Power supply monitoring system with hierarchical authority encryption function

Abstract

The invention relates to the technical field of power supply monitoring, in particular to a power supply monitoring system with a hierarchical authority encryption function, which comprises an edge calculation processing module for reading voltage, current and load data based on a power supply monitoring node, calculating voltage fluctuation in sections, monitoring current change in real time, comparing amplitude and screening a sudden change value; load balance and abnormity are judged, and power node fluctuation characteristics are generated. According to the method, the current and voltage signal characteristics are analyzed step by step by adopting the mode matching algorithm, the power abnormal event is accurately captured, the identification efficiency of the fault event is effectively improved, the false alarm and missing report conditions are reduced, and the accuracy and timeliness of power monitoring are ensured; the fault source node is positioned by using the Bayesian network in combination with the power grid topological relation, so that the fault point is quickly and accurately judged, the positioning time is shortened, the monitoring and management level of the power system is improved, and the stability and safety of the system are enhanced.

Description

Technical Field

[0001] The present invention relates to the technical field of power supply monitoring, and in particular to a power supply monitoring system with a hierarchical authority encryption function. Background Art

[0002] The field of power supply monitoring technology mainly involves real-time monitoring, management and optimization of power systems. It is widely used in industrial automation, power dispatching, building facilities and other scenarios to ensure stable power supply, reasonable distribution and power safety, including real-time monitoring of power status, intelligent distribution of power loads, automatic triggering of abnormal alarms, etc. By integrating hardware sensors, software algorithms and communication protocols, efficient management of power systems can be achieved to prevent losses caused by power overload or interruption.

[0003] The purpose of the power monitoring system with hierarchical permission encryption function is to improve the security of the system while ensuring efficient monitoring of the power system, prevent unauthorized personnel from accessing or tampering with system data, and classify user access rights to ensure that only users with specific permissions can operate or modify the corresponding power monitoring modules. The goal is to provide a safe, controllable and efficient operating environment for power management, ensure the confidentiality and integrity of data, and reduce potential safety hazards.

[0004] Traditional power supply monitoring systems rely on centralized data processing architectures. When faced with complex situations such as sudden changes in power load and fault propagation, data processing delays and monitoring lags are prone to occur, resulting in slow capture and response to power abnormalities, and inability to quickly identify and handle fault points. Simple current and voltage comparison and analysis methods are often used for fault location, lacking in-depth analysis of the grid topology and the relationship between nodes, resulting in low accuracy in fault location and prone to misjudgment or missed judgment, affecting the stability and safety of the entire power system, increasing the time cost of troubleshooting, causing large-scale interruptions in the power system, and causing serious economic losses and safety hazards. Summary of the invention

[0005] The purpose of the present invention is to solve the shortcomings existing in the prior art and to propose a power monitoring system with a hierarchical authority encryption function.

[0006] In order to achieve the above object, the present invention adopts the following technical solution: A power monitoring system with hierarchical authority encryption function includes:

[0007] Edge computing processing module: reads voltage, current, and load data based on power monitoring nodes, performs segmented calculations on node voltage data and obtains fluctuation changes. By real-time monitoring of current change trends, data amplitude comparisons and mutation value screening are performed to determine load balance and abnormal fluctuation characteristics, and generate power node fluctuation characteristics.

[0008] Distributed power supply status monitoring module: based on the fluctuation characteristics of the power supply nodes, compare the voltage balance of the edge nodes, obtain the voltage difference by comparing the voltage data between nodes, detect the current mutation distribution and load status, analyze the imbalance between nodes item by item, check the abnormal expansion and correlation of the power supply status, and generate the distributed power status monitoring results;

[0009] Power event capture module: Based on the distributed power status monitoring results, a pattern matching algorithm is used to capture current fluctuations and voltage anomalies. By gradually analyzing the current and voltage signal characteristics, over-limit values ​​and fluctuation triggering events are determined one by one, and abnormal parameters are compared for multiple event rule matching. Qualified fault events are screened to generate power fault event analysis results;

[0010] Fault fast location module: Based on the analysis results of the power fault event, a Bayesian network is used to read the topological relationship of the power grid, scan the fault current propagation path layer by layer, compare the current transmission status of each connection node, determine the voltage difference and connectivity characteristics, analyze the node position relationship, screen the key fault points layer by layer, determine the fault source node in the power grid, and generate power fault location information;

[0011] Harmonic monitoring and filtering module: based on the power supply fault location information, extract the harmonic spectrum data of the fault node, monitor the fluctuation changes of frequency and amplitude section by section, confirm the abnormal frequency section by amplitude calculation, adjust the filter to suppress the abnormal frequency, adjust the filter parameters in real time to match the harmonic changes, and generate harmonic filtering adjustment data;

[0012] Permission encryption and execution module: Based on the harmonic filtering adjustment data, the node permission level and encryption rules are retrieved, the encryption requirements of the monitoring data are analyzed one by one, the data is encrypted and matched by the permission level, the data encryption strategy is executed, the encrypted monitoring data is stored in a hierarchical and associated manner, and hierarchical permission encrypted data is generated.

[0013] As a further solution of the present invention, the power node fluctuation characteristics include voltage change value, current fluctuation amplitude, and load distribution status; the distributed power status monitoring results include node voltage balance parameters, current mutation distribution, and power load status; the power fault event analysis results include current abnormality events, voltage abnormality events, and load imbalance events; the power fault location information includes fault node identification, path propagation direction, and voltage attenuation position; the harmonic filter adjustment data includes abnormal frequency value, filter adjustment parameters, and suppressed frequency band results; the hierarchical authority encryption data includes encryption level classification, data key configuration, and authority access control.

