CN115204685A

CN115204685A - Work order distribution method and device

Info

Publication number: CN115204685A
Application number: CN202210840726.0A
Authority: CN
Inventors: 宋嘉琪
Original assignee: Bank of China Ltd
Current assignee: Bank of China Ltd
Priority date: 2022-07-18
Filing date: 2022-07-18
Publication date: 2022-10-18

Abstract

The application provides a work order distribution method and device, relates to the field of artificial intelligence, also can be used to the finance field, include: inputting the description information of the work order to be dispatched into a pre-trained work order marking model to obtain a corresponding work order label; the work order marking model is obtained by training according to historical description information of a historical work order and corresponding historical distributor information; determining the correlation between each candidate dispatcher and the work order to be dispatched according to the personnel label corresponding to each candidate dispatcher and the work order label; determining the optimal dispatching personnel of the work order to be dispatched according to the correlation; wherein the optimal dispatch personnel is one of the candidate dispatch personnel. The method and the device can automatically generate the work order label and accurately distribute the work order to the optimal distributor.

Description

Work order distribution method and device

Technical Field

The application relates to the field of artificial intelligence, can be used in the field of finance, and particularly relates to a work order distributing method and device.

Background

The work order in the field of financial business mainly refers to: documents which are proposed by the financial institution service department and are used for solving various problems in the service processing process. Generally, the operation and maintenance personnel and the development personnel of the financial business system can assist in analyzing and solving corresponding problems according to the content of the work order.

In order to solve the above problems more specifically, different kinds of work orders need to be handled by different operation and maintenance personnel or developers, which requires the work orders to be distributed. However, the current work order distribution process still relies on a large number of manual sortings, which is time consuming and labor intensive. With the continuous development of financial business systems, the types and the number of work orders are more and more, and the requirements on the processing timeliness of the work orders are higher and higher. Sometimes, the work order flow is transferred to a target operation and maintenance person or a developer of a target business system, and a plurality of intermediate flows are needed. In addition, manual sorting also often causes work order circulation errors, and greatly reduces the processing timeliness of the work orders. The existing automatic chemical order distribution system does not have a method for realizing accurate classification and distribution of work orders, so that the automatic and accurate distribution of the work orders cannot be realized, and the problems of long work order processing flow, low distribution speed, high processing cost and the like are caused.

Disclosure of Invention

Aiming at the problems in the prior art, the application provides a work order dispatching method and device, which can automatically generate a work order label and accurately dispatch the work order to the optimal dispatching personnel.

In order to solve the technical problem, the application provides the following technical scheme:

in a first aspect, the present application provides a work order dispatching method, including:

inputting the description information of the work order to be dispatched into a pre-trained work order marking model to obtain a corresponding work order label; the work order marking model is obtained by training according to historical description information of a historical work order and corresponding historical distributor information;

determining the correlation between each candidate dispatcher and the work order to be dispatched according to the staff label corresponding to each candidate dispatcher and the work order label;

determining the optimal dispatching personnel of the work orders to be dispatched according to the correlation; wherein the optimal dispatch personnel is one of the candidate dispatch personnel.

Further, the step of training the work order marking model comprises:

acquiring a historical work order label corresponding to the historical description information and a historical staff label corresponding to the historical dispatching staff information;

constructing a training set and a testing set according to the historical work order labels and the historical personnel labels;

inputting the training set into a sequence model for training to obtain an initial work order marking model;

and adjusting the initial work order marking model by using the test set to obtain the work order marking model.

Further, before inputting the description information of the work order to be dispatched into the pre-trained work order marking model and obtaining the corresponding work order label, the method further comprises the following steps:

and reading the description information of the work order to be dispatched from the kafka message queue.

Further, the personnel tag comprises: the field to which the person belongs and the person handling capacity; the personnel tag comprises: the field to which the work order belongs and the emergency degree of the work order; the determining the correlation between each candidate dispatcher and the to-be-dispatched worksheet according to the staff tag corresponding to each candidate dispatcher and the worksheet tag comprises the following steps:

respectively generating word vector expressions corresponding to the field to which the personnel belong, the personnel processing capacity, the field to which the work order belongs and the emergency degree of the work order;

calculating a first similarity between the word vector expression of the field to which the person belongs and the word vector expression of the field to which the work order belongs;

calculating a second similarity between the word vector expression of the personnel processing capacity and the word vector expression of the work order urgency degree;

and determining the correlation between the candidate dispatching personnel and the to-be-dispatched worksheet according to the first similarity and the second similarity.

