CN106874589A - A kind of alarm root finding method based on data-driven - Google Patents

A kind of alarm root finding method based on data-driven Download PDF

Info

Publication number
CN106874589A
CN106874589A CN201710073261.XA CN201710073261A CN106874589A CN 106874589 A CN106874589 A CN 106874589A CN 201710073261 A CN201710073261 A CN 201710073261A CN 106874589 A CN106874589 A CN 106874589A
Authority
CN
China
Prior art keywords
variables
data
entropy
causal relationship
calculating
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.)
Withdrawn
Application number
CN201710073261.XA
Other languages
Chinese (zh)
Inventor
陈豪
张景欣
王耀宗
张丹
蔡品隆
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Quanzhou Institute of Equipment Manufacturing
Original Assignee
Quanzhou Institute of Equipment Manufacturing
Priority date (The priority date 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 date listed.)
Filing date
Publication date
Application filed by Quanzhou Institute of Equipment Manufacturing filed Critical Quanzhou Institute of Equipment Manufacturing
Priority to CN201710073261.XA priority Critical patent/CN106874589A/en
Publication of CN106874589A publication Critical patent/CN106874589A/en
Withdrawn legal-status Critical Current

Links

Classifications

    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06FELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00Computer-aided design [CAD]
    • G06F30/20Design optimisation, verification or simulation

Landscapes

  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • Theoretical Computer Science (AREA)
  • Computer Hardware Design (AREA)
  • Evolutionary Computation (AREA)
  • Geometry (AREA)
  • General Engineering & Computer Science (AREA)
  • General Physics & Mathematics (AREA)
  • Alarm Systems (AREA)

Abstract

A kind of alarm root finding method based on data-driven disclosed by the invention, the present invention passes through data acquisition, data time stationarity is checked, data prediction and parameter optimization setting steps, set up the Mathematical Modeling of relation between variable, then NTE the and NDTE values between variable are calculated, whether there is direct causality between so as to judge system variable, the method does not rely on system physical model and priori, only relying on process measurement variable can just obtain causality, strong applicability, can be widely applied to chemical industry, weaving, the industrial circles such as metallurgy, sending the initial stage in alarm can just find root, to isolate and fixing a breakdown in time, reduce and even avoid accident from occurring, improve the safety and reliability of system operation, can also reduce environmental pollution simultaneously.

