CN113361091B - ESD strength estimation method and system - Google Patents

ESD strength estimation method and system Download PDF

Info

Publication number
CN113361091B
CN113361091B CN202110608088.5A CN202110608088A CN113361091B CN 113361091 B CN113361091 B CN 113361091B CN 202110608088 A CN202110608088 A CN 202110608088A CN 113361091 B CN113361091 B CN 113361091B
Authority
CN
China
Prior art keywords
cluster
member node
cluster member
esd
node
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
Application number
CN202110608088.5A
Other languages
Chinese (zh)
Other versions
CN113361091A (en
Inventor
胡晓琴
汪李忠
郭强
缪宇峰
胡翔
卢盛
徐昌文
伍掌
莫水良
张国连
寿坚
陈张平
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.)
Hangzhou Dianzi University
Hangzhou Power Equipment Manufacturing Co Ltd
Hangzhou Power Supply Co of State Grid Zhejiang Electric Power Co Ltd
Original Assignee
Hangzhou Dianzi University
Hangzhou Power Equipment Manufacturing Co Ltd
Hangzhou Power Supply Co of State Grid Zhejiang Electric Power Co Ltd
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 Hangzhou Dianzi University, Hangzhou Power Equipment Manufacturing Co Ltd, Hangzhou Power Supply Co of State Grid Zhejiang Electric Power Co Ltd filed Critical Hangzhou Dianzi University
Priority to CN202110608088.5A priority Critical patent/CN113361091B/en
Publication of CN113361091A publication Critical patent/CN113361091A/en
Application granted granted Critical
Publication of CN113361091B publication Critical patent/CN113361091B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/20—Design optimisation, verification or simulation
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R29/00—Arrangements for measuring or indicating electric quantities not covered by groups G01R19/00 - G01R27/00
    • G01R29/12—Measuring electrostatic fields or voltage-potential
    • H—ELECTRICITY
    • H03—ELECTRONIC CIRCUITRY
    • H03H—IMPEDANCE NETWORKS, e.g. RESONANT CIRCUITS; RESONATORS
    • H03H17/00—Networks using digital techniques
    • H03H17/02—Frequency selective networks
    • H03H17/0248—Filters characterised by a particular frequency response or filtering method
    • H03H17/0255—Filters based on statistics
    • H03H17/0257—KALMAN filters
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2111/00—Details relating to CAD techniques
    • G06F2111/08—Probabilistic or stochastic CAD
    • 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
    • Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
    • Y02D30/00—Reducing energy consumption in communication networks
    • Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks

Abstract

The application discloses an ESD intensity estimation method, which is applied to any 1 cluster head node of a sensor network with xi clusters, and comprises the following steps: receiving a consistency estimation result of each cluster member node at the current moment; determining an overall estimation value of the current time for representing the cluster member node state of the cluster based on the weight coefficient of the cluster; judging whether the number of cluster member nodes with data packet loss at the current moment in the cluster meets a preset number requirement or not; if so, taking the integral estimated value of the current moment of the cluster as the estimated value of the ESD intensity of the current moment of the area where the cluster is located; and if not, determining the ESD strength estimated value of the current time of the area where the cluster is located based on the overall estimated value of the current time of the cluster and the overall estimated value of the last time of the cluster. By applying the scheme of the application, the ESD strength estimation is effectively and accurately carried out. The application also discloses an ESD strength estimation system which has a corresponding effect.

