CN114185936A - Power grid power failure data comparison method and device based on RPA and AI - Google Patents

Abstract

Translated fromChinese

本申请公开了一种基于RPA及AI的电网停电数据对比方法及装置。其中，该方法应用于RPA机器人。该方法包括：获取停电数据及中压数据；将停电数据按照匹配规则与中压数据进行匹配；响应于停电数据与中压数据匹配，将满足匹配规则的中压数据导出并存储；响应于停电数据与中压数据不匹配，将不满足匹配规则的停电数据导出并存储。本申请可将停电数据与中压数据按照匹配规则进行对比、处理，分别得到符合匹配规则及不符合匹配规则的数据并存储。解决人工对电网停电数据进行处理时效率低、错误率高的问题，并降低人力成本。

The present application discloses a method and device for power grid outage data comparison based on RPA and AI. Among them, the method is applied to RPA robots. The method includes: acquiring power failure data and medium voltage data; matching the power failure data with the medium voltage data according to a matching rule; in response to the power failure data being matched with the medium voltage data, exporting and storing the medium voltage data satisfying the matching rule; in response to the power failure If the data does not match the medium voltage data, the power failure data that does not meet the matching rules will be exported and stored. In this application, the power failure data and the medium voltage data can be compared and processed according to the matching rules, and the data that conform to the matching rules and those that do not conform to the matching rules can be obtained and stored respectively. Solve the problems of low efficiency and high error rate when manually processing power grid outage data, and reduce labor costs.

Description

Power grid power failure data comparison method and device based on RPA and AI

Technical Field

The application relates to the field of data processing, in particular to a power grid power failure data comparison method and device based on RPA and AI.

Background

RPA (robot Process Automation) simulates human operations on a computer through specific "robot software" and automatically executes Process tasks according to rules.

AI (Artificial Intelligence) is a technical science that studies and develops theories, methods, techniques and application systems for simulating, extending and expanding human Intelligence.

Because the power failure data collected by a plurality of different systems in the power company has errors, comparison and processing of the power failure data and medium-voltage data of a large number of power transmission lines recorded by the plurality of different systems are needed to eliminate the influence of data errors on statistics and analysis of subsequent power failure data. The traditional manual data processing method has low efficiency, high error rate and higher labor cost.

Disclosure of Invention

The application provides a power grid power failure data comparison method and device based on RPA and AI, which can compare and process power failure data and medium-voltage data according to a matching rule based on RPA and AI technologies to obtain data meeting the matching rule. The problems of low efficiency and high error rate when power grid power failure data are manually processed are solved, and the labor cost is reduced.

In a first aspect, the present application provides a power grid outage data comparison method based on RPA and AI, where the method is applied to a robot process automation RPA robot, and the method includes: acquiring power failure data and medium-voltage data; matching the power failure data with the medium-voltage data according to a matching rule; responding to the power failure data and the medium-voltage data matching, and exporting and storing the medium-voltage data meeting the matching rule; and in response to the power outage data not being matched with the medium-voltage data, exporting and storing the power outage data which does not meet a matching rule.

Through the technical scheme, the power failure data and the medium-voltage data can be compared and processed according to the matching rule, and the data which accord with the matching rule and do not accord with the matching rule are respectively obtained. The problems of low efficiency and high error rate when power grid power failure data are manually processed are solved, and the labor cost is reduced.

In one implementation, the outage data includes: line blackout data; and power failure data of the transformer area.

In one implementation, the power outage data and the medium voltage data each include: line name, power failure starting time and power failure ending time; wherein, the power failure data with the medium voltage data match includes: the line name of the power failure data is matched with the line name of the medium-voltage data; the difference value of the power failure starting time in the power failure data and the power failure starting time in the medium-voltage data is within a first preset range; and the difference value of the power failure ending time in the power failure data and the power failure ending time in the medium-voltage data is within a second preset range.

In an optional implementation manner, the first preset range is ± 5 minutes, and the second preset range is ± 5 minutes.

In one implementation, the power outage data does not match the medium voltage data, including: the line name in the power failure data is not matched with the line name in the medium voltage data; or the line name in the power failure data is matched with the line name in the medium voltage data, the difference value between the power failure starting time in the power failure data and the power failure starting time in the medium voltage data is not in the first preset range, and/or the difference value between the power failure ending time in the power failure data and the power failure ending time in the medium voltage data is not in the second preset range.

In one implementation, the method further comprises: and before the power failure data is matched with the medium-voltage data according to a matching rule, cleaning the medium-voltage data.

Through this technical scheme, can save the power failure data contrast time of electric wire netting through carrying out data cleaning to middling pressure data, improve the contrast result accuracy.

In one implementation, the acquiring power outage data and medium voltage data includes: screenshot is conducted on a first system login interface based on the RPA robot to obtain a first system login interface image, Optical Character Recognition (OCR) processing is conducted on the first system login interface image based on an Artificial Intelligence (AI) technology, a first login control area in the first system login interface is determined based on an OCR processing result, and first account information is filled in the first login control area based on the simulated manual operation of the RPA robot to log in the first system to download the power failure data; and capturing a second system login interface based on the RPA robot to obtain a second system login interface image, performing Optical Character Recognition (OCR) processing on the second system login interface image based on an Artificial Intelligence (AI) technology, determining a second login control area in the second system login interface based on an OCR processing result, and filling second account information into the second login control area based on the simulated manual operation of the RPA robot to log in the second system to download the medium-voltage data.

In one implementation, the method further comprises: and generating a data table based on the stored data, and sending the data table to the terminal equipment held by the user.

Through the technical scheme, the power grid power failure data comparison result can be sent to the terminal equipment held by the user in a data table mode, and the user can check conveniently.