Description

Alarm source searching method based on data driving
Technical Field
The invention belongs to the technical field of safety monitoring, and particularly relates to a data-driven alarm root cause searching method.
Background
Due to the continuous improvement of the requirements on the safety and the reliability of the industrial system, the online and real-time monitoring of the operation process of the system becomes an essential key link in the modern industrial system. In consideration of the situations that accurate mathematical models and prior knowledge of the system are difficult to obtain, a large amount of historical operating data is generated by the industrial system, and the like, process monitoring based on data driving becomes a mainstream technology of modern industrial safety monitoring. The alarm is sent after the fault occurs, so that the staff can be helped to judge the system operation condition in time, but the method cannot determine the reason for the alarm. The alarm source searching method can be used for determining the reason of the alarm when the alarm occurs, so that the method is generally regarded.
The alarm root cause searching method accurately positions the fault through a series of measures, and assists workers to timely isolate and eliminate the fault. Through the development of many years, people have proposed various alarm source searching technologies, which are mainly divided into three categories:
1) the symbolic directed graph method, relying on the physical model and prior knowledge of the system;
2) a Granger causal analysis method based on predicted causal relationships;
3) the Transition Entropy (TE) method.
Both of the first two methods are only suitable for linear systems, and the relationship between variables is obtained by constructing a model, and the methods are not suitable for large-scale complex systems. The last method obtains the causal relationship among the variables mainly by calculating the probability density function of the process variables, can be applied to a complex nonlinear system, and has stronger practicability. The method has the defect that the method has higher requirements on the quantity of modeling data, and the characteristic that the modern industrial system generates mass data just makes up the defect.
Therefore, the inventor considers that the causal relationship among the variables is determined by using the transformation entropy method, and lays a solid foundation for searching the alarm source.
Disclosure of Invention
The invention aims to provide a data-driven alarm root cause searching method, which can obtain cause-and-effect relationships only by process measurement variables without depending on a system physical model and prior knowledge, and can search the root cause at the initial stage of alarm sending so as to isolate and eliminate faults in time, reduce or even avoid accidents and improve the safety and reliability of system operation.
In order to achieve the purpose, the invention adopts the following technical scheme:
a data-driven alarm root cause searching method comprises the following steps:
the method comprises the following steps: detecting working data of an industrial system, obtaining observation variables, storing the d observation variables into a data matrix X, checking the time stability of the data and preprocessing the data;
the working data comprises parameters reflecting the operation condition of the system;
step two: initializing the model parameters, and optimizing the model parameters by utilizing a Cao criterion or a Ragwitz criterion;
step three: calculating a transition entropy matrix P comprising:
A. selecting variables: taking any two variables from the data matrix X, marking as X and y, and d (d-1)/2 combinations in total;
B. the transition entropy between two variables is calculated:
wherein,is a joint probability density function, f (|) is a conditional probability density function, w is a random vectorSuppose the element of w is w1,w2,…,wsAnd [ alpha ] dw isAndembedded vectors, k, of historical measurements of x and y, respectively1And l1Embedding dimensions of y and x, h, respectively1Is the prediction horizon;
C. calculating standard transition entropy:
wherein, H represents the entropy of the sample,is the conditional entropy; and Tx→y≠Ty→x
If it is notIf the difference is larger than the specified threshold value, judging that the two variables x and y have causal relationship;
D. repeating the step B, C until d (d-1) is calculated, calculating the standard transition entropy of the variables of d (d-1)/2 combinations, storing the standard transition entropy into a matrix P, and then representing the variables with causal relationship by using a flow diagram;
step four: calculating standard direct transition entropy based on variable causal relationship in the information flow graph:
taking any x, y and z3 causal variables from the matrix P, wherein z is an intermediate variable, and judging the direct causal relationship between x and y comprises:
