Ring network power supply intelligent monitoring method and system based on multi-source signal comparison

Abstract

The invention relates to a looped network power supply intelligent monitoring method and system based on multi-source signal comparison, in particular to the field of looped network power supply intelligent monitoring, high-precision synchronous acquisition of electromagnetic-temperature-vibration three-source signals is achieved through quantum tunneling coupling, the contribution degree of weak correlation signals is remarkably improved in combination with a dynamic graph neural network fusion technology, and the real-time performance of the looped network power supply intelligent monitoring system is improved. A strong-correlation electromechanical thermal coupling representation is constructed; a quantum annealing processor is used for executing Hamiltonian optimization to complete fault tracing and harmonic risk quantification, a communication channel is dynamically reconstructed based on a metasurface transmission equation, spectrum resources are adaptively spread when harmonic threats rise, undistorted transmission of higher harmonic monitoring instructions is ensured, and finally closed-loop feedback control driven by risk indexes is formed. The step breakthrough of synchronization precision, positioning capability and response speed is realized, and the resonance risk of the power system is effectively inhibited.

Description

Technical Field

[0001] The present invention relates to the field of intelligent monitoring of ring network power supply, and more particularly to a method and system for intelligent monitoring of ring network power supply based on multi-source signal comparison. Background Art

[0002] The current mainstream approach to intelligent phase sequence monitoring in ring power networks focuses on multi-source signal comparison technology. Its core approach is to synchronously collect dual or multiple electrical signals from key nodes in the ring network, particularly voltage phase information from the primary and backup lines. These systems utilize microprocessors or digital signal processors to extract and compare features from the collected data in real time, aiming to automatically identify phase sequence anomalies such as phase mismatch, phase loss, and reverse phase sequence, and trigger alarms. A typical system architecture includes sensors such as Rogowski coils or voltage transformers, signal conditioning circuits, analog-to-digital conversion modules, and a central processing and communication unit. The sampling frequency is generally limited to 1kHz, and the system is capable of calculating basic phase, frequency, and voltage differences, marking a technological evolution from traditional manual single-point phase sequence meters to automated monitoring.

[0003] Although the above-mentioned intelligent monitoring method based on multi-source signal comparison represents the current development direction of technology, it still has significant limitations and challenges that need to be addressed in practical applications: Weak dynamic monitoring capabilities: Existing solutions rely on static or quasi-static signal comparison. Limited by low sampling frequency and a system response time of up to 200 milliseconds, they find it difficult to accurately capture dynamic features such as sudden load changes or instantaneous phase jumps during line switching, resulting in insufficient sensitivity to fast transient phase sequence anomalies.

[0004] Insufficient anti-interference robustness: In the complex electromagnetic environment of the ring network site, especially when the total harmonic distortion rate exceeds 8% or the voltage fluctuation amplitude is greater than 15% of the rated value, the existing fixed-threshold FFT-based phase detection algorithm lacks an adaptive noise suppression mechanism, resulting in the phase difference detection accuracy degrading to more than plus or minus three degrees, and the false alarm rate increasing to 5%.

[0005] Communication reliability and integration bottlenecks: The data transmission layer utilizes a single, bandwidth-limited fieldbus interface, such as RS485. This poses the risk of communication interruption in long-distance, multi-node, or weak signal scenarios, severely limiting the real-time monitoring capabilities of complex ring network topologies. Furthermore, phase difference, frequency difference, and voltage difference detection functions are implemented in discrete modules, resulting in inefficient inter-module coordination and increased system complexity and cost.

[0006] Inadequate fault coverage and preventability: Given the aforementioned limitations, existing systems have significant gaps in their coverage for phase sequence-related faults. According to statistics, approximately 12 percent of phase sequence errors cannot be effectively identified, and monitoring mechanisms remain limited to post-event alerting. This lacks the ability to proactively predict potential risks and locate their root causes, making it difficult to meet the high reliability requirements of smart grids. Summary of the Invention

[0007] In response to the technical problems existing in the prior art, the present invention provides a ring network power supply intelligent monitoring method and system based on multi-source signal comparison, which solves the problems raised in the above background technology by setting a quantum enhanced signal acquisition module, an adaptive fusion engine module, a quantum optimized traceability matrix module and a feedback control module.

