Power system network security real-time monitoring method and system

Abstract

The invention provides a power system network security real-time monitoring method and system, and the method comprises the steps: a first fault diagnosis unit is used for carrying out the initial fault diagnosis according to operation data, and outputting an initial fault diagnosis result; the segmentation processing unit is used for segmenting the operation data according to the initial fault diagnosis result and outputting segmented data; the second fault diagnosis unit is used for performing detail fault diagnosis according to the segmented data and outputting a detail fault diagnosis result; and the fault positioning unit outputs the minimum fault operation data segment. According to the method, the specific time of fault occurrence can be accurately positioned, so that the troubleshooting time of operation and maintenance personnel is shortened, the fault can be predicted more quickly, sudden failure of equipment is prevented, the downtime is reduced, and safe and stable operation of a power system is guaranteed.

Description

Technical Field

[0001] The present invention relates to an electric power system, and in particular to an electric power system network security real-time monitoring method and system. Background Art

[0002] Power system equipment monitoring and fault diagnosis are of key significance and important role in ensuring the safe and stable operation of the power system. Power system equipment monitoring can accurately understand the working status of the equipment by collecting various data of equipment operation (such as current, voltage, temperature, vibration, etc.) in real time. This helps to discover potential problems in advance, prevent faults from occurring, ensure the continuous and efficient operation of the power system, and reduce sudden power outages. Power system equipment fault diagnosis is to quickly locate the source of the fault and clarify the type and severity of the fault by analyzing the operating data and fault characteristics of the equipment after the fault occurs. For example, patent document CN111488947A obtains the power fingerprint data, voiceprint data, temperature data and vibration data of the power system equipment, first determines the operation mode of the equipment based on the power fingerprint data, and then uses the pre-trained fault detection model under this mode to detect the fault of the equipment based on the voiceprint, temperature and vibration data, and performs differentiated processing according to the characteristics of different working modes, thereby improving the accuracy of equipment fault detection. Patent document CN114993383A uses intelligent algorithms to confirm the state quantity of the distribution room, thereby greatly improving the accuracy of the distribution room state assessment and the reliability of fault warning. The intelligent algorithm of the transformer calculates the state quantity of the transformer and makes a more comprehensive assessment of its state, which is no longer limited to the analysis of a single state quantity, thus enhancing the accuracy of the transformer fault warning. The intelligent algorithm of the low-voltage switchgear can confirm its state from a more macroscopic perspective, significantly improving the timeliness and accuracy of the switchgear state assessment. However, the existing real-time network security monitoring of the power system has the following shortcomings: the existing monitoring method only gives a fault alarm after the power system equipment fails, and cannot determine the specific time when the fault occurs, resulting in the operation and maintenance personnel spending a lot of time to investigate the cause of the fault; the fault prediction model uses fault data for prediction, resulting in untimely fault prediction, which may cause sudden failure of equipment, and even threaten personnel safety and cause huge economic losses. Summary of the invention

[0003] In view of the problems existing in the prior art, the present invention provides a method and system for real-time monitoring of power system network security.

[0004] In one aspect, the present invention provides a power system network security real-time monitoring system, comprising:

[0005] A data acquisition unit, used to collect operation data and monitoring data of power system equipment;

[0006] a first fault diagnosis unit, configured to perform initial fault diagnosis according to the operating data and output an initial fault diagnosis result;

[0007] A segmentation processing unit, used for segmenting the operation data according to the initial fault diagnosis result and outputting segmented data;

[0008] A second fault diagnosis unit, used for performing detailed fault diagnosis according to the segmented data and outputting detailed fault diagnosis results;

[0009] The fault location unit is used to determine whether to adjust the number of segments according to the detailed fault diagnosis result; if the number of segments does not need to be adjusted, the minimum fault operation data segment where the fault occurs is output; otherwise, the number of segments is adjusted by the segment processing unit, and the second fault diagnosis unit performs detailed fault diagnosis according to the adjusted number of segments, and outputs the detailed fault diagnosis result after the number of segments is adjusted until the number of segments does not need to be adjusted.

