CN114943443A - Work order distribution method, device, equipment and storage medium - Google Patents

Abstract

The present application discloses a work order allocation method, device, equipment and storage medium, and relates to the field of cloud computing. The work order allocation method includes: acquiring work order feature information, where the work order feature information is used to represent the information technology corresponding to the work order IT problem type information, IT system level information, and user profile information of IT issues affecting users; through preset algorithms, work order service indicator values and work order service indicators are calculated based on IT system level information and user profile information that IT issues affect users The value is used to characterize the service level required for the work order; send the work order to the target processing node; solve the problem of low efficiency of traditional IT problem work order allocation methods.

Description

Work order distribution method, apparatus, device and storage medium

technical field

The present application relates to the field of cloud computing, and in particular, to a work order distribution method, apparatus, device, and storage medium.

Background technique

Information technology (IT) problems existing in the production and operation of telecom operators refer to the problems found by users, such as marketing delivery personnel, operation management personnel and other front-line IT personnel in business marketing acceptance, daily management, production operation and maintenance and other work scenarios. IT system problems or various IT problems such as problems and errors when using the system. As the telecom operator's IT architecture evolves to a mid-office architecture, the traditional method of assigning work orders for IT problems can no longer meet the IT architecture needs of existing telecom operators.

The traditional method of assigning work orders for IT problems is usually dispatched manually and relies on the experience of operation and maintenance personnel, which is inefficient.

SUMMARY OF THE INVENTION

The embodiments of the present application provide a work order allocation method, apparatus, device, and storage medium, which solve the problem of low efficiency of the traditional IT problem work order allocation method.

In order to realize the above-mentioned technical purpose, the embodiment of the present application adopts the following technical solutions:

In a first aspect, an embodiment of the present application provides a work order allocation method, including:

Obtain work order feature information, which is used to represent the information technology IT problem type information corresponding to the work order, IT system level information, and user portrait information of IT problems affecting users;

Through the preset algorithm, the work order service index value is calculated according to the IT system level information and the user profile information of the IT problem affecting the user, and the work order service index value is used to represent the service level required by the work order;

Send a work order to the target processing node; each processing node corresponds to a handler, the target processing node corresponds to the target handler, and the target handler is selected from the handler list corresponding to the IT issue type information. Single-service metric value matching.

In the embodiment of the present application, first obtain the feature information of the work order, and then use a preset algorithm to calculate the work order service index value used to represent the service level required by the work order according to the IT system level information and the user portrait information of the IT problem affecting the user, Finally, a work order is sent to the target processing node. The target processing node corresponds to the target handler, and the target handler is selected from the handler list corresponding to the IT issue type information, and the handler score of the target handler matches the work order service indicator value. , which ensures that the processing capacity of the processing node corresponding to the work order allocation can meet the work order processing requirements, and realizes the automatic allocation of work orders, which improves the matching degree between work order allocation and handler and work order allocation efficiency.

In one embodiment, the method further includes:

In the case of receiving the automatic processing instruction sent by the target processing node, the solution of the work order is queried from the preset knowledge base according to the work order; the preset knowledge base is generated by constructing a knowledge graph from the historical work orders and the solutions of the historical work orders ;

Call the operation and maintenance service to execute the solution to solve the IT problem corresponding to the work order.

In one embodiment, the user profile information includes the scope of users affected by the problem, the types of users affected by the problem, and the tendency of users to submit complaints due to the problem;

Through a preset algorithm, the work order service index value is calculated according to the IT system level information and the user profile information of the IT problem affecting the user, including:

Through preset conversion rules, the scope of users affected by the problem, the type of users affected by the problem, the tendency of users to submit complaints due to the problem, and the information at the IT system level are converted into binary codes respectively;

The binary code is converted into quantitative data that can be used to compare the size through a preset conversion algorithm, and the work order service index value is obtained.

In one embodiment, the work order service index value is calculated by the following formula:

Among them, S represents the service index value of the work order, k represents the type of the feature information of the work order corresponding to the binary code, a _i represents any code value in the binary code, and i represents the number of codes of the binary code.

In one embodiment, before sending the work order feature information to the target processing node, the method further includes:

Obtain the work order resolution efficiency index and work order resolution quality index of the handler;

The processor score is calculated according to the work order resolution efficiency index and the work order resolution quality index through the preset scoring algorithm.

In one embodiment, acquiring work order feature information includes:

Send a preset work order template to the operation and maintenance node; the operation and maintenance node corresponds to the operation and maintenance personnel, and the preset work order template requires the operation and maintenance personnel to enter the following information: problem description, screenshots of problem error prompts, problem reproduction video, and IT where the problem occurs. system;

Receive the preset work order template that has been entered;

The preset analysis algorithm is used to analyze the IT problem type information represented by the input preset work order template, the IT system level information, and the user portrait information of the user affected by the IT problem, and obtain the work order feature information.

In one embodiment, the method further includes:

In the case that the number of times of receiving the chargeback information sent by the target processing node meets the preset chargeback threshold, the work order feature information is sent to the new target processing node.

In one embodiment, the method further includes:

Record the processing time for the target processing node to process the feature information of the work order;

When the processing time exceeds the preset reminder time limit, reminder information is generated and sent to the target processing node.

In a second aspect, an embodiment of the present application provides a work order distribution device, including:

The acquisition module is used to obtain the feature information of the work order, and the feature information of the work order is used to represent the information of the information technology IT problem type corresponding to the work order, the information of the IT system level, and the user portrait information of the user affected by the IT problem;

The calculation module is used to calculate the work order service index value according to the IT system level information and the user profile information of the IT problem affecting the user through a preset algorithm, and the work order service index value is used to represent the service level required by the work order;

The sending module is used to send the work order to the target processing node; each processing node corresponds to a handler, the target processing node corresponds to the target handler, and the target handler is selected from the handler list corresponding to the IT problem type information. The handler score matches the ticket service metric value.

In one embodiment, the apparatus further includes a query module and a call module.

The query module is used for querying the solution of the work order from the preset knowledge base according to the work order under the condition of receiving the automatic processing instruction sent by the target processing node. The preset knowledge base is generated by building a knowledge graph from historical work orders and solutions of historical work orders.

The calling module is used to call the operation and maintenance service to execute the solution to solve the IT problem corresponding to the work order.

In one embodiment, the user profile information includes the scope of users affected by the problem, the types of users affected by the problem, and the tendency of users to submit complaints due to the problem.

The calculation module 820 is specifically used for:

Use preset conversion rules to convert the range of users affected by the problem, the type of users affected by the problem, the tendency of users to file complaints due to the problem, and the information at the IT system level into binary codes.

The binary code is converted into quantitative data that can be used to compare the size through a preset conversion algorithm, and the work order service index value is obtained.

In one embodiment, the work order service index value is calculated by the following formula:

Wherein, S represents the service index value of the work order, k represents the type of the feature information of the work order corresponding to the binary code, a _i represents any code value in the binary code, and i represents the number of codes of the binary code.

In one embodiment, the obtaining module 810 is further configured to obtain the work order resolution efficiency index and the work order resolution quality index of the handler before sending the work order feature information to the target processing node.

The calculation module 820 is further configured to calculate the score of the handler according to the work order resolution efficiency index and the work order resolution quality index by using a preset scoring algorithm.