[0014] As a further solution of the present invention, the edge computing processing module includes a data acquisition submodule, a current trend analysis submodule, and a load balance judgment submodule, wherein:

[0015] Data acquisition submodule: reads voltage, current, and load data based on the power monitoring node, performs segmented voltage sampling and calculates the voltage change value of each segment, gradually analyzes the voltage difference, screens abnormal fluctuation segments, obtains fluctuation data, and generates voltage fluctuation data;

[0016] Current trend analysis submodule: Based on the voltage fluctuation data, the current data is continuously monitored, the current change trend of each time period is analyzed, the current value is compared with the change of the previous time period through amplitude calculation, the high-frequency mutation segment is screened, the abnormal area is identified, and the current change characteristics are generated;

[0017] Load balance judgment submodule: Based on the current change characteristics, extract the load data of each node, compare the current and voltage fluctuation amplitudes of the node load one by one, judge the load state differences between the nodes, mark the unbalanced areas, analyze the overall load dynamics, and generate power node fluctuation characteristics.

[0018] As a further solution of the present invention, the distributed power supply state monitoring module includes a voltage balance detection submodule, a current mutation distribution submodule, and a state imbalance analysis submodule, wherein:

[0019] Voltage balance detection submodule: based on the power node fluctuation characteristics, read the voltage value of each edge node one by one, compare the voltages between nodes, calculate the balance between nodes through the voltage difference, mark the nodes that are out of the normal range, and generate node voltage difference data;

[0020] Current mutation distribution submodule: based on the node voltage difference data, extract the current mutation information of each node, calculate the current response of each node when the voltage fluctuates, identify the current change frequency and fluctuation amplitude, mark the distribution position of the mutation node, and generate the current mutation state;

[0021] State imbalance analysis submodule: Based on the current mutation state, analyze the load voltage and current imbalance between nodes, check the abnormal expansion range one by one, compare the correlation and load stability between nodes, identify the impact range, mark the expansion trend, and generate distributed power state monitoring results.

[0022] As a further solution of the present invention, the power event capture module includes a signal feature analysis submodule, a parameter comparison and matching submodule, and a fault event screening submodule, wherein:

[0023] Signal feature analysis submodule: Based on the distributed power status monitoring results, extract current and voltage signals, sample the current change amplitude and voltage fluctuation value point by point, analyze the signal characteristics of each sampling point, compare the frequency and amplitude of abnormal fluctuations, perform step-by-step data analysis, mark the abnormal fluctuation area, and generate voltage and current signal characteristics;

[0024] Parameter comparison and matching submodule: Based on the characteristics of the voltage and current signals, a pattern matching algorithm is used to determine whether each signal data point exceeds a preset threshold, and the exceeding parts of the signal are compared one by one. By matching abnormal parameters with preset rules, the conditions for triggering events are analyzed, multi-level parameter comparison and rule screening are performed, matching events are obtained, and abnormal event parameter matching results are generated;

[0025] Fault event screening submodule: Based on the abnormal event parameter matching results, screen the events that meet the fault rules one by one, extract the trigger conditions in the analysis events through data feature extraction, eliminate interference items, identify key fault events, classify and identify the events, extract the main event information, and generate power fault event analysis results.

[0026] As a further solution of the present invention, the pattern matching algorithm is according to the formula:

[0027]

[0028] Where: D(P,Q) represents the voltage and current signal data point P i and the preset threshold point Q i The sum of the weighted differences between i represents the ith actual voltage and current signal data point, Q i represents the preset threshold point corresponding to the i-th signal data point, σ i represents the standard deviation of the ith signal data point, W i represents the weight coefficient of the i-th signal point, f(V i ,T i ) represents the voltage V i and temperature T i The correlation function, V i represents the voltage value of the environment where the i-th signal point is located, T i represents the temperature value of the environment where the i-th signal point is located, and n represents the total number of signal data points.

[0029] As a further solution of the present invention, the fault rapid location module includes a current path scanning submodule, a voltage difference judgment submodule, and a fault point screening submodule, wherein:

[0030] Current path scanning submodule: based on the power fault event analysis results, read the grid topology diagram, gradually scan the current transmission path, analyze the current transmission changes of the connections between nodes according to the current flow direction, identify the node areas where the current direction suddenly changes, record the transmission anomalies in the path, and generate the fault current propagation path;

[0031] Voltage difference judgment submodule: Based on the fault current propagation path, the Bayesian network is used to compare the voltage values ​​at the connection points of each node one by one, calculate the voltage change amplitude and difference, perform correlation analysis on the voltage differences between nodes, identify the voltage nodes related to the current anomaly, mark the voltage mutation section, and generate the voltage anomaly node relationship;

[0032] Fault point screening submodule: Based on the voltage abnormal node relationship, compare the matching characteristics of current and voltage at each node, analyze the abnormal propagation trend of each node connection, determine the nodes with unstable transmission, gradually screen the key fault point locations, locate the fault source node, and generate power supply fault location information.

[0033] As a further solution of the present invention, the Bayesian network is according to the formula:

[0034]

[0035] Where: P(V i |V i-1 ,I,T i ,C i ) means that the previous node voltage V is known. i-1 , current I, current node temperature T i and capacitor C i In the case of node V i The conditional probability of the voltage at i-1 |I,T i-1 ) represents node V i-1 The voltage at the node V i-1 Temperature T i-1 Conditional probability under the condition, P(I|V i ,C i ) indicates that at node V i At, voltage V i and capacitor C i The conditional probability of the current I under the condition, P(T i |C i ) indicates that the capacitor C i Under the condition of i The temperature T i The conditional probability of current I is P(I), P(T i ) represents node V i Temperature T i The prior probability of .

[0036] As a further solution of the present invention, the harmonic monitoring and filtering module includes a harmonic data extraction submodule, a frequency fluctuation analysis submodule, and a filter parameter adjustment submodule, wherein:

[0037] Harmonic data extraction submodule: Based on the power supply fault location information, the harmonic spectrum data of the fault node is extracted segment by segment, the harmonic frequency and amplitude are read frequency by frequency, the data is segmented, the fluctuation characteristics of each frequency segment are analyzed, the high amplitude frequencies are marked one by one, the frequency position and value of abnormal fluctuations are recorded, and the fault harmonic spectrum characteristics are generated;

[0038] Frequency fluctuation analysis submodule: Based on the fault harmonic spectrum characteristics, the amplitude fluctuation of each frequency segment is compared segment by segment, the amplitude difference is calculated segment by segment, the frequency value of each segment is compared, the high-frequency abnormal amplitude is confirmed, and the abnormal frequency is marked and recorded by locating the over-limit point of the frequency fluctuation to generate abnormal frequency segment data;

[0039] Filter parameter adjustment submodule: Based on the abnormal frequency band data, the filter parameters are gradually adjusted, the amplitude of the abnormal frequency is analyzed, the suppression response of the filter is set, the frequency of a specific frequency band is suppressed, the filter is adjusted to match the abnormal frequency, the frequency suppression operation is performed, and the harmonic filter adjustment data is generated.