In a second aspect, the present application provides a power grid blackout data contrast device based on RPA and AI, the device is applied to robot process automation RPA robot, the device includes: an acquisition module: the power failure data and the medium-voltage data are acquired; a first processing module: the power failure data is matched with the medium voltage data according to a matching rule; a storage module: the medium-voltage data output device is used for responding to the matching of the power failure data and the medium-voltage data, and exporting and storing the medium-voltage data meeting a matching rule; and in response to the power outage data not being matched with the medium-voltage data, exporting and storing the power outage data which does not meet a matching rule.

In one implementation, the outage data includes: line blackout data; and power failure data of the transformer area.

In one implementation, the power outage data and the medium voltage data each include: line name, power failure starting time and power failure ending time; wherein, the power failure data with the medium voltage data match includes: the line name of the power failure data is matched with the line name of the medium-voltage data; the difference value of the power failure starting time in the power failure data and the power failure starting time in the medium-voltage data is within a first preset range; and the difference value of the power failure ending time in the power failure data and the power failure ending time in the medium-voltage data is within a second preset range.

In an optional implementation manner, the first preset range is ± 5 minutes, and the second preset range is ± 5 minutes.

In an optional implementation, the power outage data does not match the medium voltage data, including: the line name in the power failure data is not matched with the line name in the medium voltage data; or the line name in the power failure data is matched with the line name in the medium voltage data, the difference value between the power failure starting time in the power failure data and the power failure starting time in the medium voltage data is not in the first preset range, and/or the difference value between the power failure ending time in the power failure data and the power failure ending time in the medium voltage data is not in the second preset range.

In one implementation, the apparatus further comprises: and the second processing module is used for cleaning the medium-voltage data before the power failure data is matched with the medium-voltage data according to a matching rule.

In one implementation, the obtaining module is specifically configured to: screenshot is conducted on a first system login interface based on the RPA robot to obtain a first system login interface image, Optical Character Recognition (OCR) processing is conducted on the first system login interface image based on an Artificial Intelligence (AI) technology, a first login control area in the first system login interface is determined based on an OCR processing result, and first account information is filled in the first login control area based on the simulated manual operation of the RPA robot to log in the first system to download the power failure data; and capturing a second system login interface based on the RPA robot to obtain a second system login interface image, performing Optical Character Recognition (OCR) processing on the second system login interface image based on an Artificial Intelligence (AI) technology, determining a second login control area in the second system login interface based on an OCR processing result, and filling second account information into the second login control area based on the simulated manual operation of the RPA robot to log in the second system to download the medium-voltage data.

In one implementation, the apparatus further comprises: and the third processing module is used for generating a data table based on the stored data and sending the data table to the terminal equipment held by the user.

In a third aspect, an embodiment of the present application provides an electronic device, including at least one processor; and a memory communicatively coupled to the at least one processor. The memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor, so that the at least one processor can execute the RPA and AI-based grid outage data comparison method according to the first aspect.

In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium for storing instructions for implementing the method described above, and when the instructions are executed, the method described in the first aspect is executed.

In a fifth aspect, the present application provides a computer program product comprising a computer program which, when executed by a processor, performs the steps of the method of the first aspect described above.

It should be understood that the statements in this section do not necessarily identify key or critical features of the embodiments of the present application, nor do they limit the scope of the present application. Other features of the present application will become apparent from the following description.

Drawings

The drawings are included to provide a better understanding of the present solution and are not intended to limit the present application. Wherein:

fig. 1 is a flowchart of a power grid blackout data comparison method based on RPA and AI according to an embodiment of the present disclosure;

fig. 2 is a flowchart of another power grid blackout data comparison method based on RPA and AI according to an embodiment of the present disclosure;

fig. 3 is a flowchart of another power grid blackout data comparison method based on RPA and AI according to an embodiment of the present disclosure;

fig. 4 is a schematic structural diagram of a power grid outage data comparison device based on RPA and AI according to an embodiment of the present application;

fig. 5 is a schematic structural diagram of an electronic device for implementing a power grid outage data comparison method based on RPA and AI according to an embodiment of the present disclosure.

Detailed Description

The following description of the exemplary embodiments of the present application, taken in conjunction with the accompanying drawings, includes various details of the embodiments of the application for the understanding of the same, which are to be considered exemplary only. Accordingly, those of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the present application. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.

Where in the description of the present application, "/" indicates an OR meaning, for example, A/B may indicate A or B; "and/or" herein is merely an association describing an associated object, and means that there may be three relationships, e.g., a and/or B, which may mean: a exists alone, A and B exist simultaneously, and B exists alone. The various numbers of the first, second, etc. mentioned in this application are only used for the convenience of description and are not used to limit the scope of the embodiments of this application, nor to indicate the order of precedence.

In the description of the present application, the term "blackout data" refers to data generated by recording information such as a line name of a blackout event, a start time and an end time of the blackout event, and the like, but is not limited thereto.

In the description of the present application, the term "line outage data" refers to a collection of data for a plurality of outage events occurring in a power transmission line(s).

In the description of the present application, the term "block blackout data" refers to a collection of data for a plurality of blackout events occurring within a supply range or area of a transformer(s).

In the description of the present application, the term "medium voltage data" refers to operational data of a medium voltage transmission line.

In the description of the present application, the term "line name" refers to a proper noun for identifying a certain transmission line.

In the description of the present application, the term "blackout start time" refers to a specific time at which a blackout event recorded by a corresponding system starts.

In the description of the present application, the term "blackout end time" refers to a specific time at which a blackout event recorded by a corresponding system ends.

In the description of the present application, the term "data scrubbing" refers to the discovery and correction of recognizable errors in medium voltage data files, including checking for data consistency, processing invalid values, duplicate values, and the like.

