A remote intelligent monitoring system for power distribution cabinets

Abstract

The present invention discloses a remote intelligent monitoring system for a power distribution cabinet, and relates to the technical field of intelligent monitoring systems. The system comprises a network capturer, a parsing extractor, a data detector, a data regulator, a visualization controller, and a database. The network capturer receives network data packets sent by a data acquisition terminal of a power distribution cabinet in real time, and takes 10 consecutive network data packets of the same data acquisition terminal as a group. The system calculates the comprehensive deviation value of the data in this group by combining a delay trend value, a packet loss trend value, and an associated deviation value. According to the comprehensive deviation value result, on the one hand, a command signal is sent to start a spare port of the data acquisition terminal of the corresponding power distribution cabinet, and at the same time, a signal transmission channel is switched to notify relevant personnel for maintenance. On the other hand, the system compensates for the group of data and restores the complete time series value of the whole group of data packets, which can improve the continuity and efficiency of the remote intelligent monitoring system of the power distribution cabinet and enhance the user's use effect.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent monitoring systems, and in particular to a remote intelligent monitoring system for a power distribution cabinet. Background Art

[0002] The remote intelligent detection system for power distribution cabinets is an advanced system based on modern information technology and intelligent equipment, which aims to realize remote monitoring, data collection, fault diagnosis, alarm management and remote control of power distribution cabinets. The system integrates sensors, data acquisition devices, data processing and analysis platforms, and remote monitoring terminals to achieve real-time monitoring and analysis of various parameters in the power distribution cabinet, greatly improving the safety and reliability of the power distribution system.

[0003] Although the remote intelligent detection system of the power distribution cabinet has brought many conveniences and significant advantages to operation and maintenance management, its defects cannot be ignored. The system exhibits a high degree of dependence, and its smooth operation is completely based on the precise coordination of many devices and components. Once any link in this complex network fails, it will pose a direct threat to the stability and reliability of the entire system, and thus affect the continuity and efficiency of power monitoring and management. Summary of the invention

[0004] In view of the above situation, the present invention can analyze the delay trend, packet loss trend and related deviation of the network data packets sent by the data acquisition end of the distribution cabinet in real time, and can send command signals in time to start the backup port of the corresponding distribution cabinet data acquisition end, while switching the signal transmission channel and restoring the complete time series value of the entire group of abnormal data packets, thereby improving the continuity and efficiency of the remote intelligent monitoring system of the distribution cabinet.

[0005] The technical solution is to include a network capturer, a parsing extractor, a data detector, a data regulator, a visual controller, and a database. The network capturer receives network data packets sent by the data acquisition terminal of the power distribution cabinet in real time, with 10 consecutive network data packets from the same data acquisition terminal as a group;

[0006] The parsing extractor parses and extracts a group of network data packets including sending time T1, T2...T10, receiving time T11, T21...T101, number of bytes B1, B2...B10, and IP address C1, C2...C10;

[0007] The data detector first calculates the delay time of each network data packet, and calculates the jitter value of the network delay according to the delay time, and then calculates the standard deviation of the 9 jitter values, that is, the jitter deviation value;

[0008] At the same time, the corresponding threshold is retrieved according to the IP address, the abnormal ratio of the number of bytes of each network data packet is calculated, the network data packets that have been lost are identified according to the abnormal ratio, and the packet loss rate of this group is calculated;

[0009] Then, the delay threshold in the database is retrieved, and the delay trend value and packet loss trend value of the group are calculated based on the comparison between the delay time and the delay threshold. At the same time, the number of delay times greater than the threshold Q is counted, and the associated deviation value is calculated;

[0010] Finally, the comprehensive deviation value of this group of data is calculated by combining the delay trend value, packet loss trend value and associated deviation value;

[0011] According to the comprehensive deviation value result, the data controller sends a command signal to start the standby port of the corresponding distribution cabinet data acquisition terminal, switches the signal transmission channel, and notifies relevant personnel for maintenance. On the other hand, it compensates for this group of data and restores the complete time series value of the entire group of data packets.

[0012] The visual controller displays various parameters of the power distribution cabinet in real time according to the entire group of restored data packets.

