CN113361091B - A method and system for estimating ESD intensity - Google Patents

A method and system for estimating ESD intensity Download PDF

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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
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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
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Hangzhou Dianzi University
Hangzhou Power Equipment Manufacturing Co Ltd
Hangzhou Power Supply Co of State Grid Zhejiang Electric Power Co Ltd
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Hangzhou Power Equipment Manufacturing Co Ltd
Hangzhou Power Supply Co of State Grid Zhejiang Electric Power Co Ltd
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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

一种ESD强度估计方法和系统A method and system for estimating ESD intensity

技术领域technical field

本发明涉及传感器技术领域，特别是涉及一种ESD强度估计方法和系统。The present invention relates to the technical field of sensors, and in particular, to a method and system for estimating ESD intensity.

背景技术Background technique

ESD(Electro Static Discharge，静电放电)，指的是指由于电荷运动造成的静电释放。在通讯机房、开关柜、基站等封闭工作环境中，存在一些微小尘埃、水汽和腐蚀物，由于它们是移动的多电荷粒子，因此，粒子之间摩擦或设备工作便可以造成静电释放，这些物质会被静电充能。静电的特点具有长期积聚、高电压、小电流和低电量的特点。ESD强度会对设备的运行造成影响，因此需要进行ESD强度的估计，当ESD强度过大时，便需要采取一些降低ESD强度的措施。ESD (Electro Static Discharge, electrostatic discharge), refers to the electrostatic discharge caused by the movement of electric charges. In closed working environments such as communication equipment rooms, switch cabinets, base stations, etc., there are some tiny dust, water vapor and corrosive substances. Because they are moving multi-charged particles, the friction between particles or the operation of equipment can cause electrostatic discharge. These substances Charged with static electricity. Static electricity is characterized by long-term accumulation, high voltage, low current, and low battery. The ESD intensity will affect the operation of the device, so it is necessary to estimate the ESD intensity. When the ESD intensity is too high, some measures to reduce the ESD intensity need to be taken.

目前在进行ESD强度的估计时，通常是采用卡尔曼协同估计的方法，在估计信息融合时，没有考虑到ESD静电带来的干扰。ESD静电会增加通信链路的不稳定性和数据丢包的可能性，数据丢包会降低监测信息的完整性，从而降低传统方案的ESD强度的估计精度。At present, when estimating ESD intensity, the method of Kalman collaborative estimation is usually adopted, and the interference caused by ESD static electricity is not considered when estimating information fusion. ESD static electricity will increase the instability of the communication link and the possibility of data packet loss. Data packet loss will reduce the integrity of the monitoring information, thereby reducing the estimation accuracy of the ESD intensity of the traditional scheme.

综上所述，如何有效地进行ESD强度估计，提高准确性，是目前本领域技术人员急需解决的技术问题。To sum up, how to effectively estimate the ESD intensity and improve the accuracy is a technical problem that those skilled in the art urgently need to solve.

发明内容SUMMARY OF THE INVENTION

本发明的目的是提供一种ESD强度估计方法和系统，以有效地进行ESD强度估计，提高准确性。The purpose of the present invention is to provide an ESD intensity estimation method and system, so as to effectively perform ESD intensity estimation and improve the accuracy.

为解决上述技术问题，本发明提供如下技术方案：In order to solve the above-mentioned technical problems, the present invention provides the following technical solutions:

一种ESD强度估计方法，应用于具有ξ个分簇的传感器网络的任意1个簇首节点中，每个分簇具有1个簇首节点和至少1个簇成员节点，ξ为正整数，表示传感器网络中的分簇数量，包括：An ESD intensity estimation method, applied to any one cluster head node of a sensor network with ξ clusters, each cluster has one cluster head node and at least one cluster member node, ξ is a positive integer, indicating The number of clusters in the sensor network, including:

接收各个簇成员节点当前时刻的一致性估计结果；Receive the consistency estimation result of each cluster member node at the current moment;

基于该簇的权重系数，确定出用于表示该簇的簇成员节点状态的当前时刻的整体估计值；Based on the weight coefficient of the cluster, determine the overall estimated value representing the current moment of the state of the cluster member nodes of the cluster;

判断该簇中当前时刻出现数据丢包的簇成员节点的数量是否满足预设的数量要求；Determine whether the number of cluster member nodes with data packet loss at the current moment in the cluster meets the preset number requirement;

如果是，则将该簇当前时刻的整体估计值作为该簇所在区域的当前时刻的ESD强度估计值；If yes, take the overall estimated value of the cluster at the current moment as the estimated value of the ESD intensity at the current moment in the area where the cluster is located;

如果否，则基于该簇当前时刻的整体估计值以及该簇上一时刻的整体估计值，确定出该簇所在区域的当前时刻的ESD强度估计值。If not, based on the overall estimated value of the cluster at the current moment and the overall estimated value of the cluster at the previous moment, the estimated value of the ESD intensity at the current moment in the area where the cluster is located is determined.

优选的，接收的各个簇成员节点中的第i簇成员节点当前时刻的一致性估计结果，为第i簇成员节点通过以下步骤确定出的当前时刻的一致性估计结果：Preferably, the received consistency estimation result of the i-th cluster member node at the current moment in each cluster member node is the consistency estimation result at the current moment determined by the i-th cluster member node through the following steps:

建立表示工作环境内的ESD强度状态的动态离散时间系统模型；Build a dynamic discrete-time system model representing the state of ESD intensity within the work environment;

按照yi(k)＝Hixi(k)+Jivi(k)确定出第i簇成员节点在第k时刻测量到的ESD强度值；According to y i (k)=H i x i (k)+J i v i (k), determine the ESD intensity value measured by the i-th cluster member node at the k-th moment;

通过所述动态离散时间系统模型以及yi(k)得到θi(k)并将θi(k)输出至第i簇成员节点的各个邻居簇成员节点，并接收各个邻居簇成员节点发送至第i簇成员节点的各个信息传输向量，以使第i簇成员节点与各个邻居簇成员节点执行卡尔曼一致性滤波器的迭代，并得到第i簇成员节点当前时刻的一致性估计结果

Figure BDA0003094838150000021
Obtain θ i (k) through the dynamic discrete-time system model and y i (k) and output θ i (k) to each neighbor cluster member node of the i-th cluster member node, and receive each neighbor cluster member node to send to Each information transmission vector of the i-th cluster member node, so that the i-th cluster member node and each neighbor cluster member node perform the iteration of the Kalman consistency filter, and obtain the consistency estimation result of the i-th cluster member node at the current moment
Figure BDA0003094838150000021

其中，θi(k)表示第i簇成员节点在第k时刻输出的信息传输向量，且

Figure BDA0003094838150000022
zi(k)表示第k时刻的融合信息向量，Si(k)表示第k时刻的融合信息矩阵，
Figure BDA0003094838150000023
表示第i簇成员节点在第k时刻的一致性估计结果，且
Figure BDA0003094838150000024
为x(k)的估计量；Among them, θ i (k) represents the information transmission vector output by the i-th cluster member node at the k-th moment, and
Figure BDA0003094838150000022
z i (k) represents the fusion information vector at the kth moment, S i (k) represents the fusion information matrix at the kth moment,
Figure BDA0003094838150000023
represents the consistency estimation result of the i-th cluster member node at the k-th moment, and
Figure BDA0003094838150000024
is the estimator of x(k);

x(k)表示工作环境内的第k时刻的ESD强度状态，xi(k)表示第i簇成员节点周围的第k时刻的ESD强度状态，Hi和Ji均为预设的常数矩阵，yi(k)为第i簇成员节点在第k时刻测量到的ESD强度值，vi(k)表示在第k时刻具有方差Ri的零均值白噪声。x(k) represents the ESD intensity state at the k-th time in the working environment, x i (k) represents the ESD intensity state at the k-th time around the member node of the i-th cluster, and both H i and J i are preset constant matrices , y i (k) is the ESD intensity value measured by the i-th cluster member node at the k- th time, and vi (k) represents the zero-mean white noise with variance R i at the k-th time.

优选的，所述建立表示工作环境内的ESD强度状态的动态离散时间系统模型，包括：Preferably, the establishing a dynamic discrete time system model representing the ESD intensity state in the working environment includes:

按照x(k+1)＝Ax(k)+ω(k)+ρ(k)建立表示工作环境内的ESD强度状态的动态离散时间系统模型；According to x(k+1)=Ax(k)+ω(k)+ρ(k), establish a dynamic discrete-time system model representing the ESD intensity state in the working environment;

其中，A为预设的常数矩阵，x(k+1)表示工作环境内的第k+1时刻的ESD强度状态，ω(k)表示在第k时刻具有方差Q的ESD强度状态的零均值白噪声，ρ(k)表示在第k时刻具有方差W的ESD静电噪声。Among them, A is a preset constant matrix, x(k+1) represents the ESD intensity state at the k+1th time in the working environment, and ω(k) represents the zero mean of the ESD intensity state with variance Q at the kth time White noise, ρ(k) represents the ESD electrostatic noise with variance W at time k.