[0010] Furthermore, the system further comprises a monitoring processing unit, and the monitoring processing unit is used to segment the monitoring data according to the minimum fault operation data segment and output the minimum fault monitoring data segment.

[0011] Furthermore, the specific process of segmenting the operation data according to the fault diagnosis result is: dividing the operation data into equal parts;

[0012] The specific process of adjusting the number of segments by the segment processing unit is: increasing the number of segments into which the operating data is equally divided by one segment.

[0013] Furthermore, the system also includes a fault prediction unit, which is used to predict potential faults based on the operating data.

[0014] Furthermore, the fault prediction unit is trained and updated based on an operation data segment preceding the minimum fault operation data segment.

[0015] On the other hand, the present invention provides a method for real-time monitoring of power system network security, which is applied to the above-mentioned system, and the method comprises the following steps:

[0016] Step S1: The data acquisition unit acquires the operation data D and monitoring data M of the power system equipment, and the length of the operation data D and the monitoring data M are both n;

[0017] Step S2: the first fault diagnosis unit performs initial fault diagnosis on the operation data D and outputs an initial fault diagnosis result R;

[0018] Step S3: The segment processing unit divides the operation data D into k segments according to the initial fault diagnosis result R, and outputs segment data D1, D2, ..., D k ;

[0019] Step S4: The second fault diagnosis unit processes the segmented data D1, D2, ..., D k Perform detailed fault diagnosis and output detailed fault diagnosis results R1, R2, ..., R k ;

[0020] Step S5: The fault location unit locates the fault according to the detailed fault diagnosis results R1, R2, ..., R k Determine whether to adjust the number of segments k; if the number of segments k does not need to be adjusted, then output the minimum fault operation data segment D where the fault occurs F ; otherwise, the number of segments is adjusted through the segment processing unit, and the second fault diagnosis unit performs detailed fault diagnosis according to the adjusted number of segments, and outputs the detailed fault diagnosis results after the adjusted number of segments, until the number of segments does not need to be adjusted.

[0021] Furthermore, the method further comprises step S6: the monitoring processing unit is used to segment the monitoring data according to the minimum fault operation data segment, and output the minimum fault monitoring data segment M. F .

[0022] Furthermore, in step S3, the specific process of the segmentation processing unit dividing the operating data D into k segments according to the fault diagnosis result R is as follows: the operating data D is equally divided into k segments: D1, D2, ..., D k , where each segment D i The size is

[0023] In the step S5, the specific process of adjusting the number of segments by the segment processing unit is: increasing the value of the number of segments k into which the operating data is equally divided by 1.

[0024] Furthermore, the method further comprises step S7: a fault prediction unit is used to predict potential faults according to the operation data.

[0025] Furthermore, in step S7, the fault prediction unit is based on the minimum fault operation data segment D F The previous running data segment D F-1 Trained and updated.

[0026] The present invention provides a real-time monitoring method and system for power system network security, including: a data acquisition unit, a first fault diagnosis unit, a segment processing unit, a second fault diagnosis unit and a fault location unit. Through the minimum fault operation data segment output by the fault location unit, the specific time when the fault occurs can be accurately located, thereby shortening the troubleshooting time of operation and maintenance personnel and improving operation and maintenance efficiency. In addition, the fault prediction unit in the present invention is trained and updated according to the previous operation data segment of the minimum fault operation data segment, and can predict faults more quickly, prevent sudden failure of equipment, reduce downtime, ensure the safe and stable operation of the power system, and effectively reduce equipment maintenance costs and socio-economic risks. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0028] Figure 1 A structure of a real-time monitoring system for power system network security provided by an embodiment of the present invention Figure 1 ;

[0029] Figure 2 A structure of a real-time monitoring system for power system network security provided by an embodiment of the present invention Figure 2 ;

