Power system remote control method based on multimode sensing data fusion, computer device, computer readable storage medium and system

Abstract

The invention relates to an electric power system remote control method based on multimode sensing data fusion, a computer device, a computer readable storage medium and a system, and belongs to the field of wireless signal recovery. According to the method, the residual vector is calculated by using the sparseness estimation value, when the operation value of the current residual vector is greater than the first threshold value and greater than or equal to the operation value of the residual vector obtained by the last iteration, the current sparseness estimation value is increased according to the first set step length, and if the residual vector is less than or equal to the first threshold value, the current sparseness estimation value is increased according to the second set step length. Increasing the current sparseness estimated value according to a second set step length when the current sparseness estimated value is greater than a second threshold value and less than or equal to an operation value of a residual vector obtained by last iteration, and recalculating the residual vector by using the increased sparseness estimated value until the operation value of the residual vector is less than or equal to the second threshold value; and outputting an estimation signal using the sparseness estimation value, thereby effectively ensuring the requirements of high efficiency and high precision during signal reconstruction, and preventing the overfitting condition of the algorithm.

Description

Technical Field

[0001] The present invention relates to a power system remote control method, a computer device, a computer-readable storage medium and a system based on multi-mode sensing data fusion, and belongs to the field of wireless signal recovery. Background Art

[0002] As the communication field enters the 6G era, the high-quality wireless connection services and high-precision wireless sensing services provided by 6G have become the key driving force for many emerging applications such as power systems, smart cities, Internet of Vehicles, and telemedicine. In addition, with the rapid development of the Internet of Things, many practical applications require perception to collect and process data, so as to conduct high-quality monitoring of the external environment. As a tool for perceiving the material world, sensors can sense signals such as heat, force, light, electricity, sound, and displacement to provide data information for the processing and analysis of application systems. Under the construction of the power Internet of Things and digital power grids, the data in the power system is multimodal data. The integration and utilization of multimodal data is the key to playing its role as a core production factor.

[0003] Effective integration of the rich multimodal data of the power system is one of the keys to giving full play to its role as a core production factor. On the one hand, multimodal data fusion will help to accurately and comprehensively analyze various scenarios in all links of source, grid, storage and load, such as wind power and photovoltaic output forecasting at the source end, and transmission line status monitoring in the network. Fusion of multimodal data can make them complement and enhance each other, thereby effectively improving the accuracy of target perception. At the same time, when one or several types of data deviate due to aging of the perception terminal, communication failure, etc., there are still other data as supplements, so that the perception method is still effective and can improve the fault tolerance of perception to a certain extent. On the other hand, multimodal data fusion will help analyze the coupling relationship of complex networks. In the context of energy transformation and digitalization, the power system is a power system with deep coupling of source, grid, load and storage, and highly interactive various energy networks, transportation networks, and information networks. It is more complex in structure, operation mode and interactive relationship, and traditional isolated network analysis methods are no longer applicable. Therefore, we should make full use of multimodal data from different networks and map the complex dependencies into data fusion and analysis from a global and unified perspective, so as to fully explore the dependencies and coupling relationships between networks, which is conducive to the collaboration between multiple networks.

[0004] With the continuous development of science and technology, the accuracy of sensing devices is getting higher and higher. Hardware devices will inevitably have some inherent limitations. At the same time, they will also be affected by factors such as the external environment and system composition. Therefore, it is inevitable that some abnormal phenomena will occur in the sensing devices. This will undoubtedly reduce the availability of data and affect the transmission and recovery of signals by the wireless communication system.

[0005] In summary, multimodal data perception and data fusion technology can provide more comprehensive and accurate system status monitoring capabilities by integrating data information obtained by different sensors. In the power system, data of multiple parameters, such as current, voltage, temperature, etc., can be obtained through multimodal sensors, and then data fusion technology is applied to integrate and analyze these data to extract characteristic information of the system operation status. Using multimodal data perception and data fusion technology, a comprehensive perception and understanding of the operation status of the power system can be achieved, providing strong support for subsequent recovery algorithms.