优选的，第i簇成员节点与各个邻居簇成员节点执行卡尔曼一致性滤波器的迭代，包括：Preferably, the i-th cluster member node and each neighbor cluster member node perform the iteration of the Kalman consistency filter, including:

第i簇成员节点与各个邻居簇成员节点均按照

Figure BDA0003094838150000031
执行卡尔曼一致性滤波器的迭代；The i-th cluster member node and each neighbor cluster member node are in accordance with the
Figure BDA0003094838150000031
perform an iteration of the Kalman consistency filter;

其中，

Figure BDA0003094838150000032
且集合
Figure BDA0003094838150000033
表示簇ξ中第i簇成员节点的邻居节点集合，Mi(k)＝(Pi -1(k)+Si(k))-1，Mi(k)表示
Figure BDA0003094838150000034
的误差的协方差，Pi(k)表示
Figure BDA0003094838150000035
的误差的协方差；Ci表示卡尔曼一致性滤波器的共识增益，且
Figure BDA0003094838150000036
∈为预设常数，||·||F表示矩阵的Frobenius范数；
Figure BDA0003094838150000037
为x(k)的先验估计量。in,
Figure BDA0003094838150000032
and set
Figure BDA0003094838150000033
Represents the set of neighbor nodes of the i-th cluster member node in cluster ξ, Mi (k)=(P i -1 (k)+S i ( k)) -1 , Mi ( k) represents
Figure BDA0003094838150000034
The covariance of the error, P i (k) represents
Figure BDA0003094838150000035
The covariance of the error; C i represents the consensus gain of the Kalman consensus filter, and
Figure BDA0003094838150000036
∈ is a preset constant, ||·|| F represents the Frobenius norm of the matrix;
Figure BDA0003094838150000037
is the prior estimator of x(k).

优选的，第i簇成员节点所在簇的权重系数为通过以下步骤确定出的权重系数：Preferably, the weight coefficient of the cluster where the member node of the ith cluster is located is the weight coefficient determined by the following steps:

确定出表示簇成员节点与簇首节点在第k时刻的相互接近程度的支持矩阵

Figure BDA0003094838150000038
Determine the support matrix that represents the closeness of the cluster member node and the cluster head node at the kth time
Figure BDA0003094838150000038

基于所述支持矩阵Λ，得到第i簇成员节点所在簇的权重系数

Figure BDA0003094838150000039
Based on the support matrix Λ, the weight coefficient of the cluster where the member node of the i-th cluster is located is obtained
Figure BDA0003094838150000039

其中，n表示传感器网络中的第n簇，

Figure BDA00030948381500000310
ψn(k)表示第i簇成员节点所在簇的簇首节点与该簇的簇成员节点的相关度，τ表示第i簇成员节点所在簇的簇首节点参与融合的簇成员节点数量；
Figure BDA0003094838150000041
表示第i簇成员节点到第i簇成员节点所在簇的簇首节点的关联度。where n represents the nth cluster in the sensor network,
Figure BDA00030948381500000310
ψ n (k) represents the correlation between the cluster head node of the cluster where the i-th cluster member node is located and the cluster member node of the cluster, and τ represents the number of cluster member nodes that the cluster head node of the cluster where the i-th cluster member node is involved in the fusion;
Figure BDA0003094838150000041
Represents the degree of association between the i-th cluster member node and the cluster head node of the cluster where the i-th cluster member node is located.

优选的，基于该簇的权重系数，确定出用于表示该簇的簇成员节点状态的当前时刻的整体估计值，包括：Preferably, based on the weight coefficient of the cluster, an overall estimated value representing the current moment of the state of the cluster member nodes of the cluster is determined, including:

基于该簇的权重系数，确定出用于表示该簇的簇成员节点状态的当前时刻的整体估计值为

Figure BDA0003094838150000042
Based on the weight coefficient of the cluster, it is determined that the overall estimated value of the current moment used to represent the state of the cluster member nodes of the cluster is
Figure BDA0003094838150000042

其中，

Figure BDA0003094838150000043
表示确定出的用于表示第n簇的簇成员节点状态在第k时刻的整体估计值，
Figure BDA0003094838150000044
表示第i簇成员节点在第k时刻的一致性估计结果。in,
Figure BDA0003094838150000043
represents the determined overall estimated value of the state of the cluster member node of the nth cluster at the kth moment,
Figure BDA0003094838150000044
Indicates the consistency estimation result of the i-th cluster member node at the k-th time.

优选的，判断该簇中当前时刻出现数据丢包的簇成员节点的数量是否满足预设的数量要求，包括：Preferably, it is judged whether the number of cluster member nodes in which data packet loss occurs at the current moment in the cluster meets a preset number requirement, including:

判断

Figure BDA0003094838150000045
是否成立；judge
Figure BDA0003094838150000045
whether it is established;

如果是，则确定该簇中当前时刻出现数据丢包的簇成员节点的数量满足预设的数量要求；If yes, then determine that the number of cluster member nodes with packet loss at the current moment in the cluster meets the preset number requirement;

如果否，则确定该簇中当前时刻出现数据丢包的簇成员节点的数量不满足预设的数量要求；If not, it is determined that the number of cluster member nodes with packet loss at the current moment in the cluster does not meet the preset number requirement;

其中，

Figure BDA0003094838150000046
q为预设阈值，且
Figure BDA0003094838150000047
表示第i簇成员节点的邻居节点j未接收到第i簇成员节点输出的第i簇成员节点在第k时刻的一致性估计结果
Figure BDA0003094838150000048
表示第i簇成员节点的邻居节点j接收到第i簇成员节点输出的第i簇成员节点在第k时刻的一致性估计结果
Figure BDA0003094838150000049
δ(k)为触发门限因子；A为预设的常数矩阵，
Figure BDA00030948381500000410
为x(k)的先验估计量，x(k)表示工作环境内的第k时刻的ESD强度状态。in,
Figure BDA0003094838150000046
q is the preset threshold, and
Figure BDA0003094838150000047
Indicates that the neighbor node j of the i-th cluster member node does not receive the consistency estimation result of the i-th cluster member node at the k-th moment output by the i-th cluster member node
Figure BDA0003094838150000048
Indicates that the neighbor node j of the i-th cluster member node receives the consistency estimation result of the i-th cluster member node output by the i-th cluster member node at the k-th time
Figure BDA0003094838150000049
δ(k) is the trigger threshold factor; A is a preset constant matrix,
Figure BDA00030948381500000410
is an a priori estimator of x(k), where x(k) represents the ESD intensity state at the kth moment in the working environment.

优选的，基于该簇当前时刻的整体估计值以及该簇上一时刻的整体估计值，确定出该簇所在区域的当前时刻的ESD强度估计值，包括：Preferably, based on the overall estimated value of the cluster at the current moment and the overall estimated value of the cluster at the previous moment, the estimated value of the ESD intensity at the current moment in the area where the cluster is located is determined, including:

按照

Figure BDA0003094838150000051
确定出该簇所在区域的当前时刻的ESD强度估计值；according to
Figure BDA0003094838150000051
Determine the estimated value of the ESD intensity at the current moment in the area where the cluster is located;

其中，

Figure BDA0003094838150000052
表示确定出的第n簇所在区域的第k时刻的ESD强度估计值，α为调整因子，且0＜α＜1，
Figure BDA0003094838150000053
表示确定出的用于表示第n簇的簇成员节点状态在第k时刻的整体估计值。in,
Figure BDA0003094838150000052
Represents the determined estimated value of the ESD intensity at the kth time in the area where the nth cluster is located, α is the adjustment factor, and 0<α<1,
Figure BDA0003094838150000053
Indicates the overall estimated value determined to represent the state of the cluster member node of the nth cluster at the kth time.

优选的，传感器网络中的任意1个簇成员节点均为非侵入式电流传感器。Preferably, any one cluster member node in the sensor network is a non-intrusive current sensor.

一种ESD强度估计系统，应用于具有ξ个分簇的传感器网络的任意1个簇首节点中，每个分簇具有1个簇首节点和至少1个簇成员节点，ξ为正整数，表示传感器网络中的分簇数量，包括：An ESD intensity estimation system, applied to any one cluster head node of a sensor network with ξ clusters, each cluster has one cluster head node and at least one cluster member node, ξ is a positive integer, indicating The number of clusters in the sensor network, including:

一致性估计结果接收单元，用于接收各个簇成员节点当前时刻的一致性估计结果；The consistency estimation result receiving unit is used to receive the consistency estimation result of each cluster member node at the current moment;

整体估计值确定单元，用于基于该簇的权重系数，确定出用于表示该簇的簇成员节点状态的当前时刻的整体估计值；an overall estimated value determination unit, configured to determine, based on the weight coefficient of the cluster, an overall estimated value representing the current moment of the state of the cluster member nodes of the cluster;

判断单元，用于判断该簇中当前时刻出现数据丢包的簇成员节点的数量是否满足预设的数量要求；A judging unit for judging whether the number of cluster member nodes with packet loss at the current moment in the cluster meets a preset number requirement;

如果是，则执行第一触发单元，所述第一触发单元用于：将该簇当前时刻的整体估计值作为该簇所在区域的当前时刻的ESD强度估计值；If yes, execute the first trigger unit, and the first trigger unit is used for: taking the overall estimated value of the cluster at the current moment as the estimated value of the ESD intensity at the current moment of the area where the cluster is located;

如果否，则执行第二触发单元，所述第二触发单元用于：基于该簇当前时刻的整体估计值以及该簇上一时刻的整体估计值，确定出该簇所在区域的当前时刻的ESD强度估计值。If not, execute the second trigger unit, and the second trigger unit is configured to: based on the overall estimated value of the cluster at the current moment and the overall estimated value of the cluster at the previous moment, to determine the ESD at the current moment in the area where the cluster is located Intensity estimate.

