RPA fault work order grading early warning method and system

Abstract

The invention discloses an RPA fault work order grading early warning method and system, and relates to the field of information processing, after a fault work order is generated, multiple types of work order system data including work order numbers, related business types, fault description and the like are collected by means of an RPA technology, the business process key degree is quantified by constructing a business process key degree evaluation model, and the fault work order is obtained. The proportion of the number of affected users and the accuracy influence degree of key service data are calculated, a service influence degree function value is obtained through weighting so as to establish a grading standard, and faults are divided into different grades from first-grade serious faults to fourth-grade slight faults. A corresponding emergency degree evaluation scheme is formulated for each level of fault, and a differentiated notification mode is adopted. Meanwhile, an early warning information updating mechanism is arranged, so that the fault influence can be evaluated more accurately, early warning is reasonably graded, information is dynamically updated, the processing progress is controlled, the fault work order management and response efficiency is effectively improved, and stable and efficient service operation is guaranteed.

Description

Technical Field

[0001] The present invention relates to the field of information processing, and in particular to a method and system for hierarchical early warning of RPA fault work orders. Background Art

[0002] As grassroots personnel across various units face an increasing number of repetitive and tedious tasks, such as daily duty management, report generation, data transmission and reception, document processing, business flow, and exception analysis, RPA technology has gained widespread adoption. As RPA application scenarios become increasingly complex and diverse, the management and processing of troubleshooting tickets has become a major challenge facing enterprise operations and maintenance. Traditional troubleshooting ticket processing methods often lack systematicity and efficiency. The collection of ticket data often relies on manual recording and information collection, which is not only inefficient but also prone to omissions and errors.

[0003] In the past, fault assessments were often based on a single factor, which failed to fully reflect the fault's true impact on the entire business system. Faults can affect the accuracy of critical business data or impact the operations of a large number of users, potentially leading to severe knock-on effects on the overall business operation. However, traditional assessment methods struggle to accurately quantify these complex impacts, making it difficult to accurately determine the fault's urgency and priority.

[0004] Traditional models lack a refined, hierarchical approach to early warning notifications. Fault notifications are typically sent uniformly to relevant personnel, without prioritizing them based on severity. This makes it difficult for operations personnel to quickly identify which faults require immediate attention and which can be addressed later. This can lead to inappropriate resource allocation, delayed emergency response, and disruptive business operations.

[0005] Furthermore, traditional troubleshooting ticket management systems lack effective information update and feedback optimization mechanisms. Once a ticket is issued, it's difficult to track progress in real time and dynamically adjust early warning information. Furthermore, after troubleshooting is complete, detailed information about the process is rarely collected for systematic reflection and improvement. This leads to the recurrence of the same type of troubleshooting, while preventing fundamental optimization of the troubleshooting process and early warning strategies.

[0006] In summary, with the continuous improvement of enterprise digitalization and the widespread and in-depth application of RPA, existing fault ticket processing technologies can no longer meet the urgent needs of enterprises for efficient operation and maintenance, precise fault management, and business continuity assurance. Summary of the Invention

[0007] The purpose of the present invention is to provide an RPA fault ticket classification warning method and system. After the fault ticket is generated, RPA collects multiple types of data, and then conducts a series of processes such as business impact analysis and classification standard establishment, urgency assessment and notification, warning information update, order progress tracking, and post-processing feedback and adjustment to solve the above problems.

[0008] To achieve the above object, the present invention provides the following technical solutions:

[0009] A method for hierarchical early warning of RPA fault work orders, characterized by comprising the following steps:

[0010] S1: Collect work order system data; when a fault work order is generated, data collection is performed with the help of RPA technology to obtain comprehensive and critical information, which will serve as the basis for subsequent analysis and decision-making.

[0011] S2: Analyze business impact and calculate a business impact function. This introduces a multi-factor fusion business impact function, aiming to accurately quantify the impact of a failure on the business by integrating multiple factors. The criticality of business processes is quantified by constructing a business process criticality assessment model. Specifically, each business process is broken down into multiple, sequential sub-processes, which are aggregated into key business data vectors. Each sub-process is assigned a weight, and its criticality is calculated using a specific formula.