[0040] As a further solution of the present invention, the permission encryption and execution module includes a permission level parsing submodule, an encryption rule matching submodule, and a hierarchical encryption execution submodule, wherein:

[0041] Permission level analysis submodule: based on the harmonic filtering adjustment data, read the permission level of each node, extract the permission range and requirements of the node by analyzing the permission information of each node, analyze the correlation between the permission information and the monitoring data one by one, confirm the permission level that needs to be encrypted, and generate the permission level requirement;

[0042] Encryption rule matching submodule: Based on the permission level requirements, the encryption requirements of each monitoring data are analyzed one by one, and the encryption method required for the data is matched by comparing the permission requirements with the existing encryption rules, checking the correspondence between the permission conditions and the encryption rules, setting encryption parameters layer by layer, and generating encryption matching results;

[0043] Hierarchical encryption execution submodule: Based on the encryption matching results, perform encryption operations on the data, hierarchically encrypt the data of each layer of permissions, set encryption keys and policies at different levels, perform encryption operations layer by layer, store the encrypted data according to permissions, and establish hierarchical permission encrypted data.

[0044] Compared with the prior art, the advantages and positive effects of the present invention are:

[0045] In the present invention, a pattern matching algorithm is used to gradually analyze the characteristics of current and voltage signals, accurately capture abnormal power events, effectively improve the efficiency of fault event recognition, reduce false alarms and missed alarms, ensure the accuracy and timeliness of power monitoring, and use the Bayesian network in combination with the power grid topology relationship to locate the fault source node, thereby achieving rapid and accurate fault point determination, shortening the positioning time, improving the monitoring and management level of the power system, enhancing the stability and safety of the system, and reducing the negative impact of power failures on system operation and economic activities. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 is a system flow chart of the present invention;

[0047] Figure 2 Schematic diagram of the system framework of the present invention. DETAILED DESCRIPTION

[0048] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0049] Embodiment 1

[0050] See also Figure 1 The present invention provides a technical solution: a power monitoring system with hierarchical authority encryption function comprises:

[0051] Edge computing processing module: reads voltage, current, and load data based on power monitoring nodes, performs segmented calculations on node voltage data and obtains fluctuation changes. By real-time monitoring of current change trends, data amplitude comparisons and mutation value screening are performed to determine load balance and abnormal fluctuation characteristics, and generate power node fluctuation characteristics.

[0052] Distributed power supply status monitoring module: Based on the fluctuation characteristics of power supply nodes, compare the voltage balance of edge nodes, obtain voltage differences by comparing voltage data between nodes, detect current mutation distribution and load status, analyze the imbalance between nodes item by item, check the abnormal expansion and correlation of power supply status, and generate distributed power status monitoring results;

[0053] Power event capture module: Based on the distributed power status monitoring results, the pattern matching algorithm is used to capture current fluctuations and voltage anomalies. By gradually analyzing the current and voltage signal characteristics, the over-limit value and fluctuation trigger events are determined one by one, and the abnormal parameters are compared to perform multiple event rule matching, and the fault events that meet the conditions are screened to generate power fault event analysis results;

[0054] Fault fast location module: Based on the analysis results of power fault events, the Bayesian network is used to read the topological relationship of the power grid, scan the fault current propagation path layer by layer, compare the current transmission status of each connection node, determine the voltage difference and connectivity characteristics, analyze the node position relationship, screen the key fault points layer by layer, determine the fault source node in the power grid, and generate power fault location information;

[0055] Harmonic monitoring and filtering module: Based on the power supply fault location information, the harmonic spectrum data of the fault node is extracted, the fluctuation changes of frequency and amplitude are monitored section by section, the abnormal frequency section is confirmed by amplitude calculation, the filter is adjusted to suppress the abnormal frequency, the filtering parameters are adjusted in real time to match the harmonic changes, and the harmonic filtering adjustment data is generated;

[0056] Permission encryption and execution module: adjusts data based on harmonic filtering, retrieves node permission levels and encryption rules, analyzes encryption requirements of monitoring data one by one, encrypts and matches data by permission level, executes data encryption strategy, stores encrypted monitoring data in a hierarchical manner, and generates hierarchical permission encrypted data;

[0057] Power node fluctuation characteristics include voltage change value, current fluctuation amplitude, and load distribution status. Distributed power status monitoring results include node voltage balance parameters, current mutation distribution, and power load status. Power fault event analysis results include current abnormality events, voltage abnormality events, and load imbalance events. Power fault location information includes fault node identification, path propagation direction, and voltage attenuation position. Harmonic filter adjustment data includes abnormal frequency value, filter adjustment parameters, and suppressed frequency band results. Hierarchical authority encryption data includes encryption level classification, data key configuration, and authority access control.

[0058] See also Figure 2 The edge computing processing module includes a data acquisition submodule, a current trend analysis submodule, and a load balancing judgment submodule, among which:

[0059] Data acquisition submodule: reads voltage, current, and load data based on the power monitoring node, performs segmented voltage sampling and calculates the voltage change value of each segment, gradually analyzes the voltage difference, screens abnormal fluctuation segments, obtains fluctuation data, and generates voltage fluctuation data;

[0060] Current trend analysis submodule: Based on voltage fluctuation data, it continuously monitors current data, analyzes the current change trend in each time period, compares the change between the current value and the previous time period through amplitude calculation, screens high-frequency mutation segments, identifies abnormal areas, and generates current change characteristics;

[0061] Load balance judgment submodule: Based on the current change characteristics, extract the load data of each node, compare the current and voltage fluctuation amplitudes of the node load one by one, judge the load state differences between the nodes, mark the unbalanced areas, analyze the overall load dynamics, and generate the power node fluctuation characteristics;

