CN112184138A

CN112184138A - RPA and AI combined power grid work order processing method and device and electronic equipment

Info

Publication number: CN112184138A
Application number: CN202010825505.7A
Authority: CN
Inventors: 汪冠春; 胡一川; 褚瑞; 李玮; 殷志国; 张翼
Original assignee: Beijing Benying Network Technology Co Ltd; Beijing Laiye Network Technology Co Ltd
Current assignee: Beijing Benying Network Technology Co Ltd; Beijing Laiye Network Technology Co Ltd
Priority date: 2020-08-17
Filing date: 2020-08-17
Publication date: 2021-01-05
Anticipated expiration: 2040-08-17
Also published as: CN112184138B

Abstract

The application provides a power grid work order processing method and device combining RPA and AI, electronic equipment and a storage medium, and belongs to the technical field of automation. Wherein, the method comprises the following steps: inquiring a power grid work order system to obtain a work order list containing each target work order; sequentially acquiring the residual processing time of each target work order in the work order list; if the remaining processing time of any target work order is smaller than a first time threshold, acquiring a target user associated with any target work order; and pushing a work order processing reminding message to the target user. Therefore, by the power grid work order processing method combining the RPA and the AI, the work order which is about to reach the processing time limit is automatically acquired, and the work order processing reminding message is sent to the relevant user, so that the automatic monitoring and reminding of the work order are realized through the RPA technology, manual participation is not needed, the labor cost is reduced, and the work order processing efficiency and reliability are improved.

Description

RPA and AI combined power grid work order processing method and device and electronic equipment

Technical Field

The present application relates to the field of automation technologies, and in particular, to a method and an apparatus for processing a power grid work order by combining an RPA and an AI, an electronic device, and a storage medium.

Background

Robot Process Automation (RPA) is a Process task automatically executed according to rules by simulating human operations on a computer through specific robot software.

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

The power grid, as the most important component in the public utilities, supplies approximately one fourth of terminal energy for human beings, and is an important component of modern energy. In the related technology, various types of work orders exist in a work order system of a power grid, and the work orders are monitored, reminded and distributed in a manual mode. However, the manual work order processing method is not only inefficient, but also prone to errors and poor in reliability.

Disclosure of Invention

The power grid work order processing method and device combining the RPA and the AI, the electronic device and the storage medium are used for solving the problems that in the related technology, work orders in a power grid work order system are processed in a manual mode, efficiency is low, mistakes are easy to make, and reliability is poor.

An embodiment of the application provides a power grid work order processing method combining an RPA and an AI, which includes: inquiring a power grid work order system to obtain a work order list containing each target work order; sequentially acquiring the residual processing time of each target work order in the work order list; if the remaining processing time of any target work order is smaller than a first time threshold, acquiring a target user associated with any target work order; and pushing a work order processing reminding message to the target user.

Optionally, in a possible implementation manner of the embodiment of the first aspect of the present application, the work order types to which the target work orders belong are the same, and the querying the power grid work order system to obtain the work order list including the target work orders specifically includes:

acquiring keywords corresponding to the work order types to which the target work orders belong;

and querying the power grid work order system by using the keyword to obtain a work order list containing each target work order.

Optionally, in another possible implementation manner of the embodiment of the first aspect of the present application, before querying the power grid work order system with the keyword to obtain a work order list including the target work orders, the method further includes:

acquiring a second time threshold corresponding to the work order type to which each target work order belongs;

determining the current query starting time and the current query finishing time according to the second time threshold;

the querying the power grid work order system by using the keyword to obtain a work order list containing each target work order specifically comprises:

and inquiring the power grid work order system according to the keyword, the inquiry starting time and the inquiry ending time so as to obtain a work order list containing each target work order.

Optionally, in another possible implementation manner of the embodiment of the first aspect of the present application, the querying the power grid work order system to obtain a work order list including each target work order specifically includes:

and inquiring the power grid work order system at a preset frequency to obtain a work order list containing each target work order.

Optionally, in another possible implementation manner of the embodiment of the first aspect of the present application, the sequentially obtaining the remaining processing time of each target work order in the work order list specifically includes:

sequentially capturing information of each target work order from the work order list, wherein the information of the work orders comprises the deadline time of the work orders;

and determining the remaining processing time of each target work order according to the deadline of each target work order and the current time.

Optionally, in another possible implementation manner of the embodiment of the first aspect of the present application, if the remaining processing time of any one of the target work orders is smaller than the first time threshold, the obtaining a target user associated with any one of the target work orders specifically includes:

and if the remaining processing time of any target work order is less than a first time threshold, determining a target user associated with any target work order according to the type of the any target work order.

Optionally, in another possible implementation manner of the embodiment of the first aspect of the present application, the pushing a work order processing reminding message to the target user specifically includes:

determining a target pushing mode according to the work order type of any target work order;

and pushing the work order processing reminding message to the target user in the target pushing mode.

Optionally, in another possible implementation manner of the embodiment of the first aspect of the present application, the pushing, in the target pushing manner, the work order processing reminding message to the target user specifically includes:

starting a pushing system corresponding to the target pushing mode;

adding the information of any target work order into a push message of the push system to generate the work order processing reminding message;

and pushing the work order processing reminding message to the target user.

determining the target pushing mode according to the remaining processing time length of any target work order;

The utility model provides a power grid work order processing apparatus who combines RPA and AI that this application another aspect embodiment provided includes: the first acquisition module is used for inquiring the power grid work order system so as to acquire a work order list containing each target work order; the second acquisition module is used for sequentially acquiring the residual processing time of each target work order in the work order list; the third acquisition module is used for acquiring a target user associated with any target work order when the residual processing time of the target work order is less than a first time threshold; and the pushing module is used for pushing the work order processing reminding message to the target user.

Optionally, in a possible implementation manner of the embodiment of the first aspect of the present application, the types of work orders to which the target work orders belong are the same, and the first obtaining module specifically includes:

the first acquisition unit is used for acquiring keywords corresponding to the work order types to which the target work orders belong;

and the second acquisition unit is used for inquiring the power grid work order system by using the keyword so as to acquire a work order list containing each target work order.

Optionally, in another possible implementation manner of the embodiment of the first aspect of the present application, the first obtaining module further includes:

a third obtaining unit, configured to obtain a second time threshold corresponding to the work order type to which each target work order belongs;

a first determining unit, configured to determine a current query starting time and a current query ending time according to the second time threshold;

the second obtaining unit is specifically configured to:

Optionally, in yet another possible implementation manner of the embodiment of the first aspect of the present application, the first obtaining module specifically includes:

and the fourth acquisition unit is used for inquiring the power grid work order system at a preset frequency so as to acquire a work order list containing each target work order.

Optionally, in another possible implementation manner of the embodiment of the first aspect of the present application, the second obtaining module specifically includes:

the grabbing unit is used for sequentially grabbing the information of each target work order from the work order list, wherein the information of the work orders comprises the deadline of the work orders;

and the second determining unit is used for determining the residual processing time of each target work order according to the deadline of each target work order and the current time.

Optionally, in another possible implementation manner of the embodiment of the first aspect of the present application, the third obtaining module specifically includes:

and the third determining unit is used for determining the target user associated with any target work order according to the type of the any target work order when the residual processing time of the any target work order is less than the first time threshold.

Optionally, in another possible implementation manner of the embodiment of the first aspect of the present application, the pushing module specifically includes:

the fourth determining unit is used for determining a target pushing mode according to the work order type of any target work order;

and the first pushing unit is used for pushing the work order processing reminding message to the target user in the target pushing mode.

Optionally, in another possible implementation manner of the embodiment of the first aspect of the present application, the first pushing unit is specifically configured to:

starting a pushing system corresponding to the target pushing mode;

and pushing the work order processing reminding message to the target user.

a fifth determining unit, configured to determine the target pushing manner according to the remaining processing time length of any target work order;

and the second pushing unit is used for pushing the work order processing reminding message to the target user in the target pushing mode.

An embodiment of another aspect of the present application provides an electronic device, which includes: memory, processor and computer program stored on the memory and executable on the processor, characterized in that the processor when executing the program implements the grid work order processing method in combination with RPA and AI as described above.

In another aspect, the present application provides a computer-readable storage medium, on which a computer program is stored, where the program is executed by a processor to implement the method for processing a power grid work order by combining RPA and AI as described above.

In another aspect of the present application, a computer program is provided, which is executed by a processor to implement the method for processing a power grid work order by combining RPA and AI according to the embodiment of the present application.

The power grid work order processing method, device, electronic equipment, computer-readable storage medium and computer program combining RPA and AI provided by the embodiment of the application automatically acquire a work order list containing a target work order from a power grid work order system, and when the target work order with the residual processing time smaller than a first time threshold value exists in the work order list, push a work order processing reminding message to a target user associated with the target work order. Therefore, the work order which is about to reach the processing time limit is automatically obtained through the RPA, and the work order processing reminding message is sent to the related user, so that the automatic monitoring and reminding of the work order are realized through the RPA technology, manual participation is not needed, the labor cost is reduced, and the work order processing efficiency and reliability are improved.

Additional aspects and advantages of the present application will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the present application.

Drawings

The foregoing and/or additional aspects and advantages of the present application will become apparent and readily appreciated from the following description of the embodiments, taken in conjunction with the accompanying drawings of which:

fig. 1 is a schematic flowchart of a power grid work order processing method combining an RPA and an AI according to an embodiment of the present disclosure;

fig. 2 is a schematic flow chart of another power grid work order processing method combining an RPA and an AI according to an embodiment of the present disclosure;

fig. 3 is a schematic flowchart of another power grid work order processing method combining an RPA and an AI according to an embodiment of the present disclosure;

fig. 4 is a schematic flowchart of another power grid work order processing method combining an RPA and an AI according to an embodiment of the present disclosure;

fig. 5 is a schematic flowchart of another power grid work order processing method combining an RPA and an AI according to an embodiment of the present disclosure;

fig. 6 is a schematic structural diagram of a power grid work order processing device that combines RPA and AI according to an embodiment of the present disclosure;

fig. 7 is a schematic structural diagram of an electronic device according to an embodiment of the present application.

Detailed Description

Reference will now be made in detail to the embodiments of the present application, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to the like or similar elements throughout. The embodiments described below with reference to the drawings are exemplary and intended to be used for explaining the present application and should not be construed as limiting the present application.

The embodiment of the application provides a power grid work order processing method combining RPA and AI aiming at the problems that in the related technology, work orders in a power grid work order system are processed in a manual mode, the efficiency is low, mistakes are easy to occur, and the reliability is poor.

According to the power grid work order processing method combining the RPA and the AI, a work order list containing a target work order is automatically obtained from a power grid work order system, and when the target work order with the residual processing time smaller than a first time threshold value exists in the work order list, a work order processing reminding message is pushed to a target user associated with the target work order. Therefore, the work order which is about to reach the processing time limit is automatically obtained through the RPA, and the work order processing reminding message is sent to the related user, so that the automatic monitoring and reminding of the work order are realized through the RPA technology, manual participation is not needed, the labor cost is reduced, and the work order processing efficiency and reliability are improved.

The power grid work order processing method, device, electronic device, storage medium, and computer program according to the present invention and with reference to the drawings will be described in detail below.

Fig. 1 is a schematic flow chart of a power grid work order processing method combining an RPA and an AI according to an embodiment of the present disclosure.

As shown in fig. 1, the power grid work order processing method combining RPA and AI includes the following steps:

step 101, inquiring a power grid work order system to obtain a work order list containing each target work order.

It should be noted that the RPA technology can intelligently understand the existing application of the electronic device through the user interface, automate repeated regular operations based on rules and in large batch, such as automatically and repeatedly reading mails, reading Office components, operating databases, web pages, client software, and the like, collect data and perform complex calculations, so as to generate files and reports in large batch, thereby greatly reducing the input of labor cost and effectively improving the Office efficiency through the RPA technology. Therefore, in a power grid work order processing scene, the RPA program can be configured in the electronic device for processing the power grid work order, so that the electronic device can automatically monitor, remind and the like the work order in the power grid work order system according to the rule set in the RPA program.

The target work order refers to a work order obtained from a power grid work order system according to a preset work order obtaining rule. For example, the preset work order obtaining rule is "obtain all work orders existing in the power grid work order system", and the target work order may be all work orders existing at the time of obtaining the work order in the power grid work order system; for another example, the preset work order obtaining rule is "obtain all returned work orders existing in the power grid work order system", and the target work order may be all returned work orders existing at the time of obtaining the work order in the power grid work order system.

It should be noted that the above examples are only illustrative and should not be construed as limiting the present application. In actual use, the work order obtaining rule can be set according to actual needs and specific application scenarios, which is not limited in the embodiments of the present application.

In the embodiment of the application, RPA software may be configured in the electronic device for managing the power grid work order system, so that a processor of the electronic device may simulate manual operation in the power grid work order system at a preset time through RPA, and thus, the power grid work order system may be automatically queried at the preset time to obtain a target work order existing in the power grid work order system at the current query time, and a work order list may be formed by using all the target work orders.

It can be understood that the preset time for inquiring the power grid work order system can be preset according to the actual application requirements. For example, the preset time may be "obtaining a query instruction sent by a user"; or, the method can also be used for detecting that a new work order is generated in the power grid work order system; or "query the power grid work order system at time a every day", and the like, which is not limited in the embodiment of the present application.

And step 102, sequentially acquiring the residual processing time of each target work order in the work order list.

The remaining processing time of the work order refers to a time interval between the deadline of the work order and the current time.

In the embodiment of the application, when a work order is newly established in a power grid work order system, the generation time and the waiting time of the work order can be recorded. Therefore, after the target work order is acquired, the remaining processing time of the target work order can be determined according to the generation time, the waiting processing time and the current time of the target work order.

As a possible implementation manner, after a work order is established, the power grid work order system may record the remaining processing time of each work order in real time in a timer manner according to the waiting processing time of the work order. For example, if the waiting processing duration of the work order a is 24 hours, the grid work order system may establish a 24-hour countdown timer corresponding to the work order a, and use the time recorded by the countdown timer as the remaining processing time of the work order a. Therefore, after the target work order is obtained, the residual processing time of the target work order recorded in the power grid work order system can be directly obtained.

As another possible implementation manner, after a new work order is established, the power grid work order system may only record the generation time and the waiting time of the work order, so that after the target work order is obtained, the time interval between the current time and the generation time of the target work order may be determined as the existence time of the target work order, and then the difference between the waiting time and the existence time of the target work order may be determined as the remaining processing time of the target work order.

And 103, if the remaining processing time of any target work order is less than a first time threshold, acquiring a target user associated with any target work order.

In the embodiment of the application, for the target work order with shorter residual processing time, the proximity deadline of the target work order can be determined, so that related personnel can be reminded to process in time. Therefore, a first time threshold value can be preset, and if the remaining processing time of any target work order is determined to be smaller than the first time threshold value, the remaining processing time of the target work order can be determined to be shorter, so that the target user associated with the target work order can be obtained from the power grid work order system to remind the corresponding target user.

It should be noted that, when the power grid work order system establishes a work order, corresponding workers can be selected according to the service type of the work order, and the work order is used as a target user associated with the work order, and the work order and the target user are correspondingly stored.

In actual use, the first time threshold may be preset according to actual needs and specific application scenarios, which is not limited in the embodiments of the present application. For example, the first time threshold may be 7 hours.

And 104, pushing a work order processing reminding message to the target user.

In the embodiment of the application, after the target user associated with the target work order with the remaining processing time less than the first time threshold is determined, a work order processing reminding message can be pushed to the target user according to the contact information of the target user stored in the power grid work order system, so that the target user can process the corresponding target work order in time.

It should be noted that, in actual use, the contact information of the target user may include a phone number, a mailbox account, a work order processing system account, and the like, which is not limited in this embodiment of the present application.

In a possible implementation form of the method, the RPA can only monitor and remind the work order of a specific type in the power grid work order system according to actual application requirements, so that the flexibility and the practicability of work order processing are improved.

The power grid work order processing method combining the RPA and the AI provided in the embodiment of the present application is further described below with reference to fig. 2.

Fig. 2 is a schematic flow chart of another power grid work order processing method combining an RPA and an AI according to an embodiment of the present disclosure.

As shown in fig. 2, the power grid work order processing method combining the RPA and the AI includes the following steps:

step 201, obtaining keywords corresponding to the work order type to which each target work order belongs.

The work orders can be divided into different work order types according to different division rules. For example, the work order types may include a returned work order, an unprocessed work order, a processed work order, and the like, which are divided according to the processing status of the work order; for another example, the work order type may include a power distribution department work order, a customer service department work order, and the like according to the division of the processing departments of the work order, which is not limited in this application embodiment.

In the embodiment of the application, only the specific type of the work order can be monitored and reminded according to the actual application requirements, so that the work order type to which the target work order belongs can be set during the design of the RPA software, or the work order type to which the target work order belongs can be set or modified in real time according to the work order type setting instruction sent by the user and acquired in real time in the actual application process, so that the flexibility and the practicability of automatic processing of the work order are improved.

For example, according to the current application requirements, a returned work order in the power grid work order system needs to be monitored and reminded, so that when the RPA software is designed, the work order type to which the target work order belongs can be set as a returned work order; or in the actual application process, the application requirement changes, and the unprocessed work order in the power grid work order system needs to be monitored and reminded, so that when the work order type setting instruction sent by the user is obtained, the work order type 'unprocessed work order' included in the work order type setting instruction can be obtained, and the work order type to which the target work order belongs is changed from 'returned work order' to 'unprocessed work order'.

As a possible implementation manner, after the work order type to which the target work order belongs is determined, the work order type to which the target work order belongs may be determined as a keyword corresponding to the work order type; alternatively, the AI technology may be used to perform natural language processing operations such as word segmentation, part-of-speech analysis, and semantic understanding on the work order type to which the target work order belongs, so as to determine one or more segmented words included in the work order type as the keyword corresponding to the work order type.

For example, if the type of the work order to which the target work order belongs is "returned work order", it can be determined by the AI natural language processing technology that the term included in the "returned work order" includes: the "return" is associated with the "work order" and may be determined to be a keyword corresponding to the work order type.

Step 202, querying the power grid work order system by using the keywords to obtain a work order list containing each target work order.

In the embodiment of the application, after determining the keyword corresponding to the work order type to which the target work order belongs, the keyword corresponding to the work order type may be matched with all work orders in the power grid work order system, so as to obtain the work order matched with the keyword of the work order type from the power grid work order system, and the work order is used as the target work order to generate the work order list.

As a possible implementation manner, all work orders in the power grid work order system can be captured, and the work order content is subjected to operations such as character recognition, natural language processing and the like to extract the work order type information contained in the work order, so that the work order can be determined as the target work order when the matching degree between the work order type information contained in the work order and the keyword is greater than or equal to the matching degree threshold value. For example, if the keyword corresponding to the work order type to which the target work order belongs is "back", the power grid work order system is queried through the keyword' back ", and all the back work orders in the power grid work order system can be acquired as the target work order.

It should be noted that, in actual use, a specific value of the matching degree threshold may be determined according to actual needs and specific application scenarios, which is not limited in the embodiment of the present application. For example, the threshold matching degree may be 0.8.

Furthermore, only the work order established at the adjacent time can be acquired as the target work order, so that the calculation amount of work order processing is reduced, and the work order processing efficiency is improved. That is, in a possible implementation form of the embodiment of the present application, before the step 202, the method may further include:

and acquiring a second time threshold corresponding to the work order type to which each target work order belongs.

accordingly, the step 202 may include:

and inquiring the power grid work order system by using the keywords, the initial inquiry time and the end inquiry time so as to obtain a work order list containing each target work order.

The second time threshold corresponding to the work order type to which the target work order belongs may be greater than or equal to the waiting time of the work order type. For example, the type of the work order to which the target work order belongs is a "returned work order", the waiting time corresponding to the "returned work order" is 24 hours, the second time threshold corresponding to the "returned work order" may be 24 hours, or 36 hours, and the like, which is not limited in this embodiment of the present application.

As a possible implementation manner, all work orders in the power grid work order system generally have waiting processing time, that is, the work orders having the time exceeding the waiting processing time in the power grid work order system do not need to be monitored and sent with reminders. Therefore, when the target work order is obtained, whether the work order still in the waiting processing time is the target work order can be only inquired. Therefore, the waiting processing time corresponding to the work order type to which the target work order belongs can be determined according to the waiting processing time of each work order recorded in the power grid work order system, and the waiting processing time corresponding to the work order type to which the target work order belongs can be determined as the second time threshold corresponding to the work order type to which the target work order belongs. Then, the difference between the current time and the second time interval may be determined as the query start time, and the current time may be determined as the query end time.

As a possible implementation manner, after the query start time and the query end time are determined, a work order with a time between the query start time and the query end time generated in the power grid work order system may be determined as a candidate work order, and then, operations such as character recognition, natural language processing and the like are performed on the content of the candidate work order to extract work order type information included in the candidate work order, so that when the matching degree between the work order type information included in the candidate work order and the keyword is greater than or equal to a matching degree threshold value, the candidate work order may be determined as a target work order.

For example, if the type of the work order to which the target work order belongs is "returned work order", and the waiting time corresponding to the "returned work order" is 24 hours, the second time threshold corresponding to the "returned work order" may be determined to be 24 hours. If the current time is 11 o ' clock at 1/2/2020, the query start time may be determined as 11 o ' clock at 1/2020, and the query end time may be determined as 11 o ' clock at 1/2/2020, so that all return work orders whose generation time is between 11 o ' clock at 1/2020 and 11 o ' clock at 1/2/2020 may be determined as the target work order.

It should be noted that, when the waiting processing time corresponding to the work order in which the work order type included in the power grid work order system is the work order type to which the target work order belongs is different, the maximum waiting processing time may be determined as the second time threshold.

For example, the type of the work order to which the target work order belongs is "returned work order", the grid work order system includes 100 returned work orders, and the waiting time of each returned work order is 24 hours, so that the second time threshold corresponding to the type of the work order to which the target work order belongs can be determined to be 24 hours; if the 100 returned work orders include 50 returned work orders waiting for 24 hours and 50 returned work orders waiting for 36 hours, the second time threshold corresponding to the work order type to which the target work order belongs may be determined as 36 hours.

And step 203, sequentially acquiring the residual processing time of each target work order in the work order list.

And 204, if the remaining processing time of any target work order is less than the first time threshold, acquiring the target user associated with any target work order.

Step 205, pushing a work order processing reminding message to the target user.

The detailed implementation process and principle of the

step

203 and 205 can refer to the detailed description of the above embodiments, and are not described herein again.

According to the power grid work order processing method combining the RPA and the AI, a power grid work order system is queried through keywords according to keywords corresponding to the work order type to which a target work order belongs, so that a work order list containing the target work order of a specific type is obtained, and when the target work order with the residual processing time smaller than a first time threshold value exists in the work order list, a work order processing reminding message is pushed to a target user associated with the target work order. Therefore, the work order type needing to be monitored and reminded is flexibly set, so that the RPA can automatically acquire the work order of the specific type about to reach the processing time limit, and send the work order processing reminding message to the related user, thereby not only realizing the automatic monitoring and reminding of the work order through the RPA technology, reducing the labor cost, improving the efficiency and reliability of work order processing, but also improving the flexibility and practicability of work order processing.

In a possible implementation form of the method, the RPA can query the power grid work order system at a higher frequency according to actual application requirements so as to prevent missing of necessary work order processing reminding and further improve the reliability of work order processing.

The power grid work order processing method combining the RPA and the AI provided in the embodiment of the present application is further described below with reference to fig. 3.

Fig. 3 is a schematic flow chart of another power grid work order processing method combining the RPA and the AI according to an embodiment of the present disclosure.

As shown in fig. 3, the power grid work order processing method combining RPA and AI includes the following steps:

step 301, querying a power grid work order system at a preset frequency to obtain a work order list including each target work order.

In the embodiment of the application, the frequency of the power grid work order system inquired by the RPA can be set according to actual application requirements. Therefore, the preset frequency can be set when the RPA software is designed, or the preset frequency can be set or modified in real time according to a query frequency setting instruction sent by a user and acquired in real time in the actual application process.

It should be noted that, in actual use, a specific value of the preset frequency may be determined according to an actual need and a specific application scenario, which is not limited in the embodiment of the present application. For example, the predetermined frequency may be 1 minute, 5 minutes, or the like.

In the embodiment of the application, the RPA can automatically query the power grid work order system according to a preset frequency to simulate manual operation in the power grid work order system, so that a target work order existing in the power grid work order system can be obtained at each query moment.

And 302, sequentially capturing the information of each target work order from the work order list, wherein the information of the work orders comprises the deadline of the work orders.

In the embodiment of the application, when a work order is newly established in a power grid work order system, the generation time and the waiting processing time of the work order can be recorded, and the sum of the generation time and the waiting processing time of the work order is determined as the deadline time of the work order and is recorded in the information of the work order. Therefore, after the work order list is obtained, the information of each target work order in the work order list can be sequentially captured from the power grid work order system, and the deadline of each target work order is determined according to the information of each target work order.

For example, if the generation time of the work order a is 10 o 'clock at 1 month and 1 day 2020 and the waiting time is 24 hours, the deadline of the work order a may be determined to be 10 o' clock at 1 month and 2 days 2020 and recorded in the information of the work order a.

And 303, determining the residual processing time of each target work order according to the deadline of each target work order and the current time.

After the deadline of each target work order is obtained, the difference between the deadline of each target work order and the current time may be determined as the remaining processing time of each target work order.

And 304, if the remaining processing time of any target work order is less than the first time threshold, determining a target user associated with any target work order according to the type of any target work order.

In the embodiment of the application, for the target work order with shorter residual processing time, the proximity deadline of the target work order can be determined, so that related personnel can be reminded to process in time.

As a possible implementation manner, the same type of work order may be processed by the same person, so that the mapping relationship between the work order type and the target user may be preset, and thus when it is determined that the remaining processing time of the target work order is smaller than the first time threshold, that is, the remaining processing time of the target work order is short, the target user corresponding to the work order type to which the target work order belongs may be determined as the target user associated with the target work order according to the mapping relationship between the work order type to which the target work order belongs and the preset work order type and target user, so as to remind the corresponding target user.

And 305, pushing a work order processing reminding message to the target user.

The detailed implementation process and principle of the step 305 may refer to the detailed description of the above embodiments, and are not described herein again.

According to the power grid work order processing method combining the RPA and the AI, a work order list containing target work orders is automatically obtained from a power grid work order system at a preset frequency, the remaining processing time of each target work order is determined according to the deadline of each target work order and the current moment, and then when the target work orders with the remaining processing time smaller than a first time threshold value exist in the work order list, a target user associated with the target work orders is determined according to the type of the target work orders, and work order processing reminding messages are pushed to the target user. Therefore, the frequency of the RPA inquiry power grid work order system is flexibly set, the work orders needing to be reminded in the power grid work order system are obtained in time, and work order processing reminding messages are sent to related users, so that the automatic monitoring and reminding of the work orders are realized through the RPA technology, the labor cost is reduced, the work order processing efficiency is improved, and the reliability of work order processing reminding is improved.

In a possible implementation form of the method and the device, due to the fact that the types of the work orders are different, the urgency degree, the processing mode and the like of the work orders are possibly different, the pushing mode when the work order processing reminding message is pushed to the target user can be determined according to the type of the target work order, and therefore the target user can timely and efficiently process the target work order.

The power grid work order processing method combining the RPA and the AI provided in the embodiment of the present application is further described below with reference to fig. 4.

Fig. 4 is a schematic flow chart of another power grid work order processing method combining the RPA and the AI according to the embodiment of the present application.

As shown in fig. 4, the power grid work order processing method combining the RPA and the AI includes the following steps:

step 401, querying a power grid work order system to obtain a work order list including each target work order.

And step 402, sequentially acquiring the residual processing time of each target work order in the work order list.

Step 403, if the remaining processing time of any target work order is less than the first time threshold, acquiring a target user associated with any target work order.

The detailed implementation process and principle of the steps 401-403 may refer to the detailed description of the above embodiments, and are not described herein again.

And step 404, determining a target pushing mode according to the work order type of any target work order.

In the embodiment of the application, different pushing modes can be adopted for pushing the work orders to process the reminding messages for different types of target work orders, so that the pushing modes are diversified, the use preferences of different users can be met, the user can obtain the reminding messages in time, and the target work orders can be efficiently processed.

As a possible implementation manner, a mapping relationship between the work order type and the push manner may be preset, so that when it is determined that the remaining processing time of the target work order is less than the first time threshold, the push manner corresponding to the work order type may be determined as the target push manner according to the work order type to which the target work order belongs.

It should be noted that, in actual use, the type of the pushing manner and the mapping relationship between the work order type and the pushing manner may be determined according to actual needs and specific application scenarios, which is not limited in this embodiment of the present application. For example, the push mode may include a mail mode, a telephone mode, a short message mode, an audio/video mode, a software push message mode, and the like.

And 405, pushing a work order processing reminding message to a target user in a target pushing mode.

In the embodiment of the application, after the target pushing mode corresponding to the target work order is determined, the work order processing reminding message corresponding to the target work order can be generated, the work order processing reminding message is pushed to the target user in the target pushing mode,

for example, if the determined target push mode corresponding to the target work order a is a short message mode, text reminding information related to the target work order a can be generated and used as a work order processing reminding message, and the work order processing reminding message is sent to the mobile phone of the target user in the short message mode.

Further, an Office Automation (OA for short) system can be automatically started through the RPA to push messages. That is, in a possible implementation form of the embodiment of the present application, the step 405 may include:

starting a pushing system corresponding to the target pushing mode;

adding the information of any target work order into a push message of a push system to generate a work order processing reminding message;

and pushing a work order processing reminding message to the target user.

As a possible implementation manner, after determining a target push manner corresponding to a target work order, the RPA may also automatically start the OA system and log in, start a corresponding push system in the OA system according to the target push manner, add information such as deadline of the target work order, a work order type, and the like to a push message of the push system to generate a work order processing reminding message, and push the work order processing reminding message to a user through the corresponding push system.

For example, if the target push mode corresponding to the target work order a is short message reminding, the RPA may automatically start the short message push system in the OA system, add information such as the deadline of the target work order a, the work order type, etc. to the push message in the short message push system, so as to generate a work order processing reminding message in the form of a short message, and send the work order processing reminding message to the mobile phone of the user through the short message push system.

According to the power grid work order processing method combining the RPA and the AI, a work order list containing a target work order is automatically obtained from a power grid work order system, when the target work order with the residual processing time smaller than a first time threshold value exists in the work order list, a target pushing mode is determined according to the work order type of the target work order, and then a work order processing reminding message is pushed to a target user in the target pushing mode. Therefore, the work order which is about to reach the processing time limit is automatically acquired through the RPA, and the pushing mode which is matched with the work order is selected to push the reminding message according to the work order type, so that the automatic monitoring and reminding of the work order are realized through the RPA technology, the labor cost is reduced, the work order processing efficiency and reliability are improved, the pushing mode is diversified, the use preferences of different users can be met, the user can timely acquire the reminding message, and the work order is efficiently processed.

In a possible implementation form of the method and the device, a target pushing mode can be determined according to the remaining time length of the target work order, so that the target work order with the shorter remaining time length can be processed in time, and the accuracy and the reliability of work order processing reminding are further improved.

The power grid work order processing method combining the RPA and the AI provided in the embodiment of the present application is further described below with reference to fig. 5.

Fig. 5 is a schematic flow chart of another power grid work order processing method combining the RPA and the AI according to the embodiment of the present application.

As shown in fig. 5, the power grid work order processing method combining the RPA and the AI includes the following steps:

step 501, inquiring a power grid work order system to obtain a work order list containing each target work order.

Step 502, the remaining processing time of each target work order in the work order list is sequentially obtained.

Step 503, if the remaining processing time of any target work order is less than the first time threshold, a target user associated with any target work order is obtained.

The detailed implementation process and principle of the steps 501-503 can refer to the detailed description of the above embodiments, and are not described herein again.

Step 504, determining a target pushing mode according to the remaining processing time length of any target work order.

In the embodiment of the application, when the remaining processing time of the target work order is different, different pushing modes can be adopted to push the work order to process the reminding message, for example, the shorter the remaining processing time of the target work order is, a more reliable pushing mode can be adopted, so that the pushing modes are diversified, the use preferences of different users can be met, and the user can obtain the reminding message with more urgent time limit in time and efficiently process the target work order.

As a possible implementation manner, a mapping relationship between the remaining processing time length and the pushing manner may be preset, so that when it is determined that the remaining processing time of the target work order is smaller than the first time threshold, the pushing manner corresponding to the remaining processing time length may be determined as the target pushing manner according to the remaining processing time length of the target work order.

It should be noted that, in actual use, the type of the push manner and the mapping relationship between the remaining processing time length and the push manner may be determined according to actual needs and specific application scenarios, which is not limited in this embodiment of the present application. For example, the pushing mode may include a mail mode, a telephone mode, a short message mode, an audio/video mode, a software message pushing mode, and the like, and when the remaining processing time is longer than 5 hours and less than or equal to 7 hours, the corresponding pushing mode is the mail mode; when the length of the residual processing time is more than 3 hours and less than or equal to 5 hours, the corresponding pushing mode is a short message mode; when the length of the residual processing time is more than 1 hour and less than or equal to 3 hours, the corresponding pushing mode is an audio and video mode; when the length of the remaining processing time is less than or equal to 1 hour, the corresponding push mode is a telephone mode, and the like.

And 505, pushing a work order processing reminding message to a target user in a target pushing mode.

for example, if the determined target push mode corresponding to the target work order a is a telephone mode, voice prompt information related to the target work order a can be generated to serve as a work order processing prompt message, a mobile phone of the target user is dialed according to the contact mode of the target user, and the work order processing prompt message in the voice mode is automatically played after the target user answers the call.

It should be noted that, when pushing the work order processing reminding message, the pushing may also be performed by starting the pushing system corresponding to the target pushing mode in the OA system in the embodiment of the present application, and the specific implementation process and principle may refer to the detailed description of the above embodiment, which is not described herein again.

According to the power grid work order processing method combining the RPA and the AI, the work order list containing the target work order is automatically obtained from the power grid work order system, and when the target work order with the residual processing time smaller than the first time threshold value exists in the work order list, the target pushing mode is determined according to the residual processing time length of the target work order, and then the work order processing reminding message is pushed to the target user in the target pushing mode. Therefore, the work order which is about to reach the processing time limit is automatically acquired through the RPA, the pushing mode which is adaptive to the work order is selected to push the reminding message according to the length of the residual processing time, so that the automatic monitoring and reminding of the work order are realized through the RPA technology, the labor cost is reduced, the pushing mode is diversified, the use preference of different users can be met, the user can timely acquire the time-critical work order reminding message, the work order is efficiently processed, and the accuracy and the reliability of work order processing reminding are further improved.

In order to implement the above embodiments, the present application further provides a power grid work order processing device combining an RPA and an AI.

Fig. 6 is a schematic structural diagram of a power grid work order processing device combining an RPA and an AI according to an embodiment of the present application.

As shown in fig. 6, the RPA and AI combined power grid work order processing apparatus 60 includes:

the first obtaining module 61 is configured to query the power grid work order system to obtain a work order list including each target work order;

a second obtaining module 62, configured to sequentially obtain the remaining processing time of each target work order in the work order list;

a third obtaining module 63, configured to obtain a target user associated with any target work order when the remaining processing time of any target work order is smaller than the first time threshold;

and the pushing module 64 is configured to push a work order processing reminding message to the target user.

In practical use, the power grid work order processing device combining the RPA and the AI provided in the embodiment of the present application may be configured in any electronic device to execute the aforementioned power grid work order processing method combining the RPA and the AI.

The power grid work order processing device combining the RPA and the AI, provided by the embodiment of the application, automatically acquires a work order list containing a target work order from a power grid work order system, and pushes a work order processing reminding message to a target user associated with the target work order when the target work order with the residual processing time smaller than a first time threshold exists in the work order list. Therefore, the work order which is about to reach the processing time limit is automatically obtained through the RPA, and the work order processing reminding message is sent to the related user, so that the automatic monitoring and reminding of the work order are realized through the RPA technology, manual participation is not needed, the labor cost is reduced, and the work order processing efficiency and reliability are improved.

In a possible implementation form of the present application, the work orders belonging to the target work orders are of the same type; correspondingly, the first obtaining module 61 specifically includes:

and the second acquisition unit is used for inquiring the power grid work order system by using the keywords so as to acquire a work order list containing each target work order.

Further, in another possible implementation form of the present application, the first obtaining module 61 further includes:

the third acquisition unit is used for acquiring a second time threshold corresponding to the work order type to which each target work order belongs;

the first determining unit is used for determining the current query starting time and the current query finishing time according to the second time threshold;

correspondingly, the second obtaining unit is specifically configured to:

Further, in another possible implementation form of the present application, the first obtaining module 61 specifically includes:

Further, in another possible implementation form of the present application, the second obtaining module 62 specifically includes:

Further, in another possible implementation form of the present application, the third obtaining module 63 specifically includes:

and the third determining unit is used for determining the target user associated with any target work order according to the type of any target work order when the residual processing time of any target work order is less than the first time threshold.

Further, in another possible implementation form of the present application, the pushing module 64 specifically includes:

and the first pushing unit is used for pushing the work order processing reminding message to the target user in a target pushing mode.

Further, in another possible implementation form of the present application, the first pushing unit is specifically configured to:

starting a pushing system corresponding to the target pushing mode;

and pushing a work order processing reminding message to the target user.

the fifth determining unit is used for determining a target pushing mode according to the remaining processing time length of any target work order;

and the second pushing unit is used for pushing the work order processing reminding message to the target user in a target pushing mode.

It should be noted that the foregoing explanation on the embodiments of the power grid work order processing method combining RPA and AI shown in fig. 1, fig. 2, fig. 3, fig. 4, and fig. 5 is also applicable to the power grid work order processing device 60 combining RPA and AI of this embodiment, and will not be repeated here.

According to the power grid work order processing device combining the RPA and the AI, a power grid work order system is inquired through keywords according to keywords corresponding to a work order type to which a target work order belongs, so that a work order list containing the target work order of a specific type is obtained, and when the target work order with the residual processing time smaller than a first time threshold value exists in the work order list, a work order processing reminding message is pushed to a target user associated with the target work order. Therefore, the work order type needing to be monitored and reminded is flexibly set, so that the RPA can automatically acquire the work order of the specific type about to reach the processing time limit, and send the work order processing reminding message to the related user, thereby not only realizing the automatic monitoring and reminding of the work order through the RPA technology, reducing the labor cost, improving the efficiency and reliability of work order processing, but also improving the flexibility and practicability of work order processing.

In order to implement the above embodiments, the present application further provides an electronic device.

Fig. 7 is a schematic structural diagram of an electronic device according to an embodiment of the present invention.

As shown in fig. 7, the electronic device 200 includes:

a memory 210 and a processor 220, a bus 230 connecting different components (including the memory 210 and the processor 220), wherein the memory 210 stores a computer program, and when the processor 220 executes the program, the method for processing a power grid work order by combining RPA and AI according to the embodiment of the present application is implemented.

Bus 230 represents one or more of any of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, and a processor or local bus using any of a variety of bus architectures. By way of example, such architectures include, but are not limited to, Industry Standard Architecture (ISA) bus, micro-channel architecture (MAC) bus, enhanced ISA bus, Video Electronics Standards Association (VESA) local bus, and Peripheral Component Interconnect (PCI) bus.

Electronic device 200 typically includes a variety of electronic device readable media. Such media may be any available media that is accessible by electronic device 200 and includes both volatile and nonvolatile media, removable and non-removable media.

Memory 210 may also include computer system readable media in the form of volatile memory, such as Random Access Memory (RAM)240 and/or cache memory 250. The electronic device 200 may further include other removable/non-removable, volatile/nonvolatile computer system storage media. By way of example only, storage system 260 may be used to read from and write to non-removable, nonvolatile magnetic media (not shown in FIG. 7, commonly referred to as a "hard drive"). Although not shown in FIG. 7, a magnetic disk drive for reading from and writing to a removable, nonvolatile magnetic disk (e.g., a "floppy disk") and an optical disk drive for reading from or writing to a removable, nonvolatile optical disk (e.g., a CD-ROM, DVD-ROM, or other optical media) may be provided. In these cases, each drive may be connected to bus 230 by one or more data media interfaces. Memory 210 may include at least one program product having a set (e.g., at least one) of program modules that are configured to carry out the functions of embodiments of the application.

A program/utility 280 having a set (at least one) of program modules 270, including but not limited to an operating system, one or more application programs, other program modules, and program data, each of which or some combination thereof may comprise an implementation of a network environment, may be stored in, for example, the memory 210. The program modules 270 generally perform the functions and/or methodologies of the embodiments described herein.

