RPA work order processing system, system architecture and work order processing method

Abstract

The invention discloses an RPA work order processing system, a system architecture and a work order processing method, and the system comprises a user interface subsystem which is used for collecting information inputted by a user; the work order generation and management subsystem is used for managing creation of the work order; the resource allocation and scheduling subsystem manages and allocates work orders through a process automation robot; the state monitoring and notification subsystem tracks a work order processing state through a process automation robot and notifies a related informing person when the state changes; the user feedback and data analysis subsystem is used for collecting user feedback information; the data analysis engine is used for extracting insights from the work order data according to statistics and machine learning technologies and providing support for management decision making; according to the method, the problem of low-efficiency workflow and user experience caused by the existing technical problems is solved, the workflow is optimized, high efficiency, transparency and user friendliness of the work order processing flow are realized, and meanwhile, the clear responsibility and the customizability of the flow are ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of automated process processing, and in particular to an RPA work order processing system, system architecture, and work order processing method. Background Art

[0002] RPA stands for Robotic Process Automation, which is used to simulate human manipulation of different software systems and automatically execute rule-based, highly repetitive business processes. Its core principle is to simulate the interaction between human users and computer interfaces, including the following steps: 1) Task capture: identifying and recording the process of human users performing tasks; 2) Script writing: converting captured tasks into automated scripts; 3) Task execution: automatically executing tasks according to scripts; 4) Monitoring and reporting: monitoring task execution and generating reports.

[0003] At present, the existing RPA-based work order system has the following technical problems, which affect the use of the system and the user experience: 1. Long response time. In the existing system, the response time of the work order may be long because it relies on manual identification and task assignment; 2. Uneven resource allocation. The idle status of the staff may not be updated in time, resulting in uneven distribution of work orders, with some staff being overloaded while others are idle; 3. Lack of automation. The classification and allocation of work orders mainly rely on manual operations, which are prone to errors and inefficient; 4. Insufficient timeout processing. The existing system may not have an effective mechanism to monitor and remind the processing of timed work orders; 5. The notification mechanism is imperfect. When the status of a work order changes, users and related workers are not informed. 6. Low user participation: users have limited participation and right to know in the work order processing process, and lack feedback and evaluation mechanisms; 7. Manual errors: manual operations may lead to classification errors, missed notifications, or untimely information updates; 8. Lack of flexibility: the existing system may not be flexible enough to adapt to changes in different managers or business needs; 9. Poor user experience: users may be dissatisfied with the lack of transparency and communication, affecting the overall user experience; 10. Efficiency bottleneck: certain links may become bottlenecks in the entire work order processing process, affecting overall efficiency; 11. Lack of continuous improvement mechanism: the existing system may not have established a continuous improvement mechanism based on user feedback. Summary of the invention

[0004] The purpose of the present invention is to overcome the shortcomings of the prior art and propose an RPA work order processing system, system architecture and work order processing method. The system overcomes the problems of the prior art that lead to inefficient workflows and poor user experience, improves processing transparency, optimizes workflows, and achieves efficient, transparent and user-friendly work order processing, while ensuring clear responsibilities and customizability of the process.

[0005] The objective of the present invention is achieved through the following technical solution: A work order processing system based on RPA, comprising: User interface subsystem, used to collect information input by users; The work order generation and management subsystem is used to manage the creation of work orders; Resource allocation and scheduling subsystem, which manages and allocates work orders through RPA process automation robots, including: resource monitor, built-in resource allocation algorithm, real-time monitoring of workers' idle status and work order queues, tracking workers' availability, calculating the priority and type of work orders through resource allocation algorithms to dynamically allocate work orders; intelligent allocation engine, used to automatically allocate work orders to workers; The status monitoring and notification subsystem tracks the status of work order processing through RPA process automation robots and notifies relevant insiders when the status changes; User feedback and data analysis subsystem, used to generate feedback requests and collect user feedback information; The data analysis engine extracts insights from work order data based on statistical and machine learning techniques to support management decisions.

[0006] Furthermore, the resource allocation algorithm includes: Data-driven decision making: Leverage historical ticket data analysis to identify ticket processing patterns, common issues, and resolution times, thereby predicting ticket processing needs and allocating resources; Skill matching: staff are evaluated based on preset benchmark indicators. When assigning work orders, candidates are selected based on the matching degree between the technicians’ skill level and the benchmark indicators. Real-time load balancing: Real-time monitoring of the workload of the technical support team, dynamic adjustment of work order allocation, avoidance of overload and balance of workload; in dynamic adjustment, define and quantify the workload of staff, workload includes the number of work orders currently being processed by technicians, the number of unresolved work orders, the average time for work order processing and the urgency of work orders, calculated as follows: Workload = Number of work orders * Average processing time * Urgency weighted coefficient, sort all workload values ​​from high to low, the top 30% of technical support teams with high workload values ​​are fully loaded and no work order allocation is made, while the other 70% participate in work order allocation; automatically adjust the allocation of new work orders based on the current workload situation; Priority queue: Based on the urgency and business impact of the work order, a priority queue system is implemented to ensure that high-priority work orders are processed first. The urgency and business impact of the work order are mainly determined by a comprehensive calculation based on the work order type, keywords, source department, and business impact range defined by the submitter. Different work order types have different preset values, different source departments have different coefficients, and the business impact range also has a preset impact range coefficient. The priority value = (work order type * source department priority coefficient * impact range coefficient); Feedback loop: Establish a feedback mechanism to collect work order processing results and user satisfaction, and use these data to adjust and optimize the allocation algorithm; user feedback is collected through work order processing result analysis and user satisfaction surveys; the score of each work order processing result includes resolution time and first response, calculated as follows: resolution time = (resolution time of this work order / average resolution time of this type of work order); first response = (first response time of this work order / average first response time of work orders); the evaluation of the overall work order processing results includes one-time resolution rate and reopening rate, calculated as follows: one-time resolution rate = (work orders that are resolved once without redistribution / total number of work orders); reopening rate = (number of times a work order is reopened / total number of work orders); user satisfaction is counted and scored by the user checking the options in the questionnaire. Each option has a preset score, and the total value of the preset score of each option forms the satisfaction score; monitor the work order processing results and user satisfaction indicators. If the trends of the two indicators are stable or rising, it means that the automatic allocation mechanism is normal, otherwise it means that there is a problem with the automatic allocation mechanism and the indicator has dropped; Predictive analysis: Use predictive analysis to predict peak hours and workloads, and allocate resources in advance to cope with the influx of work orders; analyze and predict different types of work orders, based on the time period feature values ​​of historical work order data, supplemented by the submitting department, urgency, and the attribute values ​​of the person who handles it, calculate the average processing time, and then generate a predictive model for each type of work order based on the machine learning model; use the predictive model to predict the processing speed of the work order when it is handled by different personnel; Automated escalation paths: Preset automated escalation paths to escalate tickets to higher-level staff when primary technical support cannot resolve the issue.

