CN113395181A - Signal measurement method and device, and state monitoring method and device of Internet of things network - Google Patents
Signal measurement method and device, and state monitoring method and device of Internet of things network Download PDFInfo
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Abstract
The application relates to a signal measuring method and device based on compressed sensing, a state monitoring method and device of an Internet of things network and electronic equipment. The signal measurement method based on compressed sensing comprises the following steps: acquiring a signal to be detected, wherein the signal to be detected comprises a direct current component; multiplying a measurement matrix by a difference between the signal under test and the DC component to obtain a linear measurement value, the measurement matrix being a Bernoulli random measurement matrix; and determining the signal under test by minimizing an L0 norm of a difference between the signal under test and the dc component. Therefore, the abnormal monitoring and positioning capabilities of the information nodes of the Internet of things network can be improved by a compressed sensing algorithm containing direct-current signals and a hierarchical state monitoring model.
Description
Technical Field
The application relates to the technical field of internet of things, in particular to a signal measuring method and device based on compressed sensing, a state monitoring method and device of an internet of things network and electronic equipment.
Background
The Internet of things network relates to a huge substance sensing sensor, and comprises various sensors and professional logistics information systems, and the number of information sensing nodes is large. Meanwhile, with the development and application of the internet of things, the increase of nodes becomes inevitable. Real-time data depends on normal operation of each node of the internet of things network, and when data has errors, time delay and the like, abnormal nodes need to be quickly positioned for normal recovery, so that the node service capability of the internet of things can be guaranteed. When the state of each information node is monitored by using a traditional monitoring method, the working state of the node is usually judged directly by the value of a state variable through the traditional monitoring method including heartbeat detection and polling, and the method is simple. However, as the size of the node increases and the monitoring accuracy of the node state increases (the monitoring frequency increases), the bandwidth of the traffic data is crowded by the requirement of the data on the communication bandwidth, which causes a conflict of bandwidth congestion.
Compressed sensing is a hot spot of current signal processing field research, which can break the nyquist sampling law when processing broadband signals, and this property implies that it is still possible to maintain high accuracy when processing large-scale signals.
Therefore, it is desirable to provide a signal measurement scheme based on compressed sensing and a corresponding status monitoring scheme of the internet of things network.
Disclosure of Invention
The present application is proposed to solve the above-mentioned technical problems. The embodiment of the application provides a signal measuring method and device based on compressed sensing, a state monitoring method and device of an Internet of things network and electronic equipment.
According to an aspect of the present application, there is provided a signal measurement method based on compressed sensing, including: acquiring a signal to be detected, wherein the signal to be detected comprises a direct current component; multiplying a measurement matrix by a difference between the signal under test and the DC component to obtain a linear measurement value, the measurement matrix being a Bernoulli random measurement matrix; and determining the signal under test by minimizing an L0 norm of a difference between the signal under test and the dc component.
In the signal measurement method based on compressed sensing, the acquiring a signal to be measured includes: acquiring an original signal; and carrying out sparse transformation on the original signal through an orthogonal matrix to obtain the signal to be detected.
In the above compressed sensing-based signal measurement method, determining the signal under test by minimizing an L0 norm of a difference between the signal under test and the dc component includes: calculating an L1 norm of a difference between the signal to be measured and the DC component; and determining the signal to be measured through convex optimization linear programming.
In the above compressed sensing-based signal measurement method, determining the signal under test by minimizing an L0 norm of a difference between the signal under test and the dc component includes: and solving a suboptimal solution of an L0 norm of the difference value between the signal to be measured and the direct current component by adopting a greedy iteration algorithm to determine the signal to be measured.
In the signal measurement method based on compressive sensing, solving a suboptimal solution of an L0 norm of a difference between the signal to be measured and the dc component by using a greedy iteration algorithm to determine the signal to be measured includes: step 1: establishing a complete atom library by using the basis vectors of the measurement matrix; step 2: calculating and determining a candidate base vector corresponding to the maximum inner product through the inner product of a margin value and the base vectors in the complete atom library, wherein the margin value is initially the linear measurement value; and step 3: calculating an inner product of the residue value and the candidate base vector; and 4, step 4: calculating a product of the inner product and the candidate basis vector; and 5: calculating a difference between the residue value and the product to update the residue value; step 6: and (5) iteratively executing the steps 2 to 5 until the margin value is less than the preset threshold value.
In the compressive sensing-based signal measurement method, solving a suboptimal solution of an L0 norm of a difference between the signal to be measured and the dc component by using a greedy iteration algorithm to determine the signal to be measured further includes: determining whether the sparsity of the signal to be detected meets the sparsity requirement; and returning to the step 2 under the condition that the sparsity of the signal to be detected does not meet the sparsity requirement.
According to another aspect of the present application, a method for monitoring a status of an internet of things network is provided, including: setting a first preset number of internet-of-things network nodes with the same function as first-layer groups, wherein each first-layer group comprises an entity monitoring node; the entity monitoring node acquires a signal to be measured of the internet of things network node of the first layer of grouping, and generates a linear measurement value by using a measurement matrix; setting a second predetermined number of first layer packets as second layer packets, each second layer packet including an intermediate monitoring node; the intermediate monitoring node sums the measurement results of the entity monitoring nodes; and the monitoring center node determines the signal to be detected based on the sum of the measurement results of the middle monitoring nodes so as to monitor the state of each network node of the internet of things.
In the method for monitoring the state of the internet of things network, the determining, by the monitoring center node, the signal to be measured based on the sum of the measurement results of the intermediate monitoring nodes includes: the monitoring center node acquires the measurement results of all the intermediate monitoring nodes; the monitoring central node sums the measurement results of all the intermediate monitoring nodes; and the monitoring center node determines the signal to be measured based on the signal measurement method based on the compressed sensing.