In one embodiment, the obtaining module 810 is specifically configured to:

Send a preset work order template to the operation and maintenance node. The operation and maintenance node corresponds to the operation and maintenance personnel, and the preset work order template requires the operation and maintenance personnel to enter the following information: problem description, screenshots of problem error prompts, problem reproduction video, and the IT system where the problem occurs.

Receive preset ticket templates with completed input.

The preset analysis algorithm is used to analyze the IT problem type information represented by the input preset work order template, the IT system level information, and the user portrait information of the user affected by the IT problem, and obtain the work order feature information.

In an embodiment, the sending module 830 is further configured to send the work order feature information to the new target processing node when the number of times of receiving the chargeback information sent by the target processing node meets the preset chargeback threshold.

In one embodiment, the apparatus further includes a recording module and a generating module.

The recording module is used to record the processing time of the target processing node for processing the feature information of the work order.

The generation module is used to generate reminder information when the processing time exceeds the preset reminder time limit.

The sending module 830 is further configured to send reminder information to the target processing node.

In a third aspect, the present application provides an electronic device, comprising: a processor, and a memory for storing instructions executable by the processor; wherein the processor is configured to execute the instructions to implement the work order provided in the first aspect above allocation method.

In a fourth aspect, the present application provides a computer-readable storage medium storing a program for realizing information transfer, and when the program is executed by a processor, the method for assigning a work order as provided in the first aspect is realized.

In a fifth aspect, the present application provides a computer program product that, when the computer program product runs on a computer, causes the computer to execute the method provided in the first aspect above.

For the beneficial effects described in the second aspect, the third aspect, the fourth aspect and the fifth aspect in this application, reference may be made to the analysis of the beneficial effects of the first aspect, and details are not repeated here.

Description of drawings

FIG. 1 is a schematic structural diagram of a work order distribution system according to an embodiment of the present application;

FIG. 2 is a schematic execution flow diagram of a problem self-service acceptance module provided by an embodiment of the present application;

3 is a schematic diagram of an execution flow of a work order service SLA module provided by an embodiment of the present application;

4 is a schematic diagram of a binary encoding provided by an embodiment of the present application;

5 is a schematic diagram of an execution flow of a personnel capability matching module provided by an embodiment of the present application;

FIG. 6 is a schematic flowchart of the execution of an intelligent problem diagnosis module according to an embodiment of the present application;

FIG. 7 is a schematic flowchart of a method for assigning a work order according to an embodiment of the present application;

FIG. 8 is a schematic structural diagram of a work order distribution device according to an embodiment of the present application;

FIG. 9 is a schematic structural diagram of an electronic device according to an embodiment of the present application.

Detailed ways

The features and exemplary embodiments of various aspects of the present application will be described in detail below. In order to make the purpose, technical solutions and advantages of the present application more clear, the present application will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are only configured to explain the present application, and are not configured to limit the present application. It will be apparent to those skilled in the art that the present application may be practiced without some of these specific details. The following description of the embodiments is merely to provide a better understanding of the present application by illustrating examples of the present application.

It should be noted that, in this document, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any relationship between these entities or operations. There is no such actual relationship or order; nor should it be interpreted as indicating or implying relative importance or implying the number of technical features indicated. Thus, a feature defined as "first" or "second" may expressly or implicitly include one or more of that feature. In the description of this embodiment, unless otherwise specified, "plurality" means two or more. Moreover, the terms "comprising", "comprising" or any other variation thereof are intended to encompass a non-exclusive inclusion such that a process, method, article or device that includes a list of elements includes not only those elements, but also includes not explicitly listed or other elements inherent to such a process, method, article or apparatus. Without further limitation, an element defined by the phrase "comprises" does not preclude the presence of additional identical elements in a process, method, article, or device that includes the element.

Traditional IT problem work orders are usually dispatched manually, relying on the experience of operation and maintenance personnel, and the efficiency is low, which is reflected in the following aspects: problem positioning requires operation and maintenance personnel to interact with users many times, which is easy to cause user dissatisfaction; work order support is slow, The speed of response depends heavily on manual input; the dispatch of work orders across systems and units requires manual intervention, and the circulation time is long; the handling of work order problems relies on the experience of operation and maintenance personnel, and problem repair is time-consuming and labor-intensive; user service perception cannot be quantified, and differences are not provided service.

In order to solve the above problem, an embodiment of the present application provides a work order allocation method, which is applied to a work order allocation system provided by the embodiment of the present application. As shown in FIG. 1 , the work order allocation system may include: : Artificial intelligence (Artificial Intelligence, AI) basic platform, problem self-service acceptance module, work order Service Level Agreement (SLA) module, personnel capability matching module and intelligent problem diagnosis module.

The following describes the AI basic platform in the work order distribution system shown in Figure 1. The AI basic platform is used for:

Provide industry-wide machine learning frameworks, algorithms, software and hardware training environments, etc., connect to the data center, and provide data collection, data annotation, offline model training and optimization, problem knowledge base construction, and model deployment capabilities. Specifically, the IT problems and solutions that occur in daily operation and maintenance are solidified into knowledge, and the knowledge is expressed and retrieved through the knowledge graph to form a problem knowledge base (such as system problem knowledge base, enterprise problem knowledge base or industry problem knowledge base) library, etc.); and match the solution output from the problem knowledge base with the operation and maintenance service, and the operation and maintenance service of different solutions can be configured in advance. By calling the operation and maintenance service, the network/middle station operation instructions can be adapted, issued, executed, feedback management, etc., so as to realize the implementation of the solution. Support data automatic repair, parameter automatic configuration, system troubleshooting, etc. Train and optimize the intent recognition model for self-service acceptance of questions, and deploy the intent recognition model to the self-service acceptance module.

The following describes the problem self-service acceptance module in the work order distribution system shown in Figure 1. The problem self-service acceptance module is used for:

Through the Q&A robot in the self-service acceptance module, the IT operation and maintenance personnel require the operation and maintenance personnel to submit information related to IT problems online, such as order number, problem description, screenshots of problem error prompts, problem reproduction videos, and Problems arise with the IT system. Then, the intent recognition model is used to format and process the work order, supporting the onlineization of end-to-end full-process self-service acceptance of IT problems, intelligent processing, and work order satisfaction evaluation.

Further, the execution process of the problem self-service acceptance module is shown in Figure 2, including:

1. The problem self-service acceptance module requires operation and maintenance personnel to customize the fields of the work order template in advance. In the work order template, the fields that the operation and maintenance personnel are required to submit online include: the problem order number, a brief description of the problem, a screenshot of the problem error prompt, the problem reproduction video, and the problem occurrence system.

2. Format the work order and improve the work order information. According to the problem order number, the data is automatically collected from the middle-office data to profile the user, extract keywords from the description of the problem in the work order template, and perform image feature analysis on the screenshot of the error prompt and the video of the problem recurrence in the work order template, and finally complete Formatting and sorting out the submitted work order template content, and outputting the following work order feature information: IT support domain to which the problem belongs, scope of users affected by the problem, type of users affected by the problem, and tendency of users to file complaints due to the problem.

Further, explain the feature information of the work order:

The IT support domain to which the problem belongs is the IT support domain that needs to be determined before the system is put into production and operation. According to the IT support domain to which the system belongs, the IT support domain to which the system problem belongs is divided into three categories:

Public business issues, including IT issues such as public product application, approval, and configuration. Government and enterprise business issues, including government and enterprise business acceptance, accounting, resource allocation and other IT issues. Production and operation problems, including business support system (BSS), management support system (Manage support systems, MSS), operation support system (Operation Support System, OSS), decision support system (Decision support system, DSS) related issues IT problems.