Description

ESD strength estimation method and system
Technical Field
The invention relates to the technical field of sensors, in particular to an ESD strength estimation method and system.
Background
ESD (electrostatic Discharge) refers to electrostatic Discharge caused by movement of electric charges. In closed working environments such as communication rooms, switch cabinets, base stations and the like, some tiny dust, water vapor and corrosive substances exist, and because the tiny dust, water vapor and corrosive substances are mobile multi-charge particles, static electricity can be released due to friction among the particles or equipment working, and the substances can be charged by static electricity. The static electricity has the characteristics of long-term accumulation, high voltage, small current and low electric quantity. The ESD intensity affects the operation of the device, so it is necessary to estimate the ESD intensity, and when the ESD intensity is too large, some measures for reducing the ESD intensity are needed.
At present, when the ESD strength is estimated, a Kalman collaborative estimation method is generally adopted, and interference caused by ESD static electricity is not considered when estimation information is fused. ESD static electricity increases instability of a communication link and a possibility of data packet loss, and the integrity of monitoring information is reduced due to the data packet loss, so that the accuracy of estimating the ESD strength of the conventional scheme is reduced.
In summary, how to effectively estimate the ESD strength and improve the accuracy is a technical problem that needs to be solved by those skilled in the art.
Disclosure of Invention
The invention aims to provide an ESD strength estimation method and system, which can effectively estimate the ESD strength and improve the accuracy.
In order to solve the technical problems, the invention provides the following technical scheme:
an ESD intensity estimation method is applied to any 1 cluster head node of a sensor network with xi clusters, each cluster has 1 cluster head node and at least 1 cluster member node, xi is a positive integer and represents the number of clusters in the sensor network, and the method comprises the following steps:
receiving a consistency estimation result of each cluster member node at the current moment;
determining an overall estimation value of the current time for representing the cluster member node state of the cluster based on the weight coefficient of the cluster;
judging whether the number of cluster member nodes with data packet loss at the current moment in the cluster meets a preset number requirement or not;
if so, taking the integral estimated value of the current moment of the cluster as the estimated value of the ESD intensity of the current moment of the area where the cluster is located;
and if not, determining the ESD strength estimated value of the current time of the area where the cluster is located based on the overall estimated value of the current time of the cluster and the overall estimated value of the last time of the cluster.
Preferably, the received result of the consistency estimation of the ith cluster member node at the current time is the result of the consistency estimation of the ith cluster member node at the current time determined by the following steps:
establishing a dynamic discrete time system model representing ESD intensity states within a work environment;
according to yi(k)＝Hixi(k)+Jivi(k) Determining an ESD strength value measured by the ith cluster member node at the kth moment;
by the dynamic discrete time system model and yi(k) To obtain thetai(k) And will be thetai(k) Outputting each neighbor cluster member node to the ith cluster member node, and receiving each information transmission vector sent to the ith cluster member node by each neighbor cluster member node, so that the ith cluster member node and each neighbor cluster member node execute iteration of a Kalman consistency filter, and obtain the consistency estimation result of the ith cluster member node at the current moment
Figure BDA0003094838150000021
Wherein, thetai(k) Represents an information transmission vector output by the member node of the ith cluster at the kth time, and
Figure BDA0003094838150000022
zi(k) a vector of fusion information, S, representing the k-th time instanti(k) A matrix of fusion information representing the k-th time instant,
Figure BDA0003094838150000023
represents the consistency estimation result of the ith cluster member node at the kth moment, and
Figure BDA0003094838150000024
an estimate of x (k);
x (k) represents the ESD intensity state at the k-th moment in the working environment, xi(k) Represents the ESD strength state around the ith cluster member node at the k time point, HiAnd JiAre all a predetermined constant matrix, yi(k) ESD strength value measured for ith cluster member node at kth time, vi(k) Indicating a variance R at time kiWhite noise of zero mean.
Preferably, the establishing a dynamic discrete-time system model representing ESD intensity conditions within the working environment comprises:
establishing a dynamic discrete time system model representing the ESD intensity state in the working environment according to x (k +1) ═ ax (k) + omega (k) + rho (k);
where a is a preset constant matrix, x (k +1) represents the ESD intensity state at the k +1 th time within the working environment, ω (k) represents the zero-mean white noise of the ESD intensity state with the variance Q at the k-th time, and ρ (k) represents the ESD electrostatic noise with the variance W at the k-th time.
Preferably, the ith cluster member node and each neighbor cluster member node perform iteration of the kalman consistency filter, including:
the member node of the ith cluster and the member nodes of all the neighbor clusters are all according to
Figure BDA0003094838150000031
Performing an iteration of a Kalman consistency filter;
wherein the content of the first and second substances,
Figure BDA0003094838150000032
and are assembled
Figure BDA0003094838150000033
Set of neighbor nodes, M, representing member nodes of ith cluster in cluster ξi(k)＝(Pi -1(k)+Si(k))-1，Mi(k) To represent
Figure BDA0003094838150000034
Covariance of the error of (1), Pi(k) To represent
Figure BDA0003094838150000035
The covariance of the error of (a); ciDenotes the consensus gain of the Kalman consistency filter, an
Figure BDA0003094838150000036
Is left as a preset constant, | | write |, the calculation of the phosphorFA Frobenius norm representing a matrix;
Figure BDA0003094838150000037
is an a priori estimator of x (k).
Preferably, the weight coefficient of the cluster in which the ith cluster member node is located is determined by the following steps:
determining a support matrix representing the degree of mutual proximity of cluster member nodes and cluster head nodes at the kth time
Figure BDA0003094838150000038
Obtaining the weight coefficient of the cluster where the member node of the ith cluster is based on the support matrix lambda
Figure BDA0003094838150000039
Wherein n denotes an nth cluster in the sensor network,
Figure BDA00030948381500000310
ψn(k) the correlation degree between the cluster head node of the cluster where the ith cluster member node is located and the cluster member nodes of the cluster is represented, and tau represents the number of cluster member nodes participating in fusion of the cluster head node of the cluster where the ith cluster member node is located;
Figure BDA0003094838150000041
and the association degree from the ith cluster member node to the cluster head node of the cluster in which the ith cluster member node is positioned is shown.
Preferably, determining an overall estimation value of the current time for representing the cluster member node status of the cluster based on the weight coefficient of the cluster comprises:
based on the weight coefficient of the cluster, determining the overall estimation value of the current time for representing the states of the cluster member nodes of the cluster as
Figure BDA0003094838150000042
Wherein the content of the first and second substances,
Figure BDA0003094838150000043
indicating the determined overall estimation value of the state of the cluster member node for indicating the nth cluster at the k-th moment,
Figure BDA0003094838150000044
and representing the consistency estimation result of the ith cluster member node at the kth moment.
Preferably, the determining whether the number of cluster member nodes with data packet loss occurring at the current time in the cluster meets a preset number requirement includes:
judgment of
Figure BDA0003094838150000045
Whether the result is true or not;
if so, determining that the number of cluster member nodes with data packet loss at the current time in the cluster meets a preset number requirement;
if not, determining that the number of cluster member nodes with data packet loss at the current time in the cluster does not meet the preset number requirement;
wherein, the first and the second end of the pipe are connected with each other,
Figure BDA0003094838150000046
q is a preset threshold value, and
Figure BDA0003094838150000047
neighbor node representing member node of ith clusterj does not receive the consistency estimation result of the ith cluster member node output by the ith cluster member node at the kth moment