Referring to fig. 1, fig. 1 is a flowchart of a power grid power failure data comparison method based on RPA (robot Process Automation) and AI (Artificial Intelligence) according to an embodiment of the present disclosure, where the method may compare and Process power failure data acquired by a plurality of different systems in an electric power company, so as to obtain accurate power failure data, reduce labor cost for processing the power failure data, and improve processing accuracy. As shown in fig. 1, the RPA and AI based grid outage data comparison method may include, but is not limited to, the following steps.

Step S101: and acquiring power failure data and medium-voltage data.

The power failure data comprises line power failure data and station area power failure data; line power failure data, platform district power failure data and middling pressure data constitute by many data, and every data all includes at least: line name, blackout start time and blackout end time.

In one implementation mode, screenshot can be performed on a first system login interface based on an RPA robot to obtain a first system login interface image, Optical Character Recognition (OCR) processing is performed on the first system login interface image based on an Artificial Intelligence (AI) technology, a first login control area in the first system login interface is determined based on an OCR processing result, and first account information is filled in the first login control area based on simulated manual operation of the RPA robot to log in the first system to download power failure data;

for example, after logging in the power grid metering system, the RPA robot may capture a screenshot of the system interface, and perform OCR recognition of an artificial intelligence AI on the captured screenshot to recognize a text representing the system login in the screenshot of the interface, such as "login". And determining the area of the login control in the interface screenshot based on the position of the text, and then simulating manual operation to fill in preset account information in the corresponding area of the login control in the system interface so as to log in the system to retrieve and download the power failure data.

And capturing a second system login interface based on the RPA robot to obtain a second system login interface image, performing Optical Character Recognition (OCR) processing on the second system login interface image based on an Artificial Intelligence (AI) technology, determining a second login control area in the second system login interface based on an OCR processing result, and filling second account information into the second login control area based on the simulated manual operation of the RPA robot to log in the second system to download medium-pressure data.

For example, after logging in the grid reliability system, the RPA robot may capture a screenshot of the system interface, and perform OCR recognition of an artificial intelligence AI on the captured screenshot to recognize a text representing the system login in the screenshot of the interface, such as "login". And determining the area of the login control in the interface screenshot based on the position of the text, and then simulating manual operation to fill in preset account information in the corresponding area of the login control in the system interface so as to log in the system to retrieve and download medium-voltage data.

Step S102: and matching the power failure data with the medium-voltage data according to a matching rule.

In one implementation, the matching rule may include: the line names of the power failure data and the medium-voltage data are matched; the difference value of the power failure starting time in the power failure data and the power failure starting time in the medium-voltage data is within a preset range; the difference between the power failure ending time in the power failure data and the power failure ending time in the medium-voltage data is within a preset range.

For example, the RPA robot reads each piece of data in the blackout data one by one, obtains a line name, blackout start time and blackout end time in each piece of data, compares the line name of each piece of data with the line name of each piece of data in the medium-voltage data one by one, judges whether the medium-voltage data has data which is the same as the line name of the blackout data which is being compared, and if the medium-voltage data has the data which is the same as the line name, continuously compares the blackout start time and the blackout end time of the two pieces of data which are the same as the line name; and if the medium-voltage data does not have the data with the same name as the line name of the power failure data which is being compared, the comparison of the power failure data is completed, and the next power failure data is continuously compared with the medium-voltage data.

Step S103: and in response to the power failure data being matched with the medium-voltage data, the medium-voltage data meeting the matching rule is exported and stored.

In one implementation, matching the outage data with the medium voltage data includes: the line name of the power failure data is matched with the line name of the medium-voltage data; the difference value of the power failure starting time in the power failure data and the power failure starting time in the medium-voltage data is within a first preset range; the difference between the power outage end time in the power outage data and the power outage end time in the medium-voltage data is within a second preset range.

In an alternative implementation, the first predetermined range is ± 5 minutes, and the second predetermined range is ± 5 minutes.

For example, when a certain piece of data in the power failure data is the same as a certain piece of data in the medium voltage data in line name, and the difference between the power failure start time and the power failure end time of the two pieces of data is less than or equal to 5 minutes, the two pieces of data are matched, and at this time, the medium voltage data in the two pieces of matched data is derived and stored.

Step S104: and in response to the fact that the power failure data are not matched with the medium-voltage data, exporting and storing the power failure data which do not meet the matching rule.

In one implementation, the power outage data does not match the medium voltage data, including: the line name in the power failure data is not matched with the line name in the medium-voltage data; or the line name in the power failure data is matched with the line name in the medium-voltage data, and the difference value between the power failure starting time in the power failure data and the power failure starting time in the medium-voltage data is not in a first preset range, and/or the difference value between the power failure ending time in the power failure data and the power failure ending time in the medium-voltage data is not in a second preset range.

As an example, if the line name in the outage data does not match the line name in the medium voltage data, the outage data is determined not to match the medium voltage data.

As another example, if the line name in the blackout data matches the line name in the medium-voltage data, and the difference between the blackout start time in the blackout data and the blackout start time in the medium-voltage data is not within the first preset range, it is determined that the blackout data does not match the medium-voltage data.

As another example, if the line name in the power outage data matches the line name in the medium-voltage data, the difference between the power outage start time in the power outage data and the power outage start time in the medium-voltage data is within a first preset range, and the difference between the power outage end time in the power outage data and the power outage end time in the medium-voltage data is not within a second preset range, it is determined that the power outage data does not match the medium-voltage data.

As yet another example, if the line name in the power outage data matches the line name in the medium voltage data, and the difference between the power outage start time in the power outage data and the power outage start time in the medium voltage data is not within the first preset range, and the difference between the power outage end time in the power outage data and the power outage end time in the medium voltage data is not within the second preset range, it is determined that the power outage data does not match the medium voltage data.