[0007] Furthermore, the benchmark indicators include skill labels, experience levels, historical resolution rates, response times, user feedback, professional certifications, skill weights, skill combinations, real-time workloads, and team collaboration, where: Skill tags: Different skill tags are assigned to staff members according to their professional fields, and the skill tags are matched with keywords in the work orders; Experience level: Based on the staff's work experience and historical performance, the staff's experience level is assessed as junior, intermediate or senior. The experience level is calculated based on the working time. The staff with working time of less than 3 years is assessed as junior, the staff with working time of 3-8 years is assessed as intermediate, and the staff with working time of more than 8 years is assessed as senior; Historical resolution rate: It is evaluated based on the success rate of work orders handled during the personnel's unit working period. For those over one year, the success rate of work orders in the past year is used for calculation. For those less than one year, the success rate of work orders since the beginning of employment is used for calculation. The success rate of work orders = the ratio of completed work orders to assigned work orders. Response time: the speed at which staff respond to work orders and the average time required to resolve issues; for those with more than one year of service, the average response time in the most recent year will be used; for those with less than one year of service, the average response time since the beginning of service will be used; User feedback: User satisfaction ratings of technicians in historical work orders are used as a reference for skill matching; for those with more than one year of service, the calculation is based on the user feedback of the most recent year, and for those with less than one year of service, the calculation is based on the user feedback since the beginning of service; User satisfaction = (1-user complaints / number of related work orders); Professional certification: If a staff member holds a professional certification or qualification certificate, the staff member is deemed to have professional certification; Skill weight: For work orders that require multiple skills to solve, different skill weights are assigned according to the importance of different skills in solving specific problems and the nature of the work order. Matching skills are selected first based on the skill weight assessment. Skill combination: For combined work orders that require multiple skills, the comprehensive skill set of the worker is considered. The comprehensive skill set is multiple skill tags of a worker. Real-time workload: The worker’s current workload also influences resource allocation to ensure that tasks are not over-allocated; Teamwork: When a work order requires teamwork, it is necessary to consider the skill complementarity between team members, that is, different skills correspond to different people, and finally a team is formed to solve the work order.

[0008] Further, the resource monitor includes: Machine Learning: Apply machine learning algorithms to continuously optimize resource allocation algorithms and iteratively improve by learning from past successes and failures; Geographic and time matching: Extract the geographic location and working time attributes of the work order, and assign the work order to the staff with the corresponding geographic location and working time based on the geographic location and working time attributes; Multi-dimensional evaluation: consider the influence of other factors, including satisfaction, work fatigue, differences in development direction and non-working status of staff; set different weights for each other factor, and generate a comprehensive score based on the weight. The comprehensive score is used to determine whether a staff member is suitable when he or she has relevant skills. When the comprehensive score is greater than the preset threshold, the staff member is considered suitable; Flexible resource pool: Establish a multi-skilled flexible resource pool to quickly reallocate personnel to handle different types of work orders when needed; Continuous monitoring and optimization: Regularly review the overall effectiveness of the resource allocation algorithm, collect key performance indicators (KPIs), and adjust the resource allocation algorithm based on the key performance indicators (KPIs); among them, the key performance indicators include average response time, resolution rate, and user satisfaction index.

[0009] Furthermore, the user interface subsystem includes a user input interface for collecting work order data submitted by users and verifying the integrity and accuracy of the data; the work order data includes problem description, classification and contact information; The user interface subsystem performs the following operations: Create a form submission interface to receive the user's work order data; Implement data validation logic to ensure the completeness and accuracy of input data.

[0010] Furthermore, the work order generation and management subsystem includes: The work order creator automatically generates work orders based on user input and assigns a unique work order number. It is used to process the data submitted by the user and create a new work order record to ensure that each work order has a unique identifier. The work order database is used to store and manage the details and status of all work orders.

[0011] Furthermore, the status monitoring and notification subsystem includes: Status tracker, which is used to monitor the real-time status of the work order, track each stage of the work order processing, and update the status record in the database when the status changes; The notification sender sends notifications to relevant staff at key time points through the RPA process automation robot, that is, automatically sends email or SMS notifications to staff and users according to preset rules.

[0012] Furthermore, the user feedback and data analysis subsystem includes: User feedback collector, which is used to automatically request users to evaluate the processing process after the work order is resolved, generate feedback requests, and collect users' satisfaction and suggestions on the work order processing; The data analysis engine includes: analyzing work order processing data, identifying process bottlenecks and improvement opportunities, using statistical and machine learning techniques to extract insights from work order data, and providing support for management decisions.