Further, the determining the optimal dispatching personnel of the work order to be dispatched according to the correlation comprises:

ranking the relevance scores corresponding to the relevance between each candidate dispatcher and the work orders to be dispatched;

and selecting the candidate dispatching personnel corresponding to the highest correlation as the optimal dispatching personnel.

In a second aspect, the present application provides a work order dispatch device, comprising:

the work order label generating unit is used for inputting the description information of the work orders to be distributed into the pre-trained work order marking model to obtain corresponding work order labels; the work order marking model is obtained by training according to historical description information of a historical work order and corresponding historical distributor information;

the correlation determining unit is used for determining the correlation between each candidate dispatcher and the work order to be dispatched according to the personnel label corresponding to each candidate dispatcher and the work order label; wherein the personnel tag is preset;

the optimal dispatching personnel determining unit is used for determining the optimal dispatching personnel of the work orders to be dispatched according to the correlation; wherein the optimal dispatch personnel is one of the candidate dispatch personnel.

Further, the work order dispatching device further comprises:

the label obtaining unit is used for obtaining a historical work order label corresponding to the historical description information and a historical personnel label corresponding to the historical dispatching personnel information;

the training set and test set constructing unit is used for constructing a training set and a test set according to the historical work order labels and the historical personnel labels;

the initial model generating unit is used for inputting the training set into a sequence model for training to obtain an initial work order marking model;

and the initial model adjusting unit is used for adjusting the initial work order marking model by using the test set to obtain the work order marking model.

Further, the work order distribution device further comprises:

and the description information reading unit is used for reading the description information of the work order to be dispatched from the kafka message queue.

Further, the personnel tag comprises: the field to which the person belongs and the person handling capacity; the personnel tag comprises: the field to which the work order belongs and the emergency degree of the work order; the correlation determination unit includes:

the word vector expression generating module is used for respectively generating word vector expressions corresponding to the field to which the personnel belong, the personnel processing capacity, the field to which the work order belongs and the emergency degree of the work order;

the first similarity determining module is used for calculating first similarity between the word vector expression in the field to which the person belongs and the word vector expression in the field to which the work order belongs;

the second similarity determining module is used for calculating second similarity between the word vector expression of the personnel processing capacity and the word vector expression of the work order emergency degree;

and the correlation determination module is used for determining the correlation between the candidate dispatching personnel and the work order to be dispatched according to the first similarity and the second similarity.

Further, the optimal dispatch personnel determining unit includes:

the sorting module is used for sorting the relevance scores corresponding to the relevance between each candidate dispatcher and the work orders to be dispatched;

and the optimal dispatching person selecting module is used for selecting the candidate dispatching person corresponding to the highest correlation as the optimal dispatching person.

In a third aspect, the present application provides an electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the steps of the work order dispatch method when executing the program.

In a fourth aspect, the present application provides a computer readable storage medium having stored thereon a computer program which, when executed by a processor, performs the steps of the work order dispatch method.

In a fifth aspect, the present application provides a computer program product comprising computer programs/instructions which, when executed by a processor, implement the steps of the work order dispatch method.

Aiming at the problems in the prior art, the work order dispatching method and the work order dispatching device can automatically generate the work order label and accurately dispatch the work order to the optimal dispatching personnel, so that the work order dispatching and processing efficiency is improved, operation and maintenance personnel can process and analyze the work order according to the work order label in the follow-up process, the work order processing flow is greatly simplified, the labor cost is saved, and the time cost is reduced.

Drawings

In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.

FIG. 1 is a flow chart of a work order dispatch method in an embodiment of the present application;

FIG. 2 is a flowchart of the steps for training a marking model of a work order in an embodiment of the present application;

FIG. 3 is a flowchart illustrating an embodiment of determining a correlation between each candidate dispatcher and a work order to be dispatched;

FIG. 4 is a flowchart illustrating an embodiment of the present disclosure for determining an optimal dispatcher for a work order to be dispatched;

FIG. 5 is one of the structures of the work order dispatching device in the embodiment of the present application;

FIG. 6 is a second block diagram of the work order dispatching device in the embodiment of the present application;

fig. 7 is a structural diagram of a correlation determination unit in the embodiment of the present application;

FIG. 8 is a block diagram of an optimal distributor determination unit in the embodiment of the present application;

fig. 9 is a schematic structural diagram of an electronic device in an embodiment of the present application;

FIG. 10 is a schematic structural diagram of a work order dispatching device in an embodiment of the present application;

fig. 11 is a schematic flow chart of work order distribution in the embodiment of the present application.

