CN111010191B - Data acquisition method, system, equipment and storage medium - Google Patents
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Abstract
The embodiment of the invention provides a data acquisition method, a system, equipment and a storage medium. The method comprises the following steps: acquiring data to be acquired; sampling the data to be acquired, and determining acquisition algorithm parameters; based on the acquisition algorithm parameters, compressing and restoring the low-dimensional signals of the data to be acquired to obtain first original data; based on the first original data, compressing and restoring the high-dimensional signal of the data to be acquired to obtain second original data; and comparing the acquisition algorithm parameters with the second original data, and outputting to obtain compressed acquisition data. According to the embodiment of the invention, through a compressed sensing technology, the acquisition quantity of sensor data can be greatly reduced by utilizing the space-time correlation and sparsity among the data, the burden of acquiring and transmitting mass bottom new energy data by an energy interconnection network is reduced, and the overall performance of the system is improved.
Description
Technical Field
The present invention relates to the field of data acquisition technologies, and in particular, to a data acquisition method, system, device, and storage medium.
Background
The energy internet is a novel energy system, in order to fully and effectively access a large amount of distributed renewable energy sources and ensure the stability and the robustness operation of the system, the deep fusion of an information technology and a physical technology is required to be realized, the control performance and the reaction speed of the system are improved through fusion, the accurate perception of the network operation state is realized, and the robustness, the timeliness and the accuracy of the system strategy formulation are ensured.
The integration of information and physics in the energy internet is not separated from the collection of the underlying data, and in order to realize comprehensive and accurate perception of the system state and efficient utilization of new energy, a large number of sensors are usually required to be deployed for the same or similar new energy facilities in the system range so as to collect sample data through the sensors.
In the prior art, a large number of sample points need to be acquired in the data acquisition process, so that the acquisition, storage and transmission pressures are overlarge.
Disclosure of Invention
The embodiment of the invention provides a data acquisition method, a system, equipment and a storage medium, which are used for solving the defect that in the prior art, a large number of sample points need to be acquired in the data acquisition process, so that the acquisition, storage and transmission pressures are overlarge.
In a first aspect, an embodiment of the present invention provides a data acquisition method, including:
acquiring data to be acquired;
sampling the data to be acquired, and determining acquisition algorithm parameters;
based on the acquisition algorithm parameters, compressing and restoring the low-dimensional signals of the data to be acquired to obtain first original data;
based on the first original data, compressing and restoring the high-dimensional signal of the data to be acquired to obtain second original data;
and comparing the acquisition algorithm parameters with the second original data, and outputting to obtain compressed acquisition data.
The step of sampling the data to be acquired to determine acquisition algorithm parameters specifically comprises the following steps:
and determining the number of the distributed nodes of the data to be acquired as M, the dimension of the low-dimension data as N, and the dimension of the high-dimension data as N.
The method is characterized in that based on the acquisition algorithm parameters, the low-dimensional signals of the data to be acquired are compressed and restored to obtain first original data, and specifically comprises the following steps:
obtaining a first measurement matrix as { ψ } m1,n Respectively acquiring m1 low-dimensional data from each node in the distributed nodes according to the first measurement matrix to form a first data matrix
Acquiring sparse transforms intoCombining with the first data matrix and the first measurement matrix to obtain a relational expression:
s is a first sparse representation coefficient;
obtaining s by solving the following optimization problem, and further obtaining the first original data in n dimensions
Based on the first original data, compressing and restoring the high-dimensional signal of the data to be acquired to obtain second original data, wherein the method specifically comprises the following steps:
solving a covariance matrix C for the first original data x, and solving the following eigenvalue decomposition problem to obtain a group of new eigenvalue transformation
Where lambda represents a diagonal matrix with eigenvalues as diagonals,an n-dimensional feature transformation matrix composed of feature vectors obtained by feature decomposition is represented;
transforming the n-dimensional feature into a matrixInterpolation to high-dimensional space to obtain N-dimensional feature transformation matrix +.>
Obtaining a second measurement matrix as { ψ } m2,N Respectively acquiring m2 pieces of high-dimensional data from each node in the distributed nodes according to the second measurement matrix to form a second data matrix
Based on the second data matrix, combining the second measurement matrix and the N-dimensional feature transformation matrix to obtain a relational expression:
s is a second sparse representation coefficient;
obtaining s by solving the following optimization problem, and further obtaining second original data of N dimensions
Comparing the acquisition algorithm parameter with the second original data, and outputting to obtain compressed acquisition data, wherein the method specifically comprises the following steps:
comparing the distributed node number M in the acquisition algorithm with the second original dataRestoring to obtain M compressed acquisition data +.>
Wherein the first measurement matrix and the second measurement matrix comprise the following matrices: gaussian random matrix, bernoulli random matrix and partially orthogonal matrix.
