CN111556107A - Intelligent Internet of things application method based on stress-reflex model - Google Patents
Intelligent Internet of things application method based on stress-reflex model Download PDFInfo
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- CN111556107A CN111556107A CN202010301487.2A CN202010301487A CN111556107A CN 111556107 A CN111556107 A CN 111556107A CN 202010301487 A CN202010301487 A CN 202010301487A CN 111556107 A CN111556107 A CN 111556107A
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- H—ELECTRICITY
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Abstract
The invention discloses an intelligent Internet of things application method based on a stress-reflex model, which comprises the following steps: s1, acquiring instant exogenous data required by the equipment, the surrounding environment and the working condition by the variable-frequency equipment, and sending the data to an equipment management cloud platform; s2, the equipment management cloud platform carries out equipment Internet of things management; s3, establishing an AI learning engine, and excavating an AI analysis library for machine learning; s4, combining indexes including upper and lower limits of an index threshold value library, an artificial experience library and an optimal operation state of equipment design, and fusing and converting results of the AI analysis library to form an optimal equipment management scheme library; and S5, converting the result of the equipment management scheme library into an index system of cloud platform management and edge AI, and feeding back and guiding the data management scheme of the real-time operation of the variable-frequency equipment to provide real-time equipment management state response for the equipment. The invention can adjust the running power of the equipment according to the requirements of application conditions.
Description
Technical Field
The invention relates to the field of intelligent Internet application, in particular to an intelligent Internet of things application method based on a stress-reflection model.
Background
A new generation of intelligent equipment will lead and advance a new industrial revolution, which benefits from rapid progress in the fields of internet, big data, cloud computing and the like, and in recent years, the efficiency and accuracy of the intelligent equipment in the industrial field are continuously improved, and the intelligent equipment is more and more widely applied in life and work.
However, the existing intelligent device cannot adjust the running power of the device according to the requirements of application conditions.
Disclosure of Invention
In view of the above, in order to solve the above technical problems, the present invention aims to provide an intelligent internet of things application method based on a "stress-reflex" model, which can adjust the operating power of a device according to the requirements of application conditions.
The adopted technical scheme is as follows:
an intelligent Internet of things application method based on a stress-reflex model comprises the following steps:
s1, various sensor units on the variable-frequency equipment are utilized to acquire instant exogenous data required by the equipment, the surrounding environment and the working condition, and the data are sent to an equipment management cloud platform through an Internet of things communication unit;
s2, the equipment management cloud platform receives, analyzes and stores the exogenous data, and provides basic equipment Internet of things management;
s3, establishing an AI learning engine, cleaning and analyzing daily exogenous data, and excavating an AI analysis library for machine learning;
s4, combining indexes including upper and lower limits of an index threshold value library, an artificial experience library and an optimal operation state of equipment design, and fusing and converting results of the AI analysis library to form an optimal equipment management scheme library;
s5, converting the result of the equipment management scheme library into an index system of cloud platform management and edge AI, feeding back and guiding a data management scheme of the frequency conversion type equipment to run in real time, and providing real-time equipment management state response for the equipment through an edge AI unit, an Internet of things communication unit and a control unit;
and S6, gradually forming an equipment intelligent management scheme of the individualized equipment based on the equipment operation condition along with the data accumulation of the individualized equipment in long-term operation.
Further, in S4, the results of the AI analysis library are fused and transformed by an artificial intelligence assessment algorithm of the equipment health degree to form an optimal data management scheme library; the artificial intelligence evaluation algorithm of the equipment health degree is as follows: setting health degree threshold value tables of different parameters aiming at different types of equipment by using exogenous data including temperature, humidity, oil temperature, liquid level, height, equipment power and rotating speed obtained by various sensors; according to the data of equipment operation and repair reporting, a deep learning method is adopted, a health degree model of the learning equipment is trained in a classification mode, besides the current operation condition of the equipment is considered, historical operation and maintenance data of the equipment are introduced to serve as model reference training data, the model is corrected and revised to a certain extent according to manual experience, and finally the equipment health degree evaluation model containing a plurality of comprehensive sensor data is obtained.
Further, in S3, the machine learning is performed by using a long-short term memory artificial neural network and a deep fully-connected neural network.
Further, in S3, the equation of the long-short term memory artificial neural network method is as follows:
ft＝σ(Wf·[ht-1,xt]+bf)
it＝σ(Wi·[ht-1,xt]+bi)
ot＝σ(Wo·[ht-1,xt]+bo)
ht＝ot*tanh(Ct)
in the formula (f)t、it、otRespectively representing a forgetting gate, an input gate and an output gate, Wf、Wi、WoWeight parameters of the forgetting gate, the input gate and the output gate, respectively, bf、bi、boRespectively, offset terms of the forgetting gate, the input gate and the output gate, bfIs a meterCalculating out
A bias term of time; ct-1、CtThe states of the neuron cells at the previous moment and the current moment, ht-1、htThe outputs of the previous time and the current time are respectively, and the sigma is a tanh function.
