A power grid data acquisition and monitoring system and method based on micro-center service channels

Abstract

This invention discloses a power grid data acquisition and monitoring system and method based on a micro-center service channel. The system includes real-time acquisition of a first data curve, determination of a second data curve, integration of the difference between the first and second data curves over a preset time period, determination of the data acquisition stage and corresponding data acquisition and processing method based on the integral value, and data acquisition and processing according to the corresponding data acquisition and processing method to coordinate the accuracy and timeliness of power grid monitoring. In the event of an anomaly, a second control system is activated, and emergency data is transmitted to a data analysis and processing module based on a backup power supply, a second communication module, and a second storage module to achieve complete acquisition of the abnormal data. Data from different acquisition stages is identified and visualized, allowing maintenance personnel to quickly locate abnormal data based on the corresponding identifiers.

Description

Technical Field

[0001] This invention relates to the field of power grid data acquisition and monitoring, and specifically to a power grid data acquisition and monitoring system and method based on a micro-center service channel. Background Technology

[0002] With ever-increasing energy demands, the stability and security of power grid systems are of paramount importance. Power grid data acquisition and monitoring systems, as all-around assistants in smart grid management, provide comprehensive and efficient solutions to meet this need. A power grid data acquisition and monitoring system is a real-time monitoring system that integrates data acquisition, analysis, storage, and display functions. Through sensors, controllers, and other equipment deployed in the field, it monitors parameters such as voltage, current, and power factor of the power grid system in real time, providing strong support for the stable operation of the power grid system.

[0003] Existing power grid data acquisition and monitoring systems mainly face the following problems: First, to achieve accurate monitoring of the power grid system, these systems need to collect various types of parameter information, such as current, voltage, temperature, and power factor, from multiple devices. The massive amount of data puts enormous pressure on the timeliness of data acquisition, transmission, and processing. How to balance the accuracy and timeliness of power grid monitoring is a pressing issue. Second, in abnormal situations, such as fires or floods, the data acquisition and monitoring system often shuts down automatically for various reasons, ceasing on-site data collection. Data transmission modules also cannot continue normal data transmission due to network or power outages. However, the data information corresponding to these anomalies is often crucial for analyzing the causes of subsequent accidents. Finally, staff handle a large number of abnormal alarms daily. Enabling quick and accurate identification of abnormal data is essential for improving the efficiency of anomaly maintenance. Summary of the Invention

[0004] The purpose of this invention is to provide a power grid data acquisition and monitoring system and method based on a micro-center service channel to solve the technical problems mentioned in the background section.

[0005] To achieve the above objectives, the specific technical solution adopted by the present invention is as follows:

[0006] A power grid data acquisition and monitoring method based on a micro-center service channel includes the following steps:

[0007] S1: Real-time acquisition of the first data curve to determine the second data curve;

[0008] S2: Subtract the first data curve from the second data curve and then integrate over a preset time period;

[0009] S3: Determine the data acquisition stage and the corresponding data acquisition and processing method based on the obtained integral value;

[0010] S4: Collect and process the data according to the corresponding data acquisition and processing method.

[0011] The first data curve is a real-time data curve, and the second data curve is an ideal data curve;

[0012] The ideal data curve is pre-stored in the first storage module of the data acquisition module;

[0013] The first control module of the data acquisition module determines the second data curve according to the type and source of the real-time collected data;

[0014] The data acquisition stage includes: the first acquisition stage, the second acquisition stage, the third acquisition stage, and the fourth acquisition stage;

[0015] The determining of the data acquisition stage and the corresponding data acquisition and processing method based on the obtained integral value includes: when the integral value is less than K1 and the duration is N, enter the first acquisition stage;

[0016] When the integral value is not less than K1 but less than K2 and the duration is N, enter the second acquisition stage;

[0017] When the integral value is not less than K2 and the duration is N, and the importance of the data acquisition type is lower than H, enter the third acquisition stage;

[0018] When the integral value is not less than K2 and the duration is N, and the importance of the data acquisition type is not less than H, enter the fourth acquisition stage;

[0019] The different data acquisition stages have the following different data acquisition and processing methods:

[0020] In the first acquisition stage, the sampling rate is relatively low, the integral value is less than K1, and compared with the ideal data curve, the amplitude of the real-time data curve is relatively stable along the time axis. At this time, curve fitting is performed. When the fitting result meets the expectation, the real-time data curve is replaced with the curve function obtained by fitting, and the time node recording unit is started, and the real-time data curve in the corresponding time interval is recorded as (fitting curve function, time interval);

[0021] In the first acquisition stage, when the integral value is less than K0 (K0 < K1) and the duration is N, the ideal data curve is used to replace the real-time data curve, and the time node recording unit is started, and the real-time data curve in the corresponding time interval is recorded as (ideal curve function, time interval);

[0022] In the second acquisition phase, the sampling rate adaptive adjustment module is activated, and the sampling rate of the first sampling module is adaptively adjusted based on the real-time integral value; when the integral value increases, the sampling rate of the first sampling module increases; when the integral value decreases, the sampling rate of the first sampling module decreases; after the corresponding data is acquired, the preprocessing module is used to filter it; before data transmission, the data compression and encryption module is used to compress and encrypt the data.

[0023] In the third acquisition phase, the first and second sampling modules in the data acquisition module are simultaneously activated, and their sampling rates are both adjusted to the maximum. The activation times of the first and second sampling modules are staggered by 1 / 2 sampling period, where the sampling period is the sampling period corresponding to the maximum sampling rate. Subsequently, the acquired data is interpolated and shaped using secondary interpolation. After the corresponding data is acquired, it is filtered using a preprocessing module. Before data transmission, the data is compressed and encrypted using a data compression and encryption module.