Wherein the sparse transform comprises: discrete cosine transform, fourier transform, and discrete wavelet transform.
In a second aspect, an embodiment of the present invention provides a data acquisition system based on multiple distributed compressed sensing, including:
the acquisition module is used for acquiring data to be acquired;
the sampling module is used for sampling the data to be acquired and determining acquisition algorithm parameters;
the first processing module is used for compressing and restoring the low-dimensional signal of the data to be acquired based on the acquisition algorithm parameters to obtain first original data;
the second processing module is used for compressing and restoring the high-dimensional signals of the data to be acquired based on the first original data to obtain second original data;
and the output module is used for comparing the acquisition algorithm parameters with the second original data and outputting compressed acquisition data.
In a third aspect, an embodiment of the present invention provides an electronic device, including:
a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the steps of any one of the multiple distributed compressed sensing based data acquisition methods when the program is executed.
In a fourth aspect, embodiments of the present invention provide a non-transitory computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements the steps of any of the multiple distributed compressed sensing based data acquisition methods.
According to the technical scheme provided by the embodiment of the invention, through a compressed sensing technology, the acquisition quantity of the sensor data can be greatly reduced by utilizing the space-time correlation and sparsity among the data, the burden of acquiring and transmitting mass bottom new energy data by the energy interconnection network is reduced, and the overall performance of the system is improved.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, and it is obvious that the drawings in the following description are some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of a data acquisition method based on multiple distributed compressed sensing according to an embodiment of the present invention;
FIG. 2 is a block diagram of a data acquisition system based on multiple distributed compressed sensing according to an embodiment of the present invention;
fig. 3 is a block diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments of the present invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The compressed sensing technology is a typical technology in the information field, and can utilize the data sparsity to realize accurate and effective recovery of system state data, greatly reduce the sampling data quantity and save the system cost. In the energy internet, if the collected massive new energy data is not effectively compressed, huge data acquisition, storage and processing expenses are brought, service realization time delay is influenced, and the real-time control and the instant operation of the system are greatly influenced. How to adopt the efficient compression algorithm, the acquisition amount of new energy data is reduced while ensuring accurate perception of the system state, and the method is a challenging problem. The patent aims at the problem, the traditional compressed sensing algorithm is further improved, the sparsity representation of data is improved based on the combination of high-low dimensional compressed sensing, and the acquired data volume is effectively reduced.
The basic idea of the distributed compressed sensing method is that the space-time correlation of data in distributed nodes of the Internet of things is utilized, sample information is collected at each node through the compressed sensing method and is transmitted to a sink node for unified reduction processing, and thus the sample collection pressure of the nodes can be reduced. The multi-distributed compressed sensing method provided by the embodiment of the invention is that firstly, low-dimensional node information is collected, a traditional compressed reduction algorithm is used for reducing low-dimensional signals, then covariance matrixes of all node signals are obtained, a group of new feature transformation is obtained through feature decomposition of covariance and transformation matrix interpolation, and then distributed compressed sensing is used for reducing high-dimensional signals under the group of new feature transformation. Because the sparsity of the signals is further improved under the expression of the new characteristic basis function, the number of the sample points required to be collected for reduction is further reduced, and therefore the pressure of data on collection, storage and transportation is further reduced.