[0006] The recovery algorithm of the Modulated Wide band Converter (MWC) can respond to abnormal situations and realize the rapid recovery and optimization of wireless signals by analyzing and processing abnormal states. AMWC (Aliased Modulated Wide band Converter, AMWC) improves MWC by setting the bandwidth of the bandpass filter to be greater than the sampling rate of the low-speed analog-to-digital converter and performing intentional aliasing at the low-speed analog-to-digital converter. However, the Orthogonal Matching Pursuit (OMP) algorithm in its system needs to recover the signal under the condition of known sparsity. In actual conditions, the sparsity is unknown or changes. Therefore, some scholars have proposed the Sparsity Adaptive Matching Pursuit (SAMP) algorithm to address this problem and have made many improvements to this algorithm.

[0007] The current SAMP algorithm and its improved algorithm rarely consider the algorithm efficiency, algorithm flexibility and the problem of using multimodal data as input signals. When performing sparsity estimation, the sparsity is mainly adjusted according to a fixed step size. When the fixed step size is large, although it consumes less time, the reconstructed signal is not accurate enough, which reduces the signal reconstruction accuracy; when the fixed step size is small, although it is accurate, it consumes a lot of time, which reduces the signal reconstruction efficiency. Therefore, the current algorithm cannot meet the requirements of high-precision and high-efficiency reconstruction of multimodal signals at the same time. Summary of the invention

[0008] The purpose of the present invention is to provide a power system remote control method, a computer device, a computer-readable storage medium and a system based on multi-mode sensing data fusion, so as to solve the problem that the AMWC system cannot simultaneously meet the requirements of high-precision and high-efficiency reconstruction of signals and the diversification of power system data sources due to the limitation of fixed step size in the solution process of the SAMP algorithm.

[0009] To achieve the above object, the solution of the present invention includes:

[0010] The present invention discloses a power system remote control method based on multi-mode sensing data fusion. The power system uses an AWMC system to transmit a multi-mode signal and uses a recovery algorithm to estimate the transmitted multi-mode signal. The recovery algorithm comprises: performing residual vector calculation on the AWMC system transmission signal according to an initial sparsity estimation value. If the operation value of the residual vector currently calculated is greater than a first threshold value and greater than or equal to the operation value of the residual vector calculated in the last iteration, the current sparsity estimation value is increased according to a first set step length to obtain an increased sparsity estimation value; if it is less than or equal to the first threshold value, greater than a second threshold value and less than or equal to the operation value of the residual vector calculated in the last iteration, the current sparsity estimation value is increased according to a second set step length to obtain an increased sparsity estimation value; the increased sparsity estimation value is used to recalculate the residual vector until the iteration ends when the operation value of the residual vector calculated is less than or equal to the second threshold value, and the sparsity estimation value corresponding to the end of the iteration is used as the final sparsity estimation value, and the signal is estimated according to the final sparsity estimation value, and the final estimated signal is output; wherein the first set step length is greater than the second set step length.

[0011] Furthermore, the second set step size is equal to the first set step size multiplied by a monotonically decreasing function as the number of iterations increases.

[0012] Furthermore, the monotonically decreasing function is an exponential function, the base of the exponential function is a fixed constant greater than 0 and less than 1, and the exponent is the current number of iterations.

[0013] Furthermore, before performing signal estimation and residual vector calculation based on the initial sparsity estimate, the initial sparsity estimate is updated based on a termination condition based on the RIP condition. If the index set calculated using the current initial sparsity estimate does not meet the RIP condition, the initial sparsity estimate is increased according to a first set step size, and the index set is recalculated using the increased initial sparsity estimate until the recalculated index set meets the RIP condition, and the sparsity estimate is then used to calculate the residual vector.

[0014] Further, the initial sparsity estimation value is equal to the first set step size.