Further, in S3, the method using the deep fully-connected neural network only includes one or more hidden layers, and the formula corresponding to the network is:
y＝σ2(w2σ1(w1x+b1)+b2)
in the formula, x is an input vector of the neural network, y is an output, w is a weight matrix of neuron input, b is an error term, and is an activation function, wherein the weight is a parameter to be trained, and training adjustment is performed through back propagation.
The invention has the beneficial effects that:
1. the application model is applied to variable-frequency equipment, can adjust the running power of the equipment according to the requirements of application working conditions, and provides a more reasonable management scheme for the peak operation, economic operation and reliable use of the equipment.
2. The system is an instant response type Internet of things application architecture based on Internet of things + AI (cloud AI, edge AI).
3. Exogenous real-time data of various sensors are utilized, and an equipment management scheme library is combined, so that the AI (cloud AI and edge AI) jointly form individualized practice of the equipment.
4. One core of the method is composed of an AI-based equipment working condition management algorithm and is combined with technologies such as internet of things, automatic control, cloud computing and the like.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are only embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to these drawings without inventive exercise.
Fig. 1 is an architecture diagram of an instant response internet of things application based on internet of things + AI (cloud AI, edge AI).
FIG. 2 is a schematic diagram of a neural network structure including a hidden layer. Input in the figure is Input; a Hidden layer of a Hidden layer; output is Output; bias is error term.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only preferred embodiments of the present invention, and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 1, an intelligent internet of things application method based on a "stress-reflex" model includes the following steps:
s1, various sensor units on the variable-frequency equipment are utilized to acquire instant exogenous data required by the equipment, the surrounding environment and the working condition, and the data are sent to an equipment management cloud platform through an Internet of things communication unit;
the various sensors include temperature sensors, humidity sensors, pressure sensors, etc. The variable frequency equipment comprises various sensor units, equipment units, a control unit, an edge AI unit and an Internet of things communication unit, wherein the various sensor units are used for acquiring exogenous data of the equipment units, the ambient environment and the working condition, sending the exogenous data to the AI unit for self learning under the control of the control unit, and sending the exogenous data to an equipment management cloud platform through the Internet of things communication unit;
s2, the equipment management cloud platform receives, analyzes and stores the exogenous data, and provides basic equipment Internet of things management;
s3, the equipment Internet of things management comprises the steps of establishing an AI learning engine, cleaning and analyzing daily exogenous data, and excavating an AI analysis library for machine learning;
the machine learning adopts a Long-Short Term Memory artificial neural network (LSTM) and a deep fully-connected neural network method for learning.
Further, in S3, the equation of the long-short term memory artificial neural network method is as follows:
ft＝σ(Wf·[ht-1,xt]+bf)
it＝σ(Wi·[ht-1,xt]+bi)
ot＝σ(Wo·[ht-1,xt]+bo)
ht＝ot*tanh(Ct)
in the formula (f)t、it、otRespectively representing a forgetting gate, an input gate and an output gate, Wf、Wi、WoWeight parameters of the forgetting gate, the input gate and the output gate, respectively, bf、bi、boRespectively, offset terms of the forgetting gate, the input gate and the output gate, bfIs a calculation of
A bias term of time; ct-1、CtThe states of the neuron cells at the previous moment and the current moment, ht-1、htThe outputs of the previous time and the current time are respectively, and the sigma is a tanh function.
Further, in S3, the method using the deep fully-connected neural network only includes one or more hidden layers, and referring to fig. 2, the corresponding formula of the network is:
y＝σ2(w2σ1(w1x+b1)+b2)
in the formula, x is an input vector of the neural network, y is an output, w is a weight matrix of neuron input, b is an error term, and is an activation function, wherein the weight is a parameter to be trained, and training adjustment is performed through back propagation.
S4, combining indexes including upper and lower limits of an index threshold value library, an artificial experience library and an optimal operation state of equipment design, and fusing and converting results of the AI analysis library to form an optimal equipment management scheme library;
performing fusion transformation on the results of the AI analysis library through an artificial intelligence evaluation algorithm of the equipment health degree to form an optimal data management scheme library; the artificial intelligence evaluation algorithm of the equipment health degree is as follows: setting health degree threshold value tables of different parameters aiming at different types of equipment by using exogenous data including temperature, humidity, oil temperature, liquid level, height, equipment power and rotating speed obtained by various sensors; according to the data of equipment operation and repair reporting, a deep learning method is adopted, a health degree model of the learning equipment is trained in a classification mode, besides the current operation condition of the equipment is considered, historical operation and maintenance data of the equipment are introduced to serve as model reference training data, the model is corrected and revised to a certain extent according to manual experience, and finally the equipment health degree evaluation model containing a plurality of comprehensive sensor data is obtained.
S5, converting the result of the equipment management scheme library into an index system of cloud platform management and edge AI, feeding back and guiding a data management scheme of the frequency conversion type equipment to run in real time, and providing real-time equipment management state response for the equipment through an edge AI unit, an Internet of things communication unit and a control unit;
and S6, gradually forming an equipment intelligent management scheme of the individualized equipment based on the equipment operation condition along with the data accumulation of the individualized equipment in long-term operation.