Therefore, the embodiment of the invention provides a data acquisition method based on multiple distributed compressed sensing.
Fig. 1 is a flowchart of a data acquisition method based on multiple distributed compressed sensing according to an embodiment of the present invention, as shown in fig. 1, including:
s1, acquiring data to be acquired;
s2, sampling the data to be acquired, and determining acquisition algorithm parameters;
s3, based on the acquisition algorithm parameters, compressing and restoring the low-dimensional signals of the data to be acquired to obtain first original data;
s4, based on the first original data, compressing and restoring the high-dimensional signals of the data to be acquired to obtain second original data;
s5, comparing the acquisition algorithm parameters with the second original data, and outputting compressed acquisition data.
Specifically, firstly, acquiring data to be acquired and processed, sampling the data, determining related parameters of an adopted acquisition algorithm, such as the number of points of sampling, used data dimension and other parameter information, after determining the related parameters of the acquisition algorithm, performing compression and reduction on a low-dimensional signal of the data to be acquired to obtain first original data, then performing further conversion processing based on the obtained first original data to obtain input parameters of a high-dimensional signal, further performing compression and reduction on a high-dimensional signal of the acquired data to obtain second original data, and finally obtaining final compressed acquired data by referring to the related parameters determined at the beginning and the obtained second original data.
According to the embodiment of the invention, through a compressed sensing technology, the acquisition quantity of sensor data can be greatly reduced by utilizing the space-time correlation and sparsity among the data, the burden of acquiring and transmitting mass bottom new energy data by an energy interconnection network is reduced, and the overall performance of the system is improved.
On the basis of the above embodiment, step S2 in the method specifically includes:
and determining the number of the distributed nodes of the data to be acquired as M, the dimension of the low-dimension data as N, and the dimension of the high-dimension data as N.
On the basis of the above embodiment, step S3 in the method specifically includes:
obtaining a first measurement matrix as { ψ } m1,n Respectively acquiring m1 low-dimensional data from each node in the distributed nodes according to the first measurement matrix to form a first data matrix
Acquiring sparse transforms intoCombining with the first data matrix and the first measurement matrix to obtain a relational expression:
s is a first sparse representation coefficient;
obtaining s by solving the following optimization problem, and further obtaining the first original data in n dimensions
The algorithm is herein a low-dimensional signal compression restoration algorithm.
On the basis of the above embodiment, step S4 in the method specifically includes:
solving a covariance matrix C for the first original data x, and solving the following eigenvalue decomposition problem to obtain a group of new eigenvalue transformation
Where lambda represents a diagonal matrix with eigenvalues as diagonals,an n-dimensional feature transformation matrix composed of feature vectors obtained by feature decomposition is represented;
transforming the n-dimensional feature into a matrixInterpolation to high-dimensional space to obtain N-dimensional feature transformation matrix +.>
Obtaining a second measurement matrix as { ψ } m2,N Respectively acquiring m2 pieces of high-dimensional data from each node in the distributed nodes according to the second measurement matrix to form a second data matrix
Based on the second data matrix, combining the second measurement matrix and the N-dimensional feature transformation matrix to obtain a relational expression:
s is a second sparse representation coefficient;
obtaining s by solving the following optimization problem, and further obtaining second original data of N dimensions
Specifically, the algorithm is a high-dimensional signal compression and restoration algorithm, and before restoring high-dimensional data, the algorithm needs to utilize the feature decomposition of low-dimensional data covariance to obtain a set of feature transformation which depends on problems, and then obtains the high-dimensional feature transformation through interpolation.