[0024] In the fourth acquisition phase, the first, second, and third sampling modules in the data acquisition module are simultaneously activated, and their sampling rates are all adjusted to the maximum. The activation times of the first, second, and third sampling modules are staggered by 1 / 3 of a sampling period, where the sampling period is the sampling period corresponding to the maximum sampling rate. Subsequently, the acquired data is interpolated and shaped using three interpolation methods. After the corresponding data is acquired, it is filtered using a preprocessing module. Before data transmission, the data is compressed and encrypted using a data compression and encryption module.

[0025] Different data acquisition types have different levels of importance. The time period width, step size, and judgment thresholds for different acquisition stages are determined based on the importance of the data acquisition type.

[0026] Optionally, the method further includes S5: under normal circumstances, the first control system is used to collect and process data; when an abnormal situation occurs, the second control system is activated, and emergency data is transmitted to the data analysis and processing module based on the backup power supply, the second communication module, and the second storage module.

[0027] The first control system is the control system for normal data acquisition, and the second control system is an emergency control system for emergency data acquisition when an abnormal situation occurs.

[0028] The data transmission module includes a second control module, a first communication module, a second storage module, a backup power supply, and another second communication module. Under normal circumstances, the data transmission module receives data from the data acquisition module and sends it to the data analysis and processing module via the first communication module. In case of an abnormal situation, the backup power supply module provides power to the sensor module in the data acquisition module, the second communication module in the data transmission module, and the second storage module. The data information during transmission when the abnormality occurs is stored in the second storage module. The sensor module continues to work and collects data information for a preset time period. Finally, the emergency data information collected by the sensor module for the preset time period and the data information in transmission stored in the second storage module are sent to the data analysis and processing module.

[0029] The preset time period is obtained by multiplying the importance of the data collection type by a corresponding proportional coefficient. The higher the importance of the data collection type, the longer the preset time period.

[0030] The emergency data also includes information on the reasons that caused the first control system to shut down automatically, and information on the configuration of the first control system when it shuts down automatically.

[0031] The first communication module can be an Ethernet module; the second communication module can be a backup GPRS mobile communication module.

[0032] In the event of an abnormal situation, the backup power module can not only supply power to the second control module, the second storage module, and the second communication module in the data transmission module, but also supply power to the sensor module in the data acquisition module.

[0033] Optionally, the method further includes S6: performing anomaly detection on power grid data information, identifying and visualizing data from different acquisition stages, so that maintenance personnel can quickly locate abnormal data based on the corresponding identifiers;

[0034] The anomaly detection of power grid data information includes: the data analysis and processing module obtains the function curve of the corresponding integral value and time t, and determines the anomaly probability of the data in the corresponding time period of the second acquisition stage, the third acquisition stage and the fourth acquisition stage through corresponding calculations;

[0035] The process of determining the probability of data anomalies within the corresponding time periods of the second, third, and fourth data acquisition stages through appropriate calculations includes:

[0036] The time periods corresponding to the second, third, and fourth acquisition stages are determined. Based on the function curve of the corresponding integral value and time t, the time periods corresponding to different acquisition stages are integrated. The obtained integral value is divided by the corresponding time period length to obtain the average value of the data exceeding the limit per unit time. Based on the magnitude of the average value of the data exceeding the limit, the probability of data abnormality in the time periods corresponding to different acquisition stages is determined.

[0037] Optionally, if the average value of data exceeding the limit in the corresponding data acquisition stage exceeds a certain threshold, the data analysis and processing module will issue an alarm and prompt the corresponding handling method.

[0038] The identification and visualization of data from different acquisition stages includes:

[0039] Determine the time intervals and time nodes corresponding to the first, second, third, and fourth data collection stages;

[0040] In the visualization interface, the real-time data curves within the corresponding time intervals of different collection stages are displayed using different colors and lines, and the corresponding anomaly probability is also displayed in real time above each data curve.

[0041] In addition, there are corresponding time nodes between each collection stage. Small flags are set at the corresponding time nodes. People can directly locate the data segment corresponding to the collection stage they want to view by clicking the corresponding small flag. The color of the small flag is the same as the curve in the corresponding time interval.

[0042] Optionally, users can double-click the flag icon to select and set the real-time data curve corresponding to the collection stage to be displayed;

[0043] The visualization interface is specifically the first display.

[0044] According to another aspect of the present invention, a power grid data acquisition and monitoring system based on a micro-center service channel is also provided. The system includes: a data acquisition module, a data transmission module, a data analysis and processing module, a cloud server, and a mobile terminal.

[0045] The data acquisition module, data transmission module, data analysis and processing module, cloud server, and mobile terminal are connected in sequence.

[0046] The data analysis and processing module can transmit real-time data information to the cloud server, allowing people to monitor the working status of the power grid data acquisition and monitoring system in real time via mobile terminals;

[0047] The data acquisition module includes: a temperature sensor, a humidity sensor, a current sensor, a voltage sensor, a power factor sensor, a smoke sensor, a preprocessing module, a first control module, a sampling rate adaptive adjustment module, a data compression and encryption module, a first storage module, a first sampling module, a second sampling module, and a third sampling module;

[0048] The temperature sensor, humidity sensor, current sensor, voltage sensor, power factor sensor, and smoke sensor can collect data such as temperature, humidity, current, voltage, power factor, and smoke from relevant equipment or the environment in the power grid system, and send them to the first control module.

[0049] The first control module can acquire a first data curve in real time and determine a second data curve; calculate the difference between the first data curve and the second data curve and integrate it over a preset time period; determine the data acquisition stage and the corresponding data acquisition and processing method based on the obtained integral value; and acquire and process the data according to the corresponding data acquisition and processing method.

[0050] The first data curve is the real-time data curve, and the second data curve is the ideal data curve.

[0051] The first control module of the data acquisition module determines the second data curve based on the data type and data source acquired in real time;

[0052] The data acquisition phase includes: a first acquisition phase, a second acquisition phase, a third acquisition phase, and a fourth acquisition phase;

[0053] The step of determining the data acquisition stage and the corresponding data acquisition processing method based on the obtained integral value includes: entering the first acquisition stage when the integral value is less than K1 and the duration is N;

[0054] When the integral value is not less than K1 but less than K2 and the duration is N, the second acquisition stage begins;

[0055] When the integral value is not less than K2 and the duration is N, and the importance of the data acquisition type is less than H, the third acquisition stage begins.

