CN113361091B - ESD strength estimation method and system - Google Patents
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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
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
Wherein, thetai(k) Represents an information transmission vector output by the member node of the ith cluster at the kth time, andzi(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,represents the consistency estimation result of the ith cluster member node at the kth moment, andan 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 toPerforming an iteration of a Kalman consistency filter;
wherein the content of the first and second substances,and are assembledSet of neighbor nodes, M, representing member nodes of ith cluster in cluster ξi(k)=(Pi -1(k)+Si(k))-1,Mi(k) To representCovariance of the error of (1), Pi(k) To representThe covariance of the error of (a); ciDenotes the consensus gain of the Kalman consistency filter, anIs left as a preset constant, | | write |, the calculation of the phosphorFA Frobenius norm representing a matrix;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
Obtaining the weight coefficient of the cluster where the member node of the ith cluster is based on the support matrix lambda
Wherein n denotes an nth cluster in the sensor network,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;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
Wherein the content of the first and second substances,indicating the determined overall estimation value of the state of the cluster member node for indicating the nth cluster at the k-th moment,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:
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,q is a preset threshold value, andneighbor 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 momentThe 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 momentδ (k) is a trigger threshold factor; a is a preset constant matrix,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 toDetermining 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,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,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.
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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. 2An 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,andand 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
Wherein, thetai(k) Represents an information transmission vector output by the member node of the ith cluster at the kth time, andzi(k) a fusion information vector representing the k-th time, Si(k) A matrix of fusion information representing the k-th time instant,represents the consistency estimation result of the ith cluster member node at the kth moment, andan 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.
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
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 respectivelyAndcan be expressed as:
further, can be provided withAndandestimation error and prior estimation respectivelyAnd (4) error. Then, the user can use the device to perform the operation,covariance of error of (2) andthe covariance of the error of (a) can be expressed as:namely Mi(k) Is shown asCovariance of error with x (k), Pi(k) Is shown asCovariance of error with x (k). And consensus gain C of Kalman consistency filteriCan be expressed as: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 toPerforming an iteration of a Kalman consistency filter;
wherein the content of the first and second substances,and are assembledSet 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 representCovariance of the error of (1), Pi(k) To representThe covariance of the error of (a); ciDenotes the consensus gain of the Kalman consistency filter, anEpsilon is a preset constant, | ·| non-woven phosphorFA Frobenius norm representing a matrix;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
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
Obtaining the weight coefficient of the cluster where the member node of the ith cluster is based on the support matrix lambda
Wherein n denotes an nth cluster in the sensor network,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;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.
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 beThe 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 beTherefore, the weight coefficients in this embodiment
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
Wherein, the first and the second end of the pipe are connected with each other,indicating the determined overall estimation value of the state of the cluster member node for indicating the nth cluster at the k-th moment,and representing the consistency estimation result of the ith cluster member node at the kth moment.
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 obtainedAnd 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:
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,q is a predetermined threshold value, andindicating 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 momentThe 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 momentδ (k) is a trigger threshold factor; a is a preset constant matrix,is an a priori estimator of x (k), which represents the state of ESD strength at time k within the operating environment.
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 explainedIs 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,when the state estimation value of the ith cluster member node output is received by the node j at the moment k
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 toDetermining 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,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,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:
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
Wherein, thetai(k) Represents an information transmission vector output by the member node of the ith cluster at the kth time, andzi(k) a fusion information vector representing the k-th time, Si(k) A matrix of fusion information representing the k-th time instant,represents the consistency estimation result of the ith cluster member node at the kth moment, andan 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 toPerforming an iteration of a Kalman consistency filter;
wherein the content of the first and second substances,and are assembledSet of neighbor nodes, M, representing member nodes of ith cluster in cluster ξi(k)=(Pi -1(k)+Si(k))-1,Mi(k) To representCovariance of the error of (1), Pi(k) To representThe covariance of the error of (a); ciRepresents the consensus gain of the Kalman consistency filter, anEpsilon is a preset constant, | ·| non-woven phosphorFA Frobenius norm representing a matrix;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
Obtaining the weight coefficient of the cluster where the member node of the ith cluster is based on the support matrix lambda
Wherein n denotes an nth cluster in the sensor network,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;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
Wherein the content of the first and second substances,indicating the determined overall estimation value of the state of the cluster member node for indicating the nth cluster at the k-th moment,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:
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,q is a preset threshold value, andthe 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 momentThe 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 momentδ (k) is a trigger threshold factor; a is a preset constant matrix,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 toDetermining 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,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,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
Wherein, thetai(k) Represents an information transmission vector output by the member node of the ith cluster at the kth time, andzi(k) a fusion information vector representing the k-th time, Si(k) A matrix of fusion information representing the k-th time instant,represents the consistency estimation result of the ith cluster member node at the kth moment, andan 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 toPerforming an iteration of a Kalman consistency filter;
wherein the content of the first and second substances,and are assembled Set of neighbor nodes, M, representing member nodes of ith cluster in cluster ξi(k)=(Pi -1(k)+Si(k))-1,Mi(k) To representCovariance of the error of (1), Pi(k) To representThe covariance of the error of (a); ciDenotes the consensus gain of the Kalman consistency filter, anIs left as a preset constant, | | write |, the calculation of the phosphorFA Frobenius norm representing a matrix;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
Obtaining the weight coefficient of the cluster where the member node of the ith cluster is based on the support matrix lambda
Wherein n denotes an nth cluster in the sensor network,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;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
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:
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,q is a preset threshold value, andthe 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 nodeResultsThe 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 momentδ (k) is a trigger threshold factor; a is a preset constant matrix,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 followingDetermining 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,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,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.
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