According to the embodiment of the invention, the high-low dimensional compressed sensing problem is realized in series, so that the effective compression of the energy Internet space-time related new energy acquired data is realized, and the data transmission and storage cost is reduced.
On the basis of the above embodiment, step S5 specifically includes:
comparing the distributed node number M in the acquisition algorithm with the second original dataRestoring to obtain M compressed acquisition data +.>
On the basis of the above embodiment, the first measurement matrix and the second measurement matrix include the following matrices: gaussian random matrix, bernoulli random matrix and partially orthogonal matrix.
Specifically, the measurement matrix may be selected in various ways, and typically, a gaussian random matrix, a bernoulli random matrix, a partially orthogonal matrix, or the like may be used.
On the basis of the above embodiment, the sparse transform includes: discrete cosine transform, fourier transform, and discrete wavelet transform.
Specifically, the sparse transformation matrix may have different selection forms according to different types of acquired data, and classical sparse transformation includes Discrete Cosine Transformation (DCT), fourier transformation (FFT), discrete Wavelet Transformation (DWT), and the like.
According to the embodiment of the invention, through a compressed sensing technology, the acquisition quantity of sensor data can be greatly reduced by utilizing the space-time correlation and sparsity among the data, the burden of acquiring and transmitting mass bottom new energy data by an energy interconnection network is reduced, and the overall performance of the system is improved.
Fig. 2 is a block diagram of a data acquisition system based on multiple distributed compressed sensing according to an embodiment of the present invention, as shown in fig. 2, including: an acquisition module 21, a sampling module 22, a first processing module 23, a second processing module 24 and an output module 25; wherein:
the acquisition module 21 is used for acquiring data to be acquired; the sampling module 22 is configured to sample the data to be collected, and determine an acquisition algorithm parameter; the first processing module 23 is configured to compress and restore the low-dimensional signal of the data to be acquired based on the parameter of the acquisition algorithm, so as to obtain first original data; the second processing module 24 is configured to compress and restore the high-dimensional signal of the data to be acquired based on the first original data, so as to obtain second original data; the output module 25 is configured to compare the acquisition algorithm parameter with the second raw data, and output compressed acquired data.
The system provided by the embodiment of the present invention is used for executing the corresponding method, and the specific implementation manner of the system is consistent with the implementation manner of the method, and the related algorithm flow is the same as the algorithm flow of the corresponding method, which is not repeated here.
According to the embodiment of the invention, through a compressed sensing technology, the acquisition quantity of sensor data can be greatly reduced by utilizing the space-time correlation and sparsity among the data, the burden of acquiring and transmitting mass bottom new energy data by an energy interconnection network is reduced, and the overall performance of the system is improved.
Based on the above embodiment, the sampling module 22 is specifically configured to:
and determining the number of the distributed nodes of the data to be acquired as M, the dimension of the low-dimension data as N, and the dimension of the high-dimension data as N.
On the basis of the above embodiment, the first processing module 23 is specifically configured to:
obtaining a first measurement matrix as { ψ } m1,n Respectively acquiring m1 low-dimensional data from each node in the distributed nodes according to the first measurement matrix to form a first data matrix
Acquiring sparse transforms intoCombining with the first data matrix and the first measurement matrix to obtain a relational expression:
s is a first sparse representation coefficient;
obtaining s by solving the following optimization problem, and further obtaining the first original data in n dimensions
On the basis of the above embodiment, the second processing module 24 is specifically configured to:
solving a covariance matrix C for the first original data x, and solving the following eigenvalue decomposition problem to obtain a group of new eigenvalue transformation
Where lambda represents a diagonal matrix with eigenvalues as diagonals,representing feature decompositionAn n-dimensional feature transformation matrix formed by the obtained feature vectors;
transforming the n-dimensional feature into a matrixInterpolation to high-dimensional space to obtain N-dimensional feature transformation matrix +.>
Obtaining a second measurement matrix as { ψ } m2,N Respectively acquiring m2 pieces of high-dimensional data from each node in the distributed nodes according to the second measurement matrix to form a second data matrix
Based on the second data matrix, combining the second measurement matrix and the N-dimensional feature transformation matrix to obtain a relational expression:
s is a second sparse representation coefficient;
obtaining s by solving the following optimization problem, and further obtaining second original data of N dimensions
According to the embodiment of the invention, the high-low dimensional compressed sensing problem is realized in series, so that the effective compression of the energy Internet space-time related new energy acquired data is realized, and the data transmission and storage cost is reduced.