Scope of users affected by the problem: Refers to the classification according to the scope of users affected by IT problems, mainly divided into three categories: national users, partial users, and individual users.

Types of users affected by problems: refers to classifying users according to the types of users affected by IT problems, mainly divided into three categories: major users, important users, and ordinary users.

Problems lead to users’ tendency to submit appeals: Refers to the classification of users’ potential risk of submitting appeals based on IT problems. It is mainly divided into three categories: users have a tendency to appeal, users have a tendency to complain, and users can wait and do not ask for immediate processing.

3. Invoke the ability of the intelligent diagnosis module, query the solution, and execute the solution through the operation and maintenance service to solve the IT problem corresponding to the work order.

4. Work order satisfaction evaluation: After the problem is solved, the operation and maintenance personnel will make a closed order evaluation on the solution result.

The following describes the work order service SLA module. The work order service SLA module is used for:

Calculate the service index value of the work order according to the IT system level information and the user profile information of the user affected by the IT problem; and record the manual processing time of the processing node, send early warning and alarm notification for the work order that is about to time out or has timed out, and cancel the work order for multiple times. Make a transfer.

Further, in the above work order distribution system, the execution process of the work order service SLA module is shown in Figure 3, including:

(1) Parse the formatted work order and use it as the data input of the work order service index value.

(2) Use the integer variable function int12 to perform binary encoding on the parsing result of step 1. The binary encoding is shown in Figure 4. The binary encoding is based on IT system level information encoding (3bit) + problem affecting user range encoding (3bit) + problem affecting User type code (3bit) + problem causes users to vote in the form of appeal tendency code (3bit).

(3) Convert the 12-bit binary code to a decimal integer to obtain the work order service index value.

Among them, in the binary code representation, when each bit is 0, it means it is not of this type, and when it is 1, it means it is this type. For example, if the IT system level is the core system, 3 bits are represented as 100, if it is an important system, 3 bits are represented as 010, and if it is a general system, 3 bits are represented as 001.

(4) According to the value of the work order service index, the work order service index is divided into three levels, including: general, urgent, and urgent; the higher the work order service index level, the higher the processing priority. For example, when the IT system level of the work order is an important system, the range of users affected by the problem is some users, the type of users affected by the problem is ordinary users, and the user's tendency to appeal due to the problem is a tendency to appeal, the binary code of the work order feature is expressed as follows: 010-010-001-100, converted to a decimal integer, is 9, and the service index level of the ticket is urgent.

(5) For different work orders, set different manual processing time limit requirements. As shown in Table 1, the time limit for manual processing can include the following items:

Table 1

specific:

Intelligent diagnosis solution evaluation time limit: The processing time from the first dispatch of the work order to the operation and maintenance personnel's evaluation of the feasibility of the intelligent diagnosis solution to the operation and maintenance personnel giving evaluation opinions or upgrading to a difficult problem work order.

Response time limit for difficult work orders: The processing time from when the difficult work order is claimed until the operation and maintenance personnel reply to the solution or put forward the request for vulnerability bug/requirement research and judgment.

Time limit for BUG/demand research and judgment: The operation and maintenance personnel will assign the difficult work order that is initially judged to be a system BUG or demand problem to the R&D team, and enter the research and judgment link. Cumulative time for returning to the operation and maintenance personnel.

Time limit for replying to BUG/demand conclusions: The processing time from when the R&D team assigns the difficult work order judged to be a BUG or demand problem to the operation and maintenance personnel, until the operation and maintenance personnel reply to the user's temporary solution.

(6) Monitor the work order, detect the manual processing time data and the number of work order refunds in each link of the IT problem work order flow from the database, and calculate the long-term vector distribution of the work order manual processing.

(7) Judging whether the time distribution of manual processing in each link of the work order flow reaches the time limit threshold or time limit requirement, if not, continue to step 8, if so, skip to step 9.

Exemplarily, the time limit threshold may be set to three quarters of the corresponding manual processing time limit requirement.

(8) Determine whether the work order cancellation threshold is reached in the work order circulation. If not, skip to step 6; if so, skip to step 10.

(9) Send early warning and warning notifications to overtime work orders.

Exemplarily, reminders for overtime work orders can be sent to the processing node at a certain frequency by means of text messages, emails, online groups, etc.; for overtime work orders, in addition to sending reminder reminders, the feedback from the handler corresponding to the processing node is required. Reason for timeout.

(10) For multiple refunded work orders, call the personnel capabilities to match the module capabilities, and re-throw the orders to the matching processing nodes for processing.

The following describes the personnel capability matching module, which is used for:

According to the work order service index value, determine the target handler matching the work order service index value, and assign the work order to the target processing node corresponding to the target handler for the target handler to process the work order.

Further, the execution flow of the personnel capability matching module is shown in Figure 5, including:

(1) Read the historical data of the handler's ability to solve the work order from the database, which can be considered from three dimensions including the staff ability label, problem response efficiency, and service quality, and finally obtain the handler's work order resolution efficiency index and work order resolution. Quality Index.

Exemplarily, the work order resolution efficiency indicators may include indicators such as: 2 hours acceptance rate, 1 working day resolution rate, average delivery time and other indicators. Work order resolution quality indicators may include: work order evaluation satisfaction, work order resolution rate and other indicators.

(2) According to the work order resolution efficiency index and the work order resolution quality index, calculate the processing ability score in real time.

(3) The full score of personnel ability is 100 points, and the personnel ability level is divided into different levels according to the score, for example, it is divided into three levels: cutting-edge, backbone, and expert. For example, when the personnel ability score is not less than 80 points, the personnel ability level is the expert level; when the personnel ability score is not less than 60 points, the personnel ability level is the backbone level; when the personnel ability score is lower than 60 points, the personnel ability level is the backbone level. The personnel ability level is the cutting-edge level.

(4) Analyze the formatted work order to determine the IT support domain and qualitative classification of the problem to which the work order belongs. Personnel capability label data includes the IT support domain to which the problem belongs, and the qualitative classification of the problem. Match the personnel ability label with the work order attribute, and select the personnel with higher matching degree as the candidate personnel.

(5) The ability to call the work order service SLA module to clarify the work order service index level of the work order, and match the personnel ability level of the candidate handler with the work order service index level.

Exemplarily, in order to improve work order processing efficiency, the matching principle is as follows: experts give priority to work orders whose service index level is urgent, key personnel give priority to work orders whose service index level is urgent, and cutting-edge personnel give priority to work orders. The single service index level is a general work order; at the same time, taking into account the principle of upward compatibility, in the case of a shortage of personnel capacity, low-level personnel can process work orders with a higher work order service index level.

(6) Assign the work order to the candidate with the highest personnel ability score and the highest matching degree, and complete the automatic dispatch.

The following describes the intelligent problem diagnosis module. The intelligent problem diagnosis module is used for:

In the case of receiving the automatic processing instruction sent by the target processing node, use the knowledge graph technology to generate a solution to the work order, or throw the order to manual processing.

Further, the process executed by the problem intelligent diagnosis module can be as shown in Figure 6, including:

1. Read the input preset work order template from the database, and the input preset work order template is the formatted work order information.