For example, when the line name of a certain piece of data in the blackout data is different from the line name of each piece of data in the medium-voltage data, there is no matching item with the piece of blackout data in the medium-voltage data, and the piece of blackout data is exported and stored. When the name of a certain line in the power failure data is the same as the name of a certain line in the medium-voltage data, the difference value of the power failure starting time of the two lines is less than or equal to 5 minutes, and the difference value of the power failure ending time of the two lines is greater than 5 minutes, no matching item with the power failure data exists in the medium-voltage data, and the power failure data is exported and stored. When the name of a certain line in the power failure data is the same as the name of a certain line in the medium-voltage data, the difference value of the power failure starting time of the two lines is larger than 5 minutes, and the difference value of the power failure ending time of the two lines is smaller than or equal to 5 minutes, no matching item with the power failure data exists in the medium-voltage data, and the power failure data is exported and stored. When the name of a certain line in the power failure data is the same as the name of a certain line in the medium-voltage data, the difference value of the power failure starting time of the two lines is more than five minutes, and the difference value of the power failure ending time of the two lines is more than 5 minutes, no matching item with the power failure data exists in the medium-voltage data, and the power failure data is exported and stored.

By implementing the embodiment of the application, the power failure data and the medium-voltage data can be compared and processed according to the matching rule, and the medium-voltage data which accord with the matching rule and the power failure data which do not accord with the matching rule are respectively obtained and stored. The problems of low efficiency and high error rate when power grid power failure data are manually processed are solved, and the labor cost is reduced.

Referring to fig. 2, fig. 2 is a flowchart of another RPA and AI-based power grid outage data comparison method according to an embodiment of the present disclosure, which can compare and process line outage data with medium-voltage data. As shown in fig. 2, the RPA and AI based grid outage data comparison method may include, but is not limited to, the following steps.

Step S201: and acquiring line power failure data and medium-voltage data.

For example, after logging in a power grid metering system, the RPA robot may perform image recognition on a system page based on an artificial intelligence AI technology to obtain positions of page elements corresponding to various functions of the system, so as to simulate manual operation to click corresponding elements to enter a download page, and input data retrieval conditions at corresponding positions to retrieve and download line power failure data.

In the embodiment of the present application, the obtaining of the medium-voltage data may be implemented by any one of the embodiments of the present application, and the embodiment of the present application is not limited to this and is not described in detail again.

Step S202: and matching the line power failure data with the medium-voltage data according to a matching rule.

For example, the RPA robot reads each piece of data in line blackout data one by one, obtains a line name, blackout start time and blackout end time in each piece of data, compares the line name of each piece of data with the line name of each piece of data in medium-voltage data one by one, judges whether the medium-voltage data has data which is the same as the line name of the line blackout data being compared, and if the medium-voltage data has the data which is the same as the line name, continuously compares the blackout start time and the blackout end time of the two pieces of data which are the same as the line name; and if the medium-voltage data does not have the data with the same line name as the line power failure data which is being compared, the comparison of the line power failure data is completed, and the next line power failure data is continuously compared with the medium-voltage data.

Step S203: and in response to the matching of the line power failure data and the medium-voltage data, deriving and storing the medium-voltage data meeting the matching rule.

For example, when a certain data in the line power failure data is the same as the line name of a certain data in the medium voltage data, and the difference between the power failure start time and the power failure end time of the two data is less than or equal to 5 minutes, the two data are matched, and the medium voltage data in the two matched data are derived and stored.

Step S204: and in response to the fact that the line power failure data are not matched with the medium-voltage data, exporting and storing the line power failure data which do not meet the matching rule.

For example, when the line name of a certain piece of line power failure data is different from the line name of each piece of line data in the medium-voltage data; or the name of a certain line in the line power failure data is the same as the name of a certain line in the medium-voltage data, the difference of the power failure starting time of the two lines is less than or equal to 5 minutes, and the difference of the power failure ending time of the two lines is greater than 5 minutes; or the name of a certain line in the line power failure data is the same as the name of a certain line in the medium-voltage data, the difference of the power failure starting time of the two lines is more than 5 minutes, and the difference of the power failure ending time of the two lines is less than or equal to 5 minutes; or the name of a certain line in the line power failure data is the same as the name of a certain line in the medium-voltage data, the difference of the power failure starting time of the two lines is more than five minutes, and the difference of the power failure ending time of the two lines is more than 5 minutes; if the medium-voltage data has no matching item with the line power failure data, the line power failure data is exported and stored.

By implementing the embodiment of the application, the line power failure data and the medium voltage data can be compared and processed according to the matching rule, and the medium voltage data which accord with the matching rule and the line power failure data which do not accord with the matching rule are respectively obtained and stored. The problems of low efficiency and high error rate when power grid power failure data are manually processed are solved, and the labor cost is reduced.

Referring to fig. 3, fig. 3 is a flowchart of another power grid blackout data comparison method based on RPA and AI according to an embodiment of the present disclosure, which can compare and process blackout data of a distribution room with medium-voltage data. As shown in fig. 3, the RPA and AI based grid outage data comparison method may include, but is not limited to, the following steps.

Step S301: and acquiring power failure data and medium-voltage data of the transformer area.

For example, after logging in a power grid metering system, the RPA robot may perform image recognition on a system page based on an artificial intelligence AI technology to obtain positions of page elements corresponding to various functions of the system, so as to simulate manual operation to click corresponding elements to enter a download page, and input data retrieval conditions at corresponding positions to retrieve and download power outage data of a distribution room.

In the embodiment of the present application, the obtaining of the medium-voltage data may be implemented by any one of the embodiments of the present application, and the embodiment of the present application is not limited to this and is not described in detail again.

Step S302: and matching the line power failure data with the medium-voltage data according to a matching rule.