[0062] Data acquisition submodule: Based on the power monitoring node, the ADC sampling algorithm is adopted, and the ADC channel 0 is set to collect the voltage signal. The parameters include sampling frequency of 500Hz and resolution of 12 bits. The voltage signal is preprocessed by a low-pass filter. The filter order is 4th order and the cut-off frequency is 100Hz. The preprocessed voltage signal is sampled in sections, and the sampling section length is set to 100ms. The sampling value is calculated by differential operation for each voltage change value. The voltage difference between the adjacent sections is compared by a section-by-section comparison algorithm, and the voltage change amplitude of each section is calculated. The voltage difference threshold is set to ±5%, and the fluctuation section where the voltage change exceeds the threshold is screened out, and the fluctuation data is recorded to generate voltage fluctuation data;

[0063] Current trend analysis submodule: Based on the voltage fluctuation data, a continuous monitoring algorithm is used to monitor the current data. The Fourier transform analysis algorithm is used, and the sampling frequency is set to 1000Hz. The current signal is analyzed in the frequency domain to analyze the current change trend in each time period. Specifically, the current data is discretely segmented per second, and the current value of the current period is compared with the current value of the previous period through the amplitude calculation algorithm. The change amplitude threshold is set to ±3A, and the high-frequency mutation segment with a current change amplitude greater than the threshold is screened out. The marking algorithm is used to identify the start and end time points of the mutation segment, and further feature extraction is performed. The extracted features include peak current, average current, and change amplitude, and the current change feature is generated;

[0064] Load balance judgment submodule: Based on the current change characteristics, the node load comparison algorithm is used to extract the load data of each node, compare the fluctuation amplitude of the current and voltage of each node, and use the node load comparison formula to calculate the load state difference of each node. The load difference judgment threshold is set to 10%, and the node load states are compared one by one. If the difference is greater than the threshold, the unbalanced area is marked, and the load dynamic analysis algorithm is used to calculate the fluctuation characteristics of the overall load to generate the power supply node fluctuation characteristics.

[0065] See also Figure 2 The distributed power supply status monitoring module includes a voltage balance detection submodule, a current mutation distribution submodule, and a state imbalance analysis submodule, among which:

[0066] Voltage balance detection submodule: Based on the fluctuation characteristics of power supply nodes, read the voltage value of each edge node one by one, compare the voltage between nodes, calculate the balance between nodes through voltage difference, mark the nodes that are out of the normal range, and generate node voltage difference data;

[0067] Current mutation distribution submodule: Based on the node voltage difference data, extract the current mutation information of each node, calculate the current response of each node when the voltage fluctuates, identify the current change frequency and fluctuation amplitude, mark the distribution position of the mutation node, and generate the current mutation state;

[0068] State imbalance analysis submodule: Based on the current mutation state, analyze the load voltage and current imbalance between nodes, check the abnormal extension range one by one, and compare the correlation between nodes and load stability.

[0069] Identify the impact scope, mark the expansion trend, and generate distributed power status monitoring results;

[0070] Based on the fluctuation characteristics of power supply nodes, a multi-node voltage acquisition algorithm is used to read the voltage value of each edge node one by one. The sampling frequency is set to 200Hz, and the resolution of voltage acquisition is set to 0.01V. The voltage between each node is compared, and the voltage balance is detected using the difference calculation method. The balance threshold is set to ±0.05V. When the voltage difference exceeds the set threshold, the abnormal node identification algorithm is used to mark the node and generate node voltage difference data.

[0071] Current mutation distribution submodule: Based on the node voltage difference data, the current change extraction algorithm is used to extract the current mutation information from each node. The time window is set to 500ms, and the maximum current difference in each time window is calculated using the difference algorithm. The fluctuation amplitude is set to the difference between the maximum and minimum values ​​of the current change, the position of the current mutation node is marked, and the current mutation state is generated;

[0072] State imbalance analysis submodule: Based on the current mutation state, the load imbalance analysis algorithm is used to analyze the load voltage and current imbalance of each node, check the abnormal extension range one by one, and use the correlation analysis algorithm to compare the load status of adjacent nodes, detect the load change trend between nodes, mark the expansion trend area, and generate distributed power state monitoring results.

[0073] See also Figure 2 The power event capture module includes a signal feature analysis submodule, a parameter comparison and matching submodule, and a fault event screening submodule, among which:

[0074] Signal feature analysis submodule: Based on the distributed power status monitoring results, the current and voltage signals are extracted, the current change amplitude and voltage fluctuation value are sampled point by point, the signal characteristics of each sampling point are analyzed, the frequency and amplitude of abnormal fluctuations are compared, the data are analyzed step by step, the abnormal fluctuation area is marked, and the voltage and current signal characteristics are generated;

[0075] Parameter comparison and matching submodule: Based on the characteristics of voltage and current signals, the pattern matching algorithm is used to determine whether each signal data point exceeds the preset threshold, and the exceeding part of the signal is compared one by one. By matching abnormal parameters with preset rules, the conditions for triggering events are analyzed, multi-level parameter comparison and rule screening are performed, matching events are obtained, and abnormal event parameter matching results are generated;

[0076] Fault event screening submodule: Based on the abnormal event parameter matching results, screen the events that meet the fault rules one by one, extract and analyze the trigger conditions in the events through data feature extraction, eliminate interference items, identify key fault events, classify and identify the events, extract the main event information, and generate power fault event analysis results;

[0077] Signal feature analysis submodule: Based on the distributed power status monitoring results, a point-by-point sampling algorithm is used, the sampling interval is set to 10ms, the current and voltage signals are extracted one by one, the signal of each sampling point is analyzed in the frequency domain by the fast Fourier transform algorithm, the signal frequency component is analyzed, the abnormal frequency threshold is set to 50Hz, the amplitude comparison algorithm is used to calculate the amplitude of the abnormal fluctuation, the data is analyzed step by step, the window length is set to 1 second during the data analysis process, the labeling algorithm is used to mark the abnormal fluctuation area, and the voltage and current signal characteristics are generated;