[0056] When the integral value is not less than K2 and the duration is N, and the importance of the data acquisition type is not less than H, the fourth acquisition stage begins.

[0057] Different data acquisition stages involve the following different data acquisition and processing methods:

[0058] In the first acquisition stage, the sampling rate is relatively low, the integral value is less than K1. Compared with the ideal data curve, the amplitude of the real-time data curve is relatively stable along the time axis. At this time, curve fitting is performed. When the fitting result meets the expectation, the real-time data curve is replaced by the fitted curve function, and the time node recording unit is started to record the real-time data curve within the corresponding time interval as (fitted curve function, time interval);

[0059] Optionally, in the first acquisition stage, when the integral value is less than K0 (K0 < K1) and the duration is N, the ideal data curve is used to replace the real-time data curve, and the time node recording unit is started to record the real-time data curve within the corresponding time interval as (ideal curve function, time interval);

[0060] In the second acquisition stage, the sampling rate adaptive adjustment module is started to adaptively adjust the sampling rate of the first sampling module in combination with the real-time integral value; when the integral value increases, the sampling rate of the first sampling module is increased; when the integral value decreases, the sampling rate of the first sampling module is decreased; after the corresponding data is acquired, it is filtered by the preprocessing module; before data transmission, the data is compressed and encrypted by the data compression and encryption module;

[0061] In the third acquisition stage, the first sampling module and the second sampling module in the data acquisition module are started simultaneously, and their sampling rates are both adjusted to the maximum. The start times of the first sampling module and the second sampling module are staggered by 1 / 2 of the sampling period, and the sampling period is the sampling period corresponding to the maximum sampling rate; later, the acquired data is interpolated and shaped by quadratic interpolation; after the corresponding data is acquired, it is filtered by the preprocessing module; before data transmission, the data is compressed and encrypted by the data compression and encryption module;

[0062] In the fourth acquisition stage, the first sampling module, the second sampling module, and the third sampling module in the data acquisition module are started simultaneously, and their sampling rates are both adjusted to the maximum. The start times of the first sampling module, the second sampling module, and the third sampling module are staggered by 1 / 3 of the sampling period, and the sampling period is the sampling period corresponding to the maximum sampling rate; later, the acquired data is interpolated and shaped by cubic interpolation; after the corresponding data is acquired, it is filtered by the preprocessing module; before data transmission, the data is compressed and encrypted by the data compression and encryption module;

[0063] Different data acquisition types have different importance levels. Based on the importance of the data acquisition type, the time period width, the step size, and the judgment thresholds for different acquisition stages are determined;

[0064] The data transmission module includes: a second control module, a first communication module, a second storage module, a backup power supply, and a second communication module. Under normal circumstances, the data transmission module receives data information from the data acquisition module and sends it to the data analysis and processing module via the first communication module. In case of an abnormal situation, the backup power supply module supplies power to the sensor module in the data acquisition module, the second communication module in the data transmission module, and the second storage module. The data information during transmission when the abnormality occurs is stored in the second storage module. The sensor module continues to work and collects data information for a preset time period. Finally, the emergency data information collected by the sensor module for the preset time period and the data information in transmission stored in the second storage module are sent to the data analysis and processing module.

[0065] The preset time period is obtained by multiplying the importance of the data collection type by a corresponding proportional coefficient. The higher the importance of the data collection type, the longer the preset time period.

[0066] Optionally, the emergency data may also include relevant cause information that led to the automatic shutdown of the first control system and relevant configuration information when the first control system automatically shuts down;

[0067] The first communication module can be an Ethernet module; the second communication module can be a backup GPRS mobile communication module.

[0068] In the event of an abnormal situation, the backup power module can not only supply power to the second control module, the second storage module, and the second communication module in the data transmission module, but also supply power to the sensor module in the data acquisition module.

[0069] The data analysis and processing module includes: a third control module, a first interaction module, a first display, a third storage module, and an alarm module;

[0070] The third control module can detect anomalies in power grid data information and identify and visualize data from different acquisition stages based on the first display. Maintenance personnel can quickly locate abnormal data based on the corresponding identification.

[0071] The third control module can detect anomalies in power grid data information, including: the data analysis and processing module obtains the function curve of the corresponding integral value and time t, and determines the probability of data anomalies in the corresponding time periods of the second acquisition stage, the third acquisition stage and the fourth acquisition stage through corresponding calculations.

[0072] The process of determining the anomaly probability of data within the corresponding time periods of the second, third, and fourth acquisition stages through corresponding calculations includes: determining the time periods corresponding to the second, third, and fourth acquisition stages; integrating the corresponding time periods based on the function curve of the corresponding integral value versus time t; dividing the obtained integral value by the corresponding time period length to obtain the average out-of-limit value of data per unit time; determining the anomaly probability of data within the corresponding time periods of different acquisition stages based on the magnitude of the average out-of-limit value; and finally displaying the obtained anomaly probabilities of data within the corresponding time periods of the second, third, and fourth acquisition stages on the corresponding data curve display interface.

[0073] When the average value of data exceeding the limit during the corresponding data acquisition phase exceeds a certain threshold, the data analysis and processing module will control the alarm module to issue an alarm and prompt the corresponding handling method.

[0074] The step of identifying and visualizing data from different acquisition stages based on the first display includes: determining the time intervals and time nodes corresponding to the first, second, third, and fourth acquisition stages;

[0075] In the visualization interface, the real-time data curves within the corresponding time intervals of different collection stages are displayed using different colors and lines, and the corresponding anomaly probability is also displayed in real time above each data curve.