For example, the RPA robot reads each piece of data in the power failure data of the station area one by one, acquires a line name, power failure start time and power failure end time of each piece of data, compares the line name of each piece of data with the line name of each piece of data in the medium-voltage data one by one, judges whether the medium-voltage data has data with the same line name as the power failure data of the station area which is being compared, and if the medium-voltage data has the data with the same line name, continuously compares the power failure start time and the power failure end time of two pieces of data with the same line name; and if the medium-voltage data does not have the data with the same name as the power failure data line name of the power failure data of the power distribution area which is being compared, the power failure data of the power distribution area is compared, and the power failure data of the next power distribution area is continuously compared with the medium-voltage data.

Step S303: and in response to the matching of the power failure data of the transformer area and the medium-voltage data, exporting and storing the medium-voltage data meeting the matching rule.

For example, when a certain piece of data in the power outage data of the distribution room is the same as the line name of a certain piece of data in the medium voltage data, and the difference between the power outage starting time and the power outage ending time of the two pieces of data is less than or equal to 5 minutes, the two pieces of data are matched, and the medium voltage data in the two pieces of matched data is derived and stored.

Step S304: and in response to the fact that the power failure data of the transformer area are not matched with the medium-voltage data, exporting and storing the power failure data of the line which does not meet the matching rule.

For example, when the line name of a certain piece of data in the power failure data of the transformer area is different from the line name of each piece of data in the medium-voltage data; or the name of a certain line in the power failure data of the transformer area is the same as the name of a certain line in the medium-voltage data, the difference of the power failure starting time of the two lines is less than or equal to 5 minutes, and the difference of the power failure ending time of the two lines is greater than 5 minutes; or the name of a certain line in the power failure data of the transformer area is the same as the name of a certain line in the medium-voltage data, the difference of the power failure starting time of the two lines is more than 5 minutes, and the difference of the power failure ending time of the two lines is less than or equal to 5 minutes; or the name of a certain line in the power failure data of the transformer area is the same as the name of a certain line in the medium-voltage data, the difference of the power failure starting time of the two lines is more than five minutes, and the difference of the power failure ending time of the two lines is more than 5 minutes; and if the medium-voltage data does not have a matching item with the line power failure data, exporting and storing the power failure data of the distribution area.

By implementing the embodiment of the application, the power failure data of the transformer area and the medium-voltage data can be compared and processed according to the matching rule, and the medium-voltage data which accords with the matching rule and the power failure data of the transformer area which does not accord with the matching rule are respectively obtained and stored. The problems of low efficiency and high error rate when power grid power failure data are manually processed are solved, and the labor cost is reduced.

In some embodiments of the application, before the power failure data is matched with the medium-voltage data according to the matching rule, the medium-voltage data can be subjected to data cleaning, repeated data in the medium-voltage data are deleted, the power failure data comparison time of a power grid is saved, and the accuracy of comparison results is improved.

In some embodiments of the present application, a data table may be generated based on the stored data and sent to a terminal device held by the user.

In the embodiment of the present application, the terminal device may be a hardware device having various operating systems, such as a mobile phone, a tablet Computer, a Personal digital assistant (pda), and a Personal Computer (PC).

For example, the RPA robot may respectively generate corresponding data tables from the stored medium voltage data successfully matched, the line blackout data unsuccessfully matched, and the blackout data unsuccessfully matched station area, and send the generated data tables to the terminal device held by the user through the mail, so that the user can view the data tables conveniently.

Referring to fig. 4, fig. 4 is a schematic diagram of a comparison apparatus for power outage data of a power grid based on RPA and AI according to an embodiment of the present disclosure. The RPA and AI-based grid outagedata comparison apparatus 400 shown in fig. 4 may include: an acquisition module 401, a first processing module 402 and astorage module 403.

The obtaining module 401 is configured to obtain power outage data and medium voltage data; the first processing module 402 is configured to match the power outage data with the medium voltage data according to a matching rule; thestorage module 403 is configured to, in response to matching between the power outage data and the medium voltage data, export and store the medium voltage data meeting the matching rule; and in response to the fact that the power failure data are not matched with the medium-voltage data, exporting and storing the power failure data which do not meet the matching rule.

In one implementation, the outage data includes: line blackout data; and power failure data of the transformer area.

In one implementation, blackout data or medium voltage data includes: a line name; power failure starting time; the first processing module 402 is specifically configured to: matching the line name of the power failure data with the line name of the medium-voltage data; matching the difference value of the power failure starting time in the power failure data and the power failure starting time in the medium-voltage data with a first preset range; and matching the difference value of the power failure ending time in the power failure data and the power failure ending time in the medium-voltage data with a second preset range.

In one implementation, the outage data and the medium voltage data each include: line name, power failure starting time and power failure ending time; wherein, power failure data with the middling pressure data match, include: the line name of the power failure data is matched with the line name of the medium-voltage data; the difference value of the power failure starting time in the power failure data and the power failure starting time in the medium-voltage data is within a first preset range; the difference between the power outage end time in the power outage data and the power outage end time in the medium-voltage data is within a second preset range.

In an alternative implementation, the first predetermined range is ± 5 minutes, and the second predetermined range is ± 5 minutes.

In an alternative implementation, the power outage data is not matched with the medium voltage data, and the method includes: the line name in the power failure data is not matched with the line name in the medium-voltage data; or the line name in the power failure data is matched with the line name in the medium-voltage data, the difference value between the power failure starting time in the power failure data and the power failure starting time in the medium-voltage data is not within a first preset range, and/or the difference value between the power failure ending time in the power failure data and the power failure ending time in the medium-voltage data is not within a second preset range.

In one implementation, the apparatus further comprises: and the second processing module 404 is configured to perform data cleaning on the medium-voltage data before matching the power outage data with the medium-voltage data according to the matching rule.

In an implementation manner, the obtaining module 401 is specifically configured to: the method comprises the steps that screenshot is conducted on a first system login interface based on an RPA robot to obtain a first system login interface image, Optical Character Recognition (OCR) processing is conducted on the first system login interface image based on an Artificial Intelligence (AI) technology, a first login control area in the first system login interface is determined based on an OCR processing result, and first account information is filled in the first login control area based on simulation of manual operation of the RPA robot to log in a first system to download power failure data; and capturing a second system login interface based on the RPA robot to obtain a second system login interface image, performing Optical Character Recognition (OCR) processing on the second system login interface image based on an Artificial Intelligence (AI) technology, determining a second login control area in the second system login interface based on an OCR processing result, and filling second account information into the second login control area based on the simulated manual operation of the RPA robot to log in the second system to download medium-pressure data.

In one implementation, the apparatus further comprises: and athird processing module 405, configured to generate a data table based on the stored data, and send the data table to the terminal device held by the user.

With regard to the apparatus in the above-described embodiment, the specific manner in which each module performs the operation has been described in detail in the embodiment related to the method, and will not be elaborated here.

Based on the embodiment of this application, this application still provides an electronic equipment, includes: at least one processor; and a memory communicatively coupled to the at least one processor; the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor, so that the at least one processor can execute the RPA and AI-based grid outage data comparison method according to any one of the embodiments.

Based on the embodiments of the present application, the present application also provides a computer readable storage medium storing computer instructions. The computer instructions are used for enabling a computer to execute the RPA and AI-based power grid outage data comparison method according to any one of the embodiments provided in the present application.

Referring to FIG. 5, shown in FIG. 5 is a schematic block diagram of an example electronic device that may be used to implement embodiments of the present application. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The components shown herein, their connections and relationships, and their functions, are meant to be examples only, and are not meant to limit implementations of the present application that are described and/or claimed in the present application.

As shown in fig. 5, thedevice 500 includes acomputing unit 501 that can perform various appropriate actions and processes in accordance with a computer program stored in a Read-Only Memory (ROM) 502 or a computer program loaded from astorage unit 508 into a Random Access Memory (RAM) 503. In theRAM 503, various programs and data required for the operation of thedevice 500 can also be stored. Thecalculation unit 501, theROM 502, and theRAM 503 are connected to each other by abus 504. An Input/Output (I/O)interface 505 is also connected tobus 504.

A number of components in thedevice 500 are connected to the I/O interface 505, including: aninput unit 506 such as a keyboard, a mouse, or the like; anoutput unit 507 such as various types of displays, speakers, and the like; astorage unit 508, such as a magnetic disk, optical disk, or the like; and acommunication unit 509 such as a network card, modem, wireless communication transceiver, etc. Thecommunication unit 509 allows thedevice 500 to exchange information/data with other devices through a computer network such as the internet and/or various telecommunication networks.

Thecomputing unit 501 may be a variety of general-purpose and/or special-purpose processing components having processing and computing capabilities. Some examples of thecomputing Unit 501 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various dedicated Artificial Intelligence (AI) computing chips, various computing units running machine learning model algorithms, a Digital Signal Processor (DSP), and any suitable processor, controller, microcontroller, and the like. Thecalculation unit 501 performs the above-described methods and processes, such as the RPA and AI-based grid outage data comparison method. For example, in some embodiments, the RPA and AI based grid outage data comparison method may be implemented as a computer software program tangibly embodied in a machine-readable medium, such as thestorage unit 508. In some embodiments, part or all of the computer program may be loaded and/or installed onto thedevice 500 via theROM 502 and/or thecommunication unit 509. When loaded intoRAM 503 and executed by computingunit 501, the computer program may perform one or more of the steps of the RPA and AI based grid outage data comparison method described above. Alternatively, in other embodiments, thecomputing unit 501 may be configured to perform the RPA and AI based grid outage data comparison method by any other suitable means (e.g., by means of firmware).

Various implementations of the systems and techniques described here above may be realized in digital electronic circuitry, integrated circuitry, Field Programmable Gate Arrays (FPGAs), Application Specific Integrated Circuits (ASICs), Application Specific Standard Parts (ASSPs), System On Chip (SOC), load Programmable Logic devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs that are executable and/or interpretable on a programmable system including at least one programmable processor, which may be special or general purpose, receiving data and instructions from, and transmitting data and instructions to, a storage system, at least one input device, and at least one output device.

Program code for implementing the methods of the present application may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the program codes, when executed by the processor or controller, cause the functions/operations specified in the flowchart and/or block diagram to be performed. The program code may execute entirely on the machine, partly on the machine, as a stand-alone software package partly on the machine and partly on a remote machine or entirely on the remote machine or server.

In the context of this application, a machine-readable medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a Read-Only Memory (ROM), an Erasable Programmable Read-Only Memory (EPROM) or flash Memory), an optical fiber, a Compact Disc Read-Only Memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

To provide for interaction with a user, the systems and techniques described here can be implemented on a computer having: a Display device (e.g., a Cathode Ray Tube (CRT) or LCD (Liquid Crystal Display) monitor) for displaying information to a user; and a keyboard and a pointing device (e.g., a mouse or a trackball) by which a user can provide input to the computer. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic, speech, or tactile input.

The systems and techniques described here can be implemented in a computing system that includes a back-end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such back-end, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), Wide Area Networks (WANs), the internet, and blockchain networks.

The computer system may include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The Server may be a cloud Server, which is also called a cloud computing Server or a cloud host, and is a host product in a cloud computing service system, so as to solve the defects of high management difficulty and weak service expansibility in the conventional physical host and VPS (Virtual Private Server) service. The server may also be a server of a distributed system, or a server incorporating a blockchain.

It should be understood that various forms of the flows shown above may be used, with steps reordered, added, or deleted. For example, the steps described in the present disclosure may be executed in parallel, sequentially, or in different orders, as long as the desired results of the technical solutions disclosed in the present disclosure can be achieved, and the present disclosure is not limited herein.

The above-described embodiments should not be construed as limiting the scope of the present application. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions may be made in accordance with design requirements and other factors. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims (16)

Translated fromChinese

1.一种基于RPA及AI的电网停电数据对比方法，其特征在于，所述方法应用于机器人流程自动化RPA机器人，所述方法包括：1. a power grid outage data comparison method based on RPA and AI, is characterized in that, described method is applied to robot process automation RPA robot, and described method comprises:获取停电数据及中压数据；Obtain power outage data and medium voltage data;将所述停电数据按照匹配规则与所述中压数据进行匹配；Matching the power outage data with the medium voltage data according to a matching rule;响应于所述停电数据与所述中压数据匹配，将满足匹配规则的所述中压数据导出并存储；In response to the power failure data being matched with the medium voltage data, exporting and storing the medium voltage data satisfying the matching rule;响应于所述停电数据与所述中压数据不匹配，将不满足匹配规则的所述停电数据导出并存储。In response to the outage data not matching the medium voltage data, the outage data that does not satisfy a matching rule is derived and stored.2.根据权利要求1所述的方法，其特征在于，所述停电数据包括：2. The method according to claim 1, wherein the power outage data comprises:线路停电数据；台区停电数据。Line power outage data; Taiwan area power outage data.3.根据权利要求1所述的方法，其特征在于，所述停电数据和所述中压数据均包括：线路名称、停电开始时间和停电结束时间；3. The method according to claim 1, wherein the power outage data and the medium voltage data both include: line name, outage start time and outage end time;其中，所述停电数据与所述中压数据匹配，包括：Wherein, the power outage data matches the medium voltage data, including:所述停电数据的线路名称与所述中压数据的线路名称匹配；The line name of the power failure data matches the line name of the medium voltage data;所述停电数据中的停电开始时间与所述中压数据中的停电开始时间的差值在第一预设范围内；The difference between the power failure start time in the power failure data and the power failure start time in the medium voltage data is within a first preset range;所述停电数据中的停电结束时间与所述中压数据中的停电结束时间的差值在第二预设范围内。The difference between the power failure end time in the power failure data and the power failure end time in the medium voltage data is within a second preset range.4.根据权利要3所述的方法，其特征在于，所述第一预设范围为±5分钟；所述第二预设范围为±5分钟。4. The method according to claim 3, wherein the first preset range is ±5 minutes; the second preset range is ±5 minutes.5.根据权利要求3所述的方法，其特征在于，所述停电数据与所述中压数据不匹配，包括：5. The method of claim 3, wherein the power outage data does not match the medium voltage data, comprising:所述停电数据中的线路名称与所述中压数据中的线路名称不匹配；或者，The line names in the outage data do not match the line names in the medium voltage data; or,所述停电数据中的线路名称与所述中压数据中的线路名称匹配，且所述停电数据中的线路名称与所述中压数据中的线路名称匹配，且所述停电数据中的停电开始时间与所述中压数据中的停电开始时间的差值不在所述第一预设范围内，和/或，所述停电数据中的停电结束时间与所述中压数据中的停电结束时间的差值不在所述第二预设范围内。The line name in the outage data matches the line name in the medium voltage data, and the line name in the outage data matches the line name in the medium voltage data, and the outage starts in the outage data The difference between the time and the power outage start time in the medium voltage data is not within the first preset range, and/or the difference between the power outage end time in the power outage data and the power outage end time in the medium voltage data. The difference is not within the second preset range.6.根据权利要求1所述的方法，其特征在于，所述方法还包括：6. The method of claim 1, wherein the method further comprises:在将所述停电数据按照匹配规则与所述中压数据进行匹配之前，对所述中压数据进行数据清洗。Before the power failure data is matched with the medium voltage data according to the matching rule, data cleaning is performed on the medium voltage data.7.根据权利要求1所述的方法，其特征在于，所述获取停电数据及中压数据，包括：7. The method according to claim 1, wherein the acquiring power failure data and medium voltage data comprises:基于所述RPA机器人对第一系统登录界面进行截图，得到第一系统登录界面图像，并基于人工智能AI技术对第一系统登录界面图像进行光学字符识别OCR处理，基于OCR处理结果确定所述第一系统登录界面之中第一登录控件区域，基于所述RPA机器人模拟人工操作将第一账号信息填写至所述第一登录控件区域以登录所述第一系统中下载所述停电数据；The RPA robot takes a screenshot of the login interface of the first system to obtain an image of the login interface of the first system, and performs optical character recognition (OCR) processing on the login interface image of the first system based on artificial intelligence AI technology, and determines the first system login interface image based on the OCR processing result. A first login control area in a system login interface, filling in the first account information into the first login control area based on the simulated manual operation of the RPA robot to log in to the first system to download the power outage data;基于所述RPA机器人对第二系统登录界面进行截图，得到第二系统登录界面图像，并基于人工智能AI技术对第二系统登录界面图像进行光学字符识别OCR处理，基于OCR处理结果确定所述第二系统登录界面之中第二登录控件区域，基于所述RPA机器人模拟人工操作将第二账号信息填写至所述第二登录控件区域以登录所述第二系统中下载所述中压数据。The RPA robot takes a screenshot of the login interface of the second system to obtain an image of the login interface of the second system, and performs optical character recognition (OCR) processing on the login interface image of the second system based on artificial intelligence AI technology, and determines the login interface image of the second system based on the OCR processing result. In the second login control area in the second system login interface, the second account information is filled in the second login control area based on the simulated manual operation of the RPA robot to log in to the second system and download the medium voltage data.8.