NOMA-based electric energy quality real-time monitoring method, system and equipment

Abstract

The invention belongs to the technical field of wireless communication, and particularly relates to an electric energy quality real-time monitoring method, system and equipment based on NOMA (Non-Orthogonal Multiple Access). The real-time monitoring method comprises the following steps: firstly, constructing to obtain a distribution power optimization problem of an electric energy quality monitoring system by taking minimization of an overall error probability as a target; the optimization variable of the distribution power optimization problem is the transmitting power of each electric energy quality monitoring terminal device, then a non-convex term in the distribution power optimization problem is converted into a convex term by adopting continuous convex approximation, and the distribution power optimization problem is solved through iteration to obtain a transmitting power distribution optimization scheme; and finally, controlling the electric energy quality monitoring system to carry out electric energy quality monitoring based on the transmitting power distribution optimization scheme. According to the method, the overall error probability can be reduced by optimizing a terminal equipment transmitting power distribution scheme, so that high communication reliability in a multi-terminal equipment scene is ensured.

Description

Technical Field

[0001] The present invention belongs to the field of wireless communication technology, and specifically relates to a NOMA-based real-time power quality monitoring method, system and equipment. Background Art

[0002] With the rapid development of social economy and the continuous growth of electricity demand, the complexity of power system and power quality issues have become increasingly prominent. Power quality not only directly affects the normal operation of industrial production, commercial operations and residents' lives, but is also closely related to the safe operation of equipment and the stability of power system.

[0003] In modern power systems, the application of various nonlinear loads, renewable energy grid connection and smart grid technology has made power quality issues more complicated. For example, harmonics, voltage sags, voltage flickers and frequency fluctuations will have adverse effects on electrical equipment and even lead to equipment failure and economic losses. Therefore, it is particularly important to establish an efficient real-time power quality monitoring system.

[0004] The complexity of the power system means that there are large-scale power-consuming devices. Traditional orthogonal multiple access is difficult to support the connection of a large number of devices. Therefore, non-orthogonal multiple access (NOMA) technology is used to meet the connection requirements of a large number of power devices. At the same time, power quality monitoring requires real-time decision optimization. Traditional theoretical analysis based on Shannon domain can make the transmission error rate as low as possible, but it is difficult to meet the requirements of low latency. Therefore, short code communication is considered to be used for analysis under the limited code length domain. In the process of using short code communication, increasing the transmission power of the data stream can improve the signal-to-noise ratio of the data stream and reduce the error probability of transmitting the data stream. On the other hand, it will also be limited by the maximum transmission power, resulting in a reduction in the available transmission power of other data streams, thereby increasing the error probability of other data streams. Therefore, when using NOMA transmission, how to balance the transmission power of each data stream to reduce the overall error probability, ensure the high reliability of data transmission, and then maintain the power supply quality of the power grid is an urgent problem to be solved. Summary of the invention

[0005] The purpose of the present invention is to address the above-mentioned problems existing in the prior art and to provide a NOMA-based real-time power quality monitoring method, system and device that can reduce the overall error probability by optimizing the terminal device transmission power allocation scheme and meet the high reliability of communication in multi-terminal device scenarios.

[0006] To achieve the above objectives, the technical solution of the present invention is as follows:

[0007] In a first aspect, the present invention proposes a real-time monitoring method for power quality based on NOMA, wherein the real-time monitoring method is performed based on a power quality monitoring system, wherein the power quality monitoring system comprises a decision-making end and multiple power quality monitoring terminal devices, wherein the power quality monitoring terminal devices are used to perform power quality monitoring and transmit the monitored power quality data to the decision-making end through a NOMA communication transmission mode, wherein the decision-making end is used to decode the data streams received from the multiple power quality monitoring terminal devices in order of signal strength from strong to weak using a continuous interference elimination technology, and make a global optimization decision based on the decoding information of all power quality monitoring terminals;

[0008] The real-time monitoring method comprises:

[0009] S1. A power distribution optimization problem of a power quality monitoring system is constructed with the goal of minimizing the overall error probability, wherein the optimization variable of the power distribution optimization problem is the transmission power of each power quality monitoring terminal device;

[0010] S2. Using continuous convex approximation to convert non-convex terms in the power allocation optimization problem into convex terms, and obtaining a transmit power allocation optimization solution by iteratively solving the power allocation optimization problem;

[0011] S3. Control the power quality monitoring system to perform power quality monitoring based on the obtained transmission power allocation optimization scheme.