[0015] Furthermore, the first set step size is greater than 1 and less than or equal to 10.

[0016] Furthermore, the calculation formula of the residual vector is:

[0017]

[0018] Where y is the observation matrix, is the sensor matrix under the current support set, is the current estimated signal.

[0019] A computer device of the present invention includes a processor, and the processor is used to execute a computer program to implement the steps of a power system remote control method based on multi-mode sensing data fusion.

[0020] A readable storage medium of the present invention stores a computer program, which, when executed, implements a power system remote control method based on multi-mode sensing data fusion.

[0021] The present invention discloses a power system remote control system based on multi-mode sensing data fusion, the control system comprises a residual vector calculation module, a comparison module and a sparsity estimation value update module; the residual vector calculation module is used to calculate the residual vector of the AWMC system transmission signal according to the initial sparsity estimation value, and recalculate the residual vector using the updated sparsity estimation value until the iteration ends when the calculated residual vector operation value is less than or equal to the second threshold value; the comparison module is used to compare the currently calculated residual vector operation value with the first threshold value and the second threshold value; the sparsity estimation value update module is used to update the sparsity estimation value according to the comparison result of the comparison module: If the currently calculated value of the residual vector is greater than the first threshold and greater than or equal to the calculated value of the residual vector calculated in the previous iteration, the current sparsity estimation value is increased according to the first set step size to obtain an increased sparsity estimation value; if it is less than or equal to the first threshold, greater than the second threshold and less than or equal to the calculated value of the residual vector calculated in the previous iteration, the current sparsity estimation value is increased according to the second set step size to obtain an increased sparsity estimation value; the sparsity estimation value corresponding to the end of the iteration is used as the final sparsity estimation value, signal estimation is performed according to the final sparsity estimation value, and a final estimated signal is output; wherein the first set step size is greater than the second set step size.

[0022] The beneficial effects of the present invention are as follows: the present invention is an improved invention creation, which improves the recovery algorithm in the AMWC system and reconstructs the multimodal signal. When the operation value of the residual vector calculated using the initial sparsity estimation value is greater than the first threshold value and greater than or equal to the operation value of the residual vector calculated in the last iteration, the first set step size is used to increase the current sparsity estimation value, thereby ensuring the efficiency of the algorithm; when the operation value of the residual vector calculated using the initial sparsity estimation value is less than or equal to the first threshold value, but greater than the second threshold value and greater than or equal to the operation value of the residual vector calculated in the last iteration, the second set step size is used to increase the current sparsity estimation value, thereby ensuring the accuracy of the algorithm, until the operation value of the residual vector calculated is less than or equal to the second threshold value, and the estimated value of the multimodal signal is output; wherein the first set step size is greater than the second set step size, and the dual threshold value is used as the iteration termination condition of the algorithm, thereby effectively preventing the algorithm from overfitting. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 It is a schematic diagram of a multi-modal data source of a power system of the present invention;

[0024] Figure 2 It is a schematic diagram of the structure of a conventional modulated broadband converter;

[0025] Figure 3 It is a structural schematic diagram of an aliasing modulation broadband converter of the present invention;

[0026] Figure 4 It is a schematic diagram of the relationship between the total sampling rate and reconstruction success rate of the cMWC system and the AMWC system with different P values;

[0027] Figure 5 It is a schematic diagram comparing the reconstruction algorithms of OMP, SAMP, and ES-EVssAMP in various aspects;

[0028] Figure 6 It is a schematic diagram of the AMWC system using the SAMP algorithm and the ES-EVssAMP algorithm to perform multi-band signal reconstruction and spectrum positioning. DETAILED DESCRIPTION

[0029] In order to make the purpose, technical solutions and advantages of the present invention more clear, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments.