Figure BDA0003094838150000048
The neighbor node j representing the ith cluster member node receives the consistency estimation result of the ith cluster member node output by the ith cluster member node at the kth moment
Figure BDA0003094838150000049
δ (k) is a trigger threshold factor; a is a preset constant matrix,
Figure BDA00030948381500000410
is an a priori estimator of x (k), which represents the state of ESD strength at time k within the operating environment.
Preferably, determining the ESD strength estimate of the current time of the area in which the cluster is located based on the overall estimate of the current time of the cluster and the overall estimate of the previous time of the cluster includes:
according to
Figure BDA0003094838150000051
Determining an ESD strength estimated value of the current moment of the area where the cluster is located;
wherein the content of the first and second substances,
Figure BDA0003094838150000052
representing the ESD strength estimated value of the k-th time of the determined region where the nth cluster is located, wherein alpha is an adjusting factor and is more than 0 and less than 1,
Figure BDA0003094838150000053
and the determined overall estimation value of the node state of the cluster member of the nth cluster at the kth moment is shown.
Preferably, any 1 cluster member node in the sensor network is a non-invasive current sensor.
An ESD intensity estimation system is applied to any 1 cluster head node of a sensor network with xi clusters, each cluster has 1 cluster head node and at least 1 cluster member node, xi is a positive integer and represents the number of clusters in the sensor network, and the ESD intensity estimation system comprises:
a consistency estimation result receiving unit, configured to receive a consistency estimation result of each cluster member node at the current time;
the overall estimation value determining unit is used for determining an overall estimation value of the current time for representing the cluster member node state of the cluster based on the weight coefficient of the cluster;
the judging unit is used for judging whether the number of cluster member nodes with data packet loss at the current moment in the cluster meets the preset number requirement or not;
if yes, executing a first trigger unit, wherein the first trigger unit is used for: taking the integral estimated value of the current moment of the cluster as the estimated value of the ESD intensity of the current moment of the area where the cluster is located;
if not, executing a second trigger unit, wherein the second trigger unit is used for: and determining the ESD strength estimated value of the current time of the area where the cluster is located based on the overall estimated value of the current time of the cluster and the overall estimated value of the last time of the cluster.
By applying the technical scheme provided by the embodiment of the invention, considering that the measurement accuracy of a single sensor is easily influenced by factors such as environment, artificial change, equipment change and the like, the application adopts a sensor network with xi clusters, and different clusters are used for carrying out ESD intensity estimation in different areas. Specifically, any 1 cluster head node may receive the current-time consistency estimation result of each cluster member node in the cluster, and then determine an overall estimation value of the current time for representing the cluster member node state of the cluster based on the weight coefficient of the cluster. The method and the device can judge whether the number of cluster member nodes with data packet loss at the current moment in the cluster meets a preset number requirement, namely, the data packet loss condition caused by interference caused by ESD static electricity is considered, if the preset number requirement is not met, the data packet loss condition is shown, and the method and the device can be based on the overall estimation value of the current moment of the cluster and the overall estimation value of the last moment of the cluster, so that the ESD strength estimation value of the current moment of the area where the cluster is located is determined to be accurate. Of course, if the predetermined number requirement is satisfied, the overall estimation value of the current time of the cluster can be directly used as the estimation value of the ESD strength of the current time of the area where the cluster is located. In summary, the scheme of the application can effectively estimate the ESD strength and improve the accuracy.
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, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a flow chart of an ESD intensity estimation method according to the present invention;
FIG. 2 is a schematic diagram of a network structure of 4 clusters of a sensor network in one embodiment of the present invention;
fig. 3 is a schematic structural diagram of an ESD strength estimation system according to the present invention.
Detailed Description
The core of the invention is to provide an ESD strength estimation method, which can effectively estimate the ESD strength and improve the accuracy.
In order that those skilled in the art will better understand the disclosure, the invention will be described in further detail with reference to the accompanying drawings and specific embodiments. It is to be understood that the described embodiments are merely exemplary of the invention, and not restrictive of the full scope of the invention. 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, fig. 1 is a flowchart of an implementation of an ESD strength estimation method according to the present invention, where the ESD strength estimation method can be applied to any 1 cluster head node of a sensor network having ξ clusters, each cluster has 1 cluster head node and at least 1 cluster member node, and ξ is a positive integer and represents the number of clusters in the sensor network. The ESD strength estimation method may include the steps of:
step S101: and receiving the current-time consistency estimation result of each cluster member node.
Specifically, for example, consistency estimation of each cluster member node may be performed by a kalman collaborative estimation method to obtain a consistency estimation result of each cluster member node, and the consistency estimation result may be estimated in real time or according to a preset period, and each cluster member node may send the consistency estimation result to a cluster head node of the cluster.
The specific value of the cluster number ξ of the sensor network can be set and adjusted as required, and the specific structure in each cluster can be set as required, but it should be noted that for any 1 cluster, the cluster member nodes in the cluster are all in communication connection with the cluster head nodes in the cluster, and the communication connection can be direct connection or indirect connection, depending on the specific cluster structure and the specific position of the cluster member nodes in the cluster.
For example, fig. 2 is a schematic diagram of a network structure of 4 clusters of a sensor network in a specific situation, where 1 cluster head node is arranged in each of the 4 clusters. In FIG. 2
Figure BDA0003094838150000071
An estimated value of ESD intensity at the kth time point, which indicates the determined area in which the 1 st cluster is located, and, correspondingly,
Figure BDA0003094838150000072
and
Figure BDA0003094838150000073
and sequentially representing the determined ESD strength estimated value at the kth moment of the area where the 2 nd cluster is located, the determined ESD strength estimated value at the kth moment of the area where the 3 rd cluster is located, and the determined ESD strength estimated value at the kth moment of the area where the 4 th cluster is located. In practical applications, the number of cluster member nodes in each cluster is usually greater than or equal to 2.
In a specific embodiment of the present invention, the cluster head node described in step S101 receives the current time consistency estimation result of the ith cluster member node in each cluster member node, which is the current time consistency estimation result determined by the ith cluster member node through the following steps:
the method comprises the following steps: establishing a dynamic discrete time system model representing ESD intensity states within a work environment;
step two: according to yi(k)＝Hixi(k)+Jivi(k) Determining an ESD strength value measured by the ith cluster member node at the kth moment;
step three: by dynamic discrete time system model and yi(k) To obtain thetai(k) And will be thetai(k) Outputting each neighbor cluster member node to the ith cluster member node, and receiving each information transmission vector sent to the ith cluster member node by each neighbor cluster member node, so that the ith cluster member node and each neighbor cluster member node execute iteration of a Kalman consistency filter, and obtain the consistency estimation result of the ith cluster member node at the current moment
Figure BDA0003094838150000074
Wherein, thetai(k) Represents an information transmission vector output by the member node of the ith cluster at the kth time, and
Figure BDA0003094838150000075
