Data management robot real-time monitoring method and system based on automation technology

Abstract

The invention provides a data governance robot real-time monitoring method and system based on an automation technology, and is applied to the technical field of data processing. The method comprises the following steps: determining a plurality of to-be-monitored data sources and a to-be-monitored power grid operation parameter list according to monitoring requirements of a power system; merging the data meeting the requirements of the power grid operation parameter list in the plurality of data sources to obtain power grid operation data corresponding to the monitoring requirements; the data is collected by the data management robot; inputting the power grid operation data into an anomaly prediction model to obtain anomaly data in the power grid operation data; according to the method, the corresponding data treatment strategy is executed according to the abnormal data in the power grid operation data, the data treatment result is obtained, the power grid operation parameters can be more comprehensively obtained, the operation state of the power system can be more comprehensively reflected, and the accuracy and the real-time performance can be improved by combining and utilizing the abnormal prediction model instead of human experience judgment.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and in particular to a real-time monitoring method and system for a data governance robot based on automation technology. Background Art

[0002] The power system is an important support for the economic operation of modern society. Its stable operation is of vital importance to ensuring power safety and daily production and life. With the continuous expansion of the scale of the power system and the continuous increase of power load, the operation of the power grid faces more and more complex challenges. At the same time, the application of emerging technologies such as new energy and energy storage technology has also brought new changes and challenges to the power system. In this case, how to ensure the safe and stable operation of the power grid has become an urgent problem to be solved. Considering that the power system is a complex dynamic system involving numerous electrical equipment, sensors and complex control systems, these equipment and systems may fail and have problems during operation, such as excessive load, equipment failure and power grid failure, which will lead to instability and even system paralysis of the power system, thereby affecting the operation of the entire power grid. Therefore, in order to ensure the safe, stable and reliable operation of the power system, it is necessary to monitor, predict and assist in the management of the power system in real time.

[0003] In traditional power system monitoring methods, some parameters of the power system are often monitored. Specifically, the final monitoring results are obtained by obtaining discrete sampling data of these partial parameters and combining them with the experience and judgment of the monitoring personnel. However, some parameters cannot fully reflect the operating status of the power system, and the experience and judgment will lead to the limitation of the accuracy and real-time performance of monitoring. Therefore, the traditional power system monitoring method has the problem of low accuracy and real-time performance. Summary of the invention

[0004] In order to overcome the defects of low accuracy and real-time performance of the above-mentioned traditional power system monitoring method, the present invention provides a real-time monitoring method of a data management robot based on automation technology, the method comprising:

[0005] Determine the data range to be monitored according to the monitoring requirements of the power system; the data range includes multiple data sources;

[0006] According to the monitoring requirements of the power system, determine multiple data sources to be monitored and a list of power grid operation parameters to be monitored;

[0007] Merging the data in the multiple data sources that meet the requirements of the power grid operation parameter list to obtain power grid operation data corresponding to the monitoring requirements; the data is collected by a data governance robot;

[0008] Inputting the power grid operation data into an abnormality prediction model to obtain abnormal data in the power grid operation data;

[0009] For the abnormal data in the power grid operation data, a corresponding data governance strategy is executed to obtain a data governance result.

[0010] Optionally, when the monitoring requirements include monitoring abnormal conditions in the power system, the list of power grid operation parameters includes voltage, current and frequency; the voltage is used to monitor whether the equipment in the power system is damaged and / or whether there is an unstable power supply, the current is used to monitor whether there is an overload in the power system, and the frequency is used to monitor whether there is frequency instability in the power system.

[0011] Optionally, inputting the power grid operation data into an abnormality prediction model to obtain abnormal data in the power grid operation data includes:

[0012] inputting the power grid operation data into an abnormality prediction model;

[0013] Using an abnormal prediction model to perform pattern recognition processing on the power grid operation data to extract data features of the power grid operation data;

[0014] Based on the data features, the abnormality prediction model is used to perform abnormality detection on the power grid operation data to identify abnormal data in the power grid operation data.

[0015] Optionally, executing a corresponding data governance strategy for the abnormal data in the power grid operation data to obtain a data governance result includes:

[0016] Performing abnormal deviation processing on the abnormal data in the power grid operation data to determine the processed power grid operation data;

[0017] According to the compliance strategy and security strategy in the data governance rules, data governance is performed on the processed power grid operation data to obtain data governance results.