[0030] The idea of ​​the present invention is to reconstruct a signal using an aliasing modulated broadband converter. Taking into account the requirements for high efficiency and high precision of signal reconstruction, an exponential variable step size adaptive matching pursuit (ES-EVssAMP) algorithm based on sparsity estimation is used. A first set step size and a second set step size are added to the algorithm. By using the first set step size to update the current sparsity, the algorithm can be iterated quickly. By using the second set step size, the current sparsity can be slightly adjusted. By setting a double threshold iteration termination condition, the algorithm can be prevented from overfitting.

[0031] Method Example:

[0032] This embodiment provides a remote control method for a power system based on multi-mode sensing data fusion. Under the construction of the power Internet of Things and digital power grid, the data in the power system is multi-modal data. The fusion and utilization of multi-modal data is the key to give full play to its role as a core production factor. According to the data form and fusion purpose, this embodiment divides the fusion of multi-modal data of the power system into intra-network fusion and inter-network fusion. Figure 1 shown.

[0033] Taking the result of multimodal data perception as input, the remote trip detection result based on multimodal data perception can become a signal to trigger the start of the conventional Modulated Wideband Converter (cMWC) recovery algorithm that follows the anti-aliasing rule. The structure of cMWC is as follows: Figure 2 The analog mixing front end in the cMWC system implements spectrum aliasing, making each sub-band information appear in the baseband. The system contains m sampling channels, each of which is composed of a mixer p i (t), a low-pass filter (LPF) and a low-speed analog-to-digital converter. The multi-band signals pass through different sampling channels independently, and the mixed signal spectrum is truncated by the low-pass filter h(t) to make the channel sampling rate low enough.

[0034] In a sampling system, the mixer p i The specific form of (t) is:

[0035]

[0036] Where T p Yes i (t) period, M is the p in each period i (t) the number of pulses, α ik is in the kth interval p i (t), let α ik ∈{-1,+1}, then p i (t+nT p )=p i (t),n∈Z. Its Fourier series is:

[0037]

[0038] After the mixer processing, the signal spectrum is as follows:

[0039]

[0040] The output signal y i The discrete-time Fourier transform of (n) is:

[0041]

[0042] where F s =[-f s / 2,f s / 2], f s is the sampling rate, L0 represents all non-zero terms in the signal within the positive frequency range X(f), which satisfies:

[0043]

[0044] where f NYQ is the Nyquist rate and f p =1 / T p , the process of reconstructing the signal can be described as an optimization problem:

[0045] y(f)=AZ(f) (6)

[0046] The matrix A m×1 By p i (n), Y(f) is an m-dimensional vector, and the i-th element is Z(f) is a column vector with L elements, L=2L0+1.

[0047] Compared with cMWC, AMWC sets the bandwidth of LPF to be larger than the sampling rate to induce intentional aliasing. The structure of AMWC is as follows: Figure 3 As shown, the system includes m sampling channels, each of which is composed of a mixer p i (t), a low-pass filter and a low-speed analog-to-digital converter, and then a band-pass filter (BPF), which is then used to output a deliberately mixed signal after frequency modulation and sampling. AWMC first introduces the first aliasing at the mixer, making the signal spectrum X(f) at f p The interval overlaps with the shifted version of itself, and the aliasing effect is the same as cMWC, as shown in formula (2). The second aliasing is performed by the low-speed analog-to-digital converter to a new sampling rate f that is less than the bandwidth of the low-pass filter. s ' is the interval, and the overlapping spectrum is aliased twice. By adjusting f p and f s ', adjusting the splitting interval f I , that is, the interval f into which X(f) is split in the output of AMWC I .