[0030] Figure 3 A structure of a real-time monitoring system for power system network security provided by an embodiment of the present invention Figure 3 ;

[0031] Figure 4 A structure of a real-time monitoring system for power system network security provided by an embodiment of the present invention Figure 4 ;

[0032] Figure 5 A flowchart of a real-time monitoring method for power system network security provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0033] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution in the embodiment of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiment of the present invention. Obviously, the described embodiment is only a unit embodiment of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0034] As an embodiment of the present invention, Figure 1 As shown, this embodiment provides a power system network security real-time monitoring system, including:

[0035] A data acquisition unit, used to collect operation data and monitoring data of power system equipment;

[0036] Specifically, the power system equipment includes generators, frequency converters, inverters, transformers, mutual inductors, switchgear, etc. The operation data includes current, voltage, power, temperature, humidity, temperature rise, vibration, etc. Key power system equipment is also equipped with video monitoring to obtain monitoring data.

[0037] a first fault diagnosis unit, configured to perform initial fault diagnosis according to the operating data and output an initial fault diagnosis result;

[0038] Specifically, the first fault diagnosis unit adopts a machine learning model, which includes a support vector machine, a convolutional neural network, Q-Learning, and deep learning. It uses historical diagnostic data for training, performs initial fault diagnosis based on real-time operating data, and outputs initial fault diagnosis results.

[0039] A segmentation processing unit, used for segmenting the operation data according to the initial fault diagnosis result and outputting segmented data;

[0040] A second fault diagnosis unit, used for performing detailed fault diagnosis according to the segmented data and outputting detailed fault diagnosis results;

[0041] Specifically, the second fault diagnosis unit adopts a machine learning model, and the machine learning model includes a support vector machine, a convolutional neural network, Q-Learning, and deep learning.

[0042] The fault location unit is used to determine whether to adjust the number of segments according to the detailed fault diagnosis result; if the number of segments does not need to be adjusted, the minimum fault operation data segment where the fault occurs is output; otherwise, the number of segments is adjusted by the segment processing unit, and the second fault diagnosis unit performs detailed fault diagnosis according to the adjusted number of segments, and outputs the detailed fault diagnosis result after the number of segments is adjusted until the number of segments does not need to be adjusted.

[0043] Specifically, the initial fault diagnosis result is that a fault exists, the segmentation processing unit divides the operating data into two segments, the second fault diagnosis unit performs detailed fault diagnosis based on the two segments of segmented data, and outputs the detailed fault diagnosis result; if the detailed fault diagnosis result is that a fault exists, the segmentation processing unit divides the operating data into three segments, the second fault diagnosis unit performs detailed fault diagnosis based on the three segments of segmented data, and outputs the detailed fault diagnosis result; if the detailed fault diagnosis result is that a fault does not exist, then the data segment where the fault exists when the operating data is divided into two segments is output as the minimum fault operating data segment where the fault occurs.

[0044] Furthermore, if Figure 2 As shown, the system further includes a monitoring processing unit, which is used to segment the monitoring data according to the minimum fault operation data segment and output the minimum fault monitoring data segment.

[0045] Specifically, for some longer operating data, the operation and maintenance personnel need to retrieve a large amount of monitoring videos for troubleshooting after discovering a fault. The present invention can accurately locate the minimum fault operating data segment through the segmented processing unit combined with the first fault diagnosis unit and the second fault diagnosis unit, and can determine the corresponding minimum fault monitoring data segment, thereby shortening the fault troubleshooting time of the operation and maintenance personnel and improving the operation and maintenance efficiency.

[0046] Furthermore, the specific process of segmenting the operation data according to the fault diagnosis result is: dividing the operation data into equal parts;

[0047] The specific process of adjusting the number of segments by the segment processing unit is: increasing the number of segments into which the operating data is equally divided by one segment.

[0048] Furthermore, if Figure 3 As shown, the system further includes a fault prediction unit, which is used to predict potential faults based on the operating data.