[0078] Parameter comparison and matching submodule: Based on the characteristics of voltage and current signals, a pattern matching algorithm is used. First, the preset thresholds are set to voltage ±5V and current ±2A. A point-by-point matching algorithm is used to determine whether each signal data point exceeds the preset threshold. Secondary screening is performed by extracting the over-limit part of the signal, and the over-limit signal points are compared one by one. The conditions for triggering events are analyzed using an association rule analysis algorithm. During the parameter comparison process, the signals are screened step by step, and multi-level parameter comparisons are set. The first level is the voltage change amplitude, the second level is the current change rate, and the third level is the signal fluctuation frequency. By matching and analyzing the abnormal parameters with the preset rules, matching events that meet the rules are obtained, and abnormal event parameter matching results are generated;

[0079] Fault event screening submodule: Based on the abnormal event parameter matching results, the fault screening algorithm is used to screen events one by one, and the key trigger conditions in the events are extracted through the signal feature extraction algorithm. The condition threshold is set to a current fluctuation frequency higher than 60Hz, and interference items are eliminated layer by layer. The interference filtering algorithm is used to remove signal data that does not meet the threshold conditions, identify key events that meet the fault rules, and use the event classification algorithm to classify and identify fault events. The classification standards are voltage fluctuation amplitude, current mutation rate and fault duration. The main event information is further extracted, and the power fault event analysis results are generated.

[0080] Pattern matching algorithm, according to the formula:

[0081]

[0082] Where: D(P,Q) represents the voltage and current signal data point P i and the preset threshold point Q i The sum of the weighted differences between i represents the ith actual voltage and current signal data point, Q i represents the preset threshold point corresponding to the i-th signal data point, σ i represents the standard deviation of the ith signal data point, W i represents the weight coefficient of the i-th signal point, f(V i ,T i ) represents the voltage V i and temperature T i The correlation function, V i represents the voltage value of the environment where the i-th signal point is located, T i represents the temperature value of the environment where the i-th signal point is located, and n represents the total number of signal data points;

[0083] Execution process: First, collect the voltage and current signal data of the power supply in real time, which is expressed as each specific data point P i , according to the expected operating standards or historical data, determine the i The corresponding preset threshold Q i , as the signal anomaly judgment benchmark in power supply monitoring, the volatility of each signal point is standardized and the standard deviation σ of each signal point is calculated i , which is used to measure the fluctuation amplitude of signal data. In order to distinguish the importance of each signal data point in power supply monitoring, a weight coefficient W is set for each signal point. i Considering that the fluctuation of ambient voltage and temperature in the power supply system will affect the signal data, the correlation function f(V i ,T i ) is used to adjust the impact of these external factors on the signal and correct the voltage V i and temperature T i The impact of signal deviation is calculated, the weighted difference is determined, the abnormal signal in the power supply monitoring system is judged, and the corresponding abnormal event report is generated in combination with the hierarchical authority encryption mechanism to achieve comprehensive monitoring and abnormal warning of the power supply system.

[0084] See also Figure 2 The fault rapid location module includes a current path scanning submodule, a voltage difference judgment submodule, and a fault point screening submodule, wherein:

[0085] Current path scanning submodule: Based on the analysis results of power fault events, read the grid topology diagram, gradually scan the current transmission path, analyze the current transmission changes of the connections between nodes according to the current flow direction, identify the node areas where the current direction suddenly changes, record the transmission anomalies in the path, and generate the fault current propagation path;

[0086] Voltage difference judgment submodule: Based on the fault current propagation path, the Bayesian network is used to compare the voltage values ​​at each node connection one by one, calculate the voltage change amplitude and difference, perform correlation analysis on the voltage differences between nodes, identify the voltage nodes related to current anomalies, mark the voltage mutation sections, and generate voltage anomaly node relationships;

[0087] Fault point screening submodule: Based on the voltage abnormal node relationship, compare the matching characteristics of current and voltage at each node, analyze the abnormal propagation trend at the connection of each node, determine the nodes with unstable transmission, gradually screen the key fault point locations, locate the fault source node, and generate power supply fault location information;

[0088] Current path scanning submodule: Based on the analysis results of power fault events, the grid topology diagram is read using a step-by-step path scanning algorithm. The scanning interval is set to 100ms, and the current transmission path is scanned step by step. The current transmission change analysis is performed on the connection between each node. The current change threshold is set to ±2A. The node area where the current trend suddenly changes is identified through a differential algorithm. The current transmission anomaly in the transmission path is recorded, the abnormal nodes and areas are marked, and the fault current propagation path is generated.

[0089] Voltage difference judgment submodule: Based on the fault current propagation path, the Bayesian network algorithm is used to compare the voltage values ​​at each node connection one by one, and the voltage change probability of each node is calculated. E is a voltage mutation event, X is a current abnormality event, and the voltage change amplitude and difference are calculated one by one. The voltage difference threshold is set to ±5V, and the association analysis algorithm is used to calculate the voltage difference between nodes, identify the voltage nodes related to the current abnormality, mark the voltage mutation section, and generate the voltage abnormality node relationship;

[0090] Fault point screening submodule: Based on the voltage abnormal node relationship, a node-by-node fault screening algorithm is adopted to compare the current and voltage matching characteristics of each node, analyze the abnormal propagation trend of the node connection, set the current and voltage change thresholds one by one, the current threshold is ±2A, and the voltage threshold is ±5V. The recursive search algorithm is used to gradually screen the key fault point locations, and the depth-first search algorithm is used to determine the fault propagation path, locate the fault source node, and generate power supply fault location information.