[0048] Specifically, the new sampling rates set by AMWC are as follows:

[0049]

[0050] Where q′ is the new channel transmission parameter of the AMWC system, and the low-pass filter bandwidth is W LPF =q'f p , so for the aliasing parameter p>1,W LPF = pf s '. If p,q′ are relatively prime and q′>p, then no information loss is necessary for X(f). The new sampling rate induces additional aliasing and adjusts the splitting interval f I, thereby improving the sampling efficiency,

[0051]

[0052] The least common shifting interval (LCS) represents the minimum common shifting interval, which will become the splitting interval of AMWC, i.e., f I,AMWC =f p ′, through After filtering again, that is, passing through a bandpass filter (BPF), and then performing frequency modulation and sampling, the final output in the discrete time domain can be obtained:

[0053]

[0054] where d i,k+u (R1, p, q') is the polynomial that satisfies the minimum value R1 and p, q'. After converting (8) into a matrix equation, only when p, q are relatively prime and q'>p, the matrix equation (11) corresponding to (9) has a unique solution. At the same time, p is selected to minimize the maximum value of the sparsity K, that is, X(f) is within the splitting interval f. I,AMWC =f p ′, since the sparsity depends on the center frequency of the multi-band and the maximum bandwidth B, when f I,AWMC ≥B, each multi-band signal occupies at most two sub-bands, that is, K≤2K B On the contrary, when f I,AMWC <B, some multiband signals may occupy more than two subbands, which provides an opportunity to increase the sparsity K to 2K B Therefore, the aliasing parameter p is limited to

[0055]

[0056] In the ideal case, when the splitting interval f I becomes thinner and closer to the bandwidth, while satisfying f I ≥B, the sampling efficiency is improved, and the sampling efficiency α of the multi-band signal is taken as the ratio between the actual spectrum occupancy of the multi-band signal and the total bandwidth of the recovered sub-band, that is,

[0057]

[0058] The sparsity K is a random variable in practice. To be more certain, assume that K in X(f) B Just occupies one subband, that is, K cMWC =K AMWC =K B When f p p -1>>B and when the center frequencies of multiple frequency bands are far enough apart and small, the probability of this happening is high. Based on this assumption, the sampling efficiency of the two is obtained by the following formula:

[0059]

[0060] When p=1, the sampling efficiency of cMWC and AMWC is exactly the same. When p>1, AMWC performs intentional aliasing and improves the sampling efficiency proportionally to p.

[0061] In terms of the recovery algorithm, the SAMP algorithm is difficult to ensure high efficiency and high accuracy in the signal reconstruction process. In this invention, we propose an ES-EVssAMP algorithm. The algorithm uses the first set step size and the second set step size to improve the iterative process and quickly and accurately obtain the estimated signal.

[0062] The steps of the ES-EVssAMP algorithm are as follows:

[0063] 1. Input sensor matrix A m×n , observation matrix y m×1 , step size S, exponential function parameter β, sparsity estimation parameter δ k , a first threshold parameter θ1 and a second threshold parameter θ2, wherein the observation matrix is ​​any column in the sensor matrix;

[0064] 2. Initialization parameters, including iteration stage stage = 1, iteration number t = 1, sparsity estimate K0 = S, residual vector r0 = y, initial index set Candidate index set And the initial support set

[0065] 3. Calculate the initial index set Λ0 = max(A T y,K0), where the max(a,b) function means taking the b largest elements in the matrix a;

[0066] 4. Calculate the estimated sparsity, if Then K0=K0+S, return to step 3; otherwise K1=K0, Go to step 5.

[0067] 5. Calculate a new initial index set Λ t =max(|A T r t-1 |,K t );

[0068] 6. Merge and update candidate index set C t =F t-1 ∪Λ t ;

[0069] 7. Get the minimum estimated signal value ;

[0070] 8. Get the current support set

[0071] 9. Update the final estimate and residual of the signal

[0072] 10. If || r c If ||2≤θ1, proceed to step 9, otherwise proceed to step 12;

[0073] 11. If || r c ||2≤θ2, stop iteration and output the result, otherwise proceed to step 12;

[0074] 12. If || r c ||2≥||r t-1 ||2, then K t+1 =K t +S,stage=stage+1,F t new =F t ,t=t+1;

[0075] otherwise, Then go back to step 5.