[0012] The construction steps of the power allocation optimization problem are as follows:

[0013] S11. The decoding error probability when decoding the data stream of a single power quality monitoring terminal is:

[0014]

[0015] C(γ)=log2(1+γ);

[0016]

[0017] r = d / m;

[0018] In the above formula, ε i The decoding error probability when decoding the data stream of the power quality monitoring terminal device i; represents the right tail function of the standard normal distribution; V(γ) represents the channel dispersion; C(γ) is the channel transmission capacity of the power quality monitoring terminal device i; r represents the coding rate corresponding to the power quality monitoring terminal device i; d represents the data packet size transmitted by the data stream of the power quality monitoring terminal device i; m represents the coding block length used by the power quality monitoring terminal device i; i∈K, K is the total number of power quality monitoring terminal devices; let the decoding order be s1→s2→…→sK , where s1, s2, s K Respectively represent the data streams of the 1st, 2nd, and Kth power quality monitoring terminal devices; when i=1, 2, ..., K-1, γ is the SINR when the decision end decodes the power quality monitoring terminal device i, and when i=K, γ is the SNR when the decision end decodes the power quality monitoring terminal device i;

[0019] The SINR calculation formula is:

[0020]

[0021] In the above formula, P i , P i+1 They represent the transmission power of the power quality monitoring terminal equipment i and i+1 respectively; h represents the channel between the decision-making end and the power quality monitoring terminal equipment; Indicates the additive white Gaussian noise variance of the signals of all power quality monitoring terminal devices received by the decision-making end;

[0022] The SNR calculation formula is:

[0023]

[0024] In the above formula, P K Indicates the transmission power of the power quality monitoring terminal device K;

[0025] S12. The following power allocation optimization problem is constructed:

[0026]

[0027] In the above formula, ε o represents the overall error rate; ε i-1 The error rate of the decoding error probability when decoding the data stream of the power quality monitoring terminal device i-1; P t is the maximum transmission power; P represents the set of transmission powers of all power quality monitoring terminal devices.

[0028] The S2 includes:

[0029] S21. Perform a first-order Taylor approximation according to the following formula to convert the non-convex term into a convex term:

[0030]

[0031] In the above formula, n represents the current number of iterations;

[0032] S22, introduce the slack variable t and transform the power allocation optimization problem into the following sub-problems:

[0033]

[0034] S23, iteratively solve the above sub-problems, using the P obtained in the previous iteration in the iterative process (n-1) Solve the subproblem to get P (n) ,t (n) , P (n) ,t (n) As the starting point for the next iteration, when |t (n) -t (n-1) The iteration ends when |≤τ, where τ represents the preset threshold.

[0035] The additive white Gaussian noise variance Satisfies the following Gaussian distribution:

[0036]

[0037] In the above formula, y represents the signal received by the decision-making end from all power quality monitoring terminal devices; η represents additive Gaussian white noise.

[0038] In the second aspect, the present invention proposes a real-time monitoring system for power quality based on NOMA, wherein the real-time monitoring system includes a power allocation optimization problem building module, a calculation module, and a power quality monitoring module:

[0039] The power allocation optimization problem construction module is used to construct the power allocation optimization problem of the power quality monitoring system with the goal of minimizing the overall error probability, and the optimization variable of the power allocation optimization problem is the transmission power of each power quality monitoring terminal device;

[0040] The calculation module is used to convert non-convex terms in the power allocation optimization problem into convex terms by using continuous convex approximation, and obtain the transmission power allocation optimization solution by iterative solution;

[0041] The power quality monitoring module is used to control the power quality monitoring system to perform power quality monitoring based on the obtained transmission power allocation optimization solution.