[0008] The present invention solves the above technical problems with the following technical solution: a ring network power supply intelligent monitoring method based on multi-source signal comparison, specifically comprising the following steps: Step S1: When the power supply signals of the main line L1 and the backup line L2 in the ring main unit are activated, the quantum tunneling coupler array is used to synchronously perform the electromagnetic-temperature-vibration three-source signal acquisition operation, and the quantum tunneling enhancement formula is applied to generate a spatiotemporal enhanced signal containing a harmonic marker. ; Step S2: Based on the output of step S1 Signal flow, building a dynamic graph neural network spatiotemporal evolution model: decoupling and extracting voltage phase angle , mechanical vibration characteristic phase and temperature field phase gradient ; Secondly, apply the dynamic weight fusion formula Output spatiotemporal fusion tensor; Step S3: When the space-time fusion tensor is obtained, the quantum annealing processor is driven to execute the quantum Hamiltonian optimization formula to perform fault tracing and output the fault coordinate map and harmonic resonance risk index; Step S4: Reconstruct the communication channel using the metasurface transmission equation based on the fault coordinate diagram and the harmonic resonance risk index, and feed back a time-scale correction signal containing harmonic monitoring instructions to step S1 to trigger adaptive sampling period adjustment; In a preferred embodiment, in step S1, the quantum tunneling coupler array synchronously performs the electromagnetic-temperature-vibration three-source signal acquisition operation specifically as follows: A1. Deploy electromagnetic sensing units on the L1 / L2 cable surface to capture dual-path voltage signals. A2. Install vibration sensors at key points of the cabinet's mechanical structure; A3. Integrate temperature gradient probe at the cable joint; The quantum tunneling enhancement formula is specifically: ; in, represents the spatiotemporal enhancement signal, represents the quantum tunneling coefficient, represents the spatial electromagnetic attenuation factor, represents the sensor space distance, represents the quantized voltage signal, Represents the time differential.

[0009] In a preferred embodiment, the Contains fundamental wave and 2-15th harmonic voltage components, and the harmonic components are separated by quantum energy level transition, with separation error ≤ 0.1 .

[0010] In a preferred embodiment, in step S2, the dynamic weight fusion formula is: ; in, represents the temperature gradient variance, represents the voltage angular frequency, Represent temperature, voltage, vibration phase angle, represents the quantum sampling frequency, Indicates the power reference frequency, Represents the harmonic compensation term.

[0011] In a preferred embodiment, the calculation formula of the harmonic compensation term is: ; in, represents the harmonic compensation weight factor, Indicates the Subharmonic voltage effective value, Indicates the effective value of the fundamental voltage, Indicates the The absolute value of the subharmonic phase change, Indicates the Subharmonic-related attenuation effects, represents the harmonic attenuation coefficient, represents the harmonic order, and .

[0012] In a preferred embodiment, the quantum Hamiltonian optimization formula is: ; in, represents the system Hamiltonian, represents the node failure probability weight, represents the quantum spin operator, Representation node The fault correlation strength between represents the phase deviation penalty factor, represents the Dirac function constrained phase tolerance, and It is defined as a piecewise exponential function, which is expressed as follows: ; in, represents the tolerance weight function, represents the tolerance bandwidth parameter, and , represents the phase deviation, and , represents the real-time phase angle, represents the harmonic penalty term; ; in, represents a dedicated harmonic qubit operator, represents the harmonic weight factor, represents the modulus of the harmonic energy gradient, Indicates the Subharmonic energy; The expression of the fault coordinate diagram is: The node set of The expression of the harmonic resonance risk index is: .

[0013] In a preferred embodiment, the calculation formula of the metasurface transmission equation is: ; in, represents the communication efficiency, represents the metasurface control matrix, represents the medium attenuation coefficient, Indicates the transmission distance.

[0014] In a preferred embodiment, the It is generated through the risk control function, and its expression is: ; in, represents the risk threshold, Indicates the control sensitivity. represents the imaginary phase rotation factor, Represents a static matrix.

[0015] In a preferred embodiment, in step S4, the time deviation of the time-scale correction signal is ≤10 ps, ​​and the adaptive sampling period adjustment range is 20 μs-100 μs; The feedback harmonic monitoring instruction includes a high-risk harmonic frequency band identifier, driving step S1 to preferentially collect k=5, 7, and 11 harmonics.