[0012] S3: Establish grading standards based on business impact; based on the calculated business impact function, establish four grading standards.

[0013] S4: Establish an urgency assessment plan;

[0014] S5: Establish an early warning information update mechanism; continuously update the early warning related information, recalculate the urgency assessment function and early warning trigger function at every set time interval, and adjust the early warning level and notification method accordingly if the urgency changes. Establish an early warning upgrade mechanism. If each fault is not handled for 48 hours, it will automatically upgrade one level. This is to prevent the fault from being shelved for a long time and causing more serious consequences. By automatically upgrading the early warning level, it forces relevant personnel to pay attention and speed up the processing progress.

[0015] S6: Order Progress Tracking: This system provides real-time updates on the progress of work orders, from the moment the operator accepts the order, begins troubleshooting, implements repair measures, and ultimately completes the repair. This helps managers and other relevant personnel stay informed of the progress of troubleshooting, allowing them to coordinate, allocate resources, and adjust handling strategies. For example, if a particular step is found to be taking too long, they can promptly analyze the cause and implement measures to expedite the process.

[0016] S7: Feedback and Adjustment. After a fault ticket is processed, feedback is collected, including the actual cause of the fault, the corrective measures taken, and whether there are other potential risks. The actual cause of the fault helps analyze weaknesses in the business process or system, and feedback can be used to adjust and improve the tiered early warning method.

[0017] Step S1 collects work order system data specifically as follows: When a fault work order is generated, data is collected using RPA technology to obtain the following information: including work order number N, initiation time N, business type b, fault description D, and business process criticality C p , the number of users affected by the failure N u , the total number of users involved in the business N t .

[0018] Step S2 analyzes the business impact and calculates the business impact function. Specifically, a business impact function E based on multi-factor integration is introduced, which specifically covers the following key elements:

[0019] Business process criticality C p , by building a business process criticality assessment model to quantify, each business process is decomposed into i continuous sub-processes, and the collection is a key business data vector Each sub-process sets its weight w s , the formula for calculating its criticality is:

[0020]

[0021] in, is the weight of the ith sub-process, Is the key business data vector Evaluation function for the criticality of the i-th sub-process;

[0022] Percentage of affected users Count the number of users N affected by the failure u And the total number of users corresponding to this business Then calculate the proportion of affected users, the formula is:

[0023]

[0024] in, is the percentage of affected users, N u is the number of users affected by the failure, The total number of users corresponding to the service. This percentage directly reflects the impact of the fault on users. A higher percentage indicates a wider range of users affected by the fault, and a greater overall impact on the service.

[0025] Impact on the accuracy of key business data A d , using data deviation assessment algorithm to quantify this factor, constantly record key business data, and aggregate them into Calculate the expected mean of data under normal conditions Standard deviation σ b , when a failure occurs, the key business data actually observed is recorded as The degree of impact on the accuracy of key business data is A d Calculated by the following formula:

[0026]

[0027] in, This is the key business data observed when a failure occurs. is the expected mean of the data under normal conditions, σ b The standard deviation of the data under normal conditions is calculated by calculating the sum of squared deviations between the actual data and the expected data and comparing it with the square of the standard deviation to determine the extent to which data accuracy is affected. The larger the deviation, the more serious the impact of the fault on the accuracy of key business data.

[0028] The business impact function E is calculated by weighting, and its formula is:

[0029]

[0030] Among them, w1, w2, and w3 are weight coefficients set for key factors, and they satisfy ∑ i w i =1, by substituting the calculation results of the above factors into the service impact function E, the service impact of the fault is classified and determined according to the size of the calculated value.

[0031] Step S3 establishes a grading standard based on the business impact, specifically: based on the calculated business impact function E, establish the following grading standard:

[0032] Level 1 serious fault: when E≥E1;

[0033] Second level more serious fault: When E2≤E <E1;

[0034] Level 3 general fault: When E3≤E <E2;

[0035] Level 4 minor fault: When E4≤E <E3;

[0036] Among them, E1, E2, E3, and E4 are pre-set fault classification thresholds, which are adjusted according to actual work.

[0037] Step S4 establishes an emergency assessment plan specifically as follows: when a fault is determined to be a Level 1 serious fault, an emergency text message with special sound effects and vibration prompts is simultaneously pushed to the substation manager, director, and deputy director. The text message details the fault work order number, initiation time, involved business type, fault description, and current emergency assessment value; an alarm prompt is displayed in flashing red characters on the homepage of the enterprise internal management system, and the fault information is pinned to the top of the system notification bar;

[0038] When a fault is determined to be a Level 2, more serious fault, a text message notification is sent to the director and deputy director. The text message format is standardized and contains key fault information, work order number, business type, and a brief description of the fault. Important messages are also pushed to relevant business groups.

[0039] When a fault is identified as a Level 3 general fault, a general message will be pushed to the relevant business group to inform them of the fault. The message will include basic information about the work order and the general situation of the fault. At the same time, a prominent yellow reminder mark will be set in the work order management system, and the work order will be pinned to the top of the pending list so that the processing personnel can easily see the reminder and arrange for processing when they log in to the system.

[0040] When a fault is determined to be a Level 4 minor fault, a green low-key reminder mark will be set in the work order management system. The mark will be displayed next to the corresponding work order in the work order list and can be viewed by the processing personnel when they log in to the system.

[0041] Step S5 establishes an early warning information update mechanism, specifically: continuously updating early warning related information, recalculating the urgency assessment function and early warning trigger function at every set time interval; if the urgency changes, adjusting the early warning level and notification method accordingly, and establishing an early warning upgrade mechanism, each fault that has not been handled for 48 hours will automatically upgrade to a higher level.

[0042] Step S6 of tracking the order progress specifically includes: updating the processing progress information of the work order in real time from the time the processing personnel accepts the order, starts troubleshooting, implements repair measures to the final completion of the repair.

[0043] Step S7 feedback and adjustment is specifically as follows: after the fault work order is processed, feedback information during the processing is collected, including the real cause of the fault, the repair measures taken, and whether there are other potential risks. The graded warning method is adjusted and improved based on the feedback information. If it is found that some classification standards are unreasonable or the warning triggering rules are not suitable for the actual situation, they are revised in time to improve the accuracy and effectiveness of the fault work order graded warning.

[0044] An RPA fault work order hierarchical early warning system, characterized in that it includes a server and a processor, the server storing a system program, and characterized in that when the processor executes the computer program, it implements the steps of the method described in any one of claims 1 to 8.

[0045] The system is equipped with an information storage function, which records basic work order data such as number and initiation time, business data including process criticality and various types of user and business volume data, as well as analysis result data, warning-related data, and processing progress and feedback data. It uses a table structure to store data, which facilitates data query, retrieval and statistical analysis, and provides data support for system operation.

[0046] When a trouble ticket is generated, RPA technology rapidly collects multiple types of ticket system data, including ticket number, initiation time, and business type. The criticality of the business process is quantified using a specific model. The percentage of affected users and the degree of impact on the accuracy of key business data are also calculated using corresponding algorithms. These data are then weighted to derive a business impact function value, which is then used to establish a tiered standard for fault classification. Furthermore, an urgency assessment scheme is developed for each level of fault, and relevant personnel are notified in different ways. The system updates alert information at set intervals, adjusting the alert level and notification method based on changes in urgency. An automatic escalation mechanism is implemented for unresolved issues within 48 hours. In the order progress tracking phase, information is updated in real time at each stage, from order acceptance to repair. Once the fault is resolved, feedback is collected to optimize the tiered alerting method. Throughout this process, the information storage module stores various data in a table structure for easy query, retrieval, and statistical analysis. The server stores programs, and the processor executes them to implement the aforementioned steps. These components work together to achieve efficient, tiered alerting and handling of trouble tickets.

[0047] Compared with the prior art, the present invention has the following beneficial effects:

[0048] By introducing a business impact function based on multi-factor fusion, comprehensively considering multiple key factors such as the criticality of business processes, the proportion of affected users, and the impact of the accuracy of key business data, and performing weighted calculations, we can more comprehensively and accurately assess the actual impact of failures on the business, avoid the one-sidedness brought about by single-factor evaluation, and make subsequent graded warnings more in line with actual conditions.

[0049] Based on the accurately calculated business impact, a grading standard is established, and faults are subdivided into different levels, such as level 1 severe faults, level 2 relatively severe faults, level 3 general faults, and level 4 minor faults. An emergency assessment plan is developed to match each level, and differentiated notification and prompt methods are used to rationally allocate energy and resources to deal with faults of different levels, thereby improving overall response efficiency.

[0050] A warning information update mechanism is established to recalculate relevant functions at certain time intervals, and dynamically adjust the warning level and notification method according to changes in urgency. At the same time, a mechanism is set up to automatically upgrade the level if a fault is not handled for a long time, ensuring that the warning information can always reflect the most accurate current status of the fault, avoiding delayed or inaccurate warnings due to the development and changes of the fault, and ensuring the timeliness and effectiveness of the entire warning system.

[0051] From the time the processing personnel accept the order, troubleshoot the fault, implement the repair measures to the final completion of the progress tracking of each link of the repair, the relevant personnel can grasp the processing situation in real time, which is convenient for better coordinating the work of all parties, supervising the processing progress, preventing problems such as delayed processing procedures and lack of follow-up, and ensuring that the fault can be properly resolved in a timely manner. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 This is a flow chart of an RPA fault work order hierarchical warning system of the present invention; DETAILED DESCRIPTION

[0053] The technical solutions in the embodiments of the present invention will be fully described below in conjunction with the accompanying drawings in the embodiments of the present invention.

[0054] like Figure 1 As shown, a method for hierarchical early warning of RPA fault work orders is characterized by comprising the following steps:

[0055] S1: Collect work order system data;

[0056] S2: Analyze the business impact and calculate the business impact function;

[0057] S3: Establish grading standards based on business impact;

[0058] S4: Establish an urgency assessment plan;

[0059] S5: Establish an early warning information update mechanism;

[0060] S6: Order progress tracking;

[0061] S7: Feedback and adjustment.

[0062] Step S1 collects work order system data specifically as follows: When a fault work order is generated, data is collected using RPA technology to obtain the following information: including work order number N, initiation time N, business type b, fault description D, and business process criticality C p , the number of users affected by the failure N u , the total number of users involved in the business N t .

[0063] Step S2 analyzes the business impact and calculates the business impact function. Specifically, a business impact function E based on multi-factor integration is introduced, which specifically covers the following key elements:

[0064] Business process criticality C p , by building a business process criticality assessment model to quantify, each business process is decomposed into i continuous sub-processes, and the collection is a key business data vector Each sub-process sets its weight w s , the formula for calculating its criticality is:

[0065]

[0066] in, is the weight of the ith sub-process, Is the key business data vector Evaluation function for the criticality of the i-th sub-process;

[0067] Percentage of affected users Count the number of users N affected by the failure u And the total number of users corresponding to this business Then calculate the proportion of affected users, the formula is:

[0068]

[0069] in, is the percentage of affected users, N u is the number of users affected by the failure, The total number of users corresponding to the service. This percentage directly reflects the impact of the fault on users. A higher percentage indicates a wider range of users affected by the fault, and a greater overall impact on the service.

[0070] Impact on the accuracy of key business data A d , using data deviation assessment algorithm to quantify this factor, constantly record key business data, and aggregate them into Calculate the expected mean of data under normal conditions Standard deviation σ b , when a failure occurs, the key business data actually observed is recorded as The degree of impact on the accuracy of key business data is A d Calculated by the following formula:

[0071]

[0072] in, This is the key business data observed when a failure occurs. is the expected mean of the data under normal conditions, σ b The standard deviation of the data under normal conditions is calculated by calculating the sum of squared deviations between the actual data and the expected data and comparing it with the square of the standard deviation to determine the extent to which data accuracy is affected. The larger the deviation, the more serious the impact of the fault on the accuracy of key business data.

[0073] The business impact function E is calculated by weighting, and its formula is:

[0074]

[0075] Among them, w1, w2, and w3 are weight coefficients set for key factors, and they satisfy ∑ i w i =1, by substituting the calculation results of the above factors into the service impact function E, the service impact of the fault is classified and determined according to the size of the calculated value.

[0076] Step S3 establishes a grading standard based on the business impact, specifically: based on the calculated business impact function E, establish the following grading standard:

[0077] Level 1 serious fault: when E≥E1;

[0078] Second level more serious fault: When E2≤E <E1;

[0079] Level 3 general fault: When E3≤E <E2;

[0080] Level 4 minor fault: When E4≤E <E3;

[0081] Among them, E1, E2, E3, and E4 are pre-set fault classification thresholds, which are adjusted according to actual work.

[0082] Step S4 establishes an emergency assessment plan specifically as follows: when a fault is determined to be a Level 1 serious fault, an emergency text message with special sound effects and vibration prompts is simultaneously pushed to the substation manager, director, and deputy director. The text message details the fault work order number, initiation time, involved business type, fault description, and current emergency assessment value; an alarm prompt is displayed in flashing red characters on the homepage of the enterprise internal management system, and the fault information is pinned to the top of the system notification bar;

[0083] When a fault is determined to be a Level 2, more serious fault, a text message notification is sent to the director and deputy director. The text message format is standardized and contains key fault information, work order number, business type, and a brief description of the fault. Important messages are also pushed to relevant business groups.

[0084] When a fault is identified as a Level 3 general fault, a general message will be pushed to the relevant business group to inform them of the fault. The message will include basic information about the work order and the general situation of the fault. At the same time, a prominent yellow reminder mark will be set in the work order management system, and the work order will be pinned to the top of the pending list so that the processing personnel can easily see the reminder and arrange for processing when they log in to the system.

[0085] When a fault is determined to be a Level 4 minor fault, a green low-key reminder mark will be set in the work order management system. The mark will be displayed next to the corresponding work order in the work order list and can be viewed by the processing personnel when they log in to the system.

[0086] Step S5 establishes an early warning information update mechanism, specifically: continuously updating early warning related information, recalculating the urgency assessment function and early warning trigger function at every set time interval; if the urgency changes, adjusting the early warning level and notification method accordingly, and establishing an early warning upgrade mechanism, each fault that has not been handled for 48 hours will automatically upgrade to a higher level.

[0087] Step S6 of tracking the order progress specifically includes: updating the processing progress information of the work order in real time from the time the processing personnel accepts the order, starts troubleshooting, implements repair measures to the final completion of the repair.

[0088] Step S7 feedback and adjustment is specifically as follows: after the fault work order is processed, feedback information during the processing is collected, including the real cause of the fault, the repair measures taken, and whether there are other potential risks. The graded warning method is adjusted and improved based on the feedback information. If it is found that some classification standards are unreasonable or the warning triggering rules are not suitable for the actual situation, they are revised in time to improve the accuracy and effectiveness of the fault work order graded warning.

[0089] An RPA fault work order hierarchical early warning system, characterized in that it includes a server and a processor, the server storing a system program, and characterized in that when the processor executes the computer program, it implements the steps of the method described in any one of claims 1 to 8.

[0090] The system is equipped with an information storage function, which records basic work order data such as number and initiation time, business data including process criticality and various types of user and business volume data, as well as analysis result data, warning-related data, and processing progress and feedback data. It uses a table structure to store data, which facilitates data query, retrieval and statistical analysis, and provides data support for system operation.

[0091] The specific implementation is as follows:

[0092] S1: Collect work order system data; specifically, when a fault work order is generated, use RPA technology to collect data and obtain the following information: including work order number N, initiation time N, business type b, fault description D, and business process criticality C. p , the number of users affected by the failure N u , the total number of users involved in the business N t .

[0093] S2: Analyze the business impact and calculate the business impact function. Specifically, introduce a business impact function E based on multi-factor integration, which covers the following key elements:

[0094] Business process criticality C p , by building a business process criticality assessment model to quantify, each business process is decomposed into i continuous sub-processes, and the collection is a key business data vector Each sub-process sets its weight w s , the formula for calculating its criticality is:

[0095]

[0096] in, is the weight of the ith sub-process, Is the key business data vector Evaluation function for the criticality of the i-th sub-process;

[0097] Percentage of affected users Count the number of users N affected by the failure u And the total number of users corresponding to this business Then calculate the proportion of affected users, the formula is:

[0098]

[0099] in, is the percentage of affected users, N u is the number of users affected by the failure, The total number of users corresponding to the service. This percentage directly reflects the impact of the fault on users. A higher percentage indicates a wider range of users affected by the fault, and a greater overall impact on the service.

[0100] Impact on the accuracy of key business data A d , using data deviation assessment algorithm to quantify this factor, constantly record key business data, and aggregate them into Calculate the expected mean of data under normal conditions Standard deviation σ b, when a failure occurs, the key business data actually observed is recorded as The degree of impact on the accuracy of key business data is A d Calculated by the following formula:

[0101]

[0102] in, This is the key business data observed when a failure occurs. is the expected mean of the data under normal conditions, σ b The standard deviation of the data under normal conditions is calculated by calculating the sum of squared deviations between the actual data and the expected data and comparing it with the square of the standard deviation to determine the extent to which data accuracy is affected. The larger the deviation, the more serious the impact of the fault on the accuracy of key business data.

[0103] The business impact function E is calculated by weighting, and its formula is:

[0104]

[0105] Among them, w1, w2, and w3 are weight coefficients set for key factors, and they satisfy ∑ i w i =1, by substituting the calculation results of the above factors into the service impact function E, the service impact of the fault is classified and determined according to the size of the calculated value.

[0106] S3: Establishing a grading standard based on the business impact. Specifically, based on the calculated business impact function E, establish the following grading standard:

[0107] Level 1 serious fault: when E≥E1;

[0108] Second level more serious fault: When E2≤E <E1;

[0109] Level 3 general fault: When E3≤E <E2;

[0110] Level 4 minor fault: When E4≤E <E3;

[0111] Among them, E1, E2, E3, and E4 are pre-set fault classification thresholds, which are adjusted according to actual work.

[0112] S4: Establish an emergency assessment plan. Specifically, when a fault is determined to be a Level 1 critical fault, a text message with special sound effects and vibration alerts will be sent to the substation manager, director, and deputy director. The text message details the fault work order number, initiation time, involved business type, fault description, and current emergency assessment value. An alert will also be displayed in flashing red text on the homepage of the enterprise internal management system, and the fault information will be pinned to the top of the system notification bar.

[0113] When a fault is determined to be a Level 2, more serious fault, a text message notification is sent to the director and deputy director. The text message format is standardized and contains key fault information, work order number, business type, and a brief description of the fault. Important messages are also pushed to relevant business groups.

[0114] When a fault is identified as a Level 3 general fault, a general message will be pushed to the relevant business group to inform them of the fault. The message will include basic information about the work order and the general situation of the fault. At the same time, a prominent yellow reminder mark will be set in the work order management system, and the work order will be pinned to the top of the pending list so that the processing personnel can easily see the reminder and arrange for processing when they log in to the system.

[0115] When a fault is determined to be a Level 4 minor fault, a green low-key reminder mark will be set in the work order management system. The mark will be displayed next to the corresponding work order in the work order list and can be viewed by the processing personnel when they log in to the system.

[0116] S5: Establish an early warning information update mechanism; specifically: continuously update the early warning related information, recalculate the urgency assessment function and early warning trigger function at every set time interval; if the urgency changes, adjust the early warning level and notification method accordingly, and establish an early warning upgrade mechanism. If each fault is not handled for 48 hours, it will automatically upgrade to a higher level.

[0117] S6: Order progress tracking; specifically, from the moment the processing personnel accepts the order, begins troubleshooting, implements repair measures, to the final completion of the repair, the processing progress information of the work order is updated in real time.

[0118] S7: Feedback and Adjustment. After a fault ticket is processed, feedback is collected, including the actual cause of the fault, the corrective measures taken, and whether there are other potential risks. Based on this feedback, the tiered warning method is adjusted and improved. If any tiering criteria are found to be unreasonable or the warning triggering rules are found to be inappropriate for the actual situation, they are promptly revised to improve the accuracy and effectiveness of the tiered warning system for fault tickets.

[0119] An RPA fault work order hierarchical early warning system, characterized in that it includes a server and a processor, the server storing a system program, and characterized in that when the processor executes the computer program, it implements the steps of the method described in any one of claims 1 to 8.

[0120] The system is equipped with an information storage function, which records basic work order data such as number and initiation time, business data including process criticality and various types of user and business volume data, as well as analysis result data, warning-related data, and processing progress and feedback data. It uses a table structure to store data, which facilitates data query, retrieval and statistical analysis, and provides data support for system operation.

Claims

1. A hierarchical early warning method for RPA fault work orders, characterized in that: The following steps are involved: S1: Collect work order system data; S2: Analyze the business impact and calculate the business impact function; S3: Establish grading standards based on business impact; S4: Establish an urgency assessment plan; S5: Establish an early warning information update mechanism; S6: Order progress tracking; S7: Feedback and adjustment.

2. The RPA fault work order hierarchical warning method according to claim 1 is characterized in that: Step S1 collects work order system data specifically as follows: When a fault work order is generated, data is collected using RPA technology to obtain the following information: including work order number N, initiation time N, business type b, fault description D, and business process criticality C p , the number of users affected by the failure N u , the total number of users involved in the business N t .

3. The RPA fault work order hierarchical warning method according to claim 1 is characterized in that: Step S2 analyzes the business impact and calculates the business impact function. Specifically, a business impact function E based on multi-factor integration is introduced, which specifically covers the following key elements: Business process criticality C p , by building a business process criticality assessment model to quantify, each business process is decomposed into i continuous sub-processes, and the collection is a key business data vector Each sub-process sets its weight w s , the formula for calculating its criticality is: in, is the weight of the ith sub-process, Is the key business data vector Evaluation function for the criticality of the i-th sub-process; Percentage of affected users Count the number of users N affected by the failure u And the total number of users corresponding to this business Then calculate the proportion of affected users, the formula is: in, is the percentage of affected users, N u is the number of users affected by the failure, The total number of users corresponding to the service. This percentage directly reflects the impact of the fault on users. A higher percentage indicates a wider range of users affected by the fault, and a greater overall impact on the service. Impact on the accuracy of key business data A d , using data deviation assessment algorithm to quantify this factor, constantly record key business data, and aggregate them into Calculate the expected mean of data under normal conditions Standard deviation σ b , when a failure occurs, the key business data actually observed is recorded as The degree of impact on the accuracy of key business data is A d Calculated by the following formula: in, This is the key business data observed when a failure occurs. is the expected mean of the data under normal conditions, σ b The standard deviation of the data under normal conditions is calculated by calculating the sum of squared deviations between the actual data and the expected data and comparing it with the square of the standard deviation to determine the extent to which data accuracy is affected. The larger the deviation, the more serious the impact of the fault on the accuracy of key business data. The business impact function E is calculated by weighting, and its formula is: <h2 style=";text-align:left;direction:ltr">E=w1C<h2 style=";text-align:left;direction:ltr"> p <h2 style=";text-align:left;direction:ltr"> +w2N<h2 style=";text-align:left;direction:ltr"> r <h2 style=";text-align:left;direction:ltr"> u <h2 style=";text-align:left;direction:ltr"> +w3A<h2 style=";text-align:left;direction:ltr"> d Among them, w1, w2, and w3 are weight coefficients set for key factors, and they satisfy ∑ i w i =1, by substituting the calculation results of the above factors into the service impact function E, the service impact of the fault is classified and determined according to the size of the calculated value.