1) calculating direct transition entropy:
wherein v represents a random vectorThe prediction range h is max (h)1,h3) Embedding vectorIs a historical value of z, can provide effective information for predicting y at the moment i + h,is the historical value of x, if h ═ h1Then, thenIf h is h3Then, thenAnd calculate Tx→zWhen l is turned on2And m1Is the embedding dimension of x and z, h2Is the prediction horizon, τ2Is a time interval; calculating Tz→yWhen k is2And m2Is the embedding dimension of y and z, h3Is the prediction horizon, τ3Is a time interval;
2) calculating standard direct transformation entropy:
if it is notIf the value is larger than the specified threshold value, the direct causal relationship between x and y is shown;
carrying out the two-step calculation of 1) and 2) on the variables in the information flow diagram in the step three, and verifying the truth of the causal relationship of the variables;
and step five, establishing a variable direct causal relationship graph according to the verification result of the step four.
In the first step, the data is checked for time stability by an augmented fullerene test method.
The step of preprocessing the data comprises: and processing data noise by using a filtering method and the like.
In step four, 3 causal variables are arbitrarily taken from the matrix P, where the variable z may be null and x and y are adjacent variables.
After the scheme is adopted, the invention has the following advantages: the model is established only by mass data reflecting the operation of the system, and the method does not depend on a physical model and prior knowledge of the system, so that the limiting conditions are few and the applicability is strong; in addition, fault location is carried out at the early stage of alarming, faults can be rapidly discharged, major accidents are reduced, the safety and the reliability of the system are improved, and the economic benefit is improved.
The invention is further described below with reference to the accompanying drawings.
Drawings
FIG. 1 is a flow chart of the alarm root cause searching method based on data driving according to the present invention.
FIG. 2 is a graph of the relationship of variables x, y and z.
FIG. 3 is an information flow diagram of variables x, y and z, where z to y have a direct causal relationship.
FIG. 4 is an information flow diagram of variables x, y, and z, where z to y have no causal relationship.
Fig. 5 is an information flow diagram based on standard transition entropy.
FIG. 6 shows the steps of calculating the standard direct transition entropy.
Fig. 7 is an information flow diagram based on standard direct transition entropy.
Detailed Description
Example one
Referring to fig. 1, the present embodiment is described, and a method for searching an alarm root cause based on data driving according to an embodiment of the present invention is performed according to the following steps:
the method comprises the following steps: the working data of the industrial system is detected, observation variables are obtained, and d observation variables are stored in the data matrix X, the embodiment utilizes an augmented fullerene test method to check the time stability of the data, and preprocesses the data, wherein the preprocessing comprises processing data noise by utilizing methods such as filtering and the like; wherein the working data comprises parameters reflecting the operating conditions of the system, such as temperature, pressure, water level, etc.;
step two: initializing the model parameters, and optimizing the model parameters by using a Cao criterion; the model is a model for establishing a variable causal relationship, the model parameters are some setting parameters required for establishing the model, and the preprocessed working data are input into the model;
step three: calculating a transition entropy matrix P:
A. taking 3 variables from the data matrix X, marking as X, y and z, and calculating standard transition entropy values (NTE) between any two variables, wherein d (d-1)/2 combinations are calculated;
the 3 variables are taken as examples to explain the calculation method of the standard direct transition entropy;
B. calculate TE values for x to y:
wherein,is a joint probability density function, f (|) is a conditional probability density function, w is a random vectorSuppose the element of w is w1,w2,...,wsAnd [ alpha ] dw isAndembedded vectors, k, of historical measurements of x and y, respectively1And l1Dimension of y and x, respectively, h1Is the prediction horizon;
if T isx→yWhen x and y are not causally related, 0 is shown;
C. calculate NTE for x to y:
wherein,is the conditional entropy;
h represents entropy;
D. calculate TE values for x to z:
wherein,andis a time interval τ2η is a random vectorh2Is the prediction horizon;
if T isx→z0, indicating that x and z have no causal relationship;
E. calculate NTE values for x to z:
F. calculate the TE values for z to y:
wherein,andis a time interval τ3The embedded vector of (a) is embedded,is a random vectorh3Is the prediction horizon;
if T isz→y0, indicating that z and y have no causal relationship;
G. calculate the NTE values for z to y:
calculating TE values of any two variables of the data matrix X, and storing the TE values into a matrix P of d multiplied by d;
the diagonal elements of the matrix P are the transition entropies of the variables themselves, whose values are NA;
when the NTE value is larger than a specified threshold value, judging that the two variables have a causal relationship, and describing an information flow graph based on the NTE;
it should be noted that the probability density function is usually estimated by using a gaussian kernel function
The probability density function of a univariate can be calculated by the following formula
Where N is the number of samples, γ is the bandwidth of the reduced probability density function estimate,c=(4/3)1/5≈1.06;
for the case of d-dimensional multivariate, the probability density function estimate can be calculated using the following formula
Wherein,s=1,…,d;
step four: calculating standard direct entropy value (NDTE):
a: calculating Direct Transition Entropy (DTE):
as shown in fig. 2, x causes a change in z and y, and to determine whether x and y have a direct causal relationship, DTE is defined:
wherein v represents a random vectorThe prediction range h is max (h)1,h3) Embedding vectorIs a historical value of z, can provide effective information for predicting y at the moment i + h,is the historical value of x, if h ═ h1Then, thenIf h is h3Then, then
B. Calculating NDTE:
if DTEx→y0, x and y have no direct causal relationship;
if it is notIf the x and y are greater than the specified threshold, x and y have a direct causal relationship;
then judging the truth of the z-y causal relationship;
the DTE values for z to y are calculated as:
where upsilon represents a random vectorThe prediction range h is max (h)1,h3) Embedding vectorIs the historical value of z, can be i + hThe prediction of the time of day y provides valid information,is the historical value of x, if h ═ h1Then, thenIf h is h3Then, then
The NDTE value of z to y is calculated as
If NDTEz→yAbove a specified threshold, indicating that z to y have a causal relationship, as shown in FIG. 3; otherwise, z to y have no causal relationship, as shown in FIG. 4;
step five: and carrying out NDTE calculation on the variables with the confirmed causal relationship, verifying the direct causal relationship between the two variables, and screening out the variables with the direct causal relationship to establish a direct causal relationship diagram, namely determining the information flow diagram.
Example two: the first difference between the present embodiment and the specific embodiment is: and step two, optimizing parameters by adopting a Ragwitz criterion.
The specific embodiment is as follows: the alarm root cause searching method based on data driving in the specific embodiment is used for simulating the variable causal relationship of the Flue Gas Desulfurization (FGD) process of a petroleum company, and comprises the following specific steps;
taking the FDG process as an example, selecting the liquid levels of a reaction tank, a water tank 1 and a water tank 2 and the flow rates of pumps 2 and 3 as variables, and respectively recording the variables as y1、y2、y3y4、y53544 groups of data are collected, the data have time stability, and the data are preprocessed;
initializing model parameters, and optimizing the parameters by using a Cao criterion;
step three, calculating TE values and NTE values among variables, and showing in table 1;
TABLE 1
Selecting 0.02 as a threshold value, wherein the information flow direction path based on the standard transformation entropy is shown in FIG. 5;
step four, calculating DTE and NDTE values of the FDG part process, and showing in a table 2;
TABLE 2
If the NDTE value is too small, it is determined that the variables have no direct causal relationship, and the step of obtaining the information flow graph according to the direct transition entropy calculation result is shown in FIG. 6;
step five, obtaining the information flow diagram of the FDG process is shown in FIG. 7.
While the above description shows and describes the preferred embodiments of the present invention, it is to be understood that the invention is not limited to the forms disclosed herein, but is not to be construed as excluding other embodiments and is capable of use in various other combinations, modifications, and environments and is capable of changes within the scope of the inventive concept as expressed herein, commensurate with the above teachings, or the skill or knowledge of the relevant art. And that modifications and variations may be effected by those skilled in the art without departing from the spirit and scope of the invention as defined by the appended claims.