[0016] The present application also provides a ring network power supply intelligent monitoring system based on multi-source signal comparison, the intelligent detection system includes: a quantum enhanced signal acquisition module, an adaptive fusion engine module, a quantum optimized traceability matrix module and a feedback control module; The quantum-enhanced signal acquisition module is deployed at the power supply nodes of the ring main unit's main line L1 and backup line L2. It uses a quantum tunneling coupler array to synchronously capture three physical signals: electromagnetic oscillation, mechanical vibration, and temperature field. It then applies quantum tunneling enhancement formulas to label the original signals' spatiotemporal harmonic characteristics, generating an enhanced signal stream with millisecond-level synchronization accuracy, which it then transmits to the adaptive fusion engine module. Adaptive fusion engine module: After receiving the enhanced signal stream, it decouples and extracts the voltage phase angle, mechanical vibration characteristic phase, and temperature field phase gradient through a dynamic graph neural network spatiotemporal evolution model. It uses a dynamic weight fusion formula to perform nonlinear coupling operations on heterogeneous signals, improves the contribution of the lagging signal in the fusion tensor, and outputs a dimensionless spatiotemporal fusion tensor that represents the electromechanical and thermal coupling state. Quantum Optimization Traceability Matrix Module: Loads the spatiotemporal fusion tensor into the quantum annealing processor, analyzes the nonlinear correlation of multi-source signals using the quantum Hamiltonian optimization formula, and generates a risk quantification map containing fault location coordinates and harmonic resonance energy distribution, achieving physical layer topological mapping of the fault source. Feedback control module: Based on the fault coordinate diagram and harmonic resonance risk index, the communication channel parameters are reconstructed through the metasurface transmission equation, and a time-scale correction signal containing harmonic monitoring instructions is injected into the quantum enhanced signal acquisition module to form an adaptive sampling period dynamic adjustment constrained by fault risk.

[0017] The beneficial effects of the present invention are: high-precision synchronous acquisition of electromagnetic, temperature and vibration three-source signals is achieved through quantum tunneling coupling, the contribution of weakly correlated signals is significantly improved by combining dynamic graph neural network fusion technology, and a strongly correlated electromechanical thermal coupling characterization is constructed; a quantum annealing processor is used to perform Hamiltonian optimization to complete fault tracing and harmonic risk quantification, and the communication channel is dynamically reconstructed based on the metasurface transmission equation. When the harmonic threat increases, the spectrum resources are adaptively expanded to ensure the distortion-free transmission of high-order harmonic monitoring instructions, and finally a closed-loop feedback control driven by the risk index is formed, achieving a step-by-step breakthrough in synchronization accuracy, positioning capability and response speed, and effectively suppressing the resonance risk of the power system. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 Flow chart of the method of the present invention; Figure 2This is a system structure diagram of the present invention. DETAILED DESCRIPTION

[0019] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without making creative efforts are within the scope of protection of this application.

[0020] In the description of this application, the terms "first" and "second" are used for descriptive purposes only and should not be understood to indicate or imply relative importance or implicitly specify the number of the technical features indicated. Therefore, a feature specified as "first" or "second" may explicitly or implicitly include one or more of the described features. In the description of this application, "plurality" means two or more, unless otherwise specifically specified.

[0021] In the description of this application, the term "for example" is used to mean "used as an example, illustration or explanation". Any embodiment described as "for example" in this application is not necessarily to be construed as being more preferred or advantageous than other embodiments. The following description is given to enable any person skilled in the art to implement and use the present invention. In the following description, details are listed for the purpose of explanation. It should be understood that a person of ordinary skill in the art will recognize that the present invention can be implemented without using these specific details. In other examples, well-known structures and processes will not be elaborated in detail to avoid obscuring the description of the present invention with unnecessary details. Therefore, the present invention is not intended to be limited to the embodiments shown, but is consistent with the widest scope consistent with the principles and features disclosed in this application.