[0042] The power allocation optimization problem construction module constructs the power allocation optimization problem according to the following steps:

[0043] S11. The decoding error probability when decoding the data stream of a single power quality monitoring terminal is:

[0044]

[0045] C(γ)=log2(1+γ);

[0046]

[0047] r = d / m;

[0048] In the above formula, ε i The decoding error probability when decoding the data stream of the power quality monitoring terminal device i; represents the right tail function of the standard normal distribution; V(γ) represents the channel dispersion; C(γ) is the channel transmission capacity of the power quality monitoring terminal device i; r represents the coding rate corresponding to the power quality monitoring terminal device i; d represents the data packet size transmitted by the data stream of the power quality monitoring terminal device i; m represents the coding block length used by the power quality monitoring terminal device i; i∈K, K is the total number of power quality monitoring terminal devices; let the decoding order be s1→s2→…→s K , where s1, s2, s K Respectively represent the data streams of the 1st, 2nd, and Kth power quality monitoring terminal devices; when i=1, 2, ..., K-1, γ is the SINR when the decision end decodes the power quality monitoring terminal device i, and when i=K, γ is the SNR when the decision end decodes the power quality monitoring terminal device i;

[0049] The SINR calculation formula is:

[0050]

[0051] In the above formula, P i , P i+1 They represent the transmission power of the power quality monitoring terminal equipment i and i+1 respectively; h represents the channel between the decision-making end and the power quality monitoring terminal equipment; Indicates the additive white Gaussian noise variance of the signals of all power quality monitoring terminal devices received by the decision-making end;

[0052] The SNR calculation formula is:

[0053]

[0054] In the above formula, P K Indicates the transmission power of the power quality monitoring terminal device K;

[0055] S12. The following power allocation optimization problem is constructed:

[0056]

[0057] In the above formula, ε o represents the overall error rate; ε i-1 The error rate of the decoding error probability when decoding the data stream of the power quality monitoring terminal device i-1; P t is the maximum transmission power; P represents the set of transmission powers of all power quality monitoring terminal devices.

[0058] The calculation module is used to first perform a first-order Taylor approximation according to the following formula to convert the non-convex term into a convex term:

[0059]

[0060] In the above formula, n represents the current number of iterations;

[0061] Then, by introducing the slack variable t, the power allocation optimization problem is transformed into the following sub-problems:

[0062]

[0063]

[0064] Iterate to solve the above sub-problems, using the P obtained in the previous iteration during the iteration process (n-1) Solve the subproblem to get P (n) ,t (n) , P (n) ,t (n) As the starting point for the next iteration, when |t (n) -t (n-1) The iteration ends when |≤τ, where τ represents the preset threshold.

[0065] The additive white Gaussian noise variance Satisfies the following Gaussian distribution:

[0066]

[0067] In the above formula, y represents the signal received by the decision-making end from all power quality monitoring terminal devices; η represents additive Gaussian white noise.

[0068] In the third aspect, the present invention proposes a real-time monitoring device for power quality based on NOMA, the real-time monitoring device comprising a memory and a processor; the memory is used to store computer program code and transmit the computer program code to the processor; the processor is used to execute the aforementioned real-time monitoring method according to the instructions in the computer program code.

[0069] In a fourth aspect, the present invention proposes a computer-readable storage medium having a computer program stored thereon, and the computer program implements the aforementioned real-time monitoring method when executed by a processor.

[0070] Compared with the prior art, the present invention has the following beneficial effects:

[0071] The real-time monitoring method of power quality based on NOMA described in the present invention first constructs the distribution power optimization problem of the power quality monitoring system, then uses continuous convex approximation to convert the non-convex terms in the distribution power optimization problem into convex terms, obtains the transmission power distribution optimization scheme by iteratively solving the distribution power optimization problem, and finally controls the power quality monitoring system to perform power quality monitoring based on the obtained transmission power distribution optimization scheme; in the above design, the distribution power optimization problem is constructed with the goal of minimizing the overall error probability, and at the same time, the transmission power of each power quality monitoring terminal device is optimized, thereby meeting the high reliability requirements of communication in multi-terminal device scenarios, and being able to transmit the monitored power quality data to the decision-making end in a timely manner, make feedback optimization, and maintain the power supply quality of the power grid. Therefore, the present invention can reduce the overall error probability by optimizing the transmission power distribution scheme of the terminal device, thereby ensuring high reliability of communication in multi-terminal device scenarios. BRIEF DESCRIPTION OF THE DRAWINGS

[0072] Figure 1 The figure is a flow chart of the real-time monitoring method of the present invention.

[0073] Figure 2 It is a structural schematic diagram of the power quality monitoring system described in the present invention.

[0074] Figure 3 It is a structural schematic diagram of the real-time monitoring system of the present invention.

[0075] Figure 4 It is a structural schematic diagram of the real-time monitoring device of the present invention. DETAILED DESCRIPTION

[0076] The present invention is further described in detail below in conjunction with specific implementations and drawings.