[0076] The maintenance personnel can quickly locate abnormal data based on the corresponding identifiers, including:

[0077] There are corresponding time nodes between each collection stage. Small flags are set at the corresponding time nodes. People can directly locate the data segment corresponding to the collection stage they want to view by clicking the corresponding small flag. The color of the small flag is the same as the curve in the corresponding time interval.

[0078] Users can double-click the small flag icon to select the real-time data curve corresponding to the data collection stage they want to display.

[0079] Compared with the prior art, the present invention has the following beneficial effects:

[0080] This invention provides a power grid data acquisition and monitoring system and method based on a micro-center service channel. The system includes real-time acquisition of a first data curve, determination of a second data curve, integration of the difference between the first and second data curves over a preset time period, determination of the data acquisition stage and corresponding data acquisition processing method based on the integral value, and data acquisition and processing according to the corresponding data acquisition and processing method to coordinate the accuracy and timeliness of power grid monitoring. In the event of an anomaly, a second control system is activated, and emergency data is transmitted to a data analysis and processing module based on a backup power supply, a second communication module, and a second storage module to achieve complete acquisition of the anomaly data. Data from different acquisition stages is identified and visualized, allowing maintenance personnel to quickly locate anomalies based on the corresponding identifiers, thus improving maintenance efficiency in abnormal situations. Attached Figure Description

[0081] Figure 1 A flowchart illustrating a power grid data acquisition and monitoring method based on a micro-center service channel;

[0082] Figure 2 This is a schematic diagram of the integral based on the ideal data curve and the real-time data curve;

[0083] Figure 3 This is a schematic diagram of the structure of a power grid data acquisition and monitoring system based on a micro-center service channel. Detailed Implementation

[0084] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. The specific embodiments described herein are merely illustrative and not intended to limit the invention. Furthermore, the technical features involved in the various embodiments of the invention described below can be combined with each other as long as they do not conflict with each other.

[0085] In signal acquisition and processing systems, when the data acquisition sampling rate is high, the pressure on data acquisition, transmission, and processing will be relatively large; when the sampling rate is low, key data signals may not be able to be monitored accurately and comprehensively. How to coordinate the relationship between the pressure on data acquisition, transmission, and processing and the accuracy and comprehensiveness of data monitoring is a technical problem that urgently needs to be solved in this field.

[0086] Therefore, this application provides a power grid data acquisition and monitoring method based on a micro-center service channel, such as... Figure 1 As shown, the power grid data is obtained based on the micro-center service channel and includes the following steps:

[0087] S1: Real-time acquisition of the first data curve to determine the second data curve;

[0088] S2: Subtract the first data curve from the second data curve and then integrate over a preset time period;

[0089] S3: Determine the data acquisition stage and corresponding data acquisition and processing method based on the obtained integral value;

[0090] S4: Collect and process data according to the corresponding data collection and processing methods.

[0091] The first data curve is the real-time data curve, and the second data curve is the ideal data curve.

[0092] The real-time data curve is a curve plotted from the temperature, humidity, current, voltage, power factor, and smoke data collected in real time by the data acquisition module.

[0093] The ideal data curve is the expected control curve of temperature, humidity, current, voltage, power factor and smoke data in the power grid system, which is pre-stored in the first storage module of the data acquisition module;

[0094] The first control module of the data acquisition module determines the second data curve based on the data type and data source acquired in real time, wherein the data source is associated with the data acquisition location;

[0095] like Figure 2 As shown, the preset time period is (T1~T2), and M=T2-T1;

[0096] T1 and T2 are the time nodes before the current time t; optionally, T2 can also be the current time t.

[0097] The preset time period (T1~T2) moves forward in steps p, and the corresponding integral value is calculated.

[0098] During the data acquisition phase, the corresponding integral value and the function curve of time t are recorded in real time, and the curve information is transmitted to the data analysis and processing module to facilitate data anomaly analysis and early warning.

[0099] The width M and step size p of the preset time period can both be adjusted;

[0100] The data acquisition phase includes: a first acquisition phase, a second acquisition phase, a third acquisition phase, and a fourth acquisition phase;

[0101] The step of determining the data acquisition stage and the corresponding data acquisition processing method based on the obtained integral value includes: entering the first acquisition stage when the integral value is less than K1 and the duration is N;

[0102] When the integral value is not less than K1 but less than K2 and the duration is N, the second acquisition stage begins;

[0103] When the integral value is not less than K2 and the duration is N, and the importance of the data acquisition type is lower than H, enter the third acquisition stage;

[0104] When the integral value is not less than K2 and the duration is N, and the importance of the data acquisition type is not less than H, enter the fourth acquisition stage;

[0105] Record the time period intervals and time nodes corresponding to different acquisition stages in real time;

[0106] Different data acquisition stages have the following different data acquisition and processing methods:

[0107] In the first acquisition stage, the sampling rate is relatively low, the integral value is less than K1, and compared with the ideal data curve, the amplitude of the real-time data curve is relatively stable along the time axis. At this time, curve fitting is performed. When the fitting result meets the expectations, the curve function obtained by fitting is used to replace the real-time data curve, and the time node recording unit is started to record the real-time data curve within the corresponding time interval as (fitting curve function, time interval);

[0108] Optionally, in the first acquisition stage, when the integral value is less than K0 (K0 < K1) and the duration is N, the ideal data curve is used to replace the real-time data curve, and the time node recording unit is started to record the real-time data curve within the corresponding time interval as (ideal curve function, time interval);

[0109] The described ideal data curve can be a trigonometric function curve, a pulse function curve, a constant function curve, or other related types of function curves;

[0110] By storing the real-time data curve in the form of (fitting curve function, time interval) and / or (ideal curve function, time interval), the pressure of data transmission and storage can be reduced;

[0111] In the second acquisition stage, start the sampling rate adaptive adjustment module, and adaptively adjust the sampling rate of the first sampling module in combination with the real-time integral value; when the integral value increases, increase the sampling rate of the first sampling module; when the integral value decreases, decrease the sampling rate of the first sampling module; after collecting the corresponding data, use the preprocessing module to perform filtering processing on it; before data transmission, use the data compression and encryption module to perform compression and encryption processing on the data;

[0112] In the third acquisition phase, the first and second sampling modules in the data acquisition module are simultaneously activated, and their sampling rates are both adjusted to the maximum. The activation times of the first and second sampling modules are staggered by 1 / 2 sampling period, where the sampling period is the sampling period corresponding to the maximum sampling rate. Subsequently, the acquired data is interpolated and shaped using secondary interpolation. After the corresponding data is acquired, it is filtered using a preprocessing module. Before data transmission, the data is compressed and encrypted using a data compression and encryption module.