According to still another aspect of the present application, there is provided a signal measuring apparatus based on compressed sensing, including: the device comprises an acquisition unit, a processing unit and a processing unit, wherein the acquisition unit is used for acquiring a signal to be detected, and the signal to be detected comprises a direct current component; a measurement unit for multiplying a measurement matrix by a difference between the signal to be measured and the direct current component to obtain a linear measurement value, the measurement matrix being a bernoulli random measurement matrix; and a calculation unit for determining the signal under test by minimizing an L0 norm of a difference between the signal under test and the dc component.
According to another aspect of the present application, there is provided a status monitoring device for an internet of things network, including: the network node monitoring device comprises a first setting unit, a second setting unit and a third setting unit, wherein the first setting unit is used for setting a first preset number of network nodes of the internet of things with the same functions as first layer groups, and each first layer group comprises an entity monitoring node; the measuring unit is used for acquiring a signal to be measured of the internet of things network node of the first layer of grouping by the entity monitoring node and generating a linear measuring value by using a measuring matrix; a second setting unit configured to set a second predetermined number of first layer packets as second layer packets, each of the second layer packets including one intermediate monitoring node; a summing unit, configured to sum the measurement results of the entity monitoring nodes by the intermediate monitoring node; and the monitoring unit is used for determining the signal to be detected by the monitoring center node based on the sum of the measurement results of the middle monitoring nodes so as to monitor the state of each network node of the internet of things.
According to still another aspect of the present application, there is provided an electronic apparatus including: a processor; and a memory in which computer program instructions are stored, which, when executed by the processor, cause the processor to perform the compressed sensing-based signal measurement method as described above and the state monitoring method of the internet of things network as described above.
According to yet another aspect of the present application, there is provided a computer-readable storage medium, wherein the computer-readable storage medium has stored thereon computer program instructions, which, when executed by a computing apparatus, are operable to execute the compressed sensing-based signal measurement method as described above and the status monitoring method of the internet of things network as described above.
The signal measuring method and device based on compressed sensing, the state monitoring method and device of the internet of things network and the electronic device can improve the abnormal monitoring and positioning capacity of the information node of the internet of things network by constructing a hierarchical state monitoring model through a compressed sensing algorithm containing direct current signals.
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Various other advantages and benefits of the present application will become apparent to those of ordinary skill in the art upon reading the following detailed description of the preferred embodiments. The drawings are only for purposes of illustrating the preferred embodiments and are not to be construed as limiting the application. It is obvious that the drawings described below are only some embodiments of the application, and that for a person skilled in the art, other drawings can be derived from them without inventive effort. Also, like parts are designated by like reference numerals throughout the drawings.
FIG. 1 illustrates a flow diagram of a compressed sensing-based signal measurement method according to an embodiment of the present application;
FIG. 2 illustrates a schematic diagram of signal measurement of compressed sensing theory according to an embodiment of the present application;
FIG. 3 illustrates a schematic diagram of a comparison of a classical sparse signal and a sparse signal containing a DC component;
fig. 4 illustrates a schematic flow chart of a method for condition monitoring of an internet of things network according to an embodiment of the application;
fig. 5 illustrates a topology diagram of a hierarchical monitoring model in a status monitoring method of an internet of things network according to an embodiment of the present application;
FIG. 6 is a diagram illustrating the task completion progress of each working node at a certain time during the task processing of a status signal decoding accuracy experiment;
FIG. 7 illustrates reconstruction error cases at different numbers of measurements in a state signal compression ratio test;
FIG. 8 illustrates an abnormal node localization order in an abnormal node localization efficiency test;
FIG. 9 illustrates a block diagram of a compressed sensing-based signal measurement apparatus according to an embodiment of the present application;
fig. 10 illustrates a block diagram of a status monitoring device of an internet of things network according to an embodiment of the application;
FIG. 11 illustrates a block diagram of an electronic device in accordance with an embodiment of the present application.
Detailed Description
Hereinafter, example embodiments according to the present application will be described in detail with reference to the accompanying drawings. It should be understood that the described embodiments are only some embodiments of the present application and not all embodiments of the present application, and that the present application is not limited by the example embodiments described herein.
Exemplary Signal measurement method
Fig. 1 illustrates a flow chart of a compressed sensing-based signal measurement method according to an embodiment of the present application.
Here, the signal measurement problem of the internet of things network may also be referred to as a signal perception problem, which may be described as: k information perception nodes are distributed in the network, and in order to know whether the nodes are abnormal or not, the monitoring center regularly sends working state information according to active polling or receiving nodes to screen the abnormal nodes and determine the positions of the abnormal nodes. The main problems to be solved by the signal perception of the Internet of things are that the state of the information node can be monitored quickly and efficiently by designing a state monitoring model of the node under the conditions that the network transmission bandwidth is limited, the node processing capacity is limited, and the monitoring precision of the node has higher requirements. Since the state monitoring information is generally sparse vector, for this reason, a compressed sensing theory is introduced in the embodiment of the present application, that is, k nodes { n }1,n2…nkState information of { x }1,x2…xkRecording the signal to be measured as X, introducing a certain measurement matrix phi epsilon RM×N(M < N), the linear measurement value Y epsilon R of the signal X to be measured under the matrixMThe original signal X is reconstructed, the process of which is shown in fig. 2. Fig. 2 illustrates a schematic diagram of signal measurement of compressed sensing theory according to an embodiment of the present application.