应用本发明实施例所提供的技术方案，考虑到单个传感器的测量准确度容易受到环境、人为改变、设备变动等因素的影响，因此，本申请采用的是具有ξ个分簇的传感器网络，不同分簇用于进行不同区域的ESD强度估计。具体的，任意1个簇首节点可以接收该簇中的各个簇成员节点当前时刻的一致性估计结果，进而基于该簇的权重系数，确定出用于表示该簇的簇成员节点状态的当前时刻的整体估计值。本申请会判断该簇中当前时刻出现数据丢包的簇成员节点的数量是否满足预设的数量要求，即本申请考虑到了ESD静电带来的干扰导致的数据丢包情况，如果不满足预设的数量要求，说明出现了数据丢包情况，本申请会基于该簇当前时刻的整体估计值以及该簇上一时刻的整体估计值，使得确定出该簇所在区域的当前时刻的ESD强度估计值较为准确。当然，如果满足预设的数量要求，可以直接将该簇当前时刻的整体估计值作为该簇所在区域的当前时刻的ESD强度估计值。综上所述，本申请的方案可以有效地进行ESD强度估计，提高了准确性。Applying the technical solutions provided by the embodiments of the present invention, considering that the measurement accuracy of a single sensor is easily affected by factors such as the environment, human changes, equipment changes, etc., this application adopts a sensor network with ξ clusters. Clustering is used to perform ESD intensity estimation for different regions. Specifically, any one cluster head node can receive the consistency estimation result of the current moment of each cluster member node in the cluster, and then determine the current moment used to represent the state of the cluster member node of the cluster based on the weight coefficient of the cluster the overall estimate of . This application will determine whether the number of cluster member nodes with data packet loss at the current moment in the cluster meets the preset number requirement, that is, the application takes into account the data packet loss caused by the interference caused by ESD static electricity. This application will determine the estimated value of ESD intensity at the current moment in the area where the cluster is located based on the overall estimated value of the cluster at the current moment and the overall estimated value of the cluster at the previous moment. more accurate. Of course, if the preset quantity requirement is met, the overall estimated value of the cluster at the current moment can be directly used as the estimated value of the ESD intensity at the current moment in the area where the cluster is located. To sum up, the solution of the present application can effectively estimate the ESD intensity and improve the accuracy.

附图说明Description of drawings

为了更清楚地说明本发明实施例或现有技术中的技术方案，下面将对实施例或现有技术描述中所需要使用的附图作简单地介绍，显而易见地，下面描述中的附图仅仅是本发明的一些实施例，对于本领域普通技术人员来讲，在不付出创造性劳动的前提下，还可以根据这些附图获得其他的附图。In order to explain the embodiments of the present invention or the technical solutions in the prior art more clearly, the following briefly introduces the accompanying drawings that need to be used in the description of the embodiments or the prior art. Obviously, the accompanying drawings in the following description are only These are some embodiments of the present invention. For those of ordinary skill in the art, other drawings can also be obtained according to these drawings without creative efforts.

图1为本发明中一种ESD强度估计方法的实施流程图；Fig. 1 is the implementation flow chart of a kind of ESD intensity estimation method in the present invention;

图2为本发明一种具体场合中的传感器网络的4个分簇的网络结构示意图；2 is a schematic diagram of the network structure of 4 clusters of a sensor network in a specific occasion of the present invention;

图3为本发明中一种ESD强度估计系统的结构示意图。FIG. 3 is a schematic structural diagram of an ESD intensity estimation system in the present invention.

具体实施方式Detailed ways

本发明的核心是提供一种ESD强度估计方法，可以有效地进行ESD强度估计，提高了准确性。The core of the present invention is to provide an ESD intensity estimation method, which can effectively estimate the ESD intensity and improve the accuracy.

为了使本技术领域的人员更好地理解本发明方案，下面结合附图和具体实施方式对本发明作进一步的详细说明。显然，所描述的实施例仅仅是本发明一部分实施例，而不是全部的实施例。基于本发明中的实施例，本领域普通技术人员在没有做出创造性劳动前提下所获得的所有其他实施例，都属于本发明保护的范围。In order to make those skilled in the art better understand the solution of the present invention, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. Obviously, the described embodiments are only some, but not all, embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

请参考图1，图1为本发明中一种ESD强度估计方法的实施流程图，该ESD强度估计方法可以应用于具有ξ个分簇的传感器网络的任意1个簇首节点中，每个分簇具有1个簇首节点和至少1个簇成员节点，ξ为正整数，表示传感器网络中的分簇数量。该ESD强度估计方法可以包括以下步骤：Please refer to FIG. 1. FIG. 1 is an implementation flowchart of an ESD intensity estimation method in the present invention. The ESD intensity estimation method can be applied to any one cluster head node of a sensor network with ξ clusters. A cluster has one cluster head node and at least one cluster member node, and ξ is a positive integer, which represents the number of clusters in the sensor network. The ESD intensity estimation method may include the following steps:

步骤S101：接收各个簇成员节点当前时刻的一致性估计结果。Step S101: Receive the consistency estimation result of each cluster member node at the current moment.

具体的，例如可以通过卡尔曼协同估计的方法进行各个簇成员节点的一致性估计，得到各个簇成员节点的一致性估计结果，可以实时估计或者按照预设周期进行估计，各个簇成员节点可以将自身的一致性估计结果发送至该簇的簇首节点。Specifically, for example, the consistency estimation of each cluster member node can be performed by the method of Kalman collaborative estimation, and the consistency estimation result of each cluster member node can be obtained, which can be estimated in real time or according to a preset period. The self-consistency estimation result is sent to the cluster head node of the cluster.

传感器网络的分簇数量ξ的具体取值可以根据需要进行设定和调整，并且，每个分簇中的具体结构可以根据需要进行设定，但需要说明的是，对于任意1个分簇而言，该分簇中的簇成员节点均与该分簇中的簇首节点通信连接，该通信连接可以是直接连接，也可以是间接连接，取决于具体的分簇结构以及簇成员节点在分簇中的具体位置。The specific value of the number of clusters ξ of the sensor network can be set and adjusted as needed, and the specific structure in each cluster can be set as needed, but it should be noted that for any one cluster, the In other words, the cluster member nodes in the cluster are all connected to the cluster head node in the cluster. The communication connection can be a direct connection or an indirect connection, depending on the specific cluster structure and the cluster member nodes in specific location in the cluster.

例如图2为一种具体场合中的传感器网络的4个分簇的网络结构示意图，4个分簇中均设置了1个簇首节点。图2中的

Figure BDA0003094838150000071
表示确定出的第1簇所在区域的第k时刻的ESD强度估计值，相应的，
Figure BDA0003094838150000072
以及
Figure BDA0003094838150000073
依次表示确定出的第2簇所在区域的第k时刻的ESD强度估计值，确定出的第3簇所在区域的第k时刻的ESD强度估计值，以及确定出的第4簇所在区域的第k时刻的ESD强度估计值。在实际应用中，各个分簇的簇成员节点的数量通常大于等于2。For example, FIG. 2 is a schematic diagram of a network structure of four clusters of a sensor network in a specific situation, and one cluster head node is set in each of the four clusters. in Figure 2
Figure BDA0003094838150000071
Represents the estimated value of the ESD intensity at the k-th time of the determined area where the first cluster is located. Correspondingly,
Figure BDA0003094838150000072
as well as
Figure BDA0003094838150000073
In turn, the estimated value of the ESD intensity at the k-th time in the area where the second cluster is located, the estimated ESD intensity at the k-th time in the area where the third cluster is located, and the k-th time in the determined area where the fourth cluster is located. Estimated value of the ESD intensity at the moment. In practical applications, the number of cluster member nodes in each cluster is usually greater than or equal to 2.