[0018] Optionally, the abnormal deviation processing includes at least one of the following:

[0019] Data correction, data deletion, data replacement or data marking.

[0020] Optionally, after performing abnormal deviation processing on the abnormal data in the power grid operation data and determining the processed power grid operation data, the method further includes:

[0021] An abnormal cause assessment is performed on the abnormal data to assess the cause of the abnormal data and the potential risks brought by the abnormal data.

[0022] Optionally, after inputting the power grid operation data into the abnormal prediction model to obtain abnormal data in the power grid operation data, executing a corresponding data governance strategy for the abnormal data in the power grid operation data and before obtaining a data governance result, the method further includes:

[0023] According to the abnormal data, the power grid operation data is cleaned to obtain the cleaned power grid operation data;

[0024] The cleaned power grid operation data is standardized to obtain cleaned and standardized power grid operation data.

[0025] On the other hand, the present invention also provides a data governance robot real-time monitoring system based on automation technology, comprising:

[0026] A monitoring scope determination module, used to determine multiple data sources to be monitored and a list of power grid operation parameters to be monitored according to the monitoring requirements of the power system;

[0027] A data integration module, used to merge the data in the multiple data sources that meet the requirements of the power grid operation parameter list to obtain the power grid operation data corresponding to the monitoring requirements; the data is collected by a data governance robot;

[0028] A data monitoring module, used for inputting the power grid operation data into an abnormality prediction model to obtain abnormal data in the power grid operation data;

[0029] The data governance module is used to execute corresponding data governance strategies for the abnormal data in the power grid operation data to obtain data governance results.

[0030] Optionally, when the monitoring requirements include monitoring abnormal conditions in the power system, the list of power grid operation parameters includes voltage, current and frequency; the voltage is used to monitor whether the equipment in the power system is damaged and / or whether there is an unstable power supply, the current is used to monitor whether there is an overload in the power system, and the frequency is used to monitor whether there is frequency instability in the power system.

[0031] Optionally, the data monitoring module includes:

[0032] A pattern recognition submodule is used to input the power grid operation data into an abnormal prediction model; perform pattern recognition processing on the power grid operation data using the abnormal prediction model to extract data features of the power grid operation data;

[0033] The anomaly detection submodule is used to perform anomaly detection on the power grid operation data based on the data features and using the anomaly prediction model to identify abnormal data in the power grid operation data.

[0034] Optionally, the data governance module includes:

[0035] A processing submodule, configured to perform abnormal deviation processing on the abnormal data in the power grid operation data, and determine the processed power grid operation data;

[0036] The governance submodule is used to perform data governance on the processed power grid operation data according to the compliance policy and security policy in the data governance rules to obtain data governance results.

[0037] Optionally, abnormal deviation processing includes at least one of the following: data correction, data deletion, data replacement or data marking.

[0038] Optionally, it also includes: a data analysis module, which is used to evaluate the causes of the abnormal data, and evaluate the causes of the abnormal data and the potential risks brought by the abnormal data.

[0039] Optionally, it also includes: a data cleaning module, which is used to clean the power grid operation data according to the abnormal data to obtain the cleaned power grid operation data; and standardize the cleaned power grid operation data to obtain cleaned and standardized power grid operation data.

[0040] On the other hand, the present invention also provides an electronic device, comprising: at least one processor and a memory; the memory and the processor are connected via a bus;

[0041] The memory is used to store one or more programs;

[0042] When the one or more programs are executed by the at least one processor, the above-mentioned data governance robot real-time monitoring method based on automation technology is implemented.

[0043] On the other hand, the present invention also provides a readable storage medium having an execution program stored thereon, which, when executed, implements the above-mentioned real-time monitoring method of a data governance robot based on automation technology.