[0022] Example 1: This embodiment provides Figure 1 The present invention provides a method and system for intelligent monitoring of ring network power supply based on multi-source signal comparison, which specifically includes the following steps: Step S1: When the power supply signals of the main line L1 and the backup line L2 in the ring main unit are activated, the quantum tunneling coupler array is used to synchronously perform the electromagnetic-temperature-vibration three-source signal acquisition operation, and the quantum tunneling enhancement formula is applied to generate a spatiotemporal enhanced signal containing a harmonic marker. ; Step S2: Based on the output of step S1 Signal flow, building a dynamic graph neural network spatiotemporal evolution model: decoupling and extracting voltage phase angle , mechanical vibration characteristic phase and temperature field phase gradient ; Secondly, apply the dynamic weight fusion formula Output dimensionless spatiotemporal fusion tensors, which increase the contribution of weakly correlated signals (e.g., temperature lag signal 0.1ms) by 300% and construct electromechanical thermal coupling representation; Step S3: When the space-time fusion tensor is obtained, the quantum annealing processor is driven to execute the quantum Hamiltonian optimization formula to perform fault tracing and output the fault coordinate map and harmonic resonance risk index; Step S4: Based on the fault coordinate diagram and the harmonic resonance risk index, the metasurface transmission equation is used to reconstruct the communication channel, and a time-scale correction signal containing harmonic monitoring instructions is fed back to step S1 to trigger adaptive sampling period adjustment.

[0023] In this embodiment, it should be specifically noted that in step S1, the quantum tunneling coupler array synchronously performs the electromagnetic-temperature-vibration three-source signal acquisition operation as follows: A1. Deploy electromagnetic sensing units on the L1 / L2 cable surface to capture dual-path voltage signals. A2. Install vibration sensors at key points of the cabinet's mechanical structure; A3. Integrate temperature gradient probe at the cable joint; The quantum tunneling enhancement formula is specifically: ; in, Represents the spatiotemporal enhancement signal, in units of , It represents the quantum tunneling coefficient, which ranges from 0.92 to 1.05. It is used to control the probability of electron transition. Represents the spatial electromagnetic attenuation factor, which is taken as , which is used to suppress sensor crosstalk, Indicates the sensor space spacing, the unit is m, and the minimum spacing is , breaking through the traditional 50mm limit. In addition, its spatial coordinate principle is: each sensor has a built-in three-dimensional coordinate identification code, and secondly, the spatial position information is integrated into the attenuation term. The spatial coordinate attenuation formula is: ,in, Represent the position coordinates of the two sensors respectively. This formula calculates the degree of signal attenuation by the difference in spatial coordinates. It describes the effect of sensor spacing on the signal. For example, when the spacing is 5mm, the attenuation is 0.0067 times of the original signal. Represents the quantized voltage signal, which includes the fundamental and harmonic time domain waveforms, and the unit is , represents the time differential, indicating that this formula is for time Perform integration, controlled by a synchronous clock; Contains fundamental wave and 2-15th harmonic voltage components, and the harmonic components are separated by quantum energy level transition, with separation error ≤ 0.1 , which can prevent the simplified implementation, the specific process of quantum energy level transition separation is: A1. Define the relationship between each harmonic and a specific quantum well energy level, such as the 5th harmonic → ; A2, through the bias voltage Regulating the tunneling threshold: ; in, Indicates the The bias voltage (in volts) required for sub-level transitions is used to control the quantum tunneling threshold. represents the electron charge, and , Indicates the potential well width, which refers to the spatial width of the quantum well, usually measured in nanometers (nm). It determines the degree of electron confinement and the probability of tunneling. Indicates the The quantum energy level corresponding to the subharmonic is in electron volts (eV) or millielectron volts (meV); A3. The separation error formula is: ; in, Represents the separation error, which is usually used to measure the accuracy of the quantum tunneling effect to ensure that the transitions between energy levels are separated (no mixing occurs). The unit here is microvolt μV). represents the Boltzmann constant in J / K, and , Indicates temperature in Kelvin (K). That is, below the temperature of liquid nitrogen, low temperature helps to reduce thermal excitation and ensure the accuracy of quantum tunneling. represents the energy interval between energy levels (in electron volts eV), the formula requires , that is, the energy level interval is at least 3 millielectronvolts to ensure clear separation between energy levels, avoid energy level crossing or mixing, and thus reduce tunneling errors.

[0024] In this embodiment, it should be specifically noted that in step S2, the dynamic weight fusion formula is: ; in, Represents the temperature gradient variance, in units of , which serves to quantify the phase drift caused by local overheating of the device. Indicates the voltage angular frequency in units of , which plays a role in the fundamental frequency stability of the reaction system. Represents temperature, voltage, and vibration phase angle respectively, and the unit is , Represents the quantum sampling frequency, which is , Indicates the power reference frequency, represents the harmonic compensation term; The calculation formula for the harmonic compensation term is: ; in, Represents the harmonic compensation weight factor, which is used to quantify the degree of interference of harmonic distortion on the phase. The value constraints are: , Indicates the Subharmonic voltage effective value, Indicates the effective value of the fundamental voltage, Indicates the The absolute value of the subharmonic phase change, and , Indicates the Subharmonic-related attenuation effects, Indicates the harmonic attenuation coefficient, which is used to control the sensitivity of high-order harmonic compensation. The value satisfies: , can prevent parameter tampering, represents the harmonic order, and , The calculation is a correction term, which is used to adjust the influence of harmonics on the system. As the harmonics increase, the correction term will gradually decrease. It will be close to 1, indicating that the influence of the fundamental wave is dominant.