[0049] Furthermore, the fault prediction unit is trained and updated based on an operation data segment preceding the minimum fault operation data segment.

[0050] Specifically, the existing power system fault prediction uses fault data to establish a fault prediction unit. Although it can provide prediction accuracy, there is a certain hysteresis, which makes it impossible to carry out effective preventive maintenance on the power system, and can only passively respond to faults, affecting the operation efficiency of the power grid. Before the fault occurs, there are some fault signs in the operation data of the power system equipment. The fault prediction unit of the present invention is trained and updated based on the previous operation data segment of the minimum fault operation data segment, and can timely predict the potential faults of the power system equipment, prevent sudden failure of the equipment, reduce downtime, and ensure the safe and stable operation of the power system.

[0051] As another embodiment of the present invention, Figure 4 As shown, this embodiment provides a power system network security real-time monitoring system, including:

[0052] A data acquisition unit, used to collect operation data and monitoring data of power system equipment;

[0053] a first fault diagnosis unit, configured to perform initial fault diagnosis according to the operating data and output an initial fault diagnosis result;

[0054] A segmentation processing unit, used for dividing the operation data into equal parts according to the initial fault diagnosis result, and outputting segmented data;

[0055] A second fault diagnosis unit, used for performing detailed fault diagnosis according to the segmented data and outputting detailed fault diagnosis results;

[0056] The fault location unit is used to determine whether to adjust the number of segments according to the detailed fault diagnosis result; if the number of segments does not need to be adjusted, the minimum fault operation data segment where the fault occurs is output; otherwise, the number of segments into which the operation data is equally divided is increased by one segment, and the second fault diagnosis unit performs detailed fault diagnosis according to the adjusted number of segments, and outputs the detailed fault diagnosis result after the number of segments is adjusted until the number of segments does not need to be adjusted.

[0057] A monitoring processing unit, wherein the monitoring processing unit is used to segment the monitoring data according to the minimum fault operation data segment and output the minimum fault monitoring data segment.

[0058] A fault prediction unit is used to predict potential faults according to the operation data, and the fault prediction unit is trained and updated according to an operation data segment preceding the minimum fault operation data segment.

[0059] On the other hand, Figure 5 As shown, the present invention provides a method for real-time monitoring of power system network security, which is applied to the above-mentioned system, and the method comprises the following steps:

[0060] Step S1: The data acquisition unit acquires the operation data D and monitoring data M of the power system equipment, and the length of the operation data D and the monitoring data M are both n;

[0061] Step S2: the first fault diagnosis unit performs initial fault diagnosis on the operation data D and outputs an initial fault diagnosis result R;

[0062] Specifically, the first fault diagnosis unit adopts a machine learning model, and the machine learning model includes a support vector machine, a convolutional neural network, Q-Learning, and deep learning.

[0063] Step S3: The segment processing unit divides the operation data D into k segments according to the initial fault diagnosis result R, and outputs segment data D1, D2, ..., D k ;

[0064] Step S4: The second fault diagnosis unit processes the segmented data D1, D2, ..., D k Perform detailed fault diagnosis and output detailed fault diagnosis results R1, R2, ..., R k ;

[0065] Specifically, the second fault diagnosis unit adopts a machine learning model, and the machine learning model includes a support vector machine, a convolutional neural network, Q-Learning, and deep learning.

[0066] More specifically, the second fault diagnosis unit selects different machine learning models according to the change of k. In fault diagnosis, the selection of machine learning models has a significant impact on accuracy, especially when processing data of different lengths. Data sets of different lengths usually contain different amounts of information and feature complexity. Therefore, it is crucial to select a suitable machine learning model for each data length. For data with a small sample size, simple models can effectively avoid overfitting and provide more reliable diagnostic results. As the amount of data increases, more complex models can better capture potential patterns in the data, thereby improving accuracy. Flexibly adjusting machine learning models to adapt to data of different lengths can not only optimize model performance, but also effectively improve the accuracy of fault diagnosis. The application of this strategy in fields such as power systems is particularly important, providing strong support for ensuring equipment safety and system stability.

[0067] Step S5: The fault location unit locates the fault according to the detailed fault diagnosis results R1, R2, ..., R k Determine whether to adjust the number of segments k; if the number of segments k does not need to be adjusted, then output the minimum fault operation data segment D where the fault occurs F; otherwise, the number of segments is adjusted through the segment processing unit, and the second fault diagnosis unit performs detailed fault diagnosis according to the adjusted number of segments, and outputs the detailed fault diagnosis results after the adjusted number of segments, until the number of segments does not need to be adjusted.

[0068] Furthermore, the fault location unit locates the fault according to the detailed fault diagnosis results R1, R2, ..., R k The specific process of determining whether to adjust the number of segments k is as follows: If there is segment data D i , i∈{1,2,…,k}, such that R i =1, the number of segments k needs to be adjusted; if for all segment data D i , i∈{1,2,…,k} such that all R i =0, there is no need to adjust the number of segments.

[0069] Specifically, for example, the initial fault diagnosis result R=1, 1 indicates a fault, that is, the operating data D has a fault; the segmentation processing unit divides the operating data D into 2 segments according to the initial fault diagnosis result R, and outputs segmentation data D1, D2; the second fault diagnosis unit performs detailed fault diagnosis on the segmentation data D1, D2, and outputs detailed fault diagnosis results R1, R2; if R1=0, R2=1, it indicates that the segmentation data D2 has a fault; the segmentation processing unit divides the operating data D into 3 segments according to the initial fault diagnosis result R, and outputs segmentation data D1, D2, D3; the second fault diagnosis unit performs detailed fault diagnosis on the segmentation data D1, D2, D3, and outputs detailed fault diagnosis results R1, R2, R3; if R1=0, R2=0, R3=0, it indicates that the segmentation is terminated, and the minimum fault operating data segment D where the fault occurs is output. F It is the segmented data D2 when the operation data D is divided into two segments.

[0070] Specifically, for another example, the initial fault diagnosis result R=1, 1 indicates a fault, that is, the operating data D has a fault; the segmentation processing unit divides the operating data D into 2 segments according to the initial fault diagnosis result R, and outputs segmentation data D1, D2; the second fault diagnosis unit performs detailed fault diagnosis on the segmentation data D1, D2, and outputs detailed fault diagnosis results R1, R2; if R1=0, R2=1, it indicates that the segmentation data D2 has a fault; the segmentation processing unit divides the operating data D into 3 segments according to the initial fault diagnosis result R, and outputs segmentation data D1, D2, D3, and the second fault diagnosis unit performs detailed fault diagnosis on the segmentation data D1, D2, D3. D2, D3 perform detailed fault diagnosis, and output detailed fault diagnosis results R1, R2, R3; if R1 = 0, R2 = 0, R3 = 1, it means that the segmented data D3 has a fault; the segmentation processing unit divides the operation data D into 4 segments according to the initial fault diagnosis result R, and outputs segmented data D1, D2, D3, D4, and the second fault diagnosis unit performs detailed fault diagnosis on the segmented data D1, D2, D3, D4, and outputs detailed fault diagnosis results R1, R2, R3, R4; if R1 = 0, R2 = 0, R3 = 0, R4 = 0, it means that the segmentation is terminated, and the minimum fault operation data segment D where the fault occurs is output. F It is the segmented data D3 when the operation data D is divided into 3 segments.