[0076] 13. Output

[0077] In step 12 The process is to increase the current sparsity estimation value according to the first set step size, The process is to increase the current sparsity estimation value according to the second set step size.

[0078] The original piecewise approximation method in sparse estimation divides the iterative process into several stages, and the sparsity estimate in each stage is different. In this process, if the iterative residual does not show a downward trend, the current sparsity estimate is no longer appropriate. Before entering the next iterative operation, the piecewise approximation updates the estimated sparsity with a fixed step size. Through this stage, it makes the estimated support set close to the true support set to achieve sparsity adaptation, but the idea of ​​piecewise approximation requires that the step size is smaller than the sparsity. Since the sparsity is usually unknown in practical applications, the fixed step size is usually set to 1. Although it is accurate, it consumes a lot of time and reduces the reconstruction efficiency. In addition, the piecewise approximation does not set an upper limit on the iteration time of the algorithm. Therefore, an adaptive sparsity method based on the RIP (Restricted Isometry Property) condition is proposed. The criteria are as follows:

[0079] For any signal with a sparsity of K, the sensor matrix A satisfies the RIP condition when δ k ∈(0,1), if K0≥K, then the following formula is satisfied, where supp is the true support set and y is the observation matrix.

[0080]

[0081] Formula (14) is used as the termination condition for the large-scale iteration of the sparsity estimation. When the initial sparsity estimate does not meet the RIP condition, the first set step size is used to increase the initial sparsity estimate until it meets the RIP condition, and then the increased sparsity estimate is used to estimate the signal and calculate the residual vector, further improving the efficiency of signal reconstruction. When large-scale iterations are used to approach the true sparsity, small-scale iterations are also required to get closer to the true sparsity. In this case, the second set step size should be used. When the support set size is close to the true sparsity, the step size is gradually reduced as the iteration stage increases to improve the accuracy of signal reconstruction. This process requires the step size to gradually decrease but the step size is greater than 0, so an exponential function is chosen. The exponential function is as follows:

[0082] y=β x ,β∈(0,1),x≥0,x∈z (15)

[0083] Where β is a fixed constant. When the conditions in the formula (15) are met, it is monotonically decreasing and the function value is greater than 0. Therefore, the exponential function can be used to adjust the step size.

[0084] The initial step size is selected as a larger value (greater than 1), the length of the support set is K = K + S, and stage represents the number of iterations. When the step size needs to be reduced, the length of the support set is Indicates rounding up. When the sparsity estimate is close to the true sparsity, the support set length becomes smaller and smaller as the number of stages increases until the iteration termination condition of the double threshold is met, and the final estimated signal under the sparsity estimate is output, where the first threshold parameter is greater than the second threshold parameter.

[0085] In order to verify the algorithm performance and the compatibility of the power system integrating multimodal data perception and data fusion technology with the modulated broadband converter, a noisy electrical signal is simulated using formula (16). i ,B i ,f i ,σ irepresents the i-th energy coefficient, bandwidth, carrier spectrum and delay time. These values ​​are randomly selected. The maximum bandwidth B is set to 50MHz, the Gaussian white noise is n(t), and the signal-to-noise ratio is 10dB. In order to ensure that there is no loss of generality, the following parameters are set, where the sequence period length of the AMWC system is M=195 and the period length is T p , the number of sub-bands N is 6, and in each signal [-f NYQ / 2,f NYQ / 2] uniformly randomly selects carriers, f NYQ The carrier spectrum of each sub-band is evenly distributed in the entire bandwidth where the signal is located. In the ES-EVssAMP algorithm, we set the following parameter values. The first and second threshold parameters θ1 and θ2 are 10 -6 and 0, the base β of the exponential function in the second step is set to 0.5, and the δ of the sparsity estimation part k is 0.5, and the initial step size S of the ES-EVssAMP algorithm is 5.

[0086]

[0087] The support set reconstruction success rate of the algorithm can be calculated using (17). O ,S R represent the support sets of the original signal and the reconstructed signal respectively.