Compressed sensing as a new sampling theory can sample sparse signals and reconstruct original signals under the condition of being far lower than the Nyquist sampling rate, namely, a signal X to be detected belongs to R in a measurement matrix phi ∈ RM×N(M & lt N) projection to obtain linear measurement value Y ═ PhIX, Y ∈ RMThereby reconstructing the original signal X based on the linear measurement values and the measurement matrix.
Obviously, that is to sayThe linear measurement value Y and the measurement equation Φ are made known, but M < N, resulting in a non-unique feasible solution for X and an inaccurate reconstruction of the original signal X. But the most sparse solution exists in a series of possible solutions of X, the linear measurement dimensionality reduction meaning is the largest, and the sparsity of X | | | Y (Y) is obtained under the condition of meeting RIP (Restricted equidistant Property)0As an objective function, the reconstruction problem can be converted into a mathematical model,
s.t.ΦX＝Y (1)
generally, compressed sensing needs to sparsely represent a natural signal S, and it is common practice to perform a sparse transformation by an orthogonal matrix Ψ, that is, X ═ Ψ S, where X is a sparse representation of S in Ψ transformation domain.
Therefore, the core idea of compressed sensing is to directly compress and represent original signals by performing matrix measurement on the original signals subjected to orthogonal transformation (sparse basis representation), so that sampling samples are greatly reduced, and the signal reconstruction problem is converted into an optimization problem by solving the minimum sparsity of sparse signals, thereby breaking through the limitation of 2 times of frequency of the traditional Nyquist sampling and greatly reducing the storage space and the calculation amount of acquired signals.
Referring back to fig. 1, a compressed sensing-based signal measurement method according to an embodiment of the present application includes the following steps.
Step S110, a signal to be detected is obtained, wherein the signal to be detected comprises a direct current component.
In the embodiment of the application, the original signal of the network node of the internet of things is node state information of the network of the internet of things, and the state information is not a classical sparse signal commonly used in a compressed sensing algorithm, but a sparse signal containing a direct current component, as shown in fig. 3. Here, fig. 3 illustrates a comparative schematic diagram of a classical sparse signal and a sparse signal containing a direct current component.
In the traditional processing method, direct current components are removed through FFT (fast Fourier transform) or windowing function, so that although the direct current components can be removed, the complexity of an information acquisition end is increased, the engineering realization difficulty is high, and the cost is high. In the embodiment of the application, the measurement matrix and the reconstruction optimization model are improved, so that the improved measurement matrix and the improved reconstruction model are theoretically proved to be adaptive to the sparse signal containing the direct-current component.
Specifically, the classical reconstruction mathematical model of compressed sensing is as the above expression (1), and its objective function is to find the minimum value of the norm L0 (i.e. the number of nonzero numbers in the vector), in this embodiment of the present application, the original signal X is a signal containing a dc component C, where Δ ═ X-C, C is the dc component contained in the signal X, and this value is unknown; and delta is the sparse signal to be estimated after the direct current component of the original signal is removed. The purpose of node status monitoring of the internet of things network is to find outlier points, i.e. points of the signal X deviating from the dc component, which are obviously identical to the non-zero positions in Δ, so the objective function as in the above expression (1) is:
substituting Δ ═ X — C into constraint equation (2) yields:
φΔ＝φ(X-C)＝φX-φC (3)
when in use
Then, obtaining:
namely, it is
φΔ＝φ(X-C)＝φX-φC＝Y (5)
Thus, it is possible to obtain:
formally, expressions (1) and (6) are identical. When in use
And is also mathematically equivalent.
Therefore, in the condition monitoring problem of the internet of things network, the signal to be detected containing the direct current component can be used for signal recovery through a compressed sensing algorithm.
As described above, when the original signal is not a sparse signal, it is necessary to perform sparse transformation.
That is, in the compressed sensing-based signal measurement method according to the embodiment of the present application, acquiring a signal under test includes: acquiring an original signal; and carrying out sparse transformation on the original signal through an orthogonal matrix to obtain the signal to be detected.
And step S120, multiplying a measurement matrix by the difference value between the signal to be measured and the direct current component to obtain a linear measurement value, wherein the measurement matrix is a Bernoulli random measurement matrix.
In general, at most gaussian random measurement matrices and bernoulli random measurement matrices are used in the compressed sensing measurement matrices. The two matrix construction methods are as follows:
from a constructive point of view, both measurement matrices have a very strong randomness. When measuring the number
(c is a small constant) the measurement matrix satisfies the RIP property。
In accordance with the requirement that expressions (1) and (6) are equivalent as above, the measurement matrix should satisfy not only RIP but also RIP
I.e. the sum of each row of the matrix is zero. Selecting all N elements (N is an even number) in any row i of phi, and randomly selecting N/2 elements to make the value of the N/2 elements be equal to
Other element value is
Then the i row elements sum to
Let N be 2k (k be 1,2 …, N/2)
For all 2k elements in any phi row i, randomly selecting k elements and assigning values as
This selection combination is common to
And (4) seed preparation.
And for any element j in row i, it is selected to be assigned a value of
In combination of
Then phii,jThe probability of being selected is:
in the same way, phii,jSelected assignment
Is also 1/2, thus yielding a constructed measurement matrix of:
which is also a bernoulli random measurement matrix.
Therefore, in the embodiment of the present application, in the case of using a signal to be measured containing a direct current component for signal recovery by a compressed sensing algorithm, the measurement matrix thereof is also a bernoulli random measurement matrix.
Step S130, determining the signal to be measured by minimizing an L0 norm of a difference between the signal to be measured and the dc component.