[0071] Specifically, for another example, the initial fault diagnosis result R=1, 1 indicates a fault, that is, the operating data D has a fault; the segmentation processing unit divides the operating data D into 2 segments according to the initial fault diagnosis result R, and outputs segmentation data D1, D2; the second fault diagnosis unit performs detailed fault diagnosis on the segmentation data D1, D2, and outputs detailed fault diagnosis results R1, R2; if R1=0, R2=1, it indicates that the segmentation data D2 has a fault; the segmentation processing unit divides the operating data D into 3 segments according to the initial fault diagnosis result R, and outputs segmentation data D1, D2, D3, and the second fault diagnosis unit performs detailed fault diagnosis on the segmentation data D1, D2, D3. D2, D3 perform detailed fault diagnosis, and output detailed fault diagnosis results R1, R2, R3; if R1 = 0, R2 = 1, R3 = 0, it means that the segmented data D2 has a fault; the segmentation processing unit divides the operation data D into 4 segments according to the initial fault diagnosis result R, and outputs segmented data D1, D2, D3, D4, and the second fault diagnosis unit performs detailed fault diagnosis on the segmented data D1, D2, D3, D4, and outputs detailed fault diagnosis results R1, R2, R3, R4; if R1 = 0, R2 = 0, R3 = 0, R4 = 0, it means that the segmentation is terminated, and the minimum fault operation data segment D where the fault occurs is output. F It is the segmented data D2 when the operation data D is divided into 3 segments.

[0072] Furthermore, the method further comprises step S6: the monitoring processing unit is used to segment the monitoring data according to the minimum fault operation data segment, and output the minimum fault monitoring data segment M. F .

[0073] Specifically, when processing longer operating data, operation and maintenance personnel often need to retrieve a large number of monitoring videos to troubleshoot. To this end, the present invention proposes a new method, which realizes the precise positioning of fault data by combining a segmented processing unit with a first fault diagnosis unit and a second fault diagnosis unit. This method can accurately identify the smallest fault operation data segment and find the corresponding minimum fault monitoring data segment. This innovation not only greatly shortens the troubleshooting time of operation and maintenance personnel, but also significantly improves the operation and maintenance efficiency. By quickly locating the source of the fault, the operation and maintenance team can perform maintenance and repair more efficiently, thereby ensuring the stable operation of the equipment.

[0074] Furthermore, in step S3, the specific process of the segmentation processing unit dividing the operating data D into k segments according to the fault diagnosis result R is as follows: the operating data D is equally divided into k segments: D1, D2, ..., D k , where each segment D i The size is

[0075] In the step S5, the specific process of adjusting the number of segments by the segment processing unit is: increasing the value of the number of segments k into which the operating data is equally divided by 1.

[0076] Furthermore, the method further comprises step S7: a fault prediction unit is used to predict potential faults according to the operation data.

[0077] Furthermore, in step S7, the fault prediction unit is based on the minimum fault operation data segment D F The previous running data segment D F-1 Trained and updated.

[0078] Fault prediction of power systems mainly relies on fault data to establish prediction models. Although this method performs well in terms of accuracy, there is often a certain time delay, which makes it impossible to effectively implement preventive maintenance, and ultimately can only passively respond to faults, thereby affecting the overall operating efficiency of the power grid. However, before a fault occurs, the operating data of the power system equipment usually shows some fault signs, which provides an opportunity for early warning. The fault prediction unit of the present invention focuses on extracting these potential fault signs, especially by analyzing the previous operating data segment of the minimum fault operating data segment for training and updating. Through this method, the system can identify possible faults of the equipment more timely, thereby achieving more accurate predictions. Compared with traditional methods, this symptom-based prediction mechanism makes fault identification faster and more accurate, significantly improving the maintenance efficiency and reliability of the power system. The ability to predict in time can not only effectively prevent the sudden failure of equipment, but also significantly reduce the downtime caused by faults. This innovation enables the power system to take appropriate measures before a fault occurs, thereby ensuring the safe and stable operation of the system. In general, the present invention improves the timeliness and accuracy of prediction by extracting fault signs, providing strong support for the efficient management of the power system.