Detailed Description

The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.

It should be noted that the work order dispatching method and apparatus provided by the present application may be used in the financial field, and may also be used in any field other than the financial field.

According to the technical scheme, the data acquisition, storage, use, processing and the like meet relevant regulations of national laws and regulations.

In one embodiment, referring to fig. 1, in order to automatically generate a work order label and accurately dispatch the work order to an optimal dispatcher, the present application provides a work order dispatching method, which includes:

s101: inputting the description information of the work order to be dispatched into a pre-trained work order marking model to obtain a corresponding work order label; the work order marking model is obtained by training according to historical description information of historical work orders and corresponding historical distributor information.

It should be noted that, in an embodiment, before inputting the description information of the work order to be dispatched into the pre-trained work order marking model and obtaining the corresponding work order label, the method further includes: and reading the description information of the work order to be dispatched from the kafka message queue.

It should be noted that, in the embodiment of the present application, the "work order" may be provided by a business department of a financial institution, and specifically refers to a description document for solving a problem occurring in a business flow process. Generally, after receiving a work order distributed to the operation and maintenance personnel and developers, the operation and maintenance personnel and the developers assist in analyzing and solving corresponding problems in the work order. Considering that the fields of the problems to be solved by different operation and maintenance personnel or developers may be different, and the capabilities of solving the problems may also be different, when the work order is dispatched, the work order should be dispatched to the personnel who is the best to solve the corresponding problems, so that the problems recorded in the work order can be solved more quickly and better. The method provided by the application serves the application scenarios.

It can be understood that, with the continuous development of financial business systems, the types and the number of work orders are more and more, and the requirements on the processing timeliness of the work orders are higher and higher. Sometimes, the work order flow needs to go through a plurality of intermediate processes at the target operation and maintenance personnel or developers of the target business system. In addition, manual sorting of the work orders also often causes errors in work order circulation, and the processing timeliness of the work orders is greatly reduced. The existing automatic chemical order distribution system does not have a method for realizing accurate classification and distribution of work orders, namely, the automatic and accurate distribution of the work orders cannot be realized, so that the problems of long work order processing flow, low distribution speed, high processing cost and the like are caused.

Specifically, the step S101 is executed to label the work order and obtain a corresponding work order label. For example, work orders may be labeled as technical support, application issues, demand analysis, etc. according to their category; the work order can be marked as an IISP system, a PMMS system and the like according to the system related to the work order; the work order can be labeled as data statistics, system use, production problem troubleshooting, etc. according to the content of the work order.

In the embodiment of the application, the description information of the work order to be dispatched is input into the pre-trained work order marking model, and the corresponding work order label can be obtained. Since the work order marking model is pre-trained through Machine Learning (Machine Learning), the work order marking model can identify a new work order label (i.e., a non-historical work order label) and generate a corresponding work order label.

In one embodiment, referring to FIG. 2, the step of training the marking model of the work order includes: acquiring a historical work order label corresponding to the historical description information and a historical staff label corresponding to the historical dispatching staff information (S201); constructing a training set and a testing set according to the historical work order labels and the historical personnel labels (S202); inputting the training set into a sequence model for training to obtain an initial work order marking model (S203); and adjusting the initial work order marking model by using the test set to obtain the work order marking model (S204).

It should be noted that, in the embodiment of the present application, the training material is a historical work order and related data thereof, and specifically includes historical description information, a historical work order label, and a historical staff label corresponding to historical dispatch staff information. The "history" refers to a work order that is not currently to be distributed and is used for training the work order marking model. Historical description information includes, but is not limited to, questions documented in historical work orders, such as: the work orders proposed from 2021 to 2022 at 6 months can be used as the historical work orders at the current time (2022 at 7 months). The historical work order label is generated manually according to the historical description information in advance for model training, and can reflect the content recorded by the historical description information. The historical dispatch personnel are used for dispatching the historical work order to who to process the work order. The historical staff label refers to the characteristics of the staff who are historically dispatched to the historical work order, such as being good at handling problems in a certain field and having high-grade handling capacity.

The training set and the testing set in step S202 respectively contain a certain number of historical work order tags and the historical personnel tags. In training, it is equivalent to having known what historical work order tags should be assigned to people with what historical people tags. Through the training of step S202 and the test adjustment of step S203, the work order marking model in step S204 is finally generated. Specific machine learning algorithms can be found in the prior art.

From the above description, the work order dispatching method provided by the application can train the work order marking model.