Here, the optimization solution as in the above expressions (1) and (6) is an optimization problem of the minimum L0 norm, which is an NP problem, cannot be directly solved, and is generally solved by two ideas. Firstly, the minimum L0 norm is converted into the L1 norm, and the L1 norm is changed into a convex optimization linear programming problem. And secondly, solving the suboptimal solution by adopting greedy iteration, wherein a Matching Pursuit (MP) algorithm is a classical method for solving the suboptimal solution by adopting the greedy iteration.
Therefore, in the signal measurement method based on compressive sensing according to the embodiment of the present application, determining the signal under test by minimizing the L0 norm of the difference between the signal under test and the dc component includes: calculating an L1 norm of a difference between the signal to be measured and the DC component; and determining the signal to be measured through convex optimization linear programming.
In addition, in the signal measurement method based on compressive sensing according to an embodiment of the present application, determining the signal under test by minimizing an L0 norm of a difference between the signal under test and the dc component includes: and solving a suboptimal solution of an L0 norm of the difference value between the signal to be measured and the direct current component by adopting a greedy iteration algorithm to determine the signal to be measured.
The MP algorithm will be described in further detail below.
The basic idea of the MP algorithm is sparse approximation, and a complete atom library D is established in advance by using the basis of a measurement matrix phi; firstly, selecting atoms (basis vectors) which are most matched with the linear measurement value Y from D through inner product calculation, and calculating residual r; then iteration, i.e. continue to select the atoms matching the residual r from D until the residual r can be ignored, and the original sparse signal can be approximately linearly represented by these atoms. The algorithm process is as follows:
1) initialization: setting the residual r0Y, signal vector
Iteration counter t is 1, residue threshold epsilon0；
2) Traversing the vector of D, and searching the atom phi corresponding to the maximum inner productλ
at＝|<rt-1,Φλ>|＝supj＝1,2,…,N|<rt-1,Φj>|
Find index lambdatSo that r istAnd
has the largest inner product;
3) update margin rt：
4) Judgment of rt＞ε0The algorithm stops.
5) And t is t +1, if t is less than k, returning to the step 2), wherein k is the sparsity of the signal to be recovered.
In the embodiment of the present application, the signal to be measured is determined, that is, the expression (6) is solved, and the expression (6) and the expression (1) are mathematically equivalent according to the previous proof of the improvement of the compressive sensing model and the construction process of the measurement matrix, so that the MP algorithm may also be applied to determine the signal to be measured according to the expression (6) as described above.
Here, the MP algorithm step 2) selects the base with the largest inner product, recalculates the residual value, and if the inner product is larger, the residual value is smaller, and the iterative convergence is relatively faster. It can be seen that the MP algorithm has the property of preferentially recovering larger components during the iteration.
Therefore, in the signal measurement method based on compressive sensing according to the embodiment of the application, solving the suboptimal solution of the L0 norm of the difference between the signal to be measured and the direct-current component by using a greedy iteration algorithm to determine the signal to be measured includes: step 1: establishing a complete atom library by using the basis vectors of the measurement matrix; step 2: calculating and determining a candidate base vector corresponding to the maximum inner product through the inner product of a margin value and the base vectors in the complete atom library, wherein the margin value is initially the linear measurement value; and step 3: calculating an inner product of the residue value and the candidate base vector; and 4, step 4: calculating a product of the inner product and the candidate basis vector; and 5: calculating a difference between the residue value and the product to update the residue value; and, step 6: and (5) iteratively executing the steps 2 to 5 until the margin value is less than the preset threshold value.
In the compressive sensing-based signal measurement method, determining a sub-optimal solution of an L0 norm of a difference between the signal to be measured and the dc component by using a greedy iteration algorithm to determine the signal to be measured further includes: determining whether the sparsity of the signal to be detected meets the sparsity requirement; and returning to the step 2 under the condition that the sparsity of the signal to be detected does not meet the sparsity requirement.
Exemplary State monitoring method
Fig. 4 illustrates a schematic flow chart of a status monitoring method of an internet of things network according to an embodiment of the application.
As shown in fig. 4, a method for monitoring the status of an internet of things network according to an embodiment of the present application includes the following steps.
S210, setting a first preset number of internet-of-things network nodes with the same function as first-layer groups, wherein each first-layer group comprises an entity monitoring node; s220, the entity monitoring node acquires a signal to be measured of the internet of things network node of the first layer of grouping, and generates a linear measurement value by using a measurement matrix; s230, setting a second predetermined number of first layer packets as second layer packets, each second layer packet including an intermediate monitoring node; s240, the intermediate monitoring node sums the measurement results of the entity monitoring nodes; and S250, the monitoring center node determines the signal to be detected based on the sum of the measurement results of the middle monitoring nodes so as to monitor the state of each network node of the Internet of things.
That is, considering that the internet of things network depends on a large number of information sensing nodes to obtain various data, so that a set of scientific and reasonable monitoring models must be established for so many sensing nodes to ensure that monitoring and positioning of abnormal nodes are rapidly realized, in the state monitoring method of the internet of things network according to the embodiment of the present application, a layered structure is used to construct the monitoring model, as shown in fig. 5. Here, fig. 5 illustrates a topology diagram of a hierarchical monitoring model in a state monitoring method of an internet of things network according to an embodiment of the present application.