The invention has the beneficial effects that:
1. the application model is applied to variable-frequency equipment, can adjust the running power of the equipment according to the requirements of application working conditions, and provides a more reasonable management scheme for the peak operation, economic operation and reliable use of the equipment.
2. The system is an instant response type Internet of things application architecture based on Internet of things + AI (cloud AI, edge AI). The model is a stress-reflection model established between the variable-frequency equipment and the management cloud platform based on the Internet of things and AI.
3. Exogenous real-time data of various sensors are utilized, and an equipment management scheme library is combined, so that the AI (cloud AI and edge AI) jointly form individualized practice of the equipment.
4. One core of the method is composed of an AI-based equipment working condition management algorithm and is combined with technologies such as internet of things, automatic control, cloud computing and the like.
The above-listed detailed description is only a specific description of a possible embodiment of the present invention, and they are not intended to limit the scope of the present invention, and equivalent embodiments or modifications made without departing from the technical spirit of the present invention should be included in the scope of the present invention.
Claims (5)
1. An intelligent Internet of things application method based on a stress-reflex model is characterized by comprising the following steps:
s1, various sensor units on the variable-frequency equipment are utilized to acquire instant exogenous data required by the equipment, the surrounding environment and the working condition, and the data are sent to an equipment management cloud platform through an Internet of things communication unit;
s2, the equipment management cloud platform receives, analyzes and stores the exogenous data, and provides basic equipment Internet of things management;
s3, establishing an AI learning engine, cleaning and analyzing daily exogenous data, and excavating an AI analysis library for machine learning;
s4, combining indexes including upper and lower limits of an index threshold value library, an artificial experience library and an optimal operation state of equipment design, and fusing and converting results of the AI analysis library to form an optimal equipment management scheme library;
s5, converting the result of the equipment management scheme library into an index system of cloud platform management and edge AI, feeding back and guiding a data management scheme of the frequency conversion type equipment to run in real time, and providing real-time equipment management state response for the equipment through an edge AI unit, an Internet of things communication unit and a control unit;
and S6, gradually forming an equipment intelligent management scheme of the individualized equipment based on the equipment operation condition along with the data accumulation of the individualized equipment in long-term operation.
2. The intelligent Internet of things application method based on the stress-reflex model as claimed in claim 1, wherein in S4, the results of the AI analysis library are fused and transformed through an artificial intelligence assessment algorithm of equipment health degree to form an optimal data management scheme library; the artificial intelligence evaluation algorithm of the equipment health degree is as follows: setting health degree threshold value tables of different parameters aiming at different types of equipment by using exogenous data including temperature, humidity, oil temperature, liquid level, height, equipment power and rotating speed obtained by various sensors; according to the data of equipment operation and repair reporting, a deep learning method is adopted, a health degree model of the learning equipment is trained in a classification mode, besides the current operation condition of the equipment is considered, historical operation and maintenance data of the equipment are introduced to serve as model reference training data, the model is corrected and revised to a certain extent according to manual experience, and finally the equipment health degree evaluation model containing a plurality of comprehensive sensor data is obtained.
3. The method for applying the intelligent internet of things based on the stress-reflex model as claimed in claim 2, wherein in S3, the machine learning is performed by using a long-short term memory artificial neural network and a deep full-link neural network.
4. The intelligent Internet of things application method based on the stress-reflex model as claimed in claim 3, wherein in S3, the equation of the method adopting the long-short term memory artificial neural network is as follows:
ft＝σ(Wf·[ht-1,xt]+bf)
it＝σ(Wi·[ht-1,xt]+bi)
ot＝σ(Wo·[ht-1,xt]+bo)
ht＝ot*tanh(Ct)
in the formula (f)t、it、otRespectively representing a forgetting gate, an input gate and an output gate, Wf、Wi、WoWeight parameters of the forgetting gate, the input gate and the output gate, respectively, bf、bi、boRespectively, offset terms of the forgetting gate, the input gate and the output gate, bfIs a calculation of
A bias term of time; ct-1、CtThe states of the neuron cells at the previous moment and the current moment, ht-1、htThe outputs of the previous time and the current time are respectively, and the sigma is a tanh function.
5. The intelligent internet of things application method based on the stress-reflex model as claimed in claim 3, wherein in S3, the method using the deep fully-connected neural network only includes one or more hidden layer neural networks, and the corresponding formula of the network is as follows:
y＝σ2(w2σ1(w1x+b1)+b2)
in the formula, x is an input vector of the neural network, y is an output, w is a weight matrix of neuron input, b is an error term, and is an activation function, wherein the weight is a parameter to be trained, and training adjustment is performed through back propagation.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN113222170A (en) * | 2021-03-30 | 2021-08-06 | 新睿信智能物联研究院(南京)有限公司 | Intelligent algorithm and model for IOT (Internet of things) AI (Artificial Intelligence) collaborative service platform |
| CN113222170B (en) * | 2021-03-30 | 2024-04-23 | 新睿信智能物联研究院(南京)有限公司 | Intelligent algorithm and model for AI collaborative service platform of Internet of things |
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Application publication date: 20200818 |