[0013] Furthermore, the data detector includes a parameter analysis module, a trend analysis module, a correlation analysis module, and a comprehensive evaluation module, and the specific working steps are as follows;

[0014] S1, the parameter analysis module calculates the time difference between the receiving time and the sending time of each network data packet, that is, the delay time T12, T22...T102, and then calculates the jitter value T13, T23...T93, T13=T12-T22, T23=T22-T32, and so on, and finally calculates the jitter deviation value X, which is the standard deviation of the 9 jitter values ​​T13, T23...T93;

[0015] At the same time, the corresponding threshold K in the database is retrieved according to the IP address, and the abnormal ratios B11, B21...B101 of the number of bytes of each network data packet are calculated, where B11=(K-B1) / K, B21=(K-B2) / K...B101=(K-B10) / K. Then the abnormal ratios B11, B21...B101 are compared with 0.1 respectively. If it is less than 0.1, it means that the number of bytes of the network data packet is normal. Otherwise, it means that the network data packet data is lost and regarded as packet loss. Finally, the number of lost network data packets n is counted, and the packet loss rate M is calculated, M=n / 10;

[0016] S2, the trend analysis module first retrieves the delay time threshold Q in the database, calculates the delay time error ratio Y, Y=[(T12+T22+...T92+T102) / 10-Q] / Q, and then calculates the delay trend value Z, Z=Y×0.4+X÷Q×0.6;

[0017] At the same time, the standard deviation b of the abnormal ratios of the bytes of the 10 network data packets in this group, B11, B21...B101, is calculated, and then the packet loss trend value R is calculated, R=M×0.7+b×0.3;

[0018] S3, the association analysis module first retrieves the threshold Q of the delay time in the database, then counts the number N of delay times T12, T22...T102 that are greater than the threshold Q, and finally calculates the association deviation value L, L=N / 10×0.4+M×0.6;

[0019] S4, the comprehensive evaluation module calculates a comprehensive deviation value H, H=Z×0.3+R×0.3+L×0.4+ε, where ε represents a deviation correction value.

[0020] Furthermore, the data controller includes a redundant control module and a prediction compensation module, wherein the redundant control module receives a comprehensive deviation value H, and H is less than 0.1, indicating that the group of data is normal as a whole;

[0021] If H is greater than 0.1 and less than 0.2, it means that the whole group of data is abnormal, and there is a problem with the data acquisition terminal and / or transmission channel of the power distribution cabinet corresponding to the IP address. Then a command signal is sent to start the standby port of the corresponding data acquisition terminal of the power distribution cabinet, and the signal transmission channel is switched at the same time to notify relevant personnel for maintenance.

[0022] If H is greater than 0.2, it means that this group of data is particularly abnormal as a whole. On the one hand, a command signal is sent to start the backup port of the corresponding distribution cabinet data acquisition terminal, and at the same time, the signal transmission channel is switched to notify relevant personnel for maintenance. On the other hand, the prediction compensation module is started to compensate for this group of data.

[0023] Furthermore, the prediction and compensation module has the following specific steps:

[0024] Traverse the network data packet sequence and determine the time point corresponding to the data packet loss according to the abnormal ratios B11, B21...B101;

[0025] According to the IP address, the corresponding distribution cabinet parameter prediction model is retrieved from the database, the parameter values ​​in the adjacent and normal network data packets are selected, the parameter values ​​of the normal data packets are input into the distribution cabinet parameter prediction model, and the prediction value of the lost data packet in the time series is obtained;

[0026] Insert the predicted value into the time position where the original data packet was lost, and the entire group of data packets is restored to a complete and continuous time series state;

[0027] The originally damaged network data packets are discarded, and a sequence of restored network data packets is output.

[0028] Furthermore, the database has the function of storing system parameters, and can be called by the data detector, data regulator, and visualization controller at any time, and can update data information in real time.

[0029] Furthermore, the distribution cabinet parameter prediction model is built based on historical data and machine learning algorithms, which can predict the parameter values ​​of the distribution cabinet at a specific point in time. During the operation of the system, the visualization controller will continuously send the correct parameters of the distribution cabinet operation to the parameter prediction model and update the data in real time to ensure that the input data of the parameter prediction model is always highly consistent with the actual operating status of the distribution cabinet, so that the distribution cabinet parameter prediction model can continuously learn and adapt to the latest dynamics of the distribution cabinet.