[0044] Compared with the prior art, the present invention has the following beneficial effects:

[0045] The present invention provides a real-time monitoring method and system of a data management robot based on automation technology, including: determining multiple data sources to be monitored and a list of power grid operation parameters to be monitored according to the monitoring needs of the power system; merging the data in the multiple data sources that meet the requirements of the power grid operation parameter list to obtain the power grid operation data corresponding to the monitoring needs; the data is collected by a data management robot; the power grid operation data is input into an abnormal prediction model to obtain abnormal data in the power grid operation data; for the abnormal data in the power grid operation data, a corresponding data management strategy is executed to obtain a data management result. The present invention takes into account the monitoring needs of the power system, integrates the data in the multiple data sources that meet the monitoring needs and meet the requirements of the power grid operation parameter list, can more comprehensively obtain the power grid operation parameters, thereby more comprehensively reflecting the operating status of the power system, and combines the trained abnormal prediction model for monitoring, prediction and auxiliary management instead of human experience judgment, so as to improve accuracy and real-time performance. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 A schematic diagram of the process of the data management robot real-time monitoring method based on automation technology of the present invention;

[0047] Figure 2 It is a structural schematic diagram of the data management robot real-time monitoring system based on automation technology of the present invention;

[0048] Figure 3 It is a schematic structural diagram of the electronic device of the present invention. DETAILED DESCRIPTION

[0049] The specific implementation modes of the present invention are further described in detail below with reference to the accompanying drawings.

[0050] Embodiment 1:

[0051] The present invention provides a data management robot real-time monitoring method based on automation technology, the flow chart is as follows Figure 1 As shown, including:

[0052] Step 101: According to the monitoring requirements of the power system, multiple data sources to be monitored and a list of power grid operation parameters to be monitored are determined.

[0053] Step 102: Merge data from multiple data sources that meet the requirements of the power grid operation parameter list to obtain power grid operation data corresponding to the monitoring requirements; the data is collected by a data governance robot.

[0054] Step 103: Inputting the power grid operation data into the abnormality prediction model to obtain abnormal data in the power grid operation data.

[0055] Step 104: Execute corresponding data governance strategies for abnormal data in the power grid operation data to obtain data governance results.

[0056] Compared with the traditional power system monitoring methods that have problems such as low data collection and processing efficiency, poor accuracy, and limited information, the embodiments of the present invention take into account the monitoring needs of the power system and integrate the data that meets the requirements of the power grid operation parameter list from multiple data sources that meet the monitoring needs. It can obtain the power grid operation parameters more comprehensively, thereby more comprehensively reflecting the operating status of the power system, and combines the use of trained abnormal prediction models for monitoring, prediction, and auxiliary governance rather than human experience judgment, thereby reducing the difficulty of operation and improving accuracy and real-time performance.

[0057] The real-time monitoring method for a data governance robot based on automation technology provided in an embodiment of the present invention can be applied to a data governance robot, or can be applied to an electronic device that can communicate with a data governance robot, which is not limited here.

[0058] In the embodiment of the present invention, the monitoring demand of the power system can be a monitoring demand set manually, or it can be a monitoring demand automatically initiated according to a preset monitoring rule. If it is a monitoring demand set manually, the embodiment of the present invention can provide a user with a friendly interactive interface, in which the user can input the monitoring demand of the power system. In addition, the interactive interface can also display the real-time monitoring results of the power system. If it is a monitoring demand automatically initiated according to a preset monitoring rule, an example can be to initiate the monitoring demand according to a set time interval or at a set time point every day.

[0059] Step 101 defines the data range that needs to be detected, including but not limited to multiple data sources that need to be monitored and a list of power grid operation parameters that need to be monitored, which can provide support for key links such as data sets, data sources, and data processing. For example, in the above step 101, the data range to be monitored can be determined based on the monitoring requirements and the correspondence between the pre-set monitoring requirements and data range. For example, in the data range to be monitored, multiple data sources that can meet the monitoring requirements include but are not limited to databases, APIs (application programming interfaces), log files, etc.

[0060] In the above step 102, multiple data sources can be automatically connected and data from multiple data sources can be collected through a data governance robot, and then the data in multiple data sources that meet the requirements of the power grid operation parameter list can be integrated to achieve unified data management, thereby more comprehensively monitoring the operation status of the power system.