Specifically, the hierarchical monitoring model takes k (k is a positive integer) nodes (such as tank measurement) with the same function as one group, and may be defined as an entity monitoring node, for example. The m groups form a new monitoring node, for example, defined as an intermediate monitoring node (or referred to as an intermediate node), and the monitoring is layered, so that the highest node is a monitoring center. The entity monitoring node collects the state information of each working node and utilizes the measurement matrix phiiFor the state vector XiPerforming measurement to generate new measurement value Yi＝ΦiXi；
The intermediate node accumulates the measured values of the subordinate and gathers to the monitoring center
The monitoring center accumulates the measured values of the intermediate nodes to obtain
Φ＝[Φ1Φ2…Φc]，
c number of monitoring nodes for the entity.
Here, it may be understood by those skilled in the art that although the hierarchical monitoring model is illustrated as 4 layers in fig. 5, the hierarchical monitoring model according to the embodiment of the present application may include three layers, or five or more layers.
Compared with the traditional state monitoring method, the state monitoring method of the internet of things network has the following three obvious advantages:
1. in the state measurement process, only the entity monitoring node needs to carry out measurement coding on the state information of each working node in the detection area, and the intermediate node and the monitoring center only need to execute simple accumulation operation, so that the coding overhead of the intermediate node is greatly reduced;
2. in the process of summarizing the state information from the bottom layer to the monitoring center, the data dimension is kept unchanged, and the problem of data volume expansion when a large-scale system is monitored by a traditional heartbeat monitoring mechanism is avoided;
3. the intermediate node always keeps the last state measurement value of the lower node and reports the latest data to the upper node. The method does not need to synchronize global data, does not depend on reliable transmission of data, and has robustness to data packet loss.
And, the monitoring center periodically reconstructs the collected state measurement values, i.e., solves the sparse solution of expression (6) as described above. Moreover, it has been demonstrated above that the classical MP algorithm is still suitable for reconstructing sparse signals containing dc components under the conditions of a chosen row and zero bernoulli measurement matrix.
Therefore, in the method for monitoring the state of the internet of things network according to the embodiment of the present application, the determining, by the monitoring center node, the signal to be measured based on the sum of the measurement results of the intermediate monitoring nodes includes: the monitoring center node acquires the measurement results of all the intermediate monitoring nodes; the monitoring central node sums the measurement results of all the intermediate monitoring nodes; and the monitoring center node determines the signal to be measured based on the signal measurement method based on the compressed sensing.
Description of the effects
In the present application, model simulation is performed using Hadoop. Here, Hadoop provides a progress tracking mechanism: each working node (or called as a working node) periodically sends heartbeat messages to the central node to report the completion progress of the subtasks; the central node collects the heartbeat information and monitors the state of each subtask from the whole situation. The heartbeat cycle depends on the cluster size: the larger the cluster size, the larger the cycle. For a small cluster with 100 working nodes, the heartbeat period is typically set to 5 seconds. As the cluster size further increases, the monitoring accuracy decreases as the heartbeat cycle increases. The Hadoop progress tracking mechanism is very similar to the state monitoring of a large number of sensing nodes in the Internet of things network. For this purpose, in the present application, a monitoring method based on compressed sensing is used to replace the Hadoop original heartbeat-based progress tracking mechanism.
Next, the decoding accuracy, the compression ratio, and the positioning efficiency of the monitoring model proposed in the present application are tested by simulation experiments, and compared with the original method.
First, a state signal decoding accuracy experiment is performed. In the simulation environment, the same task is distributed to 2000 working nodes for processing, so that the test result is more objective. Fig. 6 illustrates the task completion progress of each working node at a certain time in the task processing process of the status signal decoding accuracy experiment. As can be seen from fig. 6, at this time, the task completion progress of most of the working nodes is concentrated on about 45%; the progress of a small part of working nodes (20, accounting for 1% of the total number of the nodes) is far lower than the average level, and for the completion of the whole task, the nodes belong to the shortest wood board with the bucket effect, the node numbers are determined according to state detection, and the task of the nodes is recalled for migration. If the node is a real internet of things network, if the node with abnormal state is a key node for information acquisition and processing, planning and accurate guarantee of the whole logistics are influenced very possibly, and the position of the node must be located through a monitoring system to recover or maintain and replace the node system.
Then, a status signal compression ratio test is performed. In the test, the measurement matrix phi epsilon R is changedM×NAnd (M < N) measuring times, namely changing the value of M, performing measurement and reconstruction experiments, and observing the condition that the condition monitoring model according to the embodiment of the application is used for improving the condition signal compression efficiency.
In the experiment, the sparsity is set to be 20, the measurement times M are increased from 50, the measurement times of the measurement matrix are gradually increased by taking 5 as a step length, and the reconstruction result of the state signal of each experiment is obtained. The reconstructed results were examined with the raw state signals to obtain the experimental results shown in fig. 7:
(1) when M is less than 120, the reconstructed signal has over-detection (error judgment abnormity) and under-detection (abnormal detection). Fig. 4 shows that the monitoring center reconstructs all signals, if the false detection condition of the model needs to be corrected, a secondary confirmation mechanism can be set in the monitoring center to correct the over-detection, and the under-detection condition can be corrected automatically in the second state reconstruction period.
(2) When M is more than or equal to 120, the model reconstruction state signal is completely accurate. That is, when M is 6 times of sparsity, the detection model can accurately locate all abnormal nodes, and at this time, the compression ratio of data reaches 6% (120/2000). In other words, the detection scale of the state detection model proposed in the present application is improved by about 16.7(2000/120) times while maintaining the same detection accuracy and system communication load as the conventional progress tracking method.
Here, fig. 7 illustrates the reconstruction error cases at different measurement times in the state signal compression ratio test.