S102: and determining the correlation between each candidate dispatcher and the work order to be dispatched according to the staff label corresponding to each candidate dispatcher and the work order label.

Specifically, in one embodiment, referring to fig. 3, the personnel tag includes: the field to which the person belongs and the person handling capacity; the personnel tag comprises: the field to which the work order belongs and the emergency degree of the work order; the determining the correlation between each candidate dispatcher and the work order to be dispatched according to the staff label corresponding to each candidate dispatcher and the work order label comprises the following steps: generating word vector expressions corresponding to the personnel field, the personnel processing capacity, the work order field and the work order emergency degree respectively (S301); calculating a first similarity between the word vector expression of the field to which the person belongs and the word vector expression of the field to which the work order belongs (S302); calculating a second similarity between the word vector expression of the personnel processing capacity and the word vector expression of the work order urgency degree (S303); and determining the correlation between the candidate dispatching personnel and the to-be-dispatched worksheet according to the first similarity and the second similarity (S304).

It can be understood that after the model training is completed, the corresponding work order label can be extracted by identifying the description information of the work order to be dispatched, for example: data statistics class, mobile phone bank cross-border transaction class, IISP product class and the like.

The staff labels corresponding to the candidate dispatchers are as follows:

the Xiaowang: IISP, data query, mobile banking;

xiao Li: PMMS, production problem localization, cross-border Go, etc.

By calculating the similarity between the work order label and the personnel label, the work order to be dispatched can be automatically dispatched to the queen.

Furthermore, when the work order statistical analysis is carried out at the end of the month or at the end of the year, the number of the work orders corresponding to a certain work order label can be rapidly counted through the work order label, so that the statistical analysis of the work orders is facilitated, and optimization improvement suggestions are conveniently provided for managers.

Therefore, the method provided by the application does not need manual work transfer and dispatching of the work order, and avoids the situation that the work order is forwarded to wrong candidate dispatching personnel due to the fact that the work order transfer personnel are not familiar with work order information, and therefore the processing efficiency of the work order is improved.

The specific similarity calculation method may be a variety of algorithms, including but not limited to cosine similarity calculation based on semantic recognition, as described in steps S301 to S304.

The cosine similarity is calculated as follows:

if the result of label vectorization in a certain work order field is a (a) ₁ ,a ₂ ,a ₃ ) (ii) a The result of the urgency vectorization is x (x) ₁ )。

The result of the domain label vectorization of person B is B (B) ₁ ,b ₂ ,b ₃ ) (ii) a The result of the work order processing power vectorization is y (y) ₁ )。

The result of the domain label vectorization of person C is C (C) ₁ ,c ₂ ,c ₃ ) (ii) a The result of the work order processing power vectorization is z (z) ₁ )。

According to the steps S301 to S304,

calculating cosine similarity cos theta between vector a and vector b ₁ (ii) a Calculating cosine similarity cos theta between vector x and vector y ₂ 。

Calculating cosine similarity cos theta between vector a and vector c ₃ (ii) a Calculating cosine similarity cos theta between vector x and vector z ₄ 。

Will cos theta ₁ 、cosθ ₃ As the first matching similarity, cos θ ₂ 、cosθ ₄ As the second matching similarity, the person with the highest similarity (closest match) to the work order is determined.

From the above description, the work order dispatching method provided by the application can determine the correlation between each candidate dispatcher and the work order to be dispatched according to the staff tags corresponding to the candidate dispatchers and the work order tags.

S103: determining the optimal dispatching personnel of the work order to be dispatched according to the correlation; wherein the optimal dispatch personnel is one of the candidate dispatch personnel.

In one embodiment, referring to fig. 4, the determining the optimal distributor of the work order to be distributed according to the correlation includes: sorting (S401) relevance scores corresponding to the relevance between each candidate dispatcher and the work order to be dispatched; and selecting the candidate service person corresponding to the highest correlation as the optimal service person (S402).

It will be appreciated that a higher relevance score means that the work order to be dispatched is more matched to the candidate dispatch person, i.e., the candidate dispatch person is more eligible to process the work order to be dispatched.

From the above description, the work order dispatching method provided by the application can automatically generate the work order label and accurately dispatch the work order to the optimal dispatching personnel, so that the work order dispatching and processing efficiency is improved, operation and maintenance personnel can process and analyze the work order according to the work order label, the work order processing flow is greatly simplified, the labor cost is saved, and the time cost is reduced.

For a better understanding of the embodiments of the present application, a full example will now be given.