Claims (4)

1. A data-driven alarm root cause searching method is characterized by comprising the following steps:
the method comprises the following steps: detecting working data of an industrial system, obtaining observation variables, storing the d observation variables into a data matrix X, checking the time stability of the data and preprocessing the data;
the working data comprises parameters reflecting the operation condition of the system;
step two: initializing the model parameters, and optimizing the model parameters by utilizing a Cao criterion or a Ragwitz criterion;
step three: calculating a transition entropy matrix P comprising:
A. selecting variables: taking any two variables from the data matrix X, marking as X and y, and d (d-1)/2 combinations in total;
B. the transition entropy between two variables is calculated: the formula is as follows:
T x → y = ∫ f ( y i + h 1 , y i ( k 1 ) , x i ( l 1 ) ) · log f ( y i + h 1 | y i ( k 1 ) , x i ( l 1 ) ) f ( y i + h 1 | y i ( k 1 ) ) d w
wherein,is a joint probability density function, f (|) is a conditional probability density function, w is a random vectorSuppose the element of w is w1,w2,...,wsAnd [ alpha ] dw isAndembedded vectors, k, of historical measurements of x and y, respectively1And l1Embedding dimensions of y and x, h, respectively1Is the prediction horizon;
C. calculating standard transition entropy:
NTE x → y c = H c ( y i + h 1 | y i ( k 1 ) ) - H c ( y i + h 1 | y i ( k 1 ) , x i ( l 1 ) ) H 0 - H c ( y i + h 1 | y i ( k 1 ) , x i ( l 1 ) ) = T x → y H 0 ( y ) - H c ( y i + h 1 | y i ( k 1 ) , x i ( l 1 ) ) ∈ [ 0 , 1 ]
H 0 ( y ) = - ∫ y min y max 1 y m a x - y m i n l o g 1 y m a x - y m i n d y = l o g ( y m a x - y m i n )
H c ( y i + h 1 | y i ( k 1 ) ) = - Σ f ( y i + h 1 , y i ( k 1 ) ) · log f ( y i + h 1 | y i ( k 1 ) )
wherein, H represents the entropy of the sample,is the conditional entropy; and Tx→y≠Ty→x
If it is notIf the difference is larger than the specified threshold value, judging that the two variables x and y have causal relationship;
D. repeating the step B, C until d (d-1) is calculated, calculating the standard transition entropy of the variables of d (d-1)/2 combinations, storing the standard transition entropy into a matrix P, and then representing the variables with causal relationship by using a flow diagram;
step four: calculating standard direct transition entropy based on variable causal relationship in the information flow graph:
taking any x, y and z3 causal variables from the matrix P, wherein z is an intermediate variable, and judging the direct causal relationship between x and y comprises:
1) calculating direct transition entropy:
DTE x → y = ∫ f ( y i + h , y i ( k ) , z i + h - h 3 ( m 2 ) , x i + h - h 1 ( l 1 ) ) · l o g f ( y i + h | y i ( k ) , z i + h - h 3 ( m 2 ) , x i + h - h 1 ( l 1 ) ) f ( y i + h | y i ( k ) , z i + h - h 3 ( m 2 ) ) d v
wherein v represents a random vectorThe prediction range h is max (h)1,h3) Embedding vectorIs a historical value of z, can provide effective information for predicting y at the moment i + h,is the historical value of x, if h ═ h1Then, thenIf h is h3Then, thenAnd calculate Tx→zWhen l is turned on2And m1Is the embedding dimension of x and z, h2Is the prediction horizon, τ2Is a time interval; calculating Tz→yWhen k is2And m2Is the embedding dimension of y and z, h3Is the prediction horizon, τ3Is a time interval;
2) calculating standard direct transformation entropy:
NDTE x → y c = DTE x → y H c ( y i + h | y i ( k ) ) - H c ( y i + h | y i ( k ) , z i + h - h 3 ( m 2 ) , x i + h - h 1 ( l 1 ) ) ∈ [ 0 , 1 ]
if it is notIf the value is larger than the specified threshold value, the direct causal relationship between x and y is shown;
carrying out the two-step calculation of 1) and 2) on the variables in the information flow diagram in the step three, and verifying the truth of the causal relationship of the variables;
and step five, establishing a variable direct causal relationship graph according to the verification result of the step four.
2. The alarm source finding method based on data driving according to claim 1, characterized in that: in the first step, the data is checked for time stability by an augmented fullerene test method.
3. The alarm source finding method based on data driving according to claim 1, characterized in that: the step of preprocessing the data comprises: and processing data noise by using a filtering method and the like.
4. The alarm source finding method based on data driving according to claim 1, characterized in that: in step four, 3 causal variables are arbitrarily taken from the matrix P, where the variable z may be null and x and y are adjacent variables.
CN201710073261.XA 2017-02-10 2017-02-10 A kind of alarm root finding method based on data-driven Withdrawn CN106874589A (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN201710073261.XA CN106874589A (en) 2017-02-10 2017-02-10 A kind of alarm root finding method based on data-driven

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN201710073261.XA CN106874589A (en) 2017-02-10 2017-02-10 A kind of alarm root finding method based on data-driven

Publications (1)

Publication Number Publication Date
CN106874589A true CN106874589A (en) 2017-06-20

Family

ID=59166958

Family Applications (1)

Application Number Title Priority Date Filing Date
CN201710073261.XA Withdrawn CN106874589A (en) 2017-02-10 2017-02-10 A kind of alarm root finding method based on data-driven

Country Status (1)

Country Link
CN (1) CN106874589A (en)

Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2019185039A1 (en) * 2018-03-29 2019-10-03 日本电气株式会社 A data processing method and electronic apparatus
CN113554449A (en) * 2020-04-23 2021-10-26 阿里巴巴集团控股有限公司 Commodity variable prediction method, commodity variable prediction device, and computer-readable medium
CN114175082A (en) * 2019-07-24 2022-03-11 索尼集团公司 Information processing apparatus, information processing method, and information processing program

Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
EP2789293A1 (en) * 2013-04-12 2014-10-15 Commissariat à l'Énergie Atomique et aux Énergies Alternatives Methods to monitor consciousness
CN106073702A (en) * 2016-05-27 2016-11-09 燕山大学 Many time-frequencies yardstick diencephalon myoelectricity coupling analytical method based on small echo transfer entropy