[0088]

[0089] The simulation results show that the ES-EVssAMP algorithm has more obvious advantages in complex power system environments. Under the same conditions, the ES-EVssAMP algorithm requires less time than the SAMP algorithm, and its accuracy is similar to the minimum fixed step size 1 of SAMP, and the reconstruction effect is better.

[0090] Figure 4 The sampling rate (horizontal axis in the figure) and reconstruction success rate (vertical axis in the figure) of the cMWC system after combining the ES-EVssAmp algorithm with the AMWC system are shown. Despite the presence of additive noise, AMWC still reduces the total sampling rate or improves the recovery performance. First, it is aliased by low-pass filtering, and then AMWC deliberately aliases the spectrum again. The calculation of formula (16) shows that the sampling efficiency of AMWC is improved by p times.

[0091] Compare the support set recovery of each algorithm in AMWC Figure 5As shown in the figure, the average running time and sparsity, the support set reconstruction success rate and sparsity, and the support set reconstruction success rate and measurement value are compared respectively. The vertical solid line represents the algorithm with a SAMP step size of 1, the circular solid line represents the algorithm with a SAMP step size of 5, and the triangular solid line represents the algorithm with a SAMP step size of 10. When the SAMP step size is larger, although the running time is shorter and the number of signal iterations is smaller, the reconstruction performance of the support set is poor. The ES-EVssAMP algorithm can reconstruct the signal faster and more accurately, and its success rate is equivalent to the minimum step size of SAMP. The comparison of the signals recovered by each algorithm in the AMWC system is shown in the figure. Figure 6 As shown in the figure, when the SAMP method is used in the AMWC system, the correlation coefficient between the reconstructed signal and the true signal is 0.9757, while when the ES-EVssAmp algorithm is used, the correlation coefficient is 0.9938. It can be seen from the recovered signal that the signal recovered by the ES-EVssAmp algorithm is closer to the original signal.

[0092] As for wireless signals applied in other fields, the signals can also be reconstructed with high efficiency and high accuracy according to the method steps described above of the present invention.

[0093] Device Example:

[0094] This embodiment provides a computer device, which includes a processor and a computer program running on the processor. When the device is used to recover the signal, the computer program running on the processor uses the power system remote control method based on multi-mode sensing data fusion as introduced in the method embodiment. Since the introduction of the method is clear enough, it will not be repeated here.

[0095] Medium Example:

[0096] This embodiment provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed, the power system remote control method based on multi-mode sensing data fusion as introduced in the method embodiment is used. Since the introduction of the method is clear enough, it will not be repeated here.

[0097] System Example:

[0098] This embodiment provides a remote control system for an electric power system based on multi-mode sensing data fusion, the system includes a residual vector calculation module, a comparison module and a sparsity estimation value update module; the residual vector calculation module is used to perform residual vector calculation on the AWMC system transmission signal according to the initial sparsity estimation value, and recalculate the residual vector using the updated sparsity estimation value until the iteration ends when the calculated operation value of the residual vector is less than or equal to the second threshold; the comparison module is used to compare the operation value of the currently calculated residual vector with the first threshold and the second threshold; the sparsity estimation value update module is used to update the sparsity estimation value according to the comparison result of the comparison module. When using the residual vector calculation module, the comparison module and the sparsity estimation value update module, the system respectively runs the method steps described in the method embodiment. Since the introduction of the method is clear enough, it will not be repeated here.