在本发明的一种具体实施方式中，步骤S101中描述的簇首节点接收的各个簇成员节点中的第i簇成员节点当前时刻的一致性估计结果，为第i簇成员节点通过以下步骤确定出的当前时刻的一致性估计结果：In a specific embodiment of the present invention, the consistency estimation result of the current moment of the i-th cluster member node in each cluster member node received by the cluster head node described in step S101 is determined by the i-th cluster member node through the following steps The consistency estimation result of the current moment is:

步骤一：建立表示工作环境内的ESD强度状态的动态离散时间系统模型；Step 1: Establish a dynamic discrete-time system model representing the ESD intensity state in the working environment;

步骤二：按照yi(k)＝Hixi(k)+Jivi(k)确定出第i簇成员节点在第k时刻测量到的ESD强度值；Step 2: According to y i (k)=H i x i (k)+J i v i (k), determine the ESD intensity value measured by the i-th cluster member node at the k-th moment;

步骤三：通过动态离散时间系统模型以及yi(k)得到θi(k)并将θi(k)输出至第i簇成员节点的各个邻居簇成员节点，并接收各个邻居簇成员节点发送至第i簇成员节点的各个信息传输向量，以使第i簇成员节点与各个邻居簇成员节点执行卡尔曼一致性滤波器的迭代，并得到第i簇成员节点当前时刻的一致性估计结果

Figure BDA0003094838150000074
Step 3: Obtain θ i (k) through the dynamic discrete time system model and y i (k) and output θ i (k) to each neighbor cluster member node of the i-th cluster member node, and receive the transmission from each neighbor cluster member node. Each information transmission vector to the i-th cluster member node, so that the i-th cluster member node and each neighbor cluster member node perform the iteration of the Kalman consistency filter, and obtain the consistency estimation result of the i-th cluster member node at the current moment
Figure BDA0003094838150000074

其中，θi(k)表示第i簇成员节点在第k时刻输出的信息传输向量，且

Figure BDA0003094838150000075
zi(k)表示第k时刻的融合信息向量，Si(k)表示第k时刻的融合信息矩阵，
Figure BDA0003094838150000076
表示第i簇成员节点在第k时刻的一致性估计结果，且
Figure BDA0003094838150000077
为x(k)的估计量；Among them, θ i (k) represents the information transmission vector output by the i-th cluster member node at the k-th moment, and
Figure BDA0003094838150000075
z i (k) represents the fusion information vector at the kth moment, S i (k) represents the fusion information matrix at the kth moment,
Figure BDA0003094838150000076
represents the consistency estimation result of the i-th cluster member node at the k-th moment, and
Figure BDA0003094838150000077
is the estimator of x(k);

x(k)表示工作环境内的第k时刻的ESD强度状态，xi(k)表示第i簇成员节点周围的第k时刻的ESD强度状态，Hi和Ji均为预设的常数矩阵，yi(k)为第i簇成员节点在第k时刻测量到的ESD强度值，vi(k)表示在第k时刻具有方差Ri的零均值白噪声。x(k) represents the ESD intensity state at the k-th time in the working environment, x i (k) represents the ESD intensity state at the k-th time around the member node of the i-th cluster, and both H i and J i are preset constant matrices , y i (k) is the ESD intensity value measured by the i-th cluster member node at the k- th time, and vi (k) represents the zero-mean white noise with variance R i at the k-th time.

该种具体实施方式便是通过卡尔曼协同估计的方法进行各个簇成员节点的一致性估计结果。This specific implementation is to use the Kalman collaborative estimation method to perform the consistency estimation result of each cluster member node.

具体的，在一种具体场合中，上述步骤一可以具体为：Specifically, in a specific occasion, the above-mentioned step 1 may be specifically:

按照x(k+1)＝Ax(k)+ω(k)+ρ(k)建立表示工作环境内的ESD强度状态的动态离散时间系统模型；According to x(k+1)=Ax(k)+ω(k)+ρ(k), establish a dynamic discrete-time system model representing the ESD intensity state in the working environment;

其中，A为预设的常数矩阵，x(k+1)表示工作环境内的第k+1时刻的ESD强度状态，ω(k)表示在第k时刻具有方差Q的ESD强度状态的零均值白噪声，ρ(k)表示在第k时刻具有方差W的ESD静电噪声。

Figure BDA0003094838150000081
Among them, A is a preset constant matrix, x(k+1) represents the ESD intensity state at the k+1th time in the working environment, and ω(k) represents the zero mean of the ESD intensity state with variance Q at the kth time White noise, ρ(k) represents the ESD electrostatic noise with variance W at time k.
Figure BDA0003094838150000081

该种实施方式中，是按照x(k+1)＝Ax(k)+ω(k)+ρ(k)建立表示工作环境内的ESD强度状态的动态离散时间系统模型，可以看出，模型中除了x(k)和ω(k)之外，还考虑到了在第k时刻具有方差W的ESD静电噪声ρ(k)，相较于传统的卡尔曼协同估计方法，该种具体实施方式中的工作环境内的ESD强度状态的动态离散时间系统模型会更为准确，也就使得本申请的方案可以更准确地实现的ESD强度估计。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). It can be seen that the model In addition to x(k) and ω(k), the ESD electrostatic noise ρ(k) with variance W at the kth moment is also considered. Compared with the traditional Kalman collaborative estimation method, in this specific implementation The dynamic discrete-time system model of the ESD intensity state in the working environment will be more accurate, so that the solution of the present application can more accurately realize the ESD intensity estimation.

建立了表示工作环境内的ESD强度状态的动态离散时间系统模型之后，便可以执行步骤二，即按照yi(k)＝Hixi(k)+Jivi(k)确定出第i簇成员节点在第k时刻测量到的ESD强度值，yi(k)＝Hixi(k)+Jivi(k)也就是第i簇成员节点的量测输出方程，此外，该实施方式中是视为同一簇中的各个簇成员节点的测量噪声不相关，即各传感器的测量噪声不相关。After establishing the dynamic discrete-time system model representing the ESD intensity state in the working environment, step 2 can be performed, that is, according to y i (k)=H i x i (k)+J i v i (k) to determine the first The ESD intensity value measured by the i-th cluster member node at the k-th time, y i (k)=H i x i (k)+J i v i (k) is the measurement output equation of the i-th cluster member node, in addition , in this embodiment, the measurement noise of each cluster member node in the same cluster is regarded as irrelevant, that is, the measurement noise of each sensor is irrelevant.

通过动态离散时间系统模型以及yi(k)，便可以得到第i簇成员节点在第k时刻输出的信息传输向量θi(k)并将θi(k)输出至第i簇成员节点的各个邻居簇成员节点，且第i簇成员节点可以接收各个邻居簇成员节点发送至第i簇成员节点的各个信息传输向量，以使第i簇成员节点与各个邻居簇成员节点执行卡尔曼一致性滤波器的迭代，并得到第i簇成员节点当前时刻的一致性估计结果

Figure BDA0003094838150000082
Through the dynamic discrete-time system model and y i (k), the information transmission vector θ i (k) output by the i-th cluster member node at the k-th moment can be obtained and output θ i (k) to the i-th cluster member node Each neighbor cluster member node, and the i-th cluster member node can receive each information transmission vector sent by each neighbor cluster member node to the i-th cluster member node, so that the i-th cluster member node and each neighbor cluster member node perform Kalman consistency Iteration of the filter, and get the consistency estimation result of the i-th cluster member node at the current moment
Figure BDA0003094838150000082

具体的，对于第i簇成员节点，可以给出第0时刻开始直到第k时刻的测量数据集合，表示为Yi(k)＝{yi(0),yi(1),…,yi(k)}。而状态x(k)的估计量和先验估计量可以分别表示为

Figure BDA0003094838150000091
和
Figure BDA0003094838150000092
可以表示为：
Figure BDA0003094838150000093
Specifically, for the i-th cluster member node, the measurement data set from time 0 to time k can be given, expressed as Y i (k)={y i (0),y i (1),...,y i (k)}. The estimator and prior estimator of the state x(k) can be expressed as
Figure BDA0003094838150000091
and
Figure BDA0003094838150000092
It can be expressed as:
Figure BDA0003094838150000093

进一步，可以设

Figure BDA0003094838150000094
和
Figure BDA0003094838150000095
和
Figure BDA0003094838150000096
分别为估计误差和先验估计误差。然后，
Figure BDA0003094838150000097
的误差的协方差以及
Figure BDA0003094838150000098
的误差的协方差可以分别表示为：
Figure BDA0003094838150000099
即Mi(k)表示的是
Figure BDA00030948381500000910
与x(k)的误差的协方差，Pi(k)表示的是
Figure BDA00030948381500000911
与x(k)的误差的协方差。而卡尔曼一致性滤波器的共识增益Ci可以表示为：
Figure BDA00030948381500000912
其中||·||F是矩阵的Frobenius范数，∈＞0是一个相对较小的常数。Further, it is possible to set
Figure BDA0003094838150000094
and
Figure BDA0003094838150000095
and
Figure BDA0003094838150000096
are the estimation error and the prior estimation error, respectively. Then,
Figure BDA0003094838150000097
The error covariance of and
Figure BDA0003094838150000098
The covariance of the errors can be expressed as:
Figure BDA0003094838150000099
That is, M i (k) means that
Figure BDA00030948381500000910
The covariance of the error with x(k), Pi ( k ) is
Figure BDA00030948381500000911
Covariance of error with x(k). And the consensus gain C i of the Kalman consensus filter can be expressed as:
Figure BDA00030948381500000912
where ||·|| F is the Frobenius norm of the matrix, and ∈ > 0 is a relatively small constant.