[0013] An RPA-based work order processing system architecture is used to implement the above RPA-based work order processing system, including: The data storage layer includes: the database system, which uses a relational database to store work order data, user information, work order status, and feedback data; the data warehouse, which is used to store historical work order data and user feedback for data analysis and report generation; The business logic layer includes: API server, which is used to provide RESTful API or GraphQL API for front-end applications and external system integration, and handle data creation, reading, updating and deletion operations; business logic components, which are used to implement the core business logic of work order processing, specifically work order allocation, status update and notification sending; RPA integration, which is used to integrate RPA process automation robots with the business logic layer to perform automation tasks, including work order information capture, automatic allocation and status monitoring tasks; message queue, which is used to handle asynchronous tasks, such as sending notifications and processing feedback; The service and processing layer includes: the work order management service component, which is used to handle the creation, update and management of work orders; the user feedback service component, which is used to manage the collection, storage and analysis of user feedback; the notification service component, which is used to send work order status change notifications to users and staff; the data analysis service component, which is used to perform data collection, organization and analysis, and generate service reports; The presentation layer includes: front-end applications, which are built on HTML, CSS, and JavaScript to provide users with an intuitive work order submission and query interface; front-end frameworks, which are used to build dynamic and responsive user interfaces; and mobile adaptability components, which are used to ensure that the front-end interface is compatible with mobile devices. Network and security modules, including: load balancer, used to distribute incoming network traffic to multiple servers to improve application availability and response speed; API gateway, used as an intermediary between the front-end and back-end, handling request routing, authentication and authorization; network security components, used to implement SSL / TLS encryption, firewall and DDoS protection to ensure the security of data transmission; The operation and monitoring module includes: containerization and orchestration components, which are used to containerize and automate application deployment; continuous integration components, which are used to automate testing and deployment processes to ensure code quality and rapid iteration; monitoring and logging components, which monitor application performance and log data based on monitoring tools and log management systems; The user feedback management module includes: a user feedback collector, which is integrated in the front-end application of the presentation layer and is used to collect user satisfaction and feedback on the service; a notification system, which is integrated in the front-end application of the presentation layer and is used to display the status change notifications sent by the system to the user; and a business status update component, which is used to obtain the work order status.

[0014] A work order processing method is implemented according to the above RPA-based work order processing system, comprising the following steps: S1. Submitting a work order: The user submits a work order request through the user interface subsystem and fills in the necessary information; S2. Work order information capture: The work order processing system monitors the submission action through the user interface subsystem and captures the work order information entered by the user; S3. Work order generation: Use the work order generation and management subsystem to automatically enter the captured information, create a work order record, and assign a unique work order number; S4. Work order allocation: Use the resource allocation and scheduling subsystem to automatically allocate work orders to appropriate staff members based on the work order type, urgency, and staff expertise and load; S5, Work Order Processing: After receiving the work order, the staff starts troubleshooting and repairing the problem, and the work order processing system monitors the processing progress; S6, Status Update: When the staff updates the work order status in the system, the status monitoring and notification subsystem is triggered; S7. Send status change notification: The status monitoring and notification subsystem automatically sends status change notifications to users and relevant staff via email or SMS to ensure timely delivery of information; S8. Sending questionnaire: After the work order status is updated to resolved, the work order processing system automatically sends a satisfaction survey to the user using the user feedback and data analysis subsystem; S9, user feedback processing: Users provide feedback through the survey link, and the user feedback and data analysis subsystem collects and stores these feedback data; S10, Process data collection and integration: Regularly collect data from the work order database and user feedback, and use the data analysis engine for integration and pre-processing; S11. Data analysis and reporting: The data analysis engine analyzes the collected data to identify key performance indicators and user satisfaction; S12. Report generation and presentation: Based on the analysis results, the data analysis engine automatically generates a service report and presents it to relevant staff through the user interface subsystem.

[0015] Compared with the prior art, the present invention has the following advantages and beneficial effects: 1. Quick response: Through automated work order classification and intelligent allocation, the system can quickly identify work order requirements and assign them to appropriate staff, significantly shortening response time.

[0016] 2. Balanced resource allocation: Dynamic resource management ensures that work orders are allocated according to the real-time status of staff, avoiding the imbalance of overload and idleness.

[0017] 3. Improve the level of automation: Automated work order classification reduces the need for manual operations, reduces error rates and improves efficiency.

[0018] 4. Timeout processing monitoring: The timeout reminder mechanism ensures that work orders will not be unprocessed for a long time, improving the timeliness of processing. By setting the time threshold, it ensures that more than 90% of the work orders are responded to within the specified time.

[0019] 5. Perfect notification mechanism: Status update notification ensures that all relevant personnel receive information about changes in work order status in a timely manner, enhancing the transparency of communication.

[0020] 6. Improve user participation: The user feedback loop allows users to participate in every stage of work order processing, improving user information rights and satisfaction.

[0021] 7. Reduce manual errors: Automated processes reduce manual operations, thereby reducing classification errors and untimely information updates.

[0022] 8. Enhanced flexibility: The system allows managers to adjust settings according to business needs, such as timeouts and notification methods, which improves the adaptability of the system.

[0023] 9. Improved user experience: By improving transparency and communication, user satisfaction with the service is improved.

[0024] 10. Eliminate efficiency bottlenecks: By optimizing workflows and automating processing, bottlenecks in the process are eliminated.

[0025] 11. Establish a continuous improvement mechanism: Data analysis and reporting functions help managers find improvement points in the process and establish a continuous improvement mechanism based on user feedback. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 This is an interactive flowchart of the RPA-based work order processing system.

[0027] Figure 2 This is an architecture diagram of the RPA-based work order processing system. DETAILED DESCRIPTION

[0028] The present invention will be further described below in conjunction with specific embodiments.

[0029] Example 1 See also Figure 1 As shown, the RPA-based work order processing system provided in this embodiment includes: 1) A user interface subsystem for collecting work order data input by users; the user interface subsystem includes a user input interface for collecting work order data submitted by users and verifying the integrity and accuracy of the data; the work order data includes problem description, classification and contact information; The user interface subsystem performs the following operations: creates a form submission interface to receive the user's work order data; implements data validation logic to ensure the integrity and accuracy of the input data.

[0030] 2) Work order generation and management subsystem, used to manage the creation of work orders, including: The work order creator automatically generates work orders based on user input and assigns a unique work order number. It is used to process the data submitted by the user and create a new work order record to ensure that each work order has a unique identifier. The work order database is used to store and manage the details and status of all work orders.