[0077] Embodiment 1:

[0078] A real-time monitoring method for power quality based on NOMA is proposed. Figure 2The power quality monitoring system shown in the figure is carried out, and the power quality monitoring system includes a decision-making end and K power quality monitoring terminal devices (K≥2). The power quality monitoring terminal devices are used to perform power quality monitoring, transmit the monitored power quality data such as harmonics and voltage fluctuations to the encoder for pre-coding, and then transmit them to the decision-making end through the NOMA communication transmission method. The decision-making end is used to use continuous interference elimination technology to decode the data streams received from multiple power quality monitoring terminal devices in order from strong to weak according to the signal strength, and make a global optimization decision based on the decoding information, and transmit the optimized decision data back to each power quality monitoring terminal device through the NOMA communication transmission method. Since the continuous interference elimination technology preferentially decodes stronger signals, it is assumed that the signal strength order of the data streams of the K power quality monitoring terminal devices is s1>s2>…>s K , then the decoding order of successive interference cancellation is s1→s2→…→s K , where s1, s2, s K They represent the data streams of the first, second, and Kth power quality monitoring terminal devices respectively; first decode s1 and regard other data streams as interference; if s1 is successfully decoded, remove s1 from the received signal through continuous interference elimination, then decode s2 and regard the remaining data streams as interference, and decode them in sequence until s K ;

[0079] See also Figure 1 The NOMA-based real-time power quality monitoring method is performed in the following steps:

[0080] S1. The power allocation optimization problem of the power quality monitoring system is constructed with the goal of minimizing the overall error probability. The optimization variable of the power allocation optimization problem is the transmission power of each power quality monitoring terminal device. Specifically, the steps of constructing the power allocation optimization problem are as follows:

[0081] S11. According to the finite code length domain theory, the decoding error probability when decoding the data stream of a single power quality monitoring terminal can be expressed as:

[0082]

[0083] C(γ)=log2(1+γ);

[0084]

[0085] r = d / m;

[0086] In the above formula, ε i The decoding error probability when decoding the data stream of the power quality monitoring terminal device i; represents the right tail function of the standard normal distribution; V(γ) represents the channel dispersion; C(γ) is the channel transmission capacity of the power quality monitoring terminal device i; r represents the coding rate corresponding to the power quality monitoring terminal device i; d represents the data packet size transmitted by the data stream of the power quality monitoring terminal device i; m represents the coding block length used by the power quality monitoring terminal device i. In this embodiment, each power quality monitoring terminal device uses the same coding block length and transmits at different power levels. The maximum available coding block length is M; i∈K, K is the total number of power quality monitoring terminal devices; when i=1, 2, …, K-1, γ is the SINR when the decision end decodes the power quality monitoring terminal device i, and when i=K, γ is the SNR when the decision end decodes the power quality monitoring terminal device i;

[0087] The SINR calculation formula is:

[0088]

[0089] In the above formula, P i , P i+1 They represent the transmission power of the power quality monitoring terminal equipment i and i+1 respectively; h represents the channel between the decision-making end and the power quality monitoring terminal equipment; Indicates the additive white Gaussian noise variance of all power quality monitoring terminal device signals received by the decision-making end; additive white Gaussian noise variance Satisfies the following Gaussian distribution:

[0090]

[0091]

[0092] In the above formula, y represents the signal received by the decision-making end from all power quality monitoring terminal devices; η represents additive Gaussian white noise; When is the standard Gaussian distribution;

[0093] The SNR calculation formula is:

[0094]

[0095] In the above formula, P K Indicates the transmission power of the power quality monitoring terminal device K;

[0096] S12. Since the decision-making end needs to make a global optimization decision based on the power quality data of all power quality monitoring terminal devices, the entire communication transmission process is successful only when all data streams are successfully decoded; therefore, the following allocation power optimization problem can be constructed:

[0097]

[0098] In the above formula, ε o represents the overall error rate; ε i-1 The error rate of the decoding error probability when decoding the data stream of the power quality monitoring terminal device i-1; P t is the maximum transmission power; P represents the set of transmission powers of all power quality monitoring terminal devices; when i=1, ε0=0;