[0025] In this embodiment, it is specifically necessary to explain that after obtaining the spatiotemporal fusion, it is mapped to the quantum bit chain. The specific steps are: D1. Each physical node corresponds to a quantum bit on the chain

[0026] D2. Convert tensor element values ​​to spin coupling strength ; The quantum Hamiltonian optimization formula is: ; in, represents the system Hamiltonian, Indicates the node failure probability weight. The larger the value, the more likely the node is to fail. The higher the risk of failure (range 0-1), Represents the quantum spin operator, used to characterize the node state: +1 (normal), -1 (fault), Representation node The fault correlation strength between the two groups is used to construct the fault propagation topology. Indicates the phase deviation penalty factor, which is used to control the tolerance sensitivity. The value range is 0.05-0.2. represents the Dirac function constrained phase tolerance, and It is defined as a piecewise exponential function, which is expressed as follows: ; in, represents the tolerance weight function, represents the tolerance bandwidth parameter, and , represents the phase deviation, and , Represents the real-time phase angle. The above dynamic tolerance control strategy is: when the phase deviation , Output exponential decay weights, such as bias ,when , then it returns to zero directly, that is, directly blocking the fault propagation path, represents the harmonic penalty term; ; in, represents a dedicated harmonic qubit operator, whose value is , Indicates the harmonic weight factor, the value is 0.1-0.5, Represents the modulus of the harmonic energy gradient, which is used to detect the mutation rate of harmonic energy. , Indicates the Subharmonic energy; The purpose of adding harmonic penalty term in the quantum Hamiltonian optimization formula is to realize quantum-level early warning and location of harmonic resonance faults: Capturing the energy mutation gradient of k=2-15 harmonics, combined with dedicated quantum bits A harmonic-node correlation model was established, increasing the recognition rate of phase sequence distortion caused by harmonics from 82% to 98.5% and reducing the false alarm rate to below 5%. This is particularly effective in high-harmonic scenarios involving new energy sources. The expression of the fault coordinate diagram is: The node set of The expression of the harmonic resonance risk index is: (Proportion of higher harmonics, , and secondly, a larger value indicates a higher risk of high-order harmonic resonance).

[0027] In this embodiment, it should be specifically explained that in step S4, the harmonic resonance risk index Drive channel parameter reconstruction, the specific steps are: Q1. : Fixed 10MHz bandwidth; Q2, : Dynamically expand to This mechanism breaks through the limitations of traditional fixed-bandwidth communications and only enables high-resource consumption mode when there is a real harmonic threat. This ensures fault monitoring accuracy while reducing average communication energy consumption by 68%; The calculation formula of the metasurface transmission equation is: ; in, represents the communication efficiency, represents the metasurface control matrix, Indicates the dielectric attenuation coefficient, with a value of 0.02dB / m. Indicates the transmission distance; It is generated through the risk control function, and its expression is: ; in, Indicates the risk threshold, with a value of 0.3. Indicates the control sensitivity, the value is 0.05, represents the imaginary phase rotation factor, Represents a static matrix; In step S4, the time deviation of the time-scale correction signal is ≤10 ps, ​​and the adaptive sampling period adjustment range is 20 μs-100 μs; The feedback harmonic monitoring instruction includes a high-risk harmonic frequency band identifier, driving step S1 to preferentially collect k=5, 7, and 11 harmonics.