Referring to fig. 11, a total of 8 steps are required for implementation of the embodiment of the present application, which are numbered as 1-8 below.

Wherein the step 1 and the step 2 belong to the following work order generation assembly. When the management terminal page is used, a user can input contents (including a work order title and a work order detailed description, wherein the work order detailed description is a necessary input item) in the management terminal page, and a background program reads the contents and stores the contents in a mongo database. Namely, background programs, management end pages and database design are realized through coding.

Steps 3 to 5 belong to a work order label generation component.

And 3, step 3: and the data annotation personnel check the historical work order information on the page of the management end, manually generate a historical work order label, and the program reads the marked historical work order information and the historical work order label and updates the database.

And coding personnel compile a background interface to read historical work order information and historical work order labels in the database, and coding to divide the training set and the test set according to 7:3 of the total number.

The encoding personnel writes a Python program to realize a seq2seq (sequence to sequence) model, and writes codes to realize reading of a training set and a test set and realization of model training and storage.

And 4, step 4: and coding personnel write spark streaming program to read the work order information sent by the kafka in real time, write codes to read, use the stored model and store the output result of the model in the database in real time. The front-end interface program reads the content of the database in real time and displays the content on the management end. The modification of the user at the management end is also modified in the database in real time through the interface program.

And 5, step 5: and coding personnel write a program to read the latest stored work order information and work order labels in the database and update and store the data table of the training set.

Step 6 pertains to the bottoming data reading assembly described below.

And 6, a step of: and the coding personnel writes a program to read the bottoming data of the front-end page and store the bottoming data in the database.

Steps 7-8 belong to the work order dispatch component described below.

And 7, step 7: and the coding personnel writing program reads the operation and maintenance personnel data and the product data paved in the database, and reads the work order information and the work order label. And coding personnel writes a program to calculate the similarity between the operation and maintenance personnel label and the work order label.

And 8, step 8: and coding personnel write programs to realize the matching of the work orders and the operation and maintenance personnel, store the work orders and the operation and maintenance personnel in the database and display the work orders and the operation and maintenance personnel on a management end page through an interface program. And coding personnel write a program to realize data push from the work order to the operation and maintenance personnel.

Referring to fig. 10, the embodiment of the present application includes a work order generating component, a work order label generating component, a data backing component, and a work order distributing component.

The work order generation component is mainly used for realizing the proposal of the work order (giving work order titles, work order information description and the like) by a user at a management end.

1. And setting different authorities for different users on the management end page. If business personnel can put forward a work order and add labels to the work order; the administrator can perform data bottoming, tag addition to the work order, authority management and the like.

2. The user puts forward the work order on the management end page, explains the title of the work order and the description of the work order, and stores the work order in the form of text.

The work order label generating component mainly generates a work order label aiming at the description information and the work order title of the work order, and the generated label is displayed on a page of the management end; and the user is supported to modify and select the work order tag on the page of the management terminal.

3. Reading historical work order description information, and manually marking labels. And dividing the marked data into a training set and a testing set, training a seq2seq model, continuously improving the accuracy of the model (the accuracy of testing the model by using the testing set) by adjusting parameters, and storing the model with the highest accuracy.

4. And reading the work order title and the work order description in real time through kafka, and adding a label to the work order by using the model stored in the step 3. And the work order title, the work order description and the label are displayed on a management end page in real time, and a user can modify the label on the management end page in real time.

5. And (4) taking the work order title, the work order description and the work order label finally generated in the step (4) as a part of a training data set, writing a batch program and storing the batch program into the training set for the next training of the model.

The data bottom laying component is mainly used for finishing the data information bottom laying work of operation and maintenance personnel and various products; and performing data bedding on the management end page by a user or performing data bedding on a database by a developer.

In detail, the data bottoming component mainly completes bottoming work including, but not limited to, the following data items:

the name of the operation and maintenance personnel, the labor number of the operation and maintenance personnel, the role of the personnel (such as department manager, team manager, product group leader, product group processor A corner, product group processor B corner and the like), the product group to which the personnel belong (IISP, PMMS, C-TIQ and the like), the field to which the personnel belong (data checking, data processing, operation and use, data downloading, marketing award sending and the like), and the capability level (high priority, medium priority and low priority). The data table structure is referred to as follows:

6. and reading the operation and maintenance personnel information and the product information of the bedding by the program.

The work order dispatching component can match corresponding products or operation and maintenance personnel according to the work order labels, and can automatically dispatch the work orders in real time.