[0113] In the fourth acquisition phase, the first, second, and third sampling modules in the data acquisition module are simultaneously activated, and their sampling rates are all adjusted to the maximum. The activation times of the first, second, and third sampling modules are staggered by 1 / 3 of a sampling period, where the sampling period is the sampling period corresponding to the maximum sampling rate. Subsequently, the acquired data is interpolated and shaped using three interpolation methods. After the corresponding data is acquired, it is filtered using a preprocessing module. Before data transmission, the data is compressed and encrypted using a data compression and encryption module.

[0114] Different data acquisition types have different importance, and their corresponding time period width M, step size p, and judgment thresholds for different acquisition stages are all different. The time period width, step size, and judgment thresholds for different acquisition stages are determined based on the importance of the data acquisition type.

[0115] For example, if the importance of the data collection type is Y, then its corresponding time period width, step size, and judgment threshold for different collection stages are respectively: YM, Yp, YK0, YK1, YK2, YN;

[0116] The higher the importance of the data acquisition type, the smaller the value of Y, and the more sensitive the data acquisition module is to its fluctuations during data acquisition, the higher the corresponding average sampling rate.

[0117] In abnormal situations, such as fires or floods, the data acquisition and monitoring system will often shut down automatically for various reasons and stop collecting on-site data. The data transmission module will also be unable to continue normal data transmission due to network or power outages. However, the data information corresponding to the occurrence of abnormalities is often of great significance for the analysis of the causes of the accident later.

[0118] Optionally, the method further includes S5: under normal circumstances, the first control system is used to collect and process data; when an abnormal situation occurs, the second control system is activated, and emergency data is transmitted to the data analysis and processing module based on the backup power supply, the second communication module, and the second storage module.

[0119] The first control system is the control system for normal data acquisition, and the second control system is an emergency control system for emergency data acquisition when an abnormal situation occurs.

[0120] The first and second control systems can run in the first control module of the data acquisition module, or in the second control module of the data transmission module, or they can be pre-stored in a cloud server and sent to the first control module of the data acquisition module and / or the second control module of the data transmission module when an abnormal situation occurs.

[0121] The second control system differs from the first control system; it is only used for the collection and transmission of emergency data when abnormal situations occur, and does not have other related control functions.

[0122] The data transmission module includes a second control module, a first communication module, a second storage module, a backup power supply, and another second communication module. Under normal circumstances, the data transmission module receives data from the data acquisition module and sends it to the data analysis and processing module via the first communication module. In case of an abnormal situation, the backup power supply module provides power to the sensor module in the data acquisition module, the second communication module in the data transmission module, and the second storage module. The data information during transmission when the abnormality occurs is stored in the second storage module. The sensor module continues to work and collects data information for a preset time period. Finally, the emergency data information collected by the sensor module for the preset time period and the data information in transmission stored in the second storage module are sent to the data analysis and processing module.

[0123] The preset time period is obtained by multiplying the importance of the data collection type by a corresponding proportional coefficient. The higher the importance of the data collection type, the longer the preset time period.

[0124] Optionally, the emergency data may also include relevant cause information that caused the first control system to shut down automatically and relevant configuration information when the first control system shut down automatically, so as to facilitate subsequent troubleshooting and recovery of the first control system;

[0125] The first communication module can be an Ethernet module; the second communication module can be a backup GPRS mobile communication module.

[0126] In the event of an abnormal situation, the backup power module can not only power the second control module, the second storage module, and the second communication module in the data transmission module, but also power the sensor module in the data acquisition module, thereby improving the stability of data acquisition and transmission.

[0127] Power grid staff have to handle a large number of abnormal alarms every day. Since the power grid data acquisition and monitoring system collects a large amount of data, it is of great significance to improve the efficiency of abnormal maintenance by enabling staff to quickly and accurately find abnormal data.

[0128] Optionally, the method further includes S6: performing anomaly detection on power grid data information, identifying and visualizing data from different acquisition stages, so that maintenance personnel can quickly locate abnormal data based on the corresponding identifiers; thereby improving the maintenance efficiency of abnormal situations.

[0129] The anomaly detection of power grid data information includes: the data analysis and processing module obtains the function curve of the corresponding integral value and time t, and determines the anomaly probability of the data in the corresponding time period of the second acquisition stage, the third acquisition stage and the fourth acquisition stage through corresponding calculations. That is, the integral value is used for both the adjustment of the sampling rate of the sampling module and the real-time monitoring and early warning of the data.

[0130] In one embodiment, firstly, the time periods corresponding to the second, third, and fourth acquisition stages are determined as t1~t2, t3~t4, and t5~t6, respectively. Based on the function curve of the corresponding integral value versus time t, the time periods corresponding to different acquisition stages are integrated, and the obtained integral value is divided by the corresponding time period length to obtain the average out-of-limit value of the data per unit time. The probability of data anomalies within the time periods corresponding to different acquisition stages is determined based on the magnitude of the average out-of-limit value. Finally, the probabilities of data anomalies within the time periods corresponding to the second, third, and fourth acquisition stages are displayed on the corresponding data curve display interface to facilitate subsequent troubleshooting.

[0131] The second, third, and fourth acquisition stages may each have multiple data segments, which may be continuous or discontinuous in time; the corresponding time intervals and time node information can be obtained from the data stored during data acquisition.