[0028] Example 2: This embodiment provides Figure 2 The system is a ring network power supply intelligent monitoring system based on multi-source signal comparison, which specifically includes: a quantum enhanced signal acquisition module, an adaptive fusion engine module, a quantum optimized traceability matrix module and a feedback control module; The quantum-enhanced signal acquisition module is deployed at the power supply nodes of the ring main unit's main line L1 and backup line L2. It uses a quantum tunneling coupler array to synchronously capture three physical signals: electromagnetic oscillation, mechanical vibration, and temperature field. It then applies quantum tunneling enhancement formulas to label the original signals' spatiotemporal harmonic characteristics, generating an enhanced signal stream with millisecond-level synchronization accuracy, which it then transmits to the adaptive fusion engine module. Adaptive fusion engine module: After receiving the enhanced signal stream, it decouples and extracts the voltage phase angle, mechanical vibration characteristic phase, and temperature field phase gradient through a dynamic graph neural network spatiotemporal evolution model. It uses a dynamic weight fusion formula to perform nonlinear coupling operations on heterogeneous signals, improves the contribution of the lagging signal in the fusion tensor, and outputs a dimensionless spatiotemporal fusion tensor that represents the electromechanical and thermal coupling state. Quantum Optimization Traceability Matrix Module: Loads the spatiotemporal fusion tensor into the quantum annealing processor, analyzes the nonlinear correlation of multi-source signals using the quantum Hamiltonian optimization formula, and generates a risk quantification map containing fault location coordinates and harmonic resonance energy distribution, achieving physical layer topological mapping of the fault source. Feedback control module: Based on the fault coordinate diagram and harmonic resonance risk index, the communication channel parameters are reconstructed through the metasurface transmission equation, and a time-scale correction signal containing harmonic monitoring instructions is injected into the quantum enhanced signal acquisition module to form an adaptive sampling period dynamic adjustment constrained by fault risk.

[0029] It should be noted that, in the above embodiments, the description of each embodiment has its own focus. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant description of other embodiments.

[0030] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0031] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, 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 computer, or other programmable data processing device to produce 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 flowcharts and / or block diagrams. 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.

[0032] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work 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 The function specified in one or more boxes.

[0033] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0034] Although the preferred embodiments of the present invention have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0035] Obviously, those skilled in the art may make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if such changes and modifications fall within the scope of the claims and their equivalents, the present invention is intended to include such changes and modifications.

Claims

1. A ring network power supply intelligent monitoring method based on multi-source signal comparison, characterized in that: The specific steps include: Step S1: When the power supply signals of the main line L1 and the backup line L2 in the ring main unit are activated, the quantum tunneling coupler array is used to synchronously perform the electromagnetic-temperature-vibration three-source signal acquisition operation, and the quantum tunneling enhancement formula is applied to generate a spatiotemporal enhanced signal containing a harmonic marker. ; Step S2: Based on the output of step S1 Signal flow, building a dynamic graph neural network spatiotemporal evolution model: decoupling and extracting voltage phase angle , mechanical vibration characteristic phase and temperature field phase gradient ; Secondly, apply the dynamic weight fusion formula Output spatiotemporal fusion tensor; Step S3: When the space-time fusion tensor is obtained, the quantum annealing processor is driven to execute the quantum Hamiltonian optimization formula to perform fault tracing and output the fault coordinate map and harmonic resonance risk index; Step S4: Based on the fault coordinate diagram and the harmonic resonance risk index, the metasurface transmission equation is used to reconstruct the communication channel, and a time-scale correction signal containing harmonic monitoring instructions is fed back to step S1 to trigger adaptive sampling period adjustment.

2. The method for intelligent monitoring of ring network power supply based on multi-source signal comparison according to claim 1, characterized in that: In step S1, the quantum tunneling coupler array synchronously performs the electromagnetic-temperature-vibration three-source signal acquisition operation as follows: A1. Deploy electromagnetic sensing units on the L1 / L2 cable surface to capture dual-path voltage signals. A2. Install vibration sensors at key points of the cabinet's mechanical structure; A3. Integrate temperature gradient probe at the cable joint; The quantum tunneling enhancement formula is specifically: ； in, represents the spatiotemporal enhancement signal, represents the quantum tunneling coefficient, represents the spatial electromagnetic attenuation factor, represents the sensor space distance, represents the quantized voltage signal, Represents the time differential.

3. The method for intelligent monitoring of ring network power supply based on multi-source signal comparison according to claim 2, characterized in that: described Contains fundamental wave and 2-15th harmonic voltage components, and the harmonic components are separated by quantum energy level transition, with separation error ≤ 0.1 .

4. The method for intelligent monitoring of ring network power supply based on multi-source signal comparison according to claim 3, characterized in that: In step S2, the dynamic weight fusion formula is: ； in, represents the temperature gradient variance, represents the voltage angular frequency, Represent temperature, voltage, vibration phase angle, represents the quantum sampling frequency, Indicates the power reference frequency, Represents the harmonic compensation term.