第i簇成员节点与各个邻居簇成员节点执行卡尔曼一致性滤波器的迭代，具体可以包括：The i-th cluster member node and each neighbor cluster member node perform the iteration of the Kalman consistency filter, which may specifically include:

第i簇成员节点与各个邻居簇成员节点均按照

Figure BDA00030948381500000913
执行卡尔曼一致性滤波器的迭代；The i-th cluster member node and each neighbor cluster member node are in accordance with the
Figure BDA00030948381500000913
perform an iteration of the Kalman consistency filter;

其中，

Figure BDA00030948381500000914
且集合
Figure BDA00030948381500000915
表示簇ξ中第i簇成员节点的邻居节点集合，Mi(k)＝(Pi -1(k)+Si(k))-1，如上文的描述，Mi(k)表示
Figure BDA00030948381500000916
的误差的协方差，Pi(k)表示
Figure BDA00030948381500000917
的误差的协方差；Ci表示卡尔曼一致性滤波器的共识增益，且
Figure BDA00030948381500000918
∈为预设常数，||·||F表示矩阵的Frobenius范数；
Figure BDA00030948381500000919
为x(k)的先验估计量。in,
Figure BDA00030948381500000914
and set
Figure BDA00030948381500000915
Represents the set of neighbor nodes of the i-th cluster member node in cluster ξ, Mi (k)=(P i -1 (k)+S i ( k)) -1 , as described above, Mi ( k) represents
Figure BDA00030948381500000916
The covariance of the error, P i (k) represents
Figure BDA00030948381500000917
The covariance of the error; C i represents the consensus gain of the Kalman consensus filter, and
Figure BDA00030948381500000918
∈ is a preset constant, ||·|| F represents the Frobenius norm of the matrix;
Figure BDA00030948381500000919
is the prior estimator of x(k).

步骤S102：基于该簇的权重系数，确定出用于表示该簇的簇成员节点状态的当前时刻的整体估计值。Step S102: Based on the weight coefficient of the cluster, determine the overall estimated value at the current moment used to represent the state of the cluster member node of the cluster.

如前文的描述，各个分簇中的簇成员节点均与该分簇中的簇首节点通信连接，该通信连接可以是直接连接，也可以是间接连接，因此，对于某一个簇成员节点而言，其可以直接或通过邻居节点间接将自身得到的一致性估计结果发送至簇首节点。As described above, the cluster member nodes in each cluster are connected to the cluster head node in the cluster. The communication connection can be a direct connection or an indirect connection. Therefore, for a certain cluster member node , which can directly or indirectly send the consistency estimation result obtained by itself to the cluster head node through neighbor nodes.

簇首节点不需要自己收集数据，只需要负责融合该簇的各个簇成员节点发送的数据，可以将簇首节点的状态表示为同一簇中的各簇成员节点状态的凸组合，也就是确定出用于表示该簇的簇成员节点状态的当前时刻的整体估计值，并且，本申请需要基于该簇的权重系数，确定出用于表示该簇的簇成员节点状态的当前时刻的整体估计值，可以表示为

Figure BDA0003094838150000101
The cluster head node does not need to collect data by itself, but only needs to be responsible for fusing the data sent by each cluster member node of the cluster. The state of the cluster head node can be expressed as a convex combination of the states of each cluster member node in the same cluster, that is, to determine the state of the cluster head node. is used to represent the overall estimated value of the current moment of the state of the cluster member node of the cluster, and the application needs to determine the overall estimated value of the current moment used to represent the state of the cluster member node of the cluster based on the weight coefficient of the cluster, It can be expressed as
Figure BDA0003094838150000101

这里，Ts和TH分别表示传感器网络中的簇成员节点和簇首节点的集合。权重系数σni的设定方式有多种，例如预先设定完毕，又如，可以按照簇成员节点与簇首节点的数据关联度来进行数值的优化。Here, T s and T H represent the set of cluster member nodes and cluster head nodes in the sensor network, respectively. There are many ways to set the weight coefficient σ ni , for example, it is preset, or, for example, the value can be optimized according to the degree of data correlation between the cluster member node and the cluster head node.

在本发明的一种具体实施方式中，第i簇成员节点所在簇的权重系数为通过以下步骤确定出的权重系数：In a specific embodiment of the present invention, the weight coefficient of the cluster where the i-th cluster member node is located is the weight coefficient determined by the following steps:

确定出表示簇成员节点与簇首节点在第k时刻的相互接近程度的支持矩阵

Figure BDA0003094838150000102
Determine the support matrix that represents the closeness of the cluster member node and the cluster head node at the kth time
Figure BDA0003094838150000102

基于支持矩阵Λ，得到第i簇成员节点所在簇的权重系数

Figure BDA0003094838150000103
Based on the support matrix Λ, the weight coefficient of the cluster where the member node of the i-th cluster is located is obtained
Figure BDA0003094838150000103

其中，n表示传感器网络中的第n簇，

Figure BDA0003094838150000104
ψn(k)表示第i簇成员节点所在簇的簇首节点与该簇的簇成员节点的相关度，τ表示第i簇成员节点所在簇的簇首节点参与融合的簇成员节点数量；
Figure BDA0003094838150000105
表示第i簇成员节点到第i簇成员节点所在簇的簇首节点的关联度。where n represents the nth cluster in the sensor network,
Figure BDA0003094838150000104
ψ n (k) represents the correlation between the cluster head node of the cluster where the i-th cluster member node is located and the cluster member node of the cluster, and τ represents the number of cluster member nodes that the cluster head node of the cluster where the i-th cluster member node is involved in the fusion;
Figure BDA0003094838150000105
Represents the degree of association between the i-th cluster member node and the cluster head node of the cluster where the i-th cluster member node is located.

Figure BDA0003094838150000111
即代表了第i簇成员节点与第i簇成员节点所在簇的簇首节点的接近程度，具体可以写为
Figure BDA0003094838150000112
从而可以得到如上所示的支持矩阵Λ，支持矩阵Λ用来表示簇成员节点与簇首节点在第k时刻的相互接近程度。
Figure BDA0003094838150000111
That is, it represents the proximity of the i-th cluster member node to the cluster head node of the cluster where the i-th cluster member node is located, which can be written as
Figure BDA0003094838150000112
Thereby, the support matrix Λ as shown above can be obtained, and the support matrix Λ is used to represent the mutual closeness of the cluster member node and the cluster head node at the kth time.

而该种实施方式中，考虑到传输过程中会丢包，数据的可靠性受到相互接近程度的影响，并且，簇成员节点数据权重与簇首节点的接近程度应当是正相关，因此，设定在簇首节点参与融合的簇成员节点数量τ，则第i簇成员节点所在簇的簇首节点与该簇的簇成员节点的相关度为

Figure BDA0003094838150000113
因此，该种实施方式中的权重系数
Figure BDA0003094838150000114
In this embodiment, considering that packets will be lost during transmission, the reliability of data is affected by the proximity to each other, and the data weight of the cluster member node and the proximity of the cluster head node should be positively correlated. Therefore, set at The number of cluster member nodes that the cluster head node participates in the fusion is
Figure BDA0003094838150000113
Therefore, the weight coefficient in this embodiment
Figure BDA0003094838150000114

因此，在本发明的一种具体实施方式中，步骤S102可以具体为：Therefore, in a specific implementation manner of the present invention, step S102 may be specifically:

基于该簇的权重系数，确定出用于表示该簇的簇成员节点状态的当前时刻的整体估计值为

Figure BDA0003094838150000115
Based on the weight coefficient of the cluster, it is determined that the overall estimated value of the current moment used to represent the state of the cluster member nodes of the cluster is
Figure BDA0003094838150000115

其中，

Figure BDA0003094838150000116
表示确定出的用于表示第n簇的簇成员节点状态在第k时刻的整体估计值，
Figure BDA0003094838150000117
表示第i簇成员节点在第k时刻的一致性估计结果。in,
Figure BDA0003094838150000116
represents the determined overall estimated value of the state of the cluster member node of the nth cluster at the kth moment,
Figure BDA0003094838150000117
Indicates the consistency estimation result of the i-th cluster member node at the k-th time.

按照前述具体实施方式中确定出的权重系数

Figure BDA0003094838150000118
得到整体估计值为
Figure BDA0003094838150000119
According to the weight coefficient determined in the foregoing specific implementation manner
Figure BDA0003094838150000118
The overall estimate is
Figure BDA0003094838150000119

步骤S103：判断该簇中当前时刻出现数据丢包的簇成员节点的数量是否满足预设的数量要求。Step S103: Determine whether the number of cluster member nodes in which data packet loss occurs at the current moment in the cluster meets a preset number requirement.