[0099] S2. Since the power allocation optimization problem contains ε i The variable product of , so the optimization problem is not a convex problem; therefore, the continuous convex approximation is used to convert the non-convex terms in the allocation power optimization problem into convex terms, and the transmission power allocation optimization scheme is obtained by iteratively solving the allocation power optimization problem; the specific steps are:

[0100] S21. Perform a first-order Taylor approximation according to the following formula to convert the non-convex term into a convex term:

[0101]

[0102] In the above formula, n represents the current number of iterations;

[0103] S22, introduce the slack variable t and transform the power allocation optimization problem into the following sub-problems:

[0104]

[0105] S23, iteratively solve the above sub-problems, using the P obtained in the previous iteration in the iterative process (n-1) Solve the subproblem to get P (n) ,t (n) , P (n) ,t (n) As the starting point for the next iteration, when |t (n) -t (n-1) The iteration ends when |≤τ, where τ represents the preset threshold. The specific iterative solution process is as follows:

[0106]

[0107] S3. Control the power quality monitoring system to perform power quality monitoring based on the obtained transmission power allocation optimization scheme.

[0108] Embodiment 2:

[0109] See also Figure 3A NOMA-based real-time power quality monitoring system includes a power distribution optimization problem construction module, a calculation module, and a power quality monitoring module; the power distribution optimization problem construction module is used to construct a power distribution optimization problem of a power quality monitoring system with the goal of minimizing the overall error probability, and the optimization variable of the power distribution optimization problem is the transmission power of each power quality monitoring terminal device; specifically, the power distribution optimization problem construction module constructs the power distribution optimization problem according to the following steps:

[0110] S11. The decoding error probability when decoding the data stream of a single power quality monitoring terminal is:

[0111]

[0112] C(γ)=log2(1+γ);

[0113]

[0114] r = d / m;

[0115] In the above formula, ε i The decoding error probability when decoding the data stream of the power quality monitoring terminal device i; represents the right tail function of the standard normal distribution; V(γ) represents the channel dispersion; C(γ) is the channel transmission capacity of the power quality monitoring terminal device i; r represents the coding rate corresponding to the power quality monitoring terminal device i; d represents the data packet size transmitted by the data stream of the power quality monitoring terminal device i; m represents the coding block length used by the power quality monitoring terminal device i; i∈K, K is the total number of power quality monitoring terminal devices; let the decoding order be s1→s2→…→s K , where s1, s2, s K Respectively represent the data streams of the 1st, 2nd, and Kth power quality monitoring terminal devices; when i=1, 2, ..., K-1, γ is the SINR when the decision end decodes the power quality monitoring terminal device i, and when i=K, γ is the SNR when the decision end decodes the power quality monitoring terminal device i;

[0116] The SINR calculation formula is:

[0117]

[0118] In the above formula, P i , P i+1 They represent the transmission power of the power quality monitoring terminal equipment i and i+1 respectively; h represents the channel between the decision-making end and the power quality monitoring terminal equipment; Indicates the additive white Gaussian noise variance of all power quality monitoring terminal device signals received by the decision-making end; additive white Gaussian noise variance Satisfies the following Gaussian distribution:

[0119]

[0120] In the above formula, y represents the signal received by the decision-making end from all power quality monitoring terminal devices; η represents additive Gaussian white noise;

[0121] The SNR calculation formula is:

[0122]

[0123] In the above formula, P K Indicates the transmission power of the power quality monitoring terminal device K;

[0124] S12. The following power allocation optimization problem is constructed:

[0125]

[0126] In the above formula, ε o represents the overall error rate; ε i-1 The error rate of the decoding error probability when decoding the data stream of the power quality monitoring terminal device i-1; P t is the maximum transmission power; P represents the set of transmission powers of all power quality monitoring terminal devices. ;

[0127] The calculation module is used to convert the non-convex terms in the power allocation optimization problem into convex terms by using continuous convex approximation, and obtain the transmission power allocation optimization solution by iterative solution; specifically, the calculation module is used to first perform a first-order Taylor approximation according to the following formula to convert the non-convex terms into convex terms:

[0128]

[0129] In the above formula, n represents the current number of iterations;

[0130] Then, by introducing the slack variable t, the power allocation optimization problem is transformed into the following sub-problems:

[0131]

[0132] Iterate to solve the above sub-problems, using the P obtained in the previous iteration during the iteration process (n-1) Solve the subproblem to get P (n) ,t (n) , P (n) ,t (n) As the starting point for the next iteration, when |t (n) -t (n-1) The iteration ends when |≤τ, where τ represents the preset threshold;

[0133] The power quality monitoring module is used to control the power quality monitoring system to perform power quality monitoring based on the obtained transmission power allocation optimization solution.