4. The RPA fault work order hierarchical warning method according to claim 1 is characterized in that: Step S3 establishes a grading standard based on the business impact, specifically: based on the calculated business impact function E, establish the following grading standard: Level 1 serious fault: when E≥E1; Second level more serious fault: When E2≤E <E1; Level 3 general fault: When E3≤E <E2; Level 4 minor fault: When E4≤E <E3; Among them, E1, E2, E3, and E4 are pre-set fault classification thresholds, which are adjusted according to actual work.

5. The RPA fault work order hierarchical warning method according to claim 1 is characterized in that: Step S4 establishes an emergency assessment plan as follows: when a fault is determined to be a Level 1 serious fault, a text message is simultaneously sent to the substation manager, director, and deputy director, with a special sound effect and vibration prompt. The text message details the fault work order number, initiation time, involved service type, fault description, and current emergency assessment value; The company's internal management system will issue an alarm with flashing red characters on the homepage, and the fault information will be pinned to the top of the system notification bar. When a fault is determined to be a Level 2, more serious fault, a text message notification is sent to the director and deputy director. The text message format is standardized and contains key fault information, work order number, business type, and a brief description of the fault. Important messages are also pushed to relevant business groups. When a fault is identified as a Level 3 general fault, a general message will be pushed to the relevant business group to inform them of the fault. The message will include basic information about the work order and the general situation of the fault. At the same time, a prominent yellow reminder mark will be set in the work order management system, and the work order will be pinned to the top of the pending list so that the processing personnel can easily see the reminder and arrange for processing when they log in to the system. When a fault is determined to be a Level 4 minor fault, a green low-key reminder mark will be set in the work order management system. The mark will be displayed next to the corresponding work order in the work order list and can be viewed by the processing personnel when they log in to the system.

6. The RPA fault work order hierarchical warning method according to claim 1 is characterized in that: Step S5 establishes an early warning information update mechanism, specifically: continuously updating early warning related information, recalculating the urgency assessment function and early warning trigger function at every set time interval; if the urgency changes, adjusting the early warning level and notification method accordingly, and establishing an early warning upgrade mechanism, each fault that has not been handled for 48 hours will automatically upgrade to a higher level.

7. The RPA fault work order hierarchical warning method according to claim 1 is characterized in that: Step S6 of tracking the order progress specifically includes: updating the processing progress information of the work order in real time from the time the processing personnel accepts the order, starts troubleshooting, implements repair measures to the final completion of the repair.

8. The RPA fault work order hierarchical warning method according to claim 1 is characterized in that: Step S7 feedback and adjustment is specifically as follows: after the fault work order is processed, feedback information during the processing is collected, including the real cause of the fault, the repair measures taken, and whether there are other potential risks. The graded warning method is adjusted and improved based on the feedback information. If it is found that some classification standards are unreasonable or the warning triggering rules are not suitable for the actual situation, they are revised in time to improve the accuracy and effectiveness of the fault work order graded warning.

9. An RPA fault work order hierarchical warning system, characterized by: The method comprises a server and a processor, wherein the server stores a system program, and is characterized in that the processor implements the steps of the method according to any one of claims 1 to 8 when executing the computer program.

10. An RPA fault work order hierarchical warning system, characterized in that: The system is equipped with an information storage function, which records basic work order data such as number and initiation time, business data including process criticality and various types of user and business volume data, as well as analysis result data, warning-related data, and processing progress and feedback data. It uses a table structure to store data, which facilitates data query, retrieval and statistical analysis, and provides data support for system operation.

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