method is implemented by an online monitoring and early warning system based on the PLC intelligent control, and the system is applied to a wind power material conveying device and comprises the following steps:
Acquiring basic information of a material to be conveyed, wherein the basic information of the material to be conveyed comprises the material to be conveyed, a material conveying starting point and a material conveying ending point;
Determining a material conveying pipeline path according to a wind-force material conveying device based on the material conveying starting point and the material conveying ending point;
Traversing the material conveying pipeline path, and determining a material conveying flow threshold sequence and a material conveying speed threshold sequence;
optimizing and determining pipeline valve control parameters and pipeline wind power control parameters according to the material conveying flow threshold sequence and the material conveying speed threshold sequence;
inputting the pipeline valve control parameters and the pipeline wind power control parameters into a PLC circuit control parameter matching model to generate PLC circuit control parameters;
Constructing a PLC control abnormality detection model according to the PLC circuit control parameters;
Inputting the real-time control parameter monitoring data into the PLC control abnormality detection model, generating abnormality control parameters and carrying out PLC control abnormality early warning;
wherein, according to the material conveying flow threshold sequence and the material conveying speed threshold sequence, optimizing and determining pipeline valve control parameters and pipeline wind power control parameters, comprising:
determining a valve opening interval sequence according to the material conveying flow threshold sequence;
Determining a pipeline wind power interval sequence according to the material conveying speed threshold sequence;
Obtaining a fitness function:
；
Wherein, Characterizing the valve opening of the nth conveying pipeline,The wind power of the nth conveying pipeline is characterized,The resistance coefficient of the nth conveying pipeline is represented,Characterizing fitness, N ε N;
Optimizing based on the fitness function according to the valve opening interval sequence and the pipeline wind power interval sequence to generate the pipeline valve control parameters and the pipeline wind power control parameters;
Wherein, according to the valve opening interval sequence and the pipeline wind interval sequence, optimizing is performed based on the fitness function, and the generating the pipeline valve control parameter and the pipeline wind control parameter includes:
according to the valve opening interval sequence and the pipeline wind power interval sequence, an mth valve opening sequence and an mth pipeline wind power sequence are obtained;
Inputting the mth valve opening sequence and the mth pipeline wind power sequence into the fitness function to obtain mth fitness;
Judging whether the m-th fitness is larger than or equal to the m-1 th fitness;
if the opening sequence of the mth valve and the wind power sequence of the mth pipeline are larger than or equal to each other, setting the opening sequence of the mth valve and the wind power sequence of the mth pipeline as mth optimization results; if the value is smaller than the value, setting an mth-1 valve opening sequence and an mth-1 pipeline wind power sequence as the mth optimization result;
judging whether m meets preset times, if so, determining the pipeline valve control parameters and the pipeline wind power control parameters according to the m-th optimization result.
2. The method of claim 1, wherein traversing the material delivery conduit path, determining a sequence of material delivery flow thresholds and a sequence of material delivery speed thresholds, comprises:
according to the material conveying pipeline path, a feeding device is obtained;
acquiring a plurality of first valves according to the feeding device, wherein the first valves are multi-channel valves and are in a normally closed state;
Dividing the feeding device according to the first valves to generate a first conveying pipeline and a second conveying pipeline until an Nth conveying pipeline;
traversing the first conveying pipeline and the second conveying pipeline until the N conveying pipeline, and acquiring pipeline diameter information and the type of the material to be conveyed;
And inputting the pipeline diameter information and the type of the material to be conveyed into a conveying parameter calibration table to obtain the material conveying flow threshold sequence and the material conveying speed threshold sequence.
3. The method of claim 1, wherein inputting the pipeline valve control parameters and the pipeline wind control parameters into a PLC circuit control parameter matching model to generate PLC circuit control parameters comprises:
Acquiring pipeline conveying PLC control record data and constructing a PLC circuit control parameter matching model;
inputting the pipeline valve control parameters and the pipeline wind power control parameters into the PLC circuit control parameter matching model to generate the PLC circuit control parameters.
4. The method of claim 3, wherein the obtaining pipeline transportation PLC control record data and constructing the PLC circuit control parameter matching model comprises:
acquiring pipeline valve control parameter record data and pipeline valve PLC control parameter record data according to the pipeline conveying PLC control record data;
constructing a pipeline valve PLC control parameter matching half model based on an artificial neural network according to the pipeline valve control parameter recording data and the pipeline valve PLC control parameter recording data;
Acquiring pipeline wind power control parameter record data and pipeline wind power PLC control parameter record data according to the pipeline conveying PLC control record data;
Constructing a pipeline wind power PLC control parameter matching half model based on an artificial neural network according to the pipeline wind power control parameter recording data and the pipeline wind power PLC control parameter recording data;
and combining the pipeline valve PLC control parameter matching half model and the pipeline wind power PLC control parameter matching half model to generate the PLC circuit control parameter matching model.