[0091] Bayesian network, according to the formula:

[0092]

[0093] Where: P(V i |V i-1 ,I,T i ,C i ) means that the previous node voltage V i-1 , current I, current node temperature T i and capacitor C i In the case of node V i The conditional probability of the voltage at i-1 |I,T i-1 ) represents the node V i-1 The voltage at the node V i-1 Temperature T i-1 Conditional probability under the condition, P(I|V i ,C i ) indicates that at node V i At, voltage V i and capacitor C i The conditional probability of the current I under the condition, P(T i |C i ) indicates that the capacitor C i Under the condition of i The temperature T i The conditional probability of current I is P(I), P(T i ) represents the node V i Temperature T i The prior probability of

[0094] Execution process: First, the formula starts from node V i-1 The voltage V i-1 , current I and temperature T i-1 The conditional probability P(V i-1 |I,T i-1 ), combined with the effect of temperature on voltage fluctuation, as the current propagates to the next node V i , by calculating the node V i The current I at the voltage V i and capacitor C i The conditional probability P(I|V i ,C i ), taking into account the capacitance C i The physical properties that affect the current are shown by P(T i |C i ) Calculate the capacitance C i For node temperature T i The influence of temperature, especially in high-voltage environments, may significantly change the capacitance, thus affecting the voltage stability of the node. Combining the conditional probability of each node and the prior probability P(I) and P(Ti ), calculate the node V through the Bayesian network model i The voltage V i The conditional probability of generating the relationship between voltage abnormal nodes.

[0095] See also Figure 2 The harmonic monitoring and filtering module includes a harmonic data extraction submodule, a frequency fluctuation analysis submodule, and a filter parameter adjustment submodule, among which:

[0096] Harmonic data extraction submodule: Based on the power supply fault location information, the harmonic spectrum data of the fault node is extracted segment by segment. The harmonic frequency and amplitude are read frequency by frequency, and the data is segmented. The fluctuation characteristics of each frequency segment are analyzed, and the high amplitude frequencies are marked one by one. The frequency position and value of abnormal fluctuations are recorded to generate the fault harmonic spectrum characteristics.

[0097] Frequency fluctuation analysis submodule: Based on the fault harmonic spectrum characteristics, the amplitude fluctuation of each frequency segment is compared segment by segment, the amplitude difference is calculated segment by segment, the frequency value of each segment is compared, the high-frequency abnormal amplitude is confirmed, and the abnormal frequency is marked and recorded by locating the over-limit point of the frequency fluctuation to generate abnormal frequency segment data;

[0098] Filter parameter adjustment submodule: Based on the abnormal frequency band data, the filter parameters are gradually adjusted. By performing amplitude analysis on the abnormal frequency, the filter suppression response is set, the frequency of a specific frequency band is suppressed, the filter is adjusted to match the abnormal frequency, the frequency suppression operation is performed, and the harmonic filter adjustment data is generated;

[0099] Harmonic data extraction submodule: Based on the power supply fault location information, the frequency-by-frequency extraction algorithm is used to extract the harmonic spectrum data of the fault node segment by segment. The frequency sampling interval is set to 1Hz. The harmonic frequency and amplitude are read frequency by frequency, and the fast Fourier transform algorithm is used to decompose each frequency component, calculate the harmonic amplitude one by one, set the high amplitude threshold to voltage ±5V, and use the segmented data analysis algorithm to segment the spectrum data. The fluctuation characteristics of each frequency segment are analyzed, the high amplitude frequency position is marked one by one, the value and position of each abnormal frequency are recorded, and the fault harmonic spectrum characteristics are generated;

[0100] Frequency fluctuation analysis submodule: Based on the fault harmonic spectrum characteristics, the frequency segment comparison algorithm is used to compare the amplitude fluctuation of each frequency segment segment by segment, the frequency segment width is set to 50Hz, the amplitude difference calculation is performed, the amplitude fluctuation of each frequency segment is confirmed, the frequency value of each segment is compared, and the high-frequency abnormal amplitude is determined by the over-limit judgment algorithm, the over-limit point is set to ±5%, the over-limit frequency point is marked, the value and position of the over-limit frequency are recorded, and the abnormal frequency segment data is generated;

[0101] Filter parameter adjustment submodule: Based on the abnormal frequency band data, the filter parameter optimization algorithm is used to gradually adjust the filter parameters, the filter type is set to a band-stop filter, the initial filter frequency band range is set to 50Hz to 150Hz, and the amplitude of each frequency band is calculated through amplitude analysis of the abnormal frequency band. The filter suppression response is set to -20dB, and the frequency of the out-of-limit frequency band is suppressed. The filter frequency range is adjusted using an adaptive filtering algorithm to match the abnormal frequency, the filter frequency suppression operation is performed, and harmonic filtering adjustment data is generated.

[0102] See also Figure 2 The permission encryption and execution module includes a permission level analysis submodule, an encryption rule matching submodule, and a layered encryption execution submodule, among which:

[0103] Permission level analysis submodule: Based on the harmonic filtering adjustment data, read the permission level of each node, extract the permission range and requirements of the node by analyzing the permission information of each node, analyze the correlation between the permission information and the monitoring data one by one, confirm the permission level that needs to be encrypted, and generate the permission level requirements;

[0104] Encryption rule matching submodule: Based on the permission level requirements, the encryption requirements of each monitoring data are analyzed one by one, and the encryption method required for the data is matched by comparing the permission requirements with the existing encryption rules, checking the correspondence between the permission conditions and the encryption rules, setting encryption parameters layer by layer, and generating encryption matching results;

[0105] Hierarchical encryption execution submodule: Based on the encryption matching results, perform encryption operations on the data. By hierarchically encrypting the data of each layer of permissions, setting encryption keys and policies at different levels, performing encryption operations layer by layer, storing the encrypted data according to permissions, and establishing hierarchical permission encrypted data;

[0106] Permission level analysis submodule: Based on the harmonic filtering adjustment data, the permission analysis algorithm is used to read the permission level information of each node, and the permission information extraction function is used to extract the permission range and requirements of the node one by one, and the permission level range is set to 1 to 5. The corresponding relationship between the permission information and the monitoring data of each node is analyzed through the permission association analysis algorithm, and the correlation between the permission level and the monitoring data is analyzed one by one. The permission encryption judgment function is used to confirm the permission level that needs to be encrypted, and the encryption condition is set to the permission level greater than or equal to level 3. The permission information of all matching nodes is recorded, and the permission level requirements are generated;

[0107] Encryption rule matching submodule: Based on the permission level requirements, the encryption matching algorithm is used to analyze the encryption requirements of each monitoring data one by one, and the encryption requirement parameters are set as data type and permission level. By comparing the encryption requirements with the existing encryption rules, the encryption method required for the data is matched. The rule matching algorithm is used to check the correspondence between the permission conditions and the encryption rules layer by layer. The encryption method is set to the AES algorithm, the encryption key length is 256 bits, and the encryption parameters are set layer by layer, including the key generation function, encryption block size and initial vector. The matching method is confirmed through the encryption matching function, and the encryption matching result is generated;

[0108] Hierarchical encryption execution submodule: Based on the encryption matching results, a hierarchical encryption algorithm is used to perform encryption operations on the data. The encryption level is set to level 3, and data with permission level 3 and above is encrypted. The RSA algorithm is used to double encrypt permission data above level 5. Encryption keys are distributed layer by layer. The key generation function uses the PBKDF2 algorithm with 10,000 iterations. Encryption operations are performed layer by layer, and the encrypted data is divided into three layers according to the permission level. A hierarchical storage algorithm is used to store encrypted data of different levels in independent storage paths according to permissions, and hierarchical permission encrypted data is established.