Electronic device 200 may also communicate with one or more external devices 290 (e.g., keyboard, pointing device, display 291, etc.), with one or more devices that enable a user to interact with electronic device 200, and/or with any devices (e.g., network card, modem, etc.) that enable electronic device 200 to communicate with one or more other computing devices. Such communication may occur via input/output (I/O) interfaces 292. Also, the electronic device 200 may communicate with one or more networks (e.g., a Local Area Network (LAN), a Wide Area Network (WAN), and/or a public network such as the Internet) via the network adapter 293. As shown, the network adapter 293 communicates with the other modules of the electronic device 200 via the bus 230. It should be appreciated that although not shown in the figures, other hardware and/or software modules may be used in conjunction with the electronic device 200, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems, among others.

The processor 220 executes various functional applications and data processing by executing programs stored in the memory 210.

It should be noted that, for the implementation process and the technical principle of the electronic device in this embodiment, reference is made to the foregoing explanation of the power grid work order processing method combining the RPA and the AI in the embodiment of the present application, and details are not described here again.

The electronic device provided by the embodiment of the application can execute the power grid work order processing method combining the RPA and the AI as described above, automatically acquire the work order list including the target work order from the power grid work order system, and push a work order processing reminding message to a target user associated with the target work order when the target work order with the remaining processing time smaller than the first time threshold exists in the work order list. Therefore, the work order which is about to reach the processing time limit is automatically obtained through the RPA, and the work order processing reminding message is sent to the related user, so that the automatic monitoring and reminding of the work order are realized through the RPA technology, manual participation is not needed, the labor cost is reduced, and the work order processing efficiency and reliability are improved.

In order to implement the above embodiments, the present application also proposes a computer-readable storage medium.

The computer readable storage medium stores thereon a computer program, and the program is executed by a processor to implement the method for processing a power grid work order by combining RPA and AI according to the embodiment of the present application.

In order to implement the foregoing embodiments, in yet another aspect, a computer program is provided, which when executed by a processor, implements the power grid work order processing method combining the RPA and the AI according to the embodiments of the present application.

In an alternative implementation, the embodiments may be implemented in any combination of one or more computer-readable media. The computer readable medium may be a computer readable signal medium or a computer readable storage medium. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium would include the following: an electrical connection having 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 portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this document, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.

A computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.

Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.

Computer program code for carrying out operations for aspects of the present application may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C + +, and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the consumer electronic device, partly on the consumer electronic device, as a stand-alone software package, partly on the consumer electronic device and partly on a remote electronic device, or entirely on the remote electronic device or server. In the case of remote electronic devices, the remote electronic devices may be connected to the consumer electronic device through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external electronic device (e.g., through the internet using an internet service provider).

Other embodiments of the present application will be apparent to those skilled in the art from consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of the invention following, in general, the principles of the application and including such departures from the present disclosure as come within known or customary practice within the art to which the invention pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the application being indicated by the following claims.

It will be understood that the present application is not limited to the precise arrangements described above and shown in the drawings and that various modifications and changes may be made without departing from the scope thereof. The scope of the application is limited only by the appended claims.

Claims

1. A power grid work order processing method combining RPA and AI is characterized by comprising the following steps:

inquiring a power grid work order system to obtain a work order list containing each target work order;

sequentially acquiring the residual processing time of each target work order in the work order list;

if the remaining processing time of any target work order is smaller than a first time threshold, acquiring a target user associated with any target work order;

and pushing a work order processing reminding message to the target user.

2. The method according to claim 1, wherein the work order types to which the target work orders belong are the same, and the querying of the power grid work order system to obtain the work order list including the target work orders specifically comprises:

3. The method of claim 2, wherein prior to said querying said grid work order system with said key terms to obtain a work order list containing said respective target work orders, further comprising:

4. The method of claim 1, wherein querying the grid work order system to obtain a work order list including each target work order comprises:

5. The method according to claim 1, wherein the sequentially obtaining the remaining processing time of each of the target work orders in the work order list specifically comprises:

6. The method according to claim 1, wherein if the remaining processing time of any one of the target work orders is smaller than a first time threshold, acquiring the target user associated with the any one of the target work orders specifically includes:

7. The method according to any one of claims 1 to 6, wherein the pushing of the work order processing reminder message to the target user specifically includes:

8. The method according to claim 7, wherein the pushing the work order processing reminder message to the target user in the target push manner specifically includes:

starting a pushing system corresponding to the target pushing mode;

and pushing the work order processing reminding message to the target user.

9. The method according to any one of claims 1 to 6, wherein the pushing of the work order processing reminder message to the target user specifically includes:

10. A power grid work order processing device combining RPA and AI, comprising:

the first acquisition module is used for inquiring the power grid work order system so as to acquire a work order list containing each target work order;

the second acquisition module is used for sequentially acquiring the residual processing time of each target work order in the work order list;

the third acquisition module is used for acquiring a target user associated with any target work order when the residual processing time of the target work order is less than a first time threshold;

and the pushing module is used for pushing the work order processing reminding message to the target user.

11. The apparatus according to claim 10, wherein the work order types to which the target work orders belong are the same, and the first obtaining module specifically includes:

12. The apparatus of claim 11, wherein the first obtaining module further comprises:

the second obtaining unit is specifically configured to:

13. The apparatus of claim 10, wherein the first obtaining module specifically comprises:

14. The apparatus of claim 10, wherein the second obtaining module specifically comprises:

15. The apparatus of claim 10, wherein the third obtaining module specifically includes:

16. The apparatus according to any one of claims 10 to 15, wherein the pushing module specifically includes:

17. The apparatus of claim 16, wherein the first pushing unit is specifically configured to:

starting a pushing system corresponding to the target pushing mode;

and pushing the work order processing reminding message to the target user.

18. The apparatus according to any one of claims 10 to 15, wherein the pushing module specifically includes:

19. An electronic device, comprising: memory, processor and program stored on the memory and executable on the processor, characterized in that the processor when executing the program implements a grid work order processing method in combination with RPA and AI according to any of claims 1-9.

20. A computer-readable storage medium, on which a computer program is stored, which program, when being executed by a processor, carries out a method of grid work order processing in combination with RPA and AI according to any one of claims 1-9.