zi(k) a fusion information vector representing the k-th time, Si(k) A matrix of fusion information representing the k-th time instant,
Figure BDA0003094838150000076
represents the consistency estimation result of the ith cluster member node at the kth moment, and
Figure BDA0003094838150000077
an estimate of x (k);
x (k) represents the ESD intensity state at the k-th moment in the working environment, xi(k) Represents the ith clusterESD Strength State at time k, H, around the Member nodeiAnd JiAre all a predetermined constant matrix, yi(k) ESD strength value measured for ith cluster member node at kth time, vi(k) Indicating a variance R at time kiWhite noise of zero mean.
The specific implementation mode is to carry out consistency estimation results of each cluster member node by a Kalman collaborative estimation method.
Specifically, in a specific occasion, the first step may specifically be:
establishing a dynamic discrete time system model representing the ESD intensity state in the working environment according to x (k +1) ═ ax (k) + omega (k) + rho (k);
where a is a preset constant matrix, x (k +1) represents the ESD intensity state at the k +1 th time within the working environment, ω (k) represents the zero-mean white noise of the ESD intensity state with the variance Q at the k-th time, and ρ (k) represents the ESD electrostatic noise with the variance W at the k-th time.
Figure BDA0003094838150000081
In this embodiment, a dynamic discrete-time system model representing the ESD intensity state in the working environment is established according to x (k +1) ═ ax (k) + ω (k) + ρ (k), and it can be seen that, in addition to x (k) and ω (k), the model also considers the ESD electrostatic noise ρ (k) having the variance W at the k-th time, and compared with the conventional kalman collaborative estimation method, the dynamic discrete-time system model of the ESD intensity state in the working environment in this embodiment is more accurate, so that the ESD intensity estimation of the present disclosure can be more accurately implemented.
Having established a dynamic discrete-time system model representing ESD intensity conditions within the operating environment, step two may be performed, i.e., in terms of yi(k)＝Hixi(k)+Jivi(k) Determining the ESD intensity value y measured by the ith cluster member node at the kth momenti(k)＝Hixi(k)+Jivi(k) I.e., measurement input of ith cluster member nodeIn addition, in this embodiment, the measurement noise of each cluster member node in the same cluster is not correlated, i.e., the measurement noise of each sensor is not correlated.
By dynamic discrete time system model and yi(k) So as to obtain the information transmission vector theta output by the ith cluster member node at the kth momenti(k) And will be thetai(k) Outputting to each neighbor cluster member node of the ith cluster member node, wherein the ith cluster member node can receive each information transmission vector sent to the ith cluster member node by each neighbor cluster member node, so that the ith cluster member node and each neighbor cluster member node execute iteration of a Kalman consistency filter, and obtain a consistency estimation result of the ith cluster member node at the current moment
Figure BDA0003094838150000082
Specifically, for the ith cluster member node, the measurement data set from the 0 th time to the k th time can be given, and is denoted as Yi(k)＝{yi(0),yi(1),…,yi(k) And (4) dividing. And the estimates of state x (k) and the a priori estimates may be expressed as respectively
Figure BDA0003094838150000091
And
Figure BDA0003094838150000092
can be expressed as:
Figure BDA0003094838150000093
further, can be provided with
Figure BDA0003094838150000094
And
Figure BDA0003094838150000095
and
Figure BDA0003094838150000096
estimation error and prior estimation respectivelyAnd (4) error. Then, the user can use the device to perform the operation,
Figure BDA0003094838150000097
covariance of error of (2) and
Figure BDA0003094838150000098
the covariance of the error of (a) can be expressed as:
Figure BDA0003094838150000099
namely Mi(k) Is shown as
Figure BDA00030948381500000910
Covariance of error with x (k), Pi(k) Is shown as
Figure BDA00030948381500000911
Covariance of error with x (k). And consensus gain C of Kalman consistency filteriCan be expressed as:
Figure BDA00030948381500000912
wherein | · | purpleFIs the Frobenius norm of the matrix, e > 0 is a relatively small constant.
The iteration of the kalman filter is performed by the ith cluster member node and each neighbor cluster member node, and specifically may include:
the member node of the ith cluster and the member nodes of all the neighbor clusters are all according to
Figure BDA00030948381500000913
Performing an iteration of a Kalman consistency filter;
wherein the content of the first and second substances,
Figure BDA00030948381500000914
and are assembled
Figure BDA00030948381500000915
Set of neighbor nodes, M, representing member nodes of ith cluster in cluster ξi(k)＝(Pi -1(k)+Si(k))-1As described above, Mi(k) To represent
Figure BDA00030948381500000916
Covariance of the error of (1), Pi(k) To represent
Figure BDA00030948381500000917
The covariance of the error of (a); ciDenotes the consensus gain of the Kalman consistency filter, an
Figure BDA00030948381500000918
Epsilon is a preset constant, | ·| non-woven phosphorFA Frobenius norm representing a matrix;
Figure BDA00030948381500000919
is an a priori estimator of x (k).
Step S102: based on the weight coefficient of the cluster, an overall estimation value of the current time for representing the cluster member node state of the cluster is determined.
As described above, the cluster member nodes in each cluster are in communication connection with the cluster head node in the cluster, and the communication connection may be direct connection or indirect connection, so that for a certain cluster member node, the certain cluster member node may directly or indirectly send the consistency estimation result obtained by itself to the cluster head node through the neighbor node.
The cluster head node does not need to collect data by itself, only needs to be responsible for fusing data sent by each cluster member node of the cluster, can represent the state of the cluster head node as a convex combination of the states of all cluster member nodes in the same cluster, namely, an overall estimation value of the current time for representing the state of the cluster member node of the cluster is determined, and the overall estimation value of the current time for representing the state of the cluster member node of the cluster is determined based on the weight coefficient of the cluster and can be represented as
Figure BDA0003094838150000101
Here, TsAnd THRespectively representing a set of cluster member nodes and cluster head nodes in the sensor network. Weight coefficient sigmaniThe setting method of (a) may be various, for example, the setting is completed in advance, and for example, numerical optimization may be performed according to the data association degree between the cluster member node and the cluster head node.
In a specific embodiment of the present invention, the weight coefficient of the cluster where the ith cluster member node is located is determined by the following steps:
determining a support matrix representing the mutual proximity of the cluster member nodes and the cluster head nodes at the kth time
Figure BDA0003094838150000102
Obtaining the weight coefficient of the cluster where the member node of the ith cluster is based on the support matrix lambda
Figure BDA0003094838150000103
Wherein n denotes an nth cluster in the sensor network,
Figure BDA0003094838150000104
ψn(k) the correlation degree between the cluster head node of the cluster where the ith cluster member node is located and the cluster member node of the cluster is represented, and tau represents the number of cluster member nodes participating in fusion of the cluster head node of the cluster where the ith cluster member node is located;
Figure BDA0003094838150000105
and the association degree from the ith cluster member node to the cluster head node of the cluster in which the ith cluster member node is positioned is shown.
Figure BDA0003094838150000111
That is, the proximity between the ith cluster member node and the cluster head node of the cluster in which the ith cluster member node is located is represented, and the specific write may be
Figure BDA0003094838150000112
The support matrix Λ can thus be obtained as shown above, forTo indicate the proximity of the cluster member node and the cluster head node to each other at the kth time.
In this embodiment, considering that packet loss occurs during transmission, the reliability of data is affected by the proximity of each other, and the proximity of the cluster member node data weight to the cluster head node should be positively correlated, so that the number τ of cluster member nodes in which the cluster head node participates in fusion is set, and the correlation between the cluster head node of the cluster in which the ith cluster member node is located and the cluster member node of the cluster is set to be
Figure BDA0003094838150000113
Therefore, the weight coefficients in this embodiment
Figure BDA0003094838150000114
Therefore, in an embodiment of the present invention, step S102 may specifically be:
based on the weight coefficient of the cluster, determining the overall estimation value of the current time for representing the states of the cluster member nodes of the cluster as
Figure BDA0003094838150000115
Wherein, the first and the second end of the pipe are connected with each other,
Figure BDA0003094838150000116
indicating the determined overall estimation value of the state of the cluster member node for indicating the nth cluster at the k-th moment,
Figure BDA0003094838150000117
and representing the consistency estimation result of the ith cluster member node at the kth moment.
The weight coefficients determined in the preceding embodiments
Figure BDA0003094838150000118
Obtain an overall estimate of
Figure BDA0003094838150000119
Step S103: and judging whether the number of cluster member nodes with data packet loss at the current moment in the cluster meets the preset number requirement or not.
If yes, go to step S104: taking the integral estimated value of the current moment of the cluster as the estimated value of the ESD intensity of the current moment of the area where the cluster is located;
if not, step S105 is executed: and determining the ESD strength estimated value of the current time of the area where the cluster is located based on the overall estimated value of the current time of the cluster and the overall estimated value of the last time of the cluster.
In the scheme of the application, the overall estimation value is not obtained
Figure BDA0003094838150000121
And then, directly taking the ESD intensity estimated value as the ESD intensity estimated value of the nth cluster, and judging whether data packet loss exists, namely judging whether the number of cluster member nodes with data packet loss at the current moment in the cluster meets a preset number requirement.
For example, in an embodiment of the present invention, the step S103 may specifically include:
judgment of
Figure BDA0003094838150000122
Whether the result is true or not;
if so, determining that the number of cluster member nodes with data packet loss at the current time in the cluster meets a preset number requirement;
if not, determining that the number of cluster member nodes with data packet loss at the current time in the cluster does not meet the preset number requirement;
wherein the content of the first and second substances,
Figure BDA0003094838150000123
q is a predetermined threshold value, and
Figure BDA0003094838150000124
indicating that the neighbor node j of the ith cluster member node does not receive the output of the ith cluster member nodeConsistency estimation result of ith cluster member node at kth moment
Figure BDA0003094838150000125
The neighbor node j representing the ith cluster member node receives the consistency estimation result of the ith cluster member node output by the ith cluster member node at the kth moment
Figure BDA0003094838150000126
δ (k) is a trigger threshold factor; a is a preset constant matrix,
Figure BDA0003094838150000127
is an a priori estimator of x (k), which represents the state of ESD strength at time k within the operating environment.
Figure BDA0003094838150000128
The state estimation value output by the ith cluster member node and received by the neighbor node j of the ith cluster member node is explained
Figure BDA0003094838150000129
Is less than the trigger threshold factor, i.e., it means that node j has not received data at time k, indicating that interference is triggered. On the contrary, the method can be used for carrying out the following steps,
Figure BDA00030948381500001210
when the state estimation value of the ith cluster member node output is received by the node j at the moment k
Figure BDA00030948381500001211
The number of cluster member nodes with data packet loss at the current time in the cluster is judged not to meet the preset number requirement, the ESD strength estimated value of the current time of the area where the cluster is located is determined based on the overall estimated value of the current time of the cluster and the overall estimated value of the last time of the cluster, and the ESD strength estimated value of the current time of the area where the cluster is located can be combined with the overall estimated value of the current time when the packet loss occurs in consideration of the continuity of environmental changes, so that the influence of data loss caused by data packet loss time delay on the estimated value of the current time can be reduced.
Specifically, in an embodiment of the present invention, step S105 may specifically include:
according to
Figure BDA0003094838150000131
Determining an ESD strength estimated value of the current moment of the area where the cluster is located;
wherein the content of the first and second substances,
Figure BDA0003094838150000132
representing the ESD strength estimated value of the k-th time of the determined region where the nth cluster is located, wherein alpha is an adjusting factor and is more than 0 and less than 1,
Figure BDA0003094838150000133
and the determined overall estimation value of the state of the cluster member node for representing the nth cluster at the k-th moment is shown.
Of course, if it is determined that the number of cluster member nodes with data packet loss occurring at the current time in the cluster meets the preset number requirement, the overall estimation value of the current time of the cluster can be directly used as the ESD strength estimation value of the current time of the area where the cluster is located, which can be expressed as:
Figure BDA0003094838150000134
the specific value of the preset threshold q can be set as required, which indicates the number of sensors that are allowed to lose packets at the same time.
After the estimated value of the ESD strength of the current time of the area where each cluster is located is obtained, the estimation of the ESD strength is completed, and then, whether some measures for eliminating static electricity need to be performed or not can be determined according to the estimation result of the ESD strength, for example, static electricity elimination is performed by means of dust removal and the like, that is, the ESD strength is reduced.
Any 1 cluster member node in the sensor network can be a non-invasive current sensor, and compared with an invasive current sensing device which needs to be accessed into an equipment device and is provided with a sampling resistor, the detection result of the non-invasive current sensor is not easily influenced by an equipment circuit, and meanwhile, the influence on the equipment circuit due to the damage of the sensor can be avoided.
By applying the technical scheme provided by the embodiment of the invention, considering that the measurement accuracy of a single sensor is easily influenced by factors such as environment, artificial change, equipment change and the like, the application adopts a sensor network with xi clusters, and different clusters are used for carrying out ESD intensity estimation in different areas. Specifically, any 1 cluster head node may receive the current-time consistency estimation result of each cluster member node in the cluster, and then determine an overall estimation value of the current time for representing the cluster member node state of the cluster based on the weight coefficient of the cluster. The method and the device can judge whether the number of cluster member nodes with data packet loss at the current moment in the cluster meets a preset number requirement, namely, the data packet loss condition caused by interference caused by ESD static electricity is considered, if the preset number requirement is not met, the data packet loss condition is shown, and the method and the device can be based on the overall estimation value of the current moment of the cluster and the overall estimation value of the last moment of the cluster, so that the ESD strength estimation value of the current moment of the area where the cluster is located is determined to be accurate. Of course, if the preset number requirement is met, the overall estimation value of the current time of the cluster can be directly used as the ESD strength estimation value of the current time of the area where the cluster is located. In summary, the scheme of the application can effectively estimate the ESD strength and improve the accuracy.
Corresponding to the above method embodiments, the present invention further provides an ESD strength estimation system, which can be referred to above correspondingly.
Referring to fig. 3, a schematic structural diagram of an ESD strength estimation system according to the present invention is applied to any 1 cluster head node of a sensor network having ξ clusters, each cluster has 1 cluster head node and at least 1 cluster member node, ξ is a positive integer and represents the number of clusters in the sensor network, and the ESD strength estimation system includes:
a consistency estimation result receiving unit 301, configured to receive a consistency estimation result of each cluster member node at the current time;
an overall estimation value determining unit 302, configured to determine an overall estimation value of a current time for representing a cluster member node state of the cluster based on the weight coefficient of the cluster;
a determining unit 303, configured to determine whether the number of cluster member nodes with data packet loss occurring at the current time in the cluster meets a preset number requirement;