[0031] 3) Resource allocation and scheduling subsystem, which manages and allocates work orders through RPA process automation robots, including: A) Resource monitor, with built-in resource allocation algorithm, real-time monitoring of workers' idle status and work order queues, tracking workers' availability, and calculating the priority and type of work orders through resource allocation algorithm to dynamically allocate work orders; B) Intelligent allocation engine, which is used to automatically allocate work orders to workers; Wherein, the resource monitor includes: A1) Data-driven decision making: Use historical ticket data analysis to identify ticket processing patterns, common problems, and resolution times, thereby predicting ticket processing needs and allocating resources; A2) Skill Matching: Staff are evaluated based on preset benchmark indicators. When assigning work orders, candidates are determined based on the matching degree between the technical staff’s skill level and the benchmark indicators. The benchmark indicators include skill tags, experience level, historical resolution rate, response time, user feedback, professional certification, skill weight, skill combination, real-time workload and team collaboration, among which: Skill tags: Different skill tags are assigned to staff members according to their professional fields, and the skill tags are matched with keywords in the work orders; Experience level: Based on the staff's work experience and historical performance, the staff's experience level is assessed as junior, intermediate or senior. The experience level is calculated based on the working time. The staff with working time of less than 3 years is assessed as junior, the staff with working time of 3-8 years is assessed as intermediate, and the staff with working time of more than 8 years is assessed as senior; Historical resolution rate: It is evaluated based on the success rate of work orders handled during the personnel's unit working period. For those over one year, the success rate of work orders in the past year is used for calculation. For those less than one year, the success rate of work orders since the beginning of employment is used for calculation. The success rate of work orders = the ratio of completed work orders to assigned work orders. Response time: the speed at which staff respond to work orders and the average time required to resolve issues; for those with more than one year of service, the average response time in the most recent year will be used; for those with less than one year of service, the average response time since the beginning of service will be used; User feedback: User satisfaction ratings of technicians in historical work orders are used as a reference for skill matching; for those with more than one year of service, the calculation is based on the user feedback of the most recent year, and for those with less than one year of service, the calculation is based on the user feedback since the beginning of service; User satisfaction = (1-user complaints / number of related work orders); Professional certification: If a staff member holds a professional certification or qualification certificate, the staff member is deemed to have professional certification; Skill weight: For work orders that require multiple skills to solve, different skill weights are assigned according to the importance of different skills in solving specific problems and the nature of the work order. Matching skills are selected first based on the skill weight assessment. Skill combination: For combined work orders that require multiple skills, the comprehensive skill set of the worker is considered. The comprehensive skill set is multiple skill tags of a worker. Real-time workload: Detect the current workload of workers in real time to ensure that tasks are not over-allocated; Teamwork: When a work order requires teamwork, it is necessary to consider the skill complementarity between team members, that is, different skills correspond to different people, and finally form a team to solve the work order; A3) Real-time load balancing: Monitor the workload of the technical support team in real time, dynamically adjust the work order allocation, avoid overload and balance the workload; in dynamic adjustment, define and quantify the workload of the staff, which includes the number of work orders currently being processed by the technicians, the number of unresolved work orders, the average time for work order processing, and the urgency of the work orders. The calculation formula is as follows: Workload = Number of work orders * Average processing time * Urgency weighted coefficient. All workload values ​​are sorted from high to low. The top 30% of the technical support teams with high workload values ​​are fully loaded and no work order allocation is made. The other 70% participate in work order allocation; according to the current workload situation, the allocation of new work orders is automatically adjusted; A4) Priority queue: Based on the urgency and business impact of the work order, a priority queue system is implemented to ensure that high-priority work orders are processed first. The urgency and business impact of the work order are mainly determined by a comprehensive calculation based on the work order type, keywords, source department, and business impact range defined by the submitter. Different work order types have different preset values, different source departments have different coefficients, and the business impact range also has a preset impact range coefficient. The priority value = (work order type * source department priority coefficient * impact range coefficient); A5) Feedback loop: Establish a feedback mechanism to collect work order processing results and user satisfaction, and use these data to adjust and optimize the allocation algorithm; user feedback is collected through work order processing result analysis and user satisfaction surveys; the score of each work order processing result includes resolution time and first response, calculated as follows: resolution time = (resolution time of this work order / average resolution time of this type of work order); first response = (first response time of this work order / average first response time of work orders); the evaluation of the overall work order processing results includes one-time resolution rate and reopening rate, calculated as follows: one-time resolution rate = (work orders that are resolved once without redistribution / total number of work orders); reopening rate = (number of times a work order is reopened / total number of work orders); user satisfaction is counted and scored by the user checking the options in the questionnaire. Each option has a preset score, and the total value of the preset score of each option forms the satisfaction score; monitor the work order processing results and user satisfaction indicators. If the trends of the two indicators are stable or rising, it means that the automatic allocation mechanism is normal, otherwise it means that there is a problem with the automatic allocation mechanism and the indicator has dropped; A6) Predictive analysis: Use predictive analysis to predict peak hours and workloads, and allocate resources in advance to cope with the influx of work orders. Analyze and predict different types of work orders. Based on the time period feature values ​​of historical work order data, supplemented by the submitting department, urgency, and the attribute values ​​of the person who handles the work order, calculate the average processing time, and then generate a predictive model for each type of work order based on the machine learning model. Use the predictive model to predict the processing speed of the work order when it is handled by different personnel. A7) Automated escalation path: preset automated escalation paths to escalate tickets to higher-level staff when primary technical support cannot resolve the issue; A8) Machine Learning: Apply machine learning algorithms to continuously optimize resource allocation algorithms and iteratively improve them by learning from past successes and failures. A9) Geographic and time matching: Extract the geographic location and working time attributes of the work order, and assign the work order to the staff with the corresponding geographic location and working time according to the geographic location and working time attributes; A10) Multi-dimensional evaluation: Consider the impact of other factors, including satisfaction, work fatigue, development direction differences and non-working status of staff; collect staff job satisfaction questionnaires, extract the scores of the satisfaction questionnaires, and calculate the staff's satisfaction with the work content and environment; calculate work fatigue based on the types of work orders handled by the staff and the continuous working time; extract the development direction of the staff, and calculate the difference between the development direction and the staff's existing skill tags; the non-working status of the staff includes the number of days left for vacation, the number of days after the end of vacation or other non-working status; set different weights for each other factor, and generate a comprehensive score based on the weights. The comprehensive score is used to determine whether a staff member is a suitable person when he has relevant skills. When the comprehensive score is greater than the preset threshold, the staff member is considered suitable; A11) Elastic resource pool: Establish a multi-skilled elastic resource pool to quickly reallocate personnel to handle different types of work orders when needed; A12) Continuous monitoring and optimization: Regularly review the overall effectiveness of the resource allocation algorithm, collect key performance indicators (KPIs), and make adjustments based on the key performance indicators; among them, the key performance indicators include average response time, resolution rate, and user satisfaction index.

[0032] 4) Status monitoring and notification subsystem, which tracks the status of work order processing through RPA process automation robots and notifies relevant insiders when the status changes, including: Status tracker, which is used to monitor the real-time status of the work order, track each stage of the work order processing, and update the status record in the database when the status changes; The notification sender sends notifications to relevant staff at key time points through the RPA process automation robot, that is, automatically sends email or SMS notifications to staff and users according to preset rules.

[0033] The user feedback and data analysis subsystem is used to generate feedback requests and collect user feedback information, including: a user feedback collector, which is used to automatically request users to evaluate the processing process after the work order is resolved, generate feedback requests, and collect user satisfaction and suggestions on the work order processing; Data analysis engine, used to analyze work order processing data, identify process bottlenecks and improvement opportunities, and use statistical and machine learning techniques to extract insights from work order data to support management decisions.

[0034] The technical principles and implementation methods of the RPA-based work order processing system are as follows: Based on Java Spring MVC and microservice architecture, as well as the technical selection of MySQL database, the specific technical implementation methods for each subsystem are as follows: User Interface Subsystem: Use Spring MVC framework to build RESTful API, allowing front-end applications to interact with back-end services through HTTP requests. Front-end pages use HTML / CSS / JavaScript, combined with front-end frameworks such as React or Angular to provide dynamic user interfaces.

[0035] Ticket Generation and Management Subsystem: Spring Boot is used as the basic framework of microservices to realize ticket generation and management. JPA (Java Persistence API) is used to interact with the MySQL database to perform data CRUD operations.

[0036] Resource Allocation and Scheduling Subsystem: Use Spring Cloud Eureka for service discovery to facilitate inter-service communication. Use Spring Cloud Config for centralized configuration management.

[0037] Status Monitoring and Notification Subsystem: Use Spring Integration or Spring Cloud Stream to implement an event-driven architecture for monitoring and notification of state changes. Integrate message queues such as RabbitMQ or Kafka to handle state change events.

[0038] User Feedback and Data Analysis Subsystem: Use Spring Batch or QuartzScheduler to schedule scheduled tasks for user feedback collection and data analysis. Use Spring Data JPA for complex queries and report generation.

[0039] Security: Integrate Spring Security to implement API security authentication and authorization. Use HTTPS protocol to encrypt communication between client and server.

[0040] Scalability: The microservice architecture allows each service to be independently expanded to cope with different load requirements. The database uses MySQL cluster technology to support high concurrency and data volume growth.

[0041] Deployment and operation: Use Docker containerized services and Kubernetes to orchestrate and manage services. Use Spring Boot Actuator to monitor the health status and performance indicators of services.

[0042] Example 2 See also Figure 2 As shown, the RPA-based work order processing system architecture provided in this embodiment is used to implement the RPA-based work order processing system described in Example 1, including: The data storage layer includes: the database system, which uses a relational database to store work order data, user information, work order status, and feedback data; the data warehouse, which is used to store historical work order data and user feedback for data analysis and report generation; The business logic layer includes: API server, which is used to provide RESTful API or GraphQL API for front-end applications and external system integration, and handle data creation, reading, updating and deletion operations; business logic components, which are used to implement the core business logic of work order processing, specifically work order allocation, status update and notification sending; RPA integration, which is used to integrate RPA process automation robots with the business logic layer to perform automation tasks, including work order information capture, automatic allocation and status monitoring tasks; message queue, which is used to handle asynchronous tasks, such as sending notifications and processing feedback; The service and processing layer includes: the work order management service component, which is used to handle the creation, update and management of work orders; the user feedback service component, which is used to manage the collection, storage and analysis of user feedback; the notification service component, which is used to send work order status change notifications to users and staff; the data analysis service component, which is used to perform data collection, organization and analysis, and generate service reports; The presentation layer includes: front-end applications, which are built on HTML, CSS, and JavaScript to provide users with an intuitive work order submission and query interface; front-end frameworks, which are used to build dynamic and responsive user interfaces; and mobile adaptability components, which are used to ensure that the front-end interface is compatible with mobile devices. Network and security modules, including: load balancer, used to distribute incoming network traffic to multiple servers to improve application availability and response speed; API gateway, used as an intermediary between the front-end and back-end, handling request routing, authentication and authorization; network security components, used to implement SSL / TLS encryption, firewall and DDoS protection to ensure the security of data transmission; The operation and monitoring module includes: containerization and orchestration components, which are used to containerize and automate application deployment; continuous integration components, which are used to automate testing and deployment processes to ensure code quality and rapid iteration; monitoring and logging components, which monitor application performance and log data based on monitoring tools and log management systems; The user feedback management module includes: a user feedback collector, which is integrated in the front-end application of the presentation layer and is used to collect user satisfaction and feedback on the service; a notification system, which is integrated in the front-end application of the presentation layer and is used to display the status change notifications sent by the system to the user; and a business status update component, which is used to obtain the work order status.

[0043] Example 3 The work order processing method provided in this embodiment is implemented according to the RPA-based work order processing system described in Embodiment 1, and includes the following steps: S1. Submitting a work order: The user submits a work order request through the user interface subsystem and fills in the necessary information; S2. Work order information capture: The work order processing system monitors the submission action through the user interface subsystem and captures the work order information entered by the user; S3. Work order generation: Use the work order generation and management subsystem to automatically enter the captured information, create a work order record, and assign a unique work order number; S4. Work order allocation: Use the resource allocation and scheduling subsystem to automatically allocate work orders to appropriate staff members based on the work order type, urgency, and staff expertise and load; S5, Work Order Processing: After receiving the work order, the staff starts troubleshooting and repairing the problem, and the work order processing system monitors the processing progress; S6, Status Update: When the staff updates the work order status in the system, the status monitoring and notification subsystem is triggered; S7. Send status change notification: The status monitoring and notification subsystem automatically sends status change notifications to users and relevant staff via email or SMS to ensure timely delivery of information; S8. Sending questionnaire: After the work order status is updated to resolved, the work order processing system automatically sends a satisfaction survey to the user using the user feedback and data analysis subsystem; S9, user feedback processing: Users provide feedback through the survey link, and the user feedback and data analysis subsystem collects and stores these feedback data; S10, Process data collection and integration: Regularly collect data from the work order database and user feedback, and use the data analysis engine for integration and pre-processing; S11. Data analysis and reporting: The data analysis engine analyzes the collected data to identify key performance indicators and user satisfaction; S12. Report generation and presentation: Based on the analysis results, the data analysis engine automatically generates a service report and presents it to relevant staff through the user interface subsystem.