7. And calculating the similarity between the operation and maintenance personnel labels (the work order field, the work order category, the product group and the like) and the work order labels (the work order category, the work order product, the work order emergency degree and the like).

8. And dispatching the work order to the most matched operation and maintenance personnel.

Based on the same inventive concept, the embodiment of the present application further provides a work order dispatching device, which can be used for implementing the method described in the above embodiment, as described in the following embodiments. Because the principle of the work order dispatching device for solving the problems is similar to that of the work order dispatching method, the implementation of the work order dispatching device can refer to the implementation of the software performance reference determination method, and repeated parts are not repeated. As used hereinafter, the term "unit" or "module" may be a combination of software and/or hardware that implements a predetermined function. While the system described in the embodiments below is preferably implemented in software, implementations in hardware, or a combination of software and hardware are also possible and contemplated.

In one embodiment, referring to fig. 5, a work order dispatch device comprises: a work order label generating unit 501, a correlation determining unit 502 and an optimal distributor determining unit 503.

The work order label generating unit 501 is configured to input description information of the work order to be dispatched into a pre-trained work order marking model to obtain a corresponding work order label; the work order marking model is obtained by training according to historical description information of a historical work order and corresponding historical distributor information;

a correlation determination unit 502, configured to determine a correlation between each candidate dispatcher and the to-be-dispatched worksheet according to the staff tag corresponding to each candidate dispatcher and the worksheet tag; wherein the personnel tag is preset;

an optimal dispatch personnel determining unit 503, configured to determine an optimal dispatch personnel of the work order to be dispatched according to the correlation; wherein the optimal dispatch personnel is one of the candidate dispatch personnel.

In an embodiment, referring to fig. 6, the work order dispatching device further includes: a label obtaining unit 601, a training set test set constructing unit 602, an initial model generating unit 603, and an initial model adjusting unit 604.

A tag obtaining unit 601, configured to obtain a historical work order tag corresponding to the historical description information and a historical staff tag corresponding to the historical dispatch staff information;

a training set and test set constructing unit 602, configured to construct a training set and a test set according to the historical work order tags and the historical personnel tags;

an initial model generating unit 603, configured to input the training set into a sequence model for training, so as to obtain an initial work order marking model;

an initial model adjusting unit 604, configured to adjust the initial work order marking model by using the test set, so as to obtain the work order marking model.

In one embodiment, the work order distribution apparatus further includes:

In one embodiment, referring to fig. 7, the personnel tag comprises: the field to which the person belongs and the person handling capacity; the personnel tag comprises: the field to which the work order belongs and the emergency degree of the work order; the correlation determination unit 502 includes: a word vector expression generating module 701, a first similarity determining module 702, a second similarity determining module 703 and a relevance determining module 704.

A word vector expression generating module 701, configured to generate word vector expressions corresponding to the field to which the person belongs, the person processing capability, the field to which the work order belongs, and the emergency degree of the work order, respectively;

a first similarity determining module 702, configured to calculate a first similarity between a word vector expression in the field to which the person belongs and a word vector expression in the field to which the work order belongs;

a second similarity determining module 703, configured to calculate a second similarity between the word vector expression of the staff processing capacity and the word vector expression of the work order urgency degree;

a correlation determination module 704, configured to determine a correlation between the candidate dispatch staff and the to-be-dispatched worksheet according to the first similarity and the second similarity.

In an embodiment, referring to fig. 8, the optimal distributor determining unit 503 includes: a sorting module 801 and an optimal dispatcher selecting module 802.

A ranking module 801, configured to rank relevance scores corresponding to relevance between each candidate dispatcher and the to-be-dispatched worksheet;

an optimal dispatch personnel selecting module 802, configured to select a candidate dispatch personnel corresponding to the highest correlation as the optimal dispatch personnel.

In terms of hardware, in order to automatically generate a work order label and accurately dispatch the work order to an optimal dispatcher, the present application provides an embodiment of an electronic device for implementing all or part of the content in the work order dispatching method, where the electronic device specifically includes the following content:

a Processor (Processor), a Memory (Memory), a communication Interface (Communications Interface) and a bus; the processor, the memory and the communication interface complete mutual communication through the bus; the communication interface is used for realizing information transmission between the work order distributing device and relevant equipment such as a core service system, a user terminal, a relevant database and the like; the logic controller may be a desktop computer, a tablet computer, a mobile terminal, and the like, but the embodiment is not limited thereto. In this embodiment, the logic controller may be implemented with reference to the embodiments of the work order dispatching method and the work order dispatching device in the embodiments, and the contents thereof are incorporated herein, and repeated descriptions thereof are omitted.