if yes, executing a first trigger unit 304, where the first trigger unit 304 is configured to: taking the integral estimated value of the current moment of the cluster as the estimated value of the ESD intensity of the current moment of the area where the cluster is located;
if not, a second trigger unit 305 is executed, the second trigger unit being configured to 305: and determining the ESD strength estimated value of the current time of the area where the cluster is located based on the overall estimated value of the current time of the cluster and the overall estimated value of the last time of the cluster.
In a specific embodiment of the present invention, the consistency estimation result receiving unit 301 receives a consistency estimation result of the current time of the ith cluster member node in each cluster member node, which is a consistency estimation result determined by the ith cluster member node through the following operations:
establishing a dynamic discrete time system model representing ESD intensity states within a work environment;
according to yi(k)＝Hixi(k)+Jivi(k) Determining an ESD strength value measured by the ith cluster member node at the kth moment;
by the dynamic discrete time system model and yi(k) To obtain thetai(k) And will be thetai(k) Outputting each neighbor cluster member node to the ith cluster member node, and receiving each information transmission vector sent to the ith cluster member node by each neighbor cluster member node, so that the ith cluster member node and each neighbor cluster member node execute iteration of a Kalman consistency filter, and obtain the consistency estimation result of the ith cluster member node at the current moment
Figure BDA0003094838150000151
Wherein, thetai(k) Represents an information transmission vector output by the member node of the ith cluster at the kth time, and
Figure BDA0003094838150000152
zi(k) a fusion information vector representing the k-th time, Si(k) A matrix of fusion information representing the k-th time instant,
Figure BDA0003094838150000153
represents the consistency estimation result of the ith cluster member node at the kth moment, and
Figure BDA0003094838150000154
an estimate of x (k);
x (k) represents the ESD intensity state at the k-th moment in the working environment, xi(k) Represents the ESD strength state around the ith cluster member node at the k time point, HiAnd JiAre all a predetermined constant matrix, yi(k) ESD strength value measured for ith cluster member node at kth time, vi(k) Indicating a variance R at time kiWhite noise of zero mean.
In one embodiment of the present invention, the creating a dynamic discrete-time system model representing ESD intensity conditions within a work environment comprises:
establishing a dynamic discrete time system model representing the ESD intensity state in the working environment according to x (k +1) ═ ax (k) + omega (k) + rho (k);
where a is a preset constant matrix, x (k +1) represents the ESD intensity state at the k +1 th time within the working environment, ω (k) represents the zero-mean white noise of the ESD intensity state with the variance Q at the k-th time, and ρ (k) represents the ESD electrostatic noise with the variance W at the k-th time.
In a specific embodiment of the present invention, the iteration of the kalman filter performed by the ith cluster member node and each neighbor cluster member node includes:
the ith cluster member node and eachAll the neighbor cluster member nodes are according to
Figure BDA0003094838150000155
Performing an iteration of a Kalman consistency filter;
wherein the content of the first and second substances,
Figure BDA0003094838150000161
and are assembled
Figure BDA0003094838150000162
Set of neighbor nodes, M, representing member nodes of ith cluster in cluster ξi(k)＝(Pi -1(k)+Si(k))-1，Mi(k) To represent
Figure BDA0003094838150000163
Covariance of the error of (1), Pi(k) To represent
Figure BDA0003094838150000164
The covariance of the error of (a); ciRepresents the consensus gain of the Kalman consistency filter, an
Figure BDA0003094838150000165
Epsilon is a preset constant, | ·| non-woven phosphorFA Frobenius norm representing a matrix;
Figure BDA0003094838150000166
is an a priori estimator of x (k).
In a specific embodiment of the present invention, the weight coefficient of the cluster in which the ith cluster member node is located is determined by the following steps:
determining a support matrix representing the mutual proximity of the cluster member nodes and the cluster head nodes at the kth time
Figure BDA0003094838150000167
Obtaining the weight coefficient of the cluster where the member node of the ith cluster is based on the support matrix lambda
Figure BDA0003094838150000168
Wherein n denotes an nth cluster in the sensor network,
Figure BDA0003094838150000169
ψn(k) the correlation degree between the cluster head node of the cluster where the ith cluster member node is located and the cluster member node of the cluster is represented, and tau represents the number of cluster member nodes participating in fusion of the cluster head node of the cluster where the ith cluster member node is located;
Figure BDA00030948381500001610
and the association degree from the ith cluster member node to the cluster head node of the cluster in which the ith cluster member node is positioned is shown.
In an embodiment of the present invention, the overall estimation value determining unit 302 is specifically configured to:
based on the weight coefficient of the cluster, determining the overall estimation value of the current time for representing the states of the cluster member nodes of the cluster as
Figure BDA00030948381500001611
Wherein the content of the first and second substances,
Figure BDA00030948381500001612
indicating the determined overall estimation value of the state of the cluster member node for indicating the nth cluster at the k-th moment,
Figure BDA00030948381500001613
and representing the consistency estimation result of the ith cluster member node at the kth moment.
In an embodiment of the present invention, the determining unit 303 is specifically configured to:
judgment of
Figure BDA0003094838150000171
Whether the result is true or not;
if yes, determining that the number of cluster member nodes with data packet loss at the current time in the cluster meets a preset number requirement, and executing a first triggering unit 304;
if not, determining that the number of cluster member nodes with data packet loss at the current time in the cluster does not meet the preset number requirement, and executing a second triggering unit 305;
wherein the content of the first and second substances,
Figure BDA0003094838150000172
q is a preset threshold value, and
Figure BDA0003094838150000173
the neighbor node j of the ith cluster member node does not receive the consistency estimation result of the ith cluster member node output by the ith cluster member node at the kth moment
Figure BDA0003094838150000174
The neighbor node j representing the ith cluster member node receives the consistency estimation result of the ith cluster member node output by the ith cluster member node at the kth moment
Figure BDA0003094838150000175
δ (k) is a trigger threshold factor; a is a preset constant matrix,
Figure BDA0003094838150000176
is an a priori estimator of x (k), which represents the state of ESD strength at time k within the operating environment.
In an embodiment of the present invention, the second triggering unit 305 is specifically configured to:
according to
Figure BDA0003094838150000177
Determining an ESD strength estimated value of the current moment of the area where the cluster is located;
wherein the content of the first and second substances,
Figure BDA0003094838150000178
representing the ESD strength estimated value of the k-th time of the determined region where the nth cluster is located, wherein alpha is an adjusting factor and is more than 0 and less than 1,
Figure BDA0003094838150000179
and the determined overall estimation value of the state of the cluster member node for representing the nth cluster at the k-th moment is shown.
In one embodiment of the present invention, any 1 cluster member node in the sensor network is a non-invasive current sensor.
It is further noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in the process, method, article, or apparatus that comprises the element.
Those of skill would further appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or combinations of both, and that the various illustrative components and steps have been described above generally in terms of their functionality in order to clearly illustrate this interchangeability of hardware and software. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
The principle and the implementation of the present invention are explained in the present application by using specific examples, and the above description of the embodiments is only used to help understanding the technical solution and the core idea of the present invention. It should be noted that, for those skilled in the art, it is possible to make various improvements and modifications to the present invention without departing from the principle of the present invention, and those improvements and modifications also fall within the scope of the claims of the present invention.