如果是，则执行步骤S104：将该簇当前时刻的整体估计值作为该簇所在区域的当前时刻的ESD强度估计值；If yes, then perform step S104: use the overall estimated value of the cluster at the current moment as the estimated value of the ESD intensity at the current moment of the area where the cluster is located;

如果否，则执行步骤S105：基于该簇当前时刻的整体估计值以及该簇上一时刻的整体估计值，确定出该簇所在区域的当前时刻的ESD强度估计值。If no, step S105 is performed: based on the overall estimated value of the cluster at the current moment and the overall estimated value of the cluster at the previous moment, determine the estimated value of the ESD intensity at the current moment in the area where the cluster is located.

本申请的方案中，并不是在得到整体估计值为

Figure BDA0003094838150000121
之后，直接将其作为第n簇的ESD强度估计值，而是会判断是否存在数据丢包，即会判断该簇中当前时刻出现数据丢包的簇成员节点的数量是否满足预设的数量要求。In the scheme of this application, it is not intended to obtain the overall estimated value of
Figure BDA0003094838150000121
After that, it is directly used as the estimated value of the ESD intensity of the nth cluster, but it will determine whether there is data packet loss, that is, it will determine whether the number of cluster member nodes with data packet loss at the current moment in the cluster meets the preset number requirement. .

具体的判断方式有多种，例如在本发明的一种具体实施方式中，步骤S103可以具体包括：There are various specific judgment methods. For example, in a specific implementation manner of the present invention, step S103 may specifically include:

判断

Figure BDA0003094838150000122
是否成立；judge
Figure BDA0003094838150000122
whether it is established;

如果是，则确定该簇中当前时刻出现数据丢包的簇成员节点的数量满足预设的数量要求；If yes, then determine that the number of cluster member nodes with packet loss at the current moment in the cluster meets the preset number requirement;

如果否，则确定该簇中当前时刻出现数据丢包的簇成员节点的数量不满足预设的数量要求；If not, it is determined that the number of cluster member nodes with packet loss at the current moment in the cluster does not meet the preset number requirement;

其中，

Figure BDA0003094838150000123
q为预设阈值，且
Figure BDA0003094838150000124
表示第i簇成员节点的邻居节点j未接收到第i簇成员节点输出的第i簇成员节点在第k时刻的一致性估计结果
Figure BDA0003094838150000125
表示第i簇成员节点的邻居节点j接收到第i簇成员节点输出的第i簇成员节点在第k时刻的一致性估计结果
Figure BDA0003094838150000126
δ(k)为触发门限因子；A为预设的常数矩阵，
Figure BDA0003094838150000127
为x(k)的先验估计量，x(k)表示工作环境内的第k时刻的ESD强度状态。in,
Figure BDA0003094838150000123
q is the preset threshold, and
Figure BDA0003094838150000124
Indicates that the neighbor node j of the i-th cluster member node does not receive the consistency estimation result of the i-th cluster member node at the k-th moment output by the i-th cluster member node
Figure BDA0003094838150000125
Indicates that the neighbor node j of the i-th cluster member node receives the consistency estimation result of the i-th cluster member node output by the i-th cluster member node at the k-th time
Figure BDA0003094838150000126
δ(k) is the trigger threshold factor; A is a preset constant matrix,
Figure BDA0003094838150000127
is an a priori estimator of x(k), where x(k) represents the ESD intensity state at the kth moment in the working environment.

Figure BDA0003094838150000128
时，说明第i簇成员节点的邻居节点j收到的第i簇成员节点输出的状态估计值
Figure BDA0003094838150000129
的变化水平小于了触发门限因子，即表示节点j在k时刻未收到数据，说明触发了干扰。反之，
Figure BDA00030948381500001210
时，表示节点j在k时刻受到了第i簇成员节点输出的状态估计值
Figure BDA00030948381500001211
Figure BDA0003094838150000128
When , describe the estimated state value output by the i-th cluster member node received by the neighbor node j of the i-th cluster member node
Figure BDA0003094838150000129
The change level of is less than the trigger threshold factor, which means that node j does not receive data at time k, indicating that interference is triggered. on the contrary,
Figure BDA00030948381500001210
, it means that node j receives the state estimate output from the i-th cluster member node at time k
Figure BDA00030948381500001211

判断该簇中当前时刻出现数据丢包的簇成员节点的数量不满足预设的数量要求，本申请会基于该簇当前时刻的整体估计值以及该簇上一时刻的整体估计值，确定出该簇所在区域的当前时刻的ESD强度估计值，这是考虑到环境变化存在的连续性，出现丢包时，可以将节点簇上一时刻的整体估计值与当前时刻时刻的整体估计值相结合，从而可以减少数据丢包时延造成的数据缺失对当前时刻估计值的影响。It is judged that the number of cluster member nodes with data packet loss at the current moment in the cluster does not meet the preset number requirement. The estimated value of the ESD intensity at the current moment in the area where the cluster is located, which takes into account the continuity of environmental changes. When packet loss occurs, the overall estimated value of the node cluster at the previous moment can be combined with the overall estimated value of the current moment. In this way, the impact of data missing caused by data packet loss delay on the estimated value at the current moment can be reduced.

具体的，在本发明的一种具体实施方式中，步骤S105可以具体包括：Specifically, in a specific implementation manner of the present invention, step S105 may specifically include:

按照

Figure BDA0003094838150000131
确定出该簇所在区域的当前时刻的ESD强度估计值；according to
Figure BDA0003094838150000131
Determine the estimated value of the ESD intensity at the current moment in the area where the cluster is located;

其中，

Figure BDA0003094838150000132
表示确定出的第n簇所在区域的第k时刻的ESD强度估计值，α为调整因子，且0＜α＜1，
Figure BDA0003094838150000133
表示确定出的用于表示第n簇的簇成员节点状态在第k时刻的整体估计值。in,
Figure BDA0003094838150000132
Represents the determined estimated value of the ESD intensity at the kth time in the area where the nth cluster is located, α is the adjustment factor, and 0<α<1,
Figure BDA0003094838150000133
Indicates the overall estimated value determined to represent the state of the cluster member node of the nth cluster at the kth time.

当然，如果判断出该簇中当前时刻出现数据丢包的簇成员节点的数量满足预设的数量要求，便可以直接将该簇当前时刻的整体估计值作为该簇所在区域的当前时刻的ESD强度估计值，即可以表示为：

Figure BDA0003094838150000134
Of course, if it is determined that the number of cluster member nodes with data packet loss at the current moment in the cluster meets the preset number requirement, the overall estimated value of the cluster at the current moment can be directly used as the ESD intensity of the area where the cluster is located at the current moment. The estimated value can be expressed as:
Figure BDA0003094838150000134

预设阈值q的具体取值可以根据需要进行设定，表示了在同一时刻允许丢包的传感器个数。The specific value of the preset threshold q can be set as required, and indicates the number of sensors that are allowed to lose packets at the same time.

得到了各簇所在区域的当前时刻的ESD强度估计值之后，便完成了ESD强度估计，之后，便可以根据ESD强度估计结果，决定是否需要执行一些消除静电的措施，例如通过除尘等方式进行静电消除，即降低ESD强度。After obtaining the estimated value of the ESD intensity at the current moment in the area where each cluster is located, the ESD intensity estimation is completed. After that, it is possible to decide whether to implement some measures to eliminate static electricity according to the estimated ESD intensity, such as removing static electricity by dust removal. Eliminate, i.e. reduce ESD intensity.

本申请的传感器网络中的任意1个簇成员节点可以均为非侵入式电流传感器，相较于需要接入设备装置中并且设置采样电阻的侵入式电流传感装置，采用非侵入式电流传感器的检测结果不容易受设备电路影响，同时，也不会由于传感器的损坏对设备电路造成影响。Any one cluster member node in the sensor network of the present application can be a non-intrusive current sensor. The detection result is not easily affected by the device circuit, and at the same time, the device circuit will not be affected by the damage of the sensor.

应用本发明实施例所提供的技术方案，考虑到单个传感器的测量准确度容易受到环境、人为改变、设备变动等因素的影响，因此，本申请采用的是具有ξ个分簇的传感器网络，不同分簇用于进行不同区域的ESD强度估计。具体的，任意1个簇首节点可以接收该簇中的各个簇成员节点当前时刻的一致性估计结果，进而基于该簇的权重系数，确定出用于表示该簇的簇成员节点状态的当前时刻的整体估计值。本申请会判断该簇中当前时刻出现数据丢包的簇成员节点的数量是否满足预设的数量要求，即本申请考虑到了ESD静电带来的干扰导致的数据丢包情况，如果不满足预设的数量要求，说明出现了数据丢包情况，本申请会基于该簇当前时刻的整体估计值以及该簇上一时刻的整体估计值，使得确定出该簇所在区域的当前时刻的ESD强度估计值较为准确。当然，如果满足预设的数量要求，可以直接将该簇当前时刻的整体估计值作为该簇所在区域的当前时刻的ESD强度估计值。综上所述，本申请的方案可以有效地进行ESD强度估计，提高了准确性。Applying the technical solutions provided by the embodiments of the present invention, considering that the measurement accuracy of a single sensor is easily affected by factors such as the environment, human changes, equipment changes, etc., this application adopts a sensor network with ξ clusters. Clustering is used to perform ESD intensity estimation for different regions. Specifically, any one cluster head node can receive the consistency estimation result of the current moment of each cluster member node in the cluster, and then determine the current moment used to represent the state of the cluster member node of the cluster based on the weight coefficient of the cluster the overall estimate of . This application will determine whether the number of cluster member nodes with data packet loss at the current moment in the cluster meets the preset number requirement, that is, the application takes into account the data packet loss caused by the interference caused by ESD static electricity. This application will determine the estimated value of ESD intensity at the current moment in the area where the cluster is located based on the overall estimated value of the cluster at the current moment and the overall estimated value of the cluster at the previous moment. more accurate. Of course, if the preset quantity requirement is met, the overall estimated value of the cluster at the current moment can be directly used as the estimated value of the ESD intensity at the current moment in the area where the cluster is located. To sum up, the solution of the present application can effectively estimate the ESD intensity and improve the accuracy.