[0132] Optionally, if the average value of data exceeding the limit in the corresponding data acquisition stage exceeds a certain threshold, the data analysis and processing module will issue an alarm and prompt the corresponding handling method.

[0133] The identification and visualization of data from different acquisition stages includes:

[0134] Determine the time intervals and time nodes corresponding to the first, second, third, and fourth data collection stages;

[0135] In the visualization interface, the real-time data curves within the corresponding time intervals of different collection stages are displayed using different colors and lines, and the corresponding anomaly probability is also displayed in real time above each data curve.

[0136] In addition, there are corresponding time nodes between each collection stage. Small flags are set at the corresponding time nodes. People can directly locate the data segment corresponding to the collection stage they want to view by clicking the corresponding flag, without having to fast forward or repeatedly click to locate. The color of the small flag is the same as the curve in the corresponding time period.

[0137] Optionally, users can double-click the flag icon to select and display real-time data curves corresponding to at least one data acquisition stage, and can also double-click the flag icon to select and set the real-time data curves corresponding to the data acquisition stage to be displayed.

[0138] Optionally, users can display emergency data collected by the sensor module over a preset time period on a visual interface according to their actual needs, and perform separate analysis and processing on the data.

[0139] The visualization interface is specifically the first display.

[0140] According to another aspect of the present invention, a power grid data acquisition and monitoring system based on a micro-center service channel is also provided, such as... Figure 3 As shown, the system includes: a data acquisition module, a data transmission module, a data analysis and processing module, a cloud server, and a mobile terminal;

[0141] The data acquisition module, data transmission module, data analysis and processing module, cloud server, and mobile terminal are connected in sequence.

[0142] The data analysis and processing module can transmit real-time data information to the cloud server, allowing people to monitor the working status of the power grid data acquisition and monitoring system in real time via mobile terminals;

[0143] The data acquisition module includes: a temperature sensor, a humidity sensor, a current sensor, a voltage sensor, a power factor sensor, a smoke sensor, a preprocessing module, a first control module, a sampling rate adaptive adjustment module, a data compression and encryption module, a first storage module, a first sampling module, a second sampling module, and a third sampling module;

[0144] The temperature sensor, humidity sensor, current sensor, voltage sensor, power factor sensor, and smoke sensor can collect data such as temperature, humidity, current, voltage, power factor, and smoke from relevant equipment or the environment in the power grid system, and send them to the first control module.

[0145] The first control module can acquire a first data curve in real time and determine a second data curve; calculate the difference between the first data curve and the second data curve and integrate it over a preset time period; determine the data acquisition stage and the corresponding data acquisition and processing method based on the obtained integral value; and acquire and process the data according to the corresponding data acquisition and processing method.

[0146] The first data curve is the real-time data curve, and the second data curve is the ideal data curve.

[0147] The real-time data curve is a curve plotted from the temperature, humidity, current, voltage, power factor, and smoke data collected in real time by the data acquisition module.

[0148] The ideal data curve is the expected control curve of temperature, humidity, current, voltage, power factor and smoke data in the power grid system, which is pre-stored in the first storage module of the data acquisition module;

[0149] The first control module of the data acquisition module determines the second data curve based on the data type and data source acquired in real time;

[0150] The data acquisition phase includes: a first acquisition phase, a second acquisition phase, a third acquisition phase, and a fourth acquisition phase;

[0151] The step of determining the data acquisition stage and the corresponding data acquisition processing method based on the obtained integral value includes: entering the first acquisition stage when the integral value is less than K1 and the duration is N;

[0152] When the integral value is not less than K1 but less than K2 and the duration is N, the second acquisition stage begins;

[0153] When the integral value is not less than K2 and the duration is N, and the importance of the data acquisition type is less than H, the third acquisition stage begins.

[0154] When the integral value is not less than K2 and the duration is N, and the importance of the data acquisition type is not less than H, the fourth acquisition stage begins.

[0155] Different data acquisition stages involve the following different data acquisition and processing methods:

[0156] In the first acquisition phase, the sampling rate is relatively low and the integral value is less than K1. Compared with the ideal data curve, the amplitude of the real-time data curve is relatively stable with time axis. At this time, curve fitting is performed. When the fitting result meets expectations, the fitted curve function is used to replace the real-time data curve, and the time node recording unit is started to record the real-time data curve in the corresponding time interval as (fitted curve function, time interval).

[0157] Optionally, in the first acquisition stage, when the integral value is less than K0 (K0 < K1) and the duration is N, the ideal data curve is used to replace the real-time data curve, and the time node recording unit is started to record the real-time data curve within the corresponding time interval as (ideal curve function, time interval);

[0158] The ideal data curve can be a trigonometric function curve, a pulse function curve, a constant function curve, or other related types of function curves;

[0159] By storing the real-time data curve in the form of (fitting curve function, time interval) and / or (ideal curve function, time interval), the pressure of data transmission and storage can be reduced;

[0160] In the second acquisition stage, the sampling rate adaptive adjustment module is started to adaptively adjust the sampling rate of the first sampling module in combination with the real-time integral value; when the integral value increases, the sampling rate of the first sampling module is increased; when the integral value decreases, the sampling rate of the first sampling module is decreased; after the corresponding data is acquired, it is filtered by the preprocessing module; before data transmission, the data is compressed and encrypted by the data compression and encryption module;

[0161] In the third acquisition stage, the first sampling module and the second sampling module in the data acquisition module are started simultaneously, and their sampling rates are both adjusted to the maximum. The start times of the first sampling module and the second sampling module are staggered by 1 / 2 of the sampling period, and the sampling period is the sampling period corresponding to the maximum sampling rate; later, the acquired data is interpolated and shaped by quadratic interpolation; after the corresponding data is acquired, it is filtered by the preprocessing module; before data transmission, the data is compressed and encrypted by the data compression and encryption module;