2. According to the problem order number, problem characteristics, and IT support domain to which the problem belongs to the formatted work order information, the decision tree model is used to classify the cause of the problem, that is, to clarify the IT problem type information.

Exemplarily, the IT problem type information may include the IT support domain to which the problem of the work order belongs and the qualitative classification of the problem. The processor may also preset label data, and the label data may include the IT support domain to which the problem belongs, and the qualitative classification of the problem. The qualitative classification of problems is mainly divided into consulting operation category, data repair category, parameter configuration category, program bug category, requirement rule category, and platform architecture category, among which:

Consultation operation category: Problems caused by operation.

Data repair class: data repair of non-program BUG problems.

Parameter configuration class: problems caused by unconfigured parameters, errors, etc.

Program BUG category: Problems characterized as program defects.

Requirement rules: There are clear requirements numbers, and there are doubts about the implementation of the system.

Platform architecture: problems that cannot be solved temporarily due to insufficient platform architecture and system capabilities.

3. According to the problem characteristics of the work order and the qualitative classification data of the problem, query the solution from the enterprise problem knowledge base.

Among them, the enterprise problem knowledge base is to solidify the problem work order information, solutions, and operation and maintenance services that occur in daily operation and maintenance into knowledge, using knowledge graph technology, using graph database and machine learning technology to express and retrieve knowledge.

4. Call the service SLA module to calculate the work order service index level and monitor the work order flow.

5. Invoke the ability of the personnel ability matching module, calculate the degree of matching between the service index level of the work order and the personnel ability, and transfer the solution to the handler with the matching ability.

6. The handler judges the feasibility of the solution. If the solution is confirmed to be feasible, go to step 7. If the solution is not feasible, skip to step 8.

7. Match the solution with the operation and maintenance scenario, and automatically deal with the work order problem after matching.

Among them, the solutions output from the problem knowledge base are matched with the operation and maintenance services, and the operation and maintenance services of different solutions can be configured in advance. By invoking the operation and maintenance service, the network/middle station operation instructions can be adapted, issued, executed, and feedback management, etc., so as to realize the implementation of the solution and solve the IT problem corresponding to the work order.

8. Escalate the problem to a difficult problem, update the formatted work order information, mark the work order type as a difficult work order, and the current handler is responsible for handling the problem.

9. Receive the problem processing results, invoke the AI basic platform capabilities, revise the enterprise problem knowledge base, optimize the model, and complete the IT problem knowledge storage.

In the embodiment of the present application, through the functional design of each module of the work order distribution system, the automatic allocation of work orders can be realized, and it is ensured that the processing capacity corresponding to the processing nodes to which the work orders are allocated can meet the work order processing requirements, and the work order is improved. The matching degree between the order allocation and the handler and the work order allocation efficiency.

1-6 introduce a work order distribution system provided by an embodiment of the present application, and the work order distribution system may be set up in an electronic device with data processing capabilities, such as a computer and a server. The server may be a single server, or may also be a server cluster composed of multiple servers. In some embodiments, the server cluster may also be a distributed cluster. Based on the work order allocation system, an embodiment of the present application further provides a work order allocation method, and FIG. 7 shows a schematic flowchart of the method.

As shown in Figure 7, the method may include the following steps:

S710, obtain work order feature information.

Among them, the work order feature information can be obtained from the work order, and the work order feature information is used to represent the information technology IT problem type information corresponding to the work order, the IT system level information, and the user portrait information that the IT problem affects the user.

S720: Calculate the work order service index value according to the IT system level information and the user profile information of the IT problem affecting the user through a preset algorithm.

Among them, the IT system level information and the user profile information of the users affected by IT problems can represent the service level required by the work order. Taking the IT system level information and the user profile information of the users affected by IT problems as calculation parameters, it can be calculated to represent the work order. The value of the ticket service indicator for the required service level.

S730: Send a work order to the target processing node.

Wherein, each processing node corresponds to a processing person, the target processing node corresponds to a target processing person, and the target processing person is selected from the processing person list corresponding to the IT problem type information. The agent score of the target agent matches the value of the ticket service indicator.

Exemplarily, the IT problem type information may include the IT support domain to which the problem of the work order belongs and the qualitative classification of the problem. The processor may also preset label data, and the label data may include the IT support domain to which the problem belongs, and the qualitative classification of the problem. According to the tag data, a corresponding handler list can be preset, and then a handler list corresponding to the IT issue type information can be determined according to the matching of the IT issue type information and the tag data.

Exemplarily, the IT support domain to which the problem of the work order belongs can be divided into three categories:

1) Public business issues, including IT issues such as public product application, approval and configuration.

2) Government and enterprise business problems, including IT problems such as government and enterprise business acceptance, accounting, and resource allocation.

3) Production and operation problems, including Business Support System (BSS), Management Support System (MSS), Operation Support System (OSS), Decision Support System (DSS) ) related IT issues.

Exemplarily, the qualitative classification of problems can be divided into consulting operation categories, data repair categories, parameter configuration categories, program bug categories, requirement rule categories, and platform architecture categories, in which:

(1) Consulting operation category: problems caused by operation.

(2) Data repair class: data repair of non-program BUG problems.

(3) Parameter configuration class: problems caused by unmatched parameters, errors, etc.

(4) Program BUG category: The problem is characterized as program defect.

(5) Demand rules: there are clear demand numbers, and there are doubts about the implementation of the system.

(6) Platform architecture: problems that cannot be solved temporarily due to insufficient platform architecture and system capabilities.

In the embodiment of the present application, first obtain the feature information of the work order, and then use a preset algorithm to calculate the work order service index value used to represent the service level required by the work order according to the IT system level information and the user portrait information of the IT problem affecting the user, Finally, a work order is sent to the target processing node. The target processing node corresponds to the target handler, and the target handler is selected from the handler list corresponding to the IT issue type information, and the handler score of the target handler matches the work order service indicator value. , which ensures that the processing capacity of the processing node corresponding to the work order allocation can meet the work order processing requirements, and realizes the automatic allocation of work orders, which improves the matching degree between work order allocation and handler and work order allocation efficiency.

In one embodiment, the method may further include:

S740, in the case of receiving the automatic processing instruction sent by the target processing node, query the solution of the work order from the preset knowledge base according to the work order.

Among them, the preset knowledge base is generated by building a knowledge graph through historical work orders and solutions of historical work orders. Specifically, IT problem work orders and solutions that occur in daily operation and maintenance can be solidified into knowledge, and knowledge can be expressed and retrieved through knowledge graphs to form a knowledge base.

S750, invoke the operation and maintenance service to execute the solution to solve the IT problem corresponding to the work order.

Among them, the operation and maintenance services of different solutions can be configured in advance. By invoking the operation and maintenance service, the network/middle station operation instructions can be adapted, issued, executed, and feedback management, etc., so as to realize the implementation of the solution and solve the IT problem corresponding to the work order.

In the embodiment of the present application, first obtain the feature information of the work order, and then use a preset algorithm to calculate the work order service index value used to represent the service level required by the work order according to the IT system level information and the user portrait information of the IT problem affecting the user, Finally, a work order is sent to the target processing node. The target processing node corresponds to the target handler, and the target handler is selected from the handler list corresponding to the IT issue type information, and the handler score of the target handler matches the work order service indicator value. , which ensures that the processing capacity of the processing node corresponding to the work order allocation can meet the work order processing requirements, and realizes the automatic allocation of work orders, which improves the matching degree between work order allocation and handler and work order allocation efficiency.