It is understood that the user terminal may include a smart phone, a tablet electronic device, a network set-top box, a portable computer, a desktop computer, a Personal Digital Assistant (PDA), an in-vehicle device, a smart wearable device, and the like. Wherein, intelligence wearing equipment can include intelligent glasses, intelligent wrist-watch, intelligent bracelet etc..

In practical applications, part of the work order dispatching method may be executed on the electronic device side as described above, or all operations may be completed in the client device. The selection may be specifically performed according to the processing capability of the client device, the limitation of the user usage scenario, and the like. This is not a limitation of the present application. The client device may further include a processor if all operations are performed in the client device.

The client device may have a communication module (i.e., a communication unit), and may be in communication connection with a remote server to implement data transmission with the server. The server may include a server on the side of the task scheduling center, and in other implementation scenarios, the server may also include a server on an intermediate platform, for example, a server on a third-party server platform that is communicatively linked to the task scheduling center server. The server may include a single computer device, or may include a server cluster formed by a plurality of servers, or a server structure of a distributed apparatus.

Fig. 9 is a schematic block diagram of a system configuration of an electronic device 9600 according to an embodiment of the present application. As shown in fig. 9, the electronic device 9600 can include a central processor 9100 and a memory 9140; the memory 9140 is coupled to the central processor 9100. Notably, this fig. 9 is exemplary; other types of structures may also be used in addition to or in place of the structure to implement telecommunications or other functions.

In one embodiment, the work order distribution method function may be integrated into the central processor 9100. The central processor 9100 may be configured to control as follows:

s101: inputting the description information of the work order to be dispatched into a pre-trained work order marking model to obtain a corresponding work order label; the work order marking model is obtained by training according to historical description information of a historical work order and corresponding historical distributor information;

s102: determining the correlation between each candidate dispatcher and the work order to be dispatched according to the staff label corresponding to each candidate dispatcher and the work order label;

From the above description, the work order dispatching method and the work order dispatching device provided by the application can automatically generate the work order label and accurately dispatch the work order to the optimal dispatching personnel, so that the work order dispatching and processing efficiency is improved, operation and maintenance personnel can process and analyze the work order according to the work order label, the work order processing flow is greatly simplified, the labor cost is saved, and the time cost is reduced.

In another embodiment, the work order dispatching device may be configured separately from the central processing unit 9100, for example, the work order dispatching device of the data compound transmission device may be configured as a chip connected to the central processing unit 9100, and the work order dispatching method may be implemented by the control of the central processing unit.

As shown in fig. 9, the electronic device 9600 may further include: a communication module 9110, an input unit 9120, an audio processor 9130, a display 9160, and a power supply 9170. It is noted that the electronic device 9600 also does not necessarily include all of the components shown in fig. 9; in addition, the electronic device 9600 may further include components not shown in fig. 9, which may be referred to in the prior art.

As shown in fig. 9, a central processor 9100, sometimes referred to as a controller or operational control, can include a microprocessor or other processor device and/or logic device, which central processor 9100 receives input and controls the operation of the various components of the electronic device 9600.

The memory 9140 can be, for example, one or more of a buffer, a flash memory, a hard drive, a removable media, a volatile memory, a non-volatile memory, or other suitable device. The information relating to the failure may be stored, and a program for executing the information may be stored. And the central processing unit 9100 can execute the program stored in the memory 9140 to realize information storage or processing, or the like.

The input unit 9120 provides input to the central processor 9100. The input unit 9120 is, for example, a key or a touch input device. Power supply 9170 is used to provide power to electronic device 9600. The display 9160 is used for displaying display objects such as images and characters. The display may be, for example, but is not limited to, an LCD display.

The memory 9140 can be a solid state memory, e.g., read Only Memory (ROM), random Access Memory (RAM), a SIM card, or the like. There may also be a memory that holds information even when power is off, can be selectively erased, and is provided with more data, an example of which is sometimes referred to as an EPROM or the like. The memory 9140 could also be some other type of device. Memory 9140 includes a buffer memory 9141 (sometimes referred to as a buffer). The memory 9140 may include an application/function storage portion 9142, the application/function storage portion 9142 being used for storing application programs and function programs or for executing a flow of operations of the electronic device 9600 by the central processor 9100.