[0030] Due to the adoption of the above technical solution, the present invention has the following advantages compared with the prior art:

[0031] 1. Combine the delay trend value, packet loss trend value and associated deviation value to calculate the comprehensive deviation value of this group of data. According to the comprehensive deviation value result, on the one hand, send a command signal to start the backup port of the corresponding distribution cabinet data acquisition terminal, and switch the signal transmission channel at the same time to notify relevant personnel for maintenance. On the other hand, compensate for this group of data and restore the complete time series value of the entire group of data packets, which can improve the continuity and efficiency of the remote intelligent monitoring system of the distribution cabinet and enhance the user's use effect. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] Figure 1 This is a module diagram of a remote intelligent monitoring system for a power distribution cabinet of the present invention.

[0033] Figure 2 It is a module diagram of the data detector of the present invention.

[0034] Figure 3 It is a module diagram of the data controller of the present invention. DETAILED DESCRIPTION

[0035] The above and other technical contents, features and effects of the present invention are described in detail below with reference to the attached Figures 1 to 3 The detailed description of the embodiments will clearly show that the structural contents mentioned in the following embodiments are all based on the drawings in the specification.

[0036] In order to solve the high dependence characteristics of the system, which affects the continuity and efficiency of power monitoring and management, it is necessary to ensure the continuity and accuracy of data transmission. In order to ensure the accuracy of data transmission, it is necessary to find a parameter that can predict the data transmission status in advance, that is, the parameter of the comprehensive deviation of data transmission, and be able to make corresponding processing in time to repair the damaged data, so as to ensure the continuity and accuracy of the distribution cabinet monitoring data transmission. Therefore, this solution includes a network capturer, a parsing extractor, a data detector, a data regulator, a visualization controller, and a database. The network capturer receives the network data packets sent by the data acquisition terminal of the distribution cabinet in real time, with 10 consecutive network data packets from the same data acquisition terminal as a group;

[0037] The parsing extractor parses and extracts a group of network data packets including sending time T1, T2...T10, receiving time T11, T21...T101, number of bytes B1, B2...B10, and IP address C1, C2...C10;

[0038] The data detector first calculates the delay time of each network data packet, and calculates the jitter value of the network delay according to the delay time, and then calculates the standard deviation of the 9 jitter values, that is, the jitter deviation value;

[0039] At the same time, the corresponding threshold is retrieved according to the IP address, the abnormal ratio of the number of bytes of each network data packet is calculated, the network data packets that have been lost are identified according to the abnormal ratio, and the packet loss rate of this group is calculated;

[0040] Then, the delay threshold in the database is retrieved, and the delay trend value and packet loss trend value of the group are calculated based on the comparison between the delay time and the delay threshold. At the same time, the number of delay times greater than the threshold Q is counted, and the associated deviation value is calculated;

[0041] Finally, the comprehensive deviation value of this group of data is calculated by combining the delay trend value, packet loss trend value and associated deviation value;

[0042] According to the comprehensive deviation value result, the data controller sends a command signal to start the standby port of the corresponding distribution cabinet data acquisition terminal, switches the signal transmission channel, and notifies relevant personnel for maintenance. On the other hand, it compensates for this group of data and restores the complete time series value of the entire group of data packets.

[0043] The visual controller displays various parameters of the power distribution cabinet in real time according to the entire group of restored data packets.