In one embodiment, the user profile information includes the scope of users affected by the problem, the types of users affected by the problem, and the tendency of users to submit complaints due to the problem.

Among them, as shown in Figure 4, the scope of the users affected by the problem is the scope of users affected by the IT problem of the work order record, which can be exemplarily divided into three categories: national users, partial users, and individual users. The type of users affected by the problem is the type of users affected by the IT problem of the work order record, which can be exemplarily divided into three categories: major users, important users, and ordinary users. Problems that lead to users' tendency to file complaints are situations where users have potential risk of filing complaints caused by IT problems in the work order record. Exemplarily, it can be divided into three categories: users who have a tendency to complain, users who have a tendency to complain, and users who can wait and do not ask for immediate processing.

Further, as shown in FIG. 4 , the IT system level information is the importance of the system involved in the work order record IT problem, which can be exemplarily divided into three categories: core system, important system and general system.

Based on the specific division of the above-mentioned user profile information and IT system level information, S720: Calculate the work order service index value according to the IT system level information and the user profile information of the IT problem affecting the user through a preset algorithm, which may include:

S7201, through preset conversion rules, convert the range of users affected by the problem, the types of users affected by the problem, the tendency of users to submit complaints due to the problem, and the information at the IT system level, respectively, into binary codes.

Among them, the preset conversion rules can be pre-configured, the preset conversion rules have the function of converting information into binary codes, and the application of the preset conversion rules can affect the scope of users affected by problems, the types of users affected by problems, the tendency of users to file complaints due to problems, And the IT system level information is converted into binary encoding respectively.

Further, based on the range of users affected by the above problems, the types of users affected by the problems, the tendency of users to file complaints due to the problems, and the specific classification of IT system-level information, the converted binary codes are also different. When the bit value is 0, it means it is not of this type, and when the value is 1, it means it is of this type. For example, if the problem affects users across the country, the converted binary code is 100; the problem affects some users. , the converted binary code is 010. The problem affects the type of users, the tendency of users to submit complaints due to the problem, and the binary encoding conversion mode of IT system-level information are the same, and will not be repeated here.

Exemplarily, as shown in Figure 4, the range of users affected by the problem, the type of users affected by the problem, the tendency of users to submit complaints due to the problem, and the information at the IT system level, each information can be subdivided into three types, with a total of 12 types. When coding, it can be coded as 12-bit binary code. The problem affects the scope of users, the problem affects the type of users, the problem causes users to file complaints, and the IT system level information each occupies 3 bits, and then 12-bit binary coding is used for quantitative data conversion.

S7202: Convert the binary code into quantized data that can be used for size comparison by using a preset conversion algorithm to obtain a work order service indicator value.

The preset conversion algorithm may be pre-configured, and the preset conversion algorithm has the function of converting binary code into quantized data that can be used for size comparison, and applying the preset conversion algorithm can convert binary code into quantized data that can be used for size comparison. data to get the work order service indicator value.

In the embodiment of the present application, in order to enable the work order service index value to be automatically performed, preset conversion rules and preset conversion algorithms are pre-configured, and the problem affects the scope of users, the problem affects user types, and the problem causes users to submit complaints through the preset conversion rules. Tendency and IT system level information are converted into binary codes respectively, and then the binary codes are converted into quantitative data that can be used for size comparison through a preset conversion algorithm, and the work order service index value is obtained, which realizes the quantification of the work order service index value. Further, the value of the work order service indicator can be applied to determine the target handler that matches the work order.

In one embodiment, the work order service index value is calculated by the following formula:

Among them, S represents the service index value of the work order, k represents the type of the feature information of the work order corresponding to the binary code, and the value of k ranges from 0 to 3, and there are four kinds in total, corresponding to the range of users affected by the problem, the type of users affected by the problem, The problem leads to the tendency of users to submit complaints and IT system-level information; a _i represents any coded value in the binary code, and i represents the number of codes in the binary code. Based on this formula, the work order service index value of a decimal integer can be calculated according to the binary code.

Exemplarily, the actual value calculated according to the value of the work order service index can be divided into different work order service index levels, which are divided into three levels: general, urgent and urgent, wherein: the value of the work order service index is equal to 4 , it is normal; the work order service index value is greater than 4 and not greater than 8, it is urgent; the work order service index value is greater than 8 but not greater than 16, it is urgent. According to the service index level of the work order, the corresponding handler can be matched.

Exemplarily, when the IT system level information is an important system, the scope of users affected by the problem is some users, the type of users affected by the problem is ordinary users, and the user's tendency to appeal due to the problem is a tendency to appeal, the binary code is represented as follows: 010-010- 001-100, which is 9 after being converted into a work order service index value, then the level of the work order service index value is urgent.

In the embodiment of the present application, by designing a formula with a quantized binary coding function, the binary code is converted into quantized data that can be used to compare the size through the formula, so as to obtain the work order service index value, and realize the quantification of the work order service index value. Further, the value of the work order service indicator can be applied to determine the target handler that matches the work order.

In one embodiment, before sending the work order feature information to the target processing node, the method may further include:

Obtain the work order resolution efficiency index and work order resolution quality index of the handler.

Among them, reading the historical data of the handler's ability to solve work orders from the database can be considered from three dimensions, including staff ability labels, problem response efficiency, and service quality, and finally obtain the handler's work order resolution efficiency indicators and work order resolution quality. index.

Exemplarily, the work order resolution efficiency indicators may include indicators such as: 2 hours acceptance rate, 1 working day resolution rate, average delivery time and other indicators. Work order resolution quality indicators may include: work order evaluation satisfaction, work order resolution rate and other indicators.

The processor score is calculated according to the work order resolution efficiency index and the work order resolution quality index through the preset scoring algorithm.

Among them, different indicators b _j (j=1～n) can be applied when calculating the score of the handler, j represents the type of indicators, and n represents the total number of types of indicators. For example, the indicators include five categories: 2-hour acceptance rate, 1 Working day resolution rate, average delivery time, work order evaluation satisfaction and work order resolution rate, then n is 5; different target values B _j (j=1～n) and indicator weights can be preset for each indicator W _j (j=1-n), the handler score is calculated based on B _j and W _j . Correspondingly, the calculation formula of the preset scoring algorithm can be set as:

According to the calculation formula of the above preset scoring algorithm, the full score of the handler is 100 points, and the level of the handler can be divided into three levels: cutting-edge, backbone, and expert according to the score. For example, when the score of the handler is not less than 80 points, the level of the handler is the expert level; when the score of the handler is not less than 60 points, the level of the handler is the backbone level; when the score of the handler is lower than 60 points, the level of the handler is the backbone level. Then the handler level is the cutting-edge level. According to the handler level, it can be matched with the corresponding work order service index value.

Exemplarily, the actual value calculated according to the work order service indicator value can be divided into three levels, including: if the work order service indicator value is equal to 4, it is normal, and the work order service indicator value is greater than 4 and not greater than 8 , it is urgent, and the work order service index value is greater than 8 and not greater than 16, it is urgent. Then you can assign the urgent corresponding work orders to the expert handlers, assign the urgent corresponding work orders to the backbone handlers, and assign the general corresponding work orders to the cutting-edge handlers. At the same time, taking into account the principle of upward compatibility, when there is a shortage of handlers, low-level handlers can process work orders with higher levels of work order service indicators.

In the embodiment of the present application, the handler score is calculated based on the handler's work order resolution efficiency index and the work order resolution quality index, so that the handler score matches the handler's comprehensive quality, ensuring that the application handler score matches the work order service index The value can be matched to a handler with the corresponding work order processing capability, thereby ensuring that the work order can be resolved in time.

In one embodiment, acquiring work order feature information includes:

Send a preset work order template to the operation and maintenance node.

Among them, the operation and maintenance node corresponds to the operation and maintenance personnel, and the preset work order template requires the operation and maintenance personnel to input the following information: problem description, screenshots of problem error prompts, problem reproduction video, and the IT system where the problem occurs.

Receive preset ticket templates with completed input.

The preset analysis algorithm is used to analyze the IT problem type information represented by the input preset work order template, the IT system level information, and the user portrait information of the user affected by the IT problem, and obtain the work order feature information.

Among them, the neuro-linguistic programming (NLP) algorithm can be used to extract keywords from the problem description in the input preset work order template and the IT system where the problem occurs, and use digital image processing and recognition algorithms to identify problems with errors. Prompt screenshots and problem reproduction videos are used to analyze image characteristics, and finally output IT problem type information, IT system level information, and user portrait information of IT problems affecting users.

In the embodiment of the present application, the work order-related information is collected through the preset work order template, which ensures the unification of the information format, and a unified preset algorithm can be applied to extract the work order feature information for the work order module, instead of targeting various types of work orders. Different algorithms are preset for each order, which improves the extraction efficiency of work order feature information.

In one embodiment, the work order allocation method further includes:

In the case that the number of times of receiving the chargeback information sent by the target processing node meets the preset chargeback threshold, the work order feature information is sent to the new target processing node.

In the embodiment of the present application, when the number of times of receiving the chargeback information sent by the target processing node meets the preset chargeback threshold, the work order feature information is sent to the new target processing node, so that the target processing node corresponding to the current target processing node can be sent in time. The work order that cannot be solved by the handler is sent to the new target processing node for processing by the new target handler, which can ensure that the work order is processed in time.

In one embodiment, the work order allocation method further includes:

Record the processing time for the target processing node to process the feature information of the work order.

When the processing time exceeds the preset reminder time limit, reminder information is generated and sent to the target processing node.

Wherein, different preset reminder time limits may be preset for the levels corresponding to the magnitudes of different work order service index values. Exemplarily, the actual value calculated according to the work order service index value may be divided into three levels, including : If the work order service index value is equal to 4, it is normal, if the work order service index value is greater than 4 and not greater than 8, it is urgent, and if the work order service index value is greater than 8 but not greater than 16, it is urgent. For the general level, the preset reminder time limit can be set to 24 hours; for the emergency level, the preset reminder time limit can be set to 12 hours; for the urgent level, the preset reminder time limit can be set to 3 hours.

Further, if there is more than one target processing node, each target processing node is equivalent to a link, and different preset reminder time limits can be set correspondingly for multiple links.

In the embodiment of the present application, the processing time for the target processing node to process the feature information of the work order is recorded, and when the processing time exceeds the preset reminder time limit, reminder information is generated and sent to the target processing node, which can timely remind the processor to process the work order , to optimize the user experience.

In one embodiment, after S750: invoking the operation and maintenance service to execute the solution to solve the IT problem corresponding to the work order, the method may further include:

Receive the evaluation of the solution result of the IT problem corresponding to the solution work order sent by the operation and maintenance node.

Among them, the evaluation of the solution result can reflect the effectiveness of the operation and maintenance services and solutions, and then the operation and maintenance personnel can update and adjust the operation and maintenance services and solutions.

In the embodiment of the present application, receiving the evaluation of the solution result of solving the IT problem corresponding to the work order sent by the operation and maintenance node can provide a basis for the optimization of the operation and maintenance service and the solution.

In the embodiment of the present application, first obtain the feature information of the work order, and then use a preset algorithm to calculate the work order service index value used to represent the service level required by the work order according to the IT system level information and the user portrait information of the IT problem affecting the user, Finally, a work order is sent to the target processing node. The target processing node corresponds to the target handler, and the target handler is selected from the handler list corresponding to the IT issue type information, and the handler score of the target handler matches the work order service indicator value. , which ensures that the processing capacity of the processing node corresponding to the work order allocation can meet the work order processing requirements, and realizes the automatic allocation of work orders, which improves the matching degree between work order allocation and handler and work order allocation efficiency.

FIG. 7 describes a work order allocation method, and the apparatus provided by the embodiment of the present application is described below with reference to FIG. 8 and FIG. 9 . In order to realize the above-mentioned functions, the work order distribution apparatus includes corresponding hardware structures and/or software modules for executing each function. Those skilled in the art should easily realize that the present application can be implemented in hardware or in the form of a combination of hardware and computer software, in conjunction with the algorithm steps of the examples described in the embodiments disclosed herein. Whether a function is performed by hardware or computer software driving hardware depends on the specific application and design constraints of the technical solution. Skilled artisans may implement the described functionality using different methods for each particular application, but such implementations should not be considered beyond the scope of this application.

This embodiment of the present application can exemplarily divide the function module of the work order distribution apparatus according to the above method. The work order distributing device may divide each function module corresponding to each function, and may also integrate two or more functions into one processing module. The above-mentioned integrated modules can be implemented in the form of hardware, and can also be implemented in the form of software function modules. It should be noted that, the division of modules in the embodiments of the present application is schematic, and is only a logical function division, and there may be other division manners in actual implementation.

FIG. 8 shows a schematic structural diagram of a work order distribution device provided by an embodiment of the present application. Each module in the device shown in FIG. 8 has the function of implementing each step in FIG. 7 and can achieve its corresponding technical effect. As shown in Figure 8, the apparatus may include:

The obtaining module 810 is used for obtaining work order feature information, and the work order feature information is used to represent the information technology IT problem type information corresponding to the work order, the IT system level information, and the user portrait information of the user affected by the IT problem.

The calculation module 820 is configured to calculate the work order service index value according to the IT system level information and the user profile information of the user affected by the IT problem through a preset algorithm, and the work order service index value is used to represent the service level required by the work order.

The sending module 830 is configured to send the work order to the target processing node. Each processing node corresponds to a handler, the target processing node corresponds to the target handler, the target handler is selected from the handler list corresponding to the IT issue type information, and the handler score of the target handler matches the work order service index value.

In the embodiment of the present application, first obtain the feature information of the work order, and then use a preset algorithm to calculate the work order service index value used to represent the service level required by the work order according to the IT system level information and the user portrait information of the IT problem affecting the user, Finally, a work order is sent to the target processing node. The target processing node corresponds to the target handler, and the target handler is selected from the handler list corresponding to the IT issue type information, and the handler score of the target handler matches the work order service indicator value. , which ensures that the processing capacity of the processing node corresponding to the work order allocation can meet the work order processing requirements, and realizes the automatic allocation of work orders, which improves the matching degree between work order allocation and handler and work order allocation efficiency.

In one embodiment, the apparatus further includes a query module and a call module.

The query module is used for querying the solution of the work order from the preset knowledge base according to the work order under the condition of receiving the automatic processing instruction sent by the target processing node. The preset knowledge base is generated by building a knowledge graph from historical work orders and solutions of historical work orders.

The calling module is used to call the operation and maintenance service to execute the solution to solve the IT problem corresponding to the work order.

In one embodiment, the user profile information includes the scope of users affected by the problem, the types of users affected by the problem, and the tendency of users to submit complaints due to the problem.

The calculation module 820 is specifically used for:

Use preset conversion rules to convert the range of users affected by the problem, the type of users affected by the problem, the tendency of users to file complaints due to the problem, and the information at the IT system level into binary codes.

The binary code is converted into quantitative data that can be used to compare the size through a preset conversion algorithm, and the work order service index value is obtained.

In one embodiment, the work order service index value is calculated by the following formula:

Wherein, S represents the service index value of the work order, k represents the type of the feature information of the work order corresponding to the binary code, a _i represents any code value in the binary code, and i represents the number of codes of the binary code.

In one embodiment, the obtaining module 810 is further configured to obtain the work order resolution efficiency index and the work order resolution quality index of the handler before sending the work order feature information to the target processing node.

The calculation module 820 is further configured to calculate the score of the handler according to the work order resolution efficiency index and the work order resolution quality index by using a preset scoring algorithm.

In one embodiment, the obtaining module 810 is specifically configured to:

Send a preset work order template to the operation and maintenance node. The operation and maintenance node corresponds to the operation and maintenance personnel, and the preset work order template requires the operation and maintenance personnel to enter the following information: problem description, screenshots of problem error prompts, problem reproduction video, and the IT system where the problem occurs.

Receive preset ticket templates with completed input.

The preset analysis algorithm is used to analyze the IT problem type information represented by the input preset work order template, the IT system level information, and the user portrait information of the user affected by the IT problem, and obtain the work order feature information.

In an embodiment, the sending module 830 is further configured to send the work order feature information to the new target processing node when the number of times of receiving the chargeback information sent by the target processing node meets the preset chargeback threshold.

In one embodiment, the apparatus further includes a recording module and a generating module.

The recording module is used to record the processing time of the target processing node for processing the feature information of the work order.

The generation module is used to generate reminder information when the processing time exceeds the preset reminder time limit.

The sending module 830 is further configured to send reminder information to the target processing node.

In the embodiment of the present application, first obtain the feature information of the work order, and then use a preset algorithm to calculate the work order service index value used to represent the service level required by the work order according to the IT system level information and the user portrait information of the IT problem affecting the user, Finally, a work order is sent to the target processing node. The target processing node corresponds to the target handler, and the target handler is selected from the handler list corresponding to the IT issue type information, and the handler score of the target handler matches the work order service indicator value. , which ensures that the processing capacity of the processing node corresponding to the work order allocation can meet the work order processing requirements, and realizes the automatic allocation of work orders, which improves the matching degree between work order allocation and handler and work order allocation efficiency.

FIG. 9 shows a schematic structural diagram of an electronic device provided by an embodiment of the present application. As shown in Figure 9, the apparatus may include a processor 901 and a memory 902 storing computer program instructions.

Specifically, the above-mentioned processor 901 may include a central processing unit (Central Processing Unit, CPU), or a specific integrated circuit (Application Specific Integrated Circuit, ASIC), or may be configured as one or more integrated circuits implementing the embodiments of the present application .

Memory 902 may include mass storage for data or instructions. By way of example and not limitation, memory 902 may include a Hard Disk Drive (HDD), a floppy disk drive, flash memory, optical disk, magneto-optical disk, magnetic tape, or Universal Serial Bus (USB) drive or two or more A combination of more than one of the above. In one example, memory 902 may include removable or non-removable (or fixed) media, or memory 902 may be non-volatile solid-state memory. The memory 902 may be internal or external to the integrated gateway disaster recovery device.

In one example, the memory 902 may be a read only memory (ROM). In one example, the ROM may be a mask programmed ROM, programmable ROM (PROM), erasable PROM (EPROM), electrically erasable PROM (EEPROM), electrically rewritable ROM (EAROM), or flash memory or both A combination of one or more of the above.

The processor 901 realizes the method in the embodiment shown in FIG. 7 by reading and executing the computer program instructions stored in the memory 902, and achieves the corresponding technical effect achieved by the example shown in FIG. 7 by executing its method, and is described here for brevity. No longer.

In one example, the electronic device may also include a communication interface 903 and a bus 910 . Among them, as shown in FIG. 9 , the processor 901 , the memory 902 , and the communication interface 903 are connected through the bus 910 and communicate with each other.

The communication interface 903 is mainly used to implement communication between modules, apparatuses, units and/or devices in the embodiments of the present application.

The bus 910 includes hardware, software, or both, coupling the components of the online data flow metering device to each other. By way of example and not limitation, the bus may include an Accelerated Graphics Port (AGP) or other graphics bus, an Extended Industry Standard Architecture (EISA) bus, a Front Side Bus (FSB), a hyper Transport (Hyper Transport, HT) interconnect, Industry Standard Architecture (Industry Standard Architecture, ISA) bus, Infiniband interconnect, Low pin count (LPC) bus, Memory bus, Micro Channel Architecture (MCA) bus, Peripheral component interconnect Connectivity (PCI) bus, PCI-Express (PCI-X) bus, Serial Advanced Technology Attachment (SATA) bus, Video Electronics Standards Association Local (VLB) bus or other suitable bus or two or more of these combination. Bus 910 may include one or more buses, where appropriate. Although embodiments of this application describe and illustrate a particular bus, this application contemplates any suitable bus or interconnect.

The electronic device can execute the work order allocation method in the embodiment of the present application, so as to achieve the corresponding technical effect of the work order allocation method described in FIG. 7 .

In addition, in combination with the work order allocation method in the foregoing embodiment, the embodiment of the present application may provide a computer storage medium for implementation. Computer program instructions are stored on the computer storage medium; when the computer program instructions are executed by the processor, any one of the work order allocation methods in the foregoing embodiments is implemented.

In an exemplary embodiment, an embodiment of the present application further provides a computer program product, which, when the computer program product runs on a computer, enables the computer to implement the work order distribution method in the foregoing embodiment.

From the description of the above embodiments, those skilled in the art can clearly understand that, for the convenience and brevity of the description, only the division of the above functional modules is used as an example for illustration. In practical applications, the above functions can be allocated by Different functional modules are completed, that is, the internal structure of the device is divided into different functional modules, so as to complete all or part of the functions described above.

In the several embodiments provided in this application, it should be understood that the disclosed apparatus and method may be implemented in other manners. For example, the apparatus embodiments described above are only illustrative. For example, the division of modules or units is only a logical function division. In actual implementation, there may be other division methods. For example, multiple units or components may be combined or May be integrated into another device, or some features may be omitted, or not implemented. On the other hand, the shown or discussed mutual coupling or direct coupling or communication connection may be through some interfaces, indirect coupling or communication connection of devices or units, and may be in electrical, mechanical or other forms.

Units described as separate components may or may not be physically separated, and components shown as units may be one physical unit or multiple physical units, that is, may be located in one place, or may be distributed in multiple different places. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution in this embodiment.

In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically alone, or two or more units may be integrated into one unit. The above-mentioned integrated units may be implemented in the form of hardware, or may be implemented in the form of software functional units.