[0079] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A real-time monitoring system for power system network security, characterized in that: include: A data acquisition unit, used to collect operation data and monitoring data of power system equipment; a first fault diagnosis unit, configured to perform initial fault diagnosis according to the operating data and output an initial fault diagnosis result; A segmentation processing unit, used for segmenting the operation data according to the initial fault diagnosis result and outputting segmented data; A second fault diagnosis unit, used for performing detailed fault diagnosis according to the segmented data and outputting detailed fault diagnosis results; A fault location unit, used to determine whether to adjust the number of segments according to the detailed fault diagnosis result; if the number of segments does not need to be adjusted, output the minimum fault operation data segment where the fault occurs; Otherwise, the segment number is adjusted by the segment processing unit, and the second fault diagnosis unit performs detailed fault diagnosis according to the adjusted segment number and outputs detailed fault diagnosis results after the segment number is adjusted until the segment number does not need to be adjusted.

2. The power system network security real-time monitoring system according to claim 1 is characterized by: The system further comprises a monitoring processing unit, which is used to segment the monitoring data according to the minimum fault operation data segment and output the minimum fault monitoring data segment.

3. The power system network security real-time monitoring system according to claim 1 is characterized by: The specific process of segmenting the operation data according to the fault diagnosis result is: dividing the operation data into equal parts; The specific process of adjusting the number of segments by the segment processing unit is: increasing the number of segments into which the operating data is equally divided by one segment.

4. The power system network security real-time monitoring system according to claim 1 is characterized by: The system further comprises a fault prediction unit, which is used for predicting potential faults according to the operation data.

5. The power system network security real-time monitoring system according to claim 4 is characterized by: The fault prediction unit is trained and updated according to an operation data segment preceding the minimum fault operation data segment.

6. A method for real-time monitoring of power system network security, applied to the system according to any one of claims 1 to 5, characterized in that: The method comprises the following steps: Step S1: The data acquisition unit acquires the operation data D and monitoring data M of the power system equipment, and the length of the operation data D and the monitoring data M are both n; Step S2: the first fault diagnosis unit performs initial fault diagnosis on the operation data D and outputs an initial fault diagnosis result R; Step S3: The segment processing unit divides the operation data D into k segments according to the initial fault diagnosis result R, and outputs segment data D1, D2, ..., D k ; Step S4: The second fault diagnosis unit processes the segmented data D1, D2, ..., D k Perform detailed fault diagnosis and output detailed fault diagnosis results R1, R2, ..., R k ; Step S5: The fault location unit locates the fault according to the detailed fault diagnosis results R1, R2, ..., R k Determine whether to adjust the number of segments k; if the number of segments k does not need to be adjusted, then output the minimum fault operation data segment D where the fault occurs F ; otherwise, the number of segments is adjusted through the segment processing unit, and the second fault diagnosis unit performs detailed fault diagnosis according to the adjusted number of segments, and outputs the detailed fault diagnosis results after the adjusted number of segments, until the number of segments does not need to be adjusted.

7. The method for real-time monitoring of power system network security according to claim 6, characterized in that: The method further comprises step S6: the monitoring processing unit is used to segment the monitoring data according to the minimum fault operation data segment, and output the minimum fault monitoring data segment M. F .

8. The method for real-time monitoring of power system network security according to claim 6, characterized in that: In step S3, the specific process of the segmentation processing unit dividing the operating data D into k segments according to the fault diagnosis result R is as follows: the operating data D is equally divided into k segments: D1, D2, ..., D k , where each segment D i The size is In the step S5, the specific process of adjusting the number of segments by the segment processing unit is: increasing the value of the number of segments k into which the operating data is equally divided by 1.

9. The method for real-time monitoring of power system network security according to claim 6, characterized in that: The method further comprises step S7: a fault prediction unit is used to predict potential faults according to the operation data.

10. The method for real-time monitoring of power system network security according to claim 9, characterized in that: In step S7, the fault prediction unit is based on the minimum fault operation data segment D F The previous running data segment D F-1 Trained and updated.

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