[0109] The above are only preferred embodiments of the present invention and are not intended to limit the present invention in other forms. Any technician familiar with the profession may use the technical contents disclosed above to change or modify them into equivalent embodiments with equivalent changes and apply them to other fields. However, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present invention without departing from the technical solution of the present invention still falls within the protection scope of the technical solution of the present invention.

Claims

1. A power monitoring system with hierarchical authority encryption function, characterized in that: The system comprises: Edge computing processing module: reads voltage, current, and load data based on power monitoring nodes, performs segmented calculations on node voltage data and obtains fluctuation changes. By real-time monitoring of current change trends, data amplitude comparisons and mutation value screening are performed to determine load balance and abnormal fluctuation characteristics, and generate power node fluctuation characteristics. Distributed power supply status monitoring module: based on the fluctuation characteristics of the power supply nodes, compare the voltage balance of the edge nodes, obtain the voltage difference by comparing the voltage data between nodes, detect the current mutation distribution and load status, analyze the imbalance between nodes item by item, check the abnormal expansion and correlation of the power supply status, and generate the distributed power status monitoring results; Power event capture module: Based on the distributed power status monitoring results, a pattern matching algorithm is used to capture current fluctuations and voltage anomalies. By gradually analyzing the current and voltage signal characteristics, over-limit values ​​and fluctuation triggering events are determined one by one, and abnormal parameters are compared for multiple event rule matching. Qualified fault events are screened to generate power fault event analysis results; Fault fast location module: Based on the analysis results of the power fault event, a Bayesian network is used to read the topological relationship of the power grid, scan the fault current propagation path layer by layer, compare the current transmission status of each connection node, determine the voltage difference and connectivity characteristics, analyze the node position relationship, screen the key fault points layer by layer, determine the fault source node in the power grid, and generate power fault location information; Harmonic monitoring and filtering module: based on the power supply fault location information, extract the harmonic spectrum data of the fault node, monitor the fluctuation changes of frequency and amplitude section by section, confirm the abnormal frequency section by amplitude calculation, adjust the filter to suppress the abnormal frequency, adjust the filter parameters in real time to match the harmonic changes, and generate harmonic filtering adjustment data; Permission encryption and execution module: Based on the harmonic filtering adjustment data, the node permission level and encryption rules are retrieved, the encryption requirements of the monitoring data are analyzed one by one, the data is encrypted and matched by the permission level, the data encryption strategy is executed, the encrypted monitoring data is stored in a hierarchical and associated manner, and hierarchical permission encrypted data is generated.

2. The power monitoring system with hierarchical authority encryption function according to claim 1, characterized in that: The power node fluctuation characteristics include voltage change value, current fluctuation amplitude, and load distribution status. The distributed power status monitoring results include node voltage balance parameters, current mutation distribution, and power load status. The power fault event analysis results include current abnormal events, voltage abnormal events, and load imbalance events. The power fault location information includes fault node identification, path propagation direction, and voltage attenuation position. The harmonic filter adjustment data includes abnormal frequency value, filter adjustment parameters, and suppressed frequency band results. The hierarchical authority encryption data includes encryption level classification, data key configuration, and authority access control.

3. The power monitoring system with hierarchical authority encryption function according to claim 1, characterized in that: The edge computing processing module includes a data acquisition submodule, a current trend analysis submodule, and a load balance judgment submodule, wherein: Data acquisition submodule: reads voltage, current, and load data based on the power monitoring node, performs segmented voltage sampling and calculates the voltage change value of each segment, gradually analyzes the voltage difference, screens abnormal fluctuation segments, obtains fluctuation data, and generates voltage fluctuation data; Current trend analysis submodule: Based on the voltage fluctuation data, the current data is continuously monitored, the current change trend of each time period is analyzed, the current value is compared with the change of the previous time period through amplitude calculation, the high-frequency mutation segment is screened, the abnormal area is identified, and the current change characteristics are generated; Load balance judgment submodule: Based on the current change characteristics, extract the load data of each node, compare the current and voltage fluctuation amplitudes of the node load one by one, judge the load state differences between the nodes, mark the unbalanced areas, analyze the overall load dynamics, and generate power node fluctuation characteristics.

4. The power monitoring system with hierarchical authority encryption function according to claim 1, characterized in that: The distributed power supply state monitoring module includes a voltage balance detection submodule, a current mutation distribution submodule, and a state imbalance analysis submodule, wherein: Voltage balance detection submodule: based on the power node fluctuation characteristics, read the voltage value of each edge node one by one, compare the voltages between nodes, calculate the balance between nodes through the voltage difference, mark the nodes that are out of the normal range, and generate node voltage difference data; Current mutation distribution submodule: based on the node voltage difference data, extract the current mutation information of each node, calculate the current response of each node when the voltage fluctuates, identify the current change frequency and fluctuation amplitude, mark the distribution position of the mutation node, and generate the current mutation state; State imbalance analysis submodule: Based on the current mutation state, analyze the load voltage and current imbalance between nodes, check the abnormal expansion range one by one, compare the correlation and load stability between nodes, identify the impact range, mark the expansion trend, and generate distributed power state monitoring results.