[0162] In the fourth acquisition stage, the first sampling module, the second sampling module, and the third sampling module in the data acquisition module are started simultaneously, and their sampling rates are both adjusted to the maximum. The start times of the first sampling module, the second sampling module, and the third sampling module are staggered by 1 / 3 of the sampling period, and the sampling period is the sampling period corresponding to the maximum sampling rate; later, the acquired data is interpolated and shaped by cubic interpolation; after the corresponding data is acquired, it is filtered by the preprocessing module; before data transmission, the data is compressed and encrypted by the data compression and encryption module;

[0163] Different data acquisition types have different importance levels, and the corresponding time period widths M and step sizes p, as well as the judgment thresholds for different acquisition stages, are all different. The time period width, step size, and judgment thresholds for different acquisition stages are determined based on the importance of the data acquisition type;

[0164] The data transmission module includes: a second control module, a first communication module, a second storage module, a backup power supply, and a second communication module. Under normal circumstances, the data transmission module receives data information from the data acquisition module and sends it to the data analysis and processing module via the first communication module. In case of an abnormal situation, the backup power supply module supplies power to the sensor module in the data acquisition module, the second communication module in the data transmission module, and the second storage module. The data information during transmission when the abnormality occurs is stored in the second storage module. The sensor module continues to work and collects data information for a preset time period. Finally, the emergency data information collected by the sensor module for the preset time period and the data information in transmission stored in the second storage module are sent to the data analysis and processing module.

[0165] The preset time period is obtained by multiplying the importance of the data collection type by a corresponding proportional coefficient. The higher the importance of the data collection type, the longer the preset time period.

[0166] Optionally, the emergency data may also include relevant cause information that caused the first control system to shut down automatically and relevant configuration information when the first control system shut down automatically, so as to facilitate subsequent troubleshooting and recovery of the first control system;

[0167] The first communication module can be an Ethernet module; the second communication module can be a backup GPRS mobile communication module.

[0168] In the event of an abnormal situation, the backup power module can not only power the second control module, the second storage module, and the second communication module in the data transmission module, but also power the sensor module in the data acquisition module, thereby improving the stability of data acquisition and transmission.

[0169] The data analysis and processing module includes: a third control module, a first interaction module, a first display, a third storage module, and an alarm module;

[0170] The third control module can detect anomalies in power grid data information and identify and visualize data from different acquisition stages based on the first display. Maintenance personnel can quickly locate abnormal data based on the corresponding identification.

[0171] The third control module can detect anomalies in power grid data information, including: the data analysis and processing module obtains the function curve of the corresponding integral value and time t, and determines the probability of data anomalies in the corresponding time periods of the second acquisition stage, the third acquisition stage and the fourth acquisition stage through corresponding calculations.

[0172] The time periods corresponding to the second, third, and fourth data acquisition stages are determined as t1~t2, t3~t4, and t5~t6, respectively. Based on the function curve of the corresponding integral value versus time t, the time periods corresponding to different acquisition stages are integrated. The obtained integral value is divided by the corresponding time period length to obtain the average out-of-limit value of the data per unit time. The probability of data anomalies within the time periods corresponding to different acquisition stages is determined based on the magnitude of the average out-of-limit value. Finally, the probabilities of data anomalies within the time periods corresponding to the second, third, and fourth acquisition stages are displayed on the corresponding data curve display interface to facilitate subsequent troubleshooting.

[0173] When the average value of data exceeding the limit during the corresponding data acquisition phase exceeds a certain threshold, the data analysis and processing module will control the alarm module to issue an alarm and prompt the corresponding handling method.

[0174] The step of identifying and visualizing data from different acquisition stages based on the first display includes: determining the time intervals and time nodes corresponding to the first, second, third, and fourth acquisition stages;

[0175] In the visualization interface, the real-time data curves within the corresponding time intervals of different collection stages are displayed using different colors and lines, and the corresponding anomaly probability is also displayed in real time above each data curve.

[0176] The maintenance personnel can quickly locate abnormal data based on the corresponding identifiers, including:

[0177] There are corresponding time nodes between each collection stage. Small flags are set at the corresponding time nodes. People can directly locate the data segment corresponding to the collection stage they want to view by clicking the corresponding small flag. The color of the small flag is the same as the curve in the corresponding time interval.

[0178] Users can double-click the flag icon to select and display real-time data curves corresponding to at least one data acquisition stage, and can also double-click the flag icon to select and set the real-time data curves corresponding to the data acquisition stage to be displayed.

[0179] This invention provides a power grid data acquisition and monitoring system and method based on a micro-center service channel. The system includes real-time acquisition of a first data curve, determination of a second data curve, integration of the difference between the first and second data curves over a preset time period, determination of the data acquisition stage and corresponding data acquisition processing method based on the integral value, and data acquisition and processing according to the corresponding data acquisition and processing method to coordinate the accuracy and timeliness of power grid monitoring. In the event of an anomaly, a second control system is activated, and emergency data is transmitted to a data analysis and processing module based on a backup power supply, a second communication module, and a second storage module to achieve complete acquisition of the anomaly data. Data from different acquisition stages is identified and visualized, allowing maintenance personnel to quickly locate anomalies based on the corresponding identifiers, thus improving maintenance efficiency in abnormal situations.

[0180] The above content is merely a technical concept of the present invention and should not be construed as limiting the scope of protection of the present invention. Any modifications made to the technical solution based on the technical concept proposed in this invention shall fall within the scope of protection of the claims of this invention.