相应于上面的方法实施例，本发明实施例还提供了一种ESD强度估计系统，可与上文相互对应参照。Corresponding to the above method embodiments, the embodiments of the present invention further provide an ESD intensity estimation system, which can be referred to in correspondence with the above.

参见图3所示，为本发明中一种ESD强度估计系统的结构示意图，应用于具有ξ个分簇的传感器网络的任意1个簇首节点中，每个分簇具有1个簇首节点和至少1个簇成员节点，ξ为正整数，表示传感器网络中的分簇数量，包括：Referring to FIG. 3, it is a schematic structural diagram of an ESD intensity estimation system in the present invention, which is applied to any one cluster head node of a sensor network with ξ clusters, and each cluster has one cluster head node and At least one cluster member node, ξ is a positive integer, indicating the number of clusters in the sensor network, including:

一致性估计结果接收单元301，用于接收各个簇成员节点当前时刻的一致性估计结果；The consistency estimation result receiving unit 301 is used for receiving the consistency estimation result of each cluster member node at the current moment;

整体估计值确定单元302，用于基于该簇的权重系数，确定出用于表示该簇的簇成员节点状态的当前时刻的整体估计值；The overall estimated value determination unit 302 is configured to determine, based on the weight coefficient of the cluster, an overall estimated value representing the current moment of the state of the cluster member node of the cluster;

判断单元303，用于判断该簇中当前时刻出现数据丢包的簇成员节点的数量是否满足预设的数量要求；Judging unit 303, for judging whether the number of cluster member nodes in which packet loss occurs at the current moment in the cluster meets a preset number requirement;

如果是，则执行第一触发单元304，所述第一触发单元304用于：将该簇当前时刻的整体估计值作为该簇所在区域的当前时刻的ESD强度估计值；If yes, execute the first trigger unit 304, and the first trigger unit 304 is configured to: use the overall estimated value of the cluster at the current moment as the estimated value of the ESD intensity at the current moment of the area where the cluster is located;

如果否，则执行第二触发单元305，所述第二触发单元用于305：基于该簇当前时刻的整体估计值以及该簇上一时刻的整体估计值，确定出该簇所在区域的当前时刻的ESD强度估计值。If not, execute the second triggering unit 305, and the second triggering unit is used for 305: based on the overall estimated value of the current moment of the cluster and the overall estimated value of the previous moment of the cluster, determine the current moment of the area where the cluster is located ESD intensity estimates.

在本发明的一种具体实施方式中，一致性估计结果接收单元301接收的各个簇成员节点中的第i簇成员节点当前时刻的一致性估计结果，为第i簇成员节点通过以下操作确定出的一致性估计结果：In a specific embodiment of the present invention, the consistency estimation result of the i-th cluster member node in each cluster member node received by the consistency estimation result receiving unit 301 at the current moment is determined by the i-th cluster member node through the following operations The consistency estimation results of :

建立表示工作环境内的ESD强度状态的动态离散时间系统模型；Build a dynamic discrete-time system model representing the state of ESD intensity within the work environment;

按照yi(k)＝Hixi(k)+Jivi(k)确定出第i簇成员节点在第k时刻测量到的ESD强度值；According to y i (k)=H i x i (k)+J i v i (k), determine the ESD intensity value measured by the i-th cluster member node at the k-th moment;

通过所述动态离散时间系统模型以及yi(k)得到θi(k)并将θi(k)输出至第i簇成员节点的各个邻居簇成员节点，并接收各个邻居簇成员节点发送至第i簇成员节点的各个信息传输向量，以使第i簇成员节点与各个邻居簇成员节点执行卡尔曼一致性滤波器的迭代，并得到第i簇成员节点当前时刻的一致性估计结果

Figure BDA0003094838150000151
Obtain θ i (k) through the dynamic discrete-time system model and y i (k) and output θ i (k) to each neighbor cluster member node of the i-th cluster member node, and receive each neighbor cluster member node to send to Each information transmission vector of the i-th cluster member node, so that the i-th cluster member node and each neighbor cluster member node perform the iteration of the Kalman consistency filter, and obtain the consistency estimation result of the i-th cluster member node at the current moment
Figure BDA0003094838150000151

其中，θi(k)表示第i簇成员节点在第k时刻输出的信息传输向量，且

Figure BDA0003094838150000152
zi(k)表示第k时刻的融合信息向量，Si(k)表示第k时刻的融合信息矩阵，
Figure BDA0003094838150000153
表示第i簇成员节点在第k时刻的一致性估计结果，且
Figure BDA0003094838150000154
为x(k)的估计量；Among them, θ i (k) represents the information transmission vector output by the i-th cluster member node at the k-th moment, and
Figure BDA0003094838150000152
z i (k) represents the fusion information vector at the kth moment, S i (k) represents the fusion information matrix at the kth moment,
Figure BDA0003094838150000153
represents the consistency estimation result of the i-th cluster member node at the k-th moment, and
Figure BDA0003094838150000154
is the estimator of x(k);

x(k)表示工作环境内的第k时刻的ESD强度状态，xi(k)表示第i簇成员节点周围的第k时刻的ESD强度状态，Hi和Ji均为预设的常数矩阵，yi(k)为第i簇成员节点在第k时刻测量到的ESD强度值，vi(k)表示在第k时刻具有方差Ri的零均值白噪声。x(k) represents the ESD intensity state at the k-th time in the working environment, x i (k) represents the ESD intensity state at the k-th time around the member node of the i-th cluster, and both H i and J i are preset constant matrices , y i (k) is the ESD intensity value measured by the i-th cluster member node at the k- th time, and vi (k) represents the zero-mean white noise with variance R i at the k-th time.

在本发明的一种具体实施方式中，所述建立表示工作环境内的ESD强度状态的动态离散时间系统模型，包括：In a specific embodiment of the present invention, establishing a dynamic discrete-time system model representing the ESD intensity state in the working environment includes:

按照x(k+1)＝Ax(k)+ω(k)+ρ(k)建立表示工作环境内的ESD强度状态的动态离散时间系统模型；According to x(k+1)=Ax(k)+ω(k)+ρ(k), establish a dynamic discrete-time system model representing the ESD intensity state in the working environment;

其中，A为预设的常数矩阵，x(k+1)表示工作环境内的第k+1时刻的ESD强度状态，ω(k)表示在第k时刻具有方差Q的ESD强度状态的零均值白噪声，ρ(k)表示在第k时刻具有方差W的ESD静电噪声。Among them, A is a preset constant matrix, x(k+1) represents the ESD intensity state at the k+1th time in the working environment, and ω(k) represents the zero mean of the ESD intensity state with variance Q at the kth time White noise, ρ(k) represents the ESD electrostatic noise with variance W at time k.

在本发明的一种具体实施方式中，第i簇成员节点与各个邻居簇成员节点执行卡尔曼一致性滤波器的迭代，包括：In a specific embodiment of the present invention, the i-th cluster member node and each neighbor cluster member node perform the iteration of the Kalman consistency filter, including:

第i簇成员节点与各个邻居簇成员节点均按照

Figure BDA0003094838150000155
执行卡尔曼一致性滤波器的迭代；The i-th cluster member node and each neighbor cluster member node are in accordance with the
Figure BDA0003094838150000155
perform an iteration of the Kalman consistency filter;

其中，

Figure BDA0003094838150000161
且集合
Figure BDA0003094838150000162
表示簇ξ中第i簇成员节点的邻居节点集合，Mi(k)＝(Pi -1(k)+Si(k))-1，Mi(k)表示
Figure BDA0003094838150000163
的误差的协方差，Pi(k)表示
Figure BDA0003094838150000164
的误差的协方差；Ci表示卡尔曼一致性滤波器的共识增益，且
Figure BDA0003094838150000165
∈为预设常数，||·||F表示矩阵的Frobenius范数；
Figure BDA0003094838150000166
为x(k)的先验估计量。in,
Figure BDA0003094838150000161
and set
Figure BDA0003094838150000162
Represents the set of neighbor nodes of the i-th cluster member node in cluster ξ, Mi (k)=(P i -1 (k)+S i ( k)) -1 , Mi ( k) represents
Figure BDA0003094838150000163
The covariance of the error, P i (k) represents
Figure BDA0003094838150000164
The covariance of the error; C i represents the consensus gain of the Kalman consensus filter, and
Figure BDA0003094838150000165
∈ is a preset constant, ||·|| F represents the Frobenius norm of the matrix;
Figure BDA0003094838150000166
is the prior estimator of x(k).