[0061] The data governance robot in the power system can be deployed at the key nodes of the power grid to collect data from multiple data sources in real time. Multiple data sources can include basic connection parameters, connection pool parameters and advanced parameters, etc., among which the basic connection parameters may include: database type, driver class name, URL (uniform resource locator), etc., the connection pool parameters may include: maximum number of active connections, maximum number of idle connections, minimum number of idle connections, etc., and the advanced parameters may include: auto-commit, transaction isolation level, connection test statement, etc., and may include: logs, etc.

[0062] In one possible embodiment, based on different monitoring requirements, the list of grid operation parameters may be different. For example, the monitoring requirements include monitoring abnormal conditions of the power system. The purpose of this monitoring requirement is to promptly discover and handle abnormal conditions of the power system and determine the stable operation of the power grid. The list of grid operation parameters that need to be monitored includes voltage, current and frequency. Voltage is used to monitor whether the equipment of the power system is damaged and / or whether there is unstable power supply. For example, the data management robot integrates a high-precision voltage sensor to monitor the voltage value of each node in the power grid to ensure that each node fluctuates within the normal range to avoid equipment damage or unstable power supply caused by excessively high or low voltage. Current is used to monitor whether there is an overload in the power system. For example, the data management robot integrates a high-precision current sensor to monitor the current value in the power grid and prevents overload by analyzing the load conditions. Frequency is used to monitor whether there is an unstable frequency in the power system. For example, the data management robot integrates a high-precision frequency sensor to monitor the frequency stability of the power grid to ensure that the power grid frequency is consistent with the standard frequency to prevent equipment failure caused by frequency deviation. For example, the standard frequency can be 50Hz (Hertz) or 60Hz. Optionally, the embodiments of the present invention may also use advanced encryption technology to ensure the security of data during transmission and storage, and combine it with an automated authority management system to implement fine-grained access control to complete dynamic data encryption and access control.

[0063] The abnormal prediction model in the above step 103 is an abnormal prediction model pre-trained using a machine learning algorithm. The abnormal prediction model can perform operations such as pattern recognition and abnormality detection on the data. The abnormal prediction model can be used to evaluate the data quality in real time, automatically identify the abnormalities and deviations in the power grid operation data, and determine the abnormal data. For example, the above step 103 may include: inputting the power grid operation data into the abnormal prediction model, performing pattern recognition processing on the power grid operation data using the abnormal prediction model, and extracting the data features of the power grid operation data; based on the data features, using the abnormal prediction model to perform abnormality detection on the power grid operation data and identify the abnormal data in the power grid operation data. Pattern recognition refers to the use of computer algorithms and mathematical models, that is, the trained abnormal prediction model to extract useful information and features from the data, and then subsequently classify the data (that is, divide the data into different categories or groups according to the extracted key features and laws), identify, cluster and predict operations. The abnormal prediction model can be constructed based on historical data and current patterns using pattern recognition technology. In addition to pattern recognition and abnormality inspection, the abnormal prediction model can also predict future data trends and results.

[0064] The abnormal data identified in step 103 can be used as a basis for data cleaning. In a possible implementation, after step 103 and before step 104, the power grid operation data can be cleaned according to the abnormal data to obtain cleaned power grid operation data; the cleaned power grid operation data can be standardized to obtain cleaned and standardized power grid operation data. For example, by integrating a big data processing framework such as Spark, real-time data cleaning, conversion and standardization can be achieved to ensure that the data formats of multiple integrated data sources are unified, reduce errors and redundancy, and achieve the purpose of optimizing the data set.

[0065] In one implementation, in the above step 104, abnormal deviation processing can be performed on the abnormal data in the power grid operation data to determine the processed power grid operation data; according to the compliance policy and security policy in the data governance rules, data governance is performed on the processed power grid operation data to obtain data governance results.

[0066] Abnormal deviation processing includes but is not limited to at least one of data correction, data deletion, data replacement or data marking. For example, the above-mentioned abnormal deviation processing of abnormal data in the power grid operation data and determining the processed power grid operation data include: performing data correction, data deletion, data replacement and data marking on the abnormal data in the power grid operation data to determine the processed power grid operation data.