Claims

1. A microhub service channel based power grid data acquisition and monitoring method, characterized in that, The method comprises the following steps: S1: collecting data of equipment and environment in a power grid system in real time, obtaining a first data curve, and determining a second data curve; The first data curve is a real-time data curve, and the second data curve is an ideal data curve; S2: integrating the difference between the first data curve and the second data curve over a preset time period; S3: determining a data collection stage and a corresponding data collection processing mode based on the obtained integral value; The data collection stage comprises a first collection stage, a second collection stage, a third collection stage, and a fourth collection stage; and the determination of the data collection stage and the corresponding data collection processing mode based on the obtained integral value comprises: when the integral value is less than K1 and the duration is N, entering the first collection stage; when the integral value is not less than K1 but less than K2 and the duration is N, entering the second collection stage; when the integral value is not less than K2 and the duration is N, and the importance of the data collection type is lower than H, entering the third collection stage; when the integral value is not less than K2 and the duration is N, and the importance of the data collection type is not lower than H, entering the fourth collection stage; The different data collection stages have the following different data collection processing modes: in the first collection stage, curve fitting is performed, and when the fitting result meets the expectation, a curve function obtained by fitting is used to replace the real-time data curve; in the second collection stage, a sampling rate self-adaptive adjustment module is started, and the sampling rate of the first sampling module is self-adaptively adjusted in combination with the real-time integral value; when the integral value increases, the sampling rate of the first sampling module is increased; when the integral value decreases, the sampling rate of the first sampling module is decreased; in the third collection stage, the first sampling module and the second sampling module in the data collection module are started at the same time, and the sampling rates of the first sampling module and the second sampling module are adjusted to the maximum; the starting times of the first sampling module and the second sampling module are staggered by 1 / 2 sampling period, and the sampling period is the sampling period corresponding to the maximum sampling rate; in the later period, the collected data is interpolated and shaped through quadratic interpolation; in the fourth collection stage, the first sampling module, the second sampling module, and the third sampling module in the data collection module are started at the same time, and the sampling rates of the first sampling module, the second sampling module, and the third sampling module are adjusted to the maximum; the starting times of the first sampling module, the second sampling module, and the third sampling module are staggered by 1 / 3 sampling period, and the sampling period is the sampling period corresponding to the maximum sampling rate; in the later period, the collected data is interpolated and shaped through cubic interpolation; S4: collecting and processing data according to the corresponding data collection processing mode.

2. The method according to claim 1, wherein the ideal data curve is pre-stored in a first storage module of the data collection module; and the first control module of the data collection module determines the second data curve according to the type and source of the data collected in real time. The method further comprises S5: collecting and processing data by using a first control system in a normal state, starting a second control system when an abnormal condition occurs, and transmitting emergency data to a data analysis and processing module based on a backup power supply, a second communication module, and a second storage module. ​ 3. The method of claim 2, wherein, ​ 4. The method of claim 2, wherein, The method further comprises: in the second, third and fourth acquisition stages, filtering the acquired data by using a preprocessing module; and compressing and encrypting the data by using a data compression and encryption module before data transmission.

5. A microhub service channel based power grid data acquisition monitoring system based on the method of any of claims 1-4, characterized in that, The method comprises: a data acquisition module, a data transmission module, a data analysis and processing module, a cloud server and a mobile terminal; the data acquisition module, the data transmission module, the data analysis and processing module, the cloud server and the mobile terminal are sequentially connected; the data acquisition module comprises: a temperature sensor, a humidity sensor, a current sensor, a voltage sensor, a power factor sensor, a smoke sensor, a preprocessing module, a first control module, a sampling rate self-adaptive adjustment module, a data compression and encryption module, a first storage module, a first sampling module, a second sampling module and a third sampling module; the first control module can acquire the data of the equipment and the environment in the power grid system, obtain a first data curve, determine a second data curve, and integrate the difference between the first data curve and the second data curve over a preset time period; based on the obtained integral value, the data acquisition stage and the corresponding data acquisition processing mode are determined; and the data is acquired and processed according to the corresponding data acquisition processing mode.

6. The system of claim 5, wherein, the determination of the data acquisition stage and the corresponding data acquisition processing mode based on the obtained integral value comprises: when the integral value is less than K1 and the duration is N, entering the first acquisition stage; when the integral value is not less than K1 but less than K2 and the duration is N, entering the second acquisition stage; when the integral value is not less than K2 and the duration is N, and the importance of the data acquisition type is lower than H, entering the third acquisition stage; when the integral value is not less than K2 and the duration is N, and the importance of the data acquisition type is not lower than H, entering the fourth acquisition stage.

7. The system of claim 6, wherein, the different data acquisition stages have the following different data acquisition processing modes: in the first acquisition stage, curve fitting is performed, and when the fitting result meets the expectation, the curve function obtained by fitting is used to replace the real-time data curve; in the second acquisition stage, the sampling rate self-adaptive adjustment module is started, and the sampling rate of the first sampling module is self-adaptively adjusted in combination with the real-time integral value; when the integral value increases, the sampling rate of the first sampling module is increased; when the integral value decreases, the sampling rate of the first sampling module is decreased; in the third acquisition stage, the first sampling module and the second sampling module in the data acquisition module are started at the same time, and the sampling rates of the first sampling module and the second sampling module are adjusted to the maximum; the starting time of the first sampling module and the second sampling module is staggered by 1 / 2 sampling period, and the sampling period is the sampling period corresponding to the maximum sampling rate; in the later period, the collected data is interpolated and shaped by quadratic interpolation; in the fourth acquisition stage, the first sampling module, the second sampling module and the third sampling module in the data acquisition module are started at the same time, and the sampling rates of the first sampling module, the second sampling module and the third sampling module are adjusted to the maximum; the starting time of the first sampling module, the second sampling module and the third sampling module is staggered by 1 / 3 sampling period, and the sampling period is the sampling period corresponding to the maximum sampling rate; in the later period, the collected data is interpolated and shaped by cubic interpolation.

8. The system of claim 7, wherein, In the first acquisition stage, when the integral value is less than K0 and the duration N, the real-time data curve is replaced by the ideal data curve, and the time node recording unit is started, and the real-time data curve in the corresponding time interval is recorded in the form of ideal curve function and time interval, wherein K0 is less than K1.

9. The system of claim 5, wherein, Different data acquisition types have different importance, and the time period width, the step size and the judgment threshold of different acquisition stages are determined based on the importance of the data acquisition type.

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