5. The method of claim 1, wherein constructing a PLC control anomaly detection model based on the PLC circuit control parameters comprises:
Traversing the PLC circuit control parameters, and collecting a plurality of groups of PLC circuit control parameter sample data sets, wherein the plurality of groups of PLC circuit control parameter sample data sets are control parameters for normal operation, and the characteristic value data quantity of any control parameter is more than or equal to 2;
Acquiring a first group of control parameter sample data sets according to the plurality of groups of PLC circuit control parameter sample data sets;
based on the first group of control parameter sample data sets, carrying out multi-layer division according to data characteristic values, and constructing a first PLC control abnormality detection tree, wherein any one layer of the first PLC control abnormality detection tree defines a sample data set of the characteristic values;
According to the ith group of control parameter sample data set, carrying out multi-layer division according to the data characteristic values, and constructing an ith PLC control abnormality detection tree, wherein any one layer of the ith PLC control abnormality detection tree limits a sample data set storing one characteristic value;
merging the first PLC control abnormality detection tree to the ith PLC control abnormality detection tree to generate the PLC control abnormality detection model.
6. An online monitoring and early warning system based on PLC intelligent control, which is characterized in that the system is applied to a wind power material conveying device, and the system comprises:
the information acquisition module is used for acquiring basic information of materials to be conveyed, wherein the basic information of the materials to be conveyed comprises the materials to be conveyed, a material conveying starting point and a material conveying end point;
the path determining module is used for determining a material conveying pipeline path according to the wind power material conveying device based on the material conveying starting point and the material conveying ending point;
the sequence determining module is used for traversing the material conveying pipeline path and determining a material conveying flow threshold sequence and a material conveying speed threshold sequence;
the parameter determining module is used for optimally determining pipeline valve control parameters and pipeline wind power control parameters according to the material conveying flow threshold sequence and the material conveying speed threshold sequence;
The circuit control parameter generation module is used for inputting the pipeline valve control parameters and the pipeline wind power control parameters into a PLC circuit control parameter matching model to generate PLC circuit control parameters;
the construction module is used for constructing a PLC control abnormality detection model according to the PLC circuit control parameters;
the abnormality early warning module is used for inputting real-time control parameter monitoring data into the PLC control abnormality detection model, generating abnormality control parameters and carrying out PLC control abnormality early warning;
the system further comprises:
the valve opening interval sequence determining module is used for determining a valve opening interval sequence according to the material conveying flow threshold sequence;
the pipeline wind power interval sequence determining module is used for determining a pipeline wind power interval sequence according to the material conveying speed threshold value sequence;
The fitness function acquisition module is used for acquiring fitness functions:
；
Wherein, Characterizing the valve opening of the nth conveying pipeline,The wind power of the nth conveying pipeline is characterized,The resistance coefficient of the nth conveying pipeline is represented,Characterizing fitness, N ε N;
The third execution module is used for optimizing based on the fitness function according to the valve opening interval sequence and the pipeline wind power interval sequence to generate the pipeline valve control parameters and the pipeline wind power control parameters;
the system further comprises:
the fourth execution module is used for acquiring an mth valve opening sequence and an mth pipeline wind power sequence according to the valve opening interval sequence and the pipeline wind power interval sequence;
The fitness obtaining module is used for inputting the mth valve opening sequence and the mth pipeline wind power sequence into the fitness function to obtain mth fitness;
the fitness judging module is used for judging whether the mth fitness is greater than or equal to the mth-1 fitness;
The optimization result determining module is used for setting the mth valve opening sequence and the mth pipeline wind power sequence as mth optimization results if the optimization result determining module is larger than or equal to the mth valve opening sequence and the mth pipeline wind power sequence; if the value is smaller than the value, setting an mth-1 valve opening sequence and an mth-1 pipeline wind power sequence as the mth optimization result;