[0099] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A remote control method for a power system based on multi-mode sensing data fusion, characterized in that: The power system uses an AWMC system to transmit a multimodal signal and uses a recovery algorithm to estimate the transmitted multimodal signal. The recovery algorithm includes: performing residual vector calculation on the AWMC system transmission signal according to an initial sparsity estimation value, if the currently calculated residual vector operation value is greater than a first threshold value and greater than or equal to the residual vector operation value calculated in the last iteration, increasing the current sparsity estimation value according to a first set step length to obtain an increased sparsity estimation value; if it is less than or equal to the first threshold value, greater than a second threshold value and less than or equal to the residual vector operation value calculated in the last iteration, increasing the current sparsity estimation value according to a second set step length to obtain an increased sparsity estimation value; recalculating the residual vector using the increased sparsity estimation value until the iteration ends when the calculated residual vector operation value is less than or equal to the second threshold value, using the corresponding sparsity estimation value at the end of the iteration as the final sparsity estimation value, performing signal estimation according to the final sparsity estimation value, and outputting a final estimated signal; wherein the first set step length is greater than the second set step length.

2. The method for remote control of a power system based on multi-mode sensing data fusion according to claim 1 is characterized in that: The second set step size is equal to the first set step size multiplied by a monotonically decreasing function as the number of iterations increases.

3. The method for remote control of a power system based on multi-mode sensing data fusion according to claim 2 is characterized in that: The monotonically decreasing function is an exponential function, the base of the exponential function is a fixed constant greater than 0 and less than 1, and the exponent is the current number of iterations.

4. The method for remote control of a power system based on multi-mode sensing data fusion according to claim 1 is characterized in that: Before performing signal estimation and residual vector calculation based on the initial sparsity estimate, the initial sparsity estimate is updated based on a termination condition based on the RIP condition. If the index set calculated using the current initial sparsity estimate does not meet the RIP condition, the initial sparsity estimate is increased according to a first set step size, and the index set is recalculated using the increased initial sparsity estimate until the recalculated index set meets the RIP condition, and the sparsity estimate is used to calculate the residual vector.

5. The method for remote control of a power system based on multi-mode sensing data fusion according to claim 1 is characterized in that: The initial sparsity estimation value is equal to the first set step size.

6. The method for remote control of a power system based on multi-mode sensing data fusion according to claim 1 is characterized in that: The first setting step size is greater than 1 and less than or equal to 10.

7. The method for remote control of a power system based on multi-mode sensing data fusion according to claim 1, characterized in that: The calculation formula of the residual vector is: Where y is the observation matrix, is the sensor matrix under the current support set, is the current estimated signal.

8. A computer device comprising a processor, characterized in that: The processor is used to execute a computer program to implement the steps of the power system remote control method based on multi-mode sensing data fusion as described in any one of claims 1 to 7.

9. A computer-readable storage medium, characterized in that: A computer program is stored thereon, and when the computer program is executed, the remote control method of the power system based on multi-mode sensing data fusion as described in any one of claims 1 to 7 is implemented.

10. A remote control system for a power system based on multi-mode sensing data fusion, characterized in that: The control system includes a residual vector calculation module, a comparison module and a sparsity estimation value update module; the residual vector calculation module is used to calculate the residual vector of the AWMC system transmission signal according to the initial sparsity estimation value, and recalculate the residual vector using the updated sparsity estimation value until the calculated residual vector operation value is less than or equal to the second threshold value, and the iteration ends; the comparison module is used to compare the current calculated residual vector operation value with the first threshold value and the second threshold value; The sparsity estimation value updating module is used to update the sparsity estimation value according to the comparison result of the comparison module: if the operation value of the residual vector currently calculated is greater than the first threshold and greater than or equal to the operation value of the residual vector calculated in the previous iteration, then the current sparsity estimation value is increased according to the first set step size to obtain the increased sparsity estimation value; if it is less than or equal to the first threshold, greater than the second threshold and less than or equal to the operation value of the residual vector calculated in the previous iteration, then the current sparsity estimation value is increased according to the second set step size to obtain the increased sparsity estimation value; the sparsity estimation value corresponding to the end of the iteration is used as the final sparsity estimation value, signal estimation is performed according to the final sparsity estimation value, and a final estimated signal is output; wherein the first set step size is greater than the second set step size.

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