The memory 9140 can also include a data store 9143, the data store 9143 being used to store data, such as contacts, digital data, pictures, sounds, and/or any other data used by an electronic device. The driver storage portion 9144 of the memory 9140 may include various drivers for the electronic device for communication functions and/or for performing other functions of the electronic device (e.g., messaging applications, contact book applications, etc.).

The communication module 9110 is a transmitter/receiver 9110 that transmits and receives signals via an antenna 9111. The communication module (transmitter/receiver) 9110 is coupled to the central processor 9100 to provide input signals and receive output signals, which may be the same as in the case of a conventional mobile communication terminal.

Based on different communication technologies, a plurality of communication modules 9110, such as a cellular network module, a bluetooth module, and/or a wireless lan module, may be disposed in the same electronic device. The communication module (transmitter/receiver) 9110 is also coupled to a speaker 9131 and a microphone 9132 via an audio processor 9130 to provide audio output via the speaker 9131 and receive audio input from the microphone 9132, thereby implementing ordinary telecommunication functions. The audio processor 9130 may include any suitable buffers, decoders, amplifiers and so forth. In addition, the audio processor 9130 is also coupled to the central processor 9100, thereby enabling recording locally through the microphone 9132 and enabling locally stored sounds to be played through the speaker 9131.

An embodiment of the present application further provides a computer-readable storage medium capable of implementing all the steps in the work order dispatching method with the execution subject being the server or the client in the foregoing embodiment, where the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the computer program implements all the steps in the work order dispatching method with the execution subject being the server or the client in the foregoing embodiment, for example, when the processor executes the computer program, the processor implements the following steps:

As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, apparatus, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.

The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (devices), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

The principle and the implementation mode of the invention are explained by applying specific embodiments in the invention, and the description of the embodiments is only used for helping to understand the method and the core idea of the invention; meanwhile, for a person skilled in the art, according to the idea of the present invention, there may be variations in the specific embodiments and the application scope, and in summary, the content of the present specification should not be construed as a limitation to the present invention.

Claims

1. A work order dispatching method is characterized by comprising the following steps:

determining the optimal dispatching personnel of the work order to be dispatched according to the correlation; wherein the optimal dispatch personnel is one of the candidate dispatch personnel.

2. The work order delivery method of claim 1, wherein the step of training the marking model of the work order comprises:

3. The work order dispatching method of claim 1, wherein before inputting the description information of the work order to be dispatched into the pre-trained work order marking model to obtain the corresponding work order label, further comprising:

4. The work order distribution method of claim 1, wherein the personnel tags comprise: the field to which the person belongs and the person handling capacity; the personnel tag comprises: the field to which the work order belongs and the emergency degree of the work order; the determining the correlation between each candidate dispatcher and the work order to be dispatched according to the staff label corresponding to each candidate dispatcher and the work order label comprises the following steps:

calculating a second similarity between the word vector expression of the personnel processing capacity and the word vector expression of the work order emergency degree;

5. The work order dispatch method of claim 4, wherein said determining an optimal dispatch personnel for the work order to be dispatched based on the correlation comprises:

6. A work order distribution device, comprising:

the correlation determination unit is used for determining the correlation between each candidate dispatcher and the work order to be dispatched according to the personnel label corresponding to each candidate dispatcher and the work order label; wherein the personnel tag is preset;

the optimal dispatching personnel determining unit is used for determining the optimal dispatching personnel of the work order to be dispatched according to the correlation; wherein the optimal dispatch personnel is one of the candidate dispatch personnel.

7. The work order distribution apparatus according to claim 6, further comprising:

8. The work order distribution apparatus according to claim 6, further comprising:

9. The work order distribution apparatus according to claim 6, wherein the personnel tag comprises: people belonging to domains and people handling capabilities; the personnel tag includes: the field to which the work order belongs and the emergency degree of the work order; the correlation determination unit includes:

the second similarity determining module is used for calculating a second similarity between the word vector expression of the personnel processing capacity and the word vector expression of the work order emergency degree;

and the correlation determination module is used for determining the correlation between the candidate dispatch personnel and the to-be-dispatched worksheet according to the first similarity and the second similarity.

10. The work order dispatch device of claim 9, wherein the optimal dispatch personnel determination unit comprises:

11. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the steps of the work order dispatch method of any one of claims 1 to 5 are implemented when the program is executed by the processor.

12. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the steps of the work order distribution method of any one of claims 1 to 5.

13. A computer program product comprising computer program/instructions, characterized in that the computer program/instructions, when executed by a processor, implement the steps of the work order dispatching method of any of claims 1 to 5.