And the control parameter determining module is used for judging whether m meets the preset times or not, and if so, determining the pipeline valve control parameters and the pipeline wind power control parameters according to the m-th optimization result.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211357223.4A CN115542838B (en) | 2022-11-01 | 2022-11-01 | Online monitoring and early warning method and system based on PLC intelligent control |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211357223.4A CN115542838B (en) | 2022-11-01 | 2022-11-01 | Online monitoring and early warning method and system based on PLC intelligent control |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN115542838A CN115542838A (en) | 2022-12-30 |
| CN115542838B true CN115542838B (en) | 2024-10-18 |
Family
ID=84720268
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202211357223.4A Active CN115542838B (en) | 2022-11-01 | 2022-11-01 | Online monitoring and early warning method and system based on PLC intelligent control |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN115542838B (en) |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102633371A (en) * | 2012-04-06 | 2012-08-15 | 浙江浙能能源技术有限公司 | Full-automatic boiler feedwater oxygenating device and full-automatic boiler feedwater oxygenating method |
| CN107640594A (en) * | 2017-09-29 | 2018-01-30 | 襄阳市精信催化剂有限责任公司 | A kind of zero broken negative-pressure air power conveying device and control method |
Family Cites Families (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1847110A (en) * | 2005-04-11 | 2006-10-18 | 张涉 | Garbage collecting and transporting vacuum pipeline system |
| CN102567559A (en) * | 2010-12-31 | 2012-07-11 | 贵州中烟工业有限责任公司 | Simulation method in pneumatic cut tobacco feeding process |
| CN102494524B (en) * | 2011-12-07 | 2013-10-09 | 赵扬 | Circulating airflow drying device for pilot plant test |
| CH707402A2 (en) * | 2012-12-18 | 2014-06-30 | Belimo Holding Ag | Method and device for balancing a group of consumers in a fluid transport system. |
| CN108190526A (en) * | 2018-01-31 | 2018-06-22 | 浙江省建材集团建筑产业化有限公司 | A kind of powdered building materials conveying device |
| CN113317553B (en) * | 2021-05-31 | 2022-09-23 | 浙江中烟工业有限责任公司 | SACU tobacco shred air delivery controller online monitoring and diagnosing system |
| CN217350940U (en) * | 2022-06-13 | 2022-09-02 | 中交(寿光)投资有限公司 | Intelligent aeration control cabinet for sewage treatment |
-
2022
- 2022-11-01 CN CN202211357223.4A patent/CN115542838B/en active Active
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102633371A (en) * | 2012-04-06 | 2012-08-15 | 浙江浙能能源技术有限公司 | Full-automatic boiler feedwater oxygenating device and full-automatic boiler feedwater oxygenating method |
| CN107640594A (en) * | 2017-09-29 | 2018-01-30 | 襄阳市精信催化剂有限责任公司 | A kind of zero broken negative-pressure air power conveying device and control method |
Also Published As
| Publication number | Publication date |
|---|---|
| CN115542838A (en) | 2022-12-30 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN118469352B (en) | Deep learning driven power grid line loss dynamic prediction and optimization control method | |
| CN108491404B (en) | A Bad Data Identification Method for State Estimation Based on BP Neural Network | |
| CN107169621A (en) | A kind of Dissolved Oxygen in Water Forecasting Methodology and device | |
| CN112016248B (en) | Elman neural network-based bad data identification method for SCR denitration system | |
| CN116579768B (en) | Power plant on-line instrument operation and maintenance management method and system | |
| CN116826978B (en) | Electrical variable adjustment monitoring method and system for distribution box | |
| CN113051828B (en) | Online prediction method for natural gas water dew point driven by technological parameters | |
| CN113988398A (en) | Wind turbine generator power prediction method and device, electronic equipment and storage medium | |
| CN115542838B (en) | Online monitoring and early warning method and system based on PLC intelligent control | |
| CN117951577A (en) | A method for sensing energy status in virtual power plants | |
| CN115184734B (en) | A method and system for detecting power grid line faults | |
| CN103345555A (en) | Thermal power unit coal consumption characteristic curve modeling method based on genetic programming | |
| CN115271000B (en) | State monitoring method and system for cable tunnel | |
| Wang et al. | Pm2. 5 prediction based on neural network | |
| CN115713162A (en) | Distributed low-carbon energy station health state early warning prediction method | |
| CN105300868B (en) | A kind of hole-punching huon pine paper air permeability detection method in tobacco business | |
| CN119179992A (en) | Inspection method and system combining power operation scene characteristics | |
| CN119109772A (en) | A method for identifying communication anomalies of a numerically controlled power supply | |
| CN117113267B (en) | Prediction model training method based on big data and photovoltaic power generation performance detection method | |
| CN116720854B (en) | Equipment coordination control method and system based on intelligent patrol | |
| CN116503029B (en) | Module data cooperative processing method and system for automatic driving | |
| CN117458511A (en) | An electrical load identification method and system | |
| CN116890222A (en) | Intelligent positioning method and device of automatic assembling machine | |
| CN117196883A (en) | Sewage treatment decision optimization method and system based on artificial intelligence | |
| CN112816211A (en) | Belt conveyor fault diagnosis method based on PSO-BP algorithm |
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 | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |