Search

CN-122022738-A - Work order management system and method based on artificial intelligence technology

CN122022738ACN 122022738 ACN122022738 ACN 122022738ACN-122022738-A

Abstract

The invention discloses a manual management system and method based on artificial intelligence technology, belonging to the technical field of information system of enterprise management, which extracts key information such as type, influence range, etc. by natural language processing after receiving user work order, matches and disposes script library template, calls sequence generation model to generate structured disposing script containing role, task, stage, matches with processor skill according to role capability label, and pushing a dedicated task view, monitoring task execution through two types of preset rules, dynamically pushing a flow, sharing key information, constructing a treatment time sequence causal map based on process records after a work order is closed, extracting a key path to generate a summary report containing root cause analysis, breaking information island and experience multiplexing difficult problems, and remarkably improving cross-role cooperative efficiency and organization level problem solving capability.

Inventors

  • ZHANG LUYANG
  • YANG JUNMIN

Assignees

  • 华北水利水电大学
  • 河南安普包装机械制造有限公司

Dates

Publication Date
20260512
Application Date
20260224

Claims (10)

  1. 1. A work order management system based on artificial intelligence technology, comprising: the work order receiving and analyzing module (1) is used for receiving work order data submitted by a user, carrying out natural language processing on the work order data and extracting the type, the influence range and the key description information of the work order; A disposal script library (2) is pre-stored with a plurality of structured script templates (201) which are abstracted and formed according to historical event experience, and each script template (201) defines a disposal stage sequence of a corresponding class of complex events, targets of each stage and a required disposal role set; The script generation module (3) is respectively in communication connection with the work order receiving and analyzing module (1) and the disposal script library (2) and is used for matching a target script template (201) from the disposal script library (2) according to the type and the influence range of the work order, calling an artificial intelligent model, and generating a structured disposal script (301) corresponding to the current work order by combining the key description information, wherein the structured disposal script (301) comprises a required processing role set, at least two disposal stages, a specific task target of each disposal stage associated to a specific role in the processing role set and an initial information set required for starting each disposal stage; A processor database (4) storing skill labels and current state information of a plurality of potential processors; A character configuration library (5) in which a plurality of processing character definition units (501) are prestored, each processing character definition unit (501) including a sphere of responsibility for the character, a function authority for allowing an operation, and a required capability label; The dynamic task scheduling and presenting module (6) is respectively in communication connection with the script generating module (3), the role configuration library (5) and the processor database (4) and is used for executing a required processing role set defined in the structured processing script (301), acquiring a role definition from the role configuration library (5), matching a capability label in the role definition with a skill label in the processor database (4), distributing at least one processor for each processing role, packaging task targets and initial information sets in different stages in the structured processing script (301) according to the corresponding processing roles, forming a role-specific task view, and pushing the role-specific task view to a processor terminal distributed to the role; The state monitoring and rule triggering module (7) is in communication connection with the dynamic task scheduling and presenting module (6) and is used for monitoring the operation feedback of each processor terminal to the role-specific task view of the processor terminal and judging the advancing condition of the treatment flow according to a predefined rule set, wherein the rule set at least comprises: The first type of propulsion rule triggers unlocking new task information or operation authority to a terminal of a first processing role after monitoring that a specific task belonging to the first processing role is marked as completed; A second class of pushing rules, triggering multicast sharing key information to processing role terminals participating in cooperation and activating a next processing stage defined in the structured processing scenario (301) after monitoring that specific cooperative actions respectively belonging to at least two different processing roles are completed; The processing procedure recording module (8) is in communication connection with the dynamic task scheduling and presenting module (6) and the state monitoring and rule triggering module (7) and is used for recording operation records of all processor terminals, all rule events triggered by the state monitoring and rule triggering module (7) and task view changes caused by the operation records according to time sequence; And the treatment knowledge generation module (9) is in communication connection with the treatment process recording module (8) and is used for constructing a treatment process map reflecting treatment time sequence and causal logic based on the operation record and the rule event after the work order is closed, extracting a key treatment path from the map and automatically generating a summary report containing root cause analysis and operation steps.
  2. 2. The work order management system based on artificial intelligence technology as set forth in claim 1, wherein the artificial intelligence model invoked in the scenario generation module (3) generates a model for a trained sequence, training data of the sequence generation model including historical worksheets and corresponding manual handling records thereof, the sequence generation model being used for populating the key description information to corresponding stages of the target scenario template (201) and generating logical dependencies between stages.
  3. 3. The work management system based on artificial intelligence technology according to claim 1, wherein the step of assigning processors to each processing role by the dynamic task scheduling and presenting module (6) specifically comprises: determining an allocation priority of each processing role according to the urgency degree of each processing stage in the structured processing script (301); According to the allocation priority, screening out processors with skill label matching and current status of 'available' or 'slightly busy' from the processor database (4) for each processing role in turn; when a single processing role needs to allocate multiple processors, the allocation is performed according to a load balancing policy.
  4. 4. A work management system based on artificial intelligence technology according to claim 1, characterized in that the predefined rule set in the status monitoring and rule triggering module (7) is computer executable logic stored in the form of "conditions, actions", specific tasks in the first class of propulsion rules and specific co-actions in the second class of propulsion rules, both predefined in the structured treatment script (301).
  5. 5. The workflow management system based on artificial intelligence technology as recited in claim 1, wherein the operation record recorded by the treatment process recording module (8) comprises at least an operation type, an operation object, an operation time stamp, a processor identity for executing the operation, and an operation result status.
  6. 6. The workflow management system based on artificial intelligence technology according to claim 1, wherein the disposal knowledge generation module (9) comprises: The map construction unit is used for constructing the disposal process map by taking the timestamp as an axis, taking the processor operation and rule triggering event as a node and taking the information flow and task state change relation as an edge; And the path analysis and report generation unit is used for carrying out retrospective analysis on the disposal process map, finding out the shortest critical path from the occurrence of the problem to the solution of the problem, and converting node information on the critical path into the summary report of natural language description according to a preset template.
  7. 7. A work order management method based on artificial intelligence technology, comprising application to an artificial intelligence technology based work management system as claimed in any one of claims 1 to 6, the method is characterized by comprising the following steps of: s1, receiving and analyzing a work order, namely receiving work order data submitted by a user, and performing natural language processing to extract the type, the influence range and key description information of the work order; S2, generating a script, namely matching a target script template (201) from a preset disposal script library (2) according to the type and the influence range of the work order, calling an artificial intelligent model, and generating a structured disposal script (301) corresponding to the current work order by combining the key description information; S3, task scheduling and distribution, namely matching the processor for each processing role according to the processing role set in the structured processing script (301), and packaging the tasks and information in the script into a role-specific task view and distributing the role-specific task view to the corresponding processor; s4, state monitoring and flow deduction, namely monitoring task execution feedback of each processor, dynamically propelling a treatment flow according to a predefined rule set, and unlocking a new task, releasing shared information or activating a next treatment stage; s5, recording all operation and state change events in the treatment process according to time sequence; and S6, knowledge generation, namely after the work order is closed, constructing a treatment process map based on the recorded events, and extracting a key path from the treatment process map to generate a summary report.
  8. 8. The artificial intelligence technology based worksheet management method of claim 7, wherein the step of matching and generating a structured treatment script (301) based on key information comprises: Matching the extracted worksheet type and influence range with templates in the disposal script library (2) to obtain a target script template (201); And calling a sequence generation model, taking key description information of a work order as input, refining and instantiating the target script template (201), and outputting the structured treatment script (301) containing specific tasks and information.
  9. 9. The method for managing work orders based on artificial intelligence technology according to claim 7, wherein the step of dynamically advancing the treatment process according to the rule set specifically comprises: when detecting the condition conforming to the first type of propulsion rule, unlocking the subsequent task or information to the corresponding single processor terminal; And when the conditions conforming to the second type of pushing rules are detected, the sharing information is released to a group of processor terminals, and the script flow is pushed to the next stage.
  10. 10. The method of claim 7, wherein the step of generating a treatment process map and summary report comprises: correlating the operation records of the time sequence with rule triggering events to construct a graph structure with causal relation; identifying a core action sequence for solving the problem from the graph structure; the core action sequence is converted into a structured text report.

Description

Work order management system and method based on artificial intelligence technology Technical Field The invention relates to the technical field of information systems for enterprise management, in particular to a work order management system and method based on an artificial intelligence technology. Background The work management system is a core tool in information technology operation and maintenance, customer service and business process support of modern enterprise management, and is used for tracking, distributing and processing various events, requests and faults. Currently, the mainstream work order system mostly adopts a linear flow model of "create, distribute, process, close" and is matched with a static knowledge base with the help of a simple rule engine. However, prior art solutions also suffer from drawbacks when dealing with complex events requiring multi-person, multi-persona collaboration, such as: When complex events occur, related information is generally scattered in different systems and communication channels, a processor needs to collect, screen and splice the information by itself, an information island is easy to form, the roles of different responsibilities are not convenient to quickly acquire key contexts inside and outside a responsibilities range, the communication and coordination efficiency is influenced, and even an error decision is made due to the asymmetry of the information; The disposal process relies on personal experience and is not very convenient to reuse, most of the existing systems only record final solutions or simple processing logs, and do not record the dynamic collaboration relations among decision logic, information circulation paths and roles in the whole disposal process in a structured manner, so that precious disposal experience exists in the form of implicit knowledge, can not be effectively deposited, re-coiled and converted into reusable tissue assets, and the training period of new members is prolonged, and still needs to be searched from the beginning when facing similar events. In recent years, artificial intelligence technology has been introduced into the field of work order management, such as automatic classification of work orders using natural language processing, prediction of solution time length using machine learning, or allocation advice. However, these improvements focus on the inputs (classification, dispatch) or outputs (prediction) of the process, and do not radically reconstruct the core collaborative paradigm and knowledge evolution mechanism in complex event handling, and the system still serves as a passive task distribution and recording tool, rather than an intelligent collaborative hub that can actively guide the intelligence of the collaborative, structured precipitation process. Therefore, an innovative work management system and method are needed, which can deeply integrate the artificial intelligence technology into the collaborative treatment full life cycle of complex events, realize intelligent aggregation and directional presentation of information, dynamic drastic deduction of treatment flow, and automatic extraction and solidification of whole process knowledge, so that the cross-role collaborative efficiency and the organization-level problem solving capability are remarkably improved, and therefore, we propose the work management system and method based on the artificial intelligence technology to solve the problems. Disclosure of Invention 1. Technical problem to be solved Aiming at the problems existing in the prior art, an innovative work management system and method are needed, which can deeply integrate the artificial intelligence technology into the collaborative treatment full life cycle of complex events, realize intelligent aggregation and directional presentation of information, dynamic drastic deduction of treatment flow and automatic extraction and solidification of whole process knowledge, thereby remarkably improving cross-role collaborative efficiency and organization-level problem solving capability. 2. Technical proposal In order to solve the problems, the invention adopts the following technical scheme. A work order management system based on artificial intelligence technology, comprising: The work order receiving and analyzing module is used for receiving work order data submitted by a user, carrying out natural language processing on the work order data and extracting the type, the influence range and the key description information of the work order; A script library is disposed, a plurality of structured script templates formed by abstraction according to historical event experience are prestored, and each script template defines a disposal stage sequence of a corresponding complex event, targets of each stage and a required disposal role set; The script generation module is respectively in communication connection with the work order receiving and analyzing module and the treatment script l