根据权利要求1所述的方法，其特征在于，所述方法还包括：8. The method of claim 1, wherein the method further comprises:基于所述存储的数据生成数据表，并将生成的所述数据表发送至用户所持的终端设备。A data table is generated based on the stored data, and the generated data table is sent to the terminal device held by the user.9.一种基于RPA及AI的电网停电数据对比装置，其特征在于，所述装置应用于机器人流程自动化RPA机器人，所述装置包括：9. A power grid outage data comparison device based on RPA and AI, wherein the device is applied to a robotic process automation RPA robot, and the device comprises:获取模块：用于获取停电数据及中压数据；Acquisition module: used to acquire power failure data and medium voltage data;第一处理模块：用于将所述停电数据按照匹配规则与所述中压数据进行匹配；The first processing module: used to match the power failure data with the medium voltage data according to a matching rule;存储模块：用于响应于所述停电数据与所述中压数据匹配，将满足匹配规则的所述中压数据导出并存储；响应于所述停电数据与所述中压数据不匹配，将不满足匹配规则的所述停电数据导出并存储。Storage module: used for exporting and storing the medium voltage data satisfying the matching rule in response to the power failure data matching the medium voltage data; in response to the power failure data not matching the medium voltage data, not The power outage data satisfying the matching rule is exported and stored.10.根据权利要求9所述的装置，其特征在于，所述第一处理模块具体用于：10. The apparatus according to claim 9, wherein the first processing module is specifically configured to:将所述停电数据的线路名称与所述中压数据的线路名称进行匹配；matching the line name of the power outage data with the line name of the medium voltage data;将所述停电数据中的停电开始时间与所述中压数据中的停电开始时间的差值与第一预设范围进行匹配；matching the difference between the power failure start time in the power failure data and the power failure start time in the medium voltage data with a first preset range;将所述停电数据中的停电结束时间与所述中压数据中的停电结束时间的差值与第二预设范围进行匹配。Matching the difference between the power failure end time in the power failure data and the power failure end time in the medium voltage data with a second preset range.11.根据权利要求9所述的装置，其特征在于，所述装置还包括：11. The apparatus of claim 9, wherein the apparatus further comprises:第二处理模块，用于在将所述停电数据按照匹配规则与所述中压数据进行匹配之前，对所述中压数据进行数据清洗。The second processing module is configured to perform data cleaning on the medium voltage data before matching the power outage data with the medium voltage data according to the matching rule.12.根据权利要求9所述的装置，其特征在于，所述获取模块具体用于：12. The device according to claim 9, wherein the acquiring module is specifically configured to:基于所述RPA机器人对第一系统登录界面进行截图，得到第一系统登录界面图像，并基于人工智能AI技术对第一系统登录界面图像进行光学字符识别OCR处理，基于OCR处理结果确定所述第一系统登录界面之中第一登录控件区域，基于所述RPA机器人模拟人工操作将第一账号信息填写至所述第一登录控件区域以登录所述第一系统中下载所述停电数据；The RPA robot takes a screenshot of the login interface of the first system to obtain an image of the login interface of the first system, and performs optical character recognition (OCR) processing on the login interface image of the first system based on artificial intelligence AI technology, and determines the first system login interface image based on the OCR processing result. A first login control area in a system login interface, filling in the first account information into the first login control area based on the simulated manual operation of the RPA robot to log in to the first system to download the power outage data;基于所述RPA机器人对第二系统登录界面进行截图，得到第二系统登录界面图像，并基于人工智能AI技术对第二系统登录界面图像进行光学字符识别OCR处理，基于OCR处理结果确定所述第二系统登录界面之中第二登录控件区域，基于所述RPA机器人模拟人工操作将第二账号信息填写至所述第二登录控件区域以登录所述第二系统中下载所述中压数据。The RPA robot takes a screenshot of the login interface of the second system to obtain an image of the login interface of the second system, and performs optical character recognition (OCR) processing on the login interface image of the second system based on artificial intelligence AI technology, and determines the login interface image based on the OCR processing result. In the second login control area in the second system login interface, the second account information is filled in the second login control area based on the simulated manual operation of the RPA robot to log in to the second system and download the medium voltage data.13.根据权利要求9所述的装置，其特征在于，所述装置还包括：13. The apparatus of claim 9, wherein the apparatus further comprises:第三处理模块：用于基于所述存储的数据生成数据表，并发送至用户所持的终端设备。The third processing module is used to generate a data table based on the stored data, and send it to the terminal device held by the user.14.一种电子设备，其特征在于，14. An electronic device, characterized in that,至少一个处理器；at least one processor;以及与所述至少一个处理器通信连接的存储器；and a memory communicatively coupled to the at least one processor;其中，所述存储器存储有可被所述至少一个处理器执行的指令，所述指令被所述至少一个处理器执行，以使所述至少一个处理器能够执行权利要求1至8中任一项所述的基于RPA及AI的电网停电数据对比方法。wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to execute any one of claims 1 to 8 The described power grid outage data comparison method based on RPA and AI.15.一种计算机可读存储介质，其特征在于，所述计算机指令用于使所述计算机执行权利要求1至8中任一项所述的基于RPA及AI的电网停电数据对比方法。15. A computer-readable storage medium, wherein the computer instructions are used to cause the computer to execute the RPA and AI-based power grid outage data comparison method according to any one of claims 1 to 8.16.一种计算机程序产品，包括计算机程序，所述计算机程序在被处理器执行时实现权利要求1至8中任一项所述的基于RPA及AI的电网停电数据对比方法。16. A computer program product, comprising a computer program that, when executed by a processor, implements the RPA- and AI-based power grid outage data comparison method according to any one of claims 1 to 8.

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