The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a readable storage medium. Based on such understanding, the technical solutions of the embodiments of the present application can be embodied in the form of software products in essence, or the parts that contribute to the prior art, or all or part of the technical solutions, which are stored in a storage medium , including several instructions to make a device (may be a single chip microcomputer, a chip, etc.) or a processor (processor) to execute all or part of the steps of the methods described in the various embodiments of the present application. The aforementioned storage medium includes: a U disk, a removable hard disk, a ROM, a RAM, a magnetic disk, or an optical disk and other mediums that can store program codes.

The above contents are only specific embodiments of the present application, but the protection scope of the present application is not limited thereto, and any changes or substitutions within the technical scope disclosed in the present application should be covered within the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims (18)

1. A work order allocation method, characterized in that, comprising: Obtain work order feature information, where the work order feature information is used to represent the information technology IT problem type information corresponding to the work order, the IT system level information, and the user portrait information that the IT problem affects the user; Through a preset algorithm, calculate the work order service index value according to the IT system level information and the user profile information of the user affected by the IT problem, and the work order service index value is used to represent the service level required by the work order; Send the work order to the target processing node; each processing node corresponds to a handler, the target processing node corresponds to a target handler, and the target handler is selected from the handler list corresponding to the IT problem type information, and the The handler score of the target handler matches the work order service index value. 2. The work order distribution method according to claim 1, wherein the method further comprises: In the case of receiving the automatic processing instruction sent by the target processing node, query the preset knowledge base for the solution of the work order according to the work order; The solution of the work order is generated by building a knowledge graph; The operation and maintenance service is invoked to execute the solution to solve the IT problem corresponding to the work order. 3. The work order distribution method according to claim 1, wherein the user profile information includes the scope of users affected by the problem, the type of users affected by the problem, and the tendency of users to submit complaints due to the problem; The preset algorithm is used to calculate the work order service index value according to the IT system level information and the user profile information that the IT problem affects the user, including: Converting the range of users affected by the problem, the types of users affected by the problem, the tendency of users to file complaints due to the problem, and the information at the IT system level are respectively converted into binary codes through preset conversion rules; The binary code is converted into quantized data that can be used for size comparison through a preset conversion algorithm, so as to obtain the work order service indicator value. 4. The work order distribution method according to claim 3, wherein the work order service index value is calculated by the following formula:

Wherein, S represents the work order service indicator value, k represents the type of the work order feature information corresponding to the binary code, a _i represents any code value in the binary code, and i represents the binary code. number of codes. 5. The work order distribution method according to any one of claims 1-4, wherein before sending the work order feature information to the target processing node, the method further comprises: Obtain the work order resolution efficiency index and work order resolution quality index of the handler; The handler score is calculated according to the work order resolution efficiency index and the work order resolution quality index by using a preset scoring algorithm. 6. The work order allocation method according to claim 5, wherein the acquiring the work order feature information comprises: Send a preset work order template to the operation and maintenance node; the operation and maintenance node corresponds to the operation and maintenance personnel, and the preset work order template requires the operation and maintenance personnel to input the following information: problem description, problem error prompt screenshot, problem recurrence video , and the IT system where the problem occurred; Receive the preset work order template that has been entered; The work order is obtained by analyzing the IT problem type information represented by the input preset work order template, the IT system level information, and the user profile information of the user affected by the IT problem by using a preset analysis algorithm characteristic information. 7. The work order allocation method according to claim 5, wherein the method further comprises: The work order feature information is sent to a new target processing node when the number of times of receiving the chargeback information sent by the target processing node meets the preset chargeback threshold. 8. The work order distribution method according to claim 7, wherein the method further comprises: Record the processing time of the target processing node for processing the feature information of the work order; When the processing time exceeds a preset reminder time limit, reminder information is generated and sent to the target processing node. 9. A work order distribution device, characterized in that, comprising: The acquisition module is used to acquire the feature information of the work order, and the feature information of the work order is used to represent the information of the information technology IT problem type corresponding to the work order, the information of the IT system level, and the user portrait information of the user affected by the IT problem; The calculation module is used to calculate the work order service index value according to the IT system level information and the user profile information of the user affected by the IT problem through a preset algorithm, and the work order service index value is used to represent the work order. required service level; The sending module is used to send the work order to the target processing node; each processing node corresponds to a handler, the target processing node corresponds to the target handler, and the target handler is selected from the handler corresponding to the IT problem type information Select from the list, and the handler score of the target handler matches the work order service index value. 10. The work order distribution device according to claim 9, wherein the device further comprises a query module and a call module; The query module is configured to query the preset knowledge base for the solution of the work order according to the work order in the case of receiving the automatic processing instruction sent by the target processing node; The historical work orders and the solutions of the historical work orders are generated by building a knowledge graph; The invocation module is used for invoking the operation and maintenance service to execute the solution, so as to solve the IT problem corresponding to the work order. 11. The work order distribution device according to claim 9, wherein the user profile information includes the scope of users affected by the problem, the type of users affected by the problem, and the tendency of users to submit complaints due to the problem; The computing module is specifically used for: Converting the range of users affected by the problem, the types of users affected by the problem, the tendency of users to file complaints due to the problem, and the information at the IT system level are respectively converted into binary codes through preset conversion rules; The binary code is converted into quantized data that can be used for size comparison through a preset conversion algorithm, so as to obtain the work order service indicator value. 12. The work order distribution device according to claim 11, wherein the work order service index value is calculated by the following formula:

Wherein, S represents the work order service indicator value, k represents the type of the work order feature information corresponding to the binary code, a _i represents any code value in the binary code, and i represents the binary code. number of codes. 13. The work order distribution device according to any one of claims 9 to 12, wherein the acquiring module is further configured to acquire the work order of the handler before sending the work order feature information to the target processing node. Order resolution efficiency indicators and work order resolution quality indicators; The calculation module is further configured to calculate the handler score according to the work order resolution efficiency index and the work order resolution quality index by using a preset scoring algorithm. 14. The work order distribution device according to claim 13, wherein the acquiring module is specifically used for: Send a preset work order template to the operation and maintenance node; the operation and maintenance node corresponds to the operation and maintenance personnel, and the preset work order template requires the operation and maintenance personnel to input the following information: problem description, problem error prompt screenshot, problem recurrence video , and the IT system where the problem occurred; Receive the preset work order template that has been entered; The work order is obtained by analyzing the IT problem type information represented by the input preset work order template, the IT system level information, and the user profile information of the user affected by the IT problem by using a preset analysis algorithm characteristic information. 15. The work order distribution device according to claim 13, wherein the sending module is further configured to: in the case that the number of times of receiving the chargeback information sent by the target processing node meets a preset chargeback threshold, Send the work order feature information to the new target processing node. 16. The work order distribution device of claim 15, wherein the device further comprises a recording module and a generating module; the recording module, configured to record the processing time of the target processing node for processing the feature information of the work order; The generating module is configured to generate reminder information when the processing time exceeds a preset reminder time limit; The sending module is further configured to send the reminder information to the target processing node. 17. An electronic device, comprising: a memory, a processor, and a computer program stored on the memory and running on the processor, the computer program being executed by the processor to achieve The work order distribution method according to any one of claims 1 to 8. 18. A computer-readable storage medium, wherein a computer program is stored on the computer-readable storage medium, and when the computer program is executed by a processor, the computer program according to any one of claims 1 to 8 is implemented. Work order assignment method.

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