And finally, carrying out an abnormal node positioning efficiency test. The above indicates in analyzing the MP algorithm that the MP algorithm has a characteristic of preferentially recovering a larger component in an iteration process, and this characteristic enables the monitoring method based on compressed sensing to preferentially locate a node with a more serious abnormal degree, the number of times of reconstruction of the experiment is 20, the remaining parameters are the same as those in the state signal decoding accuracy experiment, the sequence of detection of abnormal nodes is observed, and the result is shown in fig. 8. Here, fig. 8 illustrates an abnormal node locating order in the abnormal node locating efficiency test.
From the results of fig. 8, the abnormal severity of the nodes located in the order of iteration decreases in turn. Simulation results verify that the characteristic of preferentially positioning the serious abnormal node of the MP algorithm is obvious, so that great convenience is provided for positioning and troubleshooting of the serious abnormal node of the Internet of things network, and the efficiency of abnormal recovery is higher.
In conclusion, the compressive sensing theory is introduced to pertinently improve the contradiction of the state detection of the information sensing node of the internet of things, the correctness of the improved algorithm is proved through the theory, and the model simulation shows that an ideal effect is achieved.
Compared with the traditional state monitoring method, the state monitoring model provided by the application is only used for measuring at the entity node, the intermediate node is only used for simple addition operation, the monitoring center is used for signal reconstruction, the engineering implementation difficulty is low, the signal coding complexity is low, the algorithm is easy to realize, the signal compression ratio is large, and the monitoring precision can be further improved under the same scale. In addition, in the model, in the process that the measurement data are collected from the bottom layer to the monitoring center, the data dimension is kept unchanged, so that the problems of data expansion and calculation complexity increase caused by dimension increase are solved. In addition, the model is reconstructed by using an MP algorithm, and the nodes with high abnormal degree are preferentially positioned according to theories and simulations, so that the fault recovery efficiency can be greatly improved in the fault monitoring system with high automatic recovery degree.
In the simulation analysis of the application, only the monitoring method based on the compressed sensing is applied to tracking the task progress of each working node of Hadoop. In fact, the monitoring method can be applied to various internet of things networks, including military business internet of things networks.
Exemplary devices
Fig. 9 illustrates a block diagram of a compressed sensing-based signal measurement apparatus according to an embodiment of the present application.
As shown in fig. 9, a compressed sensing-based signal measurement apparatus 300 according to an embodiment of the present application includes: an obtaining unit 310, configured to obtain a signal to be detected, where the signal to be detected includes a direct current component; a measurement unit 320, configured to multiply a measurement matrix by a difference between the signal to be measured and the dc component to obtain a linear measurement value, where the measurement matrix is a bernoulli random measurement matrix; and a calculation unit 330 for determining the signal under test by minimizing an L0 norm of a difference between the signal under test and the dc component.
In an example, in the compressed sensing-based signal measuring apparatus 300, the obtaining unit 310 is configured to: acquiring an original signal; and carrying out sparse transformation on the original signal through an orthogonal matrix to obtain the signal to be detected.
In an example, in the compressed sensing-based signal measuring apparatus 300, the calculating unit 330 is configured to: calculating an L1 norm of a difference between the signal to be measured and the DC component; and determining the signal to be measured through convex optimization linear programming.
In an example, in the compressed sensing-based signal measuring apparatus 300, the calculating unit 330 is configured to: and solving a suboptimal solution of an L0 norm of the difference value between the signal to be measured and the direct current component by adopting a greedy iteration algorithm to determine the signal to be measured.
In one example, in the compressed sensing-based signal measurement apparatus 300, the sub-optimal solution of the L0 norm of the difference between the signal under test and the dc component to determine the signal under test by the computing unit 330 using a greedy iteration algorithm includes: step 1: establishing a complete atom library by using the basis vectors of the measurement matrix; step 2: calculating and determining a candidate base vector corresponding to the maximum inner product through the inner product of a margin value and the base vectors in the complete atom library, wherein the margin value is initially the linear measurement value; and step 3: calculating an inner product of the residue value and the candidate base vector; and 4, step 4: calculating a product of the inner product and the candidate basis vector; and 5: calculating a difference between the residue value and the product to update the residue value; step 6: and (5) iteratively executing the steps 2 to 5 until the margin value is less than the preset threshold value.
In one example, in the compressed sensing-based signal measurement apparatus 300, the calculating unit 330 uses a greedy iterative algorithm to solve the suboptimal solution of the L0 norm of the difference between the signal under test and the dc component to determine the signal under test further includes: determining whether the sparsity of the signal to be detected meets the sparsity requirement; and returning to the step 2 under the condition that the sparsity of the signal to be detected does not meet the sparsity requirement.
Fig. 10 illustrates a block diagram of a status monitoring device of an internet of things network according to an embodiment of the application.
As shown in fig. 10, a status monitoring apparatus 400 of an internet of things network according to an embodiment of the present application includes: a first setting unit 410, configured to set a first predetermined number of network nodes of the internet of things with the same function as first layer packets, where each first layer packet includes an entity monitoring node; a measurement unit 420, configured to obtain, by the entity monitoring node, a signal to be measured of the internet of things network node of the first layer packet, and generate a linear measurement value using a measurement matrix; a second setting unit 430 for setting a second predetermined number of first layer packets as second layer packets, each of the second layer packets including one intermediate monitoring node; a summing unit 440, configured to sum the measurement results of the entity monitoring nodes by the intermediate monitoring node; and a monitoring unit 450, configured to determine, by the monitoring center node, the signal to be detected based on a sum of the measurement results of the intermediate monitoring nodes, so as to monitor a state of each network node of the internet of things.