On the basis of the above embodiment, the output module 25 is specifically configured to:
comparing the distributed node number M in the acquisition algorithm with the second original dataRestoring to obtain M compressed acquisition data +.>
On the basis of the above embodiment, the first measurement matrix in the first processing module 23 and the second measurement matrix in the second processing module 24 include the following matrices: gaussian random matrix, bernoulli random matrix and partially orthogonal matrix.
On the basis of the above embodiment, the sparse transform in the first processing module 23 includes: discrete cosine transform, fourier transform, and discrete wavelet transform.
According to the embodiment of the invention, through a compressed sensing technology, the acquisition quantity of sensor data can be greatly reduced by utilizing the space-time correlation and sparsity among the data, the burden of acquiring and transmitting mass bottom new energy data by an energy interconnection network is reduced, and the overall performance of the system is improved.
Fig. 3 illustrates a physical schematic diagram of an electronic device, as shown in fig. 3, where the electronic device may include: processor 310, communication interface (Communications Interface) 320, memory 330 and communication bus 340, wherein processor 310, communication interface 320, memory 330 accomplish communication with each other through communication bus 340. The processor 310 may call logic instructions in the memory 330 to perform the following method: acquiring data to be acquired; sampling the data to be acquired, and determining acquisition algorithm parameters; based on the acquisition algorithm parameters, compressing and restoring the low-dimensional signals of the data to be acquired to obtain first original data; based on the first original data, compressing and restoring the high-dimensional signal of the data to be acquired to obtain second original data; and comparing the acquisition algorithm parameters with the second original data, and outputting to obtain compressed acquisition data.
Further, the logic instructions in the memory 330 described above may be implemented in the form of software functional units and may be stored in a computer-readable storage medium when sold or used as a stand-alone product. Based on this understanding, the technical solution of the present invention may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution, in the form of a software product stored in a storage medium, comprising several instructions for causing a computer device (which may be a personal computer, a server, a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
In another aspect, embodiments of the present invention further provide a non-transitory computer readable storage medium having stored thereon a computer program, which when executed by a processor is implemented to perform the transmission method provided in the above embodiments, for example, including: acquiring data to be acquired; sampling the data to be acquired, and determining acquisition algorithm parameters; based on the acquisition algorithm parameters, compressing and restoring the low-dimensional signals of the data to be acquired to obtain first original data; based on the first original data, compressing and restoring the high-dimensional signal of the data to be acquired to obtain second original data; and comparing the acquisition algorithm parameters with the second original data, and outputting to obtain compressed acquisition data.
The apparatus embodiments described above are merely illustrative, wherein the elements illustrated as separate elements may or may not be physically separate, and the elements shown as elements may or may not be physical elements, may be located in one place, or may be distributed over a plurality of network elements. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment. Those of ordinary skill in the art will understand and implement the present invention without undue burden.
From the above description of the embodiments, it will be apparent to those skilled in the art that the embodiments may be implemented by means of software plus necessary general hardware platforms, or of course may be implemented by means of hardware. Based on this understanding, the foregoing technical solution may be embodied essentially or in a part contributing to the prior art in the form of a software product, which may be stored in a computer readable storage medium, such as ROM/RAM, a magnetic disk, an optical disk, etc., including several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the method described in the respective embodiments or some parts of the embodiments.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present invention, and are not limiting; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present invention.