[0044] The data detector includes a parameter analysis module, a trend analysis module, a correlation analysis module, and a comprehensive evaluation module, and the specific working steps are as follows;

[0045] S1, the parameter analysis module calculates the time difference between the receiving time and the sending time of each network data packet, that is, the delay time T12, T22...T102, and then calculates the jitter value T13, T23...T93, T13=T12-T22, T23=T22-T32, and so on, and finally calculates the jitter deviation value X, which is the standard deviation of the 9 jitter values ​​T13, T23...T93;

[0046] At the same time, the corresponding threshold K in the database is retrieved according to the IP address, and the abnormal ratios B11, B21...B101 of the number of bytes of each network data packet are calculated, where B11=(K-B1) / K, B21=(K-B2) / K...B101=(K-B10) / K. Then the abnormal ratios B11, B21...B101 are compared with 0.1 respectively. If it is less than 0.1, it means that the number of bytes of the network data packet is normal. Otherwise, it means that the network data packet data is lost and regarded as packet loss. Finally, the number of lost network data packets n is counted, and the packet loss rate M is calculated, M=n / 10;

[0047] S2, the trend analysis module first retrieves the delay time threshold Q in the database, calculates the delay time error ratio Y, Y=[(T12+T22+...T92+T102) / 10-Q] / Q, and then calculates the delay trend value Z, Z=Y×0.4+X÷Q×0.6;

[0048] At the same time, the standard deviation b of the abnormal ratios of the bytes of the 10 network data packets in this group, B11, B21...B101, is calculated, and then the packet loss trend value R is calculated, R=M×0.7+b×0.3;

[0049] S3, the association analysis module first retrieves the threshold Q of the delay time in the database, then counts the number N of delay times T12, T22...T102 that are greater than the threshold Q, and finally calculates the association deviation value L, L=N / 10×0.4+M×0.6;

[0050] S4, the comprehensive evaluation module calculates a comprehensive deviation value H, H=Z×0.3+R×0.3+L×0.4+ε, where ε represents a deviation correction value.

[0051] Furthermore, the data controller includes a redundant control module and a prediction compensation module, wherein the redundant control module receives a comprehensive deviation value H, and H is less than 0.1, indicating that the group of data is normal as a whole;

[0052] If H is greater than 0.1 and less than 0.2, it means that the whole group of data is abnormal, and there is a problem with the data acquisition terminal and / or transmission channel of the power distribution cabinet corresponding to the IP address. Then a command signal is sent to start the standby port of the corresponding data acquisition terminal of the power distribution cabinet, and the signal transmission channel is switched at the same time to notify relevant personnel for maintenance.

[0053] If H is greater than 0.2, it means that this group of data is particularly abnormal as a whole. On the one hand, a command signal is sent to start the backup port of the corresponding distribution cabinet data acquisition terminal, and at the same time, the signal transmission channel is switched to notify relevant personnel for maintenance. On the other hand, the prediction compensation module is started to compensate for this group of data.

[0054] Furthermore, the prediction and compensation module has the following specific steps:

[0055] Traverse the network data packet sequence and determine the time point corresponding to the data packet loss according to the abnormal ratios B11, B21...B101;

[0056] According to the IP address, the corresponding distribution cabinet parameter prediction model is retrieved from the database, the parameter values ​​in the adjacent and normal network data packets are selected, the parameter values ​​of the normal data packets are input into the distribution cabinet parameter prediction model, and the prediction value of the lost data packet in the time series is obtained;

[0057] Insert the predicted value into the time position where the original data packet was lost, and the entire group of data packets is restored to a complete and continuous time series state;

[0058] The originally damaged network data packets are discarded, and a sequence of restored network data packets is output.

[0059] Furthermore, the database has the function of storing system parameters, and can be called by the data detector, data regulator, and visualization controller at any time, and can update data information in real time.

[0060] Furthermore, the distribution cabinet parameter prediction model is built based on historical data and machine learning algorithms, which can predict the parameter values ​​of the distribution cabinet at a specific point in time. During the operation of the system, the visualization controller will continuously send the correct parameters of the distribution cabinet operation to the parameter prediction model and update the data in real time to ensure that the input data of the parameter prediction model is always highly consistent with the actual operating status of the distribution cabinet, so that the distribution cabinet parameter prediction model can continuously learn and adapt to the latest dynamics of the distribution cabinet.