In an example, in the status monitoring apparatus 400 of the internet of things network, the monitoring unit 450 is configured to: the monitoring center node acquires the measurement results of all the intermediate monitoring nodes; the monitoring central node sums the measurement results of all the intermediate monitoring nodes; and the monitoring center node determines the signal to be measured based on the signal measurement method based on the compressed sensing.
Here, it can be understood by those skilled in the art that the specific functions and operations of the respective units and modules in the above-described compressed sensing-based signal measuring apparatus 300 and the state monitoring apparatus 400 of the internet of things network have been described in detail in the compressed sensing-based signal measuring method and the state monitoring method of the internet of things network described above with reference to fig. 1 to 8, and thus, a repetitive description thereof will be omitted.
As described above, the compressed sensing-based signal measurement apparatus 300 and the status monitoring apparatus 400 of the internet of things network according to the embodiment of the present application may be implemented in various terminal devices, for example, a server for monitoring nodes of the internet of things network. In one example, the compressed sensing-based signal measurement apparatus 300 and the status monitoring apparatus 400 of the internet of things network according to the embodiment of the present application may be integrated into the terminal device as one software module and/or hardware module. For example, the compressed sensing-based signal measurement apparatus 300 and the status monitoring apparatus 400 of the internet of things network may be a software module in the operating system of the terminal device, or may be an application developed for the terminal device; of course, the compressed sensing-based signal measurement apparatus 300 and the status monitoring apparatus 400 of the internet of things network may also be one of many hardware modules of the terminal device.
Alternatively, in another example, the compressed sensing-based signal measuring apparatus 300 and the status monitoring apparatus 400 of the internet of things network and the terminal device may also be separate devices, and the compressed sensing-based signal measuring apparatus 300 and the status monitoring apparatus 400 of the internet of things network may be connected to the terminal device through a wired and/or wireless network and transmit the interaction information according to an agreed data format.
Exemplary electronic device
Next, an electronic apparatus according to an embodiment of the present application is described with reference to fig. 11.
FIG. 11 illustrates a block diagram of an electronic device in accordance with an embodiment of the present application.
As shown in fig. 11, the electronic device 10 includes one or more processors 11 and memory 12.
The processor 11 may be a Central Processing Unit (CPU) or other form of processing unit having data processing capabilities and/or instruction execution capabilities, and may control other components in the electronic device 10 to perform desired functions.
In one example, the electronic device 10 may further include: an input device 13 and an output device 14, which are interconnected by a bus system and/or other form of connection mechanism (not shown).
The input device 13 may be, for example, a keyboard, a mouse, or the like.
The output device 14 may output various information, such as a signal to be measured or a status monitoring result, to the outside. The output devices 14 may include, for example, a display, speakers, a printer, and a communication network and its connected remote output devices, among others.
Of course, for the sake of simplicity, only some of the components of the electronic device 10 relevant to the present application are shown in fig. 11, and components such as buses, input/output interfaces, and the like are omitted. In addition, the electronic device 10 may include any other suitable components depending on the particular application.
Exemplary computer program product and computer-readable storage Medium
In addition to the above-described methods and apparatus, embodiments of the present application may also be a computer program product comprising computer program instructions that, when executed by a processor, cause the processor to perform the steps in the compressed sensing-based signal measurement method and the state monitoring method of the internet of things network according to various embodiments of the present application described in the "exemplary methods" section above in this specification.
The computer program product may be written with program code for performing the operations of embodiments of the present application in any combination of one or more programming languages, including an object oriented programming language such as Java, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computing device, partly on the user's device, as a stand-alone software package, partly on the user's computing device and partly on a remote computing device, or entirely on the remote computing device or server.
Furthermore, embodiments of the present application may also be a computer-readable storage medium having stored thereon computer program instructions, which, when executed by a processor, cause the processor to perform the steps in the compressed sensing-based signal measurement method and the state monitoring method of the internet of things network according to various embodiments of the present application described in the "exemplary methods" section above in this specification.
The computer-readable storage medium may take any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may include, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples (a non-exhaustive list) of the readable storage medium include: an electrical connection having one or more wires, a portable disk, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
The foregoing describes the general principles of the present application in conjunction with specific embodiments, however, it is noted that the advantages, effects, etc. mentioned in the present application are merely examples and are not limiting, and they should not be considered essential to the various embodiments of the present application. Furthermore, the foregoing disclosure of specific details is for the purpose of illustration and description and is not intended to be limiting, since the foregoing disclosure is not intended to be exhaustive or to limit the disclosure to the precise details disclosed.
The block diagrams of devices, apparatuses, systems referred to in this application are only given as illustrative examples and are not intended to require or imply that the connections, arrangements, configurations, etc. must be made in the manner shown in the block diagrams. These devices, apparatuses, devices, systems may be connected, arranged, configured in any manner, as will be appreciated by those skilled in the art. Words such as "including," "comprising," "having," and the like are open-ended words that mean "including, but not limited to," and are used interchangeably therewith. The words "or" and "as used herein mean, and are used interchangeably with, the word" and/or, "unless the context clearly dictates otherwise. The word "such as" is used herein to mean, and is used interchangeably with, the phrase "such as but not limited to".
It should also be noted that in the devices, apparatuses, and methods of the present application, the components or steps may be decomposed and/or recombined. These decompositions and/or recombinations are to be considered as equivalents of the present application.
The previous description of the disclosed aspects is provided to enable any person skilled in the art to make or use the present application. Various modifications to these aspects will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other aspects without departing from the scope of the application. Thus, the present application is not intended to be limited to the aspects shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
The foregoing description has been presented for purposes of illustration and description. Furthermore, the description is not intended to limit embodiments of the application to the form disclosed herein. While a number of example aspects and embodiments have been discussed above, those of skill in the art will recognize certain variations, modifications, alterations, additions and sub-combinations thereof.