Claims (10)

1. An ESD intensity estimation method is characterized in that the method is applied to any 1 cluster head node of a sensor network with xi clusters, each cluster has 1 cluster head node and at least 1 cluster member node, xi is a positive integer and represents the number of clusters in the sensor network, and the method comprises the following steps:
receiving a consistency estimation result of each cluster member node at the current moment;
determining an overall estimation value of the current time for representing the cluster member node state of the cluster based on the weight coefficient of the cluster;
judging whether the number of cluster member nodes with data packet loss at the current moment in the cluster meets a preset number requirement or not;
if so, taking the integral estimated value of the current moment of the cluster as the estimated value of the ESD intensity of the current moment of the area where the cluster is located;
and if not, determining the ESD strength estimated value of the current time of the area where the cluster is located based on the overall estimated value of the current time of the cluster and the overall estimated value of the last time of the cluster.
2. The ESD strength estimation method of claim 1, wherein the received consistency estimation result of the current time of the ith cluster member node in each cluster member node is a consistency estimation result determined by the following steps for the ith cluster member node:
establishing a dynamic discrete time system model representing ESD intensity states within a work environment;
according to yi(k)＝Hixi(k)+Jivi(k) Determining an ESD strength value measured by the ith cluster member node at the kth moment;
by said dynamic discrete timeSystem model and yi(k) To obtain thetai(k) And will be thetai(k) Outputting each neighbor cluster member node to the ith cluster member node, and receiving each information transmission vector sent to the ith cluster member node by each neighbor cluster member node, so that the ith cluster member node and each neighbor cluster member node execute iteration of a Kalman consistency filter, and obtain the consistency estimation result of the ith cluster member node at the current moment
Figure FDA0003565097800000011
Wherein, thetai(k) Represents an information transmission vector output by the member node of the ith cluster at the kth time, and
Figure FDA0003565097800000012
zi(k) a fusion information vector representing the k-th time, Si(k) A matrix of fusion information representing the k-th time instant,
Figure FDA0003565097800000013
represents the consistency estimation result of the ith cluster member node at the kth moment, and
Figure FDA0003565097800000014
an estimate of x (k);
x (k) represents the ESD intensity state at the k-th moment in the working environment, xi(k) Represents the ESD strength state around the ith cluster member node at the k time point, HiAnd JiAre all a predetermined constant matrix, yi(k) ESD strength value measured for ith cluster member node at kth time, vi(k) Indicating a variance R at time kiWhite noise of zero mean.
3. The ESD severity estimation method of claim 2, wherein said establishing a dynamic discrete time system model representing ESD severity conditions within an operating environment comprises:
establishing a dynamic discrete time system model representing the ESD intensity state in the working environment according to x (k +1) ═ ax (k) + omega (k) + rho (k);
where a is a preset constant matrix, x (k +1) represents the ESD intensity state at the k +1 th time within the working environment, ω (k) represents the zero-mean white noise of the ESD intensity state with the variance Q at the k-th time, and ρ (k) represents the ESD electrostatic noise with the variance W at the k-th time.
4. The ESD strength estimation method of claim 3, wherein the i-th cluster member node and each neighbor cluster member node perform an iteration of a kalman consistency filter, comprising:
the member node of the ith cluster and the member nodes of all the neighbor clusters are all according to
Figure FDA0003565097800000021
Performing an iteration of a Kalman consistency filter;
wherein the content of the first and second substances,
Figure FDA0003565097800000022
and are assembled
Figure FDA0003565097800000023
Figure FDA0003565097800000024
Set of neighbor nodes, M, representing member nodes of ith cluster in cluster ξi(k)＝(Pi -1(k)+Si(k))-1，Mi(k) To represent
Figure FDA0003565097800000025
Covariance of the error of (1), Pi(k) To represent
Figure FDA0003565097800000026
The covariance of the error of (a); ciDenotes the consensus gain of the Kalman consistency filter, an
Figure FDA0003565097800000027
Is left as a preset constant, | | write |, the calculation of the phosphorFA Frobenius norm representing a matrix;
Figure FDA0003565097800000028
is an a priori estimator of x (k).
5. The ESD strength estimation method of claim 1, wherein the weighting factor of the cluster in which the ith cluster member node is located is determined by:
determining a support matrix representing the mutual proximity of the cluster member nodes and the cluster head nodes at the kth time
Figure FDA0003565097800000031
Obtaining the weight coefficient of the cluster where the member node of the ith cluster is based on the support matrix lambda
Figure FDA0003565097800000032
Wherein n denotes an nth cluster in the sensor network,
Figure FDA0003565097800000033
ψn(k) the correlation degree between the cluster head node of the cluster where the ith cluster member node is located and the cluster member node of the cluster is represented, and tau represents the number of cluster member nodes participating in fusion of the cluster head node of the cluster where the ith cluster member node is located;
Figure FDA0003565097800000034
and the association degree from the ith cluster member node to the cluster head node of the cluster in which the ith cluster member node is positioned is shown.
6. The ESD strength estimation method of claim 5, wherein determining an overall estimate of a current time representing a state of cluster member nodes of the cluster based on the weighting coefficients of the cluster comprises:
based on the weight coefficient of the cluster, determining the overall estimation value of the current time for representing the states of the cluster member nodes of the cluster as
Figure FDA0003565097800000035
Wherein the content of the first and second substances,
Figure FDA0003565097800000036
indicating the determined overall estimation value of the node state of the cluster member representing the nth cluster at the kth time,
Figure FDA0003565097800000037
and representing the consistency estimation result of the ith cluster member node at the kth moment.
7. The ESD strength estimation method according to any one of claims 1 to 6, wherein determining whether the number of cluster member nodes with data packet loss occurring at the current time in the cluster meets a preset number requirement includes:
judgment of
Figure FDA0003565097800000038
Whether the result is true or not;
if so, determining that the number of cluster member nodes with data packet loss at the current time in the cluster meets a preset number requirement;
if not, determining that the number of cluster member nodes with data packet loss at the current time in the cluster does not meet the preset number requirement;
wherein the content of the first and second substances,
Figure FDA0003565097800000041
q is a preset threshold value, and
Figure FDA0003565097800000042
the consistency estimation of the ith cluster member node at the kth moment, which indicates that the neighbor node j of the ith cluster member node does not receive the output of the ith cluster member nodeResults
Figure FDA0003565097800000043
The neighbor node j representing the ith cluster member node receives the consistency estimation result of the ith cluster member node output by the ith cluster member node at the kth moment
Figure FDA0003565097800000044
δ (k) is a trigger threshold factor; a is a preset constant matrix,
Figure FDA0003565097800000045
an a priori estimator of x (k), which represents the state of ESD strength at time k within the operating environment; t issRepresented is a set of cluster member nodes in a sensor network.
8. The ESD robustness assessment method of claim 7, wherein determining the ESD robustness assessment value for the current time of the cluster area based on the global assessment value for the current time of the cluster and the global assessment value for a time previous to the cluster comprises:
according to the following
Figure FDA0003565097800000046
Determining an ESD strength estimated value of the area where the cluster is located at the current moment;
wherein the content of the first and second substances,
Figure FDA0003565097800000047
representing the ESD strength estimated value of the k-th time of the determined region where the nth cluster is located, wherein alpha is an adjusting factor and is more than 0 and less than 1,
Figure FDA0003565097800000048
and the determined overall estimation value of the node state of the cluster member of the nth cluster at the kth moment is shown.
9. The ESD strength estimation method of any of claims 1 to 6, wherein any 1 cluster member node in the sensor network is a non-invasive current sensor.
10. An ESD strength estimation system applied to any 1 cluster head node of a sensor network having xi clusters, each cluster having 1 cluster head node and at least 1 cluster member node, xi being a positive integer indicating the number of clusters in the sensor network, comprising:
a consistency estimation result receiving unit, configured to receive a consistency estimation result of each cluster member node at the current time;
the overall estimation value determining unit is used for determining an overall estimation value of the current time for representing the cluster member node state of the cluster based on the weight coefficient of the cluster;
the judging unit is used for judging whether the number of cluster member nodes with data packet loss at the current moment in the cluster meets the preset number requirement or not;
if yes, executing a first trigger unit, wherein the first trigger unit is used for: taking the integral estimated value of the current moment of the cluster as the estimated value of the ESD intensity of the current moment of the area where the cluster is located;
if not, executing a second trigger unit, wherein the second trigger unit is used for: and determining the ESD strength estimated value of the current time of the area where the cluster is located based on the overall estimated value of the current time of the cluster and the overall estimated value of the last time of the cluster.
CN202110608088.5A 2021-06-01 2021-06-01 ESD strength estimation method and system Active CN113361091B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202110608088.5A CN113361091B (en) 2021-06-01 2021-06-01 ESD strength estimation method and system