[0061] When the present invention is used specifically, on the basis of the prior art, it includes a network capturer, a parsing extractor, a data detector, a data regulator, a visual controller, and a database. The network capturer receives network data packets sent by the data acquisition terminal of the power distribution cabinet in real time, with 10 consecutive network data packets from the same data acquisition terminal as a group;

[0062] The parsing extractor parses and extracts a group of network data packets including sending time T1, T2...T10, receiving time T11, T21...T101, number of bytes B1, B2...B10, and IP address C1, C2...C10;

[0063] The data detector first calculates the delay time of each network data packet, and calculates the jitter value of the network delay according to the delay time, and then calculates the standard deviation of the 9 jitter values, that is, the jitter deviation value;

[0064] At the same time, the corresponding threshold is retrieved according to the IP address, the abnormal ratio of the number of bytes of each network data packet is calculated, the network data packets that have been lost are identified according to the abnormal ratio, and the packet loss rate of this group is calculated;

[0065] Then, the delay threshold in the database is retrieved, and the delay trend value and packet loss trend value of the group are calculated based on the comparison between the delay time and the delay threshold. At the same time, the number of delay times greater than the threshold Q is counted, and the associated deviation value is calculated;

[0066] Finally, the comprehensive deviation value of this group of data is calculated by combining the delay trend value, packet loss trend value and associated deviation value;

[0067] According to the comprehensive deviation value result, the data controller sends a command signal to start the standby port of the corresponding distribution cabinet data acquisition terminal, switches the signal transmission channel, and notifies relevant personnel for maintenance. On the other hand, it compensates for this group of data and restores the complete time series value of the entire group of data packets.

[0068] The visual controller displays the parameters of the power distribution cabinet in real time according to the restored whole group of data packets. The above technical solution can send a command signal in time to start the standby port of the corresponding power distribution cabinet data acquisition terminal, switch the signal transmission channel at the same time, and restore the complete time series value of the abnormal whole group of data packets, thereby improving the continuity and efficiency of the remote intelligent monitoring system of the power distribution cabinet.

[0069] The above is a further detailed description of the present invention in combination with a specific implementation method, and it cannot be determined that the specific implementation of the present invention is limited to this; for technical personnel in the technical field to which the present invention belongs and related technical fields, based on the technical solution of the present invention, the expansion and replacement of operating methods and data should all fall within the protection scope of the present invention.

Claims

1. A remote intelligent monitoring system for a power distribution cabinet, comprising a network capturer and a parsing extractor, characterized in that: It also includes a data detector, a data regulator, a visual controller, and a database. The network capturer receives network data packets sent by the data acquisition terminal of the power distribution cabinet in real time, with 10 consecutive network data packets from the same data acquisition terminal as a group; The parsing extractor parses and extracts a group of network data packets including sending time T1, T2...T10, receiving time T11, T21...T101, number of bytes B1, B2...B10, and IP address C1, C2...C10; The data detector first calculates the delay time of each network data packet, and calculates the jitter value of the network delay according to the delay time, and then calculates the standard deviation of the 9 jitter values, that is, the jitter deviation value, and at the same time calls the corresponding threshold according to the IP address, calculates the abnormal ratio of the number of bytes of each network data packet, identifies the lost network data packet according to the abnormal ratio, and calculates the packet loss rate of this group; Then, the delay threshold in the database is retrieved, and the delay trend value and packet loss trend value of the group are calculated based on the comparison between the delay time and the delay threshold. At the same time, the number of delay times greater than the threshold Q is counted, and the associated deviation value is calculated; Finally, the comprehensive deviation value of this group of data is calculated by combining the delay trend value, packet loss trend value and associated deviation value; According to the comprehensive deviation value result, the data controller sends a command signal to start the standby port of the corresponding distribution cabinet data acquisition terminal, switches the signal transmission channel, and notifies relevant personnel for maintenance. On the other hand, it compensates for this group of data and restores the complete time series value of the entire group of data packets. The visual controller displays various parameters of the power distribution cabinet in real time according to the entire group of data packets after recovery; The data detector includes a parameter analysis module, a trend analysis module, a correlation analysis module, and a comprehensive evaluation module, and the specific working steps are as follows; S1, the parameter analysis module calculates the time difference between the receiving time and the sending time of each network data packet, that is, the delay time T12, T22...T102, and then calculates the jitter value T13, T23...T93, T13=T12-T22, T23=T22-T32, and so on, and finally calculates the jitter deviation value X, which is the standard deviation of the 9 jitter values ​​T13, T23...T93; At the same time, the corresponding threshold K in the database is retrieved according to the IP address, and the abnormal ratios B11, B21...B101 of the number of bytes of each network data packet are calculated, where B11=(K-B1) / K, B21=(K-B2) / K...B101=(K-B10) / K. Then the abnormal ratios B11, B21...B101 are compared with 0.1 respectively. If it is less than 0.1, it means that the number of bytes of the network data packet is normal. Otherwise, it means that the network data packet data is lost and regarded as packet loss. Finally, the number of lost network data packets n is counted, and the packet loss rate M is calculated, M=n / 10; S2, the trend analysis module first retrieves the delay time threshold Q in the database, calculates the delay time error ratio Y, Y=[(T12+T22+...T92+T102) / 10-Q] / Q, and then calculates the delay trend value Z, Z=Y×0.4+X÷Q×0.6; At the same time, the standard deviation b of the abnormal ratios of the bytes of the 10 network data packets in this group, B11, B21...B101, is calculated, and then the packet loss trend value R is calculated, R=M×0.7+b×0.3; S3, the association analysis module first retrieves the threshold Q of the delay time in the database, then counts the number N of delay times T12, T22...T102 that are greater than the threshold Q, and finally calculates the association deviation value L, L=N / 10×0.4+M×0.6; S4, the comprehensive evaluation module calculates a comprehensive deviation value H, H=Z×0.3+R×0.3+L×0.4+ε, where ε represents a deviation correction value.