Claims (6)
1. A method of data acquisition, comprising:
acquiring data to be acquired;
sampling the data to be acquired, and determining that the number of distributed nodes of the data to be acquired is M, the dimension of low dimension is N, and the dimension of high dimension is N;
obtaining a first measurement matrix as { ψ } m1,n Respectively acquiring m1 low-dimensional data from each node in the distributed nodes according to the first measurement matrix to form a first data matrix
Acquiring sparse transforms intoCombining with the first data matrix and the first measurement matrix to obtain a relational expression:
s is a first sparse representation coefficient;
obtaining s by solving the following optimization problem, and further obtaining the first original data in n dimensions
Solving a covariance matrix C for the first original data x, and solving the following eigenvalue decomposition problem to obtain a group of new eigenvalue transformation
Where lambda represents a diagonal matrix with eigenvalues as diagonals,an n-dimensional feature transformation matrix composed of feature vectors obtained by feature decomposition is represented;
transforming the n-dimensional feature into a matrixInterpolation to high-dimensional space to obtain N-dimensional feature transformation matrix +.>
Obtaining a second measurement matrix as { ψ } m2,N Respectively acquiring m2 pieces of high-dimensional data from each node in the distributed nodes according to the second measurement matrix to form a second data matrix
Based on the second data matrix, combining the second measurement matrix and the N-dimensional feature transformation matrix to obtain a relational expression:
s is a second sparse representation coefficient;
obtaining s by solving the following optimization problem, and further obtaining second original data of N dimensions
Comparing the distributed node number M in the acquisition algorithm with the second original dataRestoring to obtain M compressed acquisition data +.>
2. The data acquisition method of claim 1, wherein the first measurement matrix and the second measurement matrix comprise the following matrices: gaussian random matrix, bernoulli random matrix and partially orthogonal matrix.
3. The data acquisition method of claim 1, wherein the sparse transform comprises: discrete cosine transform, fourier transform, and discrete wavelet transform.
4. A data acquisition system, comprising:
the acquisition module is used for acquiring data to be acquired;
the sampling module is used for sampling the data to be acquired, determining that the number of distributed nodes of the data to be acquired is M, the dimension of low-dimension data is N, and the dimension of high-dimension data is N;
a first processing module for obtaining a first measurement matrix { ψ } m1,n Respectively acquiring m1 low-dimensional data from each node in the distributed nodes according to the first measurement matrix to form a first data matrix
Acquiring sparse transforms intoCombining with the first data matrix and the first measurement matrix to obtain a relational expression:
s is a first sparse representation coefficient;
obtaining s by solving the following optimization problem, and further obtaining the first original data in n dimensions
The second processing module is used for solving a covariance matrix C for the first original data x and solving the following eigenvalue decomposition problem to obtain a group of new eigenvalue transformation
Where lambda represents a diagonal matrix with eigenvalues as diagonals,an n-dimensional feature transformation matrix composed of feature vectors obtained by feature decomposition is represented;
transforming the n-dimensional feature into a matrixInterpolation to high-dimensional space to obtain N-dimensional feature transformation matrix +.>
Obtaining a second measurement matrix as { ψ } m2,N Respectively acquiring m2 pieces of high-dimensional data from each node in the distributed nodes according to the second measurement matrix to form a second data matrix
Based on the second data matrix, combining the second measurement matrix and the N-dimensional feature transformation matrix to obtain a relational expression:
s is a second sparse representation coefficient;
obtaining s by solving the following optimization problem, and further obtaining second original data of N dimensions
An output module for comparing the distributed node number M in the acquisition algorithm with the second original dataRestoring to obtain M compressed acquisition data +.>
5. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the steps of the data acquisition method according to any one of claims 1 to 3 when the program is executed by the processor.
6. A non-transitory computer readable storage medium having stored thereon a computer program, which when executed by a processor performs the steps of the data acquisition method according to any one of claims 1 to 3.
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