[0044] Example 4 The IT work order processing method provided in this embodiment is implemented according to the RPA-based work order processing system described in Embodiment 1, and includes: In the work order processing system, once the user completes filling in the repair information on the web interface and submits the request, the entire automated workflow is initiated. At this time, the RPA capture module is monitoring the user's operation in real time with millisecond accuracy, and responds quickly to the user's submission action through the integrated event trigger mechanism. The RPA process automation robot uses the preset script to parse the web interface and capture each field filled in by the user, including key information such as fault description, device model, fault occurrence time, and user contact information.

[0045] The captured information is first temporarily stored in the internal cache of the RPA process automation robot, and a series of data cleaning and verification operations are performed to ensure the accuracy and completeness of the information. These operations include checking whether the required fields are filled in, whether the format is correct, and whether there are illegal characters. After verification, the RPA robot encapsulates this information into a structured format, such as a JSON object, and automatically enters it into the work order generation and management subsystem through API calls or direct database insertion.

[0046] The work order generation and management subsystem is the core of the entire automation process. It receives structured repair information from the RPA robot and creates a new work order record in the MySQL database based on it. This record not only contains the user's original repair information, but also generates a unique work order number. This number follows a specific format, based on a combination of date timestamp and incremental sequence to ensure uniqueness in the entire system.

[0047] When a work order record is created, the system will automatically record metadata such as the creation time and creator, providing a basis for subsequent work order management and tracking. Once the work order record and number are generated, the system will automatically update the database and pass the new work order information to the next process link, the work order allocation link, to ensure that the work order can be processed quickly and accurately.

[0048] The second is resource allocation and scheduling. The system has a built-in resource allocation algorithm that comprehensively considers the detailed information of the work order, including factors such as fault type, urgency, complexity, etc. At the same time, the system also evaluates the staff's expertise, current workload and historical processing efficiency.

[0049] During this process, the RPA process automation robot constantly interacts with the resource allocation and scheduling subsystem to obtain work order information and the status of the technical support team. Once the system determines the most suitable staff member, the RPA process automation robot automatically assigns the work order to that person and records the assignment information in the database to ensure clear ownership and responsibility of the work order.

[0050] As the work order is assigned, the RPA process automation robot immediately enters status monitoring mode. It uses scheduled queries or database triggers to detect any changes in the work order status. This real-time monitoring ensures that updates to the work order status can be quickly and accurately reflected in the system. If the staff updates the work order status to "processing" or "resolved", the RPA process automation robot will immediately capture this change.

[0051] After capturing the status change, the RPA process automation robot automatically triggers the notification mechanism. According to the preset communication protocol and user preferences, the RPA process automation robot sends status change notifications via email or SMS service. These notifications include key information such as the current status of the work order, processing progress, and estimated resolution time, ensuring that all relevant personnel and users can receive updates in a timely manner, thereby improving the transparency of the entire process.

[0052] In addition, the RPA process automation robot is also responsible for ensuring the accuracy and delivery rate of notifications. It confirms whether the information is successfully delivered by checking the email delivery status and the feedback of the SMS service. If the delivery fails, the RPA process automation robot will try to resend or take other alternative communication methods to ensure that the information can eventually reach the recipient.

[0053] In the IT work order processing system, when the staff completes the troubleshooting and repair work, they will update the work order status in the system to "solved". This status update is the key event that triggers the start of the user feedback and data analysis subsystem.

[0054] At this point, the RPA process automation robot automatically activates the user feedback mechanism. It retrieves the user contact information related to the resolved ticket from the database, and then sends a satisfaction survey to the user through the configured communication channels, such as email or SMS platforms. These surveys usually contain a series of rating questions and open-ended feedback questions, designed to collect users' views on response time, problem resolution quality, and overall service experience.

[0055] The user feedback and data analysis subsystem is not only responsible for sending surveys, but also for real-time monitoring and collecting user responses. The RPA process automation robot will automatically organize and store user feedback data, including ratings and text comments, in a dedicated area of ​​the database for subsequent analysis.

[0056] Next, the system will perform data analysis tasks regularly. RPA process automation robots use data analysis tools such as SQL queries, statistical analysis software or machine learning models to conduct in-depth analysis of the collected work order data and user feedback. This process includes calculating key performance indicators (KPIs) such as average response time, resolution rate, user satisfaction index, and identifying common problem patterns and potential areas for service improvement.

[0057] After the analysis is complete, the RPA robot integrates the results into detailed service reports. These reports usually contain charts, graphs and summaries of key findings, allowing management to quickly grasp the overall performance of the service and any areas for improvement.

[0058] In addition, the RPA process automation robot also automatically generates improvement suggestions based on the analysis results, such as adjusting the staff allocation strategy, optimizing the work order processing process, or providing additional employee training. These suggestions are included in the service report, providing decision makers with data-driven insights to help them develop evidence-based improvement measures.

[0059] Finally, these service reports will be sent to relevant staff regularly through automated report distribution mechanisms, such as automated email sending, to ensure that they can get timely updates on service performance and take necessary actions to improve service quality and user satisfaction. Through this automated and data analysis-driven approach, the IT work order processing system not only improves the efficiency of work order processing, but also enhances the ability to continuously improve and monitor service quality.

[0060] The embodiments described above are only preferred embodiments of the present invention and are not intended to limit the scope of implementation of the present invention. Therefore, all changes made according to the shape and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A work order processing system based on RPA, characterized in that: include: User interface subsystem, used to collect information input by users; The work order generation and management subsystem is used to manage the creation of work orders; Resource allocation and scheduling subsystem, which manages and allocates work orders through RPA process automation robots, including: resource monitor, built-in resource allocation algorithm, real-time monitoring of workers' idle status and work order queues, tracking workers' availability, calculating the priority and type of work orders through resource allocation algorithms to dynamically allocate work orders; intelligent allocation engine, used to automatically allocate work orders to workers; The status monitoring and notification subsystem tracks the status of work order processing through RPA process automation robots and notifies relevant insiders when the status changes; User feedback and data analysis subsystem, used to generate feedback requests and collect user feedback information; The data analysis engine extracts insights from work order data based on statistical and machine learning techniques to support management decisions.

2. According to the RPA-based work order processing system of claim 1, it is characterized in that: The resource allocation algorithm includes: Data-driven decision making: Leverage historical ticket data analysis to identify ticket processing patterns, common issues, and resolution times, thereby predicting ticket processing needs and allocating resources; Skill matching: staff are evaluated based on preset benchmark indicators. When assigning work orders, candidates are selected based on the matching degree between the technicians’ skill level and the benchmark indicators. Real-time load balancing: Real-time monitoring of the workload of the technical support team, dynamic adjustment of work order allocation, avoidance of overload and balance of workload; in dynamic adjustment, define and quantify the workload of staff, workload includes the number of work orders currently being processed by technicians, the number of unresolved work orders, the average time for work order processing and the urgency of work orders, calculated as follows: Workload = Number of work orders * Average processing time * Urgency weighted coefficient, sort all workload values ​​from high to low, the top 30% of technical support teams with high workload values ​​are fully loaded and no work order allocation is made, while the other 70% participate in work order allocation; automatically adjust the allocation of new work orders based on the current workload situation; Priority queue: Based on the urgency and business impact of the work order, a priority queue system is implemented to ensure that high-priority work orders are processed first. The urgency and business impact of the work order are mainly determined by a comprehensive calculation based on the work order type, keywords, source department, and business impact range defined by the submitter. Different work order types have different preset values, different source departments have different coefficients, and the business impact range also has a preset impact range coefficient. The priority value = (work order type * source department priority coefficient * impact range coefficient); Feedback loop: Establish a feedback mechanism to collect work order processing results and user satisfaction, and use these data to adjust and optimize the allocation algorithm; user feedback is collected through work order processing result analysis and user satisfaction surveys; the score of each work order processing result includes resolution time and first response, calculated as follows: resolution time = (resolution time of this work order / average resolution time of this type of work order); first response = (first response time of this work order / average first response time of work orders); the evaluation of the overall work order processing results includes one-time resolution rate and reopening rate, calculated as follows: one-time resolution rate = (work orders that are resolved once without redistribution / total number of work orders); reopening rate = (number of times a work order is reopened / total number of work orders); user satisfaction is counted and scored by the user checking the options in the questionnaire. Each option has a preset score, and the total value of the preset score of each option forms the satisfaction score; monitor the work order processing results and user satisfaction indicators. If the trends of the two indicators are stable or rising, it means that the automatic allocation mechanism is normal, otherwise it means that there is a problem with the automatic allocation mechanism and the indicator has dropped; Predictive analysis: Use predictive analysis to predict peak hours and workloads, and allocate resources in advance to cope with the influx of work orders; analyze and predict different types of work orders, based on the time period feature values ​​of historical work order data, supplemented by the submitting department, urgency, and the attribute values ​​of the person who handles it, calculate the average processing time, and then generate a predictive model for each type of work order based on the machine learning model; use the predictive model to predict the processing speed of the work order when it is handled by different personnel; Automated escalation paths: Preset automated escalation paths to escalate tickets to higher-level staff when primary technical support cannot resolve the issue.

3. According to claim 2, a work order processing system based on RPA is characterized in that: The benchmark indicators include skill labels, experience levels, historical resolution rates, response times, user feedback, professional certifications, skill weights, skill combinations, real-time workloads, and team collaboration, among which: Skill tags: Different skill tags are assigned to staff members according to their professional fields, and the skill tags are matched with keywords in the work orders; Experience level: Based on the staff's work experience and historical performance, the staff's experience level is assessed as junior, intermediate or senior. The experience level is calculated based on the working time. The staff with working time of less than 3 years is assessed as junior, the staff with working time of 3-8 years is assessed as intermediate, and the staff with working time of more than 8 years is assessed as senior; Historical resolution rate: It is evaluated based on the success rate of work orders handled during the personnel's unit working period. For those over one year, the success rate of work orders in the past year is used for calculation. For those less than one year, the success rate of work orders since the beginning of employment is used for calculation. The success rate of work orders = the ratio of completed work orders to assigned work orders. Response time: the speed at which staff respond to work orders and the average time required to resolve issues; for those with more than one year of service, the average response time in the most recent year will be used; for those with less than one year of service, the average response time since the beginning of service will be used; User feedback: User satisfaction ratings of technicians in historical work orders are used as a reference for skill matching; for those with more than one year of service, the calculation is based on the user feedback of the most recent year, and for those with less than one year of service, the calculation is based on the user feedback since the beginning of service; User satisfaction = (1-user complaints / number of related work orders); Professional certification: If a staff member holds a professional certification or qualification certificate, the staff member is deemed to have professional certification; Skill weight: For work orders that require multiple skills to solve, different skill weights are assigned according to the importance of different skills in solving specific problems and the nature of the work order. Matching skills are selected first based on the skill weight assessment. Skill combination: For combined work orders that require multiple skills, the comprehensive skill set of the worker is considered. The comprehensive skill set is multiple skill tags of a worker. Real-time workload: Detect the current workload of workers in real time to ensure that tasks are not over-allocated; Teamwork: When a work order requires teamwork, it is necessary to consider the skill complementarity between team members, that is, different skills correspond to different people, and finally a team is formed to solve the work order.

4. According to the RPA-based work order processing system of claim 1, it is characterized in that: The resource monitor comprises: Machine Learning: Apply machine learning algorithms to continuously optimize resource allocation algorithms and iteratively improve by learning from past successes and failures; Geographic and time matching: Extract the geographic location and working time attributes of the work order, and assign the work order to the staff with the corresponding geographic location and working time based on the geographic location and working time attributes; Multi-dimensional evaluation: extract other factors of the work order, including satisfaction, work fatigue, differences in development direction and non-working status of the staff; set different weights for each other factor, and generate a comprehensive score based on the weight. The comprehensive score is used to determine whether a staff member is suitable when he or she has relevant skills. When the comprehensive score is greater than the preset threshold, the staff member is considered suitable; Flexible resource pool: Establish a multi-skilled flexible resource pool to quickly reallocate personnel to handle different types of work orders when needed; Continuous monitoring and optimization: Regularly review the overall effectiveness of the resource allocation algorithm, collect key performance indicators (KPIs), and adjust the resource allocation algorithm based on the key performance indicators (KPIs); among them, the key performance indicators include average response time, resolution rate, and user satisfaction index.

5. According to the RPA-based work order processing system of claim 1, it is characterized in that: The user interface subsystem includes a user input interface for collecting work order data submitted by users and verifying the integrity and accuracy of the data; the work order data includes problem description, classification and contact information; The user interface subsystem performs the following operations: Create a form submission interface to receive the user's work order data; Implement data validation logic to ensure the completeness and accuracy of input data.

6. The work order processing system based on RPA according to claim 1, characterized in that: The work order generation and management subsystem includes: The work order creator automatically generates work orders based on user input and assigns a unique work order number. It is used to process the data submitted by the user and create a new work order record to ensure that each work order has a unique identifier. The work order database is used to store and manage the details and status of all work orders.

7. The work order processing system based on RPA according to claim 1, characterized in that: The status monitoring and notification subsystem includes: Status tracker, which is used to monitor the real-time status of the work order, track each stage of the work order processing, and update the status record in the database when the status changes; The notification sender sends notifications to relevant staff at key time points through the RPA process automation robot, that is, automatically sends email or SMS notifications to staff and users according to preset rules.

8. The work order processing system based on RPA according to claim 1, characterized in that: The user feedback and data analysis subsystem includes: User feedback collector, which is used to automatically request users to evaluate the processing process after the work order is resolved, generate feedback requests, and collect users' satisfaction and suggestions on the work order processing; The data analysis engine includes: analyzing work order processing data, identifying process bottlenecks and improvement opportunities, using statistical and machine learning techniques to extract insights from work order data, and providing support for management decisions.

9. An RPA-based work order processing system architecture, characterized in that: A work order processing system based on RPA for implementing any one of claims 1 to 8, comprising: The data storage layer includes: the database system, which uses a relational database to store work order data, user information, work order status, and feedback data; the data warehouse, which is used to store historical work order data and user feedback for data analysis and report generation; The business logic layer includes: API server, which is used to provide RESTful API or GraphQL API for front-end applications and external system integration, and handle data creation, reading, updating and deletion operations; business logic components, which are used to implement the core business logic of work order processing, specifically work order allocation, status update and notification sending; RPA integration, which is used to integrate RPA process automation robots with the business logic layer to perform automation tasks, including work order information capture, automatic allocation and status monitoring tasks; message queue, which is used to handle asynchronous tasks, such as sending notifications and processing feedback; The service and processing layer includes: the work order management service component, which is used to handle the creation, update and management of work orders; the user feedback service component, which is used to manage the collection, storage and analysis of user feedback; the notification service component, which is used to send work order status change notifications to users and staff; the data analysis service component, which is used to perform data collection, organization and analysis, and generate service reports; The presentation layer includes: front-end applications, which are built on HTML, CSS, and JavaScript to provide users with an intuitive work order submission and query interface; front-end frameworks, which are used to build dynamic and responsive user interfaces; and mobile adaptability components, which are used to ensure that the front-end interface is compatible with mobile devices. Network and security modules, including: load balancer, used to distribute incoming network traffic to multiple servers to improve application availability and response speed; API gateway, used as an intermediary between the front-end and back-end, handling request routing, authentication and authorization; network security components, used to implement SSL / TLS encryption, firewall and DDoS protection to ensure the security of data transmission; The operation and monitoring module includes: containerization and orchestration components, which are used to containerize and automate application deployment; continuous integration components, which are used to automate testing and deployment processes to ensure code quality and rapid iteration; monitoring and logging components, which monitor application performance and log data based on monitoring tools and log management systems; The user feedback management module includes: a user feedback collector, which is integrated in the front-end application of the presentation layer and is used to collect user satisfaction and feedback on the service; a notification system, which is integrated in the front-end application of the presentation layer and is used to display the status change notifications sent by the system to the user; and a business status update component, which is used to obtain the work order status.

10. A work order processing method, characterized in that: The implementation of the RPA-based work order processing system according to any one of claims 1 to 8 comprises the following steps: S1. Submitting a work order: The user submits a work order request through the user interface subsystem and fills in the necessary information; S2. Work order information capture: The work order processing system monitors the submission action through the user interface subsystem and captures the work order information entered by the user; S3. Work order generation: Use the work order generation and management subsystem to automatically enter the captured information, create a work order record, and assign a unique work order number; S4. Work order allocation: Use the resource allocation and scheduling subsystem to automatically allocate work orders to appropriate staff members based on the work order type, urgency, and staff expertise and load; S5, Work Order Processing: After receiving the work order, the staff starts troubleshooting and repairing the problem, and the work order processing system monitors the processing progress; S6, Status Update: When the staff updates the work order status in the system, the status monitoring and notification subsystem is triggered; S7. Send status change notification: The status monitoring and notification subsystem automatically sends status change notifications to users and relevant staff via email or SMS to ensure timely delivery of information; S8. Sending questionnaire: After the work order status is updated to resolved, the work order processing system automatically sends a satisfaction survey to the user using the user feedback and data analysis subsystem; S9, user feedback processing: Users provide feedback through the survey link, and the user feedback and data analysis subsystem collects and stores these feedback data; S10, Process data collection and integration: Regularly collect data from the work order database and user feedback, and use the data analysis engine for integration and pre-processing; S11. Data analysis and reporting: The data analysis engine analyzes the collected data to identify key performance indicators and user satisfaction; S12. Report generation and presentation: Based on the analysis results, the data analysis engine automatically generates a service report and presents it to relevant staff through the user interface subsystem.

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