Patent Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
EP2789293A1 (en) * 2013-04-12 2014-10-15 Commissariat à l'Énergie Atomique et aux Énergies Alternatives Methods to monitor consciousness
CN106073702A (en) * 2016-05-27 2016-11-09 燕山大学 Many time-frequencies yardstick diencephalon myoelectricity coupling analytical method based on small echo transfer entropy

Non-Patent Citations (3)

* Cited by examiner, † Cited by third party
Title
MICHAEL LINDNER等: "TRENTOOL: A Matlab open source toolbox to analyse information flow in time series data with transfer entropy", 《BMC NEUROSCIENCE》 *
PING DUAN等: "Direct Causality Detection via the Transfer Entropy Approach", 《IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY》 *
高慧慧等: "过程工业报警系统可视化监控技术及应用", 《化工学报》 *

Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2019185039A1 (en) * 2018-03-29 2019-10-03 日本电气株式会社 A data processing method and electronic apparatus
CN114175082A (en) * 2019-07-24 2022-03-11 索尼集团公司 Information processing apparatus, information processing method, and information processing program
CN113554449A (en) * 2020-04-23 2021-10-26 阿里巴巴集团控股有限公司 Commodity variable prediction method, commodity variable prediction device, and computer-readable medium

Similar Documents

Publication Publication Date Title
CN107941537B (en) A kind of mechanical equipment health state evaluation method
CN116625438B (en) Gas pipe network safety on-line monitoring system and method thereof
Li et al. Interval-valued reliability analysis of multi-state systems
Li et al. A sensor fault detection and diagnosis strategy for screw chiller system using support vector data description-based D-statistic and DV-contribution plots
Coble et al. Applying the general path model to estimation of remaining useful life
CN102789545B (en) Based on the Forecasting Methodology of the turbine engine residual life of degradation model coupling
CN104504296B (en) Gaussian of Mixture Hidden Markov Model and the method for predicting residual useful life of regression analysis
EP1643332A2 (en) Hybrid model based fault detection and isolation system
CN112685910B (en) Complex equipment power pack fault prediction method based on hybrid prediction model
CN111460392B (en) Magnetic suspension train and suspension system fault detection method and system thereof
CN104462757A (en) Sequential verification test method of Weibull distribution reliability based on monitoring data
CN110348150A (en) A kind of fault detection method based on dependent probability model
CN114841396A (en) Method for predicting metamorphic trend and warning catastrophe risk in petrochemical production process
CN106874589A (en) A kind of alarm root finding method based on data-driven
CN106066252A (en) The health state evaluation method of equipment subsystem level destroyed by a kind of dangerous materials
Mollineaux et al. Structural health monitoring of progressive damage
Harrou et al. Enhanced monitoring using PCA-based GLR fault detection and multiscale filtering
WO2015039693A1 (en) Method and system for data quality assessment
CN105426665A (en) Dynamic reliability determination method based on state monitoring
CN104503428A (en) Anti-interference time-variant fault diagnosis method of civil aircraft automatic flight control system
CN116907772A (en) Self-diagnosis and fault source identification method and system of bridge structure monitoring sensor
CN110009033A (en) A kind of drilling process abnormity early warning model based on dynamic principal component analysis
CN114818116B (en) Aircraft engine failure mode identification and life prediction method based on joint learning
CN109891235B (en) Method for the automatic online detection of deviations of an actual state of a fluid from a reference state of the fluid, based on statistical methods, in particular for monitoring a drinking water supply
Martí et al. YASA: yet another time series segmentation algorithm for anomaly detection in big data problems

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
CB03 Change of inventor or designer information

Inventor after: Chen Hao

Inventor after: Wang Yaozong

Inventor after: Zhang Dan

Inventor after: Zhang Jingxin

Inventor after: Cai Pinlong

Inventor before: Chen Hao

Inventor before: Zhang Jingxin

Inventor before: Wang Yaozong

Inventor before: Zhang Dan

Inventor before: Cai Pinlong

CB03 Change of inventor or designer information
WW01 Invention patent application withdrawn after publication

Application publication date: 20170620

WW01 Invention patent application withdrawn after publication