[0134] Embodiment 3:

[0135] See also Figure 4 , a real-time monitoring device for power quality based on NOMA, comprising a memory and a processor; the memory is used to store computer program code and transmit the computer program code to the processor; the processor is used to execute the real-time monitoring method described in Example 1 according to the instructions in the computer program code.

[0136] Embodiment 4:

[0137] A computer-readable storage medium having a computer program stored thereon, wherein the computer program implements the real-time monitoring method described in Example 1 when executed by a processor.

[0138] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0139] The present application is described with reference to flowcharts and / or block diagrams of methods, devices (systems) and computer program products according to embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0140] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0141] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0142] 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 NOMA-based real-time power quality monitoring method, characterized in that: The real-time monitoring method is performed based on a power quality monitoring system, which includes a decision-making end and multiple power quality monitoring terminal devices. The power quality monitoring terminal devices are used to perform power quality monitoring and transmit the monitored power quality data to the decision-making end through a NOMA communication transmission mode. The decision-making end is used to use a continuous interference elimination technology to decode the data streams received from the multiple power quality monitoring terminal devices in order from strong to weak signal strength, and make a global optimization decision based on the decoding information of all power quality monitoring terminals. The real-time monitoring method comprises: S1. A power distribution optimization problem of a power quality monitoring system is constructed with the goal of minimizing the overall error probability, wherein the optimization variable of the power distribution optimization problem is the transmission power of each power quality monitoring terminal device; S2. Using continuous convex approximation to convert non-convex terms in the power allocation optimization problem into convex terms, and obtaining a transmit power allocation optimization solution by iteratively solving the power allocation optimization problem; S3. Control the power quality monitoring system to perform power quality monitoring based on the obtained transmission power allocation optimization scheme.

2. The method for real-time monitoring of power quality based on NOMA according to claim 1, characterized in that: The construction steps of the power allocation optimization problem are as follows: S11. The decoding error probability when decoding the data stream of a single power quality monitoring terminal is: C(γ)=log2(1+γ); r = d / m; In the above formula, ε i The decoding error probability when decoding the data stream of the power quality monitoring terminal device i; represents the right tail function of the standard normal distribution; V(γ) represents the channel dispersion; C(γ) is the channel transmission capacity of the power quality monitoring terminal device i; r represents the coding rate corresponding to the power quality monitoring terminal device i; d represents the data packet size transmitted by the data stream of the power quality monitoring terminal device i; m represents the coding block length used by the power quality monitoring terminal device i; i∈K, K is the total number of power quality monitoring terminal devices; let the decoding order be s1→s2→…→s K , where s1, s2, s K Respectively represent the data streams of the 1st, 2nd, and Kth power quality monitoring terminal devices; when i=1, 2, ..., K-1, γ is the SINR when the decision end decodes the power quality monitoring terminal device i, and when i=K, γ is the SNR when the decision end decodes the power quality monitoring terminal device i; The SINR calculation formula is: In the above formula, P i , P i+1 They represent the transmission power of the power quality monitoring terminal equipment i and i+1 respectively; h represents the channel between the decision-making end and the power quality monitoring terminal equipment; Indicates the additive white Gaussian noise variance of the signals of all power quality monitoring terminal devices received by the decision-making end; The SNR calculation formula is: In the above formula, P K Indicates the transmission power of the power quality monitoring terminal device K; S12. The following power allocation optimization problem is constructed: In the above formula, ε o represents the overall error rate; ε i-1 The error rate of the decoding error probability when decoding the data stream of the power quality monitoring terminal device i-1; P t is the maximum transmission power; P represents the set of transmission powers of all power quality monitoring terminal devices.

3. The method for real-time monitoring of power quality based on NOMA according to claim 2, characterized in that: The S2 includes: S21. Perform a first-order Taylor approximation according to the following formula to convert the non-convex term into a convex term: …， In the above formula, n represents the current number of iterations; S22, introduce the slack variable t and transform the power allocation optimization problem into the following sub-problems: S23, iteratively solve the above sub-problems, using the P obtained in the previous iteration in the iterative process (n-1) Solve the subproblem to get P (n) ,t (n) , P (n) ,t (n) As the starting point for the next iteration, when |t (n) -t (n-1) The iteration ends when |≤τ, where τ represents the preset threshold.