[0067] The data correction process refers to the process of directly correcting data that can be clearly judged to be wrong; for example, correcting incorrect dates, values, or classification labels to correct values. The data deletion process refers to the process of deleting some abnormal data if they cannot be corrected or still do not meet the requirements after correction. The data replacement process refers to the process of replacing missing values ​​or abnormal values ​​with other reasonable methods, such as using the mean, median, mode, or model-based predicted values ​​to replace missing or abnormal data points. The data labeling process refers to the process of labeling or classifying abnormal data that cannot be directly corrected or deleted.

[0068] It is understandable that if the power grid operation data is cleaned and standardized before step 104, then in step 104, abnormal deviation processing can be performed on the cleaned and standardized power grid operation data.

[0069] In this implementation, abnormal deviation processing is performed on the abnormal data in the power grid operation data. After determining the processed power grid operation data, the abnormal cause evaluation can also be performed on the abnormal data to evaluate the cause of the abnormal data and the potential risks brought by the abnormal data. By performing a more in-depth data analysis on the abnormal data, especially some complex abnormal data, the cause of its generation (i.e., abnormal behavior) and the impact it brings can be determined. In addition, for the abnormal data found, an alarm notification can be triggered immediately, and the administrator / user / monitoring personnel / operation and maintenance personnel can be informed by email, SMS or system messages, and the response and processing can be quickly responded to, or corresponding measures can be taken independently. In the embodiment of the present invention, an effective data monitoring mechanism can be established to track the abnormalities and deviations in the power grid operation data in real time, and once an abnormality or a pattern that does not meet the expected pattern is found, the corresponding processing measures can be taken in time. Abnormal cause evaluation, as a data analysis method, can analyze the most basic cause of data quality problems, that is, the generation of abnormal data. Generally, there are many reasons for causing data quality problems, such as environmental conditions, human factors, system behavior, etc. Therefore, data analysis can find the root cause of abnormal data.

[0070] The compliance policies and security policies in this implementation can be data governance rules customized by users according to enterprise policies. Data governance robots can perform automated responses based on these policies, such as performing automated operations such as isolation, repair, or rejection.

[0071] Assuming that a user-friendly interactive interface is provided in the embodiment of the present invention, the data governance overview (key indicators such as data quality, processing progress, and security status), abnormality report, and governance effect analysis can be displayed through an intuitive operation interface to support decision optimization. After obtaining this information, the user can also control the abnormality prediction model and related strategies to iteratively optimize based on the monitoring results to further improve system performance.

[0072] The following is a specific example of a scenario for monitoring the power operation status. The system in this scenario includes a data governance robot and a monitoring center. The monitoring center can be an electronic device that can communicate with the data governance robot. The data governance robot is deployed at key nodes of the power grid. By integrating high-precision voltage, current and frequency sensors, it monitors the power grid operation parameters in real time, conducts a comprehensive analysis of the operation parameters, and transmits the data to the monitoring center for analysis and processing. Once an abnormality is found, the early warning mechanism is immediately triggered to notify the operation and maintenance personnel to handle it.

[0073] The system is provided with an automatic data collection module, which can be implemented by a data governance robot, and automatically integrates data through diversified data sources such as API interfaces, database connections, and log files, so as to realize real-time data collection and preliminary cleaning. And the system is provided with an intelligent analysis engine, which can be implemented through a monitoring center, and through built-in machine learning algorithms and rule engines, the collected data is deeply analyzed to identify data quality, patterns, anomalies, predict potential risks, and evaluate compliance issues. And the system also has a real-time monitoring function, by establishing a real-time data flow processing mechanism, the data flow is continuously monitored, and once abnormal or data that does not meet the preset rules is found, the response mechanism is immediately triggered. In addition, the system also has an automatic response and decision-making function. According to the preset rule base, the system automatically executes data processing actions, such as isolating problem data, sending alarms, triggering repair processes, or adjusting data processing logic. The system can also provide a visual interface, by providing users with a friendly interactive interface, displaying real-time monitoring results, data quality reports, abnormal warnings, and compliance assessments, which is convenient for decision support. In the embodiment of the present invention, the transparency of data governance is enhanced, which is conducive to auditing and compliance review.

[0074] With the acceleration of digital transformation, the exponential growth of data volume has brought unprecedented challenges to data governance, including data quality control, security compliance checks, timeliness issues, etc. Traditional manual data governance methods are inefficient and difficult to adapt to the real-time and scale requirements of modern data processing. The automated solution provided by the embodiments of the present invention can better adapt to the real-time and scale requirements of data processing and improve the efficiency of data governance.