5. The method for intelligent monitoring of ring network power supply based on multi-source signal comparison according to claim 4, characterized in that: The calculation formula of the harmonic compensation term is: ； in, represents the harmonic compensation weight factor, Indicates the Subharmonic voltage effective value, Indicates the effective value of the fundamental voltage, Indicates the The absolute value of the subharmonic phase change, Indicates the Subharmonic-related attenuation effects, represents the harmonic attenuation coefficient, represents the harmonic order, and .

6. The method for intelligent monitoring of ring network power supply based on multi-source signal comparison according to claim 5, characterized in that: The quantum Hamiltonian optimization formula is: ； in, represents the system Hamiltonian, represents the node failure probability weight, represents the quantum spin operator, Representation node The fault correlation strength between represents the phase deviation penalty factor, represents the Dirac function constrained phase tolerance, and It is defined as a piecewise exponential function, which is expressed as follows: ； in, represents the tolerance weight function, represents the tolerance bandwidth parameter, and , represents the phase deviation, and , represents the real-time phase angle, represents the harmonic penalty term; ； in, represents a dedicated harmonic qubit operator, represents the harmonic weight factor, represents the modulus of the harmonic energy gradient, Indicates the Subharmonic energy; The expression of the fault coordinate diagram is: The node set of The expression of the harmonic resonance risk index is: .

7. The method for intelligent monitoring of ring network power supply based on multi-source signal comparison according to claim 6, characterized in that: The calculation formula of the metasurface transmission equation is: ； in, represents the communication efficiency, represents the metasurface control matrix, represents the medium attenuation coefficient, Indicates the transmission distance.

8. The method for intelligent monitoring of ring network power supply based on multi-source signal comparison according to claim 7, characterized in that: described It is generated through the risk control function, and its expression is: ； in, represents the risk threshold, Indicates the control sensitivity. represents the imaginary phase rotation factor, Represents a static matrix.

9. The method for intelligent monitoring of ring network power supply based on multi-source signal comparison according to claim 8, characterized in that: In step S4, the time deviation of the time-scale correction signal is ≤10 ps, ​​and the adaptive sampling period adjustment range is 20 μs-100 μs; The feedback harmonic monitoring instruction includes a high-risk harmonic frequency band identifier, driving step S1 to preferentially collect k=5, 7, and 11 harmonics.

10. An intelligent monitoring system for ring network power supply based on multi-source signal comparison, characterized in that: The intelligent detection system adopts the ring network power supply intelligent monitoring method based on multi-source signal comparison according to any one of claims 1 to 9, and the intelligent detection system includes: a quantum enhanced signal acquisition module, an adaptive fusion engine module, a quantum optimized traceability matrix module, and a feedback control module; The quantum-enhanced signal acquisition module is deployed at the power supply nodes of the ring main unit's main line L1 and backup line L2. It uses a quantum tunneling coupler array to synchronously capture three physical signals: electromagnetic oscillation, mechanical vibration, and temperature field. It then applies quantum tunneling enhancement formulas to label the original signals' spatiotemporal harmonic characteristics, generating an enhanced signal stream with millisecond-level synchronization accuracy, which it then transmits to the adaptive fusion engine module. Adaptive fusion engine module: After receiving the enhanced signal stream, it decouples and extracts the voltage phase angle, mechanical vibration characteristic phase, and temperature field phase gradient through a dynamic graph neural network spatiotemporal evolution model. It uses a dynamic weight fusion formula to perform nonlinear coupling operations on heterogeneous signals, improves the contribution of the lagging signal in the fusion tensor, and outputs a dimensionless spatiotemporal fusion tensor that represents the electromechanical and thermal coupling state. Quantum Optimization Traceability Matrix Module: Loads the spatiotemporal fusion tensor into the quantum annealing processor, analyzes the nonlinear correlation of multi-source signals using the quantum Hamiltonian optimization formula, and generates a risk quantification map containing fault location coordinates and harmonic resonance energy distribution, achieving physical layer topological mapping of the fault source. Feedback control module: Based on the fault coordinate diagram and harmonic resonance risk index, the communication channel parameters are reconstructed through the metasurface transmission equation, and a time-scale correction signal containing harmonic monitoring instructions is injected into the quantum enhanced signal acquisition module to form an adaptive sampling period dynamic adjustment constrained by fault risk.

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