4. The method for real-time monitoring of power quality based on NOMA according to claim 2, characterized in that: The additive white Gaussian noise variance Satisfies the following Gaussian distribution: In the above formula, y represents the signal received by the decision-making end from all power quality monitoring terminal devices; η represents additive Gaussian white noise.

5. A NOMA-based real-time power quality monitoring system, characterized by: The real-time monitoring system includes a power distribution optimization problem building module, a calculation module, and a power quality monitoring module: The power allocation optimization problem construction module is used to construct the power allocation optimization problem of the power quality monitoring system with the goal of minimizing the overall error probability, and the optimization variable of the power allocation optimization problem is the transmission power of each power quality monitoring terminal device; The calculation module is used to convert non-convex terms in the power allocation optimization problem into convex terms by using continuous convex approximation, and obtain the transmission power allocation optimization solution by iterative solution; The power quality monitoring module is used to control the power quality monitoring system to perform power quality monitoring based on the obtained transmission power allocation optimization solution.

6. The NOMA-based real-time power quality monitoring system according to claim 5, characterized in that: The power allocation optimization problem construction module constructs the power allocation optimization problem according to the following steps: S11. The decoding error probability when decoding the data stream of a single power quality monitoring terminal is: C(γ)=log2(1+γ); r = d / m; In the above formula, ε i The decoding error probability when decoding the data stream of the power quality monitoring terminal device i; represents the right tail function of the standard normal distribution; V(γ) represents the channel dispersion; C(γ) is the channel transmission capacity of the power quality monitoring terminal device i; r represents the coding rate corresponding to the power quality monitoring terminal device i; d represents the data packet size transmitted by the data stream of the power quality monitoring terminal device i; m represents the coding block length used by the power quality monitoring terminal device i; i∈K, K is the total number of power quality monitoring terminal devices; let the decoding order be s1→s2→…→s K , where s1, s2, s K Respectively represent the data streams of the 1st, 2nd, and Kth power quality monitoring terminal devices; when i=1, 2, ..., K-1, γ is the SINR when the decision end decodes the power quality monitoring terminal device i, and when i=K, γ is the SNR when the decision end decodes the power quality monitoring terminal device i; The SINR calculation formula is: In the above formula, P i , P i+1 They represent the transmission power of the power quality monitoring terminal equipment i and i+1 respectively; h represents the channel between the decision-making end and the power quality monitoring terminal equipment; Indicates the additive white Gaussian noise variance of the signals of all power quality monitoring terminal devices received by the decision-making end; The SNR calculation formula is: In the above formula, P K Indicates the transmission power of the power quality monitoring terminal device K; S12. The following power allocation optimization problem is constructed: In the above formula, ε o represents the overall error rate; ε i-1 The error rate of the decoding error probability when decoding the data stream of the power quality monitoring terminal device i-1; P t is the maximum transmission power; P represents the set of transmission powers of all power quality monitoring terminal devices.

7. A NOMA-based real-time power quality monitoring system according to claim 6, characterized in that: The calculation module is used to first perform a first-order Taylor approximation according to the following formula to convert the non-convex term into a convex term: …, In the above formula, n represents the current number of iterations; Then, by introducing the slack variable t, the power allocation optimization problem is transformed into the following sub-problems: Iterate to solve the above sub-problems, using the P obtained in the previous iteration during the iteration process (n-1) Solve the subproblem to get P (n) ,t (n) , P (n) ,t (n) As the starting point for the next iteration, when |t (n) -t (n-1) The iteration ends when |≤τ, where τ represents the preset threshold.

8. The NOMA-based real-time power quality monitoring system according to claim 6, characterized in that: The additive white Gaussian noise variance Satisfies the following Gaussian distribution: In the above formula, y represents the signal received by the decision-making end from all power quality monitoring terminal devices; η represents additive Gaussian white noise.

9. A NOMA-based real-time power quality monitoring device, characterized in that: The real-time monitoring device includes a memory and a processor; the memory is used to store computer program code and transmit the computer program code to the processor; the processor is used to execute the real-time monitoring method as described in claims 1-4 according to the instructions in the computer program code.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by the processor, the real-time monitoring method according to claims 1-4 is implemented.

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