[0075] Embodiment 2:

[0076] Based on the same inventive concept, the present invention also provides a real-time monitoring system for data management robots based on automation technology, the structural diagram of which is shown in FIG. Figure 2 As shown, including:

[0077] A monitoring scope determination module, used to determine multiple data sources to be monitored and a list of power grid operation parameters to be monitored according to the monitoring requirements of the power system;

[0078] The data integration module is used to merge the data in multiple data sources that meet the requirements of the power grid operation parameter list to obtain the power grid operation data corresponding to the monitoring requirements; the data is collected by the data governance robot;

[0079] A data monitoring module is used to input the power grid operation data into the abnormal prediction model to obtain abnormal data in the power grid operation data;

[0080] The data governance module is used to execute corresponding data governance strategies for abnormal data in the power grid operation data and obtain data governance results.

[0081] In one possible implementation, when the monitoring requirements include monitoring abnormal conditions in the power system, the list of grid operation parameters includes voltage, current, and frequency; the voltage is used to monitor whether the equipment in the power system is damaged and / or whether there is unstable power supply, the current is used to monitor whether there is an overload in the power system, and the frequency is used to monitor whether there is frequency instability in the power system.

[0082] In a possible implementation, the data monitoring module includes:

[0083] The pattern recognition submodule is used to input the power grid operation data into the abnormal prediction model; use the abnormal prediction model to perform pattern recognition processing on the power grid operation data and extract data features of the power grid operation data;

[0084] The anomaly detection submodule is used to perform anomaly detection on the power grid operation data based on data features and use an anomaly prediction model to identify abnormal data in the power grid operation data.

[0085] In a possible implementation, the data governance module includes:

[0086] A processing submodule, used for performing abnormal deviation processing on abnormal data in the power grid operation data, and determining the processed power grid operation data;

[0087] The governance submodule is used to perform data governance on the processed power grid operation data according to the compliance policy and security policy in the data governance rules to obtain data governance results.

[0088] In a possible implementation, abnormal deviation processing includes at least one of the following: data correction, data deletion, data replacement or data marking.

[0089] In a possible implementation, the method further includes: a data analysis module for evaluating the causes of abnormal data, and evaluating the causes of the abnormal data and the potential risks brought by the abnormal data.

[0090] In a possible implementation, it also includes: a data cleaning module, which is used to clean the power grid operation data according to the abnormal data to obtain cleaned power grid operation data; and standardize the cleaned power grid operation data to obtain cleaned and standardized power grid operation data.

[0091] Embodiment 3:

[0092] like Figure 3 As shown, the present invention also provides an electronic device, which may be a computer device, a single-chip device, an intelligent mobile device, etc. The electronic device in this embodiment may include a processor, a memory, a transceiver component, etc. The memory, the processor, and the transceiver component are connected via a bus; the memory may be used to store an execution program, and an exemplary execution program may include instructions; the processor is used to execute the instructions stored in the memory. The memory may also be used to store data, which may be called and / or modified when the instructions are executed.

[0093] The processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. It is the computing core and control core of the terminal, which is suitable for implementing one or more instructions, specifically suitable for loading and executing one or more instructions in a storage medium to implement corresponding method processes or corresponding functions, so as to realize the steps of a real-time monitoring method for a data governance robot based on automation technology in the above-mentioned embodiment.

[0094] Embodiment 4:

[0095] Based on the same inventive concept, the present invention also provides a readable storage medium, specifically an electronic device readable storage medium (Memory), which is a memory device in an electronic device for storing programs and data. It is understandable that the storage medium here can include both built-in storage media in electronic devices and, of course, extended storage media supported by electronic devices. The storage medium provides a storage space that stores the operating system of the terminal. In addition, one or more instructions suitable for being loaded and executed by a processor are also stored in the storage space, and these instructions can be one or more execution programs (including program codes). It should be noted that the storage medium here can be a high-speed RAM memory or a non-volatile memory, such as at least one disk storage. The processor loads and executes one or more instructions stored in the storage medium, which can implement the steps of a real-time monitoring method for a data governance robot based on automation technology in the above embodiment.