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202110608088.5A CN113361091B (en) 2021-06-01 2021-06-01 ESD strength estimation method and system

Publications (2)

Publication Number Publication Date
CN113361091A CN113361091A (en) 2021-09-07
CN113361091B true CN113361091B (en) 2022-05-17

Family

ID=77530813

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202110608088.5A Active CN113361091B (en) 2021-06-01 2021-06-01 ESD strength estimation method and system

Country Status (1)

Country Link
CN (1) CN113361091B (en)

Citations (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2013111445A1 (en) * 2012-01-25 2013-08-01 国立大学法人九州工業大学 Non-contact discharge evaluation method and non-contact discharge evaluation apparatus
CN109728795A (en) * 2018-12-24 2019-05-07 哈尔滨理工大学 Time-varying event under unknown probability situation with loss of data triggers filtering method
CN110175672A (en) * 2019-05-30 2019-08-27 北斗航天信息网络技术有限公司 The battery charging state assessment system and appraisal procedure combined based on Extended Kalman filter and Genetic BP Neutral Network
CN110289989A (en) * 2019-05-27 2019-09-27 东南大学 A kind of distributed state estimation method based on volume Kalman filtering algorithm
CN110958639A (en) * 2019-01-31 2020-04-03 北京航空航天大学 Target state estimation method and system
CN111083733A (en) * 2020-02-10 2020-04-28 安徽理工大学 Congestion control method and system for wireless sensor network
CN111859773A (en) * 2020-08-05 2020-10-30 哈尔滨工程大学 Electric gate valve fault determination method and system based on regularization particle filtering
CN112733369A (en) * 2021-01-13 2021-04-30 青岛海尔科技有限公司 Intelligent equipment maintenance method, terminal and system and electronic equipment

Family Cites Families (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
EP2068259A1 (en) * 2007-12-04 2009-06-10 X-FAB Semiconductor Foundries AG Method and system for checking the ESD behaviour of integrated circuits at the circuit level
CN102122973A (en) * 2010-01-08 2011-07-13 中国科学院沈阳自动化研究所 Clustering-wireless-sensor-network-orientated two-stage adaptive frequency-hopping method
CN103052128A (en) * 2012-12-20 2013-04-17 华南理工大学 Wireless sensor network-based energy-efficient collaborative scheduling method
US10349872B2 (en) * 2015-12-28 2019-07-16 Medtronic Minimed, Inc. Methods, systems, and devices for sensor fusion
CN110087273B (en) * 2019-03-13 2022-07-22 西安电子科技大学 Wireless sensor network clustering routing method and wireless sensor network protocol platform

Patent Citations (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2013111445A1 (en) * 2012-01-25 2013-08-01 国立大学法人九州工業大学 Non-contact discharge evaluation method and non-contact discharge evaluation apparatus
CN109728795A (en) * 2018-12-24 2019-05-07 哈尔滨理工大学 Time-varying event under unknown probability situation with loss of data triggers filtering method
CN110958639A (en) * 2019-01-31 2020-04-03 北京航空航天大学 Target state estimation method and system
CN110289989A (en) * 2019-05-27 2019-09-27 东南大学 A kind of distributed state estimation method based on volume Kalman filtering algorithm
CN110175672A (en) * 2019-05-30 2019-08-27 北斗航天信息网络技术有限公司 The battery charging state assessment system and appraisal procedure combined based on Extended Kalman filter and Genetic BP Neutral Network
CN111083733A (en) * 2020-02-10 2020-04-28 安徽理工大学 Congestion control method and system for wireless sensor network
CN111859773A (en) * 2020-08-05 2020-10-30 哈尔滨工程大学 Electric gate valve fault determination method and system based on regularization particle filtering
CN112733369A (en) * 2021-01-13 2021-04-30 青岛海尔科技有限公司 Intelligent equipment maintenance method, terminal and system and electronic equipment

Non-Patent Citations (6)

* Cited by examiner, † Cited by third party
Title
MRNN:一种新的基于改进型递归神经网络的WSN动态建模方法:应用于故障检测;黄旭;《计算机工程与科学》;20150415(第04期);全文 *
传感器网络一致性分布式滤波算法;王长城等;《控制理论与应用》;20121215(第12期);全文 *
具有丢包补偿的分布式一致性融合估计器;赵国荣等;《海军航空工程学院学报》;20180630(第03期);全文 *
基于卡尔曼一致性滤波的WSN丢包补偿算法;王岩等;《仪器仪表学报》;20131115(第11期);全文 *
无线传感器网络中丢包扩散卡尔曼算法的改进;聂文梅等;《西安邮电大学学报》;20130710(第04期);全文 *
能量约束下的分布式一致性融合估计算法研究;赵国荣等;《计算机仿真》;20191215(第12期);全文 *

Also Published As

Publication number Publication date
CN113361091A (en) 2021-09-07

Similar Documents

Publication Publication Date Title
Kapoor et al. Capprobe: A simple and accurate capacity estimation technique
CN108667673B (en) Nonlinear network control system fault detection method based on event trigger mechanism
Guo et al. Find: faulty node detection for wireless sensor networks
JP4675426B2 (en) Method, computer program for analyzing and generating network traffic using an improved Markov modulation Poisson process model
Yuan et al. A comparative study of measurement-based Thevenin equivalents identification methods
CN106845820B (en) NFV system reliability evaluation method based on performance margin
CN103139804B (en) Energy-saving transmission self-adaption recursive least squares (RLS) distributed-type detection method of wireless sensor network
CN114201326A (en) Micro-service abnormity diagnosis method based on attribute relation graph
CN113361091B (en) ESD strength estimation method and system
CN109655761A (en) Internal resistance of cell measuring method and battery management system
CN101237357B (en) Online failure detection method for industrial wireless sensor network
Panda et al. Efficient fault node detection algorithm for wireless sensor networks
EP2629453A1 (en) Method, apparatus and system for setting a size of an event correlation time window
CN107728074B (en) Lithium battery state of charge estimation method considering sensor and model errors
Dai et al. Model-based on-line sensor fault detection in Wireless Sensor Actuator Networks
CN110113723B (en) Method for measuring and estimating environmental parameters in airplane cabin based on wireless sensor network
CN107797909B (en) System elastic limit index measuring method
Subhan et al. Extended gradient predictor and filter for smoothing RSSI
Xie et al. Adaptive and online fault detection using RPCA algorithm in wireless sensor network nodes
CN111683377B (en) Real-time reliable relay deployment method for power distribution network
Kapoor et al. Accuracy of link capacity estimates using passive and active approaches with CapProbe
CN108400907A (en) A kind of link packet drop rate inference method under uncertain network environment
Li Hybrid Algorithms
Lehman et al. A decentralized network coordinate system for robust internet distance
CN110149277B (en) Network congestion link diagnosis method and system based on link congestion intensity distribution

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