在本发明的一种具体实施方式中，第i簇成员节点所在簇的权重系数为通过以下步骤确定出的权重系数：In a specific embodiment of the present invention, the weight coefficient of the cluster where the i-th cluster member node is located is the weight coefficient determined by the following steps:

确定出表示簇成员节点与簇首节点在第k时刻的相互接近程度的支持矩阵

Figure BDA0003094838150000167
Determine the support matrix that represents the closeness of the cluster member node and the cluster head node at the kth time
Figure BDA0003094838150000167

基于所述支持矩阵Λ，得到第i簇成员节点所在簇的权重系数

Figure BDA0003094838150000168
Based on the support matrix Λ, the weight coefficient of the cluster where the member node of the i-th cluster is located is obtained
Figure BDA0003094838150000168

其中，n表示传感器网络中的第n簇，

Figure BDA0003094838150000169
ψn(k)表示第i簇成员节点所在簇的簇首节点与该簇的簇成员节点的相关度，τ表示第i簇成员节点所在簇的簇首节点参与融合的簇成员节点数量；
Figure BDA00030948381500001610
表示第i簇成员节点到第i簇成员节点所在簇的簇首节点的关联度。where n represents the nth cluster in the sensor network,
Figure BDA0003094838150000169
ψ n (k) represents the correlation between the cluster head node of the cluster where the i-th cluster member node is located and the cluster member node of the cluster, and τ represents the number of cluster member nodes that the cluster head node of the cluster where the i-th cluster member node is involved in the fusion;
Figure BDA00030948381500001610
Represents the degree of association between the i-th cluster member node and the cluster head node of the cluster where the i-th cluster member node is located.

在本发明的一种具体实施方式中，整体估计值确定单元302，具体用于：In a specific embodiment of the present invention, the overall estimated value determination unit 302 is specifically configured to:

基于该簇的权重系数，确定出用于表示该簇的簇成员节点状态的当前时刻的整体估计值为

Figure BDA00030948381500001611
Based on the weight coefficient of the cluster, it is determined that the overall estimated value of the current moment used to represent the state of the cluster member nodes of the cluster is
Figure BDA00030948381500001611

其中，

Figure BDA00030948381500001612
表示确定出的用于表示第n簇的簇成员节点状态在第k时刻的整体估计值，
Figure BDA00030948381500001613
表示第i簇成员节点在第k时刻的一致性估计结果。in,
Figure BDA00030948381500001612
represents the determined overall estimated value of the state of the cluster member node of the nth cluster at the kth moment,
Figure BDA00030948381500001613
Indicates the consistency estimation result of the i-th cluster member node at the k-th time.

在本发明的一种具体实施方式中，判断单元303，具体用于：In a specific embodiment of the present invention, the judgment unit 303 is specifically used for:

判断

Figure BDA0003094838150000171
是否成立；judge
Figure BDA0003094838150000171
whether it is established;

如果是，则确定该簇中当前时刻出现数据丢包的簇成员节点的数量满足预设的数量要求，并执行第一触发单元304；If yes, then determine that the number of cluster member nodes in which data packet loss occurs at the current moment in the cluster meets the preset number requirement, and execute the first trigger unit 304;

如果否，则确定该簇中当前时刻出现数据丢包的簇成员节点的数量不满足预设的数量要求，并执行第二触发单元305；If not, then it is determined that the number of cluster member nodes in which data packet loss occurs at the current moment in the cluster does not meet the preset number requirement, and the second trigger unit 305 is executed;

其中，

Figure BDA0003094838150000172
q为预设阈值，且
Figure BDA0003094838150000173
表示第i簇成员节点的邻居节点j未接收到第i簇成员节点输出的第i簇成员节点在第k时刻的一致性估计结果
Figure BDA0003094838150000174
表示第i簇成员节点的邻居节点j接收到第i簇成员节点输出的第i簇成员节点在第k时刻的一致性估计结果
Figure BDA0003094838150000175
δ(k)为触发门限因子；A为预设的常数矩阵，
Figure BDA0003094838150000176
为x(k)的先验估计量，x(k)表示工作环境内的第k时刻的ESD强度状态。in,
Figure BDA0003094838150000172
q is the preset threshold, and
Figure BDA0003094838150000173
Indicates that the neighbor node j of the i-th cluster member node does not receive the consistency estimation result of the i-th cluster member node at the k-th moment output by the i-th cluster member node
Figure BDA0003094838150000174
Indicates that the neighbor node j of the i-th cluster member node receives the consistency estimation result of the i-th cluster member node output by the i-th cluster member node at the k-th time
Figure BDA0003094838150000175
δ(k) is the trigger threshold factor; A is a preset constant matrix,
Figure BDA0003094838150000176
is an a priori estimator of x(k), where x(k) represents the ESD intensity state at the kth moment in the working environment.

在本发明的一种具体实施方式中，第二触发单元305，具体用于：In a specific embodiment of the present invention, the second trigger unit 305 is specifically used for:

按照

Figure BDA0003094838150000177
确定出该簇所在区域的当前时刻的ESD强度估计值；according to
Figure BDA0003094838150000177
Determine the estimated value of the ESD intensity at the current moment in the area where the cluster is located;

其中，

Figure BDA0003094838150000178
表示确定出的第n簇所在区域的第k时刻的ESD强度估计值，α为调整因子，且0＜α＜1，
Figure BDA0003094838150000179
表示确定出的用于表示第n簇的簇成员节点状态在第k时刻的整体估计值。in,
Figure BDA0003094838150000178
Represents the determined estimated value of the ESD intensity at the kth time in the area where the nth cluster is located, α is the adjustment factor, and 0<α<1,
Figure BDA0003094838150000179
Indicates the overall estimated value determined to represent the state of the cluster member node of the nth cluster at the kth time.

在本发明的一种具体实施方式中，传感器网络中的任意1个簇成员节点均为非侵入式电流传感器。In a specific embodiment of the present invention, any one cluster member node in the sensor network is a non-intrusive current sensor.

还需要说明的是，在本文中，诸如第一和第二等之类的关系术语仅仅用来将一个实体或者操作与另一个实体或操作区分开来，而不一定要求或者暗示这些实体或操作之间存在任何这种实际的关系或者顺序。而且，术语“包括”、“包含”或者其任何其他变体意在涵盖非排他性的包含，从而使得包括一系列要素的过程、方法、物品或者设备不仅包括那些要素，而且还包括没有明确列出的其他要素，或者是还包括为这种过程、方法、物品或者设备所固有的要素。在没有更多限制的情况下，由语句“包括一个……”限定的要素，并不排除在包括所述要素的过程、方法、物品或者设备中还存在另外的相同要素。It should also be noted that in this document, relational terms such as first and second are used only to distinguish one entity or operation from another, and do not necessarily require or imply those entities or operations There is no such actual relationship or order between them. Moreover, the terms "comprising", "comprising" or any other variation thereof are intended to encompass a non-exclusive inclusion such that a process, method, article or device that includes a list of elements includes not only those elements, but also includes not explicitly listed or other elements inherent to such a process, method, article or apparatus. Without further limitation, an element qualified by the phrase "comprising a..." does not preclude the presence of additional identical elements in a process, method, article or apparatus that includes the element.

专业人员还可以进一步意识到，结合本文中所公开的实施例描述的各示例的单元及算法步骤，能够以电子硬件、计算机软件或者二者的结合来实现，为了清楚地说明硬件和软件的可互换性，在上述说明中已经按照功能一般性地描述了各示例的组成及步骤。这些功能究竟以硬件还是软件方式来执行，取决于技术方案的特定应用和设计约束条件。专业技术人员可以对每个特定的应用来使用不同方法来实现所描述的功能，但是这种实现不应认为超出本发明的范围。Professionals may further realize that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two, in order to clearly illustrate the possibilities of hardware and software. Interchangeability, the above description has generally described the components and steps of each example in terms of functionality. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Skilled artisans may implement the described functionality using different methods for each particular application, but such implementations should not be considered beyond the scope of the present invention.

本文中应用了具体个例对本发明的原理及实施方式进行了阐述，以上实施例的说明只是用于帮助理解本发明的技术方案及其核心思想。应当指出，对于本技术领域的普通技术人员来说，在不脱离本发明原理的前提下，还可以对本发明进行若干改进和修饰，这些改进和修饰也落入本发明权利要求的保护范围内。The principles and implementations of the present invention are described herein by using specific examples, and the descriptions of the above embodiments are only used to help understand the technical solutions and core ideas of the present invention. It should be pointed out that for those skilled in the art, without departing from the principle of the present invention, several improvements and modifications can also be made to the present invention, and these improvements and modifications also fall within the protection 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,
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,
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,
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,
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.
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