Claims (12)
1. A method for signal measurement based on compressed sensing, comprising:
acquiring a signal to be detected, wherein the signal to be detected comprises a direct current component;
multiplying a measurement matrix by a difference between the signal under test and the DC component to obtain a linear measurement value, the measurement matrix being a Bernoulli random measurement matrix; and
determining the signal under test by minimizing the L0 norm of the difference between the signal under test and the DC component.
2. The compressed sensing-based signal measurement method according to claim 1, wherein the acquiring of the signal under test comprises:
acquiring an original signal; and
and carrying out sparse transformation on the original signal through an orthogonal matrix to obtain the signal to be detected.
3. The compressed sensing-based signal measurement method of claim 2, wherein determining the signal under test by minimizing an L0 norm of a difference between the signal under test and the dc component comprises:
calculating an L1 norm of a difference between the signal to be measured and the DC component; and
and determining the signal to be measured through convex optimization linear programming.
4. The compressed sensing-based signal measurement method of claim 2, wherein determining the signal under test by minimizing an L0 norm of a difference between the signal under test and the dc component comprises:
and solving a suboptimal solution of an L0 norm of the difference value between the signal to be measured and the direct current component by adopting a greedy iteration algorithm to determine the signal to be measured.
5. The method of claim 4, wherein determining the signal under test by applying a greedy iterative algorithm to solve the suboptimal solution of the L0 norm of the difference between the signal under test and the DC component comprises:
step 1: establishing a complete atom library by using the basis vectors of the measurement matrix;
step 2: calculating and determining a candidate base vector corresponding to the maximum inner product through the inner product of a margin value and the base vectors in the complete atom library, wherein the margin value is initially the linear measurement value;
and step 3: calculating an inner product of the residue value and the candidate base vector;
and 4, step 4: calculating a product of the inner product and the candidate basis vector;
and 5: calculating a difference between the residue value and the product to update the residue value;
step 6: and (5) iteratively executing the steps 2 to 5 until the margin value is less than the preset threshold value.
6. The method of claim 5, wherein determining the signal under test using a greedy iterative algorithm to solve the suboptimal solution of the L0 norm of the difference between the signal under test and the DC component further comprises:
determining whether the sparsity of the signal to be detected meets the sparsity requirement; and
and returning to the step 2 under the condition that the sparsity of the signal to be detected does not meet the sparsity requirement.
7. A state monitoring method of an Internet of things network is characterized by comprising the following steps:
setting a first preset number of internet-of-things network nodes with the same function as first-layer groups, wherein each first-layer group comprises an entity monitoring node;
the entity monitoring node acquires a signal to be measured of the internet of things network node of the first layer of grouping, and generates a linear measurement value by using a measurement matrix;
setting a second predetermined number of first layer packets as second layer packets, each second layer packet including an intermediate monitoring node;
the intermediate monitoring node sums the measurement results of the entity monitoring nodes; and
and the monitoring center node determines the signal to be detected based on the sum of the measurement results of the middle monitoring nodes so as to monitor the state of each network node of the Internet of things.
8. The method for monitoring the status of the internet of things network of claim 7, wherein the determining, by the monitoring center node, the signal to be measured based on the sum of the measurement results of the intermediate monitoring nodes comprises:
the monitoring center node acquires the measurement results of all the intermediate monitoring nodes;
the monitoring central node sums the measurement results of all the intermediate monitoring nodes; and
the monitoring center node determines the signal to be measured based on the compressed sensing-based signal measurement method according to any one of claims 1 to 6.
9. A signal measurement device based on compressed sensing, comprising:
the device comprises an acquisition unit, a processing unit and a processing unit, wherein the acquisition unit is used for acquiring a signal to be detected, and the signal to be detected comprises a direct current component;
a measurement unit for multiplying a measurement matrix by a difference between the signal to be measured and the direct current component to obtain a linear measurement value, the measurement matrix being a bernoulli random measurement matrix; and
a calculation unit for determining the signal under test by minimizing an L0 norm of a difference between the signal under test and the DC component.
10. A state monitoring device of an Internet of things network is characterized by comprising:
the network node monitoring device comprises a first setting unit, a second setting unit and a third setting unit, wherein the first setting unit is used for setting a first preset number of network nodes of the internet of things with the same functions as first layer groups, and each first layer group comprises an entity monitoring node;
the measuring unit is used for acquiring a signal to be measured of the internet of things network node of the first layer of grouping by the entity monitoring node and generating a linear measuring value by using a measuring matrix;
a second setting unit configured to set a second predetermined number of first layer packets as second layer packets, each of the second layer packets including one intermediate monitoring node;
a summing unit, configured to sum the measurement results of the entity monitoring nodes by the intermediate monitoring node; and
and the monitoring unit is used for determining the signal to be detected by the monitoring center node based on the sum of the measurement results of the middle monitoring nodes so as to monitor the state of each network node of the Internet of things.
11. An electronic device, comprising:
a processor; and
a memory having stored therein computer program instructions which, when executed by the processor, cause the processor to perform the method of compressive sensing-based signal measurement as claimed in any one of claims 1 to 6 and the method of condition monitoring of an internet of things network as claimed in claim 7 or 8.
12. A computer readable storage medium having stored thereon computer program instructions operable, when executed by a computing device, to perform a compressed sensing-based signal measurement method according to any one of claims 1 to 6 and a status monitoring method of an internet of things network according to claim 7 or 8.
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