Data processing method in big data environment based on wireless sensor network
A wireless sensor and data processing technology, applied in the field of data processing, can solve the problems of data accuracy, real-time node energy consumption data and poor post-processing ability, and achieve the effect of ensuring effectiveness
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specific Embodiment approach 1
[0058] A data processing method based on a wireless sensor network in a big data environment in this embodiment is figure 1 The wireless sensor network system structure shown, the method is implemented by the following steps, such as figure 2 Shown:
[0059] Step 1. Many control problems in engineering can be transformed into a linear matrix inequality form, through linear inequality to solve a feasibility problem or an optimization problem with multiple constraints, and invent the matrix processing problem description Is a discrete-time linear system;
[0060] Step 2: Define variables to obtain a dimensionality reduction and augmentation system;
[0061] Step 3: Design the filter parameters of the dimensionality reduction augmentation system obtained in Step 2 to obtain a filter;
[0062] Step 4. The filter parameters are obtained by reverse inference from the designed filter, and the big data signal is processed by the filter of the filter parameters, thereby completing the maximu...
specific Embodiment approach 2
[0064] The difference from the first embodiment is that in this embodiment a data processing method based on a wireless sensor network in a big data environment, the first step is specifically describing the problem as a discrete-time linear system:
[0065]
[0066] Where x k ∈R n Is the system state vector, Is the observation output, w k ∈R p Is the interference input, z k ∈R m Is the estimated state, A, B, C 1 ,C 2 ,D 1 ,D 2 It is a known constant matrix; the observation data received by the filter may have random time lag or even data packet loss. Here, the situation with one step random time lag is described as follows:
[0067]
[0068] Where y k ∈R r Is the observation received by the filter, ξ i,k (i=1, 2) is an uncorrelated random sequence that satisfies the Bernoulli distribution and satisfies the statistical probability
[0069]
[0070] among them
[0071] When ξ 1,k =1, it means that the data is received on time, the probability is When ξ 1,k =0,ξ 1,k-1 =0,ξ 2,k =1, it...
specific Embodiment approach 3
[0076] The difference from the first or second embodiment is that the data processing method in the big data environment based on wireless sensor network in this embodiment is
[0077] In the second step, the process of defining variables to obtain the dimensionality reduction and augmentation system is:
[0078] definition
[0079] Therefore
[0080] make There are the following dimensionality reduction and augmentation systems:
[0081]
[0082] among them,
[0083] And Φ i ,Γ i ,i=0,1,2,H i ,i=0,1 and It is defined as follows:
[0084]
[0085]
[0086]
[0087] The augmented system contains random variables θ i,k (i=1, 2) random parameter system, here are the following statistical characteristics:
[0088]
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