[0096] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0097] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0098] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1A function specified in one or more boxes.

[0099] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0100] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit its protection scope. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the field should understand that after reading the present invention, those skilled in the art can still make various changes, modifications or equivalent substitutions to the specific implementation methods of the application, but these changes, modifications or equivalent substitutions are all within the protection scope of the claims to be approved.

Claims

1. A real-time monitoring method for a data governance robot based on automation technology, characterized in that: include: According to the monitoring requirements of the power system, determine multiple data sources to be monitored and a list of power grid operation parameters to be monitored; Merging the data in the multiple data sources that meet the requirements of the power grid operation parameter list to obtain power grid operation data corresponding to the monitoring requirements; the data is collected by a data governance robot; Inputting the power grid operation data into an abnormality prediction model to obtain abnormal data in the power grid operation data; For the abnormal data in the power grid operation data, a corresponding data governance strategy is executed to obtain a data governance result.

2. The method according to claim 1, characterized in that When the monitoring requirements include monitoring abnormal conditions in the power system, the list of power grid operation parameters includes voltage, current and frequency; the voltage is used to monitor whether the equipment in the power system is damaged and / or whether there is an unstable power supply, the current is used to monitor whether there is an overload in the power system, and the frequency is used to monitor whether there is an unstable frequency in the power system.

3. The method according to claim 1, characterized in that The step of inputting the power grid operation data into the abnormality prediction model to obtain abnormal data in the power grid operation data comprises: inputting the power grid operation data into an abnormality prediction model; Using the abnormal prediction model to perform pattern recognition processing on the power grid operation data, and extracting data features of the power grid operation data; Based on the data features, the abnormality prediction model is used to perform abnormality detection on the power grid operation data to identify abnormal data in the power grid operation data.

4. The method according to claim 1, characterized in that The executing corresponding data governance strategy for the abnormal data in the power grid operation data to obtain the data governance result includes: Performing abnormal deviation processing on the abnormal data in the power grid operation data to determine the processed power grid operation data; According to the compliance strategy and security strategy in the data governance rules, data governance is performed on the processed power grid operation data to obtain data governance results.

5. The method according to claim 4, characterized in that The abnormal deviation processing includes at least one of the following: Data correction, data deletion, data replacement or data marking.

6. The method according to claim 5, characterized in that After performing abnormal deviation processing on the abnormal data in the power grid operation data and determining the processed power grid operation data, the method further includes: An abnormal cause assessment is performed on the abnormal data to assess the cause of the abnormal data and the potential risks brought by the abnormal data.

7. The method according to any one of claims 1 to 6, characterized in that: After inputting the power grid operation data into the abnormal prediction model to obtain abnormal data in the power grid operation data, executing a corresponding data governance strategy for the abnormal data in the power grid operation data and before obtaining a data governance result, the method further includes: According to the abnormal data, the power grid operation data is cleaned to obtain the cleaned power grid operation data; The cleaned power grid operation data is standardized to obtain cleaned and standardized power grid operation data.

8. A real-time monitoring system for data governance robots based on automation technology, characterized in that: include: A monitoring scope determination module, used to determine multiple data sources to be monitored and a list of power grid operation parameters to be monitored according to the monitoring requirements of the power system; A data integration module, used to merge the data in the multiple data sources that meet the requirements of the power grid operation parameter list to obtain the power grid operation data corresponding to the monitoring requirements; the data is collected by a data governance robot; A data monitoring module, used for inputting the power grid operation data into an abnormality prediction model to obtain abnormal data in the power grid operation data; The data governance module is used to execute corresponding data governance strategies for the abnormal data in the power grid operation data to obtain data governance results.

9. An electronic device, characterized in that: include: at least one processor and memory; The memory and the processor are connected via a bus; The memory is used to store one or more programs; When the one or more programs are executed by the at least one processor, the real-time monitoring method of the data governance robot based on automation technology as described in any one of claims 1 to 7 is implemented.

10. A readable storage medium, characterized in that: An execution program is stored thereon, and when the execution program is executed, the real-time monitoring method of the data governance robot based on automation technology as described in any one of claims 1-7 is implemented.

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