2. A remote intelligent monitoring system for a power distribution cabinet as claimed in claim 1, characterized in that: The data controller includes a redundant control module and a prediction compensation module. The redundant control module receives a comprehensive deviation value H. If H is less than 0.1, it indicates that the data set is normal as a whole. If H is greater than 0.1 and less than 0.2, it means that the whole group of data is abnormal, and there is a problem with the data acquisition terminal and / or transmission channel of the power distribution cabinet corresponding to the IP address. Then a command signal is sent to start the standby port of the corresponding data acquisition terminal of the power distribution cabinet, and the signal transmission channel is switched at the same time to notify relevant personnel for maintenance. If H is greater than 0.2, it means that this group of data is particularly abnormal as a whole. On the one hand, a command signal is sent to start the backup port of the corresponding distribution cabinet data acquisition terminal, and at the same time, the signal transmission channel is switched to notify relevant personnel for maintenance. On the other hand, the prediction compensation module is started to compensate for this group of data.

3. A remote intelligent monitoring system for a power distribution cabinet as claimed in claim 2, characterized in that: The specific steps of the prediction compensation module are as follows: Traverse the network data packet sequence and determine the time point corresponding to the data packet loss according to the abnormal ratios B11, B21...B101; According to the IP address, the corresponding distribution cabinet parameter prediction model is retrieved from the database, the parameter values ​​in the adjacent and normal network data packets are selected, the parameter values ​​of the normal data packets are input into the distribution cabinet parameter prediction model, and the prediction value of the lost data packet in the time series is obtained; Insert the predicted value into the time position where the original data packet was lost, and the entire group of data packets is restored to a complete and continuous time series state; The originally damaged network data packets are discarded, and a sequence of restored network data packets is output.

4. A remote intelligent monitoring system for a power distribution cabinet as claimed in any one of claims 1 to 3, characterized in that: The database has the function of storing system parameters, and can be called by data detectors, data regulators, and visual controllers at any time, and can update data information in real time.

5. A remote intelligent monitoring system for a power distribution cabinet as claimed in claim 3, characterized in that: The distribution cabinet parameter prediction model is built based on historical data and machine learning algorithms. It can predict the parameter values ​​of the distribution cabinet at a specific point in time. During the operation of the system, the visual controller will continuously send the correct operating parameters of the distribution cabinet to the parameter prediction model and update the data in real time to ensure that the input data of the parameter prediction model is always highly consistent with the actual operating status of the distribution cabinet, so that the distribution cabinet parameter prediction model can continuously learn and adapt to the latest dynamics of the distribution cabinet.

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