5. The power monitoring system with hierarchical authority encryption function according to claim 1, characterized in that: The power event capture module includes a signal feature analysis submodule, a parameter comparison and matching submodule, and a fault event screening submodule, wherein: Signal feature analysis submodule: Based on the distributed power status monitoring results, extract current and voltage signals, sample the current change amplitude and voltage fluctuation value point by point, analyze the signal characteristics of each sampling point, compare the frequency and amplitude of abnormal fluctuations, perform step-by-step data analysis, mark the abnormal fluctuation area, and generate voltage and current signal characteristics; Parameter comparison and matching submodule: Based on the characteristics of the voltage and current signals, a pattern matching algorithm is used to determine whether each signal data point exceeds a preset threshold, and the exceeding parts of the signal are compared one by one. By matching abnormal parameters with preset rules, the conditions for triggering events are analyzed, multi-level parameter comparison and rule screening are performed, matching events are obtained, and abnormal event parameter matching results are generated; Fault event screening submodule: Based on the abnormal event parameter matching results, screen the events that meet the fault rules one by one, extract the trigger conditions in the analysis events through data feature extraction, eliminate interference items, identify key fault events, classify and identify the events, extract the main event information, and generate power fault event analysis results.

6. The power monitoring system with hierarchical authority encryption function according to claim 1, characterized in that: The pattern matching algorithm is based on the formula: Where: D(P,Q) represents the voltage and current signal data point P i and the preset threshold point Q i The sum of the weighted differences between i represents the ith actual voltage and current signal data point, Q i represents the preset threshold point corresponding to the i-th signal data point, σ i represents the standard deviation of the ith signal data point, W i represents the weight coefficient of the i-th signal point, f(V i ,T i ) represents the voltage V i and temperature T i The correlation function, V i represents the voltage value of the environment where the i-th signal point is located, T i represents the temperature value of the environment where the i-th signal point is located, and n represents the total number of signal data points.

7. The power monitoring system with hierarchical authority encryption function according to claim 1, characterized in that: The fault rapid location module includes a current path scanning submodule, a voltage difference judgment submodule, and a fault point screening submodule, wherein: Current path scanning submodule: based on the power fault event analysis results, read the grid topology diagram, gradually scan the current transmission path, analyze the current transmission changes of the connections between nodes according to the current flow direction, identify the node areas where the current direction suddenly changes, record the transmission anomalies in the path, and generate the fault current propagation path; Voltage difference judgment submodule: Based on the fault current propagation path, the Bayesian network is used to compare the voltage values ​​at the connection points of each node one by one, calculate the voltage change amplitude and difference, perform correlation analysis on the voltage differences between nodes, identify the voltage nodes related to the current anomaly, mark the voltage mutation section, and generate the voltage anomaly node relationship; Fault point screening submodule: Based on the voltage abnormal node relationship, compare the matching characteristics of current and voltage at each node, analyze the abnormal propagation trend of each node connection, determine the nodes with unstable transmission, gradually screen the key fault point locations, locate the fault source node, and generate power supply fault location information.

8. The power monitoring system with hierarchical authority encryption function according to claim 1, characterized in that: The Bayesian network, according to the formula: Where: P(V i |V i-1 ,I,T i ,C i ) means that the previous node voltage V is known. i-1 , current I, current node temperature T i and capacitor C i In the case of node V i The conditional probability of the voltage at i-1 |I,T i-1 ) represents node V i-1 The voltage at the node V i-1 Temperature T i-1 Conditional probability under the condition, P (I|V i ,C i ) indicates that at node V i At, voltage V i and capacitor C i The conditional probability of the current I under the condition, P(T i |C i ) indicates that the capacitor C i Under the condition of i The temperature T i The conditional probability of current I is P(I), P(T i ) represents the node V i Temperature T i The prior probability of .

9. The power monitoring system with hierarchical authority encryption function according to claim 1, characterized in that: The harmonic monitoring and filtering module includes a harmonic data extraction submodule, a frequency fluctuation analysis submodule, and a filter parameter adjustment submodule, wherein: Harmonic data extraction submodule: Based on the power supply fault location information, the harmonic spectrum data of the fault node is extracted segment by segment, the harmonic frequency and amplitude are read frequency by frequency, the data is segmented, the fluctuation characteristics of each frequency segment are analyzed, the high amplitude frequencies are marked one by one, the frequency position and value of abnormal fluctuations are recorded, and the fault harmonic spectrum characteristics are generated; Frequency fluctuation analysis submodule: Based on the fault harmonic spectrum characteristics, the amplitude fluctuation of each frequency segment is compared segment by segment, the amplitude difference is calculated segment by segment, the frequency value of each segment is compared, the high-frequency abnormal amplitude is confirmed, and the abnormal frequency is marked and recorded by locating the over-limit point of the frequency fluctuation to generate abnormal frequency segment data; Filter parameter adjustment submodule: Based on the abnormal frequency band data, the filter parameters are gradually adjusted, the amplitude of the abnormal frequency is analyzed, the suppression response of the filter is set, the frequency of a specific frequency band is suppressed, the filter is adjusted to match the abnormal frequency, the frequency suppression operation is performed, and the harmonic filter adjustment data is generated.

10. The power monitoring system with hierarchical authority encryption function according to claim 1, characterized in that: The permission encryption and execution module includes a permission level analysis submodule, an encryption rule matching submodule, and a layered encryption execution submodule, wherein: Permission level analysis submodule: based on the harmonic filtering adjustment data, read the permission level of each node, extract the permission range and requirements of the node by analyzing the permission information of each node, analyze the correlation between the permission information and the monitoring data one by one, confirm the permission level that needs to be encrypted, and generate the permission level requirement; Encryption rule matching submodule: Based on the permission level requirements, the encryption requirements of each monitoring data are analyzed one by one, and the encryption method required for the data is matched by comparing the permission requirements with the existing encryption rules, checking the correspondence between the permission conditions and the encryption rules, setting encryption parameters layer by layer, and generating encryption matching results; Hierarchical encryption execution submodule: Based on the encryption matching results, perform encryption operations on the data, hierarchically encrypt the data of each layer of permissions, set encryption keys and policies at different levels, perform encryption operations layer by layer, store the encrypted data according to permissions, and establish hierarchical permission encrypted data.

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